Active labor market policy by a profit
maximizing firm
Ruud Gerards∗ , Joan Muysken§ and Riccardo Welters¶
December 20, 2009
Abstract
A wide body of literature reports that public efforts to lead the unemployed back
to work are not very successful. This paper studies the Philips Employment Scheme
(PES), a private sector program to help long-term unemployed back to work. For the
period 1999-2005 we compare the PES to the public re-integration efforts and we find
that the PES has a relatively low deadweight loss and a high net treatment effect. It is
more effective than public re-integration efforts. Furthermore we find that even with a
heterogeneous mix of participants, the chance of finding a job after the PES does not
vary much between participants. In addition we have explored the notion that Philips
might make use of the PES as an additional recruitment channel for regular vacancies
within the company, and find evidence that seems to support this. Finally we point
out some interesting avenues for further research.
JEL codes:
Keywords: Active labor market policy, policy evaluation, Philips Employment Scheme,
unemployment.
∗ Department
of Economics,
NSI and CofFEE-Europe,
Maastricht University;
email:
[email protected].
Corresponding author, Department of Economics, Maastricht
University, P.O. Box 616, NL-6200 MD Maastricht, The Netherlands. Phone: +31 43 3883821 Fax: +31 43
3884150.
§ Department of Economics and CofFEE-Europe, Maastricht University. P.O. Box 616, NL-6200 MD
Maastricht, The Netherlands.
¶ School of business, James Cook University. QLD 4811, Townsville, Australia.
2
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Gerards/Muysken/Welters
Introduction
In the last decades governments have tried a wide variety of policies to reduce unemployment
and increase employment. These policies range from tax incentives on both demand and
supply side to subsidized public jobs creation, changes in unemployment benefit generosity
and reforming institutions. To combat unemployment governments since the mid 1990s
increasingly used so called ‘active labor market policies’ (ALMPs). These policies attempt to
stimulate the unemployed to take a more active role in their return to employment. However,
an increasing amount of literature finds that public ALMPs are not very successful. The
question we ask ourselves in this paper is whether the private sector could do a better job
at re-integration the unemployed? From unique data on a firm-based employment program,
the Philips Employment Scheme (PES) run by multinational Philips Electronics, we will
show that a private program can outperform public efforts to re-integrate the unemployed.
In order to provide a background for our analysis we briefly review the literature on
the performance of active labor market policies. Literature evaluating ALMPs is growing
steadily but rarely finds only unequivocally positive effects of these policies. See for example
Calmfors, Forslund and Hemström (2001) and Roed and Raaum (2006). Even in cases in
which a positive effect is found, for example ’time in unemployment’ is reduced by a number
of weeks or the job finding rate is higher for a treatment group, the cost of running the
program often outweighs the benefits and the net welfare effects of the program are not
clear. See Kluve and Schmidt (2002) who present an overview of ALMP evaluation studies.
One of the mechanisms found in the literature that helps explain poor performance of some
ALMPs is the so called ‘locking-in effect’, whereby program participants, due to being in a
program, reduce their search efforts for a regular job (see for instance van Ours (2004) and
Rosholm and Svarer (2008)). Furthermore several papers find that some ALMPs actually
worsen a participant’s chances on the labor market. Examples of this are given in papers
by Hujer, Caliendo and Thomsen (2004) and by Rosholm and Skipper (2009). Several other
papers also highlight the importance of the so called ’threat effect’ of having to enter into
a labor market programme. This ’threat’ induces individuals to increase their job search
efforts and this apparently leads them to find a job before being treated in a labor market
program, rendering the actual program a moot point (see for instance Rosholm and Svarer
(2008) and Graversen and van Ours (2008)).
The remainder of this paper is structured as follows. We explain the Philips Employment
Scheme (PES) in Section 2 and in Section 3 we present our data. In Section 4 we analyze
the cyclical characteristics of the PES and analyze the outflow data of the program. Section
5 discusses the deadweight loss and relative performance of the PES compared to public
efforts at re-integration. Section 6 concludes.
The deadweight loss of a private ALMP
2
3
What is the Philips Employment Scheme (PES)?
To describe what the PES is we first take a look at its origins. In the early eighties the
number of unemployed in the Netherlands rose dramatically and peaked at a record high
of 10.2% of the labor force in 1983 (CPB (2009)). Among these were a large number of
youth unemployed, below age 23. In 1982 the government and unions agreed on a package
of reforms called the Wassenaar agreement. An important element in this agreement was an
economy-wide reduction in working hours to split the same amount of work into more jobs.
However, Philips preferred a different approach to fight unemployment and to do one’s bit in
these times of need. Instead of reducing working hours Philips created the ‘Youth Work Plan’
(JWP) which was the predecessor of the PES. The JWP offered unemployed youngsters a
year of work and (partly theoretical) training with Philips and already had 639 participants
by the end of 1983 (van der Bruggen and van Schagen (2001)). The JWP ran successfully
until 1986 at which time the number of youth unemployed had declined substantially and
young people had easier access to the labor market again. According to van der Bruggen
and van Schagen (2001) the apparent success of the JWP led unions to embrace it, which in
turn led Philips and the unions to incorporate the JWP into the centralized wage agreement.
This meant that part of Philips’s wage budget was allocated to the program. At the same
time the scope of the program was expanded to include other groups of unemployed; the
JWP grew into PES. The basic setup remained largely unchanged and still entailed one year
of full-time employment and training at Philips. Participants receive the legal minimum
wage and the training component is aimed to obtain a vocational qualification and therefore
includes a substantial theoretical component.
Employment in the PES scheme has always been created in addition to existing employment at Philips. At the start of the year the HRM department of Philips considers the
allocation of PES jobs over the Philips establishments. The main basis for this allocation
is the future job prospects of participants. Since Philips has factories and offices operating
in various sectors throughout the Netherlands, the future job prospects among sectors differ
and subsequently the choice as to which factories and offices are allocated to open PES jobs
matters (Welters (2005)). Due to this regional and sectoral variation, this allocation process
turns out not to be a strictly ‘top-down’ process but more a ‘two-way street’ between the
central HRM department and local HRM staff.
The performance of the program depends on three stages; selection of the candidates,
treatment and outflow counseling. To better understand the PES and the quantitative
results that we will provide in later sections of this paper, we elaborate on the design of
these three stages using data from the PES annual reports.
