Abstract
The Labor Force Survey, a large-scale government statistics, and the causal forest algorithm are used to estimate the group average treatment effect of the COVID-19 on the employment status for each month from January to June 2020. We find that (1) because of the seasonality in employment status at monthly level, whether we use January 2020 as the base month for comparison, as done in most of the studies or whether we use the same month last year as the base comparison group makes a large difference; (2) whether we include those who are absent from work among the employed or not makes a large difference in the measure of the impact of COVID-19 and its changes; (3) if we use the employment measure which does not include those who are absent from work among the employed, 25–30% among the employed are adversely affected and that 10% of the employed experienced more than 10% decline in employment probability in April, 2020; (4) those who are the most affected by the COVID-19 are those who are unemployed or work part-time in the hotel and restaurant industry and service occupations; (5) in addition, younger and female respondents are more affected than are older and male respondents; and (6) we observe no clear differences in the impacts of COVID-19 with respect to living location, education status, and firm size among the most affected.
Highlights
COVID-19 has affected the Japanese labor market by directly affecting the behaviors of employers and employees and by inducing various government policies to restrict social and economic activities in an effort to contain the spread of the virus.This paper describes how the Japanese labor market was affected by COVID-19 through June 2020
We provide basic facts based on the Labor Force Survey (LFS) using employment status as the outcome measure
The LFS is a monthly household survey conducted by the Ministry of Internal Affairs and Communications (MIC) and has a similar design to the Current Population Survey (CPS) in the United States
Summary
COVID-19 has affected the Japanese labor market by directly affecting the behaviors of employers and employees and by inducing various government policies to restrict social and economic activities in an effort to contain the spread of the virus. This paper describes how the Japanese labor market was affected by COVID-19 through June 2020. Our main interest is the difference of the employment probability in a month and that of the same month in the previous year, conditional on respondents’ background characteristics and working status in the previous month. The difference in the conditional probabilities is estimated by a causal machine learning technique (Wager and Athey 2018; Athey et al 2019). The result is used to estimate the percentiles of the conditional probabilities This is used in turn to define the most affected group.
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