GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 179

Measuring Search Frictions Using Japanese
Microdata

Masaru Sasaki, Miki Kohara and Tomohiro Machikita

February 2011; revised May 2012GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

MEASURING SEARCH FRICTIONS USING JAPANESEMICRODATA

Masaru Sasaki ǂ
Osaka University

Miki Kohara
Osaka University

Tomohiro Machikita
IDE-JETRO

February 22, 2011

Abstract: This paper estimates matching functions to measure search frictions in theJapanese labor market and presents determinants of search duration to explain the effectof unemployment benefits on a job seeker’s behavior. We employ administrative micro
data that track the job search process of individuals who left or lost their job in August
2005 and subsequently registered at their local public employment service. Our findingis that the matching function would exhibit decreasing returns-to-scale for job seekers
and vacancies, rather than constant return-to-scale. We also find that generous
unemployment benefits lengthen (shorten) the duration of job search for job seekers
who voluntarily (involuntarily) leave employment.
JEL Classification Numbers: J64, J65.
Keywords: Job Search, Matching Model, Unemployment, Unemployment Benefitsǂ

Corresponding to: ISER, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka, 567-0047,

Japan. E-mail: sasaki@econ.osaka-u.ac.jp
11. Introduction
The matching function defines the flow of new hires as a function of the stocks ofindividual job seekers and job vacancies in firms. This particular function is very usefulfor embodying unemployment in models with search frictions because of the omissionof the microfoundations for the underlying frictions when constructing these models. As
a result, there are many empirical studies that estimate the matching function to measure
the extent of search frictions in the labor market—see Petrongolo and Pissarides (2001)
for a comprehensive survey.
These studies typically employ aggregate data at a yearly, quarterly, or monthly
frequency. Needless to say, they help us better understand the job search process
between job seekers and firms with job vacancies, but the use of aggregated datasometimes causes serious aggregation bias to arise. 1 To correct for it, micro data thattrack the search process for each individual are more appropriate. However, to the bestof our knowledge, there are few studies that estimate matching functions using microdata, with the exception of Nickell (1979), Atkinson et al. (1984), and Petrongolo(2001).
Of these, Petrongolo (2001) is the first study known to investigate returns-to-scale in

1

As an alternative, Coles and Smith (1996) and Anderson and Burgess (2000) employ state-level
data. Here, the variance in state-specific factors reduces omitted variable bias. This provides more
precise estimates of the elasticity of matching formations to the stocks of job seekers andvacancies.

2the matching formation using individual data. 2 Accordingly, in the spirit of Petrongolo(2001), this paper estimates the matching function to gauge the extent of frictions in theJapanese labor market using micro data collected from job seekers with the assistance of
the Japanese public employment service office. In our approach, we use two different
segmentations to define the size of the local market faced by each individual job seeker,
namely, the prefectural area where the job seeker lives and the smaller area administeredby a local public employment service office in which the job seeker registers for
employment.
In our study, we employ two large sets of administrative micro data from theJapanese Ministry of Health, Labour, and Welfare, Report on Unemployment Insurance
and Report on Employment Services. Both sets of data include all individuals
throughout Japan who left or lost their job in August 2005. The former data set includes
the job seeker’s job history, including previous wage and the precise date when theindividual resigned or lost his/her job, while the latter data set includes the job seeker’s
individual characteristics and details about the job search process. Fortunately,individuals bear the same identification number in each data set, and this allows us tomerge the two sets of data. We then obtain history-based information on when
individuals quit or lost their old job, when they registered with the local employment
service office and started to look for a new job, and when they found a job and exited

2

Nickell (1979) and Atkinson et al. (1984) add labor market tightness as an explanatory variable toestimate the exit rate from unemployment. However, they do not address returns-to-scale in thematching formation.

3

from unemployment.
These administrative micro data sets have three main merits. The first is that, as
mentioned above, we do not need to consider the aggregation bias that arises when dataat the yearly, quarterly, or monthly frequency are used. The second is that because thedata were collected for administrative purposes, respondents are not able to self-select;that is, the selection bias problem does not arise. The final merit is that unlike interview
surveys, where it is usually impossible to cover large geographical areas
comprehensively, the data include job seekers from all of the employment service
offices located throughout Japan. Accordingly, there is no geographical bias.
This paper also estimates the duration analysis to address the effect of the generosityof unemployment benefits on a job seeker’s search behavior. As shown in theimplications of a well-known job search model (Lippman and McCall, 1976), generous
unemployment benefits increase the job seeker’s reservation wage, thus leading to anincrease in the duration of unemployment. In the Japanese welfare system, thegenerosity of unemployment benefits varies according to age, the insured period, andwhether applicants quit their employment position voluntarily or lost their job
involuntarily. These differences identify the extent to which the generosity of
unemployment benefits prevents job seekers from exiting from unemployment.
The main contribution of this paper is that we employ large and specialized micro
data sets for the first time in Japan when evaluating the individual job search process.
This allows us to measure more precisely the extent to which search frictions prevent a
job seeker and a job vacancy from forming a match. It also allows us to assess the effectof the generosity of unemployment benefits on a job seeker’s search behavior,
4

