From the Census to Satellites: New Tools for Measuring Poverty in Ecuador
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Publicado:
jun 29, 2026
Palabras clave:
poverty, spatial models, econometrics,, urbanization,, machine learning, gesopatial data
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In this study, an econometric model was developed to quantify the effects of employment and economic activity on parish-level poverty in Ecuador, explaining 63% of its variability. Geospatial data and census sources were used for this purpose, revealing that employment and economic activity have negative effects of 27.5% and 6%, respectively, on poverty. Another important finding was the identification of spatial dependence in poverty rates. In addition, an index was constructed to analyze the degree of urbanization of parishes.
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Peñafiel, M. (2026). From the Census to Satellites: New Tools for Measuring Poverty in Ecuador. Cuestiones Económicas, 36(1), Autor: Michael Peñafiel. https://doi.org/10.47550/RCE/36.1.4
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Andreano, M. S., Benedetti, R., Piersimoni, F., & Savio, G. (2021). Mapping Poverty of Latin American and Caribbean Countries from Heaven Through Night-Light Satellite Images. Social Indicators Research, 156, 533-562. https://doi.org/10.1007/s11205--020-02267-1 Asher, S., & Novosad, P. (2020). Rural Roads and Local Economic Development. American Economic Review, 110(3), 797-823. https://doi.org/10.1257/aer.20180268
Donaldson, D. (2018). Railroads of the Raj: Estimating the Impact of Transportation Infrastructure. American Economic Review, 108(4-5), 899-934. https://doi.org/10.1257/aer.20101199
Economic Commission for Latin America and the Caribbean. (2007). La medida de necesidades básicas insatisfechas (NBI) como instrumento de medición de la pobreza y focalización de programas (Estudios y Perspectivas - Oficina de la CEPAL en Bogotá LC/L.2840-P). Comisión Económica para América Latina y el Caribe. https://www.cepal.org/es/publicaciones/3544-la-medida-necesidades-basicas-insatisfechas-nbi-nstrumento-medicion-la-pobreza
Economic Commission for Latin America and the Caribbean. (2024). América Latina y el Caribe: perfil regional social-demográfico. https://statistics.cepal.org/portal/cepalstat/perfil-regional.html?theme=1&lang=esElvidge, C. D., Sutton, P. C.,
Ghosh, T., Tuttle, B. T., Baugh, K. E., Bhaduri, B., & Bright, E. (2009). A global poverty map derived from satellite data. Computers & Geosciences, 35(8), 1652-1660. https://doi.org/10.1016/j.cageo.2009.01.009
Jean, N., Burke, M., Xie, M., Davis, W. M. A., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794. https://doi.org/10.1126/science.aaf7894
Li, M., Lin, J., Ji, Z., Chen, K., & Liu, J. (2023). Evaluación de la pobreza a escala de cuadrícula mediante la integración de luz nocturna de alta resolución y big data espacial: un estudio de caso en el delta del río Pearl. Remote Sensing, 15(18), 4618. https://doi.org/10.3390/rs15184618
Masaki, T., Newhouse, D., Silwal, A. R., Bedada, A., & Engstrom, R. (2020). Small Area Estimation of Non-Monetary Poverty with Geospatial Data (Policy Research Working Paper No. 9383). World Bank. zttps://openknowledge.worldbank.org/server/api/core/bitstreams/9d4473f3-104f-5fa4-8758-0e5035dad36e/content
National Institute of Statistics and Censuses. (2023). Ficha Metodológica de Indicador: Porcentaje de personas u hogares en Pobreza por Necesidades Básicas Insatisfechas (NBI)[Ficha Metodológica]. National Institute of Statistics; Censuses. https://www.ecuadorencifras.gob.ec/
National Institute of Statistics and Censuses. (2024). Pobreza por necesidades básicas insatisfechas (NBI): resultados Censo Ecuador [Informe de Resultados]. INEC. https://www.censoecuador.gob.ec/wp-content/uploads/2024/12/Pobreza_NBI_Resultados_Dic2024.pdf
National Institute of Statistics and Censuses. (2025). Encuesta Nacional de Empleo, Desempleo y Subempleo (ENEMDU): ndicadores de pobreza y desigualdad, diciembre 2025 [Boletín Técnico]. INEC. https://www.ecuadorencifras.gob.ec/documentos/web-inec/POBREZA/2025/Diciembre/202512_PobrezayDesigualdad.pdf
Puttanapong, N., Martinez, A., Jr., Bulan, J. A. N., Addawe, M., Durante, R. L., & Martillan, M. (2022). Predicting Poverty Using Geospatial Data in Thailand. ISPRS International Journal of Geo-Information, 11(5), 293. https://doi.org/10.3390/ijgi11050293
United Nations Office for the Coordination of Humanitarian Affairs. (2024). Ecuador - Subnational Administrative Boundaries. The Humanitarian Data Exchange (HDX). https://data.humdata.org/dataset/cod-ab-ecuWang, K., Zhang, L., Cai, M., Liu, L., Wu, H., & Peng, Z. (2023). Measuring Urban Poverty
Spatial by Remote Sensing and Social Sensing Data: A Fine-Scale Empirical Study from Zhengzhou. Remote Sensing, 15(2), 381. https://doi.org/10.3390/rs15020381
Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., & Wu, J. (2019). Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh. Remote Sensing, 11(4), 375. https://doi.org/10.3390/rs11040375