AIDS
& Economics[1]
David E Bloom, Ajay Mahal, Jaypee Sevilla, and
River Path Associates
November
2001
Introduction
More than 22 million people have now died of
AIDS, including 3 million in 2000 alone.
Thirty-six million people are currently infected with the virus and,
although infection rates are stabilizing in Sub-Saharan Africa (albeit at a
very high level), the epidemic is still growing in Asia and Eastern Europe. The
lack of an imminent vaccine or cure means that many more deaths are inevitable.
The seriousness of the AIDS epidemic raises
questions about the potential impact of the epidemic on national and regional
economies. AIDS disproportionately affects people of working age, and is also
creating a huge burden of AIDS orphans. Many political leaders have expressed
alarm in light of studies showing the potential devastation of their economies,
but relatively few businesses have spoken out decisively.
This paper provides an overview of the links
between AIDS and economics. Part 1 assesses economic correlates of HIV
transmission, using macro, micro and household data. Part 2 examines the effect
of AIDS on economies, both directly and as a cause of demographic change. Part 3 explores the likely economic returns
to actions that prevent HIV infections.
Part 1 – Economic
determinants of HIV transmission
This section reviews existing studies of the
association between HIV/AIDS and economic status, at both the macro and micro
levels, and assesses the impact of development on AIDS.
The macro level
The link between income levels and AIDS
prevalence is complex and poorly understood. Data from the 1980s and early
1990s, mainly from sub-Saharan Africa, seem to indicate that the wealthy were
at highest risk from the epidemic. Two of Africa’s richest nations, Botswana
and South Africa, are among the most affected nations in the world. Figure 1,
based on data from 1997, suggests a continuing disproportionate impact on
Africa’s richer countries, possibly reflecting the role that better
infrastructure and more mobile populations seem to play in the spread of the
disease. Within all other continents, there is neither a positive nor a
negative statistical association between income levels and AIDS. Examined
continent by continent, therefore, HIV appears to be either affecting the rich
more than the poor (as in Africa) or is income neutral (everywhere else).
Between continents, however, the picture looks
different. Ninety-five percent of those infected with HIV live in less
developed countries, home to 80 percent of the world’s population. As figures 2
and 3 show, at a global level there is a statistically significant relationship
between low income and HIV prevalence rates; that is, the poorer the country
the greater the HIV prevalence. There is a similar relation between income
distribution and HIV prevalence, with countries with greater income inequality
facing a more serious epidemic. Absolute poverty rates, defined as income below
$1 a day, are strongly associated with HIV prevalence rates (figure 4), as are
low rankings on the United Nations Development Programme (UNDP) Human Poverty
Index, which takes into account mortality, literacy, malnutrition and access to
water, sanitation and health services (figure 5).
Existing data provide some indication that the
relationship between poverty and HIV is growing stronger over time, both
between and within continents. But it is not possible to infer causality from
these data. That is, it is difficult to tell whether poverty causes AIDS or
vice versa – or whether another variable, such as war, inadequate health, or
poor education, explains the relationship.
The micro level
Compared to existing macro data, micro-studies
appear to be better equipped to highlight links between economic status and
AIDS. In this regard, the intuitive
link between knowledge and HIV transmission is supported by several studies.
School enrolment rates and illiteracy rates in the majority of the developing
world, and particularly in Africa, are substantially lower than those in richer
countries, and the poor within countries are least likely to receive education.[2]
The poor are therefore less likely to be aware of the dangers of HIV/AIDS than
the rich.
-
Analysis of household data from Cambodia, Vietnam,
Nicaragua and Tanzania (see Appendix 1) shows a strong correlation between both
wealth and education and: knowledge that condoms prevent AIDS; knowledge of
where condoms can be obtained; and self-reported usage of condoms.[3]
-
Recent research in Cambodia, the country with the most
advanced epidemic in Asia, demonstrates that the poorest segments of society
have much less knowledge of how AIDS is transmitted and prevented; are more
likely to have sex at a younger age; use condoms less frequently; and, in the
case of young women, are more likely to turn to sex work as a means of
supporting themselves and their families.[4]
-
A study in Brazil showed that three-quarters of people
newly diagnosed with HIV in the early 1980s had a secondary or university
education, but by the early 1990s this share had fallen to one third.[5]
-
A study in rural Uganda, on the other hand, found that
in a cohort of almost 20,000 adults aged 15-59 years followed over 3 and
one-half years, HIV-associated mortality was highest among the better educated[6].
However, there is evidence that this pattern may be changing over time. Another
study in Uganda (see figure 6) shows that the better educated were hit hardest
in the early stages of the epidemic, but that HIV infection rates are now
falling quickest among those with more education.[7]
Education is not the only factor highlighted by
micro data. There is evidence that poverty forces many people to work in the
commercial sex industry, thereby putting them at risk of HIV infection. A
series of small-scale studies from sub-Saharan Africa, Haiti and Brazil,[8]
shows how poor women can be forced into sex work, or into providing sexual
favors in return for money. They are also shown to be less able to insist on
condom use than their wealthier clients.
