This paper presents a method for measuring economic growth using satellite data on night lights. The authors argue that night lights data can serve as a useful proxy for GDP growth, especially in countries with poor national income accounts. They develop a statistical framework that combines night lights data with existing income growth measures to improve estimates of true income growth. The framework assumes that measurement errors in using observed light as an indicator of income are uncorrelated with measurement errors in national income accounts. The authors find that for countries with good national income accounts, night lights data provide only marginal value in estimating true income growth. However, for countries with poor national income accounts, the optimal estimate of true income growth is a composite of night lights data and national income accounts data. The authors also find that night lights data allow for the measurement of income growth in sub- and supranational regions. The authors apply their method to Sub-Saharan African regions over the last 17 years. They find that real incomes in non-coastal areas have grown faster by 1/3 of an annual percentage point than coastal areas; non-malarial areas have grown faster than malarial ones by 1/3 to 2/3 annual percent points; and primate city regions have grown no faster than hinterland areas. The authors also find that night lights data can be used to measure economic growth in regions defined by geography, economic, or health metrics, rather than by political borders. They conclude that "empirical growth" need no longer be synonymous with "national income accounts." The paper also discusses the limitations of GDP data, particularly in developing countries, and the advantages of using night lights data as an alternative measure of economic activity.This paper presents a method for measuring economic growth using satellite data on night lights. The authors argue that night lights data can serve as a useful proxy for GDP growth, especially in countries with poor national income accounts. They develop a statistical framework that combines night lights data with existing income growth measures to improve estimates of true income growth. The framework assumes that measurement errors in using observed light as an indicator of income are uncorrelated with measurement errors in national income accounts. The authors find that for countries with good national income accounts, night lights data provide only marginal value in estimating true income growth. However, for countries with poor national income accounts, the optimal estimate of true income growth is a composite of night lights data and national income accounts data. The authors also find that night lights data allow for the measurement of income growth in sub- and supranational regions. The authors apply their method to Sub-Saharan African regions over the last 17 years. They find that real incomes in non-coastal areas have grown faster by 1/3 of an annual percentage point than coastal areas; non-malarial areas have grown faster than malarial ones by 1/3 to 2/3 annual percent points; and primate city regions have grown no faster than hinterland areas. The authors also find that night lights data can be used to measure economic growth in regions defined by geography, economic, or health metrics, rather than by political borders. They conclude that "empirical growth" need no longer be synonymous with "national income accounts." The paper also discusses the limitations of GDP data, particularly in developing countries, and the advantages of using night lights data as an alternative measure of economic activity.