Evaluation of the Australian Wage Subsidy Special Youth ...

i 

Evaluation of the Australian Wage Subsidy 

Special Youth Employment and Training Program, 

SYETP 

Genevieve Margaret Knight 

A thesis submitted for the Degree of Doctor of Philosophy at the School of Economics 

and Political Science, Faculty of Economics and Business, University of Sydney 

August 2002

iii 

Acknowledgements 

Dedicated to my family 

Harry, Margaret, Yolande, Melisande, Juan, José, Isobel, Joshua, Inge, Ole, Mette, Andrew. 

Special thanks to Russell Ross, my supervisor. 

Further thanks are due to 

James Richardson, Michael Lechner, Jeff Smith, Stefan Speckesser, Barbara Sianesi, 

Lorraine Dearden, Alex Heath, Henry Overman, 

Paul Gregg, Jonathon Wadsworth, 

Dave Metcalf, Richard Jackman, Richard Layard, the Centre for Economic Performance, 

Michael White, Alex Bryson, Dave Wilkinson, Richard Dorsett, 

Dorothe Bonjour, Steve Lissenburgh, 

Joan Payne, Jim Skea, Policy Studies Institute, 

Denzil Fiebig, Tony Phipps, Jeff Sheen, Gordon Mills, Judy Yates, Roslyn Joyeaux, 

Ken Tallis and The Australian Bureau of Statistics.

v 

Declaration 

I hereby declare that this submission is my own work and 

that, to the best of my knowledge and belief, it contains no 

material previously published or written by another person 

nor material which to a substantial extent has been accepted 

for the award of any other degree or diploma of the 

university or other institute of higher learning, except where 

due acknowledgment has been made in the text. All errors 

are my own.

vi 

The job subsidy Special Youth Employment and Training Program (SYETP) was 

introduced in Australia with the aim of improving the movement into work. In 1984, the 

SYETP was a flat rate subsidy of A$75 a week paid to employers for 17 weeks, 

equivalent in value to half the average teenage wage, and was available to youths aged 

15-24 who had been claiming unemployment benefits and not studying full-time for at 

least 4 of preceding 12 months. 

A review of theoretical literature indicates they can give no proof of employment gains 

for wage subsidies. The empirical ambiguity of employment gains is concluded 

unresolved, in both recent overseas and Australian literature. A contributing factor is the 

insufficiency of the evaluation methods. Appraisal of the micro-evaluation evidence for 

SYETP and other Australian wage subsidies is also found to suffer these deficiencies. 

The inadequacies of past analyses of SYETP contribute three themes to address: suitable 

modelling of selection to account for the influence of observables or unobservables, 

dealing with non-response in the observational data, and appropriate control for the 

differences between the SYETP and comparison groups. 

Past evaluation by Richardson (1998), modelling the Heckman selection bivariate probit, 

using the Australian Longitudinal Survey of Youths 1984-1987 found a very large 

positive employment effect for SYETP participants 26 months after taking part. A key 

issue with the results is that no account of sample attrition was made. Theory indicates 

bias to be a potentially serious problem with results. Two evaluation methods are 

explored – the Heckman selection bivariate probit model, and matching methods, in 

particular propensity score matching. Both identify a parameter corresponding to the 

mean effect of treatment on the treated, which can be used to decide whether the 

programme leads to employment gains. However each method uses different assumptions 

to achieve this. Selection on unobservables is assumed by the Heckman bivariate probit, 

while selection on observables is assumed by matching methods. 

A series of empirical studies assesses a number of questions – what happens to the 

evaluation outcome if selection is assumed to be based on observables instead of 

unobservables; what is the importance of sample reduction to the evaluation outcome; 

and how sensitive is the employment impact to variation in modelling. To provide a 

foundation for useful comparison Richardson (1998) is first replicated successfully. The 

more recently popular propensity score matching method (PSM) is then applied. The 

PSM results reduce the size and significance of the employment effect found. The effects 

of attrition are examined and then accounted for, and the impact on evaluation discussed. 

The results are found to be smaller and have lower statistical significance. Correctly 

accounting for weights is found to be important in applying PSM. 

The Heckman and the PSM method both make strong but very different assumptions 

about the selection into SYETP. A comparison of the employment impacts found under 

each method is undertaken, together with a discussion of the most suitable assumptions 

for this evaluation. The value of replication is validated and advocated. The research 

confirms that careful accounting for data and modelling problems is important. External 

validity for the robustness of employment gains to SYETP are provided by the variations

in method and assumptions. The orthodox approach of adopting only one potentially 

appropriate selection approach and underlying assumption of observables or 

unobservables is challenged. A sensitivity analysis showing variation to employment 

effects for changes in key modelling assumptions can give a confidence interval 

accounting for statistical modelling uncertainty. It is concluded that the benefits are an 

informed overview of the role of the assumption to the evaluation outcome. 

vii

Table of contents 

Table of contents.............................................................................................................. i 

List of Tables and Figures.............................................................................................. xi 

Glossary ....................................................................................................................... xiv 

Abstract......................................................................................................................... xv 

1: Wage subsidy theory and evaluation .............................................................................. 1 

1.1 Structure of this research .......................................................................................... 1 

1.1 General introduction ................................................................................................. 2 

1.2 Brief summary of the theory of wage subsidies........................................................ 7 

1.3 Introduction to evaluation of programs................................................................... 16 

1.3.1 Evaluation of programs........................................................................ 17 

1.3.2 Evaluation problem.............................................................................. 18 

1.3.3 Selection............................................................................................... 20 

1.3.4 Observables and unobservables ........................................................... 22 

1.4 Brief comment on recent overseas evidence of wage subsidy evaluations ............ 23 

2: Australian literature review .......................................................................................... 28 

2.1 Review of Australian wage subsidy evaluation evidence....................................... 30 

2.1.1 Adult Wage Subsidy Scheme............................................................... 31 

2.1.2 ‘General Training Assistance On-the-Job-Training’ ........................... 33 

2.1.3 Jobstart ................................................................................................. 34 

2.1.4 Discussion............................................................................................ 41 

2.2 SYETP implementation .......................................................................................... 43 

2.2.1 Historical development of SYETP........................................................... 43 

2.2.2 An overview of SYETP changes ............................................................. 48 

2.2.3 SYETP operation ..................................................................................... 51 

2.2.4 Political environment of SYETP ............................................................. 55 

2.2.5 Economic Context of SYETP at the time of our analysis........................ 58 

2.2.5.1 Role and impact of the CES.............................................................. 58 

2.2.5.2 Unemployment in the Australian economy over the 1980’s ............ 62 

2.2.5.3 Discussion......................................................................................... 69 

2.2.6 Context of SYETP environment and operation ....................................... 70 

2.2.6.1 General results for SYETP using other approaches.......................... 70 

2.2.6.2 Job characteristics ............................................................................. 71 

2.2.6.3 Characteristics of SYETP commencements ..................................... 72 

2.2.6.4 Factors affecting completion of subsidy placement.......................... 77 

2.3 Critical Review of Evaluation of SYETP ............................................................... 78 

2.3.1 Stretton (1982, 1984) .......................................................................... 79 

2.3.2 Baker (1984) ........................................................................................ 81 

2.3.3 Rao and Jones (1986)........................................................................... 84 

2.3.4 Richardson (1998)................................................................................ 87 

2.3.5 General discussion ............................................................................... 91 

3: Study 1 Replication....................................................................................................... 94 

3.1 Motivation for replication....................................................................................... 94 

3.2 Methods: The Heckman selection model................................................................ 95 

3.2.1 Self-selection and the evaluation of programmes................................ 95 

3.2.2 The estimated model............................................................................ 96 

viii

3.3 Data and variables used for estimation ................................................................... 99 

3.4 Replication results................................................................................................. 101 

3.5 Discussion............................................................................................................. 103 

4: Study 2 Propensity score matching for SYETP.......................................................... 111 

4.1 Differences between the treatment and comparison group................................... 112 

4.2 Propensity score matching methods...................................................................... 115 

4.3 Theory underlying propensity score matching methods....................................... 116 

4.4 The propensity score matching protocol implemented......................................... 119 

4.5 Estimating the probability of participation for SYETP ........................................ 122 

4.6 Distribution of the propensity score...................................................................... 127 

4.7 Common support for the treated ........................................................................... 128 

4.8 Results of Matching: one-to-one nearest-neighbour............................................. 132 

4.8.1 Discussion of the results .................................................................... 132 

4.8.2 Mean standardised bias statistic......................................................... 136 

4.9 Sensitivity analysis: all-in-Radius matching......................................................... 139 

4.10 Discussion........................................................................................................... 141 

5: Study 3 Attrition and non-response in the ALS.......................................................... 145 

5.1 Examining sample reduction in the ALS.............................................................. 145 

5.2 Theory of why attrition introduces bias................................................................ 146 

5.3 Empirical aspects of the effects of attrition on estimates ..................................... 149 

5.4 Empirical attrition test and treatment.................................................................... 150 

5.5 Examining the role of attrition in the ALS and in modelling of the SYETP 

employment effect ...................................................................................................... 152 

5.5.1 Extent of sample reduction .................................................................... 152 

5.5.2 Univariate examination of sample reduction ......................................... 155 

5.5.3 Sample reduction by SYETP treatment group....................................... 160 

5.5.3.1 Comparing those lost in attrition to those who remain................... 160 

5.5.3.2 The effect of sample reduction on the difference between SYETP and 

comparison groups...................................................................................... 164 

5.5.4 Attrition: natural attrition or analytical sample reduction?.................... 167 

5.6 Accounting for non-response and sample design ................................................. 172 

5.6.1 Survey design and non-response effects on modelling.......................... 176 

5.6.1.1 Analytical selection......................................................................... 176 

5.6.1.2 Effects of the non-response to 1984 Survey on the participation 

model........................................................................................................... 183 

5.7 Multivariate analysis of effects of sample reduction ............................................ 187 

5.7.1 Accounting for item non-response......................................................... 187 

5.7.2 Sample reduction effects on model of SYETP participation................. 189 

5.8 Model of the probability of attrition ..................................................................... 192 

5.9 Attrition Weights from the model......................................................................... 196 

5.10 Conclusions......................................................................................................... 196 

6: Study 4 Weighting to counteract attrition and non-response in ALS......................... 199 

6.1 Results of weighting Heckman bivariate probit.................................................... 199 

6.2 Results of weighting the PSM............................................................................... 207 

6.2.1 Weighting protocol ................................................................................ 207 

6.2.2 Effects of weighting PSM...................................................................... 208 

ix

6.3 Discussion............................................................................................................. 219 

7: Study 5 Sensitivity analysis ........................................................................................ 222 

7.1 Sensitivity of Heckman specification ................................................................... 222 

7.1.1 Potential heteroskedasticity ................................................................... 222 

7.1.2 Exclusion restriction in the Heckman model......................................... 231 

7.2 Varying the Propensity Score specification, effects on the match........................ 236 

7.2.1 Propensity score matching and the effect of excluding CEP referrals... 236 

7.2.2 Propensity score matching and the effect of reduced specification....... 243 

7.2.3 Further discussion and conclusions ................................................................... 246 

8: Summary and Conclusions ......................................................................................... 253 

Appendix 1 Data appendix.............................................................................................. 260 

Table 1 Description of the data....................................................................... 260 

Appendix 2 Tables .......................................................................................................... 263 

Bibliography ................................................................................................................... 302 

x

List of Tables and Figures 

Figure 1.1: Labour market equilibrium with wage subsidy..................................... 11 

Figure 1.2 Employment effect of the subsidy,......................................................... 16 

Table 1.3 Brief overview of recent European wage subsidy evidence considered.. 27 

Table 2.1 Private Sector Jobstart subsidy weekly rates 1985-1987......................... 37 

Table 2.2 Stromback and Dockery (2000) Raw employment outcomes after Jobstart 

.................................................................................................................................. 40 

Table 2.3 Average junior award rates 1977 to 1981................................................ 46 

Table 2.4 SYETP rates, period and target group/eligibility criteria 1976-December 

1985.......................................................................................................................... 50 

Table 2.5 Key SYETP provisions in 1983/84.......................................................... 55 

Table 2.6 SYETP annual expenditure and placements1976/77-1985/86 ................ 58 

Table 2.7 Active steps to find work by youths looking for work in July 1980 ....... 59 

Table 2.8 Age distribution for CES registrants in NSW 1985-1986 ....................... 62 

Table 2.9 Unemployment rate Australia 1981-1990, seasonally adjusted............... 63 

Table 2.10 Average duration of unemployment (number of weeks) by age, August 

1981-1990 ................................................................................................................ 64 

Table 2.11 Unemployment, labour force participation and employment rates, 

teenagers and total working age population March 1983 and March 1988............. 66 

Table 2.12 Value of unemployment benefits to youths 1983-1987......................... 67 

Table 2.13 Smith (1984b) summary of estimates of SYETP................................... 71 

Table 2.14 Distribution of SYETP commencements, by age 1980-81.................... 73 

Table 2.15 Distribution of SYETP commencements, by age and sex 1980-81....... 74 

Table 2.16 Completion of SYETP placement.......................................................... 76 

Table 2.17 State usage of programmes in 1980/81.................................................. 77 

Table 2.18 Baker (1984) Post-programme full-time employment outcome............ 83 

Table 2.19 Baker (1984) Estimated probabilities of labour market outcomes for 

participants from the model of employment............................................................ 84 

Table 2.20 Rao and Jones (1986) Estimated percent post-programme full-time 

continuous employment chances 1981-1983........................................................... 86 

Table 2.21 Richardson (1998) Estimated marginal effect of SYETP on employment 

from bivariate probit modelling............................................................................... 89 

Table 3.1, Part A Employment equation from bivariate probit ............................. 105 

Table 3.1 Part B Selection/participation equation of the bivariate probit.............. 108 

Table 4.1 Difference between treatment group and comparison group................. 114 

Table 4.2 Probit used to estimate propensity score for propensity score matching124 

Figure 4.3 Histograms of estimated propensity score prior to matching............... 129 

Figure 4.4 Kernel Density of propensity scores distribution for Treated SYETP and 

untreated................................................................................................................. 129 

Table 4.5 Summary statistics for distribution of propensity scores....................... 131 

Table 4.6 Matching results, Single nearest neighbour with replacement, within 

caliper..................................................................................................................... 135 

Table 4.7 Matching results, All-Within- caliper/Radius with replacement........... 141 

Table 4.8 Employment effects of Heckman versus PSM ...................................... 142 

Table 5.1 Sample Reduction for ALS List sample ................................................ 153 

xi

Table 5.2 Summary statistics for attrition effects in the final sample ................... 158 

Table 5.3 Summary statistics of attrition effects, for comparison and treated SYETP 

................................................................................................................................ 162 

Table 5.4 Difference between treatment group and comparison group before and 

after sample reduction............................................................................................ 166 

Table 5.5 Summary statistics for attrition effects by source of sample loss.......... 170 

Table 5.6: Effect of selection/response weight on 1984 survey respondents ........ 175 

Table 5.7 How the observations reduce to 1283 from those who respond to the 1986 

survey..................................................................................................................... 177 

Table 5.5a Summary statistics by source of sample loss due to analytical selection 

................................................................................................................................ 181 

Table 5.8 Probit of SYETP participation, showing sample reduction effects ....... 184 

Table 5.9: Probit results used for construction of attrition weights....................... 194 

Table 5.10: Performance of attrition weight as measured by unweighted and 

weighted profile ..................................................................................................... 198 

Table 6.1, part A Employment equation from bivariate probit.............................. 201 

Table 6.1, Part B Selection/participation equation of the bivariate probit............. 204 

Table 6.2 Weighting protocol steps ....................................................................... 207 

Table 6.3 Weighted probit used to estimate propensity score for propensity score 

matching................................................................................................................. 211 

Table 6.5 Summary statistics for distribution of weighted propensity scores, 

comparison group and SYETP............................................................................... 215 

Figure 6.6 Kernel density plot of attrition weighted propensity scores, before 

matching................................................................................................................. 216 

Table 6.7 Matching results, single nearest neighbour with replacement, within 

caliper, weighting the propensity with combined weights for attrition, non-response 

and design .............................................................................................................. 217 

Figure 6.8 Propensity Distribution after matching, 0.001 Caliper......................... 218 

Table 6.9 Employment effects of Heckman versus PSM ...................................... 220 

Table 7.1, Part A Employment equation from bivariate probit, showing effect of 

standard error estimate........................................................................................... 225 

Table 7.1, Part B Selection/participation equation of the bivariate probit, showing 

effect of standard error estimate ............................................................................ 228 

Table 7.2 summary of changes to exclusion restriction of Heckman specification235 

Table 7.3 Weighted Probit used to estimate propensity score for propensity score 

matching, exclude CEP referrals............................................................................ 238 

Table 7.4 Summary of distribution of propensity, exclude CEP referrals............. 241 

Table 7.5 Matching results, Single nearest neighbour with replacement, within 

caliper, weighting the propensity with combined weights: vary specification...... 242 


matching, new specification................................................................................... 249 

Table 7.7 Summary of distribution of propensity, new specification.................... 251 

Figure 7.8 Histograms of the propensity scores for the new specification............ 251 

Figure 7.9 Kernel densities of the propensities estimated for new specification... 252 

Table A2.0a Univariate probit for employment in 1986, as estimated in the bivariate 

probit replication of Richardson (1998)................................................................. 264 

xii

Table A2.0b Univariate Probit of participation in SYETP, as estimated in the 

bivariate probit replication of Richardson (1998).................................................. 267 

Table A2.1 Means and bias after matching, one to one nearest neighbour 0.001 and 

0.005 propensity score radius ................................................................................ 270 

Table A2.1 Continued Means and bias after matching, nearest neighbour one to one 

0.01 and 0.05 propensity score radius.................................................................... 273 

Table A2.2 Applying Different methods for the missing cases in the SYETP probit 

before sample reduction......................................................................................... 276 

Table A2.3 Probit of SYETP participation, no missing dummies......................... 279 

Table A2.4 Summary statistics for distribution of the weights constructed for 

attrition, non-response and survey design.............................................................. 282 


probit, weighted for attrition.................................................................................. 283 

Table A2.5b Univariate probit of participation in SYETP, as estimated in the 

bivariate probit, weighted for attrition................................................................... 286 

Table A2.6 Part A Employment equation from bivariate probit where age included 

in employment model, attrition weights ................................................................ 289 

Table A2.6 Part B Selection/participation equation of the bivariate probit where age 

included in employment model, attrition weights.................................................. 291 

Table A2.7 Part A Employment equation from bivariate probit where CEP referrals 

included in employment model, attrition weights.................................................. 293 

Table A2.7 Part B Selection/participation equation of the bivariate probit where 

CEP referrals included in employment model, attrition weights........................... 295 


and age included in employment model, attrition weights .................................... 297 

Table A2.8 Part B Selection/participation equation of the bivariate probit where 

CEP referrals and age included in employment model, attrition weights.............. 299 

xiii

xiv 

Glossary 

ABS Australian Bureau of Statistics 

ALMP Active Labour Market Program; see also Manpower Program. 

ALS Australian Longitudinal Survey 1984-1988 

Award wage The Award wage in Australia is agreed in Federal negotiations with 

unions, and varies by occupation and age. 

CEP Community Employment Program, an Australian job creation program 

where public projects were sponsored based on their employment 

creating capacity. 

CES Commonwealth Employment Services, the Australian public 

employment service. 

DEET Department of Employment, Education and Training Commonwealth 

Government of Australia, Canberra see also DEIR, DEETYA, DEYA 

DEETYA Department of Employment, Education, Training and Youth Affairs 

Commonwealth Government of Australia, Canberra see also DEIR, 

DEET, DEYA 

DEIR Department of Employment and Industrial Relations, Commonwealth 

Government of Australia, Canberra see also DEET, DEETYA, DEYA 

DEYA Department of Employment and Youth Affairs, Commonwealth 

Government of Australia, Canberra; see also DEIR, DEET, DEETYA 

EPUY Education Programme for Unemployed Youth; an Australian training 

program part of NEAT; courses aimed to improve basic literacy, 

numeracy and social skills. 

Manpower A labour market program consisting of training or subsidies to assist 

program movement into work. 

NEAT National Employment and Training System, Australia. An active labour 

market program of training and wage subsidies – see for example 

SYETP, EPUY. 

NWO New Work Opportunities provided direct job creation in projects where 

placements had work with some training typically in environmental, age 

care and community sectors. Existed at the same time as Jobstart. 

OECD Organisation for Economic Cooperation and Development, Paris. 

PSM propensity score matching 

SYETP Special Youth Employment and Training Program, an Australian 

employment or wage subsidy program; part of NEAT;

xv 

Abstract 

The job subsidy Special Youth Employment and Training Program (SYETP) was 

introduced in Australia with the aim of improving the movement into work. In 1984, the 

SYETP was a flat rate subsidy of A$75 a week paid to employers for 17 weeks, 

equivalent in value to half the average teenage wage, and was available to youths aged 

15-24 who had been claiming unemployment benefits and not studying full-time for at 

least 4 of preceding 12 months. 

A review of theoretical literature indicates they can give no proof of employment gains 

for wage subsidies. The empirical ambiguity of employment gains is concluded 

unresolved, in both recent overseas and Australian literature. A contributing factor is the 

insufficiency of the evaluation methods. Appraisal of the micro-evaluation evidence for 

SYETP and other Australian wage subsidies is also found to suffer these deficiencies. 

The inadequacies of past analyses of SYETP contribute three themes to address: suitable 

modelling of selection to account for the influence of observables or unobservables, 

dealing with non-response in the observational data, and appropriate control for the 

differences between the SYETP and comparison groups. 

Past evaluation by Richardson (1998), modelling the Heckman selection bivariate probit, 

using the Australian Longitudinal Survey of Youths 1984-1987 found a very large 

positive employment effect for SYETP participants 26 months after taking part. A key 

issue with the results is that no account of sample attrition was made. Theory indicates 

bias to be a potentially serious problem with results. Two evaluation methods are 

explored – the Heckman selection bivariate probit model, and matching methods, in 






A series of empirical studies assesses a number of questions – what happens to the 

evaluation outcome if selection is assumed to be based on observables instead of 

unobservables; what is the importance of sample reduction to the evaluation outcome; 

and how sensitive is the employment impact to variation in modelling. To provide a 

foundation for useful comparison Richardson (1998) is first replicated successfully. The 

more recently popular propensity score matching method (PSM) is then applied. The 

PSM results reduce the size and significance of the employment effect found. The effects 

of attrition are examined and then accounted for, and the impact on evaluation discussed. 

The results are found to be smaller and have lower statistical significance. Correctly 

accounting for weights is found to be important in applying PSM. 

The Heckman and the PSM method both make strong but very different assumptions 

about the selection into SYETP. A comparison of the employment impacts found under 

each method is undertaken, together with a discussion of the most suitable assumptions 

for this evaluation. The value of replication is validated and advocated. The research 

confirms that careful accounting for data and modelling problems is important. External

xvi 

validity for the robustness of employment gains to SYETP are provided by the variations 

in method and assumptions. The orthodox approach of adopting only one potentially 

appropriate selection approach and underlying assumption of observables or 

unobservables is challenged. A sensitivity analysis showing variation to employment 

effects for changes in key modelling assumptions can give a confidence interval 

accounting for statistical modelling uncertainty. It is concluded that the benefits are an 

informed overview of the role of the assumption to the evaluation outcome. 

Keywords: labour market program evaluation; non-experimental methods; 

panel/longitudinal survey data; attrition; missing data; Heckman bivariate probit; 

propensity score matching; replication. 

JEL classification: C14, C35, C40, J64, J68, H43.

1 

1: Wage subsidy theory and evaluation 

1.1 Structure of this research 

The first chapter introduces the motivation for the substantive research that forms the 

later chapters. Following a general introduction to the themes which are developed in the 

research, the theory of wage subsidies is reviewed. A brief preface then presents the key 

issues in evaluation of social programs such as wage subsidies. Empirical evidence for 

wage subsidies is then treated in several parts. Firstly, a selection of conclusions from 

overseas evaluations for wage subsidies is canvassed in an overview that gives a 

background for the Australian studies that follow in subsequent chapters and form the 

substance of this research. Only very recent work is included in this overseas appraisal, as 

several very comprehensive reviews have lately been published in this area. The chief 

aim is to draw out useful questions that are addressed later within this study. Australian 

wage subsidies are not covered in this chapter, but are the focus of the next chapter. 

Australian wage subsidies are reviewed in Chapter 2. A historical essay presents an 

overview of the operation and economic environment of SYETP. The context is then 

useful in assessing the processes for participation and employment for evaluation 

modelling. The past evaluation evidence relating to SYETP is critically assessed. In an 

effort to maintain clarity of exposition, the theory associated with the econometric 

techniques applied is not in the literature review. Instead, each method is expounded 

within each later study where they are applied. As a result, propensity score matching 

methods for example, are discussed in study 2 where they are first applied. 

As a result of the critical review of past evaluation attempts of SYETP, a series of studies 

present new evidence for SYETP which attempt to account for the imperfections 

identified in past evidence. This is then new evidence for the Australian wage subsidy 

program, SYETP.

2 

The first study presented in Chapter 3 is replication of the Richardson (1998) evaluation 

using Heckman bivariate modelling. Following this, propensity score matching (PSM) is 

applied in Chapter 4, with the observed differences between the SYETP and comparison 

group examined initially to establish the prospective value for propensity score matching. 

A number of variations to the specification are presented. In Chapter 5, data reduction is 

examined in detail and weights developed. The weights are then applied in Chapter 6 to 

both the Heckman bivariate probit model, and PSM. In the final empirical study, in 

Chapter 7, sensitivity of the two modelling approaches are examined. Chapter 8 

concludes, drawing together the themes of the research. 

1.1 General introduction 

The central focus of this research is the Australian wage subsidy titled the Special Youth 

Employment and Training Program (SYETP). 

Wage subsidies are one of the two major forms of labour market programs, the other 

being training. Labour market programs are a form of government intervention into the 

labour market with the express aim of benefiting a target group. Theoretical evidence 

suggests that wage subsidies may play a useful role in raising the employment prospects 

of the unemployed, especially when targeted at those disadvantaged in the labour market. 

However as this chapter will show, the theoretical models do not conclusively prove that 

wage subsidies can improve employment. The resolution of this theoretical ambiguity is 

at least one motivating force for empirical evidence that seeks to gather proof of the 

consequences of wage subsidies. Empirical evidence is also not conclusive but remains 

suggestive that wage subsidies raise employment of the target group. 

Wage subsidies 1 are generally held in high regard in real-world policy-making as a useful 

tool in labour market and social policy for the problem of the unemployed. Labour 

market policy, consisting of programs of job creation, are promoted by the OECD as 

‘active’ policies for the alleviation of unemployment, as opposed to the ‘passive’ 

1 Wage subsidies are also sometimes termed employment subsidies, recruitment subsidies, or benefit 

transfers (Snower, 1994), and these terms are applied to subsidies only in private sector jobs, not the public 

sector.

3 

programs of providing benefits and income support. In weighing the positive aspects of 

wage subsidies as a policy for treating the problem of long-term unemployment amongst 

other program choices, the OECD (1988) p58 stated: 

“…wage subsidies involve a number of advantages…the jobs which 

materialize...can be deemed to be real jobs in a mainstream labour market 

environment. Furthermore, even though one cannot impose too stringent 

conditions on employers to ensure retention beyond the period over which the 

subsidy is held, many recruits taken on under such schemes are in fact 

retained.” 

Wage subsidies, or some other form of job creation program, are in recent times often 

considered de rigueur amongst advanced economies, where there is little living memory 

of the times when they were not applied to the population. 

Theoretically, several models support the notion that wage subsidies can alleviate 

unemployment. Various authors explore the theoretical foundations of wage subsidies, 

for example Kaldor (1936), Hamermesh (1978), Johnson (1980), Jackman and Layard 

(1980), Johnson and Layard (1986), Layard et al. (1991), Layard (1997), Calmfors (1994), 

Phelps (1994, 1997), Snower (1994), Millard and Mortenson (1997). A lot of the 

literature maintains the excitement for labour market policy that existed, and was 

prevalent in the 1970’s and 1980’s 

Empirically, early evidence for the effectiveness of wage subsidies seemed very positive, 

but lack of sophistication in the methods means that many of these results are doubtful or 

flawed. Taking this into account, the empirical information about the effectiveness of 

wage subsidies is then quite sketchy. As well, although many wage subsidy programs 

have existed and do currently exist in many countries, evaluation of the employment 

consequences of these programs is more limited. Many studies that take place of these 

programs are conducted by internal government agencies and general publication of 

results is not guaranteed. While on casual overview the literature may appear to abound 

with research about labour market program evaluation, much of this stems from the US, 

is quite old, and as a result uses out-of-date methods. Additionally, most programs that 

were evaluated in the US were not wage subsidies but training programs or a mix where

4 

subsidies were not separately evaluated. Generally, the outcome of an empirical review 

for wage subsidies is a paucity of results for which there are not mitigating caveats. 

In conducting a review of the empirical wage subsidy literature, other questions arise. 

Each empirical fact requires the bigger economic context to understand the operation. For 

some programs, the evaluation outcome exists and the description of the program is 

available yet the greater picture of the economy under which the outcome was achieved is 

not readily available. The general economic context can help resolve which theoretical 

modelling assumptions are appropriate to the outcome. To this end, Australian economic 

detail for the period evaluated has been briefly summarized to augment the evaluation 

evidence for the Australian wage subsidy SYETP. 

The SYETP program operated between 1976 and 1985. While SYETP operation is 

historical, information about the employment consequences of this program are of current 

relevance for a number of reasons related to economic and econometric questions. 

Re-evaluation of program data some time later has proved productive in the US 

econometric literature. The first valuable result of reanalysis is to raise debate about the 

methods applied. Potential flaws in past program evaluation can be rectified, and the 

results re-assessed. The outcome can be useful, and often shed new light on the question 

of how the program worked. In an influential study, Smith and Todd (2000), Smith and 

Todd (2003) re-examined the program evaluation of Lalonde (1986), and found that a 

substantial amount of bias and sensitivity in the results was due to the analytical choices 

made, methods, poor data and covariate restrictions. In re-analysing, methods can then be 

updated for techniques either not available or not chosen when the data were first 

analysed. Dehija and Wahba (1998, 1999) reanalysed the Lalonde (1986) data using 

propensity score matching, but selected only a subset of the data. Smith (2000) notes that 

analysis by Smith and Todd (2000) show the very different results obtained by Dehija 

and Wahba (1998, 1999) were strongly influenced by choice of covariates and analytical 

selection of the data subset.

5 

The empirical methods used for the evaluation of labour market programs do themselves 

require assessment. It is clear that the choice of an estimator to evaluate a program 

requires judgments about the outcome equation, participation rules and their relationship 

[Heckman, Lalonde and Smith (1999) p2025]. Yet the testing of evaluation models on 

pre-program data, a specification test suggested and usually attributed to Heckman and 

Hotz (1989), is shown to be of no use due to the ‘alignment fallacy’ [Heckman et al. 

(1999): 2031]. In addition, evaluators are usually unable to test the maintained identifying 

assumptions of their empirical models. In light of this modelling uncertainty, the validity 

of the choice of a single method for evaluation is quite low. Benefits can accrue to 

varying the modelling choice in order to test the sensitivity of the outcome to the choice 

of assumptions. This re-analysis of SYETP enables the evaluation methods used to be 

assessed. 

An advantage to future literature reviews exists as a result of the re-evaluation of program 

data. For example, a recent review of the literature by Marx (2001) has already subsumed 

the results of the Richardson (1998) analysis of the Australian wage subsidy Special 

Youth Employment and Training Program (SYETP). Such reviews try to gain an 

overview and collate the evaluation findings together. Without a re-analysis, the 

published results stand as the only ones available, even if they are not ideal. 

From a scientific perspective, it is also good to ‘go back to the drawing board’. This 

enables a new examination of the material, from a different perspective. A new set of 

questions can be asked from the same data. In order to provide a sound basis for this, a 

replication of the previous analysis can allow the new analysis to build from where the 

former analysis finished. 

The econometric analyses of SYETP conducted in this research use the Australian 

Longitudinal Survey (ALS) to provide program data for evaluation of SYETP. Program 

data should be seen as a valuable resource, which can be drawn upon for analysis. 

Program data is rarely available. Although it seems many programs have information 

gathered, administratively or through surveys, most of this does not become publicly

6 

available. For example, in Australia, all microeconomic evaluations or programs have 

been carried out under the auspices of government departments. While some results have 

been published in departmental papers, the data are not usually made available for further 

analysis. Often the burden of collecting and storing the data and also collating the 

documentation needed for understanding the data are deemed too great at the time of the 

program, or initial interest in the program wanes, or resources simply aren’t made 

available. Even if good intentions exist, the government involved often seems insecure of 

making the data publicly available, or in any case don’t make it available. In this light, 

the ALS although not perfect as program data, is quite rare and should be fully utilised. 

The substantive analysis for this research is performed using the ALS survey data. This 

survey data contains information about SYETP participants and non-participants, 

however these result from ordinary variation rather than random assignment design. The 

US literature has benefited from the application of experimental design for the data 

collection, in the sense that the information relates to a randomly selected group of 

program subjects and control group who don’t receive services. Experimental data are 

held forth as the ideal data for evaluation, giving many benefits to analysis. The ‘treated’ 

and the ‘control’ or comparison outcomes can then be compared. 2 Experimental data can 

be used to develop methods and investigate comparisons of bias from the ‘true’ outcome 

for these methods. 

Although this analysis of SYETP investigates the changes in outcomes resulting from the 

application of various methods, because of the non-experimental data design it cannot 

identify a single outcome as the true outcome. Instead it provides an exploration of 

several interesting questions about SYETP and it’s effect on employment, firstly: how the 

program outcome changed as a result of adjusting for various data flaws, especially 

survey non-response and attrition; secondly: how the program outcome changed when the 

more recently popular propensity score matching (PSM) methods were applied; thirdly: 

2 It has become common in the evaluation literature to use ‘control group’ to refer to groups of program 

non-participants generated by random assignment, and to use ‘comparison group’ to refer to groups of 

program non-participants generated by naturally occurring variation in participation. This usage is applied 

throughout this work.

7 

how the outcomes from these methods changed according to the type of adjustment used 

to correct for data flaws; fourthly: what picture do the various outcomes present for the 

working of the Australian SYETP. In overview, it is a sensitivity analysis of the 

underlying methodological assumptions. 

Finally, it is useful in itself to examine the application of the various non-experimental 

methods as in this study. This is because in practice they are frequently used for program 

evaluation. Although some experimental data may exist for programs, in general 

European and Australian evaluations need to rely on non-experimental data, from 

administrative or survey sources. The reasons for this situation are likely to be many and 

diverse. However it may be influential that non-experimental data are perceived to have 

fewer associated political difficulties– and this counteracts the issue that there is no 

public consensus that it is humane to apply experimental methods to social policy 

programs. Instead, it is often true that the social policy is applied to all through law. 

Sometimes, a small ‘pilot’ of a program is run, for example the New Deal Pilots in the 

UK at the moment. The administrative and survey data can supply information that might 

roughly give a treated and control group, but without the random assignment of 

experimental design. Non-experimental data methods then need to be applied to the data 

try to approximate the scientific experiment. However, even experimental data can 

require the application of non-experimental methods. Experiments of any type can be 

very difficult to implement once people are involved, and data difficulties arising from 

loss of subject contact and other problems require adjustment through non-experimental 

analysis methods – Heckman, Lalonde and Smith (1999) detail several examples. 

1.2 Brief summary of the theory of wage subsidies 

This section outlines the main rationale for wage subsidy programs, and presents the 

basic conceptual framework within which this is usually analysed. 

Wage subsidies can be general, i.e. covering any and all workers, or specific, i.e. targeted 

to a particular group. Hamermesh (1978) defined 3 types of wage subsidy which differ 

according to what part of the firm’s total employment is covered:

8 

(1) applying to all of a firm’s employees, also called a wage bill subsidy 

(Haveman (1982)); 

(2) applicable only to net changes in the firm’s employment, also termed a marginal stock 

subsidy (Haveman (1982)); 

(3) for new hirings by a firm, which Haveman (1982) labels a recruitment subsidy. 

The first of these is not treated here. Most commonly, wage subsidies are introduced with 

the aim of being of a marginal stock subsidy, but usually it is only practicably possible to 

ensure a subsidy is a recruitment subsidy. In the later analysis of SYETP, it will be 

noticeable that it is a targeted recruitment subsidy. 

Kaldor (1936) gave the early introduction of the use of wage subsidies as a remedy for 

‘chronic’ unemployment by reducing the cost of labour. Micro-economic theory provides 

a framework for the expected effects of wage subsidies on the level and structure of 

unemployment, with partial equilibrium analysis. In general, the labour market is 

assumed to be inefficient in operation, and the subsidy acts against this inefficient 

outcome. 

A wage subsidy paid to the employer is expected to act by stimulating demand for these 

workers, and so raise employment. The wage subsidy is anticipated to act by reducing the 

cost of labour relative to other inputs. In a traditional model, the labour and capital are 

balanced to profit maximise, and the reduction in the cost of labour leads to an incentive 

for a profit-maximising firm to substitute labour for capital. There is then the possibility 

of an additional scale effect, where lower costs increase demand via reduced prices that 

stimulate an expansion of the firm’s scale of production, and in turn employment levels. 

A net employment effect then results from the interaction of the substitution and scale 

effects. 

Where the wage subsidy is targeted at a specific group of workers, then labour is split 

between subsidised and unsubsidised workers, and the substitution effect may work only 

so that subsidised workers replace unsubsidised workers. In this case, any net

9 

employment effect could be mainly due to the scale effect. The size of the substitution 

effects depends on the elasticity of demand for labour, both for subsidised and 

unsubsidised labour. The scale effect size depends on the relative cost savings against the 

total production costs. As a result, since these parameters remain unknown, the relative 

size of the substitution and scale effects remain unknown, and so theory is ambiguous as 

to whether the wage subsidy does act to increase employment. 

Johnson (1980) presented the case of a subsidy to the disadvantaged, such as long-term 

unemployed, youths without work experience, or the low-skilled. A key assumption is 

rigid wages facing the unemployed, such as minimum wages, together with a fairly 

inelastic labour supply. The model incorporates unemployment benefit payments, and 

only the employed skilled workers pay taxes to cover the expenditures of the subsidy 

program. Whether the skilled and unskilled workers are complementary or substitutes 

influences the cost-benefits of the subsidy, as well as the replacement ratio. 3 Jackman and 

Layard (1980), and Johnson and Layard (1986) found that if the supply elasticity for the 

targeted subsidy group was higher than that for the taxed group, then there was a welfare 

increase. However, the results are not unambiguous as the particular parameter values 

assumed influence the outcome. If skilled workers and unskilled workers are 

complements rather than substitutes, and unskilled labour is subsidised, there is a Pareto 

improvement. 

The following exposition draws on the description of the wage subsidy model by Dreze 

and Sneessens (1997), but is based generally in the standard microeconomic framework. 

They set up the model to allow for the labour market to involve a minimum wage, and to 

involve market segmentation where low-skill labour operates in a different market facing 

different wages. The wage subsidy is introduced for low-skilled labour only, and as such 

is targeted. This model is appealing as it more closely approximates the situation of the 

SYETP operation in Australia, with a minimum wage system and where the subsidy was 

targeted to unemployed youths and was for low-level occupations only (For a full 

description of the SYETP operation see Chapter 2 Australian Review). 

3 The ratio of unemployment benefit to the wage rate.

10 

The labour market for low-skill labour is shown in Figure 1.1. Labour demand is LD, 

while Labour supply is LS, which in a competitive market might give the wage and 

employment outcome at A (w*, L*). The minimum wage is set to w + , which gives a 

market outcome for the demand for labour at B, with employment at L + . The effective 

labour supply corresponds to D, with unemployment the difference (L # -L + ). By offering a 

wage subsidy to employers of the amount, (w + -w*), the firms raise their labour demand 

to LD2. The outcome is then drawn to point E with the wage w + equivalent to the 

minimum wage and employment L*. Unemployment is reduced and employment is 

raised. 

Dreze and Sneessens (1997) elaborate the model by describing a ‘ladder effect’ linking 

the skilled and unskilled labour markets, whereby if higher skilled wages lead to lower 

employment at the next higher skill level then the unemployed from the skilled market 

can become part of the labour supply in the low-skilled market. The labour demand then 

has skill-substitution effects, where unskilled wages are relative to other wages. Wage 

subsidy programs can then raise the employment of the person, simply by reducing the 

costs to an employer of hiring that particular person. 

Katz (1996) p7 also discusses the partial equilibrium dynamics of wage subsidies in this 

type of model shown in Figure 1.1. In the Katz model, the labour market is for low wage 

rather than low skill workers. This is also true for the Phelps (1994, 1997) subsidy. Katz 

additionally considers an infinitely elastic effective supply of labour, such as when there 

is structural unemployment, which would make the LS curve in Figure 1.1 horizontal. In 

this case, the wage subsidy does not affect wages but expands employment in proportion 

to the elasticity of labour demand for the low-skill (or low wage) workers. The effective 

wage elasticities of both labour demand and supply are key to the effects of the subsidy. 

However Katz (1996) also points out that in practice, design issues, administration, 

employer take-up, can all affect the practical effect of the subsidy.

11 

Real Wage 

Employment 

Figure 1.1: Labour market equilibrium with wage subsidy 

Dreze and Sneessens (1997) Figure 8.5 p268. 

Layard (1991, 1997) links the wage subsidy to the concept of employability within a 

model of the wage-price spiral. This is often considered a useful general model as it links 

the concepts of the NAIRU (Natural rate, or Non-accelerating inflation rate of 

unemployment) and the Phillips Curve linking inflation and unemployment, to policy for 

reducing unemployment. This gives a basic macroeconomic framework for the analysis 

of the effects of the subsidy on economic variables and processes that influence aggregate 

employment and unemployment rates. 

In defining the notion of employability, there is a mixture of job search and skills, and 

employability is linked to the capacity of a person to fill a vacancy: 

“Near one end is A: a skilled worker who is willing to take any job and 

searches every day. Near the other end is B: unskilled worker with an 

excessive reservation wage who only samples the job market once a month. If 

there are vacancies, A will probably be hired soon and B after a longer spell 

of unemployment.” (Layard (1997): 340)

12 

The labour demand in a period then results in 

(1) H=f(V , cU) with (f 1 , f 2 >0) 

Where H is the total number of unemployed hired in a given period, V is the number of 

vacancies, U the number of unemployed, f is a function and f 1 , f 2 are the first derivatives, 

and c is a weight that is the average employability of all unemployed people, with c i the 

employability of an individual. It is assumed that log prices (p) are a mark-up on 

expected log wages (w e ), giving a simple formulation of 

(2) p – w e = β 0 

Log wages are also assumed to be mark-up on expected log prices (p e ), with the 

mark-up affected by inflationary pressures indicated by (ø) so that 

(3) w - p e = y 0 + ø 

Substituting prices for expected prices from equation 2, assuming inflation is stable: 

(4) w - w e = β 0 + y 0 + ø 

In this model a random walk for price inflation means inflation is stable when wages are 

equal to expected wages (w = w e ) . 

The model is furthered by suggesting that evidence indicates that inflationary pressure 

increases with the chances of finding work for an unemployed person of given 

employability (Layard (1997): 340). Employability is then introduced via the probability 

of finding work of an unemployed person of a given employability (H/cU), in place of ø : 

(5) w - w e = β 0 + y 0 + y 1 (H/cU) 

The target real wage increases with the rate the unemployed find employment. Assuming 

unemployment is constant, in equilibrium, so that hires (H) equal separations (sN), where 

s is the separation rate, and N is employment, then wages equal expected wages and the 

real wage depends on 

(6) w = w e = β 0 + y 0 + y 1 (sN/cU) 

Thus Layard concludes that for a given inflation path, unemployment is inversely 

proportional to average employability of the unemployed. It is then assumed that there

13 

are two types of unemployment, short term and long-term unemployment, and long term 

unemployment results in people who are less employable, c L < c S and 

(7) c S U S + c L U L =constant . 

It is assumed that the wage subsidy raises the employability of the long-term unemployed, 

on whom it is targeted. The wage cost to the employer for employing the long term 

unemployed is then lowered by the subsidy, so they employ them. Short-term 

unemployment is unaffected, but long-term unemployment U L is lower by the same 

proportion that the average employability c L is raised. The time factor is the important 

key to this model, as the hiring of unemployed increases in the transition to the next 

equilibrium due to a rise in the hiring rate of long-term unemployed. A wage subsidy 

provides the link to employability, as the long term unemployed person who was targeted 

moves directly into the vacancy, albeit a subsidised employment. The subsidised 

employment raises the employability of the long-term unemployed, after which they 

either retain the job after the end of the subsidy or gain another job. 

Lewis (1963) introduced the idea of accounting for indirect effects in general equilibrium 

analysis of the macroeconomy. A number of indirect effects on wages, regular labour 

demand and the effective labour force can occur as a result of the subsidy. These need to 

be taken into account in establishing the macroeconomic net effect of the subsidy. These 

can reinforce or counteract the direct effects of the subsidy. 

The most commonly discussed indirect effects are deadweight losses, substitution effects 

and displacement effects. Calmfors (1994) p17 defines the substitution effect as “…the 

extent to which jobs created for a certain category of workers simply replace jobs for 

other categories, because relative wage costs are changed…”. This is most likely to occur 

if the subsidy is targeted on a subgroup. De La Dehesa (1997) p355 in providing a 

critique to the Layard model pointed out that substitution could occur where low wage 

jobs are switched into the subsidy scheme and away from normal employment, and thus 

making the subsidy mostly deadweight. Deadweight loss occurs when the outcome of the 

program is no different from what would have happened without the program. 

Deadweight here occurs when the subsidy is paid for those who would have been

14 

employed anyway. The displacement effect of a program occurs if it crowds-out regular 

employment. Usually, displacement is defined in reference to the fall in output for firms 

without subsidised workers where the subsidy leads to increased output for firms with 

subsidised workers. 

Millard and Mortenson (1997) develop a modified version of job creation and destruction. 

There is a two sided matching process where workers search and employers hire, but 

there is friction in the matching process and wages are determined by bargaining, and 

they simulate the effects of untargeted wages subsidies. In this framework, a small hiring 

subsidy is found to reduce equilibrium unemployment only if there are zero redundancy 

payments. It is plausible that this is practically true in a low wage or low-skill labour 

market, although this is not discussed. However, in order to examine outcomes in the 

model, it is calibrated with estimates of key parameters, and the effects of empirical mismeasurement 

are not trivial to the model outcomes. 

Snower (1994) describes the wage subsidy for his model as a ‘benefit-transfer’. This is 

due to his condition that the subsidy amount is linked to the amount of unemployment 

benefit the unemployed person would have received instead of the employment subsidy. 

The subsidy is again argued to overcome labour market inefficiencies, due to benefit 

systems, efficiency wages, insider-outsider processes or unions. Snower (1994) devises 

the subsidy value to depends positively on the length of unemployment and the amount of 

training the firm provides in the subsidy placement. This design feature is added to 

mitigate displacement and deadweight, based on the low employment probabilities faced 

by long-term unemployed and lower lay-off chance for trained employees in a firm. 

The model Snower (1994) puts forward focuses on the profit-maximizing firm. There are 

a fixed number of firms, producing output using labour input (L), with a revenue function 

that is a positive function of L, and faces constant firing cost (f): 

(8) R= aL – (½)cL 2 

Each firm hires entrants L e , and all workers have mortality rate σ set equal to the birth 

rate, and entrants become incumbents L i after one period of employment at rate (1-σ).

15 

Real wages are predetermined when firms consider employment. Incumbents get the 

market wage w*. Entrants Le have no market power and no disutility of work, so the 

wage received is equal to the exogenous unemployment benefit level (b). The subsidy 

amount (v) means the firms pay the entrant (b-v). The difference in marginal revenue and 

marginal costs for zero time discount factor is 

(9) (a-cL-w*) / σ 

The entrants are hired until the present value of the difference is zero 

(10) [(a-cL) – b +v] +(1-σ) (a-cL-w*) / σ = 0 

The incumbents’ market wage w* is set to the present value of their difference in 

marginal revenue and marginal costs plus the positive constant firing cost (f): 

(11) (a-cL-w*) / σ = - f 

The equilibrium level of employment is then 

(12) L* = (1/c)(a-b+ v- (1-σ) f ) 

The subsidy constraint is then introduced to close the model, and it ensures that the value 

of the subsidy (v L* e ) is no greater than the saving on unemployment benefits, which are 

a function of the change in employment due to the subsidy (b∆L ) where ∆L is the 

difference in equilibrium employment without the voucher L - and with the voucher L* 

( b∆L=L*- L - ): 

(13) subsidy constraint v L* e = vσL* ≤ b∆L 

Figure 1.2 shows the Snower (1994) subsidy constraint with the curve representing 

equilibrium employment (LE), in a model of the subsidy v and employment L. The 

employment gain of the subsidy (∆L) is maximized at v*, where L - < b/ c σ , that is 

where the subsidy constraint curve is steeper than equilibrium employment (LE). 

The (1994) model is then expanded to incorporate deadweight and displacement effects, 

the replacement ratio of benefits to the average wage, and subsidies are restricted to the 

long-term unemployed. There is then a new subsidy constraint that only a fraction of the 

unemployment benefits of the long-term unemployed should be reflected in the value of 

the subsidy, and that the share depend inversely on the deadweight and displacement

16 

coefficients in the model. As before the elasticity of labour demand and labour supply, 

but now also the replacement ratio, will affect the effectiveness of the employment gain 

due to the subsidy. 

V 

v* 

∆L 

L - 

b/c σ 

L 

Figure 1.2 Employment effect of the subsidy, 

Snower (1994) Figure 1 p67 

All of these theoretical models provide evidence but do not offer unequivocal proof that 

the wage subsidy can increase employment, as they retain unknown parameters in their 

final solutions. The ambiguity that these empirically unknown parameters introduce then 

leads to the conclusion that there is theoretical potential for employment gains of 

subsidies. This infers the need to empirically assess the employment gains from wage 

subsidies in practice. It is in this context that the importance of empirical analysis of 

wage subsidy programs arises. 

1.3 Introduction to evaluation of programs 

In examining the evidence for whether a wage subsidy program improves the 

employment prospects for the individual, it is necessary to conduct an evaluation. Several 

recent surveys of evaluation and evaluation techniques exist in the economic literature –

17 

Vella (1998), Heckman, Lalonde and Smith (1999), Blundell and Costa Dias (2000), 

Smith (2000). The aim here is to provide a brief introduction to the issues. 

1.3.1 Evaluation of programs 

Evaluation of the program can focus on the microeconomic level, or on the 

macroeconomic level. A microeconomic evaluation can look at the effect of the program 

upon the individual who has participated, such as whether they are in employment. A 

macroeconomic evaluation can examine the effect the program has on the economy as a 

whole, such as employment levels in the economy. In this thesis, only microeconomic 

evaluation is attempted. Substitution effects, and other indirect effects of the program, 

cannot be measured in microeconomic evaluation of program participants but only in a 

macroeconomic evaluation. However, demonstrating in a microeconomic evaluation 

whether the program has improved the employment of the targeted individuals is a 

necessary condition for establishing the potential for macroeconomic effects. 

Bryant (1978) succinctly introduces program evaluation, and the underlying concept that 

programs should be evaluated on the basis of participant outcomes. The principal 

evaluation question is stated as: 

“To what extent are the participants better off… as a result of participation 

than they would have been if there had been no such program.” (Bryant 

(1978): 44). 

In a microeconomic evaluation, participation is equated to the direct outcome of the 

program and non-participation equated to the outcome of the ‘no program’ state 

(Heckman, Lalonde and Smith (1999): 1880). The most difficult problem facing the 

evaluator is the problem of attribution. Experimental design can assist in attributing cause 

and effect, using randomisation of treatment and control status. Bryant (1978) however 

cautions that even in evaluations using experimental designs, there can be restrictions on 

the generality of results and gives examples where this can arise due to the need to obtain 

voluntary cooperation, the artificiality of the treatment in a differently organized system

18 

and the possible impact of repeated interviewing on behaviour. Additionally, there is the 

issue of overlapping social programs, which can affect even experimental program 

evaluations, and make it difficult to quantify the unique contribution of each program. 

In the absence of controlled experimentation, a comparison group is selected comprised 

of persons who are not participants in the program, but who have similar characteristics. 

The gain in employment they make serves as the benchmark against which the progress 

of the participants is compared. This is often termed the matched comparison group 

design. It is argued that because the comparisons are subjected to similar economic 

conditions at the time of the treatment, then any greater gain in employment due to 

participants than the comparisons is due to the program. The problem subsequently, is 

how to select the comparison group, and adjust the treatment and comparison groups in 

order to make them more comparable and so enable attribution of the effect to the 

program. 

1.3.2 Evaluation problem 

Heckman, Lalonde and Smith (1999) p1877 use the phrase ´the evaluation problem´ and 

succinctly describe the underlying difficulty in evaluating programs as the issue of reconstructing 

counterfactuals. To find the effect of the program on the individual, a 

comparison must be made between the observed employment for that treated individual 

and the outcome that would have occurred if that person had not participated. Yet what is 

observed is the outcome for participants and the outcome for non-participants. Inference 

about the impact of the program involves speculation about outcomes had they not 

received treatment. The counterfactual of what would have occurred if that person had 

not participated cannot be observed. Instead, all methods used for evaluation attempt to 

infer an estimate of the counterfactual from the observed data, and then use this to find 

the program effect. Hujer and Caliendo (2000) p9 describe the theoretical framework of 

this approach as the potential outcome approach, most often attributed to Rubin (1974). 

Early authors associated with this model are Fisher (1951), Roy (1951), and Quandt 

(1972) (Heckman, Ichimura and Todd (1997): 608). Identifying assumptions, which can 

differ for each method, are introduced to help identify the causal effect of the program.

19 

Each individual has two potential outcomes, Y t treatment in the program and Y c no 

treatment, i.e. they are in the comparison group. Actual participation can be denoted with 

D, with values D=1 in treatment, or D=0 no treatment. The difference between potential 

outcomes defines the treatment effect, ∆: 

(14) ∆ = Y t - Y c 

The observed outcome for each individual is: 

(15) Y= DY t + (1-D) Y c 

As noted, Y t and Y c can never be observed simultaneously. In equation (14), the 

unobserved component is then termed the counterfactual outcome. In examining 

individuals there is an underlying assumption of Stable Unit Treatment Value (SUTVA) 

(Rubin (1986): 961). This infers that the treatment effect ∆ on each individual is 

independent of the treatment of other individuals. SUTVA assumes that the outcome for 

any exposure to the treatment is the same regardless of the mechanism of assignment to 

the treatment. Rubin (1986) p961 notes that if there exist unrepresented versions of the 

treatment, so that the outcome might depend on which version of treatment received, then 

SUTVA is violated. The population has an average gain from treatment, usually termed 

average treatment effect on the treated: 

(16) E ( ∆ | D = 1 ) = E (Y t | D=1 ) - E (Y c | D=1 ) 

The second term in equation (16) is the unobservable component for the treated. To allow 

the mean outcome of non-participants to proxy for the counterfactual of participants the 

following assumption must be invoked: 

(17) E (Y c | D=1 ) = E (Y c | D=0 )

20 

This identifying assumption is valid if there is randomised assignment of individuals into 

treatment and controls. Randomised assignment can ensure that the potential outcomes 

are independent ( ╨ ) of the assignment to the treatment: 

(18) Y t , Y c ╨ D 

With conditional independence, or ignorable assignment (Rubin (1974)), then it is true 

that: 

(19) E (Y c | D=1 ) = E (Y c | D=0 ) and E (Y t | D=1 ) = E (Y t | D=0 ) 

And so the estimate of the causal effect of treatment can be estimated without bias while 

using the mean outcome of non-participants to proxy for the counterfactual of 

participants. 

1.3.3 Selection 

The economic model of the effect of the program on employment can be described in two 

parts, participation in the program and the process of job entry. In summarizing the 

process of these two parts, both observable and unobservable influences may play a role. 

The main evaluation interest is whether the program influences job entry. Heckman and 

Hotz (1989) pointed out that selection might occur on observables or unobservables. If 

some observable or unobservable component that leads to program participation also has 

a role in job entry then selection bias can arise. In this case, the assumption in equation 

(19) does not hold, and instead 

(20) E (Y c | D=1 ) ≠ E (Y c | D=0 ). 

As a result, the use of non-participants as a comparison group in an evaluation of the 

program is subject to selection bias. 

An evaluation of the program effect must account for selection. A notable exception 

might be where an experiment could assign eligible people randomly to program 

treatment or no treatment and also measure all observable influences upon outcomes. In

21 

this case, if there was no data loss such as due to loss of contact or other confounding 

problems, then the experiment might avoid the selection problem. Random assignment 

creates a control group that comprises individuals, which within sampling variation 

should have identical distributions of observable and unobservable characteristics to 

those in the treatment group. Random participation can thus defeat the potential for 

selection bias. In the absence of this, non-experimental methods for evaluation should be 

used. However, data problems of non-response, or treatment differing from assigned 

treatment, can re-introduce selection problems into even experimental data. For example 

Heckman et al. (1999) pp1905 -1914 discuss the data problems of economic program 

experimental data, such as sample attrition. Fay (1996) Table 4a p47 also lists some 

problems of experiments as randomisation bias, sample contamination, treatment 

contamination, site self-selection, substitution bias, crossover bias, and program entry 

effects. 

Heckman et al. (1999) examine in detail the form of evaluation counterfactual estimated 

using various evaluation methods. Both the Heckman selection model and matching 

methods produce the parameter corresponding to the mean effect of treatment on the 

treated, which Heckman et al. (1999) derive fully. Hujer and Caliendo (2000) p10 note 

this parameter answers the question “what is the expected outcome gain to individuals 

who received treatment, to the hypothetical situation they had not received it?”. Heckman 

et al. (1999) and Heckman et al. (1997, 1998) point out that this question focuses directly 

on actual participants, and can help decide whether the program is a success. However, 

the identifying assumptions used to estimate the unobserved counterfactual term from the 

observed non-participant outcomes, that are required for the Heckman selection model, 

differ from those required for matching to produce a valid estimation of the mean effect 

of treatment on the treated. Each of these modelling methods and their assumptions is 

treated in detail in the later chapters of this research.

22 

1.3.4 Observables and unobservables 

Friedlander et al. (1997) express succinctly the difference between selection on 

observables and selection on unobservables. The behaviour of the participants is 

expressed in the econometric model: 

(21) Y it = c t X i + b t D io + u it t>0 employment 

(22) D io = a 0 Z i + e io participation 

Again, Y it is the outcome, such as employment, for the ith person in period t , where t=0 

is the period where the treatment occurs. The X i and Zi are sets of exogenous 

characteristics for the ith individual, that can be overlapping and are measured pre – 

program. The importance of measuring pre-program is to avoid endogeneity, where the 

characteristics may change after program entry due to the program, these would confound 

measurement of the program effect. D io is the program participation binary variable, D io 

=1 if participant and D io =0 if not (the comparison group); u it, e io are error terms. The 

mean effect of the program in period t after participation is the coefficient b t . If there is 

no correlation between the participation and the error in the employment equation 

E(D io , u it ) =0, then an unbiased estimate of the coefficient b t is obtained simply from the 

employment equation (21). 

Correlations between the participation and the employment error E(D io , u it ) ≠ 0, can 

arise from selection due to observables or unobservables (Friedlander et al. (1997), 

Heckman and Robb (1985), Heckman and Hotz (1989)). The correlation between the 

participation and the employment error E(D io , u it ) ≠0 can arise through either Z i or e io, in 

the equation (22) representing participation . If the error terms for employment and 

participation are uncorrelated E(u it, e io ) = 0 , but there is correlation between the error of 

the employment equation and the characteristics affecting participation E( Z i ,u it ) ≠ 0 then 

selection is on observables. Conversely, when there is selection on unobservables, the 

error terms for employment and participation are correlated E(u it, e io ) ≠ 0 but there is no

23 

correlation for the error of the employment equation and the characteristics affecting 

participation E( Z i ,u it ) = 0. 

The Heckman selection model assumes selection on unobservables and attempts to 

control for that, while propensity score matching assumes selection on observables. In the 

later chapters, these modelling methods and their assumptions are presented and applied. 

1.4 Brief comment on recent overseas evidence of wage subsidy evaluations 

This section reviews some existing information for wage subsidy programs in other 

countries. This appraisal is limited to recent evidence since the 1990’s, as older material 

is well reviewed elsewhere. Only micro-economic evaluation evidence is addressed. The 

main aim is to consolidate a general perspective of the wage subsidy evidence found 

overseas. The exposition is then not comprehensive with regard to the details of the 

programs or a critical review of the evaluation evidence. Instead, key themes are 

identified. 

A number of published reviews provide recent overviews of wage subsidies and other 

programs. To avoid repetition, their conclusions are summarized here. 

Katz (1996) pp31-33 and Table 4 found that some targeted wage subsidies gave positive 

gains. It was found that the US Targeted Jobs Tax Credit and YIEPP private sector wage 

subsidy, might have modestly raised disadvantaged youth employment rates. However it 

was concluded that there was little satisfactory formal evidence of the impacts of wage 

subsidies, and that many studies were not compelling in their evidence due to flawed 

evaluation methods or poor data. 

Friedlander, Greenberg and Robins (1997) concentrate on US programs targeted on the 

disadvantaged, including wage subsidies. However the focus of the literature review was 

to get an overview of whether, as a whole, any social programs had gains for any groups 

of men, women and youths. The main recent wage subsidy program in the US has been 

the JTPA-II-A subsidised on-the-job training, for which a random assignment experiment

24 

was carried out. In this respect, they found that women appeared to consistently have had 

gains, while there were no youth gains and for men there was great variation and 

uncertainty of the presence of gains. 

Heckman, Lalonde and Smith (1999) noted that the major evaluations in the US have 

often focused on earnings rather than employment gains from programs. They concluded 

that the evidence from both the North American and European studies indicated only a 

modest gain in the probability of employment. However, it was found that many nonexperimental 

evaluations were lacking in their exploration of methodological issues, such 

as the appropriate choice of evaluation method. For both Friedlander et al. (1997) and 

Heckman et al. (1999) the overview encompassed an immense variety of programs, time 

periods and methods. As a result the conclusions are not specific, but instead they provide 

an impression of the uncertainty that still remains as to the empirical effectiveness of 

programs in achieving gains. 

Fay (1996) conducted a review of OECD active labour market program evidence, 

including wage subsidies to the private sector. Subsidies were concluded to be useful for 

long-term unemployed or women re-entrants, with this drawn mostly from the US JTPA- 

II-A experimental evidence. It was found impossible to harmonise the results for the 

different outcome measures and means of achieving the evaluations and it was 

commented that robust evaluation results were scarce. In particular they recommended it 

was important to make non-experimental evaluations more rigorous, and consideration 

made to testing alternative model specifications, together with greater data collection 

(Fay (1996): 33). 

Marx (2001) reviews targeted employment/wage subsidies, for OECD countries mostly 

in Europe but also Australia. The conclusion is that little evidence exists for a beneficial 

effect on employment prospects, with a variety of negative impacts, limited impact size, 

and some few positive results.

25 

Table 1.3 collects together some recent European employment effects found in evaluation 

of wage subsidies. The direction and significance of the estimated effect of the program 

is presented only, as the scope of this limited overview precludes reference to the details 

of the various programs, their targeting, and the empirical methods employed. As for the 

reviews already summarized, there is variation in the effects even when the size of the 

effect is not considered. Some are not statistically significant, some are negative and 

others are positive. Once more, the uncertainty remains of whether wage subsidies 

provide employment gains empirically. 

However, it is unlikely that any overview or literature review can account for the 

extraordinary amount of variation that could be the source of these varying evaluation 

results. A meta-analysis might generate some conclusions. Meta-analysis, in particular 

meta-regression analysis, has developed in the past two decades into one of the most 

important instruments for the synthesis of quantified evaluation findings. In metaanalysis, 

the dependent variable is a standardized measure of effect estimated in each of a 

set of studies that is representative of a field of inquiry. This dependent variable may then 

be used in two very different ways. One approach, which is dominant in medicine and 

psychology, focuses chiefly upon the derivation of an appropriately weighted average 

effect from the set of studies. Meta-analysis, in this approach, is employed to extract a 

superior estimate of what is regarded as the true effect. The other approach, which is 

more appropriate to economic evaluations, is primarily concerned with explaining the 

nature and sources of the between-studies variation in the estimates. This variation is 

regarded as representing genuine heterogeneity in impacts. This would be the more 

appropriate analysis for generating conclusions in this context. Examples are Stanley and 

Jarrell (1998), who examined estimates of the gender pay gap in the USA, and 

Ashenfelter et al. (1999) who examined rates of return to schooling from several 

industrialised countries. Although a useful proposal for future research, a meta-analysis is 

however not within the range of this research. 

However, as the economic environment within a country can set up a unique context, the 

theory of wage subsidies does not predict that wage subsidies will work in all these

26 

environments. As the former section showed, the ambiguity in the theory arises from 

whether within a given economic context, labour demand and supply elasticities, 

reservation wages and subsidy amount, a gain to employment eventuates. In this sense, 

every program needs to be assessed, and reassessed over time, in order to discover under 

what environment a gain can be found. This takes into account the dynamic aspects of 

programs, as microeconomic evaluation can hinder the appreciation of this characteristic. 

This is because the static evaluation process loses the sense of the variation in conditions 

that is naturally part of the macroeconomic setting. 

In conclusion, two strong themes are identified. Firstly, the non-experimental evidence is 

not sufficiently thorough in methodology. The key suggestion seems to be that there is 

insufficient testing of alternative methods, assumptions and model specifications. 

Secondly, there remains empirical ambiguity as to whether wage subsidies provide 

employment gains. Some of this uncertainty stems from the inadequate non-experimental 

evaluation evidence, so that the employment outcomes attributed to the wage subsidy 

cannot be deemed well established. As well, not all evaluations have found positive gains 

to employment following wage subsidies, although this to some extent is again due to the 

caveats pertinent to the quality of the non-experimental evaluation methods. 

In the following analysis, these themes are addressed via re-analysis of the Australian 

wage subsidy Special Youth Employment and Training Program (SYETP). The chief 

goal is to submit a further contribution as to whether this wage subsidy gave employment 

gains. Alongside this, by exploring alternative specifications and methods, the subsidiary 

aim is to more rigorously extend the non-experimental evaluation of SYETP.

27 

Table 1.3 Brief overview of recent European wage subsidy evidence considered 

Author Country Program 

period/data 

Method 

Wage subsidy employment 

outcome 

Bonjour et al. 

(2001) 

Britain 1998 Propensity score 

matching 

+ wage subsidy (Employment 

option) relative to other 

Bonnal et al. 

(1994) 

France 1986-88 Parametric 

Econometric 

modelling 

Breen (1991) Ireland 1982-88 Parametric 

Econometric 

modelling 

Cockx et al. 

(1996) 

Eichler and 

Lechner (1998) 

Gerfin and 

Lechner (2000) 

Harkman and 

Johansen 

(2000) 

Lalive et al. 

(2000) 

Belgium 1991-93 Parametric 

Econometric 

modelling 

East Germany 

(Sachsen- 

Anhalt) 

1991-97 Propensity score 

matching 

Switzerland 1997-98 Propensity score 

matching 

Sweden 1996 Parametric 

Econometric 

modelling 

Switzerland 1997-99 Parametric 

Econometric 

modelling 

Larsson (2000) Sweden 1991-97 Propensity score 

matching 

O’Connell and Ireland 1992 Parametric 

Mcginnity 

Econometric 

(1997) 

modelling 

Rosholm (1998) Denmark 1983-90 Parametric 

Econometric 

modelling 

options in NDYP. 

Used in most programs with 

training, not separately 

evaluated 

Used in temporary 

employment programs, not 

separately evaluated 

+ if combined with training 

+ emp (neg unemp) at 1yr 

+ emp 

+ / n.s. (emp) at 1yr 

+ females emp. transition 

n.s. males 

+ YP v. LMT at 1yr (emp and 

earnings) 

+ emp 

+ earnings/wage 

n.s priv.sector males 16-24y, 

NEG females 16-24y 

Sianesi (2001, Sweden 1994-99 Propensity score + emp 

2002) 

matching 

Notes: n.s.=not statistically significant;+ = positive effect or coefficient, neg=negative effect or coefficient 

emp=employment;

28 

2: Australian literature review 

This analysis focuses on wage subsidies, and in particular the Australian SYETP program. 

Although there have been a few past reviews, these have proceeded with the general aim 

of summarising labour market programs in Australia from various historical economic or 

socio-political perspectives [Edwards (1987), Ross (1988), Stretton and Chapman (1990), 

Webster (1998) Harris (2001)], including governmental reviews [OECD (2001), BLMR 

(June 1984)]. Some of these have also tried to assess the effectiveness of the programs by 

canvassing the evaluation evidence to date. The focus here is on extracting information 

about wage subsidies only and in particular SYETP. 

The chief intention is to summarise the results and critically assess the micro-evaluation 

evidence. Some effort is made to retain detail of the data, methods and variables used to 

achieve the evaluation results, as these can have some bearing on the outcome and can 

help place our results amongst the evidence to date. The empirical literature covered is 

limited to microeconomic evaluations. These evaluate programs by looking at the effect 

on the participant and can establish whether the program changes the outcome for the 

selected individuals after the program has ended, mainly examining employment. An 

important aspect of evaluations of this type is that they try to establish whether the 

participant would have a job anyway, and try to assess this using information about what 

happens after the program has ended. The brevity of the evaluation review reflects the 

paucity of formal evaluation in the Australian literature. 

Empirical evidence for Australian wage subsidies is treated in several parts. Only recent 

work completed during or after 1980 is included, as this corresponds to the period of 

SYETP analysed later, but also because this is when published evaluations first occurred 

in Australia. Australian wage subsidies other than SYETP are firstly briefly treated. 

