Illuminating Economic Growth

Abstract

This paper seeks to illuminate the uncertainty in official GDP per capita measures using auxiliary data. Using satellite-recorded nighttime lights as an additional measurement of true GDP per capita, we provide a statistical framework, in which the error in official GDP per capita may depend on the country's statistical capacity and the relationship between nighttime lights and true GDP per capita can be nonlinear and vary with geographic location. This paper uses recently developed results for measurement error models to identify and estimate the nonlinear relationship between nighttime lights and true GDP per capita and the nonparametric distribution of errors in official GDP per capita data. We then construct more precise and robust measures of GDP per capita using nighttime lights, official national accounts data, statistical capacity, and geographic locations. We find that GDP per capita measures are less precise for middle and low income countries and nighttime lights can play a bigger role in improving such measures.

Can we Rely on VIIRS Nightlights to Estimate the Short-Term Impacts of Natural Disasters?

“…More recent research utilizing the VIIRS nightlights also points to the limitation of monthly VIIRS in the detection of GDP and urban markets. Hu and Yao (2019) show how monthly VIIRS data have very low correlation for low income countries. 4 The overall correlation is significantly better for middle income countries and annual, corrected data.…”

“…Part of the reason for these results could be that an influx of aid and personnel would lead to a net effect of zero, but given the noise that is exhibited overall in the VIIRS data it would be hard to draw any more confident conclusions in this regard. The issue of noise in the underlying monthly data has been well illustrated in Hu and Yao (2019), where the monthly VIIRS data showed much lower coorelation with GDP than annual VIIRS and DMSP data. The fixed effects regressions were also inconclusive, as there was little if any significance across disaster types and months.…”

Measurement systems

“…It is generally recognized that officially reported GDP for developing countries may not be very accurate and Hu and Yao (2018) nicely illustrate the idea of combining multiple imperfect nonclassical measurements to address this. They consider the measurement of true unobserved per capita GDP ( * ) using (i) the World Bank's reported GDPs (), (ii) satellite measurement of night time light intensity () and (iii) two other auxiliary variables, a country's latitude () and its so-called "statistical capacity" (), which measures the data collecting abilities of the country's statistical institutions.…”

Who Gained from India’s Demonetization? Insights from Satellites and Surveys

“…where our focus is on the coefficient β T r.×g , which estimates whether there are trend To further show parallel trends in the effect of demonetization-centered deposit growth on nighttime lights prior to the shock in November 2016, Figure 6 plots the monthly β t from the following estimating equation, using (i.e., omitting) October of 2016 as the base 25 The Reserve Bank of India updates its tables periodically following the formation of new districts. We lose some districts that were split after the 2011 census for which no clear parent district could be assigned.…”