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Testing the Statistical Significance of Microsimulation Results: A Plea

Author

Listed:
  • Tim Goedemé

    () (Herman Deleeck Centre for Social Policy, University of Antwerp)

  • Karel Van den Bosch

    () (Belgian Federal Planning Bureau, Herman Deleeck Centre for Social Policy, University of Antwerp)

  • Lina Salanauskaite

    () (Herman Deleeck Centre for Social Policy, University of Antwerp)

  • Gerlinde Verbist

    () (Herman Deleeck Centre for Social Policy, University of Antwerp)

Abstract

In the microsimulation literature, it is still uncommon to test the statistical significance of results. In this article we argue that this situation is both undesirable and unnecessary. Provided the parameters used in the microsimulation are exogenous, as is often the case in static microsimulation of the first-order effects of policy changes, simple statistical tests can be sufficient. Moreover, standard routines have been developed which enable applied researchers to calculate the sampling variance of microsimulation results, while taking the sample design into account, even of relatively complex statistics such as relative poverty, inequality measures and indicators of polarization, with relative ease and a limited time investment. We stress that when comparing simulated and baseline variables, as well as when comparing two simulated variables, it is crucial to take account of the covariance between those variables. Due to this covariance, the mean difference between the variables can generally (though not always) be estimated with much greater precision than the means of the separate variables.

Suggested Citation

  • Tim Goedemé & Karel Van den Bosch & Lina Salanauskaite & Gerlinde Verbist, 2013. "Testing the Statistical Significance of Microsimulation Results: A Plea," International Journal of Microsimulation, International Microsimulation Association, vol. 6(3), pages 50-77.
  • Handle: RePEc:ijm:journl:v:6:y:2013:i:3:p:50-77
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    File URL: http://www.microsimulation.org/IJM/V6_3/4_IJM_6_3_2013_goedeme.pdf
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    References listed on IDEAS

    as
    1. John Creedy & Guyonne Kalb & Hsein Kew, 2007. "Confidence Intervals For Policy Reforms In Behavioural Tax Microsimulation Modelling," Bulletin of Economic Research, Wiley Blackwell, vol. 59(1), pages 37-65, January.
    2. Koen Decancq & Tim Goedemé & Karel Van den Bosch & Josefine Vanhille, 2013. "The Evolution of Poverty in the European Union: Concepts, Measurement and Data," ImPRovE Working Papers 13/01, Herman Deleeck Centre for Social Policy, University of Antwerp.
    3. Peter Ericson & Lennart Flood, 2012. "A Microsimulation Approach to an Optimal Swedish Income Tax," International Journal of Microsimulation, International Microsimulation Association, vol. 2(5), pages 2-21.
    4. Yves G. Berger & Chris J. Skinner, 2003. "Variance estimation for a low income proportion," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 457-468.
    5. Afshartous, David & Preston, Richard A., 2010. "Confidence intervals for dependent data: Equating non-overlap with statistical significance," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2296-2305, October.
    6. Atkinson, Tony & Cantillon, Bea & Marlier, Eric & Nolan, Brian, 2002. "Social Indicators: The EU and Social Inclusion," OUP Catalogue, Oxford University Press, number 9780199253494.
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    Cited by:

    1. Rastrigina, Olga & Leventi, Chrysa & Sutherland, Holly, 2015. "Nowcasting: estimating developments in the risk of poverty and income distribution in 2013 and 2014," EUROMOD Working Papers EM12/15, EUROMOD at the Institute for Social and Economic Research.
    2. Manos Matsaganis & Chrysa Leventi, 2014. "Distributive Effects of the Crisis and Austerity in Seven EU Countries," ImPRovE Working Papers 14/04, Herman Deleeck Centre for Social Policy, University of Antwerp.
    3. Fidel Picos & Marie-Luise Schmitz, 2016. "In-depth analysis of tax reforms using the EUROMOD microsimulation model," JRC Working Papers on Taxation & Structural Reforms 2016-06, Joint Research Centre (Seville site).

    More about this item

    Keywords

    Microsimulation; statistical inference; EUROMOD.;

    JEL classification:

    • 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
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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