DP2020-26

Urbanization Effects on
Job Search Decision*
RIEB Junior Research Fellow

Yudai HIGASHI

Revised September 1, 2022

* This Discussion Paper won the Kanematsu Prize (FY 2019).
* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional
character. In some cases, a written consent of the author may be required.

Urbanization effects on job search decision*
Yudai Higashi†
September 1, 2022

Abstract
This study examines the effect of urban agglomeration on a non-working individual’s
decision to search for a job using Japanese microdata. According to the results, urban
agglomeration raises the probability of job search for less-educated men, suggesting thatit raises the offer wages or decreases the out-of-pocket cost of job search. Urban
agglomeration also encourages unmarried women to search for a job, whereas the effectis not significant for married women. It, however, discourages married women withchildren from searching, suggesting that life events, such as marriage and childbirth, raise
women’s value of household production, especially in urban areas.
Keywords: Local labor market; Agglomeration (dis)economies; Life event; JapanJEL classification: J64; R11; R23*

I would like to thank Kazufumi Yugami, Nobuaki Hamaguchi, Tamotsu Nakamura,
Shota Fujishima, Ryo Kambayashi, Takao Kato, Bei Liao, Mariko Nakagawa, Kentaro
Nakajima, Yasuhiro Sato, and participants at the 22nd Labor Economics Conference, the2019 Autumn Meeting of the Japanese Economic Association, the 33rd Annual Meetingof the Applied Regional Science Conference, the 11th Asian Seminar in Regional Science,the 15th North American Meeting of the Urban Economics Association, the 13th Trans
Pacific Labor Seminar, the Tokyo Labor Economics Workshop, and seminars at Kobe
University and Hitotsubashi University for their helpful comments. The previous versionof this paper won the 2019 Kanematsu Prize from the Research Institute for Economics
and Business Administration, Kobe University, and I would like to thank anonymous
reviewers of this prize for their helpful comments. I am grateful to the Ministry of Internal
Affairs and Communications (MIC) of Japan for providing the microdata of the
Employment Status Survey (ESS). The results using the ESS in this paper are calculatedby the author; therefore, they are not necessarily consistent with the survey results that
the MIC makes public. Remaining errors are my own. This research was supported by
JSPS KAKENHI Grant Numbers JP18J11560 and JP20K22093.
†
Faculty of Humanities and Social Sciences, Okayama University, Japan; and ResearchInstitute for Economics and Business Administration, Kobe University, Japan.
E-mail: yudai.higashi@okayama-u.ac.jp1

1.

Introduction
Urban agglomeration generates external economies that affect workers in urban

areas. For example, it is well known that workers in larger local labor markets (i.e., urban
areas) can, on average, receive higher wages than those in smaller local labor markets(i.e., rural areas). Although spatial sorting by workers’ skills (i.e., workers in urban areas
earn high wages because their ability is high) is not negligible as a source of such nature(Combes et al., 2008), the existing studies confirm that urban agglomeration causes theurban wage premium through some mechanisms. 1 One mechanism is that urban
agglomeration fosters the accumulation of human capital (e.g., Glaeser and Maré, 2001;
Gould, 2007; Heuermann et al., 2010; De La Roca and Puga, 2017). Other studies revealthat urban agglomeration affects the matching process between workers and firms. For
example, it raises job seekers’ likelihood of finding a job, suggesting that it improvesmatching efficiency in local labor markets (Di Addario, 2011).2 In addition to job finding
rates, urban agglomeration improves the quality of matching between workers and firms,
leading to an increase in labor productivity, and thus an urban wage premium (e.g.,
Wheeler, 2006, 2008; Andersson et al., 2007; Bleakley and Lin, 2012; Abel and Deitz,
2015; Berlingieri, 2019; Dauth et al., 2022). Overall, through some mechanisms, we findagglomeration economies in labor markets.
Urban agglomeration also generates congestion, resulting in external
diseconomies, such as longer commuting times. However, little is known about the1

Duranton and Puga (2004) offer a threefold theoretical classification of the sources ofagglomeration economies: sharing, matching, and learning. These mechanisms cancause the urban wage premium (Heuermann et al., 2010).
2
Petrongolo and Pissarides (2006) show that agglomeration does not affect thelikelihood of finding a job, because agglomeration raises both firms’ offer wages andjob seekers’ reservation wages, and they offset each other.
2

negative effects of urban agglomeration on the labor market. The aforementioned studies
investigate the effects of urban agglomeration on wages and matching quality, which areobserved only for employed workers. Moreover, job finding rates are observed only forjob seekers who wish to work. The literature suggests that workers who alreadyparticipate in the labor force experience the benefits of urban agglomeration that surpass
its costs. Such workers seem to participate in the labor force because they findagglomeration economies. In other words, workers who find agglomeration diseconomies
in labor markets may not decide to search for a job.
This study aims to examine two aspects of the effects of urbanization, namely,
agglomeration economies and diseconomies. Specifically, this study explores the effectof urban agglomeration on the decision of a non-working individual to search for a job
using Japanese microdata. We suppose that some workers find agglomeration economies,
whereas others may find agglomeration diseconomies. To reveal the heterogeneity ofurban agglomeration effects, this study focuses on some individual attributes that seemto determine how an individual responds to the level of urban agglomeration. Somestudies confirm the heterogeneity of the agglomeration effect across individual attributes.
Gould (2007), for example, shows that urban agglomeration does not raise wages of bluecollar workers but those of white-collar workers. Phimister (2005) reveals that women’s
probability of employment benefits from urban agglomeration, while the same does nothold for male workers. Andini et al. (2013) show that individuals of different ages andskills exhibit different magnitudes of agglomeration effects on job finding rates. In thisstudy, we focus on gender, education, and family structure, such as marital status and thepresence of preschool children. Gender and education are factors of human capital that
directly affect labor productivity and wages. They may change the benefits of urban

