Popular DMSP night lights data are flawed by blurring, top-coding, and lack of calibration. Yet newer and better VIIRS data are rarely used in economics. We compare these two data sources for predicting Indonesian GDP at the second sub-national level. DMSP data are a bad proxy for GDP outside of cities. The city lights-GDP relationship is twice as noisy using DMSP as using VIIRS.Spatial inequality is considerably understated with DMSP data. A Pareto adjustment to correct for top-coding in DMSP data has a modest effect but still understates spatial inequality and misses key features of economic activity in Jakarta.

Night lights, as detected by satellites, are increasingly used by economists, typically as a proxy for economic activity. The growing popularity of these data reflects either the absence, or the presumed inaccuracy, of more conventional economic statistics, like national or regional GDP. Further growth in use of night lights is likely, as they have been included in the AidData geoquery tool for providing subnational data, and in geographic data that the Demographic and Health Survey links to anonymized survey enumeration areas. Yet, this ease of obtaining night lights data may lead to inappropriate use, if users fail to recognize that most of the satellites providing these data were not designed to assist economists, and have features that may threaten validity of analyses based on these data, especially for temporal comparisons, and for small and rural areas. In this paper, we review sources of satellite data on night lights, discuss issues with these data, and survey some of their uses in economics.

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Abstract:The relationship between economic growth, expansion of urban land area and the broader issue of cultivated land conversion in China has been closely examined for the late 1980s and 1990s. Much less is known about recent urban expansion and if the effects of economic growth on this expansion have changed over time. This paper updates estimates of urban expansion for China and examines the relationship with city economic growth for 1993-2012. To see if patterns are robust to different types of evidence, administrative data on the area of 225 urban cores are compared to estimates of brightly lit areas from remotely sensed night lights. The trend annual expansion rate in lit area is 8% and was significantly faster in the decade to 2002 than in the most recent decade. Expansion is slower according to administrative data, at just 5% per annum, with no change in unconditional expansion rates between decades, while conditional expansion rates have declined. The elasticity of area with respect to city economic output is about 0.3. Over time, expansion of urban land area is becoming less responsive to the growth of the local non-agricultural population.

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation.