🤖 AI Summary
This study addresses the limitations of high-resolution proxies—such as nighttime lights—in capturing unobserved local economic activity after aggregation to administrative units, a constraint rooted in aggregation bias. The authors develop an inverse regression framework and introduce a triple decomposition theorem for predictive elasticity, revealing for the first time that this bias is jointly driven by administrative unit size and internal economic heterogeneity, while also clarifying the conditions under which cross-regional transferability holds. Leveraging VIIRS nighttime lights and subnational GDP or income data across Brazil, Italy, the United States, Indonesia, and Kenya, they combine elasticity decomposition, Monte Carlo simulations, and empirical validation to demonstrate that nighttime lights reliably predict economic activity only in relatively affluent regions and only after local calibration.
📝 Abstract
This paper studies when high-resolution signals aggregated to administrative units can recover unobserved local economic activity. We develop a reverse-regression framework for signals generated by activity but used to predict it at coarser spatial supports. The main theorem decomposes predictive elasticity into elementary elasticity, reverse-regression attenuation, and a spatial aggregation term driven by unit size and within-unit dispersion, showing aggregation pulls elasticities toward one. Monte Carlo evidence confirms the decomposition and clarifies transferability conditions. Applications to VIIRS nighttime lights and local GDP or income in Brazil, Italy, the United States, Indonesia, and Kenya support local calibration mainly in richer contexts.