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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.

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Bibliographic Info

Paper provided by Southern Africa Labour and Development Research Unit, University of Cape Town in its series SALDRU Working Papers with number 38.

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Date of creation: Sep 2009
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Handle: RePEc:ldr:wpaper:38

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

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References

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  1. Joachim Merz & Henning Stolze, 2005. "Representative Time Use Data and Calibration of the American Time Use Studies 1965-1999," FFB-Discussionpaper 54, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg, revised Jan 2006.
  2. Geeta Kingdon & John Knight, 2005. "Unemployment in South Africa, 1995-2003: Causes, Problems and Policies," Economics Series Working Papers GPRG-WPS-010, University of Oxford, Department of Economics.
  3. Joachim Merz, 1994. "Microdata Adjustment by the Minimum Information Loss Principle," FFB-Discussionpaper 10, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
  4. Rulof Burger & Derek Yu, 2006. "Wage trends in post-apartheid South Africa: Constructing an earnings series from household survey data," Working Papers 10/2006, Stellenbosch University, Department of Economics.
  5. Dorrit Posel & Daniela Casale, 2003. "What Has Been Happening To Internal Labour Migration In South Africa, 1993-1999?," South African Journal of Economics, Economic Society of South Africa, vol. 71(3), pages 455-479, 09.
  6. Anne-Sophie Robilliard & Sherman Robinson, 2003. "Reconciling Household Surveys and National Accounts Data Using a Cross Entropy Estimation Method," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 49(3), pages 395-406, 09.
  7. Nicola Branson & Martin Wittenberg, 2007. "The Measurement Of Employment Status In South Africa Using Cohort Analysis, 1994-2004," South African Journal of Economics, Economic Society of South Africa, vol. 75(2), pages 313-326, 06.
  8. Iain Fraser, 2000. "An application of maximum entropy estimation: the demand for meat in the United Kingdom," Applied Economics, Taylor & Francis Journals, vol. 32(1), pages 45-59.
  9. Ozler, Berk, 2007. "Not Separate, Not Equal: Poverty and Inequality in Post-apartheid South Africa," Economic Development and Cultural Change, University of Chicago Press, vol. 55(3), pages 487-529, April.
  10. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
  11. Cally Ardington & David Lam & Murray Leibbrandt & Matthew Welch, 2005. "The Sensitivity of Estimates of Post-Apartheid Changes in South African Poverty and Inequality to key Data Imputations," SALDRU/CSSR Working Papers 106, Southern Africa Labour and Development Research Unit, University of Cape Town.
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Cited by:
  1. Taryn Dinkelman & Vimal Ranchhod, 2010. "Evidence on the impact of minimum wage laws in an informal sector: Domestic workers in South Africa," SALDRU Working Papers 44, Southern Africa Labour and Development Research Unit, University of Cape Town.
  2. Haroon Bhorat & Ravi Kanbur & Natasha Mayet, 2012. "The Impact of Sectoral Minimum Wage Laws on Employment, Wages and Hours of Work in South Africa," Working Papers 12154, University of Cape Town, Development Policy Research Unit.
  3. Haroon Bhorat & Ravi Kanbur & Benjamin Stanwix, 2012. "Estimating the Impact of Minimum Wages on Employment, Wages and Non-wage Benefits: The Case of Agriculture in South Africa," Working Papers 12149, University of Cape Town, Development Policy Research Unit.
  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. 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.

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