Abstract
Using three household surveys, the Japanese Panel Survey of Consumers (JPSC), the Working Person Survey (WPS), and the Survey of Adult Skills (PIAAC), this study estimates the returns to postgraduate education in Japan, considering potential self-selection bias. To mitigate the bias, workers’ undergraduate majors, types of university, and level of cognitive skills are controlled for. These factors explain 6.3–29.2% of the postgraduate wage premium for women, but at most 10.9% for men. Even after controlling for them, the postgraduate wage premium remains positive and significant, ranging from 16.5 to 23.7% for men and 13.5–26.4% for women.
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Data source: basic survey of schools
Data source: working person survey
Data source: working person survey
Data source: basic survey of schools
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Notes
Shimizu and Higuchi (2009) estimate the postgraduate wage premium for MBA holders in Japan.
Although the study by Morikawa (2015) takes possible sample selection bias into account by employing the Heckman two-step estimation approach, it does not address potential ability bias and the self-selection bias due to the business cycle.
Since the number of workers with a Ph.D. in the data set used is very small, the analysis focuses only on workers with a bachelor’s degree and a master’s degree.
As mentioned, while survey participants are exclusively women, the panel also includes information on the husbands of married women, to include observations about both men and women.
Social sciences include business and economics.
Part-time workers’ wage determination can be considerably different from full-time workers’ wage determination. However, postgraduate wage premium may be underestimated if part-time workers are excluded, because obtaining master’s degree may help workers to find a full-time job. To control for the wage gap between full-time and part-time workers, hours of work and part-time dummy, which takes on the value of one if the hours of work is less than one, are included in the regression models.
For 2014 survey, those aged 60–69 are also included.
This study uses annual wage, because hours of work variable in the JPSC data are a categorical variable and hourly wage is not available for the JPSC data. Moreover, previous studies use annual wage, so annual wage is preferable for comparison purposes.
Since the WPS and PIAAC data do not contain tax and social security payment, this study uses pretax wage.
The standard Mincer human capital earnings’ function contains years of education as an explanatory variable. Since the focus here is on those who have at least a bachelor's degree and the JPSC does not provide the number of years each individual spent at university, years of schooling have been omitted.
For the JPSC, we use time trend and time trend square instead of year dummy. Moreover, birth cohort dummies are replaced with 5-year birth cohort dummies for the JPSC and WPS data. This is because unemployment rate 1 year prior to graduation from the undergraduate program has small variations within the same cohort, and multi-collinearity with year and cohort dummies is suspected.
To control for the difference in work hours, we included dummy variables of hours worked category for regression with the JPSC, hours of work, and part-time dummy for regression with the WPS and PIAAC data.
The number of women with a master’s degree and men with a master’s degree in humanities and social sciences is very small, and the major at graduate schools is unavailable for the JPSC data. Thus, the postgraduate wage premium is not estimated separately for each major and type of university.
The bias due to self-selection into employment is not a serious problem for men, because most of them work full-time. Hence, the Heckman two-step estimation is applied only to women. The Heckman two-step estimation is not applied to regressions with the WPS data, because the WPS sample contains only workers.
The JPSC data contain information on each household member’s age. Thus, the number of children by age category is used for the regression with the JPSC data. For the regression with the PIAAC data, the total number of children and the age of the youngest child are used, because the age of all children is not available.
Since it is unusual to obtain a master’s degree in Japan after working full-time, the master’s dummy \(G_{i}\) is constant over time for most individuals. Therefore, it is impossible to eliminate the effect of unobserved ability by including individual fixed effects in the model.
The increase in graduate enrollment from 1990 to 1993 may not (only) reflect adverse labor market conditions, since it occurred in tandem with an expansion in graduate enrollment capacity. Moreover, graduate enrollment during this period increased not only in the natural sciences (including engineering), but also in the humanities and social sciences, which differs from the pattern observed for 1973 and 2010.
If a worker with lower employability tends to enroll in a graduate school to avoid finding jobs during the recession, the postgraduate premium will be biased downward. Since our primary concern is the over-estimation of the postgraduate wage premium, the bias due to employability is not considered a serious problem.
Since the WPS data contain only worker samples, we skip the Heckman two-step estimation.
The estimation results by the Heckman’s two-step method are not presented, because the inverse Mill’s ratio is not significant. This is presumably because the PIAAC data do not provide the number of children by age category. Marital status and age category of the youngest child are included in the selection equation, but they are not sufficient for the exclusion restriction to hold.
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Acknowledgements
I am grateful to Masahiro Hori, Daiji Kawaguchi, and Izumi Yamasaki for their helpful advice. I would like to thank the members of the research group at ESRI and the participants of the Presentation Workshop for Young Economists at Osaka University, 2016 Spring Meeting of the Japanese Economic Association, and the ESRI Seminar for their comments and suggestions. I also thank the Institute for Research on Household Economics for permitting me to use the longitudinal data from the Japanese Panel Survey of Consumers. The data of Working Person Survey, conducted by the Recruit Works Institute, were provided by the Social Science Japan Data Archive, Center for Social Research and Data Archives, Institute of Social Science, The University of Tokyo. The PIAAC data are provided by the National Institute for Educational Policy Research (NIER). I thank Ralph Paprzycki and Editage for their English editing services. All errors that remain are mine.
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Suga, F. The returns to postgraduate education in Japan. JER 71, 571–596 (2020). https://doi.org/10.1007/s42973-019-00014-x
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DOI: https://doi.org/10.1007/s42973-019-00014-x