an evaluation bart cockx and geert ridder march, 13 2000 · to stimulate t heir re -employment. ......

SOCIAL EMPLOYMENT OF WELFARE RECIPIENTS IN BELGIUM:

AN EVALUATION1

Bart Cockx 2and Geert Ridder 3

March, 13 2000

Abstract:

In Belgium, welfare agencies receive a subsidy to employ welfare recipients for a

period sufficiently long to entitle them to unemployment benefits. This work

experience program is called Social Employment (SE). We investigate the effect of SE

on the exit rate from welfare. We propose a grouping/IV estimator of the SE effect

that eliminates selection bias. The estimator is consistent, even if the selection into SE

depends on the average unobserved characteristics of welfare recipients in a region and

in a welfare duration interval. The empirical analysis suggests that there is creaming in

the selection process. Without correction for selectivity we find that SE reduces

welfare dependence, but after correction this conclusion is reversed. These results are

consistent with the adverse incentives faced by the welfare agencies.

Keywords: Welfare, selection bias, duration data, grouping, work experience

JEL classification numbers: C41, I38.

1We are grateful for the research assistance provided by Annick Persoon. This project has been financed by several grants: first, by a Human Capital and Mobility Fellowship which enabled the first author to visit the Tinbergen Institute in Amsterdam during the first semester of 1994; second, by a grant (n° 30.1.92.237) of the Belgian Fund of Collective and Fundamental Research (F.K.F.O); thirdly, by a grant "Actions de Recherches Concertées" n° 93/98-162 of the Ministry of Scientific Research of the Belgian French Speaking Community; finally, by a program, Poles d'Attraction inter-universitaires PAI P4/01, of the Belgian government. We also thank the Centre of Economic Studies of the Katholieke Universiteit Leuven, the Tinbergen Institute, and IRES of the Université Catholique de Louvain for the accommodation and the facilities provided. We thank the Minister of Health, Environment and Social Emancipation for access to the data and Frans Spinnewyn for his support in setting up this research project. We are grateful to Kenneth Y. Chay, Geert Dhaene, Thierry Magnac, Michel Mouchart, Bruno Van der Linden, and Marno Verbeek for their valuable comments. We are also indebted to, the managing editor, and especially to an anonymous referee whose detailed comments led to significant improvements of this article. The usual disclaimer applies. 2 IRES and Department of Economics, Université Catholique de Louvain, Place Montesquieu 3, B-1348 Louvain-la-Neuve, Belgium, phone: +32.10.47.34.39. E-mail: [email protected].

2

3 Department of Economics, The Johns Hopkins University, Baltimore, MD 21218, USA, phone +1.410.515.7614. E-mail: [email protected].

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1. Introduction

In this study, we evaluate the effectiveness of a public sector work experience program,

the Belgian Social Employment (SE) program. Economists have argued that work

experience programs can reduce long-term unemployment by countering the

discouragement, the loss of work habits, and the skill deterioration induced by a long spell

of inactivity (cf. e.g. Layard, Nickell and Jackman 1991). A prerequisite for the

effectiveness of a work experience program is that the responsible agencies foster the re-

employment of inactive workers. In Belgium, the local welfare agencies (WA) that are in

charge of SE, not only run the program, but they also provide community services, as

services to nursing homes, hospitals and homes for the elderly, and meals and cleaning

services for households in need of support. We argue below that, as employers, the WA

have an incentive to select the most productive welfare recipients for SE and no incentive

to stimulate their re-employment.

Not only the program administrators but also the participants in SE face adverse

incentives that may offset the beneficial effect of improved work habits. First, participants

in SE earn the minimum wage, and thus have a significantly higher income than non-

participants. This increases the wage at which they are willing to accept a regular job.

Second, participants who remain in SE until the statutorily defined end-date are

automatically entitled to unemployment benefits that are higher than welfare allowances

and not means-tested. Finally, job search effort could be directly affected, if it is harder to

search for a regular job while participating in SE.

The fact that the WA are likely to assign more employable welfare recipients to

the program complicates the estimation of the SE effect on the exit rate from welfare,

because even without the program SE participants would have a better position on the

labor market. Moreover, the effect of a work experience program may be smaller for

more employable recipients (see Gueron and Pauly 1991). To evaluate the program we

need a procedure to correct for selection bias. Research by Ashenfelter and Card (1985),

LaLonde (1986), and Fraker and Maynard (1987) has cast doubt on the ability of non-

experimental methods to correct for selection bias. Estimates are sensitive to the model

specification and the estimation method. Recently, Dehejia and Wahba (1999) have

shown that in the National Supported Work (NSW) training program, studied before by

LaLonde (1986), the selection bias is mainly determined by observable variables.

2

Conditioning on these variables (or the probability of selection) removes the selection

bias1. This requires the availability of high quality and comparable data on both

participants and non-participants, in particular data on the pre-program labor market

histories of both groups. Participants and non-participants should also be recruited from

the same local labor markets (Heckman et al. 1998).

Unfortunately, the data used in our evaluation do not contain the variables that

make the participants and non-participants comparable. We therefore follow another

strand in the evaluation literature that identifies the program effect from exogenous

variation in program participation (see Meyer 1995 or Angrist and Krueger 1999 for a

survey). In particular, we use Angrist’s (1991) observation that if participation is selective

at the individual level, aggregation may reduce or even eliminate selectivity, if variation in

the participation rate at the aggregate level is exogenous2. The grouping estimator was

first proposed by Wald (1940) to deal with measurement error. Durbin (1954) showed

that the Wald estimator is an Instrumental Variable (IV) estimator.

In our study, we group the data by duration class and region. This yields a

consistent estimator of the effect of SE if the participation rate is not correlated with the

average characteristics of the welfare recipients grouped by duration interval and region3.

This assumption may not hold in our application. Because the WA may be aware of the

variation of the average unobserved characteristics with the elapsed duration of welfare,

there may be a correlation between the average unobserved characteristics by duration

interval and the participation fraction. However, we include indicators of the duration

intervals (and of the region4), and this eliminates the correlation, at the expense of a

reduced variation in the participation fraction that is now in deviation of the mean by

duration interval (and by region).

We estimate the effect of SE on the rate of leaving welfare. In other words, the

response variable is the welfare duration. Unless we impose restrictive distributional

1 Heckman et al. (1998) find that conditioning on a similar set of variables does not remove all of the bias in an evaluation of the JTPA program and propose a conditional (on the propensity score) differences-in-differences method to remove any remaining selection on bias. 2 Angrist (1991) uses this approach to deal with the endogenous wage in a labor supply equation. He aggregates waves in the PSID to the national level and this aggregation replaces individual wages by average wages. 3 Note that this is perfectly consistent with selectivity at the individual level. To see this, assume that there are two types of welfare recipients and that the type with favorable characteristics is more likely to be selected for SE. Such a composition of welfare recipients within a group is irrelevant at the group level. 4 This eliminates the potential correlation between the participation fraction and the average unobserved "quality" of welfare recipients in a region.

3

assumptions and censoring is not important, an IV estimator is not consistent in models

for duration data5. Grouping solves this problem, since we can then base estimation on the

Minimum Chi-Square (MCS) method. The MCS method justifies estimation on the

linearized duration model (Cockx, 1997). Without further assumptions on the variation of

the program effect in the population, the probability limit of an IV estimator is not equal

to the average effect of the program. In general, an IV estimator gives the effect of SE for

marginal participants6 (Imbens and Angrist, 1994). The IV estimator gives the usual

average program effect for participants, if the program effect does not vary among

participants or if the variation in the program effect does not influence the decision to

participate in the program (Heckman and Robb 1985, p.196, Heckman and Smith 1996,

p.59-68, Heckman 1997). We test the latter assumption and show that it cannot be

rejected for our data.

Krueger (1990), Meyer (1989), and Meyer, Viscusi and Durbin (1990) analyzed

duration data that were generated by a natural experiment. Their approach cannot be used

in the evaluation of SE. First, because of censoring, restrictive distributional assumptions

must be imposed (see Krueger 1990, p.14). Second, their approach cannot account for

time-varying explanatory variables. They assume that individuals are selected into the

program prior to entry into the state of interest. WA, however, employ and therefore

select welfare recipients at some instant during the welfare spell. The participation

indicator is therefore time varying.

In the following section we discuss the main features of the institutional setting.

We compare it to the institutional setting of work experience programs in other countries

and argue that adverse incentives for administrators of the kind discussed in this paper can

explain the ineffectiveness of these programs in other countries as well. In Section 3 we

describe the data and provide a benchmark of the program effect on the basis of a

matching estimator. Section 4 presents the statistical model, justifies the identifying

assumption and presents the baseline results. Section 5 discusses the robustness of the

results. The final section contains the conclusions.

5 Only if one restricts the duration distribution to be Weibull and if there is no right censoring, duration data can be analyzed in the linear regression framework (cf. Lancaster 1990, p. 220-221). 6 This is the Local Average Treatment Effect (LATE).

4

2. Social Employment and its Relevance for other Work-Experience Programs

2.1. Social Employment

In Belgium the welfare system is a safety net for those who are not covered by social

insurance, because of insufficient work experience, delay in administrative procedures, or

because they have been punished by the social insurance administration. Individuals, who

pass a means test, can claim a supplement to their income up to the legally determined

Minimum Income Guarantee (MIG) at the welfare agency (WA) of their municipality.

