Silviase/jgov_v1.4_full_ann huggingface dataset:
Miyagi (3,574 evacuees) and Iwate (1,369 evacuees), respectively. More than half of evacuees, however, returned home in 2012. Approximately 28.3% of evacuees report that they are still away from home and 15.1% have moved to another place. The highest proportions of returnees are from Aomori, Ibraraki, and Chiba, since these three prefectures experienced only a minor impact from the earthquake. Whereas most evacuees from other prefectures returned home, 57.3% of evacuees from Iwate are still away from home. More than 20% of evacuees in Miyagi, Ibraraki, and Chiba decided to move to another place.

Table 1: Evacuee Status by Prefecture of Residence, 15 to 64 Years Old

Prefecture lived during earthquake	Type of evacuation	Total evacuees
Away	Moved	Returnee
青森県Aomori	0(0.00%)	37(13.45%)	238(86.55%)	275(100%)
岩手県Iwate	784(57.27%)	169(12.34%)	416(30.39%)	1,369(100%)
宮城県Miyagi	1,028(28.76%)	786(21.99%)	1,760(49.24%)	3,574(100%)
福島県Fukushima	1,456(27.14%)	524(9.77%)	3,384(63.09%)	5,364(100%)
茨城県Ibaraki	27(4.48%)	177(22.84%)	571(73.68%)	775(100%)
千葉県Chiba	38(9.18%)	88(21.26%)	288(69.57%)	414(100%)
Total	3,333(28.32%)	1,781(15.13%)	6,657(56.55%)	11,771

Note: The sample covered here is the working age population (15 to 64 years old) in the labor force (employed or unemployed).

Table 2 reports the labor market outcomes for the working age population by gender and evacuation status. The unemployment rates of evacuees are higher than those of non-evacuees for both men and women. Here, 6.5% of male evacuees are unemployed, while only 4.5% of the non-evacuee workforce are unemployed. Similarly, 12.1% of female evacuees are unemployed compared with 8.1% for female non-evacuees. The subgroup of evacuees shows that returnees have a higher possibility of employment. The unemployment rate for returnees is almost identical to that for
non-evacuees for male and just slightly higher for female. Among male evacuees, the highest rate of unemployment is among the “moved (evacuated and moved to another place)” workforce (8.7%), followed by workers who are still away from home (8.4%). The unemployment rates of these two groups are twice that of non-evacuees (4.5%). In addition, the highest unemployment rate among female evacuees is for the “moved” subgroup (17.5%) followed by the “away” workforce (15.1%). Women are more likely to be jobless for all evacuation statuses. Overall, the female unemployment rate is about twice as large as the male unemployment rate. Similar patterns are shown for each evacuation status. The widest gender gap is found among the “moved” subgroup (17.5% for female compared to 8.7% for male). However, the other subgroups also show a substantial unemployment gap between men and women. Men continue to have an advantage over women in terms of employment opportunities.

Table 2: Unemployment by Evacuee Status, 15 to 64 Years Old

Evacuate (those who lived in the six affected prefectures when the earthquake hit)	Do not evacuate (covering all prefectures in Japan)	Total (covering all prefectures in Japan)
Away	Moved	Retumee	Total
Male
Employed	1,768	925	3,290	5,983	596,557	607,839
Unemployed	162	88	163	413	28,332	28,983
Labor Force	1,930	1,013	3,453	6,396	624,889	636,822
% Unemployment	8.39%	8.69%	4.72%	6.46%	4.53%	4.55%
Female
Employed	1,202	648	2,889	4,749	480,688	489,076
Unemployed	213	137	305	655	42,255	43,196
Labor Force	1,415	785	3,194	5,404	522,943	532,272
% Unemployment	15.05%	17.45%	9.55%	12.12%	8.08%	8.12%
Total
Employed	2,970	1,573	6,189	10,732	1,077,245	1,096,915
Unemployed	375	225	468	1,068	70,587	72,179
Labor Force	3,345	1,798	6,657	11,800	1,147,832	1,169,094
% Unemployment	11.21%	12.51%	7.03%	9.05%	6.55%	6.17%

Notes: This table covers those of working age (15 to 64 years old) and in the labor force. The
sample in the first to fourth columns covers those who used to live in Aomori, Iwate, Miyagi, Fukushima, Ibaraki, and Chiba before the earthquake. The samples in the fifth and last columns cover all 47 prefectures.