Because of the broad extent of SYETP, there were few other subsidies and this section is

29 

necessarily brief. Then, the evidence for SYETP is reviewed. Finally, the past evaluation 

evidence relating to SYETP is critically appraised. 

As a result of the critical review of past evaluation attempts of SYETP, the later chapters 

present new evidence for SYETP which attempt to account for the imperfections 

identified in past evidence. This is then new evidence for the Australian wage subsidy 

program, SYETP. 

In an effort to maintain clarity of exposition, the theory associated with the econometric 

techniques applied is not in the literature review. Instead, each method is expounded 

within each later study where they are applied. As a result, propensity score matching 

methods for example, are discussed in study 2 where they are first applied.

30 

2.1 Review of Australian wage subsidy evaluation evidence 

The brief overseas review in the first chapter showed relatively few evaluations of wage 

subsidies exist, and empirical evidence for wage subsidies is not well founded. As this 

section will show, wage subsidy programs in Australia also have little publicly available 

evaluation evidence. Microeconomic evaluation is selected, using individual data, 

addressing the issue of whether the subsidy leads to better employment experience for 

participants after the program ends. The evidence is limited to published items that can be 

accessed by the public. 

Only recent evidence is compiled, corresponding approximately to the period starting 

with the 1980’s and onward, since this is the period of the later analysis. However it 

becomes evident in reviewing the literature that in fact most programs only started within 

the 1970’s, and publicly available evaluation work occurred mostly in the mid-1980’s 

and l990’s. Paterson (1982) in discussing evaluation activities in Australia pointed out 

that “…In Australia government involvement in the area of manpower programs is a 

relatively new phenomenon. Experience with manpower program evaluation is therefore 

limited.” (Paterson (1982): 1). The timing of this statement marked the general beginning 

of Australian evaluation activities and publications. 

Wage subsidies form only a part of the array of labour market programs available, and so 

evaluation of these in turn forms only a small part of the existing Australian evaluation 

literature available. Evaluation evidence for other program types such as training for 

Australia can be found generally reviewed in Webster (1998, 1997a). 

Although subsidised apprenticeships were available, for example CRAFT 

(Commonwealth Rebate for Apprenticeship Full-time Training), these are not dealt with 

as they are deemed to be mostly a training subsidy. 4 Also not covered are programs with 

4 In support of this, in references such as Kesteven (1987) and Hoy (1983) they are categorized as 

education programs. The design did allow for a small amount of on-the-job training. For example Merrilees 

(1984) refers to wage subsidies in CRAFT for this reason, however the grand focus was apprenticeship 

training.

31 

limited applications, such as for special needs groups only, for example the disabled or 

Aboriginal and Torres Strait Islanders. This is because the limited application means such 

programs were not reasonably comparable i.e. a wage subsidy targeted to a reasonable 

extent of the population as the SYETP was. Because so few wage subsidy evaluations 

exist, some information about the existence of other wage subsidy programs in Australia 

is also provided even if no evaluation evidence could be found for them. 

SYETP is covered separately in the next section. However, SYETP was the first 

widespread wage subsidy program applied in Australia and so also the first wage subsidy 

program in Australia for which evaluation attempts were made. SYETP was not the only 

employment based program of ‘work experience’ available that was subsidy based, in 

1983. Other, usually brief programmes, have also operated, sometimes alongside SYETP. 

SYETP was however by far the largest subsidy, making up 69.2 cent of all new 

programme placements (flow only) in 1980/81 (BLMR (1983) p16, table 3.1, final 

column). More recently, other subsidy programmes have been evaluated and the details 

of these are now presented. 

2.1.1 Adult Wage Subsidy Scheme 

The list of labour market programmes compiled by Routley (1984) describes the Adult 

Wage Subsidy Scheme (AWSS), which was introduced during 1983. This programme 

briefly coexisted with SYETP within the National Employment and Training System 

(NEAT), an active labour market programme combining training and wage subsidies. 

Chapman (1985) p101 points out that AWSS was introduced by the outgoing Fraser 

Liberal government in response to the Australian recession starting after 1981, and 

initially retained by the incoming Hawke Labour government. Subsidies in amounts of up 

to $125 per week, for an up to 52 week period, were available to long term unemployed, 

further defined as “…adults who have been unemployed for a lengthy period” (Routley 

(1984): 3). BLMR (June 1984) p168 indicates that AWSS operated in a similar fashion to 

the extended SYETP in 1983-4, with a 17 week period at $100 per week followed by 

another 17 week period at $75 per week. Those aged over 24 with 8 of the last 12 months 

registered with the CES as unemployed and away from full-time education were eligible.

32 

For those aged over 45 and similarly long-term unemployed, an ‘extended AWSS’ was 

available, at $125 per week for up to 52 weeks. 

Edwards (1987) p86 notes that there were limitations on the proportion of the employer’s 

workforce which could be employed under all/any wage subsidy programmes from the 

Federal government and cites the guidelines to CES 5 as stating that at any one time, a 

single physical location of factory, shop or office with 1-3 staff could have 1 placement; 

while those with 4-7 staff could have 2 placements; for those with 8-100 staff, not more 

than 25 per cent of staff could be placements; and for those with more than 100 staff not 

more than 10 per cent of staff could be placements. This would have limited both AWSS 

and SYETP placements. Routley (1984) indicates that in the first four months of 

operation in 1983, 1,642 of the eligible target group were approved for placements. 

Approvals then rose to 15,353 over 12 months in 1983/4 and then 14,388 in 1984/5 

(Edwards (1987) p88 Table 7.1, sourced from DEIR (1985) Annual Report figures). Ross 

(1988) p48 shows that expenditure on AWSS rose with the increase in placements: for 

the corresponding initial 4 month period to June 30 1982/3 the expenditure was 

$0.4million in current prices, while in 1983/4 it rose to $23.4 million, and 1984/5 to 

$35.1million, before $25.4 million in the part of the final year of operation to December 

1985. 

An evaluation report was due to be made for AWSS in 1985 (Routley (1984)). However 

this never achieved publication. Ross (1988) p48 Charts 1 and 2 and appendix Table of 

Acronyms , noted that having started in March 1983, in December 1985 the programme 

had already ended by being subsumed into Jobstart. An evaluation for AWSS is not 

separately listed amongst the BLMR evaluations listed in Mckay and Hope (1986) 

Attachment A pp19-21, and it is likely the evaluation was sidelined with the knowledge 

that policy interests had already determined the programme, at least in name, would end. 

Although no general analysis of AWSS was made, Edwards (1987) examined the impact 

for the Wollongong and Newcastle regions of New South Wales using a survey of firms 

5 Department of Employment and Industrial Relation (1984) Adult Wage Subsidy Scheme in CES 

Operating Manual, Volume 6 section 5; Volume 7.

33 

who participated. It was concluded that the AWSS was used mostly by small firms, and 

those firms used it for the recruitment of low wage and lower skilled workers. No 

evidence of churning was found, where employers lay off placements when the subsidy 

expired, as most placements that completed the subsidised period were retained. However, 

it was also found that most placements were at the expense of unsubsidised employment 

of other similar workers. There was no modelling of employment. 

2.1.2 ‘General Training Assistance On-the-Job-Training’ 

The ‘General Training Assistance On-the-Job-Training’ [GTA-OTJ] was also available to 

youths at the same time as SYETP in 1983, but in contrast was only 9.9 per cent of all 

placements. The GTA was a highly flexible programme, which also appears to have been 

highly discretional. This was described as an 'age tiered' wage subsidy to unemployed for 

on-the-job training in ‘occupations in demand’, with a subsidy in 1983 of $50.80 to 

juniors and $69.30 for adult award rates. It was available to all ages for those ‘at risk’, 

with eligible positions based on trade or non-trade skills which were subject to ‘labour 

market demand’ – this was tested on departmental criteria so that CES officers instigated 

a GTA position if ‘no suitably experienced qualified person is available when employers 

lodge vacancies’ (BLMR (1983) p 7 Table 1.3, footnote (c)). The period of assistance 

‘varied according to occupational skill level’. This programme likely best falls into the 

category of ‘special assistance’, as it was not generally available. Officially, it was not 

considered a true wage subsidy. It was pointed out that the primary purpose was training 

workers with past work experience in situations where there were no workers with the 

requisite skills, and it was not considered to be a marginal stock subsidy programme 

(DEYA (1983): 166). However, despite the unusual targeting, it clearly fits within the 

definition of wage subsidy outlined in section 1.2. 

GTA-OTJ was analysed alongside SYETP and other programmes and generally found to 

have a positive impact on post-programme employment. Each of these analyses is treated 

in depth later in considering SYETP evaluation, and so further details and critique of the 

methods can be found there. BLMR (1983) used administrative records for 

commencements flowing onto all government programmes to youths in 1980-81 and

34 

showed employment entry flows for GTA-OTJ, but there was no modelling of 

employment outcomes. Stretton (1982, 1984) found that GTA-OTJ performed better on 

post-programme employment than training in EPUY 6 , but was no different to SYETP. 

Baker (1984) also found that GTA-OTJ performed better than EPUY, and no different to 

SYETP. Before modelling, it was found that 58.9 per cent of GTA were in continuous 

full-time work, made up of 45.4 per cent retained in their placement job and 13.5 per cent 

not retained, with 29.8 per cent having non-continuous full-time work, while the 

estimated probability for continuous full-time employment after the programme for those 

with 17 weeks of unemployment was 0.569 (Baker (1984) p19 Table 5.2, p47 Table A8, 

p48 Table A10). Rao and Jones (1986) estimated post-programme full-time continuous 

employment chances for GTA-OTJ 1981-1983 relative to their quasi-control group as 

being 59.8 per cent for the least disadvantaged and 14.6 per cent for the most 

disadvantaged. This positive employment effect was greater than that found for SYETP. 

2.1.3 Jobstart 

Jobstart began in December 1985, when SYETP and the AWSS ended by being 

subsumed into the new Jobstart programme as all labour market programmes were 

restructured (Ross (1988) p34 footnote 9). The specialist programmes, ‘Special Needs 

Job Sector Subsidy’ and ‘Disabled on-the-job subsidy’, were also merged into Jobstart 

(Kesteven (1987): 45). All aspects of SYETP, including the Commonwealth SYETP, as 

described later in the section about SYETP, turned up as components in Jobstart. At 

inception, Jobstart had two key components, private sector Jobstart and Commonwealth 

Jobstart. The ‘Commonwealth Work Experience programme’ was identical to 

Commonwealth SYETP – it lasted for 17 weeks subsidy, and was limited to 15-24 year 

olds, with the department fully reimbursed for the wage paid to the placement. However, 

in May 1987 the Commonwealth component ceased. After this, Private Sector Jobstart 

became synonymous with the term Jobstart. 

Eligible groups for Jobstart were divided into two: Jobstart basic rates applied to those 

unemployed for 6 of the last 9 months, while Jobstart Special rates applied to those long- 

6 Education Programme for Unemployed Youth; an Australian training program part of NEAT; consisting 

of courses aimed to improve basic literacy, numeracy and social skills.

35 

term unemployed for over 12 of the last 15 months, or the disadvantaged groups listed. 

The designated disadvantaged groups were “...disabled, sole supporting parents, migrants 

with English language difficulties, Aborigines” (Kesteven (1987): 45). The positions 

were required to be full-time, but with exceptions possible for the disadvantaged groups, 

and available for 26 weeks continuous employment. Job vacancies had to be notified with 

the CES, and at least the Award wage 7 was paid to placements. There was a special 

Jobstart self-service board with all wage subsidy jobs displayed on it, and the CES could 

also give a card called, a ‘self-canvassing card’, that could be shown to employers at the 

job interview (Victorian State Office (1985) p7 and DEET (1994) p38). 

Jobstart was described as a general wage subsidy scheme, where employers received 

subsidy for placements where the rate varied with age. Private Sector Jobstart subsidy 

rates for 1985-87 are shown below in Table 2.1. The Jobstart Special rate was higher than 

the basic rate, and rates increased with age. In 1985/86 35,098 placements were approved 

in private sector Jobstart, with expenditure for the year at $18,787 million (Kesteven 

(1987): 46). 

Jobstart was expanded considerably during the Australian recession, but was more 

commonly referred to within the Job Compact package. After 1989, it was part of the Job 

Compact along with the National Training Wage [NTW]. The NTW was a training wage 

but was “...to be supplemented by subsidies to entice employers to provide training” 

(OECD (2001): 198). The Job Compact was part of the Working Nation package of 

initiatives, and centred on an offer of 6-12 months job placement in the private sector, but 

usually 9 months, to all who had been in receipt of benefits for more than 18 months 

(OECD (2001): 198). This eligibility required a longer period of unemployment than in 

1987, and so the targeting of the programme was tightened in this new form, however 

against this, the subsidy length was extended to 12 months. This had strong effects on the 

programme. There were issues of low take-up. Sheen and Trethewey (1991) p38 point 

7 Australia has extensive union wage agreements, which specify a complex system of minimum wages. 

These are termed Award Wages.

36 

out that in 1989-90 there were substantial difficulties in finding placements with private 

sector employers up to the full Budget allocation to the programme. 

During the period to 1996, there was an expansion of job creation programmes rather 

than wage subsidies, which was mostly due to the new case management incentive 

system where the case payment for outcomes was the same for entry to any programme 

placement or unsubsidised job (OECD (2001): 200). It was found that several factors 

meant that the NWO 8 direct job creation programme got most Job Compact placements 

rather than Jobstart: employers were reluctant to take on the long term unemployed and 

considered the 9 month placement too long; dismissal laws meant employers were 

reluctant to take on potentially unsuitable employees (DEETYA (1996): 46). However 

importantly, in 1994/5 there was a strong administrative push to give preference to NTW 

over Jobstart and it was perceived that there was competition between the programmes as 

employers could get a higher subsidy under NWO than Jobstart without having to 

commit to a long period of placement employment (DEETYA (1996): 41-49). Stretton 

and Chapman (1990) p42 described the selection process for Jobstart as follows: “The 

filling of Jobstart vacancies does involve some selection bias as the CES selects those 

eligible clients who are judged to be ‘job ready with assistance´ for referral to Jobstart 

vacancies. Employers then select their subsidised employee from among a number of 

referrals made by the CES.” 

The subsidy varied with age, education and length of unemployment (DEETYA (1996): 

131). In 1995, Jobstart subsidies could run for 6 to 12 months, and subsidies varied by 

unemployment length and were received in tiers so that those 18 months unemployed 

received $200 for the first 13 weeks, followed by $100 per week for the next 26 weeks, 

and a lump sum to employers of $500 after 12 months of continuous employment was 

reached (Piggot and Chapman (1995): 4). In 1994/5 there were 40,200 Jobstart 

placements for Job Compact clients, which was a fall of 25,000 from the previous year 

(DEETYA (1996): 46). Employer survey findings suggested that employers found the 

8 New Work Opportunities (NWO) provided direct job creation in projects where placements had work 

with some training typically in environmental, age care and community sectors (OECD (2001): 199).

37 

importance of the subsidy secondary to the quality and suitability of the worker, and 

references were made to the problems of low skills, poor attitudes to work and low 

motivation of long-term unemployed as perceived by employers (DEETYA (1996): 47). 

In October 1995, Jobstart was adjusted to allow shorter placements and extension of 

eligibility to those 6-12 months unemployed. 

Table 2.1 Private Sector Jobstart subsidy weekly rates 1985-1987 

Age of placement Jobstart Basic rate $ Jobstart Special rate $ 

15-17 50 75 

18-20 75 110 

21-44 100 150 

45+ 125 180 

Source: Kesteven (1987) p45 

A flurry of evaluation evidence started to exist for Jobstart from about the time of this 

programme’s inception, and through the 1990’s. These evaluations were usually by the 

government department responsible for the programme. Most of the more recent studies 

use the matched comparison group design, or exact matching methods. 9 The early 

evaluation DEET (1989) used data on 1000 participants and non-participants, and found 

post-programme continuous full-time employability increased on average by 33 

percentage points at 3,5 and 14 months. 10 

The next analysis of Jobstart was published in 1993. The raw post-programme 

employment share, sourced from the quarterly Post Program Survey, showed that for 

1992 there were 95,600 new placements and 57 per cent of Jobstart participants were in 

unsubsidised employment over the 12 months to September 1992 (DEET (1994) p38 

Table 5.3). This was the highest employment outcome amongst all programmes, with the 

average for all programmes at 32 per cent. Younger jobseekers were more likely to 

receive Jobstart 11 , and men were more likely to get Jobstart placements. 12 SYETP became 

Jobstart, and after 10 years in place it seems likely that SYETP had become a generally 

9 See earlier in Chapter 1 for the introduction to evaluation methods, and Chapter 4.4 for a description of 

matching. 

10 DEET (1989) “Jobstart evaluation” DEET Program Review and Income Support Branch, Canberra. Cited 

in Webster (1998) Table 1 p194. 

11 DEET (1994) p45 and p33 “participation by youth in Jobstart, especially 18-20 years olds was well 

above average placement rates.” 

12 DEET (1994) p33 “32 places to women per 1000 registrants compared to 44 for men”.

38 

accepted part of the labour market, so employers may have continued to use the 

programme as they had done so in the past, under the new name of Jobstart. 

The net impact analysis in 1993 used a survey of a sample of programme participants 

approximately 6 months after programme participation, and matched these on age, sex 

and unemployment duration to a similarly surveyed sample of CES registrants who had 

not been placed in that programme. The mean employment outcomes of the matched 

treated and comparison groups were then compared to create the impact measure. Two 

comparison groups were constructed, of those with no programme experience and of 

those who had received training assistance instead of Jobstart. Jobstart was found to have 

a net impact of 30 percentage points above those who had no programme assistance. It 

was found that Jobstart had a similar impact to other programmes of training assistance 

such as Jobtrain, Skillshare and Jobclub when compared to a comparison group of those 

who had taken part in a training programme and then found employment (DEET (1994): 

42). Relative to training programmes, Jobstart was concluded to be the most successful 

programme for raising post-programme employment. 

In reviewing the 1993 Jobstart evaluation, it is unclear that non-response was accounted 

for in analysing the survey data, but it is presumed that it was not. The method of directmatching 

can only account for the factors used in the matching, in this case only 3 i.e. age, 

sex and unemployment length. Although these might presumably have been considered 

the most important variables, it is likely there were restrictions on the information 

available in the data, and also limits on the number of variables it was possible to match 

on. The general difficulty of direct matching is that it becomes increasingly difficult to 

find matches as the number of factors is increased, so curbing the number of variables 

chosen. Thus there were constraints on the number of variables that could be accounted 

for. The aim of the matching is to control for observed differences in pre-programme 

characteristics between the treated and comparison groups. Matching on these three 

variables would eliminate bias due to non-random selection from other sources only to 

the extent that those other factors are correlated with these 3 variables. Any other 

variables are not controlled for entirely, even if they are observed, and so strong

39 

differences in characteristics that affect the employment outcome can remain. As a result 

it is likely that the matching performed does not fully diminish potential bias arising from 

these other sources. 

Some raw statistics suggesting the positive effect of Jobstart upon employment were 

presented in the Working Nation policy document, however no more systematic 

evaluation evidence was presented. The raw post-programme employment share, derived 

from the quarterly Post Programme Survey [PPM], showed that for 1996 there were 41.1 

per cent of Jobstart participants in unsubsidised employment over the 12 months to 

September 1992 (DEETYA (1996) p53 Table 4.5). This was higher than the employment 

share for the training programmes Jobskills and NWO 13 . The employment effect was not 

evaluated further with any modelling. 

A further evaluation of Jobstart in 1997 14 also used the matching design. Again age, sex 

and unemployment duration were used for matching with other factors such as education 

and location not accounted for, as these were not deemed as important to describing 

employment outcomes. 15 Using administrative data, participants were matched to a 

comparison group of non-participants. Jobstart participants had post-programme 

employment 3 months after the programme of 50 per cent while the matched 

comparisons had 22 per cent, a net impact of 28 percentage points. At 12 months postprogramme, 

the net employment impact had fallen to 15 percentage points. 

Stromback and Dockery (2000) used the panel data 1994-1997 Survey of Employment 

and Unemployment Patterns (SEUP) to examine employment outcomes for programme 

participants, including the Jobstart. The survey collected 3 interviews between September 

1994 and 1997, for a panel of persons aged 15-59 years. Labour market history for each 

month of the past year was collected at each interview by recall, recorded as periods of 

13 New Work Opportunities (NWO). NWO provided direct job creation in projects where placements had 

work with some training typically in environmental, age care and community sectors [OECD (2001): 199]. 

14 DEETYA (1997) “The net impact of labour market programs: improvements in the prospects of those 

assisted” DEETYA Evaluation and Monitoring Branch report 2/97, Economic and Policy Division, 

Canberra. Cited Table 1 p194 Webster (1998) and Richardson (1998) p3. 

15 DEETYA (1997) Appendix B cited Webster (1998) p200 footnote 5.

40 

working, looking for work and absence from the labour market. The survey data was 

linked to administrative records for programme participation, and the PPM survey 

outcomes. Only consenting persons could be linked, and not all linked cases had full 

administrative data or PPM survey outcomes, and all incomplete records were dropped 

for analysis. The matched data showed that raw outcomes for employment 3 months after 

the Jobstart programme were substantially different in the matched PPM outcome and the 

SEUP Labour market history, as shown in Table 2.2. The difference in the employment 

outcomes measured could be partly due to the differing data sources, collection times, 

response rates, recall biases, the shares of incomplete/missing data (as all incomplete 

cases were dropped), and also the share of cases where there was an inability to link data. 

Table 2.2 Stromback and Dockery (2000) Raw employment outcomes after Jobstart 

Matched PPM 

SEUP 

% in Employment 38.4% 51.0% 

after Jobstart 

Known outcomes 318 787 

Source: Stromback and Dockery (2000) p32 Table 8.3 Only includes episodes for which PPM outcomes are 

known. Reported labour market activity is known for all episodes. SEUP calculated as per cent of labour 

market spells, Matched PPM calculated as per cent of spells with known outcomes. 

Exact matching analysis on gender, four age groups and 8 categories of unemployment 

spell durations was used to estimate the net employment impact using the SEUP data. A 

comparison group was constructed of those registered unemployed in the administrative 

data, who were not in the programme at the point the Jobstart participants left the 

programme (note this means they could have entered the programme after this point). The 

employment outcomes 3 months after the programme participation reference period were 

then compared. For Jobstart, the net impact on employment 3 months after the 

programme in 1995 was found to be 11.8 per cent, with 19.3 per cent of Jobstart 

participants in employment and 7.5 per cent of the comparison group. During the later 

survey periods there was a change to the programme rules, which meant that employers 

were required to retain employees at least 3 months after the subsidy period, and because 

this corresponds to the post-programme evaluation period chosen for analysis these later 

periods cannot be usefully considered (OECD (2001): 217).

41 

Matching methods are theoretically based upon the consideration of characteristics prior 

to programme entry, and the definition of the comparison group used here flouts this 

fundamental premise and would bias and generally invalidate these estimates. This is 

because Stromback and Dockery (2000) argue that their comparison group definition 

excludes the ‘lock-in’ period, where placements must be in subsidised employment until 

the end of the subsidy, which the comparison group does not face. They also point out 

that defining the comparison group at programme entry as matching methods require 

would give the comparison group an employment rate nearly 3 times higher, and so 

Jobstart would end up with a negative employment impact. However, this alludes to 

choices made to achieve a desired positive employment impact rather than providing a 

satisfactory reason for choosing this econometric strategy. 

In June 1996 the new coalition government announced reductions for labour market 

programme expenditures, and abolition of several programmes. However there was some 

political confusion and substantial ambiguity about Jobstart expenditures and continuity, 

with various conflicting announcements (OECD (2001): 201 –202). It eventuated that 

Jobstart continued into 1997-98 but as a programme for the disabled, and was then 

abolished in 1998/99. There is no wage subsidy programme that operates in Australia in 

the current period. 

2.1.4 Discussion 

Australian evaluation evidence for wage subsidies is scant. The AWSS was not evaluated. 

The GTA-OTJ was only evaluated alongside SYETP. 16 It could in any case be argued, 

that the relevance of these subsidies is more limited than that of SYETP or Jobstart. Both 

AWSS and GTA-OTJ were very small in scale, with very little expenditure and few 

placements being made to these programmes. GTA-OTJ is very difficult to define clearly, 

as it was so discretionary. However, as GTA-OTJ was only for ‘jobs-in-demand’ it was 

not a general wage subsidy available to all employers. The later Jobstart evaluations 

mostly took place after Jobstart had been changed from a general wage subsidy tiered by 

age to a subsidy programme only for long-term unemployed and disadvantaged or 

16 A critical review of these evaluations is given in the later section 2.3 of this chapter

42 

targeted minority groups. The SYETP in contrast was a more generally targeted flat rate 

wage subsidy programme. 

The later Australian evaluation of the Jobstart wage subsidy used direct matching 

techniques. These methods however require a limited set of characteristics and large data 

sample in order to enable the analysis to proceed. The more characteristics it is desirable 

to match on, the fewer matches become available amongst the treatment and comparison 

groups. As the earlier modelling of SYETP showed, controlling for individual 

characteristics was important, and the subsequent Richardson (1998) evidence indicates 

the breadth of characteristics that might be useful (reviewed in the next section). 

Matching, such as that of Jobstart, which does not control for these other characteristics 

that affect both employment and participation would then be subject to bias. Importantly, 

education of participants and nonparticipants is not accounted for in the Jobstart 

evaluations. As well, for matching to be valid, definition of the characteristics must be 

prior to programme entry, and this was not the case for the comparison group constructed 

for the Stromback and Dockery (2000) Jobstart evaluation using matching. 

The review of other Australian wage subsidy evaluation evidence makes it clear that 

evaluation of the SYETP remains useful. Apart from its later namesake Jobstart, SYETP 

was the largest wage subsidy programme of general application. There is also less useful 

evaluation evidence for other wage subsidy programmes than for SYETP, as can be seen 

in the later review of SYETP evidence.

43 

2.2 SYETP implementation 

As SYETP does not now operate, this review is essentially a historical essay gathering 

together information about the programme and its process. Due to the fact that much of 

the information is contained in government publications and conference papers, some 

sources are difficult to locate, and as such the review provides a valuable resource. 

Considerable effort has been made to associate the context of other aspects of the 

Australian economy that may have influenced the working of SYETP. The policy does 

not operate within a vacuum, and although the theoretical model may outline a set of 

assumptions it is of interest when assessing the policy outcome whether in fact these 

assumptions were realistic. In particular, this is necessary as many models have the same 

outcome, but use different sets of assumptions to set up how the outcome is achieved; or 

various models can achieve different outcomes by altering the assumption set. So this 

context might help define which model best approximated the circumstances under which 

the outcome was achieved. The description of the economic context of labour market 

programme operation is often not clear, or is overlooked in a review, for example – the 

benefit system payments, the wage system, whether it operated in a recession or boom? 

Due to the wide field of influences to the labour market, it is not possible to fully convey 

the economic influences at work in a brief review. However, I have selected what I 

believe to be the most relevant aspects. The information is then reconciled in the 

discussion. 

2.2.1 Historical development of SYETP 

The SYETP was active policy in Australia from late 1976 to December 1985. The history 

and functioning of SYETP have been documented in various sources, and are 

summarised here. The terms of the scheme and operating environment are factors that are 

important to understanding the conditions under which the programme performed. They 

provide the background for the findings of possible impacts of the scheme. The

44 

characteristics of the programme and the labour market environment are the focus of 

discussion, with emphasis on details pertinent to the period of our analysis. 

SYETP was the first wage subsidy scheme to be introduced in Australia (Chapman 

(1985): 101). The scheme started operation in September/October 1976. 17 The stated 

objective was to encourage the employment of young unemployed persons. Over the 

subsequent period to December 1985 when SYETP ended, almost all labour market 

programmes in Australia were targeted at young people. 18 There was also a distinct 

emphasis on private sector involvement in SYETP. The subsidy was given at a flat rate 

for a limited time period. 

SYETP was initially quite closely targeted. However, it gradually evolved broader 

application and was tweaked to vary according to age and unemployment duration. The 

following discussion draws heavily on the historical progression of SYETP found in 

BLMR (1984), Smith (1983, 1984a), Chapman (1985), Hoy (1983), Kesteven (1987), 

Ross (1988), and Stretton and Chapman (1990). 

On initial introduction in October 1976, SYETP was for 15-19 year olds who had left 

school in 1975 (BLMR (1984): 159). Employers received $58 per week for six months. 

The eligibility was rapidly extended by December 1976 to all 15-19 year olds who had 

been unemployed and away from full-time education for at least 6 of the last 12 months. 

The age range was extended to 15-24 year olds who had been unemployed and away 

from full-time education for at least 6 of the last 12 months in July 1977, and the subsidy 

increased to $63. In October 1977, the unemployment criterion was relaxed to those 

unemployed and away from full-time education for at least 4 of the last 12 months, rather 

than 6, and the rate rose slightly to $66. 

17 BLMR (1984) p127 gives September, p159 gives October. 

18 BLMR (1984) p128. There were some exceptions, such as the Regional Employment Development 

Scheme (REDS) which started in September 1974, but was ended in 1975 and fully phased out by the end 

of 1977. This program gave grants for specific labour intensive projects, costs of wages were paid to the 

employer for up to 12 months, and award wages paid to placements. Eligible participants were unemployed 

with preference given to those with dependents and with longer unemployment (Kesteven (1987): 65).

45 

In July1978, the subsidy was calculated to be equivalent to 62 per cent of the average 

junior award pay rate for the applicable ages, as can be seen in Table 2.3. The 

information in Table 2.3 can only be used as a broad guide as award rates and weekly 

earnings in the award system vary by age, gender, occupation and industry. Subsidy 

participants were guaranteed at least the minimum award rate from employers, but could 

be offered more, although this is less likely. As the number of hours worked could vary 

the average weekly wage, the average hourly earnings can be a more useful indicator. 

Windshuttle (1985) p20 also pointed out that males and females faced differing hourly 

rates on average and so the overall average for juniors can be misleading. Windshuttle 

(1985) p21 also argued that in times of growing employment, adults were more likely to 

receive overtime than juniors. In light of this, the junior ratio to the adult average hourly 

earnings for fulltime employees is also shown for the same period, with a breakdown by 

sex as a point of comparison. The issue of the lower rate for juniors relative to adults is 

returned to in later discussion of the economic environment. 

Expansion of the eligibility criteria and subsidy amount were perceived to be the source 

of the noticeable rise in SYETP placements (see later Table 2.6 SYETP annual 

expenditure and placements1976/77-1985/86), but most noticeable to Government was 

that the expenditure on SYETP rose most dramatically. Indeed Table 2.6 shows that 

change, in the year to 78/9 from the previous year, was remarkable for a doubling of 

expenditure but little change in placement numbers. A considerable lag in stock would 

have been evident due to the 26 week placement, so although new placements (flow) did 

not change much the length of the subsidy meant that the stock on SYETP at a point in 

time would rise quite high over the period.

46 

Table 2.3 Average junior award rates 1977 to 1981 

Date 

Average 

junior award 

wage 

$ per week 

Junior males 

Average 

hourly 

earnings for 

fulltime 

employees: 

$ per hour 

Ratio to 

adult male 

Average 

hourly 

earnings for 

fulltime 

employees 

% 

Junior 

females 

Average 

hourly 

earnings for 

fulltime 

employees: 

$ per hour 

Ratio to 

adult female 

Average 

hourly 

earnings for 

fulltime 

employees 

% 

1977 1.1.77 96.08 2.71 55.19 2.66 66.66 

1.7.77 101.46 

1978 1.1.78 105.82 2.89 54.84 2.83 66.24 

1.7.78 108.82 

1979 1.1.79 113.41 3.13 54.15 3.02 66.09 

1.7.79 117.04 

1980 1.1.80 119.97 3.52 54.15 3.38 64.68 

1.7.80 125.96 

1981 1.1.81 134.07 3.72 50.81 3.61 61.50 

1.7.81 144.69 

1982 1.1.82 156.11 4.43 51.73 4.31 62.92 

Source: Column1: Bureau of Labour Market Research (1984) Table 4.14 p 160. 

Column2: Windshuttle (1985) p20 Table 2 and p21 table 3 Average hourly earnings for non-managerial 

fulltime employees in private enterprise, all industry groups, ABS Cat 6304.0 various years to 1983. The 

ratios in the 5 th and 7 th columns have been multiplied by 100 to represent percentages. 

Cutbacks in the subsidy period and amount of subsidy were introduced in August 1978. 

The subsidy period dropped from 6 months to 4 months. The subsidy amount fell to $45 19 , 

a fall from about 45 per cent of the average junior award pay rate for the applicable ages 

paid for 6 months to 30 per cent of the average junior award rate payable for only 4 

months (Ross (1988): 37). As Ross (1988) points out, this was a fall from a total of $1716 

paid to employers for a full subsidy period to only $765, a 55 per cent fall in the total 

subsidy for a full subsidy period. 

In late 1978, ministerial announcements were used to emphasise that SYETP was 

supposed to be used for additional new jobs that would not otherwise have occurred. Also, 

guidelines to this effect were issued to CES offices. 20 It is unlikely this provoked much 

change to operation however. 

19 For commencements after August 1978 the subsidy was $45 (Smith (1984)), [BLMR (1984) Table 4.14 p 

160]. The average junior award rates as estimated in BLMR (1983) and used for calculations are repeated 

in Table 2.3. 

20 Hoy (1983), cited p19 Smith (1984a).

47 

In January 1979, variations were made to the types of employer to whom the subsidy 

could apply. The State and Commonwealth government could now apply for SYETP for 

employees, as well as private sector employers. Special rates applied to the 

Commonwealth government, who received the full wage cost in SYETP subsidy. State 

and private sector employers received the flat subsidy rate. 21 However, in August 1981 

the State governments ceased to be eligible for SYETP subsidies. The States again 

became eligible in 1983. 

In February 1981 ‘extended-SYETP’ was introduced. The eligibility for this required 

longer unemployment, and employers received a doubly long subsidy period of 34 weeks, 

with the subsidy paid at a higher rate. Those unemployed and away from full-time 

education for at least 8 of the last 12 months were eligible for ‘extended-SYETP’, and 

employers got A$80 per week for the first 17 weeks, then A$55 for the next 17 weeks. 

This was equivalent to 60 per cent and then 41 per cent of the average junior award pay 

rate for the applicable ages (BLMR (1984) p160 Table 4.14). The standard rate of 

SYETP was commensurate with the second phase of extended SYETP. 

The SYETP rates were again changed in August 1982. The standard rate of SYETP and 

the second phase of extended-SYETP rose to $75, while the initial phase rate of 

extended-SYETP rose to $100 per week. The procedure for SYETP subsidies was also 

loosened at this time. Whereas before the Commonwealth Employment Service office 

was the prime intermediary, in August 1982 the young unemployed were given the right 

to make direct contact with employers. This was seen as a “liberalization of 

administrative procedures” (Smith (1984a): 20), whereby jobseekers could get a card 

indicating their eligibility for the subsidy, which they could present to prospective 

employers. Aside from this change, it was always a requirement of SYETP that the 

participant was registered with the CES, and the length of registration determined 

21 Sloan (1985) p114 pointed out that any overview of the operation of SYETP, and any labour market 

program, was likely to underestimate the important interaction between State and Federal government roles 

in funding in Australia. This aspect of expenditure can be seen here in the different treatment State and 

Commonwealth employees received at differing times in the history of SYETP.

48 

eligibility for programmes. Eligible youths were referred to employers by the CES and 

administration of the programme was carried out by the CES. 

In 1983, SYETP rates varied by age, with a lower $75 for 15-17 year olds but $100 for 

18-24 year olds. 

In 1984 programme conditions for employers included the development of a training plan 

for the new employee, and the employer had to pay the relevant award wage (Smith 

(1985): 15). In 1984, the standard rate of SYETP and the second phase of extended- 

SYETP remained at $75, and the initial phase rate of extended-SYETP stayed at $100 per 

week. In 1984, this was equivalent to “almost half the median full-time weekly earnings 

of teenagers” (Smith (1984a): 15). 

The SYETP programme ended at the end of December 1985. However, from a practical 

viewpoint all subsidised employment in place would have continued until either the 

placement ended or expiry of the subsidy period. Thus the scheme is considered to be 

completed in practice at the end of September 1986, or between at most 17-34 weeks 

after December 1985. 

2.2.2 An overview of SYETP changes 

The following Table 2.4 allows an overview of the changes to SYETP programme 

definition over time. As a result of the variations introduced the programme became 

increasingly complex. The age and unemployment duration necessary for eligibility 

altered over time, as did the subsidy period and payment. 

While ostensibly a flat rate, SYETP did have some tiers introduced. Tiers can alter the 

target group as it leads to favouring of a particular group through more advantageous 

subsidy, but in practice the effect might not be as expected due to the interaction with 

other labour market features. A key aspect of change to the programme is that at various 

stages, the subsidy rate paid varied by age, as did the unemployment benefit rate paid. 

However, while early on there was no age variation for the subsidy but unemployment

49 

benefits were paid at a slightly higher rate to older ages, later both the age variation in 

unemployment benefit and SYETP subsidy grew to be quite unfavourable for 15-17 year 

olds relative to other ages. Yet this adds to the variation in award rate payments by age, 

sex and occupation/industry so that there was a complex picture of variation in how the 

subsidy comparison worked for 15-24 year olds. In practice the subsidy to 15-19 year 

olds was positive in relation to the proportionately higher share of the award rate it 

covered for this age group. The impact of this is discussed in the evaluation evidence to 

follow where it was found that for subgroups of the eligible target group, the subsidy fell 

particularly favourably with employers leading to a greater intensity of placements for 

these groups. 22 

It becomes clear from Table 2.4 that the SYETP subsidy paid to employers was usually 

higher than the benefit payable to the unemployed participant. 23 When benefits shown in 

Table 2.4 are compared to the average award rate shown in Table 2.3, it can be seen that 

it is likely that participants were better off remunerated under the wages subsidy than 

with benefits, although non-pecuniary related benefits cannot be accounted for in this 

comparison. As well, the age/gender/occupation variation in award rates is not obvious 

from Table 2.3, and these would be important. However these figures can be used to 

roughly indicate underlying reservation wages for this group. 

22 Girls 15-17 years old got SYETP mostly, as these faced the lowest award rates, so the wage discount 

from the subsidy was greatest. 

23 In Australia in 1983, unemployment benefit was income tested on own income, but not parental benefit, 

and paid to those registered with the CES, payable to those unemployed who were able and willing to work 

and taking reasonable steps to obtain work. To gauge the scale of payments to youths: At June 1983 about 

300,000 15-24 year olds were paid unemployment benefit, benefits to 15-24 year olds were roughly 

estimated to cost $990 million in 1982/3 (DEYA (1983): xxix).

50 

Table 2.4 SYETP rates, period and target group/eligibility criteria 1976-December 1985 

Date of change Age Subsidy Rate 

$ per week 

Max weeks 

of subsidy 

Eligible group of unemployed 

CES registrants 

Unemployment 

benefit rate 

October 1976 15-17 58 26 15-19 school leavers 

36 

18-19 58 26 

41.25 

November 1976 15-17 59 26 All 15-19 at least 6 of last 12 36 

18-19 59 26 

months not in fulltime education 43.50 

August 1977 15-17 63 26 All 15-24 at least 6 of last 12 36 

18-24 63 26 


October 1977 15-17 66 26 All 15-24 at least 4 of last 12 36 

18-24 66 26 


August 1978 15-17 45 17 No change 

36 

18-24 45 17 

51.45 

November 1978 15-17 50 17 No change 

36 

18-24 50 17 

51.45 

April 1980 15-17 50 17 No change but if unemployed for 36 

18-24 50 17 

4 months continuously after 51.45 

SYETP then eligible for a second 

SYETP placement 

November 1980 15-17 55 17 No change but for ex-STWTP 24 36 

18-24 55 17 

participants who were then 53.45 

immediately eligible for a SYETP 

placement 

February 1981 15-17 55 standard rate 17 Extended SYETP introduced for 36 

18-24 

18-24 

55 standard rate 

80 extended 

17 

First 17 of 34 

18-24 unemployed at least 8 of 

last 12 months; with 2 periods of 

17 weeks, the first at the higher 

53.45 

53.45 

‘extended SYETP’ rate and the 

second 17 weeks at the standard 

rate. 

August 1982 15-17 75 standard rate 17 No change 

36 

18-24 75 standard rate 17 58.10 

August 1983 

18-24 100 extended First 17 of 34 

58.10 

15-17 75 standard rate 17 No change to eligibility, but start 40 

18-24 100 standard rate 17 of age tiers to subsidy 

68.65 


68.65 

August 1984- 15-17 50 standard rate 17 No change to eligibility 

45 

December 1985 18-19 75 standard rate 17 78.60 

20-24 100 standard rate 17 78.60 

18-19 50 extended First 17 of 34 45 


78.60 

Source: Ross (1988) p39 Table 4. The benefit rate is in $ per week. 

Notes: The SYETP became part of Jobstart from January 1986. This table relates to private SYETP rates – 

Commonwealth SYETP mainly differed only in that the employer received the full wage costs for the 

subsidy period. However see section 2.2.3 for other differences to private SYETP. 

24 School to Work Transition Program. This was a program made up of pre-employment education and 

training transition courses in Technical and Further Education (TAFE) institutes – courses generally of 12 

weeks or up to 52 weeks full-time. From October 1980 STWTP was supported by a ‘transition allowance’, 

but until then participants were not eligible for unemployment benefit (Kesteven (1987): 57).

51 

2.2.3 SYETP operation 

Earlier references to SYETP explained it in various ways. SYETP was initially described 

as a training programme that was part of the National Employment and Training System 

(NEAT 25 ), in a 1980 information pamphlet for CES services and Manpower Programmes 

(DEYA (1980)). In 1982 SYETP was listed amongst manpower programmes as a work 

experience programme, which was part of the Youth Training Programmes, with the 

following remit: 

“The work experience programme helps employers (both private and 

Commonwealth) take on young people who have found it difficult to get 

stable employment because they lack the required experience and 

qualifications by providing a subsidy for their employment.” (Paterson (1982) 

Appendix 1, p3). 

However, research documenting the programmes pointed out that the SYETP was 

essentially a wage subsidy to employers to take on unemployed young people (BLMR 

(1983): 2). In this same research, SYETP was also defined as a ‘work experience 

programme’. Yet the real emphasis was never on training. In 1984 programme conditions 

for employers included the development of a training plan for the new employee, 

however this seems the greatest extent of training under SYETP. Smith (1983) pointed 

out that this training plan could cover normal orientation for new employees. Thus it was 

a fairly straightforward employment subsidy programme with no real training provisions 

attached. 

In a submission to the OECD, the department in charge of administering manpower 

programmes described SYETP as “…one of the major programmes designed to improve 

access to and equity in the labour market”, one of two together with the job creation 

programme CEP (Community Employment Programme). It was further described as 

“…aimed at improving the employability of longer-term unemployed young people aged 

25 An active labour market ‘umbrella’ program, NEAT consisted of a mixture of separate training and wage 

subsidy programs.

52 

15-24…employers are expected to provide work experience but there is no obligation on 

them to retain subsidised young people at the end of the subsidy period…the subsidies … 

provide employers with a greater incentive to take on people...” (DEYA (1983) synopsis: 

xxvii). 

Employers received the weekly subsidy, and it was described in 1980 as a form of on-thejob-training 

where “Trainees must be 15 to 24 years of age; unemployed for 4 of the last 

12 months; away for full-time education for 4 months in the last 12 months; registered 

with the CES.” It was further stipulated that “Employers must provide proper trainee 

supervision and pay the trainee the award or going rate.” Although the chief aim was 

private employers, Commonwealth and State government could also participate in 

SYETP. There was no rule on retention of the SYETP participant after the subsidy ended, 

however “…there was an assumption that employers other than Commonwealth 

Departments and Authorities would retain the participants after the subsidy period or that 

participants would at least be able to obtain jobs on the basis of their programme 

experience” (Baker (1984): 15). 

SYETP operated as a 17 week placement for work-experience and on-the-job training, 

with a limited wage subsidy to employers (BLMR (1983) p6, Table 1.3). As the section 

tracing the historical development of SYETP shows, the subsidy limit changed in 

response to new policy directives and to keep in line with price/wage increases. In some 

cases, a ‘second-serve’ of SYETP operated as a follow-on to SYETP – where a person 

who had been on SYETP, but then following this was unemployed for 4 consecutive 

months, they then became eligible for a second 17 week placement. 26 There were several 

variations of SYETP, which existed in a small number of cases. 

For the Commonwealth Government employers, the subsidy covered full reimbursement 

of wage costs. For Commonwealth employers, placements were not subject to any ‘staff 

26 BLMR (1983) p33 and footnote 4 on page 35; Baker, S. (1984) p7 refers to this as a ‘second-serve’.

53 

ceiling constraints’ applied to Commonwealth Departments at the time. 27 Furthermore, 

there were restrictions on dismissal with a time limit so that after the first 2 weeks of 

placement, any placement dismissed could not be replaced. 28 Entrants to Commonwealth 

SYETP were restricted though and could not have already experienced a placement lost 

due to dismissal or voluntary withdrawal, thereby precluding people with former negative 

experiences. Additionally, the whole cost of the award wage paid to the participant was 

recovered from SYETP, while for private SYETP only the payment limit was paid to the 

employer by SYETP (Baker (1984): 6). 

‘Extended SYETP’ was also available, which was a slight alteration to the target group 

and subsidy structure. Extended SYETP was for those slightly older, over school age, and 

unemployed for a longer period. The structure of the subsidy for the Extended SYETP 

was changed from a flat rate for a single period to a two-tiered rate with a higher rate in 

the earlier period. The time tiered subsidy was $80 for the first 17 week period and then 

$55 for the second 17 week period, in 1983. Although the subsidy was paid to employers, 

the subsidy entitlement period was allocated to ‘trainees’, not to the placement, so if they 

left or were dismissed before the expiry of the period, then they retained eligibility for 

another placement position for the unused balance of the original period. This was termed 

a split placement (BLMR (1983) p6, Table 1.3, footnote (d)). Table 2.5 shows the key 

SYETP provisions at the time of analysis. The delivery arrangements required employers 

to respond to the programme and provide placements through the national network of 

CES offices operated by the Department of Employment and Industrial Relations. The 

CES was both the administrative and delivery mechanism, with CES offices at local level 

dealing with both the employer and the ‘trainee’. 

Employers could specify a vacancy was exclusively available for an SYETP placement 

(AIMA (1985): 90). Other operational procedures were also noted by AIMA (1985). 

Vacancies for SYETP placements could be on a special SYETP self-service notice-board, 

27 BLMR (1983) section 3 p17 footnote 2. Additionally, entrants to Commonwealth SYETP were restricted 

and could not have already experienced a placement lost due to dismissal or voluntary withdrawal 

(precluding people with former negative experiences). However, after the first 2 weeks of placement, any 

placement dismissed could not be replaced. Dismissal was then uncommon. 

28 Dismissal was then uncommon for Commonwealth SYETP as can be seen in section 2.2.6.

54 

or on the general CES vacancy display board. Alternatively, SYETP placements could be 

made by nomination of the employer, or self-canvassing by jobseekers from general 

vacancies. An informal estimate suggests that in 1984 60 per cent of SYETP placements 

were from those displayed on the SYETP self-service display board (AIMA (1985): 90). 

The display board self-service process was the same as for general vacancies, but in the 

interview and referral process for an SYETP vacancy the CES office had to confirm the 

applicant’s eligibility for the subsidy with an initial screening. A subsidy agreement had 

to be signed between the CES and the employer, the CES administered the subsidy 

monies, and CES officers could make ‘supervisory visits’ to employers. CES offices were 

directed to refer any eligible jobseeker, but after June 1984 they were to select from 

registrants suitable for referral ‘those who have the longest periods of unemployment’. 29 

There is evidence that, at least in 1984, there were limitations as to how many staff could 

be SYETP placements, or other ‘subsidy’ placements. 30 Edwards (1987) p86 notes that 

there were limitations on the proportion of the employer’s workforce which could be 

employed from wage subsidy programmes from the Federal government and cites the 

guidelines to CES 31 as stating that at any one time, a single physical location of factory, 

shop or office with 1-3 staff could have 1 placement; those with 4-7 staff could have 2 

placements; for 8-100 staff not more than 25 per cent of staff could be placements; for 

more than 100 staff not more than 10 per cent of staff could be placements. 

There is also evidence that the CES were vigilant for abuse of wage subsidy programmes 

by employers. Edwards (1987) p86 cites CES guidelines about programme subsidies with 

regard to possible termination of a programme subsidy if it was alleged that employers 

dismissed existing employees to hire the placement, or there was evidence an employer 

was seeking use of the programme for an on-going supply of subsidised labour, or where 

the employer sought to employ a former employee through the placement, or where 

29 AIMA (1985) p90 citing DEIR directive minute 84/55, 27 June 1984. 

30 Note that apprenticeship subsidy payments under CRAFT were considered by the government at the time 

as a wage ‘subsidy’, although they are classified in this analysis as ‘training’. The AWSS Adult Wage 

Subsidy Scheme operated from 1 March 1983 until 30 November 1985 as well. 

31 Department of Employment and Industrial Relations (1984) Adult Wage Subsidy Scheme CES Operating 

Manual, Volume 6 section 5; Volume 7.

55 

Award Conditions for employment were being breached. Edwards (1987) p87 also notes 

that for the Wollongong and Newcastle areas, evidence for such employer behaviour with 

regard to subsidies was ‘fairly minimal’. However, in practice the responsibility was 

there, but CES officers’ monitoring would be limited and discretional. 

Table 2.5 Key SYETP provisions in 1983/84 

Target group Subsidy structure and period Eligible positions 

SYETP 32 , private 33 

Semi-skilled 

employer 

Extended SYETP 

15-24 years, 

unemployed 

registered with 

CES and away 

from fulltime 

education for 4 of 

the past 12 months 

18-24 years 

registered with 

CES; unemployed 

and away from 

fulltime education 

for 8 of the past 12 

months 

Source: BLMR (1983) p6, Tables 1.2 and 1.3. 

flat rate for 17 weeks, for all 

placements in the age group 

(some age tiers from 1984) 

34 weeks: First 17 weeks higher 

rate, second 17 weeks lower rate 

Semi-skilled 

2.2.4 Political environment of SYETP 

SYETP operated across a long period of time, with a varying political background. As 

Stretton and Chapman (1990) p1 and section 3 point out, the expenditure on programmes 

and the mix of programmes offered changed over time due to differing government 

economic philosophies, observed changes in the unemployment structure and rate, and 

ambiguity about the role of programmes and their effectiveness. Despite this, SYETP 

continued to operate, although with resulting changes to size and operation, as 

summarized in other sections. 

Ross (1988) identifies SYETP with two key periods of government, distinguished by the 

Prime Ministers, which led them. SYETP started during the Fraser Government (1975- 

1983) – a Liberal government led by Malcolm Fraser, and continued into the time of the 

32 Also known as Standard SYETP. 

33 Also termed non-Commonwealth SYETP.

56 

Hawke Government, a Labour government led by Bob Hawke, from 1983. Harris (2001) 

also analyses in retrospect the political context for social policy during the SYETP period 

of operation. According to Harris (2001), during the Fraser period, SYETP operated 

within the context of deflationary budgets, except for during 1981-1982. Harris (2001) 

claims that little policy was implemented to combat unemployment, with a policy-making 

background where it was widely held within Treasury that unemployment levels could 

play a role in dampening inflationary expectations. Labour market programmes were 

adopted to deal with unemployment from a social perspective. 

The Fraser period was however a time during which an effort was made to develop a 

number of labour market programmes, of which SYETP was one within the NEAT 34 

umbrella. The build up to the introduction of labour market programmes started between 

1973 and the end of 1975, when unemployment rose and it became clear that it would not 

be short-lived (Ross (1988): 29). The significant rise in unemployment was particularly 

amongst young unemployed (Hoy and Paterson (1983): 2). A direct link is drawn by Ross 

(1988) p29 between the introduction of these “...small number of large-scale 

programmes...” and the then recently held government inquiries into Overseas Manpower 

and Industry Policies (1973), Labour Market Training (1973) and Unemployment 

Statistics (1973). In reference to the Fraser period, Harris (2001) cites the introduction 

during this period of a 6 week waiting period for school leavers and those who had left 

their last job, and points out that unemployment benefit criteria were tightened alongside 

the labour market programmes. Chapman (1985) p101 refers to the balancing act played 

by SYETP in policy: 

“...the conservative government needed to be seen to be concerned with 

burgeoning youth unemployment, but at the same time, there were perceived 

political risks associated with direct job creation programmes…Youth 

oriented wage subsidies accomplished the twin goals of targeted employment 

assistance and private sector support”. 

34 National Employment and Training system, an active labour market program of training and wage 

subsidies.

57 

Harris (2001) claims that during the later Hawke regime, expansionary budgets were 

introduced, and citing Watts (1999) 35 also claims that there was a ´willingness to spend´ 

on labour market programmes but this mostly went to Job Creation, such as the CEP. An 

overview of how SYETP expenditure and placements varied across these periods, indeed 

the life of the programme, can be gained from Table 2.6. This information, sourced from 

Ross (1988), gives details of the fluctuations in spending allocated to SYETP and 

numbers treated by the programme. It is clear the amount spent on SYETP doubled in 

1983/4 compared to the prior year, yet the SYETP share of labour market programme 

expenditure did not rise but fell slightly. To gauge the scale of SYETP programme 

expenditure to the cost of unemployment benefit payments to youths: at June 1983 

slightly more than 300,000 15-24 year olds were paid unemployment benefit, and 

benefits to 15-24 year olds were roughly estimated to cost $990 million in 1982/3 (DEYA 

(1983): xxix). In contrast, in the same year only $120 million was spent on SYETP. The 

expenditure on SYETP was then not great relative to the general cost of supporting the 

unemployed. This is despite the subsidy rate generally being higher than the benefit 

amount (see Table 2.4). 

35 Cited p 9, Harris (2001) referring to Watts, R. (1999) “The future of work: economists and employment 

policy 1983-1999” conference paper presented at the National Social Policy Conference Sydney, Social 

Policy Research Centre, University of New South Wales.

58 

Table 2.6 SYETP annual expenditure and placements1976/77-1985/86 

Political regime Year Expenditure 

($millions) 

Fraser government 

era 

Hawke government 

era 

Expenditure % of 

total annual 

expenditure on 

labour market 

programmes 

Number of 

placements 

1976/77 6.6 6.5 9,590 

1977/78 47.1 29.6 66,000 

1978/79 82.6 40.7 66,350 

1979/80 24.2 17.8 44,300 

1980/81 41.3 21.9 65,309 

1981/82 53.7 24.8 51,696 

1982/83 63.6 17.8 66,270 

1983/84 120.2 15.3 87,582 

1984/85 97.7 10.8 68,874 

1985/86 61.7 7.3 30,107 

Source: Ross (1988) p37 Table 3 

Note: the SYETP became part of Jobstart from January 1986 and 1985-6 figures reflect the end of the 

programme under this name. 

2.2.5 Economic Context of SYETP at the time of our analysis 

The main aim of this section is to provide a synthesis of research for selected relevant 

features of the Australian labour market for the period, in particular focusing on youths. 

2.2.5.1 Role and impact of the CES 

Wielgosz (1984) examined the referral and placement role of the Commonwealth 

Employment Service (CES). They noted that since establishment in 1946, the primary 

function of the Australian CES had been to match registered jobseekers and vacancies, 

but later the administration of manpower programmes was added to the brokerage 

function. CES effectiveness in executing such roles would affect the extent to which 

employers and jobseekers utilized the CES. In turn the CES performance in matching 

jobseekers and vacancies would be influenced by the characteristics and volume of these 

received. 

Hoy and Lampe (1982) p6 noted that after 1977, efforts were made to improve the CES 

performance of the brokerage role. They listed two initiatives: job self-service, where

59 

display boards listed details of vacancies and jobseekers could select which to apply for; 

and improved systems circulating vacancies between CES offices. Table 2.7 indicates the 

use of CES by youths looking for work. Most youths were registered with the CES, and 

most indicated they also took on active job search methods contacting employers. It was 

also observed by Hoy and Lampe (1982) that most married women were not eligible for 

unemployment benefits, which led to lower CES registration for that group. 

Table 2.7 Active steps to find work by youths looking for work in July 1980 

Active steps to find work Persons looking for work July 1980 

Unemployed males 

15-24 years 

Unemployed females 

15-24 years 

Registered with CES And took no other steps 4.3 6.9 

And applied to 

75.8 68.2 

prospective employers 

in person or by post or 

telephone 

And took other active 3.5 * 

steps 

Total registered 83.6 76.9 

Not registered with CES And applied to 

14.5 21.1 

prospective employers 

in person or by post or 

telephone 

And took other active * * 

steps 

Total not registered 16.4 23.1 

Total 100 100 

Source: Hoy and Lampe (1982) p34 Table 4 cited source ABS Cat 6222.0 ‘Persons looking for work, 

Australia’. 

A 1977 review of CES services to employers found limited employer use of the CES. It 

was established that employers estimated only 48 per cent of their current vacancies were 

notified to the CES. Limited occupations were related to the CES notified vacancies: 36 

per cent of clerical, administrative and sales worker vacancies were notified to the CES, 

and 50 per cent of vacancies for tradesmen. 36 

Wielgosz (1984) pointed out that the CES had no direct control over the volume or nature 

of vacancies received, and that only a minority of available vacancies was lodged with 

36 Hoy and Lampe (1982) citing Norgard, J.D. (1977) “Review of the Commonwealth Employment Service 

1977”, Report prepared for the Minister of Employment and Industrial Relations, Australian Government 

Printing Service, Canberra, pp253-254.

60 

the CES. In support of this they cited statistics 37 reflecting that about 30 percent of all 

vacancies were registered with the CES. In addition to the volume constraint, they noted 

that the majority of vacancies were concentrated in low-skilled occupations. In contrast to 

the low vacancy attraction, the majority of jobseekers made use of the CES. It was noted 

that 80 to 90 per cent of jobseekers looking for full-time work used the CES. 38 However, 

it was also made clear that although the majority registered with the CES, as registration 

was a condition for receipt of unemployment benefits, more than 90 per cent additionally 

used other methods, so it was not the sole method and likely not the predominant method 

for job search. 

Referral and placement 39 data for the Brisbane area, regarded as a good example of 

average CES activities, was analysed by Wielgosz (1984). In examining referral to 

notified vacancies the underlying assumption about CES functioning in referral to 

vacancies was clarified as follows: where “…employers have certain preferences and 

expectations about the type of labour they require, that the CES is aware of these 

preferences and in it’s desire to fill vacancies and fill them as quickly as possible, will 

refer those unemployed applications who are most likely to be placed easily and quickly” 

(Wielgosz (1984): 12). 

Using a probit econometric analysis, Wielgosz (1984) found referrals to have several 

statistically significant factors amongst applicant characteristics, being negatively related 

to age, educational attainment (years of schooling) and duration of unemployment 

(current spell in weeks) of applicants, with positive effects for applicants in 

clerical/administrative and skill trade occupations (relative to a base of all other 

occupations except semi-skilled and unskilled). Wielgosz (1984) separately modelled 

successful placement in a job, with a variable for the number of referrals received and 

found this significantly increased the probability of placement using the Heckman 

method to control for sample selection (Wielgosz (1984) p19 Table 7). No details of the 

Heckman sample selection model are presented, and so it is not clear what exclusion 

37 Wielgosz (1984) p4 footnote 4 citing ABS Cat.6231.0 ‘Job vacancies, Australia’ various issued to 1984. 

38 Wielgosz (1984) p4 citing ABS Cat.6222.0 ‘Persons looking for work, Australia’ various issued to 1984. 

39 Note that placement here is in subsidised or unsubsidised jobs.

61 

restriction was used. If there was no exclusion restriction applied, and identification was 

based solely on functional form, then Monte Carlo evidence in the literature indicates that 

the modelling suffers from poor performance. The estimation procedure also lacks further 

detail, and so it is not clear whether the two equations needed for the Heckman sample 

selection model were estimated simultaneously, but it appears that a two step procedure 

was applied. If both equations are estimated as probits, then this is inappropriate due to 

the nonlinearity of the probit. It was found that the factors affecting referrals did not have 

an independent affect on placement. Duration of the current unemployment spell also had 

an additional negative effect on placement if selection bias was not controlled for. 

Wielgosz found that tests for significance of the selection correction factor indicated no 

selection bias. But it was concluded that the very high correlation between the selection 

correction factor and the duration of unemployment, coupled with the significance of this 

variable when selection bias was not controlled for, suggested that that the sample 

selection bias was “…closely and solely related to duration of unemployment” (Wielgosz 

(1984): 19). It was further noted that this was because to be referred, and so included in 

the sub-sample, was almost completely dominated by the length of unemployment spell. 

As a result, due to their correlation the coefficients for selection correction and 

unemployment duration were both insignificant in the placement equation because they 

reflected the same phenomenon. 

Aungles and Stewart (1986) used the same referral and placement data as Wielgosz (1984) 

to examine referrals and the duration of unemployment, together with exits from 

unemployment. Aungles and Stewart (1986) modelled durations of unemployment, and 

also found that the number of referrals jobseekers receive is related to the probability of 

leaving unemployment. They found that jobseekers receipt of CES referrals were most 

likely to occur early in their unemployment spell, but once referred by the CES, 

differences did not exist in the probability of leaving unemployment. The hazard function 

for leaving unemployment initially rose to peak at 11 days, less than two weeks, after 

which it fell.

62 

A regional analysis of NSW registrations of unemployment and vacancies with the CES 

was conducted in 1986 by McGillicuddy et al. (1986). Over the year from June 1985 to 

June 1986, CES registrations had risen by 1.1 per cent. It was noted that regional analyses 

of the distribution of registered unemployed and vacancies emphasized geographical 

limitations to the functioning of the labour markets within NSW. This analysis could be 

extended to Australia as related to rural/urban features. The share of notified vacancies 

per unemployed was higher in Sydney (0.26 vacancies per person) than other NSW areas 

(0.13 vacancies per person) and the unfilled vacancies followed a similar pattern (Sydney 

0.06 unfilled vacancies per person; rest of NSW 0.03) (McGillicuddy et al. (1986): 6). 

Most unemployment registrations were for older age groups in the periods studied, with 

youths taking a lower share, see Table 2.8. However it was clear that those 15-19 years 

had lower registrations in Sydney than the rest of NSW, but those 20-24 years always had 

lower shares outside Sydney than within Sydney. 

Table 2.8 Age distribution for CES registrants in NSW 1985-1986 

Sydney 

Rest of NSW 

June 1985 June 1986 June 1985 June 1986 

15-19 19.5 19.1 22.5 21.8 

20-24 25.1 24.3 23.7 22.6 

25-44 41.1 42.6 39.8 41.4 

45+ 14.3 13.8 13.9 14.2 

Source: McGillicuddy et al. (1986) p6 Table 2. Columns add to 100 per cent. 

2.2.5.2 Unemployment in the Australian economy over the 1980’s 

Labour supply and demand factors, overall unemployment and the business cycle, and 

relative youth wages are factors that can be viewed as having a significant impact on 

youth unemployment. They also play a role in the operation of wage subsidies, as 

discussed in chapter 1. These issues are now considered for the Australian economic 

background to the operation of the SYETP. 

The Australian economy encountered a peak in the unemployment rate during the period 

of our data analysis, followed by improvements with falling unemployment from 1985 on

63 

to the end of the 1980’s. An overview of the change to unemployment across this period 

is shown in Table 2.9. It can be seen that the unemployment rate for those aged 15-19 

years was substantially higher than for the general population, but that it also fluctuated 

in line with the business cycle. In the context of the Australian business cycle, it was 

generally considered that the period from late 1981 to mid-1983 was a recession (see for 

example EPAC (June 1992) p14 and Windschuttle (1985) p3). 

Teenage unemployment rose dramatically between 1981 and 1983. Although teen 

participation rates may also have changed with more teenagers remaining in education 

(see later discussion) these fluctuations indicate that the share of teens looking for work 

was considerable over this period. Paterson and Mackay (1982a) make it clear that 

demographic growth was not a source of increased youth labour supply. They note that in 

Australia youths had lower than average rates of population increase. 

Table 2.9 Unemployment rate Australia 1981-1990, seasonally adjusted 

Per cent Total unemployment rate 15-19 years 

unemployment rate 

January July August 

1981 5.9 5.9 13.9 

1982 6.0 7.0 16.7 

1983 9.3 10.4 22.6 

1984 10.3 8.4 21.0 

1985 9.3 7.8 18.2 

1986 8.5 7.9 19.1 

1987 8.9 7.8 18.7 

1988 8.3 6.6 15.6 

1989 7.4 5.9 13.7 

1990 6.7 6.9 16.5 

Source: Sheen and Trethewey (1991) Total unemployment: p12 Table 2 Cited from sources ABS Cat 

6203.0 Labour Force Australia February 1986, February 1990, January 1991.15-19 years unemployment: 

Table “Labour force status of the civilian population aged 15-19: school attendance”. 

An additional understanding of unemployment during this period can be gained from the 

distribution of unemployment durations amongst the various age groups can be seen from 

Table 2.10. Youths in general usually had lower durations of unemployment than older 

groups, however school leavers would limit the average duration for teens. Over the 

changing business cycle, a rise in the share of newly unemployed would lower the 

unemployment duration average for each group.

64 

Table 2.10 Average duration of unemployment (number of weeks) by age, August 1981- 

1990 

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 

15-19 25 23 32 29 29 30 33 28 26 23 

20-24 32 32 42 46 46 43 41 46 34 33 

25-34 31 28 40 45 50 43 45 53 37 40 

35-44 38 40 44 55 50 57 52 54 56 57 

45-54 56 52 56 61 73 85 82 71 80 60 

55-59 69 46 63 79 86 89 86 115 84 99 

60-64 83 58 60 67 123 98 121 91 107 126 

Source: Sheen and Trethewey (1991) citing unpublished data ABS Cat 6203.0 Labour Force Australia. 

Analysis of unemployment by educational attainment in February 1991 also found that 

the unemployment rate was clearly higher for those with less formal education, especially 

young people. 40 It was noted that unemployment rates for those 20-24 who did not finish 

schooling to year 12 was 20 per cent, which was about twice that of those who did finish 

schooling. Overall, the observed youth unemployment rate was 23 per cent for those 15- 

24 years without schooling to year 12. 

Ross (1988) usefully examined in detail the relative performance of teenagers in the 

labour force across the period 1983-1988. The distribution of labour force activities for 

teenagers and the total working age population for Australia can be seen in Table 2.11. 

Teenagers had different activity patterns to the rest of the working age population in 

general. Ross (1988) points out that for teens, while employment rose across the period, 

and unemployment fell, their labour force participation had changed. 

Particularly for male teens there was a rise in the school participation rate, and Ross 

(1988) made clear the change to the education and employment decision was an 

important aspect of the teen labour market over the period. A breakdown in employment 

showed that full-time employment for teens fell, due to a decline in female employment, 

and part-time employment rose around 60 per cent for teens (Ross (1988): 4). It was 

found that school student’s labour force participation had risen, and while employment 

40 EPAC (1992) p 14 Chart 1.7 and citing ABS CAT 6235.0 Labour force states and educational attainment, 

Australia February 1991.

65 

for teens overall had risen, employment rose most for school students, whose 

employment was mostly part time 41 averaging 4-6 hours a week – the Saturday job effect 

(Ross (1988): 11). 

Ross (1988) found the most suitable interpretation was that employers were placing 

greater reliance on casual and part-time labour while teens were aware of lack of 

experience and qualifications affecting employment chances. Thus, teens were remaining 

in school longer, and accepting part-time employment as a first-step to full-time 

employment, which gave work-experience. The growth in in-school employment was 

interpreted as an attempt to combine work experience with higher educational attainment. 

Stricker and Sheehan (1981) and Merrilees (1981), in commenting on the rise in 

schooling participation, noted that those who wanted a job but were unable to find one 

could remain in school, and the state of the youth labour market had thus affected 

schooling rates. 

41 Fulltime employment was defined as 35 hours or more per week, with part-time less than 35 hours per 

week.

66 

Table 2.11 Unemployment, labour force participation and employment rates, teenagers 

and total working age population March 1983 and March 1988. 

Rate as 

percent 

15-19 All working age 

male female all male female all 

March Unemployment 23.2 25.6 24.3 9.9 11.2 10.4 

1983 

Labour force 

63.9 59.8 61.9 77.3 45.6 61.3 

participation 

Employment 49.1 44.5 46.9 69.6 40.5 54.9 

School 

35.2 40.0 

participation 

In school 

Unemployment 28.7 24.9 

Labour force 20.0 23.5 

participation 

Employment 14.3 17.6 

Out of school 



participation 


March Unemployment 18.2 21.2 19.7 7.3 9.1 7.5 

1988 

Labour force 

61.9 60.0 61.0 76.2 50.9 63.3 

participation 

Employment 50.6 47.3 49.0 70.6 46.3 58.2 

School 

40.9 41.3 

participation rate 

In school 



participation 


Out of school 



participation 


Source: Ross (December 1988) p2 table 1 citing ABS Cat 6203.0 Labour Force Australia March 1983 and 

March 1988. Also, selected statistics from Ross (December 1988) p5 Table 3 and p 12 Table 5. 

In concluding, Ross (1988) examined the value of unemployment benefits for teens. In 

the context of the value of benefits relative to prices and average weekly earnings, it was 

found that the real value of unemployment benefits had fallen only for those 16-17 years. 

