The response of youth unemployment to benefits, incentives, and sanctions

The response of youth unemployment to benefits,

incentives, and sanctions

Peter Jensena,b, Michael Rosholmc,*, Michael Svarerc

aThe Danish National Institute of Social Research, DenmarkbDepartment of Economics, Aarhus School of Business, Prismet, Silkeborgvej, 8000 Arhus, Denmark

cDepartment of Economics, University of Aarhus, Building 322, University Park, 8000 Arhus, Denmark

Received 21 April 2001; received in revised form 8 March 2002; accepted 28 May 2002

Abstract

The decline in the youth unemployment rate in Denmark is nearly unique among OECD countries

and merits study. In 1996, a radical labour market reform was implemented, the Youth

Unemployment Programme (YUP), directed towards unemployed, low-educated youth. This paper

analyses the effects of the implementation of the YUP. We investigate the duration of unemployment

spells and the transition rates from unemployment to schooling and employment. Three effects are

analysed: an announcement effect, a direct programme effect, and a sanction effect. We find that the

YUP has been partially successful.

D 2003 Elsevier Science B.V. All rights reserved.

JEL classification: C41; I21; J64; J65

Keywords: Youth unemployment; Education; Grouped duration model; Competing risks

1. Introduction

Youth unemployment is a source of considerable concern in many countries. Very few

countries have managed to reduce youth unemployment which remains a chronic problem

in many countries. Hence, it is of interest to look at the experiences of the countries that

have succeeded in reducing youth unemployment. Since 1993, the Danish youth

unemployment rate has declined dramatically, which is a unique experience among OECD

0176-2680/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0176-2680(02)00171-4

* Corresponding author. Tel.: +45-8942-1559; fax: +45-8613-6334.

E-mail address: [email protected] (M. Rosholm).

www.elsevier.com/locate/econbase

European Journal of Political Economy

Vol. 19 (2003) 301–316

countries. The only country experiencing a similar decline in youth unemployment is the

Netherlands. In this paper, we consider the lessons that can be learned from the Danish

case.

In 1994, a radical labour market reform was implemented in Denmark. Among the

main elements of this reform were improved targeting of the active labour market

programmes and an increase in the speed with which unemployed workers were activated.

This reform was aimed at the entire group of long-term unemployed workers. In 1996, an

even more radical reform was implemented, the Youth Unemployment Programme

(henceforth YUP), which was directed especially towards unemployed, low-educated

youth. The overall purpose of this reform was twofold: to strengthen the employment

possibilities for unemployed, low-educated youth, and to provide motivation for them to

undertake an education. The policies are considered by the European Commission as best

practices (see OECD, 1998) and have attracted substantial interest outside Denmark.

Young persons under the age of 25 without any formal education beyond secondary

school, and who have been unemployed for 6 months during the last 9 months, are offered

18 months of specially designed vocational education. Since unemployment benefits are

cut by 50% while in the special education programme, this offer contains an incentive to

undertake ordinary education on public study grants or to find a job. Refusal to participate

in the special education programmes or to enter the ordinary education system is followed

by a sanction, through a total loss of unemployment benefits.

The YUP is thus supposed to work through a combination of benefits, incentives and

sanctions. Generally, the results of active labour market programmes have been mixed.

Heckman et al. (1999) provide an overview. See Lalive et al. (2000) and van Ours (2000)

for some recent analyses of active labour market programmes. The effects of unemploy-

ment benefits on the exit rate from unemployment have been the subject of a vast amount of

literature. For some notable examples, see Atkinson et al. (1984), Narendranathan et al.

(1985), and McCall (1996); and for a very recent analysis, see Carling et al. (1999). The

effects of sanctions have been investigated by Abbring et al. (1996), van den Berg et al.

(1998), and Boone and van Ours (2000). The main lessons to be learned from previous

research are that the effects of benefits are not very robust, whereas there appear to be

strong incentive effects of sanctions. If anything, the results in the literature indicate that the

effects are stronger for youth (see Narendranathan et al., 1985; Carling et al., 1999). This

paper sheds further light on how young unemployed persons react to financial incentives.

