Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Vacancies, Job Seekers, and Minimum Wages:Evidence from Public
Employment Placement Service Data
Souichi Ohta、Kazutomo Komae

9 March, 2022
DP2022-004
https://ies.keio.ac.jp/en/publications/18177/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
9 March, 2022

Vacancies, Job Seekers, and Minimum Wages:Evidence from Public Employment
Placement Service Data
Souichi Ohta、Kazutomo Komae
Keio-IES DP2022-004
9 March, 2022
JEL Classification: J63, J64, J65
Keywords: Minimum Wages;Vacancies;Job Seekers;Labor Market Flows;Search andMatching
Abstract
This paper investigates the impact of a minimum wage increase on the number ofvacancies and job seekers using data from Japan's public employment placement service.
The results show that for 2005-2019, a rise in the minimum wage reduced the numberof vacancies. On the other hand, it increased the number of job seekers and thendecreased the vacancy-job seeker ratio. Some part of this came from the increased job
separation into unemployment. The impact of a minimum wage rise is conspicuous
during the period of the slack labor market, for prefectures with high Kaitz indices orwith a high proportion of small firms, and part-time jobs.

Souichi Ohta
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyoohta@econ.keio.ac.jp
Kazutomo Komae
Graduate School of Economics, The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyokkomae@g.ecc.u-tokyo.ac.jp

Vacancies, Job Seekers, and Minimum Wages:
Evidence from Public Employment Placement Service Data*

Souichi Ohta†
Faculty of Economics, Keio University

andKazutomo Komae‡
Graduate School of Economics, The University of Tokyo
andThe Ministry of Health, Labour, and Welfare

March 9, 2022

SummaryThis paper investigates the impact of a minimum wage increase on the number of vacancies and job seekers
using data from Japan's public employment placement service. The results show that for 2005-2019, a risein the minimum wage reduced the number of vacancies. On the other hand, it increased the number of job
seekers and then decreased the vacancy-job seeker ratio. Some part of this came from the increased job
separation into unemployment. The impact of a minimum wage rise is conspicuous during the period of theslack labor market, for prefectures with high Kaitz indices or with a high proportion of small firms, and forpart-time jobs.

JEL: E24, J63, J64, J65
Key Words: Minimum Wages, Vacancies, Job Seekers, Labor Market Flows, Search and Matching* We are grateful to Yuji Genda, Ryo Kambayashi, Daiji

Kawaguchi, Atsushi Kawakami, Masayuki
Morikawa, Yoko Takahashi, Atsuhiro Yamada, and seminar participants at the workshop held by the
Ministry of Health, Labour, and Welfare for their helpful comments and suggestions. The views expressedherein are those of the authors and do not necessarily reflect the views of the Ministry of Health, Labour,and Welfare, Japan.
† Corresponding author: 2-15-45 Mita, Minato-ku, Tokyo 108-8345 Japan (ohta@econ.keio.ac.jp)‡ 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654 Japan (kkomae@g.ecc.u-tokyo.ac.jp)

1. Introduction
This paper estimates the minimum wage elasticities of vacancies and job seekers using
monthly prefectural data registered for the public employment placement services in Japan. While
numerous previous studies have examined the impact of minimum wages on employment levels, thosestudied their effect on vacancies and job seekers are relatively scarce. Further analysis is desirable
because the number of vacancies and job seekers are the main determinants of the level of employment.
Investigating the impact of minimum wages on vacancies and job seekers opens the way forunderstanding the various routes through which minimum wages affect employment levels.
The concept of labor market flows illustrates the basic idea. Let us consider the labor market
as a site where matching takes place between vacancies and job seekers. The number of matched pairs
becomes the number of hires, which constitutes the number of flows into employment. On the other
hand, the number of separations from firms becomes the number of outflows. The difference between
the two flows determines the change in the employment level. The crux here is that the minimum wage
can affect these two flows. For example, if a rise in the minimum wage undermines firms’ incentives
to hire workers, it could bring about fewer vacancies and hence fewer hires. Firms may also lay off
their workers to save labor costs, bringing about more separations. This argument suggests thatexamining the impact of minimum wages on labor market flows is a promising strategy to unravel thenature of the minimum-wage effect on the level of employment. The basic idea of labor market flows
accords well with the model of imperfect labor markets where vacancies and job-seekers coexist due
to lack of information or search costs. In this model, and the monopsony model of the labor market aswell, an increase in the minimum wage does not always lead to employment reduction (Card andKrueger, 1995; Bhaskar et al.,2002; Flinn, 2006), which is in sharp contrast to the simple neoclassicalmodel where an increase in the minimum wage should reduce employment (Stigler, 1946).
Many empirical studies have focused on the effect of minimum wages on the level of
employment. The literature, however, has not reached a definite conclusion: Some studies find positive
or zero effects (Card and Krueger, 1994), while other studies obtain opposite results (Neumark andWascher, 2008; Clemens and Wither, 2019). Recent studies show that the minimum wage affects labormarket flows while leaving the level of employment unchanged. Dustmann et al. (2021) investigatedthe effects of the first-time introduction of the nationwide minimum wage in Germany in 2015.
According to their results, the minimum wage did not reduce employment even though the wage level
had increased. The wage increase took the form of reallocation of low-wage workers from smaller
establishments to larger establishments and from lower- to higher-paying (productive) establishments.
This observation is consistent with the prediction of search friction models that a higher minimum
wage induces the lower productivity jobs to resolve and the dismissed workers move to higher
productivity jobs (Mortensen and Pissarides, 1994; Pissarides, 2000).
2

