journal of applied econometrics volume 1 issue 2 1986 [doi 10.1002-jae.3950010202] g. ridder -- an...

8/22/2019 Journal of Applied Econometrics Volume 1 Issue 2 1986 [Doi 10.1002-Jae.3950010202] G. Ridder -- An Event Histor

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J O U R N A L O F A P P L IE D ECONOMETRICS, VOL. 1 . 109-126 1986)

AN EVENT HISTORY APPROACH TO THE EVALUATIONOF TRAINING, RECRUITMENT AND EMPLOYMENTPROGRAMMESG . R I D D E R

Department of Actuarial Science and Econometrics, University of Amsterd am, The Netherlandc

S U M M A R YIn this paper 1 eva lua te a number of t ra ining, recrui tment and em ployment program mes. The evaluat ionmethod is a combination of a quasi-experimental design (a simple pre-treatment-post-treatment design)and a stochastic process model to describe the response variables. I conclude that the programmes have noeffect on older workers . Fem ale and m inori ty worke rs benefi t most f rom the p rogramm es. Th e t rainingprogrammes are less effective than th e recruitment programm es, which ar e in turn less effective than theemployment programmes.

1 . INTRODUCTIONThis paper is concerned with the evaluation of a number of training, recruitment andemployment programmes that are used in the Netherlands. This is not the first paper o n theevaluation of labour market programmes (a short review of previous work is given insection 3), but it is different from other work in this area by employing a different evaluationmethod. In particular I use recent developments in the modelling of event histories (see e.g.Flinn and Heckman, 1982; Tuma, Hannan and Groeneveld, 1979; Burdett et a f . ,1984). Thisapproach is flexible and has only limited data requirements.The plan of the paper is as follows. Section 2 contains a description of the labour marketprogrammes evaluated in this study. Section 3 gives a review of previous evaluation studies. Insection 4 I discuss the model used in this paper and I indicate which problems are encounteredin applying the model to the data at hand. Section 5 contains the results. Moreover, it containsthe substantive conclusions that can be drawn from these results. Finally, section 6 discussessome potential improvements in the evaluation method.

2. TRAINING, RECRUITMENT AND EMPLOYMENT PROGRAMMESThe programmes evaluated in this paper fall into three categories: training, recruitment andemployment. These categories are quite heterogeneous, as can be seen from the Appendix,where I describe the separate programmes. Because of data limitations I evaluate categoriesinstead of distinct programmes. The measured effects are therefore average effects. Theprogrammes described in the Appendix reflect the situation in 1979. Since then, there havebeen a number of changes (mainly simplifications). Some summary statistics concerning theprogrammes are given in Table I which illustrates the shift from training to employmentprogrammes. The number of participants in recruitment programmes is stable.The aim of all programmes is to stimulate the re-employment of individuals, especiallyunemployed who belong to some disadvantaged group (young workers, individuals with a long0883-7252/86/020lO9-18 09.00986 by John Wiley & Sons, Ltd. Received M a y I985Final revision December I985


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110 G . R I D D E RTable I. Number of participants, program me costs and costs per participa nt 1979-1981

Training 46 84 1834 40 87 2167 33 79 2429Recruitment 14 28 2061 13 27 2044 14 21 1422Employment 10 93 91 14 12 96 8058 14 99 7295Source: Department of Social Affairs and Laborcolumn (1): number of participants (1000).column (2): programme costs (m ) .coIumn (3): programme COStS per participant S).

spell of unemployment, disabled individuals, workers who belong to an (ethnic) minority), byproviding training, by making them more attractive to potential employers (recruitment) or bydirect job creation in the public sector (employment). The programmes are directed atindividuals and should therefore be evaluated at the individual level. Their objective is toinfluence the supply side of the labour market, as opposed to policies which aim at stimulatingthe demand for labour. Recently, Layard and Nickell (1980) have advocated the introductionof a marginal employment subsidy. Evaluations of similar programmes in the United States(the New Jobs Tax Credit, see Bishop and Haveman, 1979) and the experience with theTemporary Employment Subsidy in the United Kingdom (Metcalf, 1982) suggest that theseprogrammes are quite effective. Because they subsidize the exports of firms which areprice-takers on export markets, their effects are likely to be greater in open economies such asthat of the Netherlands. Note that these programmes aim at influencing the behaviour of firms.Evaluations of these programmes should therefore measure the effect on the employmentpolicy of firms.3. SOME PREVIOUS EVALUATION STUDIES

In this section I discuss some previous evaluation studies of training, recruitment andemployment programmes. There are two reasons for this short review. First, I want toillustrate how the methodological problems associated with the use of non-experimental datato estimate programme effects have been solved. These solutions can be compared with thesolution proposed in this paper. Secondly, it is interesting to compare the results of previousevaluation studies with the results obtained in this paper.3.1. MethodologyThere is a fairly large literature on the evaluation of training programmes in the United States(Ashenfelter, 1978; Kiefer, 1979; Cooley, McGuire and Prescott, 1979; Bassi, 1984). Thedominant quasi-experimental design in these studies is the pre-treatment-post-treatmentdesign supplemented with a non-equivalent control group. For an explanation of these terms(as far as they are not self-explanatory) the reader is referred to Cook and Campbell (1979).Most studies use individual data, so that it is possible to control for differences in thecomposition of the treatment and the control group (a notable exception is the study ofCooley, McGuire and Prescott, who compare groups). The analysis of the data obtained bythis design is illustrated in Figure 1. Usually attention focuses on the effect of training o nearnings, because this allows for a direct cost-benefit analysis of the training programme. In