The purpose of the paper is to analyse the immediate effect of the implementation of the

YUP on the transition rate out of unemployment. The decline in the Danish youth

unemployment rate since 1993 can be seen in Fig. 1, which plots time series of seasonally

adjusted monthly youth and overall unemployment rates. During the same period, the

overall unemployment rate also decreased. However, this decline is much smaller than the

decline in youth unemployment, and the gap between youth and overall unemployment

rates became noticeably wider after the implementation of the YUP in April 1996.

Compared to the overall unemployment rate, the fall in the youth unemployment rate has

been more profound. This suggests that there may have been an effect on the aggregate

youth unemployment rate. Whether this pattern is due to the YUP or is simply a

consequence of youth unemployment being more cyclically sensitive than the aggregate

unemployment rate, and consequently decreasing faster during boom periods, is not

P. Jensen et al. / European Journal of Political Economy 19 (2003) 301–316302

apparent from the figure. It is, however, remarkable that the youth unemployment rate in

2002 was considerably lower than the overall unemployment rate.

The YUP was gradually implemented during 1996 and during this period not everyone

was eligible for participation. This quasi-experimental feature of the implementation

allows us to define a control group, consisting of those who are eligible but did not

participate, against which the behaviour of those who participated could be measured. We

investigate the immediate effect of the implementation of the YUP on the transition rate

out of unemployment. By examining the time unemployed individuals spend in unemploy-

ment, and the states they enter after unemployment, we investigate three effects: an

announcement effect, a direct programme effect, and a sanction effect.1

1.1. The announcement effect

We investigate whether individuals who were at risk of being affected by the YUP

behaved differently than those not at risk. Individuals in the target group receive written

information about the YUP after 4–5 months of unemployment. Given appropriate

controls, we can thus determine the change in transition intensities into employment or

ordinary education induced by the announcement.

1.2. The direct programme effect

This effect is experienced by individuals who leave unemployment to obtain an

ordinary education or to participate in the specially designed vocational education

1 A stringent definition of the effects will be given with the presentation of the econometric model in

Section 3.

Fig. 1. Youth and overall unemployment rates in Denmark (1980–2002).

P. Jensen et al. / European Journal of Political Economy 19 (2003) 301–316 303

programme (VEP). We want to answer the question: Does the start of the YUP affect the

transition rate from unemployment to schooling (either ordinary education or VEP), or was

the newly created VEP just a substitute for ordinary (vocational) education, which would

have been undertaken by the youths anyway? Results by Nord-Larsen (1997) suggest that

there is a positive direct programme effect, but due to the sampling frame and the timing of

interviews, it is difficult to identify the effect of the YUP from a seasonal effect without

using econometric models. A strong seasonal effect is expected to be present, since most

ordinary education starts in August and September.

1.3. The sanction effect

Finally, we analyse the effect from the removal of unemployment benefits after 6

months (if an offer is not accepted) on transitions from unemployment to other states in the

labour market. Of course, the direct programme effect and the announcement effect are to

some extent also a sanction effect, because the threat of being sanctioned is implicit.

Hence, we should be careful in the interpretation of our results, and we cannot expect to

identify all three effects separately.

The paper proceeds as follows. In Section 2, we describe the data used for the analysis.

In Section 3, we present the econometric methodology. Our estimation results are

presented in Section 4. Section 5 concludes the paper.

2. Data

The sample consists of two groups, called the initial group and the accession group.

The initial group contains individuals who may fulfil the criteria for participating in the

YUP from the implementation in April 1996, whereas the accession group contains

individuals who may fulfil the criteria later in 1996. Data was collected by interviewing

approximately 3500 individuals aged 16–24 from April 1996 to December 1996.

The interviews were carried out by the Danish National Institute of Social Research in

cooperation with the National Labour Market Authority. The first interviews with the

initial group were conducted in April 1996. The initial group consists of a sample of

individuals without any formal education beyond secondary school, who had been

unemployed for approximately 3 months in January 1996. If these individuals were still

unemployed 3 months later, in the beginning of April, they would fulfil the criteria for

participating in the YUP. One thousand and five hundred individuals were selected for

interview and for 77% of these (1166 individuals), an interview was obtained. They were

reinterviewed in August 1996. In the initial group, 1/3 of those eligible for an offer were

supposed to receive one in the second quarter of 1996, 1/3 in the third quarter, and 1/6 in

each of the following two quarters. We are therefore able to construct a group of people

who do not receive an offer (controls) and a group of people who do (treatments). Those in

the treatment group receive written notification of their participation in the programme

after approximately 4 months of unemployment. We assume that those in the control group

are unaware of the fact that they will later become treatments. This is obviously a

debatable assumption, but they were not given any direct individual notification of their


later eligibility for the programme, and given the complexity of the Danish system of

active labour market policies, we think it is justifiable. If the assumption is violated,

individuals in the control group may react by increasing their search intensity early on in

order to avoid the reduced unemployment benefits.

