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Comparing the Household Economic Survey to administrative records: An analysis of income and benefit receipt

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We investigate the difference between people's survey responses to the Household Economic Survey (HES) and these same people's administrative income and benefit receipt records using the Integrated Data Infrastructure (IDI). 83% of people can be linked to the IRD data within the IDI. Those that are linked have higher incomes, are more likely to be male, are more likely to be of European descent and have lower reported benefit receipt than those who are not linked. HES reported total incomes (excluding benefit income and some other categories) are typically 1.5-6% higher than administrative measures of these same people's income. Despite this difference, there is still a strong correlation between HES and administrative income (about 0.79). On the other hand, reported benefit receipt in HES correlates poorly with the administrative measure. Benefits are under-reported on average, but many people also report receiving benefits that the administrative record says that they did not receive.

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  • Chris Ball & Judd Ormsby, 2017. "Comparing the Household Economic Survey to administrative records: An analysis of income and benefit receipt," Treasury Analytical Papers Series ap17/01, New Zealand Treasury.
  • Handle: RePEc:nzt:nztaps:ap17/01
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    File URL: https://www.treasury.govt.nz/sites/default/files/2017-06/ap17-01.pdf
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    1. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    2. 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.
    3. Dean R. Hyslop & Wilbur Townsend, 2020. "Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 457-469, April.
    4. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    5. Hyslop, Dean R. & Townsend, Wilbur, 2017. "Employment misclassification in survey and administrative reports," Economics Letters, Elsevier, vol. 155(C), pages 19-23.
    6. 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.
    7. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
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    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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