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Combining Household Income and Expenditure Data in Policy Simulations

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  • Sutherland, H.
  • Taylor, R.
  • Gomulka, J.

Abstract

Analysis of the distributional impact of fiscal policy proposals often requires information on household expenditures and incomes. It is unusual to have one data source with high quality information on both, and this problem is generally overcome with statistical matching of independent data sources. In this paper Grade Correspondence Analysis (GCA) is investigated as a tool to improve the matching process. An evaluation of alternative methods is conducted using datasets from the UK Family Expenditure Survey (FES), which is unusual in containing both income and expenditure at a detailed level of disaggregation. Imputed expenditures are compared with actual expenditures through the use of indirect tax simulations using the UK microsimulation model, POLIMOD. The most successful methods are then employed to enhance income data from the Family Resources Survey (FRS) and the synthetic dataset is used as a microsimulation model dataset.

Suggested Citation

  • Sutherland, H. & Taylor, R. & Gomulka, J., 2001. "Combining Household Income and Expenditure Data in Policy Simulations," Cambridge Working Papers in Economics 0110, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:0110
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    1. Dayal N & Gomulka J & Mitton L & Sutherland H, 2000. "Enhancing Family Resources Survey income data with expenditure data from the Family Expenditure Survey: data comparisons," Microsimulation Unit Research Notes MU/RN/40, Microsimulation Unit at the Institute for Social and Economic Research.
    2. Taylor R & Sutherland H & Gomulka J, 2001. "Using POLIMOD to evaluate alternative methods of expenditure imputation," Microsimulation Unit Research Notes MU/RN/38, Microsimulation Unit at the Institute for Social and Economic Research.
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    1. van Sonsbeek, J.M. & Gradus, R.H.J.M., 2006. "A microsimulation analysis of the 2006 regime change in the Dutch disability scheme," Economic Modelling, Elsevier, vol. 23(3), pages 427-456, May.
    2. Annie Abello & Sharyn Lymer & Laurie Brown & Ann Harding & Ben Phillips, 2008. "Enhancing the Australian National Health Survey Data for Use in a Microsimulation Model of Pharmaceutical Drug Usage and Cost," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(3), pages 1-2.
    3. Nico Pestel & Eric Sommer, 2013. "Shifting Taxes from Labor to Consumption: Efficient, but Regressive?," SOEPpapers on Multidisciplinary Panel Data Research 624, DIW Berlin, The German Socio-Economic Panel (SOEP).
    4. Petr Janský & Klára Kalíšková & Daniel Münich, 2016. "Does the Czech Tax and Benefit System Contribute to One of Europe’s Lowest Levels of Relative Income Poverty and Inequality?," Eastern European Economics, Taylor & Francis Journals, vol. 54(3), pages 191-207, May.
    5. Ochnio Luiza & Rokicki Tomasz & Koszela Grzegorz & Klepacki Bogdan, 2019. "Diversification of Lamb Meat Imports in EU Countries and its Trends," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(4), pages 96-111, December.
    6. Luca Tasciotti & Natascha Wagner, 2018. "How Much Should We Trust Micro-data? A Comparison of the Socio-demographic Profile of Malawian Households Using Census, LSMS and DHS data," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 30(4), pages 588-612, September.
    7. Andreas Peichl & Thilo Schaefer, 2009. "FiFoSiM - an integrated tax benefit microsimulation and CGE model for Germany," International Journal of Microsimulation, International Microsimulation Association, vol. 2(1), pages 1-15.
    8. Cristina Borra & Almudena Sevilla & Jonathan Gershuny, 2013. "Calibrating Time-Use Estimates for the British Household Panel Survey," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(3), pages 1211-1224, December.
    9. Schaefer, Thilo & Peichl, Andreas, 2006. "Documentation FiFoSiM: integrated tax benefit microsimulation and CGE model," FiFo Discussion Papers - Finanzwissenschaftliche Diskussionsbeiträge 06-10, University of Cologne, FiFo Institute for Public Economics.
    10. Lamarche, Pierre, 2017. "Estimating consumption in the HFCS: Experimental results on the first wave of the HFCS," Statistics Paper Series 22, European Central Bank.
    11. Immervoll, Herwig, 2002. "The distribution of average and marginal effective tax rates in European Union Member States," EUROMOD Working Papers EM2/02, EUROMOD at the Institute for Social and Economic Research.
    12. 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.
    13. Matěj Bajgar & Petr Janský & Klára Kalíšková, 2019. "The poor outside the lamplight: on the prevalence of poverty among population groups not included in household surveys," Post-Communist Economies, Taylor & Francis Journals, vol. 31(2), pages 181-199, March.
    14. Dayal N & Gomulka J & Mitton L & Sutherland H, 2000. "Enhancing Family Resources Survey income data with expenditure data from the Family Expenditure Survey: data comparisons," Microsimulation Unit Research Notes MU/RN/40, Microsimulation Unit at the Institute for Social and Economic Research.
    15. 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.

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    More about this item

    Keywords

    statistical matching; clustering; income and expenditure micro-data; microsimulation;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D10 - Microeconomics - - Household Behavior - - - General

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