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Simulating Income Distribution Changes in Bolivia: a Microeconometric Approach

  • 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)

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.

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File URL: http://cedlas.econo.unlp.edu.ar/archivos_upload/doc_cedlas12.pdf
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Paper provided by CEDLAS, Universidad Nacional de La Plata in its series CEDLAS, Working Papers with number 0012.

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Length: 61 pages
Date of creation: Aug 2004
Date of revision:
Handle: RePEc:dls:wpaper:0012
Contact details of provider: Postal: Calle 48 No555 - La Plata (1900)
Phone: 21- 1466
Fax: 54-21-25-9536
Web page: http://cedlas.econo.unlp.edu.ar/

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  1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  2. Angus Deaton & Salman Zaidi, 1999. "Guidelines for Constructing Consumption Aggregates For Welfare Analysis," Working Papers 217, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
  3. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-94, July.
  4. 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.
  5. 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.
  6. 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.
  7. 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 410-42, June.
  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.
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