It has been widely assumed for many years that poverty and underdevelopment were driving factors behind the transmission of HIV, but empirical evidence does not appear to bear this out. For example:
Viewed globally, the highest prevalence of HIV is found in the poorer countries of the world, with more than two thirds of people living with HIV residing in sub Saharan Africa.
However, within sub Saharan Africa, the highest rates of HIV prevalence are found in the relatively more wealthy countries, such as Swaziland, Botswana and South Africa, and in general, the countries with higher GDP per capita have higher levels of HIV prevalence – although the country level correlation is not statistically significant.
Direct measures of poverty, such as the percentage below $1 per day are not correlated between countries. However, most of the other commonly understood poverty measures such as literacy rates, educational enrolment, under-five mortality or nutritional outcomes seem to show an inverse relationship at country level between poverty and HIV – the poorer countries (or countries with worse development indicators) have lower rates of HIV prevalence.
Household level evidence
At household level, strong evidence from population-based surveys (DHS+) has shown that HIV prevalence is lower among the poorer members of African society, and higher among those who are better off, and those living in urban areas. Note that the associations are stronger and clearer for women – i.e. women living in better-off households have clearly higher HIV prevalence in most African countries.
Therefore, it is not correct to say that HIV risk is primarily associated with severe poverty (which is also concentrated in rural areas), although the poor are also at risk. What is undoubtedly true is that the poor are the most severely affected once they do become infected with HIV, since they are unable to afford treatment, and can least afford to lose their incomes.
Evidence linking income inequality and HIV risk
There is a striking association between income inequality (measured by the GINI coefficient) and HIV – countries with higher income inequality appear to have higher HIV prevalence rates. Similar patterns (slightly less clear) can be seen with other inequality measures such as the decile or quintile ratio.
There may be many confounding factors in this apparent correlation, such as differences between countries in cultural practice (e.g. male circumcision and religion). Nevertheless, it is tempting to conclude that income inequality may give rise to social conditions that fuel the HIV epidemic – for example by providing incentives for transactional sex. However, the evidence is not yet strong enough for us to be able to say this, since the association that we observe may be a result of other underlying factors that affect both income inequality and HIV risk. The association is interesting, but we cannot yet say that it is causal, and it has not been borne out by household-level evidence.
Multivariate analysis of DHS+ data has shown that the association between wealth and HIV could be attributed to three main factors that are correlated with wealth, i.e.:
It has also been shown that education is generally associated with lower HIV risk. This, and the above analysis suggests that behavioural factors not directly related to poverty are more important drivers of HIV infection in Africa, and has clearly shown that economic status in itself is not a strong predictor of HIV status.
The primary implication is that prevention must therefore cut across all socioeconomic strata of society – especially those living in urban areas. A purely poverty-based strategy is unlikely to succeed.
There is therefore no simple explanation for the distribution of HIV risk. Poverty may be a part of the story, but it is clearly not the key. The pathways and interactions linking socio-economic status and HIV risk are complex, and predisposing factors are different for different groups – i.e. the sources of risk are not necessarily the same for poor people as they are for wealthier people. It is therefore vital to tailor interventions to the specific drivers of transmission within different groups.
Robert Greener is the senior economics advisor at UNAIDS, whose responsibilities include identifying priorities for research, intervention, and policy development on the economic aspects of HIV infection, AIDS impact and the effectiveness and consequences of the response to the epidemic. He has been involved in refining the global resource need estimates for a scaled up response to the epidemic, and in recent work on the associations between poverty, income inequality and the risk of HIV infection, and the development of sustainable financing for a long-term response in low and middle-income countries. Prior to joining UNAIDS in September 2003, he spent 7 years as a Senior Research Fellow at the Botswana Institute for Development Policy Analysis (BIDPA), where he conducted work related to rural development, poverty reduction policy, and the economic impacts of AIDS.