thus further from cities, coasts, and rivers, less suitable for agriculture, but it is only slightly more exposed to drought than the sample of 2003 cropland pixels used for the cropland loss regression.

For the second stage regression for both cropland losses and cropland gains, we use variables from a set of seven national-level explanatory variables presented in Appendix Table A3.2. These institutional and economic variables include the quality of land administration, an index of Rural land tenure insecurity, the indicator Enabling the business of agriculture, GDP per capita, the Gini index, and two measures of Cropland per capita, one for 2003 and one for 2018. Cropland per capita was calculated by dividing the country’s aggregate amount of cropland for these years in the MODIS product by the corresponding national population for the same years. Note that some of these variables, however, are not available for all countries: Whereas cropland per capita can be calculated for more than 200 economies, the indicator from Enabling the business of agriculture is only available for 101 countries, potentially limiting the size of the sample in the second stage when introducing this indicator as an explanatory variable. Table A3.2 shows that these variable vary much across countries.

# **Section 4.  Results**

We ran the first stage regression of movements out of cropland on pixel-level measures capturing local contexts. Table 2 below reports the OLS estimates for regression (1) focusing on the conversion out of cropland. Given the large dataset (over 48 million observations), all coefficients are significant at the 1 percent level. Estimated coefficients are consistent with theoretical intuitions: Cropland far from cities is likely to disappear at a more intense pace than cropland close to cities. With cropland pixels distant by 60 km from the nearest city on average, an additional one standard deviation in the distance to a city (i.e., considering locations that are an additional 55 km away from a city) increases the likelihood of cropland loss by .8 percent over the 15-year period. This could reflect greater investment incentives to keep land in production close to markets. Similarly, an increase by one standard deviation in the number of months of exposure to droughts (i.e., having 63 months of droughts instead of 38 over the period) increases the likelihood of cropland loss by 1.4 percent. A decrease by one standard deviation in cultivation suitability (i.e., a reduction of the suitability index from 84 to 66) increases the likelihood of cropland loss by 2.8 percent. An increase in one standard deviation in the distance to a river increases the probability of cropland loss by .3 percent. Proximity to a river also decreases the likelihood of cropland loss, which could capture the role of rivers in keeping soils moist as well as the likelihood of surface water irrigation close to rivers: A decrease by one standard deviation in the distance to a river decreases the probability of cropland loss by .6 percent. Finally, proximity to the coast is harmful to keeping land under cultivation. A one standard deviation in the distance to the coast increases the likelihood of land degradation by .3 percent. This could be potentially due to the salinization of coastal areas. We see that the factor which by far has the greatest impact on transitions out of cropland is the quality of soils (as measured by the cultivation suitability index), followed by climate events (as measured by droughts). Interestingly, the role of access to cities in keeping land cultivated is greater than that of proximity to rivers or distance to coasts. As a robustness check, we run the same regression on a sample including both cropland and cropland mosaics (53 million pixels) and consider movements out of either one of these two land use categories. This produces very similar results presented in Table A4.1, which all coefficients having the same sign and same order of magnitude.

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