While the micro data is suggestive of a link
between poverty and AIDS, many small scale studies are based on
non-representative samples in the hardest hit areas, and some larger scale
research, such as that conducted in Uganda, shows a negative correlation
between HIV and poverty. As with the macro data, there remain many unanswered
questions.
The impact of
development
While
poverty reduction might be thought to reduce HIV/AIDS rates, in some cases the
development process may itself strengthen epidemics. Development is associated
with infrastructure development, urbanization, increases in disposable income,
the growing importance of cash in agriculture, and growing mobility.
Furthermore, inequality often grows in the early stages of development, [9]
creating increased internal migration (as workers migrate to centers of wealth
and employment), a significant risk factor as men travel away to work, but
occasionally return to their families in their village of origin. Development
is likely to bring greater opportunities for multiple partnering and a growth
in the commercial sex industry. [10]
Finally, inequality can create changes in gender relations that may facilitate
the spread of sexually transmitted diseases. [11]
There is
currently little evidence to quantify the extent of the HIV risks caused by the
unintended consequences of development efforts. However, a strong case is
building for making HIV impact assessment a routine part of programs designed
to promote development and poverty reduction.
Poverty
to AIDS
In sum,
the link between economic status and AIDS is complex. While many micro level
studies point to a significant link between poverty and HIV prevalence rates,
macro data is unconvincing, particularly in terms of the causality of the link.
Some risk factors for HIV, such as a high level of disposable income, are more
prevalent amongst the rich than the poor. Others, such as lack of education,
are more prevalent among the poor than the rich. Both groups exhibit the kind
of mobility that appears to be associated with HIV transmission.
On
balance, it seems plausible that the rich are more at risk in the early stages
of an epidemic, and that a combination of factors, including lack of education
and other economic exigencies, put the poor at increasing risk as an epidemic
progresses. One might therefore expect HIV epidemics to become increasingly
embedded in poor communities. Although not proven, this hypothesis is broadly
consistent with patterns of HIV transmission seen in Africa and other regions,
including wealthy industrial countries such as the US.
Part 2: The impact
of AIDS on economies
The humanitarian case for taking action to
prevent HIV/AIDS is clear. However, there is also value in exploring the
economic case for action. With many problems competing for public sector
budgets, governments need guidance on where to devote their resources.
Businesses may also need to adjust their strategies to respond to the epidemic.
This section therefore explores the epidemic’s economic impact.
Macro evidence
A lack of reliable time series data on poverty
rates and AIDS makes drawing macro level conclusions about the impact of AIDS
on economies difficult. Inter-continental poverty differences predate the AIDS
epidemic, and the hardest hit continent – Africa – is mired in too complex and
deep a development trap to make disaggregating the effects of the epidemic
feasible or persuasive.
There are many mechanisms through which AIDS may
have a potential impact on the economy. Unlike most other deadly
illnesses, HIV’s prime target is people of working-age.[12]
The result is a potential reduction in savings rates and disposable income,
which may have an economic impact.[13]
New staff must be trained and recruited, a cost that would not otherwise have
been borne. Firms may also suffer a loss of valuable know-how. Moreover, AIDS
is debilitating, particularly in the final two years before death,[14]
and absenteeism for both those infected and those caring for them may have an
impact on businesses and other work organizations. Increases in health spending
could mean cuts in investment in other growth-enhancing areas, education and
infrastructure, for example. The impact on productivity may also decrease an
economy’s attractiveness to foreign investors, and diminish tax revenue.
However, there are other influences that may
counter these effects. Workers who die of AIDS may be replaced by people who
were previously unemployed and a smaller labor force may even lead to a rise in
output per capita. Although HIV/AIDS mortality can reduce overall output, it
also reduces population, so per capita productivity may not be reduced.[15] Even in the hardest hit areas, therefore, it
is possible that GDP per capita may not decline.
Macro evidence from the early phases of the
epidemic failed to substantiate the hypothesis
that AIDS would have a detrimental effect on growth rates of per capita
income. From 1980 to 1992, AIDS had no statistically significant impact on per
capita income growth.[16]
However, the epidemic has since grown rapidly and has begun to have a
significant effect on life expectancy and other human development indicators.[17]
In South Africa, for example, life expectancy is expected to fall between 18
and 25 years below its pre-AIDS level.[18]
Recent studies of AIDS in South Africa show some evidence of growing
macroeconomic impact.
-
In the Caribbean, one study projects that GDP in 2005
could be around 4.2 percent lower than it might have been in the absence of the
epidemic.[19]
-
Another study claims that AIDS will reduce output in
Botswana by a third. But since the
epidemic may cause the population to fall by about 30 percent, the effect on
per capita income may be negligible.