The value of benefits relative to prices and average weekly earnings is shown in Table 

2.12. Webster (1997b) p10 modelled the Beveridge curve of Unemployment-Vacancies

67 

for Australia using data from the period 1978-1997. This analysis found that the ratio of 

unemployment benefits to minimum award wages was an important shift variable of the 

Beveridge curve, with a rise in the ratio of benefits to wages raising the unemployment 

rate. The relative values of employment and unemployment activities in the labour 

market seem to have been detrimentally influencing youth labour participation. 

Table 2.12 Value of unemployment benefits to youths 1983-1987 

16-17 years 18-20 years 

Benefits /CPI Benefits /AWE Benefits /CPI Benefits /AWE 

1983 100.0 100.0 100.0 100.0 

1984 106.3 105.3 108.0 106.8 

1985 101.8 97.8 114.0 109.6 

1986 103.6 104.8 113.5 115.1 

1987 94.6 100.9 107.2 114.8 

Source: Ross (December 1988) p14 CPI is the Consumer Price Index for all goods weighted average for 8 

capital cities ABS Cat 6401.0. AWE is average weekly earnings from annual survey of earnings. 

Paterson and Mackay (1982) note that the share of youths aged 15-20 years in public 

sector employment suffered a large fall of 33 per cent from 1971 to 1981. Over the same 

period, public sector employment grew strongly by 29 per cent whereas private sector 

employment rose by only 8 per cent across this period, and in 1981 public sector 

employment was 30 per cent of total employment (Paterson and Mackay (1982): 28). A 

large part of this was likely due to the public sector refocusing employment towards 

graduates and skilled workers rather than taking on school-leavers and allowing 

'working-up-the-ranks'. This would have led to a contraction in what was a substantial 

part of the youth employment market. 

The role of relative labour costs can have particular relevance to the operation of a wage 

subsidy. Windshuttle (1985) presented statistics for the ratio of average hourly earnings 

for adults relative to juniors, see Table 2.3 earlier. In 1983, for full-time private sector 

employees the ratio of average hourly earnings for adults relative to juniors was 52.7 for 

males and 63.4 for females. For females, this was a fall in the ratio from 66.6 in 1977, 

while for males it was also a fall but from 55.2 in 1977. Windshuttle (1985) p6 notes that 

public sector wages were higher than those for private sector over the same period. 

Windshuttle (1985) also observed that there was a greater difference in relative hourly

68 

earnings between juniors and adults in the private sector. However, this provides no 

modelling evidence that these wage differentials were important factors influencing youth 

unemployment. 

More formal modelling by Lewis (1983) examined the role of relative wages in the 

substitution between young and adult workers in Australia. It was concluded that for 

young males, demand was not particularly elastic to any other group except adult females, 

while young females had high demand elasticity relative to any adult workers. Thus it 

was suggested a change in labour costs would have a significant effect on young women 

and unskilled young men. The SYETP wage subsidy would introduce such a change in 

the relative costs for the eligible groups. The main restriction on substitutability foreseen 

by Lewis (1983) was reasoned to be skills and work experience. 

Miller and Volker (1987) examined skills and work experience as the determinants of 

youth earnings. They found that education and experience were major determinants of 

youth earnings. They also found that recent unemployment did not have a strong 

influence on earnings for youths. They reasoned that this was due to institutional features 

and trade union effects on wages and “…while the unemployment record appears to be 

used as a cheap screen at the hire stage, it does not have much impact on wage 

determination” (Miller and Volker (1987): 35). A wage subsidy like SYETP can help 

address the screening problem by allowing a low cost trial period for unemployed. As 

SYETP operated in the period they analysed, it could have contributed to the weak 

influence of unemployment on wages by influencing human capital of participants 

through on the job training. 

Daly (1991) examined the returns to experience in a comparison of formal education and 

on-the-job training by modelling earnings in Australia 1981. It was found that the 

experience profiles of different formal education groups converged. The conclusion 

drawn was that formal education and on-the-job training were to some extent 

substitutable methods for the accumulation of human capital, and thus they were 

substitute and not complementary activities. Wage subsidies, while not ostensibly

69 

training, can provide a form of on the job training simply through work experience (for 

example 2.2.1 notes that a condition of SYETP was a training plan). This is advantageous 

for the functioning of a wage subsidy since the unemployed who benefit from the subsidy 

can develop greater human capital and subsequent improvement in their labour market 

position (in turn allowing the wage subsidy to influence employment gains). As SYETP 

existed in this period, it is difficult to determine whether this aspect arose due to SYETP, 

or pre-existed it and provided SYETP with helpful conditions in which to act as conduit 

for the activation of human capital. 

2.2.5.3 Discussion 

The analysis that our study produces operated with these background features to the 

youth labour market. Over the period this study covers, 1984-1986, the Australian youth 

labour market had several interesting aspects. It faced institutionalised lower relative 

wages than adults. Although school participation rose, overall youth unemployment was 

high relative to adults, but generally youth unemployment was falling and employment 

was rising. Regional and rural/urban differences existed in the incidence of 

unemployment. Educational attainment was related to patterns of unemployment. 

Amongst youths, teens had shorter average spells of unemployment because they were 

mostly school leavers. 

CES efficacy had a strong influence on the path out of unemployment for registrants, and 

getting referrals was an integral factor. The CES faced a limited supply of registered 

vacancies, which seemed to favour lower skilled occupations. In reviewing the evidence 

from programme evaluation for OECD countries, Fay (1996) noted that the process itself, 

relating here to the CES administration, might be an important determinant of the 

outcome from a programme (Fay (1996): 11). However it was also found in the review 

that the role of delivery was not usually included in impact studies of employment, and 

that while potentially important they might be difficult or impossible to include in 

evaluations of net employment impact.

70 

Many of the labour market features described here would form part of the model 

assumptions in theoretically ascribing a role for a youth wage subsidy. There were 

minimum wages, which would give institutionalised inefficiency of the labour market. 

Other factors, which have been reviewed here, would also have a role in affecting the 

functioning of the SYETP subsidy, including lower relative youth labour costs, elastic 

demand for youths relative to adults, and substitutability of formal education for on the 

job training in human capital. The theory of wage subsidies outlined in Chapter 1 

indicates how these might influence the potential for employment gains from a wage 

subsidy targeted at youths. Together, these features favour the suggestion that SYETP 

might have given employment gains to those eligible. However, it is impossible to 

determine whether these features were caused by SYETP, or were merely the backdrop 

influencing SYETP operation. Further consideration of the macro-economic environment 

is beyond the remit of this analysis. 

2.2.6 Context of SYETP environment and operation 

A brief exposition of useful research about the operation of SYETP is included as 

informative of the nature of SYETP close to the time of our analysis. The research 

included here is relevant to SYETP but was not an evaluation of employment, or used 

employer data, or was not based on micro-economic evaluation of individual data. 

2.2.6.1 General results for SYETP using other approaches 

Hoy and Paterson (1983) and the NEAT evaluation (BLMR (1984)) provide early BLMR 

analysis with data for the years from 1977 to 1980. Unfortunately, SYETP operation 

cannot be distinguished from other programmes and so this is not further discussed. 

Smith (1984b) conducted a macro-econometric analysis of SYETP, where administrative 

data for SYETP participants was related to aggregate unemployment survey measures for 

youth’s unemployment and duration. The chief aim was to relate the number of subsidy 

placements to variations in youth unemployment over time. The result for estimates were 

compared to employer survey estimates, and the summary is shown in Table 2.13. The

71 

employer survey estimates were the shares observed for questions asking about what 

hiring choices employers would have made if the subsidy placements had not been 

available. A useful conclusion of the analysis of SYETP was that the following extreme 

macro-outcomes for the subsidy could be considered excluded: a total windfall gain or 

complete substitution of employees for SYETP placements; that all SYETP placements 

took place without substitution of other workers; a 100 percent gain in employment per 

subsidy to the target group or the economy. 

Table 2.13 Smith (1984b) summary of estimates of SYETP 

Immediate impact of 100 subsidised jobs on Employment 

Period of data Target group Others Aggregate 

Smith (1984) 1978-83 64 -16 49 

DEIR (1984) 42 1983 42 -30 12 

Hoy and Ryan 1981 43 -24 19 

(1984) 

DEYA (1980) 43 1979 94 -61 33 

Source: Smith (1984) p 8a Table 1 

2.2.6.2 Job characteristics 

SYETP jobs were mostly low-paid, had a high job turnover, and private SYETP 

placements were more commonly for 15-17 year old girls. 44 Over the period 1976-1982, 

approximately 90 percent of placements were for 15 to 19 year olds, although the eligible 

age group was 18-24 years (Hoy and Lampe (1982): 24). Over this same period, the share 

of females always outnumbered males in placements relative to the number of 15-19 year 

olds employed full-time (Hoy and Lampe (1982) p24 and Figure 1). In August 1978, the 

number of young women in SYETP peaked at 9 per cent of those in full time 

employment, but after this fluctuated between 2-4 per cent with males slightly lower at 1 

to 3 per cent. 45 Hoy and Lampe (1982) pointed out that 49 per cent of teenage females 

assisted under SYETP were employed in Manufacturing and Retail occupations. It was 

42 DEIR “Telephone survey of wage subsidy employers conducted in November 1983” as Cited p8a Smith 

(1984), and as referenced p55 Smith (1984). This reference is unpublished. 

43 DEYA (1980) “SYETP in the private sector: follow-up survey of April 1979 placements” Melbourne, 

unpublished. Cited Smith (1984) p8a. This reference is unpublished. 

44 Cited p128 BLMR (1984) and see also Hoy and Lampe (1982) p22. 

45 Hoy and Lampe (1982). Calculations based on placement numbers relative to the ABS Labour Force 

Survey estimates of Labour Force in employment for 15-19 year olds.

72 

additionally noted that the average weekly earnings for non-managerial employees under 

21 in Retail Industry in May 1981 were lower for females at $131.40 compared to males 

at $147.20; in Manufacturing the corresponding figures for females were $149.70 while 

for males $170.50. These industries were in turn below the all-industry average earnings 

for the corresponding groups - females $154 but males $171. Thus, the bulk of industry 

and occupational placements for SYETP reflected the greater share of the wage covered 

by the subsidy. Smith (1983) concluded that this could be interpreted as employers 

responding to the wage incentives. 

2.2.6.3 Characteristics of SYETP commencements 

Departmental research in 1983 46 examined the administrative records for 

commencements flowing onto all government programmes for youths in 1980-81. 

Although this was completed as a form of ‘evaluation’, it is not included in the evaluation 

critique, as it contains no modelling. A brief exposition of this is included as informative 

of the nature of SYETP close to the time of our analysis. This is useful as it gives some 

background to the nature of SYETP placements according to administrative data sources. 

These data are less likely to suffer the problems of sample surveys, although instead 

suffering from other data problems and errors. The timing is quite close to that of our 

analysis. 

They found for all programmes, such as SYETP and EPUY, there was a substantially 

higher share of placements for 15-19 year old males (BLMR (1983): 11). They also 

concluded that 16-17 year olds were most often assisted amongst those 15-25 years. 

Commencements to extended SYETP had mean unemployment experience of 44.9 weeks, 

much greater than the minimum pre-requisite 34 weeks or more. The proportion of 

commencements that had experienced 8 months or more unemployment prior to 

programme entry was 42.7 per cent for SYETP Commonwealth placements, and 22.3 per 

cent for SYETP private placements (BLMR (1983): 14). They observed there was no 

strong age variation amongst the 2 types of SYETP programme, although the eligibility 

46 BLMR (1983) Figures only refer to ‘flow’ into the program and not the total number or ‘stock’ being 

treated at any one time.

73 

provisions for SYETP and extended SYETP had a small amount of age variation with 

extended SYETP excluding 15-17 year olds. They concluded that a smaller share of 

commencements for older ages in the target groups was likely due to the flat age payment 

of SYETP subsidy whereas employers paid wages within an age related award structure. 

Table 2.14 shows the age breakdown of SYETP commencements in 1980/81, while Table 

2.15 gives the age/sex breakdown. Total wage reimbursement to Commonwealth 

employers and the ability to avoid ‘staff ceiling constraints’ led to older target ages being 

more common in this sort of placement. The Extended SYETP was not in operation until 

the ‘March 1981’ quarter. 

In Table 2.15, the number of commencements gives an idea of the scale of the 

programmes in terms of total treated ‘flow’ cases in the period, and the proportion of the 

total each programme represents is also shown. It is clear there was a heavy reliance on 

the private SYETP, amongst all SYETP, and also there was more widespread use of 

SYETP amongst the range all programmes. 

Table 2.14 Distribution of SYETP commencements, by age 1980-81 

Quarter Sept 1980 Dec 1980 Mar 1981 Jun1981 

Age 15-19 

years 

20-24 

years 

25 47 

years 

15-19 

years 

20-24 

years 

25 

years 

15-19 

years 

20-24 

years 

25 

years 

15-19 

years 

20-24 

years 

25 

years 

SYETP 

58.6 41.4 0.0 52.3 47.5 0.2 52.2 46.6 1.2 62.7 36.5 0.8 

Commonwealth 

2nd SYETP 52.8 47.2 0.0 42.9 57.1 0.0 46.9 53.1 0.0 46.9 50.0 3.1 

Commonwealth 

SYETP private 86.8 13.1 0.1 84.8 15.0 0.2 84.1 15.7 0.2 90.1 9.8 0.1 

2nd SYETP 86.2 13.8 0.0 82.5 17.1 0.3 81.4 18.5 0.1 85.8 14.2 0.0 

private 

Extended SYETP 58.0 41.7 0.3 53.5 46.0 0.5 

Source: BLMR (1983) p42 Appendix Table B1. Data is Department of Employment and Industrial 

Relations administrative records for commencements to the programme in 1980-81, which thus only refer 

to ‘flow’ into the programme and not the total number or ‘stock’ being treated at any one time. Table shows 

proportions in the three age groups that sum to 100 per cent of commencements for each quarter and 

programme. ‘2 nd SYETP’ placements refer to commencements where this was the 2 nd eligible period of 

SYETP assistance – i.e. multiple placements. The Extended SYETP was not in operation until the ‘March 

1981’ quarter. 

47 The 25 year olds were not eligible but these commencements are explained by ‘Birth dates falling just 

prior to or after commencement dates, coding errors’ (BLMR (1983) p42 Appendix Table B1footnote (b)).

74 

Table 2.15 Distribution of SYETP commencements, by age and sex 1980-81 

15-17 years 18-19 years 20-24 years Numbers of 

commencements 

% of all 

programme 

commencements 

Male Female Male Female Male Female 

SYETP 

7.2 10.5 14.6 24.2 23.3 20.1 3315 3.8 

Commonwealth 

2nd SYETP 2.1 0.8 17.5 27.5 26.2 25.8 240 0.3 

Commonwealth 

SYETP private 27.2 32.0 12.7 14.8 7.4 5.9 47500 54.2 

2nd SYETP 19.6 21.2 20.9 22.2 8.3 7.8 23631 3.0 

private 

Extended SYETP 0.9 0.9 25.1 28.9 25.4 18.8 6825 7.8 

Total all 

87558 

programmes 

Source: BLMR (1983) p16 Table 3.1. Data is Department of Employment and Industrial Relations 

administrative records for commencements to the programme in 1980-81, which thus only refer to ‘flow’ 

into the programme and not the total number or ‘stock’ being treated at any one time. Table shows 

proportions in that sum across to 100 per cent for the programme. ‘2 nd SYETP’ placements refer to 

commencements where this was the 2 nd eligible period of SYETP assistance – i.e. multiple placements. The 

Extended SYETP was not in operation until the ‘March 1981’ quarter. 

Private SYETP placements were mostly in selling and unskilled occupations, with 14.4 

per cent in general administrative positions, 23.1 per cent in selling, and 34.4 per cent in 

semi-skilled jobs. Extended SYETP had 42 per cent of placements in semi-skilled jobs. 

Private SYETP placements occurred to a large extent in Retail trade (30.9 per cent) and 

‘other services’ which excluded public/health or education (11.8 per cent), and 

manufacturing (33.4 per cent). Extended SYETP had a similar industrial distribution. In 

comparison, Commonwealth SYETP had relatively higher shares in clerical (both 

keyboard clerical 21.5 per cent and 25.1 per cent in general administrative) and skilled 

manual jobs, influenced by the restricted range of tasks in the agencies. 48 

Not all placements were completed to the full length of their subsidy period. Overall, 

more placements terminated due to voluntary withdrawal of trainees than due to dismissal. 

However, for private sector placements only three quarters of approved subsidy time was 

used due to non-completion of the full subsidy period. 49 Overall, trainee dismissals and 

48 Of course, this meant that the industry was made up of 96.2 per cent ‘public/health or education/other 

services’. 

49 Entrants to Commonwealth placements could not have already experienced a placement lost due to 

dismissal or voluntary withdrawal (precluding people with former negative experiences). However, after

75 

withdrawals occurred at similar junctures in the approved time used, with dismissals on 

average occurring at 46.3 per cent of the allocated time, and withdrawals at 46.7 per cent 

of the subsidy period (BLMR (1983): 24). There was variation by State in the completion 

of placements, mainly due to the mix of Commonwealth/private SYETP. It was noted 

that in particular Western Australia, which had a higher share of private rather than 

Commonwealth SYETP placements, the dismissal rate was highest at 25.1% of all 

approved subsidies, whereas in Tasmania, the dismissal rate was low with 9.4 per cent of 

all subsidies ending early with dismissal. It was noted that a previous SYETP 

evaluation 50 found that voluntary withdrawal in the first 8 weeks in private SYETP 

placements was more likely to occur due to not liking the work or people, but leaving to 

go to another job was more likely to occur at 8-16 weeks of placement. As the share of 

time used is quite high for SYETP programmes, shown in Table 2.16, then it is likely 

most withdrawals would have been to go to another job. 

There was also State variation in placements to the different education and employment 

programmes available. Queensland, Western Australia and Victoria were found to have 

much higher SYETP placement rates than other states. Table 2.17 repeats information 

about the State placements to the various programmes. The greatest share of placements 

in each State went to SYETP, and mostly to private SYETP, although the concentration 

in each state varied from state to state. Western Australia had the highest SYETP 

participation rate. 

the first 2 weeks of placement, any placement dismissed could not be replaced. Dismissal was then 

uncommon in public SYETP placements. (BLMR (1983) p23 footnote 13). 

50 BLMR (1983) p33 citing Department of Employment and Youth Affairs (1980) “Special Youth 

Employment and Training Program SYETP in the private sector: follow-up survey of April 1979 

placements”, Melbourne, unpublished.

76 

Table 2.16 Completion of SYETP placement 

% completing % dismissed % voluntarily 

withdrew 

% of approved 

time used 

SYETP 

76.9 4.4 18.4 91.0 

Commonwealth 

2nd SYETP 81.2 2.9 14.6 92.1 

Commonwealth 

SYETP private 55.3 18.5 25.5 76.6 

2nd SYETP 52.6 23.7 23.1 74.9 

private 

Extended SYETP 23.1 24.9 32.7 56.7 

(1) 

For All SYETP 52.8 17.4 25.8 

Source: BLMR (1983) p 22 Table 3.6. Data is Department of Employment and Industrial Relations 

administrative records for commencements to the programme in 1980-81, which thus only refer to ‘flow’ 

into the programme and not the total number or ‘stock’ being treated at any one time. Durations are for all 

completed or terminated placements up to Jan 1982. Table shows proportion completing the full subsidy 

period, terminating for other reason, and these percentages approximately sum across to 100 for each 

programme, with any discrepancy the few who were still in training at the analysis time point. The 

percentage of approved time used is then of the eligible subsidy period. ‘2 nd SYETP’ placements refer to 

commencements where this was the 2 nd eligible 17 week period of SYETP assistance – i.e. multiple 

placements. (1) 19.4 % of extended SYETP were still in training at the time. 

Importantly, an intended sequence to programme attendance was sometimes used for 

placements. Thus, several programmes might be attended, for example combinations of 

education-based EPUY and employment in SYETP in sequence. It was found that in 

these data, which covered the period of one year and were then subject to censoring 

limitations, 8.3 per cent were multiple placements, and of these 24 per cent were 

combinations of education and employment based programmes and a further 30 per cent 

were split SYETP placements (indicating a high level of movement between SYETP 

programme placements within the 17 weeks of subsidy).

77 

Table 2.17 State usage of programmes in 1980/81 

New South Victoria Queensland South Western Tasmania 

Wales 

Australia Australia 

SYETP 

4.5 3.3 2.9 3.7 3.0 6.1 

Commonwealth 

2nd SYETP 0.2 0.2 0.2 0.4 0.7 0.3 

Commonwealth 

SYETP private 52.2 58.6 58.5 49.7 53.8 45.3 

2nd SYETP 2.0 2.4 3.3 3.6 6.5 1.7 

private 

Extended SYETP 7.3 6.6 8.5 10.6 8.3 6.9 

All SYETP 66.2 71.2 73.5 68.1 72.5 60.4 

Other programmes 33.8 28.8 26.5 31.9 27.5 39.6 

for youths 

Source: BLMR (1983) p43 Appendix Table B2. Data is Department of Employment and Industrial 

Relations administrative records for commencements to the programme in 1980-81, which thus only refer 

to ‘flow’ into the programme and not the total number or ‘stock’ being treated at any one time. Table shows 

proportions in the state that sum to 100 per cent of commencements for each state. 

2.2.6.4 Factors affecting completion of subsidy placement 

Once SYETP placements were made, the placement faced normal employment 

conditions, allowing quits and dismissals to occur within the approved subsidy period. 

Ginpil and Hoy (1984) used both employer survey and programme participant survey 

data to examine the factors affecting completion of subsidy placement. In log-linear 

analysis they modelled the completion of the full subsidy period. 

Using the employer data, they found that lower completion rates were linked to 

employers recording both poor job skills and ‘the occurrence of problems with work or 

behaviour’ for a placement, and poor work habits. However the ‘good work habits’ of a 

placement did not lead to higher completion except when there was no ‘occurrence of 

problems with work or behaviour’ for placements. Higher than average gross wages 51 to 

placements also led to higher completion rates. More placements were fully completed in 

larger firms, and completion was found to be more generally related to training and 

support to placements if problems with work or behaviour were perceived. 

51 The question in the 1982 survey based this amount around the value $140 or more per week.

78 

For the placement data, higher completion rates for private SYETP placements were 

found for school leavers, who had not held a past full-time job and those with higher 

levels of schooling (not early school leavers). The placements where it was perceived that 

no employer help was received when problems were experienced had lower chance of 

completion. Completion of placement was found to be related to the higher morale of 

placements, where they thought they had a better chance of employment now than before 

the placement. 

2.3 Critical Review of Evaluation of SYETP 

This review is limited to micro-economic evaluation evidence of the employment effect 

of SYETP, where individual record data are used. It thus covers works which were 

carried out on the same general basis as our later analysis. Some papers are not included 

in the evaluation critique, where it was not deemed an evaluation if the analysis contained 

no modelling of employment impact. Some useful information about the SYETP 

programme operation and characteristics of participants from these types of study are in 

the earlier sections, as are some macro-economic analyses such as Smith (1983, 1984b). 

Early evaluation of the SYETP during 1980 is referred to in BLMR (1983). These were 

studies carried out by the Departments and unpublished 52 , which makes it difficult to 

assess their evidence. The usefulness of their findings is however commented on in 

BLMR (1983). Reference is made to their covering various different time periods, and it 

is pointed out that “the methodologies used have made it difficult to make comparisons 

between programmes or to consider the combined impact of the programmes” (BLMR 

(1983): 3). Baker (1984) also refers to these evaluations and comments that they did not 

control for individual characteristics, but instead used contingency tables or crosstabulation. 

52 Cited p2 BLMR (1983): Department of Employment and Youth Affairs (1980) “Special Youth 

Employment and Training Program: employer study”, Melbourne, unpublished; Department of 

Employment and Youth Affairs (1980) “Special Youth Employment and Training Program: a study of the 

initial 1000 trainees”, Melbourne, unpublished; Department of Employment and Youth Affairs (1980) 

“Special Youth Employment and Training Program SYETP in the private sector: follow-up survey of April 

1979 placements”, Melbourne, unpublished.

79 

2.3.1 Stretton (1982, 1984) 53 

Stretton (1984) used survey data of participants in 5 programmes collected in November 

1981, where the 5 programmes were EPUY, GTA on-the job and 3 types of SYETP- 

Commonwealth, private, and second serve. The survey took place about 6 months after 

programme participation, was of a sample of 1500 participants who completed, 

terminated or withdrew from the placement in April/May 1981 and had a response rate of 

63 per cent with 945 cases for analysis. Stretton (1984) himself pointed out that he did 

not have a comparison group, and that the possibility of selection bias existed with regard 

to modelling employment, and non-response bias might be present. An analysis of nonresponse 

was made using the administrative data for the sample. It was found that 

younger persons, females and programme completers were more likely to respond. 

Participation in one programme was compared to participation in another programme, by 

including a set of programme dummies. Employment (two-types: full or part-time job at 

interview, or any job since programme) was modelled using a logit of labour market 

status. The regressions were run with age, continuous age, education (dummy with 1 for 

school leaver at 16 years or younger), duration of unemployment prior to programme 

entry, programme (EPUY was the base), and state (Tasmania was the base). Positive 

coefficients, statistically significant at the one and five per cent level, were present for all 

programme dummies, and for SYETP this was true in both employment at interview and 

any employment since the programme. 

To investigate retention, the same model was run on just the sample of those who were 

not retained by their placement employer. No programme dummies were significant. It 

was concluded completers not retained in their placement job did no better than those 

who did not complete. It was noted that the observed retention rates differed for the 

various programmes, with only 60 per cent of GTA retained, 70 per cent of SYETP 

private, 70 per cent or SYETP second serve, and 80 per cent of SYETP Commonwealth 

(Stretton (1984): 86). Also, less than 5 per cent of SYETP or GTA programme 

participants left early (Stretton (1984): 87). Then the employment model was adjusted to 

include dummies about completion, the resulting model interpreted as comparing those 

53 As the first of these is early findings, only the second is discussed here.

80 

who completed job-based programmes and were not retained to those who left early to no 

job. The results of this model were held to show that of those who withdrew early 

without a job to go to, those who began job-based programmes had higher probability of 

employment than those in EPUY. Overall, it was concluded SYETP and GTA had better 

employment outcomes than EPUY. Although this was the conclusion, the size of the 

employment effects for these programmes were not calculated but were instead inferred, 

with conclusions drawn from the positive and statistically significant coefficients from 

the SYETP and GTA regressions. 

Stretton (1984) did not have a control group. Instead, participation in one programme was 

compared to participation in another programme by including dummies. The main 

disadvantage to this is that it alters the evaluation question from whether the programme 

was effective in improving post-programme employment in an absolute sense, to the 

relative issue of whether the programme was effective compared to another programme. 

The participation in different programmes, which was included as right hand side 

variables, might be endogenous. The various programme participations are not modelled 

and this introduces the problem of selection bias. No adjustment for non-response is 

made, although the potential presence is briefly treated. The investigation of retention 

holds a number of problems. EPUY remained in the first model as the base, and yet postprogramme 

employer retention was impossible for this education programme. The 

dummies to reflect completion combined completion with all the programme types, and 

are not ideally constructed. The construction is problematic as retention rates varied by 

programme, as well as completion rates, and the length of each programme was very 

different. In discussion Stretton points out that two thirds of EPUY participants left early 

to start a job, but then of these 40 per cent started an SYETP job. This indicates that in 

the second retention model the ‘leaving early to a job’ is really a type of EPUY-SYETP 

programme combination. That less than 5 per cent of SYETP or GTA programme 

participants left early indicates the base would be very small. Bases for the regressions 

are not shown in the results presented so this cannot be assessed. In concluding, Stretton 

pointed out that non-response bias could also play a role in the ‘early leaver’ analysis.

81 

Stretton attributed the success of SYETP to retention. This falls in with the generally 

stated belief held in the BLMR analysis that the chief advantage of the subsidy was firstly 

the immediate impact on the employment status, where their ILO labour market status 

was now employment instead of unemployment, and the strong potential to remain in the 

job after the subsidy finished. Non-completion was however common for most 

programmes with only 54.5 per cent of all programmes completed, and a large part of 

early analysis focused on withdrawal and completion rates, together with their reasons 

(BLMR (1983) p22 Table 3.6). Earlier BLMR (1983) analysis reported that the 

programme provisions themselves were a key source of this variation. The earlier section 

2.2.6.3 and Table 2.16 gave SYETP completion rates in the administrative data and 

showed that Private SYETP had low completion rates. But for Private SYETP in 

particular, a uniquely large number, approximately half of early terminations, were 

dismissals. It was in contrast, not surprisingly, extremely rare for EPUY training 

participants to be terminated. 54 In BLMR (1983) it was concluded that job search 

accounted for the large part of voluntary withdrawal. Stretton did not account for 

dismissals in his modelling. Of early leavers with no job to go to, employment chances 

would likely be strongly affected by dismissal, in a different way to voluntary withdrawal 

for job search or other reasons. 

2.3.2 Baker (1984) 

Baker (1984) used survey data of participants to evaluate the employment impact of a 

number of programmes, including SYETP. The survey was of 3713 persons who were 

surveyed in May 1982 after they were selected from programme administrative data. The 

overall response rate was 66 per cent, but varied by the programme sampled: the response 

rate for private SYETP was 60 per cent, but 69 per cent for Commonwealth SYETP. 

Employment and labour market activity was observed at about six to eight months after 

programme participation. The raw full-time employment share at interview date for 

participants of SYETP private was 60.6 per cent, slightly lower for Commonwealth 

54 For EPUY the statistics were: 4.5 per cent dismissals, 37.6 per cent voluntary withdrawal, and a 57.5 per 

cent completion rate; Mean approved program period 14 weeks, average observed approved time used 80.8 

per cent Source: Table 3.6, p22 BLMR (1983).

82 

SYETP at 53.6 per cent, 45.6 per cent for repeat SYETP and 52.4 per cent for extended 

SYETP. A small share between 5-6 per cent was in part time employment. Fulltimeemployment 

prior to SYETP was lower for SYETP Private at 52 per cent, so before 

controlling for individual characteristics they were more likely to hold a job after than 

before the programme (Baker (1984) p16, Tables A4 and 5.1). For other SYETP, post 

programme employment was lower or the same. 

A probit model of response to the survey was found to indicate response bias might be 

present. 55 The various programme dummies were significant in modelling response, 

where response was a full response with no missing items. The post-programme full-time 

employment outcome was modelled using a variable with 3 categories: continuous 

employment, including those retained in the job and those who left after the subsidy but 

got a job within a month; non-continuous employment, including those retained in the job 

for only 1 month after subsidy expiry but who later lost it by the survey date, and those 

who left after the subsidy and took longer than one month to find a job; no employment, 

including unemployed, education or other states of not in the labour force. This variable 

combined holding a job since the programme and the durability of the jobs held since 

programme, as well as the job search success, to perhaps to reflect employability. Table 

2.18 shows the distribution of this variable for the various SYETP. It can be seen for 

example, that the continuous full-time work category for SYETP private is much lower at 

41.1 per cent than the share in employment at interview (60.6 per cent). However, about 

80 per cent of SYETP private participants had held some full-time employment. 

The employment variable with three categories (no employment, continuous and noncontinuous 

employment) was then modelled as an ordered probit, as if it were count data. 

The selection process onto programme participation was not modelled. However, the 

ordered employment probit was adjusted for non-response using the Heckman model. To 

adjust for non-response, the non-response model was used “…to estimate a correction 

factor LAMBDA…included as an additional independent variable”, and this variable was 

55 Baker (1984) p49 Table A11 Variables included age, sex, unemployment (weeks in 52 week year before 

program entry), multiple program participation, dismissal, voluntary withdrawal, 10 program dummies with 

SYETP private as Base, state dummies. State dummies were not statistically significant.

83 

included in the employment model. This is the Heckman selection correction model 

where the lambda is the inverse mills ratio. The employment model had the variables age, 

sex, year left school (but not qualifications), post-school formal training (any post-school 

qualifications), a post-school job (pre-programme), weeks unemployment (weeks in 52 

week year before programme entry), multiple programme participation, dismissal, 

voluntary withdrawal, 10 programme dummies with SYETP private as base, state 

dummies and the LAMBDA correction factor. The response model used a subset of these 

variables, so that the exclusion restriction variables were year left school, post school 

formal training and post school job (pre-programme). 

No additional results were presented showing the sensitivity of the employment model to 

whether or not nonresponse was accounted for, so it is not clear whether the additional 

Heckman selection model performed better than a simpler model without nonresponse. 

Baker estimated predicted probabilities for employment for various subgroups using the 

model outcomes. Predicted probabilities were estimated for the eligibility period of 17 

weeks unemployment in the past 52 weeks, for a participant who left school in year 10/11, 

did post-school training, took part in no other programme and completed the programme 

of participation. They found that SYETP private generally performed better than 

education based programmes, but did no better than other SYETP, and was significantly 

worse than extended SYETP. The results are shown below for SYETP in Table 2.19. It 

can be seen that the ‘out of work’ outcome had a very low probability for all types of 

SYETP. 

Table 2.18 Baker (1984) Post-programme full-time employment outcome 

Continuous 

fulltime work 

Non- 

Continuous 

fulltime work 

Retained Not-retained Total 

21.1 14.3 35.5 33.7 30.8 

SYETP 

Commonwealth 

SYETP private 34.9 6.3 41.1 38.4 20.4 

2nd SYETP 24.3 7.3 31.6 41.2 27.2 

Extended SYETP 37.1 5.5 42.6 30.6 26.8 

Source: Baker (1984) p19 Table 5.2 May 1982 post-programme Survey of participants 

Out of work

84 

Table 2.19 Baker (1984) Estimated probabilities of labour market outcomes for 

participants from the model of employment 

Continuous fulltime Non-Continuous Out of work 

work 

fulltime work 

SYETP 

Commonwealth 

.414 .400 .186 

SYETP private .517 .358 .124 

2nd SYETP .418 .399 .183 

Extended SYETP .611 .307 .082 

Source: Baker (1984) p48 Table A10 May 1982 post-programme Survey of participants. Predicted 

probabilities were estimated for the eligibility period of 17 weeks unemployment in the past 52 weeks, for a 

participant who left school in year 10/11, did post-school training, took part in no other programme and 

completed the programme of participation. 

A chief difficulty with the results of modelling is the likely endogeneity of the 

programmes. As the authors also point out, they did no selection modelling for 

programme entry. They did however try to account for the survey response problem, 

which could introduce bias and inconsistency to the estimates if unaccounted for. The 

means of implementing the Heckman selection adjustment was to enter the selection 

correction term linearly into the ordered probit model, but this is problematic due to nonlinearity 

of the probit functional form. An appropriate model combining response and 

employment would have estimated the equations jointly and allowed for correlated errors. 

A further difficulty with their nonresponse modelling is that it combines item nonresponse 

and survey non-response, which requires the assumption that the same model 

can account properly for them both. There is no non-participant comparison group for 

programme participants, only participants of other programmes are available for 

comparison. This is because the data are from a survey of programme participants only. 

2.3.3 Rao and Jones (1986) 

Rao and Jones (1986) used the same survey data as used for Baker (1984), but with 

additional data from a second follow-up survey conducted in May 1983. The additional 

survey allowed employment outcomes at 18-20 months to be examined, and they 

combined the survey data with the CES administrative data. They defined a quasi-control 

group of those who did not complete the programme, where they used the cutoff of one 

third of programme period to separate the treatment from the quasi-control of non-

85 

completers. Their argument was that such a short experience did not constitute treatment, 

because they would not have had time to benefit much from the programme (Rao and 

Jones (1986): 1). To control for differences in eligibility and characteristics of 

participants, they estimated the post-programme employment chances of “clearly defined 

socio-demographic groups in different programmes” (Rao and Jones (1986): 5). There 

were 2 groups – most disadvantaged and least disadvantaged. 56 They estimated logistic 

regressions of the probability of full-time continuous employment or not, where the 

employment variable’s basic definition was defined as for Baker (1984). The regression 

included grouped age (15-19, 20-24), sex, year of schooling, post-school training and 

completion, post-school job, pre-programme unemployment 57 , past programme 

experience, NSW resident or not, and twelve programme dummies including four SYETP 

dummies for Commonwealth, private, second serve and extended. From the regression 

estimates, the percentage probabilities were calculated. 

They found that individual characteristics as well as the type of programme affected postprogramme 

employment outcomes. Completing the programme or near-completion gave 

better employment outcomes than early withdrawal which was the quasi-control group. 

Employment based programmes performed better than training/education-based 

programmes. They found that of the employment based programmes, GTA on-the-job 

performed better than SYETP, and that this was because GTA did not prescribe prior 

unemployment or age criteria and was ‘occupational demand’ based whereas SYETP was 

aimed at disadvantaged youths. Amongst SYETP, private SYETP and extended SYETP 

were more effective than Commonwealth SYETP. A ‘second serve’ SYETP, where 

former SYETP participants had again experienced long-term unemployment and were 

given a second SYETP placement performed worst of all employment programmes. The 

most-disadvantaged were proportionally more likely to benefit from the programmes. 

However they also concluded that as the probability of post-programme employment was 

56 Rao and Jones (1986) p6. Most disadvantaged: 15-19 years, female, schooling to year 9, started but 

incomplete post-school training, no post-school full-time job, unemployed for more than 12 months prior to 

placement, no other program participation, NSW resident. Least disadvantaged: 20-24 years, male, 

completed schooling to year 11/12, completed post-school training, had a post-school job, no 

unemployment prior to placement, other past program experience, not NSW resident. 

57 The pre-program unemployment groups are: none, less than 3 months, 3-6 months, 6-12 months, >12 

months.

86 

still much lower for the most-disadvantaged, they were still worse off than the leastdisadvantaged 

after the programme. This arose because of the difference in employment 

chances for non-participant– i.e. the quasi-control group for the most-disadvantaged and 

least-disadvantaged had very different employment chances. Table 2.20 shows their 

results for SYETP. The results show a positive impact on full-time continuous 

employment for all groups after SYETP participation, however the size ranges from 5 to 

55 per cent. 

Table 2.20 Rao and Jones (1986) Estimated percent post-programme full-time 

continuous employment chances 1981-1983 

Least Disadvantaged 

Most Disadvantaged 

Completed 

programme 

Difference to 

quasi- control 

Completed 

programme 

Difference to 

quasi-control 

SYETP 

63.4 44.8 8.2 7 

Commonwealth 

SYETP private 72.0 53.4 11.7 10.5 

2nd SYETP 55.7 37.1 6.1 4.9 

Extended SYETP 74.4 55.8 13.0 11.8 

Quasi-control 18.6 1.2 

Source: Rao and Jones (1986) Table 3, p24 

This analysis made an effort to define comparison and treatment groups based on 

observed characteristics, while lacking true control data in a survey of participants. The 

quasi-control would be subject to a reasonably large extent of contamination bias, as the 

data are based on survey data recalling labour market history. Contamination bias is 

where the treatment and comparison groups are not cleanly defined and mixing can occur. 

There is also the strong possibility of selection bias. The Baker (1984) results showed 

some evidence that completers and non-completers had different characteristics, and had 

different raw employment outcomes. Rao and Jones (1986) confirmed that controlling for 

individual characteristics was essential. It is clear that individual characteristics might 

also influence which programme an individual participated in however this was not 

controlled for. There was no modelling of programme entry. Again, the various 

programmes were simply dummies in the employment equation, which is subject to the 

same difficulties as the Baker (1984) analysis. Baker (1984) showed that non-response

87 

was an issue for the data. Unlike the Baker (1984) analysis, survey non-response was 

ignored by Rao and Jones (1986). 

2.3.4 Richardson (1998) 

Richardson (1998) used panel data spanning 4 years of repeat surveys of the Australian 

Longitudinal Survey (ALS). The analysis is based upon the ALS list sample, which was a 

sample selected from the CES records for young unemployed aged 15-24. The ALS list 

sample was a nationally representative sample of Australian youths aged 15-24 who had 

been registered as unemployed with the Commonwealth Employment Service for at least 

3 months in June 1984. They were interviewed in September-October each year from 

1984-1987. 58 Using the labour market history, those who entered SYETP job placements 

during 1984 until the 1985 interview defined the SYETP treatment group and comparison 

group reference period. The subsequent survey data on labour market history until the 

1987 interview date was used to examine post-programme employment outcomes. 

An analysis of attrition was made by examination of the means. It was concluded that the 

characteristics were virtually identical and so attrition bias was treated as a minor 

problem (Richardson (1998): 5). It was however noted that those lost to attrition were 

more likely to have less than year 10 education, more likely to have held a job for at least 

1 year and less likely to have never held a job. Accordingly, no treatment of non-response 

was made. It was noted that the treated SYETP had different characteristics than the 

comparison group of non-participants. It was observed that in the raw data, employment 

in 1986 was 14 percentage points higher for those who had participated in SYETP than 

the comparison group, and in 1987 it was 5 per cent higher than the comparison group. 

First, separate probit models of employment and of participation were estimated. Then 

Richardson used a Heckman bivariate probit which controlled for programme selection 

by modelling both the participation and post-programme employment. To identify the 

model, a variable showing CES referrals to another programme, and age of the 

58 Further details of the data are in the later chapter dealing with the replication study.

88 

participant, were excluded from the employment equation and included in the 

participation equation. Additionally, some variables such as marital status were 1984 

dated in the participation equation but updated to 1985/6 values for the employment 

equation. Employment was any non-subsidised post-programme period employment. 

Additional variables that were included in both the SYETP and employment models were 

sex, children in 1984, children interacted with sex, ethnicity, State of residence in 1984, 

marital status, education: school type, year left school, post-school qualifications; initial 

labour market experience: duration of longest job to 1984 reference period; past 

programme participation, pre-programme unemployment in the year to reference period, 

health problems affecting work, attitude to women working and attitude interacted with 

sex, parental education and occupation, number of siblings, English skills, spousal 

employment in 1984, parental education and occupation, religion, urban/rural/overseas 

residence before aged 14. 

The bivariate probit results gave a negative correlation coefficient and Wald tests of the 

correlation coefficient rejected the hypothesis that the errors of the employment and 

participation equations were uncorrelated. The comparison for the bivariate probit to the 

univariate probits showed a strong difference in the measured employment effects. The 

marginal effects of SYETP on post-programme employment were calculated to give 

positive employment effects of SYETP participants over non-participants of 26 per cent 

in 1986 and 20 per cent in 1987 (Richardson (1998): 11). To examine retention of 

subsidised jobs, the modelling was repeated for employment in 1987 and the sample was 

limited to exclude those in the treatment or comparison groups who were continuously in 

a job from the 1984/5 reference period until the 1986 interview. It was noted that similar 

shares of the SYETP and comparison groups were excluded. The results were similar to 

those found from the first bivariate probit model. The marginal effect calculated for this 

equation showed again a positive employment effect for SYETP participants over the 

comparisons of 23.7 per cent (Richardson (1998): 12). Retention was concluded to be 

important, but not the only source of positive post-programme employment effects for 

SYETP participants. Table 2.21 summarises the employment effects found for 

Richardson (1998).

89 

Table 2.21 Richardson (1998) Estimated marginal effect of SYETP on employment from 

bivariate probit modelling 

effect on employment 1986 effect on employment 1987 

All data used 26.4 19.7 

Excluding jobs or placements 

retained to 1986 

23.7 

Source: Richardson (1998) p22 Table 6, and p12. 

The Richardson (1998) analysis usefully modelled both the participation in SYETP and 

the post-programme employment using the Heckman selection method in a bivariate 

probit. Earlier SYETP evaluations only modelled employment, none modelled 

participation in SYETP. The modelling included numerous variables that account for 

individual characteristics. This was by far the largest set of individual characteristics used 

in modelling the post-programme employment for SYETP. Generally, most of these have 

been used in the economics literature, if available in the data, to explain employment, 

education, or programme participation. The wide variety of variables included illustrates 

the very rich dataset the ALS has to draw on. The inclusion of a wide set of individual 

characteristics can help demonstrate that all the useful observable characteristics have 

been controlled for in modelling both the employment and programme participation. 

However some of the variables, such as religion, are possibly more unusual for 

describing economic models of employment or programme participation. 

Although the non-response was briefly examined, the examination was terse. The 

conclusions about the possible effects of non-response were based on little serious 

contemplation of non-response. Some earlier SYETP evaluations (Stretton (1984), Baker 

(1984)) modelled non-response for their observational data and found non-response was a 

potential problem. Richardson (1998) contains no modelling of non-response, yet the 

potential for non-response is much higher in a panel constructed from 4 surveys. As for 

earlier SYETP evaluations, again no treatment for non-response was made, although in 

the case of Richardson (1998) this was in line with the conclusions whereas for earlier 

evaluations this was in spite of the conclusions about the existence of a potential nonresponse 

problem for estimates.

90 

An advantage over earlier SYETP analyses is that the comparison group was nonparticipants 

rather than participants in other programmes. This allows the more pertinent 

question for evaluation to be asked: instead of whether the programme was effective 

relative to another programme to whether the programme was effective in improving 

post-programme employment in an absolute sense compared to non-participants. 

Retention in the post-programme job is addressed through sample selection for those 

where continuous employment to the end of 1986 was not the case. This has potential to 

introduce the need to further model the selection process of retention or tenure in the one 

job, as it may be endogenous. Essentially, it selects a sample with short tenure jobs, and 

durations are censored at 1986. This might introduce other difficulties related to duration 

modelling and censoring of spells too. Richardson (1998), in relation to concerns for the 

sample reduction, examines the means of the retained amongst the treated and 

comparisons, and the effect on size of the treated sample against that of the comparisons. 

The conclusion of no bias problems relies on the observation that the retained are a 

similar share of treated and of comparisons, and that the proportion of SYETP before and 

after sample reduction is the same. This examination is more related to balance of the 

retention characteristic in the treated and comparison groups than whether retention is 

related to participation in SYETP.

91 

2.3.5 General discussion 

Some general observations are apparent in comparing the SYETP analyses. All analyses 

of SYETP proceeded with survey data to enable the recording of post-programme 

employment. The earlier analyses of SYETP did not have a non-participant comparison 

group, whereas the Richardson (1998) analysis did. The results enable a gauge of whether 

participation in the SYETP programme was effective in improving post-programme 

employment. All analyses found positive effects. 

Three key themes emerge from the past analyses of SYETP. These relate to accounting 

for selection into programme participation, non-response in the observational data used, 

and controlling for differences in individual characteristics between the comparison and 

treatment groups. These themes are further developed in the later chapters of this study. 

In the following discussion, the past SYETP literature is assessed with regard to these 

points. 

Richardson (1998) is the evaluation of SYETP motivating this current study. The format 

of the Richardson (1998) evaluation forms a key basis for our analyses, which are 

contained in the later chapters starting with the Replication Study. In brief, Richardson 

used a Heckman bivariate probit which controlled for programme selection to analyse the 

SYETP using ALS panel data. A strong positive effect on post-programme employment 

was found. In this review, this evaluation is considered to contain the most sophisticated 

analysis of SYETP to date, allowing for both programme selection and individual 

characteristics. This evaluation also showed evidence that accounting for selection into 

SYETP participation in the modelling of employment was important in two ways: by 

testing the correlation coefficient and finding it statistically significant; and by showing 

the comparison to the unadjusted univariate employment results, which were also very 

different despite the employment equation being otherwise specified identically. The 

modelling of the effects on employment was the best developed of those evaluations 

reviewed, since it took into account selection into programme participation.

92 

However, the Richardson (1998) examination of non-response was less developed than 

the earlier SYETP analyses where non-response was considered. In common with all the 

SYETP analyses, survey data was used. Stretton (1984) and Baker (1984) modelled nonresponse 

in their observational data and found non-response was a potential problem. As 

both of these had the administrative sample frame to identify some variables for 

modelling non-response to the survey, they were at an advantage. Richardson (1998) 

could not benefit from the administrative sample frame as this was not available with the 

survey data deposited, and this precluded this type of analysis. However it was possible 

to choose to use the non-response weights provided with the data, which had been 

developed from the non-response analysis for the first survey using the administrative 

data. Further, the panel of data used had 3 subsequent surveys, to which non-response 59 

could be modelled from the first survey. 

In common with the earlier analyses, no treatment for non-response was applied. In the 

case of Richardson (1998) this was in line with the conclusions maintained from the 

analysis of non-response. For earlier evaluations by Stretton (1984) and Baker (1984) this 

was in spite of the conclusions about the existence of a potential non-response problem 

for estimates as a result of useful models of survey non-response from the administrative 

data sample. For Rao and Jones (1986), which used data from Baker (1984) but with 

additional survey data, ignoring non-response was in conflict with the Baker (1984) 

analysis, and the aggravation of non-response problems due to further attrition to the later 

survey was also ignored. In review it is likely that the choice of models potentially 

available with computing resources at the time limited modelling in these earlier analyses 

of SYETP. A choice had to be made as to what was the most important modelling 

consideration, given the computational capabilities of the time. As a result, non-response 

was not further accounted for. This is an adequate modelling assumption, but further 

invokes the assumption that the non-response was ignorable for their modelling. In 

Stretton (1984) and Baker (1984), the examination of non-response indicated this was not 

generally tenable. This issue is dealt with in the study of non-response that follows. 

59 Usually termed attrition in this case.

93 

Controlling for differences in individual characteristics between the comparison and 

treatment groups was carried out in all of the SYETP evaluations using econometric 

modelling of the post-programme employment. This accounts for observed differences in 

individual characteristics between the comparison and treatment groups by including 

them in the model. The earlier modelling showed that controlling for individual 

characteristics was important, but the set of characteristics was limited. The key issue 

then is whether all the observable characteristics that might affect employment have been 

included. The Richardson (1998) analysis had a very broad set of characteristics and it 

showed that some of these, such as education, are indeed important for modelling 

employment. Leaving out important variables for modelling employment is likely to lead 

to specification bias. 

Controlling for differences in individual characteristics between the control and treatment 

groups is treated further in this study when different modelling methods are used. As the 

earlier review of wage subsidy evidence shows, matching techniques can also be used to 

resolve differences in individual characteristics between the comparison and treatment 

groups. Later Australian evaluation of the Jobstart wage subsidy used direct matching 

techniques. These methods however require a limited set of characteristics and large data 

sample in order to enable the analysis to proceed. The more characteristics it is desirable 

to match on, the fewer matches become available amongst the treatment and comparison 

groups. As the earlier modelling of SYETP showed, controlling for individual 

characteristics was important, and the Richardson (1998) evidence indicates the breadth 

of characteristics that might be useful. Matching, such as that of the Jobstart evaluations, 

which does not control for these other characteristics that affect both employment and 

participation would then be subject to bias. As such, it is then most useful to control for a 

fairly broad set of characteristics. Other recent overseas evidence has used propensity 

score matching (PSM) techniques, which allow a greater number of characteristics to be 

controlled for, for example Bonjour et al (2001). This method has not been used for any 

published Australian evaluation so far. The PSM method is applied in the following study.

94 

3: Study 1 Replication 

In this section, the first aim is to first replicate the bivariate probit analysis of SYETP as 

reported by Richardson (1998). The key value of the replication is to validate the results, 

and to ensure that the later analyses are carried out using comparable data. The 

Richardson (1998) analysis was the most sophisticated of the SYETP analyses, as shown 

in the earlier review. This study has selected to build upon Richardson (1998) for this 

reason. 

Firstly, the motivation for replication is presented. Then, the methods needed to carry out 

the replication are described. The data are briefly detailed. Finally, the results of the 

replication are shown and discussed. 

3.1 Motivation for replication 

A number of papers have discussed the importance of replication. King (1995, 2002) has 

been one of the more vocal in recently maintaining the importance of the scientific step 

of replication. However, earlier work by Dewald, Thursby and Anderson (1986) also 

presented strong evidence of the value of replication. King (2002) p1 describes the 

‘Replication Standard’: 

“Sufficient information exists with which to understand, evaluate and build 

upon a prior work if a third party can replicate the results without any 

additional information from the author.” 

Empirical evidence that meets the replication standard allows the conclusions of the work 

to be maintained and built upon in accordance with the scientific method. King (1995) 

argues convincingly that “…Without complete information about where the data have 

come from and how we measured the real world and abstracted from it, we cannot truly 

understand a set of empirical results…” (King (1995): 445). King (1995) p 445 also 

suggests that replication is a prerequisite to further development and is the most 

productive method of building on existing research, by “…following the precise path

95 

taken by a previous researcher and then improve on the data or methodology in some way 

or another…”. 

Dewald et al. (1986) provide evidence of the value of the application of the replication 

standard. They point out that the confirmation of research findings through replication by 

other researchers is an essential component of scientific methodology. To examine the 

role of replication in empirical economic research they took one year's worth of articles 

from The Journal of Money, Credit and Banking and attempted replication of all the 

articles. Extensive efforts were reported but also largely failed attempts to replicate the 

findings as they were reported. They often found that replication was impossible based on 

the information in the articles, and also subject to ambiguities, errors and oversights in 

the reporting. They emphasized that replication is an essential element in the evaluation 

and unification of the results for a group of studies. 

3.2 Methods: The Heckman selection model 

The specification to be replicated is the bivariate probit of employment, where the 

selection equation modelled is that of selection into SYETP. The analysis based on the 

‘1986 data’ is attempted here, and the results as shown in Richardson (1998) Tables 4 and 

5 are replicated. 

3.2.1 Self-selection and the evaluation of programmes 

Evaluating the benefits of a social programme is one of the major uses of self-selection 

models. There are many variations of the self-selection model depending on the problem 

the model is applied to solve. The terminology is that of the experiment, where the 

programme is the treatment being evaluated. The individuals who participate in the 

programme are the treatment group and others not participating are in the comparison 

group. In the case of the SYETP, it is assumed that the programme administrator 

essentially makes the assignment of individuals to the treatment groups, but the process is 

jointly determined with the employers and the participant. Selection onto the programme 

could have led to systematic differences between those who went on the SYETP and

96 

those who did not. Modelling only the employment infers that the selection onto the 

programme was a random process. The selection problem arises where programme 

participants are not selected randomly from the population. The aim of the programme is 

to improve employability. Employability after the programme is not observed, but instead 

post-programme employment is observed. The probability of participating is not 

observed, but participation is observed. The evaluation then needs to resolve the link 

between employment and programme participation. The selection modelling problem 

occurs if there are unobserved characteristics affecting both participation and postprogramme 

employment, for example their employability before the programme. 

3.2.2 The estimated model 

Richardson (1998) states his estimated model is of the form used by van de Ven and van 

Praag (1981). In van de Ven and van Praag (1981) the model was used for the decision to 

prefer health insurance with a deductible, to one with complete coverage, and the sample 

selection problem arose as a result of non-response. Their extension to the Heckman 

(1979) method of sample selection control was to apply it in the probit analysis context, 

where not only the first but also the second stage has a discrete dependent variable. 

Maddala (1983) points out that the econometric discussion of the consequences of selfselectivity 

began with the studies of Gronau (1974), Lewis (1974) and Heckman (1974), 

but that it was not until Heckman (1976) that the two stage selection model was 

suggested as the solution. Essentially, by fully modelling the entry into participation, then 

allowing a correction factor for the participation to be included in the employment model, 

the sample selection process affecting employment can be reasonably accounted for. 

However, the bivariate probit can also be seen as a system of simultaneous equations, a 

simultaneous probit model. More recently, Knapp and Seaks (1998) describe a two 

equation recursive probit model and note that the bivariate probit model can be analogous 

to the results by Lahiri and Schmidt (1978) for the triangular system of simultaneous 

equations with cross equation error correlation.

97 

If employability is assumed to be a latent variable y*, and the probability of being 

selected onto the programme is d*, then the bivariate probit of the joint probabilities of 

selection and employment then takes the form: 

1) y i *=αd i + β′x i + ε i employment equation 

where y i =1 for y i *>0 

y i =0 otherwise 

2) d i *=γz i + v i participation equation 

where d i =1 for d i *>0 

d i =0 otherwise 

d i = dummy variable with value 1 for participation in wage subsidy, 0 otherwise 

d i *=probability of being selected for participation in wage subsidy 

x i =vector of exogenous individual characteristics 

v i = error term, distributed as standard normal 

ε i =error term, distributed as standard normal 

y i =employment with value 1 for employed in a time period, 0 otherwise 

y i *=employability 

z i = vector of exogenous individual characteristics 

Each subscript i indicating the individual. 

Employment y is observed where y=1 if the periods is employed in some period, and y=0 

otherwise. If y=1 when y*>0 and y=0 otherwise, then equation 1 can be estimated as a 

probit if it is assumed that ε i is distributed as a standard normal. However, it may be there 

is a selection problem as programme participants are not randomly selected from the 

population. If the probability of being selected onto the programme d* is determined by 

equation 2, and v i are distributed as a standard normal, and there are unobserved 

characteristics that affect programme entry and subsequent employability, then ε i and v i 

are correlated. The bivariate probit assumes that ε i and v i are correlated, and that they 

follow a bivariate normal distribution. 

E(ε i v i )=ρ 

Prob (y i =1, d i =1) = Φ (αd i + β′x i , γz i , ρ)

98 

Φ = cumulative distribution function for the standard bivariate normal 

This approach has been applied in several evaluation problems for solving the issue of 

selection bias, where programme participants are not randomly selected, as discussed in 

the brief review of Maddala (1983). It allows for selection into participation in the wage 

subsidy on the basis of variables that are unobservable, or more usually variables that are 

unobserved in the data. The model is made operational by assuming a bivariate normal 

distribution for the errors in the participation and employment equations. This assumption 

makes the unobservable characteristics that jointly influence participation and 

employment follow the bivariate normal distribution. The participation equation is 

explicitly modelled to provide a variable that is then used to control for the part of 

unobserved variation of the employment equation that is correlated with the unobserved 

variation in the participation decision. With full information maximum likelihood 

methods, the selection and employment equations are estimated jointly, in which case the 

correlation between the unobservables is estimated directly. 

The sets of exogenous variables x i , z i can be overlapping, in which case only the 

functional form assumption provides identification. However, in practice this is not 

recommended as identification based solely on functional form is likely to be empirically 

fragile. The model is more appropriately identified if at least one variable in the set z i is 

not in the set x i , as the exclusion restriction improves the identification of the parameters 

of interest. Thus, practical implementation usually requires a variable included in the 

participation equation estimated that is excluded from the employment equation. 60 This 

exogenous variable should be suitable in that it influences selection but not employability. 

An important aspect of this estimation approach is the identification of a credible 

instrument for the exclusion restriction. Also, the results of estimation rest upon the 

60 Wilde (2000) points out that earlier references presented contradictory opinions on this matter, so that 

Maddala (1983) held that a model with overlapping sets of exogenous variables x i , z i was only identified if 

at least one variable in the set z i is not in the set x i , while Heckman (1978) maintained that the functional 

form sufficed for identification. However Wilde (2000) clarifies that Maddala’s argument is only valid for 

his particular example, and is not the case generally due to the nonlinear relationships between z and the 

probability P(d=1 | z), and further concluded it could be avoided by assuming each equation contains at 

least one varying exogenous regressor. Wilde (2000) points out that this is a rather weak assumption in 

economic applications. Hence, Maddala’s argument commonly holds in econometric practice.

99 

suitability of the underlying assumption of the bivariate normal distribution for the errors 

in the participation and employment equations. 

3.3 Data and variables used for estimation 

The first stage of the replication, whereby the variables used in the analysis are reconstructed, 

is made easier by access to the final data set used as the basis for the 

Richardson (1998) analysis. 61 It was pointed out by Dewald et al. (1986) that access to 

the author’s data, with the variables in the final form used for the analysis, is an 

invaluable assistance in replication, but observed that this was an unusual occurrence. 

They attributed this to angst held by authors as they “…may interpret the very act of 

replication as a challenge to their professional competence and integrity” (Dewald et al. 

(1986): 601). In light of this, the access granted by the author James Richardson is 

strongly appreciated. 

As a result of access to the transformed data, replicating the exact construction of the 

variables is simplified. This would normally be an exacting task, as the construction of 

complex variables such as those derived from the work history is extremely difficult to 

repeat without the exact details of construction, and even the exact syntax coding. Any 

variation in construction would make replication far more difficult, and overcoming this 

hurdle also allows interpretation of the replication results to be more precise. It should 

not be overlooked that this stage of a replication would normally be most difficult. The 

variables are described in the data appendix. 

A brief introduction to the data is informative. The Australian Longitudinal Survey List 

Sample (Mcrae et al. 1984-1987) is used. This was a sample drawn from an 

administrative sample frame. The ALS list sample was a nationally representative sample 

of Australian youths aged 15-24 who had been registered as unemployed with the 

Commonwealth Employment Service for at least 3 months in June 1984. The 1984 

survey took place with interviews between September 1984 and November 1984. The 

survey was repeated in each of the later years 1985, 1986 and 1987, following up where 

61 The data were transformed from the ALS original SPSS files in extensive STATA processing by 

Lorraine Dearden, Alex Heath, Henry Overman, and James Richardson.

100 

possible the same people with whom interviews were conducted in 1984. Information 

was collected about job history, job search behaviour, job training, transitions from 

education to work, education, occupation, health, attitude to women working, parental 

education and occupation, racial origin, country of birth, age, sex, size of household, 

spousal education and occupation, religion, income, and urban/rural residence. The 

background variables available for study are then quite extensive. Further discussion of 

the data details is covered in the later sections to which it is immediately relevant. 

The breakdown of SYETP types as indicated in Chapter 2 is not available in the data. 62 

The key issue of programme heterogeneity would appear to be problematic. It is however 

possible to identify whether the SYETP job was in the public or private sector, from the 

job characteristics. All SYETP placements identified using the SYETP variable in the 

data were for private sector SYETP. The possibility of Extended SYETP placements is 

addressed and he notes that the analysis of the spells indicates that there were not spells 

of lengths indicative of extended SYETP placements (Richardson (1998): 9). 

Careful attention is paid to the description of the sample selected for analysis as reported 

by the author, in order to enable the same base to be used for the replication. The limits of 

the sample are well described detailing the exclusion from those interviewed in 1984 of 

those who were over 25 years at the interview date, those who are in full-time education, 

or for whom responses were missing in 1984, 1985 or 1986 (Richardson (1998): 5). The 

final observations number 1283, of which 104 are in the treatment group ‘SYETP’. 63 

Applying these rules, the same number of cases is successfully achieved, and the 

replication of the estimation is repeated on this data. Replication of the sample can be 

highly informative, as this is where most of the difference in results arose in the Smith 

and Todd (2000, 2003) replication of work by Dehejia and Wahba (1998,1999). 

62 Jobs which are placements to any SYETP program are simply described as SYETP, with the program 

variations not accounted for. 

63 As Richardson (1998) details, roughly half of these 104 SYETP had program participation start weeks in 

1984, with the rest in 1985. The average start week is the week beginning 13 January 1985, and the range 

of start weeks is 72 from week 40 to week 112.

101 

Heckman, Lalonde and Smith (1999) p1912 clearly point out that different choices about 

data handling such as variable choice and decisions about whom to include in the sample 

can strongly influence the measurement of treatment impacts. To avoid differences 

arising from this source, the initial replication is made as close as possible to that of the 

original. Using the reported bivariate probit equation in Richardson (1998) Tables 4 and 5, 

the variables matching those described are selected for use in the estimation. This means 

that the same identifying restrictions are applied. Thus, CEP referrals and age are 

excluded from the employment equation but included in the selection equation, 

additionally in the employment equation qualifications, children, marital status and health 

affecting work are 1985 or 1986 dated and in the selection equation they are 1984 dated. 

The same set of base variables are used for the equations as that of Richardson (1998) by 

reference to the reported bivariate probit results. The variables are described in detail in 

Appendix 1: Data Appendix, Table 1. Analysis was carried out in STATA 7.0. 

3.4 Replication results 

The results of replication for the bivariate probit are shown in Table 3.1, parts a and b. To 

facilitate comparison of the replication result with the original results, the estimates as 

shown in Richardson (1998) Tables 4 and 5 are repeated in column 1 of Table 3.1 part a 

and part b. Table 3.1 part a shows the employment equation of the bivariate probit, while 

Table 3.1 part b shows the selection equation of the bivariate probit which models the 

entry into SYETP. The second column of Table 3.1 gives the replication estimates. 

The replication is found to be generally successful. The replicated results match closely 

those of Richardson (1998). There is some minor variation, but at the 2 decimal places 

level, most estimates of the coefficients are identical. There is slightly more disparity in 

the t-statistics. The coefficient of key interest is the SYETP variable in the employment 

equation, which measures the treatment effect. In this case, the replication result is 

slightly larger, and the t-statistic is also larger. However, in no instance does the 

replication report results that are not closely comparable to those reported in Richardson 

(1998). The appendix Tables A2.0a and A2.0b show the replicated univariate probits, for

102 

which the employment equation was also reported in Richardson (1998) Table3, p16. 

These are identically specified to the bivariate probit, but of course are separate and 

independent. When compared to the Richardson estimates, these simple probit replicates 

are identical. Additionally, the means for all the variables were calculated and found to 

match those of Richardson 1998 Table 1 (these can be seen later in Chapter 5, for 

example Table 5.2). As the simpler replication exercises produced results identical to 

those of Richardson (1998), the small discrepancies observed are thought to arise from 

minor differences in the estimation algorithm. These analyses are conducted in STATA 

7.0, the Richardson (1998) analyses were in Stata 5.0, in later versions algorithm changes 

have been made to the code for implementing the bivariate probit. 

It is useful to briefly discuss the factors found to impact on employment and participation 

in SYETP. Although this is a replication, the results of the estimation are of interest for 

comparison with later results. For reference to the variable definition, see the data 

appendix. Only statistically significant coefficients are discussed. 

In the employment equation, Table 3.1 part a column 2, SYETP has a positive coefficient, 

indicating a positive effect upon employment chances, while other positive effects on 

employment were associated with the partner being in employment, having attended a 

private school, higher post-school qualifications and having left school in year 11 relative 

to the base of year 10 highest qualification 64 , and having held a job for 3 years prior to 

1984. These all represent higher levels of human capital and work experience, and greater 

labour supply and are in the direction they are expected to work. Negative effects on 

employment chances were found for women with children, those with health limiting 

their work, those with longer pre-programme unemployment, those who had other 

programme experience. Of the more unusual variables, Catholic religion had a positive 

64 Note that in the Australian schooling system, a certificate is achieved in year 10 and then another in year 

12 after successful completion of the study program. Those who left in year 10 could include those who 

achieved the certificate or left during the year prior to obtaining the certificate or failed the certificate 

studies. Those who left in year 11 had definitely obtained their year 10 certificate and so qualified to 

continue their studies. Each Australian state has a different system of education and each certificate has a 

different name and required studies.

103 

effect on employment relative to the base of Church of England. 65 Surprisingly, fair to 

poor English had a positive effect on employment relative to natural English speakers, 

however this was a self-assessed question and there might have been a tendency for those 

with non-English-speaking-background to describe their English optimistically 66 , also the 

smallest share had fair-poor English so this rests on a small number of cases. Those who 

gave opinions expressing a negative attitude towards women in work also had lower 

chances of employment – as this is attitudinal material, this may also reflect other 

attitudinal features such as a degree of anti-social behaviour. Maternal background in the 

plant operative occupations also had a negative effect on employment outcomes. 

In the participation equation, age was negative, indicating that older eligible youths had 

lower chances of placements – this is in line with the findings in other SYETP literature. 

The age of the individual was one of the variables excluded from the employment 

equation. The key variable for the selection was however CEP referrals, and this had a 

positive impact on placement chances. Other negative effects on placements were from 

health problems limiting work and childhood background in a country town. Positive 

effects on placement chances came from having highest qualification of year 12 

schooling (relative to year 10 schooling base) and a longer qualifying unemployment 

period also raised placement chances, in line with the eligibility criteria for SYETP. 

Maternal background of plant-operative occupation raised chances of a placement, which 

was opposite to the negative effect it had on employment. 

3.5 Discussion 

It is worth noting, in light of ‘the replication standard’, that no additional information was 

needed to carry out this replication. The replication was achieved using only Richardson 

(1998) and the supplied data set. For a replication data set to be useful King (1995) notes 

that it must include all information necessary to replicate the empirical results. As such, 

65 But this effect may arise together with the type of schooling being private – there is a greater share of 

Catholic private schools. 

66 The base natural English speaking held the greatest share of the 1283 cases (1161 or 91 per cent), with 

good English 93 cases or 7 per cent, and fair to poor 29 cases or 2 per cent. In the 104 SYETP cases, only 1 

had fair-poor English, while 8 had good English and the rest were native English speakers (unweighted 

percentages).

104 

the quality of this enabled the reproduced work, and so the original work met ‘the 

replication standard’. 

In summary, it is concluded that it is evident replication has a key role as a starting point 

for useful development of the analysis presented by Richardson (1998). By replicating, it 

has been shown that the results of further analysis can be isolated as not due to deviation 

in the basic data. In turn, any variations in results achieved in the following studies can be 

clearly sourced to adjustments in the methods applied.

