This paper examines whether region-specific shocks alter regional labor market tightness and matching efficiencies. We adopt the Great East Japan Earthquake of March 2011, which caused a tsunami and nuclear disaster, as a region-specific shock. We find that an increase in labor market tightness, namely, labor shortage, occurs in the damaged regions after the disaster. Matching efficiencies in the damaged regions deteriorate, suggesting that the composition of unemployment and vacancies changes, leading to higher search frictions. Such nature has spatial spillover effects because of the widespread increase in demand for reconstruction and out-migration from the damaged regions.
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See Angrist and Pischke (2009) for the DID method in detail.
To define the tsunami-hit coastal group, we find inspiration from Belasen and Polachek’s (2009) coastal dummy in their GDD models.
The evacuation zones consist of three types of areas: the difficult-to-return zone, restricted residence area, and zone in preparation for the lifting the evacuation order. The categorization in this study is 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).
The GDD adopts multiple events as natural experiments; therefore, the treatment and control groups are not fixed but change with events. This allows the GDD to ensure that the estimated effects of events hardly suffer from the characteristics of observations, unlike conventional DID (Belasen and Polachek, 2009).
The models for labor market tightness, as well as those for matching efficiency, do not contain region fixed effects because the regression analyses omit some coefficients of treatment group dummies owing to multicollinearity.
The surrounding prefectures are Aomori, Akita, Yamagata, Ibaraki, Tochigi, Gunma, and Niigata Prefectures.
To construct balanced panel data, we arrange the data as follows. First, certain types of 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.
We obtained the data on population in 2010 and 2015 from the Population Census, and those of other years from the Population Estimates. Both statistics are provided by the Ministry of Internal Affairs and Communications.
“Appendix 2” provides a list of industries.
Table 4 lists the sample PESO jurisdictions regions by the treatment and control groups.
See the press releases issued by the Ministry of Health, Labour and Welfare, “Higashinihondaishinsai ni tomonau rodokijunkantokusho, HelloWork no kaicho jokyo nitsuite, dai 1–12 hou (The status of opening of the Labor Standards Inspection Office and 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, 2018; in Japanese).
Figure 6 captures the effects of the PESOs’ shutdown.
See Table 5.
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).
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).
Table 6 contains the estimation result of elasticities in the matching function.
A region’s matching efficiency is evaluated on the basis of job seekers registered with the PESO in the corresponding region, owing to the definition of the dependent variable (i.e., outflows from unemployment) in our data (see Sect. 3). In other words, a region’s matching efficiency indicates how efficiently job seekers in the corresponding region succeed in matching with vacancies, regardless of the locations of the vacancies.
Another possibility is that the operations of the PESOs there worsened.
Matsumoto (2012) refers to the relationship between employment and the Great East Japan Earthquake in detail.
Although we are unable to confirm, we guess that some non-working individuals with low search intensities might move within a lower geographical scope, while others with high search intensities might move further. Consequently, the rate of searching persons cancels out.
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I would like to thank two anonymous reviewers, Kazufumi Yugami, Nobuaki Hamaguchi, Shinya Horie, and Tamotsu Nakamura 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 research was supported by the Grant-in-Aid for Japan Society for the Promotion of Science (JSPS) Fellows (JSPS KAKENHI Grant No. JP18J11560).
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Appendix 2: List of industries of which per capita GPPs are controlled in the DID models
Agriculture; Forestry; Fishing; Mining; Manufacturing; Construction; Electricity, gas, water supply, and waste disposal business; Wholesale and retail trade; Finance and insurance; Real estate; Transport and postal activities; Information and communications; Accommodations, eating and drinking services; Professional and science technology, business support service; Public service; Education; Public health, hygiene, and social service; Miscellaneous services.
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Higashi, Y. Effects of region-specific shocks on labor market tightness and matching efficiency: evidence from the 2011 Tohoku Earthquake in Japan. Ann Reg Sci 65, 193–219 (2020). https://doi.org/10.1007/s00168-020-00980-w