at 4 am and finishing at 4 am of the following day, using 15-minute intervals. Building on previous literature, using the time-use diary of the individuals, “market work” includes time spent on working or on the production of goods and services. In our dataset, this includes working or being employed; owning a business; staple grain farming; horticultural or high-value crop farming; raising large livestock (cattle, buffaloes), small livestock (sheep, goats, pigs), and poultry and other small animals (chickens, ducks, turkeys); and fishpond culture. “Unpaid work” activities include time spent on caregiving (including caring for children as a primary activity and caring for adults, the sick, or the elderly); shopping or getting services (including health services); cooking; other domestic work (including fetching water and fuel); and weaving, sewing, or textile work.<sup>13</sup> Lastly, we classify as “other activities” time spent on personal care, sleeping and resting, eating and drinking, attending school and doing homework, exercising, social activities and hobbies, religious activities, commuting to and from work or school, and other activities that cannot be classified as market work or unpaid work. For the econometric analysis, we use a Seemingly Unrelated Regression (SUR) framework to account for the interdependence of market work, unpaid work, and other activities that all take place within a 24-hour window. In this model, the three sets of activities are determined simultaneously by the same set of explanatory variables and are estimated jointly as a system: i (2) ii0 ii1θθ θθii iiXX ii ii where i refers to the number of hours per day spent by individual TT = ββ + ββ CC + ββ XX + εε on activity =1,2,3 corresponding to market work, unpaid work, and other activities, respectively, and , =0,1,2, and are as defined TT ii aa above. The model is estimated for each of the three activities captured in the time-use survey with an θθ CC θθ XX εε Ordinary Least Squares (OLS) estimator.<sup>14</sup> As mentioned above, the time-use model in (2) must meet restrictions to capture the interdependence of activities = 1, 2, 3 performed in any given day. Any variation in the amount of time spent on one activity due to a change in an exogenous variable must be compensated by a corresponding aa change in the time spent on other activities. Hence, the sum of the intercepts of the three equations must equal the total number of hours in a day or 24 hours. In addition, the sum of the coefficients for each of

> 13 Activities such as weaving/sewing/textile care are considered unpaid work because, in the time-use module, the paid employments are captured using the category “‘work as employed.’” As many women from low-income households work in the garment industry, the activity weaving/sewing/textile care may also capture paid labor market work rather than unpaid work. Nevertheless, in the sample studied, women reported spending 0.1 hours (6.5 minutes) on average per day on weaving/sewing/textile, suggesting that these activities referred to unpaid work rather than employment in the garment sector. 14 There are general arguments on whether to use Tobit or OLS to explain individuals’ time allocations using time diaries. We use the OLS approach because time-use specialists suggest it is more appropriate for analyzing time diary data (Stewart 2009). The reported zeroes in any activity might be just measurement error rather than non-participation. Foster and Kalenkoski (2013) find that Tobit estimations are sensitive to censoring window. Nevertheless, we run Tobit regressions on time spent on secondary childcare with full control variables to check the consistency of the results with OLS estimates and confirm that they are consistent with the findings presented in this paper.

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