105 

Table 3.1, Part A Employment equation from bivariate probit 

Employment equation from Replication results 

bivariate probit of 

Richardson (1998) 

Model of ever employed in 1986 survey 

SYETP 1.590* 1.596** 

(2.45) (2.85) 

Gender=Female -0.398** -0.397** 

(-3.99) (-4.04) 

married -0.055 -0.054 

(-0.28) (-0.28) 

children -0.238 -0.238 

(-0.98) (-0.99) 

Children*female -1.212** -1.211** 

(-3.79) (-3.80) 

Spouse employed 1984 0.542* 0.542* 

(2.40) (2.41) 

Aboriginal/Torres Strait Islander -0.272 -0.271 

(-1.17) (-1.18) 

Other ethnic minority -0.318 -0.318 

(-1.66) (-1.66) 

Work limited by health -0.306* -0.305* 

(-2.43) (-2.50) 

State interviewed in 1984 

Victoria -0.061 -0.060 

(-0.51) (-0.50) 

Queensland 0.027 0.028 

(0.19) (0.20) 

South Australia/Northern Territory -0.306 -0.305* 

(-1.94) (-1.97) 

Western Australia/Tasmania -0.101 -0.102 

(-0.67) (-0.69) 

Education school overseas -0.095 -0.095 

(-0.33) (-0.33) 

Roman Catholic school -0.010 -0.009 

(-0.06) (-0.05) 

Private school 0.603* 0.604* 

(2.07) (2.08) 

Highest qualification 

Degree/diploma 0.513** 0.512** 

(3.22) (3.26) 

Apprenticeship 0.431* 0.432* 

(2.10) (2.11) 

Other Post-School qualification 0.049 0.049 

(0.32) (0.32) 

Year 12 of school -0.063 -0.063 

(-0.40) (-0.41) 

Year 11 of school 0.357* 0.356* 

(2.18) (2.22) 

Year 9 of school or less -0.222 -0.222 

(-1.59) (-1.60) 

duration of Pre-June 1984 

unemployment 

-0.473** -0.473**

106 

(-4.19) (-4.20) 

Longest job by 1984 none 0.042 0.042 

(0.25) (0.25) 

< 1 year 0.190 0.190 

(1.40) (1.42) 

2 years 0.252 0.252 

(1.56) (1.57) 

3 years + 0.657** 0.657** 

(4.00) (4.01) 

Enter other govt prog -0.624** -0.623** 

(-4.74) (-4.90) 

Family background 

Other city before aged 14 -0.218 -0.217 

(-1.61) (-1.65) 

Country town before aged 14 -0.001 -0.000 

(-0.01) -0.00 

Rural area before aged 14 -0.128 -0.127 

(-0.65) (-0.66) 

Overseas before aged 14 0.494 0.494 

(1.36) (1.37) 

Number of siblings -0.028 -0.028 

(-1.49) (-1.51) 

English good 0.403 0.403 

(1.91) (1.91) 

English poor 1.050** 1.050** 

(2.75) (2.75) 

Sexist -0.440* -0.440* 

(-2.35) (-2.35) 

Sexist*female 0.463 0.463 

(1.25) (1.25) 

Fathers occupation when resp. 14 

Father not present when resp 14 -0.180 -0.178 

(-0.79) (-0.79) 

Labourer 0.134 0.135 

(0.55) (0.56) 

Plant operative 0.058 0.059 

(0.25) (0.26) 

Sales -0.049 -0.048 

(-0.18) (-0.18) 

Tradesperson -0.214 -0.213 

(-0.95) (-0.95) 

Manager/professional/para-professional 0.114 0.114 

(0.52) (0.53) 

Not employed 0.146 0.147 

(0.55) (0.56) 

Father holds post-school qualification 0.141 0.142 

when resp 14 

(1.26) (1.28) 

Mothers occupation when resp. 14 

Mother not present when resp 14 -0.335 -0.335 

(-1.33) (-1.34) 

Labourer -0.151 -0.150 

(-0.64) (-0.64) 

Plant operative -0.576* -0.576* 

(-2.30) (-2.31) 

Sales -0.392 -0.391

107 

(-1.80) (-1.80) 


(-0.70) (-0.70) 

Manager/professional/para-professional 0.217 .0217 

(0.97) (0.97) 

Not employed -0.087 -0.087 

(-0.49) (-0.49) 

Mother post-school qualification when -0.067 -0.067 

resp 14 

(-0.52) (-0.52) 

Religion brought up in 

Catholic 0.327* 0.327* 

(2.52) (2.56) 

Presbyterian 0.413 0.412 

(1.88) (1.92) 

Methodist 0.133 0.133 

(0.77) (0.77) 

Other Christian -0.102 -0.102 

(-0.50) (-0.50) 

Other religion -0.045 -0.045 

(-0.28) (-0.28) 

No religion 0.280 0.279 

(1.58) (1.62) 

rho -0.622 -0.626 

Observations 1283 1283 

Log likelihood -875.96 -875.96 

Wald chi 2 (degrees of freedom ) (118) 396.79 

Akaike Information Criterion 67 1.55 

Coefficient is reported with t statistic in brackets; * significant at 5%; ** significant at 1% 

NOTE 1: results Column 1 are from Table 4 and Table 5 pages 18-21 Richardson (1998). Base categories: 

European ethnic origin, state interviewed in 1984 NSW/ACT, government school, highest qualification 

year 10 at school, longest job by 1984 is 1 year, lived mostly in state capital city until respondent aged 14, 

English is first language, father clerical worker when respondent aged 14, mother clerical worker when 

respondent aged 14, and religion brought up in is Anglican. 

67 AIC=(-2log likelihood + 2 P)/ N where N= number observations and P=number of parameters estimated.

108 

Table 3.1 Part B Selection/participation equation of the bivariate probit 

Selection equation of the 

bivariate probit analysis 

by Richardson (1998) 

Model of SYETP 

participation, 1986 survey 

Replication results 

data 

Age at 1984 survey -0.107** -0.106** 

(-3.16) (-3.17) 

Gender=female 0.088 0.088 

(0.71) (0.71) 

Married 1984 -0.855 -0.854 

(-1.52) (-1.53) 

Children 1984 0.465 0.464 

(0.78) (0.78) 

Children*female -0.296 -0.295 

(-0.37) (-0.37) 

Spouse employed 1984 0.498 0.496 

(0.81) (0.81) 


(-1.01) (-1.01) 

Other ethnic minority 0.081 0.081 

(0.33) (0.33) 


(-2.53) (-2.53) 


Victoria 0.112 0.112 

(0.72) (0.72) 

Queensland -0.279 -0.280 

(-1.30) (-1.31) 

South Australia/Northern Territory -0.157 -0.157 

(-0.77) (-0.77) 

Western Australia/Tasmania 0.317 0.317 

(1.78) (1.78) 

CEP referrals 1984 0.144* 0.143* 

(1.97) (2.02) 

Education school overseas 0.078 0.078 

(0.22) (0.22) 


(-1.30) (-1.30) 

Private school -0.636 -0.635 

(-1.38) (-1.39) 

Highest qualification in 1984 

Degree/diploma 0.120 0.119 

(0.51) (0.51) 

Apprenticeship -0.129 -0.129 

(-0.42) (-0.42) 

Other Post-School qualification -0.036 -0.037 

(-0.14) (-0.14) 


(2.46) (2.46) 

Year 11 of school 0.101 0.101 

(0.53) (0.54) 


Model of SYETP 

participation, 1986 survey 

data

109 


unemployment 

(-0.35) (-0.35) 

0.487** 0.487** 

(2.92) (2.94) 

Longest job by 1984 none -0.348 -0.345 

(-1.34) (-1.35) 

< 1 year -0.020 -0.019 

(-0.11) (-0.10) 

2 years 0.173 0.173 

(0.80) (0.80) 

3 years + -0.326 -0.326 

(-1.26) (-1.26) 



(-1.48) (-1.48) 

Country town before aged 14 -0.473** -0.473** 

(-2.94) (-2.97) 


(-1.69) (-1.69) 

Overseas before aged 14 -0.757 -0.757 

(-1.48) (-1.48) 


(-0.70) (-0.70) 

English good -0.185 -0.186 

(-0.72) (-0.73) 

English poor -0.591 -0.592 

(-1.13) (-1.13) 

Sexist 0.317 0.318 

(1.22) (1.23) 

Sexist*female -0.903 -0.903 

(-1.38) (-1.39) 



(-1.11) (-1.12) 

Labourer -0.263 -0.263 

(-0.84) (-0.85) 

Plant operative -0.267 -0.267 

(-0.96) (-0.96) 

Sales -0.086 -0.086 

(-0.26) (-0.26) 


(-1.10) (-1.10) 

Manager/professional/paraprofessional 

-0.153 -0.153 

(-0.59) (-0.59) 


(-1.29) (-1.29) 

Father holds post-school 

qualification when resp 14 

-0.315* -0.315* 

(-2.14) (-2.14) 


Mother not present when resp 14 0.480 0.480 

(1.49) (1.49) 

Labourer 0.176 0.177 

(0.57) (0.57) 

Plant operative 0.697* 0.697*

110 

(2.26) (2.26) 

Sales 0.190 0.190 

(0.66) (0.66) 

Tradesperson 0.119 0.120 

(0.28) (0.29) 


-0.247 -0.246 

(-0.85) (-0.84) 


(0.17) (0.18) 

Mother post-school qualification 

when resp 14 

0.266 0.266 

(1.66) (1.66) 


Catholic 0.061 0.061 

(0.38) (0.37) 


(1.34) (1.34) 


(0.06) (0.06) 

Other Christian 0.075 0.074 

(0.27) (0.27) 

Other religion 0.176 0.176 

(0.80) (0.80) 


(0.66) (0.67) 


T statistics in brackets; * significant at 5%; ** significant at 1% 

NOTE 1: results Column 1 are sourced from Table 4 and Table 5 pages 18-21 Richardson (1998). 

Base categories: European ethnic origin, state interviewed in 1984 NSW/ACT, government school, highest 

qualification in 1984 year 10 at school, longest job by 1984 is 1 year, lived mostly in state capital city until 

respondent aged 14, English is first language, father clerical worker when respondent aged 14, mother 

clerical worker when respondent aged 14, and religion brought up in is Anglican.

111 

4: Study 2 Propensity score matching for SYETP 

In this study, the semi-parametric method of propensity score matching (PSM) is applied 

to the same data. The aim is to answer the question of what would the result have been if 

this modelling method had been applied instead of the Heckman Bivariate Probit. It is 

also a consideration of relaxation of the parameterisation. In a sense this is then a 

specification test of the modelling choice. 

The bivariate probit specification replicated in the earlier chapter is a parametric model. 

The choice of a parametric method requires full specification of the underlying model 

and relies on this set of assumptions being correct. It is of interest to consider the 

importance of the modelling method to the outcome attained. This is especially true when 

considering the SYETP coefficient in the employment equation since this provides an 

estimate of the net employment impact of SYETP for those who participated. 

In order to facilitate comparison of the results, it is necessary to maintain the conditions 

as applied to the Heckman selection modelling. In this way, ceteris paribus, the effect of 

using the different models can be isolated. Essentially, the data restrictions applied for the 

replication of the Richardson (1998) results must be repeated. As far as possible, the 

modelling variables used must also be the same. There are however limitations as to how 

similar the variables can be due to the different requirements of these two methods. This 

limitation is addressed in detail within the chapter. 

In the first section of the study, further consideration is given to the question of why it is 

useful to apply PSM to this data, and to the SYETP analysis. Having resolved the 

potential usefulness of applying the PSM method, the theory and methods of PSM for 

this study are presented. The results of the application of PSM follow; and a brief 

sensitivity analysis of the PSM protocol chosen is also implemented. The PSM result is 

then compared to the formerly presented Heckman selection modelling, and discussed.

112 

4.1 Differences between the treatment and comparison group 

An important problem for evaluation of programme effects is whether the treated are 

compared to an adequate reference group. This is the issue of selection distortion, where 

those treated have a very different profile to those in the comparison group. Selective 

recruitment onto subsidy would make the treated have a different profile than the larger 

group of those eligible. Controls for selection distortion can be made on observable 

characteristics, such as those variables shown in Table 4.1. The Table 4.1 gives the mean 

and standard deviation from the mean for a set of characteristics, with column 1 and 2 

showing these for the SYETP treated group, and columns 3 and 4 showing the 

comparison group characteristics. The absolute difference between the means is in 

column 5, with the results of the t-test at one percent level of significance indicated by an 

asterisk to show statistically significant differences. 

Richardson (1998) p6 commented that the contrast between those who took part in 

SYETP and those in the comparison group was “striking”. Indeed, column 1 and column 

3 of Table 4.1, of which the means were also presented by Richardson (1998), shows that 

the profile of the treatment and comparison groups differs strongly. In most cases, means 

were quite different for the SYETP relative to the comparisons, and in some cases the 

variation from the mean, as represented by the standard deviation was also very different 

for each group. As Richardson (1998) highlights, SYETP participants had a different 

educational attainment profile where post-school qualifications were less common, they 

were about a year younger, and had poorer labour market experiences than those in the 

comparison group. However, how different the SYETP group was from the comparisons 

is related more clearly here by the addition of the test of the statistical significance of the 

mean difference. Almost all variables have a significant divergence between the means 

for the SYETP group and the comparisons. Consideration of the many statistically 

significant differences in Table 4.1 makes the selection distortion for SYETP in the ALS 

data apparent. The literature review also highlights other evidence that it was generally 

the case for SYETP entrants to be younger than the eligible group as a whole – mostly 

teenagers. Thus it is possible that the ALS sample reflects differences that existed 

between the SYETP and comparison populations.

113 

Propensity score matching provides a method of analysis that controls for the lack of 

correspondence between the treatment and comparison group. As discussed in Chapter 1, 

evaluation methods seek to maximize the similarity of the comparison and treated groups, 

in order to return to a quasi-experimental situation where effects can be usefully 

attributed to the programme. The propensity score matching method is now further 

discussed and applied. The outcomes of the propensity score matching are then compared 

to that of the bivariate probit. The relevance of each approach to the case at hand is 

considered, and the result for the SYETP programme effect is discussed.

114 

Table 4.1 Difference between treatment group and comparison group 

SYETP 

mean 

s.d. 

Non- SYETP 

mean 

Female 43.3 0.50 41.0 0.49 2.3* 

Average age 1984 19.0 1.97 20.1 2.42 1.1* 

Aboriginal/Torres Strait Islander 1.0 0.10 3.1 0.17 2.1* 

Other ethnic minority 7.7 0.27 7.9 0.27 0.2 

Married 1984 2.9 0.17 12.5 0.33 9.6* 

Spouse employed 1984 1.9 0.14 6.3 0.24 4.4* 

Children 1984 1.9 0.14 5.8 0.23 3.9* 


Degree/diploma 7.7 0.27 12.2 0.33 4.5* 

Apprenticeship 2.9 0.17 8.6 0.28 5.7* 

Other post-school qualification 6.7 0.25 7.1 0.26 0.4 

Year 12 of school 23.1 0.42 13.9 0.35 9.2* 


Year 10 of school 31.7 0.47 31.5 0.46 0.2 


Parental background when resp. aged 14 

Father postschool qualification 26.0 0.44 34.6 0.48 8.6* 

Mother postschool qualification 20.2 0.40 18.2 0.39 2.0* 

Father manager, professional, para-professional 25.0 0.44 25.8 0.44 0.8 

Father not employed 3.8 0.19 5.6 0.23 1.8* 

Father not present 19.2 0.40 15.4 0.36 3.8* 

Mother manager, professional, para-professional 6.7 0.25 9.8 0.30 3.1* 

Mother not employed 48.1 0.50 55.3 0.50 7.2* 

Mother not present 8.7 0.28 5.0 0.22 3.7* 

Longest job ever held by 1984 

Never held a job 11.5 0.32 11.6 0.32 0.1 

< 1 year 55.8 0.49 40.1 0.49 15.7* 

1 year 13.5 0.34 13.5 0.34 0.0 

2 years 13.5 0.34 14.1 0.35 0.6 

3 years or more 5.8 0.23 19.8 0.40 14* 

Average pre-programme unemployment 69 19.0 1.97 20.1 2.42 7.5* 

Ever employed in 1986 70 86.5 0.34 72.9 0.44 13.6* 

Ever government programme 1986 71 14.4 0.35 10.7 0.31 3.7* 

Number of cases 104 1179 

NOTE: Column 5 shows the t statistic for hypothesis that difference of mean for SYETP and comparison 

is zero, where * indicates is significant at the 1 percent level of significance. 

s.d. 

SYETP 

versus 

comparison 

absolute 

difference in 

means 68 

68 The statistic for any variable is the absolute value of the difference in means for SYETP and the control 

groups. 

69 Proportion of 1984 reference period to 3 June spent unemployed. 

70 Ever held a non-subsidised, non-government program job in the 1986 reference period, after the first 17 

weeks. 

71 Ever go on a government program, including SYETP, in the 1986 reference period.

115 

4.2 Propensity score matching methods 

The propensity score matching methods, expounded in Rosenbaum and Rubin (1983), 

Dehijia and Wahba (1998), Heckman, Ichimura and Todd (1997), Heckman, Ichimura 

and Todd (1998) and Imbens (2000) have more recently been applied in the evaluation 

literature. Lechner (2000, 2001) extended the propensity score matching methods to the 

multi-treatment case. Using propensity score matching moves the emphasis away from 

specifying the selection bias towards more careful construction of the comparison group. 

It has been suggested that the key enhancement for evaluation allowed by this method is 

the comparison of comparable people (Heckman, Lalonde and Smith (1999): 2083). In 

order to do this, irrelevant comparison cases, which are not similar to the treated, are 

removed from the analysis. 

Matching methods find for each individual in the treated, at least one comparison group 

member with very similar pre-treatment characteristics. The differences in outcomes after 

the treatment are then attributed to the programme. Recalling the evaluation problem 

discussed earlier, matching is subject initially to the same difficulty of all nonexperimental 

methods where assignment to treatment is non-random. However Rubin 

(1974) showed that matching balances the distributions of all pre-treatment 

characteristics that influence assignment to the treatment, and so gives an unbiased 

estimate of treatment on the treated, as long as all relevant similar pre-treatment 

characteristics (X) are controlled for, and the Conditional Independence Assumption 

(CIA) is invoked (further explained below). 

Propensity score matching uses the propensity score to provide a single measure of the 

set of characteristics (X) that influence the probability of participating and employment. 

Rosenbaum and Rubin (1983) established that if matching on a set of observed 

characteristics is valid, then matching on the probability of selection into the programme 

conditional on these characteristics, the propensity score, is also valid. Whereas matching 

on each characteristic leads to problems with dimensions, the propensity score reduces 

the problem to a single dimension.

116 

4.3 Theory underlying propensity score matching methods 

Matching methods are steeped in experimental and evaluation terminology, and the 

following exposition which draws on the literature of Rosenbaum and Rubin (1983), 

Dehijia and Wahba (1998), Heckman, Ichimura and Todd (1997), Heckman, Ichimura 

and Todd (1998) reflects this. 

The impact of participating in the programme is defined as 

(1) ∆ = Y t – Y c 

where Y t is the potential outcome conditional on participation and Y c is the potential 

outcome conditional on non-participation. Following Rubin (1974), treatment assignment 

may be random given a set of characteristics or variables X that are unaffected by the 

treatment. The potential values of X for the different treatment states coincide, and are 

thus exogenous. 72 

The parameter most commonly estimated is the mean impact of treatment on the treated: 

(2) θ = E(∆ | D = 1, X=x) 

= E(Y t – Y c | D = 1, X=x) 

= E(Y t | D = 1, X=x) - E(Y c | D = 1, X=x) 

where D=1 denotes treatment, D=0 denotes non-treatment and X is a set of conditioning 

variables. 73 The evaluation problem arises from the term E(Y c | D = 1, X=x). This is the 

mean of the counterfactual which, since it is unobservable, must be identified and 

estimated. This issue in evaluation was earlier discussed in Chapter 1. 

The construction of a valid comparison group for matching techniques is based on the 

Conditional Independence Assumption (CIA). Under CIA, potential outcomes Y t ,Y c are 

independent of the assignment to treatment, when conditional on all pre-treatment 

72 Hujer and Caliendo (2000) p9 explain that this is not exogeneity in the econometric sense. Heckman 

Ichimura and Todd (1997) p610 note that it is not necessary to assume that the expected value of the errors 

in the model of Y conditional on X are zero, so X can fail to be exogenous in the conventional sense of the 

term. 

73 Population quantities are denoted by capital letters and sample quantities by lower-case letters.

117 

covariates that influence the assignment to treatment as well as potential outcomes. The 

precise form of CIA depends on the parameter being estimated. For the treatment on the 

treated parameter, the CIA requires that, conditional on observable characteristics, 

potential non-treatment outcomes are independent of treatment participation. 

Formally, CIA can be written as 

(3) Y c ╨ D | X = x, ∀x∈ χ 

where ╨ denotes independence and χ denotes the part of the attribute space for which 

the treatment effect is defined. This assumption is sufficient for the treatment on the 

treated parameter. The fuller CIA assumes that 

(3a) Y t ,Y c ╨ D | X. 

However, Heckman et al. (1997) show that for estimating the mean effect of treatment on 

the treated, CIA for Y c is sufficient. This is because inference of Y c for persons where 

D=1 is estimated from data on persons where D=0. Hence, after adjusting for observable 

differences, the mean of the no-treatment (potential) outcome is the same for those 

receiving treatment as for those not receiving treatment. This allows non-participants’ 

outcomes to be used to infer participants’ counterfactual outcomes. 

However, this is only valid if there are non-participants for all participants’ values of X 

(the support condition): 

(4) 0 < Pr ( D = 1 | X = x ) < 1 

where “Pr” is the probability of participating in the programme of treatment. This ensures 

that there are no values of the characteristics in X for which the propensity score is zero 

or one. Heckman, Ichimura, Smith, and Todd (1998) show that failure to ensure common 

support will give biased estimates. Rosenbaum and Rubin (1983) showed that when this 

assumption of common support holds together with the CIA, then if treatment assignment 

is strongly ignorable when X is given, then it is also strongly ignorable for any balancing 

score, such as the propensity score or probability of participating in the programme.

118 

The treatment parameter, θ, cannot be correctly identified for values of X which do not 

satisfy this support condition. Matching operates by constructing, for those participants 

with support, a counterfactual from the non-participants. There are a number of ways of 

defining this counterfactual. 74 Once the counterfactuals are identified, the mean impact of 

the programme can be estimated as the mean difference in the outcomes of the matched 

pairs. 

A refinement to the matching approach was introduced by Rosenbaum and Rubin (1983). 

Rosenbaum and Rubin (1983) suggest the use of balancing scores which are functions of 

the relevant observed covariates (here termed X). If assumptions (3) and (4) hold then 

Rosenbaum and Rubin (1983) show that the conditional independence assumption (CIA) 

extends to the use of the propensity score: 

(5) Y c ╨ D | P(X) = p(x), ∀x∈ χ 

where ( ╨ ) symbolises independence, P(X) is the propensity score more fully termed 

P(D=1| X), the probability of participating in the programme. The important advantage of 

Rosenbaum and Rubin’s (1983) innovation is that the dimensionality of the match can be 

reduced to one. 75 Rather than matching on a vector of characteristics, it is possible to 

match on just the propensity score. The information from the vector of characteristics is 

condensed within the estimated probability representing the propensity score. Having 

done so, the mean impact of the programme is again estimated as the mean difference in 

the outcomes of the matched pairs: 

(6) E[Y c | D=1, P(X)=P(x)] = E[Y c | D=0, P(X)=P(x)] = E(Y c | P(X)=P(x)] 

So then the average treatment effect can be written: 

74 See, for example, Heckman et al. (1997) for a comparison of alternative matching schemes. 

75 Matching on the propensity score removes the dimensionality problem if the propensity scores are 

estimated parametrically, and relies on parametric estimation of the probability of participation providing 

an advantage over parametric estimation of the outcome.

119 

(7) E(Y c | D=1) = E P(X) {E[Y c | P(X)=P(x)] , D=0| D=1} 

Where the outer expectation is taken over the distribution of the propensity score in the 

treated population (Hujer and Caliendo (2000)). Equation 7 can be used for estimation. 

Thus, with an estimate of the propensity {P(X)} gained from data {P(x)}, then the 

evaluation of the programme can take place by pairing participants with non-participants 

that have the same propensity score. For participants and comparisons with the same 

propensity score the distributions of the covariates are the same, and so they are balanced. 

The assumption required to identify the effect satisfactorily is generally termed CIA now. 

CIA is the term used in Lechner (2000). However the same assumption is also referred to 

as selection on observables in Heckman and Robb (1985), and as ignorable treatment 

assignment in Rosenbaum and Rubin (1983). Frölich (2001) p2 describes the validity of 

CIA as the requirement that all variables that affect simultaneously the potential 

employment outcome and the probability to be sampled from the comparison instead of 

the target population are observed and included in X. 

The estimate of the propensity is used to balance the distributions of the covariates across 

the treated and comparison groups. The empirical power of propensity score matching in 

reducing the problem of selection bias depends on the quality of the estimate of the 

propensity score. Additional to these needs, there has to exist a comparison person with a 

propensity score very similar, but preferably equal, to that of each treated person. Thus 

although in theory PSM can be powerful in estimating the effect of treatment on the 

treated, the practical implementation of PSM is subject to common parametric issues of 

how effectively the empirical estimation corresponds to the assumptions of the method. 

4.4 The propensity score matching protocol implemented 

There are several means of applying propensity score matching (PSM). One to one 

matching takes the outcome of the single most similar comparison unit, to provide a 

match to each treated unit. The comparison individual whose propensity score is the 

closest in value to the score for a treated unit is selected as the ‘nearest neighbour’. This

120 

method is also known as the single nearest neighbour method, and can additionally be 

limited using the caliper or radius method. 76 The radius provides a bound on how far the 

propensity of the nearest neighbour can be from the treated case. Selection of the 

comparison can be performed with or without replacement. Allowing the pool of 

comparison group members to be selected for a match to a treated unit more than once 

has been shown to improve the performance of the match (Dehija and Wahba (1998)). 

Also, not allowing replacement in nearest-neighbour-matching produces results which are 

not invariant to the order in which the data were sorted for matching (Friedlander et al. 

(1997): 1818). The choice of matching with or without replacement involves a tradeoff 

between bias and variance. Matching with replacement tends to lowers bias, because on 

average the matches are closer, but increases the variance, because fewer separate 

observations are used to construct the counterfactual mean. 

In this study, two types of PSM are implemented. Firstly nearest-neighbour, within 

caliper with replacement PSM is applied, then as a sensitivity analysis of the choice of 

PSM protocol, all-in-radius 77 PSM. While kernel PSM 78 is an alternative, this is not used 

here as the choice of kernel and bandwidth is not straightforward, and little literature 

exists on how best to critically implement these choices in the context of matching. 

The match is based upon observable characteristics. This is the key identifying 

assumption of matching known as the conditional independence assumption (CIA). 

Under CIA, when controlling for observable differences in the characteristics of the 

programme participants and non-participants, the outcome in the absence of treatment is 

the same for each of these groups. More generally, the conditional independence 

assumption assumes that the selection on unobservables does not affect outcomes. 

76 Caliper matching usually means single nearest neighbour matching with a maximum limit placed on the 

distance between the treated observation and the corresponding nearest neighbour from the comparison 

group. Radius matching may also be used to indicate this, or using all the comparison group observations 

within a given radius to construct the estimated counterfactual, which we term all-in-radius. The literature 

is not clear in applying these terms. 

77 Also termed all-in-caliper. 

78 Frölich suggests in monte carlo analysis that a variant of kernel matching is preferred to nearest 

neighbour, caliper and radius matching when using minimization of mean squared error as a criterion.

121 

For CIA to be plausible, a ‘rich’ dataset is needed as it is assumed that all the variables 

affecting participation and employment are observed. Under CIA, the distribution of the 

counterfactual outcome for participants is the same as that of non-participants, and the 

matching is then analogous to creating a fictional experiment where conditional upon the 

observed characteristics, the selection process is random. If CIA is satisfied, then 

selection bias ceases to be an issue. Violation of the conditional independence 

assumption cannot usually be accurately tested in non-experimentally designed 

observational data. Rosenbaum (1984) points out that theory about the causal mechanism 

being investigated can be the chief source, and that theory together with random 

assignment or experimental data can produce useful tests. Some tests are also outlined in 

Heckman and Hotz (1989), but these are most commonly carried out with experimental 

data. 

The plausibility of matching depends then on a rich set of conditioning variables 

(Heckman, Lalonde and Smith (1999): 1995). Specifically, the variables should allow 

control for all characteristics that will affect both the participation and outcomes jointly. 

In this case, the data used here provides what may be considered a suitable set of 

explanatory variables which are defined prior to treatment, covering labour market 

history, family background, education and skills, and attitudes amongst others. In order to 

enhance the comparability with the former analyses, the same variables are initially used 

to estimate the propensity score as were previously used in the SYETP participation 

equation. 79 In the later chapter 7, sensitivity analysis of this probit for the propensity 

score is performed, with some variables removed. This exploration is done in light of 

Lechner (2000) and Lechner (2001). Once again, the probit model is used to fit the 

probability of SYETP participation. Using the parametric model of the probability of 

treatment moves the matching technique from non-parametric into a semi-parametric 

approach. 

79 Including the same variables in the propensity score as are included in the participation model from the 

bivariate probit could be problematic, since the two models of probability of participation are designed with 

different intentions. In the bivariate probit, there are exclusion restrictions to capture variation in the 

probability of participation unrelated to outcomes, but a propensity score model does not usually include 

instruments – only variables that influence both employment and participation. However, in the broad 

context of this work, the exclusion restrictions, which appear to be weak, are of interest, and this issue is 

treated further later.

122 

In finding a counterfactual match for the participant, it is possible that there is no 

comparison group individual with a similar propensity score. This is termed the common 

support problem. The crux of common support is the identification and removal of treated 

individuals for which the propensity indicates no close match is available in the 

comparison group. Imposing common support ensures that the observed characteristics of 

the participants are close to that of the non-participants. 

4.5 Estimating the probability of participation for SYETP 

Initially, the main aim is to repeat the formulation of the Richardson (1998) specification 

as much as possible. In consideration of what variables to include in the propensity score 

estimation, it is then deemed appropriate to apply the probit modelling of SYETP in as 

much as possible the same specification used by Richardson (1998). 

The results for the probit analysis, which is unweighted 80 , are shown in Table 4.2. The 

variables, which are all constructed using 1984 data, are: gender, age, marital status, 

children, partner’s employment, ethnicity, location, type of schooling, qualification level, 

longest job held, CEP referrals, proportion of time spent unemployed prior to June 1984, 

urban/rural area grew up in, number of siblings had, standard of spoken English, attitude 

towards women in work, parental occupation and whether parent held a post-school 

qualification, and religion brought up in. These are all the variables in the participation 

equation of the bivariate probit estimated in chapter 3. 

The variables in the model are central to the credibility of the CIA. In regard to labour 

market evaluation, pre-programme labour market history is considered the most 

important explanatory variable, and this is part of the model applied here. In addition, the 

family background and attitudinal variables might be considered useful to help capture 

otherwise unobservable characteristics such as motivation that could influence 

80 The aim here is to find equivalent estimates for the bivariate probit model of Richardson (1998) using 

PSM. Weighting is applied to both models in chapter 6.

123 

employment and programme participation. As such, it is considered that at least with 

regard to the breadth of characteristics controlled for, the CIA has been satisfied. 

Some measures of fit for the probit model as a whole are considered. However 

interpretation of such measures of fit is limited. Scalar measures of fit have a heuristic 

nature and there is no convincing evidence that a model which maximizes the value of a 

given measure gives a model that is optimal in any sense other than the optimisation of 

the particular measure of fit. However some of these measures such as the AIC can be 

useful in comparing competing models, which is considered in later chapters. The Akaike 

Information Criterion (AIC) is a measure that can be used to compare models across 

different samples and non-nested models. All else being equal, the model with the 

smaller AIC is judged to be better. Here, the value is 0.57. Tukey’s ‘goodness of fit’ test 

is applied to check whether the specification of the probit is suitable and the results 

indicate no problems. 81 The chi squared distributed likelihood ratio test of the hypothesis 

that all coefficients except the intercept are equal to zero is 107.57 and results in this 

hypothesis being rejected. Mcfadden’s pseudo R 2 , sometimes known as the likelihood 

ratio index, also compares the model to a model with just an intercept. This tests the same 

hypothesis and equals zero if the model is equivalent to the intercept model (but can 

never precisely equal one). Here the value is 0.149, and indicates the model is reasonable. 

A summary measure of the fit of the predictions shows that 92 per cent were correctly 

estimated. 82 Thus the predictive power of the model appears acceptable within the 

context of the data. However, prediction rates are not ideal measures for assessing the 

propensity score as if the prediction rate were perfect at one, then there would be no 

common support. As such, the value of these measures for critically assessing the 

propensity are low and judging the included variables, together with the estimated model 

parameters, in the context of the theory motivating the analysis is possibly more 

important, and examining the estimated propensity scores. 

81 A probit of SYETP is run on just the predictions, and the predictions squared, sourced from the model of 

SYETP. If the model is specified correctly, the predictions squared should have no explanatory power – i.e. 

are statistically insignificant. The base reference is Tukey (1949). The results give a t value close to zero, 

which is clearly not significant. 

82 A fitted probability exceeding 0.5 is taken to indicate a predicted participation in SYETP, and these 

predicted responses are compared to the actual participants/non-participants in SYETP to check which 

cases the model correctly predicted.

124 

It is interesting to look at the factors that affect the SYETP propensity, considering only 

statistically significant coefficients. Age had a negative impact on participation in SYETP, 

with older youths less likely to take part. Those with schooling to year 12, and those with 

longer unemployment spells were more likely to take part in SYETP. Health conditions 

that limited work reduced the chance of participation in SYETP. A family background of 

living in a country town also reduced the likelihood of taking part in SYETP. Parental 

background had some influence, with those whose fathers had post school qualifications 

less likely to take part in SYETP, but those whose mother’s occupation had been a plant 

operative more likely to take part. The number of referrals by the CES to the Community 

Employment Programme, another labour market programme available, has a positive 

influence on participation in SYETP. 

Table 4.2 Probit used to estimate propensity score for propensity score matching 

Participation in SYETP 

Gender=female 0.08 

(0.67) 

Age at 1984 survey -0.11 

(3.21)** 

Married 1984 -0.97 

(1.62) 

Children 1984 0.49 

(0.74) 

Children*female -0.33 

(0.39) 

Spouse employed 1984 0.60 

(0.91) 

Aboriginal/Torres Strait Islander -0.45 

(0.95) 

Other ethnic minority 0.06 

State interviewed in 1984 (0.25) 

Victoria 0.11 

(0.73) 

Queensland -0.21 

(0.99) 

South Australia/Northern Territory -0.14 

(0.66) 

Western Australia/Tasmania 0.32 

(1.81) 

Education school overseas 0.14 

(0.38) 

Roman Catholic school -0.30 

(1.23) 

Private school -0.73

Highest qualification in 1984 (1.56) 

Degree/diploma 0.05 

(0.20) 

Apprenticeship -0.15 

(0.47) 

Other Post-School qualification 0.07 

(0.28) 

Year 12 of school 0.40 

(2.25)* 


(0.85) 

Year 9 of school or less -0.11 

(0.53) 

Longest job by 1984 none -0.42 

(1.68) 

< 1 year -0.04 

(0.24) 

2 years 0.15 

(0.70) 

3 years + -0.34 

(1.32) 

CEP referrals 1984 0.16 

(2.36)* 

duration of Pre-June 1984 unemployment 0.46 

(2.79)** 

Work limited by health -0.60 

Family background (2.32)* 

Other city before aged 14 -0.25 

(1.49) 

Country town before aged 14 -0.43 

(2.76)** 

Rural area before aged 14 -0.46 

(1.70) 

Overseas before aged 14 -0.71 

(1.38) 

Number of siblings -0.01 

(0.83) 

English good -0.12 

(0.48) 

English poor -0.56 

(1.03) 

Sexist 0.25 

(0.95) 

Sexist*female -0.76 

Fathers occupation when resp. 14 (1.24) 

Father not present when resp 14 -0.22 

(0.79) 

Labourer -0.16 

(0.52) 

Plant operative -0.21 

(0.75) 

Sales -0.10 

(0.30) 

Tradesperson -0.25 

(0.90) 

Manager/professional/para-professional -0.08 

125

126 

(0.33) 

Not employed -0.39 

(1.08) 

Father holds post-school qualification when resp 14 -0.30 

Mothers occupation when resp. 14 (2.00)* 

Mother not present when resp 14 0.52 

(1.64) 

Labourer 0.14 

(0.45) 

Plant operative 0.65 

(2.10)* 

Sales 0.19 

(0.65) 

Tradesperson 0.09 

(0.21) 


(0.76) 

Not employed 0.05 

(0.21) 

Mother post-school qualification when resp 14 0.25 

Religion brought up in (1.52) 

Catholic 0.06 

(0.38) 

Presbyterian 0.32 

(1.34) 

Methodist 0.12 

(0.46) 

Other Christian 0.10 

(0.35) 

Other religion 0.16 

(0.72) 

No religion 0.19 

(0.95) 

Constant 0.71 

(0.93) 

Observations 1283 

Log likelihood -307.19 

LR chi 2 (59) 83 107.57 

Mcfadden’s Pseudo R 2 84 0.149 

Akaike Information Criterion 85 0.57 

Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1% 

83 This is the likelihood ratio test of the hypothesis that all coefficients except the intercept are equal to 

zero. It is defined as LR = 2 (log likelihood M full – 2 log likelihood M intercept ). The degrees of freedom of this 

chi squared distributed statistic are equal to the number of constrained parameters i.e. the number of 

coefficients being tested. 

84 This measure of fit is also known as the likelihood ratio index. It compares the full model of parameters 

(M full ) to a model with just the intercept (M intercept ). It is defined as R 2 = 1 – (log likelihood M full / log 

likelihood M intercept ) . The value of Mcfadden’s Pseudo R 2 increases as new variables are added. 

85 AIC=(-2log likelihood + 2 P)/ N where N= number observations and P=number of parameters estimated.

127 

4.6 Distribution of the propensity score 

The propensity score for each case is the predicted probability of taking part in SYETP, 

as estimated from the probit model. The estimated propensity score for the SYETP and 

the comparison group are shown in Figure 4.3 as histograms. Each histogram has 10 bins 

– this is then comparable to most of the literature, such as Dehija and Wahba (1998, 

1999). The histograms show that the estimated probabilities of SYETP participation 

cover a limited area, however the histogram for the comparisons is misleading as it is so 

heavily weighted towards zero and has only 2 observations greater that 0.5, so that the 

upper bar becomes invisible it is so thin. The treated have a modest but non-trivial 

density of observations greater than 0.5. Referring to the summary statistics in table 4.5 

shows the overlap between the treated and comparisons is restricted by the comparison 

group distribution which extends slightly beyond the treated propensities. 

Figure 4.4 shows the same distributions overlaid, and with a kernel smoothing applied to 

the distribution. The shapes of the distributions differ even where they overlap. As would 

be expected, far more of the comparison group have values close to zero, with the 

comparison distribution peaking sharply here and the main bulk transpiring at this point, 

before tailing off rapidly. The comparison group more often has a propensity to 

participate in SYETP that is close to zero, i.e. no participation. The lower tail of the 

comparison group always lies above that of the SYETP. For the SYETP group, the 

distribution has a much lower peak, which is flatter and occurs between values 0.1 and 

0.2, and the upper tail falls away far more slowly. The upper tail of the SYETP lies above 

that of the comparison group where more SYETP cases have these higher propensities to 

take part in SYETP, but the upper tails approach very similar values in the final stages of 

the distributions. A good model for the propensity produces large differences of the mean 

predicted propensities across the treated and comparison groups [Larsson (2000)]. On this 

criterion, it appears that the propensity score distributions, and the underlying model, are 

acceptable. Table 4.5 shows the mean predicted propensity for SYETP is 0.17 while for 

the comparisons it is less than half of this at 0.07.

128 

4.7 Common support for the treated 

The first step is to discard from the treatment group those members whose propensity 

score is beyond the limits of that of the distribution of propensities of the comparison 

group. This form of the common support was used in the multiple treatments matching 

protocol of Gerfin and Lechner (2000) p22, where the minima and maxima of the treated 

propensities defined the common support region, in this case simplified by only one 

treatment and comparison group. Thus the propensity for the treated and comparisons 

only covers the same interval. This is the application of the rule of common support 

expressed in equation 4 of the theory in section 4.3. There is support in the comparator 

group for the treatment group member if a propensity value between 0 and 1 exists 

amongst the comparisons for each propensity of the treatment group. Those individuals 

discarded for non-support have least similarity. The members of the treatment group are 

discarded for non-support if their propensity to participate in SYETP is lower than the 

minimum score or higher than the maximum score of those in the comparison group. 

Those cases discarded for non-common support are not comparable, because for the 

treated there is no comparable non-participant. 

The distributional summaries in Table 4.5 give the four largest and smallest propensity 

scores observed in the treatment and comparison group. In this case, application of the 

rule of common support has no effect on the treatment group, as the distribution for the 

SYETP treatment propensities always lies within the bounds of the comparison group. 

The maximum comparison group propensity to take part in SYETP is 0.5458441, and the 

minimum is 0.0000598. However, there are just 2 cases in the comparison group with 

propensities greater than the maximum of the SYETP treatment group (these are not 

clearly visible in the histogram as the bar is so thin due to low number of cases). For the 

SYETP treatment group, the maximum comparison group propensity to take part in 

SYETP is 0.5258619 and the minimum is 0.0147686. In this case, there are no SYETP 

participants for whom there are not comparable non-participants.

129 

Figure 4.3 Histograms of estimated propensity score prior to matching 

1 

1 

Fra 

ctio 

n 

.5 

Fra 

ctio 

n 

.5 

0 

0 

0 .5 1 

104 observations, unweighted probit 

propensity score for treated (SYETP=1) 

0 .5 1 


propensity score for controls (SYETP=0) 

histograms of estimated propensity scores prior to matching 

Figure 4.4 Kernel Density of propensity scores distribution for Treated SYETP and 

untreated

130 

10 

Treated propensity scores, 

Untreated propensity scores, 

5 

0 

0 .2 .4 .6 

Pr(syetp) 

kernel densities of propensity scores of the treated vs untreated 

Note: Epanechnikov kernel.

131 

Table 4.5 Summary statistics for distribution of propensity scores 

Comparison group 

Percentiles Smallest 

1% .0004381 .0000598 

5% .0023942 .0000982 

10% .0056824 .0001258 Observations 1179 

25% .0174015 .0001335 

50% .0488829 Mean .073496 

Largest Std. Dev. .0784812 

75% .1021392 .4204476 

90% .1756042 .4249502 Variance .0061593 

95% .2366404 .5203477 Skewness 1.912722 

99% .3523099 .5458441 Kurtosis 7.518665 

SYETP group 


1% .0224789 .0147686 

5% .0303421 .0224789 

10% .038564 .0227128 Observations 104 

25% .0867105 .0228779 

50% .1439536 Mean .1658229 


75% .2126023 .3920572 

90% .3091516 .4926768 Variance .0117083 

95% .3695493 .5044287 Skewness 1.04344 

99% .5044287 .5258619 Kurtosis 4.192943

132 

4.8 Results of Matching: one-to-one nearest-neighbour 

Matching is now performed. On a case-by-case basis, amongst the remaining comparison 

group members, members are identified that are similar to each individual of the 

treatment group, i.e. their ‘match’. The basis for the match is the propensity score. The 

matching should produce a counterfactual for those in the treatment group. In this case, 

one-to-one nearest-neighbour within-caliper 86 matching with replacement is first 

applied. 87 For a pre-specified distance, the caliper width δ, the treated unit is matched to 

the comparison unit so that the nearest single neighbour criterion is satisfied within the 

caliper, 

1) δ > | p i - p j | = min k ∈ { D=0} { | p i – p k | } 

Where 

δ = caliper width 

i = treated unit 

j = comparison unit 

p = propensity score p(x) = Probability { D=1 | X = x } 

x = set of pre-treatment characteristics 

D = indicator of treatment, 1= treatment, 0 = no treatment 

4.8.1 Discussion of the results 

The results of the matching are shown in Table 4.6. There is no general rule as to what 

caliper width is suitable. However the theory underlying PSM indicates the caliper needs 

to provide a comparison person with a propensity score very similar but preferably equal 

to that of the treated persons. Thus a small caliper width is preferable, or so small the 

propensity scores can be deemed roughly equal. Pagan and Ullah (1999) in discussing 

bandwidth selection for kernel density, suggest inspecting the data in order to choose a 

‘suitable’ width. It can observed from the summary of the propensity scores shown in 

86 Gu and Rosenbaum (1993) introduced this pair matching technique. 

87 The STATA ado file by Barbara Sianesi is used to implement this method (psmatch.ado).

133 

Table 4.5, that the variance of the propensity score of the SYETP group is about 0.012 

while for the comparison it is 0.006. 

It could be inferred that the smaller caliper allows for the best match, with the minimum 

limit being the need to find at least one case to match within the caliper. As the 

comparison group is so much larger is size than the treated, there is the possibility of 

several matches being available within each caliper. With single-nearest neighbour 

matching, increasing the width of caliper simply widens the pool of comparisons that are 

available. The quality of the match might decrease as the pool is increased to the extent 

that more SYETP cases are then matched to dissimilar comparison cases. This would be 

the case if it were difficult to match because the support space was ‘sparse’, or had 

‘sparse’ regions. However, once a single match has been accomplished within the caliper, 

that nearest-neighbour match does not alter by widening the caliper. On the other hand, 

narrowing the caliper could improve the match when the support space has sparse regions 

by making the nearest-neighbours matched nearer to each other, because it forces the 

difference in their propensity to be smaller. This means that the caliper width can affect 

match quality, however there is a cost. The key tradeoff in choosing a caliper width is 

between match quality and the number of treated observations dropped because they do 

not have a match inside the caliper. 

In order to better answer the question of what caliper width is suitable to apply, a variety 

of caliper widths was applied. The first column shows the smallest caliper width applied 

to the difference in the propensity used to find the matched treated and comparison cases, 

the caliper width 0.001. Each column to the right of column 1 uses a wider caliper: 

Column 2 shows the caliper width 0.005, column 3 shows 0.01 caliper width, column 4 

shows 0.02 caliper width and finally column 5 shows 0.05 caliper width. To assess the 

matching, for each set of matching results is shown the matched mean difference, and the 

t statistic of the statistical significance of the difference, the number of treated and 

comparison cases matched, the number of times comparison cases were used because we 

used matching with replacement, the mean and standard deviation of the difference in the 

propensity scores and mean total bias statistic.

134 

In general, all the results pass the usual checks for performance of the matching. There is 

a reasonable, low total bias (the mean standardized bias statistic is defined fully in section 

4.9.2). The various calipers do not differ dramatically in terms of the mean standardized 

bias statistic. In general, the average difference in propensity was very low too, although 

this rises with greater caliper width as expected. The number of SYETP for which a 

match can be found within the caliper width chosen rises with caliper width as expected. 

The largest caliper width of 0.05 allows all 104 SYETP cases to be matched. 

Frölich et al. (1999) p41 point out that the advantage of replacement is clear as a 

matching algorithm that only uses a comparison case once can face difficulties when 

there is very low density for the comparison group compared to the treatment group. 

Replacement is not problematic as long as there is overlap in the propensity distributions. 

As shown in Figure 5, the propensity distributions overlap greatly, and there is no 

common support problem, as discussed earlier no cases are lost in the imposition of 

common support. The caliper also imposes a restriction that can act to impose the 

common support condition. 

A drawback of the replacement method can arise if the same observation may be used too 

often. The result of over-reliance on single observations can be a great loss in precision. 

The number of times a comparison case is used with replacement is shown in Table 4.6 to 

help assess this. The percentage of matched comparison cases used more than once 

remains less than 10 per cent, for all calipers. For example, for caliper 0.001 only 6 of the 

79 comparison cases are used more than once. All the mean differences in employment 

are significant, so efficiency is not considered problematic.

135 

Table 4.6 Matching results, Single nearest neighbour with replacement, within caliper 

Match 

with 

caliper 

width 

0.001 

Match 

with 

caliper 

width 

0.005 

Match 

with 

caliper 

width 

0.01 

Match 

with 

caliper 

width 

0.02 

Match 

with 

caliper 

width 

0.05 

Difference in employment 88 for 0.18 0.18 0.18 0.17 0.17 

matched treated and comparisons 

Student’s t statistic (2.74) (2.68) (2.68) (2.66) (2.63) 

Number of SYETP matched 87 101 102 103 104 

Number of comparison which satisfy 79 88 89 89 89 

the caliper rule 

Number of times used 

1 73 79 80 79 79 

2 5 8 8 9 8 

More than 2 1 1 1 1 2 

Mean difference in propensity score 0.0002 0.0005 0.0006 0.0007 0.001 

between single nearest neighbour 


Standard deviation 0.0002 0.0009 0.004 0.002 0.003 

Mean Standardized bias calculation 

after matching 

9.66 9.90 9.74 9.45 9.58 

88 Ever employed in 1986 survey.

136 

4.8.2 Mean standardised bias statistic 

The distributions for the remaining comparisons when compared to the SYETP group are 

not perfectly balanced in terms of the separate independent variables after propensity 

score matching. This is because the propensity score allows some variables to be less 

well matched for a case, while others variables are well matched, leading to an overall 

weighting scale across each of these. A measure that summarises the overall balance is 

the standardized mean bias. For each variable included in the probit to estimate the 

propensity score, the measure is calculated as: 

100*[ | µ treated - µ comparison | / √{( var treated + var comparison )/2} ] 

where µ is the mean and var is the variance observed for the covariates in the sample. 

The summary measure of the distribution of bias is found as the mean standardised bias 

across all the variables. 89 This measure can be used to gauge the matching performance 

(Lechner (2000)). It can be interpreted as bias as a percentage of the standard deviation. 

A smaller average bias is generally considered better. It is clear that a bias of zero is ideal, 

but the tolerance level for any non-zero standardized bias is not defined. Matching 

generally strongly improves mean bias. A poor match quality would indicate that the 

matched groups are not genuinely balanced on the observed characteristics and that no 

outcome comparisons based on these matches would be justified (Frölich et al. (2000): 

39). However there is no clear gauge as to what level of standardized bias indicates a 

match is poor. 

Comparison with other studies, which report the standardized bias, gives another gauge 

as to whether the levels are acceptable. Some recent studies that report balance measures 

are Sianesi (2001), Larsson (2000), Frölich et al. (2000), and Gerfin and Lechner (2000). 

Sianesi (2001) p47 appendix D found in Swedish administrative data analyses for 31,975 

cases a mean standardized bias of 0.86, but some variables were poor such as age at 2.86. 

This was a large data set, which enhances the ability to match. Larsson (2000) p27 and 

89 The use of the term bias here is the statistical sense, and refers to the differences in the conditioning 

variables used for matching and not bias in an estimator for a parameter.

137 

Table 6.3 using Swedish data with up to 2500 cases in matched samples, reported 

standardized bias ranging to 10 for individual variables, and reported a summary median, 

rather than the mean, of up to 6.5. Gerfin and Lechner(2000) p25 Table 7 in multiple 

treatment matching, using Swiss data for matched treatment samples of 395 to 6000 

amongst 16533 cases, found mean standardised bias ranging to 18.6 for some programme 

comparisons. They commented that their use of a greater number of variables to form the 

propensity estimate led the quality of the match to fall, but still considered these biases to 

be quite small. Frölich et al. (2000) p40 Table 6.4 in weighted Swedish data with 6287 

cases found mean standardised bias measures ranging to 8.5 and 15, but most were less 

than 10. It was advised that some caution be applied in interpreting matching results 

where the standardized bias was greater than 10. In this respect, the measure balance in 

this study is approaching the need for caution but remains less than 10, and compares 

reasonably well with other studies. Our data are of a much smaller size, and a fairly large 

number of variables are used in the propensity estimation, so the constraints to matching 

balance are greater. In light of this, the balance on the covariates represented by the 

standardized bias indicates the matching has performed reasonably. 

Examination of standardized mean bias in detail can be found by referring to Appendix 

Table A2.1, which is deemed too large to be shown here. Rosenbaum and Rubin (1983) 

p51 point out that one great advantage for propensity score matching is that even 

variables that represent quite rare events can be accounted for where it would not have 

been possible to find a match using individual matching. An example of such a variable 

in our case is children, where none of the comparison matched were observed with 

children. The average standardized bias helpfully summarises into one figure the 

variation between the treatment and comparison, but using only this measure may detract 

from quite poor individual bias measures for some of the individual variables. 

It is most useful to examine some of the variables that were found influential in the probit 

used to estimate the propensity score. Age has featured in the literature for SYETP as an 

important aspect of participation, and age had a strong statistically significant influence 

on SYETP participation in our propensity score probit estimation. The standardized bias

138 

calculation for age is very low, ranging from 2.34 for 0.001 caliper but only 0.92 for 0.05 

caliper. Prior to matching, the standardized bias figure for age was much worse at 49.85, 

which is a suitable example of the improved balance after the propensity score matching. 

Widening the caliper improved the balance score of this variable, which would be partly 

due to the number of times a comparison was used with replacement, and also partly due 

to the new comparison cases available for matching to additional SYETP cases as the 

caliper was widened. Duration of pre-programme unemployment was also well balanced 

after matching, with a standardized bias of 2.58. This did not change with caliper size, 

but the sampling of the survey design for the data, where only those of specific 

unemployment duration formed the sample frame, would be the key factor in this. 

Qualification to year 12 was badly balanced, even after matching, with the means 

showing the SYETP group remaining more likely to have this. Frölich et al. (2000) point 

out that sometimes poor standardized bias can be due to small probabilities and hence 

small standard deviation used in standardizing the standard bias. In this study, the number 

of cases of SYETP is quite small, and the variables are not continuous, so this would 

contribute to the lower balance scores. 

One step to counter poor bias, i.e. balance of the covariates, is to re-specify the 

propensity model (Larsson (2000): 54). This will be treated later in Chapter 7. 

A final consideration is how to choose between the matching results presented in Table 

4.6. The mean difference hardly varies in size, and is always significant, which means 

that the effectiveness of the match is the main criterion for selecting between the results. 

The model is identical, merely the precision of the match is adjusted in Table 4.6. The 

difference in propensity scores is smallest for the 0.001 caliper, indicating this gave the 

closest match in this sense. But in terms of the standardized bias the resulting match is 

better for the calipers 0.02 and 0.05, although the difference in bias statistics is overall 

small. As the estimate is being selected for comparison with the earlier replication 

Heckman bivariate probit result of Chapter 3, it might be said that to make the 

comparison most similar, all the SYETP cases should be included. This would favour the 

result for 0.05 caliper. However, it is claimed that the main advantage of propensity score

139 

matching is the ability to weed out the incomparable cases, and find the treatment results 

for the most comparable treatment and comparison cases. In addition, the empirical 

power of propensity score matching in reducing the problem of selection bias is improved 

with a propensity score very similar but preferably equal to that of the treated persons. 

Also, the bivariate probit employed here is based on the assumption of a common effect, 

which makes the choice among propensity score models simpler because the loss of 

treated observations due to lack of a good match does not affect the parameter being 

estimated by the propensity score matching (as opposed the scenario when heterogeneous 

effects are assumed). The 0.001 caliper result would then be chosen as the result for 

comparison upon the basis that it had no great deterioration in performance but compared 

the most comparable cases. In light of this, the 0.001 caliper results are compared to the 

bivariate probit in the discussion. 

4.9 Sensitivity analysis: all-in-Radius matching 

As a sensitivity analysis of the choice of matching algorithm, results for ‘all-in-radius’ 

matching are shown in Table 4.7. ‘All in radius’ matching might achieve some gains in 

balance, as pair-matching forces the selection from within the caliper of only one 

comparison case. This sensitivity analysis is an exercise to examine the importance of the 

choice of matching algorithm. 

Table 4.7 repeats the presentation of various calipers as used for the pair patching. The 

first column shows the smallest caliper width applied to the difference in the propensity 

used to find the matched treated and comparison cases, the caliper width 0.001. Each 

column to the right of column 1 uses a wider caliper: Column 2 shows the caliper width 

0.005, column 3 shows 0.01 caliper width, column 4 shows 0.02 caliper width and finally 

column 5 shows 0.05 caliper width. To assess the matching, for each set of matching 

results is shown the matched mean difference, and the F statistic of the statistical 

significance of the difference, the number of treat and comparison cases matched, the 

number of comparison cases unmatched and the mean standardized bias from the match.

140 

As would be expected where the same caliper widths were used, the same numbers of 

SYETP cases were matched. But the number of comparisons that could be matched from 

within the radius is much greater for every caliper, growing from 462 matched to the 87 

SYETP in the 0.001 caliper, to 920 comparisons matched to the 104 SYETP in the 0.05 

caliper. The good common support for the treated and the great size of the comparison 

group contribute to the large number of comparison that could match within each caliper. 

The standardised bias is smaller than found for the same caliper when pair matching was 

used, but not dramatically. Once again, all the mean differences in employment are 

statistically significant. The difference in employment varies in size more across the 

different caliper widths than for pair matching. The difference in employment found for 

the 0.001 caliper varies from 0.18 for pair matching to 0.14 for the all-in-radius matching. 

However, for the 0.005 caliper the difference is very small, with 0.18 for pair matching to 

0.17 for the all-in-radius matching. The mean difference in employment for the 0.05 

caliper (0.15) is more similar to that for the 0.001 caliper (0.14). The difference in 

employment does not then grow generally with the greater caliper. This suggests that 

similar propensity scores do not smoothly relate to similar employment outcomes. 

Comparing the estimate employment effects of SYETP in Table 4.8 to those of Table 4.7 

also gives variable results. At the caliper width of 0.001, ‘all in radius’ PSM gives a net 

employment effect of 0.14 percentage points while nearest neighbour PSM gives 0.18. 

However for calipers 0.005 to 0.02, the estimates are very similar in size, but diverging 

again for caliper width 0.05. Generally the ‘all in radius’ impact estimates are smaller. 

Overall, it is difficult to judge which PSM protocol is preferable. However, all-in-radius 

matching is not found used in the literature very often, and most literature apply pair 

matching, although some newer literature also applies kernel matching. From these 

results, where the differences are usually slight, the gains to be had from all-in-radius 

matching appear to be small relative to pair matching for this data. As such, pairmatching 

is concluded to be the most useful algorithm and also conforms to the great 

majority of the literature. This is then applied in the later analyses in following chapters.

141 

Table 4.7 Matching results, All-Within- caliper/Radius with replacement 

Match 

with 

caliper 

width 

0.001 

Match 

with 

caliper 

width 

0.005 

Match 

with 

caliper 

width 

0.01 

Match 

with 

caliper 

width 

0.02 

Match 

with 

caliper 

width 

0.05 



F statistic 91 13.3 13.5 16.7 15.3 14.1 

df 




Number of comparisons unmatched 458 34 3 2 0 

Mean Standardized bias calculation 8.18 6.89 7.72 7.84 7.77 

after matching 92 


The PSM results are now compared to those found for the Heckman bivariate normal 

selection model in the earlier replication chapter. Earlier, the difficulty of which PSM 

result to choose was addressed. The 0.001 caliper result was chosen as the result for 

comparison upon the basis that it had no great deterioration in performance but covered 

all SYETP cases. This facilitates similarity of conditions with the Heckman result. 

We need to compare the marginal effect of SYETP in the Heckman probit to the 

matching measure to make a common measure of the mean effect of treatment on the 

treated. The Table 4.8 shown below contains these estimates and their statistical 

significance. Both methods find a positive statistically significant effect on postprogramme 

employment from participation in SYETP. The difference between the 

employment gain estimated from the two different methods is however quite large. 

Whereas for the Heckman bivariate probit the employment gain is closer to 30 percentage 

points, for PSM the employment gain is estimated as closer to 20 percentage points. 

90 Ever employed in 1986 survey. 

91 Degrees of freedom df=n-1, n=number of matched treated and untreated. The t statistic is not produced 

for this type of PSM. 

92 For all variables entering the probit of SYETP participation.

142 

Table 4.8 Employment effects of Heckman versus PSM 

Heckman 

PSM 

selection bivariate probit 

Employment effect 0.264 93 0.18 

T statistic (2.85)** (2.74)** 

PSM: one-to-one nearest-neighbour within-caliper (0.001) matching with replacement. 

A number of factors are thought to be behind the difference in the two estimates. The 

PSM argues that it controls better for differences in individual characteristics that arise in 

the SYETP and comparison group. The differences between the groups were found to be 

large, and so the difference in part may be due to this. 

The participation equation was identical for both modelling methods. The importance of 

this is now examined. 

In the Heckman method, the participation equation is explicitly modelled to provide a 

variable that is then used to control for the part of unobserved variation of the 

employment equation that is correlated with the unobserved variation in the participation 

decision. There is some evidence to support the need to model accounting for selection 

bias in the SYETP process. For example, Stretton and Chapman (1990) p42 in discussing 

programme entry conclude that it “…does involve some selection bias as the CES selects 

those eligible clients who are judged to be ‘job ready with assistance´ for referral ... 

Employers then select their subsidised employee from among a number of referrals made 

by the CES.” Although Stretton and Chapman (1990) were describing the selection for 

the later Jobstart programme, a very similar process existed for SYETP, as can be seen 

described in the earlier Chapter 2. In the SYETP administrative data it was found that 

gaining referrals and gaining employment were closely linked in a selection process 

[Wielgosz (1984), Aungles and Stewart (1986)]. As there is no variable indicating the 

administrative value of whether individuals were ‘job- ready-with-assistance’, then this is 

93 For SYETP in the employment equation: dy/dx =0.0325919 and mean for SYETP is 0.081060. The mean 

SYETP translates to 8.1 per cent. The marginal effect calculated at the mean is then 0.26418. This is 

interpreted as a 26 per cent increase in employment. Estimated using the mfx command in STATA7.0

143 

the unobserved component. If the variables included in the propensity score model and 

participation equation of the bivariate probit were used by the caseworkers in SYETP to 

determine that an applicant is ‘job-ready-with-assistance’, then selection on 

unobservables should be negligible. 

For the exclusion restriction to identify the bivariate probit model, some variables 

included in the participation equation estimated are then excluded from the employment 

equation. An important aspect of this estimation approach is the identification of a 

credible instrument for the exclusion restriction, in this case age and referrals to CEP. 

There is some support for this role for these variables in the unweighted data, as later 

sensitivity analysis in Chapter 7 reports. Also, the results of estimation rest upon the 

suitability of the underlying assumption of the bivariate normal distribution for the errors 

in the participation and employment equations. Finally, the specification of the model is 

assumed correct, with no variables left out to cause mis-specification bias. 

For the semi-parametric method of PSM, CIA is the critical underlying assumption. 

However, again the specification of the model for the propensity to participate in SYETP 

is assumed correct. The variables in the model are central to the credibility of the CIA 

assumption. Whether the CIA is met cannot be accurately tested, however the plausibility 

of CIA can be assessed. 

In regard to labour market evaluation, pre-programme labour market history is considered 

the most important explanatory variable, and this is part of the model applied here (see 

Ham and Lalonde (1996)). There are a large number of variables generally and it is 

considered that at least with regard to the breadth of individual characteristics controlled 

for, the CIA has been satisfied. However, although a wide range of individual 

characteristics has been included, it could be argued that some are lacking that could 

affect both participation and employment. Gerfin and Lechner (2000) had the long-term 

unemployment at the regional placement office, which here would correspond to the CES. 

This sort of variable is not available in the data however. A variable in the data and which

144 

might give an approximation measure of the functioning of the administration is CES 

referrals, and also CEP referrals which have been used in analysis. 

In this analysis, age and referrals to CEP might be the key variables under suspicion for 

violating the CIA rule for PSM. This is because their use for the exclusion in Richardson 

(1998) 94 was argued as due to their plausibly not affecting employment independently 

once SYETP participation was accounted for. Indeed, in later checks carried out in this 

study it was not possible to estimate the Heckman model if these variables were included 

in the employment equation which might indicate misspecification. In later chapter 7, the 

sensitivity of the PSM specification to CEP referrals is checked. 

Furthermore, in the application of both methods, casewise deletion was used for missing 

data (item non-response) on modelled variables. This may not be the most suitable 

treatment for missing data, as briefly discussed in the next chapter. However, as later 

discussion shows, casewise deletion is preferable because it assumes the correct standard 

error. As well, in light of the replication aims, it was necessary to repeat the modelling 

assumptions of Richardson (1998). 

However, at least one strong confounding factor to discussion comparing the results is the 

issue of non-response to the surveys. The issue of attrition can introduce selection bias 

which would confound both Heckman and PSM methods unless it was treated. As a 

result, further comparison of the results is deferred. This comparison is then again treated 

in later chapters after consideration of the data problems and repairing the data to treat 

them. 

94 This was checked by modelling the univariate probit for employment, where these variables were not 

found to be statistically significant when included.

145 

5: Study 3 Attrition and non-response in the ALS 

In this chapter, data loss to the sample is examined for the Australian Longitudinal 

Survey data. The theory relating to survey attrition is broadly summarized. Literature on 

the empirical aspects of attrition and effects on estimation are then reviewed. Next, a 

practical means of investigating and treating attrition in the data is presented. Following 

this, attrition in the ALS is investigated in depth and the extent of sample reduction is 

considered. A univariate analysis of the scale of attrition effects is conducted. The 

different sources of sample loss are recognized separately. Initially the final sample and 

those lost from the sample are compared. Then, sample loss is examined within the 

SYETP treatment and comparison groups. The non-response to the first survey and 

sampling design effects are examined to check their effects on analysis. These are 

accounted for with a weight that was supplied with the data. Multivariate analysis is then 

conducted of the effects survey attrition on the participation model. Attrition is concluded 

to be a problem for the modelling. Weights are then constructed to repair the data. These 

attrition weights are then united with the sample design/non-response weights. These 

combined weights are used in subsequent chapters. 

5.1 Examining sample reduction in the ALS 

A particular concern for the analysis of longitudinal data is sample attrition. Attrition and 

survey non-response are a form of non-response usually termed ‘unit non-response’ by 

statisticians. The key worry is that attrition may lead to selective samples, which in turn 

may make interpretation of resulting estimates problematic. One potential problem for the 

analysis in the earlier studies is the possibility of attrition bias due to sample reduction 

between the first sample survey in 1984 and the 1986 survey. An important reason for 

properly accounting for attrition is that ignoring attrition can lead to biased parameter 

estimates if the attrition is related to the behaviour being modelled. 

The issue of attrition was briefly examined in Richardson (1998) by comparing the 

summary statistics of the final analysis sample (referred to as the ‘whole sample’), those

146 

of the SYETP treatment group, the non-SYETP comparison group, and of the ‘attrition’ 95 . 

It was concluded that the characteristics appeared broadly similar. As a result of this 

examination, attrition bias was treated as a minor problem. 

If attrition is random, then it is not a major problem. Attrition is considered an important 

issue if it affects endogenous variables (Hsaio (1986): 203). The key endogenous 

variables analysed are observed employment in 1986, and participation in the SYETP 

programme. Attrition is also a problem if it is correlated with individual characteristics or 

with the impact of treatment conditional on characteristics (Heckman, Lalonde and Smith 

(1999): 1914). In light of the potential importance of attrition to the interpretation of 

results, it seems sensible to perform further investigation of the impact of attrition. 

The theoretical basis for the attrition modelling carried out here, and a review of previous 

studies of attrition is also included in this section. The review focuses on recent 

econometric work on attrition. This is followed by an empirical examination of the 

sample reduction effects pertinent to the analysis. The assessment involves observing the 

univariate effects, the effects on the model of interest, modelling attrition and 

constructing weights to account for attrition. At the same time, other data problems that 

warrant scrutiny are examined. These include initial non-response, together with 

accounting for complex survey design, and missing data (item non-response). 

5.2 Theory of why attrition introduces bias 

Hsaio (1986) p198 details the issues relating to attrition in panel data concisely. At the 

heart of the concern about attrition is the problem of missing data. If the data lost through 

attrition are missing completely at random [MCAR], or it is assumed that the underlying 

process of missing-ness is random in that it does not depend on the data values, then the 

common procedure is to focus on the panel of complete observations. This is also known 

as using the balanced panel, or complete case analysis. This is the path pursued by 

Richardson (1998). Dillman et al. (2002) point out that missing-ness with an MCAR 

mechanism is usually an unrealistically strong assumption when missing data do not 

95 See discussion Richardson (1998) p.5 and tables 1 and 2, p14 and p15.

147 

occur by design, because the missing-ness usually does depend on recorded values 

(Dillman et al. (2002): 17). 

The possibility that the data are missing for reasons of self-selection raises problems in 

econometric models of outcomes. In other words, attrition is most problematic when it is 

related to the endogenous variable. The data are then not missing completely at random. 

If the probability of attrition is related to the outcomes, then the estimates are biased and 

inconsistent. 

The 2 period model is as follows: 

(1) y it = β ‘ x it + v it 

(2) a i =1 if y i2 is observed, 

a i =0 if y i2 is not observed 

(3) a i =1 if a i * = γ y i2 + θ ‘x i2 + δ ‘ w i + ε i * ≥ 0 

where time T=2, v it is the error term, y it is the endogenous variable, a i is the attrition 

dummy, a i * is the latent variable for attrition, w i is the vector of variables that do not 

enter the conditional expectation of y but affect the probability of observing y, θ and δ are 

parameters, and ε i * are normally distributed. A common procedure for analysis where 

there is attrition, is to use only those cases present in all surveys, also called a balanced 

panel. Attrition in the second period would mean that in a balanced panel, the 

observations for which y i2 is missing are discarded. 

If the probability of observing y i2 varies with the value of attrition , as well as the value of 

other variables, then the probability of observing y i2 depends on the error term v i2 . The 

reduced form, where y i2 is substituted into equation (3), becomes: 

(4) a i * = (γ β ‘ + θ ‘)x i2 + δ ‘ w i + γ v i2 + ε i * 

This can be rewritten as: 

(5) a i * = π ‘x i2 + δ ‘ w i + γ v i2 + ε i 

and finally collecting terms in matrix notation:

148 

(6) a i * = d ‘ R i + ε i 

where d ‘ = (π ‘,δ ‘) ; R i = (x i2 , w i ) ; ε i = γ v i2 + ε i * 

If the v it are assumed to be normally distributed and the variance of ε i is one, and Φ(.) is 

the standard normal distribution then the probability of no attrition is 

(7) Prob (a i = 1) = Φ(a ‘ R i ) 

When using the balanced panel of complete observations, Hsaio (1986) p199 shows that 

the conditional expectation of y i2 is 

(8) E (y i2 | x i2 , a i =1 ) = β ‘ x i2 + σ 2 ε [φ (a ‘ R i ) / Φ (d ‘ R i )] 

Where σ 2 ε is the covariance between v i2 + ε i and φ (.) is the standard normal 

distribution. It is clear that this derivation is the same as the bivariate probit model 

presented earlier in Chapter 3, but applied to the case of attrition. 

