ABSTRACT
This chapter examines recent trends in the use of geospatial data for economic inferences, with particular focus on developing countries. In those countries, which were previously data poor, the geospatial data can be used directly to enable the measurement of human economic outcomes, indirectly by addressing environment characteristics that impact the human outcomes, or through some combination. In this chapter, the authors summarize a series of recent and policy-relevant contributions, spanning the general interest economics and scientific literatures, which exhibit the value of geospatial data in supporting policy-relevant research in data-poor environments. After introducing the current remote sensing and other data collection techniques, it was noted that satellite-derived data—which have countrywide, regional, or global coverage—are often comparable across geopolitical boundaries, thus avoiding quality control problems that arise when synthesizing national surveys or census data from multiple sources. The measurement of economic outcomes based on changes in luminosity observed from space at nighttime is discussed as an application example. The approach is the extended to measuring the effects of exposure to natural phenomena from economic activities, such as pollution. In addition to the measurement of final outcomes, such as exposure and economic development, remotely sensed data have also been useful as a tool for identification in causal inference when human outcomes and exposures are jointly determined This chapter concluded with highlighting some points of caution regarding the handling of the highly processed satellite data and their associated sources.
