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Measurement Error and Poverty Rates of Widows

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  • Kathleen McGarry

Abstract

Estimates of the poverty rate and of the probability of entering or exiting poverty are biased when income is observed with error. I estimate a variance components model of income which contains a white noise error term and then treat this component as an approximation of the error in observed income. By comparing poverty rates calculated with and without this estimated measurement error, I conclude that observation error causes the poverty rate to be overestimated around two percentage points on average. However, eliminating observation error substantially reduces the probability of transiting either into or out of poverty. These reductions imply that the amount of permanent poverty is underestimated when measurement error is ignored.

Suggested Citation

  • Kathleen McGarry, 1995. "Measurement Error and Poverty Rates of Widows," Journal of Human Resources, University of Wisconsin Press, vol. 30(1), pages 113-134.
    • Handle: RePEc:uwp:jhriss:v:30:y:1995:i:1:p:113-134
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    Cited by:

    1. David R. Weir & Robert J. Willis & Purvi A. Sevak, 2002. "The Economic Consequences of Widowhood," Working Papers wp023, University of Michigan, Michigan Retirement Research Center.
    2. Nayoung Lee & Geert Ridder & John Strauss, 2017. "Estimation of Poverty Transition Matrices with Noisy Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 37-55, January.
    3. Cooper, D. & McCausland, W.D. & Theodossiou, I., 2006. "The health hazards of unemployment and poor education: The socioeconomic determinants of health duration in the European Union," Economics & Human Biology, Elsevier, vol. 4(3), pages 273-297, December.
    4. Dr Sukhan Jackson & Nerina Vecchio, 2002. "Government Policies On Employment And Superannuation: Contradictions And Consequences For Older Australians," Discussion Papers Series 308, School of Economics, University of Queensland, Australia.
    5. Daniel L. Millimet & Hao Li & Punarjit Roychowdhury, 2020. "Partial Identification of Economic Mobility: With an Application to the United States," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 732-753, October.
    6. Namkee Ahn, "undated". "Economic Consequences of Widowhood in Europe: Cross-country and Gender Differences," Working Papers 2004-27, FEDEA.
    7. Marchand, J. & Smeeding, T., 2016. "Poverty and Aging," Handbook of the Economics of Population Aging, in: Piggott, John & Woodland, Alan (ed.), Handbook of the Economics of Population Aging, edition 1, volume 1, chapter 0, pages 905-950, Elsevier.
      • Marchand, Joseph & Smeeding, Timothy, 2016. "Poverty and Aging," Working Papers 2016-11, University of Alberta, Department of Economics, revised 20 Nov 2016.
    8. Stefan Angel & Richard Heuberger & Nadja Lamei, 2018. "Differences Between Household Income from Surveys and Registers and How These Affect the Poverty Headcount: Evidence from the Austrian SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 575-603, July.
    9. ChangHwan Kim & Christopher R. Tamborini, 2014. "Response Error in Earnings," Sociological Methods & Research, , vol. 43(1), pages 39-72, February.

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