During the 1987-90 period on average 52,659 adults and 31,672 children received the

MIG at any given time (Cockx 1992, p.36). This is roughly 0.8% of the Belgian

population. To compare, in January 1989, 389,672 individuals received Unemployment

Insurance benefits (Cockx 1992, p.39). The number of welfare recipients is relatively

small, because unemployment insurance benefits have an indefinite duration7. After a

waiting period of six months, even school-leavers are entitled to unemployment insurance

benefits.

Legislation stipulates that WA may employ welfare recipients for a period that is

sufficiently long8 to entitle them to unemployment benefits. This type of employment is

called Social Employment (SE). During the 1987-90 period on average 1282 individuals

received SE at each instant of time. This is 2.4% of the adult welfare population.

The possibility to employ welfare recipients for this purpose brought legislation

into agreement with the principle that social assistance offers relief only if the main social

insurance system fails to do so. The national and regional9 authorities have gradually

widened the scope of SE as a means of enhancing the integration of the poor. This is

reflected in increased financial support to WA offering SE. The regional authorities

subsidize SE in Flanders since 1983 and in the Walloon provinces since 1989. The central

7 There is one exception to this rule. Cohabitants who are not the head of a family can lose entitlement to unemployment benefits on grounds of "excessive" duration. 8 Unemployment benefits are only paid to workers who, within a specified period prior to their claim, have been employed for at least 75 days if younger than 18, and up to 600 days if older than 50 (Van Langendonck 1991, p. 450). 9 Readers who are familiar with the Belgian institutional setting will notice that we use regional in a loose sense. As such, we can divide Belgium into 4 regions: Flanders, referring to the Flemish (Dutch) speaking community in the North; the Walloon provinces, referring to the French speaking community in the South; Brussels, referring to the region of the bilingual community of the capital city; the German speaking community in the East. Given the marginal importance of the latter region, we ignore it in the sequel.

5

authorities finance 50% of the MIG paid by the local WA. Since 1985 the WA remain

entitled to this subsidy if it socially employs the welfare recipient, and since January 1993

the subsidy is up to 100% of the MIG.

It is important to realize that the WA receive this financial support and not the

employed welfare recipients. SE participants are paid the minimum wage, so that they

earn at most as much as in an alternative job. Incentives for a welfare recipient to

participate in SE are therefore, either the (possibly higher) unemployment benefits to

which he/she is entitled after SE, or the (subjective) benefits of the work experience.

These incentives are not the sole determinants of participation, for the WA can mandate

participation, as the receipt of welfare benefits is conditional on the willingness to work.

Van de Velde (1989) demonstrates that SE is concentrated in the community

services that the WA offer to the general public. These community services consist of

domestic services to households, such as care, meal provision, cleaning, and to

institutions, i.e. nursing homes, hospitals, homes for the elderly, etc. These jobs require

few qualifications. Women are typically (95%) employed in the domestic services

provided by the WA. This work involves cleaning, cooking, washing and ironing. Men are

required to do all kinds of odd jobs (37%), to maintain roads or to plant vegetation

(13%), to help in the kitchen (10%), to do administrative work (10%) (see Van de Velde,

p.55).

WA can save on outlays by employing welfare recipients. An example clarifies this

point10. Consider a welfare recipient living alone in Flanders in January 1989. The net

monthly cost of SE for this individual is the cost of the minimum wage for the employer

(=49,354 Belgian Francs) minus the central authorities’ subsidy of 50% of the MIG

(=8,152 B.F.) and the regional authorities’ subsidy (=30,500 B.F.): 10,702 B.F.

Moreover, during the initial period of SE, in which the participant would otherwise

depend on welfare, the WA no longer needs to pay welfare benefits and therefore saves at

least11 50% of the MIG (=8,152 B.F.) minus the regional subsidy for welfare (=1,141

B.F.), that is 7,011 B.F. This implies that employing a worker, who initially produces

goods and services with a value of more than 3,691 B.F. per month and later on of more

than 10,702 B.F. per month, is financially attractive to the WA. The WA needs not worry

10 The figures in the example are taken from an internal document of a WA to which we had access. 11 WA's can offer a supplement to the MIG, but they do not receive a subsidy for this supplement.

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about costs after SE, because after SE welfare recipients are entitled to unemployment

benefits, and hence will not depend financially on the WA. We conclude that SE can result

in savings for WA. These savings are larger if the WA select the most productive

recipients for participation in the program.

This observation is not only valid for the specific example. However, the size of

the savings differs with the level of the MIG12, the time period and the region. For

instance, Brussels does not provide a specific subsidy for SE and due to the growing

popularity of the program in Flanders, the subsidy per participant has declined. In the case

of a lower subsidy the WA can still save on expenses by being more selective in the choice

of participants. The specific subsidy rules provide no incentive to the WA to use SE to

integrate welfare recipients in the labor market. Efforts to this effect will only increase its

expenditures. Consequently, the findings of Van de Velde 1990, p. iv, that SE was hardly

accompanied by training and assistance and that the WA encouraged only 6% of the

participants to apply for another job, are not surprising.

2.2. Comparison with Other Work Experience Programs

In other European countries, program administrators face similar adverse incentives. For

instance, in Germany the dramatic rise in unemployment since the early nineties has

increased the number of welfare recipients substantially. In response, several

municipalities, like Leipzig, Frankfurt and Lübeck, now offer temporary employment to

welfare recipients in municipal job-creation companies, the so-called

"Beschäftigungsgesellschaften". Feist and Schöb (1999) argue that municipalities can

benefit from these initiatives for reasons similar to those in Belgium. First, the job-

creation companies provide local public goods. Second, after being employed for a year

the participants are entitled to unemployment insurance benefits of the "Bundesanstalt für

Arbeit", a federal authority. Consequently, the German municipalities have similar adverse

incentives as the WA in Belgium13.

12 The level of the MIG differs by household type. Legislation distinguishes between singles living alone, singles living with dependent children, cohabiting individuals and cohabiting married couples. 13 We are not aware of any study evaluating this program. Eichler and Lechner (1998) find that public employment programs reduce the unemployment risk for participants in the East German State of Sachsen-Anhalt. However, this study could not distinguish employment in the municipal Beschäfti-gungsgesellschaften from other public employment programs.

7

The British Youth Training Scheme (YTS) of the second half of the eighties

provides an example in which private employers face adverse incentives. Participants in

the YTS are placed in a job where they can acquire work experience. Trainees were paid

an allowance (set slightly above the unemployment benefits level) and firms could employ

trainees without incurring any further wage costs. As the WA in Belgium, firms could

therefore gain from cheap labor. Moreover, firms did not have explicit incentives to

increase the employability of trainees. The work experience was supplemented by

classroom training, but one can have serious doubts on the quality of this training

component, as this was a constant theme in discussions of reform. Dolton et al. (1994)

find that ex-trainees obtain jobs at a slower rate than non-trainees even when the time

spent in YTS is excluded14.

Work experience programs that place participants in jobs that do not require much

training induce (public or private) employers to hang on to the participants for the subsidy

period. After this period, employers, who have not invested in the participants, have little

incentive to keep them. For example, Edin and Holmlund (1990) report that participation

in temporary jobs in the public sector in Sweden decreases the re-employment rate of

young and displaced workers15. Bonnal et al. (1997) find that in France lower educated

young workers participating in public employment programs do not have a higher

transition rate from unemployment to employment after program participation. Moreover,

participation decreases this transition rate for workers with a professional or technical

diploma16.

The SE program is, at first sight, comparable to the workfare programs in the US,

e.g. aimed at women who receive Aid to Families with Dependent Children (AFDC) (see

Gueron 1990, Gueron and Pauly 1991, Moffitt 1992 and LaLonde 1995). Typically,

welfare applicants or recipients are required to look for a job for two to four weeks. If

they do not find a job, participants may be required to work for up to three months

without pay (workfare). Usually, these are entry-level jobs in public or nonprofit

14 Female (but not male) ex-trainees obtain ‘good’ jobs at a faster rate than non-trainees when time spent on YTS is excluded. 15 The authors do find that program participation reduces the length of subsequent unemployment spells. However, the study imposes strong parametric (Weibull) restrictions on the baseline hazard and does not explicitly account for selection on unobservables, which may bias the results. 16 According to the authors, the latter finding suggests that participation in such programs may signal low job performance

8

agencies, e.g. maintenance, clerical, park upkeep, or human services functions. Monthly

working hours are equal to the welfare grant divided by the hourly minimum wage rate.

The US workfare programs, however, differ from SE in important respects. First,

both participants and program administrators face different incentives in the two

programs. Second, most US programs are not pure work experience programs, but also

include job-search assistance, education or training components. A welfare recipient on

workfare earns no more than the welfare benefits. The participant is therefore worse off

by participating, because his/her time costs are not compensated. Contrary to SE,

workfare therefore makes it more attractive to leave the welfare rolls. The US welfare

and workfare programs are administered by the states, but subsidized by the Federal

Government. In contrast to the SE-program, the funding rules of the US Federal

Government, as determined by the Family Support Act (FSA) of 1988, provide incentives

for enhancing the employability of program participants (see Gueron and Pauly 1991, pp.

55-59 for a more extensive discussion). The rules for matching grants contain provisions

that prevent states from using workfare as cheap labor. Finally, contrary to SE in

Belgium, welfare recipients who do not exit to a regular job, return to welfare, and

increase the state's welfare expenditures.

