Silviase/jgov_v1.4_full_ann huggingface dataset:
(4) 作付（栽培）延べ面積の動向をみると、昭和40年代は麦類を中心とした水田裏作の減少に加え昭和45年から始まった米の生産調整による不作付地の急増により、田を中心に大幅に減少を続けてきたものの、昭和49年以降は麦類の生産振興による作付回復等からほぼ横ばいで推移してきた。昭和60年以降は麦類に加え豆類等も減少し、平成10年からは米の需給調整対策の推進等から麦類、豆類等の作付けは増加したものの、総体的には減少傾向で推移している（図13）。

(5) 耕地利用率の動向をみると、昭和40年には123.8%であったが、その後も低下傾向で推移し、平成6年には100%を下回った。その後は平成11年に、前年に比べ0.3ポイント上昇したものの、ほぼ低下傾向で推移し、平成23年以降はほぼ横ばいで推移している。(図13)。

図13 農作物作付（栽培）延べ面積及び耕地利用率の推移
# Labor Market Outcomes of the Evacuees of the Great East Japan Earthquake

Izumi Yamasaki, Miho Takizawa, Tomohiko Inui, Rubkwan Thurmanpornphilas, Yoshio Higuchi, and Makiko Nakamuro

April 2016

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan

The views expressed in “New ESRI Working Paper” are those of the authors and not those of the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan. (Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)
# Labor Market Outcomes of the Evacuees of the Great East Japan Earthquake

Izumi Yamasaki $ ^{a} $ , Miho Takizawa $ ^{b} $ , Tomohiko Inui $ ^{c} $ , Rubkwan Thurmanpornphilas $ ^{d} $ , Yoshio Higuchi $ ^{e} $ , Makiko Nakamuro $ ^{e} $

$ ^{a} $ JICA Research Institute, Japan

$ ^{b} $ Toyo University, Japan

c Gakushuin University, Japan /Economic and Social Research Institute, Cabinet Office

d Ministry of Education, Thailand

e Keio University, Japan

April 2016

## Abstract

This study analyzes the impact of evacuation status on labor market outcomes such as employment and earnings following the Great East Japan Earthquake by using annual microdata from the 2012 Employment Status Survey in Japan. This is the first research to focus on the effect of evacuation status on labor market performance for the Great East Japan Earthquake. The evacuation status categories are (1) evacuated and still away from home, (2) evacuated and moved to another place, (3) evacuated and already returned home, and (4) did not evacuate. We applied a probit model to estimate unemployment and an interval regression to estimate earnings. To estimate unemployment, we also used propensity score matching to control for selection into evacuation status on observable characteristics. After controlling for selection into evacuation categories on observable characteristics, our findings show that those still away from home have the worst labor market performance in terms of employment. Although we could not control for selection into evacuation categories in the interval regression, current results also show those who are still away from home have the lowest earnings. The estimates suggest that we need a specific employment support for those who evacuated especially for those who are still away from home.

Keywords: natural disaster, earthquake, labor market, employment, earnings

JEL classifications: H12, J21, J30
### 1. Introduction

The Great East Japan Earthquake on March 11, 2011 caused large damage to Japanese society and its economy. The earthquake and subsequent tsunami also damaged nuclear plants in Fukushima prefecture, resulting in the release of radioactive substances into the environment. The number of people killed by the earthquake and tsunami was 15,894, with another 2,561 people missing (National Police Agency, 2016). The number of evacuees from the disaster-stricken areas and Fukushima prefecture were estimated to be about 470,000 on March 13, 2011 (Reconstruction Agency, 2014) and this number was still 174,000 in February 2016 (Reconstruction Agency, 2016). The damage to the total economy has also been serious, with the annual GDP growth rate declining to -0.5% in 2011 from 4.7% in 2010 (Cabinet Office, Government of Japan, 2016).

While five years has passed since the earthquake, the Japanese government is still struggling to accelerate the reconstruction and recovery of the economy in damaged areas to provide life support to evacuees. Groen and Polivka (2008) analyze the impact of Hurricane Katrina on the labor outcomes of evacuees and find that evacuees who were unable to return to their original residence location suffered a more disadvantageous position in the labor market than evacuees who returned home.

This study analyzes the impact on the labor market outcomes of the evacuees of the Great East Japan Earthquake by using annual microdata from the 2012 Employment Status Survey (ESS) in Japan. The ESS is a nationally representative survey of usual labor force status in Japan. It was conducted on household members aged 15 years old and over in approximately 470,000 households in October 2012. The 2012 survey included additional questions on the impact of the Great East Japan Earthquake on jobs with regular questions on household and labor force status. As far as the authors know, this is the first research focusing on the effect of evacuation status after the Great East Japan Earthquake on labor market performance by using a micro-level dataset that includes rich information on each individual. We explore the effects not only on the employment status of evacuees, but also on their earnings.

