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Economics of HIV/AIDS in Africa: Cost-Benefit Calculations, Deworming in Kenya

This lecture in African Economic Development covers the cost-benefit calculations and public health policy issues related to deworming programs in Kenya, as well as the economics of HIV/AIDS in Africa. It explores the impact of deworming on human capital investment and discusses potential explanations for low take-up rates. Additionally, it highlights the prevalence of HIV/AIDS in Sub-Saharan Africa and Kenya, highlighting the challenges of accurately counting infected individuals.

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Economics of HIV/AIDS in Africa: Cost-Benefit Calculations, Deworming in Kenya

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  1. Economics 172Issues in African Economic Development Lecture 8 February 9, 2006

  2. Outline: • Deworming in Kenya – public health policy issues • New topic: The Economics of HIV/AIDS in Africa Economics 172

  3. Cost-benefit calculations • Cost of this program: US$1.46 per pupil per year • Cost of a larger-scale program in neighboring Tanzania: only US$0.49 per pupil per year Economics 172

  4. Cost-benefit calculations • Cost of this program: US$1.46 per pupil per year • Cost of a larger-scale program in neighboring Tanzania: only US$0.49 per pupil per year • Deworming as a human capital investment: Health gains  More schooling  Higher adult wages Economics 172

  5. Cost-benefit calculations • Deworming as a human capital investment: Health gains  More schooling  Higher adult wages • Deworming led to 7% gain in school participation • Previous study: each year of school  7% higher wages • Take these gains in wages (7% x 7%) over 40 years in the workforce, discounted 5% per year Economics 172

  6. Cost-benefit calculations • Deworming as a human capital investment: Health gains  More schooling  Higher adult wages • Deworming led to 7% gain in school participation • Previous study: each year of school  7% higher wages • Take these gains in wages (7% x 7%) over 40 years in the workforce, discounted 5% per year  Deworming benefits are at least three times (3x) as large as treatment costs (using the Tanzania costs) Economics 172

  7. Given the returns, why is take-up not 100%? Economics 172

  8. Given the returns, why is take-up not 100%? • Possible explanations: (1) Free-riding / externalities -- Strong evidence people learned through their social network that the drugs were “not effective” Economics 172

  9. Given the returns, why is take-up not 100%? • Possible explanations: (1) Free-riding / externalities -- Strong evidence people learned through their social network that the drugs were “not effective” (2) Socio-cultural explanations / resistance to new technologies (evidence from anthropology) Economics 172

  10. The Impact of Higher Drug Costs • In 1998, 1999, 2000 deworming was given for free • In 2001, parents in 25 randomly chosen Group 1 and Group 2 schools paid US$0.10-0.30 per child Economics 172

  11. The Impact of Higher Drug Costs • In 1998, 1999, 2000 deworming was given for free • In 2001, parents in 25 randomly chosen Group 1 and Group 2 schools paid US$0.10-0.30 per child • 2001 deworming take-up: Free-treatment schools: 75% Cost-sharing schools: 18% Economics 172

  12. Economics 172

  13. The Economics of HIV/AIDS in Africa • Of the 42 million people worldwide thought to be infected with HIV, approximately 25 million (!) are in Sub-Saharan Africa Economics 172

  14. Economics 172

  15. The Economics of HIV/AIDS in Africa • Of the 42 million people worldwide thought to be infected with HIV, approximately 25 million (!) are in Sub-Saharan Africa • In some countries in southern Africa (e.g. Botswana, Swaziland), it is claimed that over 35% are HIV+ Economics 172

  16. Economics 172

  17. Counting HIV+ people in Kenya • Based on antenatal clinic survey data, the official UNAIDS estimate of HIV+ adults in Kenya by late 2001 was 15.0% Economics 172

  18. Counting HIV+ people in Kenya • Based on antenatal clinic survey data, the official UNAIDS estimate of HIV+ adults in Kenya by late 2001 was 15.0% • The 2003 Kenya Demographic and Health Survey (DHS) tried to survey a representative subsample of population. 73.4% agreed to be tested Economics 172

  19. Counting HIV+ people in Kenya • Based on antenatal clinic survey data, the official UNAIDS estimate of HIV+ adults in Kenya by late 2001 was 15.0% • The 2003 Kenya Demographic and Health Survey (DHS) tried to survey a representative subsample of population. 73.4% agreed to be tested • This data indicates that “only” 6.7% of Kenyan 15-49 year olds tested are HIV+! Economics 172

  20. Economics 172

  21. Counting HIV+ people in Kenya • Based on antenatal clinic survey data, the official UNAIDS estimate of HIV+ adults in Kenya by late 2001 was 15.0% • The 2003 Kenya Demographic and Health Survey (DHS) tried to survey a representative subsample of population. 73.4% agreed to be tested • This data indicates that “only” 6.7% of Kenyan 15-49 year olds tested are HIV+! • Which of the two numbers is better? Economics 172

  22. Economics 172

  23. Whiteboard #1 Economics 172

  24. Whiteboard #2 Economics 172

  25. Whiteboard #3 Economics 172

  26. Whiteboard #4 Economics 172

  27. Whiteboard #5 Economics 172

  28. Map of Africa Economics 172

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