controlling for the problems of aggregation, selection, and geographical bias.
The main findings are as follows. First, regardless of whether we define the local
labor market where a job seeker is active as the prefectural area where they live, or the
more narrowly defined area administered by a local public employment service officewhere they register for employment, the matching function would exhibit decreasing
returns-to-scale with respect to both job seekers and job vacancies, rather than constantreturn-to-scale. A possible intuition underlying this result is that a larger market
apparently lowers average productivity at least partly because of the increasing
formation of job mismatch. Lower productivity then shifts downward the wage offer
distribution, thereby discouraging job seekers from forming a match with anencountered firm. There is also a countereffect in that lower productivity decreases thejob seeker’s reservation wage, and this tends to increase the probability of exit fromunemployment.
Our estimates imply that the former effect dominates the latter, resulting indecreasing returns-to-scale in the matching function. This result is consistent with Kanoand Ohta (2005) and Sasaki (2007), though both of these studies estimated the matching
function using aggregate data. One implication is that our estimate does not ensure thata unique unemployment rate exists along the steady-state growth path (Mortensen and
Pissarides, 1998; Pissarides, 2000). According to a parametric estimate of the duration
analysis using a Weibull distribution, the hazard rate is then negatively dependent,
implying that the longer-term unemployed are less likely to exit from unemployment, inpart because of skill depreciation.
We also find, as one would expect, that more generous unemployment benefits extend
5the search duration for job seekers who quit their job voluntarily. However, we also findthat job seekers who lost their job involuntarily are more likely to return to work, eventhough they are eligible to receive more generous unemployment benefits. This may be
because those who lose their job against their will may have a stronger preference forwork, and therefore, they are eager to exit from unemployment as soon as possible,
despite the generous provision of unemployment benefits.
The remainder of the paper is organized as follows. Section 2 presents the
econometric specifications. Section 3 describes the two data sets obtained from theJapanese Ministry of Health, Labour, and Welfare and defines search duration and thecovariates. Section 4 reports the estimated results, and Section 5 provides someconcluding remarks.

2. Econometric specifications
In this section, and following Petrongolo (2001), we construct the estimating equationof an individual-level matching function when microdata are used. Many earlier studies
estimate the matching function to measure the extent of search frictions in the labormarket. 3 Importantly, these studies generally employ aggregate data on job seekers, job
vacancies, and matched jobs (Petrongolo and Pissarides, 2001). Because the micro data
capturing the individual matching process are available, we can estimate the3

There is an additional literature that addresses the issue of non-random matching. For example,

Coles and Smith (1998) and Coles and Petrongolo (2008) estimate a stock-flow matching model andshow that this performs better than the random matching function.

6

returns-to-scale in the matching formation and the effects of unemployment benefits andtheir provision period on the exit rate from unemployment.
A standard matching function is:
β γ

Mj = A�ȷ Uj Vj ,where Mj is a measure of job matches formed during a given length of time in area j, Uj
is a measure of unemployed in area j, and Vj is a measure of job vacancies in area j. The
parameters β and γ indicate the elasticity of Mj with respect to Uj and Vj, respectively.
A�ȷ is a measure of the extent of area-specific search technology and/or the average

search effort put into job search by the unemployed living in area j. We assume thisfunction is increasing in both arguments. Note that employed job seekers are notincluded in Uj; that is, a group of job seekers consists only of unemployed workers, as
explained later in detail. Similarly, Vj excludes nonregistered vacancies, and Mj
represents the number of matches only between Uj and Vj. Therefore, we do not need toaddress the problem of endogeneity that results from job competition between
unemployed and employed job searchers and vacancy competition between registeredand nonregistered firms. 4

4Broersma and van Ours (1999) and Mumford and Smith (1999) argue the need to correct for thebias caused by unobserved employed job searchers. Anderson and Burgess (2000) address
endogenous on-the-job search as a source of estimation bias (or job competition). Fahr and Sunde(2005) deal with the endogeneity problem caused by alternative vacancy postings (or vacancy
competition). Finally, Sunde (2007) uses a different approach to identify the bias caused by bothjob and vacancy competition.

7

The average exit rate of an unemployed worker living in area j is then:−(1−β) γλ� (Uj , Vj ) = A�ȷ Uj
Vj = exp�lnA�ȷ − (1 − β)lnUj + γlnVj �.

Note that this function is nonnegative. The exit rate of an unemployed individual i isthen given by:−(1−β) γ
Vj = exp�lnA�Xi , Zj � − (1 − β)lnUj + γlnVj �,λ�Uj , Vj , Xi , Zj � = A�Xi , Zj �Ujwhere Xi is a vector of individual characteristics, and Zj is a vector of area-specificcharacteristics.
We employ a parametric estimation method with a Weibull distribution and asemiparametric estimation method with a proportional hazard model (Cox, 1972) toestimate the search duration. The hazard rate is thus:h�t i ; Uj , Vj , Xi , Zj � = λ�Uj , Vj , Xi , Zj �φ(t i ),where φ(ti) > 0 represents the baseline hazard function common to all individuals. An
individual hazard therefore differs proportionally by λ. In the parametric estimate with a
Weibull distribution, φ(ti) = αtiα–1 where α is a parameter indicating the extent of
duration dependence. 5 The estimated equation follows a log-linear specification:lnh�t i ; Uj , Vj , Xi , Zj � = lnA�Xi , Zj � − (1 − β)lnUj + γlnVj + lnφ(t i ).

Many earlier empirical studies have been preoccupied with the nature of equilibrium.
One issue that needs to be considered in the estimation of a matching function is to test
whether the matching function exhibits constant returns-to-scale with respect to Uj and
Vj, implying that the rate at which a job seeker encounters a vacancy is determined by5

If α > 1 (α < 1), the hazard rate is positively (negatively) dependent.

8

labor market tightness, Vj/Uj, but not by the market size. If β + γ = 1 (that is, thecoefficients of lnUj and lnVj are equal in the absolute term), we support the hypothesisof constant returns-to-scale. This ensures that the unemployment rate is uniquelydetermined when flows into and out of the unemployment pool are equal. However, if β
+ γ > 1, the matching function exhibits increasing returns-to-scale, thus implying that anincrease in search effort on one side of the stocks encourages the other side of the stocks
to devote more search effort, as well as to decrease the search cost incurred by the otherside. This situation leads to multiple-ranked equilibria (Diamond, 1982).
The estimates also provide the extent of externalities that determine the degree of
efficiency in the decentralized labor market. For instance, the absolute value of thecoefficient on lnUj, (1 – β), indicates the extent of congestion externality in the sensethat the entry of one more unemployed person makes it more competitive for any
incumbent unemployed to look for a job. Conversely, the coefficient on lnVj (γ)
indicates the extent of search externality with the entry of one more job vacancy,
making it less competitive for the unemployed to find a job. 6
Although the measures of the unemployed and job vacancies vary over time throughtheir continuous inflow and outflow during a spell of unemployment, we treat them as
time-invariant covariates in our estimation. That is, the local labor market conditions

6

The estimate of β measures the search externality with the entry of an unemployed person making
it less competitive for firms to find an unemployed person, while (1 – γ) indicates the extent of thecongestion externality in that the entry of a job vacancy lowers the probability of other incumbent
firms finding an unemployed person.

