underemployment - NATSEM - University of Canberra

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underemployment - NATSEM - University of Canberra

Presentation outline1. Introduction and Background2. Research question3. Data4. Pathways related to Underemployment5. Descriptive Statistics6. Empirical Model7. Summary and conclusions


Introduction and Background• The 2010 Intergenerational Report highlights mature age labourforce participation as a specific objective to counter the pressuresof population ageing.• The recent Global Financial Crisis (GFC) focussed attention on thegrowing phenomenon of underemployment whereby the number ofhours of people worked declined relative to their preferences.• Underemployment represents an underutilisation of a skilledworkforce, and impacts on individuals – through lower jobsatisfaction, higher turnover, lower earnings and poorer health.• Understanding preferences for labour market participation, andspecifically the drivers of under-participation or non-participation,as well as labour market dynamics on demand and supply side, iskey to understanding both underemployment and its treatment.


Coverage and Definition of ‘mature age’• Population born between the year 1951-1965 (aged 35-59 in2010) including the Baby Boomer (1946-1960) cohort.• Definition of underemployed:part time and would like to work more hours than theycurrently usually work• We use the HILDA longitudinal data source (waves 1-10), withthe following data filtering process


Trends in underemployment over timeby gender1086420Underemployment rate (Male)Underemployment rate (Female)Underemployment rate (Total)2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012YearSource: ABS Labour Force Australia, Cat. No 6202.0


Patterns of underemploymentby age and gender108.69.087.565.7 5.86.35.26.244.33.53.84.520All Person Men WomenAge 25-34 Age 35-44 Age 45-54 Age 55+Source: ABS Labour Force Australia, Cat. No 6202.0


Cum. % freq. (n=827)Most common pathways throughunderemployment5.4%0%t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12 t13 t14 t15 t16 t17 t18Employed (Fulltime)Employed (Parttime overwork/regular)Employed (Parttime underemployed)Not working (Unemployed)Out of labour marketmissing


Cum. % freq. (n=827)Cum. % freq. (n=488)Cum. % freq. (n=85)Cum. % freq. (n=162)Cum. % freq. (n=92)Most common patterns into underemploymenthierarchical cluster analysisType 1Type 258.8%28.4%5.4%0%0%t1 t2 t3 t4 t5 t6 t7 t8 t9 t10t1 t2 t3 t4 t5 t6 t7 t8 t9 t10Type 3Type 451.8%46.7%0%t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12 t13 t14 t15 t16 t17 t180%0%t1 t2 t3 t4 t5 t6 t7 t8 t9 t10t1 t2 t3 t4 t5 t6 t7 t8 t9 t10Employed (Fulltime)Employed (Parttime overwork/regular)Employed (Parttime underemployed)Not working (Unemployed)Out of labour marketmissingEmployed (Fulltime)Employed (Parttime overw ork/regular)Employed (Parttime underemployed)Not w orking (Unemployed)Out of labour marketmissing


Characteristics of underemployment entry clustersFemaleHave apartnerDiplomaEducationor aboveHours ofworkChronicillnessForeignBorn(EnglishSpeakingCountry)ForeignBorn(NonEnglishSpeakingCountry)SpouseIllnessNumberof kidsagebetween 0 to 4Numberof kidsagebetween 5 to 14Type 1 – mixed entryTotal 0.719 0.736 0.374 26.038 0.256 0.105 0.141 0.133 0.099 0.648Type 2 – entry is dominated by out of labour marketTotal 0.833 0.656 0.293 10.639 0.358 0.130 0.154 0.177 0.138 0.841Type 3 – entry is dominated by working part timeTotal 0.953 0.818 0.404 22.515 0.239 0.129 0.094 0.184 0.124 0.978Type 4- entry is dominated by working full timeTotal 0.457 0.724 0.428 37.298 0.163 0.087 0.130 0.118 0.142 0.485Source: Authors’ calculation from HILDA waves 1-10, unit record data


