Graduate School of Economics, Kobe University
Working Paper Series No. 327

Effects of region-specific shocks on job matching efficiency:
Evidence from the 2011 Tohoku Earthquake in Japan*
Yudai Higashi†
Graduate School of Economics, Kobe University
Research Fellow of Japan Society for the Promotion of Science
February 13, 2019

Abstract
This paper examines whether region-specific shocks alter regional job matchingefficiencies, that is, how efficiently job seekers match with vacancies. We adopt the Great
East Japan Earthquake in March 2011 as a region-specific shock. We find that regional
job matching efficiencies in the damaged area deteriorated in the aftermath of the disaster.
This tendency was temporary and did not spatially diffuse. These results suggest that thecomposition of unemployment in terms of job search intensities changed in the directly
damaged area, leading to temporarily higher search frictions. No composition change inunemployment occurred in other regions owing to the different characteristics of those
local labor markets.
Keywords: Local labor market; Matching function; Spatial spillover; Difference indifferences
JEL classification: J64; R12; R23*

I would like to thank Kazufumi Yugami, Nobuaki Hamaguchi, and Shinya Horie for
helpful comments. I would also like to thank Hirokazu Fujii of the Ministry of Health,Labour and Welfare, and the Employment Policy Division of Employment Security
Bureau for their providing the data for Public Employment Security Office. Remaining
errors are my own. This work was supported by JSPS KAKENHI Grant Number
JP18J11560.
†
Address: Graduate School of Economics, Kobe University, 2-1 Rokkodai-cho, Nadaku, Kobe 657-8501, Japan. E-mail: yudai.higashi@stu.kobe-u.ac.jp

1.

Introduction
Some studies have recently revealed that the number of matches between

unemployed job seekers and vacancies by firms in one region depends on the labor market
conditions not only in their own region but also in neighboring regions (e.g., Burda andProfit, 1996; Burgess and Profit, 2001; Hynninen, 2005; Fahr and Sunde, 2006a,b; Hallerand Heuermann, 2016; Higashi, 2018). This tendency is caused by inter-regional job
search by job seekers with high search intensities. These results suggest that job seekers
compare the job finding rates in their own region with those in surrounding regions,
resulting in spatial (geographical) interdependence across local labor markets.
These findings suggest there are regional disparities in matching efficiencies,that is, how efficiently job seekers match with vacancies. Some studies find evidence
supporting this conjecture by estimating the matching function, which represents thenumber of matches is determined by the numbers of unemployed job seekers andvacancies (Petrongolo and Pissarides, 2001). Coles and Smith (1996) find that someregional demographic factors significantly affect the number of matches in their matching
function, suggesting that matching efficiencies vary across regions depending on theconditions in local labor markets. Indeed, Kano and Ohta (2005) explicitly reveal thatmatching efficiencies exhibit regional heterogeneity in Japan.
The above studies’ data or specifications provide analyses under the assumptionthat regional matching efficiencies are static. In terms of the dynamic dimension, Ibourk
et al. (2004) and Fahr and Sunde (2006a) find that the regional heterogeneity of matchingefficiencies is quite stable over time as well as that regional demographic factors affectthe regional heterogeneity of matching efficiencies.
Here, we can suppose that the regional heterogeneity of matching efficiencies
could vary over time when exogenous shocks happen based on one stream of the literature.
2

For the case of overall shocks, for example, Barnichon and Figura (2015) verify that thenegative labor demand shock of the Great Recession deteriorated the matching efficiency
in the United States through a composition change in unemployment with different searchintensities. This evidence seems to be supported by the finding of Haller and Heuermann(2016), who find that employment policy in Germany (i.e., the Hartz Reform) has
magnified the spatial extent of inter-regional job search.
On the effects of region-specific exogenous shocks on the job matching process,
Manning and Petrongolo (2017) reveal that positive region-specific labor demand shocks
generate spatial spillover effects; in other words, outflows from unemployment inneighboring regions increase through inter-regional job search. Higashi (2018) shows that
the Great East Japan Earthquake, which was a region-specific shock, boosted the spatial
extent of inter-regional job search for job seekers living around the damaged area. These
results suggest that region-specific shocks can change the regional heterogeneity ofmatching efficiencies with spatial spillovers through a composition change inunemployment.
This paper explores whether region-specific exogenous shocks alter regional
matching efficiencies using panel data on the Japanese public employment referral
service. We incorporate spatial spillover effects into our empirical models. To explore ourresearch questions, the Great East Japan Earthquake in March 2011 is adopted as a regionspecific exogenous shock. This earthquake generated a massive tsunami and a serious
accident at a nuclear power plant, decimating the Pacific Coast area in northeast Japanand changing the conditions in the labor markets around the damaged area. To identify
the effects of the earthquake on regional matching efficiencies, we use the difference-indifferences (DID) approach to compare before and after the earthquake. We also introduce
a model that explores the transition of the effects of the earthquake over time, rather than3

only before and after. To incorporate spatial spillovers, we set two types of treatmentgroups for the direct and spatial spillover effects, namely the directly damaged area andits adjacent areas, following Belasen and Polachek (2009), who examine the effects ofhurricanes on local labor markets.
We find that the regional matching efficiencies in the area directly damaged by
the tsunami temporarily deteriorated. This tendency did not exhibit spatial spillover
effects to neighboring regions unlike labor market tightness (i.e., the ratio of the stocksof vacancies to the unemployed), which became persistently larger and exhibited spatial
spillovers. These results suggest that a composition change in unemployment in terms ofsearch intensity in the area directly damaged by the tsunami generated higher search
frictions. No spatial spillover effects were observed because no composition change
might have occurred in an area not directly damaged by the tsunami, which may havedifferent industrial structures from those in the directly damaged area. The spatial
spillover effects of labor market tightness stemmed from labor shortages in the damaged
area owing to reconstruction projects, which pulled job seekers from neighboring regions
without a composition change.
The rest of this paper is organized as follows. Section 2 describes the regional
job matching efficiency in the matching function. Then, we present the empirical models
that identify the effects of the earthquake. Section 3 describes our data. Section 4 presents
the estimation results on the effects of the earthquake on local labor markets and discusses
the impacts on the matching efficiency. Section 5 concludes.