On the other hand, some studies argue that a rise in the minimum wage reduces labor market
flows. Brohu and Green (2013) show that the higher minimum wage did not change the level of
employment but reduced separation and hiring rates in Canada between 1979 and 2008. Also, Dube etal. (2016) indicate that the minimum wage change did not affect the employment level of teens andworkers who have been in a high-impact industry (restaurants) but reduced the labor market flows in
the US between 2000 and 2011. Liu (2021) finds that the rise in minimum wage decreased theoccupational mobility of younger and less-educated workers and augmented the mismatch in the US
between 2005 and 2016. Both Brochu and Green (2013) and Dube et al. (2016) argue that theirextended search friction models can explain why the minimum wage reduces layoffs even under theassumption of search friction.
These are valuable studies that focused on the role of labor market flows such as hires andseparations when examining the minimum wage effect on employment. However, these labor market
flows derive from the matching process of vacancies and job seekers in the labor market, where thelatter two indices more directly represent labor demand and supply, respectively. Therefore, the effectof minimum wages on vacancies and job seekers deserves detailed empirical investigation.
There are, in fact, a few studies that examine the relationship between minimum wage andvacancy posting. Kudlyak et al. (2020) estimated the minimum wage effect on vacancies in “at-risk
occupations” (occupations with a high employment share around the prevailing minimum wage) in
the US between 2005 and 2018. The results show that a 10% increase in the minimum wage resulted
in a 2.4% reduction of vacancies in “at-risk occupations.” For Japan, Ariga (2007) is the only study
that focuses on the relationship between minimum wage and vacancies and finds a negative correlation
between these two variables. However, one should note that this study only examined vacancies of
new high school graduates treating the minimum wage as one of the many independent variables.
Hence, a more detailed analysis using data on vacancies for general workers is necessary.
This paper is the first study that examines the impact of minimum wage on vacancies forgeneral workers in Japan. We also investigate its effect on job seekers since a higher minimum wage
may induce firms to change the number of their employees and affect the labor market participationfrom out of the labor force1. This paper also estimates the minimum wage effect on the number of newrecipients of unemployment insurance benefits to gain some insight into the relative importance ofseparations.
The remainder of this paper is structured as follows. Section 2 briefly introduces thecharacteristics of the minimum wage system in Japan. Section 3 discusses the data and estimates theeffect of minimum wage changes on the number of vacancies and job seekers. Section 4 is devoted toanalyzing differences in minimum wage effects between subperiods, regions, vacancy types, and job
1 Flinn (2006) argues that the minimum wage could encourage people out of the labor force to participate

in the job search.

3

seeker types. Section 5 concludes.

2. Minimum Wage in Japan
This section briefly introduces some characteristics of the minimum wage system in Japan.
The Japanese minimum wage system consists of two types of minimum wages: regional and specificminimum wages. The Minimum Wage Act stipulates that “regional minimum wages shall be set inconsideration of the living expenses of workers, wages of workers and the ordinary enterprises’ ability
to pay the wages in the region” (Article 9, Paragraph 2). The Central Minimum Wage Council which
consists of members of the public interest representative, worker representative, and employer
representative, provides a guideline of the amount to be raised for each prefecture. After that, the Local
Minimum Wage Council in each prefecture consisting of members of the public interest representative,
worker representative, and employer representative discusses the amount to be raised considering theguideline, then the minimum wage in that prefecture is determined.
Every year, discussions at the Central Minimum Wage Council start in early summer, andregional minimum wages come into effect around October. The regional minimum wage covers all
workers except those in particular industries and acts as a safety net. The specific minimum wages are
set based on the offer by labor and management, and their levels should be higher than the regional
minimum wage. As of March 2020, the number of workers applied is about 2.9 million, about 5% of
the number of workers employed in the same year. The specific minimum wages pertain to theindustries and occupations where the labor union organizations have strong bargaining power. Though
the law stipulates this type of minimum wage, no legal punishment exists because this system has astrong flavor of labor-management autonomy. In other words, the rationale of the regional minimum
wage as a compulsory provision lies in the fact that it covers those who cannot negotiate specificminimum wages. Considering this reason and the wide range of workers applied, this paper focuseson the effect of the regional minimum wages.
Some of the characteristics of the Japanese minimum wages are as follows. First, regional
minimum wages change every year. There has been no reduction in minimum wages, and even nochange (zero increase) has seldom occurred. At the same time, the increase had been modest when thelabor market condition was slack. For instance, the rate of change in the national average minimum
wage was only 0.17% in 2002, when many prefectures did not raise their minimum wages due to theeconomic downturn.
Second, although the negotiations at the local level determine the minimum wage change ineach region, the guidelines provided by the Central Minimum Wage Committee have a substantial
impact on the outcome. The committee divides the whole prefecture into four groups and suggests foreach group a guideline amount of increment considering the growth rate of wages, local economic and4labor market conditions, and government policy toward minimum wages. Tamada (2009) shows thatthe actual minimum wage change in each region follows closely to the guideline to that region withthe correlation coefficient of 0.98 for 2001-2009. The committee’s taking into account the regional
wage growth in determining minimum wage increase brings about a substantial correlation between
the national weighted average of minimum wage increase and the nationwide changes in wages. After
2008, however, the government policy to raise minimum wages became pronounced faced with thecriticism that minimum wages had been falling behind social welfare, especially in metropolitan areas.
In addition, the Abe cabinet announced the target level of average minimum wages (1,000 yen) in2015 to support low-income families, which the subsequent cabinets have been following since then.
For these reasons, since 2007, the national minimum wage increase has surpassed the nationwide wage
increase by 1-2 percentage points except for 2020 when the covid-19 hit the world economy.
Third, the ratio of the minimum wage to the median wage rate of full-time workers increased
from 32% in 2000 to 45% in 2021 owing to this recent effort by the government to raise minimum
wages. Despite this increment, the ratio is ranked 27th among 31 OECD countries in 2021.
Some studies have analyzed the minimum-wage impact on employment outcomes in Japan.
Among them, two recent papers are particularly noteworthy. Using individual data set on wages,
Kambayashi et al. (2013) find that the minimum-wage increase resulted in the compression of thelower tail of the wage distribution among women and that it is only partially attributable to the loss of
employment. Kawaguchi and Mori (2021) examine the impacts of the minimum wage on employment
using the minimum-wage hike induced by the introduction of the indexation of the local minimum
wage to the local cost of living. They find that the minimum-wage hike raised the wages of low-wage
workers, but reduced the employment of less-educated young men.
Figure 1 shows the minimum wages by prefecture in 2005 (average from February to
December) and 2019 (average from January to December). We choose this period because itcorresponds to the sample period for our econometric analysis in the later sections. The rise ofminimum wages mainly takes place in October. In constructing a monthly dataset, we assume that theminimum wage is raised in October when the actual day of revision is on or before October 15 and in
November if the day of revision is after October 15. This assumption applies to all the analyses in thesubsequent sections. From Figure 1, it is evident that the minimum wages are higher in core prefecturesof metropolitan areas like Tokyo, Aichi, and Osaka and their neighboring prefectures than otherprefectures. We can also observe that the minimum wages have increased in all the prefectures from2005 to 2019, especially those belonging to metropolitan areas. This disparity in the growth rate ofminimum wage across regions enables us to identify the minimum-wage impact on the number ofvacancies and job seekers.