In principle, the allocation of individuals into treatments and controls is random.

However, we suspect that the allocation is not purely random.2 First of all, there may be

some positive self-selection since the local employment offices are allowed to select those

who ask for an offer. Secondly, there may be negative selection by the employment offices,

since they attempt to treat those with most prior unemployment first. Therefore, we take

the selection process into account in the econometric specification.

As concerns the accession group, approximately 333 individuals were chosen for

interview each month from July 1996 to December 1996. Individuals were selected for an

interview if 2 months earlier they had an unemployment record of at least 4 months. This

group consists of 1565 interviewed individuals. All of these individuals were affected by

the YUP in the sense that all those becoming eligible for an offer would receive one, and

refusal to participate would lead to the removal of unemployment benefits.

For each individual, we have information about labour market transitions occurring

between the time of selection (after 3–4 months of unemployment) and the time of the

interview (2–3 months later), and about personal characteristics (age, gender, number of

children, education, ethnic status, etc.). We also know whether the individuals have

received an offer from the labour market office to participate in the YUP. This information

enables us to divide the initial group into a treatment and a control group. However, among

those in the control group, some become treatments later on as the next 1/3 is being treated

3 months later. This is accounted for in the estimation procedure by allowing the treatment

group indicator to be time-varying. In the accession group, everyone belongs to the

treatment group.

In order to make the samples homogeneous with respect to initial conditions, we choose

from the initial group only those who have experienced at least 4 months of unemploy-

ment. For the initial group, we thus have an event history for each individual covering the

period February–August 1996, i.e. a 7-month period. For the accession group, we have

event histories covering May–July 1996, June–August 1996, . . ., October–December

1996, that is, 3-month periods. The event histories record the month where a specific

transition is made, but not the exact date. Therefore, we have what is commonly known in

duration analysis as grouped durations. Selecting only the first unemployment spell for

each individual, and conditioning on non-missing treatment indicators, gender indicators,

etc., leaves us with a total of 1808 spells of unemployment.

2.1. Sample characteristics

In Table 1, we present sample means of individual characteristics for three distinct

groups: (1) those in the initial group who are treatments, (2) those in the initial group who

are controls, and (3) the accession group (treatments).

2 This suspicion arose through phone conversations with officers at selected employment offices.


The figures in Table 1 indicate that, to some extent, the local employment offices have

chosen the treatment group from among those of the unemployed who have the highest

amount of past unemployment and the lowest level of education. The sample character-

istics also indicate that the YUP has an effect on the transition from unemployment to

schooling. For both treatment groups (columns 1 and 3) the number of unemployment

spells ending with a transition to schooling exceeds the number of unemployment spells

ending with a transition to employment, whereas the opposite is true for the control group.

A more detailed look at the data reveals that many transitions occur between the

different labour market states.3 Both for the initial group and for the accession group, there

is a large reported fall in sample unemployment during the period for which we have event

histories. Large fractions of the unemployed youths have moved into schooling or

employment, e.g. only 20% of the initial group is still unemployed by August 1996,

while 37% is in education and 31% is in employment. However, such descriptive statistics

do not provide any information about the contribution from the YUP to these transitions.

First of all, there is a strong seasonal effect, because of ordinary education starting in

August or September. It is also very likely that transitions into employment are very low

during July, which is the traditional vacation month. Since the initial and accession groups

are observed over different seasons, any perceived difference may be due to seasonal

effects rather than effects of the YUP. Secondly, there may be business cycle effect

working in the same direction; since there was a general improvement in employment

possibilities over the period, those becoming unemployed later have better opportunities

for finding jobs. Thirdly, it is possible that there are seasonal composition effects in the

inflow into unemployment. It could be that, for some reason, those belonging to the initial

group on average have ‘worse’ (or ‘better’) characteristics that those in the accession

group.