2.

Model

2.1. Regional matching efficiency
To describe the regional matching efficiency, we first specify the matching4

function. We assume that the matching function is a Cobb–Douglas form, which is often
adopted by empirical studies (Petrongolo and Pissarides, 2001), as follows:
)

)

*
,
𝑀"# = 𝜇"# 𝑈"#'(
𝑉"#'(
,

(1)

where 𝑀"# denotes the outflows from unemployment in region 𝑖 during period 𝑡 .
𝑈"#'( and 𝑉"#'( are the stocks of unemployed job seekers and vacancies in region 𝑖 atthe beginning of period 𝑡, respectively. 𝜂( and 𝜂1 are the elasticities of the outflows
from unemployment with respect to the stocks of unemployed and vacancies, respectively.
Finally, 𝜇"# , our parameter of interest, represents the matching efficiency in region 𝑖during period 𝑡.
We can consider that the matching efficiency corresponds to technology in the
production function. If job seekers or vacancies have high search intensities, the matchingefficiency in the economy improves. As a result, the number of matches of job seekers
with vacancies increases (Pissarides, 2000).
Some previous studies empirically reveal that the composition of the attributesof unemployed job seekers, such as unemployment duration, reasons for unemployment,
and individual characteristics, are significant factors determining matching efficiencies
in the economy (e.g., Petrongolo, 2001; Sasaki et al., 2013; Barnichon and Figura, 2015).
Their findings suggest that the heterogeneous attributes of unemployed job seekers haveheterogeneous search intensities. We can suppose that if the composition ofunemployment in one region varies, the matching efficiency there changes. Thus, regionspecific exogenous shocks can trigger changing regional matching efficiencies through acomposition change in unemployment.
A change in the regional heterogeneity of matching efficiencies would vary theintensities of inter-regional job search, generating spatial spillover effects from regionsin which shocks are arising to neighboring regions. We describe how to empirically5measure such spatial spillover effects in the following subsection.
The matching efficiencies across regions and time periods, 𝜇"# , are unobservablefrom the data. Therefore, we predict them by estimating the matching function. The
parameters 𝜂( and 𝜂1 are estimated using a regression of the log-linear form ofequation (1). Given the estimated parameters, denoted as 𝜂̂ ( and 𝜂̂ 1 , the matchingefficiencies are predicted as follows:7ln
𝜇6# = ln 𝑀"# − 𝜂̂ ( ln 𝑈"#'( − 𝜂̂ 1 ln 𝑉"#'( .

(2)Practically, we estimate the matching function in a log-linear form with a region fixed
effect, 𝜓" , and a time fixed effect, 𝜑# , given byln 𝑀"# = 𝜂( ln 𝑈"#'( + 𝜂1 ln 𝑉"#'( + 𝜓" + 𝜑# + 𝜀"# ,(3)

7
where 𝜀"# is an error term. The predicted log of the matching efficiency, ln
𝜇6# , inequation (2) is obtained as a residual, 𝜀̂"# , derived from equation (3). The predicted
matching efficiency is a form that excludes a time-invariant region-specific matchingefficiency, 𝜓" , and an all-region common time-specific matching efficiency, 𝜑# .

2.2. Identification of the effects of region-specific shocks
To investigate the effects of region-specific exogenous shocks on regional
matching efficiencies, we adopt the Great East Japan Earthquake in March 2011 as anatural experiment. This earthquake destroyed numerous buildings and many people fell
victim. Furthermore, the resulting tsunami also caused a serious nuclear accident at the
Fukushima Daiichi Nuclear Power Station. The Japanese government set the areasurrounding the power station as an evacuation zone to protect people against radioactive
contamination.
We utilize the DID approach to identify the effects of these series of disasters on

6

local labor markets.1 Panel (a) of Figure 1 illustrates the distribution of regions by thetreatment and control groups for the DID estimation. We define two types of treatmentgroups based on the extent of damage. The first is the coastal area group, which suffereddirect and serious damage from the tsunami. The second is the nuclear disaster area group,
which contains the evacuation zones.2 In the coastal area, residents have the option toremain living there or move to another region. By contrast, in the nuclear disaster area,
residents in the evacuation zone must move because the government has prohibited themfrom living there. Moreover, the characteristics and degree of urgency of reconstruction
projects differ between these treatment groups. While the tsunami destroyed buildings
only physically, the nuclear disaster contaminated land through radioactivity. Therefore,
the impacts of the earthquake on job matching are assumed to differ between the coastal
area and nuclear disaster area.
[Figure 1 about here]
Spatial spillover effects from directly damaged regions to their neighboring
regions could also arise through the spatial interactions among local labor markets.
Focusing on the spatial spillover effects of disasters, Belasen and Polachek (2009)
examine the impact of hurricanes on local labor markets in Florida by applying the DID
approach. They define one treatment group that directly suffers from hurricanes andanother adjacent to the directly damaged area, finding that the DID estimator in terms of
the latter treatment group captures the spatial spillover effect. Applying their idea, we add

