Journal of Public Health

, Volume 25, Issue 6, pp 671–684 | Cite as

Obesity-based labour market discrimination in South Africa: a dynamic panel analysis

Original Article

Abstract

Purpose

Apart from obesity-related health care costs in South Africa, obesity is also seen to have far-reaching effects that seep into labour market outcomes. Using National Income Dynamics Survey (NIDS) panel data, this study aims to examine the relationship between body mass index (BMI) and employment status as well as wage levels of individuals to identify the optimal level of BMI from the labour market perspective in South Africa. Thereafter, the article uses ethnicity-backed obesity thresholds to measure the discrimination obese individuals face on the probability of becoming employed and their wages earned once employed.

Methods

The econometric analysis uses the OLS probit and tobit regression models as the starting point for analysis. However due to issues of reverse causality, the analysis thereafter utilises a system GMM model to take endogeneity into account. A further Blinder-Oaxaca decomposition technique is used to derive the discrimination component in the system GMM regressions for obese and non-obese individuals. Finallly, gender-specific analysis is undertaken to investigate whether obesity-related discrimination differs between males and females.

Results

The relationship between BMI and employment probability/wages is seen to be non-linear with increases in BMI leading to an increase in the probability of employment and wages up to a threshold beyond which this relationship becomes negative. Based on the system GMM estimation, the optimal BMI for employment probability and wage determination is identified as 30 and 27 respectively. Blinder-Oaxaca estimates show that 90% of the gap in employment status is accounted for by obesity-related discrimination. With regard to wages, obesity leads to a discrimination of 186%. Gen1der-specific Oaxaca analysis found that obese females face discrimination in employment probability of 109% compared to a negative discrimination of −184% for obese males. In determining wages, employed obese females face discrimination of around 73% whereas the discrimination endured by employed obese males is half of this, at 35%.

Conclusion

Our findings reiterate that increasingly obesity has adverse labour market implications. Obesity-based discrimination exists in South Africa and is predominantly faced by obese women entering the workplace and continues in the wage determination of both men and women.

Keywords

Obesity Unemployment Wages Discrimination Labour market South Africa 

JEL classification

I14 J71 J31 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest to report.

Funding

The authors acknowledge the financial support received from Economic Research Southern Africa (ERSA) for the research.

Supplementary material

10389_2017_822_MOESM1_ESM.docx (3 mb)
ESM 1(DOCX 3044 kb)

