where _Yirc i_ s a dummy variable indicating if the individual _i_ (living in region _r_ of country _c_ ) owns any property (land or housing) either alone or jointly. G _irc_ is the variable indicating whether the individual is male or female and _Xirc_ is vector of individual and household characteristics. These include age, years of education, number of children, household size, and the household wealth quintile within the country. Finally, W _c_ is a vector of country-level controls. This includes data on economic development (GDP per capita), and the ratio of female to male labor force participation rates from the WDI. In addition, we explore the role of gender discrimination in the country’s legal system by controlling for the marital property regime,<sup>29</sup> whether married men and women have equal rights to owning property<sup>30</sup> and if men and women have equal inheritance rights.<sup>31</sup> The data on legal systems are obtained from the WBL database introduced in section 2.1.<sup>32</sup> The baseline model is run as an ordinary least squares (OLS), but we run an additional model with country-region fixed effects.<sup>33</sup> The fixed effects model allows controlling for time-invariant unobservable characteristics of the country-region (based on sub-national regional variable included in the DHS data, often first-tier administrative regions), but forces us to drop all other country-level variables. We also ran the regressions with country fixed effects (not reported here) and the results are qualitatively similar.

As discussed earlier, gender gaps in property are strongly influenced by the urban-rural makeup of the sample, with gender gaps generally being much larger in rural than urban areas. We therefore run the regressions separately for urban and rural areas. Each of the regressions is run on all individuals and separately for men and women, to allow for gender-specific correlations. Finally, we also investigate individual ownership of land and housing separately (results not shown but available on request). All standard errors are clustered at the country-region level.

# **4.2 Results**

The results of our analysis of what factors are correlated with women’s and men’s ownership of property are presented in Tables 2 and 3. One of the key findings is that even after controlling for various individual, household and country-level characteristics men on average are significantly more likely to own property than women. This finding confirms the results in the previous section, and is robust to changes in specifications and across rural and urban areas. Men are 6 to 15 percent more likely to own property than women. In rural areas, this disparity in the likelihood to own property is larger (~15 percent) than in urban areas (~8 percent), in line with the descriptive statistics in the previous section. This finding holds also

> 29 Marital property regime refers to a system of property ownership between spouses that guides on how property acquired during and before marriage is managed, inherited and divided at the end of marriage. As discussed in section 2.1, countries have one of the three types of property regimes – separation, full community and partial community of property (see also Figure 2).

> 30 The dummy variable for ‘equal rights’ is set to unity when there is no legal restriction on married women or men owning property based on gender (see left panel of Figure 1).

> 31 The ‘equal inheritance rights’ dummy is set to unity if both sons and daughters, and surviving male and female spouse are treated equally in the country’s inheritance laws (i.e. combining the left and right panels of Figure 3 into a single variable). We do not include inheritance rights of sons and daughters and surviving spouses separately, because there is a high degree of overlap between the two.

> 32 We use the 2012 (and not 2016) version of the WBL in this section, because it is closer in timing to most DHS data sets (though where 2012 data are missing we resort to 2016). Two countries (Niger and the Comoros) are missing information on at least one explanatory variable in both WBL versions and hence drop out of the OLS specification.

> 33 Even though our outcome variable is binary, we estimate a linear probability model. Non-linear models (i.e. logit, probit) are more difficult to estimate with fixed effects (Greene 2004). In addition, the linear probability model eases interpretation, as the coefficients do not need to be transformed to obtain marginal effects.

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