53. Empirical Analysis
3.1 Administrative Data
This section examines the impact of the minimum wage on the number of vacancies and job
seekers registered for public employment services in Japan. The data of these variables come from the“Report on Employment Service” published monthly by the Ministry of Health, Labour, and Welfare(MHLW). Using more than 500 local agencies, the MHLW is offering the service of job placement,
employment security, and guidance towards firms to reinforce employment security. The main targets
are job applicants who have difficulty finding employment and small and medium-sized firms with alower probability of filling vacancies than larger firms. As of 2019, firms posted about 920 thousand
jobs, and about 400 thousand new job seekers registered per month, making it the most relevant
employment service provider in Japan2.
The number of vacancies at a local agency increases when a firm posts more jobs at theagency. Similarly, a person who started a job search by registering at one of the local agencies becomesa job seeker. The “new” vacancies (job seekers) in the statistics represent these newly registeredvacancies (job seekers). The MHLW collects these figures from the local agencies and publicizes thesummary statistics every month at national and regional levels. We use this highly reliable
administrative data to investigate the impact of the minimum wage on the number of vacancies andjob seekers. We use the prefectural level data because the regional minimum wages are determined at
the prefectural level, as was stated in the previous section. Note here that there are two types of vacancy
data at the prefectural level: one based on the location of an establishment where a worker works, andthe other based on that of the local agency registered. It is desirable to use the former in our analysis
because the minimum wage of each prefecture is applied based on the place where people work. Since
the MHLW publicizes this data from February 2005, our data starts from here to December 2019,
when COVID-19 has not affected the Japanese labor market. On the other hand, this kind of distinction
does not apply to job seekers: Their number is counted based on the place of the local agency registered.
Thus far, we explained the measurement of the number of new vacancies and new job
seekers, both of which are the “flow” variables. However, many firms with unfilled vacancies andunsuccessful job seekers keep searching next month, which we should consider when measuring the“stock” of vacancies and job seekers. Our data also contains the number of “active” vacancies and“active” job seekers, both of which are the sum of new vacancies (job seekers) registered each monthand the vacancies (job seekers) that have been brought forward from the previous month. The ratio of
the number of active vacancies to job seekers is one of the most frequently cited indicators of theAccording to the “Employment Trend Survey” (MHLW), those hired through this public employment
service constitute 17.8% of the total hirings in 2019, which is larger than the share of private employment
service, 5.1%.
2

6

tightness of the labor market in Japan.
Figure 2a plots the relationship between the growth rate of minimum wage and the growthrate of the number of new vacancies in each prefecture for the entire sample period. We obtained thesevariables by first calculating the year average values of the minimum wage and the number of new
vacancies for 2005 and 2019, and then calculating their growth rates using these two end-point values.
We can observe from the figure that there seems to be a negative relationship between these variables.
In Figure 2b, the vertical axis is now the growth rate of the number of new job seekers. In this case,the relationship seems more obscure. We will check these relationships formally in the next section.
3.2 Empirical Strategy
Having observed several suggestive relationships between the growth rate of minimum
wage and vacancies/job seekers, we go on to look into them more closely by using regression analysis.
The baseline model is the one that regresses the log of the number of vacancies/job seekers, both “new”
and “active,” of each prefecture on the log minimum wage in the same region, controlling forprefectural specific effects, time-specific effects, and prefectural-specific trends. We check whether
the inclusion of the prefectural-specific trends alters the impacts of minimum wage, considering somestudies stressing the importance of state-specific trends in identifying the employment effect of theminimum wage (Allegretto, Dube, and Reich 2011).
Furthermore, we also examine the specification that includes a proxy for demand shock ineach prefecture as an independent variable. We believe this specification is preferable because there
may be some demand shock that cannot be captured by the prefecture-specific effect and theprefecture-specific trend. The variable used here is the prefectural GDP obtained from the “Report on
Prefectural Accounts” published by the Cabinet Office. Since the recent series based on 2008 SNA isonly available from CY 2006 till CY 2018, the sample size becomes smaller than the specification
without this variable. This variable is also transformed in logs when included in the estimation
equation. Note also that the data of this variable are from the yearly statistic tables, and hence possible
monthly variations in the regional demand shock cannot be absorbed by this proxy measure.
The estimation equation isln 𝑌𝑖𝑡 = 𝛼 ln 𝑀𝑊𝑖𝑡 + 𝛽 ln 𝐺𝐷𝑃𝑖𝑗 + 𝛾𝑖 + 𝛿𝑡 + 𝜃𝑖 ∗ 𝑡𝑟𝑒𝑛𝑑 + 𝜀𝑖𝑡.

