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Transición Demográfica y Pobreza en América Latina. Un Análisis de Microsimulaciones

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  • Javier Alejo

    (Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS) - FCE - UNLP y CONICET)

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    Abstract

    La literatura empírica ha encontrado evidencia de una tendencia hacia el envejecimiento de la población en América Latina. Este documento analiza el impacto de los cambios demográficos sobre la pobreza utilizando las proyecciones demográficas de la Organización de las Naciones Unidas junto con distintos escenarios en la estructura educativa. La metodología utilizada en este trabajo es la de microsimulaciones econométricas. Su principal innovación consiste en proponer el método de máxima verosimilitud empírica como estrategia de simulación de ponderadores. Bajo todos los supuestos del modelo de simulación, los resultados sugieren que si la dinámica poblacional se mantiene los niveles de pobreza se verán reducidos. Sin embargo el efecto cuantitativo es muy débil, dejando un amplio margen para la planificación de políticas económicas orientadas a la reducción de la pobreza.

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    File URL: http://cedlas.econo.unlp.edu.ar/archivos_upload/doc_cedlas108.pdf
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    Bibliographic Info

    Paper provided by CEDLAS, Universidad Nacional de La Plata in its series CEDLAS, Working Papers with number 0108.

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    Length: 42 pages
    Date of creation: Nov 2010
    Date of revision:
    Handle: RePEc:dls:wpaper:0108

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    Keywords: transición demográfica; envejecimiento; pobreza; microsimulaciones; Empirical Likelihood; Quantile Regression; América Latina.;

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    1. Javier Alejo, 2006. "Desigualdad Salarial en el Gran Buenos Aires: Una Aplicación de Regresión por Cuantiles en Microdescomposiciones," CEDLAS, Working Papers 0036, CEDLAS, Universidad Nacional de La Plata.
    2. Ricardo N. Bebczuk, 2008. "Financial Inclusion in Latin America and the Caribbean: Review and Lessons," CEDLAS, Working Papers 0068, CEDLAS, Universidad Nacional de La Plata.
    3. Joshua Angrist & Alan Krueger, 1990. "Does Compulsory School Attendance Affect Schooling and Earnings?," Working Papers 653, Princeton University, Department of Economics, Industrial Relations Section..
    4. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    5. Martin Cicowiez & Javier Alejo & Luciano Di Gresia & Sergio Olivieri & Ana Pacheco, 2010. "Export Taxes, World Prices, and Poverty in Argentina: a Dynamic CGE-Microsimulation Analysis," Working Papers MPIA 2010-13, PEP-MPIA.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Gasparini, Leonardo & Alejo, Javier & Haimovich, Francisco & Olivieri, Sergio & Tornarolli, Leopoldo, 2007. "Poverty among the Elderly in Latin America and the Caribbean," MPRA Paper 42957, University Library of Munich, Germany.
    8. Lloyd-Sherlock, Peter, 2000. "Old Age and Poverty in Developing Countries: New Policy Challenges," World Development, Elsevier, vol. 28(12), pages 2157-2168, December.
    9. Leonardo Gasparini & Mariana Marchionni & Walter Sosa Escudero, 2000. "Characterization of inequality changes through microeconometric decompositions. The case of Greater Buenos Aires," Department of Economics, Working Papers 025, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata.
    10. Leonardo Gasparini & Mariana Marchionni & Federico Gutierrez, 2004. "Simulating Income Distribution Changes in Bolivia: a Microeconometric Approach," CEDLAS, Working Papers 0012, CEDLAS, Universidad Nacional de La Plata.
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