In the Australian Longitudinal Survey, attrition is a strong issue for the analysis of 

employment in 1986, as the employment in 1986 can never be known for those who are 

not interviewed in 1986 because they are lost through attrition. This introduces the 

problem of selection by virtue of survival, where the modelling of employment in 1986 

can only take place for the sample surviving from 1984 to 1986 (Hoem (1985): 260). The 

bias introduced by attrition is shown in equation 8. Unless σ 2 ε = 0 , then estimates of β 

using the balanced panel of complete observations are biased and inconsistent. 

The first formal econometric model of attrition and labour market behaviour is usually 

attributed to Hausman and Wise (1979). There was a high attrition from the Gary Income 

Maintenance experiment and they were concerned this would bias the experimental 

estimates because the attrition was likely related to income, which was their outcome of 

interest. They found evidence of attrition bias in modelling attrition alongside the income 

equations, but concluded it had little effect on the experimental effect estimated. Ridder

149 

(1990) extended and improved the Hausman-Wise model. These models incorporate 

attrition into the behavioural model. 

Selection modelling is central to our model of intrinsic interest, bivariate modelling of 

SYETP and employment outcomes. Accounting separately for attrition within this model, 

would require at least trivariate selection modelling. Recent work by Capellari (2001) 

uses the multivariate probability approach to account for endogenous panel attrition and 

selectivity in earnings, allowing estimation by application of simulated maximum 

likelihood techniques for the multi-dimensional integrals. However early examinations by 

Schmertmann (1994) found that the estimation approaches available for selectivity 

modelling of higher orders were not very good. The estimation suffers from the strict 

restrictions that need to be assumed for the bivariate correlations in the error distribution. 

It was concluded that in models with many selection criteria, or where sample sizes are 

small, the methods would not give good estimates. Furthermore, very strong assumptions 

are required about the correlations between response, SYETP participation and 

employment, and so on these grounds this method of incorporating attrition with 

selection modelling is ruled out for this application. Furthermore, considering the small 

sample size we have available, combined with the strength of the assumptions required, 

this is beyond the scope of this paper. 

5.3 Empirical aspects of the effects of attrition on estimates 

Recent examinations of the empirical effects of attrition have raised the argument that the 

theoretical potential for attrition bias does not always warrant concern. Fitzgerald et al. 

(1998a) concluded that the empirical existence and magnitude of attrition bias was not 

clearly related to the size of the attrition rate. They also concluded that attrition which is 

random in nature can result in no measurable bias arising in economic modelling, in 

accordance with earlier statistical literature (Fitzgerald et al. (1998a): 256). 

Further recent studies on the evidence for the effects of attrition bias on estimates in 

labour market analysis make stronger claims. Lillard and Panis (1998) p456 argued that 

their results for the US panel data Michigan Panel Study on Income Dynamics (PSID)

150 

indicated that even significant attrition which is observed to be selective in nature does 

not introduce strong biases in estimation results. Falaris and Peters (1998) p 531 noted 

that effects of attrition on regression estimates in general is negligible, or only affects the 

intercept. The overarching message from these studies is that attrition bias, even when 

found to act selectively on observable characteristics, does not necessarily bias the 

estimates of interest in modelling. 

Other evidence also points to the importance of validating the extent of attrition bias 

effects on the estimates of interest. Alderman et al. (2000) apply the methods set out in 

Fitzgerald et al. (1998a), and find some outcomes are affected by attrition bias while 

others are not. They find that univariate comparisons showing systematic attrition 

affecting particular variables of interest do not translate to these variables being 

significant in a probit model predicting attrition. They warn that in the relations they 

modelled attrition bias led to striking differences affecting the coefficients for some 

models of outcomes but not others. They further point out that their results indicate that 

attrition bias conclusions are not generalisable to all multivariate estimates or all data, but 

that the particular model, outcome of interest and data need to be assessed. 

5.4 Empirical attrition test and treatment 

Fitzgerald et al. (1998b) examined the Michigan Panel Study on Income Dynamics (PSID) 

of the US, and found attrition was highly selective, concentrated amongst those of lower 

socioeconomic status. Usefully, they outlined the statistical framework for tests for 

attrition bias within an econometric context. In their model, a key distinction is drawn 

between attrition where selection is on observables as opposed to selection upon 

unobservables. The background for their approach is the selection bias modelling 

econometric literature, deriving chiefly from Heckman (1979). The earlier attrition study 

of the PSID by Becketti et al. (1988) is shown to be a close relative of the direct 

modelling of attrition proposed by Fitzgerald et al. (1998a). Their model is defined as 

follows: 

(9) Y = β 0 + β 1 X + ε

151 

(10) A* = δ 0 + δ 1 x +δ 2 z + ν 

(11) A =1 if A* ≥ 0 and A=0 if A*

152 

Richardson (1998) is then extended here, by estimating the attrition of the Australian 

Longitudinal Survey and constructing weights to reduce the resultant bias. This also 

serves as a test of the impact of attrition bias on the estimated results. 

5.5 Examining the role of attrition in the ALS and in modelling of the SYETP 

employment effect 

To assess the issue of attrition bias a set of analyses are conducted, drawing on the tests 

of Fitzgerald et al. (1998a). Firstly, the univariate effects of attrition upon means are 

examined. The Fitzgerald et al. (1998a) framework for the statistical analysis of attrition 

bias applied here focuses upon sample attrition which results in selection on observables. 

The case for attrition due to observables is examined for the ALS by investigating the 

observed means. The effects of attrition upon the models of SYETP participation are also 

considered. The probability of attrition is then modelled using the probit. Weighting 

effects upon the model of employment outcomes for the SYETP programme are 

additionally explored. 

5.5.1 Extent of sample reduction 

The ALS list sample was constructed from administrative records of persons aged 15-24 

on 1 September 1984. The sample was selected based on those registered with the 

Commonwealth Employment Service offices throughout Australia who were unemployed 

and seeking full-time work for three months or more at the time of selection in June 1984. 

The original sample had 2998 cases, and the first survey collected interviews from 2,403 

cases. 

Interviews for the first survey were conducted between September 1984 and November 

1984. The survey was repeated in each of the later years 1985, 1986 and 1987, following 

up the same people with whom interviews were conducted in 1984. In 1985 1,910 

interviews were conducted in the months June to October, in 1986 1,711 interviews were 

collected between September and November, and in 1987 1,518 interviews repeated

153 

again from September to November. Of prime interest here are the first three years of 

survey observations to 1986. 

The ALS data finally used for analysis has a reasonable rate of sample reduction. Each 

year, fewer observations in ALS have survey outcomes available for analysis. The sample 

reduction due to attrition is shown in Table 5.1. The initial non-response rate for the first 

observations in 1984 is 20 per cent. Loss of sample observations between the first survey 

in1984 and the second survey in 1985, is 21 per cent. The attrition rate falls to 10 per cent 

between 1985 and 1986. By the third survey in 1986, only 57% of the original sample 

remains. Non-response was addressed in the survey design with heavy follow-up 96 . The 

final sample of 1283 cases used for analysis 97 arises due to analytical selection, where 

404 cases were discarded, amounting to 24 per cent of those who respond to all surveys 

to 1986. These different sources of sample reduction are now examined. 

Table 5.1 Sample Reduction for ALS List sample 

Number of cases 

% cases lost 

each stage 

Original sample 2998 

1984 survey interviews 2403 20 80 




% cases 

remaining of 

original 

sample 

A large part of the sample reduction in the analytical selection is due to missing data 

problems. Of those responding to the 1986 survey, that is, of those that do not attrit, 

further sample reduction occurs for several reasons. As previously described from those 

interviewed in 1984, the final sample excludes those who were over 25 years at the 

96 Interviewers were required to make 7 visits in person to the initial, 2 nd or 3 rd address of a mover, and 

were instructed to use a varying time of contact pattern, and supervisors would follow-up in attempts to 

convert refusals. Interpreters were used where necessary, and an introductory approach letter from the 

Minister and the survey firm was sent in advance of surveying. All of these took place before non-contact 

was declared. 

97 The sample is formed to match that of Richardson (1998) to facilitate replication and subsequent 

comparison of results.

154 

interview date, those who ever entered full-time education, or for whom responses were 

missing in 1984, 1985 or 1986 (Richardson (1998): 5). The first of these restrictions is 

not dealt with here, as it prescribes the eligible set, but is returned to subsequently. The 

latter of these has been dealt with in part, with reference to the natural attrition, which 

caused missing values. Apart from natural attrition, the set discarded for whom responses 

were missing in 1984, 1985 or 1986 also includes those cases for whom data on some of 

the regressor variables is missing. In particular, when information about the family 

background is missing, or when the proportion of time in unemployment is missing, these 

cases are dropped from the analysis set. Statisticians usually term this form of missing 

data ‘item non-response’. 

Greene (1991) points out there are two main scenarios to consider, depending on why the 

data on the regressors is missing. Imputation or discarding the cases is the usual approach 

to this type of missing data. Using the terminology of Griliches (1986) this type of 

incomplete data is described as the ignorable case if when using the complete 

observations the data are not missing for self-selection reasons 98 . In the ignorable case, 

using only the complete observations is not problematic if efficiency related to the 

variance is not important. The non-ignorable case, where the missing information is 

systematically related to the variable being modelled, is another case of the sample 

selection problem. 

98 Ignorable non-response means the same as selection on observables.

155 

5.5.2 Univariate examination of sample reduction 

Table 5.2 gives a univariate analysis of the differences between the characteristics of the 

final sample and those who are lost from the sample. This expands the information 

provided in Table 1 of Richardson (1998) by including the t-test of statistical significance 

for the difference between the mean of the remaining sample and those lost. Column 1 

shows the mean for the final sample, and corresponds to column 1 of Table 1, Richardson 

(1998). Allowing for rounding, the means are identical to those presented in Richardson. 

This is seen as a check that the final data matches that of Richardson (1998). The second 

column gives the standard deviation. Column 3 gives the mean of those lost from the 

sample, with column 4 the standard deviation. The absolute difference in the mean is 

given in the fifth column, with the t statistic in the sixth column. The t test of the 

hypothesis that the difference in the means is zero, accommodating unequal variance in 

the samples, is shown in column 6, with significance levels of 10 per cent, 5 per cent and 

1 per cent indicated with 1, 2 and 3 stars in each case. A number of outcome and 

regressor variables are shown. Most of these are useful variables in the modelling of 

employment outcomes. All the variables are identified using the 1984 survey data, except 

‘ever employed in 1986’, and ‘ever on a government programme in 1986’, the first of 

which is a dependent variable for analysis. SYETP participation is an important variable 

that is an outcome defined in the 1984 survey. 

Table 5.2 compares those remaining in the final sample to all those lost from the final 

sample. It is important to note that this does not differentiate between those cases lost due 

to non-response at surveys in 1985, 1986 and those cases dropped in the analytical 

selection process. The aim of initially examining the effect of sample loss on the means 

observed is not affected by this combination. The different sources of sample loss are 

subsequently dealt with separately in the further analysis of attrition. 

The two variables ‘ever employed in 1986’, and ‘ever on a government programme in 

1986’ suffer from the ‘selection by survival’ problem. The parts of the sample lost due to

156 

natural attrition have missing values for these cases, as they are defined in the 1986 

survey. Accordingly, in the table they are not presented since the overall mean would not 

be representative of the same observations as the rest of the table. Effects upon these 

variables are dealt with in the later section where the source of data reduction is 

controlled for. 

Several characteristics in Table 5.2 exhibit statistically significant differences between 

the final sample and those lost from the sample: whether the partner was employed, 

schooling to year 9 and year 11, the length of the last job held, work limiting health 

problems, state of residence at time of interview, and the rural-urban nature of the 

location. The SYETP treatment variable also shows evidence that it was affected by 

sample reduction. Thus both outcomes and regressors appear affected in the univariate 

analysis. 

It is interesting to examine how those lost from the sample compare to those remaining. 

Past studies, such as Hausman and Wise (1979) found patterns indicating particular strata 

of their dependent variable were not being observed, in their case parts of the earnings 

distribution were truncated, and certain subgroups more likely not to be surveyed. 

Fitzgerald et al. (1998a) also found lower socio-economic groups were more likely to be 

affected. Here, amongst those lost from the sample, when compared to those remaining, 

fewer had employed spouses in 1984, fewer had obtained only year 11 at school while 

more had year 9 of school, and fewer had health problems that affected their work. The 

length of the last job held differed, for more had held jobs of 1-2 years while less had 

held jobs of 2-3 years. Of those lost, fewer were first interviewed in South Australia or 

Northern Territory and more in New South Wales or the Australian Capital Territory. The 

location of the interview was important, as amongst those lost fewer came from rural 

areas or country towns, and more were from cities. Fewer of those lost from the sample 

had taken part in SYETP. 

There is no clear picture of those lost being from lower socioeconomic groups or having 

a more disadvantaged labour market position. While the educational capital is slightly

157 

lower, the job lengths are only slightly shorter with the distribution of lengths only 

appearing bulked up in the middle, and there are fewer with health problems, none of 

which indicates a preponderance of observations at the lower end of the labour market. 

This mixed picture may arise due to the different sources of sample loss. Whether this 

can be resolved is dealt with again later by further examining the differences by source of 

sample loss.

158 

s.d. Those 

Table 5.2 Summary statistics for attrition effects in the final sample 

Mean and 

sample 

s.d. 

mean 99 sample 

standard deviation (s.d.) after all 

sample 

reduction 

lost 

from 

the 

mean 

Difference in 

means 

(absolute) 

T –test 

SYETP 0.08 0.27 0.05 0.21 0.03 3.42*** 

Female 0.41 0.49 0.40 0.49 0.01 0.43 

Average age 1984 19.99 2.41 20.02 2.38 0.03 0.30 

Aboriginal/Torres Strait 0.03 0.17 0.04 0.19 0.01 1.09 

Islander 

Other ethnic minority 0.08 0.27 0.09 0.29 0.01 1.08 

Married 1984 0.12 0.32 0.10 0.30 0.02 1.21 

Spouse employed 1984 0.06 0.24 0.04 0.19 0.02 2.44*** 

Children 1984 0.05 0.23 0.04 0.20 0.01 1.27 


Degree/diploma 0.12 0.32 0.11 0.31 0.01 1.03 

Apprenticeship 0.08 0.27 0.07 0.25 0.01 1.19 

Other post-school 

0.07 0.26 0.07 0.26 0 

qualification 

Year 12 of school 0.15 0.35 0.17 0.37 0.02 1.35 

Year 11 of school 0.14 0.35 0.11 0.31 0.03 2.07** 

Year 10 of school 0.31 0.46 0.30 0.46 0.01 0.86 

Year 9 of school 0.12 0.33 0.15 0.36 0.03 2.04** 

Parental background 

when resp. aged 14 

Father postschool 

qualification 

0.34 0.47 0.35 0.48 0.01 0.30 

Mother postschool 

0.18 0.39 0.20 0.40 0.02 0.82 

qualification 

Father manager, professional, 0.26 0.44 0.24 0.43 0.02 1.02 

para-professional 

Father not employed 0.05 0.23 0.05 0.22 0.005 0.55 

Father not present 0.16 0.36 0.17 0.38 0.01 0.97 

Mother manager, professional, 0.10 0.29 0.08 0.28 0.02 0.92 


Mother not employed 0.55 0.50 0.55 0.50 0.006 0.30 

Mother not present 0.05 0.22 0.06 0.24 0.008 0.82 


Never held a job 0.12 0.32 0.11 0.31 0.01 0.42 

< 1 year 0.42 0.49 0.44 0.50 0.02 0.92 

1 year 0.13 0.34 0.17 0.37 0.04 2.22** 

2 years 0.14 0.35 0.12 0.32 0.02 1.67* 

3 years or more 0.19 0.39 0.16 0.37 0.03 1.47 

Average unemployment 100 0.62 0.41 0.64 0.41 0.03 1.55 

Ever employed in 1986 101 0.74 0.44 Na Na Na Na 

Ever government programme 

1986 102 0.11 0.31 Na Na Na Na 

99 The mean of a 0/1 binary variable is the same as the proportion. 


101 Ever held a non-subsidised, non-government programme job in the 1986 reference period, after the first 

17 weeks.

159 

Work limited by health 1984 0.10 0.30 0.12 0.33 0.02 1.82* 

Interviewed location in 1984 

New South Wales /ACT 0.37 0.48 0.41 0.49 0.04 1.88* 

Victoria 0.24 0.43 0.24 0.42 0 

Queensland 0.14 0.34 0.15 0.35 0.01 0.66 

South Australia /Northern 0.12 0.33 0.09 0.29 0.04 2.73*** 

Territory 

Western Australia / Tasmania 0.13 0.33 0.12 0.32 0.01 0.88 

Capital city 0.47 0.50 0.52 0.50 0.05 2.43*** 

Other town 0.21 0.41 0.26 0.44 0.05 2.61*** 

Country town 0.24 0.43 0.18 0.39 0.06 3.54*** 

Rural area 0.08 0.27 0.04 0.20 0.04 3.79*** 

Number of observations 1283 1085 

NOTES: Significance of t test at level 1% ***, 5%**, 10 % *.Student’s t tests of whether means are equal 

are performed with the assumption of unequal variances, n-1 degrees of freedom. 

Na = not applicable, these statistics are always missing for natural attrition. 

102 Ever go on a government programme, including SYETP, in the 1986 reference period.

160 

5.5.3 Sample reduction by SYETP treatment group 

5.5.3.1 Comparing those lost in attrition to those who remain 

In the previous section it was found that fewer of those lost from the sample had taken 

part in SYETP. Table 5.3 again examines the means for attrition effects, but focusing 

within the SYETP treatment and comparison groups. This table once again expands on 

the information provided in Table 1 of Richardson (1998) by including the t-test of 

statistical significance for the difference between the mean of the remaining sample and 

those lost. The small scale of the treatment group renders this an important means of 

identifying statistically significant differences amongst the means. The wide difference in 

the size of the comparison and treatment groups, allows the fluctuations in the 

comparison group to dominate as the main source of statistical differences. 

The first panel on the left of Table 5.3 shows the SYETP group, while the second panel 

on the right shows the comparison group. Within each panel, the same format as that of 

Table 5.2 is followed, with the mean in the remaining sample in the first column, the 

standard deviation in the second column, the mean for those lost in the third column, the 

standard deviation in the fourth, the absolute difference between the means in the fifth 

column and the t statistic in the sixth column. Column 1 in the first panel corresponds to 

Column 2 of Table 1 of Richardson (1998) p14, while Column 2 in the second panel 

reflects the same information as Column 3 of Table 1 of Richardson (1998). Generally, 

all statistics are in agreement, which is seen as a further check that the same final sample 

as Richardson (1998) has been achieved. 

The SYETP treatment group shows an important difference on two key characteristics, 

the proportion of time spent unemployed in the 1984 reference period, and the share with 

year 12 schooling. Those lost had a much smaller proportion of their time spent 

unemployed, almost half of that for those remaining in the sample. In addition, far fewer 

had the year 12 leaving certificate than those left in the sample.

161 

The characteristics of the SYETP participant profile affected by the sample reduction are 

quite different to those among the comparison group. Amongst the comparison group, 

fewer of those lost had employed partners. As well, the profile of schooling was different, 

more of those lost had year 12 and year 9 schooling, but fewer had year 11. The pattern 

of job lengths held by those of the comparisons lost was different to those remaining: 

more had held jobs of one to 2 years, but less had held jobs of three years or more. The 

locations of those lost also differ from the locations of those who remain in the 

comparison group, with more in New South Wales and Australian Capital Territory, and 

fewer in South Australia or Northern Territory, less came from rural areas or country 

towns, and more were from cities. It is conspicuous that the location of those lost within 

the comparison group reflects the overall pattern observed earlier in Table 5.2. The bulk 

of observations being in the comparison group enforces the influence of this group’s 

profile upon the overall statistics in Table 2. 

The variation in the effects within the SYETP treatment and comparison groups gives 

rise to concerns that SYETP participation as measured in the ALS data used may be 

affected by the data problems due to sample reduction. The former analysis of the overall 

effects of sample reduction also showed evidence of SYETP being affected, as well as 

potential regressor variables. The source of sample reduction is now controlled for in 

further dissection of the univariate effects.

162 

Table 5.3 Summary statistics of attrition effects, for comparison and treated SYETP 

SYETP 

Non-SYETP 

Mean and 

SYETP s.d. SYETP s.d. Difference T –test Non- s.d. Non- s.d. Difference T –test 

standard deviation (s.d.) sample 

after 

attrition 

mean 

lost to 

attrition 

mean 

in means 

SYETP 

sample 

after 

attrition 

mean 

SYETP 

lost to 

attrition 

mean 

in means 

Panel 1 Panel 2 

(1) (2) (3) (4) (5) (6) (1) (2) (3) (4) (5) (6) 

Female 0.43 0.50 0.33 0.48 0.10 1.20 0.41 0.49 0.41 0.49 0 

Average age 1984 19.03 1.97 19.16 2.41 0.13 0.33 20.07 2.42 20.06 2.37 0.01 0.12 

Aboriginal/Torres Strait 0.01 0.10 0.00 0.00 0.01 0.43 0.03 0.17 0.04 0.20 0.01 1.04 

Islander 

Other ethnic minority 0.08 0.27 0.06 0.24 0.02 0.81 0.08 0.27 0.09 0.29 0.01 1.17 

Married 1984 0.03 0.17 0.06 0.24 0.03 0.81 0.12 0.33 0.10 0.30 0.02 1.57 

Spouse employed 1984 0.02 0.14 0.00 0.00 0.02 1.42 0.06 0.24 0.04 0.20 0.02 2.48*** 

Children 1984 0.02 0.14 0.06 0.24 0.04 1.10 0.06 0.23 0.04 0.20 0.02 1.63* 


Degree/diploma 0.08 0.27 0.06 0.24 0.02 0.43 0.12 0.33 0.11 0.31 0.01 1.09 

Apprenticeship 0.03 0.17 0.02 0.14 0.01 0.36 0.09 0.28 0.07 0.26 0.02 1.32 

Other post-school qualification 0.07 0.25 0.04 0.20 0.03 0.76 0.07 0.26 0.07 0.26 0 

Year 12 of school 0.23 0.42 0.12 0.33 0.11 1.84* 0.14 0.35 0.17 0.38 0.03 1.95** 

Year 11 of school 0.17 0.38 0.14 0.35 0.03 0.58 0.14 0.34 0.11 0.31 0.03 1.90* 

Year 10 of school 0.32 0.47 0.45 0.50 0.13 1.59 0.31 0.46 0.29 0.45 0.02 1.20 

Year 9 of school 0.11 0.31 0.16 0.37 0.05 0.86 0.13 0.33 0.15 0.36 0.02 1.85* 



Father postschool qualification 0.26 0.44 0.21 0.41 0.05 0.70 0.35 0.48 0.35 0.48 0 

Mother postschool qualification 0.20 0.40 0.18 0.39 0.02 0.34 0.18 0.39 0.20 0.40 0.02 0.95 

Father manager, professional, 0.25 0.44 0.18 0.39 0.07 1.07 0.26 0.44 0.24 0.43 0.02 0.85 


Father not employed 0.04 0.19 0.04 0.20 0 0.06 0.23 0.05 0.22 0.01 0.63 

Father not present 0.19 0.40 0.16 0.37 0.03 0.55 0.15 0.36 0.17 0.38 0.02 1.19 

Mother manager, professional, 0.07 0.25 0.06 0.24 0.02 0.20 0.10 0.30 0.09 0.28 0.01 0.98

163 


Mother not employed 0.48 0.50 0.61 0.49 0.13 1.50 0.55 0.50 0.55 0.50 0 

Mother not present 0.09 0.28 0.06 0.24 0.03 0.64 0.05 0.22 0.06 0.24 0.01 1.11 


Never held a job 0.12 0.32 0.08 0.27 0.04 0.75 0.12 0.32 0.11 0.32 0.01 0.30 

< 1 year 0.56 0.50 0.63 0.49 0.07 0.83 0.41 0.49 0.43 0.50 0.02 1.02 

1 year 0.13 0.34 0.18 0.39 0.04 0.66 0.13 0.34 0.17 0.37 0.04 2.12** 

2 years 0.13 0.34 0.06 0.24 0.08 1.60 0.14 0.35 0.12 0.33 0.02 1.45 

3 years or more 0.06 0.23 0.06 0.24 0 0.20 0.40 0.17 0.38 0.03 1.77* 

Average unemployment 103 0.68 0.37 0.80 0.32 0.12 2.08** 0.61 0.41 0.63 0.41 0.02 1.38 

Ever employed in 1986 104 0.87 0.34 Na Na Na Na 0.73 0.44 Na Na Na Na 

Ever government programme 0.14 0.35 Na Na Na Na 0.11 0.31 Na Na Na Na 

1986 105 

Work limited by health 1984 0.04 0.19 0.06 0.24 0.02 0.53 0.11 0.31 0.13 0.33 0.02 1.57 


New South Wales /ACT 0.31 0.46 0.29 0.46 0.02 0.17 0.38 0.49 0.42 0.49 0.04 1.81* 

Victoria 0.27 0.45 0.24 0.43 0.03 0.43 0.23 0.42 0.24 0.42 0 

Queensland 0.09 0.28 0.20 0.40 0.11 1.75* 0.14 0.35 0.15 0.35 0.003 0.17 

South Australia /Northern 0.13 0.34 0.12 0.33 0.01 0.30 0.12 0.33 0.09 0.28 0.03 2.69** 

Territory 

Western Australia / Tasmania 0.20 0.40 0.16 0.37 0.04 0.69 0.12 0.33 0.11 0.32 0.01 0.53 

Capital city 0.58 0.50 0.55 0.50 0.03 0.33 0.46 0.50 0.52 0.50 0.06 2.73** 

Other town 0.16 0.37 0.25 0.44 0.09 1.28 0.21 0.41 0.26 0.44 0.05 2.30** 

Country town 0.21 0.41 0.12 0.33 0.09 1.54 0.24 0.43 0.18 0.39 0.06 3.38*** 

Rural area 0.05 0.21 0.08 0.27 0.03 0.70 0.08 0.27 0.04 0.20 0.04 4.10*** 

Number of observations 104 51 1179 1034 

Significance of Student’s t test at level 1% ***, 5%**, 10 % *. T tests of whether means are equal are performed with the assumption of unequal variances, n-1 

degrees of freedom. 

103 This is the proportion of the 1984 reference period to 3 June that was spent unemployed. 

104 Ever held a non-subsidised, non-government programme job in the 1986 reference period, after the first 17 weeks. 


164 

5.5.3.2 The effect of sample reduction on the difference between SYETP and comparison 

groups 

Overall, in the previous section it can be seen that the effect of the sample reduction is 

not equal on the treatment and the comparison groups’ profile for these characteristics 

examined. This is crucial as the aim of later modelling of the treatment effect is the 

comparison of the treatment and comparison groups. The effect of sample reduction on 

the difference in profiles of the treatment and comparison groups is now explored. This 

provides a different perspective on how the data reduction impacts on the analysis. 

The contrast between the treatment and comparison group is summarised in the absolute 

mean difference that is shown in Table 5.4. The first column of Table 5.4 gives the presample- 

reduction differential while the second column is the post-sample-reduction 

statistic. Statistically significant differences between these means are shown with a star 

indicating a one percent level of significance. Almost all the differences are statistically 

significant. However the differences between the treatment and comparison prior to 

sample reduction are not the same as those present after sample reduction. 

The lack of correspondence between the treatment and comparison group before sample 

reduction is quite great. Table 5.4 shows the profile prior to sample reduction to be 

markedly different for the treatment and comparison groups for certain characteristics, 

and the scale of contrast can be illustrated by considering the figures underlying column 1 

of Table 5.4. Far fewer of those who enter the SYETP were married in 1984 (3.9 per cent) 

than amongst the comparison group (11.7 per cent). The educational profile differs for 

those on SYETP compared to the comparison group. Fewer on SYETP have post-school 

qualifications (15.5 per cent) than in the comparison group (26.7 per cent), while more of 

those on SYETP have schooling of year 10 (36.1 per cent), year 11 (16.1 per cent) and 12 

(19.4 per cent) than do those in the comparison group (at 29.9 per cent, 12.1 per cent and 

15.1 per cent respectively). The work history profile of those who entered SYETP is also 

quite different to that of the comparison group. The share of those on SYETP with work 

experience of less than a year is 58.1 per cent, which contrasts strongly with the

165 

comparison group where the share is only 41.5 per cent. Those on SYETP spent more 

time prior to June 1984 unemployed (72.4 per cent) than did those in the comparison 

group (62 per cent). 

Comparison of columns1 and 2 of Table 5.4 shows the impact of the sample reduction on 

the difference between the treatment and comparison group characteristics. The effects of 

the sample reduction lead some characteristics of the treated and comparison profiles to 

diverge further, while the effect of the sample reduction on other features make the 

treated and comparison groups more similar in profile. The change in gender composition 

of the SYETP group results in raising the difference between the treated and the 

comparison gender composition. Raising the share of those with year 12 in the SYETP 

leads to a marked increase in the difference between the treated and comparison for this 

aspect. The reduction in the proportion whose mother was not employed amongst the 

SYETP, also leads to a heightened difference between the treated and the control for this 

feature. On the other hand, the fall in the share of those with year 10 in the SYETP results 

in the profile of the treated and the comparison groups being very similar for this. For 

some other traits, the overall effect is a reduction in the divergence between the treated 

and comparison, these include the proportion that held a job of less than one year, the 

average proportion of time spent in unemployment prior to June 1984, the share observed 

with employment in 1986 and the share observed in a government programme in 1986.

166 

Table 5.4 Difference between treatment group and comparison group before and after 

sample reduction 

SYETP vs. comparison 

Before 

Sample reduction Absolute 

difference in means 

Female 0.8 2.3*** 

Average age 1984 1.0*** 1.1*** 

Aboriginal/Torres Strait Islander 2.5*** 2.1*** 

Other ethnic minority 1.6*** 0.2 

Married 1984 7.9*** 9.6*** 

Spouse employed 1984 4.0*** 4.4*** 

Children 1984 1.9*** 3.9*** 


Degree/diploma 4.4*** 4.5*** 

Apprenticeship 5.4*** 5.7*** 

Other post-school qualification 1.4*** 0.4 

Year 12 of school 4.3*** 9.2*** 


Year 10 of school 6.2*** 0.2 




Father postschool qualification 10.0*** 8.6*** 

Mother postschool qualification 0.9 2.0*** 

Father manager, professional, para-professional 2.1 0.8 

Father not employed 1.3*** 1.8*** 

Father not present 2.0*** 3.8*** 

Mother manager, professional, para-professional 2.6 3.1*** 

Mother not employed 2.1*** 7.2*** 

Mother not present 2.3*** 3.7*** 


Never held a job 1.0 0.1 

< 1 year 16.6*** 15.7*** 

1 year 0.2 0 

2 years 2.3*** 0.6 

3 years or more 13.2*** 14*** 

Average unemployment 107 10.4*** 7.5*** 

Ever employed in 1986 108 15.8*** 13.6*** 

Ever government programme 1986 109 4.8*** 3.7*** 

SYETP vs. comparison After 

Sample reduction Absolute 

difference in means 106 

106 The statistic for any variable is the absolute value of the difference in means for the treatment and 

control groups. *** indicates the T statistic, for the hypothesis that the difference in mean in SYETP and 

the control group is zero, is significant at the 1 percent level of significance. 


108 Ever held a non-subsidised, non-government-program job in the 1986 reference period, after the first 17 

weeks. 


167 

5.5.4 Attrition: natural attrition or analytical sample reduction? 

Table 5.5 draws a distinction between the sources of sample reduction. Sample reduction 

resulting from attrition, where the respondent did not take part in later surveys, is here 

termed natural attrition. As outlined earlier, further sample reduction through analytical 

selection also takes place before the final sample used for analysis is reached. Zabel 

(1998) points out such analytical selection can in theory introduce biases of a similar 

nature to attrition bias. 

Indeed, Table 5.5 shows that both forms of sample reduction result in statistically 

significant differences to the means observed of those remaining in the final sample. This 

hints that both sources of sample loss may be suspected of altering the sample 

characteristics. Also, the pattern of differences varies by the form of sample reduction 

being natural attrition or analytical selection. This signals the added value of treating each 

source separately in order to allow for differential impacts. 

The first two columns of Table 5.5 show the mean and standard deviation in the final 

sample, to which the two sources of sample reduction are compared. The next four 

columns make up the first panel of the table which relates to the sample lost due to 

natural attrition, and they show the mean, standard deviation, absolute difference in the 

means and the t statistic, in a similar fashion to the former tables. The second panel gives 

the statistics for those lost though analytical selection. Only differences that are 

statistically significant at normally adopted confidence levels are discussed. 

The outcome variables ‘ever employed in 1986’ and ‘ever in a government programme in 

1986’ are not observable for those lost through natural attrition. It is useful to compare 

the statistically significant differences in the profile of those remaining in the sample to 

those lost to natural attrition. Of those not interviewed again: fewer had taken part in 

SYETP, more had year nine of school but less had year 11, more had held jobs of 1-2 

years while less had held jobs of 2-3 years, the proportion of unemployment in 1984 had

168 

been higher, and the location of the interview differed to those remaining with fewer 

from South Australia and the Northern Territory, Western Australia and Tasmania, more 

from cities and fewer from country towns and rural areas. 

The group of observations lost due to analytical selection had different features from both 

those remaining and from those lost by natural attrition. For those lost for analytical 

reasons, the means of more characteristics were affected than for those lost through 

natural attrition. Compared to those remaining, fewer of those lost were married, had 

employed spouses, apprenticeships, year 10 of school, mothers in the managerial 

occupations, or had held a job for 3 years or more, while on the other hand more had 

work limiting health problems and year 12 of school. The key outcome variable, 

employment in 1986, was significantly lower for those lost by analytical selection. 

The effects of analytical selection reinforced those of natural attrition for three variables, 

SYETP treatment, South Australia and the Northern Territory and rural areas. For each of 

these aspects, fewer cases were observed for those lost by natural attrition. The process of 

analytical selection worked in the same direction as that of natural attrition, and so of 

those lost, fewer had taken part in SYETP, fewer were from South Australia and the 

Northern Territory and fewer were from rural areas. 

There is a weak suggestion that among those lost due to natural attrition, the average 

proportion of unemployment in 1984 is lower, and of those dropped by analytical 

selection fewer were observed in employment in 1986. However, it is considered that this 

is not sufficient evidence to support the hypothesis that key subgroups are 

disproportionately affected in terms of the dependent variable labour market status. This 

question is readdressed in the multivariate analysis that follows. 

Drawing together the findings of the univariate analysis of sample reduction, it can be 

said that profiles of some important regressor variables, and the key treatment variable 

SYETP, show evidence of being statistically different after sample reduction. Segregation 

by the two types of sample reduction, shows natural attrition and analytical selection are

169 

both sources that impose changes in the profiles, and they have differing impacts. 

However, univariate analysis cannot account for the correlations between these variables 

and so the t statistics do not represent independent effects. As such, it is maintained that 

there is as yet no strong indication of serious problems arising from sample reduction. 

Further tests accounting for effects of sample reduction in a multivariate context are now 

applied. As the pattern of differences varies by the form of sample reduction being 

natural attrition or analytical selection, it seems that it will be important to maintain the 

breakdown of sample reduction by source in further examinations. Accordingly, the 

modelling of sample reduction, and any weights arising, takes separate account of natural 

attrition and analytical selection.

170 

Table 5.5 Summary statistics for attrition effects by source of sample loss 

Mean and 

standard deviation (s.d.) 

sample 

after 

attrition 

mean 110 

(1) 

s.d. 

(2) 

Those 

lost 

through 

natural 

attrition 

mean 

s.d. 

Difference 

in means 111 

(absolute) 

T –test 

Those 

lost 

through 

analytical 

selection 

mean 

s.d. 

Differenc 

e in 

means 

(absolute) 

syetp 0.08 0.27 0.05 0.22 0.03 2.91** 0.04 0.21 0.04 2.85** 

Female 0.41 0.49 0.41 0.49 0 0.40 0.49 0.01 0.49 

Average age 1984 19.99 2.41 20.13 2.39 0.16 1.30 19.82 2.36 0.17 1.25 

Aboriginal/Torres Strait 0.03 0.17 0.04 0.19 0.01 0.82 0.04 0.20 0.01 0.92 

Islander 

Other ethnic minority 0.08 0.27 0.09 0.29 0.01 1.03 0.09 0.29 0.01 0.65 

Married 1984 0.12 0.32 0.12 0.33 0 0.07 0.25 0.05 3.27*** 

Spouse employed 1984 0.06 0.24 0.05 0.21 0.01 1.02 0.02 0.14 0.04 4.12*** 

Children 1984 0.05 0.23 0.04 0.21 0.01 1.03 0.04 0.20 0.01 1.05 


Degree/diploma 0.12 0.32 0.11 0.32 0.01 0.45 0.09 0.29 0.03 1.43 

Apprenticeship 0.08 0.27 0.07 0.26 0.01 0.49 0.06 0.23 0.02 1.74* 


0.07 0.26 0.06 0.25 0.01 0.53 0.08 0.27 0.01 0.70 

qualification 

Year 12 of school 0.15 0.35 0.14 0.34 0.01 0.70 0.22 0.41 0.07 3.22*** 

Year 11 of school 0.14 0.35 0.11 0.31 0.03 1.86* 0.11 0.31 0.03 1.49 

Year 10 of school 0.31 0.46 0.32 0.47 0.01 0.04 0.27 0.44 0.04 1.76* 

Year 9 of school 0.12 0.33 0.18 0.38 0.06 2.98*** 0.12 0.32 0 




qualification 

0.34 0.47 0.34 0.47 0 0.36 0.48 0.02 0.67 


0.18 0.39 0.21 0.41 0.03 1.26 0.18 0.38 0 

qualification 

Father manager, professional, 0.26 0.44 0.24 0.43 0.02 1.01 0.24 0.43 0.02 0.57 

T –test 


111 The difference is calculated between the mean of those lost from the sample and the mean of those remaining after attrition.

171 


Father not employed 0.05 0.23 0.05 0.22 0 0.05 0.22 0 

Father not present 0.16 0.36 0.18 0.38 0.02 1.13 0.16 0.37 0 

Mother manager, professional, 0.10 0.29 0.10 0.30 0 0.06 0.24 0.04 2.14** 


Mother not employed 0.55 0.50 0.55 0.50 0 0.56 0.50 0.01 0.43 

Mother not present 0.05 0.22 0.07 0.25 0.02 1.39 0.05 0.21 0 


Never held a job 0.12 0.32 0.10 0.30 0.02 1.22 0.13 0.34 0.01 0.79 

< 1 year 0.42 0.49 0.43 0.50 0.01 0.46 0.45 0.50 0.03 1.13 

1 year 0.13 0.34 0.19 0.39 0.06 2.99** 0.13 0.34 0 

2 years 0.14 0.35 0.11 0.31 0.03 2.21** 0.14 0.34 0 

3 years or more 0.19 0.39 0.17 0.38 0.02 0.68 0.15 0.35 0.04 1.98** 

Average unemployment 112 0.62 0.41 0.66 0.40 0.04 2.36** 0.61 0.42 0.01 0.29 

Ever employed in 1986 113 0.74 0.44 Na Na Na Na 0.60 0.49 0.14 5.18*** 

Ever government programme 0.11 0.31 Na Na Na Na 0.10 0.31 0.01 0.73 

1986 114 

Work limited by health 1984 0.10 0.30 0.12 0.32 0.02 1.28 0.13 0.34 0.03 1.67* 


New South Wales /ACT 0.37 0.48 0.41 0.49 0.04 1.72* 0.41 0.49 0.03 1.22 

Victoria 0.24 0.43 0.24 0.43 0 0.23 0.42 0.01 0.28 

Queensland 0.14 0.34 0.16 0.37 0.02 1.46 0.12 0.33 0.02 0.88 

South Australia /Northern 0.12 0.33 0.09 0.29 0.03 2.29** 0.09 0.28 0.03 2.22** 

Territory 

Western Australia / Tasmania 0.13 0.33 0.09 0.29 0.04 2.38*** 0.15 0.36 0.02 1.31 

Capital city 0.47 0.50 0.54 0.50 0.07 2.78*** 0.50 0.50 0.03 0.82 

Other town 0.21 0.41 0.27 0.44 0.06 2.82** 0.24 0.42 0.03 1.06 

Country town 0.24 0.43 0.16 0.37 0.08 4.47*** 0.22 0.41 0.02 0.86 

Rural area 0.08 0.27 0.04 0.19 0.04 3.97*** 0.05 0.22 0.03 2.16** 

Number of observations 1283 681 404 

Significance of t test at level 1% ***, 5%**, 10 % *. Student’s t tests of whether means are equal 

112 The proportion of 1984 reference period to 3 June spent unemployed. 



172 

5.6 Accounting for non-response and sample design 

In the analysis so far, only sample reduction subsequent to the 1984 survey has been 

considered. It is however pertinent to ensure that the survey design and initial nonresponse 

in the 1984 survey is also accounted for. As the application of weights is a key 

factor in the subsequent analysis, the survey weight is introduced here. Although there is 

some disagreement about the quality and general applicability of survey weights, it is 

now generally accepted in the statistical community that the sampling design and nonresponse 

for a survey should be accounted for in order to obtain estimates that reference 

the population. As the ALS was not a simple random sample 115 , then it is important that 

the survey weights are applied. 

There is a selection/response weight provided with the data 116 , which weights the 1984 

data back the population: persons aged 15-24 on 1 September 1984, registered with the 

Commonwealth Employment Service offices throughout Australia who were unemployed 

and seeking full-time work for three months or more at the time of selection, 30 June 

1984 117 . To construct the weight for non-response and selection provided with the data, 

administrative data was used to weight back to the population. The weight is complex, 

adjusting for unequal probability of selection, non-response using administrative data on 

specific categories, and benchmarking adjustment to known totals 118 to reduce the 

standard errors and produce estimates comparable with other sources. Foreshadowing 

115 It was a stratified cluster sample. Those interested in the sample design details should refer to 

Kronenburg et al. (1985). Non-computerisation led to a number of issues in the ‘list’ sample which affected 

the sample design, as the records that made up the sample frame were in fact paper card records stored at 

each CES office. The key problem was double-counting and multiple registrations at several CES. 

Stratification was also not always straightforward – for the Northern Territory, in Alice Springs almost all 

the unemployed youths were Aboriginal (716 of 886), a much different proportion to all other locations and 

so only those in Darwin were chosen to represent the Northern Territory (Kronenburg et al. (1985): 7). 

116 The variable in 1984 is named ‘Weight1’. 

117 In fact, the sample was of 2 stages – (1) selection of CES offices based on the persons aged 15-24 on 1 

September 1984, registered with the CES offices throughout Australia who were unemployed and seeking 

full-time work for three months or more at 31 March. (2) samples of 50 persons [persons aged 15-24 on 1 

September 1984, registered with the CES offices throughout Australia who were unemployed and seeking 

full-time work for three months or more] selected from each office on 30 June 1984. The March and June 

numbers in scope of the survey were different, leading to differing selection probabilities. (Mcrae et al. 

(1985): 23). 

118 The benchmark data came from CES population counts for each age*sex*duration of registration 

category within the geographic selection strata (Mcrae et al. (1985: 24).

173 

work in later sections, this supplied weight does not take account of further non-response 

beyond the 1984 survey, and so additional weights will be developed to adjust for the 

non-response to the 1985 and 1986 surveys. 

Mcrae et al. (1985) examine the importance of each of the available variables 119 to the 

response rate in 1984. In univariate analysis of response rates, they found that 15-19 year 

olds were more likely to have been interviewed in 1984. Amongst this age group females 

had a higher response than males, but for 20-24 year olds there was no gender difference 

in response. The response rate was higher for those who had been unemployed longer. 

Movers had a much lower response rate than non-movers, because they could be located 

to interview less often. Certain states, Queensland and the Northern Territory, had lower 

response rates. Logistic regression analysis further examined the response, removing the 

correlation between these variables and showing the direct association with the response 

rate. This analysis identified mobility to be the chief variable related to non-response, but 

age, gender, duration of registration and state of registration had independent effects as 

well. Further analysis found that the strong differential response effect for movers and 

non-movers affected labour force variables measured in the 1984 survey. A final analysis 

compared ALS estimates to corresponding published data from the Australian Bureau of 

Statistics, which gave similar results or showed readily explainable differences. These 

results were interpreted as giving further confidence to the validity of ALS estimates. 

Comparison of the adjusted and unadjusted profiles gives an indication of the specific 

features that are most strongly affected by survey design, selection and non-response. 

Table 5.6 shows the effect on the data profile of adjusting for selection and response for 

the 1984 survey respondents. In the first column is shown the unadjusted profile of the 

data for certain key characteristics, while the second column shows how the weighted 

sample profile appears. It can be seen that there is only a very slight effect on the profile. 

119 Mcrae et al. (1985) p10: CES records held information on the age group (15-19 or 20-24), gender, 

duration of unemployment (measured by the time from the date of registration with the Commonwealth 

Employment Service [CES] to the date of sample selection, 30 June 1984; categories of less than 3 months, 

between 3-9 months, more than 9 months CES registration), State or Territory of CES registration, country 

of birth (Australia or overseas), marital status (married/defacto or unmarried), whether the address the 

person lived at when interviewed was the same as the address of CES registration.

174 

The greatest effect of the weight on the profile is for those interviewed in Queensland in 

1984. It seems that slightly lower response or selection was associated with individuals in 

Queensland.

175 

Table 5.6: Effect of selection/response weight on 1984 survey respondents 

Mean 1984 unadjusted profile 1984 weighted to sampled 

population profile 

Age at 1984 survey 20.00 19.88 


Work limited by health 1984 0.11 0.11 

Aboriginal/Torres Strait Islander 0.03 0.04 


Married 1984 0.11 0.11 


Victoria 0.24 0.24 


South Australia/Northern Territory 0.11 0.10 


New South Wales/Australian Capital Territory 0.39 0.39 

Area of residence in 1984 

Capital city 0.49 0.49 

Other city 0.23 0.23 

Country town 0.21 0.22 

Rural area 0.06 0.06 



Apprenticeship 0.08 0.07 

Other post-school qualification 0.07 0.07 





Average recorded Pre-June 1984 unemployment 120 0.63 0.62 


NOTE 1: for a 0-1 indicator variable, the mean gives the proportion. For example, the mean in the 

population, column 1, for gender=female is the proportion of females in the population. 

120 Average recorded Pre-June 1984 unemployment is missing for 27 observations, and has a base of 2341.

176 

5.6.1 Survey design and non-response effects on modelling 

Mcrae et al. (1985) found that the strong differential response effect for movers and nonmovers 

affected labour force variables measured in the 1984 survey. They pointed out 

that models and estimates of such variables might be biased by the non-response, unless 

the weights were applied. Movers, who had much lower chance of interview, were more 

likely than non-movers to be older than 20, married and in employment 121 , and had 

briefer CES registration. The definition of the SYETP treatment group relies on 1984 

survey data. The eligibility criteria for SYETP, and the other evidence 122 that they were 

more likely to be teenagers, hints that SYETP participants would more commonly have 

been non-movers, and have had a higher response rate. In light of this, the SYETP 

participation model is first examined, by exploring the effects of the survey weights. The 

aim is to study the influence of non-response, and so check whether the mobility, age, 

gender, duration of registration and state of registration were related to the variable of 

interest here, SYETP participation. 

5.6.1.1 Analytical selection 

Before continuing, the analytical selection from the full data is examined. Recall the 

limits of the sample: the exclusion from those interviewed in 1984 of those who were 

over 25 years at the interview date, those in full-time education, or for whom responses 

were missing in 1984, 1985 or 1986 (Richardson (1998): 5). For the purposes of 

evaluation analysis of SYETP, only those who were aged less than 25 and not in fulltime 

education in the 1984 survey interview can be of interest due to the eligibility restrictions 

for SYETP. 

These are discarded at different stages of the analysis however. Age was observed in the 

administrative data, and is accounted for in the non-response weight and so it is 

121 Movers were also less likely to be unemployed, but had the same proportions not in the labour force as 

non-movers. 

122 See Chapter 2, for example the results of Hoy and Lampe (1982) covered in Section 2.2.6.2.

177 

appropriate to discard these from the analysis prior to examining attrition. As a result, 

cases not meeting these age restrictions can be excluded from analysis of attrition. In all 

35 cases are affected by this restriction. Due to the very short amount of time between the 

age at sample selection date and the interviews, this is unlikely to be very different to the 

population. 123 However, those ‘not in fulltime education in the 1984 survey’ can only be 

identified after the non-response stage, and so they must be left in for the analysis of 

attrition from 1984 which is used to construct weights. In this way, the representation of 

the 1984 survey is maintained in modelling attrition to 1986. Those in fulltime education 

in the 1984 survey must be discarded in the Heckman bivariate probit and PSM 

modelling of the treatment effects of SYETP. If they are not discarded in the Heckman 

bivariate probit and PSM modelling of the treatment effects of SYETP the data for the 

treatment and comparison do not represent the eligible set. 124 This is because the 

evaluation is attempting to identify treatment on the treated by constructing a comparison 

group from those eligible but not treated. 

As can be seen in the Table 5.7 below, 287 cases are dropped because of the analytical 

restriction excluding cases in fulltime education, and 1400 of those who responded to all 

waves to 1986 remain, after which another 117 cases are dropped because they have 

missing information. 

Table 5.7 How the observations reduce to 1283 from those who respond to the 1986 

survey 

Not missing information Missing information 

Selection of cases 0 1 Total 

Not selected: in fulltime 

education in 1984 

Selected: not in fulltime 

education 1984 

264 23 287 

1283 117 1400 

Total 1547 140 1687 125 

123 Age at 1 September 1984 was used for selection, while interviews were conducted between September 

1984 and November 1984. The full sample falls from 2403 to 2368. 

124 For an example of selecting the eligible set see Frölich et al. (2000) p55. 

125 Note: remember 35 have been dropped who were older than 24 years at the 1984 survey.

178 

Table 5.5a draws a distinction between the sources of sample reduction amongst those 

dropped for the analysis –the 140 cases dropped for having missing information on 

variables used in analysis and the 264 cases dropped for other analytical reasons. 126 Table 

5.5a shows the means, standard deviation, absolute difference and t test of the 

significance of the difference from zero, using the same layout as for Table 5.5. The first 

two columns of Table 5.5a show the mean and standard deviation in the final sample, to 

which the two sources of sample reduction are compared in the t-test of the significance 

of the difference from zero. The next four columns make up the first panel of the table 

which relates to the sample lost due to missing information, and they show the mean, 

standard deviation, absolute difference in the means and the t statistic, in a similar fashion 

to the former tables. The second panel gives the statistics for those lost though further 

analytical selection. Only differences that are statistically significant at normally adopted 

confidence levels are discussed. 

Amongst those cases dropped because certain variables used in analysis are missing 

information, it is first noticeable that case-wise deletion in this manner leads to the loss of 

information from the whole case. For example ‘father post-school qualification’ is 

present for 79 of the 140 cases dropped, but while the missing information for ‘father 

post-school qualification’ was the reason for the 61 cases being dropped, these extra 79 

cases are dropped due to other variables having missing information. It is then very much 

an ‘all or nothing strategy’, where more information is lost than is actually missing. 

Column 5 of Table 5.5a indicates the number of cases that were missing information, at 

most 140. It can be seen from Table 5.5a that amongst those where all 140 cases had 

missing information for the variable in question, that for some of the variables the 

missing had a different profile to those remaining in the final sample. As a result, those 

dropped for case-wise deletion to treat missing information had undertaken less SYETP 

treatment, more often had children, less often had highest qualification of year 12 or year 

11, were much less often in employment in 1986, had more health problems affecting 

work, and came less often from Victoria and Queensland and more often from Western 

126 There are 404 cases dropped for analytical reasons, of which there are 140 cases where at least one of 

the variables used in the analysis is missing for a case, then a further 264 cases are dropped for other 

analytical reasons.

179 

Australia/Tasmania. Amongst those variables where fewer than 140 cases remained after 

the missing data were accounted for, the variables for which a different profile was 

observed were parental background where the mother had a post-school qualification, 

where the father or mother was manager, professional or para-professional. It can be seen 

that the pattern of statistical differences is different for those dropped for item nonresponse 

in comparison to those lost through attrition shown in table 5.5. Thus different 

processes might be behind these distinct sources of missing data. 

Panel 2 of Table 5.5a shows a different pattern of statistical differences for those 264 

cases dropped for other analytical reasons. It should be noted that amongst these, all cases 

are observed for all variables. The variables for which those dropped had a different 

profile to those remaining in the final sample were SYETP treatment, those married in 

1984, partner employed, children, those with apprenticeship, year 12, year 10 or year 9 as 

their highest qualification, those who had never held a job and those who had held a job 

for 3 years or more, those in employment in 1986, and those in South Australia/Northern 

Territory and Western Australia /Tasmania, and all locations other than Capital City. The 

differing profiles meant that those dropped for other analytical reasons had less often had 

SYETP treatment, were less often married or with children, had less often highest 

qualifications of apprenticeship, year 10 or year 9 and more often year 12, had more often 

never held a job, and less often had a job for 3 years or more, were less often in 

employment in 1986, lived less often in South Australia/Northern Territory and Western 

Australia /Tasmania, less often lived in country towns and rural areas and more often in 

‘other towns’. The pattern of profile differences is distinct from that found for those lost 

through attrition shown in Table 5.5. 

Both groups of cases dropped for analysis had different profiles for the key variables used 

to model SYETP treatment and employment in 1986. This indicates that as for attrition, 

further examination of the impact on analysis of the dropping of these cases is warranted. 

In the analysis so far, these cases have been treated in the same way, and so dropped for 

the analysis. Before continuing it is worth readdressing the reasons why these cases were 

dropped.

180 

The first group of cases dropped for analysis, were dropped as a treatment for missing 

information on any of the variables in analysis. The following variables have missing 

information once attrition is accounted for: parental occupation, parental qualifications, 

and unemployment spell prior to 3 June 1984. Of these, the unemployment spell 

information is generally considered the most important for evaluation analysis. However 

the parental background variables have the greatest extent of missing information. 127 

Alternatives to dropping these cases include not using those variables where there is 

missing information, and inference or imputation of the missing values. This is briefly 

considered in the next section. With regard to dropping these variables from the analysis: 

chiefly the unemployment spell would be deemed critical to modelling of participation 

and employment, and so dropping this variable would introduce misspecification bias, 

while the others may be potentially useful but could possibly be dropped. As dropping 

these variables alters the specification of the model, this is considered later in Chapter 7. 

The second group of cases dropped for analysis described themselves as those in fulltime 

education at the start of the reference period for the evaluation. The central reason 

for selecting cases not in fulltime education arises when defining the eligible group for 

the evaluation. For this rationale, those who are in full-time education at the point of 

eligibility must be excluded. 128 Retaining these cases would contaminate the comparison 

group with cases incomparable to those in the SYETP treatment group, since they would 

not have been eligible to participate in SYETP. In the context of propensity score 

matching, individuals enrolled in fulltime education have a known probability of SYETP 

participation equal to zero, and could not make good matches for any SYETP treatment 

group members, who necessarily have non-zero probabilities of SYETP participation. As 

this is survey data, and the records are based on recall there is the potential that some part 

of this may be subject to recall error, but it is not possible to further account for this. 

127 It should also be noted that it is quite plausible that respondents did not know or could not recall their 

parents’ occupation or qualification at the time the respondent was 14. 

128 The data here has eligibility set at 3 months unemployment due to the sample selection at June 1984, 

and also the reference period for the SYETP treatment variable starts on 3 June 1984. Those dropped 

indicated they were in full-time education in the period prior to June 1984, and not unemployment, which 

makes them ineligible for SYETP.

181 

Table 5.5a Summary statistics by source of sample loss due to analytical selection 

Mean and 

standard deviation (s.d.) 

sample 

after all 

sample 

loss and 

attrition 

mean 129 

(1) 

s.d. 

(2) 

Those 

dropped 

due to 

missing 

mean 

s.d. 

No. 

cases for 

that 

variable 

Difference in 

means 130 

(absolute) 

T –test 

Those 

lost – 

dropped 

for other 

analytical 

selection 

mean 

s.d. 

Difference 

in means 

(absolute) 

syetp 0.08 0.27 0.04 0.19 140 0.04 2.03** 0.05 0.21 0.04 2.38** 

Female 0.41 0.49 0.39 0.49 140 0.005 0.12 0.39 0.49 0.02 0.55 

Average age 1984 19.99 2.41 20.05 2.46 140 0.02 0.10 19.74 2.33 0.25 1.56 

Aboriginal/Torres Strait 0.03 0.17 0.06 0.23 140 0.01 0.74 0.04 0.19 0.008 0.65 

Islander 

Other ethnic minority 0.08 0.27 0.10 0.30 140 0.007 0.28 0.09 0.29 0.01 0.63 

Married 1984 0.12 0.32 0.15 0.36 140 0.04 1.25 0.02 0.14 0.10 7.97*** 

Spouse employed 1984 0.06 0.24 0.07 0.25 140 0.002 0.10 0.00 0.00 0.06 8.99*** 

Children 1984 0.05 0.23 0.11 0.31 140 0.05 1.73* 0.01 0.11 0.04 4.74*** 


Degree/diploma 0.12 0.32 0.09 0.28 140 0.03 1.29 0.10 0.30 0.02 0.98 

Apprenticeship 0.08 0.27 0.09 0.29 140 0.01 0.46 0.04 0.19 0.04 3.08*** 


0.07 0.26 0.09 0.28 140 0.07 0.26 0.001 0.06 

qualification 

Year 12 of school 0.15 0.35 0.09 0.29 140 0.05 1.70* 0.28 0.45 0.14 4.66*** 

Year 11 of school 0.14 0.35 0.08 0.27 140 0.05 1.74* 0.12 0.33 0.02 0.79 

Year 10 of school 0.31 0.46 0.34 0.47 140 0.007 0.16 0.24 0.43 0.07 2.46** 

Year 9 of school 0.12 0.33 0.20 0.40 140 0.05 1.40 0.09 0.28 0.04 1.91* 




qualification 


qualification 

0.34 0.47 0.40 0.49 79 0.07 1.15 0.34 0.48 0.006 0.18 

0.18 0.39 0.15 0.35 65 0.08 1.88* 0.19 0.40 0.01 0.38 

T –test 


130 The difference is calculated between the mean of those lost from the sample and the mean of those remaining after attrition.

182 

Father manager, professional, 0.26 0.44 0.17 0.37 135 0.11 3.30*** 0.29 0.46 0.03 1.13 


Father not employed 0.05 0.23 0.06 0.24 135 0.003 0.13 0.05 0.22 0.005 0.36 

Father not present 0.16 0.36 0.13 0.34 140 0.04 1.22 0.19 0.39 0.03 1.08 

Mother manager, professional, 0.10 0.29 0.07 0.26 113 0.06 3.13*** 0.08 0.27 0.02 1.10 


Mother not employed 0.55 0.50 0.59 0.49 113 0.06 1.32 0.54 0.50 0.009 0.27 

Mother not present 0.05 0.22 0.05 0.21 140 0.02 1.58 0.06 0.23 0.004 0.24 


Never held a job 0.12 0.32 0.09 0.28 140 0.09 1.20 0.16 0.36 0.04 1.63* 

< 1 year 0.42 0.49 0.47 0.50 140 0.06 1.29 0.44 0.50 0.02 0.55 

1 year 0.13 0.34 0.15 0.35 140 0.01 0.46 0.14 0.35 0.005 0.23 

2 years 0.14 0.35 0.14 0.35 140 0.02 0.71 0.12 0.33 0.02 0.88 

3 years or more 0.19 0.39 0.15 0.36 140 0.04 1.15 0.14 0.35 0.04 1.78* 

Average unemployment 131 0.62 0.41 0.67 0.40 125 0.05 1.42 0.58 0.42 0.04 1.27 

Ever employed in 1986 132 0.74 0.44 0.37 0.48 140 0.17 3.87*** 0.61 0.49 0.13 3.91*** 

Ever government programme 0.11 0.31 0.07 0.26 140 0.004 0.15 0.10 0.30 0.01 0.56 

1986 133 

Work limited by health 1984 0.10 0.30 0.16 0.37 140 0.09 2.53*** 0.10 0.30 0.003 0.12 


New South Wales /ACT 0.37 0.48 0.44 0.50 140 0.04 0.91 0.41 0.49 0.03 0.94 

Victoria 0.24 0.43 0.15 0.35 140 0.09 2.94*** 0.28 0.45 0.04 1.32 

Queensland 0.14 0.34 0.09 0.29 140 0.10 4.83*** 0.16 0.37 0.03 1.01 

South Australia /Northern 0.12 0.33 0.10 0.30 140 0.02 0.60 0.08 0.27 0.05 2.57*** 

Territory 

Western Australia / Tasmania 0.13 0.33 0.22 0.42 140 0.17 4.18*** 0.08 0.27 0.05 2.49*** 

Capital city 0.47 0.50 0.47 0.50 140 0.04 0.80 0.53 0.50 0.05 1.62 

Other town 0.21 0.41 0.20 0.40 140 0.02 0.69 0.26 0.44 0.05 1.76* 

Country town 0.24 0.43 0.27 0.45 140 0.07 1.62 0.17 0.38 0.07 2.54*** 

Rural area 0.08 0.27 0.06 0.24 140 0.007 0.28 0.04 0.19 0.04 2.87*** 


Significance of t test at level 1% ***, 5%**, 10 % *. Student’s t tests of whether means are equal 

131 The proportion of 1984 reference period to 3 June spent unemployed. 



183 

5.6.1.2 Effects of the non-response to 1984 Survey on the participation model 

Table 5.8 Column 1 shows the probit results for SYETP participation, for all observations 

in the 1984 survey, where no weight has been applied. Column 2 shows the probit results 

for SYETP participation for all observations in the 1984 survey, weighted with the survey 

weight (for non-response and survey design), discussed above. The results are changed 

slightly by the application of the weight. The variables that are statistically significant 

would normally be focused on, and which variables are significant is affected by applying 

the survey weight. Some variables become insignificant when the weight is applied, such 

as no job held before 1984. Some variables become significant when the weight is used, 

such as married in 1984, attended a private school, and longest job held before 1984 was 

3 years or more. 

The effects of accounting for the survey design do alter the interpretation of the results. In 

light of the analysis of Mcrae et al. (1985), the SYETP models and estimates of such 

variables appear to be biased by the non-response, unless the weights are applied. 

Pfefferman (1993) p321 points out that when the sample is selected by simple random 

sampling, the model holding for the sample data is the same as the model holding in the 

population, however with complex survey sampling designs, the two models can differ. 

The ignorability of a complex sampling design for regression analysis depends not only 

on the sample design but also the model and parameters of interest, so that if all design 

variables are included then the sampling is ignorable for the modelling. Because the 

design variables are not in the data, the design information that was used to construct the 

weights in the ALS data cannot be incorporated into the modelling without using the 

weights formed using the design information. The sampling and non-response weights 

then act as surrogates for this information set. Incorporating the weights can account for 

the differences between the complexly designed sample, and the population.

184 

Table 5.8 Probit of SYETP participation, showing sample reduction effects 

Before sample 

reduction, 

unweighted 

Before sample 

reduction, with 

response weight 

After sample 

reduction, 

unweighted 

After sample 



(1) (2) (3) (4) 

syetp syetp syetp syetp 

Gender=female -0.02 -0.03 0.08 0.06 

(0.16) (0.33) (0.66) (0.48) 

Age at 1984 survey -0.08 -0.06 -0.11 -0.08 

(3.21)** (2.41)* (3.21)** (2.86)** 

Married 1984 -0.59 -0.68 -0.97 -1.09 

(1.78) (2.66)** (1.62) (4.20)** 

Children 1984 0.48 0.38 0.49 0.33 

(1.17) (1.14) (0.73) (0.82) 

Children*female -0.17 0.16 -0.30 0.03 

(0.30) (0.35) (0.36) (0.06) 

Spouse employed 1984 0.15 0.20 0.59 0.70 

(0.34) (0.56) (0.91) (1.93) 

Aboriginal/Torres Strait Islander -0.73 -0.70 -0.45 -0.37 

(1.78) (1.73) (0.96) (0.78) 

Other ethnic minority 0.07 -0.03 0.06 0.00 

State interviewed in 1984 (0.35) (0.16) (0.24) (0.01) 

Victoria 0.13 0.15 0.12 0.11 

(1.12) (1.17) (0.75) (0.70) 

Queensland -0.05 -0.01 -0.20 -0.13 

(0.35) (0.07) (0.97) (0.66) 

South Australia/Northern Territory 0.12 0.13 -0.12 -0.11 

(0.78) (0.82) (0.61) (0.59) 

Western Australia/Tasmania 0.31 0.35 0.32 0.35 

(2.24)* (2.44)* (1.79) (2.02)* 

Education school overseas 0.07 0.24 0.14 0.38 

(0.28) (0.90) (0.40) (1.07) 

Roman Catholic school -0.38 -0.40 -0.31 -0.27 

(2.07)* (2.21)* (1.28) (1.15) 

Private school -0.75 -1.01 -0.72 -0.97 

Highest qualification in 1984 (1.85) (2.56)* (1.54) (2.32)* 

Degree/diploma -0.11 -0.12 0.05 -0.01 

(0.62) (0.65) (0.21) (0.03) 

Apprenticeship -0.33 -0.29 -0.14 -0.12 

(1.32) (1.22) (0.43) (0.41) 

Other Post-School qualification -0.09 -0.00 0.08 0.05 

(0.44) (0.01) (0.32) (0.19) 

Year 12 of school 0.15 0.23 0.41 0.41 

(1.12) (1.60) (2.27)* (2.29)* 


(0.45) (0.73) (0.85) (1.14) 

Year 9 of school or less -0.19 -0.10 -0.14 -0.02 

(1.30) (0.63) (0.68) (0.09) 

Longest job by 1984 none -0.42 -0.34 -0.41 -0.31 

(2.20)* (1.83) (1.62) (1.33) 

< 1 year -0.04 -0.04 -0.04 -0.12 

(0.30) (0.32) (0.22) (0.67) 

2 years -0.03 -0.07 0.15 0.04 

(0.18) (0.39) (0.66) (0.17)

185 

3 years + -0.35 -0.47 -0.34 -0.51 

(1.82) (2.58)** (1.30) (2.12)* 

CEP referrals 1984 0.08 0.08 0.15 0.12 

(1.66) (1.48) (2.27)* (1.64) 


unemployment 

0.40 0.30 0.47 0.34 

(3.24)** (2.64)** (2.85)** (2.46)* 

Work limited by health -0.50 -0.60 -0.59 -0.65 

Family background (2.69)** (3.33)** (2.30)* (2.84)** 

Other city before aged 14 -0.11 -0.21 -0.25 -0.40 

(0.89) (1.69) (1.51) (2.40)* 

Country town before aged 14 -0.32 -0.36 -0.43 -0.51 

(2.62)** (2.83)** (2.78)** (3.32)** 

Rural area before aged 14 -0.28 -0.39 -0.46 -0.39 

(1.38) (1.86) (1.70) (1.53) 

Overseas before aged 14 -0.73 -0.66 -0.69 -0.62 

(1.59) (1.58) (1.36) (1.32) 

Number of siblings 0.01 0.01 0.01 0.02 

(0.58) (0.60) (0.42) (0.56) 

English good -0.13 -0.18 -0.11 -0.14 

(0.68) (1.03) (0.43) (0.56) 

English poor -0.68 -0.74 -0.60 -0.73 

(1.50) (1.50) (1.11) (1.18) 

Sexist 0.11 0.19 0.25 0.31 

(0.59) (0.97) (0.95) (1.12) 

Sexist*female -0.66 -0.76 -0.76 -0.88 

Fathers occupation when resp. 14 (1.24) (1.67) (1.25) (1.60) 

Father not present when resp 14 -0.21 -0.24 -0.22 -0.22 

(1.02) (1.16) (0.82) (0.81) 

Labourer -0.06 0.00 -0.17 -0.07 

(0.28) (0.02) (0.58) (0.24) 

Plant operative -0.13 -0.09 -0.22 -0.13 

(0.65) (0.47) (0.79) (0.51) 

Sales -0.19 -0.27 -0.09 -0.25 

(0.70) (1.00) (0.27) (0.71) 

Tradesperson -0.05 -0.05 -0.25 -0.27 

(0.24) (0.27) (0.91) (1.03) 


-0.04 -0.08 -0.09 -0.19 

(0.21) (0.44) (0.35) (0.74) 

Not employed -0.31 -0.23 -0.41 -0.47 

(1.13) (0.90) (1.15) (1.42) 


-0.29 -0.26 -0.29 -0.22 


Mothers occupation when resp. 14 (2.60)** (2.24)* (1.97)* (1.61) 

Mother not present when resp 14 0.34 0.29 0.47 0.37 

(1.39) (1.25) (1.48) (1.24) 

Labourer 0.12 0.08 0.13 -0.04 

(0.55) (0.38) (0.43) (0.14) 

Plant operative 0.38 0.31 0.65 0.52 

(1.62) (1.30) (2.10)* (1.69) 

Sales 0.18 0.26 0.20 0.25 

(0.79) (1.12) (0.67) (0.89) 

Tradesperson 0.02 -0.02 0.11 0.13 

(0.05) (0.06) (0.25) (0.31) 

Manager/professional/para- -0.14 -0.19 -0.23 -0.32

186 

professional 

(0.60) (0.83) (0.78) (1.11) 

Not employed 0.08 -0.00 0.03 -0.10 

(0.46) (0.03) (0.13) (0.45) 

Mother post-school qualification 0.21 0.19 0.25 0.24 

when resp 14 

Religion brought up in (1.64) (1.45) (1.55) (1.40) 

Catholic 0.16 0.12 0.05 -0.02 

(1.27) (0.97) (0.32) (0.11) 

Presbyterian 0.28 0.21 0.32 0.25 

(1.46) (1.05) (1.31) (1.05) 

Methodist 0.12 0.24 0.11 0.23 

(0.60) (1.11) (0.44) (0.96) 

Other Christian -0.06 -0.11 0.09 0.10 

(0.26) (0.52) (0.34) (0.36) 

Other religion 0.16 0.22 0.14 0.13 

(0.94) (1.16) (0.63) (0.54) 

No religion 0.14 0.17 0.17 0.16 

(0.94) (1.08) (0.84) (0.83) 

missing fathers qualifications -0.49 -0.55 

(1.60) (1.69) 

missing Mothers qualifications 0.01 0.10 

(0.02) (0.42) 

missing proportion of time spent -0.07 -0.08 

unemployed 

(0.12) (0.16) 

missing number of siblings 0.20 0.17 

(0.79) (0.68) 

Constant -0.01 -0.33 0.66 0.39 

(0.03) (0.60) (0.86) (0.59) 

Observations 2368 2368 1283 1283 

Log likelihood -503.74 -500.54 -307.52 -307.63 

LR chi 2 (59) 134 137.31 150.58 106.91 126.23 

Mcfadden’s Pseudo R 2 135 

0.1199 0.1284 0.1481 0.1553 

Akaike Information Criterion 0.48 0.48 0.57 0.57 

Coefficient with t statistic in brackets. Absolute value of z statistics in parentheses * significant at 5%, ** 

significant at 1%. Base categories: European ethnic origin, state interviewed in 1984 NSW/ACT, 

government school, highest qualification in 1984 year 10 at school, longest job by 1984 is 1 year, lived 

mostly in state capital city until respondent aged 14, English is first language, father clerical worker when 

respondent aged 14, mother clerical worker when respondent aged 14, religion brought up in is Anglican. 