1

See Angrist and Pischke (2009) for the DID method in detail.
The evacuation zones consist of three types of areas: the difficult-to-return zone,
restricted residence area, and zone in preparation for the lifting of the evacuation order.
The evacuation zones in this study are categorized as of December 31, 2013. The
regional distribution of the evacuation zones is available from the website of the
Ministry of Economy, Trade and Industry(http://www.meti.go.jp/earthquake/nuclear/hinan_history.html, accessed on December
12, 2018; in Japanese).
2

7

another two groups: adjacent to the coastal area and the adjacent to the nuclear disasterarea. Then, our DID specification is given by
𝑌"# = 𝛼 + 𝛽( 𝐶𝑜𝑎𝑠𝑡" + 𝛽1 𝐴𝑑𝑗𝐶𝑜𝑎𝑠𝑡" + 𝛽H 𝑁𝑢𝑐𝑙𝑒𝑎𝑟" + 𝛽O 𝐴𝑑𝑗𝑁𝑢𝑐𝑙𝑒𝑎𝑟"+ 𝛾𝐴𝑓𝑡𝑒𝑟#+ (𝛿( 𝐶𝑜𝑎𝑠𝑡" + 𝛿1 𝐴𝑑𝑗𝐶𝑜𝑎𝑠𝑡" + 𝛿H 𝑁𝑢𝑐𝑙𝑒𝑎𝑟"(4)+ 𝛿O 𝐴𝑑𝑗𝑁𝑢𝑐𝑙𝑒𝑎𝑟" ) × 𝐴𝑓𝑡𝑒𝑟# + 𝜉"# ,where 𝑌"# is the outcome (i.e., the predicted matching efficiency) in region 𝑖 during
period 𝑡. 𝐶𝑜𝑎𝑠𝑡" is a dummy variable that takes one if region 𝑖 belongs to the coastal
area group, and zero otherwise. 𝐴𝑑𝑗𝐶𝑜𝑎𝑠𝑡" is a dummy variable taking one if region 𝑖
is in the adjacent to the coastal area group, and zero otherwise. Similarly, 𝑁𝑢𝑐𝑙𝑒𝑎𝑟" and𝐴𝑑𝑗𝑁𝑢𝑐𝑙𝑒𝑎𝑟" are the nuclear disaster area group dummy and adjacent to the nuclear
disaster area group dummy, respectively. 𝐴𝑓𝑡𝑒𝑟# is a dummy variable taking one if
period 𝑡 is after March 2011, when the Great East Japan Earthquake occurred, and zerootherwise. We are interested in four parameters of the interaction terms of the treatmentgroup dummies with 𝐴𝑓𝑡𝑒𝑟# , which capture the effects of the disasters on the outcomes.
𝛿( and 𝛿1 capture the direct and spatial spillover effects of the tsunami, respectively.
𝛿H and 𝛿O capture the direct and spatial spillover effects of the nuclear disaster,
respectively. Lastly, 𝛼 is a constant term, 𝛽( to 𝛽O are the coefficients of the treatment
dummies, 𝛾 is a coefficient of 𝐴𝑓𝑡𝑒𝑟# , and 𝜉"# is an error term.
Equation (4) compares the average levels of the outcomes before and after theearthquake. This equation cannot measure the transition of the level of outcomes overtime. However, the magnitudes of the effects might change over time. Our data allow us
to explore this dynamic dimension because there are a sufficient number of time periods(i.e., 72 months after the earthquake). Then, another specification is given by8

𝑌"# = 𝛼 + 𝛽( 𝐶𝑜𝑎𝑠𝑡" + 𝛽1 𝐴𝑑𝑗𝐶𝑜𝑎𝑠𝑡" + 𝛽H 𝑁𝑢𝑐𝑙𝑒𝑎𝑟" + 𝛽O 𝐴𝑑𝑗𝑁𝑢𝑐𝑙𝑒𝑎𝑟"\]^.1_(`+W[𝛾Y 𝑇𝑖𝑚𝑒Y

Yabcd.1__H(5)+ (𝛿(Y 𝐶𝑜𝑎𝑠𝑡" + 𝛿1Y 𝐴𝑑𝑗𝐶𝑜𝑎𝑠𝑡" + 𝛿HY 𝑁𝑢𝑐𝑙𝑒𝑎𝑟"+ 𝛿OY 𝐴𝑑𝑗𝑁𝑢𝑐𝑙𝑒𝑎𝑟" ) × 𝑇𝑖𝑚𝑒Y ] + 𝜉"# ,where 𝑇𝑖𝑚𝑒Y is a time period dummy. An initial period dummy (i.e., July 2003 dummyin our data) is omitted as a benchmark. We focus on the transitions of the magnitudes ofthe parameters of the interaction terms, 𝛿(Y to 𝛿OY , over time.

3.

Data
The Japanese monthly and regional panel data for our analyses come from theReport on Employment Service, provided by the Employment Security Bureau, Ministryof Health, Labour and Welfare. The monthly panel data are aggregated at the Public
Employment Security Office (PESO) jurisdiction region unit. We utilize the records for
the PESO jurisdiction regions in Iwate, Miyagi, and Fukushima Prefectures, which mostseriously suffered from the Great East Japan Earthquake, as well as their surrounding
prefectures.3 Ultimately, we analyze monthly panel data from July 2003 to March 2016,
which are the longest periods available, of 95 PESO jurisdiction regions.4
We obtain the following variables concerning job matching for our analysis: the3