3 A descriptive analysis of the data was previously presented by Nord-Larsen (1997).

Table 1

Sample means of individual characteristics

Initial group Accession group

Treatments Controls

Duration of unemployment (months) 7.99 8.23 6.29

Women 0.49 0.51 0.50

Age (years) 23.02 23.15 22.86

Length of education (years) 9.83 10.17 10.19

Missing information on education 0.05 0.03 0.02

One child 0.17 0.13 0.12

Two or more children 0.06 0.04 0.04

First-generation immigrant 0.07 0.04 0.08

Second-generation immigrant 0.09 0.06 0.09

Amount of past unemployment (years) 1.49 1.39 1.10

Number of observations 301 520 987

Number of right-censored observations 70 160 394

Number of transitions into schooling 122 166 334

Number of transitions into employment 109 194 259


From this discussion, it is obvious that any clear-cut conclusions concerning the effects

of the reform on transition rates out of unemployment must be based on a carefully

designed econometric analysis. Econometric issues are discussed in Section 3.

2.2. Empirical hazard rates

To begin our analysis, we estimated empirical hazard rates for the different groups in

order to obtain an idea of the duration dependence and a non-parametrical view of the

effect of the YUP. Fig. 2.1 presents the empirical Kaplan–Meier hazard rates for the initial

group leaving unemployment for schooling.4 For the treatment group, there is a sharp

increase in the hazard rate in the sixth month (corresponding to April in calendar time),

indicating the beginning of the YUP. The hazard rate bounces back in months 7, 8, and 9,

and in month 10 (corresponding to August, the month in which education usually begins)

both hazard rates increase dramatically.

Fig. 2.2 shows the corresponding hazard rates for transitions from unemployment to

employment, which increase for both treatment and control groups in the sixth month, but

mostly so for treatments. There is then a gradual fall for both groups until month 10, where

4 The Kaplan–Meier estimator counts in each duration interval the number of exits into for example

schooling, and divides with the number of individuals observed to have been unemployed for at least that long.

See Lancaster (1990) for details.

Fig. 2.1. Unemployment–schooling hazard rate, the Kaplan–Meier hazard rates and the 95% confidence bands

are calculated due to Lancaster (1990).


increases take place. This may be a seasonal effect, since months 8 and 9 are June and July,

which are holiday months.

The empirical hazard rates for the accession group (not shown) point towards a

profound seasonal effect in August and September in the transition rate into schooling, and

towards a positive business cycle effect increasing the transition rate into employment

throughout the period.

3. Econometric model

In order to analyse the immediate effect of the implementation of the YUP on the

transition rate out of unemployment, we specify a competing risks duration model. We

model the destination specific transition rates out of unemployment as mixed proportional

hazard functions. The hazard is then a product of a function of time being unemployed (the

baseline hazard), a function of observed, possibly time-varying, characteristics, xt, and a

function of unobserved characteristics, v;

hiðt j xt; viÞ ¼ kiðtÞexpðxtbiÞvi; ð1Þ

where i= e,s indicates whether individuals leave unemployment for schooling or employ-

ment, ki(t) is the baseline hazard and ui(xt) is the scaling function specified as exp(xtbi).Allowing for a flexible baseline hazard specified separately for treatments and controls,

Fig. 2.2. Unemployment–employment hazard rate, the Kaplan–Meier hazard rates and the 95% confidence

bands are calculated due to Lancaster (1990).


and time-varying explanatory variables enable us to analyse the announcement effect, the

direct programme effect, and the sanction effect. The announcement effect is defined as the

difference in the baseline hazards for treatments and controls in the first month of

observation (i.e. where the unemployment duration is between 4 and 5 months). The

direct programme effect is defined as the difference in the baseline hazards for treatments

and controls in the third month of observation. Finally, the sanction effect is defined as the

difference in the baseline hazards for treatments and controls in the fifth to the seventh

month of observation.5

3.1. Grouped duration hazard model

Due to the grouped nature of the duration data, we specify a model for grouped duration

data (see e.g. Kiefer, 1990). The unemployment duration T is observed to lie in one of K

intervals, with the k’th interval being (tk� 1;tk] and the convention t0 = 4.6 Let ji (i = e,s)

denote the destination state indicator. If the unemployment spell is right-censored,

je= js = 0. The probability that the duration T for an individual with explanatory variables

xt is greater than tk given that the duration is greater than tk� 1 is given by:

PðT > tk j T > tk�1; xt; vÞ ¼ exp �Z tk

tk�1

hðt j xt; vÞdt� �

: ð2Þ

The interval-specific survivor expression (Eq. (2)) is henceforth denoted ak. The proba-

bility of observing an exit out of unemployment in interval k is consequently 1� ak.Introducing competing risks and assuming that xt is constant in each interval, we find that

the probability of surviving the k’th interval given survival until tk� 1 may be expressed as

akðxk ; vÞ ¼ exp �Z tk

tk�1

heðt j xk ; veÞdt �Z tk

tk�1

hsðt j xk ; vsÞdt� �

¼ exp½�exp½xkbe�veKe;k � exp½xkbs�vsKs;k � ¼ ae;kas;k ð3Þ

where Ki;k ¼ mtktk�1kiðtÞdt, and ai,k = exp (� exp (xkbi) vi Ki,k), for i= e,s, and k = 1,. . ., K.

The probability of leaving unemployment in interval k for destination i is (1� ai,k) (seee.g. Moon, 1991 for details). If we do not specify a functional form for the baseline hazard,

the Ki,ks are just parameters to be estimated.7

6 The likelihood function employed is thus conditional on at least 4 months of unemployment.7 The same modelling approach is employed by Carling et al. (1996) for a similar issue.

5 Notice that, due to the grouped nature of the duration data, we need to employ these rather strict definitions

of the three effects. In particular, we do not want to include the differences in the baseline hazards in the second

and fourth months in the definitions. In the second month, there may be both announcement and direct

programme effects, while there may be both direct programme and sanction effects in the fourth month. In fact,

the difference in baseline hazards for treatments and controls in any given month may be a combination of various

effects. The interpretations should keep this in mind.


3.2. Likelihood function

In order to account for the possible selectivity in the allocation of individuals into the

treatment and control groups, we simultaneously model the selection process and the

transition rates out of unemployment. Failure to control for selectivity may result in biased

parameter estimates (see e.g. Heckman et al., 1998). Therefore, we define an indicator for

the selection, D, taking the value 1 when an individual belongs to the treatment group and

0 otherwise. The selection may depend on explanatory variables, xd, and an unobserved

component, vd. The selection process is specified as a logit model, i.e.

P ¼ PrðD ¼ 1 j xd ; vdÞ ¼exp½xdbd þ vd�

1þ exp½xdbd þ vd�:

The individual contribution to the likelihood function is then

L ¼Z Z Z

Pdð1� PÞ1�d : ð4Þ

ð1� ae;kÞjeð1� as;kÞjsa1�je�jsk

Yk�1

l¼1

algðvd; ve; vsÞdvddvedvs

where g(vd,ve,vs) is the joint probability density function of the unobservables.

To specify the distribution of the unobservables, we impose two restrictions:

A1 . Each of the vi, i= d,e,s, follow a discrete distribution with two points of support, vi1

and vi2.

A2 . ve and vs are perfectly correlated.

The first assumption implies that we are using the approach described by Heckman

and Singer (1985) for a given number of points of support. The second assumption

restricts the correlation between the unobservables in the two competing risks to be either

� 1 or 1.8

In order to identify the model, we impose exclusion restrictions. Specifically, we use

additional variables in the selection equation. These are an indicator for whether the

individual lives with his/her parents, an indicator for whether the individual is a home-

owner, and a set of county dummies.

The estimation results show that there is unobserved heterogeneity present in the se-

lection equation. However, the correlation between the unobserved variables in the

selection equation and the duration equation is estimated to 0.022, and therefore none

of the coefficient estimates in the duration model change when the selection process is

ignored. For reasons of efficiency, we have therefore chosen to report the estimates (and,

8 Actually, as shown by Carling and Jacobson (1995), the more general model with unrestricted correlation is

theoretically identified. However, in the empirical application the estimation results implied perfect correlation,

hence perfect correlation was imposed in the final estimations. For additional identification results, see Heckman

and Honore (1989) and Abbring and van den Berg (2000).


in particular, the standard errors) from the estimation where the selection process has not

been included.9

4. Results

We only report results where we have corrected for the presence of unobservables.10

The baseline hazards are depicted in Figs. 4.1 and 4.2. The graphs are based on the

maximum likelihood estimates of the parameters for the integrated baseline hazard, Ki,k.

