# **4. Multivariate analysis of women’s property ownership**

# **4.1Model specification**

This section investigates what factors are statistically associated with individual property ownership in a multivariate regression framework. This has the advantage that, unlike with the descriptive statistics, we can control for confounding factors. For example, despite recent gains in girls’ school enrollments, adult women still have lower levels of education than men in many of the countries in our sample, and this may have an impact on their property ownership. In addition, regression analysis is a useful way to summarize the relationship between various explanatory variables and our outcome of interest, wife’s property ownership, even as we are careful not to make causal claims due to the cross-sectional nature of the data.

We estimate the following model: where _Yirc i_ s a binary variable that equals unity if the wife (living in region _r_ , country _c_ ) owns any property, either alone or jointly with the husband or others; and zero otherwise. We estimate a linear probability model (LPM), rather than a logit or probit model for ease of exposition. Standard errors are clustered at the country level, because our main variables of interest (laws) do not vary within countries.

Wc is a vector of country - specific legal variables that might have an impact on women’s ownership of assets. These include the WBL variables discussed in section 2, which indicate the role of gender discrimination in the country’s legal system – i.e. whether the law recognizes nonmonetary contributions to marital property (Figure 1), and if men and women have equal inheritance rights (Figures 2a and 2b). Given the large degree of overlap between the countries that discriminate by gender on natal inheritance and spousal inheritance, the two variables on inheritance are merged into a single variable. We include an additional variable that measures if men and women have equal ownership rights to immovable property. This picks up discrimination by gender not only in the legal ability to own property, but also in the legal treatment of spousal property. Thus, in countries where husbands are granted the administrative right of marital property, this variable would take on a value of 0. The fourth variable in this set relates to pay equality and captures if the law mandates equal remuneration for work of equal value (Figure 3).

The WBL variables under consideration in the empirical analysis broadly reflect the pathways to property ownership by women discussed in section 2. We expect that laws discriminating against women in property use and ownership and, in the workplace, may have a negative association with their ability to own any land or housing, either solely or jointly with someone else. The variable on the valuation of nonmonetary contributions captures two related pathways to asset accumulation – via equal rights for women in marital and spousal assets (depending on full or partial community of property) and recognizing women’s contribution to social reproduction and the care economy. The gendered division of labor and expectations that women are responsible for the home in practically every country constrain women’s ability to fully realize their potential in the labor market which negatively impacts their earnings. Finally, discrimination in the workplace is captured by looking at legislations that mandate equal remuneration (including emoluments in cash or in-kind) for equal work. Such laws increase the likelihood that women in 15