The surrounding prefectures are Aomori, Akita, Yamagata, Ibaraki, Tochigi, Gunma,and Niigata Prefectures.
4
To construct balanced panel data, we arrange the data as follows. First, certain typesof PESOs that provide referral serves only for some job seekers (e.g., foreigners, older
people, and women) are eliminated from our sample because their wider jurisdiction
areas than general PESOs do not allow us to arrange a coherent regional unit. Second,some PESOs merged during the sample period; therefore, we reaggregate PESOs’
records in each period as of 2015, when the last merger occurred. Higashi (2018)
handles the same data in the same way.
9

stock of unemployed, stock of vacancies, and outflows from unemployment. The stockof unemployed (the original dataset labels these “active applicants”) is the number of job
seekers who register with the local PESOs. If job seekers are active, they remain in therecords. If job seekers are inactive, they are removed from the records within about threemonths. The stock of vacancies (the original dataset labels these “active job openings”)
is the number of vacancies registered with the PESOs in the locations of the main or
branch offices of firms with vacancies. Similar to the stock of the unemployed, inactive
vacancies are removed from the records within about three months. Outflows fromunemployment (the original dataset labels these “persons who found employment”)
represent the number of job seekers who match with vacancies through the mediation of
PESOs during one month. Outflows from unemployment are counted in the records of
the PESOs with which job seekers initially registered regardless of the vacancies withwhich they ultimately matched.
Our data have the following three features. First, they cannot identify full-timeand part-time vacancies. Second, according to the 2000–2016 Survey on Employment
Trends provided by the Ministry of Health, Labour and Welfare, only about 20% of
workers find jobs through PESOs. Third, low-skilled job seekers and low-waged
vacancies are mainly registered with PESOs (Kodama et al., 2004; Japan Institute forLabour Policy and Training, 2015). Despite such features, these data have the broadest
coverage of job matching in the Japanese labor market. However, we must be mindful ofthese features when interpreting the results of our analyses.
As mentioned in Section 2.2, sample regions are classified into four treatmentgroups, namely the coastal area, adjacent to the coastal area, nuclear disaster area, and10adjacent to the nuclear disaster area, as well as the control group.5 Table 1 shows thesummary statistics of the flow and stock variables for our analyses by these region groups.
For all groups, labor market tightness increases after the earthquake. Even so, different
region groups have different local labor market features. After the earthquake, excess
labor demand (i.e., a value of labor market tightness above one) is observed for the coastal
area, nuclear disaster area, and adjacent to the nuclear disaster area groups, but not for
the adjacent to the coastal area and control groups. Furthermore, the rates of increase inlabor market tightness are particularly large (i.e., more than double) for the coastal areaand nuclear disaster area groups. These facts suggest that the earthquake results in serious
labor shortages in those groups’ regions. Hence, labor demand related to earthquake
disaster reconstruction projects for those regions in the coastal area and nuclear disasterarea groups tends to be higher.
[Table 1 about here]
Based on the descriptive evidence, we suppose that the catastrophic earthquake
causes structural change in local labor markets. This structural change differs acrossregion groups. Therefore, earthquakes, or more generally region-specific exogenous
shocks, could have different regional effects on matching efficiencies. The next section
explores this conjecture using regression analysis.

4.

Results

4.1. The DID for the direct and spatial spillover effects
We first discuss labor market tightness to comprehend the effects of theearthquake on local labor markets. Thereafter, we focus on the effects on regional

5

Table A1 lists the sample PESO jurisdictions regions by the treatment and control
groups.
11

matching efficiency.
Table 2 provides the DID estimation results of the effects of the earthquake onlabor market tightness. Column (1) shows the result of the model only with the coastal
area as the treatment group. The parameter of the interaction term between the coastal
area group dummy and the after-earthquake dummy is positive and significant, suggestingthat local labor markets in tsunami-hit coastal areas face more labor shortages after theearthquake than the control group.
[Table 2 about here]
As shown in column (2), the DID model that also includes the adjacent to thecoastal area treatment group finds a significantly positive spatial spillover effect, as
represented by the parameter of the interaction term of the adjacent to the coastal area
group dummy with the after-earthquake dummy as well as the direct effect. The
magnitude of the spatial spillover effect is around half that of the direct effect. The
tsunami’s destruction of many buildings in coastal areas generated high labor demand forreconstruction projects.6 As a result, labor demand inequality between the tsunami hitarea and other areas occurred. Therefore, local labor markets in the coastal area pulled
job seekers from neighboring regions. At the same time, firms located adjacent to thecoastal area also received demand for reconstruction projects in the coastal area. In
addition, more out-migration from the coastal area occurred than in the other regions.7
Consequently, the effects of the labor shortages in the coastal area diffused overneighboring regions. To sum up, we should consider the spatial spillover effects whenestimating DID models using regional data since regions exhibit spatial interactions witheach other.

6
7

See Higuchi et al. (2012) and Cabinet Office (2016).
See Table A2.
12

The models in columns (1) and (2) do not identify the effects of the nuclear
disaster. In other words, the coastal area and adjacent to the coastal area contain regions
affected by the nuclear disaster. 8 The model in column (3) distinguishes the nuclear
disaster area and adjacent to the nuclear disaster area from the others. The parameters of
the interaction term of the nuclear disaster area and the interaction term of the adjacent to
the nuclear disaster area are both positive and significant. With regard to the direct effectof the tsunami, the magnitude of the parameter of the interaction term of the coastal area
falls, suggesting that if the model does not take the nuclear disaster into account, theimpact of only the tsunami is overestimated. We find that the nuclear disaster leads tolarger labor shortages than just the tsunami disaster.
The spatial spillover effect of the nuclear disaster is also larger than that for justthe tsunami disaster (see column (3)). After the earthquake, the land around the
Fukushima Daiichi Nuclear Power Station was contaminated by radioactivity,
necessitating urgent decontamination. 9 However, the extraordinary characteristics of
employment for dealing with the nuclear disaster generated more labor shortages. For
example, firms conducting decommissioning work at the power plant always had toemploy new workers because of the limitations on work days to reduce the risk to healthfrom radioactive contamination. 10 Furthermore, jobs related to the decommissioning
work and decontamination of land were unpopular for job seekers, since they were
physically demanding and carried health risks.11 Labor supply was also low because of

