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Ingreso Estructural Por Área Geográfica: una aplicación para Argentina

Author

Listed:
  • Abbate Nicolás
  • Gasparini Leonardo
  • Gluzmann Pablo Alfredo
  • Montes Rojas Gabriel
  • Sznaider Iván
  • Yatche Tobías

Abstract

El objetivo de este trabajo es obtener estimaciones del ingreso estructural para Argentina con un alto nivel de desagregación geográfica, específicamente a nivel de los más de 50.000 radios censales. Para esto estimamos una serie de modelos para el ingreso per cápita familiar en función de características observables para todas las ondas disponibles de la Encuesta Permanente de Hogares en su versión continua (2003-2022) y generamos predicciones del ingreso utilizando las características observables de los hogares en los censos 2001 y 2010. Argentina ha experimentado fuertes vaivenes económicos durante los últimos 20 años, lo que permite obtener predicciones de ingreso bajo distintos estados de la naturaleza. Al incluir todas las estimaciones, podemos predecir el ingreso estructural, entendido como un concepto de mediano plazo donde son los factores estructurales y no los coyunturales, los que tienen mayor influencia. La construcción de esta clase de mapas tiene una importante gama de aplicaciones, y su precisión, desagregación y temporalidad puede ser mejorada utilizando técnicas de inteligencia artifical sobre las imágenes satelitales de las zonas representadas.

Suggested Citation

  • Abbate Nicolás & Gasparini Leonardo & Gluzmann Pablo Alfredo & Montes Rojas Gabriel & Sznaider Iván & Yatche Tobías, 2023. "Ingreso Estructural Por Área Geográfica: una aplicación para Argentina," Asociación Argentina de Economía Política: Working Papers 4622, Asociación Argentina de Economía Política.
  • Handle: RePEc:aep:anales:4622
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    References listed on IDEAS

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    1. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    2. Ryan Engstrom & Jonathan Hersh & David Newhouse, 2022. "Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being," The World Bank Economic Review, World Bank, vol. 36(2), pages 382-412.
    3. Leopoldo Tornarolli, 2018. "Series Comparables de Indigencia y Pobreza: Una Propuesta Metodológica," CEDLAS, Working Papers 0226, CEDLAS, Universidad Nacional de La Plata.
    4. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    5. Cuong, Nguyen Viet & Truong, Tran Ngoc & van der Weide, Roy, 2010. "Poverty and inequality maps for rural Vietnam: an application of small area estimation," Policy Research Working Paper Series 5443, The World Bank.
    6. Nguyen Viet Cuong & Tran Ngoc Truong & Roy Van Der Weide, 2010. "Poverty and Inequality Maps in Rural Vietnam: An Application of Small Area Estimation," Asian Economic Journal, East Asian Economic Association, vol. 24(4), pages 355-390, December.
    7. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    8. Matías Ciaschi, 2021. "Análisis distributivo utilizando información satelital. El caso de Argentina [Distributive analysis using satellite data. The case of Argenina]," Estudios Economicos, Universidad Nacional del Sur, Departamento de Economia, vol. 38(77), pages 5-38, july-dece.
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    More about this item

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior

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