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E V A L U A T I O N OF E M P L O Y M E N T P R O G R A M M E S 111Contro l

Figure 1. The effect of training on earningsFigure 1we no te that th e allocation of t rea tments is non-random. T here is a marked differencebetween the treatment and the control group before training. Just before the selection therelative position of the treatment group shows a further deter ioration. Most l ikely this is aselection effect: only individuals with a poor labour market position (e.g. a long spell ofunemp loyment) qualify fo r the program me. Th is suggests that i t may be b etter t o comp are theparticipants with the individuals who have been selected for the programme, but for onereason or another did not participate (the no-show group). The effect of training is thedifference between the pre-training and the post- training earnings. Note that the data inFigure 1 are longitudinal. Thus it is possible to use a fixed effect estimator which eliminatesconstant personal characteristics. The model for the analysis of data from this design can berefined in various directions. It is possible to estimat e th e training effect using pre-trainin g andpost-training earnin gs differences for different periods. It is also possible to use m ore refinedmodels for the selection process (see Bassi, 1984).A different approach is used by Kaitz (1979). H e addresses the problem of evaluating theeffects of a (public) employment programme without a (non-equivalent) control group. Hefocuses o n the effect of the p rogramme on the labo ur force s ta tus (employed, unemployed ornot in the la bour force) of participants. He models the chan ge in labour force status over t imeby a stationary discrete-time Markov chain. Using a simple pre-treatment-post-treatmentdesign, he solves the problem of non-random selection of par t ic ipants ( the unemployed a remore likely to participate) by assuming that the pre-treatment da ta have been g enerated by abackward M arkov chain. In oth er words, the cond itions on the labour force sta te a t the m omentof entry into the programme. If the stochastic process is in equilibrium and if there is notreatment effect, the backward and forward Markov chains have the same equilibr iumdistribution. U nd er these assumptions shif ts in the equilibr ium distr ibution ref lect treatm enteffects . Th ere a re several problems with this approach. First , the equilibr ium assumption maynot be appro priate, so that th ere may b e differences in the pre- and post- treatment equilibr iumdistributions even in the absence of treatme nt effects . Secondly, the d iscrete time m odel is notvery elegant: th e results ar e not invariant t o the choice of the sampling period.

3.2. ResultsDespite the use of different data sets and different estimation methods, the results of theevaluation studies discussed in the previou s section a re similar . Th e mo st directly comp arable


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1 1 2 G . RIDDERstudies are those by Ashenfel ter (1978), Kiefer (1979) and Bassi (1984). They est imate theimpact of programmes on earnings. Although they compare different programmes forpart icipants from different years,* the est imated impacts of the adult programmes ar e of t hes ame o r d e r of magnitude. Women benefi t more from the programmes than men. Whitewomen benefi t m ore than minority women. T her e is som e earnings gain for minori ty males;white males d o not benefi t from th e programm es at al l .Th ere is some evidence that the earnings gain for women is due t o increased employme ntand part icipat ion in the labour force (see Kiefer, 1979, and Cooley, McGuire and Prescott ,1979). Cooley, McGuire and Prescott even a rgue that t raining only effects the employm entpattern of participants and has virtually no effect on wages.The number of evaluat ion studies in the Netherlands is small. Mo reover, t he studies thathave been conducted are plagued by methodological f laws. An example is the study byDiederen and Koekenbier (1978) of an adul t t rain ing programme. O ne of the cri teria they u seto assess the success of the program me is the average length of the unem ployme nt spel l aftertraining. This spell is compared with the average length of the incomplete unemploymentspells of all unemploy ed registered a t the labou r exchange. If the spell after completion of thetraining may be considered as an uncensored (i.e. completed) une mploym ent spell, thenDiederen and Koekenbier 's resul t that the part icipants in the programme have on averageshorter unemployment spells may be a statistical artefact. This can be shown as follows.Le t the distr ibution of a completed (i.e. uncensored) unem ployment sp el l t be given by thedistribution function F and the densi ty f.t Then under some simplifying assumptions (seeRidder, 1984) the densi ty of an incomplete unemployment spel l of an unemployed personregistered a t the labour exchange p is given by-

with = 1 F. The hazard function corresponding to g isf@)

e@) =

with O t ) = f(r)/F(t) the hazard rate corresponding to the distribution of t . Now if O t ) isdecreasing, which is quite likely if one considers heterogeneous groups (spurious durationdependence), we have that l/O(t) is increasing and we find

'The studies relate to: Manpower Development and Training Act (MDTA), participants in 1964, comparison withrandom sample from social Security Administration SSA) records (Ashenfelter); MDTA, Job Opportunities in theBusiness Sector (JOB S), Job Corps (JC), Neighborhood Youth Corp s (N YC ); participants in 1969, comparison withrandom sample from the target population of the programmes (K iefer); Comp rehensive Emp loyment and Training Act(CETA), participants in 1975 and 1976, comparison with random sample from Continuous Longitudinal ManpowerSurvey (Bassi).?Random variables are underlined.