3

agglomeration, as the literature shows. Family structure is the socioeconomic status that
affects the value of household production, a factor discouraging workers fromparticipating in the labor force. On average, women perform housework and childcare
more than men. Thus, if the value of household production varies across city sizes, it cangenerate heterogeneity in agglomeration effects on women’s job search decisions.
This study contributes to the literature by providing implications for theemployment policies of countries facing labor shortages. Some countries have recently
been facing population decline, while others are predicted to do so in the near future. For
example, the Japanese population has been declining and is projected to continue
declining.3 Two-thirds of the EU NUTS-3 regions are projected to face a population
decline by 2050. 4 Amid this depopulation trend, labor shortages are expected toconstitute a substantial issue for these countries. Indeed, many employers in Japan facelabor shortages (Japan Institute for Labour Policy and Training, 2020). One solution lies
in utilizing a potential labor force, namely, individuals who do not participate in the labor
force but are able to work. To encourage such individuals to participate in the labor force,
we must understand the factors leading them to decide to search for a job. Therefore, theinvestigation of job search decisions, that is, the step before individuals successfully findjobs and earn wages, is significant.
According to the estimation results, the agglomeration effects on job searchdecisions vary across individual attributes after controlling for observable individual
characteristics and potential endogeneity stemming from the simultaneity of individual3

See the website of the National Institute of Population and Social Security Research ofJapan(http://www.ipss.go.jp/pp-zenkoku/e/zenkoku_e2017/pp_zenkoku2017e.asp,
accessed on August 22, 2022).
4
See the website of the Eurostat (https://ec.europa.eu/eurostat/web/products-eurostatnews/-/ddn-20210430-2, accessed on August 22, 2022).
4

job search and residential location choice. The main findings of this study are twofold.
First, agglomeration raises the probability of job search for less-educated men, while
highly educated men tend to search regardless of the level of agglomeration, suggestingthat agglomeration encourages less-educated men who potentially expect low offer wages
to search for a job through an increase in the offer wages or a decrease in the out-ofpocket cost of job search. Second, life events such as marriage and childbirth change
women’s response of job search to agglomeration. The agglomeration effect on job searchdecisions is significantly positive for married women without children, while it is notstatistically significant for unmarried women. Furthermore, for married women withchildren, the effect is significantly negative. These results suggest that life events increase
women’s value of leisure, that is, time spent on other than job search, including
housework and childcare, especially in urban areas.
The remainder of this paper is organized as follows. Section 2 presents theempirical model to estimate the agglomeration effect on job search decisions. Section 3
presents the data. Section 4 presents the estimation results. Finally, Section 5 concludes
the study.

2.

Model
According to Mortensen (1986), non-working individuals face a decision problem

of job search. If an individual finds that the present value of the search is sufficiently large,
he/she decides to search for a job, that is, to participate in the labor force as an
unemployed searching worker. In this case, the benefit of job search is larger than the outof-pocket cost of job search. This benefit increases when an individual finds a higher offer
wage or a higher offer arrival rate, but decreases when an individual encounters a higher

5value of leisure. Here, the value of “leisure” represents the value of time other than thatspent on searching for a job, implying that it contains the value of household production(e.g., housework and childcare) as well as leisure activities. Hereafter, we label this value
as the “value of leisure/household production.”
Based on this framework, we consider the effects of urban agglomeration on job
search decisions and individual attributes. First, urban agglomeration is assumed toincrease the offer arrival rate because individuals can meet potential vacancies more often,
resulting in a higher benefit of job search. Thus, in more agglomerated areas, individuals
can receive more offers by paying a certain fixed search cost, resulting in a lower out-ofpocket cost of job search.5 Consequently, urban agglomeration encourages individuals tosearch for a job through the offer arrival rate and the out-of-pocket cost of job search.
Next, in the benefit of job search, the offer wage can be assumed to increase withthe level of urban agglomeration, as many studies show the urban wage premium.
Furthermore, a wage gap arises among individual attributes of human capital, which candirectly affect labor productivity. For example, highly educated individuals earn morethan the less educated, and men earn more than women do. The value of leisure, the factor
that decreases the benefit of job search, can be assumed to increase with the level of urban
agglomeration because the value of leisure/household production seems high in urban
areas. This assumption reflects how the market prices of services concerning householdproduction (e.g., housekeeping services and childcare facilities) can be high. Additionally,
longer commuting times in urban areas can cause a larger value of leisure/householdproduction. We focus on individual socioeconomic status, such as family structure, as the5

Wheeler (2001) develops the firm’s search model assuming that urban agglomeration
determines a firm’s search cost. We consider that this assumption can be applied directly
to a worker’s job search.
6

individual attribute determining this value. Suppose that, as a consequence of a divisionof labor in the household, a wife will engage in household production, while a husband
will perform paid work in the labor market after they marry. When a woman experiences
marriage and childbirth, her value of leisure/household production increases because she
spends her time doing housework and childcare.
Overall, we find that urban agglomeration can both increase and decrease thebenefit of job search because it consists of the offer wage and the value of
leisure/household production. Whether urban agglomeration increases or decreases thebenefit of job search may depend on individual attributes, implying that the urban
agglomeration effect on the benefit of job search is heterogeneous across individual
attributes. Consequently, how urban agglomeration affects job search decisions is also
heterogeneous across individual attributes, such as gender, education, marital status, andthe presence of children.
Based on this conjecture, we present an empirical specification to estimate theeffects of urban agglomeration on job search decisions. The source of individual
microdata for the empirical analyses is a pooled cross-sectional survey; therefore, wecannot directly observe the process of job search decisions from the data. Instead, we
examine the relationship between a non-working individual’s job search status and thelevel of urban agglomeration where he/she resides. Therefore, the empirical model
estimates the probability of searching for a job after controlling for the observable
individual and household characteristics.
Let 𝑠! denote the job search status taking one if a non-working individual 𝑖 issearching for a job and zero otherwise. Owing to this binary outcome, the empirical
specification is a probit model given by7

