To measure urban growth, we therefore rely on data that are not limited by India’s statistical infrastructure, namely night-time lights, as detected by satellite. These data are originally from the Defence Meteorological Satellite Program (DMSP) and are processed by the National Oceanic and Atmospheric Administration. They provide annual estimates over 1992-2012 at a one square kilometer resolution (in some years two satellites are in orbit so there are 33 satellite-year observations). Gibson et al (2015) use these data to estimate rates of area expansion over 1992-2012 for 47 Indian agglomerations with a population of at least one million in the 2011 census.<sup>12</sup> These agglomerations are shown in Figure 4, in terms of their lit area in 2012. We treat these 47 agglomerations as India’s “big cities” and contrast the effects of their growth with the effects of the growth of smaller towns.<sup>13</sup> The threshold of one million population is also used by Chatterjee et al (2015) and Li and Rama (2015) to demarcate between India’s large and small urban areas.

Figure 4 also shows the NSS regions; 39 of 59 have one or more of the big cities in them. For the others, the city effects come indirectly, with these spatial spillovers captured by our econometric model. We consider city growth on the extensive and intensive margins, since it is _a priori_ unclear which matters more to poverty reduction. For example, extensive margin growth may offer easier commuting from rural areas but more sprawling cities may be less efficient. Specifically, we use the night lights to both demarcate the edge of cities for examining effects of expanded areas (the extensive margin), and to calculate the intensity of light—the “digital number” (DN)— emanating from within these boundaries (the intensive margin).<sup>14</sup> There is also a measurement reason for distinguishing between lit area and light output within an area. Some DMSP satellites distribute measured light less intensely over a wider area and others focus it into a smaller area.<sup>15</sup> For example, Liu et al (2012) note that

> 12 The census lists 53 million-plus cities but two are within the Delhi agglomeration (Faridabad and Ghaziabad) and four others (Malappurum, Srinigar, Kollam and Kannur) were too dim to be detected at the 50% threshold used for the other cities. At the 20% luminosity threshhold we use to distinguish towns from rural areas, these four cities contribute just 0.4% of total lit area (and 1.3% of big city lit area, if measured at that threshold). 13 The area expansion rate of the 23 cities that were million plus in 1991 was not significantly different from the others that crossed the million threshhold between 1991 and 2011, indicating that there is no evident selection or survivorship bias by defining the sample of big cities by their meeting a criteria set at the end of the period. 14 Technically, we measure areas by starting at the center of each big city, where lights should be brightest, and as the algorithm moves outwards and comes across pixels less illuminated than the brightness threshold it searches in a different direction. If the algorithm finds no contiguous pixels with light above the threshold except those closer to the city center that it has already scanned over, it sets a boundary.

> 15 To briefly digress, DMSP was to detect clouds, for daily weather forecasts to help Air Force pilots, rather than to detect ground level activity to help economists. Photons from the observed area enter a scanner and create a pulse of electrons that map to a DN ranging from 0-63; this range was because on-board data holding could spare just six bits of memory (2<sup>6</sup> =64) for each datum (Abrahams et al 2015). Given the original purpose, sensors on board DMSP satellites were adjusted over the lunar cycle to keep the brightness of cloud tops the same, with

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