(1)

The subscripts 𝑖 and 𝑡 denote a prefecture and time (month), respectively. 𝑌𝑖𝑡 is the number ofvacancies/job seekers in each prefecture (both fulltime and parttime), 𝑀𝑊𝑖𝑡 is the prefectural
minimum wage, and 𝐺𝐷𝑃𝑖𝑗 is the prefectural GDP (million yen). 𝛾𝑖 is a prefectural fixed effect, 𝛿𝑡 is
a month fixed effect, and 𝜃𝑖 ∗ 𝑡𝑟𝑒𝑛𝑑 is a prefectural specific trend. 𝜀𝑖𝑡 is an error term. We will reportcases with or without the prefectural GDP in the regression equation. When 𝑌𝑖𝑡 is the number of7

vacancies, the coefficient of the log of the minimum wage, 𝛼, would become negative if a rise in theminimum wage in a prefecture leads to a decrease in labor demand through reduced hiring of workersof the firms in the region. In the same vein, the coefficient would become positive when the dependent
variable is the number of job seekers if a higher minimum wage leads to the increased separation ofworkers or participation from the out of labor force3. The estimation method is the OLS using clustered
standard errors at the level of the prefecture4. See Table A-1 for some of the summary statistics.
3.3 Results
Panel A of Table 1 presents the estimation results for new vacancies. Columns (1)-(4) show
the estimated coefficients when we do not include the prefectural GDP as an independent variable,
while (5)-(6) are the cases with that variable. We can observe the strong negative impact of the regional
minimum wage variable irrespective of the specification. The results show that a 1% increase in theminimum wage leads to a 1.2-2.4% decrease in new vacancies. Controlling for the prefectural specifictrends strengthens the impact of the minimum wage, while the addition of the prefectural GDP reduces
the impact only marginally. The prefectural GDP has a significantly positive coefficient, which accords
with our prediction stated earlier. Our preferred specifications with prefectural specific trends showthat a 1% increase in the minimum wage results in a 2.1-2.4% decrease in the number of new vacancies,
which seems large in magnitude. Panel B shows the cases where the dependent variable is the numberof new job seekers. We find positive estimated coefficients of the minimum wage variable, while theabsolute values of the estimated minimum wage effect are smaller than those of Panel A within thecorresponding specification. Again, the coefficients become larger when controlling for the
prefectural-specific trends. The estimated coefficient of the prefectural GDP is negative, which isconsistent with our prediction. The estimated coefficients of the log minimum wage show that a 1%
increase in the minimum wage brings about a 1.8-2.2% increase in job seekers, which may be due tothe higher separations resulting from decreased labor demand. Panels C and D are for active vacancies
and job seekers, respectively. These panels show that the estimated coefficients of the minimum wage
for the active vacancies (job seekers) tend to be larger than those for the new vacancies (job seekers)in absolute value when the prefectural specific trends are absent. The estimated coefficients of theminimum wage of the active vacancies/job seekers are similar to those of the new vacancies/job
seekers for the specifications with the prefectural specific trends. In the next section, we willconcentrate on new vacancies/job seekers.

3 We will return to this issue in section 4.4, where we examine the impact of the prefectural minimum wage

on the number of new recipients of the unemployment insurance benefit in the same prefecture.
4
Note here that using the OLS could bias the estimates of the impact of minimum wages if the labor market
tightness is a factor to be considered when setting the minimum wage. We do not pursue this issue here
because it is hard to cope with this endogeneity problem. However, the direction of the bias is likely toward
underestimation, meaning that the OLS coefficient becomes the lower bound of the minimum wage effect.

84. Further Investigation
4.1 Differences in Minimum Wage Effects between Subperiods
Here we investigate whether the impact of the minimum wage differs across different
subperiods. It seems reasonable to conjecture that an increase in the minimum wage during a recession
is more detrimental to labor demand because firms tend to be more vulnerable to the exogenous
increase in labor costs when consumer demand for their products/services is weaker. This conjectureleads to the hypothesis that a rise in the minimum wage decreases labor demand more in the slacker
labor market resulting from a recession. We divide the entire sample period into two subperiods,
February 2005 to December 2013 and January 2014 to December 2019, and consider the former (latter)as the slack (tight) labor market period. This choice of threshold is natural because average active job
openings rates5 in 2014 and after are higher than any year between 2005 and 2013. Moreover, theunemployment rate in 2014 fell below an annual average of 4% for the first time in 17 years since1997.
Figure 3a shows the relationship between the minimum wage growth rates and the newvacancy growth rates during the period of the slack labor market. It seems that a negative relationshipexists between these variables. The relationship between minimum wage growth rates and the new job
seeker growth rates shown in Figure 3b is unclear. Figures 4a and 4b are similar figures for the tightlabor market period. The dots in the figures are more scattered for this period, and there are no apparent
relationships.
Now we estimate new vacancies/job seekers equations for each subperiod and look at thechanges in the estimated impact of the minimum wage. The estimation model is the same as the oneused in the previous section. The results are in Table 2. Panel A shows the results for the slack labormarket period, and Panel B shows those for the tight labor market period. The difference between thepanels is conspicuous. We find a substantial impact of the minimum wage both on new vacancies andjob seekers during the first half period, while we find no such effect on those variables during the latter
half period except for the cases of new job seekers without prefectural specific trends. For the first
subperiod, the estimated minimum wage elasticity is about -4 for vacancies and about 3.5 for job
seekers, which are more pronounced than found for the entire sample period in Table 1. The results
are consistent with the hypothesis that the negative impact of the minimum wage on labor demand
becomes severer when the labor market is loose. One can also see that the minimum wage effects on
vacancies/job seekers found in the previous section come almost entirely from the inclusion of thisslack labor market period. In addition, Panel A shows that the estimated minimum wage effects during
the first half become larger in absolute value when we exclude the data for 2005 from the sample,
5 This index is the ratio of the number of active vacancies to that of active job seekers.