P(𝑠! = 1) = Φ)𝛼 + 𝛽 ln 𝐴"(!),&(!) + 𝑋! 𝛾 + 𝑌"(!) 𝛿 + 𝑍&(!) 𝜂6,(1)

where 𝐴"(!),&(!) , a measure of the level of urban agglomeration, is the employment density
in region 𝑟 , where individual 𝑖 in survey year 𝑡 resides; 𝑋! is a vector of theindividual and household characteristics; 𝑌"(!) is a vector of regional block dummies;
𝑍&(!) is a vector of survey year dummies; 𝛼 is a constant; 𝛽, 𝛾, 𝛿, and 𝜂 are thevectors of the parameters; finally, Φ(∙) is the normal cumulative distribution function.
Our interest parameter 𝛽 establishes a distinction among the individual types
concerning the relationship between urban agglomeration and job search decisions. To
identify the heterogeneity of the urban agglomeration effects across individual attributes,
the model is estimated by subsample based on three dimensions: gender, educational
background (i.e., less than a university degree and a university degree or higher), and life
events (i.e., marital status and the presence of preschool children under six years old).
Focusing on these attributes is useful not only for verifying the conjecture but also for
detecting policy targets to utilize the potential labor force. If the estimate is significantly
positive, the level of urban agglomeration increases the probability of searching for a job.
This suggests that the individuals in the subsample find agglomeration economies in theirjob search decisions. In contrast, if the estimate is significantly negative, agglomeration
discourages individuals in the subsample from searching for a job. Individuals findagglomeration diseconomies. Lastly, if the estimate is not significantly different fromzero, the individual is unlikely to fall under either category. In this case, we interpret thatthe individuals in the sample tend to exhibit one of the two types: individuals always
search or never search, independent of the level of urban agglomeration. There is also
another possibility that the sample contains two types of individuals, that is,agglomeration encourages some individuals to search, while discouraging others from8

searching, resulting in a statistically insignificant estimate.
A concern about the estimation of equation (1) is that the level of urban
agglomeration (i.e., the log of employment density) might be endogenous because theindividual job search and residential location choice exhibit simultaneity; therefore, theestimate may be biased. Combes et al. (2011) and Combes and Gobillon (2015) point out
that endogeneity occurs at both regional and individual levels. First, if the agglomerated
areas attract workers owing to, for example, high wages or a high likelihood of matchingjobs, the local labor markets become denser. In this case, the unobservable regional
factors are correlated with the level of urban agglomeration. Second, individuals with ahigh intention to work, determined by their unobservable factors, might choose to live indenser areas to obtain more opportunities to match jobs. This case induces a correlation
between the unobservable individual factors and the level of urban agglomeration.
To cope with this endogenous problem, the existing literature investigating
agglomeration effects has used long-lagged values of agglomeration variables as an
instrument since Ciccone and Hall (1996). The literature related to this study on
agglomeration effects on job search behavior and matching also uses such instruments(e.g., Di Addario, 2011; Bleakley and Lin, 2012; Andini et al., 2013; Abel and Deitz,
2015; Berlingieri, 2019). Following previous studies, we adopt the log of population
density in 1920 as the instrument from the Population Census that was first conducted inJapan. Using this, we run instrumental variable (IV) probit estimations in addition toprobit estimations. Such long-lagged values of agglomeration are considered valid
instruments for the following reasons (Combes et al., 2011; Combes and Gobillon, 2015).
The instrument satisfies the relevance because the stock of facilities, such as housing,office buildings, factories, and infrastructure, lasts over time, resulting in persistence in9regional patterns of agglomeration. Furthermore, it satisfies the exclusion restriction
because individuals’ job search behaviors in the present should be quite different from thepast owing to changes in the industrial structure and job search method.

3.

Data
We construct a dataset for the estimations by combining individual-level

microdata and regional-level agglomeration data from Japan. First, individual microdata
is obtained from the Employment Status Survey (ESS), conducted every five years by the
Ministry of Internal Affairs and Communications (MIC). We use a pooled cross-sectional
dataset from eight waves of ESS between 1982 and 2017. Each wave covers
approximately one million members aged 15 years and over in 500,000 households,
which were collected through a stratified two-stage sampling method. They were asked
about their employment status as of October 1 of the survey year. The ESS reports
individuals’ places of residence at the municipality level, a minimum administrative
district in Japan, which allows one to merge this data with the regional-level
agglomeration data. The study sample contains individuals who answered not working.
Their records report whether they were searching for a job. Individuals who answeredthat they were preparing to open a business instead of searching for a job are omitted. The
sample is restricted to working age (i.e., 15–64 years) and students are omitted.
Second, to create the variable of employment density as a measure of the level ofurban agglomeration, we take the number of employees at the municipality level from six
waves between 1981 and 2006 of the Establishment and Enterprise Census (EEC),
conducted every five years, and two waves in 2009 and 2014 of the Economic Census for
Business Frame (ECBF), a successor to the former EEC. These censuses were conducted10

in all establishments in Japan through the MIC. As a measure of the level of urban
agglomeration, we calculate employment density, namely, the number of employees
working at establishments in the region per square kilometer of inhabitable area.6
Here, the municipality, the available minimum regional unit from the study’s data,
may not be valid as the regional unit for the estimations, because it is not considered tocorrespond to the local labor market, but an administrative unit. Indeed, many workers
commute beyond the boundaries of municipalities in Japan, implying that local labormarkets have widened.7 To deal with this problem, the regional unit for this study follows
the Urban Employment Area (UEA) proposed by Kanemoto and Tokuoka (2002), which
is defined by commuting rates across municipalities in Japan. Each UEA consists of coreand outlying municipalities. The former is determined by the size of the densely
inhabitable district population and the latter is defined by the rates of employed workers
commuting to the core.8 We thus consider that the UEAs represent a good proxy for theJapanese local labor markets. The number of UEAs is 222. Finally, we create a dataset
for the estimations by merging each wave of the ESS with its immediately preceding yearof EEC or ECBF, based on the individuals’ residential UEAs.9
Table 1 shows the summary statistics of job search status and the variables to

