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Reweighting Household Surveys for Tax Microsimulation Modelling: An Application to the New Zealand Household Economic Survey

Author

Listed:
  • John Creedy

    (The University of Melbourne)

  • Ivan Tuckwell

    (New Zealand Treasury)

Abstract

This paper reports a reweighting exercise for the New Zealand Household Economic Survey, which is the basis of the Treasury’s microsimulation model, TaxMod. Comparisons of benefit expenditures in a variety of demographic groups, along with population data, reveal that TaxMod estimates differ substantially from totals based on administrative data, when the weights provided by Statistics New Zealand are used. After describing the method used to compute new weights, the calibration requirements are reported. These relate to the age structure of the population and the number of beneficiaries for Unemployment Benefit, Domestic Purposes Benefit, Invalid’s and Sickness Benefits and Family Support and Tax Credits. The revised weights and expenditure estimates are reported and the resulting distribution of income examined. The new weights are found to produce much improved expenditure estimates, while having little effect on the resulting income distribution. The effects of reweighting are demonstrated using a simple policy simulation.

Suggested Citation

  • 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.
    • Handle: RePEc:ozl:journl:v:7:y:2004:i:1:p:71-88
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    References listed on IDEAS

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    1. 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.
    2. Anders Klevmarken, 2022. "Statistical Inference in Micro-simulation Models: Incorporating External Information," International Journal of Microsimulation, International Microsimulation Association, vol. 15(1), pages 111-120.
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    Cited by:

    1. Bourguignon, François & Bussolo, Maurizio, 2013. "Income Distribution in Computable General Equilibrium Modeling," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 1383-1437, Elsevier.
    2. Michal Myck & Mateusz Najsztub, 2015. "Data and Model Cross-validation to Improve Accuracy of Microsimulation Results: Estimates for the Polish Household Budget Survey," International Journal of Microsimulation, International Microsimulation Association, vol. 8(1), pages 33-66.
    3. Miriam Hortas Rico & Jorge Onrubia Fernández, 2014. "Renta personal de los municipios espanoles y su distribución: Metodología de estimación a partir de microdatos tributarios," Studies on the Spanish Economy eee2014-12, FEDEA.
    4. Tobias Schoch & André Müller, 2020. "Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(3), pages 267-304, December.
    5. Marko Ledic & Ivica Rubil & Ivica Urban, 2022. "Missing top incomes and tax-benefit microsimulation: evidence from correcting household survey data using tax records data," Working Papers 2201, The Institute of Economics, Zagreb.
    6. Miriam Hortas-Rico & Jorge Onrubia, 2023. "Renta personal de los municipios españoles y su distribución: Actualización de la estadística 2004-2016," Studies on the Spanish Economy eee2023-26, FEDEA.
    7. John Creedy & Joseph Mercante & Penny Mok, 2018. "The Labour Market Effects of ‘Working for Families’ In New Zealand," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 51(2), pages 211-231, June.
    8. Nicola Curci & Marco Savegnago & Marika Cioffi, 2017. "BIMic: the Bank of Italy microsimulation model for the Italian tax and benefit system," Questioni di Economia e Finanza (Occasional Papers) 394, Bank of Italy, Economic Research and International Relations Area.
    9. John Creedy & Rosanna Scutella, 2004. "The Role of the Unit of Analysis in Tax Policy Return Evaluations of Inequality and Social Welfare," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 7(1), pages 89-108, March.
    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. Miriam Hortas-Rico & Jorge Onrubia & Daniele Pacifico, 2013. "Personal Income Distribution at the Local Level. An Estimation for Spanish Municipalities Using Tax Microdata," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper1314, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    12. Stefano Boscolo, 2019. "Quantifying the Redistributive Effect of the Erosion of the Italian Personal Income Tax Base: A Microsimulation Exercise," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2019(2), pages 39-80.

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    More about this item

    Keywords

    Labor Force and Employment; Size; and Structure (by industry; occupation; demographic characteristics; etc.) Survey Methods Model Evaluation and Testing;
    All these keywords.

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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