individuals and their households are engaged in. There is a longstanding literature on how proxies for individual socioeconomic status (or bargaining power, in specific studies), including earnings, age, education, and marital status, affect individuals’ labor allocation.<sup>3</sup> Thus far, however, nationally representative studies examining the association between individual-level time use measures and ownership of physical and financial assets — and not only of a specific type, but a diverse set — have been limited. This has in turn hampered a broader understanding of how time use is linked with wealth.

The available studies have focused on the impacts associated with transfer of specific assets and property rights in specific sub-populations. Examples include the randomized trials to show how expanded property rights (see Field, 2007, in a study of an urban land titling program in Peru) and creation of financial accounts (Field et al., 2021, in a study of a cluster of villages in central India) can boost women’s labor force participation. Shocks to assets that women bring into marriage has been examined by Brown (2009), in a study of four counties from China, to understand the effects on women-focused expenditures, intrahousehold allocation of housework, as well as women’s leisure time. Wang (2014) also examines the effects of transferring property rights to men and women on time use, consumption and children’s health stemming from a 1994 housing reform in China, and documents that property rights transfer to men, as opposed to women, increases women’s time devoted to housework.

Beyond the gaps in our understanding of individual wealth and time allocation, a related gap in the literature is the lack of systematic assessments of the estimated relationships between time use measures and the covariates that are commonly used in empirical research as proxies for individual socioeconomic status (or bargaining power, in the context of intra-household analyses). These knowledge gaps are in part due to the weaknesses in the availability and quality of individual-disaggregated survey data collected in large-scale, multi-topic household surveys. These surveys seldom collect individual-disaggregated information on men’s and women’s asset ownership and time use, together with data on employment and complementary socioeconomic and demographic characteristics. Without such integrated data collection, it is not possible to fill the aforementioned knowledge gaps.

However, even when the data are available, poor data quality can impede development research. For example, in large-scale household surveys, it is common for the data on asset ownership to be either collected at the household-level – even when assets are owned by individuals – or collected at the assetlevel, with individual disaggregation to identify owners and right holders, whom would, however, be identified by only one “most knowledgeable” household member. Distortionary effects of proxy respondents have in turn been documented in the measurement of not only men’s and women’s asset ownership but also employment (Kilic et al. 2021; Kilic et al., 2020), underlining the importance of eliciting self-reported survey data on these topics.<sup>4</sup> Proxy reporting and other measurement challenges on individuals’ time allocation have also marred a clear understanding of the varied activities women and men are involved in throughout the day, and how their time is linked with other socioeconomic factors (Floro, 2021).

> 3 This includes how time in paid and unpaid work is affected by individuals’ share of earnings in the household (see Bittman, 2003, using data from the U.S. and Australia, and Sevilla-Sanz et al., 2010, using data from Spain); unearned income from transfers and property (Schultz, 1990); cash transfers (Hasan, 2010, examining a program targeted towards girls’ education in the Punjab province of Pakistan); as well as adult household members’ age, education, and marital status (see Fengdan et al., 2016, in a study from China).

> 4 Evolving approaches to measuring time allocation and effort can also allow for a broader view on the links with asset ownership — Friedman et al. (2021), for example, collect physical activity tracking data from men and women in agriculture in two districts of Malawi, and find, among other results, that women tend to exert more physical effort among couples with larger gender inequalities in the number of plots owned.

3