Panel Data Research Center at Keio University
DISCUSSION PAPER SERIES
DP2014-007
March, 2015

- Policy Evaluation of the Job Café Related Projects –Isamu Yamamoto＊
Yasuhiro Nohara＊【Abstract】

This paper conducts a policy evaluation of the Job Café support programs that had been
implemented as the regional active labor market policy for the youth since the 2000s in
Japan. First, we estimate job matching function using prefectural panel data to
examine whether the efficiency of job matching increased in the target model regions
covered by the programs. The results show that the matching efficiency may haveincreased in the target model regions as a result of the Job Café support programs
between FY 2005 and 2007. Next, we conduct a difference-in-differences analysis basedon regression model, using household panel data, to examine whether the individual
employment probability increased. The results do not show the strong evidence that theprobability of regular and non-regular employment increased in the target model
regions. Thus, we interpret that while the Job Café support programs may have createdemployment for Job Café users, the programs did not significantly improved the overall
employment environment of the youth in the region.

＊

Graduate School of Business and Commerce, Keio University

Panel Data Research Center at Keio University
Keio University

Active Labor Market Policy and Youth Employment in Japan∗– Policy Evaluation of the Job Café Related Projects –Isamu Yamamoto*

Yasuhiro Nohara

Keio University

Keio University
Abstract

This paper conducts a policy evaluation of the Job Café support programs that
had been implemented as the regional active labor market policy for the youth
since the 2000s in Japan. First, we estimate job matching function using
prefectural panel data to examine whether the efficiency of job matching increasedin the target model regions covered by the programs. The results show that thematching efficiency may have increased in the target model regions as a result of
the Job Café support programs between FY 2005 and 2007. Next, we conduct a
difference-in-differences analysis based on regression model, using household panel
data, to examine whether the individual employment probability increased. The
results do not show the strong evidence that the probability of regular and nonregular employment increased in the target model regions. Thus, we interpret thatwhile the Job Café support programs may have created employment for Job Café
users, the programs did not significantly improved the overall employment
environment of the youth in the region.
Keywords: Active labor market policy, Youth employment, Matching function,
Difference-in-differences analysis, Job Café
* Corresponding Author: yamamoto@fbc.keio.ac.jp, Tel/Fax: +81-3-5427-1085
The authors deeply appreciate Yoshio Higuchi, Michio Naoi, and Hirotaka Ito for their valuablecomments. We are grateful for access to the micro data from the “Keio Household Panel Survey,”
provided by the Panel Data Research Center at Keio University, and the prefectural data fromthe JEPS Statistics, provided by the Ministry of Health, Labor, and Welfare. Any remaining errors
are entirely our own.
1

1. Introduction
What effects can we expect for the active labor market policy targeting the youthof specific regions? In this paper, we examine the effect on youth employment of
the Job Café support programs, the regional active labor market policy for the
youth implemented since the 2000s in Japan, based on prefectural panel data on
public job placements and individual household panel data.
The employment environment for the youth has continued to deterioratein the Japanese labor market since the 1990s, a time known as the employment ice
age. The unemployment rate for those between the ages of 15 and 24 shifted between
3 and 5% until the 1980s. However, this rate increased much faster than it did forother age groups after the burst of the bubble economy, reaching approximately
10% by the early 2000s. In addition to the unemployment rate, the non-regular
employment rate increased rapidly from the late 1990s and, by the 2000s, one outof three people between the ages of 15 and 24, excluding students, were employedin this category. On the other hand, there were notable differences in theemployment environment for the youth among the regions. For example, in 2003,
the unemployment rate for those between the ages of 15 and 24 was 7.4% in the
Hokuriku region, 12.9% in the Hokkaido region, and 12.7% in the Kyushu andOkinawa regions. As a result, the employment environment for the youth has drawn
social attention as an issue that affects the foundations of the economy, such as
economic disparity, economic growth, and social security.
Given the circumstances, the government launched intensive active labormarket policies for the youth. In April 2003, the “Strategy Council to Foster a Spiritof Independence and Challenge in Youth” was established, in which the members
included the Minister of Education, Culture, Sports, Science, and Technology, the2

Minister of Economy, Trade, and Industry(hereafter, “METI”), and the State
Minister in Charge of Economic and Fiscal Policy. Then, in June of the same year,
the “youth independence and challenge plan” was compiled. The plan was crossministerial and the scope of the policy incorporated a wide range of fields, such as
education, employment, and industry, in order to comprehensively implement
measures to address the youth employment issues. Komikawa (2010) and Arai
(2006) pointed out that the plan is the first full-scale employment measures for the
youth in the post-war era in Japan. Subsequently, several measures targeting youth
employment have been established, including the “action plan for the independence
and challenges of youth” in December 2004, and the “enhanced action plan for the
independence and challenges of youth” in October 2005.
As part of the “youth independence and challenge plan,” which covered awide range of fields, “Job Cafés” were established as the core of youth employment
measures for each region. The Job Cafés are one-stop services for the youth, throughmeasures under the initiative of each region, as a new system to collect actual
opinions from the youth and to develop careful and effective measures. Specifically,
Job Cafés provide career counseling, job seeking information, and job placement incollaboration with the Public Employment Security Office established together with
the Job Cafés. Since the start of the program in 2004, Job Cafés have operated in
46 prefectures, excluding the Kagawa Prefecture.
A number of support programs have been implemented to promote the
Job Café program: the “model program,” implemented from FY 2004 through 2006;
the “program for the network between youth and SMEs to support the Job Café
function,” implemented from FY 2006 through 2007; the “Job Café regional network
support program,” implemented from FY 2008 through 2010; the “program forpersonnel support to respond to the employment situation in SMEs,” implemented
3

from FY 2009 through 2010; and the “program for promoting the employment
environment development in SMEs,” implemented in FY 2011.
Through these support programs, the METI has provided intensive
support to specific regions between FY 2004 and 2011. Specifically, the support
programs implemented in FY 2009 and 2010, “Job Café regional network support
program” and the “program for personnel support to respond to the employment
situation in SMEs,” were designed against the deterioration in the employment
situation after the bankruptcy of Lehman Brothers and the necessity of
improvements in employment in local SMEs. These programs were implemented by
entrusting the operations to the Japan Chamber of Commerce and Industry(hereafter “JCCI”), based on SMEs across the country. In addition to the budget
allocated by the Ministry of Health, Labour, and Welfare (hereafter “MHLW”) to
the Job Cafés across the country, the budget for these related programs wasallocated intensively to 15 to 20 target regions selected by the METI and the Japan
Chamber of Commerce and Industry. The scale of the budget was JPY 2.5 to 3
billion from the MHLW. In addition, the following additional budgets wereallocated: JPY 5 to 7 billion for the “model program;” approximately JPY 1.5
billion for the “program for the network between youth and SMEs to support the
Job Café function;” JPY 0.5 to 1.5 billion for the “Job Café regional network
support program;” approximately JPY 2 billion for the “program for personnel
support to respond to the employment situation in SMEs;” and approximately JPY
1 billion for the “program for promoting the employment environment developmentin SMEs.”
The Job Café programs can be classified as an active labor market policy(ALMP). In contrast to a passive labor market policy, which has conventionallycentered on the payment of unemployment benefits, an ALMP is a policy that aims
4

