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Longitudinal statistical matching: transferring consumption expenditure from HBS to SILC panel survey


  • Baris Ucar


  • Gianni Betti



The aim of this study is to look for an appropriate procedure to conduct statistical matching for longitudinal data sets. To our knowledge, among the studies, which are associated to statistical matching with longitudinal data, no such issue has been specifically covered or identified in literature. The longitudinal data set, which is used in this study, involves a longitudinal weight at individual level, which requires further procedures before the matching application. The study will discuss and propose ways to deal with the statistical matching issue for such data sets. In the process, a four-year longitudinal data set is used and data from each year is matched with a cross-sectional data set for the corresponding year. The matching procedure is comprised of two steps, respectively the Renssen (1998) method followed by nearest neighbor distance hot deck matching, proposed by D’Orazio (2016) and Donatiello et al. (2015). The application is undertaken on Turkish data to impute consumption expenditure variable from Household Budget Survey (HBS) to Statistics on Income, and Living Conditions (SILC) Survey. A synthetic longitudinal data set is created by using these two survey data sets. The two data sets have many variables in common including income variable, which enables Conditional Independence Assumption (CIA) to be more likely. The study also carries out validation analyses to determine the quality of the matching procedures and the findings achieved with the proposed itinerary, indicate that the distribution of consumption expenditure estimate in synthetic data set is well preserved with all estimates. The poverty indicators in general and at household level is also looked for and the results indicate a good quality match

Suggested Citation

  • 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.
  • Handle: RePEc:usi:wpaper:739

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    References listed on IDEAS

    1. Brodaty, Thomas & Crépon, Bruno & Fougère, Denis, 2000. "Using Matching Estimators to Evaluate Alternative Youth Employment Programs: Evidence from France, 1986-1988," CEPR Discussion Papers 2604, C.E.P.R. Discussion Papers.
    2. 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.
    3. Radner, Daniel B, 1981. "An Example of the Use of Statistical Matching in the Estimation and Analysis of the Size Distribution of Income," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 27(3), pages 211-242, September.
    4. Thomas Masterson, 2013. "Quality of Statistical Match and Simulations Used in the Estimation of the Levy Institute Measure of Time and Consumption Poverty (LIMTCP) for Turkey in 2006," Economics Working Paper Archive wp_769, Levy Economics Institute.
    5. Lisa Keister, 2003. "Sharing the wealth: The effect of siblings on adults’ wealth ownership," Demography, Springer;Population Association of America (PAA), vol. 40(3), pages 521-542, August.
    6. Gabriella Donatiello & Marcello D’Orazio & Doriana Frattarola & Antony Rizzi & Mauro Scanu & Mattia Spaziani, 2014. "Statistical Matching of Income and Consumption Expenditures," International Journal of Economic Sciences, University of Economics, Prague, vol. 2014(3), pages 50-65.
    7. Gabriella Donatiello & Marcello D'Orazio & Doriana Frattarola & Antony Rizzi & Mauro Scanu & Mattia Spaziani, 2014. "Statistical matching of income and consumption expenditures," Proceedings of International Academic Conferences 0100965, International Institute of Social and Economic Sciences.
    8. Anika Rasner & Joachim R. Frick & Markus M. Grabka, 2011. "Extending the Empirical Basis for Wealth Inequality Research Using Statistical Matching of Administrative and Survey Data," SOEPpapers on Multidisciplinary Panel Data Research 359, DIW Berlin, The German Socio-Economic Panel (SOEP).
    9. 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".
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    Cited by:

    1. Ozlem Albayrak & Thomas Masterson, 2017. "Quality of Statistical Match of Household Budget Survey and SILC for Turkey," Economics Working Paper Archive wp_885, Levy Economics Institute.

    More about this item


    Statistical matching; Consumption Expenditure; SILC; Household Budget Survey;

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

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