Notes: Variables miss 1 (mothers occupation missing) and miss 2 (fathers occupation missing) predict 

failure perfectly, and cannot be added to the regression. 







likelihood M intercept ) . The value of Mcfadden’s Pseudo R 2 increases as new variables are added.

187 

5.7 Multivariate analysis of effects of sample reduction 

The univariate evidence on the sample reduction effects treated earlier suggested that 

SYETP participants and non-participants were differentially affected. Accordingly, the 

model of SYETP is also examined in light of the further sample reduction after the 1984 

survey. The estimation is repeated on both samples, all available in 1984 and those cases 

available after sample reduction. This has the benefit of revealing whether the estimation 

would have looked different using the entire sample available for the 1984 survey. 

5.7.1 Accounting for item non-response 

As previously pointed out, there are some cases who respond at the 1984 survey for 

whom certain regressor variables have missing information. To best regain the lost 

information of the incomplete observations, the modified zero order regression is usually 

adopted (Greene (1991): 288). The missing cases are filled with zeros, and the regression 

includes for each instance a dummy variable which takes the value of 1 for missing 

observations and 0 for complete cases. 

Recent work by King et al. (2001) examines the role of missing data in the explanatory 

variables. King et al. (2001) examines the validity of this form of imputation (mean 

substitution), as compared to deleting the observations where missing data occurs, or an 

alternative form of multiple imputation algorithm proposed. The algorithm outperforms 

both more commonly used alternatives. In particular it is pointed out that mean 

substitution gives standard errors that are too small because it assumes in estimation that 

the substituted values are known with the same certainty as the observed values (King et 

al. (2001): 66). On the other hand, dropping those cases with missing covariates (or 

listwise deletion) gives the correct standard error, although estimates do suffer the 

problems of bias/inefficiency as recounted in our discussion. It is commented that for this 

reason, deletion can be a preferable approach. 

In light of this, Appendix Table A2.2 shows the effect of applying each of the mean 

substitution and casewise deletion approaches. In order to detract from the issue of the

188 

weighting, both the weighted (columns 2 and 4) and unweighted (columns 1 and 3) 

results are shown for each missing data approach. Of main interest is the comparison of 

the estimates using each missing data approach, thus column 1 versus column 3, or 

column 2 versus column 4. Discussion of Table A2.2 generalises the results to 

comparison between the first panel (using mean substitution) and the second panel 

(deletion of cases with missing data – this gives the smaller base of 2150 cases where 

1984 survey information has no information missing on any explanatory variables) by 

discussing the weighted results. Moving from mean substitution to the deletion approach 

for the weighted data does change which variables are statistically significant, as the t- 

statistic size changes – for example ’other city before aged 14’ becomes statistically 

significant, and ‘age in 1984’ becomes insignificant. It also leads to a change in the size 

of coefficients for statistically significant variables – for example the coefficient for 

‘proportion of pre-June unemployment’ falls, and the t-statistic falls. In a probit, the 

coefficient size is not clearly interpretable, so the interpretation of a positive influence of 

this variable on participation in SYETP is not changed by the various missing data 

approaches, however the calculated marginal effect would be affected. When the variable 

is not statistically significant, changing the missing data approach can also lead to change 

in the sign, such as for ‘children 1984’. Although not discussed in detail here, it can be 

seen that substantively important changes in the estimates arise depending on each 

approach used. The chief variation is in which variables are statistically significant, so 

that choice of treatment of missing data affects which coefficients are interpreted as 

statistically significant. The tradeoff between bias and efficiency and using more 

information affects the interpretation of results. In light of this, it may be worth pursuing 

the application of the imputation algorithm of King et al. (2001) in future research, which 

is argued to outperform the mean substitution and deletion methods. However, only the 

more commonly accepted approaches are dealt with here. 

The effects of a set of dummy variables for mean imputation is shown in the first 2 

columns of Table 5.8. Missing information on the parental occupation predicts failure 

perfectly, the problem of collinearity, and these variables must be dropped from the 

regression to enable estimation. The missing information for parental qualifications,

189 

proportion of time spent unemployed and number of siblings is controlled for using the 

dummies. The estimation on the data where those cases with missing information in these 

variables are dropped is given in Appendix Table A2.3. The results in columns one and 

two are slightly different to that of Table 5.8. Of course the number of observations in 

columns one and two of Appendix Table A2.3 are lower at 2150 because the observations 

with missing information are dropped, whereas in Table 5.8 they are 2368. The Akaike 

Information Criterion does not vary much in size between the models, and so does not 

assist much in model selection here (because the sample and variables change between 

the models, this fit measure is more relevant).The arguments of King et al. (2001) suggest 

that dropping those cases, casewise deletion, gives the correct standard error, although 

estimates do suffer the problems of bias. It is then a subjective choice as to whether the 

analyst prefers to trade-off bias, however correct standard error estimation is essential if 

the statistical significance of the coefficients is important to analysis. In light of this, it is 

deemed more useful to apply casewise deletion than mean imputation dummies. 

5.7.2 Sample reduction effects on model of SYETP participation 

Columns 3 and 4 of Table 5.8 give the probit results for SYETP participation for the final 

data set after sample reduction. Column 3 136 shows the unweighted results, and column 4 

shows the results weighted with the survey weight. 137 As for the whole sample discussed 

earlier, the variables that are statistically significant alter with the use of the weight. The 

variables that gain significance when using the weight are married in 1984, attended a 

private school, interviewed in Western Australia/ Tasmania, longest job held before 1984 

was 3 years or more, mostly lived in a city until aged 14. The variables that lose 

statistical significance are CEP referrals in 1984, father held a post-school qualification 

when respondent aged 14, mother worked as plant operative when respondent aged 14. A 

worrying change is the loss of statistical significance for CEP referrals in 1984. This is 

further discussed later in the modelling of the treatment effect of SYETP, because this is 

a key element of the identifying restriction in the bivariate probit of employment 

136 The results in column 3 are equivalent to the univariate probit estimated in Richardson (1998). 

137 Note that no account has been made of sample reduction from the 1984 survey in this weight. This is 

treated next.

190 

outcomes and SYETP participation. These changes to the estimation are interpreted as 

signifying the importance of accounting for the survey design and non-response in the 

data, 

Finally, Table 5.8 examines the effects of applying the SYETP model to the full sample 

and the sample after sample reduction. This is a further examination of the sample 

reduction effects upon the desired model of SYETP participation. The importance of the 

sampling weight has been shown. The results weighted by the survey weight are 

discussed first, in a comparison of columns 2 and 4, followed by a comparison of the unweighted 

data in columns 1 and 3. The non-response weighting is not fully appropriate 

for the final sample as it does not include further effects of attrition, which are yet to be 

constructed, however it is presented here to allow the opportunity to disregard the share 

due to non-response weight effects when comparing before and after sample reduction. 

The table shows that whether or not the non-response weight was used, the results are 

affected by the use of only the reduced sample compared to all those cases available 

before sample reduction. In using the full sample compared to the reduced sample, two 

important types of changes are found that would alter interpretation of the estimates: 

changes in sign, from positive coefficient to negative and vice versa, and changes in 

which variables are statistically significant. These changes occur whether the weight is 

used or not, however the incidence is different in the weighted data to that in the unweighted 

data. The changes in sign occur only for variables that were not statistically 

significant. No change in significance is associated with a change in sign. 

The results weighted by the survey weight change sign for gender, other ethnicity, South 

Australia/Northern Territory, fathers occupation was labourer, mothers occupation was 

labourer or tradesperson, and religion brought up in was Catholic or other Christian. The 

variables that change statistical significance, either falling out of statistical significance or 

becoming significant, are Roman Catholic schooling, highest qualification was schooling 

to year 12, mostly lived in a city to age 14, and father held a post school qualification 

when respondent was 14. The unweighted results change sign for gender, South 

Australia/Northern Territory, highest qualification in 1984 is degree or diploma or other

191 

post-school qualification, and longest job held by 1984 is 2 to 3 years. The variables that 

change statistical significance, are interviewed in Western Australia/ Tasmania in 1984, 

Roman Catholic schooling, highest qualification was schooling to year 12, no job held by 

1984, CEP referrals in 1984, mothers occupation was plant operative, and brought up in 

other Christian religion. 

Sample reduction is found to affect the results of modelling SYETP quite substantially. 

Comparison between the full data and reduced data estimates shows significant 

differences between the results of the models, and these are interpreted as representative 

of the presence of attrition bias. This supports the theoretical position that bias is 

introduced to the estimates. It also concurs with the empirical material of Alderman et al. 

(2000) discussed earlier where similar impacts on modelling were also found. The next 

section deals with multivariate modelling of the sample reduction. Weights are then 

produced to account for the sample reduction.

192 

5.8 Model of the probability of attrition 

To construct the weight for non-response and selection that was provided with the data, 

administrative data was used to weight back to the population. Only survey data is 

available to be applied to estimates of the sample attrition weight. As the attrition only 

occurs after the 1984 survey, it is likely the survey information is the most relevant for 

describing the characteristics of the respondents, as it is gathered at a more recent 

juncture while the administrative data would be more dated. One useful piece of relevant 

information that is not available in the data would be survey collection information about 

movers. This variable had a central role in describing non-response. However, 

unavailability means the importance of this item to attrition cannot be assessed. The 

variables selected for explaining attrition are chosen from amongst those variables 

measured in the 1984 survey. The model is weighted by the initial nonresponse/design/selection 

weight in order to represent the 1984 data, so that the estimates 

which will be used for the attrition weights will only account for the further non-response 

to the later surveys to 1986. In this following model, it is assumed that the same process 

as that of attrition to 1986 defines attrition to the 1985 survey. 138 

The results of estimation for the probit model of attrition are shown in column 1 of table 

5.9. Those who were interviewed in South Australia and the Northern Territory, Western 

Australia and Tasmania, were more likely to also be interviewed in later years to 1986. 

Additionally, those in country towns and rural areas, and those with a highest 

qualification to year 12, had a higher tendency to be interviewed again in later years. 

Those with longer unemployment prior to June 1984 were less likely to be surveyed 

again in later years, as were those who were missing the unemployment variable, had 

held longer jobs in the past, and those who were married. Participants in SYETP were 

more likely to respond to later surveys. 

138 In exploration of this, results for which are not shown, separate modelling of the attrition to each year 

was not found to have different estimated models, with the same coefficients statistically significant and of 

similar magnitude, and so common modelling of the attrition was found appropriate.

193 

Generally, those variables found to be statistically significant in the multivariate model of 

attrition reflect those pointed out by the univariate analysis of difference in mean arising 

from attrition (see section5.5.4 and Table 5.5). 

As a summary measure of fit for the probit model, the predictions of the model are 

examined. A fitted probability exceeding 0.5 is taken to indicate a predicted response to 

the survey. When these predicted responses are compared to the actual interviews in 1986 

the model correctly predicted the attrition to 1986 for 72 per cent of the 1984 

observations. The other measures of fit indicate no serious problems. 

These weights are now introduced into a single final weight that can be used for 

modelling. This combined weight could then be used to reflect the total effect due to nonresponse, 

survey design, and survey attrition.

194 

Table 5.9: Probit results used for construction of attrition weights 

Model of Response to 

1986 survey 139 

syetp syetp 0.25 

(2.05)* 

age84 Age at 1984 survey -0.03 

(1.71) 

female Gender=female -0.05 

(0.83) 

roatsi Aboriginal/Torres Strait Islander -0.06 

(0.40) 

rones Other ethnic minority -0.00 

(0.04) 

health84 Work limited by health 0.02 

(0.17) 

mar84 Married 1984 -0.36 

State interviewed in 1984 (2.66)** 

vic Victoria 0.05 

(0.62) 

qld Queensland -0.03 

(0.41) 

sant South Australia/Northern Territory 0.26 

(2.58)** 

watas Western Australia/Tasmania 0.26 

Area of residence in 1984 (2.62)** 

oc other town 0.02 

(0.33) 

ct Country town 0.40 

(5.10)** 

ra Rural area 0.54 

Highest qualification in 1984 (4.06)** 

hqdd84 Degree/diploma 0.15 

(1.46) 

hqtra84 Apprenticeship 0.06 

(0.54) 

hqoth84 Other Post-School qualification 0.16 

(1.37) 

hq12_84 Year 12 of school 0.23 

(2.55)* 

hq11_84 Year 11 of school 0.16 

(1.60) 

hq9_84 Year 9 of school or less -0.16 

(1.86) 

upropjn duration of Pre-June 1984 

unemployment 

-0.16 

(2.20)* 

child84 Children in 1984 0.45 

(2.78)** 

spemp Spouse employed in 1984 0.30 

(1.71) 

139 Dependent variable is response to 1986 survey, with value 1 if respondent at survey in 1986, zero 

otherwise.

195 

longj0 Longest job by 1984 < 1 year -0.08 

(0.63) 

longj1 1

196 

5.9 Attrition Weights from the model 

As a check on the performance of the weights, the profile of a number of explanatory 

variables is considered. The application of the weights constructed is shown in Table 5.10. 

The 1984 survey data is shown in the first column, while the 1986 survey data is shown 

in the second column. The constructed attrition weights should approximately reproduce 

the profile of the 1984 data for variables significantly affected by attrition. 

Applying the weights from the probability model of attrition yields the profile in column 

3 of Table 5.10. Column 3 shows that the attrition weight has restored the profile of the 

1984 survey quite well. 

The final two columns in Table 5.10 examine the combination of the attrition weight with 

the response/selection weight. The fourth column shows the 1984 survey weighted to 

restore the sampled population profile. The final column shows the profile of the 1986 

data restored to the sampled population, where the response/selection weight has been 

multiplied by the attrition weight. The profile of the 1986 data in column five is now 

more similar to that of the population. Thus the weights constructed have performed the 

desired repair to the dataset so that it better reflects the original sample eligible for 

SYETP. This combined weight is to be used in the analysis of the treatment effects of 

SYETP, to restore the sampled population characteristics where the ‘1986 panel’ data is 

used. This is performed in the next study. 

The summary statistics describing the distribution of the weights for the 1283 cases used 

in analysis is shown in the Appendix 2 Table A2.4. 

5.10 Conclusions 

Attrition was found to have a substantial effect on participation in SYETP, one of the 

chief variables for modelling. Non-response and sampling design were investigated, and 

also found to have important effects on modelling, and so the weight to account for this 

was adopted. Item non-response was considered and the casewise deletion approach

197 

adopted in order to maintain correct standard errors in estimation. Analytical sample 

reduction was found to suitable for the analysis when it reflected the eligibility criteria for 

the programme. Weights were constructed to treat attrition, which were combined with 

the non-response and sampling design weight. The combined weight is to be used for 

analysis in later chapters.

198 

Table 5.10: Performance of attrition weight as measured by unweighted and weighted 

profile 

mean 1984 

unadjusted 


1986 

unadjusted 


1986 

weighted 

to 1984 


1984 

weighted to 

sampled 

population 


1986 

adjusted to 

sampled 

population 

Age at 1984 survey 20.13 19.95 20.01 19.88 19.92 

Gender=female 0.41 0.41 0.41 0.39 0.39 

Work limited by health 0.12 0.11 0.11 0.11 0.11 

Aboriginal/Torres Strait 0.03 0.03 0.03 0.04 0.03 

Islander 

Other ethnic minority 0.08 0.08 0.08 0.08 0.08 

Married 1984 0.12 0.10 0.11 0.11 0.11 


Victoria 0.24 0.24 0.24 0.24 0.24 

Queensland 0.16 0.13 0.14 0.16 0.16 

South Australia/Northern 0.11 0.11 0.11 0.10 0.10 

Territory 

Western Australia/Tasmania 0.12 0.13 0.12 0.11 0.11 

New South Wales/Australian 0.39 0.38 0.39 0.39 0.39 

Capital Territory 

Area of residence in 1984 

Capital city 0.49 0.47 0.49 0.49 0.49 

Other city 0.23 0.22 0.23 0.23 0.23 

Country town 0.21 0.24 0.21 0.22 0.22 

Rural area 0.06 0.07 0.06 0.06 0.06 


Degree/diploma 0.11 0.11 0.11 0.11 0.11 

Apprenticeship 0.08 0.08 0.08 0.07 0.08 


0.07 0.07 0.07 0.07 0.07 

qualification 





Average recorded Pre-June 

1984 unemployment 142 0.63 0.61 0.62 0.62 0.61 

observations 2368 1687 1687 2368 1687 

NOTE 1: for a 0-1 indicator variable, the mean gives the proportion. For example, the mean in the 

population, column 1, for gender=female is the proportion of females in the population. 

142 Average recorded Pre-June 1984 unemployment is missing for 27 observations, and has a base of 2341.

199 

6: Study 4 Weighting to counteract attrition and nonresponse 

in ALS 

In this chapter the combined weights are applied, which were constructed in the previous 

chapter to repair the data for loss due to attrition, non-response and to account for the 

survey design. First the weights are applied to the Heckman bivariate probit modelling, 

and the effects discussed. Then the weights are applied to the propensity score matching 

(PSM). The application of weights to PSM is more complex, and the full protocol is first 

presented and then the weighted results are discussed. Finally, the employment effects 

found from the Heckman and PSM models in the repaired data are contrasted with those 

formerly found. The discussion focuses on the limitations to each application. 

6.1 Results of weighting Heckman bivariate probit 

The weights accounting for attrition, non-response and survey design were constructed in 

the previous section. Table 6.1 shows the effect of applying this sample reduction weight 

to the bivariate probit of employment and SYETP participation. The first column shows 

the unweighted replication results to better enable comparison while the final column 

gives the results for the regression weighted for sample reduction and survey design. 

Attention is focused on the treatment effect, as indicated by the SYETP variable. 

An important change brought in by accounting for the sample reduction is the loss of 

statistical significance for CEP referrals in 1984 in the SYETP participation equation. It 

is now less significant, although it would pass the t test at the significance level of 10 per 

cent. In the modelling of the treatment effect of SYETP, this is a key element of the 

identifying restriction in the bivariate probit. Along with this change, the SYETP 

treatment dummy loses size and statistical significance in the employment equation. The 

change introduced by weighting for sample reduction strongly affects the interpretation 

of SYETP treatment effect. Given the coincident change in the CEP referrals in the 

participation equation, and it’s role in the identifying restrictions for the bivariate probit, 

it is highly likely that adjusting for the selection effects due to attrition of the sample, has

200 

reduced the need to account for selection into SYETP, with subsequent impacts on the 

treatment effect measured. This potential problem, where attrition seemed related to 

SYETP participation, was hinted at in earlier analysis of the sample reduction effects on 

the univariate probit of SYETP participation. 143 

The Wald test of significance of the selection correction also gives a value which in the 

chi square test that correlation is zero, leads to the conclusion of failing to reject the null 

hypothesis. This might suggest that a simple probit of employment might be an 

acceptable modelling format, when the attrition has been accounted for with weighting. 

The basic univariate probit results, similarly weighted and specified, are shown in the 

Appendix Tables A2.5a and A2.5b. In the basic probit analysis of employment, the 

employment effect of SYETP is positive i.e. marginal effect of 13 percentage points, and 

statistically significant. This is slightly higher than the marginal effect of SYETP in the 

bivariate probit, which gives a 10 percentage point gain in employment (see Table 6.9 

later). 

Other differences also occur in comparison of the weighted to the former unweighted 

Heckman bivariate probit results. A number of variables formerly statistically 

insignificant, are statistically significant in the weighted equation; these include: being of 

other ethnic minority, holding highest qualification of up to year 9 schooling, having 

good English, no religion and Presbyterian religion. Other variables lose statistical 

significance once weighting is applied: holding highest qualification of apprentice, 

mother’s occupation of plant operative, and negative attitude to women in work. Similar 

effects are also evident in the participation equation, but are not detailed. Following 

Fitzgerald et al. (1998a), the interpretation of these changes with the introduction of 

attrition weighting is that sample reduction lead to bias in the estimates. Accounting for 

the sample reduction with weights reduces the bias. 

143 See section 5.8 on attrition and the discussion of the Probit Model of SYETP and effects of sample 

reduction.

201 

Table 6.1, part A Employment equation from bivariate probit 

Replication results Replicated equation 

weighted with 

combined weight 

Model of ever employed 

in 1986 survey 

Dropping cases with attrition, missing 

information or not eligible for SYETP 


SYETP 1.596** 0.933 

(2.85) (0.79) 

Gender=Female -0.397** -0.463** 

(-4.04) (-4.48) 

married -0.54 0.033 

(-0.28) (0.14) 

children -0.238 -0.395 

(-0.99) (-1.47) 

Children*female -1.211** -1.271** 

(-3.80) (-3.75) 

Spouse employed 1984 0.542* 0.554* 

(2.41) (2.25) 


(-1.18) (-1.38) 

Other ethnic minority -0.318 -0.373* 

(-1.66) (-1.99) 

Victoria -0.060 -0.126 

(-0.50) (-1.00) 


(0.20) (0.32) 

South Australia/Northern Territory -0.305* -0.488** 

(-1.97) (-3.03) 


(-0.69) (-0.63) 


(-0.33) (-0.62) 


(-0.05) (-0.11) 

Private school 0.604* 0.623* 

(2.08) (1.97) 

Degree/diploma 0.512** 0.512** 

(3.26) (3.13) 

Apprenticeship 0.432* 0.371 

(2.11) (1.74) 


(0.32) (0.53) 


(-0.41) (-0.44) 


(2.22) (2.38) 

Year 9 of school or less -0.222 -0.291* 

(-1.60) (-2.03) 

Longest job by 1984 none 0.042 -0.033 

(0.25) (-0.19) 

< 1 year 0.190 0.225 

(1.42) (1.64) 

2 years 0.252 0.222 

(1.57) (1.34) 

3 years + 0.657** 0.572** 

(4.01) (3.13) 

Enter other govt prog -0.623** -0.730** 

(-4.90) (-5.33)

202 

duration of Pre-June 1984 unemployment -0.473** -0.442** 

(-4.20) (-3.55) 


(-2.50) (-2.53) 


(-1.65) (-1.83) 


-0.00 (-0.59) 


(-0.66) (-1.91) 


(1.37) (1.06) 


(-1.51) (-1.68) 

English good 0.403 0.430* 

(1.91) (2.08) 

English poor 1.050** 1.021** 

(2.75) (2.58) 

Sexist -0.440* -0.395 

(-2.35) (-1.93) 


(1.25) (1.55) 


(-0.79) (-0.89) 

Father Labourer 0.135 0.180 

(0.56) (0.75) 

Father Plant operative 0.059 0.152 

(0.26) (0.68) 

Father Sales -0.048 0.05 

(-0.18) (0.18) 

Father Tradesperson -0.213 0.241 

(-0.95) (-0.72) 

Father Manager/professional/paraprofessional 

0.114 0.241 

(0.53) (1.12) 

Father Not employed 0.147 0.087 

(0.56) (0.33) 

Father holds post-school qualification 

when resp 14 

0.142 0.089 

(1.28) (0.75) 


(-1.34) (-0.65) 

Mother Labourer -0.150 -0.149 

(-0.64) (-0.60) 

Mother Plant operative -0.576* -0.503 

(-2.31) (-1.80) 

Mother Sales -0.391 -0.399 

(-1.80) (-1.75) 

Mother Tradesperson -0.229 -0.329 

(-0.70) (-0.97) 

Mother Manager/professional/paraprofessional 

.0217 0.066 

(0.97) (0.28) 

Mother Not employed -0.087 -0.07 

(-0.49) (-0.37) 

Mother post-school qualification when 

resp 14 

-0.067 -0.012 

(-0.52) (-0.08) 

Catholic 0.327* 0.430** 

(2.56) (3.18) 

Presbyterian 0.412 0.504* 

(1.92) (2.24)

203 


(0.77) (1.24) 


(-0.50) (-0.51) 

Other religion -0.045 0.009 

(-0.28) (0.05) 

No religion 0.279 0.412* 

(1.62) (2.30) 

rho -0.626 -0.206 

Wald test of Rho=0 (chi2 (1) statistic) 0.09 



LR chi 2 (degrees of freedom) 144 (118) 379.79 (118) 429.85 

Akaike Information Criterion 1.55 1.52 

Coefficient is reported with Student’s t statistic in brackets; * significant at 5%; ** significant at 1% 

NOTE 1: Results in Column 1 are from Table 4 and Table 5 pages 18-21 Richardson (1998). Base categories: European 

ethnic origin, state interviewed in 1984 NSW/ACT, government school, highest qualification year 10 at school, longest 

job by 1984 is 1 year, lived mostly in state capital city until respondent aged 14, English is first language, father clerical 

worker when respondent aged 14, mother clerical worker when respondent aged 14, religion brought up in is Anglican. 




coefficients being tested.

204 

Table 6.1, Part B Selection/participation equation of the bivariate probit 

Replication results Replicated 

equation with 

combined weight 

Model of SYETP participation, 1986 

survey data 

Dropping cases with attrition, missing 

information or not eligible for SYETP 



(0.71) (0.53) 

Age at 1984 survey -0.106** -0.081* 

(-3.17) (-2.78) 

Married 1984 -0.854 -1.008** 

(-1.53) (-3.57) 

Children 1984 0.464 0.347 

(0.78) (0.86) 


(-0.37) (-0.16) 


(0.81) (1.80) 


(-1.01) (-0.79) 


(0.33) (0.03) 

Victoria 0.112 0.088 

(0.72) (0.57) 


(-1.31) (-0.66) 


(-0.77) (-0.63) 

Western Australia/Tasmania 0.317 0.381* 

(1.78) (2.20) 


(0.22) (0.89) 


(-1.30) (-1.10) 

Private school -0.635 -0.910* 

(-1.39) (-2.19) 


(0.51) (0.00) 


(-0.42) (-0.38) 

Other Post-School qualification -0.037 0.025 

(-0.14) (0.08) 


(2.46) (2.21) 


(0.54) (0.93) 

Year 9 of school or less -0.73 0.015 

(-0.35) (0.07) 


(-1.35) (-1.08) 

< 1 year -0.019 -0.068 

(-0.10) (-0.34) 

2 years 0.173 0.065 

(0.80) (0.29) 

3 years + -0.326 -0.491 

(-1.26) (-1.99)*

205 

CEP referrals 1984 0.143* 0.128 

(2.02) (1.78) 145 

Proportion of time Pre-June 1984 spent in 0.487** 0.335* 

unemployment 

(2.94) (2.40) 

Work limited by health -0.633* -0.673** 

(-2.53) (-2.90) 

Other city before aged 14 -0.243 -0.395* 

(-1.48) (-2.34) 

Country town before aged 14 -0.473** -0.521** 

(-2.97) (-3.28) 


(-1.69) (-1.59) 


(-1.48) (-1.26) 


(-0.70) (-0.89) 


(-0.73) (-0.61) 


(-1.13) (-1.09) 

Sexist 0.318 0.373 

(1.23) (1.22) 


(-1.39) (-1.57) 


(-1.12) (-0.82) 

Father Labourer -0.263 -0.084 

(-0.85) (-0.27) 

Father Plant operative -0.267 -0.141 

(-0.96) (-0.51) 

Father Sales -0.086 -0.217 

(-0.26) (-0.62) 

Father Tradesperson -0.300 -0.280 

Father Manager/professional/paraprofessional 

(-1.10) (-1.05) 

-0.153 -0.182 

(-0.59) (-0.70) 

Father Not employed -0.456 -0.435 

(-1.29) (-1.33) 


when resp 14 

-0.315* -0.229 

(-2.14) (-1.60) 


(1.49) (1.22) 

Mother Labourer 0.177 -0.034 

(0.57) (-0.11) 

Mother Plant operative 0.697* 0.575* 

(2.26) (1.81) 

Mother Sales 0.190 0.216 

(0.66) (0.77) 

Mother Tradesperson 0.120 0.197 

(0.29) (0.46) 

Mother manager/ prof /para-professional -0.246 -0.320 

(-0.84) (-1.11) 

Mother Not employed 0.041 -0.124 

(0.18) (-0.59) 

Mother post-school qualification when 0.266 0.218 

145 A t-test probability of 0.076, so this is statistically significant for a test set at the 10 per cent level of 

significance.

206 

resp 14 

(1.66) (1.27) 

Catholic 0.061 -0.006 

(0.37) (-0.03) 


(1.34) (1.33) 


(0.06) (1.01) 


(0.27) (0.21) 


(0.80) (0.58) 


(0.67) (0.74) 


Student’s t statistics in brackets; * significant at 5%; ** significant at 1% 

Weighted with combined weight for attrition, non-response and survey design. 

NOTE 1: Results Column 1 are sourced from Table 4 and Table 5 pages 18-21 Richardson (1998). 




clerical worker when respondent aged 14, religion brought up in is Anglican.

207 

6.2 Results of weighting the PSM 

Weighting the Heckman bivariate probit is a relatively simple task of ensuring the 

weights are used in estimating the model. In contrast, weighting the PSM requires a little 

more attention. Frölich et al. (2001) is one of the few applications of PSM found where 

weights have been applied to reflect the selective nature of the sample surveyed. As the 

application of weights in PSM is not generally treated in the literature, details of the 

approach are made clear. 146 

6.2.1 Weighting protocol 

In order to correctly adjust the PSM for the attrition, two steps need to be taken. The 

weighting protocol steps are shown in Table 6.2. The probit model of SYETP used to 

estimate the propensity scores, must be weighted with the attrition weight. The common 

support for the weighted propensity needs to be checked. Once again, the minimum and 

maximum of the weighted SYETP propensity scores are used to define the common 

support. Once the matches have been found for these estimated propensity scores, then 

the final match needs to be re-weighted. The balance of the treatment group in the 

population is the only consideration. The weights that formerly applied to the comparison 

cases are then discarded. Instead, the weight that applies to the SYETP case to which 

they are matched is donated to the comparison case. In this way, the final match is 

weighted using only the weights of the treated. This is done so that only the 

characteristics of the treated have influence, as the parameter of interest is the impact of 

treatment on the treated. One other important step also applies when weighting. In 

assessing the standardized bias for the performance of the match, weighted mean and 

variance statistics must be used to construct the standardized bias. 

Table 6.2 Weighting protocol steps 

• Weight the propensity probit 

• Weight the final match, using only the weight of the treated; where comparison 

case used with replacement, sum the applicable treated case weights. 

146 I am grateful to Michael Lechner who in response to an email query made clear the steps in the protocol.

208 

6.2.2 Effects of weighting PSM 

The first effect of the weighting was to produce slightly different propensity score 

distributions for the SYETP and comparison than the unweighted estimations. The weight 

used is the combined weight developed in Chapter 5. The results for the estimation of the 

weighted probit used to create the propensity scores are shown in Table 6.3. The 

application of the weight produces no real change to the fit of the model. The model is 

estimated to have fit 147 of 92 per cent, which indicates that by this measure it has the 

same predictive power as the unweighted model. The Wald chi squared and pseudo R 

squared indicate no problems with the fit of the model and are only slightly large than for 

the unweighted model. The Akaike Information Criterion is very similar to that of the 

unweighted model. More coefficients are statistically significant in the weighted model 

than was the case for the unweighted model of the propensity. A number of variables 

formerly statistically insignificant, are statistically significant in the weighted equation; 

these include: marital status, partner in employment, the location of Western 

Australia/Tasmania, private schooling, past job held for more than 3 years, and 

background living in a non-Capital city. The CEP referral variable which was significant 

in the unweighted propensity, is not statistically significant in the weighted propensity 

model. The direction of the coefficients remains plausible in the weighted propensity. It 

is reasonable to expect that for SYETP age has a negative influence, additionally negative 

influences on SYETP participation are from being married, or having private schooling, 

past job held for more than 3 years, poor health, and background living in a non-Capital 

city or country town. Longer spells of unemployment retain a positive impact on 

participation, with further positive influences from partner in employment, school-leavers 

with highest qualification of year 12, living in Western Australia (which evidence in 

chapter 2 shows is the state had the highest usage of SYETP). 

147 A fitted probability exceeding 0.5 is taken to indicate a predicted response to the survey; these predicted 

responses are compared to the actual participants/non-participants in SYETP to check which cases the 

model correctly predicted.

209 

The distribution of the propensity scores is shown using the histogram in Figure 6.4; 

kernel density plots of the propensities before matching are shown in Figure 6.6. The 

statistics summarizing the distribution of the new propensity scores are also shown in 

Table 6.5 for both the comparison group and SYETP participants. The weighted 

propensity distributions, when compared to the unweighted shown previously in Figures 

4.3, 4.4 and Table 4.5, do not appear very much altered by the application of the weights. 

However, the distributions have had slight changes. The SYETP propensities have grown 

slightly at the upper end, with the largest cases greater in size than when unweighted, 

while at the lower end of the distribution the smallest cases have shrunk in size. The 

median is roughly the same as when unweighted, but the mean propensity is now slightly 

larger. For the comparison group of nonparticipants, it is also true that the tails have been 

slightly altered, with the largest cases greater in size than when unweighted, and the 

smallest cases smaller than when unweighted. However, the mean and median of the 

comparisons are largely unaffected by the introduction of weighting. 

Comparison with the former unweighted propensity scores shows only very small change. 

The propensities still have good properties, in that the distributions overlap, and the range 

of the distributions gives a probability roughly from zero up to 0.5 for both treated and 

comparisons. The kernel density mapping doesn’t look too different to that of the 

unweighted propensity model. A result of the new weighted propensity scores is that now 

the application of the common support condition leads to one case being dropped from 

the SYETP group, the SYETP case with the largest propensity score value. The 

implication is that for that SYETP case, there was no comparison case which had a 

propensity score this high. Indeed the distributions shown in the histogram and kernel 

density plot indicate most comparison cases had near-zero propensity values. 

The matching results are shown in the Table 6.7, where the PSM implemented was single 

nearest –neighbour –with replacement, within-caliper, with the additional protocol of 

weighting the propensity with survey weights for attrition. Comparison with the former 

results shows that the weighting has led to much smaller results for the employment 

effect. The statistical significance of the employment effects is also low. Although

210 

reasonable for smaller calipers, for the wider calipers of 0.01, 0.02 and 0.05, the 

statistical significance falls off dramatically. This would indicate that applying a wider 

caliper was far more consequential in the weighted data. Compared to the former 

unweighted results, there is a much greater variation in the size of employment effect 

estimated. However, none of the new PSM results for the employment effect have 

statistical significance at normal test levels. To an extent this would be due to the use of 

weights as the variance estimate is then less efficient. When the weights are used, the 

more conservative estimate of the variance is used in order to account for the fact that it 

is assumed the missing data follows the same pattern as that of the non-missing data. 

The effectiveness of the matching as assessed by the standardized bias measure gives a 

slightly poorer performance than for the former unweighted results. The weighted 

matches have standardized mean biases which are between 11 and 12 in magnitude, 

whereas before they were between 9 and 10. This change in size is very slight. As 

discussed in the preceding chapter, this would indicate that caution should be applied in 

accepting the performance of the match. The mean difference in the propensity scores 

after matching is very small, which would indicate close matching as a small mean 

difference is consistent with good matches. The number of comparison group 

observations used for the matching has slightly risen in the weighted results, compared to 

the unweighted, which would indicate slightly less reliance on a few comparison cases. 

However, taken as a whole, the effectiveness of the weighted match is not too different 

from that of the unweighted matching. 

A graphical summary of the matched propensity scores for the 0.001 caliper is shown in 

Figure 6.8. Although a kernel density plot can only give a rough guide, the distributions 

of the matched propensity scores overlap very well. It would seem that at the upper and 

lower extremes, the overlap is better, but in the middle regions of the distributions there 

are two gaps. In the first gap, at lower propensities, the untreated lie outside the SYETP, 

while in the second gap, the SYETP lie outside the untreated. In these gaps, the 

difference in the propensity scores of the treated and comparison would be greater, and in 

these gap regions the match is slightly poorer.

211 

Table 6.3 Weighted probit used to estimate propensity score for propensity score 

matching 

Variable 

Model of 

SYETP 

participation 

Weighted for 

Attrition 

non-response 

and survey 

design 

syetp 

Gender=female female 0.07 

(0.52) 

Age at 1984 survey age84 -0.08 

(2.87)** 

Married 1984 mar84 -1.03 

(3.91)** 

Children 1984 child84 0.34 

(0.85) 

Children*female ch84fem -0.09 

(0.14) 

Spouse employed 1984 spemp84 0.72 

(1.96)* 

Aboriginal/Torres Strait Islander roatsi -0.37 

(0.78) 

Other ethnic minority rones -0.00 


Victoria vic 0.09 

(0.58) 

Queensland qld -0.12 

(0.62) 

South Australia/Northern Territory sant -0.12 

(0.63) 

Western Australia/Tasmania watas 0.38 

(2.18)* 

Education school overseas lsos 0.38 

(1.04) 

Roman Catholic school lsrcs -0.25 

(1.09) 

Private school lspriv -0.93 

Highest qualification in 1984 (2.25)* 

Degree/diploma hqdd84 -0.02 

(0.11) 

Apprenticeship hqtra84 -0.11 

(0.38) 

Other Post-School qualification hqoth84 0.06 

(0.24) 

Year 12 of school hq12_84 0.39 

(2.19)* 

Year 11 of school hq11_84 0.20 

(1.05) 

Year 9 of school or less hq9_84 -0.00

(0.02) 

Longest job by 1984 none longjno -0.32 

(1.37) 

< 1 year longj0 -0.08 

(0.47) 

2 years longj2 0.05 

(0.25) 

3 years + longj3p -0.50 

(2.13)* 

CEP referrals 1984 cepref84 0.13 

(1.75) 

duration of Pre-June 1984 unemployment upropjn 0.33 

(2.40)* 

Work limited by health health84 -0.66 

Family background (2.91)** 

Other city before aged 14 oc14 -0.40 

(2.39)* 

Country town before aged 14 ct14 -0.51 

(3.34)** 

Rural area before aged 14 ra14 -0.42 

(1.61) 

Overseas before aged 14 os14 -0.60 

(1.25) 

Number of siblings nsib2 -0.01 

(0.91) 

English good egood -0.14 

(0.56) 

English poor epoor -0.66 

(1.06) 

Sexist ksink5p 0.35 

(1.26) 

Sexist*female ks5pfem -0.85 


Father not present when resp 14 fnpres14 -0.22 

(0.79) 

Labourer flabo -0.06 

(0.19) 

Plant operative fplan -0.12 

(0.46) 

Sales fsale -0.23 

(0.66) 

Tradesperson ftrad -0.27 

(1.02) 

Manager/professional/para-professional fmpp -0.17 

(0.66) 

Not employed fnemp -0.42 

(1.31) 

Father holds post-school qualification when resp 14 fpsq -0.22 

Mothers occupation when resp. 14 (1.59) 

Mother not present when resp 14 mnpres14 0.37 

(1.24) 

Labourer mlabo -0.05 

(0.16) 

Plant operative mplan 0.56 

(1.80) 

Sales msale 0.21 

212

213 

(0.76) 

Tradesperson mtrad 0.20 

(0.46) 

Manager/professional/para-professional mmpp -0.31 

(1.10) 

Not employed mnemp -0.12 

(0.57) 

Mother post-school qualification when resp 14 mpsq 0.22 


Catholic cath -0.01 

(0.05) 

Presbyterian pres 0.31 

(1.29) 

Methodist meth 0.29 

(1.19) 

Other Christian othx 0.06 

(0.21) 

Other religion othrel 0.14 

(0.56) 

No religion norel 0.16 

(0.84) 

Constant Constant 0.44 

(0.65) 

Observations Observations 1283 


Wald chi 2 (59) 148 131.99 


Akaike Information Criterion 0.56 

Coefficient with robust t statistics in parentheses * significant at 5%; ** significant at 1% 

Weighted with the combination weights developed in Chapter 5. 








214 

1 

1 

Fra 

ctio 

n 

.5 

Fra 

ctio 

n 

.5 

0 

0 

0 .5 1 


propensity score for treated(SYETP=1) 

0 .5 1 


propensity score for control(SYETP=0) 

with combined weight 

histograms of weighted estimated propensity scores prior to matching 

Figure 6.4 Propensity histogram with combined weight for attrition, non-response and 

survey design

215 

Table 6.5 Summary statistics for distribution of weighted propensity scores, comparison 

group and SYETP 

SYETP 

Comparison 


1% 0.011806 0.008913 

5% 0.025983 0.011806 

10% 0.037321 0.014426 Obs 104 

25% 0.080427 0.018845 Sum of Wgt. 104 

50% 0.143067 Mean 0.167949 

Largest Std. Dev. 0.119193 

75% 0.22751 0.443881 

90% 0.30302 0.525781 Variance 0.014207 

95% 0.409976 0.565907 Skewness 1.225549 

99% 0.565907 0.591027 Kurtosis 4.833109 


1% 0.000322 1.08E-05 

5% 0.002353 1.53E-05 

10% 0.005012 4.85E-05 Obs 1179 

25% 0.016947 5.74E-05 Sum of Wgt. 1179 

50% 0.047517 Mean 0.074077 


75% 0.104098 0.455594 

90% 0.177439 0.473631 Variance 0.00648 

95% 0.242457 0.491513 Skewness 2.004687 

99% 0.376687 0.572013 Kurtosis 8.090501 

Weighted with combined weight for attrition, non-response and survey design.

216 

10 

Treated propensity scores, 


5 

0 

0 .2 .4 .6 

attrition weight 

kernel densities of weightd propensity scores, treated vs untreated 

Figure 6.6 Kernel density plot of attrition weighted propensity scores, before matching 


217 

Table 6.7 Matching results, single nearest neighbour with replacement, within caliper, 

weighting the propensity with combined weights for attrition, non-response and design 

Match 

with 

caliper 

width 

0.001 

Match 

with 

caliper 

width 

0.005 

Match 

with 

caliper 

width 

0.01 

Match 

with 

caliper 

width 

0.02 

Match 

with 

caliper 

width 

0.05 



T statistic 1.57 151 1.19 0.14 0.14 0.14 





1 73 80 81 81 82 

2 7 8 9 9 9 

More than 2 1 1 1 1 

Mean difference in propensity score 0.0002 0.0004 0.0005 0.0005 0.0009 



Standard deviation 0.0002 0.0006 0.0012 0.0012 0.0035 

Mean bias 11.04 11.85 11.73 11.73 11.84 

Common support – 1 case dropped. Weighting protocol: weight propensity, weight the match using the 

treated weight only. The weighted mean bias is calculated using svymean in Stata. 


151 Probability of (0.22) for acceptance of the null hypothesis.

218 

6 

Treated propensity scores 


4 

2 

0 

0 .1 .2 .3 .4 

combined weight, after matching 

kernel densities of weighted propensity scores, treated vs untreated 

Figure 6.8 Propensity Distribution after matching, 0.001 Caliper 


219 


The comparison of the employment effects of Heckman versus PSM is shown in 

Table 6.9. The former unweighted results are in the first columns, to facilitate comparison. 

The weighted results are shown in the last two columns, shaded. 

Comparison between the weighted and unweighted results provides an additional 

indicator for possible attrition bias. D’Agostino and Rubin (2000) consider the issue of 

missing data in item non-response, and note that most propensity score methods are based 

on complete data. They suggest that missingness needs to be controlled in PSM. The fall 

in the employment gain estimated under PSM once attrition is accounted for, is in line 

with the need to account for the missing data that attrition introduces. The significant 

differences between the results of the weighted and unweighted models are interpreted as 

indicative of the presence of attrition bias. In a general comparison with the earlier 

unweighted results, it is noticeable that the weighted results are much smaller in size, 

although still positive, and are also of much lower statistical significance. Whereas before 

the weighting was applied, the Heckman and PSM gave very different sizes for the 

employment effect, once weighted to account for attrition the effects gained are very 

similar in size. This would indicate that under the different modelling assumptions of 

PSM and the Heckman bivariate probit, similar results for the employment effect are 

gained once selection due to attrition is accounted for. 

The PSM result is significant at the 22 per cent level of significance, which is outside 

normal test bounds. However, the variance estimates in the weighted estimates are also 

far more conservative than for the unweighted. 152 If the unweighted Heckman probit 

results had used the more conservative estimate for variance, the t statistic for the 

employment effect falls to 1.49. This is then similar in size to the weighted PSM, and 

also gives about a 22 per cent level of significance. Under the more conservative variance 

estimates, neither Heckman probit gives statistically significant results at normal test 

sizes. 

152 See chapter 7 sensitivity analysis for a fuller discussion of the variance estimates used.

220 

As both the Heckman bivariate probit and PSM are not statistically significant when 

weighted, there would seem to be a substantial loss of efficiency in the application of the 

weights. 

Table 6.9 Employment effects of Heckman versus PSM 

unweighted 

Weighted for attrition 

Heckman PSM 

Heckman PSM 

selection bivariate 

probit 

selection 

bivariate 

probit 

Employment effect 0.26 153 0.18 0.10 154 0.08 

t statistic (2.85)** (2.74)** (0.79) (1.50) 155 

PSM: one-to-one nearest-neighbour within-caliper (0.001) matching with replacement. 

The key assumption where weighting is applied is that the non-response has a missing at 

random [MAR] process. Violation of this assumption would invalidate the results. It 

should be noted that not treating for non-response, as in the unweighted results, adopts 

the stricter missing completely at random [MCAR] assumption. Thus, weighting the data 

is a relaxation of this assumption. Part of the MAR assumption for the weighting 

treatment of attrition and non-response is that the observed variables used for modelling 

can accurately represent the non-response and there are no unobserved variables 

involved. 156 

This assumption is added to the modelling assumptions applicable to each of the 

Heckman and PSM models, as discussed earlier. In the Heckman bivariate probit model, 

the unobservable information is assumed to be correlated suitably with the observed 

variables used, in order to solve the selection problem. For the PSM, CIA assumes that all 

observable variables that jointly influence employment and participation are both in the 

data and in the model. Unobserved factors, such as lack of job search effort, could affect 

153 For SYETP in the employment equation: dy/dx =0.0325919 and mean for SYETP is 0.081060. The 

mean SYETP translates to 8.1 per cent. The marginal effect calculated at the mean is then 0.26418. This is 

interpreted as a 26 per cent increase in employment. Estimated using the mfx command in STATA7.0. 

154 For SYETP in the employment equation: dy/dx =0.0126509 and mean for SYETP is 0.080747. The 

mean SYETP translates to 8.1 per cent. The marginal effect calculated at the mean is then 0.10215. This is 

interpreted as a 10 per cent increase in employment. Estimated using the mfx command in STATA7.0. 

155 Probability of (0.22). 

156 In other words, MAR implies the absence of ‘attrition on unobservables’.

221 

the selection into SYETP and the employment outcome jointly, invalidating the CIA 

(Frölich et al. (2000): 29). A key change to the Heckman bivariate probit results was that 

the test of the rho correlation factor in the selection modelling failed to reject that it was 

zero whereas in the former analysis this hypothesis was rejected. Furthermore, in the 

application of both methods, and for both the weighted and unweighted analyses, 

casewise deletion was used for missing data (when there was item non-response) on 

modelled variables. This may not be the most suitable treatment for missing data, as was 

discussed in the previous chapter. 

All of these considerations limit the validity of the results. Sensitivity of some of these 

modelling assumptions is addressed in the next chapter.

222 

7: Study 5 Sensitivity analysis 

In this chapter, the specifications modelled so far are further investigated. Firstly, 

specification of the Heckman bivariate probit is examined. As a part of this, a potential 

source of confounding for the results is initially considered. Then the reliance of the 

exclusion restriction on the variable CEP referrals is explored. These analyses test some 

of the limitations of the Heckman bivariate probit modelling. The robustness of the 

propensity score matching (PSM) is then looked at by varying the specification of the 

probit used to estimate the propensity. This explores the importance of the 

parameterisation of the propensity to the measured employment outcome. 

7.1 Sensitivity of Heckman specification 

The Heckman modelling in Chapter 6, where weighting for attrition was applied, showed 

a big fall in the size of the SYETP coefficient and a dramatic loss of it’s statistical 

significance. Although the size of the probit coefficient is not directly interpretable, it 

does impact on the marginal effect calculable from the coefficient as a measure of the 

employment impact of SYETP. Mostly the impact of weighting was not found to affect 

other coefficients, and where other coefficients were affected, the change was not so 

dramatic. The first sensitivity check examines a possible source of this effect. 

7.1.1 Potential heteroskedasticity 

It is of interest to briefly consider the possibility of one of the modelling assumptions 

being violated. One way of allowing for this is through considering the Huber-White 

variance estimation for the equation. The Huber-White estimates of variance 157 provide a 

more conservative estimate that can allow for potential heteroskedasticity of undefined 

form amongst the residuals. Homoskedasticity is the standard assumption in modelling, 

such that the disturbances have the same variance. The variance of each disturbance term 

conditional for the chosen values of the explanatory variables is constant. 

157 Also known as the ‘sandwich estimator of variance’, or ‘robust estimator of variance’, key recent 

development papers are White (1980) and White (1982).

223 

Heteroskedasticity is a violation of this assumption. Heteroskedasticity can introduce a 

form of mis-specification if the modelling assumes homoskedasticity. The worth of 

allowing the heteroskedasticity to be of undefined form is that the form it takes is usually 

unknown. Furthermore, as Gujurati (1988) p326 points out, due to data problems the 

investigation of heteroskedasticity is often sheer speculation. The possibility of 

heteroskedasticity in cross-sectional or longitudinal data, involving heterogeneous units is 

very high however. 

The Huber-White variance estimates can be interpreted as trading off efficient estimation 

of the variance-covariance matrix for estimates that accommodate a degree of slack and 

so potentially allow for the issue of minor mis-specification. The point estimates of 

coefficients are unaffected. The replication results applying the Huber-White variance 

estimation are shown in Column 3 of Table 7.1. The first two columns show the 

Richardson (1998) results and the earlier replication estimation to better enable 

comparison. The t-statistics for some variables are quite different, smaller in size, 

reflecting more cautious estimates of error. The impact is not even across variables, 

which suggests some variables might be more affected by heteroskedasticity than others. 

The SYETP treatment variable loses statistical significance under the Huber-White 

variance estimates, with the significance level falling to 14 per cent from 1 per cent. The 

conventional levels of statistical significance are not now met, and the 95 per cent 

confidence interval for the SYETP coefficient now ranges from –0.50 to 3.69 whereas 

before it was 0.50 to 2.69. Other variables generally were not affected strongly in this 

way. 

The results above indicate that the assumption of homoskedasticity may be violated in the 

data. Given that the survey design used a clustered sample, one potential source of 

heteroskedasticity may be due to spatial correlation. 158 Maddala (1983) points out that 

little work studies the effects of violations of the different assumptions for this type of 

model. For the general class of limited dependent variable models, he reports that 

findings show the estimates are neither consistent nor efficient when there is 

158 As the clusters are not known in the data, this cannot be examined.

224 

heteroskedasticity of the residuals (Maddala (1983): 178). Yatchew and Griliches (1985) 

examine the effect of heteroscedastic errors on the probit model. They found that 

heteroscedastic errors led the usual estimators to be inconsistent but that for small 

departures from homoskedasticity the parameter vector is simply rescaled if the variance 

of the residual is uncorrelated with the explanatory variable (Yatchew and Griliches 

(1985): 135). They reported that numerical simulations in their earlier work showed that 

biases tended to increase as the heteroskedasticity increased, and the biases also tended to 

be larger when the explanatory variable was correlated with the heteroskedasticity 

(Yatchew and Griliches (1985): 138). 

Applying this interpretation to these results, indicates that SYETP may be correlated with 

heteroskedasticity of the variance introducing some bias. One source of 

heteroskedasticity that involves SYETP may be that the group of SYETP placements are 

very heterogeneous units relative to the comparison group. The heteroskedasticity would 

seem to be small however. The solutions for heteroskedasticity usually rely on specifying 

the form it takes, which in this case we are not prepared to do. The Huber-White 

estimates offer an alternative that roughly adjusts for this problem. These more 

conservative estimates of variance are applied when weighting is used, and to some 

extent this introduces the lower statistical significance of the SYETP employment effect. 

Other implications of heteroskedasticity, although these may be broad, are not further 

accounted for here as they are beyond the scope of this work.

225 

Table 7.1, Part A Employment equation from bivariate probit, showing effect of standard 

error estimate 

Employment 

equation 

from 

bivariate 

probit of 

Richardson 

(1998) 

Replication 

results 

Model of ever employed 


Replication 

results 

with 

Huber- 

White 

estimates 

of 

Standard 

error 

SYETP 1.590* 1.596** 1.596 

(2.45) (2.85) (1.49) 

Gender=Female -0.398** -0.397** -0.397** 

(-3.99) (-4.04) (-3.74) 

Married -0.055 -0.054 -0.054 

(-0.28) (-0.28) (-0.26) 

Children -0.238 -0.238 -0.238 

(-0.98) (-0.99) (-0.93) 

Children*female -1.212** -1.211** -1.211** 

(-3.79) (-3.80) (-3.79) 

Spouse employed 1984 0.542* 0.542* 0.542* 

(2.40) (2.41) (2.29) 

Aboriginal/Torres Strait Islander -0.272 -0.271 -0.271 

(-1.17) (-1.18) (-1.08) 

Other ethnic minority -0.318 -0.318 -0.318 

(-1.66) (-1.66) (-1.78) 

Work limited by health -0.306* -0.305* -0.305* 

(-2.43) (-2.50) (-1.96) 


Victoria -0.061 -0.060 -0.060 

(-0.51) (-0.50) (-0.53) 

Queensland 0.027 0.028 0.028 

(0.19) (0.20) (0.19) 

South Australia/Northern Territory -0.306 -0.305* -0.305 

(-1.94) (-1.97) (-1.70) 

Western Australia/Tasmania -0.101 -0.102 -0.102 

(-0.67) (-0.69) (-0.60) 

Education school overseas -0.095 -0.095 -0.095 

(-0.33) (-0.33) (-0.31) 

Roman Catholic school -0.010 -0.009 -0.009 

(-0.06) (-0.05) (-0.05) 

Private school 0.603* 0.604* 0.604* 

(2.07) (2.08) (2.07) 

Highest qualification 

Degree/diploma 0.513** 0.512** 0.512** 

(3.22) (3.26) (2.95) 

Apprenticeship 0.431* 0.432* 0.432* 

(2.10) (2.11) (2.27) 

Other Post-School qualification 0.049 0.049 0.049

226 

(0.32) (0.32) (0.32) 

Year 12 of school -0.063 -0.063 -0.063 

(-0.40) (-0.41) (-0.37) 

Year 11 of school 0.357* 0.356* 0.356* 

(2.18) (2.22) (1.98) 

Year 9 of school or less -0.222 -0.222 -0.222 

(-1.59) (-1.60) (-1.49) 

Duration of Pre-June 1984 

unemployment 

-0.473** -0.473** -0.473** 

(-4.19) (-4.20) (-4.22) 

Longest job by 1984 none 0.042 0.042 0.042 

(0.25) (0.25) (0.26) 

< 1 year 0.190 0.190 0.190 

(1.40) (1.42) (1.29) 

2 years 0.252 0.252 0.252 

(1.56) (1.57) (1.53) 

3 years + 0.657** 0.657** 0.657** 

(4.00) (4.01) (4.16) 

Enter other govt prog -0.624** -0.623** -0.623** 

(-4.74) (-4.90) (-3.78) 


Other city before aged 14 -0.218 -0.217 -0.217 

(-1.61) (-1.65) (-1.39) 

Country town before aged 14 -0.001 -0.000 -0.000 

(-0.01) -0.00 -0.00 

Rural area before aged 14 -0.128 -0.127 -0.127 

(-0.65) (-0.66) (-0.53) 

Overseas before aged 14 0.494 0.494 0.494 

(1.36) (1.37) (1.34) 

Number of siblings -0.028 -0.028 -0.028 

(-1.49) (-1.51) (-1.61) 

English good 0.403 0.403 0.403* 

(1.91) (1.91) (2.12) 

English poor 1.050** 1.050** 1.050** 

(2.75) (2.75) (2.92) 

Sexist -0.440* -0.440* -0.440* 

(-2.35) (-2.35) (-2.33) 

Sexist*female 0.463 0.463 0.463 

(1.25) (1.25) (1.30) 


Father not present when resp 14 -0.180 -0.178 -0.178 

(-0.79) (-0.79) (-0.79) 

Labourer 0.134 0.135 0.135 

(0.55) (0.56) (0.59) 

Plant operative 0.058 0.059 0.059 

(0.25) (0.26) (0.28) 

Sales -0.049 -0.048 -0.048 

(-0.18) (-0.18) (-0.19) 

Tradesperson -0.214 -0.213 -0.213 

(-0.95) (-0.95) (-0.93) 

Manager/professional/para-professional 0.114 0.114 0.114 

(0.52) (0.53) (0.57) 

Not employed 0.146 0.147 0.147 

(0.55) (0.56) (0.58) 


when resp 14 

0.141 0.142 0.142

227 

(1.26) (1.28) (1.16) 


Mother not present when resp 14 -0.335 -0.335 -0.335 

(-1.33) (-1.34) (-1.27) 

Labourer -0.151 -0.150 -0.150 

(-0.64) (-0.64) (-0.67) 

Plant operative -0.576* -0.576* -0.576* 

(-2.30) (-2.31) (-2.29) 

Sales -0.392 -0.391 -0.391 

(-1.80) (-1.80) (-1.87) 

Tradesperson -0.229 -0.229 -0.229 

(-0.70) (-0.70) (-0.74) 

Manager/professional/para-professional 0.217 .0217 .0217 

(0.97) (0.97) (1.02) 

Not employed -0.087 -0.087 -0.087 

(-0.49) (-0.49) (-0.52) 

Mother post-school qualification when -0.067 -0.067 -0.067 

resp 14 

(-0.52) (-0.52) (-0.52) 


Catholic 0.327* 0.327* 0.327* 

(2.52) (2.56) (2.29) 

Presbyterian 0.413 0.412 0.412 

(1.88) (1.92) (1.63) 

Methodist 0.133 0.133 0.133 

(0.77) (0.77) (0.72) 

Other Christian -0.102 -0.102 -0.102 

(-0.50) (-0.50) (-0.53) 

Other religion -0.045 -0.045 -0.045 

(-0.28) (-0.28) (-0.29) 

No religion 0.280 0.279 0.279 

(1.58) (1.62) (1.32) 

rho -0.622 -0.626 -0.626 

Observations 1283 1283 1283 

Log likelihood -875.96 -875.96 -875.96 

Coefficient is reported with Student’s t statistic in brackets; * significant at 5%; ** significant at 1% 

NOTE: results Column 1 are from Table 4 and Table 5 pages 18-21 Richardson (1998). Base categories: 

European ethnic origin, state interviewed in 1984 NSW/ACT, government school, highest qualification 

year 10 at school, longest job by 1984 is 1 year, lived mostly in state capital city until respondent aged 14, 

English is first language, father clerical worker when respondent aged 14, mother clerical worker when 

respondent aged 14, religion brought up in is Anglican.

228 

Table 7.1, Part B Selection/participation equation of the bivariate probit, showing effect 

of standard error estimate 

Selection 

equation of 

the bivariate 

probit 

analysis by 

Richardson 

(1998) 

Replication 

results 

Replication 

results 

with 

Huber/White 

estimates of 

Standard 

error 

Model of SYETP participation, 1986 survey 

data 

Age at 1984 survey -0.107** -0.106** -0.106** 

(-3.16) (-3.17) (-3.38) 

Gender=female 0.088 0.088 0.088 

(0.71) (0.71) (0.77) 

Married 1984 -0.855 -0.854 -0.854** 

(-1.52) (-1.53) (-2.16) 

Children 1984 0.465 0.464 0.464 

(0.78) (0.78) (1.21) 

Children*female -0.296 -0.295 -0.295 

(-0.37) (-0.37) (-0.52) 

Spouse employed 1984 0.498 0.496 0.496 

(0.81) (0.81) (1.26) 

Aboriginal/Torres Strait Islander -0.451 -0.451 -0.451 

(-1.01) (-1.01) (-1.03) 

Other ethnic minority 0.081 0.081 0.081 

(0.33) (0.33) (0.33) 

Work limited by health -0.633* -0.633* -0.633** 

(-2.53) (-2.53) (-2.80) 


Victoria 0.112 0.112 0.112 

(0.72) (0.72) (0.77) 

Queensland -0.279 -0.280 -0.280 

(-1.30) (-1.31) (-1.25) 

South Australia/Northern Territory -0.157 -0.157 -0.157 

(-0.77) (-0.77) (-0.82) 

Western Australia/Tasmania 0.317 0.317 0.317 

(1.78) (1.78) (1.93) 

CEP referrals 1984 0.144* 0.143* 0.143 

(1.97) (2.02) (1.52) 

Education school overseas 0.078 0.078 0.078 

(0.22) (0.22) (0.23) 

Roman Catholic school -0.310 -0.310 -0.310 

(-1.30) (-1.30) (-1.39) 

Private school -0.636 -0.635 -0.635 

(-1.38) (-1.39) (-1.53) 


Degree/diploma 0.120 0.119 0.119 

(0.51) (0.51) (0.48) 

Apprenticeship -0.129 -0.129 -0.129 

(-0.42) (-0.42) (-0.46)

229 

Other Post-School qualification -0.036 -0.037 -0.037 

(-0.14) (-0.14) (-0.13) 

Year 12 of school 0.433* 0.433* 0.433* 

(2.46) (2.46) (2.54) 

Year 11 of school 0.101 0.101 0.101 

(0.53) (0.54) (0.47) 

Year 9 of school or less -0.074 -0.73 -0.73 

(-0.35) (-0.35) (-0.36) 


unemployment 

0.487** 0.487** 0.487** 

(2.92) (2.94) (3.14) 

Longest job by 1984 none -0.348 -0.345 -0.345 

(-1.34) (-1.35) (-1.32) 

< 1 year -0.020 -0.019 -0.019 

(-0.11) (-0.10) (-0.11) 

2 years 0.173 0.173 0.173 

(0.80) (0.80) (0.83) 

3 years + -0.326 -0.326 -0.326 

(-1.26) (-1.26) (-1.37) 


Other city before aged 14 -0.244 -0.243 -0.243 

(-1.48) (-1.48) (-1.54) 

Country town before aged 14 -0.473** -0.473** -0.473** 

(-2.94) (-2.97) (-2.73) 

Rural area before aged 14 -0.466 -0.446 -0.446 

(-1.69) (-1.69) (-1.74) 

Overseas before aged 14 -0.757 -0.757 -0.757 

(-1.48) (-1.48) (-1.55) 

Number of siblings -0.011 -0.011 -0.011 

(-0.70) (-0.70) (-0.97) 

English good -0.185 -0.186 -0.186 

(-0.72) (-0.73) (-0.68) 

English poor -0.591 -0.592 -0.592 

(-1.13) (-1.13) (-1.04) 

Sexist 0.317 0.318 0.318 

(1.22) (1.23) (1.16) 

Sexist*female -0.903 -0.903 -0.903 

(-1.38) (-1.39) (-1.58) 


Father not present when resp 14 -0.309 -0.309 -0.309 

(-1.11) (-1.12) (-1.05) 

Labourer -0.263 -0.263 -0.263 

(-0.84) (-0.85) (-0.77) 

Plant operative -0.267 -0.267 -0.267 

(-0.96) (-0.96) (-0.98) 

Sales -0.086 -0.086 -0.086 

(-0.26) (-0.26) (-0.27) 

Tradesperson -0.300 -0.300 -0.300 

(-1.10) (-1.10) (-1.14) 


-0.153 -0.153 -0.153 

(-0.59) (-0.59) (-0.57) 

Not employed -0.457 -0.456 -0.456 

(-1.29) (-1.29) (-1.45) 



-0.315* -0.315* -0.315*

230 

(-2.14) (-2.14) (-2.41) 


Mother not present when resp 14 0.480 0.480 0.480 

(1.49) (1.49) (1.55) 

Labourer 0.176 0.177 0.177 

(0.57) (0.57) (0.59) 

Plant operative 0.697* 0.697* 0.697* 

(2.26) (2.26) (2.30) 

Sales 0.190 0.190 0.190 

(0.66) (0.66) (0.71) 

Tradesperson 0.119 0.120 0.120 

(0.28) (0.29) (0.32) 


-0.247 -0.246 -0.246 

(-0.85) (-0.84) (-0.90) 

Not employed 0.041 0.041 0.041 

Mother post-school qualification 

when resp 14 

(0.17) (0.18) (0.20) 

0.266 0.266 0.266 

(1.66) (1.66) (1.70) 


Catholic 0.061 0.061 0.061 

(0.38) (0.37) (0.39) 

Presbyterian 0.322 0.322 0.322 

(1.34) (1.34) (1.46) 

Methodist 0.017 0.015 0.015 

(0.06) (0.06) (0.05) 

Other Christian 0.075 0.074 0.074 

(0.27) (0.27) (0.27) 

Other religion 0.176 0.176 0.176 

(0.80) (0.80) (0.77) 

No religion 0.138 0.138 0.138 

(0.66) (0.67) (0.69) 

Observations 1283 1283 1283 

Student’s t statistics in brackets; * significant at 5%; ** significant at 1% 

NOTE 1: results Column 1 are sourced from Table 4 and Table 5 pages 18-21 Richardson (1998). 




clerical worker when respondent aged 14, religion brought up in is Anglican.

231 

7.1.2 Exclusion restriction in the Heckman model 

The weighted results for the Heckman model are very different to those of the 

unweighted, especially for the size and significance of the employment effect on SYETP. 

As section 5.6.1.1 found, the profile of SYETP treatment varied amongst those lost to 

selection and attrition, and so the weights that treat this would rebalance to restore the 

SYETP profile. Thus the weighted specifications are likely to differ from the unweighted 

as a result of this. Some further potential sources for changes to the employment effect 

are now investigated. As well, the robustness of the Heckman bivariate probit modelling 

is explored with sensitivity analysis of the specification. 

One of the chief considerations of the Heckman bivariate probit modelling is the 

exclusion restriction. As was noted, in the weighted Heckman results the key variable for 

the restriction has no independent effect in the participation modelling, whereas in the 

unweighted model it had been strongly statistically significant and large in size. In 

addition, in the weighted modelling, the hypothesis tests of the correlation due to 

selection fail to reject the null hypothesis that no selection is present. The sensitivity 

analysis here examines the role of the variables chosen for the exclusion restriction, and 

the validity of the restriction in the weighted and unweighted Heckman model. 

The two key variables forming the exclusion restriction are age and number of CES 

referrals to the CEP programme. Tables A2.6, A2.7, A2.8 in the appendix show the effect 

on the model of changing the specification by including these in the employment 

equation. The specification remains identical to that previously estimated, but for the 

change. Table 7.2 shows a summary of key aspects of the changes to the specification. 

The first two columns show the original results, unweighted and weighted. The table is 

then formed of a further three panels, with two columns in each panel, unweighted and 

weighted, where the exclusion restriction is tested by inclusion of the variables in the 

employment equation. The first panel of new results shows inclusion of age in the 

employment equation, the second panel gives results when CEP referrals are included in

232 

the employment equation, and the final panel considers the effect of including both age 

and CEP referrals in the employment equation as well as the participation equation. 

Table A2.6 in the appendix shows the effect on the weighted model of changing the 

specification by including age in the employment equation. The first column in the first 

new results panels in Table 7.2 gives the lack of results for the unweighted equation – it 

proved impossible to estimate the unweighted equation with age in the employment 

equation. This is interpreted as indicative that because the selection arising from data loss 

is unaccounted for, there is misspecification when age is included in the unweighted 

equation. In the second column of this panel is shown the weighted Heckman bivariate 

probit results where age is in the employment equation and participation equation, but 

CEP referrals remains as an exclusion restriction. The weighted specification is estimable. 

However, age does not have a reasonably sized coefficient nor is it statistically significant 

in the employment equation. The size of the SYETP coefficient is slightly affected by 

this alteration to the specification – with the size increased, although remaining 

statistically insignificant at conventional test levels. 