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E V A L U A T I O N OF E M P L O Y M E N T P R O G R A M M E S 113Thus

No w f rom D iederen and Koekenbier (1978, p. 14, Table 1) we find the data in Table I1 for theempirical hazard of the participants an d the unem ployed registered at the labour exchange.Although the results in Table I1 a re not fully consistent with (3) they indicate thatcomparison of E ( e ) and E ( 1 ) is not a good procedure to assess the effect of participation.The prob lem with D iederen a nd Koeken biers study (and oth er evaluation studies) is that i tis difficult to construct a suitable control gro up. Earning s histories of participants or mem bersof a potential control group a re often not available (and cannot be constructed) . These datalimitations demand an evaluation approach which does not require a control group or earningshistories. Th e next sections propose an app roach with these m odest data requirements.Table 11. Empirical hazard of participants and unem ployedin Diederen and Koekenbier 1 978)

MonthGroup 1 1-


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114 G. R I D D E RThe quantities q, , r ) are the transition intensities of the stochastic process. Their interpretationfollows directly from 5) . The quantity

is the escape rate from state i; it is the hazard rate of the waiting time distribution in state i.Given that state i is left at time t , the probability is that the process jumps to statej is given by

From (6) i t follows that the distribution of the length of stay in i given that i is entered at time t ois given by

A labour market history of an individual is a sequence of states and waiting times in states{ i l r f , , i 2 , tZ , }. Note that this description is equivalent to the description { x t ) , t 3 0 . sing(7) and (8) it is easy to construct the distribution of labour market histories.I assume that the stochastic process is a non-stationary Markov chain with a continuous timeparameter. Let 0 < t , < t , < . < fN be arbitrary then the Markov assumption is equivalent to

(9)This implies that the transition intensities at t do not depend on the sample path before t .Moreover, I assume that the Markov chain in non-stationary, i.e.

Pr(_x(t,) = i N ( t N - J = i,-,, _ x ( t l ) = i,) = Pr(g(tN)= iNIz tN- , )iN-])

Pr(z(t + s ) = j { ~ ( s ) i) Pr(x_(t)= Ix 0) = i). s 0 (10)This implies that the transition intensities are not constant over time. This allows for theinclusion of external influences, such as general labour market conditions, treatment effects,participation effects, etc.We use the model to describe labour market histories of heterogeneous individuals. Thisheterogeneity is described by specifying the transition intensities as

q i , Q , x ; B ) = exTpqjj(t) (11)where x is a vector of covariates and l is a vector of parameters.4.2. Applying the ModelThe data one encounters in practice rarely have the form of complete labour market histories.Usually the data are obtained by some observational plan, which allows one to observe onlypart of a labour market history. It is important to assess the influence of the observational planon the distribution of the observed part of the labour market histories (see, for instance, thediscussion of Diederen and Koekenbier's (1978) study in section 3). The observational planused in this paper is illustrated in Figure 2.The observational plan is fully retrospective: there is only one interview. However, it ismore natural to consider the moment of selection for the programme as the point of departure.The reason for this is that the participants are not randomly selected from the labour force at


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E V A L U A T I O N OF E M P L O Y M E N T P R O G R A M M E S 115

Figure 2 . Observational plan

the moment of selection for the programm e. Unem ployed individuals are much more likely tobe selected then employed individuals. Therefore, we derive the distribution of the observedsample paths ( i .e. observed according to the observational plan of Figure 2) conditional on theobserved state at the moment of selection. Starting from the mo men t of selection the observedlabour market history can be divided into a retrospective part (before selection) and aprospective part (after selection).I f irst consider the prospective part conditional on the retrospective part . In the case ofrecruitment and emp loyment programmes the prospective part s tar ts with a transition: anunemployed person is placed in a job. Of course this transition is not a normal transition, butthe consequence of an intervention. Therefore, I proceed conditional on this transition. Thedistribution of the conditional prospective labour market history can be derived using (7) and(8). Th e only complication is the censored waiting time in the state occupied at the mom ent ofinterview. The contr ibution of this spe ll is

where N i s the num ber of completed spells af ter selection, the mo men t of selection is time 0,and r is the length of the cen sored spell. This deals with th e right censoring of the prospectivelabour mark et history.Next I consider the retrospective part of the la bour m arket history. The distr ibution of thispart is more complicated. Especially the f ixed num ber of spells observed before selection (twoin o ur observational plan) causes some problem s. To ma ke this clear I int roduce th e entry ra tein state i, the retrospective counterpart of the escape ra te

e , ( t ) = limd r lOPr(x(t dt ) i, ) = i)

d twe have

K

, = I/ I

Pr(x_(t - dr) i , x ( t ) = i) = p , ( t dr)q,,(t dt)dt o(dt)


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116 G . RlDDERwherepi(t) = Pr(g(t) = i). Now if the transition intensities are left continuous in t , that is if thetime paths of the external variables (general labour market conditions etc.) are left-continuous, we have

This follows from

so thatlim p , ( t dt) = p i ( r )d i p

Now the observation of two spells before selection is equivalent to observing {il,tl i2,t2}.However, there are two complications: first, the event i, is entered at time - t , f:theselection takes place at epoch 0) is part of the observed event; secondly, the spell 1 , istruncated at the time of selection. Noting that the probability of entry in i l at - r 1 - t 2 ise l , ] -tl - t 2 and that the truncation can be incorporated as in (12), we find that the probabilityof the observed event is given by