to help the unemployed find jobs through occupational training and job placement.
ALMPs have been widely implemented since the 1990s, mainly in European
countries. The shift from a passive to an active labor market policy wasrecommended in the “OECD Jobs Strategy” in 1994, as well as in the “Restated
OECD Jobs Strategy” in 2006.
However, ALMPs have received some criticism. For example, Boeri andBruda (1996) cast doubt on the effects of ALMPs by pointing out the possibility ofcreating a negative signal on the unemployed who participate in the program. 1
Furthermore, they note the possibility that the high initial productivity of those
who obtained jobs through the program meant they would have found a job bythemselves without participating in the program. 2 In addition, they point out thepossibility that the overall employment across the labor market may not increase,because employment for those outside the scope of the policy could be crowded out.
In response to these criticisms, many studies have examined the effects of
ALMPs in the field of labor economics. For example, based on a meta-analysis of
137 experimental studies that examined ALMPs in 19 EU countries, Kluve (2010)
finds that the “efficient job search service” has relatively the greatest positive effecton the employment probability among four ALMPs. The ALMPs were the following:(1) occupational training program; (2) employment subsidy system; (3) direct
employment in public sectors; and (4) efficient job search service. In addition, Cardet al. (2010) conduct a meta-analysis of 97 experimental studies in 26 countries,
including countries outside the EU. They find that positive effects can be obtained1

For example, Burtless (1985) measures the effects of the wage subsidy program in the UnitedStates, finding that the employment probability of workers on the program decreased. A possible
interpretation is that companies tend to estimate the productivity of the workers on the program
as low and thus, use the program participation as a screening device.
2

Calmfors (1994) refers to this situation as a deadweight loss.
5

in terms of employment and wages using the “job search assistance program” withinthe “efficient job search service.” 3
Although the contents of the Job Café related programs are similar to
those of the “efficient job search service,” which Kluve (2010) and Card et al. (2010)
show to be effective, it is not obvious that the Job Café related programs are also
effective in Japan. To the best of our knowledge, only two academic studies provide
an empirical analysis of the effects of Job Café related programs: Takahashi (2005)
and Nagase and Mizuochi (2011). Takahashi (2005) estimates the job matching
function using monthly panel data by prefecture and by age from the“job/employment placement services statistics” (hereafter, “JEPS Statistics”;
MHLW) between July 1996 and August 2004. The study finds no statisticallysignificant result that matching efficiency had improved for the 19–29 age groupsince the start of Job Café programs. On the other hand, Nagase and Mizuochi(2011) examine whether the increase in the Job Café use ratio in the prefectures
improves the probability of regular employment. By using monthly micro data fromthe “Labour Force Survey” (Ministry of Internal Affairs and Communications)
between 2002 and 2007, they find a significant effect for men.
These studies offer no consensus on the positive effects of Job Café
programs on youth employment. In addition, while the two studies evaluated the
Job Café programs as a whole, they did not examine the extent to which the “model
program” and other support programs, which allocate a large amount of the budget
to specified regions, had an effect. As described earlier, in Japan, there is a marked
3

The results of these two meta-analyses include many experimental studies on ALMPs for people

other than the youth. Blundel et al. (2002) conduct a study on the efficient job search service forthe youth using a matching difference-in-differences analysis of the New Deal for Young People
Program implemented since 1998 in the U.K. They find that both male and female program
participants who searched for a job while receiving counseling during the four-month gateway
period had a higher probability of obtaining employment.
6difference among regions in youth unemployment rate. Therefore, examining
whether the regional ALMP can improve the youth employment should be
important when designing future employment policies and regional policies.
Therefore, this paper measures empirically the effects of Job Café support
programs classified as ALMPs for the target model regions. Our analysis uses
prefectural panel data from the JEPS Statistics and household individual panel
data, based on the two studies described earlier. The former data are used to
examine whether the implementation of Job Café support programs increased thematching efficiency of public job placement. The latter data are used to examine
whether the employment probability for regular and non-regular employment
improved. 4
Our results can be summarized as follows. First, from the estimation of
the job matching function using prefectural panel data, we found an increase inmatching efficiency between FY 2005 and 2007 in the Job Café support program
target regions (model regions). Next, from the DD analysis using a random effectprobit model based on individual panel data, we did not find strong evidence of anincrease in the probability of employment for regular and non-regular employment
in the model regions. Thus, we point out that although there is a possibility of
employment creation for Job Café users through the Job Café support programs,
the effects were not significant enough to improve the youth employment
environment in the regions as a whole.
The rest of the paper are structured as follows. In Section 2, we outline
the Job Café programs as an ALMP and explain our analysis approach. In Section
4

This paper is similar to Boeri and Bruda (1996), which studies whether budgetary injections to

76 regions in the Czech Republic under the active labor market policy in the 1990s yielded asignificant result. Boeri and Bruda (1996) report that higher budgets tended to be associated withincreased employment.
7

3, we introduce the data and variables used in our analysis and use graphs to show
the trends in youth employment. Then, in Section 4, we describe the results of theestimations using the matching function and the employment probability function.
Finally, Section 5 concludes the paper with a summary of our results and a
description of areas of possible future research.

2. Job Café Programs and Analysis Approach
2.1 Target regions of the support programs and the Job Café target age
Under the “Youth independence and challenge plan,” the Job Café programs are
positioned as the core of the youth employment measures for each region. In thispaper, we measure the effects of the “model program” in which government supports
the selected region’s Job Café programs by designating the specific target regions
as the “model regions.”
The model program began in FY 2004 when the Job Café programs started.
In all, 15 regions (prefectures) were selected as model regions at that time, with anadditional five regions specified in FY 2005 and continuing to FY 2006. The model
regions then changed from FY 2007 depending on the type of programs. By FY
2011, when the model program ended, 27 prefectures had been designated at least
once as model regions. The list of the model regions is shown in Table 1.
The age of workers covered by the programs varies among regions. Job
Café programs specify an upper age limit, usually those under 35. However, the
Job Cafés in each region set their own target age to respond flexibly to the regional
unique labor market situation. The upper age limit for the different regions isshown in Table 2.
8

2.2 Analysis approach

Estimation of the matching function
We take two approaches to measure the effects of model programs of Job Café. The
first approach measures the job matching efficiency in the youth labor market.
Specifically, by using the JEPS Statistics as monthly panel data by prefecture,5 weestimate the job matching function to examine whether the matching efficiency has
increased as a result of the model support programs.
As introduced by Petrongolo and Pissarides (2001), we formulate the job
matching function as follows.

𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽1 + 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 𝑻𝑻𝒕𝒕𝒕𝒕 𝜷𝜷𝟐𝟐 + 𝛽𝛽3 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 + 𝑻𝑻𝒕𝒕𝒕𝒕 𝜷𝜷𝟒𝟒

+𝛽𝛽5 𝑈𝑈𝑖𝑖t𝑚𝑚 + 𝛽𝛽6 𝑉𝑉𝑖𝑖𝑖𝑖𝑖𝑖 + 𝛽𝛽7 𝑑𝑑𝑡𝑡𝑡𝑡 + 𝒅𝒅𝒎𝒎 𝜷𝜷𝟖𝟖 + 𝑓𝑓𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 ,(1)

where 𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖 denotes the number employed in prefecture i in month m of fiscal yeart (natural logarithmic value); 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 is a dummy variable indicating the model

regions under the model programs; 𝑻𝑻𝒕𝒕𝒕𝒕 is a vector of year dummy variables; 𝑈𝑈𝑖𝑖𝑖𝑖𝑖𝑖

denotes the effective number of monthly job seekers (natural logarithmic value);

𝑉𝑉𝑖𝑖𝑖𝑖𝑖𝑖 denotes the effective number of monthly job offers (natural logarithmic value);𝑑𝑑𝑡𝑡𝑡𝑡 is a dummy variable indicating the period of an economic trough; 𝒅𝒅𝒎𝒎 is avector of month dummy variables; 𝑓𝑓𝑖𝑖 is a time-invariant prefecture

specific factor;and 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 is an error term.

Equation (1) is a Cobb–Douglas matching function in which the residuals

5

In addition to the study by Takahashi (2005), introduced in the preceding section, the studies

estimating the matching function based on the JEPS Statistics include Kambayashi and
Mizumachi (2014) and Sasaki (2007).
9

can be identified as a matching efficiency when the number of job seekers and job
offers are controlled. By putting fiscal year dummies, model region dummy, and thecross-terms of those dummies in the matching function, we conduct a difference-indifferences (DD) analysis where the coefficients of the cross-terms can beinterpreted as the effect of model program.
Since the model programs ended in FY 2012, we set the period from FY
2013 as a base of fiscal year dummies 𝑇𝑇𝑡𝑡𝑡𝑡 , so that we can identify the coefficientsof the cross-terms of fiscal year dummies (between FY 2005 and FY 2011) 𝜷𝜷𝟐𝟐 as

the average treatment effect (ATE). 6,7 If the coefficients 𝜷𝜷𝟐𝟐 for FY 2005 to 2011

are significantly positive, we interpret that the matching efficiency was high in themodel regions during the period when the model program was implemented, andthe Job Café support programs did produce an effect.
In addition, we take into account the possibility that the matchingefficiency was increased by the efficient job search and job offers as examined in
Petrongolo and Pissarides (2001) and Ohta (2010). Specifically, we allow thecoefficients of job seekers 𝑈𝑈𝑖𝑖𝑖𝑖𝑖𝑖 and the job offers 𝑉𝑉𝑖𝑖𝑖𝑖𝑖𝑖 to vary due to the effect ofthe model program by estimating the following equation.

𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛾𝛾1 + 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 𝑻𝑻𝒕𝒕𝒕𝒕 𝜸𝜸𝟐𝟐 + 𝛾𝛾3 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 + 𝑻𝑻𝒕𝒕𝒕𝒕 𝜸𝜸𝟒𝟒+(𝛾𝛾5 +𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 ∙ 𝑻𝑻𝒕𝒕𝒕𝒕 𝜸𝜸𝟔𝟔 + 𝛾𝛾7 𝐷𝐷𝑖𝑖 + 𝑻𝑻𝒕𝒕𝒕𝒕 𝜸𝜸𝟖𝟖 )𝑈𝑈𝑖𝑖t𝑚𝑚+(𝛾𝛾9 +𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 ∙ 𝑻𝑻𝒕𝒕𝒕𝒕 𝜸𝜸𝟏𝟏𝟏𝟏 + 𝛾𝛾11 𝐷𝐷𝑖𝑖 + 𝑻𝑻𝒕𝒕𝒕𝒕 𝜸𝜸𝟏𝟏𝟏𝟏 )𝑉𝑉𝑖𝑖t𝑚𝑚

6(2)+𝛾𝛾13 𝑑𝑑𝑡𝑡𝑡𝑡 + 𝒅𝒅𝒎𝒎 𝜸𝜸𝟏𝟏𝟏𝟏 + 𝑓𝑓𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 .

Although the model programs started in FY 2004, the data available for our study starts fromJanuary 2005. Therefore, the period between April 2004 and December 2004 is not covered by thisanalysis.
7

As a priority budget is not allocated to specific regions after the end of support programs, we

assume that any direct policy effects are not seen in FY 2013, and focus on a comparison with
FY 2013 after the end of programs.
10

If either 𝜸𝜸𝟔𝟔 or 𝜸𝜸𝟗𝟗 is significantly positive, we interpret this to mean that the model
support programs increased the matching efficiency, particularly through theenhanced intensity of the job search and job offer behaviors.

Estimation of the employment probability function
Another approach to investigate the effects of the model programs is the estimationof the employment probability function using the annual panel data at theindividual level from the household panel survey. Specifically, we conduct a DD
analysis by estimating the following equation.

𝑌𝑌𝑖𝑖𝑖𝑖 = 𝛿𝛿1 + 𝐷𝐷𝑖𝑖 𝑻𝑻𝑡𝑡 𝜹𝜹𝟐𝟐 + 𝛿𝛿3 𝐷𝐷𝑖𝑖 + 𝑻𝑻𝒕𝒕 𝜹𝜹𝟒𝟒 + 𝛿𝛿5 𝑆𝑆𝑖𝑖 + 𝑿𝑿𝒊𝒊𝒊𝒊 𝜹𝜹𝟔𝟔 + 𝑓𝑓𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 ,(3)

where 𝑌𝑌𝑖𝑖𝑖𝑖 is a dummy variable indicating employment status of individual i in year

t. Specifically, 𝑌𝑌𝑖𝑖𝑖𝑖 takes the value 1 if the individual is employed as a regular or

non-regular employee. 𝐷𝐷𝑖𝑖 is a model region dummy variable that takes the value 1

if the individual lives in a model region. 𝑻𝑻𝒕𝒕 is a vector of year dummy variables.

