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Imputation of missing expenditure information in standard household income surveys

Listed author(s):
  • Massimo Baldini

    ()

  • Daniele Pacifico

    ()

  • Federica Termini

    ()

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.

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File URL: http://155.185.68.2/campusone/web_dep/CappPaper/Capp_p116.pdf
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Paper provided by Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi" in its series Center for the Analysis of Public Policies (CAPP) with number 0116.

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Length: pages 21
Date of creation: Jan 2015
Handle: RePEc:mod:cappmo:0116
Contact details of provider: Web page: http://www.capp.unimore.it

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  1. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
  2. 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, 02.
  3. 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.
  4. 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).
  5. 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.
  6. 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.
  7. 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, 01.
  8. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
  9. 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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