Duration of UI periods and the perceived threat effect from labour market programmes

Labour Economics 14 (2007) 639 – 652 www.elsevier.com/locate/econbase

Duration of UI periods and the perceived threat effect from labour market programmes Lars Pico Geerdsen a , Anders Holm b,⁎ a

b

The Rockwool Foundation Research Unit, Denmark Department of Sociology and Centre for Applied Microeconometrics (CAM), University of Copenhagen, Denmark Received 1 February 2005; received in revised form 17 June 2006; accepted 19 June 2006 Available online 1 August 2006

Abstract In this paper, we study whether the prospect of compulsory programme participation motivates individuals to leave the unemployment insurance (UI) system prior to participation. In some systems, individuals may experience very different risks of enrolment even when they face identical formal rules. If individuals learn that programme enrolment does not deterministically follow regulations, estimated effects based solely on institutional regulations may be downward biased. This means that the true effect of potential enrolment may be underestimated. We analyse data from the Danish labour market which includes information on a series of reforms that have enforced programme participation in return for unemployment benefit entitlement. First, we find that unemployed individuals do indeed have different risk of compulsory enrolment even when regulations indicate that the risk should be identical. Second, we find that individuals do react strongly and significantly to the prospect of programme enrolment. However, since individuals experience different risks of programme enrolment, the resulting response observed in individuals' hazard out of unemployment is also different as the unemployment spell progresses. © 2006 Elsevier B.V. All rights reserved. JEL classification: C41; I21; J64 Keywords: Active labour market measures; Threat effect; Hazard rates; Unobserved heterogeneity

1. Introduction This paper studies the effect of individual level variation in enrolment probabilities into labour market programmes. The effect of enrolment probabilities is measured in terms of exit probabilities out of unemployment. ⁎ Corresponding author. E-mail addresses: [email protected] (L.P. Geerdsen), [email protected] (A. Holm). 0927-5371/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.labeco.2006.06.001

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In response to the high and sustained unemployment in the beginning of the 1990s, many countries made UI benefits payment conditional on participation in a labour market programme. This principle of compulsory programme participation after some pre-defined duration of unemployment was introduced in Europe by Denmark in 1994 and followed by Great Britain, Switzerland, and Sweden (Gerfin and Lechner, 2002). Similar principles are also in use in countries such as the US and Australia (Black et al., 2003; Richardson, 2002). There is a recent and growing literature on how individuals respond to the prospect of compulsory programme participation, henceforth denoted “the threat effect” (Bjørn et al., 2004). The basic idea is that unemployed individuals may find that participation lower their welfare. This could be the case if they regard programme participation to be a tax on leisure or if individuals believe there is a risk of scarring when participating in a programme. One notable example of studies that finds threat effects is Black et al. (2003) who, on US data, find increases of up to 50 percent in terms of the hazard of employment around the time when individuals receive a letter about forthcoming programme participation. A similar effect is found on Danish data by Geerdsen (2006). Both these studies model the threat effect as the timing of compulsory programme participation as set by institutional regulations. However, in some systems individuals may experience very different risks of enrolment even when they face identical formal rules for entering a labour market programme. There are various reasons why this might be the case. First, case officers may assess that some individuals will gain less from programme participation and hence be less likely to allocate them to programmes. Second, some unemployed individuals may be better at negotiating with case officers in order to avoid programme participation. Finally, some types of programmes may be supplied in limited numbers thereby forcing case officers to single out individuals. If indeed individuals experience that programme enrolment does not entirely comply with the regulations and that they respond accordingly, then estimated threat effects based solely on institutional regulations may be downward biased. This means that the true threat effect for those who actually face the risk of enrolment may be underestimated. This bias obviously depends on how different actual enrolment is from institutional regulation and how many unemployed individuals know and exploit this. The determination of this is an empirical matter. In this paper, we examine whether individuals' risk of enrolment in a labour market programme is identical to what we would expect according to the institutional regulations. We apply individuals' predicted probability of programme enrolment in a model describing their hazard of leaving UI unemployment. We use the model to estimate the threat effect of programme enrolment for different types of individuals. The estimation is based on Danish register data describing individuals' unemployment history. Since 1994, the period in which individuals can receive UI benefits without participating in a labour market programme has been reduced several times. This yields a quasi-experimental set up where the threat effect of compulsory programme participation can be (locally) identified. Our estimation produces two findings. First, it appears that unemployed individuals do indeed have different risks of compulsory enrolment in a labour market programme. These differences are found across individuals to whom institutional regulations would otherwise assign identical risk of programme participation. Second, we find that individuals do react strongly and significantly to the prospect of programme enrolment. However, since individuals experience different risks of programme enrolment, the resulting reaction in individuals' hazard out of the UI is also different as the UI spell progresses. The paper is organised as follows. Section 2 provides details about the labour market under study. Section 3 presents the data applied in the study. Section 4 introduces an econometric model

