Appendix B. Further statistics of all samples

Is less more? A dynamic look at the effectiveness of
Abstract
Swiss active labour market policy
Heidi Steiger
Date of this version: 13 February 2004
Past studies found active labour market programmes to be ineffective compared to a state of
nonparticipation and more intense job search for Switzerland. One policy conclusion would be
to do away with the programmes because they are very costly, and public money would only
be wasted. But is less really more? This study re-evaluates Swiss active labour market programmes
by means of the "conventional" non-dynamic matching estimator as well as a dynamic
variant of it. The non-dynamic method leads to negative effects of almost all programmes
compared to nonparticipation. The dynamic method reverses the findings, and nonparticipation
loses any superiority compared to all other programmes. Not considering dynamics
of treatment assignment therefore leads to biased conclusions. Effectiveness in this paper is
measured in terms of a couple of outcomes that have not been considered so far, in order to
give a clearer picture of the effects. They cover unemployment, employment, earnings, out of
labour force, and welfare dependency measures. The programmes are found to be effective in
every possible sense. In aggregation, we find clearly positive effects in the sense of total income
for language courses, computer courses, vocational training, and other courses, as well
as for employment programmes in single workplaces and interim jobs. The aggregated total
income of participants within 12 months after programme start is between 1700 and 3250 CHF
higher for effective programmes. Subject to unemployment benefits, effects are positive and
significant for interim jobs.
Keywords: labour market programmes, evaluation, quasi-dynamic evaluation, nonparticipation,
Switzerland
JEL Classification: C14, J64
Heidi Steiger, Swiss Institute for International Economics and Applied Economic Research (SIAW), University
of St. Gallen, Dufourstrasse 48, CH-9000 St. Gallen, Switzerland; phone +41 71 224 22 99 fax +41
71 224 22 98; e-mail heidi.steiger@unisg.ch; homepage http://www.siaw.unisg.ch/lechner/steiger

1 Introduction
1
This paper contributes to the evaluation of active labour market policies in Switzerland in two
ways: first, programmes are re-evaluated with regard to a variety of outcomes such as unemployment,
employment, earnings, out of labour force, and welfare dependency status. This
provides a more complete insight of how labour market programmes affect participants. Secondly,
for this purpose two different matching methods are used, one a non-dynamic estimator,
the other a quasi-dynamic variant first suggested by Sianesi (2004). The results of the two
methods are compared.
Evaluation of active labour market programmes with respect to their "effectiveness" asks for a
definition of the target variable, i.e. the context in which effectiveness is measured. Former
work by Gerfin and Lechner (2002) and Lalive, van Ours and Zweimüller (2002) used the outcome
"deregistered from unemployment" as efficiency measure. This paper doesn't want to
restrict this term to either a single indicator or some composite welfare measure, but uses a
variety of separate outcomes and analyzes them separately. For this purpose, a combination of
pension system and unemployment insurance records is used. This has partly been done in
Gerfin, Lechner and Steiger (2002) already, but the focus of that study was different, and an indepth
analysis was not the purpose of that study. Here, the data are used more efficiently to
construct additional outcomes They provide an insight not only in how the employment status
of participants evolves within and after their unemployment spells, but also gives earnings
effects and indicators for dropping out of the labour force and/or welfare dependency. Therefore
it gives a more complete picture of the effectiveness of different programmes than what is
available until now.
Furthermore, measuring effectiveness is always a comparison of different states. This paper has
a micro perspective, i.e. the focus is on how a programme affects the participants compared to
a state where they would not have participated in the corresponding treatment. Therefore, a
natural comparison is a state "nonparticipation". For Gerfin and Lechner (2002) persons were
defined as nonparticipants if they did not participate in any programme throughout their unemployment
spell. Only nonparticipation and subsidised interim jobs by them were found to
be superior programmes in terms of leaving unemployment and finding a job. Gerfin, Lechner
and Steiger (2002) used updated data of the same sample to have a closer look at the different
effects of different kinds of temporary subsidised employment in form of employment programmes
and subsidised interim jobs; with better outcomes that also accounted for earnings
effects they found that interim jobs to be superior for almost all possible subgroups; however,
the superiority of nonparticipation compared to employment programmes was challenged
though a clear picture could not be drawn. Lalive, van Ours and Zweimüller (2002) evaluated
Swiss active labour market policies by means of a parametric duration model; nonparticipation
was the base category and treated in a dynamic way. Almost all programmes were found to be

2
effective, and employment programmes in particular turned out to be superior in all respects,
more so for women.
The research question posed here is how programmes affect the participants compared to
nonparticipation. It is maybe the most interesting policy question in terms of active labour
market programmes, because it directly asks if public spending is effective at all: all programmes
are costly except for interim jobs which are less expensive than just passive maintenance.
Therefore, efficient spending means that there is a return on active labour market programmes.
This question was addressed in the earlier studies mentioned above also. However,
recent work by Fredriksson and Johansson (2002) showed that both ways of treating nonparticipation
give biased results. The method of Sianesi (2004) is an empirical strategy that is in
line with these findings; she applied a quasi-dynamic approach to an evaluation of Swedish
programmes that are very similarly set up as in Switzerland. It applies another definition of
nonparticipation altogether: every person is a nonparticipant until she participates in a programme
or leaves unemployment. This method is adjusted here for the needs of evaluating
Swiss programmes. Moreover, none of the previous studies really gave a comprehensive picture
of the effects in terms of different outcomes.
This paper is structured in the following way: section 2 gives an overview of active labour
market policy in Switzerland. Section 3 explains the two estimation strategies, section 4 describes
the data and how it is sampled for this study. Section 5 shows and discusses estimation
results, and section 0 concludes.
2 Active labour market policy in Switzerland
2.1 Design
Traditionally, Switzerland has seen low unemployment, which facilitates high public spending
on the unemployed. Besides comparatively high passive unemployment benefits (between 70
and 80 percent of former salary), Switzerland maintains a comprehensive toolbox of active
programmes also.
Counselling and placement services are available to every job seeker in Switzerland, regardless
of his or her unemployment status. Actual unemployed persons are entitled to more intensive
programmes such as personality, computer, and language courses, or other vocational training,
in order to re-fresh and improve their professional abilities. For work experience different
kinds of employment subsidies or specifically designed temporary jobs are available. The duration
of programmes varies substantially: full-time courses may not last longer than two months
whereas employment programmes normally are assigned for 6 months.
An unemployed person registers with the corresponding regional unemployment office. The
responsible caseworker takes up the case and meets the unemployed on a regular basis to

3
evaluate the endeavours for finding a job and to chart a roadmap to success. Caseworkers
counsel the unemployed and decide about participation in active labour market programmes.
Once decreed by the officer, a programme is compulsory: non-attendance by the unemployed
is sanctioned by benefit cancellation.
2.2 Nonparticipation
Why do certain unemployed participate in a programme, but others do not? Nonparticipation
in the Swiss setup arises, (1) if the unemployed has not been assigned to a programme by the
caseworker, (2) if the unemployed has not come up with the idea of a programme himself, or
(3) if the unemployed wanted to participate in a programme, but his request was not granted
by the caseworker.
ad (1): The policy for assignment of unemployed into programmes is not standardised in Switzerland;
cantons have a considerable amount of autonomy in executing the federal law. Assignment
of persons into programmes furthermore varies between employment offices and
officers themselves. A person might not be assigned to a programme by the caseworker because
there is no programme available (limited supply), because the caseworker doesn't see
any need for training, or because he wants to give the person more time to search for a job
instead of keeping her busy with participation in a programme.
ad (2) and (3): An unemployed person can apply herself for programmes. The request has to
be granted by the caseworker or – in case of expensive programmes – by the cantonal authority.
The programme by law has to increase the unemployed person's employability in order to
be granted. Another programme that is mostly organised by the unemployed themselves is an
interim job: a job is taken up interim in order to avoid joblessness, though the job is not "suitable"
in the sense of the unemployment insurance act (more than 2 hours away from home,
pays less than actual unemployment benefits, or has unacceptable working conditions for the
person, e.g. work on call).
Participation furthermore is hardly related to a point of time within the total unemployment
spell. There is no rule which says that after a certain point of time a person is obliged to participate
in a specific programme or the opposite, i.e. participation is not possible any more.
The federal authority gives a general recommendation to assign people into programmes early
in their spell. However, this recommendation is not binding and often not followed.
If a person has not participated in any programme, this therefore might be more a combination
of dynamic decisions at every meeting with the caseworkers than a predefined and intentional
strategy. Since meetings with caseworkers normally take place at least once in a month,
a monthly decision basis is appropriate for evaluation purpose also.