2.1
Selection
Ever since the start of the PES, Philips has selected the PES candidates in close cooperation with the relevant public and private authorities dealing with unemployment and
reintegration. In January 2009 several (semi-)government agencies were merged into one
4
Gerards/Muysken/Welters
new organization named UWV WERKbedrijf which is the administrative and executive
body of the Ministery of Social Affairs and Unemployment in charge of all employee based
social insurances. Together with local municipal governments and a variety of private firms,
UWV WERKbedrijf tries to reintegrate or otherwise help the unemployed, sick and disabled
as best as they can within the legal framework. So naturally the first criterion for selection
into the PES is that a person is registered as unemployed with the UWV.1 An exception to
this rule was made for women reentering the labor market who could enter the PES without
formally being registered as unemployed. Within the relevant group of unemployed the PES
explicitly targets those persons with the largest distance to the labor market: that is, long
term unemployed, unemployed from ethnic minorities, persons with a disability and higher
educated persons with weak ties to the labor market. Thus the distance of a person to the
labor market is the second criterion for selection. As third criterion a candidate should have
the potential to successfully complete the practical and theoretical training component that
is part of the PES treatment. For testing these first three selection criteria; being unemployed, having a certain distance to the labor market and having the minimum required
intellectual capacity, Philips relies on its cooperation with the UWV and its private partners. They present candidates that match these three criteria to the Philips outlet at which
they are supposed to fulfil a PES position. Naturally a candidate should possess a certain
level of willingness and motivation to participate in the PES and this constitutes the fourth
criterion, which is tested at the relevant Philips outlet. Philips aims at a yearly inflow into
the PES of 1% of the total number of people employed at Philips.
The data used to construct the figures in this section originate from the PES annual
reports from 1987 to 2008. From the data in these annual reports we compiled a comprehensive dataset. Therefore, this section also gives an overview of the first of our three
datasets concerning the PES. In the remainder of the paper we will refer to this first dataset
as the ‘Time series dataset’. Table 1 presents summary statistics of the most important
variables in this dataset. The other three datasets will be described in detail in Section 3.
What immediately strikes from the table is the job finding rate of almost 70% when meaTable 1: Summary of ‘Time series dataset’
Variable (N=22 years)
Total inflow
% males in inflow
Average age of inflow
Job finding rate 6 weeks after PES exit
Job finding rate 1 year after PES exit
Years
Mean
Min
Max
1987-2008
1988-2008
1988-2008
1987-2007
1995-2007
440
56.8
31.2
60.7
69.6
165
38
23.5
40
50
768
76
35.8
79
84
sured one year after PES exit. Thus at first glance the PES seems to be quite successful.
But let’s not jump to conclusions, the remainder of this paper will provide more subtle and
in depth evidence as to the performance of the PES.
1 We
will refer to UWV WERKbedrijf as ‘UWV’
The deadweight loss of a private ALMP
5
We continue our description of the PES with Figure 1. This figure shows the number
of participants entering the PES compared to total Philips employment.2 We see that on
Figure 1: PES inflow
1,4
40000
1,3
35000
30000
1,2
25000
1,1
20000
1
15000
0,9
10000
0,8
5000
0,7
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
PES inflow as % total Philips employment (left hand scale)
Total empl. Philips NL (right hand scale)
2008
0
Based on data from our ‘Time series dataset’
average the 1% target is successfully attained, but the inflow varies substantially with time.
One reason which is mentioned several times by PES managers from Philips is that they
had to make more effort to attract suitable candidates during economic upswings. Figure 2
reveals even more hints about the relationship with the business cycle. It shows the inflow
percentage of males, the unemployment rate and the average age of total inflow into the
PES. The percentage of males entering the PES moves along a very similar path as the
unemployment, trailing behind it roughly one year. This means that in economic upswings
the gender balance tends to shift in favor of female entrants. A possible explanation for
this phenomenon is that when the stock of stereotype male long term unemployed becomes
relatively exhausted due to an economic upswing, other disadvantaged groups (including
women reentering the labor force) are automatically more prone to be selected for the PES.
Another obvious trend is the slowly increasing average age.
2.2
Treatment and outflow counseling
As mentioned before, the treatment in the PES consists of one year of work experience
combined with a formal training that leads to a vocational qualification. Since the end of
the nineties, half of the PES participants start with a five month pre-program because their
initial qualifications are too low to enter the PES directly. They would not be able to attain
the vocational qualification within the standard PES timeframe of one year. After this
pre-program they enter the PES as regular (van der Bruggen (2004)). Many participants
are also not able to complete the entire PES treatment in one year and are offered a 6-12
months extension. These developments exemplify the large distance to the labor market of
many candidates.
2 Unfortunately
total employment of Philips (Netherlands) is only available from 1998 onwards.
6
Gerards/Muysken/Welters
Figure 2: Average age and gender mix of inflow
80
9
8
70
7
60
6
50
5
4
40
3
30
2
1
20
Males % of total inflow (left hand scale)
Unemployment rate (right hand scale)
Average age of total inflow (left hand scale)
Based on data from our ‘Time series dataset’
As said, PES participants follow a formal training program as part of the PES treatment. The type of training that a participants receives depends on the nature of the PES job
the participant works in and the future job prospects that were essential in allocating this
PES job (as mentioned earlier in this section). Many of the PES participants still complete
the VaPro degree, which is a widely recognized qualification in the Dutch process industry.
However, due to changes in the labor market and in the qualifications of participants, there
has been an increase in the number of participants that complete a degree in other fields
such as administrative, secretarial and ICT skills. The knowledge and skills acquired during the training are brought into practice on the job. Throughout the entire PES period
the participant receives regular supervision, counseling and guidance primarily from within
Philips but also from the UWV. Table 2 shows participant’s levels of satisfaction with the
guidance on the workfloor. Only six percent of the respondents answer strongly negative.
Table 2: Did you receive sufficient guidance/counseling on the workfloor?a
Yes, this was good
Yes, but could be more intensive
I would be fine with less
No
N (1017)
Percent
669
262
25
61
66
26
2
6
a Average
responses from participants that started in the PES between 1997 and 2006.
Based on data from our ‘Interview dataset’ which is described in Section 3.