Figure 1 reports the proportion of workers at each earning level, starting from less than 500,000 yen per annum to greater than 15,000,000 yen per annum. The data are defined by evacuation status. A person who reports being away because of the earthquake is more likely to have lower earnings. Less than 4% of this group have annual earnings between 5,000,000 and 5,990,000 yen compared with more than 6% of non-evacuees. Evacuees who moved to another place tend to receive higher annual earnings than those away since the group has a smaller proportion of workers in the low earnings category. Returnees seem to earn less (more) than those who moved (are still away). Non-evacuees, on average, enjoy higher earnings than all other types of evacuees. The data indicate a higher proportion of non-evacuees at almost every wage level above 4 million yen per annum. In addition, a lower proportion of non-evacuees tends to be found in the lower earnings category.

Segregated by gender, Figure 2 shows the differences in wage level between male and female workers by evacuation status. The figure illustrates that women receive lower earnings regardless of their evacuation status. Over 50% of female workers report earning less than 1.5 million yen per annum. As seen in all four panels of Figure 2, there is a higher proportion of female workers in the lower earnings category compared with male workers. This pattern seems to switch at an earning level of 1.5 million yen per annum where male workers start to have a higher proportion. The earning distributions of returnees and non-evacuees are similar for both male and female workers. The two upper panels, on the contrary, show steeper slopes on the left, especially for women. This finding indicates that female workers in the “away” and “moved” statuses are more likely to have lower annual earnings compared with female workers in the “returnee” and “do not evacuate” groups. A similar pattern is observed for the distribution of male earnings; however, the difference is more prominent in the “away” group in which many male workers report receiving lower earnings.
Figure 1: Proportion of Workers by Earnings Level, 15 to 64 Years Old (in 1,000 yen)

Notes: The sample covered here is the working age population (15 to 64 years old) in the labor force (employed or unemployed). Those who are either “away”, “moved” or “returned” are people who used to live in Aomori, Iwate, Miyagi, Fukushima, Ibaraki, and Chiba before the earthquake. Those who “do not evacuate” cover people in all 47 prefectures.

Figure 2: Proportion of Workers by Earnings Level, Segregated by Evacuation Status and Gender, 15 to 64 Years Old (in 1,000 yen)
Figure 2: Proportion of Workers by Earnings Level, Segregated by Evacuation Status and Gender, 15 to 64 Years Old (in 1,000 yen) (continued)
Figure 2: Proportion of Workers by Earnings Level, Segregated by Evacuation Status and Gender, 15 to 64 Years Old (in 1,000 yen) (continued)

Notes: The sample covered here is the working age population (15 to 64 years old) in the labor force (employed or unemployed). Those who are either “away”, “moved” or “returned” are people who used to live in Aomori, Iwate, Miyagi, Fukushima, Ibaraki, and Chiba before the earthquake. Those who “do not evacuate” cover people in all 47 prefectures.

Table 3 reports the characteristics of workers by evacuation status. Some of these observed characteristics might have resulted in differences in employment status and earnings even before the Great East Japan Earthquake. The illustration shows that evacuees who decide to move, on average, are several years younger than workers of other evacuation statuses. They are also more likely to be male and non-married compared with non-evacuees and returnees. Moreover, evacuees who report being away are less likely to have a high level of education. More than 80% of “away” evacuees graduated from senior high school or lower. Evacuees who decide to move have similar educational characteristics to non-evacuees and are more likely to have a higher education level than the “away” and “returnee” groups. The industry variables do not show substantial differences among evacuees and non-evacuees; some industries are worth mentioning, however. For example, evacuees are more likely to be in the fishing industry, especially the “away” group, although the percentages are small. The percentage gap of being in the fishing industry between non-evacuees and the “away”
group is more than triple. Another industry that shows a similar pattern is construction. Altogether, 15.8% of workers in the “away” group are involved in the construction industry compared with only 8.0% of non-evacuees.

