are visible in the overwhelming majority of the land area. In Figure 3, a closer view of Dar es Salaam, Tanzania’s largest city, shows a contiguously lit area 20–30 km across, extending farther in a few directions along main intercity roads just as the city’s built up area does.

Henderson, Storeygard and Weil (2012) show that light growth is a good proxy for income growth at the national level. Annual changes in gross domestic product (GDP) are correlated with changes in DN, with an elasticity of approximately 0.3 for a global sample as well as a sample of low and middle income countries. In both samples, the lights explain about 20 percent of the variation in log GDP net of country and year The chief strength of the lights lies in their geographic specificity—they are highly local measures. To proceed with lights as a measure of city-level GDP, it must first be shown that the strong national relationship holds for _subnational_ regions. This is problematic because of a mismatch in data availability. Rich countries tend to have good local economic data, but the lights data are heavily topcoded in their cities. Lights topcoding is less of a problem in most poorer countries, and especially in Sub-Saharan Africa, where almost no pixels are topcoded (15 per 100,000, or 3 per 100,000 outside South Africa and Nigeria). However, good local economic data are rarely available. China and South Africa represent good compromises, with relatively little topcoding but relatively high quality income data for a short panel of regions.

China has panel GDP data for two relevant types of subnational regions: cities proper and prefectures.<sup>17</sup> Columns 1 and 2 in Table 1 show, at the city proper and prefecture level, respectively, that the elasticity of GDP with respect to light is significantly positive in a 1990/1992–2005 long difference specification.<sup>18</sup> The point estimate is very similar to the one for the global sample.

South Africa also has household income data in its 1996 and 2001 censuses that can be used to form an income panel for administrative units called magisterial districts (MD).<sup>19</sup> When lights and income are summed within 284 MD, the elasticity of changes decreasing proportionally with the cosine of latitude. The data are processed and distributed by the United States National Oceanic and Atmospheric Administration (NOAA), http://www.ngdc.noaa.gov/dmsp/downloadV4composites.html. Accessed 22 January 2010.

> 17I am grateful to Vernon Henderson and Qinghua Zhang for providing the China evidence based on their work in progress with Nathaniel Baum-Snow, Loren Brandt, and Matthew Turner.

> 18The lights are from 1992 but the GDP data are from 1990—the closest year with good data.

> 19Produced by Statistics South Africa and distributed as: Minnesota Population Center. Integrated Public Use Microdata Series (IPUMS), International: Version 6.1 [Machine-readable database]. Minneapolis: University of Minnesota, 2011. https://international.ipums.org/. Accessed 28 April 2011. The 2007 census also includes household income data, but the smallest comparable geography in the IPUMS sample is the province, and there are only nine of them. The IPUMS data for both 1996 and

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