9prevailing at the time an individual starts to search for a job serve as the covariates. To
counter the argument that local labor market conditions at the time an individual starts
to look for a job are not relevant to the decision of exiting from unemployment after aspell of searching for a job, we specify local labor market conditions just one month
before each individual exits as the time-invariant covariates. For individuals whose spellof unemployment is incomplete, we use local labor market conditions at the end of theobserved interval.

3. Data
We employ two administrative micro data sets constructed by the Employment Security
Bureau, Japanese Ministry of Health, Labour, and Welfare. The first is the Report on
Unemployment Insurance. This data set contains the job history of an insured person,
including the wage previously earned and the actual date when the person resigned from
his/her employment or lost it between August 1 and August 31, 2005. This data set does
not include those ineligible for unemployment insurance, including the self-employed,housewives, and part-time workers (those working fewer than 20 hours per week). The
data set also does not include disadvantaged persons and those with a disability. 7 The
second data set is the Report on Employment Services, which captures the job search
process conducted by the individuals who quit or lost their job between August 1 andAugust 31, 2005. This data set tracks and monitors the process of job search from the7

Disadvantaged persons are: (1) persons aged more than 65 years, (2) seasonal workers, (3) daily
workers, and (4) job seekers who have been unemployed for more than a year.

10date when a job seeker registered at his/her local employment service office to the datewhen he/she found a job, or the last date in the observed interval (July 13, 2006).
Both data sets include a common identification (ID) number for each job seeker, andthis allows us to merge the data sets using the ID number. This data processing
operation leaves us with only job seekers who are eligible to receive unemployment
benefits during their job search and have registered at the public employment service
office and frequently visit to receive benefits. 8 Note that those who join theunemployment insurance program cannot actually receive benefits without firstregistering with the public employment service office. Because the date an individual
quit or lost his/her last job does not exactly correspond with the date he/she registered asa job seeker at the local employment office, as a robustness check, we use not only thedate when individuals registered with the employment office but also the date when
they quit or lost their previous employment as the starting date for job search. Therefore,
two separate sets of search duration data are available in our estimates. In addition,
because the Report on Employment Services tracks and monitors job search on a weekly
basis, the frequency of the search duration data is weekly.
Our concern now moves to the covariates. We begin with the local labor market
conditions. Two methods are employed to measure the tightness of the local labor

8

Some of the literature focuses on identifying the effect of search methods on the search duration.
See, for example, Blau and Robin (1990) in the US, Gregg and Wadsworth (1996) in the UK, andAddison and Portugal (2002) in Portugal. The public employment service is popular for job
seekers and productive in forming matches in the UK (Jobcentre), but not in the US and Portugal.
In this paper, we do not explore which particular search methods are more productive.

11

market. The first is simply to use monthly measures of job seekers and job vacancies ina prefecture where an individual resides. The merit of using prefectural data is that we
can obtain prefectural labor market conditions at the time of just one month before eachindividual exits and at the end of the observed interval, as well as when the individual
starts to search for a new job. This is because the release of the prefectural-level data on
job seekers and job vacancies is monthly, allowing us to observe the effect on the exit
rate of the most recent labor market conditions. However, one problem is that theprefectural labor market is so geographically large that it does not necessarily match up
with the exact area where an individual is actively searching for a job.
To compensate for this shortcoming, we instead use measures of job seekers and job
vacancies registered at the local public employment service office where an individual
registered to receive unemployment benefits. This precisely pins down the labor market
where individuals are actually looking for a job. It is of course possible that anindividual actively searches for a job in, for example, an urban area, despite his/her
registration being with the local employment service office nearest to their suburban
residence. The main problem of using this method is, additionally, that the aggregate
data on job seekers and job vacancies registered at each employment service office areavailable only at a yearly frequency. We use the 2005 data on job seekers and job
vacancies. The data on job seekers and job vacancies in the local labor market (both at
the prefectural level and in the area administered by the local employment service
office) are obtained from the Japanese Ministry of Health, Labour, and Welfare.
Unemployment benefits and individual characteristics are crucial covariates
determining the search duration. This is because we consider these as proxies indicating
12the extent of job search effort, the level of human capital and the level of the reservation
wage. As background, we outline the Japanese unemployment insurance system before
we explain how we include unemployment benefits in the vector of covariates. In sum,
the unemployed in Japan receive 50–80% of their wage prior to becoming unemployedfor 90 to 360 days. The amount of benefits and the provision period then depend on the
person’s age, the length of the insured period, and whether they resigned from theirprevious employment voluntarily or lost their job involuntarily.
All other things being equal, an employed person receives more generous benefits ifthey are older, if they have become involuntarily unemployed, and/or when insured for alonger period. When an unemployed person exits from unemployment at an early stage(before the last day for the provision of benefits), the benefits remaining are partiallypaid if certain conditions are met. After the provision of unemployment benefits ends,
alternative allowances are not available for any unemployed still looking for a job. 9
It is expected that the generosity of unemployment benefits affects the level of search
effort; that is, when benefits are more generous, the unemployed put less effort into
searching for a job. We employ several dummy variables indicating the types of
unemployment benefits: 90, 120, 150 days of benefits with voluntary job turnover, and90, 120, 180, 240, and more than 240 days of benefits with involuntary job turnover.
Finally, some individual characteristics are included in the vector of covariates,
including age, sex, marital status, education, prior job tenure, and the prior wage. These9

In contrast, in Europe, public assistance continues for the unemployed still looking for a job, evenafter the provision of unemployment benefits ends.

13

covariates determine an individual’s level of human capital and the reservation wage.
The Appendix provides some descriptive statistics.