9which happened to be caused by the availability of the prefectural GDP data. One possible
interpretation is that since the labor market condition in 2005 is comparatively better than thesubsequent years in the same subperiod, the property of the slackness of this subperiod was reinforcedby dropping the observations that belong to this year. This observation leads to a question: Which partof the first subperiod is responsible for the substantial negative minimum wage effect on labordemand?
To answer this question, we perform a rolling regression. Specifically, we repeatedly run a
regression while sliding the sample period by one month, keeping constant the length of the sample
period (this length is called the “window”). Choosing a window entails a tradeoff. A wide window
does not help identify the relevant part within the subperiod. On the other hand, a narrow window canbring about the problem of a small sample size. After some experimental trials, we chose 62 months(6 years) as the window width. The specification of the model is that of (2) and (4) in Tables 1 and 2.
Figures 5a and 5b show the results of the rolling regression for new vacancies and job
seekers, respectively. The horizontal axis is the stating period of the sample, and the solid line denotes
the estimated coefficients of the log minimum wage. The estimated minimum wage elasticity of the
new vacancies is positive during the sample periods that start in 2005 but forms a fairly sharp troughin the late 2000s with a bottom figure around -5. Then the estimated elasticity quickly returns to zerofor the sample periods starting from 2011 and becomes stable after that (Figure 5a). On the other hand,the estimated minimum wage elasticity of new job seekers is about two at the starting periods andmakes a sharp peak near 5 in the late 2000s. After that, the level drops to near zero in 2010 and thenslightly moves upward, exceeding 1 in 2013 (Figure 5b). The concurrence of the sharp peak and troughseems to support the view that there is a particular period when the minimum wage affected the labormarket. We can now figure out the intersection of the sample periods that bring about substantial
estimates in absolute value. If we choose 4 for this threshold, the intersection period is from April or
May 2008 to April or May 2012. During this period, the Japanese economy was seriously damaged by
the Great Recession of 2008 and the Great East Japan Earthquake of 2011, resulting in the highest
unemployment rates among the entire sample period 2005-2019. This finding also supports the
hypothesis that the negative impact of minimum wages on labor demand becomes strongest during aslack labor market.
4.2 Differences in Minimum Wage Effects between Regions
The effect of minimum wage could vary across regions. The reason is that each has its
distinct economic environment such as industry or firm size composition, wage level, labor force
composition. Among the various economic variables that characterize regional differences, we focus
on two variables: the Kaitz index and the share of small establishments. We examine these in turnbelow.
10

The Kaitz index is a frequently used measure of the wage distribution in the literature ofminimum wages. Its definition is the ratio of a regional minimum wage of a particular region to theaverage wage rate of that region multiplied by 100. Figure 6 shows the average Kaitz index calculatedusing the “Monthly Labor Survey” (MHLW). The prefecture with the highest index is Okinawa, while
Tokyo is the prefecture with the lowest index. If this index is high, it suggests the minimum wage isrelatively higher than the local wage rate in that region, which may bring about a more detrimental
effect of a minimum wage increase on labor demand in that region. To test this hypothesis, we divide
the 47 prefectures into two groups: the group with low Kaitz indices and the other with high Kaitz
indices. The threshold value of the index is set to 45 so that the number of prefectures is roughly equal
across the two groups. We then run regressions explaining the log of new job vacancies and new job
seekers for each group using the entire sample period and compare the estimated coefficients of theminimum wage. The specifications used here are those using prefectural specific trends, with andwithout the regional GDP.
Table 3 shows the estimation results. Panel A shows that the estimated coefficients of theminimum wage are negative for all the groups and the specifications. It is interesting to observe thatthe negative impact of the minimum wage on the number of new vacancies is higher in absolute value
for the group with high Kaitz indices than for that with low Kaitz indices. The difference is stark, thatis, nearly twice in elasticity. This result is consistent with our hypothesis that the impact of minimum
wage is more pronounced in the regions with minimum wage. Panel B of the table shows the estimation
results for the number of job seekers. We can see that the coefficients of the minimum wage arepositive for both groups irrespective of the specifications. Again, the estimated elasticities for thegroup with high Kaitz indices are higher than that with low Kaitz indices, supporting our hypothesis.
Let us now turn to the case when we group the prefectures according to the size ofestablishments. It is reasonable to conjecture that small firms are more vulnerable to the rise in theminimum wage. A plausible reason is that since such small firms do not usually have competitivenessof their products like large firms, they face difficulty adding the burden of the minimum wage rise to
their product prices. Considering that the minimum wage applies to the establishments in eachprefecture, we divide them into two groups according to the share of small establishments. The data
source of establishment size is “Economic Census of Business Activities” (Ministry of Internal Affairs
and Communications). We use the share of establishments that are less than 30 employees averaged
over the survey years 2009, 2011, 2013, and 2016 as a benchmark of establishment size distribution.
We calculate this value for each prefecture and divide them into two groups. The threshold value is
54.8% to make the number of regions in each group roughly the same. See Table A-2 for the
prefectures included in each group.
Table 4 shows the estimation results. We can observe that the minimum wage elasticity of
new vacancies in absolute value is substantially higher for the group of the higher share of small
11establishments (Panel A). The difference is even more conspicuous than when we group theprefectures according to the Kaitz index. The estimated elasticities are not statistically significant for
the group with a lower share, while they are smaller than -4 and statistically significant for the groupwith a higher composition of small firms. We can also see that the impact of the minimum wage on
the number of job seekers is higher in the group with a higher share of small firms. The difference isagain more conspicuous than when we group the prefectures according to the Kaitz index.
In sum, the results in this section show that the minimum wage effect on the number ofvacancies and job seekers in a region varies according to the minimum wage level relative to theaverage wage in that region (Kaitz index) and the share of small establishments: The minimum wage
effects is higher in prefectures with higher Kaitz index or higher composition of small establishments.
4.3 Differences in Minimum Wage Effects between Vacancy Types
The effect of a rise in minimum wage can be different according to the type of vacancies.
Among many possible aspects of them, the distinction between full-time and part-time jobs seems to
be intriguing. One can conjecture that the absolute value of minimum wage elasticity of posted
vacancies is higher for part-time jobs for two reasons. First, wage rates of part-time jobs are generally
lower than those of full-time jobs, resulting in a higher impact of the minimum wage rise on thenumber of vacancies of part-time jobs. That is because the proportion of vacancies needed to adjust
their posted wage rates to the minimum wage increase is likely to be higher for part-time jobs. Second,the elasticity of labor demand for part-time workers tends to be higher than for full-time workers
because the former is usually an atypical type of labor with high own wage elasticity of labor demand(Lichter, Peichl, and Siegloch 2015).
The full-time and part-time vacancies data are available only for the posted location, rather
than the work location as in the previous sections. Therefore, we should first check the extent to which
the difference in location treatment brings about the difference in the estimated coefficients bycomparing the results for total vacancies in both cases. The estimation specifications are those withthe prefecture-specific trend terms with and without the prefectural GDP variable. Table 5 shows theestimation results of the new vacancies. From Panel A columns (1) and (2), we can observe that theestimated minimum wage elasticities for the entire sample period are significantly negative, andcontrary to our intuition, their absolute values are higher than those using new vacancies classified by
the location of work (cf. Table 1). The results in columns (3)-(6) indicate that the estimated coefficientsof the minimum wage are higher for part-time than for full-time jobs. Panels B and C show theestimation results for the two subperiods introduced in Section 4.1. The results for the period of slack
labor market do not give us conclusive evidence. On the other hand, we can observe some marginallysignificant effects only for part-time vacancies during the tight labor market period, contributing to
the higher minimum wage elasticities for the entire sample period.
12

