Do higher-quality nighttime lights and net primary productivity predict subnational GDP in developing countries? Evidence from the Philippines

Nighttime lights (NTL) data from satellites are a useful proxy for local economic activity in developing countries where economic data are sparse. Yet most analyses use the flawed DMSP NTL data, a poor proxy for GDP in less densely populated and highly agricultural rural areas. In this article, we augment a novel NTL dataset of the newer and better VIIRS NTL data with more ubiquitous remotely sensed data, namely, net primary productivity (NPP) and land cover, and we test whether these satellite data predict subnational GDP in both urban and rural sectors of the Philippines. The results confirm that the higher-quality VIIRS NTL data predict urban economic activity sufficiently well for both light-intense and dimly lit regions but still do not explain rural economic activity very well. The use of croplands NPP as an intensive measure of agricultural productivity, however, dramatically improves the performance of land cover as a proxy. We demonstrate that remotely sensed data can be useful in various applications, including evaluating the long-run dynamics of province-level GDP growth, the local impact of natural disasters, and the effects of infrastructure projects at the city and municipal levels. Such applications point toward the need for empirical analysis of growth at finer scales of aggregation.

Automated summary

This article explores the use of satellite data, including nighttime lights (NTL) data and other remotely sensed data, in predicting subnational GDP in urban and rural sectors of the Philippines. The results show that the higher-quality VIIRS NTL data perform well in predicting urban economic activity, but not rural economic activity. However, incorporating croplands net primary productivity (NPP) as a measure of agricultural productivity significantly improves the performance of land cover as a proxy.