woman.<sup>9</sup> Data on current and previous reservation status allows us to test for persistence of such effects, i.e., if -in line with the notion that gender attitudes change slowly with individuals altering their attitude only after having been exposed to female leadership for some time (Beaman et al. 2012)- past reservation of a village for female leadership affects current outcomes. Synergies between political and economic empowerment (Deininger _et al._ 2019) would yield the same result. Letting _v_ denote villages, _i_ individuals, and _t_ time, we assess the impacts of female reservation on outcome variables relating to individual _i_ ’s labor force participation as well as other outcome variables by estimating the following equation.

force participation as well as other outcome variables by estimating the following equation. i 0 1 2 3 i 4 i (1) ii<sup>1</sup> ii<sup>2</sup> vv dd where _Yiv_ is the outcome variable of interest for individual YY = ββ + ββ RR + ββ RR + ββ XX + ββ _i_ in village VV + uu _v_ , + εε is an indicator variable that equals one if council leadership in village _v_ was reserved for women in the most recent election (i.e., the ii<sup>1</sup> RR _pradhan_ at the time of the survey was a woman who assumed her position as a result of reservation) and zero otherwise; is an indicator variable that equals one if council leadership in village _v_ had been reserved for a woman in the previous election and zero otherwise;ii<sup>210</sup> **_X_** is a vector of household and RR individual controls; **_V_** is a vector of village and _pradhan_ characteristics; a district fixed effect; and _εiv_ an error term. Our main interest is in _β1_ and _β2_ , the parameter estimates of current or past reservation on dd uu individual outcomes relative to the base category of a village never having been reserved.

To explore the gender dimension of reservation, we let _fiv_ be an indicator variable taking a value of one if the respondent is female and zero otherwise. With interactions between respondent’s gender and current or past reservation, our estimating equation becomes: i 0 1 3 i 2 4 i 5 i 6 i (2) ii<sup>1</sup> ii<sup>1</sup> ii ii<sup>2</sup> ii<sup>2</sup> ii dd where parameters are as above and the main difference from other studies is that the parameters estimated YY = ββ + ββ RR + ββ (RR × ff ) + ββ RR + ββ (RR × ff ) + ββ XX + ββ VV + uu + εε are gender-specific. In other words, _β1_ and _β2_ are the estimated impact of current or past reservation on men and _β1_ + _β3_ , as well as _β2_ + _β4_ are estimated impacts of current and past reservation on women so that the household-level impact of current reservation is given by _β2_ + _β4_ . The significance of linear combinations of estimated parameters can be tested via F-tests which are reported in the results tables throughout.

> 9 In 2009/10 all states in our sample except Bihar and Madhya Pradesh (where the share was 50 percent) required a third of villages to reserve the _pradhan_ position for a woman. By 2015 all except Haryana and Uttar Pradesh had increased the share of _panchayats_ required to reserve seats for women to 50 percent. Whatever the overall share, because a village’s reservation status is exogenously given it does not affect our analysis. For a detailed discussion of how randomization is implemented, see Dunning and Nilekani (2013) and Chattopadhyay and Duflo (2004).

> 10 To illustrate: _R1v_ for villages in Andhra Pradesh equals one if, in this village, the 2011 election was reserved for a woman and _R2v_ equals one if in this village the 2006 election had been reserved. Similarly, for villages in Orissa _R_<sup>_1_</sup> _v_ and _R_<sup>_2_</sup> _v_ equal 1 if the 2012 or 2007 elections were reserved.

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