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How Reliable Are Income Data Collected with a Single Question?

Author

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  • Micklewright, John

    () (University College London)

  • Schnepf, Sylke V.

    () (European Commission, DG Joint Research Centre)

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 issues of quality, and these are heightened when individuals are asked about the household total rather than own income alone. Data are typically banded, implying a loss of information. We investigate the reliability of ‘single-question’ data using the ONS Omnibus and British Social Attitudes (BSA) surveys as examples. We first compare the distributions of income in these surveys – individual income in the Omnibus and household income in the BSA – with those in two other much larger UK surveys that measure income in much greater detail. Second, we investigate an implication of restricting the single question to individual income and interviewing only one adult per household: total income in respondents’ households is unobserved. We therefore examine the relationship between individual and household income in one of the comparator surveys. Third, after imposing bands on comparator survey data, we measure the information loss from banding with Generalised Entropy indices. We then assess its impact on the use of income as a covariate. Disaggregation by gender proves fruitful in much of the analysis.

Suggested Citation

  • Micklewright, John & Schnepf, Sylke V., 2007. "How Reliable Are Income Data Collected with a Single Question?," IZA Discussion Papers 3177, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp3177
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Elvire Guillaud & Michaël Zemmour, 2017. "The redistributive preferences of the well-off," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01652706, HAL.
    10. 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).
    11. 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.
    12. 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.
    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

    Omnibus survey; information loss; banding; income data; British Social Attitudes survey;

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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