4. Results
4.1 Labor market conditions
Table 1 reports estimated results of the matching function where prefectural data on job
seekers and job vacancies as of August 2005 define the local labor market conditions. In
the first two columns of Table 1, we define the starting date of job search by when an
individual registered at the local public employment service office, and in the second
two columns by when they quit or lost their previous job. Columns [1] and [3] provide
the semiparametric estimates using Cox’s proportional hazard model, while columns [2]and [4] present the parametric estimates using a Weibull distribution.
Regardless of the starting date of job search, the estimated coefficient on job seekers
is negative while that on job vacancies is positive according to the semiparametricestimations (columns [1] and [3]). This is consistent with our expectations. However,while the job seekers’ coefficient is significant at the 1% level, the coefficient on job
vacancies is statistically insignificant. In addition, the former is larger in absolutemagnitude than the latter, and the sum of these coefficients is less than zero. We
therefore reject the hypothesis that the sum of the coefficients is zero, suggesting thatthe matching function does not exhibit constant returns-to-scale, rather decreasing
returns-to-scale. This result is inconsistent with the existence of a unique unemployment
rate along the steady-state growth path (Mortensen and Pissarides, 1998; Pissarides,
2000).
14

However, this result is consistent with the estimated matching functions in Kano andOhta (2005) and Sasaki (2007) when using aggregate data by prefecture. 10 Nevertheless,
Sasaki (2008) still shows that the matching function exhibits constant returns-to-scale
after correcting for the temporal aggregation bias that arises when discrete-time data areused to estimate a continuous-time matching formation. 11,12 Ueno et al. (2004) also
estimate the job matching function by including the individual characteristics of job
seekers,

and

they

conclude

that

the

matching

function

exhibits

constant

returns-to-scale. 13 Lastly, Petrongolo and Pissarides (2001) survey the international
literature and summarize on this basis that the job matching function across countries
and over time generally exhibits constant returns-to-scale. 14
In columns [1] and [3], the elasticity of job matches with respect to job seekers lies
within the range 0.765–0.778, while the elasticity with respect to job vacancies isrelatively smaller in magnitude, from 0.070 to 0.084. This implies that an additional job

10

Kano and Ohta (2005) find that β + γ = 0862 and Sasaki (2007) that β + γ = 0.830. This isapparently despite some differences in sampling, where Kano and Ohta (2005) include regional
data from 1973 to 1999 and Sasaki (2007) uses quarterly regional data from 1998 q1 to 2007 q1.

11

Sasaki (2008) suggests that the matching function formation should be nonrandom (stock–flow
matching); however, the estimation advantage appears trivial.

12

Burdett et al. (1994) and Coles and Petrongolo (2008) both show how to correct for the temporal
aggregation bias.

13This study covers data from 1991 to 2001.

14

Yet other studies find that the matching function exhibits increasing returns-to-scale (Blanchard
and Diamond, 1989; Yashiv, 2000).

15

vacancy has only a minor effect on the probability of forming a job match.
Similar results are obtained in the parametric estimations with a Weibull distribution,
as shown in columns [2] and [4] in Table 1. This part of the analysis shows that thehazard rate of a job seeker is negatively dependent, implying that the exit rate of a job
seeker from unemployment becomes lower as the search duration becomes longer. In
turn, this suggests that job seekers are more likely to become long-term unemployed, atleast partly because the job seekers’ human capital depreciates as the duration ofunemployment becomes longer.
Table 2 provides the estimated results of the matching function using the data on
prefectural labor conditions either one month before the job seeker exited fromunemployment or at the end of the observed interval (July 13, 2006). As shown incolumns [1] and [3] of the semiparametric estimations, job vacancies have a positive
impact on search duration at the 5% level of significance, while job seekers maintain anegative effect at the 1% level of significance. As in Table 1, the sum of the coefficientson lnUj and lnVj is negative, thus indicating that the matching function exhibits
decreasing but not constant returns-to-scale.
However, unlike the estimates in Table 1, the magnitudes of these coefficients arelarger in the sense that the elasticity of job match to job seekers is smaller (0.399–0.510),
while the elasticity with respect to vacancies is larger (0.287–0.381). Given the
difference in data sampling between these two sets of estimates, this implies that searchand congestion externalities are larger when we specify data one month before a job
match or at the end of the interval, and but in August 2005. The reason why theexternalities are small using the data as of August 2005 is as follows. An increase in job
16vacancies in August 2005 increases the likelihood that a job seeker exits fromunemployment, but may also discourage the job seeker from job search hastily because
he/she believes many vacancies remain available. We expect the latter effect torelatively dominate when job search activity begins in August 2005, while the former
effect relatively dominates one month before the formation of a job match.
We can interpret an increase in the coefficient on lnUj in a similar manner. Because
job seekers are not very serious in searching for a job as of August 2005 when search
activity actually begins, the entry of an additional job seeker does not generate a serious
congestion externality for other job seekers. However, because job seekers become
gradually more serious in searching for a job as they keep searching, the entry of anadditional job seeker generates a large congestion externality for incumbent job seekers.
The inclusion of independent variables indicating a job seeker’s individual
characteristics should help control for the extent of individual search effort to somedegree, but it apparently does not do this perfectly.
Regardless of the starting date of job search, the same results are obtained in theparametric estimations with a Weibull distribution, as shown in columns [2] and [4] of
Table 2. As in Table 1, the hazard rate of a job seeker is negatively dependent, thereby
indicating that in the Japanese labor market, the longer-term unemployed have greaterdifficulty in exiting from unemployment.
Table 3 presents the estimated matching function where job seekers and job vacancies
registered at each employment service office in 2005 are used as the local labor market
conditions. As a result, the labor market where a job seeker is actively searching isdefined by the area administered by the local employment service office at which they17