It can be shown that if the starting point of the process is sufficiently far in the past, then themarginal probability of i 2 in (18) is equal to P , . ~ O ) .Therefore the conditional distribution ofil,t,,t2has density

We now make a number of simplifying assumptions. First, there are only two states:employed and unemployed. Secondly, before selection the transition intensities are constantover time. In other words, we assume that the labour market conditions were stable beforeselection. The participation in the programme took place in 1979 and the first half of 1980.The dramatic deterioration of labour market conditions started in the second half of 1980.Thirdly, the entry rates are constant over time. From (15) we see that this is equivalent to theassumption that the process was in equilibrium at the time of entry. Under these assumptions itfollows that if U denotes the unemployment state and E the employment state


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E V A L U A T I O N OF E M P L O Y M E N T P R O G R A M M E S 117Substitution of (20) and (18) into (19) gives

f E,rE,iU U ) = qEe-qE' que-quu (21)Note that the entry rate cancels out.The density in (21) is just the product of the densities of the first and second spells beforeselection. However, it is important to remember how (21) was derived. In particular, thetreatment of the last truncated unemployment spell as a complete spell depends on theassumed exponential character of the duration distribution. If the escape rate from the state ofunemployment decreases over time, which, as noted above, may be due to failure to controlfor heterogeneity, then the observed escape rate may be an overestimate of the escape rateof a complete spell, causing a downward bias in the estimate of the treatment effect (see thediscussion in section 3). The derivation in this section is specific to the observational plan ofFigure 2. Other observational plans lead to different distributions of the observed samplepaths.

It should be noted that I have made some assumptions concerning the selection ofparticipants by the programme administrators. First, I have assumed that the selection isnon-random with respect to the labour force state. Further, I have assumed that the selection israndom with respect to other aspects of the labour market history. This seems in conflict withthe selection criteria indicated in the Appendix. However, from the data and from directinformation from the programme administrators, it is clear that the application of these criteriais not very strict. Of course it is possible (at the cost of increased complexity of the model) toallow for other forms of selectivity.

5 . RESULTS OF THE EVALUATION5.1. Some Further Specifications and Data UsedIn section 4 i t was shown that a non-stationary Markov chain with continuous time parameteris characterized by the transition intensities q , , t ) where t refers to calendar time. Thespecification of the transition intensities in this paper is

4,(hx;B) = exp{xTB,,,+ z(t) B*,L i , j = U , E ; j (22)where x is a vector of covariates describing the heterogeneity of the population and z t ) is avector of (calendar) time varying variables; f i l l ,and B2,/ are vectors of parameters.The time constant covariates are: age (AGE), sex (SEX), number of years at school(SCHOOL), a dummy for minority workers (MIGRA), a dummy for frequent spells ofunemployment (DUNEMP) and a dummy for long spells of unemployment (DLONG).DUNEMP and DLONG are included to test and correct for unobserved heterogeneity (inaddition to the heterogeneity captured by AGE, SEX, SCHOOL and MIGRA). Table I11gives some sample statistics. From this table we see that there are some differences betweent he participants in the programmes. Participants in training programmes are younger thanparticipants in other programmes; they are also less likely to complete the programme(COMPL is a dummy variable that indicates completion of the program). Trainingprogrammes have a longer duration than recruitment and employment programmes(DUURM measures the duration of the programme in months). The highest fraction ofminority workers and the lowest fraction of females are found among the participants inrecruitment programmes. Participants in recruitment programmes tend to have long spells of


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118 G. RIDDERTable 111. Some sample characteristics. Standard deviations in parentheses

~ ~VariableCategory AGE SEX SCHOOL M I G R A D U N E M P DLONG COMPL DURTraining 25.5 0.21 10.3 0.21 0.11 0.28 0.65 11.0programmes (6.3) - (1.7)Recruitment 37.1 0.13 9.4 0.41 0.07 0.54 0.68 7.4programmes (10.5) - (2.8)Employment 36.6 0.34 10.3 0.31 0.11 0.49 0.84 9.6programmes (12.3) - (3.5)

(7.8)(3.6)(4.5)