3 Econometrics
3.1 Identification and Estimation
4
The definition of an individual causal effect of a treatment 1 is the difference between what a
person achieves when she got the treatment and what she would have achieved in the absence
of it. The average effect then is the mean of all individual effects if macro effects are
ruled out (stable unit treatment value assumption, SUTVA). By denoting the outcome (achieve-
ment) as Y we thus can define two states for a person:
the presence of a treatment. The average treatment effect then is
1 0
E ⎡
⎣Y −Y
⎤
⎦.
0
Y is the state in the absence,
1
Y in
In the presence of multiple (R) treatments the causal effect can be defined following Lechner
(2001b) between any two programmes m and l as the effect of participating in one of the R
programmes m compared to participation in any other programme l ≠ m. A natural effect of
interest would then be the comparison to nonparticipation:
m 0
E ⎡
⎣Y −Y
⎤
⎦ .
People now can be observed in only one programme (or in no programme), such that the
specific outcome for them is known after actual programme participation. If we denote participation
as D, we thus can observe
m
E ⎡
⎣Y D = m ⎤
⎦
The possible outcome after another treatment or after not participating at all is unknown, since
those people didn’t take place in any of them. They are referred to as counterfactuals, that is,
hypothetical outcomes in a hypothetically existing parallel world.
l
E ⎣
⎡Y D = 0⎤
⎦ or
⎡
⎣
⎤
⎦
0
E Y D = l
In the remaining part of this paper the focus will be on average treatment effects on the
treated (ATT):
⎣
⎡ ⎤
⎦
⎡
⎣
⎤
⎦
⎡
⎣
⎤
⎦
m 0 m
0
E Y − Y D = m = E Y D = m − E Y D = m
The outcomes of people who have not participated is
⎡
⎣ = 0⎤
⎦ . What we observe thus is
0
E Y D
⎡
⎣ = ⎤
⎦ − ⎡
⎣ = 0⎤ ⎦ = ⎡
⎣ − = ⎤
⎦+ ⎡
⎣ = ⎤
⎦− ⎡
⎣ = 0⎤
������������� ⎦
m 0 m 0 0 0
E Y D m E Y D E Y Y D m E Y D m E Y D
selection bias
1 Active labour market programmes are referred to as “treatments” in this literature (following the biostatistical literature).

5
The second part of this is the selection bias, i.e. the difference between their (hypothetical or
actual) outcomes after nonparticipation. In non-experimental studies this bias is not expected
to be zero; i.e. persons who actually participated in programme m might have a different expected
outcome after nonparticipation than those who actually did not participate. In other
words, people might have been selected into programmes according to their expected profits
of the programme. Would this selection bias be zero, we just could take the average outcomes
in both participant groups of programmes 0 and m, and it would be the causal effect. This is –
under further assumptions – the case in experimental studies. However, experimental data are
not at hand. For the identification of treatment effects from non-experimental data in principle
two strategies are possible: the first strategy is to assume that all relevant information about the
connection between programme selection and outcome is given. This is called “selection on
observables” or “conditional independence assumption” (CIA). If this assumption is not credible
with the data at hand, other strategies based on “selection on unobservables” have to be
applied (Instrument variable, difference-in-difference, distributional assumptions of selection
bias components, etc.). For an overview of strategies see Heckman, LaLonde, Smith (1999).
The CIA states that, given all known characteristics X that jointly influence participation decision
and outcome after participation, the (hypothetical) outcome after participation in a programme
is independent of the actual participation D:
l
Y � D X l ∈ { m,0}
For the matching estimator we need a second assumption, namely that everybody has a positive
probability to be in either programme m or programme 0:
P( D = r ) > 0 r ∈ { m,0}
With this assumption we can identify
⎡
⎣
⎤
⎦
⎡
⎣
⎤
⎦
0 0
E Y D = m, X = x = E Y D = 0, X = x
Integrating over x we get:
∫
⎡
⎣
⎤
⎦
⎡
⎣
⎤
⎦
0 0
E Y D = m = E Y D = 0, X = x f( x D = m) dx
The average treatment effect of m on those who participated in m compared to those partici-
m,0
pating in 0 ( θ ) then is
= E ⎣Y D = m⎦− E ⎣Y D = 0, X = x⎦f( x D = m) dx
m,0 m
0
θ ⎡ ⎤ ⎡ ⎤
∫
m
E ⎡
⎣Y D = m ⎤
⎦ is estimated by the sample mean outcome of all participants in treatment m.
The second part is a little bit more difficult to estimate because X is multi-dimensional. It is
shown that instead of the whole vector of X it is sufficient to condition on the participation
probabilities P ( D = m X = x ) and P ( D 0 X x)
= = (cf. Lechner 2001a). In this paper the

6
conditioning is based on all participation probabilities as well as on other important variables
(age, gender, duration of unemployment at beginning of programme, employability, mother
tongue, qualification, canton), because the availability of additional programmes effects the
choice of the actual programme. For every person in the sample of participants in treatment m
one or several very similar persons in treatment 0 are found and the (weighted average) outcome
is calculated. “Similarity” is in terms of the participation probabilities and the other variables
also. The matching procedure can be based on one nearest neighbour only or on a
weighted average of several similar persons. A comprehensive overview of matching estimators
is given in Frölich (2004). In this application, nearest-neighbour-matching is done.
3.2 Quasi-dynamic matching
A non-dynamic variant of the matching method defines participants as those who participated
in the programme once in their total spell, and nonparticipants as those who never participated
in any programme. Fredriksson and Johansson (2002) showed that this leads to negative
effects of treatments compared to nonparticipation, because the conditional independence
assumption does not hold: The treatment indicator itself is defined based on future outcomes.
Sianesi (2004) developed and applied an empirical approach to account for the dynamics of
the treatment decision: she treats nonparticipants as “waiters”. Nonparticipation is not treated
as an own programme, but as a “default” state with more intensive job search. This approach
resembles the hazard model with multiple risks: at any time after registration a person decides
whether to participate in any programme m or to wait. All persons who haven’t participated
until time t and who are still unemployed are “waiters”.
The CIA that is required for this is
�
Y D X = x, D = D = ... = D = 0, still unemployed in τ, t > τ
0 τ 1 2
τ
t
This CIA states that how the (hypothetical) outcome evolves after not participating up to time
τ is independent of a programme participation at time τ, given characteristics X.
The treatment effect of having participated in programme m at time τ instead of waiting at
least one period longer, for those who participated, is (note that the outcome is measured at
different points of times t after programme participation):
⎡ ⎤
⎣ ⎦
m, τ m,
τ 0, τ τ
θt = EYt − Yt D = m t > τ
The composite treatment effect at time t for all persons having participated at any time before t
compared to having waited therefore is:
= ∑
τ<
τ
⋅
τ
= =
,
m
t
t
m
t
θ θ
( )
P D m D m
Note that meaning of “causality” in this context is not straightforward. m
θ is the overall treatment
effect on those who participated in programme m at any time in their unemployment

7
spell instead of waiting longer, which might include another programme participation later in
their spell as well.
3.3 Matching procedure
Conventional nearest-neighbour-matching in a multiple treatment setup according to Gerfin
and Lechner (2002) as well as an adjusted version for the Sianesi (2004) approach are used.
The following subsections explain the steps.
1. Propensity scores
The suggested multinomial probit model for calculating the propensity scores is computationally
very expensive in the case of 10 treatments. Therefore 9 binomial probits based on all
observations are used here (dependent variable: “participant in treatment j”). Lechner (2002)
found that this procedure leads to results very close to those obtained using the multinomial
probit estimates.
On the basis of the computed coefficients, for every person and every programme their re-
0 9
spective participation probabilities are computed ( P ,... P ).
i i
For the quasi-dynamic approach the same participation probabilities are computed. 2
2. Common support
The Common support restriction follows directly Gerfin and Lechner (2002). More work on
common support and how it effects estimates is provided in Lechner (2001a). It eliminates all
observations that have propensity scores smaller than the maximum of all minima or bigger
than the minimum of all maxima within the treatment groups. This criterion guarantees comparability
between treatment groups.
In the quasi-dynamic approach the common support restriction is not applied, because regularly
there are very few participants in every month, and a quite large set of corresponding
nonparticipants.
3. Matching procedure
The matching procedure is nearest-neighbour matching with replacement, as used in GL:
Within a treatment group m for every observation (person) i the most similar person j from the
treatment group 0 is chosen. “Similarity” is defined in terms of the Mahalanobis distance based
0
on all propensity scores P – 9
P as well as a subset of outcome specific covariates x� i : beginning
of programme, age, gender, duration of unemployment at beginning of programme, em-
2 A propensity score for participation at the corresponding time τ cannot be used because there are too few single
observations in the corresponding cells starting a programme.