registered for employment. Note that we specify yearly data for 2005. Despite thesedifferences in sampling, the estimated results are close to those in Table 2 in the sensethat the coefficient on job vacancies remains positive at the 5% level of significance,
while the coefficient on job seekers remains negative at the 1% level of significance.
Moreover, the sum of these coefficients is also negative, again indicating that thematching function exhibits decreasing returns-to-scale.
The results are also similar to those in Table 1 in the sense that the search andcongestion externalities are both relatively small when we specify yearly data for 2005
from the local labor market as administered by the local employment service office.
Because the data on local labor market conditions are weighted averages from January
to December 2005, the discouraged job seeker effect—in the sense that an additional
vacancy discourages a job seeker from beginning job search hastily and that anadditional job seeker does not yet generate a serious congestion externality to other job
seekers—is relatively strong, and this reduces the magnitude of these externalities.
According to the parametric estimates with a Weibull distribution (columns [2] and[4] in Table 3), the hazard rate remains negatively dependent, when the starting date ofjob search is defined as when an individual registered with the public employment
service office (column [2]). However, it becomes positively dependent when we specify
the date at which an individual quit or lost his/her job as the starting date of job search(column [4]).
Overall, Tables 1–3 provide robust results indicating that the matching function
exhibits decreasing returns-to-scale, thereby implying that doubling the number of job
seekers and job vacancies delivers fewer than twice as many job matches. One of the18

possible reasons for this is negative scale effects at the level of the local labor market.
For instance, Petrongolo and Pissarides (2006) hypothesize that a larger market size
increases productivity on average to support increasing returns-to-scale at the structurallevel. Our view is contrast with Petrongolo and Pissarides (2006) in that a larger market
size somehow decreases productivity on average, possibly because the limited numberof staff at employment service offices cannot efficiently coordinate productive job
matching formations between too many unemployed and job vacancies. Alternatively, it
could be because the transfer of job information in a larger market to both the
unemployed and firms is not as efficient, resulting in unproductive job matches (ormismatches). A decrease in productivity then shifts downward the wage offer
distribution, thereby discouraging job seekers from forming a match with anencountered firm. A countereffect is that lower productivity decreases the reservation
wage of job seekers, encouraging them to exit from unemployment. Based on ourestimates, we thus conclude that the former effect dominates the latter, thereby leading
to decreasing returns-to-scale.
4.2 Unemployment benefits
This subsection focuses on the effects of unemployment benefits on a job seeker’s
search behavior. Tables 1–3 provide the estimated results, assuming that the reference
dummy is “the provision of benefits for 90 days because of voluntary turnover”. Whilesome of the results obtained are qualitatively alike in these tables, the effects differ tosome extent by the level of significance. One noteworthy finding is that according to theparametric and semiparametric estimates of the duration analysis in columns [1]–[4],job seekers who resign (are removed) from their previous employment voluntarily
19

(involuntarily) are less (more) likely to exit from unemployment. In particular, those
who lose their previous job involuntarily and are eligible to receive benefits for 120
days are the quickest to exit from unemployment. In addition, even though job seekers
are eligible to receive benefits for more than 90 days, those job seekers who quit theirjob against their own will are generally eager to get back to work as soon as possible.
Focusing attention on job seekers who quit their previous job voluntarily, we can seethat generous benefits encourage them to spend more time looking for a job, and thisextends the duration of unemployment. This is consistent with the predictions of thebenchmark search model. We can interpret this result as meaning that unemployment
benefits lower matching effectiveness in the sense that it takes more time to form amatch between a job seeker and a job vacancy.
However, we should also note that matching effectiveness is not only measured by
the time it takes to form a match (the search duration) but also by the wage rate earned
after matching with a new firm and the length of tenure experienced at the new firm. To
account for this, we predict that greater benefits increase an individual’s reservation
wage, leading to both a longer duration of unemployment and a higher wage if
eventually hired at a new firm. There are, of course, trade-offs in terms of matchingeffectiveness between the duration of unemployment and the expected wage at the new
firm (or matching quality). In terms of work in this area, Kohara et al. (2010) explores
the effect of the unemployment insurance system on matching effectiveness asmeasured by the length of tenure at a newly matched firm, using the same micro data as
the present analysis.
4.3 Other covariates
20

Finally, we report on the extent to which individual characteristics affect the exit ratefrom unemployment (the duration of job search). The covariates include proxies
indicating the level of human capital, the reservation wage, and the search effort. Onceagain, many of the results are common in Tables 1–3. To start with, compared withmarried women, single women and single and married men are more likely to exit fromunemployment. This supports the notion that the reservation wage of married women isrelatively high because these women receive income earned exogenously by theirhusbands and because the customary division of labor in Japan (husbands concentratingon market work while wives specializing in household duties) may remain prevalent.
We also find that the more educated find a new job at earlier stages of the job search
process. One intuition behind this result is that because the more educated incur agreater burden of the opportunity cost of being unemployed, they put more effort into
searching for a job. In addition, a higher wage in the previous job also induces job
seekers to exit from the unemployment pool more quickly. We interpret this as meaningmuch the same, namely, that the more educated who are expected to receive a higher
wage in their previous job incur a higher opportunity cost of being unemployed.
Finally, job seekers who are older or have enjoyed longer tenure in their previous job
are less likely to exit from unemployment. This could be because they are skillful
enough to set their reservation wage sufficiently high. Alternatively, it could be because
as they have worked longer, they have saved sufficient funds, which allows them todelay the process of finding a new job.

