ecological potential across EAs, regions and countries. To integrate population density with AEP, for each EA we therefore also compute an agro-ecological population pressure that takes account of the AEP, namely the AEP per sqkm divided by the population density, which gives us the AEP per person in the EA. Unlike for raw population density, the lower this number, the higher is the population pressure.

Rural population for each EA has not been collected in the LSMS-ISA surveys. We therefore use the data for rural population density collected by Harvest Choice<sup>5</sup> . These data are disaggregated to the level of communities which contain the EA.

The EAs in the surveys have been chosen with probability proportional to their population at the last census. Our measure of rural population density will differ significantly from rural population density data derived from census data. The reason is that the sample gives preference to EAs with higher population, and low population density areas will be presented less. What we are measuring is the rural population density of the areas in which most of the rural people actually live.

# **Urban Gravity**

We follow Henderson et al. (2009) Gallup et al. (1999), Kiszewski et al. (2004) in using the correlation between the intensity of light emitted by a particular pixel of space. While light intensity is not a direct measure of economic activity, it is highly correlated with it. If GDP data are flawed, they may be a superior measure of economic activity at the national level.<sup>6</sup> A great advantage of light intensity is that they can be used as proxies for GDP for sub-units of countries for which GDP data are not available, as is the case for most cities in Africa. The data for light intensity come from the Defense Meteorological Satellite Program (DMSP) of the National Geophysical Data Center.<sup>7</sup> To measure the aggregate emission of light at night from a city, we aggregate the light intensity of each pixel over all pixels of the city. The resulting light emission should be correlated to the economic output and income of the city, and therefore be a proxy for the food demand emanating from it. To take account of the distance decay of economic pull of cities over space, we weigh the light intensity of each city with respect to A negative exponential function. (Deichman 1997). We then aggregate the resulting UG by summing it kk<sup>by travel time in hours to each of these cities, using a</sup> over all cities in the country or across the border of neighboring countries with population above 500,000.<sup>8</sup> We adjust the light intensity of cross-border cities by the composite index of the difficulty of movement of people, goods and information across the respective borders, using the higher difficulty of cross-border movement of the two respective countries. The result is the aggregate UG to which each EA is exposed.

5 We use of Rural population density (pers./sq. km) in 2005. Calculations based on data from: Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IFPRI); The World Bank; and Centro Internacional de Agricultura Tropical (CIAT). 2004. 6 The authors present estimates across countries and over time of reported GDP and light intensity, and also present an estimator of GDP which optimally combines the two.

7 The data source is the DMSP F16 the U.S. Air Force’s research project called the Defense Meteorological Satellite Program (DMSP) established in 1960. Since 1994 DSMP produce a time series of annual cloud-free composites of DMSP nighttime lights. Together with the NGDC –EOG (the National Geographic Data Center – Earth Observation Group .

8We choose cities over 500,000 population at the current time, because smaller cities often are centers of agricultural services and markets, and therefore their population is influenced by the AEP of the surrounding area, which makes it endogenous 9