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Testing the Statistical Significance of Microsimulation Results: Often Easier than You Think. A Technical Note

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  • Tim Goedemé
  • Karel Van den Bosch
  • Lina Salanauskaite
  • Gerlinde Verbist

Abstract

In the microsimulation literature, it is still uncommon to test the statistical significance of results. In this note 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.

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Paper provided by Herman Deleeck Centre for Social Policy, University of Antwerp in its series ImPRovE Working Papers with number 13/10.

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Date of creation: Aug 2013
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Handle: RePEc:hdl:improv:1310

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Keywords: Statistical inference; significance tests; microsimulation; covariance; t-test; EUROMOD;

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  6. Atkinson, Tony & Cantillon, Bea & Marlier, Eric & Nolan, Brian, 2002. "Social Indicators: The EU and Social Inclusion," OUP Catalogue, Oxford University Press, number 9780199253494.
  7. Peter Ericson & Lennart Flood, 2012. "A Microsimulation Approach to an Optimal Swedish Income Tax," International Journal of Microsimulation, Interational Microsimulation Association, vol. 2(5), pages 2-21.
  8. Sutherland, Holly & Figari, Francesco, 2013. "EUROMOD: The European Union Tax-Benefit Microsimulation Model," EUROMOD Working Papers EM8/13, EUROMOD at the Institute for Social and Economic Research.
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  10. 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.
  11. Robert Tanton & Yogi Vidyattama & Justine McNamara & Quoc Ngu Vu & Ann Harding, 2009. "Old, Single and Poor: Using Microsimulation and Microdata to Analyse Poverty and the Impact of Policy Change among Older Australians," Economic Papers, The Economic Society of Australia, vol. 28(2), pages 102-120, 06.
  12. 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.
  13. 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.
  14. Howes, Stephen & Lanjouw, Jean Olson, 1998. "Does Sample Design Matter for Poverty Rate Comparisons?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 99-109, March.
  15. Levy, Horacio & Morawski, Leszek & Myck, Michal, 2008. "Alternative tax-benefit strategies to support children in Poland," EUROMOD Working Papers EM3/08, EUROMOD at the Institute for Social and Economic Research.
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