Satellite images of nighttime lights have contributed to our understanding of economic activity for more than a decade. For example, nighttime lights can help approximate economic activity in small spatial units over time. Recent technological innovations have expanded potential applications. Data is now available at a monthly frequency, in a finer spatial grid, and with more sensitive measurement of low-light areas.
Early publications have demonstrated powerful applications of this new data to economics. Studies of natural disasters, lockdown measures during the COVID-19 pandemic, domestic policy measures (such as demonetization in India), and trade shocks (such as tariff escalation between China and the United States) have all been undertaken using nighttime light data.
Since nighttime lights and gross domestic product (GDP) are measured in different units, changes in the former cannot be converted directly into changes in the latter. In a new paper, ). This novel analysis builds on the seminal work of Henderson, Storeygard, and Weil (2012), who pioneered the estimation of an annual elasticity between nighttime light intensity and GDP with the old data a decade ago.
Even after aggregating nighttime light data to the country level and quarterly frequency, substantial statistical noise remains. This noise mainly stems from atmospheric conditions like cloud cover that impact the effective number of observations in each quarter. Our novel framework can identify the elasticity because nighttime lights are not measured equally well across countries: countries with more clouds have worse nighttime light data. Using information from the average number of effective observations, we provide a regression equation that estimates the elasticity precisely.
In emerging markets and developing economies (EMDEs), a 1 percent change in quarterly GDP is associated with a 1.55 percent change in the nighttime light intensity. While the elasticity varies somewhat with a country’s income status and economic structure, deviations from this average estimate are small. This elasticity can be used to translate changes in nighttime lights into changes in economic activity. For example, a 10 percent decline of nighttime light intensity suggests an economic impact of 6.5 percent of GDP.
We conclude our paper with two applications. First, we construct a nighttime light–adjusted measure of GDP growth and show that more developed countries and those with higher statistical capacity tend to have smaller measurement errors in their national accounts.However, we also find that low voice and accountability results in higher overestimation of GDP growth. This finding highlights the crucial role of academia, think tanks, and the media to ensure that statistics reflect reality. Second, we assess the economic impact of the COVID-19 pandemic and show that regions with higher levels of development and population density experience larger declines in economic activity.
The new data are still underused. They can shed even more light on the impact of economic events. Our elasticity will be helpful to approximate their impact on GDP from observed changes in nighttime lights.
Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring Economic Growth from Outer Space. American Economic Review, 102(2), 994-1028. https://www.aeaweb.org/articles?id=10.1257/aer.102.2.994