those for the more recent group, as we might expect, given that the total measured forest cover loss for this group is higher.

Using the narrower discontinuity within 10 points of the cutoff (Panel B), we lose around 3,000 observations, but the results are broadly similar for the rate of cover loss indicator: all point estimates indicate greater conservation of forest in participant parcels than non-participant parcels. The magnitudes range from 24% (0.151/0.634) for the full sample, 35% (0.324/0.934) for 2011-12, and 8% (0.025/0.329) for 2013-14. For the binary variable indicating cover loss greater than 2 hectares, the linear probability model indicates similar trends. For both bandwidth windows, the point-estimates for all the combined and individual cohorts also indicated avoided forest cover loss. Moreover, for the whole sample we find a marginally significant impact of 7.2 percentage points.

# _Heterogeneous Effects_

Knowing for what subgroups an intervention works for is particularly relevant for program design and program targeting. We tested for heterogeneous effects across category of deforestation risk and regions. **Table 6** shows heterogeneity in results by the risk of forest cover loss and **Appendix Figure A7** shows the coefficients of interest. With regards to deforestation risk (where high is defined as above the median level of risk in the sample), we observe a significant impact in areas of high deforestation risk. **Table 6** shows the estimates for the combined and individual cohorts. For the combined sample (2011-2014), the deforestation rate in the rejected group with high risk of deforestation is 0.865 percent, and the sum of the main effect with the interaction term is -0.256, so the total reduction in forest cover lost for this type of land overall is 29.6% (0.256/0.865). For the cohorts that have been enrolled for longer in the program (2011-2012), this effect is 38.1% (0.491/1.294). For the combined and the 2011-2012 cohorts, the impact on avoided deforestation is statistically significant at the 1 and 5 percent level respectively. We also observe a smaller (but not significant) effect in the expected direction for program recipients that have only been in the program a short period of time (2013-2014). We believe that the larger and statistically significant impacts in high risk areas can be ascribed to the fact that there is more scope for behavioral change in an area with high risk of deforestation – in other words, it is easier to have an impact when there is greater possibility for change.

In addition, the program has differential impacts across regions (see **Appendix Figure A8)** . While effects on percent of avoided deforestation were not detected in the north, center and southwest regions, the program’s effectiveness was high in the Yucatan peninsula, which is an area of high risk of deforestation.

# _Robustness Checks_

The above results are consistent across a series of robustness checks. Results for the fuzzy design (i.e., the instrumental variables approach where the discontinuity is used as an instrument while controlling for the point score) are shown in **Appendix Table A3** . The point estimates for the different outcomes and for the different windows are very similar to our intent to treat estimates. We conducted two further robustness checks. First, we ran a placebo test using deforestation outcomes from the years 2007-2010. These estimates are shown in the **Appendix Table A4** . As expected, this effect shows no pattern of program impacts in the years prior to enrollment. Second, our results are robust to excluding those properties just over the threshold, i.e. with point scores between 0 and .5 (see **Appendix Table A5** ).

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