𝑆𝑆𝑖𝑖 is a dummy variable indicating a junior or senior high school graduate. 𝑿𝑿𝒊𝒊𝒊𝒊 is avector of control variables such as age, spouse dummy, child dummy (1 if having achild under 6), living with parents dummy, the number of people living together,
non-work income, GDP growth rate of the prefecture. 𝑓𝑓𝑖𝑖 is a time-invariant
individual specific factor; and 𝜀𝜀𝑖𝑖𝑖𝑖 is an error term.

In the same manner as the equations (1) and (2), we interpret thecoefficients of the cross term of the model region dummy and the year dummy
variables 𝜹𝜹𝟐𝟐 as indicating the ATE. Since we use the panel data from the household

panel survey as of January of each year, the data for 2004 apply before the model
program began in April 2004. Therefore, we set year 2004 as the base for year
11

dummy variables so that we can compare the years before the model program (2004)
and the year after the program (2005~12). Accordingly, if the coefficient of eachyear between 2005 and 2012 is significant and positive, we interpret this to meanthat the employment probability is high in the model regions and that the Job Café
support program has a positive effect on the youth employment.
We also estimate the equation (4), in which the cross term of the model
region dummy 𝐷𝐷𝑖𝑖 and a vector of year dummy variables 𝑻𝑻𝒕𝒕 are incorporated in

the junior/senior high school graduate dummy 𝑆𝑆𝑖𝑖 , to examine whether or not theeffect of the model program differs across the academic background.

𝑌𝑌𝑖𝑖𝑖𝑖 = 𝜃𝜃1 + 𝐷𝐷𝑖𝑖 𝑻𝑻𝒕𝒕 𝜽𝜽𝟐𝟐 + 𝜃𝜃3 𝐷𝐷𝑖𝑖 + 𝑻𝑻𝒕𝒕 𝜽𝜽𝟒𝟒+(𝜃𝜃5 +𝐷𝐷𝑖𝑖 𝑻𝑻𝒕𝒕 𝜽𝜽𝟔𝟔 + 𝜃𝜃7 𝐷𝐷𝑖𝑖 + 𝑻𝑻𝒕𝒕 𝜽𝜽𝟖𝟖 )𝑆𝑆𝑖𝑖 + 𝑿𝑿𝒊𝒊𝒊𝒊 𝜽𝜽𝟗𝟗 + 𝑓𝑓𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 .

(4)

We estimate the equations (3) and (4) as a random effect probit model.

3. Data
3.1 Data and variables
The data used in this paper are the prefectural panel data from the JEPS Statistics
and individual panel data from the Keio Household Panel Survey (KHPS).
The JEPS Statistics are released by the MHLW at the end of every month,and provide an outline of the job search, job offers, and employment situations,handled by the Public Employment Security Offices (excluding new graduates).
The data available for this paper are by prefecture and by age group, and further
narrowed down according to the Job Café target age, which varies among12

prefectures, as shown in Table 2. Kagawa Prefecture, which does not have a Job
Café, is excluded from the sample. The data period is between January 2005 andDecember 2013. Although we should use the data before 2004, when the model
programs began, we are not able to use the data before 2004 due to the data
availability. We thus use the data since 2005 only.
The variables used in the estimation are as follows: the number of monthly
new employment, 𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖 ; the active number of monthly job seekers, 𝑈𝑈𝑖𝑖𝑖𝑖𝑖𝑖 ; the active

number of monthly job offers, 𝑉𝑉𝑖𝑖𝑖𝑖𝑖𝑖 ; the model region dummy, 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 ; the fiscal year
dummy, 𝑻𝑻𝒕𝒕𝒕𝒕 ; as well as the other variables explained in the equations (1) and (2).

The basic statistics are shown in Table 3.

The KHPS is the longitudinal household survey conducted at the end ofJanuary each year from 2004 for individuals randomly extracted from men andwomen between 20 and 69. We use the data from the survey between 2004 and 2014,
during which the GDP growth rate, a control variable for the economic situation ineach prefecture, was available to us. In the same manner as the JEPS Statistics,
the sample is selected according to the Job Café target ages, which vary among
prefectures.
From the KHPS, we can get the information whether the individual is
working or not, employed or self-employed, a regular worker or non-regular worker(contract worker, part-time, temporary worker from an agency, fixed-term
employee). We focus on the employment dummy, regular employment dummy, andnon-regular employment dummy. The basic statistics of the KHPS are shown in
Table 4.

3.2 Data Overview: Transition of the youth labor market from 2004
Prior to the DD analysis, this section graphically shows the basic trends in youth
13

employment provided by the data. First, we present an overview of major trends
in the youth labor market at the prefectural level using the JEPS Statistics. Figures
1 to 3 show the annual transition of the average number of monthly new
employment, the number of monthly job seekers, and the number of monthly job
offers for the Job Café target ages, respectively. The vertical lines for each marker
in the figure show the 95% confidence intervals.
In Figure 1, we see that the number of monthly new employment in themodel regions is greater than that in non-model regions in every fiscal year.
Furthermore, it seems that the gap increased from FY 2009 through FY 2010. On
the other hand, the number of job seekers in Figure 2 shows no significant gap until
FY 2009. However, the number is greater in the model regions from FY 2010
through FY 2011. In addition, the number of job offers, shown in Figure 3, is greaterin the model regions from FY 2010 onward though it is greater in the non-model
regions until FY 2009.
These results imply that the number of job seekers and job offers throughthe Public Employment Security Offices has increased since FY 2010, and that thismay have increased the number of new employment. On the other hand, thenumber of new employment was greater than that in the non-model regions
although the number of job offers was smaller in the model regions until FY 2009.
Therefore, we can point out the possibility that the Job Café support programs
increased the matching efficiency.
From these figures, however, it is hard for us to judge whether the relative
increase in the number of new employment in the model regions was caused by theincrease in inputs, such as job seekers and job offers, or whether it was due to better
matching efficiency. In addition, the confidence intervals indicate that thestatistical significance of the gap observed in these figures is not necessarily strong.
14

Furthermore, we have not controlled for other factors. Therefore, to examine
whether the Job Café support programs indeed increased the matching efficiency,
we conduct a DD analysis as described in the next section.
Figures 4 and 5 present the transition of employment rate for male andfemale, respectively, in the model regions and non-model regions. According to thesefigures, the employment rate for male in the model regions (Figure 4) tends to belower than that in the non-model regions. It seems that the gap increased since2008, which coincides with the end of the model programs with the largest budgets.
Therefore, it is likely that the injection of funds through the model programs
increased the employment rate for male in the model regions. On the other hand,the employment rate for female, as shown in Figure 5, decreased in the model
regions until 2008, then increased until 2010, before decreasing until 2012. We
examine these trends in the DD analysis described in the next section.