5. Concluding remarks
21

Using administrative job search micro data from the Japanese Ministry of Health,Labour, and Welfare, this paper estimates matching functions to measure the extent ofsearch frictions in the Japanese labor market and presents a duration analysis toelucidate the effect of unemployment benefits on a job seeker’s behavior. It is important
to note that the administrative data are extremely valuable in that they track the job
search process of job seekers who resigned from their employment or lost it in August
2005 and subsequently registered at one of the local public employment service offices
located throughout Japan. Because these are administrative micro data sets, neither
aggregate, sample selection nor geographical bias potentially arises. This is the firstresearch attempt known to use this particular data set for the purpose outlined in thispaper.
We found that the matching function does not exhibit constant but rather decreasing
returns-to-scale with respect to the numbers of job seekers and job vacancies. This result
is consistent with Kano and Ohta (2005) and Sasaki (2008) who estimate matching
functions in Japan using aggregate data. According to the duration analysis using a
Weibull distribution, we also find that the hazard rate of a job seeker is negatively
dependent, implying that the longer-term unemployed have more difficulty in exitingfrom unemployment, partly because of the depreciation of human capital during theperiod unemployed.
Another finding is that more generous unemployment benefits extend the search
duration of job seekers who quit their previous job voluntarily. In contrast, job seekers
who lost their job involuntarily are more likely to return to work, thereby shortening theduration of unemployment. This is perhaps because the involuntarily unemployed may
22exhibit a greater preference for work and therefore put more effort into searching for a
new job, even though they are technically eligible to receive more generous
unemployment benefits.
A number of policy implications concerning matching effectiveness arise from ouranalysis. To start with, because the matching function exhibits decreasing
returns-to-scale, fewer than twice the number of job matches result from double thenumbers of job seekers and job vacancies. This implies that mismatch between job
seekers and job vacancies lowers matching effectiveness. To reduce the incidence of job
mismatch, advertisements for job vacancies could be more frequent and more accurate.
Job training with the unemployed could also focus on instilling the skills firms areseeking. Alternatively, as far as the available budget permits, an increase in the numberof staff in public employment service offices could encourage more productive (notmismatched) coordination between job seekers and job vacancies. Finally, the negative
dependency of the hazard rate shows that it takes more time for an individual who has
been unemployed longer to leave the unemployment pool. This suggests the need to
implement a policy that targets the long-term unemployed to make them sufficiently
skillful to get a job as soon as possible.

Acknowledgement:
The authors would like to thank the Japanese Ministry of Health, Labour, and Welfare
and the Japan Institute for Labour Policy and Training (JILPT) for their assistance incollecting and granting permission for the use of the data in this research. We are also
grateful to participants at a National Institute of Population and Social Security
23

Research seminar for their valuable comments. The usual disclaimer applies. The Japan
Society for the Promotion of Science (grants No. 20730156, No. 21730212 and No.
20530196) financially supported part of this research.

24

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26

Petrongolo, B. (2001) “Re-Employment probabilities and return to Matching”, Journalof Labor Economics, Vol. 19, pp. 716–741.
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elasticities”, Economica, Vol. 74, pp. 537–560.
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Economic Review, 90(5), pp. 1297–1322.

27

Table 1
Estimations of the matching function using the prefectural data
Search duration
[1]
[2]
[3]
[4]
Cox
WeibullCox
Weibullln(vacancies)
0.070
0.079
0.084
0.085
(0.069)(0.071)(0.061)(0.062)ln(job seekers)-0.222 ***-0.234 ***-0.235 ***-0.238 ***(0.080)(0.083)(0.072)(0.073)ln(age)-0.521 ***-0.540 ***-0.377 ***-0.387 ***(0.022)(0.023)(0.022)(0.022)Single man
0.519 ***
0.523 ***
0.556 ***
0.565 ***
(0.032)(0.032)(0.030)(0.031)
Single woman
0.465 ***
0.469 ***
0.516 ***
0.527 ***
(0.023)(0.024)(0.023)(0.024)
Married man
0.859 ***
0.870 ***
0.873 ***
0.885 ***
(0.034)(0.034)(0.033)(0.033)ln(education yrs)
0.386 ***
0.399 ***
0.417 ***
0.434 ***
(0.038)(0.039)(0.035)(0.036)ln(prev. tenure)-0.118 ***-0.121 ***-0.160 ***-0.160 ***(0.008)(0.008)(0.007)(0.007)ln(prev. wage)
0.068 ***
0.068 ***
0.060 ***
0.063 ***
(0.016)(0.017)(0.016)(0.016)
UI benefits
120 days with
-0.034 **-0.035 **-0.025 *-0.025 *voluntary quit
(0.014)(0.015)(0.014)(0.015)150 days with
-0.168 ***-0.176 ***-0.151 ***-0.151 ***voluntary quit
(0.029)(0.029)(0.028)(0.029)90 days with
0.025
0.021
0.001
0.004
involuntary quit
(0.021)(0.022)(0.021)(0.022)120 days with
0.188 ***
0.193 ***
0.230 ***
0.237 ***
involuntary quit
(0.049)(0.051)(0.049)(0.051)180 days with
0.112 ***
0.110 ***
0.095 ***
0.100 ***
involuntary quit
(0.025)(0.026)(0.025)(0.025)240 days with
0.067 **
0.063 *
0.103 ***
0.107 ***
involuntary quit
(0.034)(0.035)(0.032)(0.033)More than 240 days with
0.057 **
0.050 *
0.105 ***
0.106 ***
involuntary quit
(0.028)(0.029)(0.028)(0.028)Constant-2.868 ***-3.488 ***(0.281)(0.254)
Dependency
0.935
0.959
Log-likelihood-674075-151058-810689-174887
Wald Chi24334.694512.325923.915806.78
F-valueN
119115119115135347135347*** 1% ** 5% * 10% significant[1][2]: The search duration is from the date when a job seeker registered at the employment
service office to when he/she found a job, or July 13, 2006. [3][4]: The search duration isfrom the date when a job seeker left or lost his/her previous job to when he/she found a job,or July 13, 2006. The numbers of job seekers and job vacancies are measured when a job
seeker starts to look for a job (August, 2005).