Outflow counseling starts towards the end of the PES period when the participants take
a course in how to apply for jobs and are counseled in their job search efforts. Six weeks
The deadweight loss of a private ALMP
7
and one year after exiting the PES participants are interviewed with regard to their PES
experiences. The aggregate results of these interviews have been Philips’s main source to
base their PES annual reports on. One question in the interview is if the former PES
participant is currently employed and the aggregate answers to this question are shown in
Figure 3.
Figure 3: Aggregate job success of PES
90
20
80
18
70
16
14
60
12
50
10
40
8
30
6
20
4
10
2
0
0
% with job 6 weeks after program end (left hand scale)
% with job at Philips 6 weeks after PES
% with job 1 year after program end (left hand scale)
NL unemployment rate (right hand scale)
Based on data from our ‘Time series dataset’ and unemployment data from Statistics Netherlands
On average sixty percent of ex-participants report to be employed when interviewed six
weeks after exiting the PES whereas this is seventy percent after one year. On average 15%
of participants were employed by Philips itself after the end of the PES period, while this
figure has varied from 11 to 34 percent.3 When asked about their general opinion on their
PES period, on average seventy percent of respondents answer ‘satisfied’ or ‘very satisfied’
(on a five point scale). Table 3 presents the five highest ranking answers that were given
when asked to be more specific about what it was that they benefitted most from during
their PES period. The renewed work experience and social contacts appear to be the most
valuable contributions the PES made to the participants’ lives.
Table 3: What is it you benefitted most from during your PES period?
1.
2.
3.
4.
5.
‘I have (re)gained work-experience’
‘Social contacts’
‘I have been able to show what I’m capable of’
‘I have applied in practice what I have learned in training’
‘I have gotten more self-esteem’
Based on data from our ‘Time series dataset’
3 In Section 4 we present estimates showing that young and academically qualified participants are more
likely to be employed by Philips after the PES than others.
8
2.3
Gerards/Muysken/Welters
About the PES so far
The aggregate time series data presented in this section paint a quite positive picture of
the performance of the PES. Overall the large majority of PES participants report to be
satisfied with the program and the chance to be in a job six weeks and one year after ‘being
treated’ is at least fifty percent. As mentioned already in a footnote, these aggregate data
stem from Philips PES annual reports. To see whether this positive picture holds up when
we take a deeper look at individual data, we first elaborate in Section 3 on what other data
we have used.
3
Data
Due to the lifespan of the PES program itself, which started in 1983 and is still ongoing, individual participant progress and job success were never recorded methodically. Therefore,
apart from the ‘Time series dataset’ described in the previous section, we have used two
other datasets, each from a different source. For our analysis we combined all the datasets
described in this section with longitudinal unemployment measures from Statistics Netherlands, the rate of unemployment and the number of individuals receiving unemployment
benefits, both on several levels of regional aggregation from municipality to COROP (a
regional classification especially suited for labor market analysis) to province.
3.1
‘8900 dataset’
The dataset we will refer to as ‘8900 dataset’ contains data on 8928 individual participants
of the PES. This dataset was originally extracted from Philips’ administrative systems and
is the most integral collection of PES participants possible, containing 75 percent of all
PES participants ever. This large sample size comes with a trade-off since the information
we have per participant is limited to a number of ‘bread and butter’ variables. Table 4
summarizes the most important variables in this dataset. Looking at the various statistics
Table 4: Summary of main variables ‘8900 dataset’.
Variable (N=8928)
N
Startyear PES treatment
Endyear PES treatment
Duration PES treatment (months)
Age
Level of prior education (years of school)
8928
8712
8712
8928
7150
Gender
Residential address
N
8928
8928
Mean
Std. Dev.
Min
Max
10.0
28.5
10.3
4.5
8.2
2.9
1983
1983
0
16
6
2009
2009
24
60
16
Male
5187
Female
3741
in the table, especially the means and standard deviations, we can state that the PES inflow
is quite heterogeneous. Moreover, although this was never recorded good enough to be
used as variable, we know from working with the data and from qualitative sources that
The deadweight loss of a private ALMP
9
there is also a large variation in ethnicity of participants. The level of prior education was
originally defined in eight categories specific to the Dutch education system. Table 5 provides
the number of PES participants from each educational background. For later analysis we
translated these categories into ’years of school’ as shown in the last column.
Table 5: Prior education level of PES participants.
Prior education
LO (Primary school)
LBO (Low level vocational)
MAVO (low level secondary)
MBO (middle level vocational)
HAVO (middle level secondary)
VWO (top level secondary)
HBO (high level vocational)
WO (university)
ª
LO + LBO: Primary
)
MAVO - VWO: Secondary
ª
HBO + WO: Tertiary
N
1280
1581
1022
1302
466
183
813
503
%
17.9
22.1
14.3
18.2
6.5
2.6
11.4
7.0
Years of school
6
9
10
10.5
11
12
15
16
In addition, Figure 4 shows the education level of the PES inflow over time if we rescale
it to a standard three category scale. Especially the nineties were characterized by a marked
increase in participants with tertiary education. Facing 7,000 high skilled unemployed in
the area of South-east Brabant (where Philips has its origins and many of its factories and
offices), Philips and the local UWV-predecessor agreed to target this group with the PES.
As a consequence in 1994 and 1995 the inflow of high skilled unemployed is at its peak and
remains high throughout the second half of the decade.4
Figure 4: Education level of PES inflow over time
100
90
80
70
60
50
40
30
20
10
0
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Primary education
Secondary education
Tertiary education
Based on data from our ‘8900 dataset’
4 Comparing
Figures 4 and 3 we also see an all-time peak in former PES participants being employed by
Philips in the same period. This already hints at a possible relation between a high skill level at entry and
chances of being employed by Philips, as we will formally show in Section 4.
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Gerards/Muysken/Welters
3.2
‘Interview dataset’
The most detailed information on individuals who have participated in the PES comes from
our third dataset, which we will refer to as the ‘Interview dataset’. This dataset stems from
Philips databases in which the answers to the aforementioned interviews were recorded. It
contains the answers of 1042 former PES participants interviewed six weeks after the end of
their PES treatment. Table 6 summarizes the main variables.
Table 6: Summary of main variables ‘Interview dataset’.
Variable (N=1038)
N
Mean
Std. Dev.
Min
Max
2006
2007
58
16
Startyear PES
Endyear PES
Age
Prior education (years of school)
1038
1038
1038
1012
35.2
11.6
9.6
2.8
1997
1999
17
6
Unemployment before PES
N
1025
n.r.
