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

Author

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  • Pier Luigi Conti

    (Sapienza Università di Roma)

  • Daniela Marella

    (Università Roma TRE)

  • Andrea Neri

    (Banca d’Italia)

Abstract

Among the goals of statistical matching, a very important one is the estimation of the joint distribution of variables not jointly observed in a sample survey but separately available from independent sample surveys. The absence of joint information on the variables of interest leads to uncertainty about the data generating model since the available sample information is unable to discriminate among a set of plausible joint distributions. In the present paper a short review of the concept of uncertainty in statistical matching under logical constraints, as well as how to measure uncertainty for continuous variables is presented. The notion of matching error is related to an appropriate measure of uncertainty and a criterion of selecting matching variables by choosing the variables minimizing such an uncertainty measure is introduced. Finally, a method to choose a plausible joint distribution for the variables of interest via iterative proportional fitting algorithm is described. The proposed methodology is then applied to household income and expenditure data when extra sample information regarding the average propensity to consume is available. This leads to a reconstructed complete dataset where each record includes measures on income and expenditure.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stmapp:v:26:y:2017:i:3:d:10.1007_s10260-016-0374-7
    DOI: 10.1007/s10260-016-0374-7
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    1. Caballero, Ricardo J., 1990. "Consumption puzzles and precautionary savings," Journal of Monetary Economics, Elsevier, vol. 25(1), pages 113-136, January.
    2. Martin Browning & M. Dolores Collado, 2001. "The Response of Expenditures to Anticipated Income Changes: Panel Data Estimates," American Economic Review, American Economic Association, vol. 91(3), pages 681-692, June.
    3. Guiso, Luigi & Jappelli, Tullio & Terlizzese, Daniele, 1992. "Earnings uncertainty and precautionary saving," Journal of Monetary Economics, Elsevier, vol. 30(2), pages 307-337, November.
    4. Erich Battistin & Raffaele Miniaci & Guglielmo Weber, 2003. "What Do We Learn from Recall Consumption Data?," Journal of Human Resources, University of Wisconsin Press, vol. 38(2).
    5. Tedeschi, Simone & Pisano, Elena, 2013. "Data Fusion Between Bank of Italy-SHIW and ISTAT-HBS," MPRA Paper 51253, University Library of Munich, Germany.
    6. 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.
    7. Michael G. Palumbo, 1999. "Uncertain Medical Expenses and Precautionary Saving Near the End of the Life Cycle," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(2), pages 395-421.
    8. Orazio Attanasio & Luigi Pistaferri, 2014. "Consumption Inequality over the Last Half Century: Some Evidence Using the New PSID Consumption Measure," American Economic Review, American Economic Association, vol. 104(5), pages 122-126, May.
    9. Martin Browning & Thomas F. Crossley & Joachim Winter, 2014. "The Measurement of Household Consumption Expenditures," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 475-501, August.
    10. Rodgers, Willard L, 1984. "An Evaluation of Statistical Matching," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 91-102, January.
    11. James Banks & Zoe Oldfield, 2007. "Understanding Pensions: Cognitive Function, Numerical Ability and Retirement Saving," Fiscal Studies, Institute for Fiscal Studies, vol. 28(2), pages 143-170, June.
    12. Richard Blundell & Luigi Pistaferri & Ian Preston, 2008. "Consumption Inequality and Partial Insurance," American Economic Review, American Economic Association, vol. 98(5), pages 1887-1921, December.
    13. Giulia Cifaldi & Andrea Neri, 2013. "Asking income and consumption questions in the same survey: what are the risks?," Temi di discussione (Economic working papers) 908, Bank of Italy, Economic Research and International Relations Area.
    14. Skinner, Jonathan, 1987. "A superior measure of consumption from the panel study of income dynamics," Economics Letters, Elsevier, vol. 23(2), pages 213-216.
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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. Lamarche, Pierre, 2017. "Estimating consumption in the HFCS: Experimental results on the first wave of the HFCS," Statistics Paper Series 22, European Central Bank.
    7. 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.
    8. 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.
    9. 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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