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Statistical Matching of Income and Consumption Expenditures

Author

Listed:
  • Gabriella Donatiello
  • Marcello D’Orazio
  • Doriana Frattarola
  • Antony Rizzi
  • Mauro Scanu
  • Mattia Spaziani

Abstract

The purpose of this paper is to evaluate the possibility of applying statistical matching on two different data sources to create an integrated database with detailed information on households income and consumption expenditures in Italy. The data to integrate are those of EU-SILC (EU Statistics on Income and Living Condition) 2012, with income reference year 2011, and the HBS (Household Budget Survey) 2011. This paper explores which are the matching approaches more suitable with the final objective and provides insights concerning some important steps of the integration process. In order to avoid the statistical matching under the conditional independence assumption (CIA) it is evaluated the usage of the available auxiliary information (household monthly income) and the main results are also presented.

Suggested Citation

  • Gabriella Donatiello & Marcello D’Orazio & Doriana Frattarola & Antony Rizzi & Mauro Scanu & Mattia Spaziani, 2014. "Statistical Matching of Income and Consumption Expenditures," International Journal of Economic Sciences, Prague University of Economics and Business, vol. 2014(3), pages 50-65.
    • Handle: RePEc:prg:jnljes:v:2014:y:2014:i:3:id:16:p:50-65
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    References listed on IDEAS

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    1. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    2. Brewer, Mike & O'Dea, Cormac, 2012. "Measuring living standards with income and consumption: evidence from the UK," ISER Working Paper Series 2012-05, Institute for Social and Economic Research.
    3. Bruce D. Meyer & James X. Sullivan, 2011. "Viewpoint: Further results on measuring the well-being of the poor using income and consumption," Canadian Journal of Economics, Canadian Economics Association, vol. 44(1), pages 52-87, February.
    4. Mike Brewer & Cormac O'Dea, 2012. "Measuring living standards with income and consumption: evidence from the UK," IFS Working Papers W12/12, Institute for Fiscal Studies.
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    Citations

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    Cited by:

    1. Esposito Laura & Fioroni Livia & Guandalini Alessio, 2019. "Gross income projection in Labour Force Survey Data," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 73(4), pages 41-52, October-D.
    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. Marcello D’Orazio, 2015. "Integration and imputation of survey data in R: the StatMatch package," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 57-68, June.
    4. Baris Ucar & Gianni Betti, 2016. "Longitudinal statistical matching: transferring consumption expenditure from HBS to SILC panel survey," Department of Economics University of Siena 739, Department of Economics, University of Siena.
    5. D'Alberto, Riccardo & Zavalloni, Matteo & Raggi, Meri & Viaggi, Davide, 2021. "A Statistical Matching approach to reproduce the heterogeneity of willingness to pay in benefit transfer," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    6. Luca Gandullia & Lucia Leporatti, 2019. "Distributional effects of gambling taxes: empirical evidence from Italy," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(4), pages 565-590, December.
    7. 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.
    8. Menyhért, Bálint, 2024. "Energy poverty in the European Union. The art of kaleidoscopic measurement," Energy Policy, Elsevier, vol. 190(C).

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    More about this item

    Keywords

    NA; Statistical matching; Survey data integration; Income; Consumption;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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