The baseline parameters are calculated as the expected hazard rate, where the expectation

is with respect to the unobservables’ distribution. From Fig. 4.1 it is clear that the

implementation of the YUP has a significant positive effect on the hazard rate from

unemployment to schooling in the 6–7-month interval. This effect is present even after

correcting for seasonal effects and for the observable and unobservable characteristics of

the unemployed. Hence, we find evidence of a strong direct programme effect of the

implementation of the YUP. After the seventh month, the baseline hazard is not

9 The full set of estimation results are available on request from the authors.10 Estimation results where no correction has been made for the presence of unobservables are available

on request from the authors. The same applies to estimation results that are only reported as graphs in the

paper.

Fig. 4.1. Unemployment–schooling hazard, (interval-specific integrated baseline hazard rates with 95%

confidence bands. Due to the unit length intervals, this corresponds to the average baseline hazard).


significantly different from the baseline hazard in the pre-YUP period. However, there is a

strong increase in the baseline hazard for the treatments after the ninth month, indicating a

sanction effect, although it is very imprecisely estimated due to few observations in this

interval. In Fig. 4.2, we find the same overall pattern for the hazard rates from

unemployment to employment, but the direct programme effect in the 6–7-month interval

is not significant in this case (at a 5% significance level). We also see an increase in the

baseline hazard for the controls in the 6–7-month interval, which may reflect the fact that

the controls are not fully aware of their position, but fear that they could also be subjected

to a sanction or be pushed into an educational programme (even though they have not

received any advance notice).11 Figs. 4.1 and 4.2 show no evidence of an announcement

effect of the YUP, since the baseline hazards for treatments and controls are not

significantly different in the 4–5- and 5–6-month intervals.

Table 2 presents the maximum likelihood estimates of the coefficients of the

explanatory variables in the competing risks model. As described earlier, the distribution

of the unobservables is specified as a discrete distribution with two points of support for

each destination state. In addition, the unobservables in the two destination states are

restricted to be perfectly correlated. In the present context, our estimation results imply

that persons with a high value of unobserved characteristics have this for both destination

states (the correlation is equal to 1). P(ve1,vs

1) then gives the fraction of persons with high

11 This would produce a downward bias in the difference between controls and treatments.

Fig. 4.2. Unemployment–employment hazard, (interval-specific integrated baseline hazard rates with 95%

confidence bands. Due to the unit length intervals, this corresponds to the average baseline hazard).


values of unobserved characteristics in both competing risks. The estimated parameters of

the unobserved heterogeneity distribution show that the hazard rate for the second group is

0, indicating that the persons in this group will never experience a transition. During the

estimation, the low value of the unobserved characteristic went towards minus infinity

(implying a hazard rate of 0 for the second group). The final estimation was therefore done

with this hazard rate set to 0.12

The transition rate from unemployment to schooling is significantly negatively affected

by length of education, which is in line with the a priori expectations given the purpose of

the YUP. In addition, being a woman and the presence of one child has a major impact on

the hazard rate from unemployment to schooling. For the monthly indicators, the ex-

pected pattern emerges, i.e. a strong positive effect on the hazard rate in August and

September.

For the transition from unemployment to employment, there is a significantly negative

effect on the hazard rate from length of education and the amount of past unemployment.

Regarding the monthly indicators, the correction for unobserved characteristics implies a

positive, and to some extent, increasing effect over the calendar year. The latter indicates

that not correcting for unobservables induces a negative bias on the effect of the improved

employment possibilities during 1996. This improvement is primarily exploited by

persons with high levels of unobserved characteristics.