18.2%
< 6m
21.7%
6-12m
20.6%
12-24m
17.6%
24-48m
11.7%
Yes
Philips
13.6%
Male
55.6%
Yes
Elsewhere
20.6%
Female
44.4%
Yes
Temp
21.8%
No
Study
3.0%
No
No
41.0%
Job after PES
Gender
PES location/Philips outleta
N
1022
N
1038
1035
> 48m
10.2%
The substantial variations in age, prior education and unemployment duration confirm
the heterogeneity of participants observed already from Table 4. In addition to the variables
reported in the table, the ‘Interview dataset’ contains data on participant satisfaction with
the PES treatment and answers to questions that enquire about the intensity of supervision
participants received from both Philips as well as the Labor Office.5
In Appendix B we show that the ‘Interview dataset’ can be considered a representative
sample of the large ‘8900 dataset’ as it displays very similar movements in gender and
age composition of participants. Due to differences in how prior education was defined in
both datasets, we could not construct a reasonable comparison of this characteristic (even
though we managed to translate them both into ’years of education’). In the ‘8900 dataset’,
prior education was classified in 8 levels whereas the classification in the ‘Interview dataset’
contained only 5 levels. Especially the lower education levels were more compressed into
aggregates in the ‘Interview dataset’, leading to less accuracy there.
As a conclusion to this section, Table 7 presents a summary overview of all datasets.
Table 7: Datasets summary.
Dataset
Time series dataset
8900 dataset
Interview dataset
5 As
used in Table 2.
Source
PES Annual reports
Philips HRM systems
Databases of phone-interviews
Period
1987-2008
1983-2009
1997-2007
N
22 (years)
8928
1038
Record of success
Average per year
No
Yes,for N=1022
The deadweight loss of a private ALMP
4
11
What drives the Philips Employment Scheme’s job find rate?
In Section 2 we found that on average seventy percent of ex-participants hold a job when
interviewed one year after leaving the PES. In this section we take a closer look at the PES
to investigate what factors influence its job finding rate and the quality of its participants.
4.1
The aggregate job finding rate of PES
Our analysis starts with a simple OLS regression using data from our ‘Time series dataset’
based on yearly averages of the participants. Table 8 presents estimates of several key
variables on the (aggregate) job finding rate (this variable was shown in Figure 3).6 We find
Table 8: Determinants of aggregate job finding rate of the PES, 1988 - 2007
Dependent variable
Job finding rate
Unemployment at time of inflow
Unemployment at time of outflow
Average educational attainmenta
Average age
Percentage of males
Constant
9.01 (3.61)∗∗
−10.03 (2.48)∗∗∗
0.09 (0.03)∗∗∗
0.00 (0.01)
−0.00 (0.00)
−0.24 (0.34)
N = 19 years
R2
Durbin-Watson D-statistic
∗∗∗
= significant at 1%, ∗∗ = significant at 5%,
number of years of schooling
Standard errors in parentheses
based on our ‘Time series dataset’
0.77
2.09
∗
= significant at 10%
a Average
a positive relation between the job finding rate and unemployment at time of inflow and a
negative relation with unemployment at time of outflow. The first relation indicates that
participants who enter the PES when unemployment is high, have a higher chance to find
a job after their PES training than participants who enter the PES in a low unemployment
period. The second relation suggests that higher unemployment at time of outflow from the
PES means a lower chance to find a job. We discuss two possible explanations for these
findings. The latter finding can be argued to be a business cycle effect: more unemployment
at time of outflow means more competition among job applicants and this reduces chances
of finding a job. The former finding consists of a business cycle effect and a selection
effect. A recent study by Lechner and Wunsch (2009), who studied ten years of German
administrative data on ALMP participants and non-participants, shows that -all else equal- a
higher unemployment at time of inflow into an ALMP correlates positively with the ALMPs
effects. Thus the correlation we find for the PES between unemployment at time of inflow
and the job finding rate might be similar in nature. However we believe this correlation
also entails a selection component. When a slowdown of the economy causes a higher stock
6 The variable ‘Average educational attainment’ was taken from our ‘8900 dataset’ which contains a longer
and more accurate history of educational attainment than the ‘Time series dataset’.
12
Gerards/Muysken/Welters
of unemployed (at time of inflow into the PES) the quality of the participants that enter
into the PES increases which suggests that inflow is not random and that some selection
occurs. Lechner and Wunsch also find that the characteristics of participants relate positively
with unemployment. In addition we find that a higher average educational attainment
corresponds with a higher average job finding rate.
4.2
A closer look at the inflow of the PES
To further investigate these business cycle and selection effects we provide some additional
analyses that zoom in on the (dynamics of) the inflow quality in the PES. For these analyses
we use the more detailed information available in our other ‘8900 dataset’. In Table 9 we
present OLS estimates of the effect of ‘unemployment at time of inflow’, gender and age
on the quality of participants. The quality of participants is measured by the ratio of the
participant’s years of schooling to the labor force average years of schooling in the year
of inflow. We constructed this ratio to correct for possible bias that might be introduced
by the rising trend in the labor force’s overall education level. The first result we observe
Table 9: Effect of labor market conditions on participants quality, 1987-2006
Educational attainmenta
Unemployment at time of inflow
Gender:
Male
Female
Age groups:
16 - 30 years
31 - 40 years
41 - 50 years
> 50 years
Constant
R2
N
0.04 (0.00)∗∗∗
reference
−0.04 (0.01)∗∗∗
reference
−0.02 (0.01) ∗ ∗
−0.04 (0.01)∗∗∗
−0.06 (0.03) ∗ ∗
0.74 (0.02)∗∗∗
0.06
6611
∗∗∗
= significant at 1%, ∗∗ = significant at 5%, ∗ = significant at 10%
Participant’s years of schooling to labor force average years of schooling ratio
Standard errors in parentheses
a
is that, the higher the unemployment rate at time of inflow, the higher the educational
attainment of participants entering the PES. This supports our suspicion, that when an
economic slowdown increases the stock of unemployed, selection occurs because the quality
of inflow increases. Whether this is self-selection or selection by UWV/Philips cannot be
answered with certainty. Albeit neither gratifying nor original to argue, it’s most likely a
combination of both. We also find that, relative to participants from the age group of 16
- 30 year, participants from older age groups have a lower educational attainment relative
to the labor force average. This is not surprising since recent generations stay in education
longer and hence the average educational attainment of the labor force has risen steadily
during the last decades. Finally we observe that female PES participants appear to have a
The deadweight loss of a private ALMP
13
lower educational attainment.7
By analyzing the duration participants spent in the PES we can provide an alternative
analysis which more clearly shows the business cycle effect on the quality of inflow.8 A
‘bread-and-butter’ PES treatment takes up to a maximum of twelve months. Thus when a
person takes more than twelve months he or she apparently needs more time to be deemed
ready for outflow, and is likely to have been relatively less qualified at time of inflow.9
Based on this reasonable assumption we constructed a dummy variable equal to ‘0’ when
participants spent at maximum twelve months in the PES, and equal to ‘1’ for participants
who spent more than twelve months in the PES. We tested the sensitivity of this dummy
to the business cycle and the characteristics of the inflow with a probit model. Table 10
contains the results.