Evacuation status was categorized as (1) evacuated and still away from home, (2) evacuated and moved to another place, (3) evacuated and already returned home, and (4) did not evacuate. We applied a probit model to estimate unemployment and interval regressions to estimate earnings. To estimate unemployment, we used propensity score matching (PSM) to control for selection into evacuation status on observable characteristics. After controlling for selection into evacuation categories on observable characteristics, our findings show that those still away from home have the worst labor market performance in terms of employment. Although we could not control
for selection into evacuation categories in the interval regression, current results also show those who are still away from home have the lowest earnings as well. Finally, for the analysis of the effect of evacuation status by affected prefecture, we could not find any systematic results for both employment and earnings. This topic could be investigated further in future studies.

The remainder of this paper is organized as follows. Section 2 reviews previous studies. Section 3 describes the analysis methods. Section 4 is an introduction to the data used. Section 5 provides the descriptive statistics, while section 6 states the estimation results, summary, discussion of the findings and policy implications. The last section presents the main conclusions.

### 2. Literature Review

Given the increasing trend of natural disasters globally, the amount of research on their economic impact has been rising. Earthquakes, storms, floods, and tsunamis cause average economic losses of USD 250–300 billion each year. Assuming everyone equally shares the risk of exposure to hazardous events, this would be equivalent to an annual loss of approximately USD 70 for each individual in the working age group (UNISDR, 2015). The economic impacts caused by natural disasters are estimated to be not only the direct damage, including the loss of lives and infrastructure, but also the indirect damage such as the effects on businesses, tourism, labor markets, and economic growth. While the mass media and national governments focus overwhelmingly on the direct damage, the latter is a greater concern for economics researchers.

In the United States where natural disasters such as hurricanes have hit several times, economics researchers have examined such exogenous shocks in the given market. Among the various statistical techniques used, the difference-in-difference estimation is commonly adopted (Belasen & Polachek, 2009; McIntosh, 2008; Groen & Polivka, 2008). For example, to measure the effects of hurricanes on employment and earnings in Florida, Belasen and Polachek (2009) compare counties hit by hurricanes with those counties not hit. They also take into account the possibility of labor demand and labor supply changing in the neighboring county. The exogenous shock represented by a hurricane seems to shift the labor supply of stricken counties inward, thus decreasing employment in the stricken county and increasing earnings substantially. At the same time, workers leave the devastation and flee to neighboring counties. Those counties experience a sudden increase in labor supply, moving the equilibrium downward and thereby reducing workers' earnings. If labor demand in the neighboring county is inelastic, the migration of workers does not lead to higher employment in that
county. However, the study does not investigate the characteristics of workers who evacuated or explore whether these determine the possibility of employment and the amount of earnings.

McIntosh (2008) confirms the change in the labor market equilibrium of a neighboring area following an evacuation. The Hurricane Katrina migration to Houston, Texas was associated with a decline in wages and in the probability of being employed among the native population. Workers in sectors or occupations that faced greater labor market competition after the arrival of evacuees suffered the most. On the contrary, the inflow of low-skilled immigrants from Central America to the United States seemed to complement high-skilled native male workers and led to higher hourly wages for this group (Kugler & Yuksel, 2008).

A significant body of research examines the effects of Hurricane Katrina on the labor market because of its severe damage to human capital, not only those killed but also those forced to leave their hometowns. Some studies emphasize measuring hurricane impact by migration status (Groen & Polivka, 2008; Zissimopoulos & Karoly, 2010). After a hurricane hits, people have to evacuate to new areas and new labor markets with which they might be unfamiliar and lacking in social networks. Thus, they face higher costs of seeking jobs, which put them into a disadvantageous position. Specifically, the effects of Hurricane Katrina lowered the labor force participation rate, lowered the employment-population ratio, and raised the unemployment rate of evacuees according to these studies.

Further, among evacuees after such a natural disaster, workers who do not return home have worse labor outcomes. Although individual and family background accounts for some extent of the differences, the primary reason is that non-returnees come from areas that experience greater residence damage (Groen & Polivka, 2008). For example, Zissimopoulos and Karoly (2010) compare non-evacuees, returnee evacuees, and non-returnee evacuees by affected states and find that a natural disaster leads to different experiences for different subgroups of the population and state. Non-returnee evacuees are more severely affected by a hurricane, with many pushed into self-employment.

