urban areas).<sup>10</sup> Mean statistics for all variables used in the regressions are shown in Table A4 for the urban and rural subsamples.

Besides the DHS data for the couple sample, the paper draws on the following country-level databases (see Table A1 for data definition). The _Women Business and the Law_ (WBL) program provides data on laws and regulations constraining women’s property ownership and economic opportunities (World Bank 2020a).<sup>11</sup> The _World Development Indicators_ (WDI) provide data on GDP per capita (World Bank 2020b). Data on historical cultural attitudes and social norms are from Alesina et al (2013), while the data on the country’s largest religions are drawn from _The Association of Religion Data Archives_ (Harris et al 2019).

# **4.2 Results**

Considering the WBL variables we see that the equal remuneration law has the lowest coverage in the sample -- only about 15 to 18 percent in urban and rural areas, respectively (Table A4). Effectively, the share of the population benefitting from such laws is likely even lower, as employment-related legislations do not extend to the informal sector that employs a large proportion of workers in developing countries. Equal ownership rights to immovable property has the widest coverage and applies to more than 90 percent of the sample. Women’s incidence of housing ownership is higher than land ownership, and a greater proportion of rural than urban women own some property. Turning to our regression analysis, we find that women living in countries with more gender-egalitarian legal regimes are more likely to own property in both urban and rural areas. We first discuss the results for housing ownership (Table 1). In urban areas, three legal variables, except for the laws providing for equal inheritance rights, are positively and significantly associated with women’s chances of owning a house across all models. More genderequitable legislation providing for equal ownership rights for men and women is associated with a 15 to 18 percent increase in the probability to own the house; legislation valuing non-monetary contributions to the marriage is associated with a 11 to 16 percent increase; and legislation mandating equal remuneration for equal work is associated with a 10 percent increase in women’s chances of owing the house. In rural areas, equal ownership rights are associated with a 21 percent increase in women’s housing ownership, while the valuation of non-monetary contributions is associated with 12 to 20 percent increase in women’s housing ownership across the models.

In the case of land, the results are qualitatively similar, but the associations are weaker and significant for only a few legal variables (Table 2). In urban areas, women living in countries where laws provide for the valuation of non-monetary contributions are 8 to 11 percent more likely to own land. Controlling for current religion, historical cultural attitudes and a rich set of couple and household attributes, the other legal variables no longer remain significant. In rural areas, the laws providing for equal ownership rights are significantly associated with women’s ownership. For land, the fact that most WBL variables lose significance once the analysis controls for current religion and historical cultural variables (i.e. comparing columns (2) and (3) for both urban and

> 10 As noted in section 3, two countries (South Africa and Liberia) only collect data on housing and not on land ownership, which explains the smaller sample size for the regressions on land ownership. A few additional observations drop out once we control individual, spouse, couple and household characteristics due to missing values.

> 11 Unlike in section 2, where we reported the most recent WBL data (referring to 2020), the multivariate analysis draws on the data referring to 2015 (from the 2020 WBL edition), which are closer in timing to the DHS data in our sample (see Table A2).

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