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Survey Reweighting For Tax Microsimulation Modelling

In: Studies on Economic Well-Being: Essays in the Honor of John P. Formby

Author

Listed:
  • John Creedy
Abstract
Tax microsimulation models are based on large-scale cross-sectional survey data. Each individual or household has a sample weight provided by the statistical agency responsible for collecting the data. The typical starting point is to use weights that are inversely related to the probability of selecting the individual in a random sample, with some adjustment for non-response. It has become common for agencies, using “minimal” adjustments, to produce revised weights to ensure that, for example, the estimated population age/gender distributions match population totals obtained from other sources, in particular census data. Such calibration methods appear to be well known among survey statisticians, a highly influential paper being that byDeville and Särndal (1992).2

Suggested Citation

  • John Creedy, 2004. "Survey Reweighting For Tax Microsimulation Modelling," Research on Economic Inequality, in: Studies on Economic Well-Being: Essays in the Honor of John P. Formby, pages 229-249, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:reinzz:s1049-2585(04)12009-7
    DOI: 10.1016/S1049-2585(04)12009-7
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    Cited by:

    1. Michael Christl & Silvia Poli & Tine Hufkens & Andreas Peichl & Mattia Ricci, 2023. "The role of short-time work and discretionary policy measures in mitigating the effects of the COVID-19 crisis in Germany," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(4), pages 1107-1136, August.
    2. Tindara Addabbo & Rosa García-Fernández & Carmen Llorca-Rodríguez & Anna Maccagnan, 2011. "The impact of the crisis on unemployment and household income in Italy and Spain," Working Papers 235, ECINEQ, Society for the Study of Economic Inequality.
    3. John Creedy & Catherine Sleeman, 2004. "Adult Equivalence Scales, Inequality and Poverty in New Zealand," Treasury Working Paper Series 04/21, New Zealand Treasury.
    4. Jekaterina Navicke & Olga Rastrigina & Holly Sutherland, 2014. "Nowcasting Indicators of Poverty Risk in the European Union: A Microsimulation Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 119(1), pages 101-119, October.
    5. John Creedy & Ivan Tuckwell, 2004. "Reweighting Household Surveys for Tax Microsimulation Modelling: An Application to the New Zealand Household Economic Survey," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 7(1), pages 71-88, March.
    6. John Creedy & Ivan Tuckwell, 2003. "Reweighting the New Zealand Household Economic Survey for Tax Microsimuilation Modelling," Treasury Working Paper Series 03/33, New Zealand Treasury.
    7. Massimiliano Ferraresi & Leonzio Rizzo, 2015. "L?impatto della contabilità euro-compatibile in un?auspicabile evoluzione del patto di stabilità interno," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2015(3), pages 31-57.
    8. Jekaterina Navicke, 2020. "Driving factors behind the changes in income distribution in the Baltics: income, policy, demography," GRAPE Working Papers 44, GRAPE Group for Research in Applied Economics.
    9. Tindara Addabbo & Anna Maccagnan, 2011. "The Italian Labour Market and the Crisis," Center for the Analysis of Public Policies (CAPP) 0086, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    10. Miriam Hortas-Rico & Jorge Onrubia & Daniele Pacifico, 2014. "Estimating the Personal Income Distribution in Spanish Municipalities Using Tax Micro-Data," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper1419, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    11. Navicke, Jekaterina & Kump, Nataša, 2014. "Re-weighting EUROMOD for demographic change: an application on Slovenian and Lithuanian data," EUROMOD Working Papers EM13/14, EUROMOD at the Institute for Social and Economic Research.
    12. Mihály Szoboszlai, 2018. "Disaggregated Household Incomes in Hungary Based on the Comparative Analysis of the Reweighted Household Surveys of 2010 and 2015," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 17(2), pages 99-123.
    13. Daniele Pacifico, 2014. "Reweight: a stata module to reweight survey data to external totals," Working Papers 5, Department of the Treasury, Ministry of the Economy and of Finance.
    14. Giovanna Messina & Marco Savegnago, 2015. "Le imposte sulla prima casa in Italia, un equilibrio difficile fra decentramento e redistribuzione," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2015(3), pages 5-29.
    15. Mihály Szoboszlai & Zoltán Bögöthy & Pálma Mosberger & Dávid Berta, 2018. "Assessment of the tax and transfer changes in Hungary between 2010 and 2017 using a microsimulation model," MNB Occasional Papers 2018/135, Magyar Nemzeti Bank (Central Bank of Hungary).
    16. Maria Cozzolino & Marco Di Marco, 2015. "Micromodelling Italian Taxes and Social Policies," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 17(2), pages 17-26.

    More about this item

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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