-
An early World Bank study, of 30 sub-Saharan African
countries, concludes that the net effect of AIDS will be a reduction in GDP
growth of between 0.8 and 1.4 percentage points per year.[20]
Conversely, however, a recent assessment of AIDS
in Asia concluded that the region’s low prevalence rates are likely to mean
that the impact of the virus on Asian economies remains minimal.[21]
Data are far from adequate, but calculations
made for Thailand may be instructive for understanding the potential economic
effect of AIDS in Sub-Saharan Africa. Thailand’s ratio of working-age to total
population is projected to be 0.70 in 2015.[22]
We estimate that cumulative AIDS deaths by that year will be about 1 million, a
relatively small number because risky behaviors have declined as a result of
Thailand's highly successful anti-HIV policies. Yet if we simulate cumulative
AIDS deaths in the absence of these substantial behavioral improvements, they
could be as high as 10 million. Add to
this an estimate of the number of children that would not have been born
because of these deaths and the population could be about 11.6 million smaller
than it otherwise would have been. AIDS
mortality is disproportionately selective of adults, and we project that of the
10 million deaths, 92% or 9.2 million would be among adults. To this number, we add the .75 million
children these adults would have had, and who would have had the chance to
reach working age by 2015, and we find that this high-risk scenario causes the
working age population to be smaller by about 9.95 million.[23] This combined effect on the total and
working age population would result in a decline in the working-age share of
the population to 0.67. This difference
could reduce the average growth rate of per capita GDP between 1990 and 2015 by
about .65 percentage points, such that annual growth rates are projected to be
2.81 percent instead of 3.46 percent.
As a result, the level of GDP per capita in 2015 would be $1272 lower
than its projected $8500. At Thailand’s
current prevalence rates, still among the highest outside Africa at an adult
rate of 2.15 percent, the impact on GDP is minimal. Nevertheless, the example
demonstrates that an unchecked AIDS epidemic – as some African countries are
experiencing – can have a substantial effect on the growth of income per capita
because it is so highly concentrated in working-age individuals.[24]
In reviewing all the available evidence, UNAIDS
states that, “despite incomplete data, there is growing evidence that as HIV
prevalence rates rise, both total and growth in national income – GDP – fall
significantly.”[25] It is
important to emphasize, however, that the data are incomplete; many of
the studies forecast rather than report impacts; and the methodology of some
studies can be questioned.
The impact on
business
The effects of HIV/AIDS on a business are likely
to be felt in three areas: a firm’s labor force, its customer base, and the
reputation of the company.
There have been a number of studies on the
impact of HIV/AIDS on the workforce. In the early stages of the epidemic in
Africa, the spread of the epidemic appeared to correlate with wealth, and firms
therefore seemed to be losing their most skilled, productive, and
expensive-to-replace workers. More recently, however, a study has suggested
that overall infection rates will peak at nearly three times the rate for
highly skilled workers.[26]
Generally, turnover in Africa was minimally affected in the early stages of the
epidemic,[27] but as the
epidemic has matured, companies in hard-hit areas have begun to feel an impact.[28]
Some multinational organizations in South Africa, for example, claim to have
hired 3 workers for each position to replace those who die.[29]
Outside Africa, there is as yet no evidence that the epidemic is
disproportionately targeting the skilled. Companies in certain sectors may
suffer – trucking companies in India, for example[30]
– but large-scale decimation of workforces is unlikely.
There are other potential effects that are even
more difficult to quantify. Studies in Kenya and Thailand, for example,
suggest, not surprisingly, that motivation and productivity are adversely
affected by AIDS-related illnesses and death, for example.[31]
In the future, it is also possible that the quality of available workers will
deteriorate, as AIDS orphans (of whom there are currently 13 million) receive
less education and are poorly socialized. [32]
Studies are not yet able to show the likely economic impact of these effects.
The impacts on the customer base will be felt
most keenly in Africa, although countries that trade extensively with African
countries (as well as multinationals with franchises in Africa) may feel a
ripple effect. Studies in Cote d’Ivoire and Rwanda have shown how health
expenditure significantly reduces the household consumption of families living
with AIDS.[33] A study of
the epidemic in Thailand claims that it may cost Japan as much as 1.2 percent
of its gross national product (GNP) due to the weakening of this important
market for Japanese exports.[34]
The methodological basis of this and similar studies is questionable however.
Even without proof that the virus will devastate
labor forces and markets, however, there might be other reasons for some firms
to take action against the disease. Pharmaceutical companies have profited from
the disease in terms of sales, but many have suffered harmful effects to their
reputations as protests by individuals, NGOs, and national governments have
attacked AIDS drug patents.[35]
Other companies, such as Levi Strauss and MTV, have been widely praised for
their prompt action to educate their workers and their customers as to the
dangers of the disease. As a recent study on corporate responsibility and
HIV/AIDS for the American Foundation for AIDS Research suggested, there are
both positive and negative motivations for companies to intervene on behalf of
the public interest (see model at figure 7). “Negative” interventions will be a
response to demands from employees, customers or shareholders, whereas
“positive” actions will be driven by a desire to stand out above competitors
and search out new business opportunities. As the paper says, “boosting
employee morale, raising corporate profile and contributing to society are
powerful drivers for many of today’s most innovative companies.”[36]
The high profile of the AIDS virus, particularly among the young generation
that has grown up with the disease, presents a powerful opportunity for
corporate action.
The effect on
households
The effect of AIDS on affected households is
substantial. AIDS is an expensive illness to treat, and caring costs are high.