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of the threat effect, whereas Section 5 presents the estimation results of this model. Finally, conclusions are drawn in Section 6. 2. The Danish labour market The Danish UI system covers the majority of the workforce. Approximately 80 percent is member of the UI system. Individuals, who do not have the UI are referred to means-tested benefits, which in most cases are well below the level of the UI benefits. The Danish UI period is divided into two sub periods — a “passive” period and an “activation” period.1 Individuals who become unemployed first enter the passive period. In this period they are mostly left to their own job search. Case officers can, however, enlist individuals for early enrolment in a programme if individuals do not appear to be searching for employment. The passive period is followed by the activation period. In this period all individuals, according to legislation, have to participate in a labour market programme in order to receive benefits. Individuals, who do not comply with regulations, for instance miss a meeting or reject to participate in a programme, can be met with sanctions. In most cases (80 percent of all cases), individuals lose a couple of days of UI benefits. In the most extreme cases, individuals can lose their right to UI all together. This is, however, a very rare outcome.2 Hence, over all the threat effect, if present, consist mainly in the perception by the unemployed, that programme participation is regarded as undesirable.3 In 1994, when the UI system was reformed, the passive period was set to a maximum of four years and the activation period was set to three years. Roughly, this meant that individuals could stay unemployed and receive benefits for up to seven years. The maximum passive period has since then been shortened. From mid 1996, individuals who gained or regained the right to a “fresh” UI period were only entitled to a two-year passive period.4 For all other individuals, the passive period was reduced to three years in mid 1996 and in 1998 to two years. Since 1994, the activation period has been unchanged, i.e. three years. The requirement for obtaining a fresh UI period was up until mid 1996 half a year of unsupported employment within a three-year period. After mid 1996, the requirement was changed to one year of unsupported employment within a three-year period. If individuals do not regain the right to a fresh UI period between two unemployment spells, the previous spell will be included in the individuals' seniority in the UI period. This means that two individuals who begin an unemployment spell at the same time may have different seniority in the UI period and hence (by legislation) different risk of being enrolled in a labour market programme.

1 This system was introduced in 1994. Before 1994, individuals who were eligible for UI benefits could - when unemployed - receive benefits for up to 9 years. 2 In 1999, approximately 20 percent of the sanctions applied were a minimum of 3 weeks of quarantine from the UI system. The remaining 80 percent were a loss of - on average- 2–3 days of UI benefits. In 1999, the number of sanctions were 13,800, cf. Arbejdsdirektoratet (2005). 3 The case officer and the unemployed individuals have to make an “action plan” together, which describes the individual’s employment goals and the training needed in order to achieve these goals. The aim is to offer programme participation which fulfils this training requirement. The unemployed individual, therefore has some influence on which types of programmes he or she is enrolled in. 4 The term “fresh” UI period is inspired by Rogers (1998).

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The labour market programmes offered consist of a wide range of options. The programmes can be divided into the following categories: • Labour market training • Subsidised employment in a private or public firm • Assistance with self-employment Individuals within training programmes receive income equal to the benefits they received prior to commencing in the labour market programme. Individuals in both public and private subsidised employment receive the minimum wage set by collective bargaining in the given sector. The working hours for individuals in public subsidised employment are restricted so that the wage does not surpass the maximum benefit level. Individuals in private subsidised employment can have normal working hours and thereby have an income higher than maximum benefit level. Between 1994 and 1998, individuals could also receive financial support for selfemployment under the rules of the ALMP. The support was given for up to two and half years and was fifty percent of maximum benefits. In 1998, labour market training constituted 40 percent of the offered programmes. Subsidised employment in a public firm constituted 40 percent whereas subsidised employment in private firms constituted 8 percent of all programmes. Finally, assistance with self-employment constituted 12 percent of all programmes (Statistics Denmark, 2004). 3. Data and descriptive analysis The data used in this analysis are collected by different public bodies and supplied by Statistics Denmark. Since data are based entirely on registers, there is no non-response. The analysis is performed on a balanced 10 percent sample of the population for the period 1994 to 1998. We have restricted the population to males who in 1994 were between the age of 25 and 47. We only include individuals in the sample who commence a fresh UI unemployment spell. However, all individuals in the sample are followed for as long as data permit. This means that we may observe several unemployment spells for each individual. Individuals who do not leave UI unemployment after programme participation are regarded as right censored and the remainder of the spell is not included in the sample.5 This is done in order to avoid mixing up the threat effect with effects from programme participation. Our spell measure is monthly observations. In order to construct the UI spells for the analysis, we have assumed that a spell consists of minimum two weeks of unemployment within a month. A spell is broken if an individual is not receiving benefits for more than two weeks in a month.6 The sample is described in Table 1. A total of 10,184 individuals appear in the sample. These individuals have a total of 27,010 UI spells between 1994 and 1998. A little more than half of these spells (52.7 percent) are fresh spells, meaning that the right to a new UI period has been

5

If individuals leave unemployment and subsequently become unemployed again, this following spell is included in the sample. 6 We do not have information on exit state of the individual. However, by restricting the analysis to males in the age group 25 to 47 years the overwhelming number of spells in the analysis ends in employment.

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Table 1 Descriptive statistics Variables Spouse = 1 Unskilled = 1 Up.sec./vocational = 1 Tertiary education = 1 Age (years) Spell length (months) No. of spell per individual Initial entitlement (months) ⁎

Min

25.00 0.53 1.00 −31.43

Max

Mean

st.dev.

51.00 53.53 16.00 47.46

0.60 0.31 0.47 0.15 37.05 3.83 2.42 35.73

6.82 5.51 1.64 13.74

Note: Number of individuals = 10,184. Number of spells = 27,010. Number of fresh spells = 14,234. Number of right censored spells = 1,495. Number of spells in July 1996 = 2,239. Number of spells in January 1998 = 2,733. ⁎ A negative number means that individuals have used up their passive period and commenced their unemployment spell x months into the activation period.

gained or regained prior to unemployment. On average, individuals have 2.4 unemployment spells in the sample period and the average duration of a spell is slightly less than 4 months. On average, individuals commencing an unemployment spell have approximately 36 months left until entering the activation period (denoted “initial entitlement” in Table 1). Out of the 27,010 spells in the sample 2,239 spells are located in July 1996 when the first cut in the passive period is implemented, and 2,733 are located in January 1998 when the second cut is implemented, cf. Section 2. One simple way of examining whether individuals are indeed treated differently in the UI system is by plotting the proportion of individuals in labour market programmes against the time

Fig. 1. Proportion of different educational groups enrolled in a labour market programme as a function of number of months remaining in the passive period, 1994–1998.

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they have spent on their entitled UI period for different types of individuals. This plot is displayed in Fig. 1 for different educational groups. The figure is based on a 10 percent sample of all UI recipients in the period from 1994 to 1998. Fig. 1 indicates that individuals with different education are not treated identically in the UI system.7 Especially individuals with a vocational, lower tertiary or intermediate tertiary education are located in the top when it comes to programme participation. In contrast, individuals with either no education, no education information or- to some extend- long tertiary education are the ones who have the lowest programme participation rate. 4. The econometric model In this section, we propose an econometric model of the threat effect of labour market programmes on job search. As we described in the previous Section in Fig. 1, the risk of programme participation is by no means uniform across UI benefit receivers. We therefore propose a model which describes individuals' risk of programme participation and use the estimated risk as an explanatory variable in a hazard model describing individuals' hazard of leaving the UI system. As indicated by Fig. 1, the risk of programme enrolment is in part a function of each individual's entitlement (the number of months remaining in the passive period) when they begin an unemployment spell. In the following, we denote this variable “initial entitlement”, cf. Ham and Rea (1987). Initial entitlement draws its variation from two sources. First of all, the passive period has been reduced from 4 to 3 years in mid 1996 and from 3 to 2 years in January 1998. Thus, initial entitlement varies across calendar time. Second, if individuals do not regain a fresh UI period between two unemployment spells, previous unemployment will be deducted from their initial entitlement. The first source of variation is exogenous but the second is not. In order to address this problem, individuals are only included in the sample when they commence an unemployment spell with full initial entitlement (also denoted “a fresh spell”). By following Gritz (1993) and van den Berg et al. (2002) we subsequently model their labour market history as long as they are observed in the data. By taking this approach we model the process of endogenous variation in initial entitlement.8 More specifically, our model consists of three endogenous variables, observed for J consecutive spells for each individual.9 1. The risk of leaving UI unemployment (END) 2. The risk of entering a labour market programme during the UI unemployment spell (LMP) 3. The probability of regaining the right to a fresh UI period between two unemployment spells (REGA)

7 The reason why some unemployed individuals participate in a programme before entering the activation period is that if the unemployed show no job search activity at all, case managers at the unemployment agencies are entitled to offer programme participation to these individuals. Non-compliance in this case induced a 20 percent reduction in benefits. 8 The model still suffers from an initial condition problem as described by Gritz (1993). 9 Note that J may vary by individual.

