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Regular employment and intergenerational income mobility in Japan

Abstract

Using 1995 and 2005 Survey of Social Stratification and Mobility (SSM), this paper analyzes the effect of being regularly employed on intergenerational income mobility (IGM) from fathers to sons in Japan. Using Two Sample Two Stage Least Square (TS2SLS) method by which fathers’ income is imputed by their characteristics since it is unobservable, intergenerational income elasticity (IGE) between fathers and sons who are employees is estimated, and IGE is decomposed following a well-established framework. Focusing on sons born from 1935 to 1976, this paper finds that IGE increased from 1995 to 2005. The contribution rate of whether being regular employees in current job or not to IGE increased from less than 2% in 1995 to over 9% in 2005, and the increasing in IGE explained by regular employment status accounts for around 30% of increasing in IGE between father and sons who are employees from 1995 to 2005. The dominating reason for this pattern is that it had become difficult for sons from less prosperous families to find regular positions after the collapse of traditional employment structure since late 1990s. This study indicates that “how are people employed” had impeded equality of opportunities for youth and had exacerbated intergenerational transmission of income inequality in Japan.

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Fig. 1

Availability of data and code

Data and code are available upon request.

Notes

  1. 1.

    For instance, an IGE of 0.3 indicates that if parental income is changed by 1%, then children’s income is changed by 0.3%. In other words, 30% of income inequality in parents’ generation is transmitted to children’s generation. \(dIGM/dIGE<0\).

  2. 2.

    Besides these three factors, Gong et al. (2012)’s study in urban China considered industry and party membership. Bevis and Barrett (2015)’s study in rural Philippines considered land. Kubota (2017)’s Japanese study on intergenerational transmission of wealth considered family income, bequest, risk tolerance, time preference and altruism.

  3. 3.

    Besides lower income, irregular staffs also suffer from less job securities and employee benefits. Section 2 provides details.

  4. 4.

    Blanden et al. (2014) studied U.S. and Great Britain and considered education as well as labor market attachment, marital status, health and social classes at different ages.

  5. 5.

    TS2SLS is asymptotically the same as 2SLS. Its statistical properties were reviewed in Pacini and Windmeijer (2016).

  6. 6.

    Ueda (2009) used Japanese Panel Survey of Consumers (JPSC) in which parents’ income is observable. However, parental income in JPSC is too poorly measured to be used when estimating IGE (See Lefranc et al. (2014) for detailed discussion), which leads Ueda (2009) to rely on 2SLS and TS2SLS to impute parental income in most of its specifications. Kubota (2017) used Parent and Children Survey (PCS) in which both children’s wealth and parents’ wealth are observable. However, in most of its specifications, sample size is smaller than 300. In addition, wealth is not a commonly used measurement of economic status in IGM studies. Kubota (2017) also reported 2SLS results.

  7. 7.

    Symmetrically, if children’s (parental) income is observed in elder (younger) ages, then \(\beta\) will be overestimated. However, this is less likely to happen in empirical IGM studies.

  8. 8.

    The definition of pseudo father is introduced in Sect. 1.

  9. 9.

    Chu and Lin (2020) used fathers’ education, occupation and industry to impute, in which occupation and industry refer to those when sons were 15 years old. Therefore they could calculate the exact years when sons were 15 and extracted auxiliary sample from surveys conducted around those years. In this study, fathers’ occupation and other job characteristics refer to those in their main job, therefore Chu and Lin (2020)’s accurate approach cannot be applied.

  10. 10.

    Empirically, the direction of this bias is always positive. Lefgren et al. (2012)’s model pointed out that such inconsistency is not bias. In fact, IV/2SLS/Two Sample IV (TSIV)/TS2SLS estimators which are larger than OLS estimator capture multi-mechanisms through which income inequality is transmitted over generations. Section 6.1 discusses this issue in detail.

  11. 11.

    Although it is commonly believed that \(\widehat{\beta }\) becomes much higher when parental income is imputed based only on education, it is not the case in Japanese data. Section 6.3 presents relevant results.

  12. 12.

    The latest wave, 2015’s survey, has not been released yet. Females are not included until the fourth survey (1985).