4. Estimation Results
4.1 Matching function
The estimation results of the equations (1) and (2) are shown in Table 5. Columns(a) to (b) show the estimation results for the equation (1), and columns (c) to (d)show that for the equation (2). The result of the Hausman test is also listed inTable 5, which shows that the fixed effect model is supported for both the equations(1) and (2).
First, focusing on the row (b), supported by the Hausman test, we findthat the coefficients of the number of job seekers and job offers are significantly

15

positive, 8 and that the coefficients of the cross term of the model region dummy
and the fiscal year dummies between FY 2005 and FY 2007 are also significantly
positive. According to this result, we can point out that the matching efficiency
increased between FY 2005 and FY 2007 in the model regions. The coefficients
indicate that the matching efficiency increased in the model regions by 3 % becauseof these programs.
Next, focusing on row (d), we find that the coefficient of the cross term of
the model region dummy and the fiscal year dummy for FY 2007 is significantly
positive, although the coefficients of the cross term of the FY 2005 dummy and FY
2006 dummy are no longer statistically significant. In addition, most of thecoefficients of the cross term for the number of job seekers are not statisticallysignificant, and the cross term of the FY 2010 dummy and the FY 2012 dummy
are significantly negative. On the other hand, although the coefficient of the crossterm of the number of job offers for FY 2010 is significantly positive, it is notstatistically significantly different from zero for the other fiscal years. These results
indicate that the matching efficiency in the model regions between FY 2005 andFY 2007 increased, but we could not specify that the increase in the efficiency wascaused either by the job offer actions or by the job search actions.
As for the reason why we observe the increase in the matching efficiency
only in the FY 2005, 2006, and 2007, we can point out the followings. The firstreason is the large budget. The budget of the model programs implemented between
FY 2004 and 2006 was approximately 5 to 7 billion yen, which was three to fourtimes larger than that implemented from FY 2007 onward. Since the support of a8

The total of the coefficient of the number of job seekers and the job offers (both natural

logarithmic values) is between 0.8 and 0.9, which is almost consistent with the estimation resultsof the preceding studies, including Takahashi (2005).
16

budget at a certain minimum level is necessary to improve the matching efficiency,
this may be why the policy effects appear mainly in the period between 2004 and2006.
A second possible reason is the difference among the programs and theimplementation bodies. In FY 2008, the program changed from the “program forthe network between youth and SMEs to support Job Café function” to the “Job
Café regional network support program.” In addition, in FY 2009 and 2010, thesupport provider changed from the METI to the JCCI. These changes in the content
of the programs and the support systems, owing to the changes in programs andimplementation bodies, may have blocked the expected increase in matchingefficiency.
A third possible reason is external factors such as the economic downturn.
In Figure 3, the number of job offers exhibits a rapid decrease from FY 2007, andbegan recovering from FY 2010. However, it has still not recovered to its level priorto FY 2006. Conversely, the number of job seekers has increased since FY 2008 andmaintained a high level between FY 2009 and 2011. Thus, it is likely that the job
matching function for the youth deteriorated because employment agencies such as“Hello Work (the Public Employment Security Office)” became crowded with manyjob seekers owing to the financial crisis.

4.2 Employment probability function
Tables 6 and 7 provide the estimation results of the equations (3) and (4). Table 6is the estimation results for men and Table 7 for women, and the both tables show
the marginal effect. In each table, columns (a) to (c) show the estimation results ofequation (3) and columns (d) to (f) show the estimation results of equation (4).
Focusing on the results for male in Table 6, we find no significant marginal17

effects of the cross term of the model region dummy and the year dummies, as wellas the cross term of the junior/senior high school-graduate dummy, regarding theemployment rate and regular employment rate. In addition, we see that themarginal effects of the cross term of the model regions dummy for 2005 dummy and2012 dummy are significantly negative. Furthermore, the cross term of the model
regions dummy and the year dummy or the junior/senior high school- graduate
dummy are significantly negative.
Based on these results, we cannot say that employment situation for the
young male worker improved during the period of Job Café support program
implementation in the model regions, if any, there was a limited improvement.
Table 7, showing the estimation results for female, indicates that themarginal effects of the cross term of the model regions dummy and 2007 and 2008
dummies are significantly negative. The table also shows that the marginal effectsof the cross term of the junior/senior high school graduate dummy and each year
dummy from 2009 through 2012 are significantly negative. Thus, the positive effectsof the Job Café support programs on the young female employment cannot be
confirmed from these estimation results.

5. Concluding remarks
In this paper, we examined the influence of the ALMP, Job Café support programs,
on the youth regional employment by estimating the matching function and theemployment probability function. The estimations of the matching function basedon prefectural panel data indicate that the Job Café support programs increased
the matching efficiency in the model regions between FY 2005 and 2007. However,18

the estimations of the employment probability function do not show the evidenceof the improvement in the probability of regular and non-regular employment in
the model regions. Based on these two results, we could conclude that the Job Café
support programs possibly created the employment for Job Café users, but theeffect was not significant enough to improve the overall employment environmentof the youth for the model regions.
The different results for the matching function and employment
probability function can be interpreted as follows. First, there is a possibility thatnot many workers receive the benefit of the programs because the Job Café
programs are based on the employment agencies such as “Hello Work (the Public
Employment Security Office).” The use rate of the Hello Work is low, as shown in
the Survey on Employment Trends (MHLW, 2008), which indicates that only about
23% of those newly employed in the year used the Hello Work, including its Internet
services. Therefore, even if the Job Café programs have a positive effect for theirusers in terms of matching efficiency, it is likely that the effect did not spread to all
workers living in the regions because of the low use rate.
Second, as described in the Section 1, there is a criticism of the ALMP in
which even if the employment probability of Job Café users increased, it may be
the case that the employment probability of non-users decreased because of a
crowding out effect. Since household panel data includes randomly selectedindividuals in each region, the estimation results may reflect the overall influence
including the crowding out.