Table 10: Effect of business cycle and inflow characteristics on time spent in
PES, 1987-2006
Duration dummy
Unemployment at time of inflow
Gender:
Male
Female
Age groups:
Age 16-30
Age 31-40
Age 41-50
Age 51+
−0.20 (0.02)∗∗∗
reference
−0.40 (0.05)∗∗∗
reference
0.12 (0.05) ∗ ∗
0.27 (0.07)∗∗∗
0.23 (0.15)
Prior education:
Primary school
Low level vocational
Low level secondary
Middle level vocational
Middle level secondary
Top level secondary
High level vocational
University
Constant
reference
0.08 (0.07)
−0.10 (0.08)
−0.05 (0.07)
−0.16 (0.11)
−0.17 (0.16)
−0.37 (0.10)∗∗∗
−0.46 (0.12)∗∗∗
0.03 (0.11)
Correctly specified by probit model
N
∗∗∗ = significant at 1%, ∗∗ = significant at 5%,
Standard errors in parentheses
91.45%
6611
∗
= significant at 10%
The business cycle effect manifests itself as the negative relation between unemployment
at time of inflow and the duration dummy. When the labor market is tight and unemployment is low at time of inflow, participants have a higher chance to spend more than twelve
7 We tested several models with unemployment data on national, provincial and municipal levels and did
not find this geographical distinction to be significant (see Table 15 in Appendix A for details). Therefore,
we only report analyses based on national unemployment data.
8 As presented in Table 4, our ‘8900 dataset’ contains a variable measuring the time spent in the PES.
This is measured as duration in months.
9 In Section 2.2 we already mentioned that less qualified participants are regularly offered a 6-12 months
extension, supporting the assumption we make here.
14
Gerards/Muysken/Welters
months in the PES and are likely to be less qualified. Thus participants are ‘locked-in’ the
PES longer when unemployment was low at time of inflow, a result that is also found by
Lechner and Wunsch (2009). Although our explanation (a person needs more time in the
PES to be qualified enough to successfully exit the PES) differs from the usual explanation that ‘locking-in’ occurs due to reduced job search efforts on behalf of the participant.
Apart from this we observe that women are less likely to spend more than twelve months
in the PES and that middle aged participants a more likely to stay longer in the PES than
the youngest participants. Furthermore both high level vocational and university educated
participants are less prone to spend more than twelve months in the PES.
4.3
Who gets a job after PES?
To assess whether these differences in entry qualifications of participants also lead to differences in job finding chances we provide a combined analysis of business cycle and quality
indicators on a dummy variable that expresses whether a persons is in a job after the PES
(dummy=1) or not (dummy=0). This analysis is based on our ‘Interview dataset’ which
is our best source of individual outflow data. The results are presented in Table 11. The
variable ‘Unemployment status’ is a dummy variable that distinguishes participants that
entered the PES without having been formerly registered as unemployed (dummy=0, remember from Section 2.1 that these are mainly women reentering the labor market) from
those that had a registered unemployment history (dummy=1). We observe that participants without a registered unemployment history seem to have a higher chance to find a
job after the PES. A possible explanation for this could be that these participants are more
intrinsically motivated to get back to work (because they make the deliberate choice to
re-enter the labor force) whilst participants with a registered unemployment history were
looking for work by definition. With regard to unemployment we find very similar results to
those in Table 8, a positive relation of the job finding chance with unemployment at time of
inflow and a negative relation with unemployment at time of outflow. Overall, an increase
in age has a negative effect on the chances of finding a job, although participants between
age 31 and 40 have a higher chance of finding a job after the PES than the reference group
of 16 to 30 year old’s. The role of education is limited to a slightly higher chance of finding
a job for participants with a university degree. There is no significant difference between
men and women. This suggests the PES is quite successful, given the intrinsic qualities of
the candidates.10
10 We tested this model on several levels of geographical aggregation. See Table 16 in Appendix A. The
results do not change dramatically. We only observe smaller coefficients for the unemployment variables as
we analyze smaller geographic entities such as municipalities. Since variations in regional unemployment
rates are larger than variations in national unemployment rates (as if looked at through a magnifying glass)
we argue that the smaller coefficient on municipal level also has to be looked at through a magnifying glass.
Hence the lower coefficient on municipal level does not mean that the regional business cycle sensitivity of
the PES is less that the national business cycle sensitivity.
The deadweight loss of a private ALMP
15
Table 11: Explaining participants’ job finding chances, 1999-2007
Job finding dummy
Unemployment at time of outflow
Unemployment at time of inflow
Age
Gender
Prior education:
Primary school
Low level vocational/secondary
Middle level vocational/secondary
High level vocational
University
Unemployment status
Unemployment duration:
0-6 months
6-12 months
12-24 months
24-48 months
48+ months
If unemployed, what age:
Age 16-30
Age 31-40
Age 41-50
Age 51+
Counseling from UWV
Constant
−0.54∗∗∗
0.36∗∗∗
−0.03∗∗∗
0.04
−0.09
−0.04
(0.16)
(0.10)
reference
−0.01
(0.13)
0.29∗
(0.15)
−0.51∗∗∗
0.03
−0.11
0.17
0.04
reference
(0.13)
(0.14)
(0.15)
(0.16)
−0.08
2.15∗∗∗
= significant at 1%, ∗∗ = significant at 5%,
Standard errors in parentheses
4.4
(0.16)
reference
0.35 ∗ ∗
(0.15)
0.31
(0.22)
0.30
(0.31)
N
Correctly classified by probit model
∗∗∗
(0.10)
(0.10)
(0.01)
(0.09)
(0.05)
(0.40)
982
63.03%
∗
= significant at 10%
The PES as a recruitment channel for Philips
In Section 2.2 we already mentioned that a significant number of PES participants ends up
becoming regular Philips employees. Figures 3 and 4 already suggested there might be a
link between education level and the chance to stay with Philips after participating in the
PES. Table 12 gives the results of a more formal way of testing this. From our ‘Interview
dataset’ we constructed a subset of participants who found a job after their PES treatment.
