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Survey nonresponse and the distribution of income

  • Anton Korinek
  • Johan Mistiaen
  • Martin Ravallion


The authors examine the distributional implications of selective compliance in sample surveys, whereby households with different incomes are not equally likely to participate. They discuss poverty and inequality measurement implications for monotonically decreasing and inverted-U compliance-income relationships. The authors demonstrate that the latent income effect on the probability of compliance can be estimated from information on response rates across geographic areas. On implementing the method on the Current Population Survey for the United States, they find that the compliance probability falls monotonically as income rises. Correcting for non-response appreciably increases mean income and inequality, but has only a small impact on poverty incidence up to poverty lines common in the United States.

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Article provided by Springer in its journal The Journal of Economic Inequality.

Volume (Year): 4 (2006)
Issue (Month): 1 (April)
Pages: 33-55

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Handle: RePEc:kap:jecinq:v:4:y:2006:i:1:p:33-55
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  1. Lee Lillard & James P. Smith & Finis Welch, 2004. "What Do We Really Know About Wages: The Importance of Nonreporting and Census Imputation," Labor and Demography 0404005, EconWPA.
  2. Xavier Sala-i-Martin, 2002. "The World Distribution of Income (estimated from Individual Country Distributions)," NBER Working Papers 8933, National Bureau of Economic Research, Inc.
  3. van Praag, Bernard M S & Hagenaars, Aldi J M & van Eck, Wim, 1983. "The Influence of Classification and Observation Errors on the Measurement of Income Inequality," Econometrica, Econometric Society, vol. 51(4), pages 1093-108, July.
  4. Korinek, Anton & Mistiaen, Johan A. & Ravallion, Martin, 2005. "An econometric method of correcting for unit nonresponse bias in surveys," Policy Research Working Paper Series 3711, The World Bank.
  5. Surjit Bhalla, 2002. "Imagine There's No Country: Poverty, Inequality, and Growth in the Era of Globalization," Peterson Institute Press: All Books, Peterson Institute for International Economics, number 348.
  6. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," Review of Economic Studies, Oxford University Press, vol. 69(2), pages 357-378.
  7. Eichhorn, Wolfgang & Funke, Helmut & Richter, Wolfram F., 1984. "Tax progression and inequality of income distribution," Journal of Mathematical Economics, Elsevier, vol. 13(2), pages 127-131, October.
  8. Ravallion, Martin, 1994. "Poverty rankings using noisy data on living standards," Economics Letters, Elsevier, vol. 45(4), pages 481-485, August.
  9. Atkinson, A B, 1987. "On the Measurement of Poverty," Econometrica, Econometric Society, vol. 55(4), pages 749-64, July.
  10. Nijman, T.E. & Verbeek, M.J.C.M., 1992. "Non-response in panel data : The impact on estimates of a life cycle consumption function," Other publications TiSEM 3c661e33-2cd1-47f1-a7d9-3, Tilburg University, School of Economics and Management.
  11. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  12. Nijman, Theo & Verbeek, Marno, 1992. "Nonresponse in Panel Data: The Impact on Estimates of a Life Cycle Consumption Function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(3), pages 243-57, July-Sept.
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