NIGHT LIGHTS IN ECONOMICS: SOURCES AND USES¹

Abstract

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.

Better Night Lights Data, For Longer*

“…Moreover, a recent trend in economics is to use DMSP night lights data for ever smaller areas and so concern about blurring should have become more apparent, yet it remains largely ignored, as seen in the review by Gibson et al . (2020). For example, the spatial units used by Heger and Neumayer (2019) are about one‐tenth of the size of the area mapped in Figure 1, and those used by Lee (2018) are about one‐hundredth of the size of Figure 1, yet the issue of blurring is ignored in these papers.…”

“…Another data quality diagnostic is the number of nights used in the annual composite, which is often fewer in the first and last year of the series for a satellite as it is either bedded‐in or retired. For F18 the number of nights used in the 2013 annual composite was more than in the previous 3 years from the same satellite (Gibson et al ., 2020). Finally, if the 2012 DMSP data are related to 2012 GDP for the NUTS2 regions, the results are about the same as what is reported in Table 2 using the 2013 DMSP data, which reflects the high correlation between the DMSP estimates for the 2 years ( r = 0.98).…”

“…For example, of the 41 IDEAS/RePEc records from 2019 or 2020, all but four use DMSP data. The inaccuracies in DMSP data include: blurred images (Abrahams et al ., 2018) and geo‐location errors (Tuttle et al ., 2013), so light is attributed to places other than where it is emitted; top‐coding, where brightly lit city centres get the same data values as low‐density, dimmer suburbs (Bluhm and Krause, 2018); and uncalibrated variation in DMSP sensor amplification and inter‐satellite differences that impair comparability over time and space (Gibson et al ., 2020). Also, DMSP data are increasingly out of date, as production of these data ended in 2013.…”

“…Inter‐satellite differences also create inconsistency. The superiority of VIIRS has resulted in a rapid switch in the scientific literature; now almost twice as many articles per year publish using the VIIRS night lights data compared to those using the older and less accurate DMSP data, yet economists have continued to persist with DMSP data and largely ignore VIIRS (Gibson et al ., 2020).…”

Estimating Rural Electric Power Consumption Using NPP-VIIRS Night-Time Light, Toponym and POI Data in Ethnic Minority Areas of China

“…Therefore, methods used in previous studies may not be usable in small scale and undeveloped areas, such as rural areas. Secondly, previous research shows that VIIRS-DNB data may threaten validity of analyses especially in small and rural areas because of the low density [31]. In order to reduce the influence in small or rural areas and provide more accurate predictions of poverty, we can efficiently combine disparate data sources [32].…”