use of remote sensing data, especially night-time lights (NTL) data, has flourished in recent decades as a proxy for measuring economic activity and the spatial distribution of population. The availability of these data can make up for the absence of and possible inaccuracy in traditional economic statistics, especially because NTL data are available at fine scale and with high frequency. The growing use of these data raises the question of how accurate is this night-time lights proxy, and for what purposes is it best suited when studying economic development and population changes at sub-national levels. In the first part of this thesis, a 20-year time-series of GDP at China’s third sub-national level (counties, county-level cities and districts) and census population counts at the same level from 2000, 2010 and 2020, are used as a benchmark to examine the performance of multiple sources of NTL data as proxies for local economic activity and inequality. I also test the accuracy of gridded population estimates, some of which rely on NTL data, for estimating inter-census changes in local population. Based on the comprehensive evaluation of different NTL sources at different levels of spatial aggregation, the second part of the thesis uses NTL and other remote sensing data, in conjunction with the traditional GDP and population statistics, to study a set of economic issues that are pertinent to regional development in China. The impact of administrative upgrading of county-level units (a type of regional policy practiced in China) and of modern transport infrastructure is studied, using both GDP and NTL data to measure economic impacts. These studies use spatial econometric models that allow for spillovers. The changes in the city size distribution, in terms of land and people, is also studied because of the possibility that China’s cities are physically expanding in places other than where the population is moving. Overall, the thesis contributes a nuanced understanding of how NTL data and other remote sensing data can assist in studying complex social and economic problems, as well as raising some caveats about the limits to the uses of such data.