19

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Petrongolo, Barbara and Christopher A. Pissarides (2001): “Looking into the blackbox: A survey of the matching function,” Journal of Economic Literature,38, pp.390-431.
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employment support center,’” Bulletin of Takasaki University of Healthand Welfare, 5, pp.169-180. (in Japanese)20

Ohta, Souichi (2010): The economics of youth employment, Nikkei Publishing Inc.
(in Japanese)
Kambayashi, Ryo and Mizumachi, Yuichiro (2014): “Policy evaluation on Worker
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Sasaki, Masaru (2007): “Measuring efficiency of matching and an incentive to searchthrough the Public Employment Agency in Japan,” The Japanese
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Takahashi, Yoko (2005): “Current situation of ‘Job Cafés’ as employment support
by municipalities,” The Japanese Journal of Labour Studies, 47(6),pp.56-67. (in Japanese)
Nagase, Nobuko and Mizuochi, Masaaki (2011): “Temporary to permanent
employment, the effect of economic recovery, the previous work
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Table 1 List of model regions
FY

Program name

FY2004Model program

FY2005Model program

Target regions (Prefecture)
Hokkaido, Aomori, Iwate, Gunma, Chiba, Ishikawa, Gifu, Kyoto, Osaka,
Shimane, Yamaguchi, Ehime, Fukuoka, Nagasaki, Okinawa
Hokkaido, Aomori, Iwate, Gunma, Chiba, Ishikawa, Gifu, Kyoto, Osaka,
Shimane, Yamaguchi, Ehime, Fukuoka, Nagasaki, Okinawa, Miyagi, Ibaraki,
Niigata, Fukui, Oita

FY2006

Model program
Hokkaido, Aomori, Iwate, Gunma, Chiba, Ishikawa, Gifu, Kyoto, Osaka,
Program for the network between
Shimane, Yamaguchi, Ehime, Fukuoka, Nagasaki, Okinawa, Miyagi, Ibaraki,
youth and SMEs to support Job Caf
Niigata, Fukui, Oitaé function

FY2007

Model program
Hokkaido, Aomori, Iwate, Gunma, Chiba, Ishikawa, Gifu, Kyoto, Osaka,
Program for the network between
Shimane, Yamaguchi, Ehime, Nagasaki, Okinawa, Miyagi, Ibaraki, Niigata,youth and SMEs to support Job Caf
Fukui, Oita, Hiroshima
é function

FY2008

Hokkaido, Aomori, Iwate, Gunma, Chiba, Ishikawa, Gifu, Kyoto, Osaka,
Job Café regional network support
Shimane, Yamaguchi, Ehime, Nagasaki, Okinawa, Miyagi, Ibaraki, Niigata,programFukui, Oita, Hiroshima
Job Café regional network support Aomori, Miyagi, Ibaraki, Gunma, Ishikawa, Wakayama, Yamaguchi, Ehime,
programOita, Kagoshima, Okinawa

FY2009FY2010FY2011FY2012
onwordProgram for personnel support torespond to the employment
situation in SMEs

Hokkaido, Iwate, Tochigi, Chiba, Kyoto, Osaka, Hiroshima, Nagasaki,
Kumamoto

Aomori, Miyagi, Ibaraki, Iwate, Gunma, Ishikawa, Aichi, Wakayama,
Job Café regional network support
Yamaguchi, Ehime, Oita, Kagoshima, Okinawa
program
Program for personnel support torespond to the employment
situation in SMEs

Hokkaido, Tochigi, Chiba, Kyoto, Osaka, Hiroshima, Nagasaki, Kumamoto

Program for promoting theemployment environment
development in SMEs

Hokkaido, Aomori, Iwate, Miyagi, Ibaraki, Tochigi, Gunma, Chiba, Ishikawa,Gifu, Aichi, Kyoto, Osaka, Nara, Wakayama, Hiroshima, Yamaguchi, Ehime,
Nagasaki, Kumamoto, Oita, Okinawa

No support provided from the Ministry of Economy, Trade and Industry to specific regions

Table 2 Upper age limit of the programs in each regions
Target age
29 or younger Fukuoka

Prefecture

34 or younger Tokyo, Gifu, Mie, Kyoto, Osaka, Iwate, Fukushima, Tochigi, Toyama,(under 35) Ishikawa, Shiga, Nara, Wakayama, Kumamoto, Oita, Kagoshima
39 or younger Ibaraki, Chiba, Kanagawa, Fukui, Yamanashi, Shizuoka, Hyogo,(under 40) Yamaguchi, Ehime, Kochi, Saitama, Okayama, Nagasaki
44 or younger

Hokkaido, Miyazaki, Niigata, Nagano, Hiroshima, Okinawa, Aomori,
Yamagata, Gunma, Aichi, Tottori, Shimane, Tokushima, Saga

22

Table 3 Basic statistics: JEPS
Variable name
Sample size
The number of monthly new
4968
employment
The number of monthly job
seekers
4968
The number of monthly job
offers4968
Program target region
dummy (prefecture)
4968
Economic trough dummy
4968

Mean

SD

Min

Max

1851.765

1118.031

408

6531

24760.560 20116.870

4875 106987

20719.450 22251.330

3059 176811

0.472
0.185

0.499
0.388

0
0

1
1

Table 4 Basic statistics: KHPS
Variable name
Employment rate
Regular employment rate
Non-regular employment rate
Dummy program target region(municipality)
Age
Married dummy
With a child under 6 dummy
Junior high school-/senior highschool-graduate dummy
Dummy living with parents
Number of family members
living together
Non-work income
GDP growth rate of the placeof residence

Sample size
9943
9943
9943

Mean
0.660
0.441
0.205

SD
0.474
0.497
0.403

Min
0
0
0

Max
1
1
1

9943

0.079

0.270

0

1

9943
9943
9943

33.239
0.748
0.456

5.649
0.434
0.498

19
0
0

44
1
1

9943

0.522

0.500

0

1

9943

0.240

0.427

0

1

9943

3.766

1.425

1

10

9943

12.40777.595

0

3000

9943

1.169

2.657

-9.193148

9.514758

23

Table 5 Estimation results of job matching function
Model regions dummy
×FY05 dummy
×FY06 dummy
×FY07 dummy
×FY08 dummy
×FY09 dummy
×FY10 dummy
×FY11 dummy
×FY12 dummyln Number of job seekers
ln Number of job seekers
×Model regions dummy(a)Random Effect
0.0172(0.0110)0.0286**(0.0137)
0.0266**(0.0134)
0.0270*(0.0152)
0.0186(0.0123)
0.00282(0.0125)-0.0123(0.0140)-0.000854(0.0141)-0.00153(0.00746)
0.523***(0.0218)(b)
Fixed Effect
0.0164(0.0109)0.0279**(0.0137)
0.0262*(0.0135)
0.0268*(0.0153)
0.0186(0.0124)0.00308(0.0124)-0.0122(0.0140)-0.00102(0.0140)-0.00179(0.00740)
0.538***(0.0285)0.280***(0.0157)