Despite these differences, the finding of this paper that SE increases rather than

reduces welfare dependence is not in conflict with the evaluations of US workfare

programs. First, there is limited and inconsistent evidence on the effect of pure unpaid

work experience: Studies have problems in disentangling the independent effects of each

of a sequence of program components. Nevertheless, one finding is robust. No effect is

found, when resources are so limited that staff can provide almost no direct assistance. As

participants in SE hardly receive any assistance, this is in line with our results.

A noteworthy example of a successful work experience program is the National

Supported Work (NSW) demonstration targeted at extremely disadvantaged welfare

recipients17. In contrast with other work experience programs, NSW offered highly

structured, full-time work experience positions for up to 18 months with close

supervision and peer group support (Gueron and Pauly 1991, p.101). Hollister, Kemper

and Maynard (1984) and Grossman, Maynard and Roberts (1985) give evidence that

participation in NSW led to a sizable earnings increase for former AFDC recipients, and

17 NSW was not only offered to AFDC women, but also to disadvantaged youth. Evaluation studies do not find

9

that it significantly reduced the probability of being on welfare. Couch (1992) shows that

the initial positive earnings effect is even maintained 8 years after the program.

The experience with NSW is similar to that of SE pilot projects in Belgium

evaluated by Wouters, Van Meensel and Nicaise (1994). In these pilot projects work

experience is complemented by training and intensive assistance. Wouters, Van Meensel

and Nicaise find that these projects increase the employment rates, even in the long run.

3. The Data We analyze administrative data on recipients of the MIG that have been collected since

June 1987 by the Ministry of Social Integration. From these data we calculate the length

of welfare and SE spells (in days18) for all MIG recipients, who claim benefits after June

1st 1987. In the analysis we consider only spells that started in the period June 1987 -

July 1990. On July 1st 1990 additional measures to stimulate the re-employment of

welfare recipients were introduced. We only consider welfare recipients who were

younger than fifty at the start of the welfare spell.

Our data do not allow us to distinguish between exits to regular employment and

other states. Hence, a positive effect of SE on the total exit rate from welfare need not

correspond to a larger transition rate to regular employment. For example, Dehaes (1994,

p.119 and 128) found that 26% of the exits involve a change in labor market status19,

35% involve entitlement to other social benefits, 19% a move to another municipality,

11% a change in the family composition20, 4% a withdrawal of benefits by the WA, and

finally 4% other reasons.

Table 1 summarizes some characteristics of the population under consideration.

As the data were collected for administrative purposes and not for analysis, the

information is limited. The first column refers to all welfare spells, the second to those

spells in which some time was spent in SE.

INSERT TABLE 1 APPROXIMATELY HERE

positive short- or long-term effects for the latter group (cf. Hollister et al. 1984 and Couch 1992). 18 In Table 2 we express statistics in months by dividing the number of days by 30.4375. 19 This refers to events such as re-employment, a transition from a part-time to a full-time job, an increase of self-employment earnings, etc. 20 This refers to events such as marriage to or cohabitation with a partner with a sufficiently high income, moving into the house of parents or children with sufficient means, etc.

10

In contrast to Garcia (1990) we do not find an age difference between SE

participants and non-participants. This is a consequence of the restriction to welfare

recipients younger than fifty. On the other hand, the over-representation of women

among the socially employed is confirmed. In addition, individuals who are legally

married but living alone are over-represented among the participants in SE. Furthermore,

participants are more likely to have dependent children. The rate of participation in SE is

larger in small municipalities and in Flanders.

INSERT FIGURE 1 APPROXIMATELY HERE

The median welfare spell is 5 months. This is somewhat smaller than the number

reported in Cockx (1997). The difference can be attributed to the exclusion of recipients

older than fifty. Welfare spells of participants in SE are much longer. The median

duration for this group is 13 months. Moreover, as can be seen from Figure 1, the

survival fraction of participants in SE is uniformly higher than that of non-participants.

From this we cannot conclude, that participation in SE increases welfare dependence.

First, as reported in Table 2, the median duration until selection into SE is 6 months, and

this increases the survival fraction of participants. However, even if we subtract the time

until selection, the median duration of participants (=13-6=7 months) exceeds the median

duration of all welfare spells. Second, if transitions from welfare exhibit negative duration

dependence, the difference of the medians understates the effect, because the exit rates of

participants are lower due to delayed entry in SE. Third, even at an equal elapsed

duration, participants and non-participants differ in both observed and unobserved

characteristics. This induces the well-known selection bias. Note that, if the WA indeed

cream, i.e. select the best welfare recipients, the difference between the two survivor

functions in Figure 1 under- rather than overestimates the impact of SE.

INSERT TABLE 2 APPROXIMATELY HERE The finding that the median duration until selection into SE is larger than the

median welfare duration seems to be evidence against the creaming hypothesis of Section

2. However, WA will not offer SE to welfare recipients who are likely to leave welfare

shortly, because of the fixed costs associated with SE. As a consequence, SE is only

11

offered to welfare recipients who have been on welfare for some period. The creaming

hypothesis applies to this subgroup of the welfare recipients, which is a negative selection

from all welfare recipients.

Some of the biases associated with a simple comparison of participants and non-

participants in SE are reduced if we consider a matched comparison. Despite recent

pessimistic evidence of the validity of matching methods (cf. Friedlander and Robins

1995), the interest in their application has recently revived. Dehejia and Wahba (1995a,b),

for instance, reproduce, by matching on the probability of selection or propensity score,

the treatment effect estimated by a randomized experimental design. This requires that the

treatment assignment is independent of the outcome conditionally on observed

variables21. The administrative data used in our study are clearly not rich enough. For this

reason, the matching estimator gives only a benchmark estimate of the effect of SE on

welfare duration. Rather surprisingly, however, the estimate found by this method are

consistent with those found by the grouping/IV estimator proposed in Section 5 below.

Participants in SE are matched with non-participants on the basis of the

characteristics reported in Table 1. We only retain exact matches22. This explains the

significantly lower number of individuals in the matched sample. In the matched sample,

welfare recipients living in small municipalities are underrepresented. This should not

come as a surprise, as the number of welfare recipients is much smaller in these

municipalities, and consequently the probability of finding an exact match. For the same

reason foreigners, widow(er)s, women, families with dependent children, married

persons, and persons living in Flanders are underrepresented This feature can bias the

matching estimator of SE to the extent that the treatment effect varies systematically with

these characteristics. In Section 5 we argue that this is unlikely to be the case.

INSERT FIGURE 2 APPROXIMATELY HERE

In order to account for the bias induced by the elapsed duration until selection, we

match only to non-participants who are still on the rolls after the selection time of the SE

participant. Moreover, as SE can only produce effects from the moment of enrollment,

21 Heckman et al. (1998), using a rich dataset, reject the hypothesis of conditional independence given only observables in favor of the hypothesis that unobservables are important for treatment assignment. 22 Hence, we do not match on the propensity score. The matching procedure is nonparametric.

12

we subtract this elapsed duration from the welfare duration in both groups. In Figure 2

the survival functions of the welfare durations are plotted for participants and non-

participants23. This shows that participants in SE are more likely to be on welfare for up

to one year. Afterwards, they seem to be more likely to have left. This pattern is

consistent with (i) a lower transition rate out of welfare of SE participants (in comparison

to individuals who have been on welfare for the same time period) (ii) creaming of SE

participants. Initially the lower transition rate dominates, but over time the positive

selection into SE diminishes this effect among the survivors, and even reverses the

effect24.

4. Dealing with Bias: Discrete Durations, Competing Risks and Unobservables Nonrandom selection of participants leads to differences in the (average) characteristics

of participants and non-participants. Part of this is due to the delay in selection into SE.

The matching procedure of the previous section makes the two groups comparable and

eliminates the bias due to selection on observables and the delay in participation. It does

not eliminate bias due to selection on unobservable characteristics, which as argued is

potentially important in SE.

In this section we introduce a model for discrete duration data with competing

risks that deals with both types of bias simultaneously. The competing risks specification

allows for an arbitrary starting time in SE. In a first step, we control for even fewer

observable characteristics then in the matching procedure. If our hypothesis of creaming

in the selection process holds, this benchmark estimator of the effect of SE on the

transition rate hazard out of welfare will be even more biased upwards. This is confirmed

below. In a second step, we present the grouping/IV estimator that controls for selection

on both observables and unobservables.

We group the observed durations by duration interval and explanatory variables.

The advantage of grouping is that we do not need parametric assumptions on the joint

distribution of unobservables that affect both the participation decision and the transition

rate out of welfare. Grouped durations can be modeled in continuous or in discrete time.

23 As a consequence of right censoring, we cannot simply take differences of durations between two matched individuals as a measure of the treatment effect. 24 We also matched the SE participants to non-participants in WA’s that do not have welfare recipient in SE. This resulted in a somewhat larger matched sample. The results are the same as in Figure 2.

13

Following Prentice and Gloeckler (1978) and Cockx (1997) we use time-aggregation in a

continuous time proportional hazards model. In the resulting model the parameters are

invariant to the grouping. After a simple transformation of the dependent variable, the

model can be written as a (heteroskedastic) linear regression model.

4.1. Time-Varying Covariates and Competing Risks with Grouped Duration Data

Let the duration data be grouped into K+1 intervals: [ t

0, t

1), ...., [ t

k −1, t

k ), ..., [ tK −1,tK),

[ tK , tK+1 ) of (possibly unequal) length ∆ k = tk − tk −1 with t0 = 0 , tK +1 = ∞ . In the simplest

grouped duration model it is assumed (Prentice and Gloeckler 1979) that the (base-line)

hazard λ is constant on these intervals

(1) kk ttt <≤= 1-k , (t) λλ Let d (t ) be the indicator of participation in SE at t. In this section, we assume that the

process of participation is exogenous, an assumption that will be relaxed later. Note that

the time-scale is time spent on welfare. This is the time-scale used in the rest of this

paper.

