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Big Data y Algoritmos para la Medición de la Pobreza y el Desarrollo

Author

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  • Walter Sosa Escudero

    (UDESA, CONICET, CEDLAS-IIE-UNLP)

Abstract

La revolución del combo big data-machine learning-inteligencia artificial ha invadido todos los campos del conocimiento y, esperablemente, el de la medición del bienestar no es una excepción. Y, naturalmente, urge preguntar si los enormes problemas de cuantificación de la pobreza o la desigualdad no encontraran una solución rápida y efectiva que provenga de la combinación de datos masivos de big data y los poderosos algoritmos de machine learning y la inteligencia artificial. Esta nota es una introducción técnicamente accesible a los logros y desafíos del uso big data y machine learning para la medición de la pobreza, el desarrollo, la desigualdad y otras dimensiones sociales. Se basa en Sosa Escudero, Anauati y Brau (2022), un artículo abarcativo y técnico, que estudia con detalle el estado de las artes en lo que se refiere al uso de machine learning para los estudios de desarrollo y bienestar, al cual remitiremos para mayores detalles y referencias específicas.

Suggested Citation

  • Walter Sosa Escudero, 2023. "Big Data y Algoritmos para la Medición de la Pobreza y el Desarrollo," CEDLAS, Working Papers 0319, CEDLAS, Universidad Nacional de La Plata.
    • Handle: RePEc:dls:wpaper:0319
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