8
ployability, mother tongue, qualification, canton. The person with the minimum Mahalanobis
distance to person i from the pool of participants in treatment 0 is defined as her “counterfac-
tual”. A person in 0 might be the closest match for more than one observation in m.
counts the numbers of times a person in the pool l is used as the closest match for a person in
pool m. The counterfactual outcome for a person is computed as:
1
Y = w ⋅Y
∑
cf m, l
i
N m j∈Il j j
The treatment effect is computed as:
ml , 1
cf
θ = ∑ ( Yi −Yi
)
N m i∈Im Simple standard errors of the ATT ignoring the variance of the first estimation step (propensity
scores) are computed following Lechner (2001b) as the square root of
1 � 1 ml ,2
Var Y + �
2 ∑ w j ⋅Var
Y ,
N i∈Im N
j∈Il m m
j∈Il l is always used for the ‘untreated’ and m for the ‘treated’.
This matching procedure in principle is independent of the outcome measured. However, in
this framework there is a problem with the outcomes 2, 3, and 4: both of them are measured
in months after programme start. For persons with an early programme start date, e.g. January
1998, this outcome is observed for a very long time. For persons with a late programme start
only a short time interval after programme start is observed, and later outcomes are missing.
The same problem arises for persons whose benefit entitlement period started late (for outcome
3) or whose registration was late (outcome 4). The following solution is chosen: Persons
here are matched only once. For later time periods, the counterfactual outcome exists only if
the counterfactual person also still has an outcome. The variance takes into account only the
observations with positive weights.
The quasi-dynamic matching estimator m
θ is the following:
t −
�m � m,
τ τ
θ = θ ⋅ ˆ
t t P ( D = m D = m, τ < t )
∑
τ =
1
1
N N
τ τ
with Pˆ ( D = m D = m, τ < t) = ∑1 ( D = m, τ < t) / ∑1
( D = m, τ < t )
i= 1 i=
1
where �, m τ
θ is the conventional matching estimator for the treatment effect on the treated of
t
starting a programme m at time τ compared to not starting a programme until and by the end
of month τ, as described above. The aggregate effect sums up over all persons having already
started their programme before t. ˆ ( τ = , τ < )
m starting their treatment at time τ of all those starting before t.
The variance is computed the same way:
ml
w j
P D m t is the share of participants in programme

−
� � τ τ
( θ ) = ∑ θ ⋅ ( = = τ < )
1 t
m m,
Var ˆ
t Var t P D m D m, t
4. Validity of CIA
τ = 1
9
CIA is a very strong assumption that states that all information that influences the participation
decision and the outcome at the same time are observed and controlled for. Nullity would
mean that there are unobservable differences between the treated and untreated group that are
not controlled for but that jointly influence participation and outcome. In that case the effects
don’t have any causal interpretation, but are a composite between the selection bias and the
true causal effect. In the non-dynamic setup the CIA has proved to be violated. For the quasidynamic
setup, scepticism about the validity of the CIA might come up. Nevertheless, we refer
readers to the comprehensive data that among other variables also contains earnings profiles
of the past 10 years as well as caseworkers’ subjective rating of the unemployed person’s employability.
With these variables a lot of generally unobserved but important heterogeneity
should be accounted for.
4 Data and Sampling
4.1 Data
The sample contains a cross section of all people who were unemployed at the end of December
1997. 3 The data is a combination of two information systems of the unemployment
insurance (placement/statistics and payments) and pension system records. The different data
sets can be combined through a unique personal identification number.
The following information is available in the data:
Placement and labour market statistics database: Monthly information about corresponding
placement office, personal characteristics, characteristics of previous job and desired job, status
of job seeking, work experience, skills and caseworker’s rating of chances to find a job, duration
of unemployment spell, participation in active labour market programmes/job introduction
allowances. 4 We use pre-programme information from December 1997 as well as monthly information
for the definition of unemployment status from January 1998 to December 1999.
Payment system: Total benefits and other supplementary allowances paid by the unemployment
insurance, sanctions, interim jobs, benefit exhaustion (on a monthly basis). We use pre-
3 The data is an update and extension of the one used by Gerfin and Lechner (2002). More information about the
original sample is given there.
4 Though the data is very rich and contains a lot of information, it has to be mentioned that information about education
is not available for the time covered here.

10
programme information from December 1997 as well as monthly information for the definition
of interim jobs and unemployment status from January 1998 to December 1999.
Pension system: All payments paid to the pension system by an employer, by the person for
self or by the unemployment insurance (the source can be identified); data gives yearly information
indicating months of begin and end of each income spell (maximum January and December).
This data is available from 1988 to 1999 for this study. Data from 1988 to 1997 is
used for the construction of unemployment and employment history, data from 1996-1999
additionally is used for identification of employment on a monthly basis.
For privacy protection rules access to all of these data bases is rigorously restricted. They are
not publicly available.
The pension system data were available for a subset of 81'399 persons. After applying diverse
selection criteria (see Appendix A for the exact selection criteria) and applying the minimum
duration criterion of 15 days for a programme 33'700 people remain.
4.2 Outcomes
This paper uses 11 different outcomes and three time frames in which outcomes are measured.
The outcomes a-f are computed using raw pension system data only; outcome g-k use pension
system data combined with unemployment insurance data.
Outcome a: A person has a positive entry of a salary in the pension system for the corresponding
month.
Outcome b: A person has a positive entry of an unemployment benefits payment in the pension
system for the corresponding month.
Outcome c: A person has no entry in the pension system for the corresponding month.
Outcome d: "Raw" income from employment in corresponding month according to the pension
system.
Outcome e: "Raw" income from unemployment in corresponding month according to the pension
system.
Outcome f: Total income from unemployment in corresponding month according to the pension
system.
Outcome g: A person is employed according to the pension system data in an employment
spell that lasts at least three consecutive months; at the same time that person doesn’t receive
any payments from the unemployment insurance.
Outcome h: A person is employed according to the same criteria as in outcome g (at least 3
consecutive months, no benefits at the same time); additionally, the earnings are at least 90
percent of the last income which is the basis for the computation of benefits.
Outcome i: Earnings in employment according to the definition in outcome g (at least 3 consecutive
months, no benefits at the same time), this variable is 0 if outcome g is 0.

11
Outcome j: A person is employed according to the same criteria as in outcome g (at least 3
consecutive months, no benefits at the same time); additionally, the earnings are “sustainable”
in the sense that they exceed a minimal income criteria that is computed according to the
guidelines of the social welfare (basic needs, additional needs, health insurance, flat rent) on
the basis of the number of persons in the same household and the area the person lives.
Outcome k: A person is registered at the regional employment office. This variable is set to 0 if
the person at the same time has an ongoing earnings spell with earnings of at least 3000
francs.
Outcomes are measured in three different time frames:
1. After programme start: maximum 23 months after programme start
The first 6 outcomes (a-f) are measured in two more time frames:
2. Entitlement period: months of entitlement period (maximum 48 months).
3. Registration period: months since last registration (before beginning of 1998) in unemployment
(maximum 36 months).
Time frame 2 and 3 are not the same. The entitlement period starts with an unemployment
spell. If the person re-registers unemployed after a short period of unemployment, the old
entitlement period is still going on.
For the time frames 2 and 3 only outcomes purely based on the pension system (i.e. a-f) exist
because monthly data from the other systems is not complete for the time period January 1996
– December 1997. Therefore, in total 23 outcomes are defined, each of them for the available
time periods.
4.3 Programmes
In line with earlier evaluation work for Switzerland, the first significant programme after
1.1.1998 is considered, i.e. the first programme that exceeds two weeks. Shorter programmes
are not taken into account.
The programme code in the unemployment insurance data differentiates between more than
30 programmes. Here they are aggregated to 8 programme groups; this aggregation is similar
to the one used by Frölich, Lechner and Steiger (2003): personality and other basic courses (1),
language courses (2), basic computer courses (3), higher vocational training (4), lower vocational
training (5), other training (6), employment programmes in single workplaces (7), employment
programmes in collective workplaces (8). Basic programmes are not taken into account
at all because they are often followed by another programme and the effects should be
assigned to the predominant programme. A 9 th programme is defined according to the unemployment
insurance payment data if the computed length exceeds 2 weeks: interim jobs (9).