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The three endogenous variables are modelled using the following recursive structure. The first variable, the risk of leaving UI unemployment (END), is modelled using a logistic regression model, cf. Eq. (1).   s¼K P s exp a þ ds t þ k1 PðLMPtþ1 ¼1Þð1  LMPt Þ þ k2 LMPt þ k3 Xt þ uEnd  s¼1s¼K : Pð ENDt juEnd Þ ¼ P s 1 þ exp a þ ds t þ k1 PðLMPtþ1 ¼1Þð1  LMPt Þ þ k2 LMPt þ k3 Xt þ uEnd s¼1

ð1Þ

P 2 Duration dependency is modelled through a polynomial ( Ks¼1 dlk t s ) where t describes the duration of the UI spell in months. The threat effect of programme participation (λ1) is modelled as individuals' reaction to the hazard of being enrolled in a programme (P(LMP)). The variable is leaded one period in order to capture individuals' response to the risk of programme enrolment in the following month.10 In order to ensure that the estimated parameter only captures the threat effect, i.e. when individuals are not in a programme, P(LMP) is set to zero when individuals are in a programme (1-LMP). The parameter λ2 captures the effect of being in a labour market program. If λ2 is negative, it indicates a locking-in effect from programme participation. λ3 is a parameter vector capturing the effects of exogenous individual and institutional characteristics, Xt. Finally, uEnd captures any unobserved individual effect. In the following we suppress conditioning on observed variables on the left hand side for simplicity. The endogenous variable capturing individuals' risk of enrolment in a programme (LMP) is modelled using a logistic form. It is assumed to be a function of individual characteristics (X) such as education (cf. Fig. 1), the duration of the unemployment spell (t), and the number of passive period months individuals are entitled to at the beginning of their unemployment spell (IENT), cf. Eq. (2).   s¼J P exp b þ us t s þ g1 IENTt þ g2 CUTUt þ g3 Xt þ uLMP  s¼1s¼J  : ð2Þ Pð LMPt¼1 juLMP Þ ¼ P 1 þ exp b þ us t s þ g1 IENTt þ g2 CUTUt þ g3 Xt þ uLMP s¼1

The initial entitlement variable (IENT) is included in order to capture the effect from seniority in the UI system which, as indicated by Fig. 2, may affect the risk of programme enrolment. uLMP captures any unobserved individual effect. Furthermore, everyone who was unemployed in mid 1996 or January 1998 experienced a discrete cut in their UI entitlement period as dictated by legislation. These cuts are modelled by dummy variable (CUTU).11 Notice that this dummy variable only captures the effect for individual's unemployed when the cuts were implemented. They do not describe the cuts in entitlement for individuals who were not unemployed mid 1996

10 One could, in principle, test the dynamics of how P(LMP) works on probability of leaving unemployment. However, in practice, it is very difficult to ascertain the precise leading of P(LMP) as successive values of P(LMP) are highly serial correlated. 11 The dummy variables switch to 1 from 0 when the cuts are implemented (mid 1996 and January 1998). We thereby assume that individuals do not expect the cuts until they are implemented. Geerdsen (2003) tests various expectation models using the same data as in this study. Geerdsen finds that the expectation model applied in this study gives the best presentation of individuals’ behaviour.