6

Table A1 shows the summary statistics of the urban agglomeration variables at the UEAlevel.
7
According to the 2015 Population Census, 44 percent of employed persons work inmunicipalities other than the ones where they reside.
8
Based on municipal population and commuting rates across municipalities from the
Population Census conducted every five years, the definition of UEA is revised after the
census by the Center for Spatial Information Science (CSIS) of the University of Tokyo.
To keep consistency of the regional unit and to deal with municipal mergers (seeAppendix A for details), we adopt the 2015 standards of UEA unit, which is the latest
during the study’s sample periods. The UEA list is available through the website of the
CSIS (http://www.csis.u-tokyo.ac.jp/UEA/index.htm, accessed on August 19, 2020).
9
See Appendix A for how to construct the dataset in detail.
11control for the individual and household characteristics by the subsample for theestimations, defined by gender, educational background (i.e., less than a university degree
and a university degree or higher), and life events (i.e., marital status and the presence ofpreschool children under six years old). In addition to the variables in this table, theestimation models control for cohort group dummies as individual characteristics.10 In
the sample, individuals who lack one or more necessary variables are omitted.
[Table 1 about here]
The proportion of non-working individuals searching for a job is quite different
across the subsamples. To consider this gap by understanding the characteristics of nonworking individuals in the sample, Table 2 presents the proportion of non-working
individuals to the whole population, as well as the proportion of searchers to non-working
individuals by subsample. Panel A for men shows that, for both educational backgrounds,
the proportion of non-working individuals to the whole population and the proportion of
searchers to non-working individuals for married men with no children are lower thanthose for the unmarried men. This tendency indicates that married men tend to engage inpaid work because of the division of labor in the household. Many men finally seem toengage in paid work after their spouse has children because the proportion of nonworking individuals to the whole population and the proportion of searchers to nonworking individuals for married men with children are the smallest and largest,
respectively.
Panel B for women shows that, unlike men, the proportion of non-working
individuals to the whole population is the smallest for unmarried women, followed inorder by married women with no children and married women with children, for both10

See Table A2 for the cohort group dummies in detail.
12

educational backgrounds. In the reverse order, the proportion searchers to non-working
individuals is the largest for unmarried women and the smallest for married women withchildren. Each order of the proportions is consistent, suggesting that life events such as
marriage and childbirth discourage women from participating in the labor force.
[Table 2 about here]
Altogether, life events may indeed raise women’s value of leisure/householdproduction, whereas they have little effect on or decrease men’s one. The following
section investigates whether such heterogeneous effects of life events lead toheterogeneous effects of urban agglomeration on job search decisions of non-working
individuals.

4.

Results

4.1. Probit estimations
This section presents the estimation results of the empirical model (equation (1))
that examines the effect of urban agglomeration on the probability of non-working
individuals’ job search. Table 3 presents the probit model estimation results. All themodels in this table control for observable individual and household characteristics. First,
we consider the results for men, shown in panel A. Columns (1) to (3) for less than auniversity degree holders show that the marginal effect of the log of employment density
on job search decisions is positive and significant at the 10 percent level, regardless ofmarital status and the presence of children. This result suggests that urban agglomeration
can trigger a less-educated man’s decision to search for a job. However, when thesignificance level is set at 1 percent, the effect for the unmarried group is not statisticallysignificant. In addition, the magnitudes of the marginal effect for married men without

13

children (0.0254) and with children (0.0209) are larger than that for unmarried men(0.0049). These results suggest that marriage and childcare encourage less-educated men
in urban areas to work in labor markets because of the comparative advantage in theirhouseholds; that is, husbands and wives tend to specialize in paid work in the labor market
and household production (e.g., housework and childcare), respectively. This division oflabor in the household may encourage husbands in more urban areas to decide to search
for a job because they can expect to benefit from agglomeration, such as the urban wage
premium as well as the lower out-of-pocket cost of job search.
[Table 3 about here]
The estimation results for men with a university degree or higher, shown incolumns (4) to (6) of panel A, are similar to those for less-educated men. The
agglomeration effect for the unmarried men is not statistically significant (column (4)),
while those for married men both without and with children are significantly positive(columns (5) and (6)).
Next, panel B of Table 3 presents the estimation results for women. For less thana university degree holders in columns (1) to (3), the results show a significant gap in the
agglomeration effect on job search decisions among the three groups with different
marital statuses and the presence of children. For unmarried women, the agglomeration
effect is significantly positive (column (1)), but not statistically significant for married
women without children (column (2)). Finally, for married women with children, theagglomeration effect becomes negative and significant (column (3)). With regard to themagnitude of the effect, a 10 percent increase in employment density raises the likelihoodof searching for a job by 0.14 percentage points for the unmarried. By contrast, it
decreases the likelihood by 0.07 percentage points for married women with children.

14

These results suggest that life events, such as, marriage and childbirth, change theless-educated women’s response of job search decisions to agglomeration. First, nonworking unmarried women in urban areas tend to search for jobs because they find alower out-of-pocket cost of job search there and higher benefits of job search, such as theurban wage premium. After they marry, but do not yet have children, based on thecomparative advantage in the household, married couples allocate more of the female
spouse’s time to housework and the male spouse’s time to paid work in the labor market.
This household decision increases the women’s value of leisure/household production,
leading to a higher opportunity cost of job search. Their offer wages are supposed toremain unchanged, as marriage could not affect human capital.
Finally, if couples have children, the burden of childcare further increases
women’s value of leisure and household production. This tendency is large enough tosurpass the offer wages, especially in urban areas, suggesting that childcare costs arehigher in urban areas. Indeed, the labor force participation rates of married women withchildren in Japan are lower in urban areas than in others (Abe, 2013, 2018). The factors
leading to this regional disparity could be longer commuting times in urban areas (Blacket al., 2014; Kawabata and Abe, 2018) and supply shortages of childcare facilities in urban
areas (Hashimoto and Miyagawa, 2008). Consequently, these features of urban areas cangenerate agglomeration diseconomies in job search decisions for married women withchildren.
Columns (4) to (6) of panel B present the estimation results for women with auniversity degree or higher. Unlike less-educated women, the agglomeration effect is not
significant for unmarried women and married women with children (columns (4) and (6)),
but is significantly positive for married women without children (column (5)). Highly

15educated unmarried women may search for jobs, regardless of the level of urban
agglomeration, because their offer wages are high. Two possibilities are considered to be
the factors causing the insignificant agglomeration effect for married women withchildren. First, even if the highly educated women’s value of leisure/householdproduction increases owing to marriage and childcare, their offer wages may be still highenough. They thus search for jobs regardless of the level of urban agglomeration. Second,
highly educated women tend to marry highly educated men who earn high wages;
therefore, wives may specialize in household production more. They thus do not searchfor jobs regardless of the level of urban agglomeration. The latter might be morereasonable because, regardless of the presence of children, the proportion of married
women with a university degree or higher who are searching for a job is lower than thatof less than a university degree holders, as shown in Table 2. However, it remains difficult
to interpret the significant agglomeration effect for married women without children.