--- - - -

unemployment. They have on average the smallest number of years at school. Theemployment programmes have the highest proportion of female participants. The participantsin these programmes also have the largest probability of completing the programme.Besides variables that correct for the heterogeneity of the population there are a number oftime-varying variables. First, there are the programme effects. The pre-treatment-post-treatment effect is captured by a time-varying dummy variable DEFFECT that is 0 beforeselection and 1 after selection. To allow for differences in the treatment effect for differentdemographic groups I include interactions of DEFFECT with DAGE (a dummy variable thatis 1 for participants above 35 years of age), SEX and MIGRA. The dummy variable DPAR is1 during participation and 0 before and after participation. This variable captures theimmediate participation effect.. The variable COMPL is 1 after participation if the participantcompleted the programme. To control for varying labour market conditions 1 also include thevariables D80, D81 and D82. D80 is 1 during 1980/7-1980/12, D81 is 1 during1981/1-1981/12 and D82 is 1 during 1982/1-1982/6. I chose 1980/7 as the first monthincluded in D80 because it was the beginning of a period of rapid deterioration of labourmarket conditions.The data were obtained from a survey of participants in May and June 1982. Theparticipants all live in Rotterdam and the participation took place mainly in 1979. The labourmarket histories were constructed according to the observational plan in Figure 2. The numberof observations was 337.5.2. ResultsThe parameters of the model were estimated by a maximum likelihood procedure. Thelikelihood function was constructed from the densities of the prospective and retrospectiveparts of the labour market histories (see the discussion preceding (12) and (21)). The estimatesof the treatment effects are summarized in Table IV. In this table we find the multiplicativefactors that must be applied to the pre-treatment average spells of employment andunemployment to obtain the post-treatment spells. I first consider the treatment effect on theaverage spell of employment.It is clear that no programme increases the length of an average spell of employment ofworkers older than 35 years of age. Females and minority workers benefit from trainingprogrammes and still more from employment programmes. Recruitment programmes have afavourable effect on the average employment spell of young workers and females. Minorityworkers do not benefit from these programmes. Employment programmes lengthen theaverage employment spells of young workers, females and minority workers (in this order).


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E V A L U A T I O N OF E M P L O Y M E N T P R O G R A M M E S 119Table IV. Multiplicative effect of treatment on the average spell of employment and unemployment.Different demographic groups

Average spell of employment Average spell of unemploymentdemographic group effect 95 per cent interval effect 95 per cent intervalCategory/

~ _ _

TrainingAGE


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120 G . RIDDERaverage spell of unemployment. This is due to the selection by the programme administrators.In section 4 I show that only i the escape rate from unemployment is constant over time andthe administrator draws a random sample from the unemployed, the comparison of thepre-programme and post-programme unemployment spells gives an unbiased estimate of theprogramme effect.The results for the unemployment spells indicate that the selection for the programmesoccurs before the average spell of unemployment has elapsed. This implies that there is arather large windfall effect. The observational plan used to collect information onpre-treatment unemployment spells yields spells that are truncated by the programmeadministrator. A better plan is to collect information on the last three spells beforeparticipation and to neglect the truncated unemployment spell (see also the discussion insection 6).

We now summarize the results of Table IV: employment programmes are more effectivethan recruitment programmes, and these are more effective than training programmes.Females, minority workers and young workers (in this order) benefit most from theprogrammes (one exception: minority workers do not benefit from recruitment programmes).Older workers do not benefit from any programme. These results are consistent with theresults cited in section 3 which were obtained by entirely different methods.Besides the pre-treatment-post-treatment effects of Table IV, the programme has othereffects which are captured by the time-varying variables DPAR and COMPL. These effectscan be found in Table V. During the participation in a training programme one has a largerprobability of finding a job, and a smaller probability of losing one (if employed). Participantsof recruitment programmes have a higher risk of losing their job. The largest effects are foundfor participants in employment programmes. During participation they have an increased riskof losing their job. They also have a higher probability of changing jobs. Participants oftraining programmes who complete their training have a reduced probability of becomingunemployed. The same is true for participants of recruitment programmes.

Table V. The effect of DP AR a n d COMPL on the transitionintensities; coefficients an d (r-values)Categoryvariabletransition Training Recru itment Em ploym entD P A RE+E'E+U'U + E

C O M P LE+EE+UU + E

0.062(0.074)-0.58(- 1.04)0.52(1.44)

-0.95- 1.95(-5.06)-0.028(-0.066)

(-1.20)

0-18(0.25)0.68(1.86)0.33(0.31)

0.51(0.64)-0.69(-1.98)-0.054(- 0.10)

1.681.90(2.06)

( 3 . 3 5 )

-0.060(-0.075)-0.71(-0.81)

~

U = unemployed; E = employed.


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E V A L U A T IO N OF EMPLOYMENT PROGRAMMES 121The model and the parameter estimates can be used to simulate the changes in labour

market status over time for selected groups. From the preceding discussion it is clear that wecan simulate only the behaviour after participation. The results can be found in TablesVI-VII. These tables are constructed as follows. We have chosen a standard participantcharacterized by a set of values of the covariates. Next we consider the changes that occur if wevary the characteristics of this participant one at a time. The variables that characterize thelabour market history are self-explanatory. Although the estimates of the labour markethistory characteristics are subject to large sampling error, due to the small number of observedtransitions (this affects in particular the estimates for D81 = l , some patterns emerge fromthese simulations.

First, the labour market position of participants in training programmes is better than that ofparticipants in recruitment programmes, which again is better than that of participants inemployment programmes. The number of years at school has a positive effect on the labourmarket position of participants. Among the participants in training programmes, females andminority workers have a relatively weak position (after participation). However, the groupsthat do not benefit from the programmes have a relatively strong labour market position. Notealso the weak position of participants who did not complete the program me. I n Table VI thelabour market position of participants is decomposed into average durations and transitionprobabilities. It is seen that the variations in the labour market position are mainly due to

Table VI. Simulation results. Training programmes. Training during employment (unemployment)Percentage employed after Average duration in months Transitionprobabilities

