Abstract
AbstractThis study explores the potential and the limits of medium-resolution satellite data as a proxy for economic activity at small geographic units. Using a commune-level dataset from Vietnam, it compares the performance of commonly used nightlight data and higher resolution Landsat imagery, which measures daytime light reflection. The analysis suggests that Landsat outperforms nighttime lights at predicting enterprise counts, employment, and expenditure in simple regression models. A parsimonious combination of the first two moments of the Landsat spectral bands can explain a reasonable share of the variation in economic activity in the cross-section. There is, however, poor prediction power of either satellite measure for changes over time.
Highlights
The analysis of satellite imagery is a key methodology in economics and other applied scientific research
The analysis is based on regressions of the measures of economic activity on nightlight data from DMSP-ordinary least squares (OLS)
This paper follow the sum of lights (SOL) approach that is commonly applied for indicating light intensity
Summary
The analysis of satellite imagery is a key methodology in economics and other applied scientific research. The study adds to the literature on remote sensing by showing that parsimonious linear models of medium-resolution satellite data can capture the spatial variation of economic activity to a fair degree and are vastly superior to simple specifications of nightlight data in the context of a developing country. This provides a viable and inexpensive alternative for applied researchers to study areas with inferior data availability.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
