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Divorce and the duality of marital payoff

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Abstract

Empirical studies on the determinants of divorce are scarce in economics. The literature has focused on the impact of income differentials between partners. We extend the model of marital dissolution to integrate time-varying non-pecuniary quality of the match. We use a unique Russian dataset to measure shocks to the non-economic components of the value of marriage. Our estimates suggest that the monetary and non-monetary components enter additively into marriage surplus, but with gender-specific marginal rates of substitution: divorce hazard is more sensitive to the non-pecuniary dissatisfaction of the wife than to that of the husband; impacts of the monetary components are also gender-specific and highly non-linear. We link these findings to remarriage prospects and partial risk sharing.

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Notes

  1. The selection hypothesis implies that happier people are more outgoing and are more successful in finding a good match, while unhappy people are more likely to have trouble finding a stable mate. The social role hypothesis implies that divorced individuals are less likely to have social networks and more likely to have financial difficulties. Both hypotheses were rejected based on the instability in the differences in spouses’ happiness levels and short-term correlations in levels of happiness and marriage or divorce occurrences.

  2. See Becker (1991) for an early survey, and Browning et al. (2014) for a more recent presentation.

  3. See Chiappori and Mazzocco (forthcoming) for a survey.

  4. There are also child and community-level questionnaires. The latter provides information on region-specific prices and community infrastructure.

  5. Individual evaluations are again given on a scale ranging from 1, the highest level of satisfaction, to 5.

  6. Unfortunately, this is the only round of the survey designed to report these aspects.

  7. We attempted to estimate more complex wage equations with highly persistent shock (modeled by a random walk) or serially correlated temporary shocks. Unfortunately, the panel dimension does not seem to be strong enough to identify such structures.

  8. They can affect the divorce hazard via the probability of singlehood. However, the wage rate increased over the whole period, and the time dummies are likely to also reflect the effects of inflation despite the CPI used to adjust the nominal wages.

  9. The within transformation implied by the fixed effect approach would yield demeaned match quality which would distort between and overall variation of the match quality variable because the panel data in hand are strongly unbalanced. It would therefore mislead the estimates of the divorce model described below.

  10. Unfortunately, most rounds of the survey do not provide data on marriage duration. This makes it impossible to use a duration model of the divorce hazard. However, the duration of marriage is highly correlated with age, which we include in X ti (estimation based on the few rounds that have information on marriage duration show that the correlation with age is 0.9).

  11. The specification using the tail terms is motivated by our analysis of the distributions of the wage components obtained from (1), which we discuss in section 6.

  12. The 10th and 90th percentiles of the log-wage shock distributions are roughly −0.5 and 0.5 for both gender groups, so that our “strong shocks” are larger than approximately 50% in absolute value.

  13. The equations are estimated using a sample of working individuals which is considerably larger than our main sample based on a selection of the rounds providing the necessary information. Using this extended sample allows us to obtain precise and efficient estimates of wage determinants while including a rich set of fixed effects.

  14. The return to human capital is stable over the observational window. The parsimonious model reported excludes interactions between human capital variables and time. A weak association between wages and education in Russia is quite a stylized fact (Cheidvasser and Benítez-Silva, 2007), it relates to (1) oversupply of highly educated workers in Russia since the transition period and (2) USSR inheritance of wage equalization with no or very slight differentiation based on education.

  15. The corresponding specification is not reported but is available on request.

  16. For the sake of estimation efficiency, we also excluded housing price changes from the baseline model. The housing prices are frequently associated with the cost of divorce. However, the coefficients associated have been found statistically insignificant. On the one hand, it is likely to be a statistical result: house prices are self-reported starting with round 9 (2004) implying more than 30% loss of observations. The sample reduction concerns in particular transitions from marital to divorce status and leads to high efficiency loss of the estimation. On the other hand, the insignificance is consistent with housing markets failures in Russia documented by Zavisca (2012) who qualifies the Russian housing regime as “property without markets, in which housing is privately owned but not fully commodified”. Housing ownership relates to privatization process of 1990–2000 s; exchange institutions and mortgage markets fail due to very high interest rates and housing prices relative to earnings, high level of economic instability and uncertainty, and strong population distrust in regards to financial institutions. Housing wealth remains a “frozen asset” for majority of the population; housing mobility is very limited in the 2000s and is mainly driven by inheritances and infra-family wealth transfers (Zavisca 2012). In this context, changes of housing economic value, prices, do not yet immediately translate into the cost of divorce unlike economies where housing mobility is driven by housing market conditions.

  17. Excluded from the parsimonious estimation reported in Table 4.

  18. Recall that as defined in (5), our shock variables are algebraic: a large negative value denotes a large negative shock rather than its absolute value.

  19. We should note here that Weiss and Willis measure “predicted earnings” somewhat differently.

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Authors and Affiliations

  1. Department of Economics, Columbia University, 1009 International Affairs Building 420, West 118th St., New York, NY, 10027, USA

    Pierre–André Chiappori

  2. Department of Economics, American University, 4400 Massachusetts Avenue NW, Washington, DC, 20016, United States

    Natalia Radchenko

  3. Department of Economics, Columbia University, 1022 International Affairs Building 420, W 118th Street, New York, NY, 10027, USA

    Bernard Salanié

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  1. Pierre–André Chiappori
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  2. Natalia Radchenko
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  3. Bernard Salanié
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Correspondence to Natalia Radchenko.

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Appendix

Appendix

Table 5

Table 5 Descriptive statistics on the key variables
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Chiappori, P., Radchenko, N. & Salanié, B. Divorce and the duality of marital payoff. Rev Econ Household 16, 833–858 (2018). https://doi.org/10.1007/s11150-017-9382-0

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