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Truncation Bias and the Ordinal Evaluation of Income Inequality

Author

Listed:
  • Bishop, John A
  • Chiou, Jong-Rong
  • Formby, John P

Abstract

Lorenz dominance analysis is used to examine the effect of top-coding on the ordinal evaluation of U.S. income inequality across time. Current Population Survey microdata are adjusted for truncation bias, and statistical inference procedures are used to examine biennial changes in unadjusted and adjusted Lorenz curves. Beginning in 1985, the truncation bias has a significant effect on ordinal rankings of income inequality.

Suggested Citation

  • Bishop, John A & Chiou, Jong-Rong & Formby, John P, 1994. "Truncation Bias and the Ordinal Evaluation of Income Inequality," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(1), pages 123-127, January.
    • Handle: RePEc:bes:jnlbes:v:12:y:1994:i:1:p:123-27
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    Citations

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    Cited by:

    1. Richard Burkhauser & Shuaizhang Feng & Stephen Jenkins & Jeff Larrimore, 2009. "Recent Trends in Top Income Shares in the USA: Reconciling Estimates from March CPS and IRS Tax Return Data," Working Papers 09-26, Center for Economic Studies, U.S. Census Bureau.
    2. Ellis Scharfenaker, Markus P.A. Schneider, 2019. "Labor Market Segmentation and the Distribution of Income: New Evidence from Internal Census Bureau Data," Working Paper Series, Department of Economics, University of Utah 2019_08, University of Utah, Department of Economics.
    3. Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2008. "Measuring Labor Earnings Inequality using Public-Use March Current Population Survey Data: The Value of Including Variances and Cell Means When Imputing Topcoded Values," NBER Working Papers 14458, National Bureau of Economic Research, Inc.
    4. Philip Armour & Richard V. Burkhauser & Jeff Larrimore, 2016. "Using The Pareto Distribution To Improve Estimates Of Topcoded Earnings," Economic Inquiry, Western Economic Association International, vol. 54(2), pages 1263-1273, April.
    5. Zheng, Buhong & J. Cushing, Brian, 2001. "Statistical inference for testing inequality indices with dependent samples," Journal of Econometrics, Elsevier, vol. 101(2), pages 315-335, April.
    6. Richard Burkhauser & Shuaizhang Feng & Stephen Jenkins & Jeff Larrimore, 2011. "Estimating trends in US income inequality using the Current Population Survey: the importance of controlling for censoring," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(3), pages 393-415, September.
    7. Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2009. "Measuring Inequality Using Censored Data: A Multiple Imputation Approach," Discussion Papers of DIW Berlin 866, DIW Berlin, German Institute for Economic Research.
    8. Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2011. "Measuring inequality using censored data: a multiple‐imputation approach to estimation and inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 63-81, January.
    9. Stephanie Aaronson, 2002. "The rise in lifetime earnings inequality among men," Finance and Economics Discussion Series 2002-21, Board of Governors of the Federal Reserve System (U.S.).
    10. John Bishop & K. Chow & John Formby & Chih-Chin Ho, 1997. "Did Tax Reform Reduce Actual US Progressivity? Evidence from the Taxpayer Compliance Measurement Program," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 4(2), pages 177-197, May.
    11. Ellis Scharfenaker & Markus P. A. Schneider, 2023. "Labor Market Segmentation and the Distribution of Income: New Evidence from Internal Census Bureau Data," Working Papers 23-41, Center for Economic Studies, U.S. Census Bureau.

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