8

See panel (b) of Figure 1.
See the website of the Japan Atomic Energy Commission(http://www.aec.go.jp/jicst/NC/iinkai/teirei/siryo2011/siryo33/siryo1-2.pdf, accessed on
January 17, 2019; in Japanese).
10
See the website of the Tokyo Electric Power Company Holdings, Inc.
(http://www.tepco.co.jp/decommission/progress/environment/index-j.html, accessed on
January 17, 2019; in Japanese).
11
See Kitamura (2014), Fukushima Minpo (2012), and Fukushima Minyu Shinbun
9

13

large out-migration from evacuation zones.
Second, we consider the effects on regional matching efficiencies predicted fromthe matching function of equation (3).12 Table 3 contains the estimation results of the
DID analysis for these regional matching efficiencies. According to the model only
examining the effect of the tsunami in column (1), the parameter of the interaction term
is significantly negative. One possible factor causing this negative effect on regional
matching efficiencies is the change in the composition of the attributes of the unemployedregistered with the PESOs in the coastal area.13 In the case of overall shocks, Barnichon
and Figura (2015), using U.S. data, reveal that a negative labor demand shock such as theGreat Recession deteriorates matching efficiencies through the composition change inunemployment. We consider that a similar tendency arises in regions in the coastal area.
This negative effect is also verified in the model by considering the spatial spillover effectin column (2). However, unlike labor market tightness, the spatial spillover effect isinsignificant.
[Table 3 about here]
After incorporating the nuclear disaster, the spatial spillover effect of the tsunami
becomes significantly negative, as shown in column (3). In terms of the nuclear disaster,
the direct and spatial spillover effects are significantly negative and positive, respectively.
The reason why the insignificant spatial spillover effect of the tsunami becomessignificant after incorporating the nuclear disaster is that the positive spatial spillover
effect of the nuclear disaster cancels out the negative spatial spillover effect of the tsunami.
The positive spatial spillover effect of the nuclear disaster suggests that after the(2018).
12
Table A3 contains the estimation result of elasticities in the matching function.
13
Another possibility is that the operations of the PESOs in the coastal area worsened.
14

earthquake, it became easier for job seekers in regions adjacent to the nuclear disasterarea to succeed in matching vacancies. 14 To explore this point further, in the next
subsection, we consider the transition of the level of outcomes over time.

4.2. Transition of the direct and spatial spillover effects over time
Here, as before, we first focus on labor market tightness. Then, we discuss thematching efficiencies.
Figure 2 shows the estimation results of the transition of the effects of theearthquake on labor market tightness over time from equation (5). This figure plots themarginal effects by region group. Panel (a) provides the result of the model containing
only the coastal area group as the treatment group to examine the direct effect of thetsunami. Before the earthquake in March 2011, labor market tightness in the coastal area
and control groups exhibit a similar trend. After mid-2012, the gap in labor market
tightness between the coastal area and control groups becomes statistically significant.
Labor market tightness in the coastal area is persistently and significantly larger than thatin the control group thereafter. Some periods exhibit a statistically significant gap
between the coastal area and control groups before the earthquake; nevertheless, weconclude that the earthquake creates a structural change in labor market tightness in the
coastal area compared with the control group, since the magnitude relation of labormarket tightness is reversed.15 This result for the coastal area (i.e., the direct effect) is

14

A region’s matching efficiency is evaluated on the basis of job seekers registeredwith the PESO in the corresponding region, owing to the definition of the dependent
variable (i.e., outflows from unemployment) in our data (see Section 3). In other words,
a region’s matching efficiency indicates how efficiently job seekers in thecorresponding region succeed in matching with vacancies regardless of the locations of
the vacancies.
15
The same occurs for panels (b) and (c).
15also confirmed after taking the spatial spillover effect into account, as shown in panel (b).
[Figure 2 about here]
In terms of the spatial spillover effect of the tsunami, panel (b) illustrates thatlabor market tightness in the adjacent to the coastal area group also becomes significantly
larger than that in the control group after mid-2012. This magnitude is lower than that forthe coastal area. The significant gap in labor market tightness between the adjacent to thecoastal area and control groups disappears until 2014. In early 2014, the gap between the
coastal and adjacent to the coastal areas also becomes not significantly different fromzero. These results suggest that the tsunami directly generates larger labor shortages in
the coastal area. This effect spreads to the adjacent to the coastal area, not immediately
after the earthquake in March 2011, but about 15 months later.
Panel (c) shows the result of the model incorporating the nuclear disaster. Labor
market tightness in the nuclear disaster area group is significantly larger than that for thecontrol group after mid-2011. This change happens earlier than for the coastal area group.
This large effect in the nuclear disaster area continues to be significant compared with thecontrol group over time. Furthermore, after the earthquake, the effect in the nuclear
disaster area is significantly larger than that in the coastal area until mid-2012. In termsof the adjacent to the coastal area and adjacent to the nuclear disaster area groups, we
hardly find a statistically significant gap compared with the control group in most periods,
even after the earthquake. Similar to the case for the DID in Table 2, the model ignoring
the nuclear disaster overestimates the spatial spillover effect of the tsunami. Althoughstatistical significance is incomplete, the ranking of the point estimates is stable over timeafter the earthquake. That is, the nuclear disaster area is the highest, followed by thecoastal area, adjacent to the nuclear disaster area, adjacent to the coastal area, and control
group in that order. These results are consistent with the DID estimations in Table 2,16

