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The importance of choosing the data set for tax-benefit analysis

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  • V. Fiorio, Carlo
  • Ceriani, Lidia
  • Gigliarano, Chiara

Abstract

Given the increased availability of survey income data, in this paper we analyse the pros and cons of alternative data sets for static tax-benefit microsimulation in Italy. We focus on all possible alternatives, namely using (a) SHIW or (b) IT-SILC data using a consistent net-to-gross microsimulation model, or (c) IT-SILC data using the gross incomes provided since 2007. Our results suggest that IT-SILC improves in the regional representativeness of the Italian population and does not perform worse than SHIW as for most demographic characteristics, SHIW provides more information regarding building and real estate incomes. Gross income variables simulated by using the net-to-gross module included in the TABEITA microsimulation model and calibrating for tax evasion provide a very precise fit with external statistics, improving on results which could be obtained using the same TABEITA model on SHIW data. Simulated IT-SILC gross income data fit external aggregate data even better than gross income data provided in IT-SILC, which tend to largely overestimate self-employment income. Finally, we suggest to match IT-SILC with SHIW to include in the former the information on building and real estate incomes that are contained only in the latter. This allows us to reach a very satisfactory validation of the final data set.

Suggested Citation

  • V. Fiorio, Carlo & Ceriani, Lidia & Gigliarano, Chiara, 2013. "The importance of choosing the data set for tax-benefit analysis," EUROMOD Working Papers EM5/13, EUROMOD at the Institute for Social and Economic Research.
  • Handle: RePEc:ese:emodwp:em5-13
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    Cited by:

    1. Olivier Bargain, 2017. "Welfare analysis and redistributive policies," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(4), pages 393-419, December.
    2. Gemma Wright & Helen Barnes & Michael Noble & David McLennan & Faith Masekesa, 2018. "Assessing the quality of the income data used in SAMOD, a South African tax-benefit microsimulation model," WIDER Working Paper Series wp-2018-173, World Institute for Development Economic Research (UNU-WIDER).
    3. Carlos Felipe Balcázar & Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2017. "Rent‐Imputation for Welfare Measurement: A Review of Methodologies and Empirical Findings," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 881-898, December.
    4. Sarah Kuypers & Francesco Figari & Gerlinde Verbist & Dorien Verckist, 2017. "EWIGE - European Wealth data InteGration in EUROMOD," JRC Working Papers on Taxation & Structural Reforms 2017-04, Joint Research Centre.
    5. Andrea Albarea & Michele Bernasconi & Cinzia Di Novi & Anna Marenzi & Dino Rizzi & Francesca Zantomio, 2015. "Accounting for Tax Evasion Profiles and Tax Expenditures in Microsimulation Modelling. The BETAMOD Model for Personal Income Taxes in Italy," International Journal of Microsimulation, International Microsimulation Association, vol. 8(3), pages 99-136.
    6. Helen Barnes & Gemma Wright & Michael Noble & David McLennan & Faith Masekesa, 2018. "Assessing the quality of the income data used in SAMOD, a South African tax-benefit microsimulation model," WIDER Working Paper Series 173, World Institute for Development Economic Research (UNU-WIDER).
    7. Doorley, Karina & Callan, Tim & Savage, Michael, 2018. "Inequality in EU crisis countries. How effective were automatic stabilisers?," EUROMOD Working Papers EM10/18, EUROMOD at the Institute for Social and Economic Research.
    8. Sarah Kuypers & Francesco Figari & Gerlinde Verbist, 2016. "The Eurosystem Household Finance and Consumption Survey: A New Underlying Database for EUROMOD," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 35-65.
    9. Bavaro, Michele & Boscolo, Stefano & Tedeschi, Simone, 2024. "Simulating Long-Run Wealth Distribution and Transmission: The Role of Intergenerational Transfers," INET Oxford Working Papers 2024-01, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    10. Holly Sutherland & Francesco Figari, 2013. "EUROMOD: the European Union tax-benefit microsimulation model," International Journal of Microsimulation, International Microsimulation Association, vol. 1(6), pages 4-26.
    11. 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.
    12. Gerlinde Verbist & Francesco Figari, 2014. "The Redistributive Effect and Progressivity of Taxes Revisited: An International Comparison across the European Union," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 70(3), pages 405-429, September.
    13. Figari, Francesco & Fiorio, Carlo, 2015. "Fiscal consolidation policies in the context of Italy’s two recessions," EUROMOD Working Papers EM7/15, EUROMOD at the Institute for Social and Economic Research.
    14. Fernando Di Nicola & Giorgio Mongelli & Simone Pellegrino, 2015. "The static microsimulation model of the Italian Department of Finance: Structure and first results regarding income and housing taxation," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2015(2), pages 125-157.
    15. Michael Christl & Monika Köppl-Turyna & Dénes Kucsera, 2017. "A Tax-Benefit Model for Austria (AUTAX): Work Incentives and Distributional Effects of the 2016 Tax Reform," International Journal of Microsimulation, International Microsimulation Association, vol. 10(2), pages 144-176.
    16. Lidia Ceriani & Carlo V. Fiorio & Chiara Gigliarano, 2013. "The importance of choosing the data set for tax-benefit analysis," International Journal of Microsimulation, International Microsimulation Association, vol. 1(6), pages 86-121.
    17. Paolo Caro, 2020. "Decomposing Personal Income Tax Redistribution with Application to Italy," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(1), pages 113-129, March.
    18. Paolo Di Caro, 2018. "Redistribution in real-world PIT: Evidence from Italian tax records," Working Papers wp2018-2, Ministry of Economy and Finance, Department of Finance.
    19. Karina Doorley & Tim Callan & Michael Savage, 2021. "What drove income inequality in EU crisis countries during the Great Recession?," Fiscal Studies, John Wiley & Sons, vol. 42(2), pages 319-343, June.
    20. Figari, Francesco & Paulus, Alari & Sutherland, Holly, 2014. "Microsimulation and policy analysis," ISER Working Paper Series 2014-23, Institute for Social and Economic Research.
    21. Paolo Di Caro, 2017. "The contribution of tax statistics for analysing regional income disparities in Italy," Journal of Income Distribution, Ad libros publications inc., vol. 25(1), pages 1-27, March.
    22. 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

    JEL classification:

    • H20 - Public Economics - - Taxation, Subsidies, and Revenue - - - General
    • H24 - Public Economics - - Taxation, Subsidies, and Revenue - - - Personal Income and Other Nonbusiness Taxes and Subsidies
    • H26 - Public Economics - - Taxation, Subsidies, and Revenue - - - Tax Evasion and Avoidance
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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