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Gender norms and women’s decision to work: evidence from Japan

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Abstract

Using individual-level data from the National Family Research of Japan Survey (1999, 2004 and 2009) and exploiting variation in the share of individuals with non-traditional gender norms across birth-cohorts, survey year, education, and prefecture, we find that an increase in the share of individuals with non-traditional beliefs by one standard deviation is associated with an increase in Japanese women’s decision to work by 0.016 percentage points, the equivalent of an increase of 3.4% standard deviation. Our measure of non-traditional gender norms is the share of women who disagree with the statement “men should work outside and women should look after the family”. As we conduct a battery of sensitivity analyses and placebo tests, our findings suggest an impact of non-traditional norms on Japanese women’s decision to work full-time.

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Notes

  1. For instance, the Prime-Minister’s office announced in 2016 several measures to promote female labor force participation.

  2. Source OECD 2017— https://stats.oecd.org/Index.aspx?DataSetCode=LFS_SEXAGE_I_R, and Japanese Population Census.

  3. Data from Census tabulations by Abe (2016) shown in Tables 1A and 1B.

  4. See Angrist and Lang (2004); Friesen and Krauth (2007); Hanushek et al. (2003); Hoxby (2000); Lavy and Schlosser (2011); Lavy, Paserman and Schlosser (2012); Bifulco, Fletcher and Ross (2011); and Olivetti, Patacchini, and Zenou (2020).

  5. Fortin’s 2005 analysis uses both individual- and aggregate-level data to measure the outcome variable, namely women’s decision to work and female labor force participation, respectively. In another paper, Fortin (2015) exploits 30 years of time variation and focuses only on one country, the US, to study how individuals’ own beliefs about gender roles affects women’s decision to work. She addresses the endogeneity concerns using an instrumental variable approach.

  6. Antecol (2000) conducts a similar analysis using first-generation immigrants instead of second-generation immigrants.

  7. To do so, the authors, first, estimate for each married women a distribution of the wife’s potential earnings by assigning her a random draw of the population of working women in her demographic group. The distribution is based on 19 percentiles as the authors estimate it based on every 5th percentile from the 5th to the 95th. Thereafter, using this distribution of potential earnings, they define the likelihood that each married women earns more than her husband with the following formula: \(\frac{1}{{19}}\mathop {\sum}\nolimits_p {1_{\left\{ {w_i^p > husbandIncome_i} \right\}}}\).

  8. Rodríguez-Planas, Sanz-de-Galdeano and Terskaya (2018) do not find an effect on female labor force participation.

  9. In the same spirit, Fernández et al. (2004) find that US husbands raised by working mothers were more likely to be married to full-time working wives suggesting that husbands’ gender stereotypes affect their wives’ labor force participation.

  10. However, using the Social Stratification and Social Mobility Survey, Shirahase (2005) finds that a mother’s labor force participation affects neither her sons’ nor her daughters’ opinion regarding gender roles.

  11. The signs are consistent with Fernández et al. (2004) but not statistically significant.

  12. As we have 5-year cohort dummies, the age variable controls for age variation within cohorts.

  13. Specifications that control for presence of small children and grandparents in the household, as well as household income are restricted to married women.

  14. The wage ratios are estimated at the industry/cohort/survey year/prefecture level. Their construction is explained with detail in the next section.

  15. While the Northern Coast of Japan and the prefecture of Chubu have a large share of female workers in manufacturing (25% and 24%, respectively, versus 25% and 34% of male workers); the prefecture of Kansai only has 19% of female workers in manufacturing (versus 26% of male workers) with female workers concentrating in the service sector (36% of female workers versus 21% of male workers). Not surprisingly, the service sector is also strong in Tokyo with a large share of both male (30%) and female (42%) workers employed in it.

  16. Our results are robust to using 1981 weights instead. Results are available from authors upon request.

  17. For example, the maximum shares of workers in prefecture-cohort-gender cell in 1995 are 0.64 percent for 25-29 years old female working in service sector in Tokyo prefecture, and 0.53 percent for 25-29 years old male working in the same sector and prefecture.

