I am excited to share with you the final product, which is in fact not at all final! I acknowledge the immense room for improvement and further exploration, however I feel it is a solid start to better understanding the financing of HIV/AIDS, and proposes some valuable strategies on more effective financing initiatives.
I present to you, "Financing an End to AIDS: Conditions of AIDS Aid Efficacy"! To facilitate reading such a relatively large "post" I have broken it down into sections. If you would like a PDF I'm happy to e-mail it to you! E-mail me at katherine.otto@gmail.com
xo Kate
Monday, April 28, 2008
Abstract
The destruction caused by the global AIDS pandemic has sparked rich nations and international organizations to commit billions of dollars in AIDS-specific foreign aid to over 100 national governments. The provision of resources to address HIV/AIDS has exploded in recent years, dominated by three main donors: the World Bank Multi-Country HIV/AIDS Program, the Global Fund to Fight AIDS, Tuberculosis and Malaria, and the United States President’s Emergency Plan for AIDS Relief. Despite the massive AIDS-aid increase, I argue that HIV rates have continued to increase in many recipient nations, and that no significant correlation exists between changes in HIV rates and pure AIDS-specific aid. However under certain political and institutional conditions, pure AIDS-aid can be effective. OLS Regression of aid on changes in HIV rates, controlled for selection bias with a Treatment Effects model, reveals that in countries with certain institutional and political conditions AIDS-aid does correlate with decreasing levels of HIV. In response to the recent influx of AIDS-specific giving, this paper suggests that political preconditions of aid exist if the eradication of AIDS is to be financed with foreign aid.
Acknowledgments
I thank my teaching assistant Yael Zeira, professors Peter Rosendorff and John Gershman, and mentor Helen Epstein for their academic guidance and insight throughout this project. I would also like to acknowledge the resilience of the HIV/AIDS affected communities with which I have been privileged to work, specifically my friends in Ghana, Tanzania, South Africa, and Uganda. I am inspired by those using available resources to their best ability to diminish the destruction of AIDS in their communities, despite the burdensome politics of aid. Your tireless efforts have motivated me to search for significant insight into how to best give and use AIDS-aid.
1. Introduction
In a matter of decades, the AIDS pandemic has left more than 25 million dead, thereby threatening national economic security and destroying traditional family and societal structures. In the West, availability and affordability of antiretroviral medications, surrounding medical care, and prevention mechanisms like sexual health education and social counseling, have allowed society to keep transmission levels relatively low and transform HIV from a death sentence into a chronic, manageable illness. However across the globe AIDS continues to cause early death in communities who lack access to these services.
Powerful nations and international organizations have responded to the crisis of HIV/AIDS with foreign aid earmarked for AIDS programs, providing health services like those that have reduced HIV rates in the West. Aggregate “AIDS-aid” from major donors has increased notably from a commitment of $200 million in 1986 to $8.9 billion in 2005.
Yet AIDS continues to ravage the globe. Are committed financial resources falling short of actual need, or is aid destined to fail within the current system of aid distribution? Boone, Alesina, Burnside, Dollar, Easterly, Stiglitz, and many other scholars agree that foreign aid alone will not solve the problems of the poor. I argue, by extension, that aid alone will also not solve the AIDS pandemic. This paper examines the effect of AIDS-aid financing on changes in HIV rates in recipient nations. I argue that AIDS-aid is only effective in decreasing HIV rates if the recipient nation upholds certain policies that allow aid to be spent effectively. I test for a selection bias that determines who receives aid to examine the possibilities that aid is endogenous to change in HIV rates, and that aid recipients could have been worse off without aid.
Powerful nations and international organizations have responded to the crisis of HIV/AIDS with foreign aid earmarked for AIDS programs, providing health services like those that have reduced HIV rates in the West. Aggregate “AIDS-aid” from major donors has increased notably from a commitment of $200 million in 1986 to $8.9 billion in 2005.
