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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.

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
    DOI: 10.1111/j.1813-6982.2007.00122.x
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    References listed on IDEAS

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    1. Gustavsson, Magnus, 2004. "Trends in the Transitory Variance of Earnings: Evidence from Sweden 1960-1990 and a Comparison with the United States," Working Paper Series 2004:11, Uppsala University, Department of Economics.
    2. 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.
    3. 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.
    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, 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.
    2. Claire Vermaak, 2010. "The Impact of Multiple Imputation of Coarsened Data on Estimates on the Working Poor in South Africa," WIDER Working Paper Series wp-2010-086, World Institute for Development Economic Research (UNU-WIDER).
    3. Martin Wittenberg, 2017. "Wages and Wage Inequality in South Africa 1994–2011: Part 1 – Wage Measurement and Trends," South African Journal of Economics, Economic Society of South Africa, vol. 85(2), pages 279-297, June.
    4. 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.
    5. 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 Kiel).
    6. 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.
    7. Snower, Dennis J. & Görlich, Dennis, 2013. "Multitasking and Wages," IZA Discussion Papers 7426, Institute of Labor Economics (IZA).
    8. 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.
    9. Bridgman, Grace & von Fintel, Dieter, 2022. "Stunting, double orphanhood and unequal access to public services in democratic South Africa," Economics & Human Biology, Elsevier, vol. 44(C).
    10. Adel Bosch & Steven F. Koch, 2021. "Individual and Household Debt: Does Imputation Choice Matter?," Working Papers 202141, University of Pretoria, Department of Economics.
    11. 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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