4.4 Job-to-Unemployment Separation
The estimation results obtained up to this point show that a rise in the minimum wage
increased the number of job seekers during the slack labor market period. This phenomenon could be
due to the increased separation of workers (dismissals, for example) or participation from out of thelabor force. Since our data contains information on the number of new recipients of the unemployment
insurance benefit, we can examine whether or not the number of job-to-unemployment separations
increases with a rise in the minimum wage. Testing this effect is valuable because confirmation of thispoint becomes another evidence showing that the labor demand decreases with a minimum wage rise.
In addition to analysis on the entire sample period, we conduct estimation for two subsample periods
defined earlier.
Table 6 provides estimation results. We can see from Panel A that all the estimatedcoefficients of the minimum wage for the entire sample period are significantly positive, showing thatthe job-to-unemployment separation increases with a minimum wage rise. The results for the slack
labor market period exhibit a starker minimum wage effect (Panel B). Interestingly, we observe somesignificant estimated coefficients even for the tight labor market period (Panel C). These results are
consistent with the view that a rise in the minimum wage leads to decreased labor demand via
increased job separations.

5. Conclusion
The results obtained in this paper show that for 2005-2019, a rise in the minimum wage
reduced the number of vacancies. On the other hand, it increased the number of job seekers and thendecreased the vacancy-job seeker ratio. Some part of this came from the increased job separation into
unemployment. The impact of a minimum wage rise is conspicuous during the period of the slack
labor market, for prefectures with high Kaitz indices or with a high proportion of small firms, and forpart-time jobs.
One should note that the vacancies and job seekers analyzed in this paper are not necessarily
representative of those in the Japanese labor market as a whole. Since the main targets of theemployment service are job applicants who have difficulty finding employment and small andmedium-sized firms with a lower probability of filling vacancies than larger firms, the impact of aminimum wage rise obtained in this paper is likely to be higher than the national average. Also, since
we did not have access to the raw data of vacancies and job seekers due to its administrative nature,
we could not identify the detailed mechanisms of the impact of a minimum wage rise on the numberof vacancies and job seekers. Richer data on vacancies are necessary to examine these issues.

13

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14

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15

Table 1 Minimum Wage Effects on Vacancies and Job Seekers (2005-2019)
Specification
PeriodA. New vacancies (log)Log of minimum wage

(1)
(2)
Feb. 2005 - Dec. 2019

(3)

(4)
(5)
Apr. 2006 - Mar. 2018

-1.772***(0.374)-2.176***(0.728)-1.251***(0.373)-2.483***(0.787)

0.984

0.989

0.985

0.990

0.709*(0.371)1.808***(0.437)0.657*(0.337)2.209***(0.522)

0.986

0.989

0.987

0.989

-2.014***(0.450)-2.053***(0.751)-1.540***(0.455)-2.459***(0.810)

0.985

0.991

0.985

0.992

1.116***(0.384)1.752***(0.427)1.160***(0.365)2.256***(0.535)(6)-1.344***(0.391)0.815***(0.303)0.986-2.442***(0.749)0.529**(0.226)0.990

0.676*(0.338)-0.165(0.136)0.987

2.180***(0.515)-0.374**(0.140)0.989-1.654***(0.473)0.996***(0.352)0.987-2.402***(0.757)0.737***(0.224)
0.992

1.195***(0.376)
Log of GDP-0.303*(0.153)R-squared
0.992
0.995
0.993
0.995
0.993
Number of observations
8,413
8,413
6,768
6,768
6,768
Prefectural specific time trends
NO
YES
NO
YES
NO
Notes: New vacancies (job seekers) stand for those that appear in a particular month, while active vacancies(job seekers) are the ones that exist that month. The prefecture of vacancies pertains to the location of work.
The sample period for (3)-(6) is shorter than (1) and (2) due to the availability of regional GDP data.
All specifications include prefecture fixed effects and time fixed effects.
Clustered standard errors at the level of the prefecture in parentheses*** p<0.01, ** p<0.05, * p<0.12.201***(0.522)-0.717***(0.131)
0.995
6,768
YES

Log of GDP
R-squared
B. New job seekers (log)Log of minimum wage
Log of GDP
R-squared
C. Active vacancies (log)Log of minimum wage
Log of GDP
R-squared
D. Active job seekers (log)Log of minimum wage