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Fig. 2. The risk of participating in a labour market programme for individuals with different levels of education. Note: The hazard rates are calculated using the estimation results from above for a married individual who is 30 years of age in 1996, who begins his unemployment spell after mid 1996, and who at the beginning of the spell is entitled to 12 months of UI benefits in the passive period. They are furthermore among the 85 percent of the sample with mass points different from 0.

and January 1998. For this group the cuts are imbedded in the initial entitlement variable (IENT) for subsequent unemployment spells. Individuals' initial entitlement, IENTj, in a given spell is a deterministic function of initial entitlement in the previous unemployment spell, IENTj−1, and the duration of the previous unemployment spell Tj−1. Furthermore, it is a non-deterministic function of whether the unemployed individual has regained a fresh UI period. All together we have: IENTj ¼ A  REGAj þ ðIENTj1  Tj1 Þ  ð1  REGAj Þ;

ð3Þ

where A is the maximum attainable passive period entitlement and where REGAj denote the binary indicator of whether the individual regains a fresh UI period between unemployment spell j − 1 and j.12 As individual variation in REGA is potentially endogenous, we model whether individuals regain entitlement using a logistic regression model:   P REGAj ¼ 1juREGA ¼

expðg þ g1 Xj þ CUTNUj þ uREGA Þ : 1 þ expðg þ g1 Xj þ CUTNUj þ uREGA Þ

ð4Þ

Xj are observed covariate affecting the probability of regaining entitlement between unemployment spell j and j − 1. Due to the legislative cuts in the UI passive period in mid 1996 and January 1998, the duration of the UI period that individuals can regain differs over time.

12

Note that REGA1, the indicator between the first spell in the sample and the last spell prior to entering the sample is 1 for all observations, due to the selection criteria of the sample.

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This may influence individuals' labour market behaviour. The cuts in the UI period are modelled by dummy variables (CUTNU). Finally, uREGA captures any unobserved individual effect. The full model, Eqs. (1), (2) and (4), is entirely recursive, and the relation between the three endogenous variables (END, LMP and REGA) is non-linear. Identification is obtained from the variables CUTU and CUTNU. The Variable CUTU describes the cut in the passive period for individuals who were unemployed when the cuts were implemented. We believe that this variable has an exogenous effect on individuals' risk of enrolment (P(LMP = 1) in Eq. (2)). The variable CUTNU describes the cuts in the passive period for individuals who were not unemployed when the cuts were implemented. This variable, we believe, has an exogenous effect on individuals' probability of regaining a fresh UI period prior to unemployment (P(REGA = 1) in Eq. (4)). Since we model individuals' entire labour market history from they enter our sample and onwards, we also control for any selection bias from being either unemployed or employed when the cuts in the passive period were implemented. From Eqs. (1), (2) and (4) we construct the likelihood function contributions that include each individual's sequence of UI spells in the time period we analyse. In order to form a log-likelihood function only conditional on observed information, we integrate out the unobserved random effects. We do this by applying a discrete non-parametric multivariate distribution of the random effects. That is, the distribution of random effects is represented by a finite number of masspoints, uk ¼ fukEND ; ukLMP ; ukREGA g;

k ¼ 1; N ; K;

ð5Þ

where K is the number of mass-points, each associated with a particular probability, pk. Integration of the random effects then becomes a summation over a finite number of mass-points. Identification of the distribution of random effect is facilitated by the presences of multiple spells for each individual. Furthermore, van den Berg et al. (2002) show that this approach allows for correlated random effects between the different states in the model. This is further elaborated in Van den Berg (2001). Using the specified equations we reach the following full information log-likelihood function for n individuals, each with Ji number of unemployment spells and each spell having duration of Tij: LnL ¼

n X

K X i ln pk jJj¼1 ð1  PðREGAij jukREGA Þ1REGAij ÞPðREGAij jukREGA ÞREGAij

i¼1



k¼1 Tij jt¼1 ð1

 PðLMPijt ¼ 1jukLMP ÞÞ1LMPijt PðLMPijt ¼ 1jukLMP ÞLMPijt

 ð1  PðENDijt ¼ 1ÞjukEND Þ1ENDijt PðENDijt ¼ 1jukEND ÞENDijt

ð6Þ

where subscript “j” indicates the j'th spell for the i'th individual. Note that individuals' initial passive period entitlement (IENT) is constant for each spell. 5. Estimation results The estimation results of the model presented in Section 4 are presented in Table 2. We present parameter estimates for the entire model, that is, both the model for whether the individual regains the right to a fresh UI period, the probability of entering a labour market programme and the probability of leaving unemployment.