Regarding research on the impact of the Great East Japan Earthquake, Genda (2014) addresses similar topics to us and uses the same dataset as that presented herein. Genda (2014) analyzes a number of factors:

(1) The determinants of being affected by the earthquake on employment among those employed when the earthquake hit, using a probit model,

(2) The determinants of the changes in employment (taking a leave of absence,
leaving one’s job) among those employed when the earthquake hit, using a multinomial logit model,

(3) The determinants of being workless among those who took a leave of absence or left one’s job, using a probit model and conducting analyses separately for all regions and affected municipalities,

(4) The determinants of willingness to work among those who took a leave of absence or left one’s job, having no job in October 2012, using a probit model,

(5) The determinants of looking for a job among those took a leave of absence or left one’s job, having no job in October 2012, using a probit model, and

(6) The effect of evacuation, change of residence, and place of living affected by the earthquake on whether a respondent is employed, willing to work, and looking for a job among those who left a job or took a leave of absence because of the earthquake.

Based on the results, Genda (2014) concludes that the earthquake affected not only those in Iwate, Miyagi, and Fukushima but also those in all prefectures in eastern Japan excluding Hokkaido, especially middle-aged and older generations and less educated groups. In addition, permanent employees tended to be protected and less affected. The manufacturing sector was greatly affected but manufacturing workers were less affected in terms of being workless in fall 2012. He also suggests that those who left a job or took a leave of absence because of the earthquake were much more likely to be without a job in the affected municipalities than those in other municipalities. The negative effect was the strongest in municipalities that included evacuated areas in Fukushima. However, the people in these municipalities did not lose their willingness to work, although they were less likely to look for a job.

Genda (2014) also finds that evacuation and change of residence because of the earthquake were greatly associated with being jobless after leaving a job or taking a leave of absence after the earthquake. Those who left a job or took a leave of absence as well as those who evacuated or moved to other municipalities were less likely to have a job, want to work, and look for a job.

This study replicates the previous analyses by Groen and Polivka (2008) and Zissimopoulos and Karoly (2010), but measures the impacts of the Great East Japan Earthquake in 2011. It segregates evacuees into three groups, namely evacuees who were still away, evacuees who decided to move, and returnees, while previous studies have divided them into only returnees and non-returnees. Unlike Groen and Polivka (2008), Zissimopoulos and Karoly (2010) and Genda (2014) that focus on employment, our study extends the investigation to additionally assess the impacts of the earthquake on earnings.
Although the topic is similar, this study is different from Genda (2014) in several aspects. We focus more on the effect of evacuation status (evacuated and returned home, evacuated and moved, evacuated and still evacuating, did not evacuate). This study also looks at differences by affected prefecture on the effect of evacuation status. Further, our study looks at the effect on earnings. Lastly, we attempt to control for selection into evacuation status by PSM on observable characteristics when we estimate the effect of evacuation status on employment.

### 3. Methods

We estimate the impact of the Great East Japan Earthquake on two labor market outcomes, namely employment status and earnings, by evacuation status. This study investigates the differences among three types of evacuees: 1) those who evacuated and are still away from home, 2) those who evacuated and decided to move to another place when the earthquake hit, and 3) those who evacuated and returned home compared with those who did not evacuate. This segregation extends the work of Groen and Polivka (2008) and Zissimopoulos and Karoly (2010), which divides Katrina evacuees into returnees and non-returnees.

### 3.1 Estimating the Probability of Unemployment by Evacuation Status

First, we perform a probit estimation of the probability of being unemployed and employed by evacuation status, controlling for demographic characteristics. The baseline probit model in our analysis is

$$ \Pr(Y_{i}=1|\mathbf{X}_{i})=\Phi(\mathbf{X}_{i}\boldsymbol{\beta}) $$

where $ Y_{i} $ is a binary dependent variable where 1 means being unemployed and 0 means being employed for individual i. $ \Phi $ is the cumulative density function of a standard normal random variable. $ X_{i} $ is a vector of the explanatory variables affecting the unemployment status decision of individual i, including dummy variables on evacuation status (still away, moved, and back home), female dummy, age, age squared, marital status, the number of children under 15 years old, unemployment rate by prefecture where individual i lives in October 2012, and education level. $ \beta $ is the parameter vector of each explanatory variable.

To control for sample selection bias into evacuation status as much as possible based on observable characteristics, we use PSM. Since PSM is used to compare the outcomes of two groups and we cannot construct a statistical model, we control for selection bias in two groups. We estimated the probabilities of unemployment among those (i) who evacuated and are still away from home compared with those who did not.
evacuate, (ii) who evacuated and moved compared with those who did not evacuate, and (iii) who evacuated and returned home compared with those who did not evacuate.

