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A non-parametric microsimulation approach to assess changes in inequality and poverty

Author

Listed:
  • Rob Vos

    () (United Nations Department of Economic and Social Affairs, New York, NY 10017, USA;)

  • Marco V. Sánchez

    () (United Nations Department of Economic and Social Affairs, New York, NY 10017, USA;)

Abstract

This paper presents a non-parametric microsimulation methodology for assessing the impact of labour market changes and government transfers on income inequality and poverty at the household level. The approach assumes that labour markets are segmented and determines (as part of a randomized process) which individuals are expected to move in or out of employment and which move from one employment segment to another based on either known or counterfactual information of aggregate labour market changes. The methodology assumes that the distribution of earnings of those who become employed in a particular segment resembles that of the individuals observed to be employed in that segment. The approach can be effectively combined in top-down fashion with static or dynamic computable general equilibrium (CGE) models, which typically provide insufficient information about household income distribution. The paper discusses the virtues and limitations of applying this methodology and further explains to practitioners how to implement it as a stand-alone methodology or in combination with a CGE model. It also shows how the methodology can be generalized to also capture the poverty and inequality effects of changes in non-labour incomes, such as government transfers. One great advantage of this method is that it is not very demanding in terms of modelling labour supply and household behaviour as compared with alternative parametric approaches, while at the same time providing a plausible link between changes in overall labour market conditions and the full household income distribution.

Suggested Citation

  • Rob Vos & Marco V. Sánchez, 2010. "A non-parametric microsimulation approach to assess changes in inequality and poverty," International Journal of Microsimulation, International Microsimulation Association, vol. 3(1), pages 8-23.
  • Handle: RePEc:ijm:journl:v:3:y:2010:i:1:p:8-23
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    File URL: http://ima.natsem.canberra.edu.au/IJM/V3_1/IJM_25.pdf
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    References listed on IDEAS

    as
    1. Sattinger, Michael, 1993. "Assignment Models of the Distribution of Earnings," Journal of Economic Literature, American Economic Association, vol. 31(2), pages 831-880, June.
    2. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters,in: Schooling, Experience, and Earnings, pages 1-4 National Bureau of Economic Research, Inc.
    3. Hartog, Joop, 1985. "Earnings functions : Testing for the demand side," Economics Letters, Elsevier, vol. 19(3), pages 281-285.
    4. Luc Savard, 2003. "Poverty and Income Distribution in a CGE-Household Micro-Simulation Model: Top-Down/Bottom Up Approach," Cahiers de recherche 0343, CIRPEE.
    5. François Bourguignon & Anne-Sophie Robilliard & Sherman Robinson, 2003. "Representative versus real households in the macro-economic modeling of inequality," Working Papers DT/2003/10, DIAL (Développement, Institutions et Mondialisation).
    6. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, June.
    7. Almeida dos Reis, Jose Guilherme & Paes de Barros, Ricardo, 1991. "Wage inequality and the distribution of education : A study of the evolution of regional differences in inequality in metropolitan Brazil," Journal of Development Economics, Elsevier, vol. 36(1), pages 117-143, July.
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    Citations

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    Cited by:

    1. Sánchez, Marco V. & Cicowiez, Martín, 2014. "Trade-offs and Payoffs of Investing in Human Development," World Development, Elsevier, vol. 62(C), pages 14-29.
    2. Carmen Estrades & Cecilia Llambí, 2013. "Lessons from the 2008 Financial Crisis: Policy Responses to External Shocks in Uruguay," The Developing Economies, Institute of Developing Economies, vol. 51(3), pages 233-259, September.
    3. Edward, Batte Sennoga & John Mary, Matovu, 2016. "Growth and Welfare Effects of Macroeconomic Shocks in Uganda," Occasional Papers 244096, Economic Policy Research Centre (EPRC).
    4. repec:eee:touman:v:51:y:2015:i:c:p:157-173 is not listed on IDEAS
    5. Debowicz, Darío, 2016. "Does the microsimulation approach used in macro–micro modelling matter? An application to the distributional effects of capital outflows during Argentina's Currency Board regime," Economic Modelling, Elsevier, vol. 54(C), pages 591-599.
    6. Breisinger, Clemens & Ecker, Olivier, 2014. "Simulating economic growth effects on food and nutrition security in Yemen: A new macro–micro modeling approach," Economic Modelling, Elsevier, vol. 43(C), pages 100-113.
    7. Debowicz, Darío & Golan, Jennifer, 2014. "The impact of Oportunidades on human capital and income distribution in Mexico: A top-down/bottom-up approach," Journal of Policy Modeling, Elsevier, vol. 36(1), pages 24-42.
    8. Renato Vargas & Pamela Escobar & Maynor Cabrera & Javier Cabrera & Violeta Hernández & Vivian Guzmán & Martin Cicowiez, 2017. "Climate risk and food security in Guatemala," Working Papers MPIA 2017-01, PEP-MPIA.
    9. Ramírez, Nerys F., 2016. "Determinantes del Desempleo en la República Dominicana: Dinámica Temporal y Microsimulaciones
      [Determinants of Unemployment in the Dominican Republic: Temporal Dynamics and Microsimulations]
      ," MPRA Paper 76998, University Library of Munich, Germany.
    10. Sánchez Cantillo, Marco Vinicio, 2015. "Macroeconomic trade-offs and external vulnerabilities of human development in Nicaragua," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), April.
    11. Lofgren, Hans & Cicowiez, Martin & Diaz-Bonilla, Carolina, 2013. "MAMS – A Computable General Equilibrium Model for Developing Country Strategy Analysis," Handbook of Computable General Equilibrium Modeling, Elsevier.
    12. Banerjee, Onil & Cicowiez, Martin & Gachot, Sébastien, 2015. "A quantitative framework for assessing public investment in tourism – An application to Haiti," Tourism Management, Elsevier, vol. 51(C), pages 157-173.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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