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Poverty, Income Distribution and CGE Modeling: Does the Functional Form of Distribution Matter?

Author

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  • Dorothée Boccanfuso
  • Bernard Decaluwé
  • Luc Savard

Abstract

In this paper, we provide an overview of approaches used to model income distribution and poverty in CGE models. CGE models have started to use income distribution functional forms such as the lognormal, Pareto, beta distribution and Kernel non-parametric methods to apply GFT poverty indices. None of the authors of these papers have gone into much detail to justify the use of one method or functional form over the other, within the context of this type of work. Extensive literature exists on the choice of functional forms to estimate income distribution; however it has not been utilized in the CGE context. Given the fact that the desegregation of groups of households can be important in CGE analysis and the fact that the impact on income of policy simulations are often small in CGE models, we investigate the importance of othe choice of the functional form used to estimate the income distribution of groups of households. We compare six functional forms with parametric estimation and on a non-parametric method. Results show that no single form is more appropriate in all cases or groups of households. The characteristics of samples and subgroups play an important role and the choice shoudl be guided by the best fitting distribution.

Suggested Citation

  • Dorothée Boccanfuso & Bernard Decaluwé & Luc Savard, 2003. "Poverty, Income Distribution and CGE Modeling: Does the Functional Form of Distribution Matter?," Cahiers de recherche 0332, CIRPEE.
  • Handle: RePEc:lvl:lacicr:0332
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    Cited by:

    1. Calvin Z. Djiofack & Eric W. Djimeu & Matthieu Boussichas, 2014. "Editor's choice Impact of Qualified Worker Emigration on Poverty: A Macro–Micro-Simulation Approach for an African Economy," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 23(1), pages 1-52.
    2. Andrew Feltenstein & Luciana Lopes & Janet Porras Mendoza & Sally Wallace, 2013. "“The Impact of Micro-simulation and CGE modeling on Tax Reform and Tax Advice in Developing Countries”: A Survey of Alternative Approaches and an Application to Pakistan," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper1309, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    3. Luc Savard, 2005. "Poverty and Inequality Analysis within a CGE Framework: A Comparative Analysis of the Representative Agent and Microsimulation Approaches," Development Policy Review, Overseas Development Institute, vol. 23(3), pages 313-331, May.
    4. Boccanfuso, Dorothée & Cabral, François & Cissé, Fatou & Diagne, Abdoulaye & Savard, Luc, 2007. "Stratégies de réduction de la pauvreté au Sénégal : une analyse par la modélisation en équilibre général calculable microsimulé," L'Actualité Economique, Société Canadienne de Science Economique, vol. 83(4), pages 483-528, décembre.
    5. Pierre-Richard AGÉNOR & Derek H. C. CHEN & Michael GRIMM, "undated". "Linking Representative Household Models with Household Surveys for Poverty Analysis: A Comparison of Alternative Methodologies," EcoMod2004 330600002, EcoMod.
    6. Dorothée Boccanfuso & G. Rodolphe A. Missinhoun & Luc Savard, 2010. "Réformes economiques et croissance pro-pauvre : une application macro-micro aux Philippines," Recherches économiques de Louvain, De Boeck Université, vol. 76(3), pages 257-288.
    7. Dorothée Boccanfuso & François Joseph Cabral & Luc Savard, 2004. "Une analyse préliminaire d'impacts de la libéralisation de la filière arachide au Sénégal: un modèle d'équilibre général calculable multi-ménages," Cahiers de recherche 0406, CIRPEE.
    8. Liyanaarachchi, Tilak S. & Naranpanawa, Athula & Bandara, Jayatilleke S., 2016. "Impact of trade liberalisation on labour market and poverty in Sri Lanka. An integrated macro-micro modelling approach," Economic Modelling, Elsevier, vol. 59(C), pages 102-115.
    9. 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.
    10. Nabil Annabi & H. Khondker Bazlul & Selim Raihan & John Cockburn & Bernard Decaluwe, 2005. "Implications of WTO Agreements and Domestic Trade Policy Reforms for Poverty in Bangladesh: Short vs. Long Run," Working Papers MPIA 2005-02, PEP-MPIA.
    11. Dorothée BOCCANFUSO & Tambi Samuel KABORE, 2004. "Macroeconomic Growth, Sectoral Quality Of Growth And Poverty In Developing Countries: Measure And Application To Burkina Faso," Cahiers de recherche 04-07, Departement d'Economique de l'École de gestion à l'Université de Sherbrooke.
    12. Davies, James B., 2004. "Microsimulation, CGE and Macro Modelling for Transition and Developing Economies," WIDER Working Paper Series UNU-WIDER Research Paper , World Institute for Development Economic Research (UNU-WIDER).
    13. Lorenza Campagnolo & Fabio Eboli & Marinella Davide, 2016. "Can Paris deal boost SDGs achievement? An assesment of climate-sustainabilty co-benefits or side-effects," EcoMod2016 9635, EcoMod.
    14. Dorothée Boccanfuso & Luc Savard, 2005. "Analyse d’Impact de la Construction de l’Autoroute Dakar-Thies : un Modèle Equilibre Géneral Calculable Multi-Ménages Intégrés," Cahiers de recherche 05-11, Departement d'Economique de l'École de gestion à l'Université de Sherbrooke.
    15. Nkang, Nkang & Omonona, Bolarin & Yusuf, Suleiman & Oni, Omobowale, 2012. "Simulating the Impact of Exogenous Food Price Shock on Agriculture and the Poor in Nigeria: Results from a Computable General Equilibrium Model," Sustainable Agriculture Research, Canadian Center of Science and Education, vol. 1(2).
    16. D. Boccanfuso & F. Cabral & F. Cissé & A. Diagne & L. Savard, 2003. "Pauvreté et distribution de revenus au Sénégal: une approche par la modélisation en équilibre général calculable micro-simulé," Cahiers de recherche 0333, CIRPEE.
    17. John Cockburn & Erwin Corong & Caesar Cororaton, 2010. "Integrated Computable General Equilibrium (CGE) microsimulation approach," International Journal of Microsimulation, International Microsimulation Association, vol. 3(1), pages 60-71.

    More about this item

    Keywords

    Computable general equilibrium models; estimation; personal income and wealth distribution; measurement and analysis of poverty;

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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