Satellite nighttime lights reveal how poorer districts in India catch up. Accounting for connections between neighboring districts raises the estimated pace of catch-up and shortens the time needed to close half the gap from about 23 to 13 years.
Does obesity fall as countries become wealthier? Evidence from 84 countries points to a more complex relationship with multiple turning points, rather than a single income threshold.
Economic growth does not reduce unemployment equally across Indonesia: it helps a lot in some districts and barely at all in others. Because growth in one district also affects jobs in its neighbors, job policies work best when they fit local conditions and are coordinated across nearby regions.
Around Jakarta, many people live in one district and work in another, and each district sets its own minimum wage. We find that workers are more likely to commute to a neighboring district when its minimum wage is higher, so wage rules drawn along district borders shape where people travel to work.
Satellite images of lights at night can track how China's provinces grow, but the link between light and income shifts over time and weakens during downturns. Newer satellite data measure economic activity more accurately than older data, especially for industry and services.
Up-to-date poverty data are scarce in Cambodia. We combine household surveys with satellite data, such as nighttime lights, and machine learning to map, down to the household level, where people lack basics like clean water, sanitation, electricity, and education.
Do regional gaps within a country first widen and then narrow as the country gets richer? We find that this inverted-U pattern holds, and that natural resource wealth, farmland, and ethnic inequality are also among the most reliable predictors of regional inequality.
Through the lens of a multiscale geographically weighted regression (MGWR) and an updated inference framework, we assess the spatial scale at which political and socioeconomic factors affect violence.
This study constructs a novel subnational GDP dataset for Vietnam by integrating nighttime lights, agricultural land, and climate data through machine learning methods