12
1 Personality and other basic courses: They are courses who help persons to position themselves
and their needs in the labour market. Additionally, there are courses for basic qualifications.
2 Language courses: They can be either courses in a foreign language for persons with good
knowledge in the corresponding Swiss language, or German/French courses for persons with
another mother tongue.
3 Basic computer course: This computer course gives basic knowledge, e.g. Word courses,
beginners courses, internet courses, etc.
4 Higher vocational training: These courses are on a higher level than an actual vocational
education in Switzerland; they include computer courses on an advanced level for specialists,
commercial, and technical courses.
5 Lower vocational training: These courses are not beyond the level of a vocational education.
They include commercial, technical, hotel/restaurant industry, nursing, and cleaning courses.
6 Other training: This is a group that subsumes all other courses: laboratory firms, internship,
self-employment courses.
7 Employment programmes single workplace: Participants work in the public administration, in
another public service, or in a non-profit organisation. They work together with other “normally”
employed persons.
8 Employment programmes collective workplace: Participants work in facilities specially designed
for unemployed persons, e.g. recycling.
9 Interim jobs: A job in the labour market that is not suitable for the unemployed but that
he/she takes up temporarily to avoid joblessness. It is subsidised by the unemployment insurance
such that the unemployed is better off than with unemployment benefits only. This programme
is observable only month-wise; here participation is defined as having received the
subsidy for at least 2 weeks. 5
4.4 Definition of nonparticipants and programme start dates
Nonparticipants in the non-dynamic matching part are defined according to Gerfin and
Lechner (2002) as persons who don't participate in any programme in the observable time
frame. For the outcome after programme start a hypothetical programme start date is assigned
to persons in the following way: the programme start variable for all other persons is regressed
on all available X variables. Based on the estimated coefficients and the corresponding X characteristics
of the nonparticipants hypothetical starts for nonparticipants are computed. Nonparticipants
who are already employed or don't have any entry in their pension system records
are excluded from the sample.

13
For the quasi-dynamic method nonparticipation is defined on a monthly basis: a person in
month t is a nonparticipant if she has not yet participated in a programme and at the same
time is not employed or out of labour force yet.
In both cases a person falls out of the comparison sample if she has already participated in a
programme, if she is employed according to a criterion combining pension system records and
unemployment insurance data, or if she doesn't have any information in her pension system
records, which is an indicator of being out of labour force or welfare dependent. In both cases
a person in a strict sense is not part of the target group of a programme any more and hence
is excluded.
4.5 Descriptive statistics
Some selected descriptive statistics of the whole sample are given in Table 4-1. An overview of
all descriptive statistics of all variables available is given in Appendix B.
Table 4-1 Selected descriptive statistics
Sample A Sample B
Current unemployment spell
Duration of current unemployment, 31.12.1997 151.03 147.12
Unemployment benefit, daily, CHF 139.09 137.63
Programme participation (in %)
No course 0.32 0.47
Personality course 0.05 0.04
Language course 0.09 0.08
Basic computer course 0.08 0.06
Vocational training high level 0.02 0.02
Vocational training low level 0.03 0.02
Other training 0.02 0.01
Employment programme public 0.07 0.05
Employment programme private 0.07 0.05
Temporary wage subsidy 0.25 0.20
Duration of current unemployment spell at begin of first programme
Other socio-economic characteristics
94.66 206.26
Female 0.44 0.46
Marital status single 0.28 0.26
Marital status married 0.59 0.61
Nationality Swiss 0.56 0.54
Foreigner with permanent permit 0.29 0.30
Qualification: skilled 0.57 0.54
Qualification: unskilled 0.27 0.31
Mother tongue German 0.35 0.34
Monthly earnings in last job in CHF 3883 3840
Employability easy or very easy 0.20 0.21
Employability medium
Employment history from pension system data
0.58 0.55
Number of months unemployed since entry into pension system or since 1988 10.21 10.48
Number of months employed since entry into pension system or since 1988 87.10 85.74
5 For a comprehensive description of temporary interim jobs the reader is referred to Gerfin, Lechner and Steiger
(2002).

Never unemployed 0.08 0.08
Month of entry into pension system (1=January 1988) 13.49 14.13
Number of unemployment spells since 1988 1.80 1.87
Mean duration of unemployment spell in months 5.18 5.17
Duration of last employment spell 52.60 50.23
Graph 4-1 Descriptive statistics of outcomes
Calendar time
After programme start
In benefit entitlement period
Since registration
%
20 40 60 80 100
%
20 40 60 80 100
%
20 40 60 80 100
%
20 40 60 80 100
14
Outcomes in CHF Outcomes in %
2 5 8 11 14 17 20 23
calendar time in months (1 = Jan 98)
Empl. >= 3 months
Empl. w/o earnings loss
Empl. with sust. earnings
Searching for job
Pension: empl>0
Pension: unempl>0
No info in AHV
2 5 8 11 14 17 20 22
months after programme start
Empl. >= 3 months
Empl. w/o earnings loss
Empl. with sust. earnings
Searching for job
Pension: empl>0
Pension: unempl>0
No info in AHV
2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47
months in benefit entitlement period
Pension: empl>0
Pension: unempl>0
No info in AHV
2 5 8 11 14 17 20 23 26 29 32 35
months since registration
Pension: empl>0
Pension: unempl>0
No info in AHV
CHF
0 1000 2000 3000 4000
CHF
0 1000 2000 3000 4000
CHF
0 1000 2000 3000 4000
CHF
0 1000 2000 3000 4000
2 5 8 11 14 17 20 23
calendar time in months (1 = Jan 98)
Earnings in empl. >=3 m
Income from empl.
Income from unempl.
Total income
2 5 8 11 14 17 20 22
months after programme start
Earnings in empl. >=3 m
Income from empl.
Income from unempl.
Total income
2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47
months in benefit entitlement period
Income from empl.
Income from unempl.
Total income
2 5 8 11 14 17 20 23 26 29 32 35
months since registration
Income from empl.
Income from unempl.
Total income
Note: All graphs are based on Sample A with 21'555 observations (after common support restriction and dropping out
nonparticipants based on their assigned starting date).

15
Graphs with descriptive statistics of outcomes are displayed in Graph 4-1. At the beginning of
1998, nobody is employed and all persons are searching for a job according to the unemployment
insurance data. The pension system records show that still persons receive income from
employment, which most probably comes from interim jobs or employment programmes. The
amount of persons without information in the pension system increases by time. Typically, the
share of persons without information jumps up after the benefit entitlement period is exhausted
(after 24 months). Similarly, the total available income drops after benefit exhaustion.
Surprisingly, at a first glance no employment effect close to benefit exhaustion can be found.
The share of persons with a "stable" or "sustainable" job increases with time. Towards the end
of the observation period, the income mainly comes from stable income of at least 3 months.
5 Results
5.1 Propensity Scores
The computation of propensity scores should contain all variables X that potentially influence
both the participation decision and the later outcome. In principle all available variables are
used for estimation; variables not used are either collinear to other variables, or they predict a
participation decision perfectly. As already described, 10 binomial probit models are estimated
for the dynamic and the non-dynamic models. The coefficients are not of particular interest
and therefore not given here.
5.2 Common Support
The common support criterion described above shall guarantee comparability of the people in
the treatment groups by excluding observations that are not comparable to observations in
other programme participant groups. For this purpose, all those with a propensity score
smaller than the maximum of all minima of propensity scores within the 10 participant groups
are deleted as well as those with a propensity score bigger than the minimum of all maxima.
This restriction guarantees comparability between all groups, i.e. all pairwise comparisons are
based on the same observations. This leads to a significant loss of observations (cf. Table 5-1)
of around 18 percent. Participants in language courses, employment programmes in collective
workplaces, and interim jobs lose more observations than the average. That means that the
participants in these programmes are less comparable to others. They might be more specifically
selected into these programmes.
This selection possibly alters the underlying sample considerably. Tests for equality of means
show that there are significant differences. Table 5-2 shows the number of outcome means that
are significantly different before and after common support. In the time frame "calendar time"