suggesting that, as mentioned in the previous subsection, the nuclear disaster leads tomore severe labor shortages than the tsunami disaster. Such spatially heterogeneous labormarket tightness owing to the earthquake could be associated with the different regional
matching efficiencies.
Again, Figure 3 considers the effects on the matching efficiency. According to
the result of the model with one treatment group (i.e., the coastal area) in panel (a), a
significantly spectacular and temporal negative shock in the coastal area seems to arisein March and April 2011. However, we do not focus on this shock because it only captures
the fact that some PESOs in the coastal area could not avoid shutting down during thesemonths because of the effects of the tsunami.16 It does not reflect the economic state but
rather physical problems.
[Figure 3 about here]
Except for the shutdown effects of some PESOs, the tsunami disaster exhibits anegative direct effect on the matching efficiencies in the coastal area group between the
middle and end of 2011. Other periods, however, show almost no differences in matchingefficiencies between the coastal area and control groups. This direct effect in the coastal
area group is also confirmed for the model taking the spatial spillover effect of the tsunami
into account, as shown in panel (b).
In the coastal area, the marine product industry was damaged by the tsunami,
causing a negative labor demand shock. As a result, the number of unemployed job

16

See the press releases issued by the Ministry of Health, Labour and Welfare,“Higashinihondaishinsai ni tomonau rodokijunkantokusho, HelloWork no kaicho jokyonitsuite, dai 1–12 hou (The status of opening of the Labor Standards Inspection Officeand the HelloWork due to the Great East Japan Earthquake, no. 1–12)”(https://www.mhlw.go.jp/stf/houdou/2r98520000015q3n.html, accessed on December 1;
in Japanese).
17seekers who had previously worked in that industry increased there.17 This fact allows
us to make the following conjectures. Many unemployed workers in the coastal area
might not be immediately able to find jobs other than in the marine product industry. A
few months after the earthquake, they register themselves as job seekers with their local
PESOs. In other words, the composition of the attributes of unemployed job seekers interms of search intensities changes relative to before the earthquake. Such a compositionchange generates larger search frictions within the corresponding local labor markets, asmentioned in the previous subsection. After a while, matching efficiencies return to theprevious level. This reflects that job seekers gradually find jobs by spending sufficient
time searching. At the same time, job seekers with a better ability to find jobs, such as theunemployed who can afford to pay relatively high search costs and have higher skills,
might leave the coastal area to search for jobs. Then, the average ability to find jobs by
the job seekers remaining in the coastal area lowers. This is also a composition change interms of the search intensities.
Unlike the direct effect of the tsunami, the negative spatial spillover effect of thetsunami is not observed, except for the effects of the shutdown of PESOs. This result
indicates that the search frictions of the above conjecture do not spatially diffuse overregions. Job seekers residing in regions adjacent to the coastal area have relatively more
success in finding jobs. It reflects that the significant composition change inunemployment is small. This is because the industrial structure there is different from thatin the coastal area or those firms hardly suffered from the tsunami.
Panel (c) illustrates the result of the model incorporating the nuclear disaster.
The transitions of the effects in the coastal area and adjacent to the coastal area groups

17

Matsumoto (2012) refers to the relationship between employment and the Great East
Japan Earthquake in detail.
18

are still similar to those in panels (a) and (b).
The result of the insignificant spatial spillover effect of the tsunami for theadjacent to the coastal area group in panel (c) conflicts with the DID result in column (3)of Table 3, reporting significantly negative effects. The reason is that the DID model
captures the impacts of the PESOs’ shutdown, which seems to be noise for our research.
This DID result can result in misleading interpretations for this study. We find that
exploring the transitions of the effects is therefore useful.
To be easily viewable, panel (c.1) abstracts only the lines for the nuclear disasterarea, adjacent to the nuclear disaster area, and control groups from panel (c). The effectof the PESOs’ shutdown is also observed in the nuclear disaster area. Unlike the case forthe coastal area group, we cannot find a significant gap in matching efficiencies among
the nuclear disaster area, adjacent to the nuclear disaster area, and control groups.
Although statistical significance is not reached, the point estimates of the nuclear disasterarea group seem to be lower than those of the control group for several months in 2011
and 2012. This result indicates that the search frictions in the nuclear disaster area riseslightly. However, it is difficult for us to distinguish this using our data since the standard
errors in terms of the nuclear disaster area group, which contains only three regions, tend
to be large.

5.

Conclusion
This paper examined whether region-specific exogenous shocks alter regional

job matching efficiencies using monthly regional panel data from the Japanese public
employment referral service. As a region-specific shock, we adopted the Great East Japan
Earthquake in March 2011. Sample regions were classified into two treatment groups,
namely the coastal area, directly suffering from the tsunami, and nuclear disaster area
19

groups. To capture the spatial spillover effects, we additionally introduced the adjacent tothe coastal area and adjacent to the nuclear disaster area as further treatment groups.
Before discussing the regional matching efficiencies, we confirmed that theearthquake indeed exhibited persistent direct and spatial spillover effects on local labormarket conditions; that is, labor market tightness in the four treatment groups turned out
to be larger after the earthquake. These facts suggested that labor shortages owing toreconstruction projects arose in the directly damaged area and spatially diffused overother regions.
In terms of regional matching efficiencies, we found that those in the coastal area
group temporally deteriorated after the earthquake, suggesting that higher search frictions
arose in the area directly damaged by the tsunami, caused by a composition change inunemployment with regard to the search intensities there. After a while, the matchingefficiencies recovered because job seekers gradually succeeded in finding jobs by
spending sufficient time searching. Unlike labor market tightness, the spatial spillover
effect of the tsunami was statistically insignificant, suggesting that the composition ofunemployment did not change in the area not directly damaged by the tsunami owing to
the different industrial structures. For the nuclear disaster, both the direct and the spatial
spillover effects were insignificant. The point estimate for the direct effect seemed to be
similar to that for the coastal area, although it was statistically insignificant. Regardless
we could not emphasize, we considered that a composition change in unemployment
might also have occurred in the nuclear disaster area.
Our findings provide regional employment policy implications to deal with
region-specific exogenous shocks such as natural disasters. To improve regional matchingefficiencies that have fallen because of exogenous shocks, it seems helpful forpolicymakers to center moderating search frictions owing to a composition change in20

