# **6.1 Time Use of Employed Women**

We estimate equation (7) for employed women, based on their current work status, and report the results in panel A of Table A3.<sup>11</sup> As shown in column 1, employed women in treated states spent less time traveling after the implementation of the free bus scheme compared to those in control states, which is consistent with potential substitution from slower modes of travel (cheaper but slower buses or walking) to faster buses. These travel time savings can be allocated to household chores, additional work, or a combination. Surprisingly, we find that it is primarily the former: hours spent on domestic tasks increased by 19.1% (column 2), with minimal change in the time spent working (column 3).<sup>12</sup> We then explore the potential heterogeneity of this pattern across employed women at various skill levels. We define low-skill women as individuals who have not received education beyond primary school, medium-skill women as those who have attended middle or secondary schools, and high-skill women as those who hold bachelor’s degrees or above.<sup>13</sup> We document the results by skill levels in panel B of Table A3.<sup>14</sup> Although all women save time on travel, employed women across various skill levels allocate this saved time to different activities. Specifically, employed low-skill women reduce their 11While the baseline results do not consider the flows in and out of employment, in Appendix Table D8, we present a matrix of women’s labor market status before and after the program. Among those employed in the post-treatment periods, 65.8% had also been employed before the program deployment. We thus also estimate equation (7) for women continuously employed during the study period to examine if the results are influenced by changes in employment status. The results are reported in Appendix Table D9. One can see that the estimated coefficients are similar to those in panel A of Table A3. In Appendix Table D10, we cluster the standard errors at the district level and the significance levels of the estimated coefficients are similar to those in panel A of Table A3 and Appendix Table D9.

> 12The result in working hours may prompt inquiries about wage effect. We, therefore, also use the InP section data to examine the impact on employed women’s wages and find a statistically insignificant increase in treated states compared to control states following the scheme’s implementation, as shown in column 1 of Appendix Table D11. However, it should be noted the wage results presented in Appendix Table D11 are only suggestive evidence. We observe wages for 64 % of the employed women featured in Table A3. Most of this attrition is not due to selection bias (i.e., respondents refusing to answer the income survey). Instead, when matching the PoI and the InP datasets, we find that over 70 % of the unmatched observations result from interviewers being unable to reach the household within the available fixed period.

> 13Goel (2017) and Thomas (2011) classify skill levels by education levels. Asuyama (2012) categorizes “people who were illiterate or have only received education below the primary level” as individuals with the lowest skill level. Mehrotra, Gandhi and Sahoo (2013) also group people who have below primary or only primary level of education as individuals with low level of skill. In this paper, we follow previous literature and group skill level by education level, setting individuals who have not received education or have only attended primary school as low-skill individuals.

> 14These results are based on women’s skill levels before the implementation of the scheme. As shown in Appendix Table D12, only minor changes in skill status occurred from the pre- to the post-period. We further verify this point in a regression in columns 2 and 4 in Appendix Table D13.

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