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The Effect of Household Characteristics on Living Standards in South Africa 1993 - 98: A Quantile Regression Analysis with Sample Attrition

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Listed author(s):
  • Farshid Vahid

    ()

  • Pushkar Maitra

    ()

Abstract

This paper examines whether the dismantling of apartheid has resulted in the improvement in the standard of living for the vast majority of South Africans. The study is based on a panel data set from the Kwazulu-Natal province. Despite the best efforts of the interview team, the attrition rate in this panel is around 16%. We find that household income and size in 1993, several community characteristics and survey quality in 1993 significantly affect the probability of attrition. We use weighted quantile regressions to examine the distribution of standards of living, which corrects for the potential bias arising from non-random sample attrition. Our results show that there has been a significant increase in the spread of the distribution of household expenditure of the Non-White households residing in Kwazulu-Natal province. We argue that the stretch to the right of the upper tail of distribution can be attributed to significant increase in returns to primary and high school education, while movement to the left of the lower quantiles can be associated with the increase in the proportion of female headed households and household size.

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File URL: https://www.cbe.anu.edu.au/researchpapers/econ/wp452.pdf
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Paper provided by Australian National University, College of Business and Economics, School of Economics in its series ANU Working Papers in Economics and Econometrics with number 2005-452.

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Length: 39 pages
Date of creation: May 2005
Handle: RePEc:acb:cbeeco:2005-452
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Find related papers by JEL classification:

  • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
  • D1 - Microeconomics - - Household Behavior
  • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
  • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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  1. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-473, March.
  2. Mwabu, Germano & Schultz, T Paul, 2000. "Wage Premiums for Education and Location of South African Workers, by Gender and Race," Economic Development and Cultural Change, University of Chicago Press, vol. 48(2), pages 307-334, January.
  3. O'Connell, Philip J. & Russell, Helen & FitzGerald, John, 2006. "Human Resources," Book Chapters,in: Morgenroth, Edgar (ed.), Ex-Ante Evaluation of the Investment Priorities for the National Development Plan 2007-2013 Economic and Social Research Institute (ESRI).
  4. Harold Alderman & Jere Behrman & Hans-Peter Kohler & John A. Maluccio & Susan Watkins, 2001. "Attrition in Longitudinal Household Survey Data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 5(4), pages 79-124, November.
  5. Marianne Bertrand & Sendhil Mullainathan & Douglas Miller, 2003. "Public Policy and Extended Families: Evidence from Pensions in South Africa," World Bank Economic Review, World Bank Group, vol. 17(1), pages 27-50, June.
  6. Carter, Michael R. & May, Julian, 1999. "Poverty, livelihood and class in rural South Africa," World Development, Elsevier, vol. 27(1), pages 1-20, January.
  7. Duncan Thomas & Elizabeth Frankenberg & James P. Smith, 2001. "Lost but Not Forgotten: Attrition and Follow-up in the Indonesia Family Life Survey," Journal of Human Resources, University of Wisconsin Press, vol. 36(3), pages 556-592.
  8. Mwabu, Germano & Schultz, T Paul, 1996. "Education Returns across Quantiles of the Wage Function: Alternative Explanations for Returns to Education by Race in South Africa," American Economic Review, American Economic Association, vol. 86(2), pages 335-339, May.
  9. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  10. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
  11. Thomas, Duncan, 1996. "Education across Generations in South Africa," American Economic Review, American Economic Association, vol. 86(2), pages 330-334, May.
  12. John A. Maluccio, 2004. "Using Quality of Interview Information to Assess Nonrandom Attrition Bias in Developing-Country Panel Data," Review of Development Economics, Wiley Blackwell, vol. 8(1), pages 91-109, 02.
  13. Case, Anne & Deaton, Angus, 1998. "Large Cash Transfers to the Elderly in South Africa," Economic Journal, Royal Economic Society, vol. 108(450), pages 1330-1361, September.
  14. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition and stratification," CeMMAP working papers CWP11/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  15. Moshe Buchinsky, 2001. "Quantile regression with sample selection: Estimating women's return to education in the U.S," Empirical Economics, Springer, vol. 26(1), pages 87-113.
  16. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  17. Deolalikar, Anil B & Evenson, Robert E, 1989. "Technology Production and Technology Purchase in Indian Industry: An Econometric Analysis," The Review of Economics and Statistics, MIT Press, vol. 71(4), pages 689-692, November.
  18. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
  19. Newey, Whitney K. & McFadden, Daniel, 1986. "Large sample estimation and hypothesis testing," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 36, pages 2111-2245 Elsevier.
  20. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
  21. Klasen, Stephan, 2000. "Measuring Poverty and Deprivation in South Africa," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 46(1), pages 33-58, March.
  22. Maitra, Pushkar & Ray, Ranjan, 2003. "The effect of transfers on household expenditure patterns and poverty in South Africa," Journal of Development Economics, Elsevier, vol. 71(1), pages 23-49, June.
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Cited by:

  1. Essama-Nssah, B. & Bassole, Leandre, 2010. "A counterfactual analysis of the poverty impact of economic growth in Cameroon," Policy Research Working Paper Series 5249, The World Bank.
  2. Martine Mariotti & Juergen Meinecke, 2009. "Nonparametric Bounds on Returns to Education in South Africa: Overcoming Ability and Selection Bias," ANU Working Papers in Economics and Econometrics 2009-510, Australian National University, College of Business and Economics, School of Economics.

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