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Income Distribution in Computable General Equilibrium Modeling

Author

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  • François Bourguignon

    (PSE - Paris-Jourdan Sciences Economiques - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Maurizio Bussolo

    (Banque Mondiale - Banque Mondiale)

Abstract

Studying the poverty and income distribution effects of macroeconomic policies or shocks requires a methodology that accounts on the one hand for the nature of the policy or shock being studied and their aggregate impact on the economy and, on the other hand, the heterogeneity of their overall effects among individuals or households at the micro level. Standard evaluation methods are of little use here. Methods based on the comparison between individuals exposed and not exposed to the policy or shock are clearly not applicable since, by definition, all individuals are affected by any policy with some macroeconomic dimension. The evaluation methodology thus needs to rely not only on a micro but also on a macro counterfactual within some kind of general equilibrium setting. This chapter reviews the growing literature that addresses this challenge by combining macro and micro modeling in various ways. It describes existing methodologies and their underpinning links with economic theory and presents some applications. The chapter is structured according to the nature of the computable general equilibrium (CGE) macro model being used and how the macro and micro components of the methodology are connected together and/or integrated. It thus starts from a simple framework where the two components are handled separately: the CGE model is static and Walrasian and the unidirectional link from the macro to the micro component essentially consists of changes in prices. It then moves to more integrated variants, where results from both the CGE and micro models are looped back and forth, and also to non-Walrasian specifications where both price and aggregate quantity changes link the macro and the micro components. Finally the chapter considers dynamic settings where the static CGE model on the macro side is replaced by a recursivedynamic model, whereas the micro data base is allowed to change over time through both exogenous demographic and policy-induced endogenous economic transitions. Illustrations of that approach are drawn from the Global Income Distribution Dynamics (GIDD) model recently developed at the World Bank.

Suggested Citation

  • François Bourguignon & Maurizio Bussolo, 2013. "Income Distribution in Computable General Equilibrium Modeling," PSE-Ecole d'économie de Paris (Postprint) hal-00812905, HAL.
  • Handle: RePEc:hal:pseptp:hal-00812905
    DOI: 10.1016/B978-0-444-59568-3.00021-3
    Note: View the original document on HAL open archive server: https://hal-pjse.archives-ouvertes.fr/hal-00812905
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    10. van Ruijven, Bas J. & O’Neill, Brian C. & Chateau, Jean, 2015. "Methods for including income distribution in global CGE models for long-term climate change research," Energy Economics, Elsevier, vol. 51(C), pages 530-543.
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    15. Facundo Alvaredo & Leonardo Gasparini, 2013. "Recent Trends in Inequality and Poverty in Developing Countries," CEDLAS, Working Papers 0151, CEDLAS, Universidad Nacional de La Plata.
    16. Devarajan, Shantayanan & Go, Delfin S. & Maliszewska, Maryla & Osorio-Rodarte, Israel & Timmer, Hans, 2015. "Stress-testing Africa's recent growth and poverty performance," Journal of Policy Modeling, Elsevier, vol. 37(4), pages 521-547.
    17. Amer Ahmed & Maurizio Bussolo & Marcio Cruz & Delfin S. Go & Israel Osorio-Rodarte, 2020. "Global Inequality in a more educated world," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(4), pages 585-616, December.
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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • D33 - Microeconomics - - Distribution - - - Factor Income Distribution
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • I30 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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