Development Research at High Geographic Resolution: An Analysis of Night-Lights, Firms, and Poverty in India Using the SHRUG Open Data Platform

Abstract

The SHRUG is an open data platform describing multidimensional socioeconomic development across 600,000 villages and towns in India. This paper presents three illustrative analyses only possible with high-resolution data. First, it confirms that nighttime lights are highly significant proxies for population, employment, per capita consumption, and electrification at very local levels. However, elasticities between night-lights and these variables are far lower in time series than in cross section, and vary widely across context and level of aggregation. Next, this study shows that the distribution of manufacturing employment across villages follows a power law: the majority of rural Indians have considerably less access to manufacturing employment than is suggested by aggregate data. Third, a poverty mapping exercise explores local heterogeneity in living standards and estimates the potential targeting improvement from allocating programs at the village—rather than at the district—level. The SHRUG can serve as a model for open high-resolution data in developing countries.

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

“…The between estimator R 2 values were 15 points higher (at 0.86 vs. 0.71) when using the masked VNL data products rather than their unmasked counterparts. Prior studies have shown that NTL data are more powerful cross-sectional predictors of differences in GDP (and other economic activity indicators) between areas than they are predictors of time-series changes [26,28,45]. This pattern also holds for the masked V.2 VNL data, where the R 2 values for the between estimator in the cross-section were almost 30 times as high as for the within-estimator of the time-series changes.…”

“…The results in Table 2 are atypical of studies that relate NTL data to GDP data. While there are some county-level results for China [25], the validation studies with GDP data as a benchmark are mostly for spatially aggregated data at the national or first subnational level, even as applied studies increasingly use NTL data locally [45]. It is therefore of interest to see how the results for estimating Equation (1) change when the GDP and NTL data are at the state-level.…”

“…In other words, estimates of the impact of the treatment on NTL data may not be very informative about the impact of the treatment on economic activity. In particular, treatment effects may be far smaller than presumed from econometric estimates using NTL data, especially if the researchers assume that cross-sectional elasticities hold in the time-series context [45].…”

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

“…The results in Table 1 are atypical of studies that relate NTL data to GDP data. Apart from county-level in China [25] and commune-level in Vietnam [28] validation studies are mostly for aggregated data, such as the national or first subnational level, even as applied studies use NTL data locally [45]. It is therefore of interest to see how equation (1) changes with state-level data.…”

“…In other words, estimates of the impact of the treatment on NTL data may not be very informative about the impact of the treatment on economic activity. In particular, treatment effects may be far smaller than presumed from econometric estimates using NTL data, especially if researchers assume that cross-sectional elasticities hold in the time-series context [45].…”