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

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

Abstract

In the microsimulation literature, it is still uncommon to test the statistical significance of results. In this paper 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 EUROMOD at the Institute for Social and Economic Research in its series EUROMOD Working Papers with number em18/13.

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Date of creation: 14 Nov 2013
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Publication status: published
Handle: RePEc:ese:emodwp:em18-13

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  1. Atkinson, Tony & Cantillon, Bea & Marlier, Eric & Nolan, Brian, 2002. "Social Indicators: The EU and Social Inclusion," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780199253494, October.
  2. Ericson, Peter & Flood, Lennart, 2009. "A Microsimulation Approach to an Optimal Swedish Income Tax," IZA Discussion Papers, Institute for the Study of Labor (IZA) 4379, Institute for the Study of Labor (IZA).
  3. Klevmarken, N. Anders, 1998. "Statistical Inference in Micro Simulation Models: Incorporating external information," Working Paper Series, Uppsala University, Department of Economics 1998:20, Uppsala University, Department of Economics.
  4. repec:ese:emodwp:em3-08 is not listed on IDEAS
  5. repec:ese:emodwp:em11-13 is not listed on IDEAS
  6. Immervoll, Herwig & Kleven, Henrik & Kreiner, Claus Thustrup & Saez, Emmanuel, 2004. "Welfare Reform in European Countries: A Micro-Simulation Analysis," CEPR Discussion Papers, C.E.P.R. Discussion Papers 4324, C.E.P.R. Discussion Papers.
  7. repec:ese:emodwp:em6-09 is not listed on IDEAS
  8. John Creedy & Guyonne Kalb & Hsein Kew, 2007. "Confidence Intervals For Policy Reforms In Behavioural Tax Microsimulation Modelling," Bulletin of Economic Research, Wiley Blackwell, Wiley Blackwell, vol. 59(1), pages 37-65, 01.
  9. 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, The Economic Society of Australia, vol. 28(2), pages 102-120, 06.
  10. Afshartous, David & Preston, Richard A., 2010. "Confidence intervals for dependent data: Equating non-overlap with statistical significance," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(10), pages 2296-2305, October.
  11. Holly Sutherland & Francesco Figari, 2013. "EUROMOD: the European Union tax-benefit microsimulation model," International Journal of Microsimulation, Interational Microsimulation Association, Interational Microsimulation Association, vol. 1(6), pages 4-26.
  12. 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, International Association for Research in Income and Wealth, vol. 44(1), pages 99-109, March.
  13. 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, Herman Deleeck Centre for Social Policy, University of Antwerp 13/01, Herman Deleeck Centre for Social Policy, University of Antwerp.
  14. Martin Biewen & Stephen P. Jenkins, 2006. "Variance Estimation for Generalized Entropy and Atkinson Inequality Indices: the Complex Survey Data Case," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, Department of Economics, University of Oxford, vol. 68(3), pages 371-383, 06.
  15. 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, Royal Statistical Society, vol. 52(4), pages 457-468.
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