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Who replies in brackets and what are the implications for earnings estimates? An analysis of earnings data from South Africa

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  • Dorrit Posel
  • Daniela Casale

Abstract

In household surveys, earnings data typically can be reported as point values, in brackets or as 'missing'. In this paper we consider South African household survey data that contain these three sets of responses. In particular, we examine whether there are systematic differences between the sample of the employed with earnings reported as point values and those with earnings responses in brackets; we compare five different methods of reconciling bracket and point responses so as to generate descriptive measures of earnings; and we investigate empirically how earnings measures differ by approach.

Suggested Citation

  • 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.
  • Handle: RePEc:rza:wpaper:07
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    Cited by:

    1. Reza C. Daniels, 2012. "Questionnaire Design and Response Propensities for Employee Income Micro Data," SALDRU Working Papers 89, Southern Africa Labour and Development Research Unit, University of Cape Town.
    2. Chicoine, Luke, 2012. "AIDS mortality and its effect on the labor market: Evidence from South Africa," Journal of Development Economics, Elsevier, vol. 98(2), pages 256-269.
    3. Martin Gustafsson & Firoz Patel, 2009. "Managing the teacher pay system: What the local and international data are telling us," Working Papers 26/2009, Stellenbosch University, Department of Economics.
    4. 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.
    5. Haroon Bhorat & Sumayya Goga, 2012. "The Gender Wage Gap in the Post-apartheid South African Labour Market," Working Papers 12148, University of Cape Town, Development Policy Research Unit.
    6. 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).
    7. 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.
    8. Reza C. Daniels, 2012. "A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys," SALDRU Working Papers 90, Southern Africa Labour and Development Research Unit, University of Cape Town.
    9. Adel Bosch & Steven F. Koch, 2021. "Individual and Household Debt: Does Imputation Choice Matter?," Working Papers 202141, University of Pretoria, Department of Economics.
    10. 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.

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