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Simulating Income Distribution Changes in Bolivia: a Microeconometric Approach

Author

Listed:
  • Leonardo Gasparini

    ()

  • Mariana Marchionni

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

  • Federico Gutierrez

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

Abstract

This paper uses microeconometric simulations to characterize the distributional changes occurred in the Bolivian economy in the period 1993-2002, and to assess the potential distributional impact of various alternative economic scenarios for the next decade. Wage equations for urban and rural areas estimated by both OLS and quantile regression are the main inputs for the microsimulations. A sizeable increase in the dispersion in worker unobserved wage determinants is the main factor behind the significant increase in household income inequality in the 90s. The results of the microsimulations suggest a small poverty-reducing effect of several potential scenarios, including education upgrading, sectoral transformations, labor informality reduction, gender and race wage gap closing, and changes in the structure of the returns to education. Sustainable and vigorous productivity growth seems to be a necessary condition for Bolivia to meet the poverty Millennium Development Goal by 2015.

Suggested Citation

  • 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.
  • Handle: RePEc:dls:wpaper:0012
    as

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    File URL: http://www.cedlas.econo.unlp.edu.ar/wp/wp-content/uploads/doc_cedlas12.pdf
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    References listed on IDEAS

    as
    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    2. 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.
    3. Juhn, Chinhui & Murphy, Kevin M & Pierce, Brooks, 1993. "Wage Inequality and the Rise in Returns to Skill," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 410-442, June.
    4. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-694, July.
    5. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    6. Angus Deaton & Salman Zaidi, 2002. "Guidelines for Constructing Consumption Aggregates for Welfare Analysis," World Bank Publications, The World Bank, number 14101.
    7. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
    8. Oaxaca, Ronald L. & Ransom, Michael R., 1994. "On discrimination and the decomposition of wage differentials," Journal of Econometrics, Elsevier, vol. 61(1), pages 5-21, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Carlos Foronda Rojas, 2006. "Evaluación de los efectos de las políticas de exportación y de productividad en la distribución del ingreso a partir de microsimulaciones," Investigación & Desarrollo 0206, Universidad Privada Boliviana, revised Mar 2006.
    2. Monserrat Bustelo, 2004. "Caracterización de los Cambios en la Desigualdad y la Pobreza en Argentina Haciendo Uso de Técnicas de Descomposiciones Microeconometricas (1992-2001)," CEDLAS, Working Papers 0013, CEDLAS, Universidad Nacional de La Plata.
    3. Jing Dai & Stefan Sperlich & Walter Zucchini, 2011. "Estimating and Predicting Household Expenditures and Income Distributions," MAGKS Papers on Economics 201147, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    4. Ricardo Bebczuk, 2009. "SME Access to Credit in Guatemala and Nicaragua: Challenging Conventional Wisdom with New Evidence," CEDLAS, Working Papers 0080, CEDLAS, Universidad Nacional de La Plata.
    5. Javier Alejo, 2010. "Transición Demográfica y Pobreza en América Latina. Un Análisis de Microsimulaciones," CEDLAS, Working Papers 0108, CEDLAS, Universidad Nacional de La Plata.
    6. Eid, Ahmed & Aguirre, Rodrigo, 2013. "Tendencias en desigualdad de ingreso y consumo en Bolivia: un cuento de hadas de gigantes empequeñeciendo y enanos. en crecimiento," Revista Latinoamericana de Desarrollo Economico, Instituto de Investigaciones Socio-Económicas (IISEC), Universidad Católica Boliviana, issue 20, pages 75-110, Noviembre.
    7. World Bank, 2005. "Peru : Opportunities for All, Peru Poverty Assessment," World Bank Other Operational Studies 8809, The World Bank.
    8. Yanez-Pagans, Monica, 2008. "Culture and Human Capital Investments: Evidence of an Unconditional Cash Transfer Program in Bolivia," IZA Discussion Papers 3678, Institute for the Study of Labor (IZA).
    9. Carlos Alberto Foronda Rojas & Milenka Ocampo, 2008. "Retornos de la educación pública y privada: Inferencia asintótica y bootstrap en medidas de desigualdad," Investigación & Desarrollo 0308, Universidad Privada Boliviana, revised Mar 2008.
    10. Jing Dai & Stefan Sperlich & Walter Zucchini, 2016. "A Simple Method for Predicting Distributions by Means of Covariates with Examples from Poverty and Health Economics," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 152(I), pages 49-80, March.
    11. repec:aru:wpaper:201304 is not listed on IDEAS

    More about this item

    Keywords

    distribution; Bolivia; wages; decompositions; quantile; education; MDG;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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