16
we can hardly find any differences. In the time frame "entitlement period" there are differences,
especially in the two groups of vocational training. These groups however are small,
and the difference might come especially from higher time periods were numbers of observations
are very low.
The common support in terms of propensity scores is displayed in Graph 5-1 in the form of
Kernel densities for the probability of not participating, within the subgroups, respectively. The
support is reasonable equal among groups. Comparability between groups might be reasonably
good.
Table 5-1: Observations deleted due to common support criterion
Before After (%)
Total observations 26'333 21'555 18
No programme 8'331 6'936 17
Personality or other basic course 1'293 1'076 17
Language course 2'704 2'007 26
Basic computer course 1'891 1'647 13
Higher vocational training 609 519 15
Lower vocational training 707 607 14
Other training 376 334 11
Employment programme single workplace 1'764 1'496 15
Employment programme collective workplace 1'826 1'486 19
Interim job 6'832 5'447 20
Note: (%) denotes the percentage of observations lost.
Table 5-2: Test for equality of outcome means before and after common support
Outcome a b c d e f g h i j k
No programme 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1
Personality course 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0
Language course 0 1 0 1 0 2 0 1 0 1 0 1 0 0 0 1 0
Computer course 0 2 0 1 0 2 0 1 0 1 0 1 0 0 0 1 0
Higher voc. training 0 4 0 6 0 4 0 1 0 6 0 1 0 0 0 0 1
Lower voc. training 0 3 0 9 0 3 0 3 0 9 0 3 0 2 0 2 1
Other training 0 4 0 3 0 4 0 1 0 3 0 1 0 1 0 0 0
Empl. pr. single 0 1 0 1 0 4 0 1 0 1 0 1 0 0 0 0 0
Empl. pr. collective 0 2 0 2 0 2 0 1 0 1 0 1 0 0 0 0 0
Interim job 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Note: For definitions of outcomes see above (4.2). Outcomes a-f are measured in calendar time since 1/1998 as well as in
benefit entitlement period (maximal 48 periods). Outcomes g-k are measured in calendar time since 1/1998 (24
months).
Tests for comparing means before and after common support: ( ybefore − yafter ) /
ber of significantly different means out of all time periods (23/48) are given.
Var � / �
before Nbefore+ Varafter/ Nafter
>1.96; num

Graph 5-1 Kernel densities of propensity scores after common support restriction
0 1 2 3 4 5
17
0 .2 .4 .6 .8
no prog
personality
language
computer
voc. high
voc. low
other
EP single
EP coll.
interim job
Note: Kernel densities for Pr(no programme). The underlying sample is the final sample for the non-dynamic matching
estimator, after dropping nonparticipants on the basis of computed programme start dates and after common support
restriction, 21'555 observations (no programme: 8'331; personality: 1'293; language: 2'704; computer: 1'891; voc.
high: 609; voc. low: 707; other training: 376; empl. prog. single: 1'764; empl. prog. collective: 1'826; interim job:
6'832).
5.3 Matching results
In the following subsections the results of the different outcomes are discussed separately.
Every section gives results from the non-dynamic as well as the quasi-dynamic setup. Every
displayed point shows a significant average treatment effect on the treated (ATT, significance
level 5 percent). For both setups, ATTs are given compared to nonparticipation (treatment
effect on the participants compared to nonparticipation). "Nonparticipation" is defined by the
corresponding non-dynamic and quasi-dynamic approaches, respectively. A point below 0
means that for people having participated in the corresponding employment programme the
outcome was lower than without any programme.
The graphs display average treatment effects on the treated (ATT) for different treatments,
points of times, and outcomes. Every point shows an ATT that is significant on the 5 percent
level. Points above the 0 line mean that the reference treatment lead to a significantly higher
outcome compared to the treatment defined by the symbol at the corresponding point of time,
for the people having participated in the treatment the point is assigned to.

5.3.1 Employment and income based only on pension system data
Table 5-3 Results for outcome "Employment based only on pension system data"
%
-30 -20 -10 0 10 20 30
%
-30 -20 -10 0 10 20 30
%
-30 -20 -10 0 10 20 30
18
Non-dynamic Quasi-dynamic
After programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32
months since registration
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23
months after programme start
In benefit entitlement period
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
Since registration
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23 26 29 32
months since registration
The first as well as the following outcomes are based purely on pension system data. A person
is "employed" if she has a positive income that comes from a regular employer according to
the data. A person is also "employed" by this definition when she is in an interim job or an
employment programme, based on the way these earnings are defined. This is the reason for
the positive effects after programme start (see Table 5-3): an employment programme regularly
has a duration of around 6 months; this effect drops after 6 months for these programmes
which means that this effect is "causal" only in the sense that these persons are in the programme
whereas others are not. In general, programmes don't have negative effects in the
quasi-dynamic framework compared to nonparticipation. In general, the effects are reversed.
The earnings related to this employment are shown in Table 5-4. They mirror the employment
effects displayed above.

Table 5-4 Results for outcome "Income from employment according to pension system"
CHF
-800-400 0 400 800
CHF
-800-400 0 400800
CHF
-800 -400 0 400 800
19
Non-dynamic Quasi-dynamic
After programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32
months since registration
CHF
-800 -400 0 400 800
2 5 8 11 14 17 20 23
months after programme start
In benefit entitlement period
CHF
-800 -400 0 400 800
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
Since registration
CHF
-800-400 0 400800
2 5 8 11 14 17 20 23 26 29 32
months since registration
5.3.2 Unemployment and income from unemployment based only on pension system
data
Income from unemployment in the definition here is benefit payments by the unemployment
insurance, without earnings from employment programmes. For interim jobs the subsidy is
counted as benefits, the employer's part itself not. In terms of unemployment a similar picture
arises as for employment. A specific finding here is that after expiration of the official benefit
entitlement period (24 months) persons with interim jobs are more likely to receive unemployment
benefits than others. Earnings in interim jobs allow for extension of the benefit entitlement
period whereas other programmes do not.

Table 5-5 Results for outcome "Employment based only on pension system data "
%
-30 -20 -10 0 10 20 30
%
-30 -20 -10 0 10 20 30
%
-30 -20 -10 0 10 20 30
20
Non-dynamic Quasi-dynamic
After programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32
months since registration
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23
months after programme start
In benefit entitlement period
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
Since registration
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23 26 29 32
months since registration

21
Table 5-6 Results for outcome "Income from unemployment according to pension system"
CHF
-800 -400 0 400 800
CHF
-800 -400 0 400 800
CHF
-800 -400 0 400 800
Non-dynamic Quasi-dynamic
After programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32
months since registration
CHF
-800 -400 0 400 800
2 5 8 11 14 17 20 23
months after programme start
In benefit entitlement period
CHF
-800 -400 0 400 800
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
After registration
CHF
-800-400 0 400 800
2 5 8 11 14 17 20 23 26 29 32
months since registration

5.3.3 No information in the pension system
Table 5-7 Results for outcome "No information in the pension system "
%
-30 -20 -10 0 10 20 30
%
-30 -20 -10 0 10 20 30
%
-30 -20 -10 0 10 20 30
22
Non-dynamic Quasi-dynamic
After programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32
months since registration
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23
months after programme start
In benefit entitlement period
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
Since registration
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23 26 29 32
months since registration
The outcome "no information in pension system" is directly related to welfare dependency and
out of labour force status, though these two issues can't be disentangled with the underlying
data (welfare payments are not subject to pension system contributions). For the first time the
effects are not reversed between the two methods. In fact, the non-dynamic variant shows an
effect that would be more in favour of programmes. However, effects are negative throughout,
which means that persons in programmes are less likely to "drop out of the system".