12 60 mmonths months months unemployment employment E + E E + U

AGE=29;MALE;SCHOOL= lO;MIGRA=O;DUNEMP=O;DLONG=O;COMPL= 1 D80=0;D81=O;D82=0AGE=21AGE=37FEMALESCHOOL=8SCHOOL= 11MIGRA= 1DUNEMP= 1DLONG= 1COMPL=OD81=1D81= l;AGE = 21D81= 1 AGE=37D8 1= 1 ;FEMALED81= l;SCHOOL=8D81=1;SCHOOL=12D81= 1 MIGRA= 1D81=1;DUNEMP=1D81= 1 DLONG= 1D81 =l;COMPL=O

94(71) 96(96) 96

92(73)96(69)87(60)90( 68)88(62)83(66)91(39)89(67)86(47)81(48)90(46)

97(74)

74(37)77(43)92(50)75(38)64(40)83(22)78(42)

95(95)97(97)93(93)91(90)98(98)92(91)89(88)91(81)76(76)88(84)84(82)Yl(86)74(71)80(77)93(89)76(73)b4(68)49(48)77(57)

95979193989289917688849175809377707948

11.8 112.0

10.4 89.712.8 136.715.1 89.712.4 86.911.3 133.014.3 84.011.8 60.730.7 112.012.6 24.522.4 127.720.6 91.124.3 176.128.6 74.823.4 79.721.4 192.427.1 76.222.4 47.058.1 127.723.0 20.8

0.62 0.38

0.54 0.460.69 0.310.40 0.600.47 0.530.75 0.250.46 0.540.33 0.670.62 0.380.31 0.690.22 0.780.18 0.820.28 0.720.11 0.890.14 0.860.34 0.660.13 0.870.08 0.920.22 0.780.08 0.92


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122 G . RIDDERTable VII. Simulation results. Recruitment programmes

TransitionPercentage employed after After duration in months probabilities12 60 WType months months months unemployment employment E+E E-+U

AGE=37;MALE;SCHOOL=B;MIGRA=O;DUNEMP=O;DLONG=O;COMPL= 1;D80=0;D81 =O;D82=OAGE=29AGE=45FEMALESCHOOL=7SCHOOL= 11MIGRA= 1DUNEMP= 1DLONG= 1COMPL=OD81=1D8 1= 1 AGE=29D81=1;AGE=45D81 =l;FEMALED81=1;SCHOOL=7D81= 1 SCHOOL= 11D81=1;MIGRA=lDBl=l;DUNEMP=lD81 =l;DLONG=lD81= l;COMPL=O

86

86878983916963838273707579717546357269

90

91899082947572837060546166536931255434

90

91899182947672836452544954366727243721

16.5

13.021.121.631.28.819.516.524.430.695.875.1122.2125.0180.550.8113.095.8170.1177.0

66.6 0.55 0.45

45.393.972.355.977.631.431.766.650.991.575.5109.1122.487.994,538.628.891.545.0

0.64 0.360.44 0.560.65 0.350.62 0.380.47 0.530.48 0.520.26 0.740 . 5 5 0.450.23 0.730.10 0.900.14 0.860.07 0.930.15 0.850.13 0.870.08 0.920.08 0.920.03 0.970.10 0.900.03 0.97

variations in the average durations of jobs and the transition probabilities if one leaves a job.For instance, the strong labour market position of participants with a relatively large numberof years at school is due to the long duration of jobs and the high probability of transition intoanother job once a job is left. From Table IV, we see that minority workers who, according tothe results in Table IV, do not benefit from recruitment programmes have a weak labourmarket position. This weak position is caused by short job durations and a large probability ofbecoming unemployed. It isclear that recruitment programmes are of no help in improving theposition of minority workers. Indeed, recruitment programmes tend to stimulate turnover.Note again the weak position of participants who dropped out of the programme. Afterparticipation in employment programmes the position of females and minority workers is quitestrong (Table VIII). Here participants with a small number of years at school have a relativelyweak position (mainly due to their long unemployment spells). Note also that the position ofparticipants who did not complete the programme is relatively strong. An explanation for thismay be that most premature terminations of employment programmes are voluntary.The effects of the business cycle on the labour market position of participants is dramatic.Although the results may be subjected to a large sampling variation due to the small number oftransitions for the distinct groups in 1981, it is clear that participants are heavily affected bythe deterioration of the iabour market conditions in 1981.It is interesting to note that trainingand recruitment programme participants with a relatively large number of years at school are


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EVALUATION OF EMPLOYMENT PROGRAMMES 123Table VIII. Simulation results. Employment programmes

~ ~~ TransitionPercentage employed after Average duration in months probabilities12 60 mype months months months unemployment employment E+E E+U