0.288***(0.0182)

×FY05 dummy
×FY06 dummy
×FY07 dummy
×FY08 dummy
×FY09 dummy
×FY10 dummy
×FY11 dummy
×FY12 dummyln Number of job offers
ln Number of job offers
×Model regions dummy
×FY05 dummy
×FY06 dummy
×FY07 dummy
×FY08 dummy(Continue)

24(c)Random Effect-0.283**(0.138)-0.00206(0.209)0.231(0.201)
0.362*(0.193)0.113(0.167)0.192(0.192)0.186(0.156)0.108(0.154)0.153*(0.0881)0.516***(0.0388)
0.0337(0.0357)
0.0593(0.0480)0.0286(0.0496)0.0285(0.0490)0.0118(0.0410)-0.0419(0.0446)-0.110**(0.0520)-0.0869(0.0641)-0.0794*(0.0427)0.265***(0.0213)-0.00279(0.0274)-0.0560(0.0425)-0.0491(0.0483)-0.0628(0.0480)-0.0224(0.0384)(d)
Fixed Effect-0.239*(0.126)-0.0243(0.200)0.219(0.194)0.349*(0.188)0.0805(0.171)
0.176(0.191)
0.168(0.155)0.103(0.155)0.153*(0.0870)
0.562***(0.0456)
0.0259(0.0343)0.0640(0.0472)
0.0328(0.0493)0.0334(0.0479)
0.0222(0.0406)-0.0355(0.0443)-0.106**(0.0510)-0.0842(0.0634)-0.0748*(0.0416)0.278***(0.0206)
0.000247(0.0267)-0.0587(0.0423)-0.0520(0.0488)-0.0665(0.0478)-0.0297(0.0389)

×FY09 dummy
×FY10 dummy
×FY11 dummy
×FY12 dummyln Number of effective job seekers
×each FY dummyln Number of effective job offers
×each FY dummy
Each FY dummy
Economic trough dummy

Month dummy
Constant

Hausman(prob>chi2)
Sample size
Adj-R20.0250(0.0353)
0.0956*(0.0509)0.0791(0.0615)
0.0653(0.0430)0.0203(0.0352)
0.0940*(0.0501)
0.0770(0.0608)0.0606(0.0415)

No

No

Yes

Yes

No
Yes
Yes
Yes
-0.702**(0.281)

No
Yes
Yes
Yes
-0.925**(0.387)

Yes
Yes
Yes
Yes
-0.497*(0.275)

Yes
Yes
Yes
Yes
-1.065**(0.399)

4,968
0.864

4,968
0.868

4,968
0.864

0.00000.0000Notes: 1. Numbers in parentheses are robust standard errors.
2. ***, **, and * indicate statistical significance at the 1%, 5%, and 10 % levels,
respectively.
3. Base for fiscal year dummies is 2013.

25

4,968
0.873

Table 6 Estimation results of the employment probability function: Male

(a)
(b)
(c)
(d)
(e)
(f)
RegularNon-regular
RegularNon-regular Employme
Employment
employment employmentnt
employment employment
Rate
Rate
rate
rate
rate
rate
-0.00100-0.02530.00186-0.000553
0.0115-0.00146*
Model regions dummy(municipality)(0.0453)(0.00376)(0.00742)(0.0210)(0.000833)
0.000769
0.0217-0.00157*
0.000404
0.0207-0.00130*
×2005 dummy(0.0100)(0.0286)(0.000840)(0.00528)(0.0280)(0.000717)
0.000505
0.0189-0.00144-0.0122-0.00897-0.00120*×2006 dummy(0.00612)(0.0244)(0.000877)(0.101)(0.0444)(0.000684)
0.000704
0.00739-0.000154
0.000598
0.000304-0.000808
×2007 dummy(0.00906)(0.0222)(0.00269)(0.00781)(0.0324)(0.00105)0.000498
0.0183-0.00144-0.000437
0.0165-0.00122*
×2008 dummy(0.00613)(0.0240)(0.000925)(0.00513)(0.0233)(0.000672)
0.000769
0.01850.003480.000600
0.0175-0.000406
×2009 dummy(0.0101)(0.0244)(0.0104)(0.00780)(0.0242)(0.00248)
0.000549
0.0168-0.000123
0.000338
0.0177-0.00107
×2010 dummy(0.00684)(0.0228)(0.00392)(0.00467)(0.0245)(0.000794)-0.00373
0.00937-0.000952-0.0309
0.0186-0.00132*
×2011 dummy(0.0404)(0.0263)(0.00196)(0.230)(0.0255)(0.000729)-0.00135
0.0209-0.00171*-0.01180.0194-0.00130*
×2012 dummy(0.0152)(0.0277)(0.000902)(0.0977)(0.0265)(0.000717)
J.H.S./S.H.S.grad dummy
0.000242-0.1030.0829×Model regions dummy(municipality)(0.00299)(0.155)(0.0991)
0.000717
0.00782
0.0448×2005 dummy(0.00948)(0.0322)(0.0988)
0.000728
0.02130.0113×2006 dummy(0.00966)(0.0298)(0.0394)0.000418
0.01680.0166
×2007 dummy(0.00477)(0.0254)(0.0510)0.000699
0.0183-0.00125*×2008 dummy(0.00921)(0.0266)(0.000694)
0.000708
0.0178-0.00124*
×2009 dummy(0.00940)(0.0297)(0.000690)-0.000763-0.0946-0.00122*
×2010 dummy(0.0129)(0.241)(0.000682)
0.000721-0.5900.999***
×2011 dummy(0.00959)(0.459)(0.000895)
0.000718
0.0211
0.358
×2012 dummy(0.00955)(0.0296)(0.281)Age-9.32e-05
0.00221-0.000479** -8.20e-05
0.00226-0.000382**(0.00114)(0.00226)(0.000222)(0.00101)(0.00240)(0.000185)
Married dummy
0.01320.157-0.00885
0.01220.158-0.00755(0.120)(0.123)(0.00581)(0.113)(0.126)(0.00508)
With a child under 6 dummy
0.00103
0.0239-0.000312
0.000971
0.0218-9.80e-05(0.0123)(0.0274)(0.00121)(0.0117)(0.0259)(0.000939)
J.H.S./S.H.S.grad dummy-0.00120-0.00769-0.00222*-0.000549
0.00596-0.00181(0.0139)(0.0161)(0.00128)(0.00639)(0.0158)(0.00127)
Living with parents dummy-0.000101-0.0177
0.00251-2.26e-06-0.0151
0.00207(0.00192)(0.0276)(0.00234)(0.000917)(0.0255)(0.00198)
Number of family members living-0.000854-0.0125-0.000392-0.000829-0.0124-0.000329
together(0.0100)(0.0138)(0.000434)(0.00979)(0.0141)(0.000349)
Non-work income-8.06e-07-1.83e-05
1.02e-06-7.43e-07-1.66e-05
5.30e-07(1.00e-05)(2.96e-05)(1.57e-06)(9.29e-06)(2.84e-05)(1.26e-06)
GDP growth rate
8.93e-05
0.000852
0.000195
8.87e-05
0.001120.000122(0.00107)(0.00172)(0.000163)(0.00107)(0.00193)(0.000124)
J.H.S./S.H.S.grad dummyNo×each year dummy
No
No
Yes
Yes
Yes
each year dummy
Yes
Yes
Yes
Yes
Yes
Yes
Sample size
4,535
4,535
4,535
4,535
4,535
4,535
log pseudolikelihood-1303.2552-1574.071-802.90645 -1293.4415 -1560.3689-791.57082