28

Table 2
Estimations of the matching function using the prefectural data
Search duration
[1]
[2]
[3]
[4]
Cox
WeibullCox
Weibullln(vacancies)
0.381 **
0.399 **
0.287 **
0.301 **
(0.154)(0.158)(0.131)(0.136)ln(job seekers)-0.601 ***-0.624 ***-0.490 ***-0.510 ***(0.198)(0.204)(0.168)(0.174)ln(age)-0.522 ***-0.542 ***-0.381 ***-0.392 ***(0.022)(0.023)(0.021)(0.022)Single man
0.528 ***
0.533 ***
0.560 ***
0.570 ***
(0.031)(0.032)(0.030)(0.030)Single woman
0.468 ***
0.472 ***
0.518 ***
0.529 ***
(0.024)(0.024)(0.023)(0.024)
Married man
0.869 ***
0.880 ***
0.878 ***
0.891 ***
(0.034)(0.034)(0.033)(0.033)ln(education yrs)
0.358 ***
0.371 ***
0.399 ***
0.415 ***
(0.041)(0.042)(0.037)(0.038)ln(prev. tenure)-0.127 ***-0.131 ***-0.163 ***-0.163 ***(0.009)(0.009)(0.007)(0.007)ln(prev. wage)
0.041 **
0.041 **
0.044 **
0.045 **
(0.018)(0.018)(0.018)(0.018)
UI benefits
120 days with
-0.028 **-0.029 **-0.024 *-0.024
voluntary quit
(0.014)(0.014)(0.014)(0.014)150 days with
-0.169 ***-0.177 ***-0.153 ***-0.153 ***voluntary quit
(0.028)(0.028)(0.028)(0.028)90 days with
0.038 *
0.035
0.013
0.016
involuntary quit
(0.021)(0.022)(0.022)(0.022)120 days with
0.203 ***
0.208 ***
0.239 ***
0.247 ***
involuntary quit
(0.049)(0.051)(0.049)(0.050)180 days with
0.127 ***
0.125 ***
0.107 ***
0.112 ***
involuntary quit
(0.027)(0.028)(0.026)(0.026)240 days with
0.087 ***
0.084 **
0.116 ***
0.120 ***
involuntary quit
(0.033)(0.034)(0.032)(0.033)More than 240 days with
0.079 ***
0.072 **
0.118 ***
0.120 ***
involuntary quit
(0.029)(0.030)(0.029)(0.029)Constant(0.028)-1.829 ***-2.730 ***(0.572)(0.488)
Dependency
0.940
0.963
Log-likelihood-673427-150362-810193-174351
Wald Chi2
4599.964774.585182.395130.28
F-valueN
119115119115135347135347*** 1% ** 5% * 10% significant[1][2]: The search duration is from the date when a job seeker registered at the employment
service office to when he/she found a job, or July 13, 2006. [3][4]: The search duration isfrom the date when a job seeker left or lost his/her previous job to the when he/she found ajob, or July 13, 2006. The numbers of job seekers and job vacancies are measured just one
month before the job seeker found a job, or at the end of the interval (July 13, 2006).

29

Table 3
Estimations of the matching function using the data of employment service office
Search duration
[1]
[2]
[3]
[4]
Cox
WeibullCox
Weibullln(vacancies)
0.061 **
0.064 ***
0.064 ***
0.064 **
(0.024)(0.025)(0.025)(0.025)ln(job seekers)-0.192 ***-0.197 ***-0.199 ***-0.204 ***(0.028)(0.029)(0.028)(0.029)ln(age)-0.518 ***-0.538 ***-0.456 ***-0.472 ***(0.018)(0.018)(0.017)(0.018)Single man
0.524 ***
0.528 ***
0.598 ***
0.613 ***
(0.018)(0.018)(0.018)(0.018)Single woman
0.469 ***
0.473 ***
0.550 ***
0.567 ***
(0.014)(0.014)(0.014)(0.014)
Married man
0.871 ***
0.882 ***
0.948 ***
0.971 ***
(0.018)(0.019)(0.018)(0.019)ln(education yrs)
0.406 ***
0.420 ***
0.429 ***
0.453 ***
(0.031)(0.032)(0.031)(0.032)ln(prev. tenure)-0.122 ***-0.125 ***-0.115 ***-0.117 ***(0.006)(0.006)(0.006)(0.006)ln(prev. wage)
0.049 ***
0.049 ***
0.039 ***
0.040 ***
(0.013)(0.014)(0.013)(0.014)
UI benefits
120 days with
-0.034 **-0.035 **-0.022-0.020voluntary quit
(0.017)(0.017)(0.017)(0.017)150 days with
-0.169 ***-0.177 ***-0.144 ***-0.144 ***voluntary quit
(0.022)(0.022)(0.022)(0.023)90 days with
0.022
0.018 **
0.078 ***
0.080 ***
involuntary quit
(0.019)(0.020)(0.020)(0.021)120 days with
0.191 ***
0.195 ***
0.246 ***
0.258 ***
involuntary quit
(0.049)(0.051)(0.050)(0.052)180 days with
0.096 ***
0.093 ***
0.148 ***
0.153 ***
involuntary quit
(0.027)(0.028)(0.028)(0.028)240 days with
0.077 **
0.072 **
0.127 ***
0.134 ***
involuntary quit
(0.031)(0.032)(0.032)(0.033)More than 240 days with
0.056 *
0.048
0.103 ***
0.106 ***
involuntary quit
(0.031)(0.032)(0.033)(0.033)
Constant-3.351 ***-4.801 ***(0.157)(0.159)Dependency
0.936
1.106
Log-likelihood-636154-143350-638947-134598
Wald Chi26637.32
6722.156590.836563.28
F-valueN113219113219113363113363*** 1% ** 5% * 10% significant[1][2]: The search duration is from the date when a job seeker registered at the employment
service office to when he/she found a job, or July 13, 2006. [3][4]: The search duration isfrom the date when a job seeker left or lost his/her previous job to when she found a job, or
July 13, 2006. The numbers of job seekers and job vacancies registered at each employment
service office are measured in 2005.

30

Appendix Table 1

Descriptive statistics (prefectural data, the date when starting to look for a job)

Search duration
Proportion of those who find a job
within the interval
Individual characteristics
Age
Single man
Single woman
Married man
Married woman
Education years
Tenure of the previous jobs (days)
Previous wage (monthly thousands)
UI benefits
90 days with voluntary quit
120 days with voluntary quit
150 days with voluntary quit
90 days with involuntary quit
120 days with involuntary quit
180 days with involuntary quit
240 days with involuntary quit
More than 240 days withinvoluntary quit

N

Average

Std. Dev.

