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Re-weighting the OHS and LFS National household Survey Data to create a consistent series over time: A Cross Entropy Estimation Approach

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  • Nicola Branson

    (SALDRU, School of Economics, University of Cape Town)

Abstract

In the absence of South African longitudinal data for the ten years post apartheid, national cross-sectional household survey data is frequently used to analyse change over time. When these data are stacked side-by-side however, they reveal inconsistencies both in trends across time and between the household and person level data. These inconsistencies can introduce biases into research which analyse change. This study calculates a new set of person and household weights for the October Household Surveys between 1995 and 1999 and the Labour Force Surveys between 2000 and 2004. A cross entropy estimation approach is used. This approach is favoured because the calculated weights are similar to the initial sample weights (and hence retain the survey design benefits) while simultaneously being consistent with aggregate auxiliary data. A consistent series of aggregates from the Actuarial Society of South Africa (ASSA) model and the 1996 and 2001 South African Census data are used as benchmarks. The new weights result in consistent demographic and geographic trends over time and greater consistency between person and household level data.

Suggested Citation

  • Nicola Branson, 2009. "Re-weighting the OHS and LFS National household Survey Data to create a consistent series over time: A Cross Entropy Estimation Approach," SALDRU Working Papers 38, Southern Africa Labour and Development Research Unit, University of Cape Town.
    • Handle: RePEc:ldr:wpaper:38
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    Cited by:

    1. Neil Rankin & Rulof Burger & Friedrich Kreuser, 2015. "The elasticity of substitution and labour-displacing technical change in post-apartheid South Africa," WIDER Working Paper Series 101, World Institute for Development Economic Research (UNU-WIDER).
    2. Haroon Bhorat & Ravi Kanbur & Natasha Mayet, 2013. "The impact of sectoral minimum wage laws on employment, wages, and hours of work in South Africa," IZA Journal of Labor & Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 2(1), pages 1-27, December.
    3. Dinkelman, Taryn & Ranchhod, Vimal, 2012. "Evidence on the impact of minimum wage laws in an informal sector: Domestic workers in South Africa," Journal of Development Economics, Elsevier, vol. 99(1), pages 27-45.
    4. 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.
    5. Magejo, Prudence & Benhura, Miracle, 2015. "A Detailed Decomposition Analysis of the Public-Private Sector Wage Gap in South Africa," IZA Discussion Papers 9271, Institute of Labor Economics (IZA).
    6. Rulof Burger & Rachel Jafta & Dieter von Fintel, 2016. "Affirmative action policies and the evolution of post-apartheid South Africa's racial wage gap," WIDER Working Paper Series 066, World Institute for Development Economic Research (UNU-WIDER).
    7. Haroon Bhorat & Ravi Kanbur & Benjamin Stanwix, 2014. "Estimating the Impact of Minimum Wages on Employment, Wages, and Non-Wage Benefits: The Case of Agriculture in South Africa," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(5), pages 1402-1419.
    8. 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.
    9. repec:pri:rpdevs:dinkelman_ranchhod_minwages_0710 is not listed on IDEAS

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    Keywords

    South African national household survey data; Post-stratification; Reweighting; Cross entropy estimation.;
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