(see below), our final sample includes 444 village heads, 1,067 village bureaucrats, and 8,880 citizens. Appendix B provides additional details on our survey design.

**Electoral Data.** As part of our survey, we collected official voting tallies for all candidates running in the last village head election. We obtained complete electoral data for 799 among the 852 villages in our sample (94%). Under the Village Law, village heads are elected every six years via first-past-the-post voting, and local elections are staggered across districts, with all village elections occurring in the same year within a district. Thus, elections were held in different years across villages in our sample: less than 1% were held before 2016, 11% in 2016, 13% in 2017, 16% in 2018, 28% in 2019, 2% in 2020, 27% in 2021, and 1% in 2022. On average, 3.6 candidates competed in these elections with a turnout of 82% (calculated as votes cast divided by the number of registered voters in each village).<sup>2</sup> We report various checks on the electoral data in Section 3.3 (see also Appendix Table A.1 and Appendix Figures A.1–A.2).

The voting data indicates which candidate was the incumbent at the time of the last election. Overall, 512 village elections featured an incumbent candidate. These villages constitute the main sample for our empirical analysis.<sup>3</sup> Women comprised only 5% of incumbent candidates, and 6% of all candidates. Figure 1 (panel a) plots the density of the difference between the vote share received by the highestranking challenger candidate and the incumbent’s vote share. We use this difference as the running variable in our regression discontinuity (RD) design, described in Section 3.2.

**Administrative Data.** To measure bureaucratic performance, in addition to survey outcomes, we use administrative data from the 2014 and 2021 rounds of _Podes_ , a triennial village census, which we match to our survey sample. We use the 2014 data as the baseline and the 2021 data as the endline. When studying endline outcomes, we restrict the sample to villages that held their last election before 2021 and use the remaining villages for placebo checks. We also use predetermined geographic and socioeconomic characteristics from _Podes_ to conduct balance and other validity checks.

# **3.2 Regression Discontinuity Design**

Our goal is to identify how bureaucratic processes and service provision are influenced by turnover in the most recent village election. Before introducing our identification strategy, Table 1 presents correlations between some of these outcomes and the presence or the victory of an incumbent candidate in the last election. We first examine two measures of bureaucratic performance: the quality of public goods in the 2021 _Podes_ data, and the growth in service quality between the 2014 and 2021 _Podes_ waves. While there is no significant correlation between these outcomes and whether an incumbent runs (columns 1 and 3), incumbent wins are followed by a decline in public goods provision (columns 2 and 4). As one might expect, incumbent reelections are also associated with lower bureaucratic turnover (column 6).

> 2A small fraction (4%) of elections in our sample featured turnout greater than 100%. We later use this as a measure of data quality and show that this is uncorrelated with the occurrence of an electoral turnover.

> 3In Table 1, discussed in Section 3.2, we use the full sample of villages to document how villages where an incumbent competed in the last election differ from other villages. In this table and the RD analysis, we exclude 8 villages where the incumbent and the highest-ranked challenger candidate are tied in the electoral data.

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