Davide Fiaschi, Angela Parenti, Cristiano Ricci
arXiv 16 Jul 2026 · Econometrics
arXiv:2607.14825 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Huber, J. D. and L. Mayoral (2024, February) (2024) Economic development in pixels: The limitations of nightlights and new spatially disaggregated measures of consumption and poverty | 0.811 | 4 | 2 | 100% |
| 2 | Abbes, A. B., J. Machicao, P. L. Corrêa, A. Specht, R. Devillers, J.… (2024) Deepwealth: A generalizable open-source deep learning framework using satellite images for well-being estimation | 0.737 | 3 | 2 | 100% |
| 3 | Gibson, J., B. Kim, and C. Li (2024) Luminosity and local economic growth | 0.737 | 3 | 2 | 100% |
| 4 | Jean, N., M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S. Ermon (2016) Combining satellite imagery and machine learning to predict poverty | 0.737 | 3 | 2 | 100% |
| 5 | Galimberti, J. K (2020) Forecasting gdp growth from outer space | 0.585 | 3 | 1 | 100% |
| 6 | Bluhm, R. and G. C. McCord (2022) What can we learn from nighttime lights for small geographies? measurement errors and heterogeneous elasticities | 0.511 | 2 | 1 | 100% |
| 7 | Durbin, J (1954) Errors in variables | 0.511 | 2 | 1 | 100% |
| 8 | Henderson, J. V., A. Storeygard, and D. N. Weil (2012) Measuring economic growth from outer space | 0.511 | 2 | 1 | 100% |
| 9 | Wald, A (1940) The fitting of straight lines if both variables are subject to error | 0.511 | 2 | 1 | 100% |
| 10 | Cannari, L. and G. Iuzzolino (2009) Le differenze nel livello dei prezzi al consumo tra nord e sud | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.