If we assume that the effect of SE on the exit rate is independent of t, then the exit

rate h can be written as

(2) )())(exp()( ttdth λα= This means that participation in an individual's SE status at time t leads to a proportional

change in the baseline hazard that is equal to α . Let Pk be the conditional probability of

exit in the k-th duration interval [ t k −1 , tk ) given that the individual is still on welfare at t k −1 .

With (1) and (2) we have

(3) ( )

−−= ∫

−

k

k

t

t

kk dssdP1

)(expexp1 αλ

This probability depends on the evolution of d in the k-th duration interval. With grouped

duration data this evolution is unknown. We could assume that exit from SE is only

possible by leaving welfare and that all selection into SE occurs at time t k −1 . Then we

14

only need to know the SE-status at the start of the k-th interval to determine Pk , because

the SE-indicator is constant on that interval. However, these assumptions are

counterfactual, because individuals may leave SE and remain on welfare, and they may be

selected into SE at any point during the welfare spell. For that reason we make the more

reasonable assumption that there is at most one transition in a time interval.

Let θ0 (t) , θ1 (t ) be the transition intensities into and out of SE at welfare duration

t. As in (1) we assume that these intensities are piece-wise constant, and θ0k , θ1k are the

values on the k-th interval. We condition on the SE-status at the beginning of the k-th

interval. Let PkWSE ,OW be the conditional probability of leaving welfare in the k-th duration

interval while being in SE until exit from welfare, given that the individual is on welfare

and in SE at t k −125. Analogously, let Pk

WNSE ,OW be the conditional probability of leaving

welfare in the k-th duration interval while not being in SE until exit from welfare, given

that the individual is on welfare and not in SE at t k −1 . These probabilities are transition

probabilities in the k-th interval from the state welfare with SE (WSE) to the state out of

welfare (OW) and from the state welfare without SE (WNSE) to the state out-of-welfare

(OW). With (1) and (2) we have

(4)

( ) ( )

( )[ ]kkkkkk

k

kkkOWWSE

k dsssPk

∆−∆−−+

=

−−= ∫∆

11

1

0

,

)exp(exp1)exp(

)exp(

exp)exp(exp)exp(

θαλθαλ

αλ

θαλαλ

In a similar way we obtain

(5) ( )[ ]kkkkkk

kWNSEWSEkP ∆−∆−−

+= 1

1

1, )exp(exp1)exp(

θαλθαλ

θ

The conditional probability of all other events is the complement of the sum of these

probabilities.

For welfare recipients who are not in SE at the start of the k-th interval we have

25 Combining the event of leaving welfare with the event of being in SE until the transition, implies that we only consider the first transition within the interval. With the states defined below, we assume that there is only one transition in a time interval.

15

(6) ( )[ ]kkkkkk

kOWWNSEkP ∆−∆−−

+= 0

0

, exp1 θλθλ

λ

and

(7) ( )[ ]kkkkkk

kWSEWNSEkP ∆−∆−−

+= 0

0

0, exp1 θλθλ

θ

The expressions (4)-(7) show that our model for selection into SE is equivalent to

two competing risks models with piece-wise constant transition intensities: one for the

origin state WSE and destination states OW and WNSE, and one for the origin state

WNSE and destination states OW and WSE.

4.2. Modeling Grouped Durations with Competing Risks

It will be convenient to attach numbers to the three states introduced in Section 4.1

1. Welfare without SE

2. Welfare with SE

3. Out of welfare

In the sequel we identify the states by these numbers. We are interested in comparing the

transition intensity from state 1 to state 3 with that from state 2 to state 3. This identifies

the effect of SE on the exit rate from welfare. State 3 is assumed to be an absorbing state.

Our data do not contain multiple welfare spells.

Origin/destination states are indicated by superscripts u and v, and duration

intervals and types of individuals by subscripts k and m, respectively. We make the

following assumptions on the transition intensities between the states

(8) ( ) uvuh uv

kmuvv

mvk

uvkm ≠==+++= 3,2,1 ,2,1 ,log εαβγ

There are K+1 duration intervals, where the last interval is open, and M types. The

duration effect γ kv and the type effect v

mβ are assumed to depend only on the destination

state, i.e. they are the same for SE-participants and non-participants. We normalize the

type effects and set β1

v = 0 . The parameter α uv gives the effect of SE on the exit rate

from welfare. The effect is assumed to be independent of the type m. By definition

16

α12 = α 21 = 0 , and by normalization α13 = 0 . Note that α 23 is the parameter of interest.

Finally, the ε kmuv are unobserved variables, which are similar to the specification errors

introduced by Amemiya and Nold (1975) in their grouped logit model (see also Parks

1980). If we consider these unobserved variables as parameters, we obtain a saturated

model. In the sequel we impose restrictions on the unobserved variables, by assuming that

they are draws from some distribution. The specification in (8) is a proportional hazard

model for the continuous-time transition intensities.

The specification allows for unobserved heterogeneity in the transition intensities.

Unobservables may be important determinants of the transition intensities, because the

number of included observed characteristics is small. Because unobservables may also be

important for the selection into SE and correlation between unobservables in the

transition intensities and in the selection process biases the estimate of the SE-effect, we

must assess their contribution to the explanation of the transition intensities. Because the

data are grouped, the unobservables vary between, but not within groups. Hence, the

analogy between the group-specific unobserved heterogeneity in our model and the

individual unobserved heterogeneity in continuously observed duration data is imperfect.

An important advantage of our approach is that it is not necessary to specify the

distribution of the unobservables. An attempt to distinguish between the distribution of

unobservables and duration effects may bias the estimate of the SE effect (see e.g. Baker

and Melino 1999).

Under these assumptions the transition probability Pkmuv , i.e. the probability of a

transition of an individual of type m from state u to state v in duration interval k, is equal

to

(9) uvuh

h

hP k

uww

uwkm

uww

uwkm

uvkmuv

km ≠==

∆−−= ∑

∑ ≠=

≠=

3,2,1 ,2,1 ,exp13

,13

,1

This expression maps transition intensities to transition probabilities. The inverse mapping

from transition probabilities to transition intensities is

(10) ( )( ) ( ) uvuh

P

PPz uv

kmuukmk

uukm

uvkmuv

km ≠===

−∆−

= 3,2,1 ,2,1 , log1

loglog

17

with

(11) ∑≠=

−=3

,1

1uww

uwkm

uukm PP

the probability of staying in u in the k-th duration interval.

Let rkmu be the number of individuals of type m in state u at the start of duration

interval k, i.e. at time t k −1 , and let q kmuv of these individuals make a transition from state u

to state v in the k-th duration interval. Then we estimate Pkmuv by

(12) uvur

qP

ukm

uvkmuv

km ≠=== 3,2,1 ,2,1 ,ˆ

Upon substitution of these estimates in the middle expression of (10), we obtain uv

kmz . If in

(10) we replace the transition probabilities by their estimates, then the second equality

does not hold exactly. However, a Taylor series expansion around Pkmuv yields

(13) u32,,1 ,2,1 ,)log(ˆ ≠==+= vuhz uvkm

uvkm

uvkm ω

with

(14) ∑≠

−=3

w1,=w

)ˆ(u

uwkm

uwkm

uvwkm

uvkm PPbω ,

where uvw

kmb is defined in the Appendix. We may ignore the remainder of the Taylor

expansion as it can be shown that its probability limit for rkmu tending to infinity converges

to zero at a faster rate than uvkmω (Amemiya 1985, p. 276-77). Hence, omission of the

remainder does not affect the consistency of the estimator nor its asymptotic distribution.

Conditional on rkmu the random vector of the number of transitions of individuals

of type m from u to states v ≠ u in the k-th interval has a multinomial distribution with

parameters rkmu and Pkm

u , which is the vector of transition probabilities from state u to

states v ≠ u in interval k for type m. The errors of the system of regression equations

(13) for v ≠ u have mean 0, but they are heteroskedastic and correlated. The variance

matrix is given in the Appendix.

18

Upon substitution of (8) into (13) we obtain a linear regression model. More

precisely, we obtain for each origin state u = 1, 2 two regression equations that

correspond to the two destination states uv ≠= 3,2,1

(15) 2,1,3,2,1 ,ˆ =≠=++++= uuvz uvkm

uvkm

uvvm

vk

uvkm ωεαβγ

In the sequel we use the notation uv

kmuvkm

uvkm ωεν += .

We assume that the 4xMxK unobserved variables uvkmε are random variables26 with

a joint distribution such that

(16) ( ) 0=uv

kmE ε

(17) ( ) uvw

uummkkwumk

uvkmE σδδδεε ′′′

′′′ =

The unobserved variables corresponding to different duration classes, types, and origin

states are assumed to be uncorrelated. The unobserved variables corresponding to

different destination states may be correlated. Note that the disturbances uvkmω have the

same pattern of zero correlations. Hence, for origin state u and for duration interval k and

type m, the distribution of the 2-vector of disturbances of the two regression equations

(15) with v = 1, 2, 3 ≠ u , vkmu ≡ vkm

uvvkm

uw[ ] , which is the sum of the unobserved group

effect and the approximation error has a variance-covariance matrix with typical element

σ uvw + skmuvw , with v, w = 1, 2, 3 ≠ u . We denote this 2x2 matrix by Vkm

u . This completes the

specification of the regression equations.

The regression equations (15) are heteroskedastic and have correlated

disturbances. In the Appendix we discuss the estimation of these equations. Because we

have grouped data, we can use a χ 2 -goodness-of-fit test to evaluate the specification of

the model. This statistic can be interpreted as a GMM test for the null hypothesis of

regressor-error orthogonality. Since we do not control for selection on unobservables, we

anticipate a rejection of the null hypothesis on the basis of this test statistic. The details

can be found in the Appendix.

26 The assumption that the omitted variables are random is analogous to the treatment of unobserved heterogeneity in duration (and other) models. The expectation is with respect to the distribution of the omitted variables.

19

The estimation results for model (15) are reported in Table 3. Durations are

measured in months. We have chosen K=4 duration intervals: {[0,3), [3,6), [6,12),

[12,24)}. There are three types (M=3) that correspond to the three regions: Flanders,

Walloon Provinces and Brussels. Hence, 4 duration intervals, three regions, two origin

states and two destination states make up 48 empirical hazard estimates and 19 regression

parameters.


We obtain negative estimates for the variances of the unobserved group effects for the

transitions from state 1 to state 2 and from state 2 to state 1. Following Parks (1980,

p.299, footnote 5) we set these variances to 0. Hence, the unobserved group effects are

set to 0. We only find an unobserved group effect in the transitions from welfare with SE

and welfare without SE to out-of welfare.

The matching estimators of Section 4 suggested that SE decreases the exit rate

from welfare. Table 3, which corrects for the timing of selection and the resultant lower

exit rate due to the duration dependence of the exit rate, leads to the opposite conclusion:

participation in SE increases the exit rate by a factor 1.25 (= exp(.224)). This difference

can be explained by the fact that the matching estimator controls for more observable

characteristics and hence reduces the selection bias induced by creaming. Nevertheless,

both estimators are biased if selection in SE is based on unobservable characteristics. If

this is the case, then the distribution of the unobserved determinants of the exit rates ε kmuv

depends on the type of transition. In particular, creaming implies that >),( 23 mkE kmε

),( 13 mkE kmε , and as a result the effect of participation in SE is overestimated. The

specification is rejected by the chi-square goodness-of-fit test.

4.3. Correcting for Selection on Unobservables

In this subsection we derive a grouping/IV estimator that corrects for selection on

unobservables. We show that aggregation over the program participation indicator

identifies the effect of SE under a weaker assumption than the one invoked in the

previous section.

20

Because we observe all welfare spells that start between June 1 1987 and July 1

1990, the population fraction of spells that start out in the two origin states fkmu is known

exactly27. The fraction given k and m is kmu

km ff , where ∑ =≡

2

1u

ukmkm ff . Hence, if we

average regression equation (15) over the origin state u, for the destination state of

interest, i.e. for v=3, and if we recall that 013 =α , we obtain:

(18) 3323333ˆ kmkmkmmkkm pz ωεαβγ ++++=

where kmkmkm ffp 2≡ is the fraction of welfare recipients in k, m that participate in SE.

The new unobserved group effect, 3kmε , is the average of the unobserved

characteristics of the total welfare population in a particular region and duration class,

irrespective of participation in SE. Selective participation in SE in a region and duration

class implies that, in the case of creaming, welfare recipients with above average

unobserved characteristics are more likely to be selected for SE, so that ),( 13 mkE kmε

),( 23 mkE kmε> , i.e. the average unobserved characteristics of participants are better than

those of non-participants. This correlation between the participation indicator and the

average unobserved characteristics of participants and non-participants is absent in (18):

even if SE divides the welfare population into two subgroups on the basis of

(unobserved) characteristics, the fraction in SE can be unrelated to the average

characteristics of the welfare population. Hence (18) gives an unbiased estimate of the SE

effect, if the participation fraction is uncorrelated with 3kmε , a much weaker assumption

than the uncorrelatedness of characteristics and participation invoked in (15).

If 3kmε and kmp are uncorrelated, then the SE effect can be estimated from (18).

The resulting estimator is a grouping estimator, similar to the estimator first proposed by

Wald (1940) to deal with measurement error. Durbin (1954) showed that the Wald

estimator is an IV estimator. Angrist (1991) used this estimator to estimate a labor supply

equation with an endogenous wage rate, and in his application the instruments are year

dummies. In our application the instruments are the region and duration class indicators.

27 Hence, the averaging does not result in an errors-in-variables problem as in Deaton (1985).

21

The regression equation (18) includes region and duration class dummies, because

we want to allow for differences in the exit rate from welfare by region and by welfare

duration. As a consequence, the regressor in (18) is the residual kmp~ after regressing the

participation rate in SE on region and duration class dummies. The residual participation

rate is uncorrelated with the average participation rate by region and the average

participation rate by duration interval. Hence, if the SE participation rate is correlated

with 3kmε through the average unobserved characteristics of the welfare recipients in the

regions or through the average unobserved characteristics of welfare recipients in the

duration intervals, the residual rate kmp~ is uncorrelated with 3kmε . The inclusion of

regional and duration interval indicators makes the SE effect in (18) thus unbiased under

even weaker assumptions.

The price for these weaker assumptions is that the variation in the residual

participation rate kmp~ is less than the variation in kmp , and this increases the variance of

the estimator. If we include interactions between region and duration class in (18), there

is no variation, and (18) cannot be used for the estimation of the SE effect.

Although we can not estimate (18) if we include all interactions between region

and duration class indicators, there is indirect evidence that these are not important. First,

the chi-square goodness-of-fit test does not reject model (18). If the interactions were

important the model should be rejected. Second, in the next section we experiment with

the inclusion of some interactions and we find that the estimates do not change. The

regression equation (18) can be estimated by OLS, or, because of the heteroskedastic

errors, preferably by GLS. The details of the estimation procedure can be found in the

Appendix28.

The GLS estimates for regression equation (18) and the regression equations for

the other transitions are reported in Table 4. A comparison with the results in Table 3

shows that the parameter estimates for the destination states welfare without SE and

welfare with SE are almost identical in the two tables. Also the duration effects in the

transition intensity to the state out-of-welfare do not differ by much between these tables.

The only significant differences are in the regional effects and the effect of SE.

28 An alternative is to use difference-in-difference estimators where the differences are between duration intervals and regions. With our data these estimators are imprecise. Following Angrist (1991) we combine the difference-in-difference estimates to increase the precision. Angrist’s Efficient Wald estimator is our GLS estimator.

22

Eliminating unobserved differences between participants and non-participants makes the

transition intensity to the state out-of-welfare significantly smaller in the Walloon

provinces as compared to Flanders. This is reassuring given the structurally weaker

economy of the Walloon Provinces. More importantly, the effect of SE is negative in

Table 4, while it was significantly positive in Table 3. Note that the GMM test statistic

only rejects at a significance level of more than 27%.


5. Sensitivity Analysis

The standard error of the (efficient) GLS estimator of the SE-effect is relatively large.

Hence, it is important to investigate the sensitivity of this estimate to changes in the

specification. First, we consider the use of (stochastic) prior information. Cockx (1992,

1997) gives estimates of the regional effects on the hazard out of welfare. On the basis of

his estimates, taking Brussels as the region of reference, we calculate29 the following

point estimates (and standard errors) for Flanders and the Walloon Provinces: .0541

(.0165) and -.0797 (.0170). Following Theil (1963) or Judge et al. (1985, p.58), we

impose these stochastic linear restrictions on the parameters in the estimation. The results

are reported in Table 5.


The standard error of the effect of SE is smaller, and the point estimate becomes even

more negative.

In the estimation we group the durations in a relatively small number of intervals,

and we assume that the transition intensities are constant during these intervals. To

investigate the sensitivity to the specification of the duration dependence we estimate (18)

29 Cockx (1992,1997) reports separate estimates for men and women and considers other regressors besides region. The above point estimates were calculated by taking the appropriate transforms of the mixture of the duration distribution of men and women. As information on the covariances is not available, the standard errors are calculated by assuming a zero covariance between the estimated coefficients.

23

and the regressions for the other two transition intensities with 8 instead of 4 duration

intervals. The results are reported in Table 6.


Upon comparison with the estimates of Table 4 we conclude, that only the estimates of

the duration effects differ between these two tables. In particular, the estimate of the SE-

effect is similar to that in Table 4. Note that the model now is rejected against the

saturated model.

The estimates of Table 6 indicate that there is a problem with the specification.

We already noted that Brussels is rather different from Flanders and the Walloon

provinces. We re-estimate the model with a separate duration dependence for Brussels.

The estimate of the SE-effect for this specification is reported in the column (1) of Table

7.


The goodness-of-fit statistic improves considerably. The point estimate of the SE-effect is

hardly affected and its standard error is significantly reduced.

During the 1987-90 period the demand for labor was rising, peaking in 1990. One

may question to what extent the effect of SE is influenced by labor market conditions. In

particular, the review of the literature suggests that work experience programs are more

likely to be effective in periods of high labor demand (see e.g. Ridder 1986). To test this

hypothesis we estimate a model that allows for a different the SE-effect for 1987-88 and

for 1988-90. Column (2) of Table 7 reports the estimated SE-effects. The point estimates

are both negative, and in line with expectations, less negative in the second sub-period.