Yet AIDS continues to ravage the globe. Are committed financial resources falling short of actual need, or is aid destined to fail within the current system of aid distribution? Boone, Alesina, Burnside, Dollar, Easterly, Stiglitz, and many other scholars agree that foreign aid alone will not solve the problems of the poor. I argue, by extension, that aid alone will also not solve the AIDS pandemic. This paper examines the effect of AIDS-aid financing on changes in HIV rates in recipient nations. I argue that AIDS-aid is only effective in decreasing HIV rates if the recipient nation upholds certain policies that allow aid to be spent effectively. I test for a selection bias that determines who receives aid to examine the possibilities that aid is endogenous to change in HIV rates, and that aid recipients could have been worse off without aid.
2. Discussion
AIDS-Specific Aid
Global funding to combat HIV/AIDS in low- and middle-income countries has nearly quadrupled since 2001, most likely due to heavy political pressure. Three major funding mechanisms have fueled the overall financing and their bureaucratic organizational procedures foreshadow the politics of AIDS-aid that have emerged in recent years.
In fiscal year 2001 The World Bank Multi-Country HIV/AIDS Program (MAP) began using International Development Association (IDA) funds and interest on other World Bank loans to finance AIDS-specific projects and programs. MAP aid is disbursed most often to World Bank-created national entities known as National AIDS Control Councils (NACC) for 3-5 yearlong programs. In this paper, MAP aid is defined by the date on which the aid was approved and the amount at that time allocated to the recipient NACC.
In 2002 The Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) began giving grants for 2-year programs, offering renewals to good performers for 1-3 years. Money comes from governments on the Global Fund Board as well as from foundations, private companies, and rich individuals. Although given to a variety of both government and non-governmental partners, in this paper Global Fund aid is defined by the Program Start Date and the Total Amount Disbursed, and only includes HIV/AIDS or HIV/Tuberculosis earmarked funds given to the category of Government.
In fiscal year 2004 President Bush launched The President’s Emergency Plan for AIDS Relief (PEPFAR) as the umbrella of all United States international AIDS-related funding. PEPFAR allocated $15 billion from 2003-2008 for AIDS specific programs, predominantly to 15 ‘focus countries,’ and is set to approve and additional $50 billion for the next 5 years. Funds are received by non-governmental organizations (NGOs), for-profits contractors, and Ministries of Health, however in-country American coordinators exclusively manage the aid. In this paper, PEPFAR aid is defined as the aid given in the fiscal year it is recorded under in PEPFAR public records.
The world collectively spent over $8 billion on AIDS relief in 2005 , led by MAP, Global Fund, and PEPFAR Funds, however the United Nations Joint Programme on HIV/AIDS (UNAIDS) recently reported that it will take $42 billion a year by 2010 to properly address the AIDS pandemic. The Report specifies that wise investments might include “427,500 medical personnel and 1.5 million teachers…. 10 billion condoms and 2.5 million circumcisions,” resources and approaches that are all evidenced to prevent new HIV infection, intimating that health outcomes will rely on smart spending decisions. In an analysis of AIDS-aid efficacy the institutional and political environment in which such spending decisions are made must be important.
HIV Transmission
I measure aid efficacy as the change in national HIV prevalence rate, so understanding the potential drivers of HIV is necessary to understanding under what conditions AIDS-aid will succeed. Although UNAIDS reports national infection rates of nearly 40% in countries like Swaziland and Botswana, with many sub-Saharan communities suffering startling 80-90% infection rates, HIV transmission is well understood. General methods of transmission of and protection from HIV are biologically established, however little consensus exists on the social, political, and economic factors that can influence changes in HIV rates. Beyond the establishment of AIDS-specific medical and social services that can decrease HIV, more general factors that decrease prevalence include management of ethnic fractionalization and political stability , promotion of gender equality , and reduction of general poverty, including the availability of adequate nutrition, water, and sanitation and access to education.