Now, the same form of specification change is shown in panel 2 of Table 7.2, with CEP 

referrals dropped from the exclusion restriction and included in the employment equation 

as well as the participation equation. Again, Table A2.7 in the appendix shows the effect 

on the weighted model of changing the specification by including CEP referrals in the 

employment equation. The first column of the second panel again shows no results for 

the unweighted model, as again this specification proved impossible to estimate for the 

unweighted data. In the second column is shown the weighted Heckman results where 

CEP referrals are in the employment equation and participation equation, but age remains 

as an exclusion restriction entered only in the participation equation. The consequences 

are similar to those found for age – the equation is estimable, and the coefficient of CEP 

referrals is not of reasonable size and it is not statistically significant, but the change to 

specification raises the size of the SYETP coefficient although there is no gain in 

statistical significance.

233 

Finally, the third panel of new results in Table 7.2 considers reintroduction of both age 

and CEP referrals to the employment equation, with no exclusion restriction operation. 

Once more, Table A2.8 in the appendix shows the effect on the weighted model of 

changing the specification by including both age and CEP referrals in the employment 

equation. The results are very similar to those already discussed. The unweighted 

equation is inestimable in this specification, and the same interpretation is given as before. 

Again, although introducing these variables to the employment specification raises the 

size of the SYETP coefficient it does not gain statistical significance, and neither variable 

age or CEP referrals is of significant size or statistical significance. 

Overall, the results shown for varying the specification of the exclusion restriction in both 

the weighted and unweighted data indicate a number of helpful conclusions. 

In the weighted data, the alteration of the exclusion restriction shows that age was not 

particularly important to the exclusion restriction, but that it had no independent effect in 

the employment equation specification. However, including CEP referrals is likely to be a 

mis-specification as it has no independent effect in the equation and also dramatically 

reduces the SYETP coefficient. On the other hand, in the unweighted data, age and CEP 

referrals provided a useful exclusion restriction, and accounted for selection in the data. 

Once selection due to sample reduction from non-response, sample attrition, and sample 

design effects was accounted for using the weights, the selection problem in the data 

appears to have been reduced. The hypothesis tests indicate that the weighted equation 

does not have evidence for selection. It is further concluded that the original Heckman 

specification was not greatly improved by any of these specification changes, as 

evidenced by the only slightly lower AIC. The goodness of fit assessment shows not 

strong basis for preferring one model over the other. The SYETP coefficient increased in 

size slightly but there was no improvement in statistical significance. 

There is a suggestion that the unweighted model is particularly sensitive to the variables 

in the exclusion restriction, since only when they are excluded is the equation estimable. 

The weighted equation is more robust to changes in these variables, but it is posited that

234 

this is mostly because there is no substantive selection to account for once weighted, as 

evidenced by the hypothesis tests. This interpretation suggests that the sample attrition, 

which affected the profile of characteristics including SYETP, may have acted as a form 

of selection that the bivariate probit incorporated via selection into the program. As 

section 5.6.1.1 showed, SYETP participation was associated with the data loss. As the 

bivariate probit historically has been associated with applications where the data loss 

from non-response has been accounted for in this way, this is not unreasonable. The 

serious difficulties in obtaining an estimable specification for the bivariate probit are 

highlighted by the failure of estimation for each of the alternative unweighted 

specifications put forward. This indicates that the search for a suitable estimable 

specification amongst plausible alternatives can be arduous. This can be a difficulty for 

instrumental variables models, of which the Heckman bivariate probit is an example due 

to the use of the exclusion restriction.

235 

Table 7.2 summary of changes to exclusion restriction of Heckman specification 

Model of ever 

employed 


Original results Panel 1 Panel 2 Panel3 

Add age to employment Add CEP referrals to Add both age and CEP 

equation 

employment 

referrals to employment 

Replication 

results 

Replicated 

equation 

weighted 

for all 

attrition 

No weights 

weighted 

for all 

attrition 

equation 

No weights 

weighted 

for all 

attrition 

equation 

No weights 

weighted 

for all 

attrition 

Observations 1283 1283 1283 1283 1283 1283 1283 1283 

SYETP 1.596** 0.933 Not 1.06 Not 0.32 Not 0.38 

estimable 

estimable 

estimable 

(2.85) (0.79) (0.77) (0.23) (0.25) 

Rho=-1 Rho=-1 Rho=-1 

Age coefficient 

0.01 0.00 

in employment 

equation 

(0.30) (0.17) 

CEP referral 

-0.03 -0.03 

coefficient in 

employment 

equation 

(-0.39) (0.40) 

Estimated rho -0.75 -0.21 -0.28 0.14 0.11 

Wald test of 0.34 0.08 0.11 0.41 0.02 

Rho=0 (chi2 

(1) statistic) 

Log likelihood -875.96 -858.74 -858.68 -858.64 -858.55 

LR chi 2 (df) 159 (118) 

396.79 

(118) 

429.85 

(119) 

443.03 

(119) 

436.99 

(120) 

457.81 

Akaike 1.55 1.52 1.52 1.52 1.53 

Information 

Criterion 

Coefficient is reported with absolute value of t statistic in brackets. 


zero. It is defined as LR = 2 (log likelihood M full – 2 log likelihood M intercept ). The degrees of freedom (df) of 

this chi squared distributed statistic are equal to the number of constrained parameters i.e. the number of 

coefficients being tested.

236 

7.2 Varying the Propensity Score specification, effects on the match 

The specification of the propensity score estimated by probit is vital to the propensity 

score matching outcome. Some variation to this specification is now examined. In this 

section, the variable CEP referrals is excluded from the model for the propensity score, 

and the effect on the matching outcome examined. Additionally, a more concise 

specification for the probit is estimated, using only those variables thought to have most 

plausible economic influence on SYETP participation and employment. The combined 

weighting for sample reduction due to attrition and non-response and survey design is 

applied in all models, with the weighting protocol for the propensity score matching as 

shown in the earlier chapter. 

7.2.1 Propensity score matching and the effect of excluding CEP referrals 

Recall that the originally estimated propensity specification was designed to be as similar 

as possible to the Heckman modelling of Richardson (1998), as the aim had been to 

discover what the result would have been if propensity score matching had been used. It 

was earlier noted, that at least one variable in the propensity score then estimated might 

not satisfy the requirement that it influence both employment and participation in SYETP: 

namely CEP referrals. 

Table 7.3 shows the full results for the weighted probit when the variable CEP referrals is 

excluded from the model. The first column shows the former results with the original 

specification, while the second column is for the specification when CEP referrals is 

excluded. The predicted fit of the model, calculated as before 160 , shows 85 per cent of 

cases are predicted correctly. Thus the predictive power of the model remains acceptable, 

although lower than for the former weighted specification. Other scalar fit measures 

presented all indicate the new model to be similarly acceptable as the former model. Most 

changes to the coefficients and t statistics are very slight. Only the variable private 

160 A fitted probability exceeding 0.5 is taken to indicate a predicted response to the survey; these predicted 

responses are compared to the actual participants/non-participants in SYETP to check which cases the 

model correctly predicted.

237 

schooling that was statistically significant in the former model changes with a move to 

non-significance at conventional levels when CEP referrals is excluded. 

The distribution of the estimated propensity score prior to matching is shown in Table 7.4. 

It is useful to compare this distribution to that found for the weighted results using the 

original specification (see Table 6.5 in the previous chapter). The distribution of the 

estimated propensity for those in the SYETP treatment group appears only a little 

changed by the altered specification. In the SYETP treatment group, the limits of the 

distribution are very similar to those for the former specification, with only very small 

adjustments to the size of the largest and smallest observed propensities. The mean 

propensity for the SYETP treatment group is also very similar; however the median had 

fallen in size from roughly 0.143 to 0.135, which would indicate a slight shift down in the 

central peak of the distribution. The standard deviation of the propensity for the SYETP 

treatment group is hardly changed by the new specification. The propensity score 

distribution for the comparison group is also only changed slightly overall. The greatest 

effect appears for the lower tail of the distribution for the comparisons, where the 

smallest estimated propensities have greater size in the altered specification. The mean 

propensity, median and standard deviation for the comparisons remains very similar to 

that found for the former specification.

238 


matching, exclude CEP referrals 

Coefficient, (t statistic) Original specification Original specification, 

Don’t include cep referrals 

Model of SYETP participation Model of SYETP participation 


(0.52) (0.51) 

Age at 1984 survey -0.08 -0.08 

(2.87)** (2.90)** 

Married 1984 -1.03 -1.04 

(3.91)** (4.02)** 

Children 1984 0.34 0.32 

(0.85) (0.81) 


(0.14) (0.16) 


(1.96)* (2.04)* 


(0.78) (0.78) 

Other ethnic minority -0.00 0.00 

State interviewed in 1984 (0.00) (0.02) 


(0.58) (0.46) 


(0.62) (0.68) 


(0.63) (0.57) 


(2.18)* (2.05)* 


(1.04) (1.06) 


(1.09) (1.12) 


Highest qualification in 1984 (2.25)* (1.83) 

Degree/diploma -0.02 -0.04 

(0.11) (0.17) 


(0.38) (0.41) 


(0.24) (0.24) 


(2.19)* (2.14)* 


(1.05) (0.99) 

Year 9 of school or less -0.00 0.00 

(0.02) (0.01) 


(1.37) (1.32) 

< 1 year -0.08 -0.09 

(0.47) (0.52) 

2 years 0.05 0.05

239 

(0.25) (0.21) 

3 years + -0.50 -0.52 

(2.13)* (2.24)* 

CEP referrals 0.13 

(1.75) 


unemployment 

0.33 0.33 

(2.40)* (2.42)* 

Work limited by health -0.66 -0.64 

Family background (2.91)** (2.76)** 


(2.39)* (2.33)* 


(3.34)** (3.35)** 


(1.61) (1.57) 


(1.25) (1.28) 


(0.91) (0.65) 


(0.56) (0.44) 


(1.06) (1.12) 

Sexist 0.35 0.31 

(1.26) (1.14) 


Fathers occupation when resp. 14 (1.54) (1.53) 


(0.79) (0.73) 

Labourer -0.06 -0.06 

(0.19) (0.21) 


(0.46) (0.49) 

Sales -0.23 -0.23 

(0.66) (0.66) 


(1.02) (1.01) 


-0.17 -0.17 

(0.66) (0.66) 


(1.31) (1.34) 


-0.22 -0.21 


Mothers occupation when resp. 14 (1.59) (1.55) 


(1.24) (1.25) 

Labourer -0.05 -0.03 

(0.16) (0.12) 


(1.80) (1.79) 

Sales 0.21 0.25 

(0.76) (0.90) 


(0.46) (0.58)

240 


-0.31 -0.31 

(1.10) (1.09) 


(0.57) (0.55) 

Mother post-school qualification 0.22 0.21 

when resp 14 

Religion brought up in (1.26) (1.21) 

Catholic -0.01 0.00 

(0.05) (0.03) 


(1.29) (1.20) 


(1.19) (1.18) 


(0.21) (0.15) 


(0.56) (0.53) 


(0.84) (0.72) 

Constant 0.44 0.47 

(0.65) (0.70) 



LR chi 2 (59) 161 131.99 118.83 


0.1572 0.1528 

Akaike Information Criterion 0.56 0.57 

Robust z-statistics in parentheses* significant at 5%; ** significant at 1% 





162 

This measure of fit is also known as the likelihood ratio index. It compares the full model of parameters 

(Mfull) to a model with just the intercept (Mintercept). It is defined as R2 = 1 – (log likelihood Mfull / log 

likelihood Mintercept). The value of Mcfadden’s Pseudo R2 increases as new variables are added.

241 

Table 7.4 Summary of distribution of propensity, exclude CEP referrals 

Distribution of estimated propensity for SYETP treatment group 


1% .0096799 .0080714 

5% .0209195 .0096799 

10% .0391096 .011608 Obs 104 

25% .0818465 .0178796 Sum of Wgt. 104 

50% .1352024 Mean .1653081 


75% .2308325 .4278012 

90% .3124363 .456956 Variance .0134977 

95% .4151957 .4614879 Skewness 1.059129 

99% .4614879 .6016818 Kurtosis 4.17753 

Distribution of estimated propensity for comparison group 


1% .0003765 .0000136 

5% .0025803 .0000267 

10% .005414 .0000539 Obs 1179 


50% .0492093 Mean .0743632 


75% .1044401 .4422829 

90% .1755594 .4788667 Variance .0063036 

95% .2357188 .4821222 Skewness 2.031504 

99% .3915129 .5711446 Kurtosis 8.35458 

The new matching results are shown in column 2 of Table 7.5. To better facilitate 

comparison, the former results with CEP referrals included are shown in the first column. 

The matching results are now compared, to show the effect of removing CEP referrals 

from the specification. The employment effect falls slightly in size, from eight percentage 

points to six, and statistical significance drops very low. The number of common support 

cases discarded from the SYETP treatment group does not change. However the number 

of SYETP matched falls slightly, and the number of comparison cases used to match to 

them also falls, with more comparison cases used with replacement than before. The 

mean difference in the propensity scores after matching is of the same magnitude as 

formerly, however the standard deviation is now slightly larger. The mean standardized 

bias has risen from 11.04 to 16.15. Overall, the efficiency of the match is judged to be 

poorer than before.

242 

The magnitude of the employment effect falls, but only slightly. The dramatic changes to 

the statistical significance of the employment effect when CEP referrals is dropped might 

be attributed to the relatively large fall in the number of comparison cases used to match 

against the SYETP. This has a role in reducing the efficiency that in turn causes the fall 

in statistical significance. However, further discussion of the results is now deferred until 

after the estimation of a new specification. 

Table 7.5 Matching results, Single nearest neighbour with replacement, within caliper, 

weighting the propensity with combined weights: vary specification 

Match with 

caliper width 

0.001 

Original 

specification 

Match with 

caliper width 

0.001 

Original 

specification, 

Don’t include 

cep referrals 

Match with 

caliper width 

0.001 

New reduced 

specification 

Difference in employment 163 for 0.08 0.06 0.18 


t statistic 1.57 164 0.53 4.72** 

Total SYETP available 104 104 109 

Number of SYETP matched 87 85 104 

% SYETP matched 84% 82% 95% 

Number of comparison which satisfy 80 75 93 



1 73 68 83 

2 7 5 9 

More than 2 2 1 

Mean difference in propensity score 0.0002 0.0002 0.0002 



Standard deviation 0.0002 0.0003 0.0002 

Mean standardized bias 11.04 16.15 14.48 

Common support cases dropped from 

syetp before matching 

1 1 0 


Weighting protocol: weight propensity, weight the match using the treated weight only. The weighted mean 

bias is calculated using svymean in Stata. * significant at 10 % l.o.s., ** 5% l.o.s. 


164 Probability of (0.22).

243 

7.2.2 Propensity score matching and the effect of reduced specification 

Augurzky and Schmidt (2001) argue that although all available variables that rule the 

selection process are usually included in the participation equation, it may be better to 

remove variables of lesser importance from the specification. Their analysis showed that 

only including variables that were strongly significant could help combat the unnecessary 

effort of trying to balance these variables, which they found came at the expense of 

balance of the most relevant variables. In light of this, variables with low significance, or 

with only a theoretically minor role in employment, are candidates for exclusion from the 

probit for participation. Heckman, Ichimura and Todd (1997) also suggest that it is useful 

to compare the matching results from a reduced model of participation to that of the fuller 

model, in order to better assess the different approach to modelling entailed. 

In light of this a reduced specification of the probit for the propensity is proposed. The 

new matching results are shown in column 3 of Table 7.5, already shown. Only those 

variables with a potentially strong role in both employment and programme participation 

are included in the probit. Labour market outcomes for programmes are most often based 

on gender, age and unemployment experience which are included in the reduced model. 

The human capital effects of education, represented by highest qualification, and work 

experience are added. Marital status, children and partner’s labour market behaviour are 

included as variables that usually influence labour supply. Health, ethnicity and location 

are other factors commonly entered as constraints to labour supply or demand. The 

variation by location (state) was found to strongly influence SYETP participation [see 

section 2.2.6]. The rural/urban location is based on background prior to programme entry, 

retained from the former specification, because it is highly related to location in 1984 and 

later surveys. But this variable might act as a better instrument for the influences of 

personal background on labour market behaviour because location for young people is 

mostly due to parental choice. All of these variables form a subgroup of the fuller original 

specification. Note that CEP referrals is not included. Only one variable that was 

formerly statistically significant in the full model was excluded: nature of schooling 

(private, government, overseas, Roman Catholic). All other variables excluded from the 

reduced model were not statistically significant in the estimated probit.

244 

Table 7.6 shows the results of the new specification of the probit including only these 

variables. The estimated coefficients that are statistically significant are compared to 

those in the original specification. Age and marital status remain statistically significant 

at conventional levels; however partner’s employment loses statistical significance. The 

location of Western Australia/Tasmania retains a positive influence on participation. This 

is consistent with the administrative data that showed Western Australia to have a much 

higher SYETP placement rate than other states [see section 2.2.6]. Qualifications lose 

their impact on SYETP participation in the reduced form model, whereas in the original 

model year 12 schooling had a positive impact. A longer qualifying spell of 

unemployment still has a positive impact, and past experience of a job held for more than 

3 years still has a negative impact on SYETP participation. Other city or country town 

kept their negative impact on SYETP participation in the reduced model. The only key 

change for variables included in both the full and reduced models is that partner’s 

employment and highest qualification of year 12 lose statistical significance in the 

reduced form. 

The predictive power of the probit is 92 per cent. This is the same power as the original 

model. Thus the excluded variables do not change the predictive performance of the 

probit. This measure of predictive power is only very rough however. Other measures of 

fit indicate the reduced specification to be slightly better, with the AIC lower at 0.53 

compared to the former models values of 0.56 and 0.57 (note the sample size has 

increased as certain variables dropped had missing cases). 

The propensity scores, for both SYETP participants and comparisons, from the new 

specification are shown in Table 7.7. These are compared to those found earlier [see 

section 6.2.2]. This change to the specification has quite a strong impact on the range of 

the distribution. Figure 7.8 shows the histograms for the propensities estimated. Both 

propensities for SYETP and the comparison group now have a range less than 0.4. The 

distributions have been shifted towards zero. The upper tails have generally disappeared, 

with the remainder of the distribution shaped similarly to the distribution from the

245 

original model, but with the peaks at lower points in the distribution. The central peak of 

the SYETP propensities has been changed most. The mean of the propensities for the 

treatment SYETP has been shifted much lower down than for the original specification, 

while the mean of the comparisons has hardly changed. Figure 7.9 shows the kernel 

densities of the SYETP and comparison distribution of propensities overlaid. The peak of 

the SYETP is raised higher than before. This is likely due to the bunching effect of 

reducing the range. The peak of the distribution of propensities for the comparison is 

lower than before, but the upper tail is slightly fatter, so the reduction in range for the 

comparisons has a more diffuse impact. The change to the specification has brought the 

peaks of the SYETP and comparisons closer in height than before. The different locations 

of the propensity peaks for the SYETP and comparisons indicate that the participation 

model is still producing an adequate model that distinguishes participation. 

The effects of the new specification on the matching results are shown in Table 7.5. The 

number of SYETP matched is raised to 104. No cases have been dropped due to 

application of the common support rule. The number of cases of comparisons matched to 

the SYETP is higher than before at 93 cases, while more are also used with replacement. 

The mean difference in propensities is no different to that found before. 165 The mean 

standardised bias after matching is now worse than before however, as it has risen from 

11.04 to 14.48. However, in comparison to the other specification change, where CEP 

referrals was excluded from the original model, shown in column 2 Table 7.5, the bias 

has improved. As the bias was much worse where CEP referrals was excluded than the 

original model, CEP referrals seems to have helped balance the bias. 

D’Agostino and Rubin (2000) consider the issue of missing data in item non-response, 

and note that most propensity score methods are based on complete data. They suggest 

that missing-ness needs to be controlled in PSM. This suggests the new specification may 

also benefit from the reduction in missing data due to item non-response, which may help 

explain the great gain in size and statistical significance of the measured employment 

effect. In the new specification, only the proportion of time unemployed contains missing 

165 Although rounding hides that the mean is very slightly smaller at 0.00016.

246 

information, whereas in the old specification family background variables contributed to 

a large number of item non-response cases. This raises the number of observations usable 

in the new specification. The number of SYETP cases rises slightly as a result of the 

change in specification, as the item non-response is resolved. The percentage of SYETP 

matched rises from 85 per cent in the old specification to 95 per cent in the new 

specification. 

7.2.3 Further discussion and conclusions 

Lechner (2001) p23 also examined the effects on propensity score matching of reduction 

in the covariate set, and the results were found to be substantially different, but not 

monotone, so that the conclusion was that it is not necessarily better to control for more 

variables. Rosenbaum and Rubin (1985b) p115 noted that in practice nearest neighbour 

matching algorithms are subject to the trade-off between finding matches for all treated 

units and obtaining matched treated-comparison pairs that are extremely similar. The 

failure to match all treated units can give incomplete matching bias, while inexact pair 

matching can result from greater differences in matched pairs. They advocated the use of 

complete matching in order to avoid incomplete matching bias. It was noted that 

incomplete matching can lead to loss of efficiency. 

In comparing the original specification to the reduced specification, it appears that 

reduction of the incomplete matching bias leads to a dramatic gain in the employment 

effect of SYETP, and the statistical significance is much greater. The percentage of 

SYETP matched rises from 85 per cent in the old specification to 95 per cent in the new 

specification. These results are in accordance with the bias and inefficiency arising from 

incomplete matching, referred to by Rosenbaum and Rubin (1985b). However, some of 

this is also attributable to effects of missing data on PSM suggested in D’Agostino and 

Rubin (2000). At the same time as these gains, the standardized bias is not much worse, 

an indication that the incomplete matching bias is of a lesser degree. As the reduced 

specification had fewer variables, it appears that trying to balance the included variables 

is very difficult with these data. It is possible that this might be interpreted as evidence 

that selection on unobservables is more consistent with the data. If this belief is

247 

maintained, then the Heckman bivariate probit modelling results give a modelling result 

in harmony with the role of unobservables in SYETP participation. But while this may be 

true, the form which the unobservables take is also highly restricted in the Heckman 

bivariate probit modelling, and the reasonableness of this must in turn be considered. 

Rosenbaum and Rubin (1985b) p 109 also note that the assumption of strongly ignorable 

treatment (CIA) needs to be consistent with the data and the causal mechanism, through 

which the SYETP treatment is thought to operate to produce employment effects, and 

without which the matching results are biased. CEP referrals is questioned as potentially 

violating the CIA requirements, because of the use of this variable for the exclusion in 

Richardson (1998). 166 It was argued earlier that CEP referrals credibly did not affect 

employment independently once SYETP participation was accounted for. The effects of 

the CEP referrals for the replicated Heckman modelling in the same data have been 

shown to be strongly diminished by accounting for attrition with weighting. The 

theoretical view against the inclusion of CEP referrals in the weighted model for the 

propensity can however also be argued on the basis of the evidence of Wielgosz (1984) 

and Aungles and Stewart (1986), discussed in section 2.2.6. If this conviction is upheld, 

then the sensitivity results where CEP referrals are excluded, or the reduced specification, 

provide matching results consistent with this view. 

In conclusion, the sensitivity analysis shows that the employment effects found are 

subject to the specifications and modelling assumptions adopted. This was true for both 

the Heckman and PSM modelling approaches. In an overview of the various results 

gained which disregards the magnitude of point estimates, it can be determined that no 

negative employment effects were found for SYETP, even in sensitivity analysis. 

Friedlander et al. (1997) p1819 note that in debates about non-experimental evaluations 

there is no clear-cut way of determining which modelling assumption is valid. Within the 

range of alternatives explored, and as can be determined within the limits of the ALS data, 

the direction of employment effect is robustly positive. Statistical significance in this 

166 This was checked by modelling the univariate probit for employment, where these variables were not 

found significant.

248 

analysis was however strongly affected by the small sample size and reductions to 

efficiency from various assumptions. As such the sensitivity analysis indicates that it 

would be useful to provide a form of ‘confidence interval’ for model selection in 

evaluation results, showing the variation employment effects are subject to for changes to 

key modelling assumptions. Provision of such a confidence interval can allow for the 

statistical modelling uncertainty, without which evidence for the employment effects can 

be masked. This can be particularly helpful when there is no clear evidence favouring one 

model and it’s assumptions over the other. In final consideration of the evidence for 

SYETP found in this analysis, the conclusions of Heckman et al. (1999) are deferred to: 

“…every estimator relies on identifying assumptions about the 

outcome and participation processes. When a particular estimator 

is applied to data, where those assumptions fail to hold, bias 

results. This bias can be substantial. When different estimators are 

applied to the same data, the estimates they produce will vary 

because at most one set of underlying assumptions is consistent 

with the data. Only if there is no problem of selection bias would 

all estimators identify the same parameter.” (Heckman et al. 

(1999): 2007) 

As the limitations of the non-experimental data used here bound the extent of conclusions, 

it is perhaps the evaluation policy which needs to be addressed. However, debating the 

role of experimentation in social policy is beyond the scope of this study, and inevitably 

becomes a political and ethical decision.

249 


matching, new specification 

New specification 

syetp 


(0.17) 


(2.52)* 

Married 1984 -0.91 

(2.91)** 


(1.20) 


(1.52) 


(1.07) 

Other ethnic minority -0.08 

(0.37) 

Victoria at 1984 survey 0.21 

(1.41) 

Queensland at 1984 survey 0.01 

South Australia/Northern Territory at 

1984 survey 

(0.04) 

-0.03 

(0.17) 

0.35 

Western Australia/Tasmania at 1984 

survey 


Degree/diploma -0.16 

(0.74) 


(0.78) 

Other Post-School qualification -0.03 

(0.11) 


(1.74) 


(0.80) 


(0.27) 


(1.87) 

< 1 year -0.16 

(0.89) 

2 years 0.01 

(0.05) 

3 years + -0.62 

(2.79)** 

Proportion of time Pre-June 1984 spent 

in unemployment 

0.34 

(2.43)* 


(2.84)** 

Other city before aged 14 -0.28

250 

(1.75) 


(3.16)** 


(1.97)* 


(0.84) 

Constant 0.09 

(0.15) 



LR chi 2 (27) 167 80.04 


0.1029 


Robust z-statistics in parentheses* significant at 5%; ** significant at 1% 





168 




251 

Table 7.7 Summary of distribution of propensity, new specification 

Distribution of estimated propensity for SYETP treatment group 


1% .0206492 .0041307 

5% .037368 .0206492 

10% .0438178 .0270285 Obs 109 


50% .1295373 Mean .1298514 


75% .1679348 .280174 

90% .2276545 .2934951 Variance .0049677 

95% .2737046 .2991401 Skewness .4874995 

99% .2991401 .3021699 Kurtosis 2.585342 

Distribution of estimated propensity for comparison group 


1% .000886 .0002578 

5% .0043483 .0003094 

10% .0096206 .0003152 Obs 1280 


50% .059004 Mean .0743514 


75% .1078141 .3332027 

90% .1600628 .3395213 Variance .003906 

95% .1981402 .3596305 Skewness 1.224251 

99% .2763645 .3648924 Kurtosis 4.590139 

1 

1 

Fraction 

.5 

Fraction 

.5 

0 

0 

0 .5 1 


propensity score for treated(SYETP=1) 

0 .5 1 


propensity score for control(SYETP=0) 

histograms of estimated propensity scores prior to matching 

Figure 7.8 Histograms of the propensity scores for the new specification.

252 

Treated propensity scores, Epan Untreated propensity scores, Ep 

8 

6 

4 

2 

0 

0 .1 .2 .3 .4 

Pr(syetp) 

kernel densities of propensity scores of the treated vs untreated 

Figure 7.9 Kernel densities of the propensities estimated for new specification

253 

8: Summary and Conclusions 

The underlying motivation for this work is the desire for a closer examination of the 

Australian wage subsidy SYETP. The main theoretical rationale for wage subsidies is 

that they stimulate a gain in employment. However the review of the theoretical models 

of wage subsidies shows they cannot provide unambiguous proof of an increase in 

employment. The importance of empirical evaluation of wage subsidies in part stems 

from this uncertainty. The central question remains: do wage subsidies improve 

employment prospects for the individual? 

Microeconomic evaluation evidence can demonstrate whether SYETP has led to a gain in 

employment for participants. While the main evaluation interest is the programme 

influence on job entry, the processes of participation and job entry can involve both 

observable and unobservable components that can introduce selection bias. An evaluation 

of the program effect needs to account for this selection. Two evaluation methods are 

explored here – the Heckman selection bivariate probit model, and matching methods, in 






The review of recent overseas evidence for wage subsidies shows a variety of positive 

and negative impacts. Two key themes are identified. The empirical ambiguity as to 

employment gains from wage subsidies remains unresolved. Contributing to this, in many 

studies the non-experimental evaluation methods insufficiently test the assumptions, 

alternative methods, and data issues. It was noted that meta-analysis might be more 

satisfactory in accounting for the deviations in evaluation evidence, since it was hard to

254 

separate these from the wide dissimilarities in the differing data, methods, environments, 

subsidy conditions, and other sources of variation. 

The appraisal of micro-evaluation evidence in Australia also shows the empirical 

evidence for wage subsidies is not well established and suffers from similar deficiencies. 

SYETP existed with a broad remit from 1976 to 1985. In the early 1980’s, the ‘Adult 

Wage Subsidy Scheme’ (AWSS), and ‘General Training Assistance On-Job-Training’ 

(GTA-OTJ) briefly coexisted with SYETP, and then from 1985 to 1996, Jobstart replaced 

SYETP. As well as being short-lived, both AWSS and GTA-OTJ were very small in 

scale, with very little expenditure and few placements and GTA-OTJ was uniquely 

discretionary in targeting making the eligible group difficult to define. The AWSS was 

never evaluated and GTA-OTJ was only evaluated relative to other programs. The more 

recent Jobstart was evaluated several times, mostly using exact matching but with very 

limited variables, and data on comparisons drawn from surveys but no accounting for 

non-response. Before 1980, evaluation of SYETP was not published and possibly did not 

take place. Several evaluations of SYETP undertaken by Stretton (1982, 1984), Baker 

(1984), Rao and Jones (1984), and Richardson (1998) were appraised in detail. All found 

positive effects of SYETP on employment. All used survey data, but although most 

discussed the potential for non-response and even modelled it, none acceptably accounted 

for it in their evaluation estimations. Some evaluated SYETP relative to other programs, 

such as GTA-OTJ and other training programs instead of a non-participant comparison 

group, although the eligible groups do not always clearly overlap. Various forms of 

regression modelling, such as logit, probit or ordered probit, were used, however only 

Richardson (1998) accounted for selection using the Heckman selection model of SYETP 

participation and employment. The inadequacies of past analyses of SYETP contributed 

three themes to address: suitable modelling of selection to account for the influence of 

observables or unobservables, dealing with non-response in the observational data, and 

appropriate control for the differences between the SYETP and comparison groups. 

The evidence for the economic environment and operation of SYETP provided a useful 

understanding of the issues affecting the evaluation of SYETP. During the long period

255 

over which it ran, the review makes it clear SYETP underwent repeated adjustment, with 

the eligible groups, amount and length of subsidy, political setting, and economic 

conditions constantly changing. This gave perspective into the dynamic aspects of labour 

market programmes, which is often ignored. Micro-evaluations are essentially static, so it 

is important to maintain awareness that they provide only a snapshot of the employment 

effects relevant to a particular macroeconomic setting. 

The summary of the economic context during the period of our empirical studies, 1984- 

1986 provided characterisation of the Australian youth labour market that SYETP 

addressed. Youth minimum wages were institutionally set by the award wage system, and 

were lower relative to adults. Youths earned about 50-60 per cent of adult wages. 

Although youth unemployment was high relative to adults, this was countered by rising 

school participation, and employment. The regional and rural/urban contrasts in Australia 

contributed to wide variation in the incidence opportunities and unemployment. The CES 

administered SYETP, but faced a limited supply of registered vacancies that were 

generally for lower-skilled occupations. 

Other factors, which have been reviewed here, would also have had a role in affecting the 

functioning of the SYETP subsidy, including lower relative youth labour costs, elastic 

demand for youths relative to adults, and substitutability of formal education for on the 

job training in human capital. There were minimum wages, which would give 

institutionalised inefficiency of the labour market. Finally, the value of SYETP payments 

to employers was greater than the unemployment benefits to the eligible unemployed 

youths, and in turn participants received Award wages that were greater than benefit 

payments (usually substantially greater). The theory of wage subsidies outlined in 

Chapter 1 indicates how these might influence the potential for employment gains from a 

wage subsidy targeted at youths. Together, these features favour the suggestion that 

SYETP might have given employment gains to those eligible. However, it is impossible 

in this context to determine whether these features were caused by SYETP, or were 

merely the backdrop influencing SYETP operation.

256 

A series of empirical studies was presented, forming an evaluation of the employment 

gains attributable to SYETP. The central aims of the analysis were threefold: to examine 

the results of the Heckman selection modelling against those of propensity score 

matching, and so relax the specification from fully parameterised to semi-parametric; to 

account for attrition and non-response in the data; and to compare the employment 

impact of SYETP under the different modelling assumptions. The analysis has produced 

new results for the employment gains of SYETP. The new results account for potential 

bias due to survey attrition, and provide more evidence as to the robustness of the 

employment gains to varying the modelling assumptions. The new evaluation evidence 

for SYETP thus improves the knowledge relating to the employment gains for SYETP. 

The Richardson (1998) evaluation using the Heckman selection probit was first replicated 

successfully. The propensity score matching (PSM) methods were applied using the 

nearest-neighbour-within-caliper-with-replacement protocol. The choice of caliper width 

was explored by the application of several plausible caliper widths. To examine the 

sensitivity of the results to the choice of Propensity Score Matching protocol, all-incaliper 

matching was also applied, and it was concluded that the results were not strongly 

affected by the choice of matching protocol. The PSM results reduce the size and 

significance of the employment effect found when compared to the Heckman modelling. 

It was concluded that potential attrition bias might affect the results by introducing 

selection bias. 

Sample reduction in the Australian Longitudinal Survey data was explored. The effects of 

survey design, initial non-response, and subsequent panel attrition were examined and 

found to have substantial impacts on the participant and comparison group characteristics, 

introducing potential bias. This was then accounted for, and the impact on evaluation 

assessed. Weights were constructed to deal with attrition, non-response and complex 

survey design in the ALS data. The weighted results were found to be smaller and to have 

low statistical significance. Accounting for attrition in both the Heckman bivariate probit 

and PSM models reduces the size of the employment effect found, and results have low 

significance. However, after accounting for attrition, the employment effect found using

257 

the Heckman and PSM methods were very similar. It is concluded this is due to the 

removal of selection effects due to attrition. Sensitivity analysis explores alternative 

specifications for each model. The employment effects for the models were found to be 

susceptible to the specification changes. 

In light of past research for SYETP, this analysis provides a number of improvements. 

Propensity score matching is applied for the first time to the evaluation data. No past 

wage subsidy evaluation in Australia has applied this method, although exact matching 

has been applied. This evaluation then accounts for more potentially influential variables 

in the use of matching methods, accommodated within the propensity score matching. 

Selection due to survey attrition is accounted for with weights, at the same time as 

selection modelling to account for selection due to participation using the Heckman 

bivariate probit. Past SYETP analyses were only able to deal at most with one of these 

sources of selection, as shown in section 2. The role of attrition and data loss in 

Propensity Score Matching was also dealt with. Few papers account for sample reduction 

in Propensity Score Matching. The analysis of PSM and Heckman bivariate probit with 

weighting for attrition shows that both these methods produce strongly different measures 

of employment gains once attrition is accounted for. The dependence of the evaluation 

strategy on the assumption of selection relating to observable or unobservable variables is 

relaxed by considering employment gains in both the Heckman and PSM methods. 

The value of replication has been validated in this case. Having the original data, the 

replication of the sample and methods proved highly informative, revealing the important 

role of sample reduction, through attrition and sample selection. This finding concurs 

with the literature, where Smith and Todd (2000, 2003), Dehijia and Wahba (1998, 1999) 

also found key influences on the results arose from the data sample and approaches to 

data issues. The conclusions accordingly advocate the adoption of replication as an 

important initial point for research. 

Comparing the different models gives increased external validity to the research findings 

for SYETP employment gains. SYETP had positive employment impacts in all past

258 

evaluations, as shown in the review, although most of the results can only be considered 

with strong caveats, due to the inadequate methods employed. At least partially, due to 

the varying timeframes for the analyses, this gives evidence that SYETP gave 

employment gains in a dynamic context. 

In all variations, the research here found positive impacts of SYETP, even if some were 

not statistically significant, a difficulty influenced by the small sample and efficiency 

losses from some of the methods. Regardless of whether selection was based on the 

assumption of observables or unobservables, the impact remained positive. This 

contributes evidence of the robustness of the positive effect on employment for SYETP 

participants. 

The sensitivity of the specifications of both the bivariate probit and the propensity score 

used for the PSM were explored. In the bivariate probit this centered around the 

exclusion restriction, and in the propensity score matching the implications of the 

exclusion restriction for the comparative analysis were investigated. In the case of the 

bivariate probit, comparisons of the weighted and unweighted results found that the 

specification has great bearing on the results. An interpretation advanced here is that 

because sample attrition was confounded with the SYETP program treatment, the 

bivariate probit accounted for this with the modelling of the selection into the program. 

Hence, the strong differences in the outcomes of the weighted and unweighted analyses. 

There were serious difficulties in estimating alternative specifications in the unweighted 

data, indicating that the search for a plausible specification could be problematic. In the 

PSM, in line with recent contributions to the matching literature, the variants examined 

the restriction of the propensity to those variables expected on empirical or theoretical 

grounds to have significant relations with both SYETP participation and employment. 

The substantial changes to the results provide a useful illustration of the importance of 

the modelling assumptions, and indicate that the assumptions involved in matching are 

not simple. The results show that the statistical modelling uncertainty can be difficult to 

resolve without clear evidence of a strong basis for favouring one model over another, 

unless the goodness of fit assessment undertaken provides obvious resolution. It is

259 

posited that that such sensitivity analysis, comparing bivariate probit and PSM where the 

data can lend support to both models, should be provided to enhance evaluation results 

and provide a form of confidence interval for model selection, and so account for the 

statistical modelling uncertainty. 

This exploration has confirmed some relationships suggested in the literature and 

revealed some new insights into SYETP and wage subsidy employment effects. The 

research challenges orthodox evaluation methods by applying not one potentially 

appropriate selection approach, and underlying assumption about observables and 

unobservables, but both. The benefits are informed overview of the role of the 

assumption to the evaluation outcome. To the extent that the data allows both methods to 

be attempted, such as sufficiently extensive data on characteristics of employment and 

participation, as well as variables that can act as instruments, this allows the conclusion 

that this approach should be adopted wherever possible. Deliberative attempts were made 

to constructively account for data and modelling issues. The constraints of the nonexperimental 

data limit the conclusions for SYETP, as without a benchmark from 

experimental data, it is not possible to further choose between the model of the Heckman 

and PSM methods. Although unsatisfying to a small extent, the remaining limitations of 

this research can inform further work on the evaluation of labour market programmes 

such as SYETP.

260 

Appendix 1 Data appendix 

Table 1 Description of the data 

Description Derivation and details of construction Variable 

name in 

data 

SYETP participation 

indicator 

Derived from the weekly calendar files for the 4 survey years, which 

cover the survey ‘reference period’. Dummy identifier with code 1 for 

all those that have a spell on SYETP in the 1984 survey reference 

period after 3 June 1984 or in 1985 survey reference period up until the 

1985 interview date. All spells in the job calendar which are a job 

where there is a wage received were asked if they got the job through a 

government programme (SYETP, then later Jobstart), up to a 

maximum of 4 spells in each year had this information collected 

although up to 8 spells in a year had start and end dates. The reference 

period in the 1984 survey started 1 January 1984 OR the week in 

which entered the labour force if later. Reference periods in the 3 

surveys 1985-1987 started a week after the interview in the former 

survey year, and ended in the week of the current interview. The 

calendars correspond to roughly covering a yearly period. The 

treatment group, described by the SYETP variable, has differing 

programme participation start dates, as described by Richardson (1998) 

p5. 

SYETP 

Sex indicator Sex dummy, with values of 0 for male, 1 for female. female 

Marital status 

indicator, 1984 

survey 

Dummy with value of 1 if married or de facto, zero otherwise. Mar84 

State initial interview 

conducted in 1984 

Ethnicity indicator 

English language 

skills indicator 

Main area of 

residence before 

respondent age 14 

Main area of 

residence 1984 

survey 

Time varying marital status dummy, takes the value for marital status 

for each survey year, such as 1985 or 1986. Same construction as for 

mar84. 

Set of dummies. For each state dummy, value of 1 for the state. New 

South Wales/ACT, nswact; Victoria, vic; Queensland, qld; South 

Australia/Northern Territories, sant; Western Australia/Tasmania, 

watas; 

Ethnic origin, equals stated ethnic origin if born Australia; otherwise 

Asian if respondent or either parent born Asia, Other if not Asian and 

respondent or either parent born Middle East, Pacific Oceania or other 

(principally Africa, Latin America), European if not Asian nor Other. 

Set of 4 indicator dummies: aboriginal/Torres Strait Islander, roatsi; 

Asian, roasian; European, roeur; Other, rooth. 

Set of dummies for English proficiency with value of 1 for efl if 

English natural language, else egood if good, epoor if fair, poor or very 

poor . 

Set of dummies with value of 1 for capital city, cc14; other city, oc14; 

country town, ct14; rural area, ra14; overseas, os14. 

Set of dummies with value of 1 for capital city, cc84; other city, oc84; 

country town, ct84; rural area, ra84; 

mar 

Nswact; vic 

qld; sant; 

watas 

Roeur, 

roatsi, 

roasian, 

rooth. 

Efl, egood, 

epoor. 

Cc14, oc14, 

ct14, ra14, 

os14. 

Cc84, oc84, 

ct84, ra84.

261 


name in 

data 

Lived with parents at The prefix m indicate mother, the prefix f indicates father. mother not Mnpres14, 

respondent age 14 

Mother’s and 

Father’s occupation 

at age respondent 14 

if lived with them at 

14 

Mother’s and 

Father’s highest 

educational 

qualification at 

respondent age 14 if 

lived with them 

Attitude to working 

women 

Number of other 

children in family 

Religion 

Type of school last 

attended 

Highest overall 

qualification in 1984 

present, mnpres14; father not present, fnpres14 

The prefix m indicate mother, the prefix f indicates father. The 2-digit 

ASCO 169 codes, where dummies with value 1 for categories, for 

mother labourer, mlabo; plant operative, mplan; sales, msale; clerical, 

mcler; tradespeople, mtrad; not employed, mnemp; mmpp, for mother 

manager, professional or para-professional. For father labourer, flabo; 

plant operative, fplan; sales, fsale; clerical, fcler; tradespeople, ftrad; 

not employed, fnemp; fmpp, for father manager, professional or paraprofessional. 

The prefix m indicate mother, the prefix f indicates father. Mpsq and 

fpsq dummy where value of 1 for any post-school qualification 

including degree, trade/apprenticeship, other post-school qualification. 

Those with zero attended secondary school, primary school, no school. 

Based on questions that examine attitude to women in work. An 

underlying variable is created, where Sexist is an index between 0–7 of 

how many questions respondents agreed or strongly agreed with 

reactionary attitudes to women and work. ksink5p is then a dummy 

with value 1 for sexist greater than five respectively. An interaction 

variable ks5pfem interacts ksink5p with female. 

Total number of older and younger siblings (nsib). 

Religion brought up in as a set of dummies, with value 1 where Church 

of England, coe; Roman Catholic, cath; Presbyterian, pres; Methodist, 

meth; other Christian, othx; other religion, othrel; no religion, norel). 

Type of last school if went to school in Australia, dummies for 

government, lsgov; Roman Catholic, lsrcs; other type, including 

private, lspriv; lsos if last attended school overseas. 

Highest overall qualification in 1984 survey. Set of dummies, with 

value of 1 for degree, diploma/other cert Hq_dd84; 

apprenticeship/trainee/ATS, hq_tra84; other post-school, hq_oth84; 

years of school, year 12 hq12_84; years of school year 11 hq11_84; 

years of school year 10 hq10_84; hq9_84=1 if year school 9 or less. 

Time varying dummies for overall highest qualification. Takes the 

values as above, for each later survey year 1985, 1986. 

fnpres14 

Mlabo, 

mplan. 

Msale, 

mcler, 

mtrad, 

mnemp, 

mmpp; 

Fabo, fplan. 

fsale, fcler, 

ftrad, 

fnemp, 

fmpp; 

Mpsq, fpsq 

Ksink5p; 

ks5pfem 

nsib 

Coe, cath, 

pres, meth, 

othx, othrel 

Lsgov, 

lsrcs, 

lspriv, lsos. 

Hqdd84, 

hqtra84, 

hqoth84, 

hq12_84, 

hq11_84, 

hq10_84, 

hq9_84 

Hq_dd, 

hq_tra, 

hq_oth, 

hq12, hq11, 

hq10, hq9 

169 Australian Standard Classification for Occupation, a skill-based classification of occupations as defined 

by the Australian Bureau of Statistics ABS Catalogue number: 1221.0 ASCO First Edition which was used 

to code occupation data in surveys to May 1996.

262 


name in 

data 

Work limited by Work limited by health, dummy with value of 1 if work restricted by Health84 

health in 1984 health in type or amount. 

Time varying ‘Work limited by health’, takes the values as above, for health 

Partner’s 

employment in 1984 

Children in 1984 

Past work experience 

to 1984 

each later survey year 1985, 1986. 

Spouse employed if living in household, 1984 survey. Dummy with 

value 1 if employed, 0 otherwise. 

Time varying dummy for Spouse employed if living in household. 

Takes the values as above, for each later survey year 1985, 1986 

Own children living in household, 1984 survey. 

Also the interaction term ch84fem, where gender interacted with 

children female with child84. 

Time varying dummy. Takes the values as above, for each later survey 

year 1985, 1986 

Length of longest recorded job/business held up to the first interview 

in 1984. Includes jobs or self-employment spells. A set of dummies 

with value of 1 for work where, longj0, for less than 1 year; longj1 for 

1 year to less than 2 years; longj2 for 2 years to less than 3 years; 

longj3p for 3 or more years; longjno for never had a job/business. 

Spemp84 

spemp 

Child84, 

Ch84fem 

Child, 

childfem 

Longj0, 

Longj1, 

long j2, 

longj3p, 

long jno. 

Employment 

outcome 

Ever studying fulltime 

Referrals 

Other programme 

participation 

Proportion of time 

spent unemployed in 

1984 survey 

reference period, 

prior to June 1984 

Those not in FTED 

ALS List sample 

Data syntax editors 

Employed in an unsubsidised, non-government programme job, at any 

time during the 1986 wave. This includes ‘retained’ jobs that were 

initially subsidised during 1984/85, excluding the first 17 weeks of 

their duration. 

A dummy taking the value 1 if studying full-time in any wave up to 

that point, and also if ever studying full-time in the final wave. 

Referred to a Community Employment Programme place by the CES 

in 1984/1985 

Go on other government programme in 1984 or 1985, after 3 June 

1984. 

Government programme job in 1986 

Constructed from the calendar, where number of weeks unemployed to 

June 1984 is converted to a proportion of the time period, which is the 

reference period in 1984 survey. The reference period in the 1984 

survey started 1 January 1984 OR the week in which entered the labour 

force if later. 

This variable identifies panel entry and exit, and when they leave fulltime 

education, for example those in school or aged 25+ when 

interviewed in first wave. A value of zero means they are not observed 

to leave FTED, ‘84’ they had left FTED when observed in the 1984 

survey, and similarly for the later survey years. So those with value 

‘84’ have left FTED, and this is one of the exclusions used for the 

analytical selection – only those with enteryr=84 are used, ie. Only 

those who had left FTED. 

Mcrae, I.; Parkinson, G.; Woyzbun, L. (1984-1987) Australian 

Longitudinal Survey Waves 1 to 4, Level 1. Principal Investigators: 

Mcrae, I.; Parkinson, G.; Woyzbun, L.; Data collected by Roy Morgan 

Research Centre, Canberra. Ian Mcrae, Bureau of Labour Market 

Research, [producer], Canberra. Social Science Data Archives, The 

Australian National University, Canberra [distributor]. 

npjob18 

stever 

Cepref84 

gpoth 

Gpjob86 

upropjn 

enteryr 

SSDA 

study 

numbers 

377, 410, 

420, 491. 

The authors of the syntax used to the derive the variables were Lorraine Dearden, 

Alex Heath, Henry Overman, James Richardson, and the syntax they wrote takes the 

ALS original SPSS files through extensive STATA processing.

Appendix 2 Tables 

263

264 


probit replication of Richardson (1998) 

npjob18 

Coefficient 

(t stat) 

Marginal 

effects 170 

syetp 0.59 0.13 

(3.26)** (3.26)** 

Gender=female -0.41 -0.12 

(4.15)** (4.15)** 

Married 1984 -0.10 -0.03 

(0.51) (0.51) 

Children 1984 -0.25 -0.08 

(1.00) (1.00) 


(3.84)** (3.84)** 


(2.48)* (2.48)* 


(1.48) (1.48) 

Other ethnic minority -0.33 -0.10 


Victoria -0.05 -0.02 

(0.44) (0.44) 


(0.05) (0.05) 


(2.33)* (2.33)* 


(0.22) (0.22) 


(0.16) (0.16) 


(0.37) (0.37) 

Private school 0.53 0.12 

Highest qualification in 1984 (1.79) (1.79) 


(3.41)** (3.41)** 

Apprenticeship 0.42 0.10 

(1.97)* (1.97)* 


(0.35) (0.35) 


(0.01) (0.01) 


(2.62)** (2.62)** 


(1.79) (1.79) 

Longest job by 1984 none 0.02 0.01 

(0.11) (0.11) 

< 1 year 0.23 0.07 

(1.79) (1.79) 

170 Log likelihood for the probit gives Marginal effect is {δΦ (xb) / δx i }| x = µ , where Φ is the cumulative 

standard normal, µ= mean.

2 years 0.28 0.07 

(1.70) (1.70) 

3 years + 0.63 0.15 

(3.71)** (3.71)** 

Enter other govt programme -0.66 -0.22 

(5.55)** (5.55)** 

duration of Pre-June 1984 unemployment -0.45 -0.13 

(3.87)** (3.87)** 


Family background (3.07)** (3.07)** 


(2.15)* (2.15)* 


(0.63) (0.63) 


(1.25) (1.25) 


(1.09) (1.09) 


(1.64) (1.64) 

English good 0.39 0.10 

(1.77) (1.77) 

English poor 0.97 0.17 

(2.44)* (2.44)* 

Sexist -0.42 -0.14 

(2.19)* (2.19)* 




(0.94) (0.94) 

Labourer 0.12 0.03 

(0.48) (0.48) 


(0.19) (0.19) 

Sales -0.07 -0.02 

(0.27) (0.27) 


(1.18) (1.18) 


(0.50) (0.50) 


(0.36) (0.36) 

Father holds post-school qualification when resp 14 0.10 0.03 

Mothers occupation when resp. 14 (0.87) (0.87) 


(0.99) (0.99) 

Labourer -0.13 -0.04 

(0.53) (0.53) 


(1.92) (1.92) 

Labourer -0.38 -0.12 

(1.68) (1.68) 


(0.59) (0.59) 


(0.91) (0.91) 

265

266 


(0.41) (0.41) 

mother post-school qualification when resp 14 -0.04 -0.01 


Catholic 0.36 0.10 

(2.85)** (2.85)** 


(2.35)* (2.35)* 


(0.84) (0.84) 


(0.38) (0.38) 

Other religion -0.03 -0.01 

(0.16) (0.16) 


(2.11)* (2.11)* 

constant 1.16 

(3.65)** 



LR chi 2 (59) 171 329.72 








172 


(Mfull) to a model with just the intercept (Mintercept). It is defined as R2 = 1 – (log likelihood Mfull / log 

likelihood Mintercept). The value of Mcfadden’s Pseudo R2 increases as new variables are added.

267 

Table A2.0b Univariate Probit of participation in SYETP, as estimated in the bivariate 

probit replication of Richardson (1998) 

SYETP 

Marginal effects 

Coefficient 

(t stat) 


(0.67) (0.67) 

Age at 1984 survey -0.11 -0.01 

(3.21)** (3.21)** 

Married 1984 -0.97 -0.06 

(1.62) (1.62) 

Children 1984 0.49 0.07 

(0.74) (0.74) 


(0.39) (0.39) 


(0.91) (0.91) 


(0.95) (0.95) 




(0.73) (0.73) 


(0.99) (0.99) 


(0.66) (0.66) 


(1.81) (1.81) 


(0.38) (0.38) 


(1.23) (1.23) 


Highest qualification in 1984 (1.56) (1.56) 


(0.20) (0.20) 


(0.47) (0.47) 


(0.28) (0.28) 


(2.25)* (2.25)* 


(0.85) (0.85) 


(0.53) (0.53) 


(1.68) (1.68) 

< 1 year -0.04 -0.00 

(0.24) (0.24) 

2 years 0.15 0.02 

(0.70) (0.70) 

3 years + -0.34 -0.03

(1.32) (1.32) 

CEP referrals 1984 0.16 0.02 

(2.36)* (2.36)* 

duration of Pre-June 1984 unemployment 0.46 0.05 

(2.79)** (2.79)** 


Family background (2.32)* (2.32)* 


(1.49) (1.49) 


(2.76)** (2.76)** 


(1.70) (1.70) 


(1.38) (1.38) 


(0.83) (0.83) 


(0.48) (0.48) 


(1.03) (1.03) 

Sexist 0.25 0.03 

(0.95) (0.95) 




(0.79) (0.79) 

Labourer -0.16 -0.01 

(0.52) (0.52) 


(0.75) (0.75) 

Sales -0.10 -0.01 

(0.30) (0.30) 


(0.90) (0.90) 

Manager/professional/para-professional -0.08 -0.01 

(0.33) (0.33) 


(1.08) (1.08) 

Father holds post-school qualification when resp 14 -0.30 -0.03 

Mothers occupation when resp. 14 (2.00)* (2.00)* 


(1.64) (1.64) 

Labourer 0.14 0.02 

(0.45) (0.45) 


(2.10)* (2.10)* 

Sales 0.19 0.02 

(0.65) (0.65) 


(0.21) (0.21) 

Manager/professional/para-professional -0.23 -0.02 

(0.76) (0.76) 


(0.21) (0.21) 

Mother post-school qualification when resp 14 0.25 0.03 

268

269 


Catholic 0.06 0.01 

(0.38) (0.38) 


(1.34) (1.34) 


(0.46) (0.46) 


(0.35) (0.35) 


(0.72) (0.72) 


(0.95) (0.95) 

constant 0.71 

(0.93) 



LR chi 2 (59) 173 107.57 








174 




270 

Table A2.1 Means and bias after matching, one to one nearest neighbour 0.001 and 0.005 propensity score radius 

Matching results shown in 

Table 4.6 

0.001 propensity score radius 0.005 propensity score radius 

Means var Means var Bias Means var Means 

in the 




compari 

SYETP 

compari 

SYETP 

son 

group 

son 

group 

group 

matched 

group 

matched 

matched 

matched 

var 

Bias 

female Gender=female 0.49 0.25 0.45 0.25 8.00 0.5 0.25 0.45 0.25 10.00 

age84 Age at 1984 survey 19.16 5.34 19.11 3.78 2.34 19.03 5.27 19.1 3.83 3.28 

mar84 Married 1984 0.04 0.04 0.03 0.03 5.35 0.03 0.03 0.03 0.03 0.00 

child84 Children 1984 0 0 0.02 0.02 20.00 0 0 0.02 0.02 20.00 

ch84fem Children*female 0 0 0.01 0.01 14.14 0 0 0.01 0.01 14.14 

spemp84 Spouse employed 1984 0.04 0.04 0.02 0.02 11.55 0.03 0.03 0.02 0.02 6.32 

roatsi 

Aboriginal/Torres Strait 

Islander 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.00 

rones Other ethnic minority 0.06 0.06 0.08 0.07 7.84 0.08 0.07 0.08 0.07 0.00 

vic 

State interviewed in 

1984Victoria 0.33 0.22 0.29 0.21 8.63 0.33 0.22 0.28 0.2 10.91 

qld Queensland 0.11 0.1 0.09 0.08 6.67 0.11 0.1 0.08 0.07 10.29 

sant 

South Australia/Northern 

watas 

Territory 0.13 0.11 0.14 0.12 2.95 0.14 0.12 0.14 0.12 0.00 

Western 

Australia/Tasmania 0.1 0.09 0.15 0.13 15.08 0.09 0.08 0.2 0.16 31.75 

lsos Education school overseas 0.06 0.06 0.02 0.02 20.00 0.06 0.05 0.02 0.02 21.38 

lsrcs Roman Catholic school 0.04 0.04 0.07 0.06 13.42 0.05 0.04 0.06 0.06 4.47 

lspriv Private school 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.00 

hqdd84 

Highest qualification in 

1984 Degree/diploma 0.1 0.09 0.09 0.08 3.43 0.09 0.08 0.08 0.07 3.65 

hqtra84 Apprenticeship 0.03 0.02 0.03 0.03 0.00 0.02 0.02 0.03 0.03 6.32 

hqoth84 

Other Post-School 

qualification 0.11 0.1 0.07 0.06 14.14 0.11 0.1 0.07 0.07 13.72 

hq12_84 Year 12 of school 0.14 0.12 0.2 0.16 16.04 0.14 0.12 0.23 0.18 23.24 

hq11_84 Year 11 of school 0.13 0.11 0.2 0.16 19.05 0.15 0.13 0.18 0.15 8.02

271 

hq9_84 Year 9 of school or less 0.09 0.08 0.1 0.09 3.43 0.09 0.08 0.11 0.1 6.67 

longjno Longest job by 1984 none 0.16 0.14 0.14 0.12 5.55 0.15 0.13 0.12 0.11 8.66 

longj0 < 1 year 0.48 0.25 0.52 0.25 8.00 0.49 0.25 0.54 0.25 10.00 

longj2 2 years 0.19 0.16 0.14 0.12 13.36 0.22 0.17 0.14 0.12 21.01 

longj3p 3 years + 0.05 0.05 0.07 0.06 8.53 0.05 0.04 0.06 0.06 4.47 

cepref84 CEP referrals 1984 0.49 1.48 0.31 1.03 16.07 0.5 1.59 0.27 0.9 20.61 


unemployment 0.66 0.16 0.67 0.14 2.58 0.67 0.16 0.68 0.14 2.58 

health84 Work limited by health 0.03 0.02 0.05 0.04 11.55 0.02 0.02 0.04 0.04 11.55 

oc14 

Family background Other 

ct14 

city before aged 14 0.28 0.2 0.18 0.15 23.90 0.27 0.2 0.17 0.14 24.25 

Country town before aged 

14 0.19 0.16 0.21 0.17 4.92 0.18 0.15 0.18 0.15 0.00 

ra14 Rural area before aged 14 0.03 0.02 0.05 0.04 11.55 0.02 0.02 0.04 0.04 11.55 

os14 Overseas before aged 14 0 0 0.01 0.01 14.14 0 0 0.01 0.01 14.14 

nsib2 Number of siblings 4.29 120.03 3.14 4.47 14.58 4.09 108.22 2.99 4.17 14.67 

egood English good 0.09 0.08 0.08 0.07 3.65 0.09 0.08 0.08 0.07 3.65 

epoor English poor 0 0 0.01 0.01 14.14 0 0 0.01 0.01 14.14 

ksink5p Sexist 0.08 0.07 0.07 0.06 3.92 0.07 0.06 0.06 0.06 4.08 

ks5pfem Sexist*female 0.04 0.04 0.01 0.01 18.97 0.03 0.03 0.01 0.01 14.14 

fnpres14 

flabo 

Father not present when 

resp 14 0.14 0.12 0.18 0.15 10.89 0.16 0.14 0.19 0.15 7.88 

Fathers occupation 

Labourer 0.06 0.06 0.09 0.08 11.34 0.08 0.07 0.09 0.08 3.65 

fplan Plant operative 0.16 0.14 0.16 0.14 0.00 0.16 0.14 0.17 0.14 2.67 

fsale Sales 0.08 0.07 0.06 0.05 8.16 0.07 0.06 0.05 0.05 8.53 

ftrad Tradesperson 0.1 0.09 0.16 0.14 17.69 0.1 0.09 0.15 0.13 15.08 

fmpp 

Manager/professional/para 

-professional 0.3 0.21 0.22 0.17 18.35 0.3 0.21 0.25 0.19 11.18 

fnemp Not employed 0.08 0.07 0.05 0.04 12.79 0.07 0.06 0.04 0.04 13.42 

fpsq 

mnpres14 


qualification when resp 14 0.32 0.22 0.26 0.2 13.09 0.3 0.21 0.26 0.19 8.94 

Mother not present when 

resp 14 0.09 0.08 0.07 0.06 7.56 0.11 0.1 0.08 0.07 10.29 

mlabo Labourer 0.06 0.06 0.08 0.07 7.84 0.06 0.05 0.07 0.07 4.08 

mplan Plant operative 0.09 0.08 0.09 0.08 0.00 0.11 0.1 0.1 0.09 3.24 

msale Sales 0.11 0.1 0.09 0.08 6.67 0.1 0.09 0.09 0.08 3.43

272 

mtrad Tradesperson 0.03 0.02 0.02 0.02 7.07 0.05 0.04 0.03 0.03 10.69 

mmpp 



mnemp Not employed 0.49 0.25 0.48 0.25 2.00 0.45 0.25 0.5 0.25 10.00 

mpsq 

Mother post-school 


cath 


Catholic 0.3 0.21 0.28 0.2 4.42 0.3 0.21 0.27 0.2 6.63 

pres Presbyterian 0.04 0.04 0.07 0.06 13.42 0.03 0.03 0.08 0.07 22.36 

meth Methodist 0.03 0.02 0.06 0.05 16.04 0.02 0.02 0.06 0.06 20.00 

othx Other Christian 0.08 0.07 0.07 0.06 3.92 0.08 0.07 0.07 0.07 3.78 

othrel Other religion 0.06 0.06 0.08 0.07 7.84 0.07 0.06 0.08 0.07 3.92 

norel No religion 0.19 0.16 0.13 0.11 16.33 0.22 0.17 0.14 0.12 21.01 

Average bias 9.66 9.90

273 

Table A2.1 Continued Means and bias after matching, nearest neighbour one to one 0.01 and 0.05 propensity score radius 

Matching results shown in 

Table 4.6 

0.01 propensity score radius 0.05 propensity score radius 

Means var Means var Bias Means var Means 





compari 

SYETP 

control 

SYETP 

son 

group 

group 

group 

group 

matched 

matched 

matched 

matched 

var 

Bias 

female Gender=female 0.51 0.25 0.44 0.25 14.00 0.51 0.25 0.43 0.25 16.00 

age84 Age at 1984 survey 19.01 5.26 19.08 3.84 3.28 19.01 5.26 19.03 3.89 0.94 

mar84 Married 1984 0.03 0.03 0.03 0.03 0.00 0.03 0.03 0.03 0.03 0.00 

child84 Children 1984 0 0 0.02 0.02 20.00 0 0 0.02 0.02 20.00 

ch84fem Children*female 0 0 0.01 0.01 14.14 0 0 0.01 0.01 14.14 

spemp84 Spouse employed 1984 0.03 0.03 0.02 0.02 6.32 0.03 0.03 0.02 0.02 6.32 

roatsi 


Islander 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.00 

rones Other ethnic minority 0.08 0.07 0.08 0.07 0.00 0.08 0.07 0.08 0.07 0.00 

vic 

State interviewed in 

1984Victoria 0.33 0.22 0.27 0.2 13.09 0.33 0.22 0.27 0.2 13.09 

qld Queensland 0.11 0.1 0.09 0.08 6.67 0.11 0.1 0.09 0.08 6.67 

sant 


watas 

Territory 0.13 0.12 0.14 0.12 2.89 0.13 0.12 0.13 0.12 0.00 

Western 

Australia/Tasmania 0.09 0.08 0.2 0.16 31.75 0.09 0.08 0.2 0.16 31.75 

lsos Education school overseas 0.06 0.05 0.03 0.03 15.00 0.06 0.05 0.04 0.04 9.43 

lsrcs Roman Catholic school 0.04 0.04 0.06 0.06 8.94 0.04 0.04 0.06 0.05 9.43 

lspriv Private school 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.00 

hqdd84 

Highest qualification in 

1984 Degree/diploma 0.09 0.08 0.08 0.07 3.65 0.09 0.08 0.08 0.07 3.65 

hqtra84 Apprenticeship 0.02 0.02 0.03 0.03 6.32 0.02 0.02 0.03 0.03 6.32 

hqoth84 


qualification 0.11 0.1 0.07 0.06 14.14 0.11 0.1 0.07 0.06 14.14 

hq12_84 Year 12 of school 0.13 0.12 0.23 0.18 25.82 0.13 0.12 0.23 0.18 25.82 

hq11_84 Year 11 of school 0.16 0.13 0.18 0.15 5.35 0.16 0.13 0.17 0.14 2.72

274 

hq9_84 Year 9 of school or less 0.09 0.08 0.11 0.1 6.67 0.09 0.08 0.11 0.1 6.67 

longjno Longest job by 1984 none 0.15 0.13 0.12 0.1 8.85 0.15 0.13 0.12 0.1 8.85 

longj0 < 1 year 0.49 0.25 0.55 0.25 12.00 0.49 0.25 0.56 0.25 14.00 

longj2 2 years 0.21 0.17 0.14 0.12 18.38 0.21 0.17 0.13 0.12 21.01 

longj3p 3 years + 0.04 0.04 0.06 0.06 8.94 0.04 0.04 0.06 0.05 9.43 

cepref84 CEP referrals 1984 0.49 1.57 0.29 0.96 17.78 0.49 1.57 0.3 0.95 16.93 


unemployment 0.67 0.16 0.68 0.14 2.58 0.67 0.16 0.68 0.14 2.58 

health84 Work limited by health 0.02 0.02 0.04 0.04 11.55 0.02 0.02 0.04 0.04 11.55 

oc14 

Family background Other 

ct14 

city before aged 14 0.27 0.2 0.17 0.14 24.25 0.27 0.2 0.16 0.14 26.68 


14 0.19 0.16 0.18 0.15 2.54 0.19 0.16 0.17 0.14 5.16 

ra14 Rural area before aged 14 0.02 0.02 0.04 0.04 11.55 0.02 0.02 0.04 0.04 11.55 

os14 Overseas before aged 14 0 0 0.01 0.01 14.14 0 0 0.01 0.01 14.14 

nsib2 Number of siblings 4.07 107.04 2.98 4.14 14.62 4.07 107.04 2.97 4.07 14.76 

egood English good 0.09 0.08 0.08 0.07 3.65 0.09 0.08 0.08 0.07 3.65 

epoor English poor 0 0 0.01 0.01 14.14 0 0 0.01 0.01 14.14 

ksink5p Sexist 0.07 0.06 0.06 0.06 4.08 0.07 0.06 0.07 0.06 0.00 

ks5pfem Sexist*female 0.03 0.03 0.01 0.01 14.14 0.03 0.03 0.01 0.01 14.14 

fnpres14 

flabo 


resp 14 0.16 0.13 0.2 0.16 10.50 0.16 0.13 0.19 0.16 7.88 

Fathers occupation 

Labourer 0.08 0.07 0.09 0.08 3.65 0.08 0.07 0.1 0.09 7.07 

fplan Plant operative 0.17 0.14 0.17 0.14 0.00 0.17 0.14 0.16 0.14 2.67 

fsale Sales 0.07 0.06 0.05 0.05 8.53 0.07 0.06 0.05 0.05 8.53 

ftrad Tradesperson 0.1 0.09 0.15 0.13 15.08 0.1 0.09 0.14 0.12 12.34 

fmpp 



fnemp Not employed 0.07 0.06 0.04 0.04 13.42 0.07 0.06 0.04 0.04 13.42 

fpsq 

mnpres14 




resp 14 0.11 0.1 0.09 0.08 6.67 0.11 0.1 0.09 0.08 6.67 

mlabo Labourer 0.06 0.05 0.07 0.06 4.26 0.06 0.05 0.08 0.07 8.16 

mplan Plant operative 0.12 0.11 0.1 0.09 6.32 0.12 0.11 0.1 0.09 6.32 

msale Sales 0.1 0.09 0.09 0.08 3.43 0.1 0.09 0.1 0.09 0.00

275 

mtrad Tradesperson 0.04 0.04 0.03 0.03 5.35 0.04 0.04 0.03 0.03 5.35 

mmpp 



mnemp Not employed 0.45 0.25 0.49 0.25 8.00 0.45 0.25 0.48 0.25 6.00 

mpsq 



cath 


Catholic 0.29 0.21 0.26 0.2 6.63 0.29 0.21 0.26 0.19 6.71 

pres Presbyterian 0.04 0.04 0.08 0.07 17.06 0.04 0.04 0.08 0.07 17.06 

meth Methodist 0.02 0.02 0.06 0.06 20.00 0.02 0.02 0.06 0.05 21.38 

othx Other Christian 0.08 0.07 0.07 0.06 3.92 0.08 0.07 0.07 0.06 3.92 

othrel Other religion 0.07 0.06 0.09 0.08 7.56 0.07 0.06 0.09 0.08 7.56 

norel No religion 0.21 0.17 0.14 0.12 18.38 0.21 0.17 0.15 0.13 15.49 

Average bias 9.74 9.57

276 

Table A2.2 Applying Different methods for the missing cases in the SYETP probit before 

sample reduction 

Mean substitution method 

for missing values for some 

Regressor variables 

Using Missing dummy 

Before 

sample 

reduction, 

unweighted 

Before 

sample 

reduction, 

with weight 

Deletion method for 

missing values for some 

Regressor variables 

No missing dummy, 

where missing cases 

dropped 

Before 

sample 

reduction, 

unweighted 

Before 

sample 

reduction, 

with weight 

(1) (2) (3) (4) 


Gender=female -0.02 -0.03 -0.02 -0.03 

(0.16) (0.33) (0.23) (0.33) 

Age at 1984 survey -0.08 -0.06 -0.07 -0.05 

(3.21)** (2.41)* (2.84)** (1.94) 

Married 1984 -0.59 -0.68 -0.61 -0.72 

(1.78) (2.66)** (1.69) (2.31)* 

Children 1984 0.48 0.38 -0.01 -0.11 

(1.17) (1.14) (0.02) (0.24) 

Children*female -0.17 0.16 0.06 0.43 

(0.30) (0.35) (0.09) (0.69) 


(0.34) (0.56) (0.52) (0.81) 


(1.78) (1.73) (1.64) (1.61) 

Other ethnic minority 0.07 -0.03 0.08 -0.03 


Victoria 0.13 0.15 0.05 0.08 

(1.12) (1.17) (0.43) (0.61) 

Queensland -0.05 -0.01 -0.08 -0.06 

(0.35) (0.07) (0.53) (0.38) 

South Australia/Northern Territory 0.12 0.13 0.12 0.14 

(0.78) (0.82) (0.76) (0.89) 


(2.24)* (2.44)* (2.18)* (2.47)* 

Education school overseas 0.07 0.24 -0.05 0.15 

(0.28) (0.90) (0.18) (0.50) 


(2.07)* (2.21)* (1.92) (2.01)* 


Highest qualification in 1984 (1.85) (2.56)* (1.90) (2.71)** 

Degree/diploma -0.11 -0.12 -0.15 -0.18 

(0.62) (0.65) (0.80) (0.94) 


(1.32) (1.22) (1.13) (0.98) 

Other Post-School qualification -0.09 -0.00 -0.13 -0.11 

(0.44) (0.01) (0.61) (0.55) 


(1.12) (1.60) (0.83) (1.25) 


(0.45) (0.73) (0.42) (0.56)

277 

Year 9 of school or less -0.19 -0.10 -0.09 0.00 

(1.30) (0.63) (0.61) (0.01) 


(2.20)* (1.83) (1.63) (1.29) 

< 1 year -0.04 -0.04 0.03 0.01 

(0.30) (0.32) (0.22) (0.08) 

2 years -0.03 -0.07 -0.01 -0.08 

(0.18) (0.39) (0.04) (0.43) 

3 years + -0.35 -0.47 -0.30 -0.45 

(1.82) (2.58)** (1.51) (2.43)* 

CEP referrals 1984 0.08 0.08 0.09 0.09 

(1.66) (1.48) (1.84) (1.63) 

Duration of Pre-June 1984 unemployment 0.40 0.30 0.36 0.24 

(3.24)** (2.64)** (2.85)** (2.03)* 


Family background (2.69)** (3.33)** (2.53)* (3.14)** 


(0.89) (1.69) (1.18) (1.97)* 


(2.62)** (2.83)** (2.77)** (3.16)** 


(1.38) (1.86) (1.15) (1.61) 


(1.59) (1.58) (1.53) (1.54) 

Number of siblings 0.01 0.01 -0.00 0.00 

(0.58) (0.60) (0.29) (0.10) 

English good -0.13 -0.18 -0.24 -0.27 

(0.68) (1.03) (1.18) (1.40) 

English poor -0.68 -0.74 -0.63 -0.68 

(1.50) (1.50) (1.36) (1.37) 

Sexist 0.11 0.19 0.04 0.08 

(0.59) (0.97) (0.20) (0.36) 

Sexist*female -0.66 -0.76 -0.57 -0.62 

Fathers occupation when resp. 14 (1.24) (1.67) (1.06) (1.30) 


(1.02) (1.16) (0.88) (1.00) 

Labourer -0.06 0.00 -0.07 0.01 

(0.28) (0.02) (0.31) (0.04) 


(0.65) (0.47) (0.75) (0.61) 

Sales -0.19 -0.27 -0.16 -0.24 

(0.70) (1.00) (0.56) (0.85) 

Tradesperson -0.05 -0.05 -0.09 -0.09 

(0.24) (0.27) (0.42) (0.46) 

Manager/professional/para-professional -0.04 -0.08 -0.04 -0.09 

(0.21) (0.44) (0.19) (0.42) 

Not employed -0.31 -0.23 -0.28 -0.20 

(1.13) (0.90) (0.98) (0.75) 

Father holds post-school qualification when resp -0.29 -0.26 -0.29 -0.25 

14 

Mothers occupation when resp. 14 (2.60)** (2.24)* (2.49)* (2.08)* 

Mother not present when resp 14 0.34 0.29 0.31 0.21 

(1.39) (1.25) (1.23) (0.87) 

Labourer 0.12 0.08 0.08 -0.04 

(0.55) (0.38) (0.33) (0.20) 

Plant operative 0.38 0.31 0.36 0.25

278 

(1.62) (1.30) (1.46) (1.01) 

Sales 0.18 0.26 0.05 0.10 

(0.79) (1.12) (0.22) (0.44) 

Tradesperson 0.02 -0.02 -0.05 -0.13 

(0.05) (0.06) (0.13) (0.36) 

Manager/professional/para-professional -0.14 -0.19 -0.19 -0.27 

(0.60) (0.83) (0.79) (1.14) 

Not employed 0.08 -0.00 -0.02 -0.13 

(0.46) (0.03) (0.13) (0.74) 

Mther post-school qualification when resp 14 0.21 0.19 0.20 0.19 


Catholic 0.16 0.12 0.10 0.06 

(1.27) (0.97) (0.81) (0.46) 

Presbyterian 0.28 0.21 0.30 0.24 

(1.46) (1.05) (1.57) (1.22) 

Methodist 0.12 0.24 0.11 0.22 

(0.60) (1.11) (0.51) (1.03) 

Other Christian -0.06 -0.11 -0.04 -0.08 

(0.26) (0.52) (0.17) (0.36) 


(0.94) (1.16) (0.94) (1.14) 

No religion 0.14 0.17 0.08 0.11 

(0.94) (1.08) (0.55) (0.69) 

missing fathers qualifications -0.49 -0.55 

(1.60) (1.69) 

missing mothers qualifications 0.01 0.10 

(0.02) (0.42) 

missing proportion of time spent unemployed -0.07 -0.08 

(0.12) (0.16) 

missing number of siblings 0.20 0.17 

(0.79) (0.68) 

Constant -0.01 -0.33 0.04 -0.26 

(0.03) (0.60) (0.07) (0.45) 

Observations 2368 2368 2150 2150 


LR chi 2 175 (63) 137.31 (63) 150.58 (59) 125.41 (59) 134.91 


0.1199 0.1284 0.1169 0.1253 


Coefficient with t statistic in brackets. Absolute value of z statistics in parentheses * significant at 5%, ** 

significant at 1%. Base categories: European ethnic origin, state interviewed in 1984 NSW/ACT, 

government school, highest qualification in 1984 year 10 at school, Longest job by 1984 is 1 year, lived 

mostly in state capital city until respondent aged 14, English is first language, father clerical worker when 

respondent aged 14, mother clerical worker when respondent aged 14, religion brought up in is Anglican. 


zero. 

