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

Author

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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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    File URL: https://mpra.ub.uni-muenchen.de/51253/1/MPRA_paper_51253.pdf
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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. 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.
    3. 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.
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    Cited by:

    1. repec:spr:stmapp:v:26:y:2017:i:3:d:10.1007_s10260-016-0374-7 is not listed on IDEAS
    2. Elena Dalla Chiara & Martina Menon & Federico Perali, 2015. "An Integrated Data Base to Measure Living Standards," Working Papers 28/2015, University of Verona, Department of Economics.
    3. repec:eee:ecolec:v:138:y:2017:i:c:p:109-125 is not listed on IDEAS
    4. 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.

    More about this item

    Keywords

    data fusion; propensity score; household consumption; income; wealth;

    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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