The AIDS-Aid Paradox
The sudden large-scale response of rich nations and international organizations suggests that AIDS-aid is intended for AIDS relief. Indeed, in many cases this has appeared to be true. Djibouti received $33 million from the MAP from 2000-2003, and between 2001 and 2003 saw a 78.72% decrease in their HIV rate, from 11.75% to 2.9%. Similarly, Ethiopia received $126.8 million from the MAP from 2002-2003, as well as $270 million from the Global Fund and $131.8 million from PEPFAR between 2004-05. Ethiopia observed a 50% decrease in HIV rate, from 4.4% in 2003 to 2.2% in 2005.
However this is not always the case. Curiously, many HIV-burdened nations have observed a decrease in HIV prevalence independent of AIDS-aid, while some countries receiving massive AIDS-aid experience an increase in national HIV prevalence. Table 1 shows the breakdown of changes in HIV rates for observations included in this study.
Interestingly, decreases in HIV rate are slightly more prevalent when AIDS Aid is not given, rather than when AIDS aid is given. Table 2 more specifically highlights a few instances of the paradox in which three geopolitically diverse AIDS-aid recipients have witnessed increased HIV levels, while one nation who with relatively small aid has witnessed a decrease in HIV.
Even with substantially less aid than Angola and Vietnam, Cote d’Ivoire managed to oversee an overall decrease in HIV over the time period, in which three nations with large amounts of aid saw tragic increases in national HIV prevalence. More aid is clearly not a guarantee of AIDS relief, and this study intends to better understand the factors that ultimately determine AIDS-Aid efficacy.
Global funding to combat HIV/AIDS in low- and middle-income countries has nearly quadrupled since 2001, most likely due to heavy political pressure. Three major funding mechanisms have fueled the overall financing and their bureaucratic organizational procedures foreshadow the politics of AIDS-aid that have emerged in recent years.
In fiscal year 2001 The World Bank Multi-Country HIV/AIDS Program (MAP) began using International Development Association (IDA) funds and interest on other World Bank loans to finance AIDS-specific projects and programs. MAP aid is disbursed most often to World Bank-created national entities known as National AIDS Control Councils (NACC) for 3-5 yearlong programs. In this paper, MAP aid is defined by the date on which the aid was approved and the amount at that time allocated to the recipient NACC.
In 2002 The Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) began giving grants for 2-year programs, offering renewals to good performers for 1-3 years. Money comes from governments on the Global Fund Board as well as from foundations, private companies, and rich individuals. Although given to a variety of both government and non-governmental partners, in this paper Global Fund aid is defined by the Program Start Date and the Total Amount Disbursed, and only includes HIV/AIDS or HIV/Tuberculosis earmarked funds given to the category of Government.
In fiscal year 2004 President Bush launched The President’s Emergency Plan for AIDS Relief (PEPFAR) as the umbrella of all United States international AIDS-related funding. PEPFAR allocated $15 billion from 2003-2008 for AIDS specific programs, predominantly to 15 ‘focus countries,’ and is set to approve and additional $50 billion for the next 5 years. Funds are received by non-governmental organizations (NGOs), for-profits contractors, and Ministries of Health, however in-country American coordinators exclusively manage the aid. In this paper, PEPFAR aid is defined as the aid given in the fiscal year it is recorded under in PEPFAR public records.
The world collectively spent over $8 billion on AIDS relief in 2005 , led by MAP, Global Fund, and PEPFAR Funds, however the United Nations Joint Programme on HIV/AIDS (UNAIDS) recently reported that it will take $42 billion a year by 2010 to properly address the AIDS pandemic. The Report specifies that wise investments might include “427,500 medical personnel and 1.5 million teachers…. 10 billion condoms and 2.5 million circumcisions,” resources and approaches that are all evidenced to prevent new HIV infection, intimating that health outcomes will rely on smart spending decisions. In an analysis of AIDS-aid efficacy the institutional and political environment in which such spending decisions are made must be important.