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Table 2 Estimation results

t t2 t3 Age Couple Primary education Upper secondary Vocational Short tertiary Int. tertiary. Long tertiary No educ. Inf. Unempl. Level Unempl. level2 Constant P(LMP) In LMP Unempl. Level3 Cut(July 96) Cut(Jan 98) Prev. UI ent. Prev UI ent.2 IENT IENT2 t⁎IENT IENT⁎Up. Secondary IENT⁎Vocational IENT⁎Short tertiary IENT⁎Int. tertiary. IENT⁎Long tertiary IENT⁎No educ. Inf. IENT⁎couple IENT⁎age t⁎upper secondary t⁎vocational t⁎short tertiary t⁎int. tertiary. t⁎long tertiary t⁎no educ. inf. t2⁎upper sec. t2⁎vocational t2⁎short tertiary t2⁎int. tertiary. t2⁎long tertiary t2⁎no educ. inf. t3⁎upper sec. t3⁎vocational t3⁎short tertiary t3⁎int. tertiary.

P(leaving the UI system)

P(regaining a fresh UI period)

P(participating in a LMP)

Coef.

Coef.

St.error

Coef.

St.error

0.004 0.301⁎⁎⁎ – − 0.457 0.039⁎⁎⁎ − 0.340⁎⁎⁎ − 0.487⁎⁎⁎ − 0.551⁎⁎⁎ − 0.454⁎⁎⁎ 5.050⁎⁎⁎ − 43.487⁎⁎⁎ − 23.556⁎⁎⁎

0.003 0.041 – 0.145 0.043 0.112 0.116 0.114 0.095 0.536 4.427 2.113

0.516⁎⁎⁎ − 5.176⁎⁎⁎ 6.815⁎⁎⁎ − 0.029⁎⁎⁎ 0.758⁎⁎⁎ – − 0.004 − 0.114 0.470⁎⁎ 0.848⁎⁎⁎ − 0.554⁎⁎ − 0.483⁎⁎⁎ 2.614⁎⁎⁎ − 23.280⁎⁎⁎ − 10.537⁎⁎⁎

0.018 1.002 1.614 0.007 0.088 – 0.232 0.126 0.236 0.233 0.227 0.166 0.248 1.854 1.115

11.661⁎⁎⁎ − 0.393⁎⁎⁎ 1.632⁎⁎⁎

1.186 0.052 0.081

6.504⁎⁎⁎ 0.582⁎⁎⁎ − 0.199⁎⁎⁎ 0.066⁎⁎⁎ 0.001⁎⁎⁎ − 0.040⁎⁎⁎ 6.199⁎⁎⁎ − 0.005⁎⁎⁎ 0.003 − 0.004 − 0.012⁎⁎ − 0.026⁎⁎⁎ 0.010⁎⁎ 0.001 − 0.010⁎⁎⁎ 0.000 0.068 0.164⁎⁎⁎ 0.215⁎⁎⁎ 0.134⁎⁎⁎ 0.038 − 0.026 − 5.076⁎⁎ − 8.430⁎⁎⁎ − 14.653⁎⁎⁎ − 7.396⁎⁎⁎ − 1.720 − 2.075 8.013⁎⁎ 11.974⁎⁎⁎ 25.538⁎⁎⁎ 10.106⁎⁎

0.451 0.047 0.060 0.009 0.000 0.008 0.723 0.000 0.005 0.003 0.005 0.005 0.005 0.004 0.002 0.000 0.043 0.023 0.046 0.041 0.040 0.033 2.516 1.409 3.075 2.477 2.415 2.035 3.993 2.320 5.580 3.984

− 0.233⁎⁎⁎ 8.713⁎⁎⁎ − 9.546⁎⁎⁎ − 0.005⁎⁎⁎ 0.237⁎⁎⁎ – − 0.292⁎⁎⁎ 0.112⁎⁎⁎ − 0.096⁎⁎ − 0.095⁎⁎⁎ − 0.124⁎⁎⁎ − 0.376⁎⁎⁎ 0.049⁎⁎ − 0.123 − 1.625⁎⁎⁎ 1.082⁎⁎⁎ − 0.343⁎⁎⁎

St.error 0.006 0.402 0.717 0.001 0.015 – 0.046 0.017 0.038 0.036 0.036 0.031 0.022 0.081 0.161 0.113 0.053

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Table 2 (continued)

t3⁎long tertiary t3⁎no educ. inf.

P(leaving the UI system)

P(regaining a fresh UI period)

P(participating in a LMP)

Coef.

Coef.

Coef.