AGE=36;MALE; 73 78 79 44.2 71.1 0.62 0.38SCHOOL= lO;MIGRA=O;DUNEMP=O;DLONG=O;COMPL= 1;D80=0;DSO=O:D80=0AGE=28 72 80 82 38.8 69.4 0.59 0.41AGE=44 74 76 77 62.3 72.6 0.65 0.35FEMALE 76 84 86 33.1 70.6 0.64 0.36SCHOOL=8 70 69 68 85.7 73.5 0.59 0.41SCHOOL= 12 78 86 88 28.2 68.6 0.65 0.35MIGRA = 1 77 83 85 36.4 78.1 0.63 0.37DUMEMP= 1 53 62 64 49.2 49.8 0.44 0.56DLONG= 1 69 62 5 2 172.7 71.1 0.62 0.38COMPL=O 74 89 91 25.6 77.9 0.68 0.32D81=1 27 27 27 138.1 42.0 0.16 0.84D8 1= 1 AGE= 28 25 28 29 108.9 38.6 0.14 0.86D81= 1 AGE=44 29 26 24 175.1 45.7 0.18 0.82D81= 1 FEMALE 30 34 36 92.9 42.9 0.17 0.83D81 =l; SCHOO L=8 23 19 17 240.7 40.9 0.14 0.86D81= l;SCHOOL= 12 32 38 40 79.2 43.1 0.18 0.82D81= l;MIGRA= 1 32 34 35 102.2 46.3 0.16 0.84D81= DLONG = 1 23 14 9 485.1 42.0 0.16 0.84D8 1= 1 DUNEMP= 1 8 14 15 138.1 21.4 0.08 0.02D81 =l;COMPL=O 28 43 48 71.8 52.5 0.20 0.80

less affected because the increase in the average duration of jobs com pensates the increase inthe length of spells of unemployment .Com bining the evidence in the Tables IV-VIII we conclude that the participation of olderworkers in th e programmes does not serve any purpose . They d o not benefi t f rom th eprogramm es an d, moreover , their labour marke t position af ter participation is not weak. Th eparticipation of females in all programmes and of minority workers in training andemployment programmes is justif ied. The participation of minority workers in recruitmentprogramm es doe s not reduce their turno ver , which is the main cau se of their weak position onthe labour market . Young workers do benef i t f rom the programmes. However , theirlabour market position is not particular ly weak. Of course , the remarks on the ( re la t ive)labour market position of specif ic dem ographic group s only applies to th e participants in theprogrammes.

6. F U R T H E R I M P R O V E M E N T S IN M E T H O D O L O G Y - C O N C L U S I O N SIn this paper I have taken a rather novel approach to the evaluation of labour marketprogrammes. In several places I have indicated that the re a re deficiencies in the model or inthe d ata . O ne can ask wh ether these def ic iencies can b e remedied.


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124 G . RIDDERFirst, as is clear from section 4, the observational plan used in this paper is not ideal. Abetter plan is to chose a fixed point in the past and to reconstruct the labour market historyfrom this point until the time of interview. This yields observed retrospective sample paths that

can be considered as right-censored sample paths. Of course, this leaves t he problem ofnon-random selection by the programme administrator. However, this problem can be solvedby neglecting the last spell of unemployment before selection. Care must be taken that weobserve enough transitions. If we use this observational plan we can show that the entry ratedoes not enter the distribution of the observed sample paths. Secondly, as was noted insection 5, unobserved heterogeneity causes a (downward) bias in the estimated treatmenteffects. However, one expects by analogy with the ordinary linear model that i t is possible toexploit the panel character of the data more fully to eliminate time-constant individualdifferences. Indeed, i t can be shown that the Cox 1972) estimator can be used as awithin-estimator and under the assumption of proportional hazards this estimator does notdepend on time-constant individual differences. This allows for a directpre-treatment-post-treatment comparison which is not affected by omitted heterogeneity.The estimates obtained in this way could be compared with the estimates obtained from a(designed) experiment. If one is interested in differences in the participation effect betweendemographic groups, the randomized block design seems to be the most appropriateexperimental design. An advantage of the experimental approach is that we only need toanalyse prospective labour market histories so that the selection effect is eliminated. Aproblem is that common methods for the analysis of censored durations are affected byomitted heterogeneity (Ridder and Verbakel, 1986). However, this problem is notinsurmountable (Heckman and Singer, 1984). Given the flexibility of the evaluation methodproposed in this paper and the substantive conclusions that can be drawn (as summarized insection 5 ) , we conclude that there is scope for further application of the method, especially ifthe suggestions made above are followed.

ACKNOWLEDGEMENTSThis paper is based on work for a project o n the evaluation of labour market programmessponsored by the Dutch Department of Social Affairs and Labour. The study was performedby the Foundation for Economic Research of the University of Amsterdam. The full resultscan be found in van der Vegt et al. 1984). The author is indebted to J.S. Cramer for helpfulcomments and suggestions.

REFERENCESAshenfelter, 0 197 8). Estimating the effect of training programs on earnings, Review of EconomicsBassi, L. J . 1 984), Estimating the e ffect of training programs with non -random selection, Review ofBishop, J. and R. Haveman (1979), Selective employment subsidies: can Okuns law be repealed?Burdett, K., N. M. Kiefer, D. T. Mortensen and G . R . Neumann (1 984) . Earnings, unemployment, andCook, T. D . and D. T. Campbell (1979), Quasi-experimenration, Houghton Mifflin, Boston.Cooley, T. F., T. W. McGuire and E . C. Prescott (19 79) , Earnings and employment dynamics ofmanpow er trainees: and exploratory econom etric analysis, in F. E. Bloch (ed.), Research in LabourEconomics, JAl Press, Greenwich.Cox, D . R . (19 72) , Regression models and life tables,Journal of he Royal Statistical Society Series B ,Diederen, J . H . 1. W . and H. A. J . Koekenbier (1978), Effects of schooling in a center for vocational

and Statistics. 60, 47-57.Economics and statistics, 66, 36-43.American Economic Review, 69, 124-130.the allocation of time over time, Review of Economic Studies, LI, 559-578.