Throughout this paper we have made the assumption of effect homogeneity. If the

SE-effect varies among participants, then the grouping estimator does not estimate the

effect of SE on the average participant, but rather the effect on the marginal participant.

There are two cases, in which the grouping estimator does estimate the usual effect on

the average participant. Either the SE effect does not vary between participants or

selection into the program is unrelated to the individual specific variation in the benefit

difference between participation and non-participation, but is related to the average

24

difference in benefits between these two options (Heckman and Robb 1985, p. 196,

Heckman and Smith 1996, p.59-68, Heckman 1997). If the SE effect varies between

participants, then this induces within group unobserved heterogeneity in the exit rate. We

perform two tests for the presence of unobservables in the exit rate.

It is well known (see e.g. Lancaster (1990), p.64-65), that unobserved individual

heterogeneity induces a trend towards 0 in the SE effect. Hence, the absence of such a

trend, is an indication that within-group heterogeneity is not important. If we introduce a

time-varying SE effect in model (15), correcting for selection on observables only, we

find that the effect increases with duration.

Second, if within-group heterogeneity is important the hazard rate at a particular

duration is known to depend on the hazard rates of the past. This suggests testing for

within-group heterogeneity by checking whether the (weighted) residuals of our

regression equation are autocorrelated. For this purpose we calculated the Durbin-

Watson and the (modified) Lagrange multiplier (LM) test statistic.

Denote the weighted residuals corresponding to the estimated regression model

reported in Table 4 by ekmv . We adapt the Durbin-Watson statistic (d) to account for the

panel structure of the data in the following way:

(21) ( )

( )∑ ∑ ∑∑ ∑ ∑

= = =

= = = −−=

3

1 1 1

2

3

1 1 2

2

1 v

M

m

K

k

vkm

v

M

m

K

k

vmk

vkm

e

eed

The finite sample properties of this statistic are not obvious. However, asymptotically we

have d ~ N(2, 4/(3MK )) . The Durbin-Watson statistic corresponding to the model of

Table 4 is 2.4027. Consequently, we cannot reject the null hypothesis of zero

autocorrelation of the residuals at a significance level of 11%.

The Breusch (1978)-Godfrey (1978) LM test of autocorrelation provides an

alternative procedure. Operationally, the test is carried out by regressing the (weighted)

least squares residuals, ekmv , on the contemporaneous regressors and ek−1m

v . 3M (K − 1)R2

is then distributed as a chi-square random variable with one degree of freedom. Kiviet

(1986) finds that such a test can be poorly behaved when the regression model is

overspecified. He therefore suggests that it is safer to employ a modified LM procedure,

by testing the significance of the estimated autocorrelation coefficient in the regression

25

(see Godfrey 1988, p.116-117 for a discussion). Given the flexible parameterization of

our model we follow Kiviet's suggestion. The autocorrelation coefficient is estimated to

be equal to -.192 with an asymptotic T-ratio of -1.014. The hypothesis of zero

autocorrelation can therefore not be rejected at a significance level of 33%. The test

statistics for the model in Table 6 lead to similar conclusions.

6. Conclusion In this paper we estimate the effect of participation in social employment (SE) on welfare

duration in Belgium. For this purpose we extend the Minimum Chi-Square approach that

Cockx (1997) proposed for the analysis of grouped duration data. First, we show that the

time-varying participation in SE can be modeled in a competing risks framework.

Secondly, by allowing for a specification error, that captures the effect of unobservables

on the transition rates, we can study selection on these unobservables. The proposed

estimator deals with the selection bias by aggregation to a level where the participation

rate is exogenous. Inclusion of specific indicators eliminates the correlation between the

participation rate and the average unobserved differences between regions and duration

intervals. The resulting GLS estimator is efficient under these assumptions. The

estimation results can be summarized as follows. If we correct for selection on

observables only (Section 5.3, Table 3) participation in SE reduces the median welfare

spell from 13.1 to 11.6 months30. The corresponding coefficient is significantly different

from 0. If we correct for selection on unobservables, the change in the estimate of the SE

effect suggests that there is substantial creaming in the selection process. Participation in

SE is now reduces the exit rate from welfare, be it that the effect is not significantly

different from 0. The median duration of a welfare spell is 12.5 months for non-

participants and 14.8 months for participants in SE.

These results are in line with expectations. Welfare agencies (WA) face adverse

incentives. Since the welfare recipient who is in SE will eventually become entitled to

unemployment benefits, and will therefore no longer depend on the WA, the WA has no

incentive to enhance the professional integration of the participants in SE. Moreover,

since the SE is heavily subsidized, the WA, as an employer and provider of community

services, has an incentive to keep welfare recipients in the program. With the current

30 These median durations refer to a welfare recipient in Flanders, who is selected into SE after 6 months.

26

incentives to the WA, welfare recipient do not benefit and are likely to be hurt by

participation in SE. Moreover, international comparison of work experience programs

reveals that adverse incentives for administrators of the kind discussed in this paper can

explain the ineffectiveness of these programs in other European countries.

Appendix: The system of SUR regression equations (13) In (14)

(A.1) uv

kmvwuu

kmuu

kmuu

km

uvwkm

PPPPb

1

)log(

1

1

1δ+−

−=

and δvw the Kronecker delta. If we order the errors in (13) for v ≠ u in a vector, we have

for all k, m, and u = 1, 2

(A.2) 0)( =ukmE ω

so that the disturbance in (13) has expectation 0. The variance-covariance matrix of the

vector ukmω has typical element

(A.3) [ ] == uw

kmuvkm

uvwkm Es ωω

−−−++

−−∑ ∑ ∑

≠= ≠= ≠=

ukmvw

uvkm

uvkmvw

uukm

ukm

uxx uxx xyy

uykm

uxkm

uxkm

uxkm

ukm rPPPbPPPPbE )1()1(2)1()(

3

,1

3

,1

3

,1

2 δδ

with v, w = 1, 2, 3 ≠ u , )log(

1

1

1uu

kmuu

kmuu

km

ukm

PPPb −

−= , and where the expectation is taken

with respect to the distribution of rkmu . A consistent estimate is obtained by omitting the

expectation and replacing the transition probabilities by their estimates. Estimation of SUR system (15) Under assumption (16) which in particular implies that there are no unobserved

differences in the exit rates of SE-participants ( 3,2 == vu ) and non-participants

( 3,1 == vu ), we can estimate the parameters of these equations by OLS, or more

efficiently by GLS. Because we do not observe the transitions that occur in the open

27

K+1-th duration interval, we omit the regression equations that correspond to that

interval. As a result, we have MxKx2x2 equations. If we order the regressions by m, k, u,

v, in this order, we obtain a block-diagonal variance-covariance matrix of the MxKx2x2

vector of disturbances with the diagonal blocks being 2x2 matrices Vkmu .

The feasible GLS procedure consists of two steps. In the first step we use OLS to

estimate the 3xK+3x(M-1)+1 parameters γ kv , k=1,...,K, v=1,2,3, βm

v m=2,...,M,

v=1,2,3, and 23α . Next, we estimate the 2x2 variance-covariance matrix of the

unobserved group effects by

(A.4) [ ]

−= ∑∑

= =

M

m

K

k

uvwkm

uwkm

uvkm

uvw svvMK 1 1

ˆˆˆ1

σ

with ˆ v km

uv the residuals of the regression (15), and ˆ s kmuvw the estimate of (A.3). Substitution

of this consistent estimate gives a consistent estimate of Vkmu , which is used in the second

step of the feasible GLS procedure.

Let ˆ v be the 4KM-vector of residuals of the regression equation (15) ordered by

m, k, u, v, in that order. Let the estimated variance-covariance matrix of the disturbances

in (15) be denoted by ˆ V . This is a block diagonal matrix, which simplifies the

computation of its inverse. Under the assumption that the model is correctly specified we

have that ˆ ′ v ˆ V −1 ˆ v follows a χ 2 -distribution with 4KM-3K-3(M-1)-1 degrees of freedom.

As the regression equations can be seen as a set of 4KM moment conditions, this statistic

can be interpreted as a goodness-of-fit test statistic.

GLS estimation of the grouped regression equation (18)

In (15) we had four regression equations for each k, m. In (18) two of those are

combined, so that we have in total 3xKxM equations. If we order these equations by m, k

and for each such pair in the order: transition from 1 to 2, from 2 to 1, combined 1 and 2

to 3, then the variance matrix of the 3xKxM vector of disturbances is block-diagonal with

blocks

28

(A.5)

( )( )( )

+

++++

∑=

2

1

33233

2132213123211

1231123211122

......

0

0

u

ukmkm

ukm

kmkmkmkm

kmkmkmkm

sff

sffs

sffs

σ

σσσσ

In the two-step feasible GLS procedure we first use OLS to estimate the residuals. Next,

σ 122 and σ 211 are estimated as in (A.4). If we denote the OLS residuals of (23) by ˆ v km3 ,

then we have

(A.6) [ ]

−= ∑∑

= =

M

m

K

kkmkmkmkmkm sffvv

MK 1 1

1231312123 ˆ)(ˆˆ1

σ

with an analogous estimator for σ 213 , and

(A.7)

−= ∑∑ ∑

= = =

M

m

K

k u

ukmkm

ukmkm sffv

MK 1 1

2

1

3322333 ˆ)()ˆ(1

σ

In the second step we estimate the variance matrix (A.5) and estimate the parameters by

GLS.