HIV Transmission
I measure aid efficacy as the change in national HIV prevalence rate, so understanding the potential drivers of HIV is necessary to understanding under what conditions AIDS-aid will succeed. Although UNAIDS reports national infection rates of nearly 40% in countries like Swaziland and Botswana, with many sub-Saharan communities suffering startling 80-90% infection rates, HIV transmission is well understood. General methods of transmission of and protection from HIV are biologically established, however little consensus exists on the social, political, and economic factors that can influence changes in HIV rates. Beyond the establishment of AIDS-specific medical and social services that can decrease HIV, more general factors that decrease prevalence include management of ethnic fractionalization and political stability , promotion of gender equality , and reduction of general poverty, including the availability of adequate nutrition, water, and sanitation and access to education.
The AIDS-Aid Paradox
The sudden large-scale response of rich nations and international organizations suggests that AIDS-aid is intended for AIDS relief. Indeed, in many cases this has appeared to be true. Djibouti received $33 million from the MAP from 2000-2003, and between 2001 and 2003 saw a 78.72% decrease in their HIV rate, from 11.75% to 2.9%. Similarly, Ethiopia received $126.8 million from the MAP from 2002-2003, as well as $270 million from the Global Fund and $131.8 million from PEPFAR between 2004-05. Ethiopia observed a 50% decrease in HIV rate, from 4.4% in 2003 to 2.2% in 2005.
However this is not always the case. Curiously, many HIV-burdened nations have observed a decrease in HIV prevalence independent of AIDS-aid, while some countries receiving massive AIDS-aid experience an increase in national HIV prevalence. Table 1 shows the breakdown of changes in HIV rates for observations included in this study.
Interestingly, decreases in HIV rate are slightly more prevalent when AIDS Aid is not given, rather than when AIDS aid is given. Table 2 more specifically highlights a few instances of the paradox in which three geopolitically diverse AIDS-aid recipients have witnessed increased HIV levels, while one nation who with relatively small aid has witnessed a decrease in HIV.
Even with substantially less aid than Angola and Vietnam, Cote d’Ivoire managed to oversee an overall decrease in HIV over the time period, in which three nations with large amounts of aid saw tragic increases in national HIV prevalence. More aid is clearly not a guarantee of AIDS relief, and this study intends to better understand the factors that ultimately determine AIDS-Aid efficacy.3. Literature Review
Aid Efficacy
Aid effectiveness literature to date primarily analyzes the macroeconomic impacts of aid on recipient country economic growth, and consistently yields inconclusive results. Similar uncertainty exists in the growing literature on AIDS-aid of what determinants cause changes in HIV rates.
This study diverts from mainstream uncertainty in two notable ways. First, I use HIV rates instead of economic growth as a unique dependent variable to measure aid efficacy. Boone (1994, 1996) pioneered new indicators of growth - infant mortality, primary school ratios, and life expectancy- although he confirmed the same weak relationship between aid and investment that the literature upholds. Secondly, I build this study off of Burnside and Dollar’s (BD) 2000 conclusion that aid can impact macroeconomic growth, but only when recipient nations endorse specific “good” policies.
BD found that aid has a positive impact on growth in developing countries when interacted with a Policy Index of “good” fiscal, monetary, and trade policies, but has no effect on growth in countries with “poor” policies. Easterly et al challenged BD’s theory in 2004, using the same model and an expanded data set to prove that not even in countries with “good” policies could aid be proven to promote economic growth. Although the effects of aid remain inconclusive, the Policy Index and subsequent Interaction Term (Aid x Policy) remain useful tools to measure if and how policies influence the efficacy of aid. Bueno de Mesquita (BDM) et al (2003) suggest that aid effectiveness is determined in part by political and institutional conditions in the recipient country, as a result of leaders’ tendency to prioritize political survival. If a leader’s political survival is not dependent on provision of public good as specific as AIDS services, AIDS-aid may not be used as intended. I therefore adopt BD’s hypothesis and create a Policy Index of conditions under which AIDS relief is possible , and an Interaction Term of AID-aid and Policy. Health Economics literature theorizes that AIDS-aid is more likely to work in countries with ‘good governance,’ strong health systems, and gender equality, and these factors help develop an appropriate Policy Index for this study.