4.2. IV probit estimations
The simple probit models in the previous subsection may induce biased estimates
because of the simultaneity of individual job search and residential location choice. To
address this problem, we consider the estimation results using the 1920 population density
as the instrument. Table 4 presents the marginal effects of the IV probit estimation. The
instrument accurately predicts the agglomeration variable because the coefficient of the
instrument in the first stage is significantly positive and the partial R-squared isapproximately 0.5 for all models. Thus, the instrument is considered to satisfy therelevance.
[Table 4 about here]

16

For men with less than a university degree, columns (1) to (3) of panel A show
similar results to the probit estimations in Table 3; that is, there are significant positive
agglomeration effects on job search decisions regardless of marital status and the presenceof children. In this case, we consider the endogenous bias to be a minor problem.
For men with a university degree or higher, column (4) shows that theagglomeration effect for unmarried men is not statistically significant, as in the probit
estimation. For married men without children in column (5), the effect is significant only
when the statistically significant level is set at the 10 percent level. Column (6) for
married men with children does not indicate a statistically significant effect, unlike in the
probit estimation. Probit estimation without the instrument may have been contaminatedby simultaneous bias. In other words, men with a high search intensity may move to urban
areas to search for jobs after they marry. The results after coping with this bias suggestthat agglomeration does not change the job search decisions of highly educated men,regardless of marital status and the presence of children. We thus consider that highly
educated non-working men search for jobs regardless of the agglomeration level because
they expect to receive high offer wages.
Next, we consider the results for women in panel B. Columns (1) to (3) for womenwith less than a university degree confirm results similar to those in Table 3; that is,agglomeration encourages less-educated women to search for jobs when they are
unmarried. The agglomeration effect becomes insignificant when they marry andbecomes significantly negative when they have children. Consequently, even after copingwith the endogeneity of agglomeration, the agglomeration effect on the job searchdecisions of less-educated women depends on their life events, such as marriage andchildbirth.

17

For women with a university degree or higher, as shown in columns (4) to (6), theagglomeration effect is not statistically significant for all groups with different marital
statuses and the presence of children. The significantly positive effect for married womenwith children confirmed by the probit estimations without the instrument in Table 3 may
capture the simultaneity of their job search and residential location choice after marriage.
After coping with this simultaneity, the job search decisions of highly educated womenare confirmed not to depend on agglomeration, regardless of their marital status and thepresence of children. These results reinforce the interpretation provided in the previous
subsection for probit estimations without the instrument; that is, highly educated women
do not change their job search decisions based on agglomeration, because they can expect
high offer wages that offset the value of leisure/household production. Another possibility
is that they do not search for a job after they marry because they tend to marry highly
educated men who earn high wages, resulting in more specialized household production.
Overall, these estimations suggest that life events, such as marriage and childbirth,increase women’s value of leisure/household production sufficiently enough to change
the agglomeration effect on job search decisions. Ultimately, married women withchildren in urban areas do not decide to search for jobs.

5.

Conclusion
This study examined the effect of urban agglomeration on the decision of a non-working individual to search for a job, a step before job matching and earning wages,
using Japanese microdata. It also focused on the differences in this effect acrossindividual attributes, such as gender, education, marital status, and the presence ofchildren.

18

The estimation results showed that the agglomeration effect differs acrossindividual attributes, even after coping with potential endogeneity stemming from thesimultaneity of individuals’ job search and residential location choice. The main findings
were twofold. First, urban agglomeration encourages less-educated men to search for ajob, suggesting that they find the benefit of agglomeration, such as higher offer wages.
The effect of urban agglomeration is not significant for highly educated men, indicatingthat they search for jobs regardless of agglomeration because they can potentially expect
high offer wages.
Second, life events, such as marriage and childbirth, remarkably change the job
search decision responses of less-educated women to agglomeration. For unmarried
women, the agglomeration effect on job search decisions is significantly positive, but
becomes insignificant after marriage. This result suggests that the burden of housework
increases the value of leisure/household production. When married women have children,
the agglomeration effect becomes significantly negative, suggesting that childcare further
increases the value of leisure/household production, especially in urban areas. A
significant agglomeration effect was not confirmed for highly educated women,
indicating that their high offer wages enable them to search for a job, or that marriage
with highly educated men reduces their likelihood of searching for a job, regardless of
agglomeration.
Finally, the results lead to the following implications for employment policy toutilize the potential labor force. To utilize inactive men, especially those who are lesseducated, it seems helpful to decrease the out-of-pocket cost of job search in rural areas
by policies inducing high offer arrival rates, such as reinforcement of public employment
referral, because those who reside in non-urban areas do not tend to search for a job.

19

Vocational training for male workers in rural areas might also be helpful because humancapital accumulation is expected to increase offer wages, which leads to more job
searching. To utilize inactive women, especially those who are married, the gender wage
gap should be decreased, as comparatively lower wages may discourage wives fromsearching for jobs. In addition, to utilize inactive married women with children who
encounter negative agglomeration effects, policymakers should support them in urban
areas by solving the long commuting times or the shortages of childcare facilities in orderto reduce these women’s value of leisure/household production.

Appendix A. Construction of the dataset
First, some municipal mergers occurred in Japan during the sample period (1981–2017). To construct a consistent regional unit over the sample periods, we arranged themunicipality unit as of October 1, 2015, when the latest Population Census was conductedduring the sample periods. After this period, no municipal mergers occurred. Then, weconverted the municipality unit to the 2015 standards of the UEA unit, which is defined
based on the municipal population and commuting rates across municipalities from the
Population Census. Exceptionally, Kamikuishiki-mura separated into two areas: one
merged with Kofu-shi and the other merged with Fujikawaguchiko-machi on March 1,
2006. Furthermore, these two municipalities are situated in different UEAs. Then,
individuals who resided in Kamikuishiki-mura from the ESS between 1982 and 2002
were dropped from the sample because their residential UEAs could not be identified.
Regarding the agglomeration data, the number of employees from the EEC between 1981
and 2001 was distributed based on the areas after this separation. 11 The population

