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An Integrated Database to Measure Living Standards

Author

Listed:
  • Chiara Elena Dalla

    (University of Verona, Department of Economics, via Cantarane, 24, 37129, Verona, Italy.)

  • Menon Martina

    (University of Verona, Department of Economics, via Cantarane, 24, 37129, Verona, Italy.)

  • Perali Federico

    (University of Verona, Department of Economics, via Cantarane, 24, 37129, Verona, Italy.)

Abstract
This study generates an integrated database to measure living standards in Italy using propensity score matching. We follow the recommendations of the Commission on the Measurement of Economic Performance and Social Progress proposing that income, consumption of market goods and nonmarket activities, and wealth, rather than production, should be evaluated jointly in order to appropriately measure material welfare. Our integrated database is similar in design to the one built for the United States by the Levy Economics Institute to measure the multiple dimensions of well-being. In the United States, as is the case for Italy and most European countries, the state does not maintain a unified database to measure household economic well-being, and data sources about income and employment surveys and other surveys on wealth and the use of time have to be statistically matched. The measure of well-being is therefore the result of a multidimensional evaluation process no longer associated with a single indicator, as is usually the case when measuring gross domestic product. The estimation of individual and social welfare, multidimensional poverty and inequality does require an integrated living standard database where information about consumption, income, time use and subjective well-being are jointly available. With this objective in mind, we combine information available in four different surveys: the European Union Statistics on Income and Living Conditions Survey, the Household Budget Survey, the Time Use Survey, and the Household Conditions and Social Capital Survey. We perform three different statistical matching procedures to link the relevant dimensions of living standards contained in each survey and report both the statistical and economic tests carried out to evaluate the quality of the procedure at a high level of detail.

Suggested Citation

  • 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.
    • Handle: RePEc:vrs:offsta:v:35:y:2019:i:3:p:531-576:n:3
    DOI: 10.2478/jos-2019-0023
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Elizaveta A. Belousova, 2022. "Economic well-being: Semantic environment and research contexts at a municipal level," Journal of New Economy, Ural State University of Economics, vol. 23(4), pages 46-68, January.
    3. Elena Dalla Chiara & Federico Perali, 2022. "What Causes Juvenile Crime? A Case-Control Study," Working Papers 9, SITES.
    4. Elena Dalla Chiara & Federico Perali, 2022. "Relational Well-being and the Many Dimensions of Poverty in Italy," Working Papers 6, SITES.
    5. Leonardo Ciambezi & Alessandro Pietropaoli, 2024. "Relative price shocks and inequality: evidence from Italy," Questioni di Economia e Finanza (Occasional Papers) 883, Bank of Italy, Economic Research and International Relations Area.
    6. Eleftherios Giovanis & Martina Menon & Federico Perali, 2023. "Disability specific equivalence scales: a case–control approach applied to the cost of acquired brain injuries," International Journal of Health Economics and Management, Springer, vol. 23(4), pages 643-672, December.

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    More about this item

    Keywords

    Propensity score; statistical matching; well-being; fused data; multidimensional poverty;
    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
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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