References

  1. Adeboye B, Bermano G, Rolland C (2012) Obesity and its health impact in Africa: a systematic view. Cardiovasc J S Afr 23(9):512–521CrossRefGoogle Scholar
  2. Arellano M, Bond S (1991) Some tests of specication for panel data:Monte carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277-297Google Scholar
  3. Arellano M, Bover O (1995) Another look at the instrumental variables estimation of error components models. J Econ 68:29-51Google Scholar
  4. Asgeirsdottir TL (2011) Obesity & Employment: the case of Iceland. University of Iceland, Department of Economics, ReykjavíkGoogle Scholar
  5. Barnett A, Kumar S (2009) Obesity & Diabetes, Second edn. John-Wiley & Sons, Southern Gate, United KingdomCrossRefGoogle Scholar
  6. Baum C, Ford W (2004) The wage effects of obesity: a longitudinal study. Health Econ 13(9):885–899CrossRefPubMedGoogle Scholar
  7. Blinder AS (1973) Wage discrimination: reduced form and structural estimates. J Hum Resour 8:436–455CrossRefGoogle Scholar
  8. Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econ 87:115-143Google Scholar
  9. Cawley J (2004) The impact of obesity on wages. J Hum Resour 39(2):451–474CrossRefGoogle Scholar
  10. Cawley J, Han E, Norton EC (2009) Obesity and labor market outcomes among legal immigrants to the United States from developing countries. Econ Hum Biol 7(2):153–164CrossRefPubMedGoogle Scholar
  11. Dackleburg M, Gerdtham UG, Nordin M (2014) Productivity or discrimination? An economic analysis of excess-weight penalty in the Swedish labor market. Eur J Health Econ 16(6):589–601Google Scholar
  12. Dasgupta P, Ray D (1986) Inequality as a determinant of malnutrition and unemployment: theory. Econ J 96(384):1011–1034CrossRefGoogle Scholar
  13. Department of Health (2015) Strategy for the Prevention & Control of obesity in South Africa 2015–2020. Department of Health, Republic of South AfricaGoogle Scholar
  14. Fairbrother KA (2009) Healthcare burden of obesity in South Africa: a reflection on the role of government. University of Witwatersrand, Johannesburg Retrieved on 2/03/2017 at: http://wiredspace.wits.ac.za/bitstream/handle/10539/8792/AmyFairbrother_ResearchReport_FinalSubmission.pdf?sequence=1 Google Scholar
  15. Garcia J, Quintana-Domeque C (2006) Obesity, employment, and wages in Europe. Adv Health Econ Health Serv Res 17Google Scholar
  16. Greve J (2008) Obesity and labor market outcomes in Denmark. Econ Hum Biol 6(3):350–362CrossRefPubMedGoogle Scholar
  17. Harkonen J, Rasanen P, Nasi M (2011) Obesity, unemployment, and earnings. Nordic Journal of working life studies 1(2):23–38CrossRefGoogle Scholar
  18. Harper B (2000) Beauty, stature and the labour market: a British cohort study. Oxf Bull Econ Stat 62:771–801CrossRefGoogle Scholar
  19. Holtz-Eakin D, Newey W, Rosen HS (1988) Estimating vector autoregressions with panel data. Econometrica 56:1371–1395CrossRefGoogle Scholar
  20. Johansson E, Böckerman P, Kiiskinen U, Heliövaara M (2009) Obesity and labour market success in Finland: the difference between having a high BMI and being fat. Econ Hum Biol Mar 7(1):36–45CrossRefGoogle Scholar
  21. Kollamparambil U, Razak A (2016) Trends in gender wage gap and discrimination in South Africa: a comparative analysis across races. Indian Journal of Human Development 10(1):5–9CrossRefGoogle Scholar
  22. Lechtenfeld T & Zoch A (2014) Income Convergence in South Africa: Fact or Measurement Error? Stellenbosch Economic Working Papers: 10/14, South AfricaGoogle Scholar
  23. Lindeboom M, Lundborgc P, der Klaauw Bc V (2010) Assessing the impact of obesity on labor market outcomes. Econ Hum Biol 8(3):309–319CrossRefPubMedGoogle Scholar
  24. Morris S (2007) The impact of obesity on employment. Labour Econ 14(3):413–433CrossRefGoogle Scholar
  25. National Institute for Health and Clinical Excellence (NICE) (2013) Assessing body mass index and waist circumference thresholds ethnic groups in the UK (PH 46) [Online]. Available from: http://www.thehealthwell.info/node/522752 [Accessed: 1st May 2016]
  26. Ntuk U, Gill J, Mackay D, Sattar N, Pell J (2014) Ethnic-specific obesity cutoffs for diabetes risk: cross-sectional study of 490, 288 UK biobank participants. Diabetes Care 37(9):2500–2507CrossRefPubMedGoogle Scholar
  27. Oaxaca RL (1973) Male-female wage differentials in urban labor markets. Int Econ Rev 14:693–709CrossRefGoogle Scholar
  28. Pinkston JC (2015) The dynamic effects of obesity on the wages of young workers. University of Louisville, Department of Economics, LouisvilleGoogle Scholar
  29. Roodman D (2009) How to do xtabond2: an introduction to difference and system GMM in Stata. Stata J 9(1):86–136Google Scholar
  30. Sargent J, Blanchflower D (1994) Obesity and stature in adolescence and earnings in young adulthood. Analysis of a British birth cohort. Arch Pediatr Adolesc Med 148(7):681–687 681–687.147CrossRefPubMedGoogle Scholar
  31. Sarlio-Lahteenkorva S, Lahelma E (1999) The association of body mass index with social and economic disadvantage in women and men. Int J of Epidemiol 28:445–449CrossRefGoogle Scholar
  32. Sen B (2014) Using the Oaxaca–Blinder decomposition as an empirical tool to analyze racial disparities in obesity. Obesity 22(7):170–1755CrossRefGoogle Scholar
  33. Some M, Rasheed N, Ohonba A (2016) The impact of obesity on employment in South Africa. Stud Econ Econ 40(2):87–104Google Scholar
  34. Statistics South Africa (2014) Mid-year population estimates 2014. Statistics South Africa, PretoriaGoogle Scholar
  35. Treasury N (2016) Taxation of sugar sweetened beverages, economics tax analysis chief directorate policy paper. Government of South Africa, PretoriaGoogle Scholar
  36. Tugendhaft A, Hofman K (2014) Empowering healthy food and beverage choices in the workplace. Occupational Health Southern Africa 20(5)Google Scholar
  37. Villar JG, Oreffice S & Quintana-Domeque C (2011) Physical Activity and Obesity in Spain: Evidence from the Spanish National Health Survey. The Economics of Sport, Health and Happiness: The Promotion of Well-being Through Sporting ActivitiesGoogle Scholar
  38. Wittenberg M (2011) The weight of success: the body mass index and economic well-being in South Africa. School of Economics. SALDRU and Data First University of Cape Town, Cape TownGoogle Scholar
  39. Woolridge JM (2002) Econometric analysis of cross section and panel data. MIT Press, Cambridge (MA)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of Economic and Business SciencesUniversity of WitwatersrandJohannesburgSouth Africa

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