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Imputation of missing expenditure information in standard household income surveys

Author

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  • Massimo Baldini

    ()

  • Daniele Pacifico

    ()

  • Federica Termini

    ()

Abstract

The aim of this paper is to present a new methodology for dealing with missing expenditure information in standard income surveys. Under given conditions, typical imputation procedures, such as statistical matching or regression-based models, can replicate well in the income survey both the unconditional density of household expenditure and its joint density with a set of socio-demographic variables that the two surveys have in common. However, standard imputation procedures may fail in capturing the overall relation between income and expenditure, especially if the common control variables used for the imputation have a weak correlation with the missing information. The paper suggests a two-step imputation procedure that allows reproducing the joint relation between income and expenditure observed from external sources, while maintaining the advantages of traditional imputation methods. The proposed methodology suits well for any empirical analysis that needs to relate income and consumption, such as the estimation of Engel curves or the evaluation of consumption taxes through micro-simulation models. An empirical application shows the makings of such a technique for the evaluation of the distributive effects of consumption taxes and proves that common imputation methods may produce significantly biased results in terms of policy recommendations when the control variables used for the imputation procedure are weakly correlated with the missing variable.

Suggested Citation

  • Massimo Baldini & Daniele Pacifico & Federica Termini, 2015. "Imputation of missing expenditure information in standard household income surveys," Center for the Analysis of Public Policies (CAPP) 0116, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
  • Handle: RePEc:mod:cappmo:0116
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    File URL: http://155.185.68.2/campusone/web_dep/CappPaper/Capp_p116.pdf
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    References listed on IDEAS

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    1. repec:ags:stataj:116250 is not listed on IDEAS
    2. Abadie, Alberto & Drukker, David M. & Herr, Jane Leber & Imbens, Guido W., 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 1-22.
    3. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    4. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    5. André Decoster & Jason Loughrey & Cathal O'Donoghue & Dirk Verwerft, 2011. "Microsimulation of indirect taxes," International Journal of Microsimulation, International Microsimulation Association, vol. 4(2), pages 41-56.
    6. repec:ags:stataj:116022 is not listed on IDEAS
    7. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
    8. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    9. Becker, Sascha O. & Ichino, Andrea, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 1-20.
    10. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
    11. Elena Pisano & Simone Tedeschi, 2014. "Micro Data Fusion of Italian Expenditures and Incomes Surveys," Working Papers 164, University of Rome La Sapienza, Department of Public Economics.
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    Citations

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    Cited by:

    1. 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.
    2. Lamarche, Pierre, 2017. "Estimating consumption in the HFCS: Experimental results on the first wave of the HFCS," Statistics Paper Series 22, European Central Bank.
    3. repec:eee:ecolec:v:138:y:2017:i:c:p:109-125 is not listed on IDEAS

    More about this item

    Keywords

    expenditure imputation; matching; propensity score; tax incidence;

    JEL classification:

    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F20 - International Economics - - International Factor Movements and International Business - - - General
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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