29

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Deaton, A. (1985), “Panel Data from Time Series of Cross-sections”, Journal of Econometrics, 30, 109-126. Dehaes, Viviane (1994), Leven van de bijstand. Een onderzoek naar de persistentie van armoede in Vlaanderen, CBGS-monografie, 1, Centrum voor Bevolkings en Gezinsstudiën, Brussel. Dehejia, Rajeev H. and Wahba, Sadek (1995a), "A Matching Approach for Estimating Causal Effects in Non-Experimental Studies", mimeo, Department of Economics, Harvard University, Cambridge, MA. Dehejia, Rajeev H. and Wahba, Sadek (1995b), "Causal Effects in Non-Experimental Studies", mimeo, Department of Economics, Harvard University, Cambridge, MA. Dolton, Peter J., Makepeace, Gerald H., Treble, John G. (1994), "The Youth Training Scheme and the School-to-work Transition", Oxford Economic Papers, 46(4), 629-57. Durbin, J. (1954), "Errors in variables," Review of the International Statistical Institute, 22, 23-32. Edin, Pers-Anders and Holmlund, Bertil (1990), "Unemployment, Vacancies and Labour Market Programmes: Swedish Evidence", in Padoa Schioppa, Fiorella (ed.), Mismatch and Labour Mobility, Cambridge University Press, Cambridge. Eichler, Martin and Michael Lechner (1998), "An Evaluation of Public Employment Programmes in the East German State of Sachsen-Anhalt", mimeo, Universität Mannheim/Universität St. Gallen. Feist, Holger and Ronnie Schöb (1999), "Workfare in Germany and the Problem of

Vertical Externalities", paper presented at the 11th EALE-conference, Regensburg, 23-26 September, CES, Ludwig-Maximilians-Universität München. Fraker, T. and Maynard, R. (1987), "The Adequacy of Comparison Group Designs for Evaluations of Employment-Related Programs", Journal of Human Resources, 22, 194-227. Friedlander, Daniel and Robins, Philip K. (1995), "Evaluating Program Evaluations: New Evidence on Commonly Used Nonexperimental Methods", The American Economic Review, 85(4), 923-37. Garcia, A. (1990), La remise au travail des bénéficiaires du Minimex dans le cadre de l'article 60 §7, Département des Sciences Politiques et Sociales, Université Catholique de Louvain, Louvain-la-Neuve. Godfrey, L. G. (1978), "Testing Against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, 46, 1293-1302.

31

Godfrey, L.G. (1988), Misspecification tests in econometrics, Cambridge University Press, Cambridge. Grossman, Jean Baldwin, Maynard, Rebecca and Roberts, Judith (1985), Reanalysis of the Effects of Selected Employment and Training Programs for Welfare Recipients, Mathematica Policy Research, Inc., Princeton. Gueron, Judith M. (1990), "Work and Welfare: Lessons on Employment Programs", Journal of Economic Perspectives, 4(1), Winter, 79-98. Gueron Judith M. and Edward Pauly (1991), From Welfare to Work, Russell Sage Foundation, New York. Heckman, James J. (1997), "Instrumental Variables. A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations", Journal of Human Resources, 32(3), 441-62. Heckman, James J., Heinrich, Carolyn and Smith, Jeffrey (1997), "Assessing the Performance of Performance Standards in Public Bureacracies", The American Economic Review, Papers and Proceedings, 87(2), 389-95. Heckman, James J., Ichimura, Hidehiko, Smith, Jeffrey, and Todd, Petra (1998), "Characterizing Selection Bias Using Experimental Data", Econometrica, 66(5), September, 1017-98. Heckman, James J. and Robb, Richard, Jr. (1985), "Alternative Methods for Evaluating the Impact of Interventions", in Heckman, James J. and Singer, B. (eds.), Longitudinal Analysis of Labor Market Data, Econometric Society Monograph series, Cambridge University Press, New York. Heckman, James J. and Smith, Jeffrey A. (1995), "The Effects of JTPA Performance Standards in Efficiency, Equity and Participant Outcomes", mimeo, University of Chicago, Chicago. Heckman, James J. and Smith, Jeffrey A. (1996), "Experimental and Nonexperimental Evaluation", in Schmid, G., O'Reilly, J. and Schöman K. (eds.), International Handbook of Labour Market Policy and Evaluation, Cambridge University Press, Cambridge, 37-88. Hollister, Robison G., Jr., Kemper, Peter, Maynard, Rebecca A. (eds.) (1984), The National Supported Work Demonstration, University of Wisconsin Press, Madison, Wis. Imbens, Guido W. and Angrist, Joshua D. (1994), "Identification and Estimation of Local Average Treatment Effects", Econometrica, 62, no. 2, 467-475. Judge, George G., W.E. Griffiths, R. Carter Hill, H. Lütkepohl and T.-C. Lee (1985), The Theory and Practice of Econometrics, (2nd ed.), John Wiley and Sons, New York.

32

Kiviet, J. (1986), "On the Rigour of Some Specification Tests for Modeling Dynamic Relationships," The Review of Economic Studies, 53, 241-262. Krueger, Alan B. (1990), "Workers' Compensation Insurance and the Duration of Workplace Injuries", NBER Working Paper Series, no.3253. Lalonde, R. (1986), "Evaluating the Econometric Evaluations of Training Programs with Experimental Data", American Economic Review, 79, 604-620. Lalonde, R. (1995), "The Promise of Public Sector-Sponsored Training Programs", Journal of Economic Perspectives, 9(2), Spring, 149-68. Lancaster, Tony (1990), The Econometric Analysis of Transition Data, Cambridge University Press, Cambridge. Layard, R., Nickell, S. and Jackman, R. (1991), Unemployment, Oxford University Press, Oxford. Meyer, Bruce D. (1989), "A Quasi-Experimental Approach to the Effects of Unemployment Insurance", NBER Working Paper Series, no. 3159. Meyer, Bruce D., Viscusi, W. Kip and Durbin, David L. (1990), "Workers' Compensation and Injury Duration: Evidence from a Natural Experiment", NBER Working Paper Series, no. 3494. Meyer, Bruce D. (1995), “Natural and Quasi-Experiments in Economics”, Journal of Business and Economic Statistics, 13(2), April, 151-161. Moffitt, Robert (1992), "Incentive Effects of the US Welfare system: A Review", Journal of Economic Literature, 30 (March), 1-61. O.C.M.W.-Visies (1993), "Het Bijzonder Fonds voor Maatschappelijk Welzijn: een overzicht van de gehanteerde criteria", O.C.M.W.-Visies, 1, 14-22. Parks, Richard W. (1980), "On the Estimation of Multinomial Logit Models from Relative Frequency Data", Journal of Econometrics, 13, 293-303. Prentice, R. L. and L.A. Gloeckler (1978), " Regression Analysis of Grouped Survival Data with Application to Breast Cancer Data," Biometrics, 34, 57-67. Ridder, Geert (1986), "An Event History Approach to the Evaluation of Training, Recruitment and Employment Programmes", Journal of Applied Econometrics, 1(2), 109-126. Theil, H. (1963), "On the Use of Incomplete Prior Information in Regression Analysis", Journal of the American Statistical Association, 58, 401-414.

33

Van de Velde, Veerle (1990), Wat na de sociale tewerkstelling? Evaluatie van de toepassing van art. 60 par.7 van de organieke wet op het OCMW, Hoger Instituut voor de Arbeid, Katolieke Universiteit Leuven, Leuven. Van Langendonck, J. (1991), Handboek Sociale Zekerheid, Acco, Leuven. Wald, Abraham (1940), "The fitting of straight lines if both variables are subject to error", Annals of Mathematical Statistics, 11, 284-300. Wouters, Martine, Rien Van Meensel and Ides Nicaise (1994), De TOK-projecten en hun cursisten, drie jaar later. Follow-up onderzoek van de projecten van 1989, Hoger Instituut voor de Arbeid, KU Leuven, Leuven.

34

Table 1: Population characteristics

all welfare spells (including SE)

spells with some time in SE

matched sample: Controls in same

WA Number of spells 80,621 2,813 681* Average age in years 30.0 30.9 29.3 Male 39.1% Belgian 92.2% 94.3% 97.8% Children dependent children present

39.2% 51.7% 39.8%

Number of children 1.8 1.8 1.7 Marital status unmarried 45.3% 37.5% 46.8% married 30.6% 38.5% 34.6% widow(er) 1.2% 0.6% 0.3% divorced 22.8% 23.4% 23.4% Household type Living alone 69.5% 73.1% ** Married / cohabiting. 10.1% 9.8% ** Unmarried / cohabiting 20.4% 17.2% ** Region Flanders 39.4% 59.4% 52.0% Walloon Provinces 47.0% 25.7% 29.5% Brussels 13.6% 14.9% 18.5% Size of municipality > 100,000 36.0% 28.4% 42.4% 50-100,000 18.3% 18.1% 27.5% < 50,000 45.7% 53.5% 30.1% * 2 groups of 681 individuals ** This information was not available at the moment that the groups were matched.

