Variances in HIV Prevalence: An African Case Study

1. Variances in HIV Prevalence: An African Case Study William G. Tuleu wt8213a@american.edu American University School of International Service

2. Research Question & Research hypothesis • Research Question: What are the social, political, and economic factors that contribute to varying HIV prevalence rates in African countries? • Research Hypothesis: African countries with poor access to healthcare, low social development, and uneven economic growth contribute to higher HIV prevalence rates on the African continent.

3. Literature Review • Larry Sawers & Eileen Stillwaggon, “Understanding the Southern African ‘Anomaly’: Poverty, Endemic Disease, and HIV”. Development and Change, 41(2): 195-224 (2010) • Hypothesis: Economic studies alone do not adequately account for divergent HIV infection rates and future research must incorporate social and historical factors. • Findings: Six variables are determined to significantly effect HIV rates in South Africa: Ginicoeffficient, age of the epidemic, Muslim per cent of population, adult literacy, gender discrimination, income per capita • Gaps: To what extent are these findings replicable to the African continent as a whole? South Africa is typically not included in African regression analyses as it often skews results. As South Africa has higher institutional and social development, in addition to, generally, more economic success. As such, these independent variables may have different results when tested on the continent as a whole • Alan Whiteside, “Poverty and HIV/AIDS in Africa”, Third World Quarterly, 23(2): 313-332 (2002) • Hypothesis: In order to effectively understand the history of, HIV/AIDS in Africa, and subsequently break the cycle, research must look beyond just monetary poverty and consider all aspects of poverty. • Findings: GDP per capita, HDI score, and poverty have significant correlations with HIV rates on the continent • Gaps:To what extent is macroeconomics a successful independent variable? The author does not dig deeper from macro indicators, such as GDP, HDI, and nation-wide poverty levels to test micro-indicators such as health expenditures by household or availability of physicians. Furthermore, it is quite possible that economic variables alone do not account for all HIV divergence.

4. Gaps in existing literature • Theoretical & Empirical Gaps • Sawers & Stillwaggon: To what extent are these findings replicable to the African continent as a whole? South Africa is typically not included in African regression analyses as it often skews results. As South Africa has higher institutional and social development, in addition to, generally, more economic success. As such, these independent variables may have different results when tested on the continent as a whole • Whiteside: To what extent is macroeconomics a successful independent variable? The author does not dig deeper from macro indicators, such as GDP, HDI, and nation-wide poverty levels to test micro-indicators such as health expenditures by household or availability of physicians. Furthermore, it is quite possible that economic variables alone do not account for all HIV divergence.

5. Data • Unit of analysis/study: Country • Source of the data: World Bank, World Development Indicators • Dependent variable: • Y= HIV prevalence rate (ages 15-49) as a percentage of the total population • Independent Variables • Χ1 Physicians per 1000 People (total) • Χ2 Out of Pocket Health Expenditures (percent of total expenditure on health) • Χ3 Fertility Rate(Births per woman) • Χ4 GDP Per Capita Growth (annual percentage) • Χ5 Log of Urban Population (Total)

6. Descriptive Statistics: Tables of Central Tendency

7. Why it Matters: Global HIV Prevalence vs. African HIV Prevalence

8. Descriptive Statistics: Graphs

9. Descriptive Statistics: Matrix

10. Regression Analysis

11. Multivariate- I-R LOM dependent variable

12. Findings of the research • Findings: • Finding #1: Availability of healthcare to individuals has a significant, negative correlation with HIV prevalence. As availability of [affordable] healthcare increases, then HIV rates decrease. • Finding #2: Social factors, such as fertility rate, seem to have a weak, negative relationship. This suggest other social factors, which have not been included, may more significant correlations. • Finding #3: Macroeconomic indicators, on their own, do not account for significant variance in HIV prevalence. Only once included with significant healthcare variables in Model 4 does GDP growth help account for variance. • Findings #4: Urban population as a social factor has no significant effect on HIV prevalence.

13. Policy Implications • Macroeconomic variables, such as GDP growth, do not adequately explain HIV prevalence. As such, a general focus on economic improvement on the state level will not have a significant impact on reducing HIV infection rates. • African governments or NGOs that aim to reduce HIV prevalence should focus on affordable and accessible healthcare. As economic policy alone will not affect rates, a more nuanced, individual level of policy that promulgates effective, affordable, and available healthcare has the potential to successfully reduce HIV infection rates. • More research is needed to gauge the effect of social variables. Fertility rates, on their own, do not account for significant variance. Similarly, urban population did not seem to have a significant impact. Future research should include social conflict, women’s rights, and attitudes towards sex.