2003-2018 period. We empirically estimate a model of cropland changes accounting for the impact of biophysical, socio-economic, and institutional contexts. To our knowledge, our study is the first to embed a high-resolution analysis of global cropland change in an econometric setting. Closest to our approach is the paper by Haney and Cohen (2015), who developed a grid cell level model of cropland transitions explained by global historical trends and global population dynamics. However, our paper significantly departs from their approach given our consideration of pixel and national level determinants—as opposed to assessing how each pixel responds to global demographic dynamics—and given the breadth of the variables included in our analysis. More importantly, we use measures of actual cropland changes rather than modeled data. Our paper is as follows: In sections 1 and 2 below, we present the datasets used in our analysis as well as stylized facts regarding patterns of cropland distributions and transitions into and out of cropland. Section 3 details our empirical approach and discusses how we deal with potential endogeneity concerns. Section 4 presents the results, and Section 5 concludes.

# **Section 1.  Data**

We measure land cover at the scale of the world using satellite imagery. This provides a global and consistent measure of cultivated land. Our “direct” approach circumvents the issues that emerge with the aggregation of disparate national-level statistics for the whole world. Above all, it allows us to focus on actual cropland location, which would be impossible from national-level statistics as published in a database such as FAOSTAT. We use the MCD12Q1 product from Moderate Resolution Imaging Spectroradiometer (MODIS) available since 2001 (see Sulla-Menashe and Friedl, 2018), which we chose for its relatively high temporal resolution and high level of accuracy (Potapov et al., 2021). For the analysis, we regroup the 16 land cover classes based on the Annual University of Maryland (UMD) Classification into 8 categories. These categories include: water bodies, forests, shrub and grasslands, wetlands, croplands, cropland/natural vegetation mosaics, urban, and non-vegetated/barren. This gives an annual breakdown of land cover by pixels of approximately 500 meters by 500 meters for the whole world. In order to smooth away biases from potential misclassification, we allocated each pixel to the predominant class over these 3 consecutive years. For the analysis, we use a restricted sample that covers the years 2003-2018. Our main focus is on “cropland” defined as pixels that have at least 60 percent of their area cultivated. The category of “cropland mosaic” (pixels that have between 40 and 60 percent of cropland with natural tree, shrub, or herbaceous vegetation) is a hybrid category that can capture intermediate steps towards cultivation or degradation or mixed uses such as agricultural and residential at the periphery of cities. Figure A1.1 in the Appendix shows the 2018 global land cover by land cover category.

The MODIS dataset provides an estimate of global cropland of 1.2 billion hectares in 2018, which is very close to that of Potapov et al. (2021), who combined MODIS and Landsat to enhance the accuracy of cropland extent measurement. If we include cropland mosaics, we obtain a total mass of cultivated land of slightly more than 1.3 billion hectares. This is smaller than FAO’s estimate of global “cropland” of 1.6 billion hectares based on a broader definition, which includes temporary meadows and land temporary fallow. This gives us confidence in focusing on the raw MODIS product and its cropland category for our analysis. Cropland mosaic is a hybrid category that can be considered a transitional category in the dynamic processes whereby land is put into or out of cultivation and thus needs to be treated separately for the purpose of studying cropland dynamics.

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