16

Table 2 Minimum Wage Effects by Subperiod
Specification
A. Period of slack labor market
PeriodNew vacancies (log)Log of minimum wage

(1)

(2)

(3)

Feb. 2005 - Dec. 2013(4)

-2.557***(0.522)-2.239(1.406)-1.963***(0.512)-4.411***(1.364)

0.982

0.989

0.983

0.990

1.407***(0.434)2.703***(0.762)

1.328***(0.419)3.827***(0.862)

0.984
5,029

0.987
5,029

0.985
4,371

0.988
4,371

Log of GDP
R-squared
Number of observations
B. Period of tight labor market
PeriodNew vacancies (log)Log of minimum wage

Jan. 2014 - Dec. 2019
0.915
(1.011)-0.365(0.556)0.540(0.816)0.0444(0.444)

0.994

0.996

0.995

0.997

1.756**(0.761)

1.523(1.167)1.890*(0.964)1.808(1.145)Log of GDP
R-squared
0.992
0.993
0.992
0.993
Number of observations
3,384
3,384
2,397
2,397
Prefectural specific time trends
NO
YES
NO
YES
Notes: New vacancies (job seekers) stand for those that appear in a particular month.
The prefecture of vacancies pertains to the location of work.
The sample period for (3)-(6) is shorter than (1) and (2) due to the availability of regional GDP data.
All specifications include prefecture fixed effects and time fixed effects.
Clustered standard errors at the level of the prefecture in parentheses*** p<0.01, ** p<0.05, * p<0.1

17-1.999***(0.500)0.746*(0.379)0.984-4.019***(1.317)0.697***(0.216)0.990

1.340***(0.420)-0.241(0.194)
0.985
4,371

3.502***(0.797)-0.577***(0.130)
0.988
4,371

Jan. 2014 - Mar. 2018

Log of GDP
R-squared
New job seekers (log)Log of minimum wage

(6)

Apr. 2006 - Dec. 2013

Log of GDP
R-squared
New job seekers (log)Log of minimum wage

(5)

0.469
(0.776)
0.688***(0.234)0.995

0.0446(0.445)0.0524(0.166)0.997

1.902*(0.949)-0.120(0.136)
0.992
2,397
NO

1.807
(1.139)-0.136(0.144)
0.993
2,397
YES

Table 3 Minimum Wage Effects by Regional Group (Kaitz Index)
Specification
RegionA. New vacancies (log)Log of minimum wage

(1)

(2)
All

-2.176***(0.728)R-squared
B. New job seekers (log)Log of minimum wage

0.989

(4)
Kaitz <45

-2.442***(0.749)0.529**(0.226)
0.990

Log of GDP(3)-1.541*(0.780)0.991

1.808***(0.437)2.180***
1.683***(0.515)(0.544)Log of GDP-0.374**(0.140)R-squared
0.989
0.989
0.995
Number of observations
8,413
6,768
4,296
Prefectural specific time trends
YES
YES
YES
Notes: New vacancies (job seekers) stand for those that appear in a particular month.
The prefecture of vacancies pertains to the location of work.
All specifications include prefecture fixed effects and time fixed effects.
Clustered standard errors at the level of the prefecture in parentheses*** p<0.01, ** p<0.05, * p<0.1

(5)

(6)
Kaitz ≥ 45

-1.752**(0.778)0.569(0.344)0.992-3.223*(1.591)

1.953***(0.601)-0.480***(0.169)
0.995
3,456
YES

2.049**(0.799)

0.986

0.979
4,117
YES

-3.529**(1.607)0.393*(0.198)0.987
2.448**(1.026)-0.135(0.182)
0.979
3,312
YES

Table 4 Minimum Wage Effects by Regional Group (Establishment Size)
Specification(1)

RegionA. New vacancies (log)Log of minimum wage

All
-2.176***(0.728)Log of GDP
R-squared
B. New job seekers (log)Log of minimum wage

(2)

0.989

-2.442***(0.749)0.529**(0.226)0.990

1.808***(0.437)(3)(4)
Share of small
establishments: low(5)(6)
Share of small
establishments: high-0.491(0.628)-0.729(0.635)0.183(0.370)0.988-4.240***(1.379)1.007***(0.336)-0.322(0.198)
0.989
3,456
YES

2.794*(1.455)0.987

2.180***
0.856**(0.515)(0.334)Log of GDP-0.374**(0.140)R-squared
0.989
0.989
0.988
Number of observations
8,413
6,768
4,296
Prefectural specific time trends
YES
YES
YES
Notes: New vacancies (job seekers) stand for those that appear in a particular month.
The prefecture of vacancies pertains to the location of work.
All specifications include prefecture fixed effects and time fixed effects.
Clustered standard errors at the level of the prefecture in parentheses*** p<0.01, ** p<0.05, * p<0.1

18

0.977

0.977
4,117
YES

-4.571***(1.194)0.557***(0.178)0.977
3.656**(1.409)-0.208(0.159)
0.976
3,312
YES

Table 5 Minimum Wage Effects by Vacancy Type
Dependent variable: log of the number of new vacancies
Specification(1)
Type of job
All
A. Entire period
Log of minimum wage

-2.697***(0.918)Log of GDP
R-squared
Number of observations
B. Period of slack labor market
Log of minimum wage

0.989
8,413
-2.154(1.444)Log of GDP
R-squared
Number of observations
C. Period of tight labor market
Log of minimum wage

0.988
5,029

(2)

(3)
(4)
Full-time job-3.157***(0.857)0.545**(0.226)
0.990
6,768

-2.304**(0.912)-4.193***(1.313)0.728***(0.228)
0.989
4,371

-2.170(1.747)-0.241(0.615)

0.987
8,413

0.985
5,029

(5)
(6)
Part-time job-2.901***(0.901)0.641**(0.240)
0.987
6,768

-3.090***(1.029)-4.942***(1.588)0.830***(0.270)
0.987
4,371

-2.638***(0.838)

