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Dealing With Earnings Bracket Responses In Household Surveys - How Sharp Are Midpoint Imputations?

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  • Dieter Von fintel

Abstract

Earnings functions form the basis of numerous labour market analyses. Non-response (particularly among higher earners) may, however, lead to the exclusion of a significant proportion of South Africa's earnings base. Earnings brackets built into surveys intend to maintain response rates. Econometric tools to incorporate brackets vary from "simplistic" imputation to interval regressions. Coefficient differences are investigated here to establish reliable remedies. Monte-Carlo simulations suggest that "simple" methods fail only under extreme skewness and when a substantial number of right-censored observations appear in the sample. Testing procedures applied to LFS data reveal that in practice coefficients are virtually invariant to the proposed methods. Copyright (c) 2007 The Author; Journal compilation (c) Economic Society of South Africa 2007.

Suggested Citation

  • Dieter Von fintel, 2007. "Dealing With Earnings Bracket Responses In Household Surveys - How Sharp Are Midpoint Imputations?," South African Journal of Economics, Economic Society of South Africa, vol. 75(2), pages 293-312, June.
  • Handle: RePEc:bla:sajeco:v:75:y:2007:i:2:p:293-312
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    References listed on IDEAS

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    1. Dieter von Fintel, 2006. "Earnings bracket obstacles in household surveys – How sharp are the tools in the shed?," Working Papers 08/2006, Stellenbosch University, Department of Economics.
    2. Dorrit Posel & Daniela Casale, 2005. "Who replies in brackets and what are the implications for earnings estimates? An analysis of earnings data from South Africa," Working Papers 07, Economic Research Southern Africa.
    3. Doubell Chamberlain & Servaas van der Berg, 2002. "Earnings functions, labour market discrimination and quality of education in South Africa," Working Papers 02/2002, Stellenbosch University, Department of Economics.
    4. Rulof Burger & Rachel Jafta, 2006. "Returns to Race: Labour Market Discrimination in Post-Apartheid South Africa," Working Papers 04/2006, Stellenbosch University, Department of Economics.
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    Cited by:

    1. Dieter von Fintel & Eldridge Moses, 2017. "Migration and gender in South Africa: following bright lights and the fortunes of others?," Working Papers 09/2017, Stellenbosch University, Department of Economics, revised 2018.
    2. Görlich, Dennis & Snower, Dennis J., 2010. "Wage inequality and the changing organization of work," Kiel Working Papers 1588, Kiel Institute for the World Economy (IfW).
    3. Derek Yu, 2013. "Some factors influencing the comparability and reliability of poverty estimates across household surveys," Working Papers 03/2013, Stellenbosch University, Department of Economics.
    4. Dieter von Fintel, 2017. "Institutional wage-setting, labour demand and labour supply: Causal estimates from a South African pseudo-panel," Development Southern Africa, Taylor & Francis Journals, vol. 34(1), pages 1-16, January.
    5. Snower, Dennis J. & Goerlich, Dennis, 2013. "Multitasking and Wages," IZA Discussion Papers 7426, Institute for the Study of Labor (IZA).
    6. Claire Vermaak, 2012. "Tracking poverty with coarse data: evidence from South Africa," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(2), pages 239-265, June.
    7. repec:bla:sajeco:v:85:y:2017:i:2:p:279-297 is not listed on IDEAS
    8. Vermaark, Claire, 2010. "The Impact of Multiple Imputation of Coarsened Data on Estimates on the Working Poor in South Africa," WIDER Working Paper Series 086, World Institute for Development Economic Research (UNU-WIDER).

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