We constructed a dummy with value ‘1’ if this was a job with Philips and a value of ‘0’ for
a job elsewhere. Interesting to notice is the negative relation between the chance of getting
a job with Philips and unemployment at time of outflow. Although this is the same relation
as found in our previous tables we believe it may portray additional meaning in the context
of the table at hand. One could generally argue that in an economic upswing the costs
for recruitment and selection of new personnel are high. Normally this will also hold for
Philips, recruiting suitable candidates to fill a vacancy is more difficult when Philips has a
16
Gerards/Muysken/Welters
Table 12: Who stay with Philips? 1999-2007
Job with Philips
Unemployment at time of outflowa
Gender
Prior education:
Primary school
Low level vocational/secondary
Middle level vocational/secondary
High level vocational
University
−0.17 ∗ ∗
−0.03
(0.09)
(0.12)
−0.34
0.14
(0.28)
(0.15)
reference
−0.26
(0.19)
0.31∗
(0.18)
Age groups:
Age 16-30
Age 31-40
Age 41-50
Age 51+
reference
−0.25∗
(0.14)
−0.35 ∗ ∗
(0.17)
0.21
(0.27)
Counseling from UWV
Constant
−0.04
−0.06
N
Correctly classified by probit model
∗∗∗
= significant at 1%, ∗∗ = significant at 5%,
Standard errors in parentheses
a On municipal level
(0.07)
(0.28)
549
75.96%
∗
= significant at 10%
tight labor market to compete in. Especially in these periods it might prove to be a cost
saver if Philips could fulfil a vacancy with a former PES participant, circumventing or at
least cutting short the usual recruitment efforts and associated costs.
In addition, albeit with somewhat weak significance, we find confirmation for our earlier
suspicion that academically qualified PES participants tend to have a higher chance to
stay with Philips. Futhermore, middle aged participants have a lower chance to become
employed by Philips compared to the reference group of 16-30 year old’s. So the younger
participants seem to be preferred by Philips when it comes to offering them a chance to
stick around. This seems logical if you consider their PES treatment as an investment; the
youngest participants have the longest possible return on investment window. Together, all
these findings at least fuel the suggestion that the PES has come with a positive externality
for Philips in acting as a recruitment channel and cost saver. In future research we hope to
be able to quantify this using data not yet available to us at the moment.
5
Relative performance and deadweight loss of the PES
To compare the performance of the PES to the performance of public treatment we first
need a basic understanding of the workings of the social security system with regard to
unemployment. Given that the social security system has undergone ongoing reforms since
the early 1980s, we will only discuss the current situation. If a person becomes unemployed
he or she is eligible for unemployment benefits (UB) under the ‘Werkloosheidswet’ (WW:
The deadweight loss of a private ALMP
17
Unemployment law). Depending on the person’s employment history one may receive UB
for up to 38 months. The replacement rate is 75% for the first two months of unemployment
and 70% thereafter. The salary to which this replacement rate applies is capped at just
under 48,000 euro yearly, leading to a maximum UB of just over 34,000 euro per year. Thus
for those with a yearly income above the maximum, the replacement rate turns out lower
than 70%. If a person does not find employment within their UB eligible period they are left
with residual social assistance under the ‘Wet Werk en Bijstand’ (WWB: Work and social
assistance law’). These residual benefits are means tested and further depend on a number
of factors such as a person’s age and living situation. Needless to say these residual benefits
are much lower than UB. However, there is no duration limit to these residual benefits.
From the moment a person becomes unemployed and registers with the UWV, the latter
is responsible for leading the person (then ‘client’) back to employment. A basic requirement
to receive UB is that the client periodically submits a certain number of job applications.
Of course counselors at UWV will also try to match their clients to vacancies. However, this
situation reflects the ‘standard’ client approach and hence will be labeled as ‘not (specifically)
treated’ for the remainder of this paper. Depending on individual circumstances a specific
re-integration treatment can be started for the client. There are numerous variations to a
re-integration treatment and, for the time being, we will not distinguish between these.11
Instead, as a large amount of evaluation studies of re-integration treatment does, we will
only distinguish between untreated and treated clients.
5.1
Performance of public re-integration efforts
We now turn to comparing the PES to untreated and treated UWV clients. In 2008 a
detailed study on re-integration policies, commissioned by the Council for Work and Income
(an important advisory body to the Dutch government and social partners), was published.
This study uses the integral social security database, observing every person that has been
unemployed between 1999 and 2005. After linking the social security data with data on reintegration projects, they were able to measure the effect of the re-integration projects using
a duration model. For details we kindly refer to the original study (Groot et al. (2008)). In
Table 13 we reproduce some of the numbers calculated by Groot et al. (2008) and we use
these to calculate the job finding rate for treated and untreated clients.
Column one shows the percentage of clients that flow out of the WW within the mentioned duration. Subsequently, column 2 shows what percentage of this outflow is outflow
to work. Hence multiplying these first two columns gives the job finding rate within the
specified duration for untreated clients. Finally, column four reports the treatment effect of
public re-integration efforts. Focussing on the first row, we see that persons who become
unemployed and enter the WW have a 57.7% chance of finding a job within the first year of
unemployment. If a public re-integration treatment is started in this first year, this increases
the chance of finding a job within 1.5 years after start of the treatment with 0.9%. (Groot
11 As
will be mentioned in our conclusions, one further avenue for research we intend to pursue is to
distinguish between -and elaborate on- different forms of re-integration treatment.
