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The changing intra-household resource allocation in Russia


During the transition period, Russian workers witnessed important changes in their real earnings. In the process, the wage gap between men and women has varied wildly and the family decision-making process may have been significantly altered. To investigate this issue, we estimate a collective labour supply model using data from the RLMS. The specification allows the sharing rule to change in a discrete manner between the pre- and post-1998 financial crisis. Our results indicate that the parameters of the sharing-rule have shifted to a new equilibrium in the post-1998 period. Indeed, when their relative wage increases, husbands (wives) transfer relatively less (more) to their spouse than was previously the case.

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Fig. 1


  1. Gerry et al. (2004) provide evidence that the wage gap is unevenly distributed, with women at the lower end of the distribution suffering most.

  2. Recently, Vermeulen et al. (2005, 2008) have used consumption data from the RLMS to test the collective model using non-parametric tests. Their results indicate that the collective model is compatible with the data.

  3. Information on the RLMS can be found at

  4. The period 1994–2004 was plagued by very high inflation. During the transition phase, sellers would post prices in “units” that needed to be translated into roubles using the rouble/US dollar exchange rate. We thus convert the wage rates into US$ using the official exchange rates (see Goskomstat Rossii 2005).

  5. Glinskaya and Mroz (2000) report very similar wage gaps for the years 1992–1995 using RMLS data. See also Gerry et al. (2004) for a detailed analysis of the gender wage gap for the years 1994–1998.

  6. The stability of the wage and participation equations has been investigated thoroughly by Radtchenko (2006) based on regressions similar to those reported in Table 2. She finds that the participation equations are stable over the 1994–1996 and 1998–2004 periods, but that the parameter estimates are distinct between the two periods. On the other hand, there does not appear to be any structural break in the wage equations of both husbands and wives.

  7. See the aforementioned papers by Vermeulen et al. (2008), who find the collective model to be consistent with RLMS consumption data.

  8. We do not allow the sharing-rule to vary yearly to avoid over-parameterising the model. Furthermore, we do not account for home production for two separate reasons. First, time-use data are no longer available as of round IX of the RLMS. Second, as shown by Donni (2008) and Chiappori (1997), if one is willing to assume that the home production is separable in time inputs of spouses, then the collective model is valid even if home production is not explicitly taken into account.

  9. The presence of elderly parents is frequent in Russian households. We acknowledge that elderly parents and grown-up children may influence the decision-making process (see, e.g. Fortin et al. 2008). We omit this possibility to keep the model tractable.

  10. We could alternatively assume that each spouse has “caring” preferences, i.e. \(W_{j} = W_{j} \left[U(h_{j},C_{j}), U(h_{k},C_{k})\right], j\ne k\). These preferences allow for interdependence of altruistic utility but impose weak separability between goods consumed by a household member and those consumed by his or her spouse. The assumption of egotistic or “caring” preferences is necessary to identify the collective model in our framework.

  11. We index the variables in the maximisation problem by t to highlight the fact that we use panel data when estimating the model. We remove them in the remainder of the section to ease reading.

  12. The specification does not include constants for the time being. They will be introduced later on through fixed effects.

  13. We index the variables with t to underline the fact that the model is estimated with panel data.

  14. Additive heterogeneity can be shown not to affect the identification of the sharing rule since additive constants are not identified.

  15. In earlier work, we estimated the model using a standard random effects model. Because over half of our sample is only observed once (1,040 out of 1,953 households), we deem it preferable to use a parsimonious discrete specification, thus avoiding turning to specific parametric distributions. Michaud and Vermeulen (2006) have recently estimated a discrete-choice collective household labour supply model in which unobserved heterogeneity is modelled in a similar fashion.

  16. The likelihood function is relatively involved. It is omitted from the paper for the sake of brevity. The interested reader may consult Lacroix and Radtchenko (2008).

  17. The P values of the null assumption that the wages (\(\ln w_{j}, (\ln w_{j})^2\)) have no effect on labour supply are the following:

    Table 4
  18. The wage equations include regional dummy variables that are absent from the hours equations. This exclusion restriction is motivated by the fact that auxiliary regressions have shown that, once we condition on wages, there is little regional variation in weekly hours of work. On the other hand, children variables are included in the hours regressions but not in the wage regressions.

  19. The low rate of return was traditionally attributed to government “wage-squeezing” policies. It was conjectured that the rate of return would increase as Russia moved towards market democracy (see Brainerd 1998). Cheidvaaser and Benitez-Silva (2007) attribute the low rate of return to education in post-communist Russia to an excess supply of well-educated workers.

  20. One could also argue the opposite: wives with inactive husbands are willing to work at lower-than-average wage rates. While plausible, this situation is more likely when husbands are involuntarily unemployed. In principle, there are no involuntarily unemployed individuals in our sample.

  21. The model is estimated for only two pairs (\(\pi_{j}^k,\lambda_{j}^k\)). The data support up to three pairs of parameters. Unfortunately, one of the pairs always has a very small probability of realisation. To avoid over-parameterising the model, we focus on the more parsimonious specification.

  22. For the sake of brevity, we do not report the parameter estimates of Σ . A more detailed discussion can be found in Lacroix and Radtchenko (2008). It suffices to mention that we find little correlation between own wages and own labour supply but that the labour supply functions of both spouses are relatively strongly correlated.

  23. Just as we did with the reduced-form parameters, we could test whether the structural parameters change between pre- and post-1998. The structural parameters all depend upon the parameter estimates of Φ(·). Unfortunately, because this parameter is only statistically significant for men in the post-1998 period, the tests are of little quantitative value.

  24. Most of these are highly non-linear functions of the structural parameters. So while few of these are individually statistically significant, it may be he case that these non-linear functions turn out to be significant once the covariance between the parameter estimates are taken into account. Furthermore, the elasticities in Table 5 are intimately related to the parameters of the sharing-rule, i.e. γ mD and γ fD .

  25. Recall that the spouse’s wage rate intervenes only through the sharing rule.


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We are grateful to Thierry Kamionka and Catherine Sofer for useful comments on a preliminary version of the paper. Comments from participants at the ESPE 2006 Meeting in Verona, Italy, from the editor and two anonymous referees are gratefully acknowledged.

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Correspondence to Natalia Radtchenko.

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Lacroix, G., Radtchenko, N. The changing intra-household resource allocation in Russia. J Popul Econ 24, 85–106 (2011).

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  • Collective model
  • Sharing rule
  • Russian economic crisis

JEL Classifications

  • D1
  • J22
  • C5