23
5.3.4 Total income according to pension system data
Table 5-8 Results for outcome "Total income according to pension system"
CHF
-800-400 0 400 800
CHF
-800-400 0 400 800
CHF
-800 -400 0 400 800
Non-dynamic Quasi-dynamic
After programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32
months since registration
CHF
-800 -400 0 400 800
2 5 8 11 14 17 20 23
months after programme start
In benefit entitlement period
CHF
-800 -400 0 400 800
2 5 8 11 14 17 20 23 26 29 32 35 38 40
months in benefit entitlement period
After registration
CHF
-800 -400 0 400 800
2 5 8 11 14 17 20 23 26 29 32
months since registration
Total income from the pension system data is an indicator of what the total purchasing power
of a person is. Both methods show a similar picture: participants in general are better off after
participation, and the positive effect goes on for a long time.

24
5.3.5 Employment for at least 3 consecutive months
Table 5-9 Results for outcome "Employed for at least 3 consecutive months"
%
-30 -20 -10 0 10 20 30
Non-dynamic Quasi-dynamic
2 5 8 11 14 17 20 22
months after programme start
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23
months after programme start
This outcome variable rates employment as "stable" if the income spell in the pension system
lasts for at least 3 months. Using the quasi-dynamic approach, the results are almost reversed
compared to the non-dynamic picture. In fact, the non-dynamic method might be biased by
nonparticipants who just leave unemployment after their assigned start date (one month after
programme start the effect is already minus 20 percent which is hardly credible to be a causal
effect). The quasi-dynamic approach does not show negative effects for any programme after
four months.
5.3.6 Employment with at least 90 percent of former earnings
Table 5-10 Results for outcome "Employed with at least 90 percent of former earnings"
%
-30 -20 -10 0 10 20 30
Non-dynamic Quasi-dynamic
2 5 8 11 14 17 20 22
months after programme start
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23
months after programme start
In addition to the stability criterion, this outcome variable puts a "quality" restriction on the job
by applying a minimum criterion of 90 percent of former income on the earnings. For this outcome
again the conclusions are just reversed. The quasi-dynamic approach shows significantly

25
positive effects only after four months. None of the programme had a negative effect in the
longer run.
5.3.7 Earnings if not unemployed
Table 5-11 Results for outcome "Earnings if not unemployed"
CHF
-800-400 0 400 800
Non-dynamic Quasi-dynamic
2 5 8 11 14 17 20 22
months after programme start
CHF
-800 -400 0 400 800
2 5 8 11 14 17 20 23
months after programme start
personality
language
computer
voc. high
voc. low
other
EP single
EP coll.
int_job
Aggregated average Treatment Effects on the Treated compared to waiting, significance level 5%;
Outcome variable: Earnings in employment of at least 3 consecutive months
This outcome shows the earnings only if a person is not registered at the unemployment office
or doesn't receive any unemployment payments at the same time. Positive effects of up to almost
800 francs are found (which of course are driven by the employment effect of the programmes).
5.3.8 Employment with sustainable earnings
Table 5-12 Results for outcome "Employment with sustainable earnings"
%
-30 -20 -10 0 10 20 30
Non-dynamic Quasi-dynamic
2 5 8 11 14 17 20 22
months after programme start
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23
months after programme start

26
Employment with "sustainable earnings" is classified based on family and regional information
of the unemployed (number of dependent persons, canton, and agglomeration). "Sustainable"
means that the earnings are higher than a computed equivalence income which defines the
existence minimum in Switzerland. The results based on the quasi-dynamic approach show
positive effects on this classification of employment also whereas the non-dynamic approach
rates nonparticipation much higher.
5.3.9 Registered at the unemployment office
Table 5-13 Results for outcome "Registered at the unemployment office"
%
-30 -20 -10 0 10 20 30
Non-dynamic Quasi-dynamic
2 5 8 11 14 17 20 22
months after programme start
%
-40 -20 0 20 40
2 5 8 11 14 17 20 23
months after programme start
The outcome "registered at the unemployment office" is 1 if the corresponding person still is
enrolled with the regional placement office and doesn't have a "stable" job with 3000 CHF income
at the same time. According to the non-dynamic approach, persons are more likely to be
registered whereas the quasi-dynamic approach shows that participants in almost all programmes
are less likely to be registered than nonparticipants.
5.3.10 Bounds
One relevant policy question is the size of aggregated effects over time, for the public as well
as for the unemployed. This subsection picks out two outcomes and gives lower and upper
bounds for aggregated effects of programmes over time. The chosen outcomes are "income
from unemployment" and "total income". The first is related to the public expenses, the second
is the private profit of programmes. The bounds are "worst case" and "best case" bounds, i.e.
simple aggregation of single time 5 percent confidence bounds. An example how to read this
table: A personality course leads in average to 101 CHF more unemployment benefits aggregated
over 12 months after programme start; a "worst case" bound for this is 1556 CHF more
unemployment benefits, "best case" would be 1354 CHF less unemployment benefits. The total
income of a participant in a personality course aggregated over 12 months after programme
start is 124 CHF lower than without the programme, with an lower bound of -2020 and an
upper bound of 1772 CHF.

27
Grey shaded areas show "effective" programmes in the sense that even the worst case bound
shows lower unemployment income or higher total income. Note that the lower income from
unemployment for employment programmes are not shaded because they might be due to the
income during the programme which is not counted as "income from unemployment" in the
pension system data. We find clearly positive effects in the sense of total income for language
courses, computer courses, vocational training, and other courses, as well as for employment
programmes in single workplaces and interim jobs. The aggregated total income of participants
within 12 months after programme start is between 1700 and 3250 CHF higher for effective
programmes.
In terms of benefit payments only interim jobs lead to less public expenses. For the other programmes
the aggregated effects are not clear. Participants and computer courses and high vocational
training in average received less unemployment benefits within 12 months after programme
start than if they had not participated. The effect for personality, language, and low
vocational courses is to the other side: in average they need more unemployment benefits.
However, these effects are not significant. For employment programmes this effect is not determinable
for reasons explained above.
Table 5-14 Upper and lower bounds for aggregated effects
Income from unemployment Total income
1-23 months 1-12 months 1-23 months 1-12 months
lower mean upper lower mean upper lower mean upper lower mean upper
Personality -3350 -729 1891 -1354 101 1556 -5164 -808 3549 -2020 -124 1772
Language -2105 -326 1452 -564 430 1423 1703 4608 7514 393 1692 2991
Computer -4299 -2044 211 -1921 -659 604 2100 5802 9504 359 2015 3670
Voc. high -6736 -2516 1704 -3488 -1081 1327 2357 9460 16563 70 3145 6220
Voc. low -4727 -1282 2163 -1725 155 2035 232 6028 11823 -137 2283 4702
Other -4664 997 6659 -2037 907 3851 295 9461 18627 -431 3258 6948
Empl. sing. -8542 -5985 -3428 -6013 -4829 -3644 -408 4449 9306 656 2314 3972
Empl. coll. -9851 -7509 -5168 -6893 -5787 -4682 -2701 1758 6216 -811 735 2281
Interim job -6831 -5685 -4538 -5326 -4721 -4117 5609 7600 9591 2192 3047 3902
Note: Aggregation is after programme start. Treatment effects are based on the quasi-dynamic matching estimator.
6 Conclusion
Active labour market programmes are evaluated in terms of a variety of outcomes that give a
clear picture of the effectiveness of active labour market policy. The focus of the study is the
effect of the treatment on the treated compared to nonparticipation. Treatment assignment and
therefore nonparticipation was modelled non-dynamically as well as in the quasi-dynamic design
of Sianesi (2004) where nonparticipation is modelled as "waiting" and more intense job
search status compared to participation. The two methodologies' estimated effects for active
labour market programmes in Switzerland are compared. Indeed the consideration of dynamics
reverse the results compared to a non-dynamic approach.

28
The outcomes show that persons who have participated in programmes are better off than
nonparticipants in almost every sense: they are more likely to be employed, where "employment"
was analyzed not only quantitatively, but also qualitatively in terms of stability, purchasing
power, and sustainability. Moreover, participants tend to keep in touch with the labour
market longer than untreated persons, in the sense that they don't leave the labour force and
they don't become welfare dependent. In general effects don't change or jump close to benefit
exhaustion, so a possible effect in terms of benefit exhaustion can be ruled out or is similar in
all groups.
In aggregation, we find clearly positive effects in the sense of total income for language
courses, computer courses, vocational training, and other courses, as well as for employment
programmes in single workplaces and interim jobs. The aggregated total income of participants
within 12 months after programme start is between 1700 and 3250 CHF higher for effective
programmes. Subject to unemployment benefits, effects are clear only for interim jobs: they
lead to less unemployment benefits.

