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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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    References listed on IDEAS

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    2. Giovanni D'Alessio & Ivan Faiella, 2002. "Non-response behaviour in the Bank of Italy�s Survey of Household Income and Wealth," Temi di discussione (Economic working papers) 462, Bank of Italy, Economic Research and International Relations Area.
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    Cited by:

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    2. 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).
    3. 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.
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    5. 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.

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    More about this item

    Keywords

    Tax-benefit microsimulation; Italy; TABEITA; SILC; SHIW;
    All these keywords.

    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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