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Statistical matching and uncertainty analysis in combining household income and expenditure data

Author

Listed:
  • Pier Luigi Conti

    (Dipartimento di Scienze Statistiche, Sapienza Università di Roma)

  • Daniela Marella

    (Dipartimento di Scienze della Formazione, Università Roma Tre)

  • Andrea Neri

    (Bank of Italy)

Abstract

The availability of microdata on both income and expenditure is highly recommended if one wants to assess the distributional consequences of policy changes. In Italy, the main sources used for estimating household income and expenditure are the Bank of Italy's Survey on Household Income and Wealth and the Italian National Institute of Statistics Household Budget Survey. However, there is no single data source containing information on both expenditure and income. The problem is generally overcome with statistical matching procedures based on the conditional independence (CIA) assumption. The aim of this paper is to present a method to combine information coming from different databases relaxing the CIA assumption. In particular we propose a method to combine household income and expenditure data under logical constraints regarding the average propensity to consume. We also propose an estimate of a plausible joint distribution function for household income and expenditure.

Suggested Citation

  • Pier Luigi Conti & Daniela Marella & Andrea Neri, 2015. "Statistical matching and uncertainty analysis in combining household income and expenditure data," Temi di discussione (Economic working papers) 1018, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:wptemi:td_1018_15
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    References listed on IDEAS

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    Cited by:

    1. Ton de Waal & Arnout van Delden & Sander Scholtus, 2020. "Multi‐source Statistics: Basic Situations and Methods," International Statistical Review, International Statistical Institute, vol. 88(1), pages 203-228, April.
    2. 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.
    3. Claramunt González, Juan & van Delden, Arnout & de Waal, Ton, 2023. "Assessment of the effect of constraints in a new multivariate mixed method for statistical matching," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    4. Lamarche, Pierre, 2017. "Estimating consumption in the HFCS: Experimental results on the first wave of the HFCS," Statistics Paper Series 22, European Central Bank.
    5. Daniela Marella & Danny Pfeffermann, 2023. "Accounting for Non‐ignorable Sampling and Non‐response in Statistical Matching," International Statistical Review, International Statistical Institute, vol. 91(2), pages 269-293, August.
    6. 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.
    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. Perchinunno, Paola & Mongelli, Lucia & d’Ovidio, Francesco D., 2020. "Statistical matching techniques in order to plan interventions on socioeconomic weakness: An Italian case," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).

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    More about this item

    Keywords

    statistical matching; uncertainty; matching error; iterative proportional fitting;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods

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