11

To calculate the areas after the separation, we used the Municipality Map Maker for20

density in 1920 for the instrument was calculated in the same manner.12 Although somemunicipalities are classified into wards (ku) in ordinance-designated cities (seirei-shitei
toshi) separated during the sample periods, these separations are not problematic forregional unit arrangements because the UEAs containing such municipalities cover all
wards. Table A1 shows the summary statistics of the urban agglomeration variables (i.e.,
the log of employment density and log of population density in 1920) at the UEA level.
Second, the regional block of a UEA was determined based on the prefecture towhich the core municipalities belong. The 11 regional blocks are as follows (prefectures
are in parentheses): Hokkaido (Hokkaido), Tohoku (Aomori, Iwate, Miyagi, Akita,
Yamagata, and Fukushima), Minami-Kanto (Saitama, Chiba, Tokyo, and Kanagawa),
Kita-Kanto/Koshin (Ibaraki, Tochigi, Gumma, Yamanashi, and Nagano), Hokuriku(Niigata, Toyama, Ishikawa, and Fukui), Tokai (Gifu, Shizuoka, Aichi, and Mie), Kinki(Shiga, Kyoto, Osaka, Hyogo, Nara, and Wakayama), Chugoku (Tottori, Shimane,
Okayama, Hiroshima, and Yamaguchi), Shikoku (Tokushima, Kagawa, Ehime, andKochi), Kyushu (Fukuoka, Saga, Nagasaki, Kumamoto, Oita, Miyazaki, and Kagoshima),
and Okinawa (Okinawa).
Third, the cohort group dummies for the estimations were constructed based on
the age of individuals in each wave of the ESS, as shown in Table A2.
[Tables A1 and A2]

Web (http://www.tkirimura.com/mmm/, accessed on March 11, 2021), developed by
Kirimura et al. (2011).
12
We obtained the population data at the municipality level of the 1920 Population
Census from a CD-ROM attached to Sinfonica and JSA (2005).
21

References
Abe, Y. (2013). Regional variations in labor force behavior of women in Japan. Japanand the World Economy, 28, 112–124.
Abe, Y. (2018). Effects of demographic compositional changes on the convergence offemale participation rates. Journal of the Japanese and International Economies,
48, 97–104.
Abel, J. R., & Deitz, R. (2015). Agglomeration and job matching among college graduates.
Regional Science and Urban Economics, 51, 14–24.
Andersson, F., Burgess, S., & Lane, J. I. (2007). Cities, matching and the productivity
gains of agglomeration. Journal of Urban Economics, 61(1), 112–128.
Andini, M., de Blasio, G., Duranton, G., & Strange, W. C. (2013). Marshallian labour
market pooling: Evidence from Italy. Regional Science and Urban Economics,
43(6), 1008–1022.
Berlingieri, F. (2019). Local labor market size and qualification mismatch. Journal of
Economic Geography, 19(6), 1261–1286.
Black, D. A., Kolesnikova, N., & Taylor, L. J. (2014). Why do so few women work in
New York (and so many in Minneapolis)? Labor supply of married women across
US cities. Journal of Urban Economics, 79, 59–71.
Bleakley, H., & Lin, J. (2012). Thick-market effects and churning in the labor market:
Evidence from US cities. Journal of Urban Economics, 72(2–3), 87–103.
Ciccone, A., & Hall, R. E. (1996). Productivity and the Density of Economic Activity.
American Economic Review, 86(1), 54–70.
Combes, P.-P., Duranton, G., & Gobillon, L. (2008). Spatial wage disparities: Sorting
matters! Journal of Urban Economics, 63(2), 723–742.

22

Combes, P.-P., Duranton, G., & Gobillon, L. (2011). The identification of agglomeration
economies. Journal of Economic Geography, 11(2), 253–266.
Combes, P.-P., & Gobillon, L. (2015). The Empirics of Agglomeration Economies. In G.
Duranton, J. V. Henderson, & W. C. Strange (Eds.), Handbook of Regional and
Urban Economics (Vol. 5, pp. 247–348). Amsterdam: Elsevier.
Dauth, W., Findeisen, S., Moretti, E., & Suedekum, J. (2022). Matching in Cities. Journalof the European Economic Association, forthcoming.
De La Roca, J., & Puga, D. (2017). Learning by Working in Big Cities. The Review of
Economic Studies, 84(1), 106–142.
Di Addario, S. (2011). Job search in thick markets. Journal of Urban Economics, 69(3),303–318.
Duranton, G., & Puga, D. (2004). Micro-Foundations of Urban Agglomeration
Economies. In J. V. Henderson, & J.-F. Thisse (Eds.), Handbook of Regional and
Urban Economics (Vol. 4, pp. 2063–2117). Amsterdam: Elsevier.
Glaeser, E. L., & Maré, D. C. (2001). Cities and Skills. Journal of Labor Economics,
19(2), 316–342.
Gould, E. D. (2007). Cities, Workers, and Wages: A Structural Analysis of the Urban
Wage Premium. The Review of Economic Studies, 74(2), 477–506.
Hashimoto, Y., & Miyagawa, S. (2008). Why the Female Labor Force Participation Ratein Metropolitan Areas is Low: A reexamination of the current situation and of theissues. RIETI Discussion Paper Series, No. 08-J-043 (in Japanese).
Heuermann, D., Halfdanarson, B., & Suedekum, J. (2010). Human Capital Externalities
and the Urban Wage Premium: Two Literatures and their Interrelations. Urban
Studies, 47(4), 749–767.

23

Japan Institute for Labour Policy and Training. (2020). Challenges Facing Japan: Work
Styles and Labor Shortages: MHLW’s White Paper on the Labor Economy 2019.
Japan Labor Issues, 4(23), 2–9.
Kanemoto, Y., & Tokuoka, K. (2002). Proposal for the standards of metropolitan areas ofJapan. Journal of Applied Regional Science, 7, 1–15 (in Japanese).
Kawabata, M., & Abe, Y. (2018). Intra-metropolitan spatial patterns of female labor force
participation and commute times in Tokyo. Regional Science and Urban
Economics, 68, 291–303.
Kirimura, T., Nakaya, T., & Yano, K. (2011). Building a Spatio-temporal GIS Database
about Boundaries of Municipalities: Municipality Map Maker for Web. Theory
and Applications of GIS, 19(2), 83–92 (in Japanese).
Mortensen, D. T. (1986). Job Search and Labor Market Analysis. In O. C. Ashenfelter, &R. Layard (Eds.), Handbook of Labor Economics (Vol. 2, pp. 849–919).
Amsterdam: Elsevier.
Petrongolo, B., & Pissarides, C. (2006). Scale Effects in Markets with Search. The
Economic Journal, 116(508), 21–44.
Phimister, E. (2005). Urban effects on participation and wages: Are there genderdifferences? Journal of Urban Economics, 58(3), 513–536.
Statistical Information Institute for Consulting and Analysis [Sinfonica], & Japan
Statistical Association [JSA]. (2005). Shikuchoson Jinko no Chokikeiretsu: Heisei
no Daigappeigo no Shikuchosonkyoiki niyoru Sokyujinkokeiretsu (Long-term
Series of Municipal Population: Retroactive Population by Municipal boundaries
after the Great Heisei Consolidation). Tokyo: Japan Statistical Association (inJapanese).