unemployment on damaged areas. Specifically, policymakers should encourage job
seekers to change occupations or industries if they are reluctant to do so even though they
have the relevant skills. This policy seems to be feasible since our evidence shows thatmatching efficiencies recover after a while. What policymakers must do is induce thistendency earlier.
Another contribution is finding that we must pay attention when adopting the
DID approach. If researchers estimate the DID model for panel data including uncertain
spectacular shocks other than the research interest, they have a risk of misleadinginterpretations. To deal with this, we should confirm the transitions of the effects overtime if the data have sufficient time periods.
Finally, a limitation of this study is that our analyses do not explicitly measure acomposition change in unemployment. For further research, it would be useful to analyze
panel data disaggregated at the levels of some attribute of the unemployed as well as at
the region level. Such data must include periods with significant region-specificexogenous shocks.

Appendix[Tables A1–A3 here]

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24

Table 1: Summary statistics of the flow and stock variables for the estimations by region
group
Before the earthquake
After the earthquake(July 2003–February 2011)(March 2011–March 2016)
Mean Std.
Max
Mean Std.
Max
Variable
Dev. Min
Dev. Min(a) Coastal area (7 regions)
Outflows from
272
109
87
605
292
137
42
814
unemployment
Stock of2493 1083 1195 5484 2463 1653
856 11205
unemployment
Stock of
1285
819
353 5154 2408 1381
550 6123
vacancies
Labor market
0.50
0.15
0.16
1.30
1.13
0.48
0.17
2.26
tightness(b) Adjacent to coastal area (11 regions)
Outflows from432
281
95 1472432
293
81 1588
unemployment
Stock of4618 3303 1144 14015 4001 3077
722 14032
unemployment
Stock of2891 2531
351 11642 3805 3047
469 12518
vacancies
Labor market
0.62
0.26
0.19
1.41
0.96
0.26
0.32
1.67
tightness(c) Nuclear disaster area (3 regions)
Outflows from
530
124
277
858
501
204
141
944
unemployment
Stock of6390 2235 2853 12671 5231 2574 1266 10930
unemployment
Stock of4510 2043 1626 9554 6601 2759 1812 11455
vacancies
Labor market
0.72
0.25
0.27
1.35
1.48
0.60
0.39
2.96
tightness(d) Adjacent to nuclear disaster area (7 regions)
Outflows from500
353
90 1655553
405
119 1907
unemployment
Stock of6905 6847 1084 28934 6364 6632 1190 30522
unemployment
Stock of
5418 7374
453 32038 8007 10950
920 39148
vacancies
Labor market
0.67
0.29
0.12
1.61
1.08
0.41
0.27
2.11
tightness(e) Control group (67 regions)
Outflows from304
215
53 1426305
213
41 1591
unemployment
Stock of3565 2742
575 16869 3245 2549
486 15735
unemployment
Stock of2697 2897
291 22875 2956 2797
424 16984
vacancies
Labor market
0.75
0.39
0.08
4.39
0.90
0.31
0.16
2.29
tightness

25

Table 2: Estimation results of the DID for the direct and spatial spillover effects of theearthquake on labor market tightness
Dependent variable: Labor market tightness
(1)
(2)
(3)
VariableAfter
0.188***
0.144***
0.141***(0.006)(0.007)(0.007)
Coast-0.136***-0.162***-0.254***(0.009)(0.009)(0.008)
Coast×After
0.492***
0.536***
0.487***(0.025)(0.025)(0.025)
Adjacent to coast-0.136***-0.137***(0.008)(0.009)Adjacent to coast×After
0.236***
0.205***(0.014)(0.015)
Nuclear-0.032**(0.016)
Nuclear×After
0.616***(0.047)
Adjacent to nuclear-0.084***(0.012)
Adjacent to nuclear×After
0.269***(0.024)
Constant
0.727***
0.752***
0.754***(0.004)(0.005)(0.005)
Number of observations
14535
14535
14535
2
Adj. R
0.135
0.149
0.158
Notes: Robust standard errors are reported in parentheses. ***, **, and * represent
statistical significance at the 1%, 5%, and 10% levels, respectively.

26

Table 3: Estimation results of the DID for the direct and spatial spillover effects of theearthquake on the matching efficiency
Dependent variable: Matching efficiency
(1)
(2)
(3)
VariableAfter0.005**
0.004
0.004(0.002)(0.003)(0.003)Coast
0.017***
0.017***
0.019***(0.005)(0.005)(0.007)
Coast×After-0.043***-0.043***-0.047***(0.009)(0.009)(0.012)
Adjacent to coast-0.0010.006(0.004)(0.004)
Adjacent to coast×After
0.002-0.016**(0.005)(0.007)
Nuclear0.028***(0.005)
Nuclear×After-0.071***(0.011)
Adjacent to nuclear-0.021***(0.004)
Adjacent to nuclear×After
0.052***(0.007)
Constant-0.002-0.002-0.001(0.002)(0.002)(0.002)
Number of observations
14535
14535
14535
2
Adj. R
0.002
0.002
0.007
Notes: Robust standard errors are reported in parentheses. ***, **, and * represent
statistical significance at the 1%, 5%, and 10% levels, respectively.

