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Data Fusion Between Bank of Italy-SHIW and ISTAT-HBS

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  • Tedeschi, Simone
  • Pisano, Elena

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. In particular, we combine information from the Historical Database (integrated with information from the original cross sectional files) of SHIW 2010 with the wave 2010 of HBS. The work offers a review of the main matching methodologies, coupled with a discussion of the underlying hypotheses (such as the CIA) which, in our case, are less demanding to assume given the presence of aggregate consumption as common variable 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 provides an integrated synthetic dataset which allows to jointly analyze income, wealth and consumption distributions with a high degree of detail for both incomes/assets and consumption expenditure items. This source is expected to allow better multidimensional-distributional analyses on consumption income and wealth and to provide a basis for an integrated microsimulation analysis of direct, indirect and wealth tax reforms which, so far, has not been feasible taking available sample surveys separately.

Suggested Citation

  • Tedeschi, Simone & Pisano, Elena, 2013. "Data Fusion Between Bank of Italy-SHIW and ISTAT-HBS," MPRA Paper 51253, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:51253
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    References listed on IDEAS

    as
    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. Edwin Leuven & Barbara Sianesi, 2003. "PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing," Statistical Software Components S432001, Boston College Department of Economics, revised 01 Feb 2018.
    3. Barbara Sianesi, 2001. "Propensity score matching," United Kingdom Stata Users' Group Meetings 2001 12, Stata Users Group, revised 23 Aug 2001.
    4. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    5. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
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    Citations

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    Cited by:

    1. Chiara Elena Dalla & Menon Martina & Perali Federico, 2019. "An Integrated Database to Measure Living Standards," Journal of Official Statistics, Sciendo, vol. 35(3), pages 531-576, September.
    2. Teixidó, Jordi J. & Verde, Stefano F., 2017. "Is the Gasoline Tax Regressive in the Twenty-First Century? Taking Wealth into Account," Ecological Economics, Elsevier, vol. 138(C), pages 109-125.
    3. Pier Luigi Conti & Daniela Marella & Andrea Neri, 2017. "Statistical matching and uncertainty analysis in combining household income and expenditure data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 485-505, August.
    4. Andrea Cutillo & Mauro Scanu, 2020. "A Mixed Approach for Data Fusion of HBS and SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 150(2), pages 411-437, July.

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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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