References
29
Fredriksson, Peter and Per Johansson (2002), Program evaluation and random program starts, IFAU Working Paper
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Frölich, Markus (2004), Programme evaluation with multiple treatments, forthcoming in Journal of Economic Surveys.
Frölich, Markus, Michael Lechner, and Heidi Steiger (2003), Statistically Assisted Programme Selection – International
Experiences and Potential Benefits for Switzerland, Swiss Journal of Economics and Statistics, 139, 311-331.
Gerfin, Michael, and Michael Lechner (2002); Microeconometric Evaluation of the Active Labour Market Policy in Switzerland,
The Economic Journal, 112 (482), 854-893
Gerfin, Michael, Michael Lechner, and Heidi Steiger (2002), Does subsidised temporary employment get the unemployed
back to work? An econometric analysis of two different schemes, Discussion Paper 2002-22, Department of Economics,
University of St. Gallen.
Heckman, James J., Robert LaLonde, and Jeffrey Smith (1999), The Economics and Econometrics of Active Labor Market
Programs, in O. Ashenfelter and D. Card (eds.), Handbook of Labor Economics, Volume 3A. Amsterdam: North-
Holland: 1865-2097.
Lalive, Rafael, Jan van Ours, and Josef Zweimüller (2002), The Effect of Benefit Sanctions on the Duration of Unemployment,
IZA Discussion Papers 469, Institute for the Study of Labor (IZA).
Lechner, Michael (2001a), A note on the common support problem in applied evaluation studies, Discussion Paper 2001-
01, Department of Economics, University of St. Gallen
Lechner, Michael (2001b), Identification and Estimation of Causal Effects of Multiple Treatments under the Conditional
Independence Assumption, in: Lechner, M., Pfeiffer, F. (eds), Econometric Evaluation of Labour Market Policies,
Heidelberg: Physica/Springer, 2001, p. 43-58
Lechner, Michael (2002), Some practical issues in the evaluation of heterogeneous labour market programmes by matching
methods, Journal of the Royal Statistical Society - Series A, 165/1, 59-82
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forthcoming in the Review of Economics and Statistics.

30
Appendix A. Sample selection rules
The selection procedure starts from the total sample of pension system records available for
the corresponding sample and selects step by step people with described characteristics.
Table A-1 Sample restrictions
Sample restrictions according to Lechner and Gerfin
Initial number of observations 81'399 †
First selection: personal characteristics
No missing data about insured earnings in current unemployment spell
Age 25 to 55
No disability
Earnings in last job > 100 CHF
Only Swiss citizens and foreigners with at least yearly permit
No homeworkers/trainees/students
Information about duration of entitlement period available
Remaining observations 70'680
Second selection: unemployment duration
Not long-term unemployed on 31 st of December 1997
Beginning of spell is earlier than end of spell
Has been registered as unemployed on 31 st December 1997
Benefit entitlement not yet exhausted before 31 st December 1997
Remaining observations 67'191
Third selection: programme participation
No significant programme participation according to AVAM (courses and empl. programmes) or
ASAL (interim jobs) before 1998
Programme doesn’t start on 1 st of January 1998
Person has not participated in any other programme not considered in this study
Remaining observations 34'393
Fourth selection: pension system data
Has been employed before at least once
Remaining observations SAMPLE A 33'700
After randomising programme start dates for nonparticipants (-7'367) 26'333
After common support restriction (-4'778) SAMPLE B 21'555
Note: † This sample is a random sub-sample of the underlying population, but after already having applied some of the here
mentioned selection criteria on the population. The numbers of observations deleted due to these criteria hence
should not be interpreted as shares in the total population of unemployed in Switzerland.

Appendix B. Further statistics of all samples
Table B-1: Descriptive Statistics
Sample A Sample B
21'555 33'700
Programme participation (in %)
No course 0.32 0.47
Personality course 0.05 0.04
Language course 0.09 0.08
Basic computer course 0.08 0.06
Vocational training high level 0.02 0.02
Vocational training low level 0.03 0.02
Other training 0.02 0.01
Employment programme public 0.07 0.05
Employment programme private 0.07 0.05
Temporary wage subsidy 0.25 0.20
Begin of programme in calendar time (1=1.1.1998) 101.06 59.14
Duration of current unemployment spell at beginning of first programme 94.66 206.26
Remaining time of benefit entitlement at beginning of programme (only people in 412.46 442.59
courses and empl. programmes) in days
Duration of first programme (only people in courses and empl. programmes) 40.28 94.20
Current unemployment spell
Duration of current unemployment, 31.12.1997 151.03 147.12
Unemployment benefit, daily, CHF 139.09 137.63
Beginning of benefit entitlement period in months since 1996 (1=Jan1996) 17.68 17.31
Number of days of cancelled benefits (as sanction) in 1997 4.72 4.75
Other personal characteristics
Age in years 37.64 37.75
Younger than 30 0.20 0.20
Older than 50 0.12 0.13
Female 0.44 0.46
Number of additional persons in same household 1.41 1.44
At least one additional person in same household 0.69 0.70
Mother tongue
German 0.35 0.34
French 0.20 0.18
Italian 0.10 0.11
Other 0.35 0.37
Language spoken in canton of residence 0.54 0.52
German/French/Italian, but not canton language 0.11 0.11
Foreign languages
Number of foreign languages 1.42 1.43
Marital status
Single 0.28 0.26
Married 0.59 0.61
Widowed 0.01 0.01
Divorced 0.13 0.12
Nationality
Swiss 0.56 0.54
Foreign with permanent permit 0.29 0.30
Foreign with yearly permit 0.14 0.17
Qualification
Skilled 0.57 0.54
Semi-skilled 0.16 0.15
Unskilled 0.27 0.31
table continues on next page
31

table continued
Sample A Sample B
Caseworker's subjective rating of employability
No information 0.05 0.05
Very easy 0.05 0.06
Easy 0.15 0.15
Medium 0.58 0.55
Difficult 0.15 0.15
Special case
Mobility
0.02 0.02
Not mobile 0.09 0.09
Daily commuter 0.87 0.86
Mobile within parts of Switzerland 0.03 0.02
Mobile within whole of Switzerland 0.01 0.02
Mobile abroad
Looking for ... job
0.01 0.01
Full-time 0.36 0.36
Part-time 0.14 0.15
No information
Unemployment status
0.49 0.49
Full-time 0.82 0.81
Part-time 0.16 0.16
In part-time employment 0.01 0.02
Other
Monthly earnings in last job
0.01 0.01
Monthly earnings in CHF 3882.88 3839.75
Less than 1000 CHF 0.02 0.02
Between 1000 and 2000 0.09 0.10
Between 2000 and 3000 0.19 0.20
Between 3000 and 4000 0.25 0.25
Between 4000 and 5000 0.24 0.22
Between 5000 and 6000 0.11 0.11
More than 6000
Job position
0.09 0.10
Self-employed 0.01 0.01
High (management, etc.) 0.06 0.06
Medium 0.58 0.55
Low
Canton
0.35 0.38
Zurich 0.20 0.21
Berne 0.09 0.09
Lucerne 0.04 0.04
Uri 0.00 0.00
Schwyz 0.01 0.01
Obwalden 0.00 0.00
Nidwalden 0.00 0.00
Glarus 0.00 0.00
Zug 0.01 0.01
Fribourg 0.03 0.03
Solothurn 0.03 0.03
Basle-Town 0.03 0.04
Basle-Country 0.02 0.02
Schaffhausen 0.01 0.01
Appenzell Outer Rhodes 0.00 0.00
Appenzell Inner-Rhodes 0.00 0.00
St. Gall 0.05 0.05
Grisons 0.01 0.01
Argovia 0.06 0.05
Thurgovia 0.02 0.02
Ticino 0.05 0.08
Vaud
table continues on next page
0.14 0.12
32

table continued
Sample A Sample B
Valais 0.05 0.05
Neuchâtel 0.03 0.03
Geneva 0.08 0.08
Jura
Canton language
0.01 0.01
French 0.34 0.32
Italien 0.07 0.09
German
Region
0.59 0.59
East 0.10 0.10
Central 0.07 0.06
North-West 0.14 0.14
Middle (Bern) 0.09 0.09
Romandie (French speaking part) 0.34 0.32
Zurich 0.20 0.21
Italian speaking part
Size of town where worked before
0.05 0.08
>=200’000 0.08 0.08

