calculating the size of area affected by fire for each fire event and subsequently taking the average thereof.

We use the Synthetic Control Method to estimate the FIP program’s impact on each of the four forest fire measures: forest fire occurrences, the share of a forest grid cells having been on fire at least once, the number of forest fires started, and the extent to which forest fires spread out.<sup>5</sup> As forest characteristics that predict fire incidences, we use the annual panel of 2006-2013 pre-treatment outcomes<sup>6</sup> , the average annual precipitation in the forest before the treatment, and the size of the forest (measured at baseline, in 2013) to construct the synthetic control forest for each of the treatment forests. Although the relationship between each of these two forest characteristics and the actual forest fire occurrences is ex-ante ambiguous<sup>7</sup> , including them in the weighting process will improve the fit, independent of the exact relationship. Regarding the composition of the synthetic control forests, 34 of the 65 non-intervention forests received a positive weight in at least one of the 12 synthetic control forests; on average, a project forest’s synthetic control

> 5While it would also be interesting to have information on the duration of forest fires, the MODIs data set does not contain the necessary information (Balboni et al., 2021). We can approximate it, however, by taking the ratio of the number of forest fire occurrences and the share of forest grid cells that were affected at least once during the month. This would provide an upper-bound estimate of the average duration of fires because it does not take into account whether grid cells were on fire on consecutive days or not. An analysis of this fifth (and imprecise) indicator would not yield any new insights because we already analyze the numerator and denominator separately.

> 6Despite having data on forest fires from 2003 onward, we do not include all pre-treatment periods as predictors. This is because if we were to do so, the “relevance weights” ( **_V_** _k_ ) that assign positive weights to the pre-treatment outcomes and zero to the the forest characteristics would minimize the pre-treatment RMSPE in Equation (B.5) (cf. Kaul et al. (2021)). Instead of including all pre-treatment outcomes, we aim to find synthetic controls that fit well to the project forests in the period leading up to the beginning of the FIP program.

> 7Forest size is expected to affect forest fire occurrences because of two reasons. First, man-made fires are more likely to be started on the forest fringe than in the interior (especially if agriculture is the main activity causing forest fires), so that larger forests may have, on average, fewer forest fires per grid cell. Alternatively, larger forests may have more fire occurrences because they are more difficult to monitor. And also the relationship between precipitation and forest fires is also ambiguous ex ante. In drier forests the vegetation is more prone to catching fire and the fire is likely to spread wider too. But then it may also be the case that agricultural activity is higher in areas with more precipitation, so that forest fire occurrences are more pronounced in forests with higher precipitation rates. Whichever of the two opposing mechanisms is dominant for each of these two variables, including precipitation rates and forest size in the SCM’s weighting process will improve the accuracy of the synthetic control.

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