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Micro Data Fusion of Italian Expenditures and Incomes Surveys

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  • Elena Pisano
  • Simone Tedeschi

Abstract

The aim of this work is to match household consumption information from Indagine sui Consumi delle Famiglie (Household Budget Survey, HBS) by the Italian National Statistical Institute (ISTAT) with Indagine sui Bilanci delle Famiglie Italiane (Survey of Households’ Income and Wealth, SHIW) by the Bank of Italy for the year 2010. The work offers a review of the main matching methodologies, coupled with adiscussion of the underlying hypotheses (such as the CIA) which, in our case, are less demanding to assume given the presence consumption aggregates as common variables between the two surveys. Moreover, some tests measuring the validity of the matching procedure are presented in order to check the preservation of joint distributions.The resulting sample is expected to allow better distributional and micro-econometric analyses onconsumption income and wealth (e.g. Engel curves, consumption age/income profiles). Moreover, the very detailed integrated dataset would constitute a platform for an integrated microsimulation analysis of direct, indirect and wealth tax reforms which, so far, has not been feasible taking available sample surveys separately.Our matching achieves a good preservation of the marginal distributions of all consumption aggregates from the donor survey. However, a thorough comparison of the original distributions suggests that the HBS is a convenient donor for the imputation of non-durable commodities only. Consumption aggregates closer to the concept of wealth (such as durables and the extraordinary expenditure for dwelling maintenance) or savings (such as mortgages and private pensions) prove to be better assessed by the longer - and more issue-specific - recall of the SHIW. As secondary outcomes, the information derived from HBS on non-durables entails an increase in the dispersion and an upward adjustment of consumption profiles in the synthetic distribution relative to SHIW. This implies also a downsized average propensity to save for the household sector which gets closer to the National Accounts figures.

Suggested Citation

  • Elena Pisano & Simone Tedeschi, 2014. "Micro Data Fusion of Italian Expenditures and Incomes Surveys," Working Papers in Public Economics 164, Department of Economics and Law, Sapienza University of Roma.
    • Handle: RePEc:sap:wpaper:wp164
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    References listed on IDEAS

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    1. Andrea Brandolini, 1999. "The Distribution of Personal Income in Post-War Italy: Source Description, Data Quality, and the Time Pattern of Income Inequality," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 58(2), pages 183-239, September.
    2. Erich Battistin & Raffaele Miniaci & Guglielmo Weber, 2003. "What Do We Learn from Recall Consumption Data?," Journal of Human Resources, University of Wisconsin Press, vol. 38(2).
    3. Barbara Sianesi, 2001. "Propensity score matching," United Kingdom Stata Users' Group Meetings 2001 12, Stata Users Group, revised 23 Aug 2001.
    4. Sisto, Andrea, 2006. "Propensity Score Matching: un'applicazione per la creazione di un database integrato ISTAT-Banca d'Italia," POLIS Working Papers 56, Institute of Public Policy and Public Choice - POLIS.
    5. Giulia Cifaldi & Andrea Neri, 2013. "Asking income and consumption questions in the same survey: what are the risks?," Temi di discussione (Economic working papers) 908, Bank of Italy, Economic Research and International Relations Area.
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    Cited by:

    1. Massimo Baldini & Daniele Pacifico & Federica Termini, 2015. "Imputation of missing expenditure information in standard household income surveys," Center for the Analysis of Public Policies (CAPP) 0116, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    2. Cristina Cirillo & Lucia Imperioli & Marco Manzo, 2021. "The Value Added Tax Simulation Model: VATSIM-DF (II)," Working Papers wp2021-12, Ministry of Economy and Finance, Department of Finance.
    3. Nicola Curci & Marco Savegnago, 2019. "Shifting taxes from labour to consumption: the efficiency-equity trade-off," Temi di discussione (Economic working papers) 1244, Bank of Italy, Economic Research and International Relations Area.
    4. Massimo Baldini & Daniele Pacifico & Federica Termini, 2015. "Imputation of missing expenditure information in standard household income surveys," Department of Economics 0049, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    5. 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.
    6. Romero-Jordán, Desiderio & del Río, Pablo, 2022. "Analysing the drivers of the efficiency of households in electricity consumption," Energy Policy, Elsevier, vol. 164(C).

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    More about this item

    Keywords

    data fusion; propensity score; household consumption; income; wealth;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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