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How Reliable are Income Data Collected with a Single Question?

Author

Listed:
  • John Micklewright

    () (Depatment of Quantitative Social Science - Institute of Education, University of London.)

  • Sylke V. Schnepf

    () (School of Social Sciences and Southampton Statistical Sciences Research Institute, University of Southampton)

Abstract

Income is an important correlate for numerous phenomena in the social sciences. But many surveys collect data with just a single question covering all forms of income. This raises questions over the reliability of the data collected. Issues of reliability are heightened when individuals are asked about the household total rather than own income alone. We argue that the large literature on measuring incomes has not devoted enough attention to ‘single-question’ surveys. We investigate the reliability of single-question data using the ONS Omnibus survey and British Social Attitudes (BSA) survey as examples. We compare the distributions of income in these surveys – individual income in the Omnibus and household income in the BSA --- with those in two larger UK surveys that measure income in much greater detail. Distributions compare less well for household income than for individual income. Disaggregation by gender proves fruitful in much of the analysis. We also establish levels of item non-response to the income question in single-question surveys from a wide range of countries.

Suggested Citation

  • John Micklewright & Sylke V. Schnepf, 2009. "How Reliable are Income Data Collected with a Single Question?," DoQSS Working Papers 09-03, Department of Quantitative Social Science - UCL Institute of Education, University College London.
  • Handle: RePEc:qss:dqsswp:0903
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    Cited by:

    1. Lourdes Diaz Olvera & Didier Plat & Pascal Pochet, 2015. "Assessment of mobility inequalities and income data collection. Methodological issues and a case study (Douala, Cameroon)," Post-Print halshs-01205776, HAL.
    2. Andrew J. Healy & Mikael Persson & Erik Snowberg, 2016. "Digging into the Pocketbook: Evidence on Economic Voting from Income Registry Data Matched to a Voter Survey," CESifo Working Paper Series 6171, CESifo Group Munich.
    3. Kirstine Hansen & Dylan Kneale, 2013. "Does How You Measure Income Make a Difference to Measuring Poverty? Evidence from the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(3), pages 1119-1140, February.
    4. Thomas F. Crossley & Joachim K. Winter, 2014. "Asking Households about Expenditures: What Have We Learned?," NBER Chapters,in: Improving the Measurement of Consumer Expenditures, pages 23-50 National Bureau of Economic Research, Inc.
    5. Engzell, Per, 2017. "What Do Books in the Home Proxy For? A Cautionary Tale," Working Paper Series 1/2016, Stockholm University, Swedish Institute for Social Research.
    6. Ana Rute Cardoso & Annalisa Loviglio & Lavinia Piemontese, 2016. "Misperceptions of unemployment and individual labor market outcomes," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-22, December.
    7. Jake Anders, 2012. "Using the Longitudinal Study of Young People in England for research into Higher Education access," DoQSS Working Papers 12-13, Department of Quantitative Social Science - UCL Institute of Education, University College London.
    8. 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.
    9. Jack Britton & Neil Shephard & Anna Vignoles, 2015. "Comparing sample survey measures of English earnings of graduates with administrative data during the Great Recession," IFS Working Papers W15/28, Institute for Fiscal Studies.
    10. Elvire Guillaud & Michaël Zemmour, 2017. "The redistributive preferences of the well-off," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01652706, HAL.
    11. Gustafsson, Björn & LI, Shi & Sato, Hiroshi, 2014. "Data for studying earnings, the distribution of household income and poverty in China," China Economic Review, Elsevier, vol. 30(C), pages 419-431.
    12. Cardoso, Ana Rute & Loviglio, Annalisa & Piemontese, Lavinia, 2015. "Information Frictions and Labor Market Outcomes," IZA Discussion Papers 9070, Institute for the Study of Labor (IZA).
    13. Lourdes Diaz Olvera & Didier Plat & Pascal Pochet, 2015. "Assessment of mobility inequalities and income data collection. Methodological issues and a case study (Douala, Cameroon)
      [Evaluation des inégalités de mobilité et recueil des revenus. Questions mé
      ," Post-Print halshs-01235185, HAL.

    More about this item

    Keywords

    income measurement; validity;

    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

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