St.error

St.error

1.682 6.502⁎

St.error 3.909 3.411

Note: “t” denotes the number of months have been unemployed in the given spell. The variables starting with “Primary education” and ending with “No educ. inf.” are education level dummies. “P(LMP)“ denotes each individual’s estimated probability of being enrolled in a LMP. “IENT” is the number of passive period UI months a person is entitled to at the beginning of the unemployment spell. ”Previous UI entitlement” is the number of passive period UI months a person was entitled to at the end of last previous unemployment spell. “Cut(July 96)” and “Cut(January 98)” are two dummy variables indicating whether a given observed unemployment month is located after these two months. “Unemployment level” denotes the general UI unemployment level at any given month in the sample. For reasons of computational accuracy the variable t2 is divided by 1,000, t3 is divided by 100,000, Unempl. level2 is dived by 100, and Unempl. level3 is divided by 1,000.

First, looking at the model for leaving unemployment, we find a large positive and significant effect from the probability of entering a labour market programme (P(LMP)). This indicates a strong threat effect from the prospect of entering a labour market programme. Furthermore, we find evidence of strong negative duration dependence (t, t2 and t3). We find negative age effects, and individuals with a partner (Couple) have a higher probability of leaving unemployment. We also find that individuals with vocational education (Vocational) have a higher probability of leaving unemployment and, somewhat surprisingly, that individuals with tertiary education have a lower probability of leaving unemployment. The reason for this result may be due to the fact that many individuals with no or vocational qualifications are only temporarily laid off and hence have a high turnover rate between employment and unemployment. Turning to the model for the probability of entering a labour market programme, we expect, as indicated by Fig. 1, highly non-proportional unemployment duration effects for individuals with different background characteristics. We therefore allow for a very detailed modelling of various interaction effects. It turns out that a substantial number of interaction terms are significant. The full implications of these effects are depicted in Fig. 2 below. We shall return to this. Finally, we look at the model for regaining a fresh UI period. Here we find that individuals with a partner (Couple) have a higher probability of regaining a fresh UI period, whereas individuals with upper secondary education (upper sec.) or academic educational qualifications (short, int. or long tertiary) have a lower probability of regaining a fresh UI period. At first sight, this might seem counter intuitive, but is probably due to two factors. First, as mentioned above, individuals with no or vocational qualifications typically operate on a labour market with a high turnover rate. Hence, these individuals meet more job openings, and it may therefore be easier for these individuals to regain a fresh UI period. Second, due to the fact that many individuals with academic qualifications are never unemployed, these individuals never enter unemployment and consequently neither our sample. In Table 3, we present the results for the discrete distribution of random effects. Our estimations resulted in two mass points.13

13

Further mass points have been tried without significant improvement of the fit of the model.

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Table 3 Estimation results Parameter

Estimate

Std. Error

u1REGA u2REGA u1LMP u2LMP u1END u2END P(u1END, P(u2END,

0 0.766 0 − 5.066 0 0.617 0.154 0.846

– 0.038 – 0.046 – 0.076 – 0.034

u1LMP, u1REGA) u2LMP, u2REGA)

Distribution of random effects.   eðaÞ where a is the estimated parameter. Note: P u2END ; u2LMP ; u2REGA ¼ 1 þ eðaÞ

From Table 3 we see that there is a small group of individuals (approximately 15 percent) who has a relatively low hazard of leaving UI unemployment (uEND), a relatively high probability of being in a labour market programme (uLMP), and a lower probability of regaining UI entitlement (uREGA). This means that the remaining approximately 85 percent of the population holds the adverse characteristics. The distribution of unobserved characteristics seems to indicate a cluster of individuals who have low employment prospects, but who also have a high risk of entering a labour market programme and another group with high employment prospect, but who also have a low risk of entering a labour market programme. If we had ignored these unobserved characteristics, we may had underestimated the threat effects as we would find that those with a high risk of programme enrolment (due to unobserved characteristics) also have a low escape rate out of the UI.

Fig. 3. The hazard of leaving unemployment with and without the threat effect calculated for two different types of individuals. Note: The hazard rates are calculated using the estimation results from above for individuals who begin their unemployment spell after mid 1996, and who at the beginning of the spell are entitled to 12 months of UI benefits before entering the activation period. They are furthermore among the 85 percent of the sample with mass points different from 0.

