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

  • Tedeschi, Simone
  • Pisano, Elena

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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 51253.

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Date of creation: Oct 2013
Date of revision:
Handle: RePEc:pra:mprapa:51253
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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," Temi di discussione (Economic working papers) 350, Bank of Italy, Economic Research and International Relations Area.
  2. Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
  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 19 Jan 2015.
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