35

Table 2: Quintiles of Survivor Functions by Region and Participation Indicator (95% confidence intervals between brackets)

1. Duration in Months until the Beginning of SE

Quintiles Flanders Walloon P. Brussels Belgium 75% 1.4

[1.1,1.8] 1.0

[0.6,1.2] 1.0

[1.0,1.2] 1.1

[1.0,1.3] 50% 6.8

[6.0,7.5] 5.3

[4.5,6.5] 4.5

[3.7,5.9] 6.0

[5.5,6.5] 25% 16.5

[15.3,17.9] 14.1

[12.8,15.5] 12.0

[10.4,14.0] 15.0

[14.1,15.9] 2. Duration in Months of SE spells

75% 4.0 [3.9,4.8]

2.5 [2.0,3.0]

2.4 [1.9,3.0]

3.1 [3.0,3.6]

50% 10.5 [9.0,11.5]

6.0 [6.0,6.8]

6.5 [6.0,7.0]

7.6 [7.0,8.2]

25% 18.0 [17.9,18.2]

12.5 [12.0,14.0]

13.0 [12.0,17.0]

15.9 [14.9,17.9]

3. Welfare Durations in Months for Non-Matched Groups

75% Non-participants

1.9 [1.9,1.9]

2.0 [1.9,2.0]

2.0 [2.0,2.0]

1.9 [1.9,2.0]

Participants in SE

7.6 [7.0,8.0]

5.0 [4.4,6.0]

5.6 [4.0,6.7]

6.7 [6.0,7.0]


4.0 [4.0,4.2]

5.0 [4.9,5.0]

4.8 [4.5,4.9]

4.8 [4.6,4.9]

Participants in SE

14.8 [14.0,15.5]

11.8 [10.5,12.8]

11.4 [10.3,12.8]

13.1 [12.8,14.0]


11.0 [10.9,11.5]

13.0 [12.9,13.3]

11.4 [11.0,12.0]

12.0 [11.9,12.0]

Participants in SE

23.7 [22.4,25.0]

20.0 [18.6,22.0]

20.7 [19.0,23.4]

22.3 [21.3,23.4]

4. Welfare Durations in Months for Matched(i) Groups - Controls in Participating WA


2.0 [1.8,2.4]

1.8 [1.4,2.4]

1.5 [1.0,2.3]

1.9 [1.7,2.0]

Participants in SE

3.7 [3.0,4.2]

2.8 [1.9,3.5]

1.7 [1.0,2.0]

2.8 [2.2,3.1]


4.4 [3.9,5.9]

4.0 [3.0,5.1]

3.3 [3.0,4.0]

4.0 [3.8,4.5]

Participants in SE

7.0 [6.0,9.0]

5.5 [4.4,6.0]

5.2 [3.0,6.9]

6.0 [5.9,6.9]


14.5 [10.6,19.9]

8.8 [8.0,12.3]

7.0 [5.1,13.6]

11.0 [9.2,13.6]

Participants in SE

13.0 [12.0,15.0]

9.3 [9.0,11.9]

10.0 [7.2,12.0]

12.0 [11.9,12.5]

(i) The individuals are matched on the year of birth, gender, nationality, number of children, marital status (see Table 1 for a definition), and WA (panel 4). The welfare duration of non-participants is required to be greater than the elapsed duration of participants at the time of selection in SE. We redefine welfare duration by subtracting the time elapsed until selection in SE for each pair of matched individuals.

36

Table 3: GLS estimates of the parameters of the transition intensities (standard error in parentheses)

Destination state (v) 1: Welfare

without SE 2: Welfare with

SE 3: Out of welfare

Duration interval (months)

0-3 (γ1v) -3.084***

(.188) -4.977***

(.064) -1.838***

(.095) 3-6 (γ 2

v − γ 1v) -.224

(.236) -.011

(.095) -.341***

(.108) 6-12 (γ 3

v − γ 1v) -.610**

(.236) -.035

(.095) -.465***

(.107)

12-24 (γ 4v − γ 1

v ) -.693** (.265)

.089 (.107)

-.579*** (.109)

Region

Walloon Prov. (β2v) .448**

(.184) -1.190***

(.086) -.016

(.092) Brussels (β3

v) -.364

(.318) -.359***

(.104) .086

(.095) SE-effect

α 23 - - .224**(i)

(.093) Unobserved vars.

ˆ σ 1vw 0 - 0.00868

ˆ σ 2vw

- 0 0.01986

Weighted sum of squared residuals (degrees of freedom)

72.54 (29)

P-value .00 (i) This is the parameter of interest, signifying that SE increases the transition rate out of welfare by 25% (= *100

( )[ ]1224.exp − ).

* significant at the 10% level ** significant at the 5% level *** significant at the 1% level

37

Table 4: GLS estimates of the parameters of the transition intensities with participation rate in SE instead of participation indicator

(standard errors in parentheses) Destination state v

1: Welfare without SE

2: Welfare with SE

3: Out of welfare


0-3 (γ1v) -3.095***

(.127) -4.977***

(.043) -1.716 ***

(.029) 3-6 (γ 2

v − γ 1v ) -.222

(.159) -.011

(.064) -.321***

(.025)

6-12 (γ 3v − γ 1

v) -.608***

(.159) -.035

(.064) -.509***

(.033)

12-24 (γ 4v − γ 1

v) -.614***

(.180) .088

(.072) -.709***

(.052) Region

Walloon Prov. (β2v ) .469***

(.124) -1.191***

(.058) -.116***


v ) -.349 (.215)

-.360*** (.070)

-.007 (.026)

SE-effect

α 23 - - -.258(i)

(.814) Unobserved vars

ˆ σ 1vw 0 - -

ˆ σ 2vw

- 0 -

ˆ σ 33

- - .000464


19.36 (17)

P-value .31 (i) This is the parameter of interest, signifying that SE decreases the transition rate out of welfare by 23% (= *100

( )[ ]258.exp1 −− ).


38

Table 5: GLS estimates of the parameters of the transition intensities with participation rate in SE instead of participation indicator and

imposing the stochastic linear restrictions β13 = .0541 (.0165) and

β 23 = -.0797 (.0170).

(standard errors in parentheses) Destination state v


2: Welfare with SE

3: Out of welfare


0-3 (γ1v) -3.446***

(.238) -5.337***

(.071) -1.742 ***

(.018) 3-6 (γ 2

v − γ 1v ) -.222

(.162) -.011

(.065) -.318***

(.025)

6-12 (γ 3v − γ 1

v) -.607***

(.162) -.035

(.065) -.503***

(.029)

12-24 (γ 4v − γ 1

v) -.613***

(.183) .088

(.074) -.698***

(.043) Region

Flanders (β1v ) .351

(.219) .360***

(.071) .042***

(.014) Walloon Prov. (β2

v ) .821*** (.228)

-.830*** (.081)

-.086 (.014)

SE-effect

α 23 - - -.499(i)

(.621) Unobserved vars

ˆ σ 1vw 0 - -

ˆ σ 2vw

- 0 -

ˆ σ 33

- - .000464

Weighted sum of squared residuals

21.79

(i) This is the parameter of interest, signifying that SE decreases the transition rate out of welfare by 39% (= *100

( )[ ]499.exp1 −− ).


39

Table 6: GLS estimates of the parameters of the transition intensities with participation rate in SE instead of participation indicator: 8

duration intervals (standard errors in parentheses)

Destination state v


2: Welfare with SE 3: Out of welfare


0-3 (γ1v) -3.103***

(.148) -4.977***

(0.052) -1.706***

(.015)

3-6 (γ 2v − γ 1

v) -.234

(.191) -.011

(.077) -.335***

(.015) 6-9 (γ 3

v − γ 1v) -.211

(.190) -.087

(.092) -.619***

(.023) 9-12 (γ 4

v − γ 1v) -.118

(.195) -.115

(.109) -.532***

(.027)

12-15 (γ 5v − γ 1

v ) -.205 (.210)

-.047 (.124)

-.868*** (.036)

15-18 (γ 6v −γ 1

v ) -.176 (.226)

-.070 (.145)

-.975*** (.042)

18-21 (γ 7v − γ 1

v) -.214

(.263) -.097

(.175) -.977***

(.047) 21-24 (γ 8

v − γ 1v) -.207

(.304) -.225

(.222) -.936***

(.053) Region

Walloon Prov. (β2v) .472***

(.119) -1.180***

(.069) -.112***


v) -.128

(.191) -.374***

(.085) -.020

(.018) SE-effect α 23

- - -.171(i)

(.502)


67.463 (41)

P-value .0057 (i) This is the parameter of interest, signifying that SE decreases the transition rate out of welfare by 16% (= *100

( )[ ]171.exp1 −− ).


40

Table 7: Efficient Wald Estimates of the SE-effect for various models*

(standard errors in parentheses)

(1) (2) SE-effect 87-88 89-90

23α -.248 (.316)

-1.663 (1.860)

-0.458 (1.400)


7.730 (14)

47.873 (34)

P-value .90 .058 * The complete results can be obtained from the authors on request. (1) GLS estimates of the parameters of the transition intensities with participation rate in SE instead of participation indicator, allowing the duration effects to be different in Brussels (2) GLS estimates of the parameters of the transition intensities with participation rate in SE instead of participation indicator: proportionality over calendar time and SE-effect varies over calendar time.

41

Figure 1: Survival Functions of Welfare Durations

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 3 6 9 12 15 18 21 24 27 30 33 36

months

non-participants participants in SE

42

Figure 2: Survival Functions of the Welfare Durations of the Matched Groups - Contols in Participating WA's

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 3 6 9 12 15 18 21

months

non-participants participants in SE

The individuals are matched on year of birth, gender, nationality, number of children, marital status (see Table 1 for a definition), and WA. The welfare duration of non-participants is required to be greater than the elapsed duration of participants at the time of selection in SE. We redefine welfare duration by subtracting the time elapsed until selection in SE for each pair of matched individuals

an evaluation bart cockx and geert ridder march, 13 2000 · to stimulate t heir re -employment. ......

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