We then turn to explicitly testing Prediction 5. In columns (5) and (6), we examine whether the increase in women’s likelihood of winning occurs in the same districts where the program impact on economic growth is larger. To preserve statistical power, the specification that we use in column (6) is different from the one used in previous columns. Instead of bifurcating the sample into above and below-median districts like we do in columns (1)(4), in column (5) the outcome variable is constructed by interacting two indicator variables: whether a woman wins in an election times whether the district had above median luminosity (total light per square kilometer) in 1994. Similarly, in column (6), the outcome variable is constructed by interacting: whether a woman wins in an election times whether the district had below median luminosity in 1994. This implies that the outcome variable in column (5) (column (6)) equals one only when the district has a female winner and the district had above (below) median luminosity in 1994, and equals zero otherwise. Consistent with our model, we find the effect of the DPEP on women’s likelihood of being elected to be stronger in districts where the program was more successful at improving local economic outcomes. In fact, women’s probability of winning increases only in below-median districts (at the start of the DPEP) by a statistically significant 14.4 p.p.

# **B. Educational Performance**

In Table 6, we conduct a similar analysis as in Table 5; instead of night lights, we now investigate heterogeneity by the DPEP’s impacts on school infrastructure and student participation at the district level. Using a principal-component analysis and DISE data, we first construct a district-level educational performance index.<sup>19</sup> We then split the districts into two groups (above or below median) based on their pre-DPEP education performance proxied by their 2004-05 educational performance index. Ideally, we would have liked to split the sample based on the index values in a pre-DPEP year, like we do for night lights; however, 2004-05 is the first year for which DISE data is available for all districts in India. Thus, the results in Table 6 should be valid to the extent that the below vs above median ranking of districts was preserved despite improvements in the education performance of treated districts due to the DPEP.

With this caveat in mind, in columns (1) and (2), we estimate the impact of the DPEP on district performance index scores in 2004-05.<sup>20</sup> In column (1) (column (2)), the outcome is

> 19The index is based on the following variables: the share of primary schools built since 1995, the number of pupils appearing for the grade eight final exam, the share of pupils that pass the grade eight final exam, the share of pupils that are enrolled in government primary schools, the share of primary pupils that are girls, the share of pupils enrolled in schools with no constructed building, and the share of primary schools that have only a single teacher.

> 20As the DISE data is a cross-sectional district data set, we include several district-level covariates from the 2001 National Census such as the child sex ratio, decadal population growth, the share of the population from scheduled castes or tribes, and the share of villages with a paved road approach, a primary health

25