Table 3: Characteristics by Evacuation Status, 15 to 64 Years Old

Characteristics	Type of evacuation(those who lived in the six affected prefectures when the earthquake hit)	Do not evacuate (covering all prefectures in Japan)
Away	Moved	Returnee
Age	44.41	36.64	41.77	43.46
Female (%)	42.30%	43.66%	48.13%	50.63%
Married (%)	61.79%	55.97%	66.58%	64.81%
Number of children under 15 years	0.50	0.62	0.70	0.51
Unemployment rate by current prefecture	4.21	4.32	4.25	4.06
Junior high and lower (%)	15.33%	5.46%	8.98%	7.49%
Senior high (%)	64.71%	46.38%	56.61%	48.67%
Post secondary vocational education and training (%)	5.55%	11.70%	7.80%	7.72%
Junior college (%)	5.82%	9.36%	8.14%	10.01%
College (%)	8.13%	22.13%	16.73%	23.63%
Graduate school (%)	0.24%	3.95%	1.50%	2.18%
Years of experience	26.30	17.18	22.94	24.24
[Among those who have jobs]
Agriculture and woods (%)	1.89%	1.34%	1.62%	3.04%
Fishing (%)	2.86%	0.70%	2.14%	0.57%
Mining (%)	0.00%	0.00%	0.00%	0.05%
Construction (%)	15.82%	9.92%	10.08%	8.03%
Manufacturing (%)	17.34%	14.49%	18.55%	17.65%
Energy (%)	1.31%	0.95%	0.44%	0.61%
Information and communication (%)	1.21%	3.12%	2.13%	2.07%
Transportation and postal service (%)	5.35%	5.91%	5.45%	5.23%
Retail (%)	13.80%	16.27%	16.90%	15.54%
Finance (%)	1.45%	2.48%	1.65%	2.60%
Real estate and leasing (%)	1.08%	0.76%	1.10%	1.42%
R&D and engineering (%)	1.62%	2.80%	3.05%	2.91%
Hotels and restaurants (%)	4.01%	6.04%	7.14%	5.51%
Life service and leisure (%)	2.56%	4.58%	4.15%	3.44%
Education (%)	2.86%	4.64%	4.59%	5.01%
Health (%)	9.36%	14.18%	10.07%	12.57%
Multiple service (%)	1.45%	0.57%	0.57%	1.09%
Other service (%)	9.46%	4.70%	5.33%	5.56%
Government (%)	4.04%	3.37%	1.81%	4.18%
Not classified (%)	2.53%	3.18%	3.23%	2.90%

### 6. Results

In this section, we present the results of the estimated impact of the Great East Japan Earthquake on labor market outcomes (employment status and earnings) for evacuees and non-evacuees.

### 6.1 Effect of Evacuation Status and Other Characteristics on Employment

Table 4 shows the results of the probit estimation of the probability of being unemployed and employed by evacuation status. As shown in Table 4, compared with non-evacuees, those who evacuated and are still away from home have a 3.3% higher unemployment rate. Similarly, those who evacuated and moved have a 3.1% higher unemployment rate than non-evacuees. Further, compared with men, women have a 3.6% higher unemployment rate. If they are married, they have a 2.7% lower unemployment rate. Finally, Table 4 shows that the higher their educational backgrounds are, the lower are their unemployment rates.

Table 4: Probit Regression Estimating Unemployment by Evacuee Status, Marginal Effects

Dependent variable: Unemployed=1, Employed=0

Marginal Effects	Standard Errors	P>z
Away	0.033***	0.003	0.000
Moved	0.031***	0.005	0.000
Returned	-0.001	0.003	0.744
Female	0.036***	0.000	0.000
Age	-0.003***	0.000	0.000
Age squared	0.000***	0.000	0.000
Married	-0.027***	0.001	0.000
Number of children under 15 years	0.002***	0.000	0.000
Unemployment by prefecture	0.009***	0.000	0.000
Senior high	-0.025***	0.001	0.000
Post secondary vocational education and training	-0.040***	0.001	0.000
Junior college	-0.030***	0.001	0.000
College	-0.045***	0.001	0.000
Graduate school	-0.076***	0.002	0.000
Constant
Number of obs	1125221
LR chi2(33)	17842.63
Prob > chi2	0.000
Pseudo R2	0.037

Table 5 shows the results by evacuation status and prefecture. Those who evacuated and returned to pre-earthquake homes are less likely to be unemployed, excluding Miyagi and Ibaraki prefectures. Those who evacuated from Miyagi and Fukushima and are still away from home have a higher unemployment rate. Also those who evacuated from Aomori, Miyagi, Fukushima, and Ibaraki and have moved have a higher unemployment rate.