34, 183-202.training (in Dutch ), Report I T S , Nijmegen.


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E V A L U A T I O N OF E M P L O Y M E N T P R O G R A M M E S 125Flinn, C. J. and J. J . Heckman (1982) Models for the analysis of labor force dynamics, in Advances inHeckman, J . J. (1979), Sample selections bias as a specification error, Economerrica, 47 , 153-162.Heckmann, J . J. and B. Singer (1984), A method for minimizing the impact of distributionalKaitz, H . B. (1979), Potential use of Markov process models to determine program impact, in F. E.Keifer, N. M. (1979), The economic benefits from four government training programs, in F. E. BlochLancaster, T. and S. J . Nickell (1980). The analysis of re-employment probabilities for theLayard, P. R. G. and S. J . Nickell (1980), The case for subsidizing extra jobs, Economic Journal, 90Metcalf, D. (1 982), Special employment measures, Midland Bank Review, Autumn.Ridder, G. (1984), The distribution of single-spell duration data, Studies in labor market dynamics,G. R. Neumann and N . C. Westergard-Nielsen, Springer, Berlin.Ridder, G. and W . Verbakel (1986), On the estimation of proportional hazards model in the presence of

unobserved heterogeneity, Journal of the American Statistical Association, forthcoming.Tuma. N., M Hannan and L. Groeneveld (1979), Dynamic analysis of event histories,American Journalof Sociology, 84 20-854.Vegt, C. van der, C. H. M. Lutz, W. C. G . M. van Paridon and G. Ridder (with J . M. Meijering and R.Giebels) (1984), The application of labor market programs in the urban labor market (in Dutch),Report of the Foundation for Economic Research, Amsterdam.

Econometrics, vol 1 , JAl Press, Greenwich.

assumptions in econometric models of duration data, Economerrica, 52 271-320.Bloch (ed.), Research in Labour Economics, JAl Press, Greenwich.(ed.), Research in Labour Economics, JAl Press, Greenwich.unemployed, Journal of rhe Royal Statistical Society, Series A 143 141-165.51-73.

APPE NDIX: TR AINING, R E C R UIT M E NT AND E M PL OYM E NT PR OG R AM M E S I N1979

Th e program mes evaluated in this pape r areTraining Programmes

Programme( n o . of participantsin 1979) Description

participantosts per ,average1979-1981

Vocational training(SVS)

Subsidy to employers. An incentive to provide on-the-job 1329for school-leavers(25,757)Centre forvocationaltraining ofadults (CVV)(3517)

training for school-leavers under 26 who have beenunemployed for at least 2 weeks and at most 12 months.Participants mainly in construction and industry.These centres provide training traditionally for occupationsin construction and the metal industry for unemployed over17 and under 51 years of age. Since a number of years theyalso provide training for other occupations (mainly clerical).Participants receive a supplement to their unemploymentbenefits.

697

Training in Training aimed at adaptation of unemployed to specific 3455co-operation betweenemployers and publicagencies (SOB)(5206)Specific trainingsubsidy (SKR)(9752)

job openings. The training is also provided for individualsthreatened with unemployment. Parts of the trainingcosts and wages are subsidized.

A subsidy for training costs incurred by individualunemployed who participate in training programmes notspecifically aimed at the unemployed.34h


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126 G. IDDERRecruitment Programmes

Programme(no. of participantsin 1979) Description

Costs perparticipant ,average1979- 1981

30 per cent wagesubsidy (30 L R)(2861)Wage subsidy forunemployed with aprolonged spell ofunemployment (LW)(2631)Wage subsidy forthe d isabled (H AN D)(1 034)Wage subsidy foryoung unemployed(2271)Recruitmentprogramme forworkers from ethnicminorities (MIGRA)

(LKJ)

(114)

Wage subsidy for older unem ployed and unemployed with aprolonged spell of unemployment (6 m onths or longer) . Thesubsidy is 30 per cent of the wage during the first 6 mo nths.Wage subsidy aimed at unemployed with an unemploymentspell of at least 12 months. The subsidy is 75 per cen t of thewage du ring the first 1 2 months.

1 7 1 0

6855

Th e subsidy is 60 per cent of the wage during the first 12months and 30 during the next 3 months. 4 3 0 6

Aimed at young workers (17-21 years of age) who have beenunemployed for more than 6 months. The subsidy is 210per m onth during the first 12 mo nths.2195

Adaptation courses on-the-job. The minority worker is notpaid a wage but receives unemployment benefits. Theem ployer receives a small subsidy. Th e duration of theprogramme is 6 months.

768

(Public) Employment ProgrammesProgramme(no. of participantsin 1979) Description

Costs perparticipant ,average1979-1981Temporaryjobs programme(3023)Creation of extrajobs (WVM )(6559)

(TAP)A job is created in the non-profit se ctor. Eligible areindividuals unde r 45 years of age with an unemploym entspell of at least 6 months, and all unemployed over 45years of age. Th e duration of the job is 1 2 months.Extra jo bs are cre ated in the public sector. Eligibleare all those w ith an unem ploym ent spell of at least 6months. The duration of the job is 1 2 months.

5186

8680

journal of applied econometrics volume 1 issue 2 1986 [doi 10.1002-jae.3950010202] g. ridder -- an...

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