Cum. % freq. (n=185)Cum. % freq. (n=110)Cum. % freq. (n=193)Common exits out of underemploymentType 1 – recent entrantsSubtype 1Subtype 236.2%14.5%0%0%t10 t12 t14 t16 t18t10 t12 t14 t16 t18Subtype 326.4%Employed (Fulltime)Employed (Parttime overwork/regular)Employed (Parttime underemployed)Not working (Unemployed)Out of labour marketmissing0%t10 t12 t14 t16 t18


Cum. % freq. (n=64)Cum. % freq. (n=26)Cum. % freq. (n=72)Common exits out of underemploymentType 2 – entrants from non-participationSubtype 1Subtype 245.3%61.1%0%0%t10 t12 t14 t16 t18t10 t12 t14 t16 t18Subtype 346.2%Employed (Fulltime)Employed (Parttime overwork/regular)Employed (Parttime underemployed)Not working (Unemployed)Out of labour marketmissing0%t10 t12 t14 t16 t18


Cum. % freq. (n=18)Cum. % freq. (n=22)Cum. % freq. (n=45)Common exits out of underemploymentType 3 – entrants from part-time (regular) employmentSubtype 1Subtype 294.4%91.1%0%0%t10 t12 t14 t16 t18t10 t12 t14 t16 t18Subtype 350%Employed (Fulltime)Employed (Parttime overwork/regular)Employed (Parttime underemployed)Not working (Unemployed)Out of labour marketmissing0%t10 t12 t14 t16 t18


Cum. % freq. (n=63)Cum. % freq. (n=13)Cum. % freq. (n=16)Common exits out of underemploymentType 4 – entrants from full-time employmentSubtype 1Subtype 2100%100%0%0%t10 t12 t14 t16 t18t10 t12 t14 t16 t18Subtype 3100%Employed (Fulltime)Employed (Parttime overwork/regular)Employed (Parttime underemployed)Not working (Unemployed)Out of labour marketmissing0%t10 t12 t14 t16 t18


Characteristics of underemployment entry clustersFemaleHave apartnerDiplomaEducationor aboveHours ofworkChronicillnessForeignBorn(EnglishSpeakingCountry)ForeignBorn(NonEnglishSpeakingCountry)SpouseIllnessNumberof kidsagebetween 0 to 4Numberof kidsagebetween 5 to 14Type 1 – mixed entryTotal 0.719 0.736 0.374 26.038 0.256 0.105 0.141 0.133 0.099 0.648Type 2 – entry is dominated by out of labour marketTotal 0.833 0.656 0.293 10.639 0.358 0.130 0.154 0.177 0.138 0.841Type 3 – entry is dominated by working part timeTotal 0.953 0.818 0.404 22.515 0.239 0.129 0.094 0.184 0.124 0.978Type 4- entry is dominated by working full timeTotal 0.457 0.724 0.428 37.298 0.163 0.087 0.130 0.118 0.142 0.485Source: Authors’ calculation from HILDA waves 1-10, unit record data