Savings rates in affected households are likely to suffer as a result.[37]
A series of micro level studies shows the impact of AIDS on households:
-
A large-scale World Bank survey of households in
Tanzania, Cote d’Ivoire and Thailand found that household expenditures on AIDS
care were much higher than on other illnesses.[38]
-
Household income in the poorest quarter of households
in Botswana is projected to fall by 13 percent from current levels in the next
ten years as a result of the disease.[39]
-
A study in Cambodia shows the poor are forced to sell
limited family assets to pay for the cost of caring for a family member with
AIDS. They are also likely to borrow, at high rates of interest.[40]
Again, however, the literature is patchy. Many
of the micro studies are carried out in extremely hard-hit areas using
non-representative samples and none gives a wholly reliable picture of the
effect of the virus.[41]
AIDS and economics
The lack of conclusive evidence on the economic
impact of the AIDS epidemic reflects the lack of investment in research by
governments and donors and a failing in the academic community. Further studies
will therefore be needed to provide conclusive evidence of the size and nature
of any effects.
Part 3: The Economic
Return on HIV Prevention Programs
Irrespective of whether one can (or cannot)
measure all of the economic impacts of HIV/AIDS epidemic, it can nonetheless be
demonstrated that investments in HIV/AIDS prevention have the potential of
yielding high rates of economic return.
This is because, independent of any other consideration, increased
numbers of AIDS cases and deaths require medical expenditures for treatment and
impose a clear loss to society in the form of lost output. In this section we
provide rough estimates of the rate of return to HIV prevention and compare it
to returns to other investments, in the health sector and elsewhere.
Rate of return
Without reliable data, it is difficult for
governments to set investment priorities. The standard approach adopted by
economists advising governments is to calculate the rate of return (ROR) on
competing demands for resources and direct the funds to investments that yield
the highest return, followed by the second highest, and so on, until the budget
is exhausted. If an activity offers higher rates of return than alternative
uses, the case for investing in that activity is strengthened. Benefits from
HIV prevention accrue from both the medical costs averted (by both private and
public sectors) and the value of lives saved on account of the intervention.[42]
Research conducted for this paper provides an attempt at assessing the ROR from
HIV prevention efforts based on data from Thailand, whose efforts in the 1990s
were successful in reducing the number of new AIDS cases, which had doubled to
26,000 from 1994 to 1997, back to 1994 levels in just two years (see Appendix 2
for full details).[43]
The time period from 1990, when Thailand’s
prevention activities began, to 2020, when the full effects will be measurable,
was chosen for the analysis. Public sector and donor expenditures on HIV/AIDS
jumped from US$0.68 million in 1998 to nearly US$10 million in 1991 and $82
million by 1997. It is estimated that roughly 15 percent of these expenditures
were on prevention activity.[44]
The private sector spent an estimated US$80 million on prevention messages in
1991.[45]
Data on changes in behavior suggests that if behaviors had remained unchanged
at 1990 levels, there would have been more than 12 million extra deaths due to
AIDS in Thailand, cumulatively, by the year 2020 compared to current behavioral
patterns.
Even with conservative estimates of the impact of the
prevention campaigns on the changes in behavior, ROR is calculated at between
12 percent and 380 percent annually, depending on the scenario posited:
-
If we focus only on benefits in terms of medical
expenditures avoided, the rates of return range from 12 percent to 33 percent
over a 30-year period (the lower bound
is an outcome of assuming growth of medical expenditures in tune with per
capita income).
-
If we include averted income losses as additional
benefits resulting from the reduced number of AIDS deaths (i.e., in addition to
savings in medical expenditures), the rate of return jumps upwards very sharply
– with the range now from 37 percent to 55 percent.
-
If, however, instead of income losses, we consider the
value of an averted AIDS death to be equal to the statistical value of a life,
the rates of return of HIV prevention programs jump to upwards of 380 percent
per year.
Estimates of the rate of return for some other
health interventions are available. The rate of return (inclusive of income
losses due to disability) of the global guinea-worm eradication program, for
example, is roughly 29 percent, compared to 37-55 percent for Thailand for HIV
prevention, calculated by an equivalent methodology. Our estimates for the rate
of return on HIV prevention expenditures (inclusive of income losses) also
exceed the range of rates of return from interventions for river blindness
eradication in Africa (which the World Bank estimates at 6-17 percent[46]).
The World Bank considers an annual rate of return of greater than 10 percent to
be acceptable.[47]
Integrated action
It is clear, therefore, that intervention to
prevent HIV/AIDS provides a potentially high rate of return on investment (even
the lowest estimate is above World Bank criteria). However, there remains the
need for further research in this area, to explore the rate of return in
different countries, with different intensity of epidemic, at different stages
in the development process, and with different intervention portfolios.
It is worth noting that one of the features of
Thailand’s success has been the reliability of its data on HIV/AIDS. This has
enabled the nature of the virus to be tracked over time and spot emerging
changes of profile in the epidemic.[48]
In the absence of such surveillance, governments are unlikely to be able to
keep up with HIV, and resource allocation will inevitably be inefficient.
Conclusion
The connection between AIDS and economics is complex, and drawing firm conclusions is complicated by the lack of concrete data in many areas. The poor appear to be most vulnerable to AIDS, but it is possible that this is not just because they are poor, but because of the interaction between poverty and other factors such as poor education, migration and weak health systems. Poverty reduction may decrease risk from the epidemic, but it is also possible that ill-planned development efforts will temporarily increase the risk that poor people face.
