IDEAS home Printed from https://ideas.repec.org/a/bla/revinw/v48y2002i4p517-536.html
   My bibliography  Save this article

Combining Household Income and Expenditure Data in Policy Simulations

Author

Listed:
  • Holly Sutherland
  • Rebecca Taylor
  • Joanna Gomulka

Abstract

The 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 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. GCA draws out the relationships between the common variables to enable the sample to be partitioned into more homogeneous groups, prior to matching. An evaluation is conducted using the UK Family Expenditure Survey, 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. The most successful methods are then employed to enhance data from the Family Resources Survey and the synthetic dataset is used as a microsimulation model database.

Suggested Citation

  • Holly Sutherland & Rebecca Taylor & Joanna Gomulka, 2002. "Combining Household Income and Expenditure Data in Policy Simulations," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 48(4), pages 517-536, December.
    • Handle: RePEc:bla:revinw:v:48:y:2002:i:4:p:517-536
    DOI: 10.1111/1475-4991.00066
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1475-4991.00066
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1475-4991.00066?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Francesconi & Holly Sutherland & Francesca Zantomio, 2011. "A comparison of earnings measures from longitudinal and cross‐sectional surveys: evidence from the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 297-326, April.
    2. Sutherland, Holly & Mantovani, Daniela, 2003. "Social indicators and other income statistics using the EUROMOD baseline: a comparison with Eurostat and National Statistics," EUROMOD Working Papers EM1/03, EUROMOD at the Institute for Social and Economic Research.
    3. Haroon Mumtaz & Angeliki Theophilopoulou, 2015. "Monetary Policy and Inequality in the UK," Working Papers 738, Queen Mary University of London, School of Economics and Finance.
    4. 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.
    5. 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.
    6. Mumtaz, Haroon & Theophilopoulou, Angeliki, 2017. "The impact of monetary policy on inequality in the UK. An empirical analysis," European Economic Review, Elsevier, vol. 98(C), pages 410-423.

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:revinw:v:48:y:2002:i:4:p:517-536. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/iariwea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.