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We now return to the probability of entering a labour market programme. In Fig. 2, the estimation results have been used to calculate the enrolment risk for individuals with different levels of education. The individuals are assumed to have 12 months of passive UI entitlement left at the beginning of their spell, be married and thirty years of age when the spell commences. According to the estimation results, the risk of enrolment is close to zero the first six months of the spell for most individuals. From the sixth month and onwards the risk of programme enrolment increases dramatically for some individuals. This is especially the case for individuals with a short and intermediate tertiary education. After 14–15 months of unemployment (2– 3 months into the activation period), individuals with a short or intermediate tertiary education are almost certainly enrolled in a programme. Other groups such as individuals with either no education or a long tertiary education have a significantly smaller risk of programme enrolment. This indicates that case officers mainly target training programmes at individuals with a short or intermediate tertiary education, perhaps because they believe these groups will benefit the most from participation. The substantial differences in risk of programme enrolment indicate that a threat effect, if present, will not be located at the same point in the UI period for all individuals. We have calculated the hazard of leaving unemployment for different types of individuals. The hazard is calculated with and without the threat effect. The results are shown in Fig. 3 below: Fig. 3 reveals two things. First, the risk of programme enrolment does indeed affect individuals' hazard of leaving unemployment before they enter a programme. The estimated threat effect results in an up to 100 percent increase in the hazard out of unemployment. Second, the large differences found in the risk of programme enrolment lead to different patterns for the threat effect. For instance, young individuals with a short tertiary education experience the threat effect approximately 6 months earlier than older individuals with a long tertiary education. Furthermore, due to a relatively high risk of programme enrolment, young individuals with a short tertiary education experience a lift in the hazard out of unemployment which is much stronger than what we find for individuals with a long tertiary education. 6. Conclusion First, the results clearly indicate that there is a significant and substantial variation in the risk of enrolment in a labour market programme. This variation is found for individuals who should have the same risk according to labour market regulations. Hence, knowledge of institutional factors is not sufficient when trying to capture threat effects from labour market programmes. Models of the threat effect based on legislation alone may risk downward biased estimates. Second, we find a significant and strong threat effect. A 50 percentage point increase in the risk of enrolment results in an approximately 50 percent increase in the hazard of leaving unemployment. The size of this effect is comparable with the effect found in studies of individuals' reaction to the prospect of benefit exhaustion (Meyer, 1990; Rogers, 1998). This indicates that it is indeed possible to obtain a strong incentive effect without inflicting a large income drop on individuals who cannot find employment. Finally, we find that it is important to take into account the potential endogeneity of entitlement and the risk of participation, when modelling the incentive effects of compulsory programme participation.

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Acknowledgments This paper has benefited from comments by Bo Honoré, Martin Browning, and Michael Rosholm. We would like to thank the Centre for Applied Microeconometrics and the Danish National Institute of Social Research for research support and access to data. In addition, we would also like to thank the Danish Ministry of Employment for financial support. The results and views expressed in this paper are solely those of the authors. References Arbejdsdirektoratet, 2005, Rådighedsstatistikken 2004. Rådigheden hos forsikrede ledige”. Bjørn, N.H., Geerdsen, L.P., Jensen, P., 2004. The threat of compulsory participation in active labour market programmes —a survey. Working paper. Black, D.A., Smith, J.A., Berger, M.C., Brett, J.N., 2003. Is the threat of reemployment services more effective than the services themselves? Evidence from random assignment in the UI system. American Economic Review 93, 1313–1327. Geerdsen, L.P., 2003. Marginalisation processes in the Danish labour market. PhD thesis. The Danish National Institute of Social Research. 03:24. Geerdsen, L.P., 2006. Is there a threat effect of labour market programmes? A study of ALMP in the Danish UI system. Economic Journal 116, 738–750. Gerfin, M., Lechner, M., 2002. A microeconometric evaluation of the active labour market policy in Switzerland. The Economic Journal 112, 854–893. Gritz, R.M., 1993. The impact of training on the frequency and duration of employment. Journal of Econometrics 57, 21–51. Ham, J.C., Rea, S.A., 1987. Unemployment insurance and male unemployment duration. Canada Journal of Labor Economics 5, 325–353. Meyer, B.D., 1990. Unemployment insurance and unemployment spells. Econometrica 58, 757–782. Richardson, L.L., 2002. Impact of the mutual obligation initiative on the exit behaviour of unemployment benefit recipients: the threat of additional activities. The Economic Record 78, 406–421. Rogers, C.L., 1998. Expectations of unemployment insurance and unemployment duration. Journal of Labor Economics 16, 630–666. Statistics Denmark, 2004. Homepage. www.dst.dk. Van den Berg, G.J.A., 2001. Duration models: specification, identification and multiple durations. In: Heckman, J.J., Leamer, E. (Eds.), Handbook of Econometrics, vol. 5, pp. 3381–3460. van den Berg, G.J., Holm, A., van Ours, J., 2002. Do stepping stone jobs exist? Early career paths in the medical profession. Journal of Population Economics 15.