The impact of AIDS on economies is also hard to
measure. It seems clear that there were limited effects early in the epidemic.
Although some studies now project increasing impact, they are speculative, even
if there is a strong intuition that very badly affected countries will see a
significant economic deterioration. It is possible that this effect will only
be felt when the prevalence of HIV/AIDS surpasses a certain threshold, however.
Finally, there is a need for better data to help
track the development of the epidemic, to judge the most effective
interventions, and to help decision-makers decide between competing priorities.
Our preliminary estimates suggest a high potential return to investments in HIV
prevention.
Our understanding of the epidemic seems to be somewhat weak, given the time since the discovery of HIV, the global nature of the epidemic, and its ferocity. At a time of huge political interest in health as a tool of development, it is clear that further work is badly needed.
Figure 6
Source: UNAIDS (2000)
Figure 7
Source: Bloom et al (2001) ibid
Appendix
1
We ran binary logits on each of the
following outcome variables:
1. knowledge of the HIV-preventive benefits of
condom use
2.
knowledge of the HIV-preventive benefits of having just one sexual
partner
3. knowledge of the HIV-preventive benefits of
avoiding sex with prostitutes
4.
knowing of a source for condoms
5.
knowing about condoms
6.
ever having used a condom
Each logit contained dummy variables for
wealth quintile, highest level of education achieved, and 5-year age
cohort. We report odds ratios for the
effect of being in the wealthiest quintile and having the highest level of
education. In all countries but
Tanzania, the highest education level is higher education. In Tanzania, the highest education level is
secondary education.
v754c byte percent18.0g V754C AIDS: use condoms during sex
|
Country |
Wealthiest quintile |
Highest Education |
|
Cambodia n=15,351 |
2.139 (0.259)*** |
1.953 (1.142) |
|
Vietnam n=1658 |
2.684 (1.157)** |
6.455 (4.284)** |
|
Nicaragua n=1334 |
1.970 (0.377)*** |
1.876 (0.723) |
|
Tanzania n=1675 |
3.031 (0.894)*** |
3.771 (1.355)*** |
v754d byte
percent20.0g V754D AIDS: only one sex partner
|
Country |
Wealthiest
quintile |
Highest
Education |
|
Cambodia n=15,351 |
1.127 (0.095) |
1.109 (0.291) |
|
Vietnam n=1658 |
1.959 (0.797) |
4.144 (2.552)** |
|
Nicaragua n=1334 |
1.084 (0.283) |
3.206 (1.300)*** |
|
Tanzania n=1675 |
1.577 (0.384)* |
1.254 (0.359) |
v754e byte percent17.0g V754E AIDS: avoid sex with prostitutes
|
Country |
Wealthiest quintile |
Highest Education |
|
Cambodia n=15,351 |
0.799 (0.073)** |
1.398 (0.423) |
|
Vietnam n=1658 |
2.233 (0.588)*** |
0.967 (0.655) |
|
Nicaragua n=1334 |
2.251 (0.836)** |
1.103 (0.630) |
|
Tanzania n=1675 |
1.069 (0.510) |
0.960 (0.563) |
v762 byte percent24.0g V762 Source for condoms
|
Country |
Wealthiest quintile |
Highest Education |
|
|
|
|
|
Vietnam n=1867 |
2.175 (0.771)** |
34.132 (36.592)*** |
|
Nicaragua n=1416 |
2.042 (0.440)*** |
12.417 (7.988)*** |
|
Tanzania n=1698 |
2.462 (0.669)*** |
16.005 (6.815)*** |
v764 byte percent18.0g V764 Knowledge of condom
|
Country |
Wealthiest quintile |
Highest Education |
|
|
|
|
|
Vietnam n=1832 |
2.504 (0.970)** |
26.720 (15.523)*** |
|
Nicaragua n=1416 |
4.713 (2.051)*** |
6.984 (7.403)* |
|
Tanzania n=1530 |
17.996 (19.313)*** |
8.664 (3.894)*** |
v765 byte percent17.0g V765 Ever use of condom
|
Country |
Wealthiest quintile |
Highest Education |
|
|
|
|
|
|
|
|
|
Nicaragua n=1419 |
1.766 (0.424)** |
2.046 (0.916)* |
|
Tanzania n=1698 |
2.779 (1.834) |
3.457 (2.529)* |
Summary of results:
1. Knowledge of HIV preventive benefits of
condom use
In all countries, the wealthy and better
educated are better informed of the HIV-preventive benefits of condom use.
2. Knowledge of HIV preventive benefits of
having just one sexual partner
In all countries, the wealthy and better
educated are better informed of the HIV-preventive benefits of having just one
sexual partner but results are statistically significant only in Vietnam and
Nicaragua for education, and Tanzania for wealth.
3. Knowledge of HIV preventive benefits of
avoiding sex with prostitutes
Knowledge of the HIV-preventive benefits
of avoiding sex with prostitutes does not seem to vary by level of
education. The correlation between this
knowledge and wealth is ambiguous. It
is positive in Vietnam and Nicaragua, negative in Cambodia, and roughly zero in
Tanzania.
