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Desagregación de datos en encuestas de hogares: metodologías de estimación en áreas pequeñas

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  • Molina, Isabel

Abstract

Las encuestas de hogares son un instrumento ampliamente utilizado para obtener información sobre la situación socioeconómica y el bienestar de las personas. Sin embargo, la precisión de las estimaciones de las encuestas de hogares decrece sustancialmente cuando se trata de realizar inferencias para grupos poblacionales que representan desagregaciones para las cuales la encuesta no fue diseñada. En este contexto, es posible utilizar procesos de estimación que combinan la información de las encuestas de hogares con información auxiliar existente a nivel poblacional como censos o registros administrativos. Este documento presenta una guía metodológica de la conjunción de técnicas estadísticas de las encuestas y modelos probabilísticos con el fin de producir desagregaciones para grupos de interés, conocidas como técnicas de estimación de áreas pequeñas.

Suggested Citation

  • Molina, Isabel, 2019. "Desagregación de datos en encuestas de hogares: metodologías de estimación en áreas pequeñas," Estudios Estadísticos 44214, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
  • Handle: RePEc:ecr:col027:44214
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    File URL: http://repositorio.cepal.org/handle/11362/44214
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    Cited by:

    1. Corral Rodas,Paul Andres & Kastelic,Kristen Himelein & Mcgee,Kevin Robert & Molina,Isabel, 2021. "A Map of the Poor or a Poor Map ?," Policy Research Working Paper Series 9620, The World Bank.
    2. Corral Rodas,Paul Andres & Molina,Isabel & Nguyen,Minh Cong, 2020. "Pull Your Small Area Estimates up by the Bootstraps," Policy Research Working Paper Series 9256, The World Bank.
    3. Paul Corral & Kristen Himelein & Kevin McGee & Isabel Molina, 2021. "A Map of the Poor or a Poor Map?," Mathematics, MDPI, vol. 9(21), pages 1-40, November.
    4. Guadarrama, María & Morales, Domingo & Molina, Isabel, 2021. "Time stable empirical best predictors under a unit-level model," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).

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