18
Gerards/Muysken/Welters
Table 13: Performance of public re-integration efforts (1999-2005)
Duration in WW
up to 1 year
1-3 years
3+ years
1
Total exit
percentage
2
Exit to work
3
Job finding rate
(untreated)
4
Effect of public
treatment
74%
15%
10%
78%
63%
29%
57.7%
9.5%
2.9%
0.9%
1.3%a
0%b
a
Effect of treatment if treatment started in 2nd year
Effect of treatment if treatment started later than the 2nd year
Source: Columns 1,2 and 4 from Groot et al. (2008).
b
et al. (2008)). The remainder of the table is read in the same manner.
Interesting to observe is that after one year in unemployment (WW) the chance of
finding work is severely reduced. In fact, Groot et al. (2008) find that already after 3
months in unemployment an individual’s job finding rate starts to decline rapidly. A part
of this is of course a selection-effect, which means that the unemployed with the best set
of characteristics find a job fast and hence the chances to find work for the remaining
unemployment population decline. However, they argue that persons entering the WW are
quite homogeneous in the sense that they all have recent employment history. So initially
their distance to the labor market is not that large and thus the observed decline of the job
finding rate after three months is likely to be more dominated by a duration-effect than a
selection-effect. The longer one is unemployed, the larger the distance to the labor market
becomes (Groot et al. (2008)).
5.2
Deadweight loss and relative performance of PES
When analyzing re-integration efforts or active labor market policies it is necessary to correct
the program’s gross performance for the fact that some participant’s of the program would
have found a job even without the help of the program. This is called the ‘deadweight
loss’. We can quantify the deadweight loss of the PES using the information that we have
presented so far. First, we know the job finding rate for untreated unemployed by duration
of unemployment from Table 13. Second, recall from Table 6 (row ‘Unemployment before
PES’) that we also know how many participants have entered the PES by unemployment
duration. If we combine these two figures we can calculate how many of the participants of
the PES would have found a job without PES treatment while correcting for unemployment
duration.12 This is the deadweight loss of the PES. In Table 14 we present our calculation.
Column one shows the job finding rate for untreated unemployed. Column two shows
the corrected percentage of PES participants per category of unemployment duration.13
12 Since our PES duration categories distinguish between 12-24 months and 24-48 months we could not
directly compare this to the 1-3 years and 3+ years of Groot et al (2008). We chose to split the 24-48 months
category in half and thus add 5.85 percent to both 12-24 and 48+ to obtain a comparable number.
13 For an unbiased comparison we have left out the PES participants that had no unemployment registration. We only want to observe those participants with a registered unemployment duration and compare
The deadweight loss of a private ALMP
19
Table 14: Deadweight loss of PES (1999-2005)
Duration in WW
up to 1 year
1-3 years
3+ years
Total PES DWL
1
Job finding rate
untreated
2
PES population
by duration
3
Deadweight loss
57.7%
9.5%
2.9%
51.7%
28.7%
19.6%
29.8%
2.7%
0.6%
33.1%
In column three we have multiplied the numbers from columns one and two which gives
the percentage of PES participants that would have found a job without treatment, by
unemployment duration (thus the deadweight loss per category of unemployment duration).
Finally, adding the numbers in column three gives us the weighted average deadweight loss
of the PES. We find that 33.1% of the PES participants would have found a job without
the PES. Recall from Table 1 that the average job finding rate of PES participants is 69.6%
when measured one year after exiting the PES.14 For the period 1999-2005 this is 61.0%
(the same period as covered by the study by Groot et al. (2008)). Thus the (net) treatment
effect of the PES is 61-33.1=27.9% for the period 1999-2005. Suffice it to say that this
contrasts sharply with the treatment effects of public treatment as reported by Groot et al
(2008) and reproduced here in column 4 of Table 13.
Finally, we would like to point out an argument that suggests that the PES deadweight
loss we find is in fact smaller and hence the treatment effect is even bigger. From the
preceding paragraphs we have learned that 57.7% of all unemployed find a job without
treatment within the first year of their unemployment spell. At the end of Section 5.1 we
already mentioned that after three months in unemployment, the job finding rate starts to
decline rapidly. This means that a large portion of the 57.7% already find their job in the
first three months and the remainder of the 57.7% is spread over months 4 to 12. But PES
participants that have only spent up to three months in unemployment before entering the
PES are spread thin for two reasons. First, if apparently such a relatively large part of
unemployed find a job within the first three months of unemployment, these persons are out
of unemployment so fast on their own that they have therewith selected themselves out of
the ‘selection pool’ of the PES. Second, even if they would not have left unemployment so
fast, by design the PES targets predominantly those with the largest distance to the labor
market and they would therefore not pass the selection criteria. All this implies that if
we would be able to further break down the PES population and the job finding rates as
reported in Table 14, we would expect to find a lower deadweight loss and a higher treatment
these against untreated unemployed of equal unemployment duration. Therefore we have divided the numbers from Table 6 by 1-0.182=0.818.
14 We use the job finding rate after one year because this compares best with Groot et al. (2008), who
also measure their treatment effects by looking at 1,5 years after treatment.
20
Gerards/Muysken/Welters
effect.
6
Conclusion
Overall the evidence in Section 5 of this paper provides an affirmative answer to our research question. Yes, a private program can outperform public efforts at re-integrating the
unemployed. In a comparison to public re-integration efforts over the period of 1999-2005
we found that the Philips Employment Scheme has a net treatment effect of almost 28% and
a deadweight loss of just over 33%. This greatly surpasses the performance of the public
efforts in the same period. We are currently developing further research plans focussed on
clarifying what underlying factors lead to the success of the PES.
Apart from this comparison of the PES to a public scheme in a given time period, we have
also taken an in depth look at the PES itself. We find that the business cycle influences the
chance of finding a job after the PES since higher unemployment at time of inflow correlates
positively with the job finding dummy. However, we found no significant difference in chance
of finding a job for participants with short or long unemployment history, suggesting the
PES works equally well for participants of varying unemployment duration. We found mixed
effects of the age of participants on the chance of finding a job after the PES. While in general
age seems negatively related to the chance of finding a job after the PES, the participants
between age 31 and 40 perform better than ones aged 16-30. We found no evidence of
different job finding chances after the PES for men or women and only a weak positive effect
of having university education on chances to find a job after the PES (compared to all other
levels of education). This further supports the notion that the PES works almost equally
well for unemployed of all characteristics. In fact, if the characteristics of the participants
seem to matter so little for the chance of them finding a job after the PES, this may imply
that a positive ‘Philips stigma’ is all there is to it. As said revealing the underlying success
factors is an avenue for research we are currently developing.
