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Intergenerational earnings mobility in Japan among sons and daughters: levels and trends

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Abstract

This paper estimates the extent of intergenerational income mobility in Japan among sons and daughters born between 1935 and 1975. Our estimates rely on a two-sample instrumental variables approach using representative data from the Japanese Social Stratification and Mobility surveys, collected between 1965 and 2005. Father’s income is predicted on the basis of a rich set of variables, and we discuss changes in the Japanese earnings structure for cohorts born between the early 1900s and the 1960s. Our main results indicate that the intergenerational income elasticity (IGE) for both sons and daughters in Japan lies around 0.35, which is an intermediate value, by international standards. We discuss the sensitivity of the IGE to using either personal or family income as the income variable for both fathers and children. We also examine changes across cohorts in the IGE. Results indicate that intergenerational mobility has been roughly stable over the last decades.

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Notes

  1. Whether Japan still is, in the most recent period, an “equal” society seems highly debatable, as discussed in Tachibanaki (2009).

  2. The relevance of this view was challenged with great force in general-audience debates by the book of Sato (2000) who made the (controversial) claim that the Japanese society had been closing down after the bubble-growth era of the 1990s.

  3. A third paper is that of Yoshida (2008) but it is only available in Japanese.

  4. An alternative approach is to estimate siblings correlation (e.g., Björklund et al. 2002).

  5. For general discussions and surveys of the causal mechanisms underlying the intergenerational transmission of inequality, see in particular Bowles and Gintis (2002) and Black and Devereux (2010). For recent analysis, see for instance Anger and Heineck (2010) and Liu and Zeng (2009).

  6. The classical measurement error case refer to the situation where measurement error is independent of the true value.

  7. Since their study is based on US data, one should of course question the relevance of their results in the Japanese context. In the absence of direct replication of their study using Japanese data, this question cannot be addressed directly.

  8. In principle, the use of polynomial function for age would allow to simultaneously include time and cohort dummies. Cohort dummies, however, turn out to be insignificant when added to this specification, and their inclusion does not affect the results.

  9. A larger set of interaction terms could be introduced, such as occupation–age interactions. This is done in Section 6. In practice, they turned out to be nonsignificant.

  10. In most specifications, the characteristics whose effects are allowed to vary across cohorts are age, education, and self-employment status. We consider other variables in Section 6.

  11. Lefranc et al. (2008) uses interval regression to deal with the bracketed form of income and show that the impact on the estimated IGE is negligible.

  12. Among others, Katz and Revenga (1989) and Kambayashi et al. (2008) analyze the Japanese earnings structure and its evolution using BSWS data.

  13. Our analysis relies on the coding developed by Shirakawa in the SSM research group.

  14. The reason for not using earlier waves of the SSM for the second-step is that women are only interviewed starting in 1985. Furthermore, some information on family background is missing or not recoded homogenously before 1985.

  15. Since our estimation does not control for job seniority, one may argue that firm-size effects partly reflect differences in seniority by firm size. However, the controversy on the extent of returns to job seniority has also suggested that seniority effects might themselves partly arise from firm fixed effects (see in particular Topel 1991; Altonji and Williams 2005). Hashimoto and Raisian (1985) discuss seniority distribution and its effect on earnings in Japan.

  16. Tokyo, Yokohama, Osaka, Nagoya, Kyoto, and Kobe.

  17. In the case of the piecewise linear and stepwise functions, the inflection is of course constrained a priori by the parametric hypothesis, but this is not the case for the quadratic function.

  18. Other evidence on the incidence of the measure of family economic status in the USA can be found in Altonji and Dunn (1991) and Mulligan (1999). Our findings of a smaller gap between the IGEs find using both variables may be partly attributable to the fact that our measure of father’s income, contrary to earnings measures used in the US, already incorporates the asset earnings of the father.

  19. Earnings are only measured in continuous form for single daughters still living with their parents.

  20. According to the 2005 census, 30 % of men between 30 and 59 years old and 25 % of women are single.

  21. As already discussed, classical measurement error also introduce an attenuation bias. TSIV estimation corrects for this attenuation bias but is still subject to the life-cycle bias.

  22. Lee and Solon (2009) suggest including the interaction between child’s age and father’s income, in an OLS context. A further issue arises in our case from the fact that we are implementing a TSIV estimator, i.e., using predicted father’s permanent income instead of the true value (or a good proxy of it, as provided by multi-year averages). As discussed in Nybom and Stuhler (2011), the results of Haider and Solon (2006) (henceforth HS) carry over to the TSIV context. Under the assumptions of HS, predicted father’s income at age 40 will consistently estimate lifetime permanent income differentials among fathers. Substituting father’s predicted income for father’s lifetime income will still be distorted by the presence of nonclassical measurement error in the right-hand side variable compared to the TSIV estimate one would obtain in the absence of measurement error in the RHS. However, correcting for life-cycle bias can also be undertaken in this context using the rule of thumb of HS, or using the parametric interaction term of Lee and Solon, provided that the age-profile of the life-cycle bias can be proxied by a parametric functional taking value zero at age 40.

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Authors and Affiliations

  1. Université de Cergy Pontoise, THEMA, 33 boulevard du Port, 95011, Cergy Pontoise, France

    Arnaud Lefranc

  2. IZA, Bonn, Germany

    Arnaud Lefranc

  3. Department of Sociology, Doshisha University, Shinmachi-dori Imadegawa Agaru, Kamigyo-ku, Kyoto, 602-8580, Japan

    Fumiaki Ojima

  4. Faculty of Humanities and Social Sciences, Shizuoka University, 836 Ohya, Suruga-ku, Shizuoka, 422-8529, Japan

    Takashi Yoshida

Authors
  1. Arnaud Lefranc
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  2. Fumiaki Ojima
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  3. Takashi Yoshida
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Correspondence to Arnaud Lefranc.

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Responsible editor: Erdal Tekin

We thank Jaap Dronkers, Nobuo Kanomata, Tadashi Yagi, seminar participants at Keio University and Doshisha University and one anonymous referee for useful comments. Access to the SSM data was granted through the SSM 2005 Research Committee, whose support is gratefully acknowledged. This research received financial support from the French National Research Agency, under the grant TRANSINEQ (ANR-08-JCJC-0098-01) and from the Japanese Society for the Promotion of Science (Grant-in-Aid for Scientific Research #22330161).

Appendix: Early fathers cohorts

Appendix: Early fathers cohorts

Given the age restrictions we impose and the survey years available, our oldest cohort of children was born in 1935 and our oldest cohort of fathers was born in 1905. The tabulation of father’s birth cohort by child’s birth cohort, in Table 11 below, indicates that for the oldest child cohorts, a significant fraction of the fathers were born before 1905. This raises the question of how their income should be predicted given the limited information exploited in the first-step equation.

Table 11 Father’s birth cohort by child’s birth cohort
Full size table
Table 12 IGE estimates for various treatments of fathers born before 1905
Full size table

We considered four options. The first, which we use in the rest of the paper, was to assume that the earnings equation estimated for the 1905 cohort was also valid for the cohorts born before 1905. The second one amounts to retropolate the cohort trends in the earnings structure estimated after 1905. The third one was to drop all individuals whose father was born before 1905. The fourth one is to exclude all child’s cohorts in which a significant share of the fathers was born before 1905.

All four procedures produce very similar results, as shown in the table below, in the case of the piecewise linear trends specification.

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Lefranc, A., Ojima, F. & Yoshida, T. Intergenerational earnings mobility in Japan among sons and daughters: levels and trends. J Popul Econ 27, 91–134 (2014). https://doi.org/10.1007/s00148-012-0464-2

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