Characteristics of underemployment entry clustersFemaleHave apartnerDiplomaEducationor aboveHours ofworkChronicillnessForeignBorn(EnglishSpeakingCountry)ForeignBorn(NonEnglishSpeakingCountry)SpouseIllnessNumberof kidsagebetween 0 to 4Numberof kidsagebetween 5 to 14Type 1 – mixed entrySubtype 1 0.546 0.751 0.395 35.124 0.209 0.097 0.178 0.123 0.098 0.628Subtype 2 0.860 0.720 0.356 18.378 0.304 0.093 0.119 0.147 0.102 0.742Subtype 3 0.764 0.738 0.372 24.197 0.250 0.136 0.118 0.127 0.095 0.517Total 0.719 0.736 0.374 26.038 0.256 0.105 0.141 0.133 0.099 0.648Type 2 – entry is dominated by out of labour marketSubtype 1 0.797 0.656 0.313 10.430 0.375 0.094 0.141 0.221 0.129 0.736Subtype 2 0.903 0.683 0.299 8.324 0.369 0.167 0.139 0.125 0.169 0.981Subtype 3 0.731 0.583 0.231 17.565 0.290 0.115 0.231 0.211 0.070 0.713Total 0.833 0.656 0.293 10.639 0.358 0.130 0.154 0.177 0.138 0.841Type 3 – entry is dominated by working part timeSubtype 1 1.000 0.810 0.722 27.910 0.192 0.167 0.056 0.156 0.214 1.188Subtype 2 0.933 0.818 0.281 20.679 0.229 0.156 0.133 0.207 0.107 0.841Subtype 3 0.955 0.823 0.394 21.856 0.300 0.045 0.045 0.159 0.086 1.085Total 0.953 0.818 0.404 22.515 0.239 0.129 0.094 0.184 0.124 0.978Type 4- entry is dominated by working full timeSubtype 1 0.429 0.705 0.468 36.118 0.172 0.079 0.159 0.134 0.124 0.495Subtype 2 0.375 0.817 0.300 43.072 0.144 0.188 0.000 0.100 0.140 0.501Subtype 3 0.692 0.700 0.392 35.909 0.139 0.000 0.154 0.062 0.231 0.420Total 0.457 0.724 0.428 37.298 0.163 0.087 0.130 0.118 0.142 0.485Source: Authors’ calculation from HILDA waves 1-10, unit record data


Hierarchical cluster analysis – take-homes• A high degree of heterogeneity in pathways to and fromunderemployment●clear gender differences in both entry and (conditional) exit• Potential duration dependence●underemployment begets further underemployment• Significant path dependencies●different exits conditional on entry into underemployment


Empirical Specification• Two jointly estimated equations• For the main equation●y it = X it β + a i y + ε iy• For the selection equation●S it = Z it γ + a i s + ε is• Allow correlations in a and ϵ• Initial condition controlled using Wooldridge’s (2005) method• Estimated using Maximum Simulated Likelihood


Estimation• Separate models by gender• Variables included● Basic individuals social economical variable (e.g. age )●●Family characteristics (partnership, number of kids)Interactions with known labour market characteristics• Variables to capture path dependency●1 st and 2 nd order lag


Variable Female MaleUnderemployment (t-1) 0.717*** (0.071) 0.482** (0.161)Underemployment (t-2) 0.517*** (0.069) 0.628*** (0.144)Underemployment in both t-1 and t-2 -0.126 (0.099) 0.023 (0.202)Part-time employed (t-1) 0.203 (0.119) 0.131 (0.197)Unemployment (t-1) 0.255* (0.117) -0.114 (0.185)Fulltime employment (t-1) -0.553*** (0.123) -0.784*** (0.190)Non labour income in t-1 0.006 (0.006) 0.010 (0.009)last wage rate times number of kids age 0 to 4 0.091 (0.049) -0.024 (0.043)last wage rate times number of kids age 5 to 14 -0.025 (0.014) -0.0405* (0.019)Live in urban area 0.078 (0.144) -0.244 (0.216)Number of kids age between 0 to 4 -0.103 (0.153) 0.098 (0.144)Number of kids age between 5 to 14 0.089 (0.057) 0.203* (0.080)Foreign born (English speaking country) -0.020 (0.067) -0.128 (0.112)Foreign born (non-English speaking country) 0.159* (0.064) 0.197* (0.096)Chronic illness 0.111 (0.066) -0.014 (0.101)Have a partner -0.103 (0.115) 0.039 (0.167)Education (Some college or above) -0.069 (0.286) -0.026 (0.457)Age 2.133** (0.814) 1.355 (1.199)Age square -0.227** (0.084) -0.124 (0.123)Spouse Illness -0.007 (0.057) 0.074 (0.090)Constant -6.395** (1.959) -5.228 (2.903)(Correlation between selection and main) 0.631*** (0.098) 0.493** (0.157)


Future Work• Structural econometric model explaining underemployment• Model the full range of employment transitions• Explore scenarios/simulations that lead to different labourmarket pathways


Thank youjinjing.li@natsem.canberra.edu.aualan.duncan@curtin.edu.auriyana.miranti@natsem.canberra.edu.au

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