IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v47y2013i4p1957-1969.html

The long and short of asking questions about income: a comparison using data from Hungary

Author

Listed:
  • Wim Jansen

  • Willem-Jan Verhoeven

  • Peter Robert

  • Jos Dessens

Abstract

A lot of research on income mobility and income inequality is based on survey questions about income. Various question formats are being used. Researchers seem to assume that the actual format used delivers the best estimate of the “true” income. However, surprisingly little empirical support is available for this claim. We implemented an experimental design using the short and long versions of income questions in a Hungarian survey. Results show an overall positive difference between the long and short version. The differences are related to the income components (wages and salaries, transfers, and assets), and respondent characteristics, controlling for the effect of the order of the two versions of income questions. Based on the results, we provide some recommendations for implementing income questions in surveys. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Wim Jansen & Willem-Jan Verhoeven & Peter Robert & Jos Dessens, 2013. "The long and short of asking questions about income: a comparison using data from Hungary," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 1957-1969, June.
    • Handle: RePEc:spr:qualqt:v:47:y:2013:i:4:p:1957-1969
    DOI: 10.1007/s11135-011-9636-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-011-9636-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-011-9636-5?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    2. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
    3. Ecob, Russell & Davey Smith, George, 1999. "Income and health: what is the nature of the relationship?," Social Science & Medicine, Elsevier, vol. 48(5), pages 693-705, March.
    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. Wenbin Du & Muyuan Luo & Zheng Zhou, 2022. "A Study on the Relationship Between Marital Socioeconomic Status, Marital Satisfaction, and Depression: Analysis Based on Actor–Partner Interdependence Model (APIM)," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(3), pages 1477-1499, June.

    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. Diogo G. C. Britto & Alexandre Fonseca & Paolo Pinotti & Breno Sampaio & Lucas Warwar, 2022. "Intergenerational Mobility in the Land of Inequality," CESifo Working Paper Series 10004, CESifo.
    2. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    3. Qing Li, 2014. "Identifiability of mean-reverting measurement error with instrumental variable," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(2), pages 118-129, May.
    4. Javier Escobal & Sonia Laszlo, 2008. "Measurement Error in Access to Markets," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(2), pages 209-243, April.
    5. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    6. Stella Martin & Kevin Stabenow & Mark Trede, 2024. "Measurement Error in Earnings," CQE Working Papers 10824, Center for Quantitative Economics (CQE), University of Muenster.
    7. John C. Ham & Kevin T. Reilly, 2002. "Testing Intertemporal Substitution, Implicit Contracts, and Hours Restriction Models of the Labor Market Using Micro Data," American Economic Review, American Economic Association, vol. 92(4), pages 905-927, September.
    8. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    9. Abay, Kibrom A. & Abate, Gashaw T. & Barrett, Christopher B. & Bernard, Tanguy, 2019. "Correlated non-classical measurement errors, ‘Second best’ policy inference, and the inverse size-productivity relationship in agriculture," Journal of Development Economics, Elsevier, vol. 139(C), pages 171-184.
    10. Geert Ridder & Yingyao Hu, 2004. "Estimation of Nonlinear Models with Measurement Error Using Marginal Information," Econometric Society 2004 North American Summer Meetings 21, Econometric Society.
    11. Arnaud Lefranc, 2003. "On the sensitivity of returns to seniority to the measurement of earnings," International Journal of Manpower, Emerald Group Publishing Limited, vol. 24(7), pages 789-811, November.
    12. Alejandro Badel & Mark Huggett, 2014. "Interpreting Life Cycle Inequality Patterns as an Efficient Allocation: Mission Impossible?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(4), pages 613-629, October.
    13. Lisa M. Dragoset & Gary S. Fields, 2006. "U.S. Earnings Mobility: Comparing Survey-Based and Administrative-Based Estimates," Working Papers 55, ECINEQ, Society for the Study of Economic Inequality.
    14. Cyrille Hagneré & Arnaud Lefranc, 2006. "Étendue et conséquences des erreurs de mesure dans les données individuelles d'enquête : une évaluation à partir des données appariées des enquêtes emploi et revenus fiscaux," Economie & Prévision, La Documentation Française, vol. 0(3), pages 131-154.
    15. Sule Alan, 2012. "Do disaster expectations explain household portfolios?," Quantitative Economics, Econometric Society, vol. 3(1), pages 1-28, March.
    16. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    17. Bruce D. Meyer & James X. Sullivan, 2003. "Measuring the Well-Being of the Poor Using Income and Consumption," NBER Working Papers 9760, National Bureau of Economic Research, Inc.
    18. Nicoletti, Cheti & Peracchi, Franco & Foliano, Francesca, 2011. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 61-72.
    19. Mark C. Berger & Dan A. Black & Frank A. Scott, 1998. "How Well Do We Measure Employer‐Provided Health Insurance Coverage?," Contemporary Economic Policy, Western Economic Association International, vol. 16(3), pages 356-367, July.
    20. Ana Cinta G. Cabral & Norman Gemmell & Nazila Alinaghi, 2021. "Are survey-based self-employment income underreporting estimates biased? New evidence from matched register and survey data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 28(2), pages 284-322, April.

    More about this item

    Keywords

    ;
    ;
    ;

    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:spr:qualqt:v:47:y:2013:i:4:p:1957-1969. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.