table continued
Sample A Sample B
Paper 0.00 0.00
Graphics 0.02 0.02
Leather 0.00 0.00
Chemistry 0.01 0.01
Stone 0.01 0.01
Metals 0.03 0.02
Machinery 0.03 0.03
Electronics 0.02 0.02
Watches/jewellery 0.01 0.01
Other industry 0.01 0.01
Construction 0.13 0.12
Trade 0.16 0.15
Hotel industry 0.13 0.15
Repairing 0.02 0.02
Transportation 0.03 0.03
Communication 0.00 0.00
Banking 0.03 0.03
Real estate 0.01 0.01
Consulting 0.12 0.11
Teaching 0.02 0.02
R&D 0.00 0.00
Health care 0.04 0.04
Other services 0.10 0.10
Civil services 0.04 0.04
Unemployment rate in industry, 12/1997
Employment history from pension system data
6.38 6.60
Uninterrupted history in pension system since registration or 1988 0.70 0.68
Uninterrupted history in pension system since 1993 0.74 0.72
Person was self-employed at least once since 1988 0.08 0.08
Person was self-employed at least once since 1993 0.05 0.05
Number of unemployment spells since registration or 1988 0.28 0.30
Number of unemployment spells since 1993 0.83 0.89
Was out of labour force since registration or 1988 0.15 0.15
Number of months out of labour force since registration or 1988 1.87 1.89
Was out of labour force since registration or 1993 0.18 0.18
Income from employment (in 1000 CHF, inflation corr.) 1988 29.94 29.92
1989 32.49 32.30
1990 36.79 35.61
1991 36.58 36.66
1992 35.59 36.52
1993 36.55 35.53
1994 38.53 36.65
1995 39.59 38.35
1996 24.86 38.91
1997 24.86 25.09
Income from unemployment (in 1000 CHF, inflation corr.) 1988 0.14 0.20
1989 0.13 0.18
1990 0.17 0.22
1991 0.56 0.60
1992 1.66 1.70
1993 3.50 3.54
1994 2.87 2.94
1995 2.30 2.40
1996 1.91 2.29
1997 12.74 12.39
Immigrated to Switzerland 1988-1992 .05 0.06
Immigrated to Switzerland 1993-1997
table continues on next page
.05 0.05
34

table continued
Sample A Sample B
No information in pension system for 1998 0.00 0.01
No information in pension system for 1999 0.09 0.10
Number of months unemployed since entry into social security system or since 1988 10.21 10.48
Number of months employed since entry into social security system or since 1988 87.10 85.74
Never unemployed 0.08 0.08
Month of entry into pension system (1=January 1988) 13.49 14.13
Number of employment spells since 1988 2.30 2.38
Number of unemployment spells since 1988 1.80 1.87
Mean duration of employment spell in months 55.63 53.64
Mean duration of unemployment spell in months 5.18 5.17
Max. duration of employment spell in month 70.86 69.25
Max. duration of unemployment spell in month 6.84 6.88
Standard deviation of wages 1232.31 1313.86
Duration of last employment spell in months 52.60 50.23
Wage growth during last employment spell (inflation corrected) 0.01 0.01
Proportion of time unemployed in % 0.10 0.11
Proportion of time employed in % 0.81 0.80
Mean wage 1988 2703.77 2716.2
1989 2928.22 2927.58
1990 3197.88 3219.62
1991 3298.35 3306.75
1992 3303.71 3313.09
1993 3233.26 3242.38
1994 3322.18 3451.41
1995 3476.28 3483.09
1996 3622.91 3596.84
1997 3050.08 3078.53
Graph B-1: Numbers of observations left in different outcome measures for Sample A
number of observations
5000 10000 15000 20000
35
2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47
months
after prog. start
in entitl. period
after registration

Graph B-2: Descriptive statistics of outcomes by treatment categories
Outcome a
Outcome c
Outcome e
Outcome g
Outcome i
Outcome k
%
20 40 60 80
%
20 40 60 80
CHF
1000 2000 3000
%
20 40 60 80
CHF
1000 2000 3000
%
20 40 60 80
After programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 22
months after programme start
graphs continued on next page
36
Outcome b
Outcome d
Outcome f
Outcome h
Outcome j
%
20 40 60 80
CHF
1000 2000 3000
CHF
1000 2000 3000
%
20 40 60 80
%
20 40 60 80
After programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 22
months after programme start
2 5 8 11 14 17 20 22
months after programme start

graphs continued
Outcome f
Outcome g
Outcome h
Outcome i
Outcome j
Outcome k
%
20 40 60 80
%
20 40 60 80
%
20 40 60 80
CHF
100020003000
CHF
1000 2000 3000
CHF
100020003000
37
In entitlement period After registration
2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47
months in benefit entitlement period
2 5 8 11 14 17 20 23 26 29 32 35 38 41 44 47
months in benefit entitlement period
%
20 40 60 80
%
20 40 60 80
%
20 40 60 80
CHF
1000 2000 3000
CHF
1000 2000 3000
CHF
1000 2000 3000
2 5 8 11 14 17 20 23 26 29 32 35
months since registration
2 5 8 11 14 17 20 23 26 29 32 35
months since registration
2 5 8 11 14 17 20 23 26 29 32 35
months since registration
2 5 8 11 14 17 20 23 26 29 32 35
months since registration
2 5 8 11 14 17 20 23 26 29 32 35
months since registration
2 5 8 11 14 17 20 23 26 29 32 35
months since registration

Table B-2: Numbers of persons starting programme at what time (quasi-dynamic matching)
38
Personal. Lang. Comp. Voc. high Voc. low Other EP single EP coll. Int. job
t (1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)
1
2 33 42 107 121 80 88 14 25 13 17 9 7 13 21 8 20 241 424
3 88 108 189 278 219 232 40 44 34 47 17 31 29 49 33 62 435 663
4 109 145 242 310 217 267 56 71 45 62 15 21 67 96 59 86 487 708
5 110 143 278 316 175 235 49 68 52 69 29 39 75 116 74 102 421 661
6 97 114 225 225 146 179 48 53 48 60 23 29 96 122 109 116 427 548
7 82 88 200 219 149 161 43 40 50 63 23 26 93 112 97 118 392 526
8 65 69 165 196 119 137 34 44 51 53 21 28 104 127 133 143 366 473
9 73 85 147 162 97 100 41 46 41 41 17 18 116 132 104 129 344 380
10 74 79 144 156 93 95 40 39 43 50 23 22 104 103 111 126 336 361
11 77 89 154 164 91 80 29 37 47 46 16 16 133 128 109 120 316 336
12 65 59 142 137 67 75 23 27 36 48 23 23 128 132 112 117 333 324
13 83 82 153 142 88 84 36 34 48 43 24 22 125 119 113 119 378 345
14 72 54 124 71 58 35 25 22 29 27 27 25 118 117 120 122 381 236
15 43 28 88 65 52 33 21 18 32 20 20 22 100 78 124 102 285 160
16 42 35 70 38 50 26 13 10 33 16 13 11 98 89 97 92 291 152
17 27 21 57 27 28 20 19 11 18 8 17 10 96 64 86 65 241 139
18 28 9 35 23 25 16 13 7 16 11 14 7 80 52 87 59 184 93
19 32 21 42 15 24 8 17 5 18 3 8 3 57 36 84 47 176 79
20 28 42 35 11 17 9 40 61 160
21 25 31 16 6 12 11 31 37 122
22 18 25 22 14 11 11 20 31 146
23 9 23 21 5 7 11 8 124
24 3 7 8 7 3 3 114
25 8 4 3 4 60
26 3 3 7 5 17
27 3 5 10
28 3 9
29 5 5
30 4 6
31 4
tot 1286 1271 2701 2665 1884 1871 607 601 704 684 370 360 1755 1693 1815 1745 6811 6608
Note: Only cells with at least 3 observations are counted. Time frame (1): months in entitlement period. (2): months since
registration. The number of participants in (1) and (2) differ slightly because both time frames were "cut" above (31
and 19 months, respectively). This cut affected the number of participants a bit stronger in the registration time
frame.