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

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  • Lidia CERIANI

    ()

  • Carlo V. FIORIO

    ()

  • Chiara GHIGLIARANO

    ()

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) using the gross incomes provided in IT-SILC 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 procedure provided 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 harmonized 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

  • Lidia CERIANI & Carlo V. FIORIO & Chiara GHIGLIARANO, 2013. "The importance of choosing the data set for tax-benefit analysis," Departmental Working Papers 2013-05, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2013-05
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    Cited by:

    1. Tony Addison & Yukka Pirttilä & Finn Tarp & 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.
    2. 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.
    3. 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 (Seville site).
    4. repec:kap:jecinq:v:15:y:2017:i:4:d:10.1007_s10888-017-9369-3 is not listed on IDEAS
    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. 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.
    7. 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.
    8. Callan, Tim & Doorley, Karina & 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.
    9. 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.
    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. Figari, Francesco & Paulus, Alari & Sutherland, Holly, 2014. "Microsimulation and policy analysis," ISER Working Paper Series 2014-23, Institute for Social and Economic Research.
    12. 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.

    More about this item

    Keywords

    Tax-benefit microsimulation; Italy; TABEITA; SILC; SHIW;

    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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