Within our community sample, more than 97% of communities above the cutoff received the program as intended, while no communities having point totals below the cutoff received the program. Although our analysis is based on an intent-to-treat analysis, the high levels of compliance with the program cutoffs for both treatment and control groups means that our ITT estimates are similar to (local) average treatment effects.

# _Tests for Discontinuities of Covariates_

To assess the covariate discontinuity and therefore validity of the RD design, this section presents a series of visual and parametric tests using the satellite and survey data. While the assumption that no determinants of the outcomes jump discontinuously at the threshold is untestable for unobservable characteristics, **Figures 5** and **6** show kernel regressions of several observable characteristics, recentered by subtracting their year and state mean, on the point scores. **Figure 5** shows these graphs for the survey data, while **Figure 6** contains those for the environmental data. In neither case do we observe substantial visual discontinuities in key observable environmental and social characteristics. Parametric tests of discontinuity for these same covariates generally confirm the visual inspection. These tests are presented in **Tables 3a and 3b** for the environmental polygon data set and the survey data set respectively. We do reject the null of no discontinuity for some indicators, but we do not think that these differences drive our results for two reasons. First, most of the differences are small in magnitude with respect to the control means.<sup>14</sup> Second, when we include poverty and the other controls in our final regressions, we find that the results are unaffected, even when we allow for differential slopes on either side of the discontinuity. Furthermore, the correlation between the poverty index and our social capital outcomes, controlling for state effects, is 0.005-0.009 across the cohorts and is not statistically significant.

**Table 4a** shows summary statistics for participant and non-participant groups for a series of geographic characteristics for the sample of polygons submitted in each cohort.  The first two columns show means and the third column of the table shows normalized differences of means (Imbens and Wooldridge 2009) between treatment and control polygons. It is important to note that in a regression discontinuity design, differences in average covariates across groups above and below the cutoff threshold are to be expected if it is the case that the running variable used for prioritization is correlated with these characteristics. The normalized differences were above the 0.25 standard deviation threshold (Imbens and Rubin, 2015) for only two of the 36 indicator-cohort comparisons. Enrollees tend to be located in higher elevations and to be ejidos for the 2011-2012 cohorts (normal differences of 0.342 and 0.334 respectively), with differences persisting for the 2013-2014 cohorts (0.186-0.196). The higher prevalence of ejidos is a direct result of the targeting criterion, and elevation in Mexico tends to be correlated with forest cover, another targeting criteria of the program.

**Tables 4b** and **4c** present summary statistics of community and household level characteristics, as reported in the leader and household surveys respectively. Similarly, we find that enrollees and rejected applicants have generally small normalized differences in means.

> 14 For the polygon environmental data (Appendix Table A2a), we run OLS of the covariates on the treatment threshold, a quadratic of the running variable with slopes that vary on either side of the threshold, polygon size, an ejido dummy variable, ecosystem indicator variables, and state and year fixed effects. For the community and household-level survey data (Appendix Table A2b), we run regressions that include the threshold dummy variable, the first and second order polynomials of the de-meaned point score and their interactions with the threshold dummy, as well as state fixed effects.

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