  18. In 1995, the standard deviation of the share of workers in service sector across prefecture and cohort is 2.7 percent for male and 2.8 percent for female. The coefficients of variation are 14 percent for male and 18 percent for female.

  19. This is calculated as: \({{\alpha _2 \ast {{Non}\,{Traditional}\,{Beliefs}_{StDev}}}{{Y_{StDev}}}} = {{0.0827 \ast 0.1986}}{{0.4771}} = {{0.0164}}\)

  20. This is calculated as \(\frac{{\alpha _2\, \ast \,{{Non}\,{Traditional}\,{Beliefs}_{StDev}}}{{Y_{StDev}}}} = \frac{{0.0827\, \ast \,0.1986}}{{0.4771}} = \frac{{0.0164}}{{0.4771}} = 0.0344\)

  21. This is calculated as \(\frac{{\alpha _2\, \ast \,WAGERATIO_{StDev}}}{{Y_{StDev}}} = \frac{{0.3340\, \ast \,0.105}}{{0.4771}} = \frac{{0.0351}}{{0.4771}} = 0.0736\)

  22. Olivetti, Patacchini and Zenou (2020) find that “one standard deviation increase in the average number of hours worked by mothers’ of the students in the same school and same cohort translates into an additional 1/20th of a standard deviation in women’s weekly hours worked in their late twenties.” Fernández (2007) finds that an increase of one standard deviation in the FLFP of parents’ source country was associated with an increase of 8% standard deviation in second-generation immigrant women’s hours worked in the US.

  23. Unfortunately, information on whether they lived in a three-generation household or whether their child attends childcare is not available in the National Family Research of Japan Survey.

  24. This is calculated as \(\frac{{( {0.0121\, \ast \,5.2} )}}{3} = 0.021\)

  25. The female labor force participation estimates are calculated using our sample and hence differ slightly from those mentioned in the Introduction that use the Japanese Population Census.

  26. This is calculated as \(\frac{{( {0.5022\, \ast \,2.59})}}{3} = 0.4336\)

  27. We replicated the analysis using the share of women in part-time work as the left-hand-side variable (results available from the authors upon request), and found that none of the coefficients on the non-traditional gender norms variable are statistically significantly different from zero. As reported in the white paper by the Ministry of Health, Labor and Welfare (1998), in Japan, female part-time work has been very much socially accepted since late 1990s. This would be consistent with our finding of no effect of our non-traditional gender norms variable on part-time. The lack of results on the decision to work part-time implies that earlier findings on the decision to work are driven by full-time work.

  28. In contrast with non-regular employment, regular employment in Japan is full-time and allows workers to progress within the firm, have salary promotions, job benefits, and job security until retirement.

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Acknowledgements

This study has benefitted from excellent suggestions and feedback from co-editor Sonia Oreffice and two anonymous referees. We would like to thank Yukiko Abe, Hideo Owan, Daiji Kawaguchi, Ayako Kondo, Shintaro Yamaguchi, and Kazufumi Yugami for comments that helped us improve the paper, as well as comments from participants at the 2nd Society of Economics of Household in Paris, and seminar participants Osaka University and Hokkaido University. Ryuichi Tanaka acknowledges financial support from Japan Society for the Promotion of Science through the Grant-in-Aid for Scientific Research (JSPS KAKENHI Grant Numbers JP24330077).

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  1. Economics Department, City University of New York (CUNY), Queens College, Powdermaker Hall, 65-30 Kissena Blvd., Queens, New York, NY, 11367, USA

    Núria Rodríguez-Planas

  2. Institute of Social Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Ryuichi Tanaka

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  1. Núria Rodríguez-Planas
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Table 7

Table 7 Determinants of women’s decision to work in details
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Rodríguez-Planas, N., Tanaka, R. Gender norms and women’s decision to work: evidence from Japan. Rev Econ Household 20, 15–36 (2022). https://doi.org/10.1007/s11150-021-09543-0

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