12 Similar empirical results (with v =�l) are found by Lalive et al. (2000) and van Ours (2000).

Table 2

Maximum likelihood estimates of coefficients of explanatory variables

Unemployment to schooling Unemployment to employment

Coefficient Standard error Coefficient Standard error

Women 0.2102 0.1082 0.0983 0.1038

Age 0.1353 0.3516 0.5944 0.3392

Length of education � 0.5069 0.2399 � 1.4619 0.3978

Missing information on education � 0.0012 0.1438 � 1.5861 0.5561

One child 0.2906 0.1510 � 0.1162 0.1706

Two or more children � 0.4185 0.4121 0.0142 0.2622

First-generation immigrant � 0.1062 0.2349 � 0.5303 0.2547

Second-generation immigrant � 0.0269 0.2053 0.0912 0.2083

Amount of past unemployment � 0.4817 0.7234 � 4.0666 0.7921

May 1.6052 0.3856 1.4286 0.3265

June 1.3186 0.3223 1.2142 0.2592

July 0.1226 0.3234 0.3595 0.2446

August 2.3609 0.1864 0.9762 0.2178

September 2.4913 0.2067 1.6637 0.2107

October 1.5767 0.2566 1.4615 0.2121

November 1.9197 0.2944 1.7960 0.2494

December 2.9058 3.9524 4.4344 27.9679

P(ve1,vs

1) 0.6516 0.0165

Log-likelihood value � 3127.2


5. Conclusion

The main result of this analysis is that the transition rate from unemployment to

schooling is significantly raised by the YUP. This happens mainly through a direct

programme effect and to a smaller extent through a sanction effect. These effects are found

after correcting for the strong seasonality in the transition rate from unemployment to

schooling. In addition, we find somewhat weaker effects on the transition rate from

unemployment to employment. We find no evidence of an announcement effect of the

YUP.

These results of our analysis are closely related to the findings of previous research on

the behaviour of unemployed workers who approach the date of benefit exhaustion.

Carling et al. (1996) find some evidence that the transition rate from unemployment rises

just before benefits are exhausted in Sweden, and Katz and Meyer (1990) find the same for

the US. For Sweden, there is also a very pronounced increase in the transition rate to

labour market programmes around the time of benefit exhaustion. This corresponds to our

result regarding transitions into schooling.

Our results are subject to a number of qualifications. First, it is difficult to identify all

three effects separately. Hence, even though we have shown that the YUP has a significant

impact on the transition rates, this may be a consequence of a combination of various

effects intertwined in a complicated manner. We have, however, labelled the effects

according to the time intervals in which they occur. Secondly, we have addressed a number

of econometric problems in our estimations. Since we are using quasi-experimental data, it

is important to allow for selection into the treatment group. There are difficult selection

problems that do not have ideal solutions. We have chosen to assume that certain variables

only affect the selection decision. In the same way, we have chosen to model the strong

seasonality in our data by allowing the seasonal effects to be fully flexible across time, but

to be restricted to be equal for the control and the treatment group. Both of these choices

may, of course, influence the results. Nevertheless, we are confident that our results

provide evidence that the YUP has been, at least partially, successful in lowering youth

unemployment in Denmark.

Whether the YUP should be judged as a success may be discussed further. In the sense

that the programme is only shifting young people away from ‘‘waiting on the dole’’ to

‘‘waiting in the classroom’’ it is not necessarily successful. Clearly, a significant increase

in the transition to employment would have been more satisfactory. However, the possible

‘‘scarring’’ effect of unemployment suggests that it could be welfare-improving to move

the youth out of unemployment and into the classroom. Thereby, the long-term con-

sequences of unemployment may be mitigated. In fact, the Danish YUP was made

politically viable by the combination of benefits, incentives and sanctions, combined with

a strong belief that training is beneficial and improves long-term employment possibilities.

A lesson that emerges from the Danish case, and which is also a common point for

Denmark and the Netherlands, is that a successful strategy for reducing unemployment

includes the implementation of a broad policy package (see Elmeskov et al., 1998 for an

illuminating discussion). The Danish experience illustrated in our analysis should there-

fore be applicable to other countries as well, but should be part of a more comprehensive

labour market reform. Finally, it should be noted that we have only evaluated the short-run


effects of the YUP. The long-run effects on subsequent labour market histories will have to

await future data collection.

Acknowledgements

Financial support from the Danish National Research Foundation is gratefully

acknowledged. Dennis Andersen, formerly of the Danish National Institute of Social

Research, was a co-author on an earlier version of the paper. He is thanked for his

contributions to the paper. We are also grateful to Per-Anders Edin, Nina Smith, Editor

Arye L. Hillman and three anonymous referees for very useful comments. The Danish

National Institute of Social Research and the National Labour Market Authority are

thanked for making the data available.

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The response of youth unemployment to benefits, incentives, and sanctions

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