Table 5: Probit Regression Estimating Unemployment with the Interaction of Evacuee Status and Affected Prefecture, Marginal Effects

Dependent variable: Unemployed=1, Employed=0

Marginal Effects	Standard Errors	P>z
Away Aomori	(omitted)
Away Iwate	0.004	0.009	0.623
Away Miyagi	0.019 $ ^{***} $	0.006	0.002
Away Fukushima	0.054 $ ^{***} $	0.005	0.000
Away Ibaraki	(omitted)
Away Chiba	0.022	0.033	0.510
Moved Aomori	0.108 $ ^{***} $	0.024	0.000
Moved Iwate	-0.028	0.022	0.199
Moved Miyagi	0.037 $ ^{***} $	0.007	0.000
Moved Fukushima	0.023***	0.009	0.009
Moved Ibaraki	0.053***	0.014	0.000
Moved Chiba	0.005	0.024	0.843
Returned Aomori	-0.036**	0.017	0.030
Returned Iwate	-0.035**	0.014	0.011
Returned Miyagi	-0.004	0.005	0.465
Returned Fukushima	0.009**	0.005	0.020
Returned Ibaraki	-0.001	0.010	0.911
Returned Chiba	-0.061***	0.023	0.005
Female	0.036***	0.000	0.000
Age	-0.003***	0.000	0.000
Age squared	0.000***	0.000	0.000
Married	-0.027***	0.001	0.000
Number of children under 15
years	0.002***	0.000	0.000
Unemployment by prefecture	0.009***	0.000	0.000
Senior high	-0.025***	0.001	0.000
Post secondary vocational education and training	-0.040***	0.001	0.000
Junior college	-0.031***	0.001	0.000
College	-0.045***	0.001	0.000
Graduate school	-0.076**	0.002	0.000
Number of obs	1125191
LR chi2(47)	17939.74
Prob > chi2	0.000
Pseudo R2	0.037

Notes: The prefecture is where the person was living when the earthquake hit. The interaction of away and Aomori is dropped because of collinearity (actually no one from Aomori is still away from home). The interaction of away and Ibaraki is dropped because it predicts failure perfectly (For more details please see Stata.com- probit http://www.stata.com/manuals13/rprobit.pdf).

Finally, Table 6 shows the impact of evacuation status on employment status estimated by PSM on observable characteristics. The results of those still away and moved do not significantly differ from the results in previous models; however, the estimated effect of being away is greater than that in the other models. In addition, we now find a statistically significant negative (estimated) impact of “evacuated and
“returned home” on employment after controlling for selection into this category on observable characteristics, although the estimated effect for this group is still the weakest. As we can see in Table 6, those still away have higher unemployment rate by 5.6%, those moved have higher unemployment rate by 3% and those returned home have higher unemployment rate by 1.3%, than those did not evacuate.

Table 6: PSM of the Linear Probability Model: The Probability of being Unemployed among those who Evacuated Compared with those who did not

Dependent Variable	Away	Moved	Returned
Unemployed=1, 0 otherwise
Evacuated and Still Away, Moved or Returned	0.056 $ ^{***} $	0.030 $ ^{**} $	0.013 $ ^{***} $
(0.012)	(0.014)	(0.008)
Number of treated	3,284	1,674	6,426
Number of untreated	1,113,837	1,113,837	1,113,837

### 6.2 Effect of Evacuation Status and Other Characteristics on Earnings

Table 7 presents the result of the interval regression of the impact of evacuation status on earnings. It shows that compared with non-evacuees nationally (i.e., those who did not evacuate in any prefecture), annual earnings are not statistically significantly different among those who evacuated and moved or returned home compared with non-evacuees. By contrast, those who evacuated and are still away from home have lower annual earnings compared with those who did not evacuate by 322,500 yen. This finding suggests that the labor market situation is worse for those who are still away from home. The results for the other individual attributes including educational background were as expected.

Table 7: Interval Regression: The Impact of Evacuation Status and Individual Characteristics on Earnings

Dependent variable: lower bound of earnings, upper bound of earnings (in 10,000 yen)

Coefficient	Standard Error	P>z
Away	-32.25***	3.72	0.000
Moved	7.79	5.24	0.137
Returnee	2.99	2.61	0.251
Female	-199.98***	0.44	0.000
Experience	14.70***	0.07	0.000
Experience squared	-0.26***	0.00	0.000
Married	39.48***	0.51	0.000
Number of children under 15 years	-1.28***	0.26	0.000
Average earnings by prefecture	0.78***	0.01	0.000
Senior high	42.67***	0.80	0.000
Post secondary vocational education and training	81.95***	1.08	0.000
Junior college	71.85***	1.02	0.000
College	175.88***	0.91	0.000
Graduate school	314.20***	1.59	0.000
Constant	-187.44***	2.27	0.000
Number of obs	1071049
LR chi2(33)	516992.35
Prob > chi2	0.000

Note: Industry dummies are included as independent variables in the estimation but the results are not shown here for brevity.