4. Knowing of a source for condoms
The wealthy and better educated are
considerably more likely to know a source for condoms. (Note, there is no Cambodia data on this)
5. Knowledge of condoms
The wealthy and better educated are
considerably more likely to know about condoms. (Note, there are no Cambodia data on this)
6. Ever used condoms
The wealthy and better educated are considerably
more likely to have ever used condoms, though the effect is insignificant in
Tanzania. (Note, there are no Cambodia
data on this)
Appendix 2
What is the rate of return to investments in HIV
prevention?
The
average annual returns from HIV prevention expenditures over a 30-year period
are:
- If benefits include only averted medical
expenditures (12-33 percent)
- If averted income losses and medical
expenditures (37-55 percent)
- If averted value of statistical lives and
medical expenditures (>380 percent)
A. Relevance
The
importance of an answer to this question stems from the fact that ministries of
health and finance in developing countries are resource constrained, and face
competing demands for their resources.
For instance, Ministries of Health are often faced with the policy
challenge of whether to spend the marginal dollar on diarrhea and/or malaria
prevention, or HIV prevention? At other
times a policy choice may have to be made between investments on treating the
elderly versus HIV prevention. The
often unenviable nature of these choices may make it desirable to seek
additional funds from ministries of finance, that in turn, also face competing
demands for funds.
The
standard approach economists’ think about for allocating scarce resources in an
efficient way is to assess the rate of return on competing demands for
resources and direct the funds to investments that yield the highest return,
following by the second highest, and so on, till the budget is fully exhausted. An economist advising a government whose
objective function includes not just allocative efficiency, but also equity and
environmental protection (for example), would modify the analysis so that the
rate of return as referred to here is evaluated after including the value of these
additional objectives. Whether
governments do this in practice is another matter. What does matter is that if an activity offers higher rates of
return in comparison to alternative uses, the case for investing in that
activity will likely be strengthened.
Although
it is often asserted that investments in HIV prevention yield relatively high
rates of return, these assertions appear not to be empirically grounded.[49] In the absence of such grounding, it is
difficult to argue (against competing claims) that governments ought to spend
more on HIV. This note is a first
attempt in the direction of providing a rate of return on investments in HIV
prevention.
B. Approach
The
technique that we use to estimate the rate of return is a classic one used in
the cost-benefit analysis literature.
Specifically, we seek to estimate the “internal rate of return” to
investments in HIV infection.
If
It are the investments in HIV prevention in year ‘t’ and Bt
are a money value of the benefits in terms of avoided HIV infections and AIDS
cases that can be attributed to those investments, then the rate of
return to HIV prevention is the ‘r’ that solves the following equation, where
the summation is over the time horizon of interest.
St(Bt
– It)/(1+r)t = 0
Expenditures
on HIV prevention (It) in any given year ‘t’ are the sum,
after taking account of inter-institutional flows, of public sector
expenditures, private sector
expenditures (for-profit, non-profit, and households), and expenditures
incurred by the international donor community.
Benefits
(Bt) from HIV prevention occur in two main ways – the first being
the medical care expenditures incurred (by the public and private sectors) in
the treatment of AIDS cases. This can
be estimated to be the number of AIDS cases for each year (in year-equivalent
terms) times the cost of treating an AIDS case for that year. A second benefit is the monetary value of
lives that are “saved” on account of the intervention. This can be measured as the reduction the
number of AIDS deaths due to the HIV prevention program times the value
of a “life.” One approach to estimating
the value of a life is to look at the income losses that result from the
death. Another is to estimate the value
of a “statistical life” obtained from studies of the additional amounts people
need (as compensation) to accept small increases in the risk of dying.
Calculation
of benefits from a specific HIV prevention program requires AIDS-related data
of the following type for the relevant time horizon: (a) the number of AIDS
cases averted on account of the intervention in each year; (b) the medical cost
of treating a single case of AIDS in each year; (c) the number of AIDS deaths
(annual and cumulative) for each year; (d) the value of a statistical life in
any given year; and/or (e) income “lost” for each future year on account of an
AIDS death in any given year.
C. Data and
Rationale
To
calculate the rate of return from interventions in HIV prevention, we used data
from Thailand that, since the early 1990s put in a tremendous effort in HIV
prevention activity. We chose the time
period 1990-2020 as the horizon over which to make calculations of the rate of
return. This way, we could inquire how
much Thailand is likely to have “earned” for the investments in HIV prevention
that it undertook starting 1990.
As
one illustration of Thailand’s efforts towards HIV control, public sector and
donor HIV/AIDS expenditures (not all on prevention) jumped from a level of
US$0.68 million in 1988 to nearly US$10 million in 1991 and to a level of US$82
million by 1997 (Ainsworth et al. 2001).