24Wheeler, C. H. (2001). Search, Sorting, and Urban Agglomeration. Journal of Labor
Economics, 19(4), 879–899.
Wheeler, C. H. (2006). Cities and the growth of wages among young workers: Evidence
from the NLSY. Journal of Urban Economics, 60(2), 162–184.
Wheeler, C. H. (2008). Local market scale and the pattern of job changes among youngmen. Regional Science and Urban Economics, 38(2), 101–118.

25

Table 1: Summary statistics of non-working individual and household characteristics
Less than university degree
Unmarried
MarriedNo children
With children
(1)
(2)
(3)
Mean Std. dev. Mean Std. dev. Mean Std. dev.

Variable
Panel A: Men
Searching for a job
0.380
0.485
0.341
0.474
Age
38.034 15.396 58.477
6.902
Household head
0.358
0.479
0.948
0.222
Working a year ago
0.196
0.397
0.304
0.460
Previous job experience
0.644
0.479
0.962
0.191
Number of household members aged 15 or over 1.747
1.455
1.793
1.002
Number of children aged 0–5
0.028
0.209
0.000
0.000
Number of children aged 6–14
0.084
0.356
0.154
0.523
Household yearly income (JPY in millions)Less than 1
0.230
0.421
0.073
0.261
1–1.99
0.180
0.385
0.154
0.361
2–2.99
0.139
0.346
0.230
0.421
3–3.99
0.105
0.307
0.171
0.377
4–4.99
0.077
0.267
0.119
0.324
5–6.99
0.109
0.312
0.143
0.350
7–9.99
0.094
0.292
0.083
0.276
10–14.99
0.049
0.217
0.024
0.153
15 or higher
0.015
0.122
0.003
0.058
Observations
82,75469,293
Notes: Pooled data across eight waves of the ESS between 1982 and 2017.

26

0.422
51.622
0.838
0.392
0.960
2.640
1.396
0.492
0.080
0.099
0.122
0.124
0.125
0.211
0.175
0.056
0.008

0.494
13.405
0.369
0.488
0.197
1.163
0.571
0.748

0.271
0.299
0.327
0.329
0.331
0.408
0.380
0.230
0.090
8,350

University degree or higher
Unmarried
MarriedNo children
With children
(4)
(5)
(6)
Mean Std. dev. Mean Std. dev. Mean Std. dev.
0.518
38.207
0.294
0.259
0.736
1.818
0.013
0.019

0.500
13.301
0.455
0.438
0.441
1.335
0.145
0.169

0.148
0.355
0.129
0.335
0.150
0.358
0.125
0.331
0.087
0.281
0.126
0.332
0.117
0.321
0.085
0.278
0.034
0.180
13,0710.289
58.719
0.945
0.345
0.982
1.720
0.000
0.097

0.453
7.051
0.227
0.475
0.134
0.957
0.000
0.410

0.580
44.162
0.838
0.575
0.961
1.937
1.311
0.368

0.494
12.908
0.369
0.495
0.193
1.219
0.513
0.651

0.059
0.235
0.092
0.289
0.170
0.375
0.175
0.380
0.132
0.338
0.178
0.382
0.129
0.335
0.053
0.225
0.013
0.114
11,928

0.131
0.104
0.147
0.107
0.113
0.148
0.130
0.091
0.030

0.338
0.306
0.354
0.309
0.317
0.355
0.336
0.288
0.169
778

Table 1 (continued)
Less than university degree
Unmarried
MarriedNo children
With children
(1)
(2)
(3)
Mean Std. dev. Mean Std. dev. Mean Std. dev.

Variable
Panel B: Women
Searching for a job
0.304
0.460
0.170
0.376
Age
44.304 16.053 50.110 10.438
Household head
0.432
0.495
0.023
0.148
Working a year ago
0.205
0.404
0.110
0.312
Previous job experience
0.674
0.469
0.710
0.454
Number of household members aged 15 or over 1.623
1.393
1.853
1.025
Number of children aged 0–5
0.116
0.413
0.000
0.000
Number of children aged 6–14
0.199
0.562
0.423
0.800
Household yearly income (JPY in millions)Less than 1
0.186
0.389
0.021
0.142
1–1.99
0.190
0.392
0.059
0.236
2–2.99
0.145
0.352
0.128
0.334
3–3.99
0.116
0.320
0.141
0.348
4–4.99
0.093
0.290
0.134
0.340
5–6.99
0.125
0.331
0.211
0.408
7–9.99
0.091
0.288
0.187
0.390
10–14.99
0.042
0.201
0.093
0.290
15 or higher
0.013
0.114
0.027
0.163
Observations
124,134
448,214
Notes: Pooled data across eight waves of the ESS between 1982 and 2017.

27

0.127
34.648
0.008
0.094
0.858
1.662
1.397
0.457

University degree or higher
Unmarried
MarriedNo children
With children
(4)
(5)
(6)
Mean Std. dev. Mean Std. dev. Mean Std. dev.