27

Table A1: List of sample regions by groupNo.
PESO(a) Coast
3020
Kamaishi3030
Miyako3080
Ofunato
3100
Kuji(b) Adjacent to coast2020
Hachinohe
3010
Morioka3040
Hanamaki
3050
Ichinoseki
3060
Mizusawa
3090
Ninohe(c) Nuclear
7010
Fukushima
7040
Koriyama(d) Adjacent to nuclear
4010
Sendai
4050
Ogawara6020
Yonezawa
7020
Taira(e) Control
2010
Aomori
2030
Hirosaki
2040
Mutsu
2050
Noheji
2060
Goshogawara
2080
Misawa2090
Kuroishi
3070
Kitakami
4060
Tsukidate
5010
Akita
5020
Noshiro
5030
Odate
5040
Omagari
5050
Honjo
5060
Yokote(Pref.)No.

PESO(Pref.)

(3)
(3)
(3)
(3)

4020
4030
4080

Ishinomaki
Shiogama
Kesennuma

(4)
(4)
(4)

(2)
(3)
(3)
(3)
(3)
(3)

4040
4070
6010
6070
7050

Furukawa
HasamaYamagata
Murayama
Shirakawa

(4)
(4)
(6)
(6)
(7)

(7)
(7)

7120

Soso

(7)

(4)
(4)
(6)
(7)

7030
7060
7080

Aizuwakamatsu
Sukagawa
Nihonmatsu

(7)
(7)
(7)

(2)
(2)
(2)
(2)
(2)
(2)
(2)
(3)
(4)
(5)
(5)
(5)
(5)
(5)
(5)

9010
9020
9030
9040
9050
9060
9070
9080
9090
9110
9120
10010
10020
10030
10040

Utsunomiya
KanumaTochigi
Sano
Ashikaga
Moka
Yaita
Ohtawara
Oyama
Nikko
Kuroiso
MaebashiTakasaki
Kiryu
Isesaki

(9)
(9)
(9)
(9)
(9)
(9)
(9)
(9)
(9)
(9)
(9)
(10)
(10)
(10)
(10)

28

5070
5080
6030
6040
6050
6060
6080
8010
8020
8030
8040
8050
8060
8080
8090
8100
8110
8120

YuzawaKazunoSakataTsuruoka
Shinjo
Nagai
Sagae
Mito
HitachiChikusei
Tsuchiura
Koga
Joso
Ishioka
Hitachiomiya
Ryugasaki
Takahagi
Hitachikashima

(5)
(5)
(6)
(6)
(6)
(6)
(6)
(8)
(8)
(8)
(8)
(8)
(8)
(8)
(8)
(8)
(8)
(8)

10050
Ota
(10)
10060
Tatebayashi
(10)
10070
Numata(10)
10080
Gunmatomioka
(10)
10090
Fujioka(10)
10100
Shibukawa
(10)
15010
Niigata(15)
15020
Nagaoka(15)
15030
Joetsu
(15)
15040
Sanjo
(15)
15050
Kashiwazaki
(15)
15060
Shibata(15)
15070
Niitsu(15)
15080
Tokamachi
(15)
15100
Itoigawa
(15)
15110
Maki
(15)
15120
Minamiuonuma
(15)
15130
Sado
(15)
15140
Murakami(15)
Notes: Prefecture number is reported in parentheses. 2: Aomori, 3: Iwate, 4: Miyagi,
5: Akita, 6: Yamagata, 7: Fukushima, 8: Ibaraki, 9: Tochigi, 10: Gunma, and 15:
Niigata.

29

Table A2: Net-migration by region group (persons in thousands)
Coast

Adjacent tocoast

NuclearAdjacent tonuclearControl

FY2010
-3.4
-6.3
-1.6
-2.2
-15.6
FY2011
-18.4
1.6
-21.5
-3.6
-15.9
FY2012
-4.4
-3.0
-8.2
4.0
-21.9
FY2013
-3.2
-2.9
-1.9
2.9
-26.9
FY2014
-2.5
-4.3
0.1
1.4
-26.8
FY2015
-3.2
-5.7
0.2
-0.8
-29.0
FY2016
-2.5
-5.2
-2.6
-1.8
-28.0
Notes: The Japanese fiscal year is from April 1 to March 31. A record of Yamatsurimachi in Fukushima Prefecture, controlled by the Shirakawa PESO, is omitted owing
to a loss of data.
Source: Author’s calculation based on the Report on Internal Migration in Japan
2010–2016, provided by the Statistics Bureau, Ministry of Internal Affairs andCommunications.

30

Table A3: Estimation result of the matching function
Dependent variable: log of outflows from unemployment
Variableln 𝑈"#'(
0.479***(0.040)ln 𝑉"#'(
0.162***(0.021)
Constant
0.565*(0.323)
Number of observations
14535
2
Adj. R
0.607
Notes: This model contains the region fixed effects andtime dummies. Robust standard errors are reported inparentheses. ***, **, and * represent statisticalsignificance at the 1%, 5%, and 10% levels, respectively.

31

Figure 1: Distribution of the region groups

Notes: The region unit is the PESO jurisdiction. We create this figure by using the map
data of the Administrative Zones Data 2015 (Gyousei Kuiki Data Heisei 27 nen) fromthe National Land Numerical Information download service, provided by the National
Land Information Division, National Spatial Planning and Regional Policy Bureau,
Ministry of Land, Infrastructure, Transport and Tourism(http://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-N03-v2_3.html, accessed on March 3,2017).

32

Figure 2: Transition of the direct and spatial spillover effects of the earthquake on labormarket tightness33

Notes: Shading represents the 95% confidence interval. Vertical line represents March
2011.

34

Figure 3: Transition of the direct and spatial spillover effects of the earthquake on thematching efficiency

35

Notes: Shading represents the 95% confidence interval. Vertical line represents March2011.
36