Table 8 shows the results of the impact of evacuation status on earnings by prefecture. We find that those who evacuated from Iwate, Miyagi, Fukushima and Ibaraki and are still away from home, those who evacuated from Ibaraki and moved, and those who evacuated from Miyagi and returned home have lower earnings than the other groups. As we can see those who are still away from home have the lowest average earnings in any prefecture subgroups except for Chiba. However, the number of people who are from Chiba and away from home is small and we cannot draw any strong conclusion from the result.

Table 8: Interval Regression: The Impact of Evacuation Status and Individual Characteristics on Earnings by Prefecture

Dependent variable: Dependent variable: lower bound of earnings, upper bound of earnings (in 10,000 yen)

Coef.	Std. Err.	P>z
Away Aomori	(omitted)
Away Iwate	-46.04***	7.39	0.000
Away Miyagi	-33.54***	6.69	0.000
Away Fukushima	-23.07***	5.76	0.000
Away Ibaraki	-107.80***	36.93	0.004
Away Chiba	3.11	35.76	0.931
Moved Aomori	31.61	40.63	0.437
Moved Iwate	15.89	15.97	0.320
Moved Miyagi	15.59*	7.99	0.051
Moved Fukushima	6.08	9.63	0.528
Moved Ibaraki	-33.52*	17.77	0.059
Moved Chiba	19.73	22.97	0.390
Returnee Aomori	49.33***	13.54	0.000
Returnee Iwate	17.11*	10.15	0.092
Returnee Miyagi	-13.41***	5.09	0.008
Returnee Fukushima	4.86	3.66	0.184
Returnee Ibaraki	-7.30	8.88	0.411
Returnee Chiba	38.70***	12.31	0.002
Female	-199.98***	0.44	0.000
Experience	14.70***	0.07	0.000
Experience squared	-0.26***	0.00	0.000
Married	39.47***	0.51	0.000
Number of children under 15 years	-1.27***	0.26	0.000
Average earnings by prefecture	.7838***	0.01	0.000
Senior high	42.68***	0.80	0.002
Post secondary vocational education and training	81.97***	1.08	0.000
Junior college	71.86***	1.02	0.000
College	175.87***	0.91	0.000
Graduate school	314.20***	1.59	0.000
Constant	-187.67***	2.27	0.000
Number of obs	1071043
LR chi2(47)	517052.68
Prob > chi2	0.000

Note: Industry dummies are included as independent variables in the estimation but the results are not shown here for brevity. Aomori is dropped because of collinearity (actually no one from Aomori is still away from home).

### 6.3 Summary, Discussion and Policy Implications

In summary, our findings show that those still away from home have the worst labor
market performance in terms of employment, followed by those who moved, and then those who returned home. This is understandable because it is hard to live in temporary houses or new places and look for a job in an unfamiliar city. Also, those still away from home have least earnings than other groups while the earnings of those who moved and returned are not statistically significantly different from those of non-evacuees. There are no additional systematic findings using the interactions of evacuation status and affected prefecture for employment and earnings. However, we need to further investigate the impact of evacuation status by affected prefecture in the future studies.

The results in our study suggest that those who are still away from home have the worst labor market situations in terms of employment and earnings. We could possibly create programs to support evacuees, especially those who are away from home in addition to existing employment support for affected people and prefectures so that they can find jobs or better jobs which match for their qualifications. The important thing is that we need to support those who are living outside of affected prefectures as well. We need further investigations on the details of the situation of those who evacuated to form concrete programs.

### 7. Conclusion

This study analyzed the impact on the labor market outcomes of evacuees of the Great East Japan Earthquake by using annual microdata from the 2012 ESS in Japan. We estimated the effects not only on the employment status of evacuees, but also on their earnings. Our estimates suggest that those still away from home have the worst labor market performance in terms of employment and earnings. However, further studies are required to investigate the reasons for our findings, such as how education level affects the employment and earnings of those who evacuated after the earthquake. We would also like to note that improved identification strategies are necessary because PSM could not control for selection into evacuation status based on unobserved characteristics. Also, PSM used in this paper could only compare two groups not more than two groups. In addition, we were unable to construct a model to control for selection into evacuation status in the estimation of the effect of evacuation status on earnings because using an interval regression prevents us from using PSM $ ^{1} $ or other methods. In addition, we need to find a way in which to estimate the effect of evacuation status on employment and earnings taking into account the multinomial
categories of evacuation status. The results suggest that we need more employment support for those who are away from home including people those who are living outside of affected prefectures. We need further investigation on evacuees to formulate a more concrete employment support program.

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