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The asymmetric housing wealth effect on childbirth


The literature has shown that an increase in housing wealth, driven by unexpected shocks to house prices, exerts a positive effect on the birthrates of homeowners. According to canonical models, a decrease in housing wealth has a symmetric negative impact on the fertility behavior of households. That is, housing gains and losses of the same size have identical quantitative effects on fertility. In comparison, the reference-dependent preferences in prospect theory suggest that people care more about housing losses than equivalent gains, leading to an asymmetric effect of housing wealth on the fertility decision. We propose a theoretical model where household utility depends on both childbirth and housing wealth. In this model, the utility from housing wealth is reference dependent in the sense that we assume the housing value in the previous year is the reference. The theoretical model suggests that a decrease in housing wealth would have a larger impact on the probability of childbirth than an equivalent increase. We test this theoretical prediction using longitudinal data on Japanese households. Consistent with the theoretical prediction, our empirical results show that the fertility responses of homeowners are substantially larger for housing losses than equivalent gains.

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  1. According to the 2013 Housing and Land Survey, the homeownership rate of Japanese aged 25–29 years was 11.3%, while that of those in their 30s and 40s was 38.6 and 59.2%, respectively.

  2. Paiella (2009) provides a detailed review of this literature.

  3. Bracing for demographic change: Japan’s fertility rate headed for long-term decline. Nikkei Asian Review, June 6, 2015. (accessed June 12, 2015).

  4. Related to our focus in this analysis, Lovenheim and Mumford (2013) provided some evidence that the effects of housing booms and busts are not symmetric. However, as their data do not cover the housing bust period, the sample size was not sufficiently large to examine the asymmetric effects directly. As a result, they suggested that more work on the effect of the housing bust on fertility was needed. Dettling and Kearney (2014), using data from the housing bust period, showed that the empirical results from this period were qualitatively similar to the housing boom period, although a detailed, quantitative comparison was not made.

  5. In addition, there were random refreshment samples of approximately 1400 and 1000 new respondents in 2007 and 2012, respectively.

  6. Women’s employment careers are likely to be interrupted by childbirth and infant care, leading to a typical reverse-causality problem. Therefore, for employment status, we specify our dummy variables according to the status prior to the childbirth. In addition, we do not use total household income as it includes mother’s income. Nevertheless, even using total household income does not change our main empirical results.

  7. The JHPS categorizes a respondent’s location of residence across seven regions (Hokkaido, Tohoku, Kanto, Chubu, Kinki, Chugoku/Shikoku, and Kyushu) and three city sizes (20 major cities, other smaller cities, and towns/villages).

  8. There are few homeowners that reported extremely large fluctuations in their home values. Including these observations introduces measurement error in our key explanatory variable, which could bias our estimates. We therefore check the robustness of our results by excluding these outliers from our regression sample, the results for which do not change our primary conclusion.

  9. Excluding these households from our estimation sample does not fundamentally change our results. See the discussion in Section 3.4.

  10. Given the panel structure of our dataset, we also apply linear probability models with individual fixed effects. This allows us to eliminate any time-constant heterogeneity at the individual household level. Furthermore, we also analyze decisions on childbirth using a duration model allowing for the heterogeneity of the baseline hazard. These alternative modeling assumptions do not change our main empirical findings.

  11. In the estimation, we classify female respondents into four groups based on their birth year (born in and before 1964, born between 1965 and 1969, born between 1970 and 1974, and born in and after 1975) and into seven groups based on the year in which a married woman moved into her current residence (in and before 1979, between 1980 and 1984, between 1985 and 1989, between 1990 and 1994, between 1995 and 1999, between 2000 and 2004, and in and after 2005).

  12. Table 8 in the Appendix presents the marginal effects for the regression controls omitted from Table 3.

  13. Because households can purchase an expensive house by taking out a large mortgage, contemporaneous housing price levels may not appropriately measure real household resources. In fact, we found that the level of housing value has no significant impact on fertility.

  14. As shown in Table 2, women experiencing housing gains are significantly older than those suffering losses. To see whether this would bias our estimates, we estimated an alternative model that better controls for the age effects, including quadratic age terms or age dummies. These alternative specifications, however, did not change our main results.

  15. The interpretation here is also supported by our additional empirical results. Following Lovenheim and Mumford (2013), we also estimate the model including both the one-year housing wealth changes, and the lagged two-year changes, (W ir,t−1 − W ir,t−3)/W ir,t−3. The results indicate that long-term gains and losses have a significant impact on fertility. However, even after controlling for the longer-term effects, the impact of housing wealth changes appears to be strongest for short-term losses. In addition, we still find that there is an asymmetry in the impact of short-term gains and losses.

  16. There are few households that experienced repeated births during our sample period. We estimated the benchmark model excluding households with multiple births, without showing any significant changes in the main findings.

  17. Marginal effects for housing gains and losses are evaluated at the point where W ir t  = W ir,t−1 i.e., the reference wealth level.


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We would like to thank two anonymous reviewers, Mototsugu Fukushige, Hassan Gholipour Fereidouni, Robert Jahoda and Yuko Nozaki, seminar participants at Gakushuin University, Kanagawa University and the National Institute of Population and Social Security Research, and conference attendees at ARSC, AsRES, ENHR, ERES and JEA for their valuable comments and suggestions. We are also grateful to the Panel Data Research Center at Keio University for access to its microdata. This research was supported by JSPS KAKENHI Grant Numbers JP25380288 (Iwata) and JP25870238 (Naoi), and by Seimei-Kai (Naoi).

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Correspondence to Shinichiro Iwata.

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See Table 8.

Table 8 Benchmark results for logit estimates (Table 3 continued)

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Iwata, S., Naoi, M. The asymmetric housing wealth effect on childbirth. Rev Econ Household 15, 1373–1397 (2017).

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  • Childbirth
  • Housing prices
  • Wealth
  • Homeownership
  • Reference-dependent preferences
  • Loss aversion

JEL classification

  • D11
  • D12
  • J13
  • R21