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Data Integration Techniques for the Identification of Poverty Profiles

Author

Listed:
  • Francesco D. d’Ovidio

    (University of Bari “Aldo Moro”)

  • Paola Perchinunno

    (University of Bari “Aldo Moro”)

  • Laura Antonucci

    (University of Foggia)

Abstract

Economic connotation of poverty concerns mainly the level of household’s spending and income as privileged indicators. Sometimes, data sources have several lack of information, but such information is available in other data sources related to the same statistical units, or to other households. In order to identify records relating to the same or similar units belonging to two data archives, some methodologies (record linkage in the first case, statistical matching in the latter) are used for integrating data. In this study we illustrate a model of data integration between two surveys conducted by the Italian National Institute of Statistics (Statistics on Income and Living Conditions and Survey on Household Expenses). The construction of an integrated database starting from those two surveys is useful for studying consumer behaviours in relation to specific groups of goods, analysing decisions related to household savings, examining economic and social inequalities, such as studying the impact of public policies through simulations.

Suggested Citation

  • Francesco D. d’Ovidio & Paola Perchinunno & Laura Antonucci, 2021. "Data Integration Techniques for the Identification of Poverty Profiles," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 515-531, August.
    • Handle: RePEc:spr:soinre:v:156:y:2021:i:2:d:10.1007_s11205-019-02255-0
    DOI: 10.1007/s11205-019-02255-0
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    References listed on IDEAS

    as
    1. J. B. Copas & F. J. Hilton, 1990. "Record Linkage: Statistical Models for Matching Computer Records," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(3), pages 287-312, May.
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    3. Nancy Ruggles & Richard Ruggles, 1974. "A Strategy for Merging and Matching Microdata Sets," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 2, pages 353-371, National Bureau of Economic Research, Inc.
    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.
    5. Silvestro Montrone & Antonella Massari & Paola Perchinunno & Stefania Girone, 2013. "An Integrated Archive Of The Lifestyles Of Families," 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. 67(3-4), pages 168-175, July-Dece.
    6. Pier Luigi Conti & Daniela Marella & Mauro Scanu, 2016. "Statistical Matching Analysis for Complex Survey Data With Applications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1715-1725, October.
    7. Larsen M. D & Rubin D. B, 2001. "Iterative Automated Record Linkage Using Mixture Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 32-41, March.
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    More about this item

    Keywords

    Data integration; Record linkage; Statistical matching; Poverty profiles; Income; Expenses;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty

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