0.986
8,413

0.986
5,029

-0.1180.198
0.461-0.813(0.449)(0.822)(0.564)(0.558)Log of GDP-0.0343-0.0101(0.171)(0.183)R-squared
0.996
0.996
0.995
0.996
0.991
3,384
2,397
3,384
2,397
3,384
Prefectural specific time trends
YES
YES
YES
YES
YES
Notes: New vacancies (job seekers) stand for those that appear in a particular month.
The prefecture of vacancies pertains to the posted location.
Entire period: Feb. 2005 - Dec. 2019 for (1),(3), and (5), Apr. 2006 - Mar. 2018 for (2),(4), and (6)Period of slack labor market: Feb. 2005 - Dec. 2013 for (1),(3), and (5), Apr. 2006 - Mar. 2013 for (2),(4), and (6)Period of tight labor makert: Apr. 2014 - Dec. 2019 for (1),(3), and (5), Apr. 2014 - Mar. 2018 for (2),(4), and (6)All specifications include prefecture fixed effects and time fixed effects.
Clustered standard errors at the level of the prefecture in parentheses*** p<0.01, ** p<0.05, * p<0.1

19-3.406***(0.900)0.334(0.223)0.986
6,768-3.153***(0.873)0.482***(0.168)0.986
4,371-0.896*(0.512)-0.0478(0.204)
0.992
2,397
YES

Table 6 Minimum Wage Effects on Job-to-Unemployment Separations
Dependent variable: log of the number of new recipients of UI benefit
Specification(1)(2)
A. Entire period
PeriodFeb. 2005 - Dec. 2019
Log of minimum wage

(3)

(4)

Log of minimum wage

0.682**(0.266)2.262***(0.522)0.699**(0.266)2.936***(0.638)

0.987
8,413

0.988
6,768

0.987
8,413

0.988
6,768

Feb. 2005 - Dec. 2013

Log of minimum wage

4.659***(1.225)1.463***(0.392)6.264***(1.388)

0.984
5,029

0.986
4,371

0.983
5,029

0.985
4,371

Jan. 2014 - Dec. 2019
0.635
(0.631)1.048(0.675)0.727**(0.283)-0.240*(0.129)
0.987
8,413

1.486***(0.410)-0.470**(0.192)
0.984
5,029

5.786***(1.305)-0.848***(0.167)
0.986
4,371

Jan. 2014 - Mar. 2018
1.372*(0.699)2.051***(0.639)1.378*(0.696)
Log of GDP-0.0593(0.119)R-squared
0.993
0.993
0.993
0.994
0.993
3,384
2,397
3,384
2,397
3,384
Prefectural specific time trends
NO
YES
NO
YES
NO
Notes: The sample period for (3)-(6) is shorter than (1) and (2) due to the availability of regional GDP data.
All specifications include prefecture fixed effects and time fixed effects.
Clustered standard errors at the level of the prefecture in parentheses*** p<0.01, ** p<0.05, * p<0.1

202.889***(0.629)-0.612***(0.115)
0.988
6,768

Apr. 2006 - Dec. 2013

1.507***(0.394)Log of GDP
R-squared
Number of observations
C. Period of tight labor market
Period(6)

Apr. 2006 - Mar. 2018

Log of GDP
R-squared
Number of observations
B. Period of slack labor market
Period(5)2.051***(0.634)-0.134(0.145)
0.994
2,397
YES

Table A-1 Summary Statistics (2005-2019)
VariableRaw values
New vacancies (location of work)
New vacancies (location of register)
New vacancies (full time)
New vacancies (part time)
New job seekers
Active vacancies (location of work)
Active job seekers
New recipients of UI benefit
Minimum wage
Prefectural GDP (one million yen)Log of the variables
New vacancies (location of work)
New vacancies (location of register)
New vacancies (full time)
New vacancies (part time)
New job seekers
Active vacancies (location of work)
Active job seekers
New recipients of UI benefit
Minimum wage
Prefectural GDP

Obs

Mean

Std. dev.

Min

8,413
8,413
8,413
8,413
8,413
8,413
8,413
8,413
8,413
6,768

16952.74
16955.47
10588.24
6367.223
11191.19
45833.92
46204.98
3456.67
707.3459
1.13E+07

15389.2
18277.6
11811.45
6666.745
10177.58
43050.4442816.56
3436.857
71.98013
1.61E+07

2455
1060482617
139399
1521
96115
767
59212
1698
82505
6708
2876448378
299017
439
34447
606
1013
1713879 1.06E+08

8,413
8,413
8,413
8,413
8,413
8,413
8,413
8,413
8,413
6,768

9.458251
9.415816
8.923644
8.455889
9.040554
10.43095
10.44742
7.807856
6.556602
15.78122

0.707115
0.735648
0.761001
0.712897
0.710088
0.737223
0.719347
0.779392
0.098009
0.850634

7.805882
7.869784
7.327123
6.642487
7.437206
8.811056
9.033364
6.084499
6.40688
14.35427

Table A-2 Classification of Prefectures Based on Establishment Size
proportion of small establishments: low
Tokyo
Aichi
Kanagawa
Shiga
Mie
Chiba
IbarakiHyogo
Tochigi
Kyoto
Hiroshima Saitama
ToyamaOkayama
FukuokaMiyagi
Yamaguchi Fukushima Nara
Ishikawa
proportion of small establishments: high
Kochi
ShimaneWakayama Aomori
Akita
TottoriKagoshima Tokushima
Gifu
Yamagata
Iwate
Yamanashi
Okinawa
Oita
Kumamoto Fukui
NaganoNiigata
Kagawa21

Osaka
Shizuoka
Gunma
Hokkaido

MiyazakiNagasaki
Ehime
Saga

Max

11.57165
11.8451
11.4733
10.98888
11.32061
12.56948
12.60826
10.44718
6.920671
18.4812

22

23

24

25

26

27