0.333
9.852
0.088
0.292
0.349
1.097
0.560
0.704

0.431
36.894
0.259
0.329
0.747
1.947
0.040
0.048

0.008
0.090
0.039
0.194
0.133
0.339
0.193
0.395
0.187
0.390
0.237
0.425
0.137
0.344
0.052
0.222
0.014
0.117
188,121

0.101
0.101
0.128
0.110
0.098
0.131
0.147
0.114
0.069

0.495
13.568
0.438
0.470
0.435
1.324
0.248
0.268

0.302
0.302
0.334
0.312
0.297
0.337
0.354
0.318
0.254
7,777

0.135
46.579
0.033
0.122
0.808
1.650
0.000
0.535

0.342
10.441
0.177
0.327
0.394
0.939
0.000
0.858

0.012
0.110
0.016
0.126
0.048
0.213
0.077
0.266
0.097
0.295
0.196
0.397
0.261
0.439
0.201
0.401
0.093
0.290
30,2830.094
33.559
0.009
0.082
0.908
1.236
1.376
0.384

0.291
4.778
0.093
0.275
0.289
0.715
0.549
0.641

0.006
0.077
0.009
0.094
0.049
0.217
0.128
0.334
0.172
0.378
0.313
0.464
0.211
0.408
0.082
0.275
0.030
0.170
20,174

Table 2: Fractions of non-working individuals and those who are searching
Less than university degree
Unmarried

Married

Unmarried

No children

With children

(1)

(2)

(3)

Non-working/Whole population

0.162

0.073

Searching/Non-working

0.380
0.213

Variable

University degree or higher
MarriedNo children

With children

(4)

(5)

(6)

0.031

0.087

0.040

0.007

0.341

0.422

0.518

0.289

0.580

0.355

0.541

0.099

0.330

0.486

Panel A: Men

Panel B: Women
Non-working/Whole population

Searching/Non-working
0.304
0.170
0.127
0.431
0.135
0.094
Note: Pooled data across eight waves of the ESS between 1982 and 2017. Sample of the working individuals is restricted to those whohave no missing values of the same variables, which are used for the estimations for the non-working individuals.

28

Table 3: Probit marginal effects of agglomeration on job search decisionLess than university degree
Unmarried

University degree or higher

Married

Unmarried

No children

With children

(1)

(2)

(3)

0.0049*0.0254***(0.0030)

MarriedNo children

With children

(4)

(5)

(6)

0.0209***0.00030.0184***0.0541***(0.0040)(0.0067)(0.0068)(0.0070)(0.0172)

82754

69293

8350

13071

11928

778

-42841.29-36350.46-3919.27-7239.34-5471.87-322.29

0.220

0.182

0.311

0.200

0.237

0.391

0.0141***0.0014-0.0074***

0.00710.0107***-0.0006(0.0026)(0.0018)(0.0018)(0.0085)(0.0036)(0.0042)

Observations

124134448214

188121

7777

30283

20174

Log pseudo likelihood-57844.24-177329.05-68044.23-4182.89-10456.54-6004.36

Panel A: Menln(Employment density)
Observations
Log pseudo likelihood
Pseudo R2
Panel B: Womenln(Employment density)

Pseudo R2
0.241
0.132
0.051
0.213
0.129
0.042
Notes: Standard errors clustered at the regional level are in parentheses. ***, **, and * represent statistical significance at 1%, 5%, and
10% level, respectively. All models contain a constant, individual and household characteristics, cohort group dummies, regional blockdummies, and the survey year dummies.

29

Table 4: IV Probit marginal effects of agglomeration on job search decision
Unmarried(1)
Panel A: Menln(Employment density)
First stage resultsln(Population density in 1920)
Observations
Log pseudo likelihood
Partial R2
Wald test of exogeneity (chi2)(p-value)
Panel B: Womenln(Employment density)

Less than university degree
MarriedNo children
With children(2)(3)

Unmarried(4)

University degree or higher
MarriedNo children
With children(5)(6)

0.0094**(0.0044)0.0287***(0.0059)

0.0367***(0.0097)0.0077(0.0092)

0.0204*(0.0108)0.0246(0.0280)

1.3053***(0.1102)82754-82657.48
0.495
2.083
0.149

1.3230***(0.1108)
69293-68816.88
0.514
0.865
0.352

1.3496***(0.0985)
8350-7451.38
0.543
6.216
0.013

1.3788***(0.1218)13071-12930.73
0.474
1.051
0.305

1.4080***(0.1213)11928-10230.27
0.504
0.082
0.775

1.4616***(0.1218)778-656.29
0.519
2.323
0.127

0.0135***(0.0037)0.0005(0.0023)-0.0059**(0.0027)0.0094(0.0125)

0.0077(0.0049)-0.0061(0.0053)First stage resultsln(Population density in 1920)

1.3440***1.3675***1.3737***1.4116***1.4245***1.4106***(0.1126)(0.1137)(0.1051)(0.1369)(0.1381)(0.1281)
Observations
124134448214
188121
7777
30283
20174
Log pseudo likelihood-116927.38-369116.86-144527.55-7317.31-19282.89-12256.61
Partial R2
0.507
0.528
0.549
0.478
0.503
0.521
Wald test of exogeneity (chi2)
0.064
0.241
0.736
0.049
0.722
1.667
(p-value)
0.800
0.624
0.391
0.824
0.395
0.197
Notes: Standard errors clustered at the regional level are in parentheses. ***, **, and * represent statistical significance at 1%, 5%, and 10% level, respectively. All
models contain a constant, individual and household characteristics, cohort group dummies, regional block dummies, and the survey year dummies.

30

Table A1: Summary statistics of urban agglomeration variables
Variable

Mean

Std. dev.

Min

Max

ln(Employment density)

5.496

0.753

3.112

7.734

ln(Population density in 1920)
6.021
0.647
3.692
7.287
Notes: Numbers of observations are 1776 (= 222 UEAs * 8 waves) and 222 for the log of
employment density and the log of population density in 1920, respectively. Employment
and population density are calculated by the number of the employees and population per
square kilometer of inhabitable area, respectively.

31

Table A2: Cohort groups for dummy variables for the estimations
Aged
15–20

Aged
20–25

Aged
25–30

Aged
30–35

Aged
35–40

Aged
40–45

Aged
45–50

Aged
50–55

Aged
55–60

Aged
60–65

1982

10

9

8

7

6

5

4

3

2

1

1987

11

10

9

8

7

6

5

4

3

2

1992

12

11

10

9

8

7

6

5

4

3

1997

13

12

11

10

9

8

7

6

5

4

2002

14

13

12

11

10

9

8

7

6

5

2007

15

14

13

12

11

10

9

8

7

6

2012

16

15

14

13

12

11

10

9

8

7

2017
17
16
15
Notes: Numbers represent the cohort groups.

14

13

12

11

10

9

8

ESS wave32