The propensity score is the conditional probability of assignment to a particular treatment given the observed covariates (Rosenbaum & Rubin, 1983). PSM constructs a statistical comparison group based on the propensity score estimated by the observed characteristics. People in the treatment group are matched to those not in the treatment group based on the propensity score. Propensity score is the probability of being in the treatment, D give the observed characteristics: $ P(X) = \Pr(D = 1|X) $ . Then, average treatment effect on the treated (ATT) is estimated by calculating the mean difference in outcomes across the two groups. The PSM estimator for ATT can be written as below assuming that conditional independence holds and that there is common support (overlap between both groups).

$$ \tau_{A T T}^{P S M}=E_{P(X)|D=1}\{E[Y(1)|D=1,P(X)]-E[Y(0)|D=0,P(X)]\} $$

where $ \tau_{ATT}^{PSM} $ is defined as the unemployment probability differential between those who evacuated and are still away (moved or returned) and those who did not evacuate. $ Y(1) $ means outcomes for those who are in treatment group (one of three evacuation statuses) and $ Y(0) $ outcomes for those who are in control group (non-evacuee). D=1 means “evacuated and still away (moved or returned),” D=0 means “did not evacuate,” Y is unemployment status (1 if unemployed, 0 otherwise), and X is individual characteristics, which are the gender, age, age squared, marital status, number of children under 15, official unemployment rate by prefecture where respondents live, and education dummies. PSM is the only decent method we could currently think of in order to control for selection into evacuation status as much as possible. However, as suggested from the explanation above, we have to note that PSM cannot control for selection bias on unobserved characteristics and therefore the estimates by PSM could still have some bias. In any case, since we match people in two groups on the same observable characteristics using PSM, and drop those who cannot be matched, the estimates of PSM should have less bias than those of probit models.

### 3.2 Estimating Earnings by Evacuation Status

Second, we estimate the effects of evacuation status on earnings by using an interval regression. We cannot run a conventional ordinary least squares regression because data on earnings are categorical. To estimate the effect of evacuation status and the other characteristics on earnings, we use the interval regression because the earnings variable is an ordered categorical variable. The interval regression used is the “exactly ordered probit with the cut points fixed and with $ \beta $ and $ \sigma^{2} $ estimated by maximum
"likelihood" (Wooldridge, 2002, p. 509). As Wooldridge (2002) explains, in an interval regression, “ $ \beta_{j} $ are interpretable as if we had observed $ y_{i}^{*} $ for each i and estimated

$ E(y^{*}|x)=x\beta_{i} $ by OLS" (p. 509).

The more detailed equations below are based on the study by Bettin and Lucchetti (2012). The dependent variable of interest, annual gross earnings from the main job or main business, $ y_{i}^{*} $ is unobserved. The observed information is an interval as below:

$$ m_{i}\leq y_{i}^{*}
### 4. Data

The dataset used in this study is the 2012 ESS in Japan, a nationally representative survey of labor force status in Japan, and it was conducted on approximately 470,000 household members aged 15 years old or more in October 2012. The 2012 survey includes additional questions on the impact of Great East Japan Earthquake on the job with the regular questions on the household and labor force status. In the probit model used to estimate the effect of evacuation status and the other characteristics on unemployment, the dependent variable is binary and equals 1 if individual i is unemployed in October 2012 and 0 otherwise. The independent variables are the three types of evacuation status and female dummy (1 if female, 0 otherwise), age, age squared, marital status dummy (1 if married, 0 otherwise), number of children under 15 years old, official unemployment rate of the prefecture where an individual is living, and education level dummies.

In the interval regression used to estimate earnings, the dependent variable is the categories of annual earnings (wages/salaries or profits from business). The earnings categories start from “no earnings or less than 500,000 yen” followed by “500,000 to 990,000 yen” and “1,000,000 to 1,490,000 yen.” After the second category, we have categories for every 500,000 yen until “2,500,000 to 2,990,000 yen.” From 3,000,000 yen, we have categories for every 1,000,000 yen (“3,000,000 to 3,990,000 yen”) up to “9,000,000 to 9,990,000 yen.” The last three categories are “10,000,000 to 12,490,000 yen,” “12,500,000 to 14,990,000 yen,” and “More than 15,000,000 yen.” The independent variables in the model are three types of evacuation status (explained later), female dummy, years of experience, years of experience squared, marital status dummy (1 if married, 0 otherwise), number children under 15 year, average earning of the prefecture where an individual is living calculated by authors, education level dummies, and industry dummies.

Lastly, evacuation status (in October 2012) in both models is categorized into three groups: (1) those who evacuated after the earthquake and still live away from home, (2) those who evacuated after the earthquake and have already moved to another place, and (3) those who evacuated after the earthquake and returned home. The base category is those who did not evacuate.

### 5. Descriptive Statistics

According to the ESS, 11,771 evacuees of the earthquake are in the labor force (Table 1). The majority of these are from Fukushima, from which many people were evacuated fearing radiation sickness. The second and third largest groups of evacuees come from