the labor force will have an opportunity to accumulate savings to acquire property through market transactions.

Kc is the second vector of country-specific variables representing both historical and current cultural attitudes and social norms surrounding gendered roles and responsibilities, and gender equality more broadly, that may enhance or diminish the effectiveness of current antidiscriminatory legislations (Deere et al 2013; Giuliano 2017). While gender inequality can be all encompassing and affects women from the cradle to the grave, we focus on those cultural attributes that we believe could be correlated with property rights. Following the definitions of Alesina et al (2013), we control for the absence of private property, rules governing post-marital residence, and family structure. The authors revisit Engels (1902) argument regarding the emergence of private property being detrimental for women’s autonomy. According to Engels (1902), the control of private property by men also led to control and suppression of women to protect paternity. In certain regions, such as South Asia, patrilocality is often accompanied by residence with the husband’s extended family with differential impacts on women’s agency and wellbeing (Khalil and Mookerjee 2019); this could also impact their property rights. It is also plausible that in patrilocal societies, girls may be denied a share of natal inheritance, particularly in rural areas, citing their inability to administer the property. Lastly, this set includes religion of the current population to pick up deep-seated social attitudes to gender equality including women’s rights to own and use property. Even in countries with constitutional guarantees for gender equality, religious beliefs may drive much of what is commonly accepted and practiced in societies and therefore affect women’s property ownership (see, for example, Evans 2015 for qualitative evidence from Senegal).

We also control for GDP per capita and its square, _G_ c; this is a proxy for a potentially nonlinear relationship between the economic development of the country and gender gaps in property ownership.

_Xirc_ is vector of characteristics of the couple _i_ . These include (separately) the wife’s and husband’s number of sons and daughters ever born, age and age squared, years of education (as a categorical variable) and employment status as well as the husband-wife education gap and a variable that equals unity if both spouses are working. _Zirc_ is a vector of household characteristics and includes who in the survey is assigned the head of the household.

We run three different models, all with world region fixed-effects that control for timeinvariant unobservable regional characteristics. The first model includes the WBL variables and per capita GDP (linear and squared); the second one adds the cultural and social norms related variables; the final third model adds characteristics of the couple and household.

As discussed earlier, gender gaps in property ownership are strongly influenced by the urbanrural 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. We also run the models separately for ownership of land and housing. Each of the regressions is run on the couple sample, pooling observations from 41countries for housing ownership, and from 39 countries for land ownership. In total, our analysis amounts to 213,898 couples for housing (142,585 in rural areas and 71,313) and 211,310 couples for land ownership (141,024 in rural areas and 70,286 in 16