Notes: 1. Numbers are marginal effect and numbers in parentheses are robust standard errors.
2. ***, **, and * indicate statistical significance at the 1%, 5%, and 10 % levels, respectively.
26

Table 7 Estimation results of the employment probability function: Female
(a)
(b)
(c)
(d)
(e)
(f)
RegularNon-regular
RegularNon-regular
Employment
Employment
employment employment
employment employment
Rate
Rate
rate
rate
rate
rate
0.115
0.00363
0.09620.07010.00395
0.0842Model regions dummy(municipality)(0.102)(0.00656)(0.0860)(0.156)(0.00797)(0.110)-0.1180.00120-0.0938**-0.09420.00130-0.0868*
×2005 dummy(0.136)(0.00401)(0.0446)(0.139)(0.00416)(0.0445)-0.1540.000703-0.0709-0.1170.000465-0.0546
×2006 dummy(0.128)(0.00428)(0.0645)(0.142)(0.00385)(0.0742)-0.237*-0.00161-0.103**-0.228-0.00115-0.107**
×2007 dummy(0.143)(0.00233)(0.0481)(0.146)(0.00186)(0.0479)-0.399***-0.00206-0.141***-0.409***-0.00140-0.147***×2008 dummy(0.110)(0.00280)(0.0328)(0.107)(0.00211)(0.0243)-0.243-0.00222-0.0448-0.0825-0.00178
0.0716×2009 dummy(0.155)(0.00304)(0.0885)(0.146)(0.00261)(0.136)-0.121-0.00225-0.0170-0.0887-0.00197
0.0642×2010 dummy(0.201)(0.00311)(0.116)(0.206)(0.00288)(0.165)-0.257-0.00212-0.111**-0.230-0.00170-0.0716
×2011 dummy(0.183)(0.00290)(0.0566)(0.185)(0.00249)(0.0856)-0.291-0.00217-0.104-0.317-0.00175-0.0607×2012 dummy(0.213)(0.00297)(0.0641)(0.209)(0.00256)(0.0961)
J.H.S./S.H.S.grad dummy
0.0836-0.000790
0.0175×Model regions dummy(municipality)(0.171)(0.00175)(0.110)-0.104-0.000714-0.0313×2005 dummy(0.301)(0.00198)(0.150)-0.190-9.78e-05-0.0843
×2006 dummy(0.274)(0.00357)(0.0993)-0.0757-0.00147
0.101
×2007 dummy(0.339)(0.00249)(0.212)0.0600-0.00177
0.347
×2008 dummy(0.343)(0.00261)(0.421)-0.541***-0.00171-0.165***×2009 dummy(0.127)(0.00250)(0.0169)-0.1490.998***-0.162***×2010 dummy(0.461)(0.00255)(0.0167)-0.222-0.000746-0.163***×2011 dummy(0.465)(0.00328)(0.0167)0.118
0.00260-0.163***×2012 dummy(0.484)(0.0122)(0.0167)Age
0.000580-0.000235
0.001190.000526-0.000185
0.00109(0.00454)(0.000284)(0.00273)(0.00457)(0.000238)(0.00267)
Married dummy-0.401***-0.0187-0.102**-0.402***-0.0161-0.101**(0.0428)(0.0218)(0.0468)(0.0427)(0.0200)(0.0458)
With a child under 6 dummy-0.387***-0.00306-0.212***-0.382***-0.00249-0.205***(0.0392)(0.00377)(0.0232)(0.0392)(0.00328)(0.0229)
J.H.S./S.H.S.grad dummy-0.0635-0.00932
0.0761***-0.112*-0.00671
0.0434(0.0488)(0.0105)(0.0292)(0.0605)(0.00839)(0.0371)
Living with parents dummy
0.222***
0.0146-0.0235
0.219***
0.0141-0.0292(0.0518)(0.0179)(0.0335)(0.0521)(0.0179)(0.0322)
Number of family members living-0.0540***-0.00205
0.0255**-0.0536***-0.00176
0.0255**
together(0.0193)(0.00261)(0.0122)(0.0194)(0.00238)(0.0119)
Non-work income-0.000451**-6.45e-06-0.000172-0.000426**-5.42e-06-0.000165(0.000194)(8.98e-06)(0.000123)(0.000191)(7.95e-06)(0.000119)
GDP growth rate
0.00294-2.43e-05
0.001310.00304-3.26e-05
0.00149(0.00600)(0.000166)(0.00388)(0.00602)(0.000141)(0.00378)
J.H.S./S.H.S.grad dummy
×each year dummy
No
No
No
Yes
Yes
Yes
each year dummy
Yes
Yes
Yes
Yes
Yes
Yes
Sample size
5,408
5,408
5,408
5,408
5,408
5,408
log pseudolikelihood-2447.4927-1523.5508-2509.598-2441.1537-1513.3294-2492.7845

Notes: 1. Numbers are marginal effect and numbers in parentheses are robust standard errors.
2. ***, **, and * indicate statistical significance at the 1%, 5%, and 10 % levels, respectively.
27

Figure 1 Annual transition of the number of monthly new employment

Notes: The vertical lines in the figures represent the 95% confidence interval.

Figure 2 Annual transition of the number of monthly job seekers

Notes: The vertical lines in the figures represent the 95% confidence interval.
28

Figure 3 Annual transition of the number of monthly job offers

Notes: The vertical lines in the figures represent the 95% confidence interval.

Figure 4 Annual transition of employment rate: Male

Notes: The vertical lines in the figures represent the 95% confidence interval.

29

Figure 5 Annual transition of employment rate: Female

Notes: The vertical lines in the figures represent the 95% confidence interval.

30