subnational units into grid cells with a 10x10-kilometer resolution. We use the most recent SPAM statistics, for 2010.

The OECD database (OECD 2022) provides the crop-level data on output subsidies. We use the Producer Single Commodity Transfer (PSCT) indicator, which measures the annual monetary value of gross transfers from consumers and taxpayers to agricultural producers. PSCT includes both MPS—which results largely from policies (especially trade policies such as import or export bans) that create a gap between internal market prices and border prices—and output subsidies that are fiscal in nature, such as direct transfers to farmers per unit of crop produced. PSCT can be positive or negative: while fiscal subsidies can only be positive, MPS can be positive or negative, depending on whether trade policies increase or decrease market prices. Positive values represent benefits to farmers, and negative values represent costs to farmers. To be comparable with the SPAM dataset, we use 2010 PSCT data.

OECD reports PSCT for 65 commodities in 20 countries. After matching SPAM with the OECD dataset, 23 commodities in 16 countries remain in our analysis. They are wheat, rice, maize, barley, potatoes, sorghum, cassava, lentils, other pulses, soybeans, groundnuts, coconut, palm oil, sunflowers, rapeseed, sugar, cotton, coffee, cocoa beans, tea, tobacco, bananas, and plantains. These 23 commodities represent 91 percent of PSCT paid to all commodities in 2010 in countries in the OECD dataset, excluding livestock, meat, and dairy products. The intersection of the 23 crops in the 16 countries account for 34 percent of total agricultural production values worldwide.

The analysis rests on the assumption that the subsidy for each crop is spatially neutral. This means that the spatial distribution of output subsidies in a country for a particular crop is equal to the spatial distribution of that crop. Put another way, if a particular region in a country produces 5 percent of that country’s maize, then that region also likely receives 5 percent of the output subsidy for maize. This assumption will generally be true enough to make inferences about the aggregate distribution of subsidies, but may not always hold. We discuss reasons why it may not hold, and the implications of this, in section 7.

Based on the above assumption, we spatially allocate PSCT transfers for each crop in the same way as crop production, aggregating data into 0.5x0.5-degree grid cells (approximately 56x56 kilometers at the equator) and summing them across all crops in the country for which there are PSCT and crop production data. The result is a PSCT distribution map within each of the 16 countries in our analysis. To isolate grid cells with significant crop production, we only include those where land use is at least 50 percent cropland according to the European Space Agency’s land-cover/land-use database. Figure 2 shows total PSCT transfers measured for each grid cell and summed by crop. We construct the spatial distribution of MPS, a subset of PSCT, in the same way.

With a few additional assumptions, we extend the analysis to examine the spatial incidence of input subsidies. While crop-specific input subsidy data are not available, the OECD dataset provides countrylevel data on input subsidies. Since these are reported at country, rather than crop level, we make two additional assumptions to estimate their spatial incidence. First, we assume that input subsidies are mainly used to produce five crops: potatoes, sugarcane, maize, rice, and wheat. This assumption is based on literature surveys (Dorward 2009; Hemming et al. 2018), which found that these crops are most likely to be targeted by such programs, and the fact that they tend to be among the highest-yielding crops.

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