A final interesting conclusion from this paper is that Philips -intendedly or unintendedlyseems to have gained a useful recruitment channel with the PES, as they apparently pick
the youngest and highest educated participants to offer a regular job to after the PES. We
plan to investigate how much this saves Philips on their recruitment and selection costs, to
hopefully answer the question whether this is also a net profitable program for the company.
We suspect this could very well be the case and that would mean the PES is both a profitable
and effective way of helping unemployed back to work.
The deadweight loss of a private ALMP
21
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22
A
Gerards/Muysken/Welters
Regional analysis
Table 15: Effect of labor market conditions on participants quality, 1995-2006
Dependent variable
Estimation number
Unemployment:
National
Province
Corop
Municipality
Gender:
Male
Female
Age cohorts:
16 - 30 years
31 - 40 years
41 - 50 years
> 50 years
Constant
Adj. R2
N
1
Educational attainmenta
2
3
4
0.08 (0.00)∗∗∗
0.07 (0.00)∗∗∗
0.07 (0.00)∗∗∗
0.05 (0.00)∗∗∗
reference
−0.00 (0.01)
reference
−0.03 (0.01)∗∗∗
−0.05 (0.01)∗∗∗
−0.04 (0.03)
0.73 (0.02)∗∗∗
0.08
4139
reference
0.00 (0.01)
reference
−0.03 (0.01)∗∗∗
−0.05 (0.01)∗∗∗
−0.05 (0.03)∗
0.75 (0.02)∗∗∗
0.07
4139
reference
−0.01 (0.01)
reference
−0.00 (0.01)
reference
−0.04 (0.01)∗∗∗
−0.06 (0.03)∗∗∗
−0.07 (0.03) ∗ ∗
0.76 (0.01)∗∗∗
0.09
4136
reference
−0.04 (0.01)∗∗∗
−0.06 (0.03)∗∗∗
−0.06 (0.03) ∗ ∗
0.81 (0.02)∗∗∗
0.05
4032
∗∗∗
= significant at 1%, ∗∗ = significant at 5%, ∗ = significant at 10%
Participant’s years of schooling to labor force average years of schooling ratio
Standard errors in parentheses
a
For this table we used the number of unemployment benefit claimants as unemployment
measure. This variable is available for all geographical levels. In Table 9 we used a different
unemployment measure (the unemployment rate), which we have available for more years
but not on all geographical levels. Both measures show the same pattern and trend.
The deadweight loss of a private ALMP
23
Table 16: Explaining participants’ job finding chances, 1995-2006
Job finding dummy
Unemployment at time of outflow:
National
−0.54∗∗∗
(0.10)
Province
−0.46∗∗∗
(0.08)
Corop
−0.49∗∗∗
(0.09)
Municipality
Unemployment at time of inflow:
National
−0.36∗∗∗
(0.07)
0.36∗∗∗
(0.10)
Province
0.30∗∗∗
(0.08)
Corop
0.30∗∗∗
(0.09)
Municipality
Age
Gender
Prior education:
Primary school
Low level vocational/secondary
Middle level vocational/secondary
High level vocational
University
Unemployment status
Unemployment duration:
0-6 months
6-12 months
12-24 months
24-48 months
48+ months
If unemployed, what age:
Age 16-30
Age 31-40
Age 41-50
Age 51+
Counseling from UWV
Constant
N
∗∗∗
−0.03∗∗∗
(0.01)
0.04
(0.09)
−0.03∗∗∗
(0.01)
0.04
(0.09)
−0.09
(0.16)
−0.04
(0.10)
−0.11
(0.16)
−0.04
(0.10)
−0.01
(0.13)
0.29∗
(0.15)
−0.08
−0.08
(0.16)
(0.16)
−0.03
−0.03
(0.10)
(0.10)
reference
−0.01
−0.03
(0.13)
(0.13)
0.28∗
0.27∗
(0.15)
(0.15)
−0.51∗∗∗
(0.16)
−0.51∗∗∗
(0.16)
−0.49∗∗∗
(0.16)
0.03
(0.13)
−0.11
(0.14)
0.17
(0.15)
0.04
(0.16)
reference
0.03
0.02
(0.13)
(0.13)
−0.11
−0.11
(0.14)
(0.14)
0.17
0.17
(0.15)
(0.15)
0.05
0.04
(0.16)
(0.16)
0.05
(0.13)
−0.08
(0.13)
0.19
(0.15)
0.07
(0.16)
0.35 ∗ ∗
(0.15)
0.31
(0.22)
0.30
(0.31)
−0.08
(0.05)
2.12∗∗∗
(0.40)
982.00
reference
0.35 ∗ ∗
0.37 ∗ ∗
(0.15)
(0.15)
0.32
0.34
(0.22)
(0.22)
0.29
0.33
(0.31)
(0.31)
−0.07
−0.09∗
(0.05)
(0.05)
2.07∗∗∗
2.22∗∗∗
(0.39)
(0.38)
982.00
982.00
0.34 ∗ ∗
(0.15)
0.30
(0.22)
0.29
(0.31)
−0.07
(0.05)
1.96∗∗∗
(0.38)
982.00
= significant at 1%, ∗∗ = significant at 5%,
Standard errors in parentheses
∗
−0.03∗∗∗
(0.01)
0.01
(0.09)
0.24∗∗∗
(0.07)
−0.03∗∗∗
(0.01)
0.05
(0.09)
−0.53∗∗∗
(0.16)
= significant at 10%
−0.00
(0.12)
0.29∗
(0.15)
24
B
Gerards/Muysken/Welters
Representativeness of ‘Interview’ dataset
Figure 5: Mean of variable gender by year and dataset
600
300
500
250
400
200
300
150
200
100
100
50
0
0
N 8900 (left hand scale)
N Interview (right hand scale)
Figure 6: Mean age of participants by year and dataset
40
35
30
25
20
15
Mean age 8900
Mean age Interview
The deadweight loss of a private ALMP
25
Figure 7: Number of participants (N) by year and dataset
600
300
500
250
400
200
300
150
200
100
100
50
0
0
N 8900 (left hand scale)
N Interview (right hand scale)