  13. 13.

    The author thank Yoichi Murase and Takunori Terasawa for providing the code converting original 3 digit occupation data to these eight categories.

  14. 14.

    Self-employed sons, sons who are executives, and sons who have no jobs are dopped.

  15. 15.

    Besides, Kubota (2017)’s 2SLS results showed intergenerational wealth elasticity between parents and children is about 0.35.

  16. 16.

    Results based on \(\widehat{\beta }\) in Columns (1) of Table 4 are similar. These results are available upon request.

  17. 17.

    Although their studies focused on the difference between the estimate of IGE (\(\widehat{\beta }\)) by OLS and those by IV, 2SLS, TSIV and TS2SLS, the estimates of effects of parental income on pathway variables (\(\widehat{\gamma }\)’s) follow the same intuition.

  18. 18.

    Lefranc (2018)’s French study using TS2SLS found education only explains 30–50% of IGE between fathers and sons. Education is the only pathway factor in Lefranc (2018).

  19. 19.

    The test here and similar tests below are based on Seemingly Unrelated Regression (SUR). \({\chi }^{2}\left(1\right)\) statistic here is 2.90.

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Acknowledgements

I would like to thank Midori Wakabayashi, Michio Suzuki and Akira Hibiki for their help. I would also like to thank the editor and the anonymous reviewer. This paper was presented at the 35th Conference of Eurasia Business and Economics Society (EBES), and was awarded Best Paper Award. I am greatly indebted to the conference committee and all the participants of the conference. I am particularly grateful to the discussant of my session, Ilker Kaya, for his insightful comments. I would also like to thank Social Science Japan Data Archive (SSJDA) for providing the data.

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This research is not funded (it is self-funded).

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Correspondence to Zhi-xiao Jia.

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Appendix: First stage results

Appendix: First stage results

Dependent variable: log(pseudo fathers’ annual income) (\({I}_{a}\))
Upper secondary 0.228***
(0.038)
Upper secondary × age − 0.002
(0.004)
Upper secondary × age2 0.001
(0.001)
Tertiary 0.292***
(0.051)
Tertiary × age − 0.010
(0.007)
Tertiary × age2 0.002**
(0.001)
Professional 0.490***
(0.088)
Professional × age 0.020
(0.014)
Professional × age2 − 0.002
(0.001)
Managerial 0.774***
(0.082)
Managerial × age 0.019*
(0.011)
Managerial × age2 − 0.002
(0.001)
Clerical 0.416***
(0.080)
Clerical × age 0.005
(0.010)
Clerical × age2 − 0.001
(0.001)
Sales 0.290***
(0.073)
Sales × age 0.003
(0.009)
Sales × age2 0.001
(0.001)
Manual 0.266***
(0.067)
Manual × age − 0.005
(0.008)
Manual × age2  < 0.0005
(0.001)
Self-employment 0.186***
(0.053)
Self-employment × age 0.004
(0.006)
Self-employment × age2 − 0.002***
(0.001)
Big firms 0.244***
(0.032)
Big firms × age 0.010***
(0.003)
BIG firms × age2 − 0.001*
(< 0.0005)
Public sector 0.025
(0.040)
Public sector × age 0.013***
(0.005)
Public sector × age2  < 0.0005
(0.001)
   Age 0.007
(0.008)
Age2  < 0.0005
(0.001)
n = 2893, R2 = 0.64, F = 173.12***
  1. Constant term and year dummy are controlled, and year dummy = 1 if pseudo fathers are from 1965’s survey. Robust standard deviations are in parentheses
  2. ***P < 0.01, **P< 0.05, *P < 0.1

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Jia, Zx. Regular employment and intergenerational income mobility in Japan. Eurasian Econ Rev (2021). https://doi.org/10.1007/s40822-021-00186-1

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Keywords

  • Decomposition
  • Intergenerational income elasticity (IGE)
  • Intergenerational income mobility (IGM)
  • Japan
  • Regular employment
  • Two Sample Two Stage Least Square (TS2SLS)

JEL Classifications

  • D31
  • J62