Min

Max

150391150391

197.4880.510

110.479
0.500

0
0

365
1

15039115039115039115039115039112164115039114735139.128
0.228
0.280
0.229
0.263
12.2882426.850
187.724

12.914
0.419
0.449
0.420
0.440
1.891
3009.704
112.608

16
0
0
0
0
9
7
1

65
1
1
1
1
18
18043
9190

150391150391150391150391150391150391150391150391

0.618
0.108
0.148
0.041
0.005
0.030
0.024
0.026

0.486
0.310
0.355
0.198
0.069
0.172
0.153
0.159

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

15039115039190351.02
83438.51

85327.71
59384.25

8492
10986

343101237491

Local labor market
Prefectural data
When starting for a job
Job vacancies
Job seekers

One month before when finding a job
Job vacancies
150391 92170.14 86291.82
8497
343101
Job seekers
150391 83429.04 59118.20
10986
222826The search duration is from the date when a job seeker registered at the employment service office towhen he/she found a job, or the end of the interval (July 13, 2006). The numbers of job seekers andjob vacancies are measured from the prefectural data. We here limit the data on job seekers who
found a job within one year.

31Appendix Table 2

Descriptive statistics (prefectural data, the date when leaving or losing the previous job)
N
Search duration
Proportion of those who find a job
within the interval
Individual characteristics
Age
Single man
Single woman
Married man
Married woman
Education years
Tenure of the previous jobs (days)
Previous wage (monthly thousands)
UI benefits
90 days with voluntary quit
120 days with voluntary quit
150 days with voluntary quit
90 days with involuntary quit
120 days with involuntary quit
180 days with involuntary quit
240 days with involuntary quit
More than 240 days withinvoluntary quit

Average

Std. Dev.

218.9610.532

116.2091
0.499

1
0

346
1

17051638.8411705160.230
1705160.284
1705160.229
1705160.257
13799312.298170489 2209.864
167358188.01112.835
0.421
0.451
0.420
0.437
1.885
2946.843
110.782

16
0
0
0
0
9
1
1

64
1
1
1
1
18
18043
9190

170516170516Min

Max

170516170516170516170516170516170516170516170516

0.663
0.095
0.130
0.036
0.004
0.027
0.021
0.023

0.473
0.294
0.337
0.187
0.065
0.161
0.144
0.149

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

17051617051686926.74
84236.61

81836.80
60045.03

8790
13091

307820219737

Local labor market
Prefectural data
When leaving or losing the prior job
Job vacancies
Job seekers

One month before when finding a job
Job vacancies
170516 91478.67 85849.61
8497
343101
Job seekers
170516 83235.24 58931.52
10986
222826The search duration is from the date when a job seeker left or lost the previous job to when he/she
found a job, or the end of the interval (July 13, 2006). The numbers of job seekers and job vacancies
are measured from the prefectural data. We here limit the data on job seekers who found a job within
one year.

32

Appendix Table 3

Descriptive statistics (data from employment service office, the date when starting
to look for a job)

Search duration
Proportion of those who find a job
within the interval
Individual characteristics
Age
Single man
Single woman
Married man
Married woman
Education years
Tenure of the previous jobs (days)
Previous wage (monthly thousands)
UI benefits
90 days with voluntary quit
120 days with voluntary quit
150 days with voluntary quit
90 days with involuntary quit
120 days with involuntary quit
180 days with involuntary quit
240 days with involuntary quit
More than 240 days withinvoluntary quit

N

Average

Std. Dev.

Min

Max

143494143494

197.8070.509

110.385
0.500

0
0

365
1

14349414349414349414349414349411562514349414058639.086
0.228
0.281
0.228
0.263
12.2982419.786
188.227

12.900
0.419
0.450
0.419
0.440
1.894
3008.113113.376

16
0
0
0
0
9
7
1

65
1
1
1
1
18
18043
9190

143494143494143494143494143494143494143494143494

0.620
0.108
0.148
0.041
0.005
0.030
0.024
0.025

0.485
0.310
0.355
0.198
0.068
0.171
0.152
0.157

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

Local labor market
Data from employment service offices
When starting for a job
Job vacancies (2005 average)
143494 110671.30 108264.00
3159
782957
Job seekers (2005 average)
143494 107607.50 72852.77
4677
314853The search duration is from the date when a job seeker registered at the employment service office towhen he/she found a job, or the end of the interval (July 13, 2006). The numbers of job seekers and job
vacancies are measured from each employment service office. We here limit the data on[ job seekers
who found a job within one year.

33

Appendix Table 4

Descriptive statistics (data from employment service office, the date when leaving or losing
the previous job)

Search duration
Proportion of those who find a job
within the interval
Individual characteristics
Age
Single man
Single woman
Married man
Married woman
Education years
Tenure of the previous jobs (days)
Previous wage (monthly thousands)
UI benefits
90 days with voluntary quit
120 days with voluntary quit
150 days with voluntary quit
90 days with involuntary quit
120 days with involuntary quit
180 days with involuntary quit
240 days with involuntary quit
More than 240 days withinvoluntary quit

N

Average

Std. Dev.

143677143677228.4900.508

110.728
0.500

1
0

346
1

14367738.9681436770.228
1436770.282
1436770.228
1436770.263
11576912.298143677 2419.031
140767188.223

12.901
0.419
0.450
0.419
0.440
1.894
3007.740
113.351

16
0
0
0
0
9
7
1

64
1
1
1
1
18
18043
9190

0.485
0.310
0.355
0.198
0.068
0.171
0.152
0.157

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

143677143677143677143677143677143677143677143677

0.620
0.108
0.147
0.041
0.005
0.030
0.024
0.025

Min

Max

Local labor market
Data from employment service offices
When leaving or losing the prior job
Job vacancies (2005 average)
143677 110493.80 108110.00
3225
782957
jJb seekers (2005 average)
143677 107879.60 73059.33
4677
314853The search duration is from the date when a job seeker left or lost the previous job to when he/she
found a job, or the end of the interval (July 13, 2006). The numbers of job seekers and job vacancies
are measured from each employment service. We here limit the data on job seekers who found a job
within one year.office.

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