176 

This measure of fit is also known as the likelihood ratio index.

279 

Table A2.3 Probit of SYETP participation, no missing dummies 

Before sample 

reduction, 

unweighted 

Before sample 



After sample 

reduction, 

unweighted 

After sample 



(1) (2) (3) (4) 


Gender=female -0.02 -0.03 0.08 0.06 

(0.23) (0.33) (0.66) (0.48) 

Age at 1984 survey -0.07 -0.05 -0.11 -0.08 

(2.84)** (1.94) (3.21)** (2.86)** 

Married 1984 -0.61 -0.72 -0.97 -1.09 

(1.69) (2.31)* (1.62) (4.20)** 

Children 1984 -0.01 -0.11 0.49 0.33 

(0.02) (0.24) (0.73) (0.82) 

Children*female 0.06 0.43 -0.30 0.03 

(0.09) (0.69) (0.36) (0.06) 


(0.52) (0.81) (0.91) (1.93) 


(1.64) (1.61) (0.96) (0.78) 

Other ethnic minority 0.08 -0.03 0.06 0.00 


Victoria 0.05 0.08 0.12 0.11 

(0.43) (0.61) (0.75) (0.70) 

Queensland -0.08 -0.06 -0.20 -0.13 

(0.53) (0.38) (0.97) (0.66) 


Territory 

0.12 0.14 -0.12 -0.11 

(0.76) (0.89) (0.61) (0.59) 


(2.18)* (2.47)* (1.79) (2.02)* 

Education school overseas -0.05 0.15 0.14 0.38 

(0.18) (0.50) (0.40) (1.07) 


(1.92) (2.01)* (1.28) (1.15) 


Highest qualification in 1984 (1.90) (2.71)** (1.54) (2.32)* 

Degree/diploma -0.15 -0.18 0.05 -0.01 

(0.80) (0.94) (0.21) (0.03) 


(1.13) (0.98) (0.43) (0.41) 

Other Post-School qualification -0.13 -0.11 0.08 0.05 

(0.61) (0.55) (0.32) (0.19) 


(0.83) (1.25) (2.27)* (2.29)* 


(0.42) (0.56) (0.85) (1.14) 

Year 9 of school or less -0.09 0.00 -0.14 -0.02 

(0.61) (0.01) (0.68) (0.09) 


(1.63) (1.29) (1.62) (1.33) 

< 1 year 0.03 0.01 -0.04 -0.12 

(0.22) (0.08) (0.22) (0.67) 

2 years -0.01 -0.08 0.15 0.04

280 

(0.04) (0.43) (0.66) (0.17) 

3 years + -0.30 -0.45 -0.34 -0.51 

(1.51) (2.43)* (1.30) (2.12)* 

CEP referrals 1984 0.09 0.09 0.15 0.12 

(1.84) (1.63) (2.27)* (1.64) 


unemployment 

0.36 0.24 0.47 0.34 

(2.85)** (2.03)* (2.85)** (2.46)* 


Family background (2.53)* (3.14)** (2.30)* (2.84)** 


(1.18) (1.97)* (1.51) (2.40)* 


(2.77)** (3.16)** (2.78)** (3.32)** 


(1.15) (1.61) (1.70) (1.53) 


(1.53) (1.54) (1.36) (1.32) 

Number of siblings -0.00 0.00 0.01 0.02 

(0.29) (0.10) (0.42) (0.56) 

English good -0.24 -0.27 -0.11 -0.14 

(1.18) (1.40) (0.43) (0.56) 

English poor -0.63 -0.68 -0.60 -0.73 

(1.36) (1.37) (1.11) (1.18) 

Sexist 0.04 0.08 0.25 0.31 

(0.20) (0.36) (0.95) (1.12) 

Sexist*female -0.57 -0.62 -0.76 -0.88 

Fathers occupation when resp. (1.06) (1.30) (1.25) (1.60) 

14 


(0.88) (1.00) (0.82) (0.81) 

Labourer -0.07 0.01 -0.17 -0.07 

(0.31) (0.04) (0.58) (0.24) 


(0.75) (0.61) (0.79) (0.51) 

Sales -0.16 -0.24 -0.09 -0.25 

(0.56) (0.85) (0.27) (0.71) 

Tradesperson -0.09 -0.09 -0.25 -0.27 

(0.42) (0.46) (0.91) (1.03) 


-0.04 -0.09 -0.09 -0.19 

(0.19) (0.42) (0.35) (0.74) 

Not employed -0.28 -0.20 -0.41 -0.47 

(0.98) (0.75) (1.15) (1.42) 



Mothers occupation when resp. 

14 

Mother not present when resp 

14 

-0.29 -0.25 -0.29 -0.22 

(2.49)* (2.08)* (1.97)* (1.61) 

0.31 0.21 0.47 0.37 

(1.23) (0.87) (1.48) (1.24) 

Labourer 0.08 -0.04 0.13 -0.04 

(0.33) (0.20) (0.43) (0.14) 

Plant operative 0.36 0.25 0.65 0.52 

(1.46) (1.01) (2.10)* (1.69) 

Sales 0.05 0.10 0.20 0.25

281 

(0.22) (0.44) (0.67) (0.89) 

Tradesperson -0.05 -0.13 0.11 0.13 

(0.13) (0.36) (0.25) (0.31) 


-0.19 -0.27 -0.23 -0.32 

(0.79) (1.14) (0.78) (1.11) 

Not employed -0.02 -0.13 0.03 -0.10 

(0.13) (0.74) (0.13) (0.45) 

Mother post-school qualification 0.20 0.19 0.25 0.24 

when resp 14 


Catholic 0.10 0.06 0.05 -0.02 

(0.81) (0.46) (0.32) (0.11) 

Presbyterian 0.30 0.24 0.32 0.25 

(1.57) (1.22) (1.31) (1.05) 

Methodist 0.11 0.22 0.11 0.23 

(0.51) (1.03) (0.44) (0.96) 

Other Christian -0.04 -0.08 0.09 0.10 

(0.17) (0.36) (0.34) (0.36) 


(0.94) (1.14) (0.63) (0.54) 

No religion 0.08 0.11 0.17 0.16 

(0.55) (0.69) (0.84) (0.83) 

Constant 0.04 -0.26 0.66 0.39 

(0.07) (0.45) (0.86) (0.59) 

Observations 2150 2150 1283 1283 


LR chi 2 177 (59) 125.41 (59) 134.91 (59) 106.91 (59) 126.23 


0.1169 0.1253 0.1481 0.1553 


Coefficient with t statistic in brackets. Absolute value of z statistics in parentheses * significant at 5%; ** 

significant at 1%. 


qualification in 1984 year 10 at school, Longest job by 1984 is 1 year, lived mostly in state capital city until 


clerical worker when respondent aged 14, religion brought up in is Anglican. 


zero. 

178 


282 

Table A2.4 Summary statistics for distribution of the weights constructed for attrition, 

non-response and survey design 

wt86 


1% 0.457385 0.350283 

5% 0.619839 0.356677 

10% 0.769777 0.413743 Obs 1283 

25% 0.927171 0.421961 Sum of Wgt. 1283 

50% 1.158263 Mean 1.286986 


75% 1.50368 3.763253 

90% 1.955461 4.14118 Variance 0.355917 

95% 2.445457 4.843395 Skewness 3.083631 

99% 3.289642 9.10959 Kurtosis 28.41954

283 


probit, weighted for attrition 

Dy/Dx 179 , (t statistic) 

Employment 

(npjob18) 

syetp 180 0.13 

(3.06)** 

Gender=female -0.13 

(4.49)** 

Married 1984 0.01 

(0.08) 

Children 1984 -0.13 

(1.48) 


(3.74)** 


(2.27)* 


(1.47) 


State interviewed in 1984 (2.00)* 

Victoria -0.04 

(0.98) 

Queensland 0.01 

(0.31) 


(3.15)** 

Western Australia/Tasmania -0.03 

(0.56) 

Education school overseas -0.05 

(0.54) 


(0.18) 

Private school 0.13 


Degree/diploma 0.12 

(3.11)** 

Apprenticeship 0.09 

(1.73) 


(0.58) 

Year 12 of school -0.02 

(0.34) 


(2.53)* 


(2.02)* 


(0.22) 


standard normal, µ= mean. 

180 Mean of SYETP is (.080747); 95 per cent confidence interval for dydx gives (0.066445, 0.190978).

1 year 0.06 

(1.70) 

2 years 0.06 

(1.31) 

3 years + 0.13 

(3.24)** 

Other govt programme -0.25 

(5.44)** 

Duration of Pre-June 1984 unemployment -0.12 

(3.55)** 




(2.27)* 


(0.95) 


(2.31)* 

Overseas before aged 14 0.09 

(1.01) 


(1.72) 

English good 0.10 

(2.03)* 

English poor 0.17 

(2.56)* 

Sexist -0.12 

(1.84) 

Sexist*female 0.12 



(0.93) 

Labourer 0.05 

(0.74) 


(0.68) 

Sales 0.01 

(0.14) 


(0.79) 

Manager/professional/para-professional 0.06 

(1.10) 

Not employed 0.02 

(0.27) 

Father holds post-school qualification when resp 14 0.02 


Mother not present when resp 14 -0.04 

(0.58) 


(0.61) 


(1.73) 

Sales -0.12 

(1.69) 


(0.93) 

284

285 

Manager/professional/para-professional 0.02 

(0.24) 


(0.39) 

Mother post-school qualification when resp 14 -0.00 


Catholic 0.11 

(3.22)** 

Presbyterian 0.12 

(2.40)* 

Methodist 0.07 

(1.35) 

Other Christian -0.03 

(0.49) 

Other religion 0.00 

(0.08) 

No religion 0.10 

(2.49)* 



LR chi 2 (59) 181 288.06 





zero. 

182 


286 

Table A2.5b Univariate probit of participation in SYETP, as estimated in the bivariate 

probit, weighted for attrition 

Dy/Dx 183 , (t statistic) 

syetp 


(0.52) 


(2.87)** 

Married 1984 -0.06 

(3.91)** 


(0.85) 


(0.14) 


(1.96)* 


(0.78) 



Victoria 0.01 

(0.58) 

Queensland -0.01 

(0.62) 


(0.63) 

Western Australia/Tasmania 0.05 

(2.18)* 

Education school overseas 0.05 

(1.04) 


(1.09) 

Private school -0.05 


Degree/diploma -0.00 

(0.11) 


(0.38) 


(0.24) 


(2.19)* 


(1.05) 


(0.02) 


(1.37) 

< 1 year -0.01 

(0.47) 

2 years 0.01 

(0.25) 


standard normal, µ= mean.

3 years + -0.04 

(2.13)* 

CEP referrals 1984 0.01 

(1.75) 

Duration of Pre-June 1984 unemployment 0.03 

(2.40)* 




(2.39)* 


(3.34)** 


(1.61) 


(1.25) 


(0.91) 

English good -0.01 

(0.56) 

English poor -0.04 

(1.06) 

Sexist 0.04 

(1.26) 

Sexist*female -0.04 



(0.79) 


(0.19) 


(0.46) 

Sales -0.02 

(0.66) 


(1.02) 


(0.66) 


(1.31) 

Father holds post-school qualification when resp 14 -0.02 


Mother not present when resp 14 0.05 

(1.24) 


(0.16) 


(1.80) 

Sales 0.02 

(0.76) 

Tradesperson 0.02 

(0.46) 


(1.10) 


(0.57) 

287

288 

Mother post-school qualification when resp 14 0.02 

(1.26) 

Religion brought up in -0.00 

Catholic (0.05) 

0.04 

Presbyterian (1.29) 

0.03 

Methodist (1.19) 

0.01 

Other Christian (0.21) 

0.01 

Other religion (0.56) 

0.02 

No religion (0.84) 



LR chi 2 (59) 184 131.99 





zero. 

185 This measure of fit is also known as the likelihood ratio index.

289 

Table A2.6 Part A Employment equation from bivariate probit where age included in 

employment model, attrition weights 

ever employed in 1986 

survey 

coefficient t statistic probability of t 

SYETP 1.06 0.77 0.440 

Age at 1984 survey 0.01 0.30 0.768 

Gender=female -0.46** -4.34 0.000 

Married 0.03 0.14 0.891 

Children -0.40 -1.48 0.140 

Children*female -1.27** -3.73 0.000 

Spouse employed 0.55** 2.22 0.027 


Islander 

-0.34 -1.36 0.174 

Other ethnic minority -0.38* -2.00 0.045 

Victoria -0.12 -0.99 0.324 

Queensland 0.05 0.34 0.731 


Territory 

-0.48** -2.90 0.004 

Western 

Australia/Tasmania 

-0.11 -0.64 0.523 

Education school overseas -0.22 -0.63 0.531 

Roman Catholic school -0.02 -0.08 0.935 

Private school 0.63* 1.95 0.051 

Degree/diploma 0.50** 2.84 0.004 

Apprenticeship 0.37 1.73 0.083 


qualification 

0.09 0.50 0.616 

Year 12 of school -0.09 -0.49 0.621 

Year 11 of school 0.38* 2.17 0.030 

Year 9 of school or less -0.29* -2.03 0.043 

Longest job by 1984 none -0.01 -0.06 0.949 

< 1 year 0.24 1.68 0.093 

2 years 0.21 1.26 0.208 

3 years + 0.56** 3.11 0.002 

Enter other govt prog -0.73** -5.14 0.000 


unemployment 

-0.45** -3.46 0.001 

Work limited by health -0.34* -2.42 0.016 

Other city before aged 14 -0.26 -1.58 0.114 


14 

-0.07 -0.42 0.674 

Rural area before aged 14 -0.40 -1.73 0.084 


Number of siblings -0.04 -1.66 0.097 

English good 0.43* 2.08 0.037 

English poor 1.03** 2.60 0.009 

Sexist -0.40* -1.95 0.051 

Sexist*female 0.61 1.56 0.119 


respondent 14 

-0.19 -0.85 0.398 

Father Labourer 0.18 0.75 0.451 

Father Plant operative 0.16 0.70 0.484 

Father Sales 0.05 0.19 0.848 

Father Tradesperson -0.15 -0.69 0.492

290 

Father 

0.24 1.13 0.259 


Father Not employed 0.10 0.36 0.717 

Father holds post-school 0.10 0.78 0.437 


Mother not present when -0.18 -0.67 0.501 

resp 14 

Mother Labourer -0.15 -0.61 0.539 

Mother Plant operative -0.52 -1.81 0.070 

Mother Sales -0.41 -1.77 0.076 

Mother Tradesperson -0.33 -0.98 0.326 

Mother 

0.07 0.30 0.766 


Mother Not employed -0.07 -0.37 0.711 

Mother post-school -0.02 -0.14 0.890 


Catholic 0.43** 3.12 0.002 

Presbyterian 0.49* 2.10 0.036 

Methodist 0.25 1.14 0.253 

Other Christian -0.10 -0.49 0.623 


No religion 0.41* 2.24 0.025 

rho -0.28 

Wald test of Rho=0 (chi2 0.11 





qualification year 10 at school, Longest job by 1984 is 1 year, lived mostly in state capital city until respondent aged 14, 

English is first language, father clerical worker when respondent aged 14, mother clerical worker when respondent 

aged 14, religion brought up in is Anglican. 

Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1%.

291 

Table A2.6 Part B Selection/participation equation of the bivariate probit where age 

included in employment model, attrition weights 


SYETP 

participation, 

1986 survey 

data 


Gender=female 0.07 0.53 0.596 

Age at 1984 survey -0.08** -2.90 0.004 

Married 1984 -1.00** -3.30 0.001 

Children 1984 0.35 0.86 0.389 

Children*female -0.10 -0.17 0.866 



Islander 

-0.38 -0.79 0.429 


Victoria 0.09 0.56 0.574 

Queensland -0.14 -0.67 0.504 


Territory 

-0.12 -0.63 0.526 

Western 

Australia/Tasmania 

0.38* 2.20 0.028 



Private school -0.90* -2.14 0.032 


Apprenticeship -0.11 -0.38 0.706 


qualification 

0.01 0.04 0.967 

Year 12 of school 0.41* 2.19 0.028 


Year 9 of school or less 0.02 0.10 0.919 


< 1 year -0.06 -0.33 0.745 

2 years 0.07 0.31 0.760 

3 years + -0.48* -1.92 0.054 

CEP referrals 1984 0.13 1.79 0.074 

Proportion of time Pre-June 0.34* 2.39 0.017 

1984 spent in 

unemployment 

Work limited by health -0.68** -2.92 0.003 

Other city before aged 14 -0.39* -2.33 0.020 


14 

-0.53** -3.21 0.001 


Overseas before aged 14 -0.61 -1.26 0.209 


English good -0.17 -0.63 0.530 

English poor -0.68 -1.10 0.272 

Sexist 0.38 1.23 0.220 

Sexist*female -0.90 -1.59 0.111 


respondent aged 14 

-0.25 -0.84 0.403 

Father Labourer -0.09 -0.29 0.772

292 

Father Plant operative -0.15 -0.53 0.596 

Father Sales -0.21 -0.60 0.546 

Father Tradesperson -0.28 -1.07 0.287 


-0.19 -0.71 0.475 


Father Not employed -0.44 -1.34 0.179 



-0.23 -1.61 0.108 


resp 14 

0.37 1.22 0.222 


Mother Plant operative 0.58 1.81 0.070 

Mother Sales 0.22 0.78 0.435 

Mother Tradesperson 0.20 0.46 0.648 

Mother manager/ prof 

/para-professional 

-0.32 -1.12 0.265 




0.22 1.27 0.203 

Catholic 0.00 -0.03 0.977 

Presbyterian 0.33 1.34 0.179 

Methodist 0.25 0.93 0.351 



No religion 0.14 0.69 0.492 






293 


included in employment model, attrition weights 

ever employed in 1986 


SYETP 1.03 0.83 0.404 

CEP referrals -0.03 -0.36 0.716 

Gender=female -0.46 -4.45 0.000 

Married 0.03 0.14 0.886 

Children -0.39 -1.46 0.143 


Spouse employed 0.55 2.23 0.026 


Islander -0.34 -1.36 0.175 

Other ethnic minority -0.37 -1.96 0.051 

Victoria -0.13 -1.01 0.312 

Queensland 0.05 0.34 0.736 


Territory -0.48 -2.97 0.003 

Western 

Australia/Tasmania -0.12 -0.67 0.504 



Private school 0.64 1.96 0.050 




qualification 0.10 0.54 0.590 



Year 9 of school or less -0.29 -2.03 0.043 


< 1 year 0.22 1.57 0.116 

2 years 0.22 1.32 0.185 

3 years + 0.57 3.14 0.002 

Enter other govt prog -0.72 -5.07 0.000 


unemployment -0.44 -3.58 0.000 

Work limited by health -0.34 -2.44 0.014 



14 -0.08 -0.53 0.595 




English good 0.43 2.10 0.036 

English poor 1.03 2.61 0.009 

Sexist -0.40 -1.97 0.049 



respondent 14 -0.20 -0.87 0.383 

Father Labourer 0.18 0.75 0.451 


Father Sales 0.05 0.18 0.856

294 




0.24 1.13 0.257 



qualification when resp 14 0.09 0.78 0.436 


resp 14 -0.17 -0.64 0.519 







0.07 0.30 0.761 



qualification when resp 14 -0.02 -0.11 0.910 

Catholic 0.43 3.19 0.001 


Methodist 0.25 1.19 0.235 



No religion 0.40 2.19 0.028 

1.13 2.83 0.005 

rho -0.26 










295 

Table A2.7 Part B Selection/participation equation of the bivariate probit where CEP 

referrals included in employment model, attrition weights 

Participation equation 

from bivariate probit 


SYETP 


1986 survey 

data 



Age at 1984 survey -0.08 -2.74 0.006 

Married 1984 -1.00 -3.43 0.001 

Children 1984 0.35 0.86 0.389 




Islander -0.38 -0.79 0.430 


Victoria 0.09 0.57 0.571 

Queensland -0.14 -0.67 0.504 


Territory -0.12 -0.63 0.527 

Western 

Australia/Tasmania 0.38 2.20 0.028 



Private school -0.90 -2.16 0.031 









< 1 year -0.06 -0.32 0.752 

2 years 0.07 0.30 0.763 

3 years + -0.49 -1.97 0.049 

CEP referrals 1984 0.13 1.75 0.081 

Proportion of time Pre-June 

1984 spent in 

unemployment 0.34 2.41 0.016 




14 -0.52 -3.29 0.001 




English good -0.17 -0.63 0.526 

English poor -0.68 -1.10 0.273 

Sexist 0.38 1.24 0.215 

Sexist*female -0.90 -1.59 0.111 


respondent aged 14 -0.25 -0.84 0.402 


296 






-0.19 -0.71 0.475 





resp 14 0.37 1.22 0.223 






/para-professional -0.32 -1.11 0.265 




Catholic -0.01 -0.03 0.975 


Methodist 0.26 0.96 0.336 



No religion 0.14 0.72 0.473 

constant 0.41 0.60 0.546 





Absolute value of z statistics in parentheses * significant at 5%, ** significant at 1%.

297 

Table A2.8 Part A Employment equation from bivariate probit where CEP referrals and 

age included in employment model, attrition weights 

ever employed in 

1986 survey 


SYETP 1.19 0.81 0.416 

CEP referrals -0.03 -0.40 0.692 

Age 1984 0.01 0.33 0.739 

Gender=female -0.46 -4.18 0.000 

Married 0.03 0.14 0.891 

Children -0.39 -1.47 0.143 


Spouse employed 0.54 2.18 0.029 


Islander -0.34 -1.32 0.186 

Other ethnic minority -0.37 -1.95 0.051 

Victoria -0.13 -1.00 0.318 

Queensland 0.05 0.36 0.716 


Territory -0.48 -2.75 0.006 

Western 

Australia/Tasmania -0.13 -0.68 0.497 



Private school 0.65 1.93 0.054 







Year 9 of school or less -0.29 -2.02 0.043 


< 1 year 0.23 1.64 0.102 

2 years 0.21 1.22 0.221 

3 years + 0.57 3.14 0.002 

Enter other govt prog -0.72 -4.64 0.000 


unemployment -0.46 -3.53 0.000 




14 -0.06 -0.33 0.742 




English good 0.43 2.10 0.035 

English poor 1.04 2.64 0.008 

Sexist -0.41 -2.00 0.045 



respondent 14 -0.19 -0.81 0.419 

Father Labourer 0.18 0.76 0.449

298 


Father Sales 0.05 0.20 0.844 




0.24 1.14 0.254 





resp 14 -0.18 -0.68 0.497 







0.08 0.32 0.745 




Catholic 0.43 3.09 0.002 


Methodist 0.24 1.05 0.292 



No religion 0.40 2.07 0.038 

0.89 0.96 0.338 

rho -0.36 










299 

Table A2.8 Part B Selection/participation equation of the bivariate probit where CEP 

referrals and age included in employment model, attrition weights 


SYETP 


1986 survey 

data 



Age at 1984 survey -0.08 -2.91 0.004 

Married 1984 -0.98 -2.99 0.003 

Children 1984 0.35 0.87 0.386 




Islander -0.38 -0.80 0.426 


Victoria 0.09 0.56 0.576 

Queensland -0.15 -0.68 0.494 


Territory -0.12 -0.63 0.526 

Western 

Australia/Tasmania 0.38 2.19 0.029 



Private school -0.89 -2.08 0.038 









< 1 year -0.06 -0.29 0.770 

2 years 0.07 0.32 0.749 

3 years + -0.48 -1.86 0.062 

CEP referrals 1984 0.13 1.76 0.078 

Proportion of time Pre-June 

1984 spent in 

unemployment 0.34 2.38 0.017 




14 -0.53 -3.20 0.001 




English good -0.18 -0.65 0.515 

English poor -0.69 -1.11 0.267 

Sexist 0.39 1.25 0.211 

Sexist*female -0.92 -1.62 0.106 


respondent aged 14 -0.26 -0.85 0.394 


300 






-0.19 -0.73 0.464 





resp 14 0.37 1.21 0.226 






/para-professional -0.32 -1.12 0.261 




Catholic 0.00 -0.03 0.979 


Methodist 0.24 0.85 0.393 



No religion 0.13 0.64 0.521 

constant 0.43 0.66 0.512 






301

302 

Bibliography 

AIMA (1985) "Reducing the risk: unemployed migrant youth and labour market programs, 

Report to the Minister for Immigration and Cultural Affairs", Australian Institute of 

Multicultural Affairs, Melbourne Australia. 

Alderman, H.; Behrman, J.; Kohler, H.; Maluccio, J.; and Watkins, S. (2000) "Attrition in 

longitudinal household survey data: some tests for three developing country samples" 

World Bank Policy research working paper no. 2447, Washington. 

Ashenfelter, O., Harmon, C. and Oosterbeek, H. (1999) “A review of estimates of the 

schooling/earnings relationship, with tests for publication bias”, Labour Economics 

Vol. 6, pp453-470. 

Augurzky, B and Schmidt, C.M. (2001) “The propensity score: a means to an end” Institute for 

the Study of Labor (IZA) Discussion Paper no. 271, Institute for the Study of Labor 

(IZA), Bonn 

Aungles, P. and Stewart, B.P. (1986)"The duration of unemployment of CES registered 

jobseekers: an econometric analysis" Bureau of Labour Market Research Working 

Paper no. 66 Department of Employment and Industrial Relations, Commonwealth of 

Australia, Canberra. 

Baird, C. E.; Gregory, R.H.; and Gruen, F. H. (eds.) (1981) "Youth employment, education 

and training" Conference Papers Academy of the Social Sciences in Australia, Centre 

for Economic Policy Research, Australian National University, Canberra. 

Baker, S. (1984) "A comparative evaluation of the impact on participants of selected youth 

labour force programs" Bureau of Labour Market Research Working Paper no. 50 

Department of Employment and Industrial Relations, Commonwealth of Australia, 

Canberra. 

Baker, S.; Nicoll, P. and Yik, W.F. (1985) "Aboriginal participation in the Community 

Employment Program State/Territory element in the Northern Territory and Australia" 

Bureau of Labour Market Research Conference Paper no 56, Department of 

Employment and Industrial Relations, Commonwealth of Australia, Australian, 

Government Publishing Service Canberra. 

Becketti, S.; Gould, W.; Lillard, L. and Welch, F. (1988) "The panel study of Income 

Dynamics after 14 years: an evaluation", Journal of Labor Economics Vol. 6 (4) pp 

473-492. 

BLMR Bureau of Labour Market Research (1983) "Employment and training programs for 

young people: analysis of assistance in 1980-81" Bureau of Labour Market Research 

Research Report no.2, Department of Employment and Industrial Relations, 

Commonwealth of Australia, Canberra. 

BLMR Bureau of Labour Market Research (1984) "The National Employment and Training 

System (NEAT): an evaluation" Bureau of Labour Market Research Monograph Series 

no.3, Department of Employment and Industrial Relations, Commonwealth of 

Australia, Australian, Government Publishing Service Canberra. 

BLMR Bureau of Labour Market Research (June 1984) "Submission to committee of enquiry 

into labour market programs", Bureau of Labour Market Research, Research Report no. 

5 Australian Government Publishing Service, Canberra.

BLMR Bureau of Labour Market Research (1986) "The first wave of the Australian 

Longitudinal Survey: facts and figures about young CES registrants", Bureau of Labour 

Market Research Monograph Series no.12, Department of Employment and Industrial 

Relations, Commonwealth of Australia, Australian Government Publishing Service 

Canberra. 

Blundell, R. and Costa Dias, M. (2000) "Evaluation methods for non-experimental data" Fiscal 

Studies Vol. 21 (4) pp 427-468. 

Bonjour, D., Dorsett, R., Knight, G., Lissenburgh, S., Mukherjee, A., Payne, J., Range, M., 

Urwin, P., and White, M. (2001) New Deal for Young People: National Survey of 

Participants: Stage 2, Employment Service Research and Development Report ESR67, 

Employment Service, Sheffield, UK. 

Bonnal, L., Fougere, D. and Serandon, A. (1994) "L'impact des pispositifs d'emploi sur le 

devenir des jeunes chomeurs: une evaluation econometrique sur donnees 

longitudinales", Economie et Prevision no.115 pp1-28, Crespe, Universite d'Orleans. 

Bonnal, L., Fougere, D. and Serandon, A. (1997) "Evaluating the impact of French 

employment policies on individual labour market histories", Review of Economic 

Studies 64, 683-713. 

Breen, R. (1991) "Education, Employment and Training in the Youth Labour Market", General 

Research Series Paper No. 152, Economic and Social Research Institute, Dublin. 

Brooks, C. and Volker, P. (1984) "The probability of leaving unemployment: the evidence 

from Australian gross flows data" Bureau of Labour Market Research Conference 

paper no 47, Department of Employment and Industrial Relations, Commonwealth of 

Australia, Australian, Government Publishing Service Canberra. 

Bryant, E.C. (1978) " Survey statistics in social program evaluation" pp41-55 in David, 

H.A.(ed) "Contributions to survey sampling and applied statistics: papers in honour of 

H. O. Hartley" Academic Press, New York. 

Burtless, G. (1990) "The economists' lament: public assistance in America" Journal of 

Economic Perspectives, Vol.4 Issue 1 pp57-78. 

Calmfors, L. (1994) "Active labour market policy and unemployment - a framework for the 

analysis of crucial design features" OECD Economic Studies V 22, pp7-47. 

Capellari, L. (2001) "Earnings mobility among Italian low paid workers", Institute for Social 

and Economic Research Working paper 2001-13, Institute for Social and Economic 

Research, University of Essex. 

Chapman, B. (1985) "Continuity and change: labour market programs and education 

expenditure", The Australian Economic Review 71, pp98-113. 

Cochran, W. and Rubin, D.B. (1973), "Controlling Bias in Observational Studies", Sankyha, 

35, 417-446. 

Cockx, B., Van der Linden B., and Karaa, A. (1996) "On active labour market policies and job 

duration: a microeconometric evaluation for Belgium" 1996 Conference Proceedings, 

Vol.4. European Association of Labour Economists (EALE). 

D'Agostino, R.B. and Rubin, D.B. (2000) "Estimating and using propensity scores with 

partially missing Data" Journal of the American Statistical Association, September 

2000, Vol. 95 no. 451 pp749-759. 

Daly, A. (1991) "Are education and on-the-job training complementary or substitute activities? 

Evidence from Australia, Great Britain and the US", Centre for Aboriginal Economic 

Policy Research Discussion Paper no 260, Australian National University, Canberra. 

303

Friedlander, D.; Greenberg, D.H. and Robins, P.K. (1997) "Evaluating Government Training 

Programs for the Economically Disadvantaged", Journal of Economic Literature, Vol. 

35, No. 4., pp1809-1855. 

De La Dehesa, G. (1997) "Preventing long-term unemployment: discussion" p349-356 in 

Snower, D.J. and De La Dehesa, G. [eds.](1997) "Unemployment policy: government 

options for the labour market", Cambridge University Press. 

DEET (1994) "Newstart evaluation: interim report on effectiveness" Sakkara, M.; Beyers, S.; 

Buchanan, K.; Gatenby, P.; Patston, K; Department of Employment, Education and 

Training, Evaluation and Monitoring Branch, Economic Policy Analysis Division, 

Australian Government Publishing Service, Canberra. 

DEETYA (1996) "Working Nation: evaluation of the employment, education, and training 

elements", Department of Employment, Education, Training and Youth Affairs, 

Australian Government Publishing Service, Canberra. 

Dehija, R. and Wahba, S. (1998) "Propensity score matching methods for non-experimental 

causal studies" NBER Working Paper no. 6829. 

Dehija, R. and Wahba, S. (1999) "Causal effects in non-experimental studies: re-evaluating the 

evaluation of training programs", Journal of the American Statistical Association 94 

(448), pp 1053-1062. 

Dewald, W. G.; Thursby, J. G; and Anderson, R.G.; (1986), “Replication in Empirical 

Economics: The Journal of Money, Credit and Banking Project", The American 

Economic Review, Vol. 76, No. 4. pp 587-603. 

DEYA (1980) "Employer and manpower services: a guide issued by the Department of 

Employment and Youth Affairs", pamphlet March 1980, Department of Employment 

and Youth Affairs, Canberra. 

DEYA (1983) "Youth Policies, programs and issues an Australian background paper", 

Department of Education and Youth Affairs Australian Government Publishing 

Service, Canberra. 

Dillman, D.A.; Eltinge, J.; Groves, R.M. and Little, R.J.A.;(2002) "Survey nonresponse in 

design, data collection and analysis" pp3-26 in Groves, R.M.; Dillman, D.A.; Eltinge, 

J.; Little, R.J.A.; [eds.] (2002)"Survey non-response", Wiley Series in Survey 

Methodology, John Wiley New York. 

Dreze, J.H. and Sneessens, H. (1997) "Technological development, competition from low 

wage economies and low-skilled unemployment" Chapter 8 pp250-277 in Snower, D.J. 

and De La Dehesa, G. [eds.](1997) "Unemployment policy: government options for the 

labour market", Cambridge University Press. 

Edwards, S. (1987) "Do manpower programmes enhance employment opportunities? An 

evaluation of two programmes in Wollongong and Newcastle", Industrial Relations 

Research Centre Monograph (May 1987) Industrial Relations Research Centre 

University of New South Wales. 

Eichler, M. and Lechner, M. (1998) "An evaluation of public employment programmes in the 

East German state of Sachsen-Anhalt" Discussion Paper no.9815, Volswirtschaftliche 

Abteilung, University of St. Gallen, Switzerland. 

EPAC (June 1992) "Unemployment in Australia" Economic Planning Advisory Council, 

Council Paper no.51, Australian Government Publishing Service, Canberra. 

Falaris, E.M. and Peters, H.E. (1998) "Survey attrition and schooling choices", The Journal of 

Human Resources Vol. 33 (2) pp 531-554. 

304

Fay, R.G. (1996) "Enhancing the effectiveness of active labour market policies: evidence from 

Programme evaluations in OECD countries", Labour market and social policy 

Occasional Papers no.18, OECD Working Papers Vol. 4, no.56, Organisation for 

Economic Cooperation and Development, Paris. 

Fisher, R. (1951)"The design of experiments", 6th edition, Oliver and Boyd, London. 

Fitzgerald, J.; Gottshalk, P. and Moffitt, R, (1998a) "An analysis of sample attrition in panel 

data", The Journal of Human Resources Vol. 33 (2) pp 251-299. 

Fitzgerald, J.; Gottshalk, P. and Moffitt, R, (1998b) "An analysis of the impact of sample 

attrition on the second generation of respondents in the Michigan Panel Study of 

Income Dynamics", The Journal of Human Resources Vol. 33 (2) pp 300-344. 

Friedlander, D. and Robins, P.K. (1995) "Evaluating Program Evaluations: New Evidence on 

Commonly Used Non-experimental Methods" The American Economic Review, Vol. 

85, No. 4. , pp 923-937. 

Friedlander, D.; Greenberg, D.H. and Robins, P.K. (1997) "Evaluating government training 

programs for the economically disadvantaged" Journal of economic Literature, Vol.35 

(December 1997) pp1809-1855. 

Frölich, M. (2001) "Non-parametric covariate adjustment: pair-matching versus local 

polynomial matching", Discussion Paper 2000-17, Department of Economics 

University of St. Gallen, Switzerland. 

Frölich, M.; Heshmati, A. and Lechner, M.; (2000) "A microeconometric evaluation of 

rehabilitation of long-term sickness in Sweden" Discussion Paper 2000-04, Department 

of Economics University of St. Gallen, Switzerland. 

Frölich, M.; Heshmati, A. and Lechner, M.; (2001) "A microeconometric evaluation of 

rehabilitation of long-term sickness in Sweden, revised" Discussion Paper 2000-04, 

Department of Economics University of St. Gallen, Switzerland. 

Gerfin, M. and Lechner, M. (2000) "Microeconometric evaluation of the active labour market 

policy in Switzerland" Swiss Institute for International Economics and Applied 

Research (SIAW) mimeo www.siaw.unisg.ch/lechner. 

Ginpil, S. and Hoy, M. (1984) "Some factors affecting the completion rates for young people 

under wage subsidy" Bureau of Labour Market Research Working paper no.46, 


Australian, Government Publishing Service Canberra. 

Greene, W.H. (1991) "Econometric Analysis", Macmillan, New York. 

Griliches, Z. (1986) "Economic Data Issues", in Griliches, S. and Intrilligator, M. (eds.) (1986) 

"Handbook of econometrics Volume 3", North Holland, Amsterdam. 

Gronau, R. (1973) "The effect of children on the housewife's value of time" Journal of Political 

Economy Vol. 81, no 2, part 2, pp S168-S199. 

Gronau, R. (1974) “Wage comparisons – a selectivity bias” Journal of Political Economy Vol. 

82 no. 6, pp 1119-1143. 

Groves, R.M.; Dillman, D.A.; Eltinge, J.L.; Little, R.J.A.; (eds.)(2002)"Survey nonresponse" 

Wiley series in Survey Methodology, John Wiley, New York. 

Gu, X. and Rosenbaum, P. (1993) "Comparison of multivariate matching methods: structures, 

distance and algorithms" Journal of computational and Graphical Statistics 2, pp405- 

420. 

Gueron, J.M. (1990) "Work and welfare: lessons on employment programs" Journal of 

Economic Perspectives, Vol.4 Issue 1 pp79-98 

305

Gujurati, D.N. (1988) "Basic Econometrics" McGraw-Hill, Singapore. 

Ham, J.C. and Lalonde, R.J. (1996) “The effect of sample selection and initial conditions in 

duration models: evidence from experimental data on training”, Econometrica 64 

pp175-205. 

Hamermesh, D.S. (1978) "Subsidies for jobs in the private sector", pp87-122 in Palmer, J. L. 

(1978) [ed] "Creating jobs: public employment programs and wage subsidies" 

Brookings Institute, Washington D.C. 

Harkman, A., and Johansson, A. (2000) "Training or Subsidised Jobs - What Works? 

Employment Impact of Seven Swedish Labour Market Programmes", 

Arbetsmarknadsstyrelsen, mimeo. 

Harris, M. (1996) "Modelling the probability of youth unemployment in Australia" Economic 

Record, Vol. 72 no 217 p118-130. 

Harris, P. (2001) “From relief to mutual obligation: welfare rationalities and unemployment in 

20 th century Australia” Journal of Sociology Vol. 37 (1) pp5-24. 

Hausman, J.A. and Wise, D. (1979) "Attrition bias in experimental and panel data: the GARY 

income maintenance experiment", Econometrica 46 (6) pp931- 959. 

Haveman, R.H. (1982) "Marginal employment subsidies" Organisation for Economic 

Cooperation and Development, Paris. 

Heckman, J.J. (1974) "Shadow prices, market wages and labor supply" Econometrica Vol.42 

pp 679-694. 

Heckman, J.J. (1976) "The common structure of statistical models of truncation, sample 

selection and limited dependent variables and simple estimator for such models" 

Annals of Economics and Social Measurement Vol. 5 pp475-492. 

Heckman, J.J. (1978) “Dummy endogenous variables in a simultaneous equation system” 

Econometrica 46, pp931-959. 

Heckman, J.J. (1979) "Sample selection bias as specification error", Econometrica 47 pp 153- 

161. 

Heckman, J.J. and Hotz, V.J. (1989) "Choosing among alternative non-experimental methods 

for estimating the impact of social programs: the case of manpower training" Journal of 

the American Statistical Association Vol. 84 no. 408 pp862-874. 

Heckman, J.J.; Ichimura, H.; Smith, J. and Todd, P.E. (1998) “Characterising selection bias 

with experimental data” Econometrica 66 1017-1098. 

Heckman, J.J., Ichimura, H. and Todd, P.E. (1997) "Matching as an econometric evaluation 

estimator: evidence from evaluating a job training programme", Review of Economic 

Studies 64(4), pp605-654. 

Heckman, J.J.; Ichimura, H. and Todd, P.E. (1998) "Matching as an econometric evaluation 

estimator" Review of Economic Studies 65(1), pp261-294. 

Heckman, J.J.; Lalonde, R.J.; Smith, J. A. (1999) "The economics and econometrics of active 

labour market programs" Chapter 31 pp1865-2097 in Ashenfelter, O. C. and Card, D. 

[eds.] (1999) "Handbook of labour economics Volume 3A", Elsevier Science B.V. 

Amsterdam. 

Heckman J.J. and Robb, R. Jr. (1985) "Alternative methods for evaluating the impact of 

interventions: an overview" Journal of Econometrics Vol. 30, pp239-267. 

306

Heckman J.J. and Smith, J. A. (1998) "The sensitivity of experimental impact estimates: 

evidence from the National JTPA study" in Freeman, R. and Katz, L. [eds.] (1998) 

"Youth employment and unemployment in the OECD countries, University of Chicago 

Press, Chicago Illinois. 

Hoem (1985) "Weighting, misclassification and other issues in the analysis of survey samples 

of life histories" Chapter 5 in Heckman, J.J. and Singer, B. (Eds.) (1985) "Longitudinal 

analysis of labor market data", Cambridge University Press. 

Hoy, M. (1983) "Review of five years of operation of the Special Youth Employment Training 

Program" Bureau of Labour Market Research Conference Paper No. 18, Department of 

Employment and Industrial Relations, Commonwealth of Australia, Canberra. 

Hoy, M.A. and Lampe, G.L. (1982) "Women in national training and employment programs" 

Bureau of Labour Market Research Conference Paper no.13, Department of 


Hoy, M and Paterson, P. (1983) "Data on national training and employment programs" Bureau 

of Labour Market Research Technical Paper no.6, Department of Employment and 

Industrial Relations, Commonwealth of Australia, Canberra. 

Hoy, M.A. and Ryan, C.A. (1984) "Participating employers responses to the SYETP wage 

subsidy scheme", Bureau of Labour Market Research Working Paper no. 40, Australian 

Government Publishing Service, Canberra. 

Hsaio, C. (1986) "Analysis of panel data" Cambridge University Press. 

Hujer, R. and Caliendo, M. (2000) "Evaluation of active labour market policy: methodological 

concepts and empirical estimates" Institute for the Study of Labor (IZA) Discussion 

Paper no. 236, Institute for the Study of Labor (IZA), Bonn. 

Imbens, Guido (2000) “The Role of the Propensity Score in Estimating Dose-Response 

Functions” Biometrika. 87: 706-710. 

Jackman, R.A and Layard, P.R.G (1980) "The efficiency case for long run labour market 

policies", Economica V 47 pp331-349. 

Johannesson, J. (1985) "The Swedish experience with labour market policy" Bureau of Labour 

Market Research Working Paper no.62, Department of Employment and Industrial 

Relations, Commonwealth of Australia, Canberra. 

Johnson, G.E. and Layard, P.R.G. (1986) " The natural rate of unemployment: explanations 

and policy" Chapter 16 pp921-999 Volume 2 Ashenfelter, O. and Layard, R. [Eds.] 

"Handbook of Labor economics" North-Holland, Amsterdam. 

Johnson, G.E. (1980) "The theory of labour market intervention", Economica V 47, pp309- 

329. 

Junankar, P.N. (1986) "Program of research and actions on the development of the labour 

market: costs of unemployment", Commission of the European Communities, 

Luxembourg. 

Kaldor, N. (1936) "Wage Subsidies as a Remedy for Unemployment" The Journal of Political 

Economy, Vol. 44, No. 6. (Dec., 1936), pp. 721-742 

Katz, L.F. (1996) "Wage subsidies for the disadvantaged" National Bureau of Economic 

Research Working Paper 5679, National Bureau of Economic Research, Cambridge 

Massachusetts. 

307

Kesteven, S. (1987) "Commonwealth employment and training schemes since 1973" 

Education and Welfare Group, Legislative Research Service Current Issues Paper 1 

1987-88, Department of the Parliamentary Library, The Parliament of the 

Commonwealth of Australia, Commonwealth of Australia. 

King, G. (1995) "replication, replication '' with comments from nineteen authors and a 

response, "a revised proposal, proposal,'' PS: Political Science and Politics, Vol. 

XXVIII, No. 3 (September, 1995): Pp. 443-499. 

King, G. (2002) "The future of the replication movement", April 2002 meetings of the 

International Studies Organisation, mimeo http://GKing.Harvard.Edu. 

King, G.; Honaker, J.; Joseph, A.; and Scheve, K. (2001) "Analysing incomplete political 

science data: an alternative algorithm for multiple imputation" American Political 

Science Review Vol.95 No.1 pp49-69. 

Kirby, P.E.F. (1981) "An overview of Australian experience with manpower programs" pp4.1- 

4.45 in Baird, C. E.; Gregory, R.H.; Gruen, F. H. (eds.) "Youth employment, education 

and training" (1981) Conference Papers, Academy of the Social Sciences in Australia, 

Centre for Economic Policy Research, Australian National University, Canberra. 

Knapp, L. and Seaks, T. (1998) “A Hausman test for a dummy variable in probit” Applied 

Economics Letters Vol.5 pp321-323. 

Kronenburg, N.; McRae, I. and Nicoll, P; (1985) "The Australian longitudinal survey - sample 

design" Bureau of Labour Market Research Technical paper no 23, Department of 

Employment and Industrial Relations, Commonwealth of Australia, Australian, 


Lahiri, K. and Schmidt, P. (1978) “On the estimation of triangular structural systems”, 

Econometrica 46, pp1217-1221. 

Lalive, R.; van Ours, J.C. and Zweimuller, J. (2000) " The impact of active labor market 

programs and benefit entitlement rules on the duration of unemployment" Centre for 

Economic Research (IEW) Working Paper no.2000-41, University of Zurich, 

Switzerland. 

LaLonde, R. J. (1986) "Evaluating the Econometric Evaluations of Training Programs with 

Experimental Data", The American Economic Review, Vol. 76, No. 4. (Sep 1986) pp. 

604-620. 

Larsson, L. (2000) "Evaluation of Swedish youth labour market programs", Uppsala 

University mimeo. 

Layard, R (1997) "Preventing long-term unemployment" chapter 11 pp333-351 in Snower, 

D.J. and De La Dehesa, G. [eds.](1997) "Unemployment policy: government options 

for the labour market", Cambridge University Press. 

Layard, R.; Nickell, S. and Jackman, R; (1991) "Unemployment, macroeconomic performance 

and the labour market" Oxford University Press. 

Lechner, M. (2000) "A note on the common support problem in applied evaluation studies" 

Swiss Institute for International Economics and Applied Economic Research (SIAW) 

Discussion Paper no.2001-01, University of St. Gallen, Switzerland. 

Lechner, M. (2001) "Some practical issues in the evaluation of heterogeneous labour market 

programmes by matching methods" Swiss Institute for International Economics and 

Applied Economic Research (SIAW) Discussion Paper 2000-14, University of St. 

Gallen, Switzerland. [forthcoming in Journal of the Royal Statistical Society Series A, 

165(1) pp59-82.] 

308

Lewis, H.G. (1963) "Unionism and relative wages", University of Chicago Press, Chicago 

Illinois. 

Lewis, H.G. (1974) "Comments on selectivity biases in wage comparisons" Journal of Political 

Economy Vol. 82. (6) pp1145-1155. 

Lewis, P.E.T. (1983) "The role of relative wages in the substitution between young and adult 

workers in Australia" Bureau of Labour Market Research Working Paper no.19, 


Canberra. 

Lillard, L. and Panis, C.W.A. (1998) “Panel Attrition from the Panel Study of Income 

Dynamics” The Journal of Human Resources 33, no. 2: 437-57. 

Lindbeck, A. (1994)"The Welfare State and the Employment Problem" The American 

Economic Review, Vol. 84, No. 2, Papers and Proceedings of the Hundred and Sixth 

Annual Meeting of the American Economic Association. (May, 1994), pp. 71-75. 

Maddala, G.S. (1983)"Limited dependent and qualitative variables in econometrics" 

Econometric Society Monograph no 3, Cambridge University Press. 

Martin, J.P. (1998) "What works among active labour market policies: evidence from OECD 

countries´ experiences", Labour market and social policy Occasional Papers no.35, 

OECD Working Papers Vol. 6, no.82, Organisation for Economic Cooperation and 

Development, Paris. 

Marx, I. (2001) "Job subsidies and cuts in employers' social security contributions: the verdict 

of empirical evaluation studies" International Labour Review Vol. 140 No. 1, 

International Labour Organisation, Geneva. 

McGillicudy, R.; Athanasou, J. and Longford, R. (1986) “Unemployment registrations with 

the CES in NSW: a regional Analysis" September 1986, NSW Department of Industrial 

Relations, Information and policy unit, Human Resources Division. 

Mckay, R. and Hope, C. (1986) "Advances in the evaluation of labour force programs: issues 

and methodological approached" Bureau of Labour Market Research Conference Paper 

no.61, Department of Employment and Industrial Relations, Commonwealth of 

Australia, Canberra. 

Mcrae, I. (1984) "The Australian National Longitudinal Survey" Bureau of Labour Market 

Research Conference Paper no.44, Department of Employment and Industrial 

Relations, Commonwealth of Australia, Canberra. 

Mcrae, I. (1986) "What happens to the young unemployed - some facts from the ALS", 

Australian Longitudinal Survey Working Paper no.9, The Office of the Australian 

Council for Employment and Training. 

Mcrae, I.; Parkinson, G.; Woyzbun, L. (1984-1987) Australian Longitudinal Survey Waves 1 

to 4, Level 1. Principal Investigators: Mcrae, I.; Parkinson, G.; Woyzbun, L.; Data 

collected by Roy Morgan Research Centre, Canberra. Ian Mcrae, Bureau of Labour 

Market Research, [producer], Canberra. Social Science Data Archives, The Australian 

National University, Canberra [distributor]. 

Mcrae, I.; Adena, M.; Montesin, H.; Parkinson, G.; Kronenbug, N. (1985) "The Australian 

Longitudinal survey: response rates and ad hoc technical issues related to the 1984 

survey" Bureau of Labour Market Research Technical Paper no.30, Bureau of Labour 

Market Research, Canberra. 

Merrilees, W. (1981)"The effects of labour market conditions on school enrolment rates", 

Australian Economic Review, third quarter 1981, pp56-60. 

309

Merrilees, W.J. (1984) "Do Wage subsidies stimulate training? An evaluation of the CRAFT 

rebate scheme", Australian Economic Papers pp235-248. 

Millard, S. and Mortenson, D. (1997)" The unemployment and welfare effects of labour 

market policy: a comparison of USA and the UK" chapter 17 pp545-572 in Snower, 

D.J. and De La Dehesa, G. [eds.](1997) "Unemployment policy: government options 


Miller, P. and Volker, P. (1987) "The youth labour market in Australian: a survey of the issues 

and evidence", Discussion paper no. 171 Department of Economics, Research School 

of Social Sciences, Australian National University. 

O'Connell, P.J. and McGinnity, F. (1997) "What works, who works? The employment and 

earnings effects of active labour market programmes among young people in Ireland", 

Work, Employment and Society, Vol. 11, No. 4, pp. 639-661. 

OECD (1988) "Measure to assist the long-term unemployed: recent experience in some OECD 

countries" Organisation for Economic Cooperation and Development, Paris. 

OECD [Grubb, D.; Lippoldt, D. and Tergeist, P.;] (2001) "Innovations in labour market 

policies: The Australian way" Organisation for Economic Cooperation and 

Development, Paris. 

Pagan, A. and Ullah, A. (1999) "Nonparametric Econometrics", Cambridge University Press, 

Cambridge. 

Parkinson, G. and Mcrae, I. (1985) "The Australian longitudinal survey - questionnaire design 

aspects" Bureau of Labour Market Research Technical paper no 24, Department of 

Employment and Industrial Relations, Commonwealth of Australia, Australian 


Paterson, P. (1982) "Manpower program evaluation activities of the Bureau of Labour Market 

Research", Bureau of Labour Market Research Conference Paper no.12, Department of 


Paterson, P.R. and Mackay, K.R. (1982a) "Youth employment in Australia", Bureau of Labour 

Market Research Working Paper no.8, Department of Employment and Industrial 

Relations, Commonwealth of Australia, Australian, Government Publishing Service 

Canberra. 

Paterson, P.R. and Mackay, K.R. (1982b) "Changes in the youth labour market: 1971 to 1981" 

Bureau of Labour Market Research Working Paper no.11, Department of Employment 

and Industrial Relations, Commonwealth of Australia, Canberra. 

Pfefferman, D. (1993) "The role of sampling weights when modelling survey data" 

International Statistical Review Vol. 61 (2), pp317-337. 

Phelps, E.S. (1994) "Low-Wage Employment Subsidies versus the Welfare State" The 

American Economic Review, Vol. 84, No. 2, Papers and Proceedings of the Hundred 

and Sixth Annual Meeting of the American Economic Association. (May, 1994), pp. 

54-58. 

Phelps, E.S. (1997) "Wage subsidy programmes: alternative designs" chapter 7 pp206-244 in 

Snower, D.J. and De La Dehesa, G. [eds.](1997) "Unemployment policy: government 

options for the labour market", Cambridge University Press. 

Piggot, J and Chapman, B. (1995) “Costing the Job Compact” Economic Record V.71 (215) 

pp313-328. 

Puhani, P. (2000) "The Heckman correction for sample selection and it's critique a short 

survey" Journal of Economic Surveys V.14, pp53-68. 

310

Quandt, R. (1972) "A new approach to switching regressions", Journal of the American 

Statistical Association Vol. 67, pp306-310. 

Rao, G.L. and Jones, F.L. (1986) "Effectiveness of youth manpower programs” Bureau of 

Labour Market Research Working paper no.61, Department of Employment and 

Industrial Relations, Commonwealth of Australia, Australian, Government Publishing 

Service Canberra. 

Richardson, J. (1998) "Do wage subsidies enhance employability? Evidence from Australian 

youth" Discussion Paper no.387, April 1998, Centre for Economic Performance, 

London School of Economics. 

Ridder, G. (1990) "Attrition in multi-wave panel data" pp45-67 in Hartog, J., Ridder, G. and 

Theeuwes, J. (eds.)(1990) "Panel data and labour market studies", North-Holland, 

Amsterdam. 

Rosenbaum, P.R. (1984) “From association to causation in observational studies: the role of 

tests of strongly ignorable treatment assignment”, Journal of the American Statistical 

Association Vol.79 No.38, pp41-48. 

Rosenbaum, P.R. and Rubin, D.B. (1983), "The Central Role of the Propensity Score in 

Observational Studies for Causal Effects", Biometrika, 70, 1, 41-55. 

Rosenbaum, P.R. and Rubin, D.B. (1985a) "Constructing a control group using multivariate 

matched sampling methods that incorporate the propensity score" The American 

Statistician Vol. 39, pp33-38. 

Rosenbaum, P.R. and Rubin, D.B. (1985b) "The bias due to incomplete matching", Biometrics 

41 (March 1985) pp103-116. 

Rosholm, M. (1998) "Transitions in the labour market", University of Aarhus Department of 

Economics PhD Thesis, University of Aarhus, Denmark. 

Ross, R.T. (1988) "Teenagers and the labour market 1983-1988", Social Welfare Research 

Centre Discussion paper no.8 (December 1988) University of NSW. 

Ross, R.T. (1989) "Labour market programs and income support: The Australian Experience" 

in Saunders, P. and Jamrozik, A. (eds.)(September 1989) "Social Policy and Inequity in 

Australia and New Zealand", Social Welfare Research Centre, University of New 

South Wales, Reports and Proceedings no 78. 

Routley, V.C. (1984) "Register of government employment and training programs", Bureau of 

Labour Market Research Technical Paper no.19 (March 1984), Department of 


Roy, A.D (1951) "Some thoughts on the distribution of earnings" Oxford Economics Papers 

Vol.3 pp 135-146. 

Rubin, D.B. (1974) "Estimating causal effects to treatments in randomized and nonrandomized 

studies", Journal of Educational Psychology 66, pp688-701. 

Rubin, D.B. (1980), "Bias Reduction Using Mahalanobis-Metric Matching", Biometrics, 36, 

293-298. 

Rubin, D.B. (1986) "Statistics and causal inference, Comment: Which ifs have causal 

answers?" Journal of the American Statistical Association Vol. 81 Issue 396 pp 961- 

962. 

Scherer, P. (1985) "The Kirby report: implications for training, education and work" Bureau of 

Labour Market Research Conference Paper no.54, Department of Employment and 

Industrial Relations, Commonwealth of Australia, Canberra. 

311

Schmertmann, C. P. (1994) "Selectivity bias correction methods in polychotomous sample 

selection models", Journal of Econometrics 60 pp101-132. 

Stricker, P. and Sheehan, P. (1981) "Hidden unemployment: the Australian experience" 

Institute of Applied Economic and Social Research, Melbourne. 

Sheen, V. and Trethewey, J. (1991) "Unemployment in the recession: policies for reform", 

Brotherhood of St. Lawrence, Melbourne. 

Sianesi, B. (2001)"An evaluation of the Swedish system of active labour market programmes 

in the 1990's" Institute of Fiscal Studies Working paper no.02/01, University College 

London. 

Sianesi, B. (2002)" Swedish system of active labour market programmes in the 1990's: overall 

effectiveness and differential performance" Institute of Fiscal Studies Working paper 

no.02/03, University College London. 

Sloan, J. (1985) "Comment on Continuity and change: labour market programs and education 

expenditure", The Australian Economic Review 71, pp114-115. 

Smith, R.E. (1983) "Employment subsidies in theory and practice: the Special youth 

Employment Training Program?" Centre for Economic Policy Research Discussion 

Paper no.69, Australian National University, Canberra. 

Smith, R.E. (1984a) "Estimating the impacts of job subsidies on the distribution of 

unemployment: reshuffling the queue?" Centre for Economic Policy Research 

Discussion Paper no.95, (may 1984) Australian National University, Canberra. 

Smith, R.E. (1984b)"How effective has the SYETP job subsidy really been" Discussion paper 

no 104, (August 1984) Department of economics, Research School of Social Sciences, 

Australian National University. 

Smith, J. (2000) "A critical survey of empirical methods for evaluating active labour market 

policies", Schweiz Zeitschrift für Volkswirtschaft und Statistik Vol. 136(3) pp1-22. 

Smith, J. and Todd, P. (2000) "Does matching overcome Lalonde's critique of nonexperimental 

estimators", University of Western Ontario Mimeo. 

Smith, J. and Todd, P. (2003) "Does matching overcome Lalonde's critique of nonexperimental 

estimators", Working paper no. 2003/5 CIBC Working Paper Series 

University of Western Ontario Department of Economics Social Science Centre, 

University of Western Ontario, Canada. 

http://www.ssc.uwo.ca/economics/centres/cibc/wp2003/Smith05.pdf 

Snower, D.J. and De La Dehesa, G. [eds.] (1997) "Unemployment policy: government options 


Snower, D.J. (1994) "Converting Unemployment Benefits into Employment Subsidies" The 

American Economic Review, Vol. 84, No. 2, Papers and Proceedings of the Hundred 

and Sixth Annual Meeting of the American Economic Association. (May, 1994), pp. 

65-70. 

Stanley, T.D. and Jarell, S.B. (1998) “Gender Wage Discrimination Bias? A Meta-Regression 

Analysis”, The Journal of Human Resources, Vol. 33, pp947-973. 

Stretton, A. (1984) "The short term impact on participants of selected labour force programs", 

The Journal of Industrial Relations Vol. 26(1), March 1984 pp 76-90. 

Stretton, A. and Chapman, B.J. (1990) "An analysis of Australian labour market programs" 

Centre for Economic Policy Research Discussion Paper no.247, Australian National 

University, Canberra. 

312

Stromback, M. and Dockery, A. (2000) "Labour market programs, unemployment and 

employment hazards: an application using the 1994-1997 Survey of Employment and 

Unemployment Patterns" Australian Bureau of Statistics {ABS} Occasional Paper Cat. 

No. 6293.0.00.002, ABS, Canberra. 

Tukey, J.W. (1949) "One degree of freedom for non-additivity", Biometrics 5, 232-242. 

van de Ven, W.P.M.M. and van Praag, M.S. (1981)"The demand for deductibles in private 

health insurance: a probit model with sample selection" Journal of Econometrics Vol. 

17 pp229-252. 

Vella, F. (1998) "Estimating models with sample selection bias: a survey", The Journal of 

Human Resources Vol. 33 (1) pp127-169. 

Victorian State Office (1985) "Leaving School of out of work: a guide for young people" 

pamphlet, Victorian State Office, Australian, Government Publishing Service 

Canberra. 

Wainer, H. (1987) "Eelworms, bullet holes and Geraldine Ferraro: some problems in 

statistically adjusting for survey non-response", Program Statistics Research, Technical 

Report no 87-76 (research report no 87-12), Educational Testing Service, Princeton, 

New Jersey. 

Webster, E. (1997a) "Labour market programs: a review of the literature" Working paper no 

28/97, Melbourne Institute of Applied economic and Social Research, University of 

Melbourne. 

Webster, E. (1997b) "Labour Market Programs and the Australian Beveridge curve: 1978 

to1996" Working paper no 25/97, Melbourne Institute of Applied economic and Social 

Research, University of Melbourne. 

Webster, E. (1998) "Microeconomic evaluations of Australian labour market programs", The 

Australian Economic Review, June 1998, vol. 31, no. 2, pp. 189-201. 

Webster, E. and Summers, P. (1999)"The effect of labour market programs on wage inflation" 

Working paper no 16/99, Melbourne Institute of Applied economic and Social 

Research, University of Melbourne. 

White, H. (1980) "A heteroskedasticity consistent covariance matrix estimator and a direct test 

for heteroskedasticity" Econometrica 48 (4) pp817-830. 

White, H. (1982) "Maximum likelihood estimation of misspecified models" Econometrica, 50 

(1) pp1-25. 

Wielgosz, J.B. (1984)"The referral and placement activity of the Commonwealth Employment 

Service: a case study of the Brisbane Metropolitan Area" Bureau of Labour Market 

Research Working paper no.45, Department of Employment and Industrial Relations, 

Commonwealth of Australia, Australian Government Publishing Service Canberra. 

Wilde, J. (2000) “Identification of multiple equation probit models with endogenous dummy 

regressors” Economics Letters 69 (2000) pp 309-312. 

Windshuttle, K. (1985)"Youth wages, employment and unemployment: critique of a study by 

the Bureau of Labour Market Research", Department of Industrial Relations Working 

paper no.60, University of New South Wales. 

Yatchew, A. and Griliches, Z. (1985) “Specification error in probit models”, The Review of 

Economics and Statistics, Vol. 67 No.1, pp134-139. 

Zabel, J. E. (1998) "An analysis of attrition in the Panel Study of Income Dynamics and the 

survey of Income and Program Participation with an application to a model of labor 

market behaviour", The Journal of Human Resources Vol. 33 (1) pp479-506. 

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