# **3.1 LAND-COVER DATA**

To analyze land cover change, we used the Hansen et al. global analysis data (2013), version 1.2 (accessed in 2016).<sup>9</sup> The data set provides forest cover in 2000 and loss of forest cover from 20002014. The Hansen data are the only available data source with annual variation in land-cover during our period of study. The data set is based on a combination of Landsat (30m resolution) and MODIS (250m resolution) satellite images. We use the reported annual changes from Hansen’s data, summing reported deforestation for each polygon after application to the program. This means that for those polygons that enter in 2011, we are summing deforestation from 2011 to 2014, while for those entering in 2012, we sum from 2012 to 2014, etc. To calculate percent changes, we take the total forest cover loss divided by the area of forest in the data set baseline year of 2000. Given that our land cover data are measured across different time periods for each cohort, they are not comparable to the government official rates. Furthermore, they are not easily comparable across cohorts, since for the earliest cohorts we sum deforestation across four years and for the later ones only across two.

The Hansen et al. data set has important limitations. It was intended for global/regional change analysis, not change analysis at the small parcel-level, as is necessary to evaluate the PES program. It has been shown that the accuracy of the classification algorithm varies across different countries and ecosystem types (Hansen et al., 2014; Bellot et al., 2014; Burivalova et al., 2015). For example, assessments by the CONAFOR remote sensing team suggest that the Hansen product offers better results in Mexico when the percentages of forest cover are below 30 or above 60 percent. The data are likely to understate loss of natural forest because they may classify plantations and agroforestry crops as forested areas, and they may also understate selective logging--an important source of forest degradation --or very small areas of deforestation. Furthermore, the data do not not capture degradation (and no forest cover change data set captures other important ecosystem services such as conservation of biodiversity, soil conservation, or watershed functions). For these reasons, our estimates are likely to be conservative with respect to forest cover change and do not measure multiple other potential environmental impacts. Most importantly, all conclusions are limited by the lack of power in the analysis. Although pre-analysis power calculation estimates suggested that we could detect medium sized effects, the actual power based on the Hansen data from the study years and the entire universe of applicants reveals substantial power limitations (further discussed below).

For the analysis of land-cover impacts, the unit of analysis for the environmental estimates is the parcel (polygon). These polygons were created by dividing applicant parcels into smaller units so that they have unique application histories and we avoid any possible issues of double-counting areas. For example, if a landowner submitted a parcel in 2011 and was rejected, and the following year submitted an imperfectly overlapping parcel that was accepted, these two applications would generate three polygons: one rejected in 2011, one rejected in 2011 and accepted in 2012, and one accepted in 2012.<sup>10</sup> We cluster the standard errors by common property to account for the spatial clustering of these units. **Figure 4** shows a visual representation of these units within various communities with repeated applications.

> 9 The high-resolution MAD-Mex system being developed by CONAFOR was originally the preferred option, but postprocessing of this layer was not completed by the time of analysis.

> 10 A preliminary analysis of changes in submissions within the same ejido over time indicates that landowners are responding to the point system (e.g. higher points for higher risk land) when they resubmit applications. A full analysis is outside the scope of this project.

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