We estimate that roughly 15 percent of these expenditures in any given
year were on prevention activity (Ainsworth et al. 1997). By contrast the private corporate sector spent
about US$80 million on HIV prevention messages in 1991 (Viravaidya, Myers and
Obremsky 1992). There were additional
small amounts spent by non-governmental organizations, but we assume these were
mostly funds obtained from international donors and the government. No data were available about HIV prevention
spending by the private corporate sector for years other than 1991, so we
estimated the rate of return under many different assumptions about its time
profile.[50]
There
are good data in Thailand on changes in behavior that put people at high risk
for HIV infection, and that has helped permit reliable calculations of AIDS
cases and deaths with the help of sophisticated epidemiological models. In particular, these models can help predict
the profile of HIV and AIDS cases, and deaths linked to AIDS, over time. As a consequence we can construct a profile
of AIDS cases and deaths over time had behaviors remained unchanged at their
1990 levels, and compare them with the profile that actually emerged following
1990. Comparing the two series yields
startling numbers. For instance,
unchanged behaviors at their 1990 levels would have led to more than 12 million
extra deaths due to AIDS in Thailand, cumulatively, by the year 2020 compared
to current behavioral patterns.
Whether
the difference in the time profile of AIDS cases and deaths (with and without
the behavior change following 1990) was due solely to HIV prevention
interventions is another matter. In
particular, faced with high risks of HIV infection, individuals might seek on
their own to acquire the knowledge and adopt some of the steps necessary to
prevent HIV infection to themselves and people they care about, without any
public intervention. If only a portion
of the difference in the two profiles (with and without behavior change) can be
attributed to HIV prevention expenditures, then our method of calculating the
rate of return must account for that fact.
It is also possible that some of the reductions in HIV/AIDS infections
arise out of increased expenditures on AIDS treatment. This might occur, if individuals when
infected individuals come into contact with the formal health system,
counseling and the like and adopt protective measures that lead to a reduction
of secondary infections. Exactly, how
much of the averted cases arise from treatment-related expenditures is
uncertain, but not insignificant.
The
approach that we use in this note relies on the reasonable premise that
compared to a public campaign, individuals acting alone or in small groups will
take longer to adopt preventive measures and in less intensive ways than would
result from the sort of all-out public campaign so characteristic of
Thailand. Specifically, we assume that
during the period 1990-2000 all of the reduction in AIDS cases and deaths were
due to HIV prevention expenditures of the public and private corporate
sectors. During the period 2000-2020,
however, we attributed only half of the averted AIDS cases and deaths to
prevention expenditures.
Calculating
benefits requires assigning money values to the averted AIDS cases and
deaths. Consider first medical
expenditures due to AIDS. Data on the
annual cost of treatment of an AIDS case (about 25,700 Baht at 1995 prices –
US$837) was obtained from a 1991 study of Thailand (Viravaidya, Obremsky and
Myers 1992) and forecasted forward (and backward) in two ways: (a) based on
regression analysis linking the log of the annual costs of AIDS treatment to
the log of per capita income using cross-country data for 9 Asian countries
(authors’ calculations); and (b) from the rate of growth of the annual costs of
AIDS cases in the United States over the period 1992-1997 (slightly higher than
the growth rate based on per capita income changes) (Hellinger 1998). This scenario was considered necessary since
costs of treating AIDS will likely increase over time as AZT (and various
combination therapies) are used with greater frequency in Thailand.[51]
To
forecast the cost of AIDS cases, we needed to know the expected rate of growth
of real GDP per capita. The rate of
growth of real GDP per capita in Thailand during 1999-2020 was forecasted to be
its average annual rate of growth during 1989-1998 – about 3.6 percent per
year. This is somewhat lower than
forecasts reported in a recent IMF report and so leads to conservative
estimates of benefits in terms of the amounts of medical expenditures avoided
through HIV prevention programs.
Next,
there were benefits from averted AIDS deaths as the money values of lives
saved. Benefits in terms of lost
incomes averted from a person dying of AIDS in a specific year were calculated
to be the per capita GDP for that year; and for subsequent years, the
forecasted levels of real per capita GDP until the year 2020. In the year, 1995, for instance, the per
capita GDP (at 1995 prices) was about Baht 71 thousand, rising to Baht 138
thousand by the year 2020.
On
the other hand, the gains in terms of the value of a statistical life
(estimated to be Baht 38.73 million in 1993 at 1995 prices) were directly
allocated in the year in which the AIDS death occurred and assumed to grow at
3.6 percent per year in line with the rate of growth of real GDP per
capita. This estimate of the value of a
statistical life was based on US numbers but multiplied by the ratio of
Thailand’s income per capita to the US income per capita in PPP terms (UNDP
1996, Moore and Viscusi 1998).
Estimates of the rate of return based on benefits measured in terms of
the value of statistical lives saved turned out to be very high.
We
did not have any data on forecasted HIV prevention expenditures during the
period 2000-2020 and on private corporate prevention expenditures for years
other than 1991, so we considered two scenarios. Both assumed that for the period 2000-2020 annual HIV prevention
expenditures would remain the same (in real terms) as the mean of expenditures
during the period 1990-2000. However,
one scenario assumed that private corporate expenditures would be fixed at
their 1991 levels for the period 1990-2000.
Another assumed that the ratio of private corporate prevention
expenditures to public sector (and donor) prevention expenditures would remain
constant during 1990-2000.
