We study the distribution of economic activity, as proxied by lights at night, across 250,000 grid cells of average area 560 square kilometers. We first document that nearly half of the variation can be explained by a parsimonious set of physical geography attributes. A full set of country...

This paper studies the maturity and stream of payments of sovereign debt. Using Bloomberg bond data for eleven emerging economies, we document that countries react to crises by issuing debt with shortened maturity but back-load payment schedules. To account for this pattern, we develop a...

We use state-of-the-art, satellite-based PM2.5 estimates to assess the extent to which the EPA's existing, monitor-based measurements over- or under-estimate true exposure to PM2.5 pollution. Treating satellite-based estimates as truth implies a substantial number of "policy...

In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy. We demonstrate how advancements in satellite imagery and machine learning can help ameliorate these data and inference challenges. In the context of an...

This paper proposes a methodology for defining urban markets based on economic activity detected by satellite imagery. We use nighttime lights data, whose use in economics is increasingly common, to define urban markets based on contiguous pixels that have a minimum threshold of light intensity....

The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional approaches rely heavily on repeated in-person field surveys to measure program effects. However, this is costly, time-consuming, and often logistically challenging. Here we provide the first...

We apply deep learning to daytime satellite imagery to predict changes in income and population at high spatial resolution in US data. For grid cells with lateral dimensions of 1.2km and 2.4km (where the average US county has dimension of 55.6km), our model predictions achieve R2 values of 0.85...

Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine...

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of...