D. Results
The
estimates of the rates of return were obtained under three main benefit profile
scenarios, and four main time profiles for prevention expenditures:
1. Benefits
evaluated in terms of medical expenditures averted only (with two possibilities
– growth at US levels, or based on per capita income growth);
2. Benefits
evaluated in terms of medical expenditures averted plus income losses averted;
3. Benefits
evaluated in terms of medical expenditures averted plus losses in the value of
a statistical life averted;
4. Private
prevention expenditures were fixed at their 1991 levels; and during 2001-2020,
prevention expenditures (public and private) were the mean of all prevention
expenditures during the period 1990-2000.
5. Private
prevention expenditures were fixed at a constant proportion of public (and
donor spending) based on their 1991 levels; and for 2001-2020 prevention
expenditures (public and private) were the mean of all prevention expenditures
during the period 1990-2000.
In total, 12 cases
were considered and are summarized in the 6 scenarios reported below. The main findings are reported below.
Scenario I:
Medical Expenditures per AIDS case grow over
time; Private corporate expenditures on HIV prevention are fixed at their 1991
levels; NO income losses, or value of statistical lives lost on account of AIDS
deaths
Rate of return on HIV prevention expenditures
31.5 percent if rate of growth of per person AIDS expenditures was linked to
per capita income growth in Thailand, but 32.6 percent if AIDS medical
expenditures grow at the rate reported for the US.
Scenario II:
Medical Expenditures per AIDS case grow over
time; Private corporate expenditures on HIV prevention are a ratio of public
expenditures; NO income losses, or value of statistical lives lost on account
of AIDS deaths.
Rate of return on HIV prevention expenditures
12.3 percent if rate of growth of per person AIDS expenditures was linked to
per capita income growth in Thailand, but 14.0 percent if AIDS medical
expenditures grow at the rate reported for the US.
Scenario III:
Medical Expenditures per AIDS case grow over
time; Private corporate expenditures on HIV prevention are fixed at their 1991
levels; Losses on account of AIDS deaths included and measured in terms of per
capita income losses for each year subsequent to an AIDS death.
Rate of return on HIV prevention expenditures
55.0 percent if rate of growth of per person AIDS expenditures was linked to
per capita income growth in Thailand, but 55.1 percent if AIDS medical
expenditures grow at the rate reported for the US.
Scenario IV:
Medical Expenditures per AIDS case grow over
time; Private corporate expenditures on HIV prevention are a ratio of public
expenditures; Losses on account of AIDS deaths included and measured in terms
of per capita income losses for each year subsequent to an AIDS death.
Rate of return on HIV prevention expenditures
37.0 percent if rate of growth of per person AIDS expenditures was linked to
per capita income growth in Thailand, but 37.2 percent if AIDS medical
expenditures grow at the rate reported for the US.
Scenarios V and VI:
Medical Expenditures per AIDS case grow over
time; Private corporate expenditures on HIV prevention are fixed at their 1991
levels/Taken as a fixed proportion of public expenditures; Include value of
statistical lives lost on account of AIDS deaths
Rate of return on HIV prevention expenditures
was in the range of 385-415 percent.
In sum: If we
focus only on benefits in terms of medical expenditures avoided, the rates of
return range from 12.0 percent to 33.0 percent over a 30-year period. The lower bound of our numbers is an outcome
of assuming growth of medical expenditures in tune with per capita income.
If we include
averted income losses as additional benefits resulting from the reduced number
of AIDS deaths (in addition to savings in medical expenditures), the rate of
return jumps upwards very sharply – with the range now from 37 percent to 55
percent.
If instead of income
losses, we consider the value of an averted AIDS death to be equal to the
statistical value of a life, the rates of return of HIV prevention programs
jump to upwards of 380 percent per year!
E. Cautionary Remarks
- We are not fully confident about what proportion
of the reduction in AIDS cases and deaths in Thailand can be attributed to
HIV prevention programs. We do believe
that we have been conservative about HIV risk reduction that can be
attributed to prevention expenditures.
- Even though returns from HIV prevention programs
appear high, for a fair comparison with other heath interventions and
programs, rates of return from the latter ought to be evaluated using the
same methodology.
It is interesting in this context that our estimates
of the internal rate of return with only avoided medical expenditures as
benefits (12 percent-33 percent) bracket the 26.7 percent estimate based on
scenario prevention analyses carried out for India and reported in Dayton
(1998).
Estimates of the rate of return (inclusive of income
losses due to disability) of the global guinea-worm eradication program are
roughly 29 percent, which is below the range of estimates of the rate of return
that we report for Thailand for HIV prevention (37-55 percent). Our estimates
for the rate of return on HIV prevention expenditures (inclusive of income
losses) also exceed the range of rates of return from interventions for
Riverblind disease eradication in Africa (6-17 percent, World Bank 2000).
- We had to make several assumptions about the
level of private corporate spending during the period 1990-2020 and
beyond, with obvious implications for the estimates of the rate of return
that we report here.
- The fact that we observe high rates of return on
HIV prevention does not imply that expenditures for much needed treatments
for people with HIV and AIDS ought to be ignored or placed on a lower
level of priority. Care for
people living with HIV/AIDS, aside from being crucial from basic
humanitarian and equity concerns, is potentially important for HIV
prevention (as noted earlier).
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