Selection Bias
In addition to the conditions of recipient nations, the bias of donors also plays a role in the ultimate efficacy of aid. How is AIDS-aid allocated and what is the motivation behind donor behavior? Alesina and Dollar (2000) show that aid is driven primarily by strategic interests, a view supported by Mascarenhas and Sandler (2006) who claim aid is not given altruistically to those in most need, but rather bureaucratically, according to donor interests. PEPFAR programs are openly criticized for giving in this way, as they are accused of funding scientifically ineffective programs, like promotion of abstinence to appease conservative funders, which often means restricting access to birth control and other sexual health services to those in need. Ulterior motives are surely at play when AIDS-aid is given to programs that are scientifically evidenced to be ineffective.
Vreeland’s (2004) findings that nations who align their UN votes with those of the US get greater access to IMF funds suggests there is indeed a selection bias in the aid world, and that perhaps rich nations give for policy concessions instead of based on pure need. Even aside from pursuit of policy concessions to satiate rich nations, Easterly (2005) suggests that AIDS donors may not even consider AIDS relief a desired outcome of donations. He suggests that bureaucratic hierarchies of any large international organization perpetuate giving without requiring results, and that top-down efforts have bad incentives built in because high-level agents are not held accountable when their programs fail, as they remain employed as long as poverty persists. Indeed studies show that PEPFAR disbursements are marred with allegations of bureaucratic delays, resulting in insufficient and untimely disbursements , and the Global Fund shares a record of low disbursement rates and has publicly disclosed in-country experiences that recipient governments were often unable to spend money due to delayed use of resources and reliance on unreliable actors outside the government to implement the grant.
BDM’s selectorate theory (2003) suggests regime type and donor desires for policy concessions as potentially relevant variables for a selection equation if donors do give for reasons other than altruism. If leaders can ensure their political survival by providing a small amount of private goods to their core winning coalition, the chance of receiving aid increases as a country’s winning coalition size decreases, relative to the greater body of all citizens eligible to be part of the elite winning coalition. This occurs because donors can obtain desired policy concessions more cheaply from leaders who will keep and not actually disburse aid as national public goods.
This paper fills an existing gap in aid effectiveness literature that leaves unanswered the effects of specific HIV/AIDS-aid, and explores new measures of aid efficacy, substituting HIV prevalence rates as a dependant variable instead of traditional measures of economic growth, while controlling for selection bias.
Aid effectiveness literature to date primarily analyzes the macroeconomic impacts of aid on recipient country economic growth, and consistently yields inconclusive results. Similar uncertainty exists in the growing literature on AIDS-aid of what determinants cause changes in HIV rates.
This study diverts from mainstream uncertainty in two notable ways. First, I use HIV rates instead of economic growth as a unique dependent variable to measure aid efficacy. Boone (1994, 1996) pioneered new indicators of growth - infant mortality, primary school ratios, and life expectancy- although he confirmed the same weak relationship between aid and investment that the literature upholds. Secondly, I build this study off of Burnside and Dollar’s (BD) 2000 conclusion that aid can impact macroeconomic growth, but only when recipient nations endorse specific “good” policies.
BD found that aid has a positive impact on growth in developing countries when interacted with a Policy Index of “good” fiscal, monetary, and trade policies, but has no effect on growth in countries with “poor” policies. Easterly et al challenged BD’s theory in 2004, using the same model and an expanded data set to prove that not even in countries with “good” policies could aid be proven to promote economic growth. Although the effects of aid remain inconclusive, the Policy Index and subsequent Interaction Term (Aid x Policy) remain useful tools to measure if and how policies influence the efficacy of aid. Bueno de Mesquita (BDM) et al (2003) suggest that aid effectiveness is determined in part by political and institutional conditions in the recipient country, as a result of leaders’ tendency to prioritize political survival. If a leader’s political survival is not dependent on provision of public good as specific as AIDS services, AIDS-aid may not be used as intended. I therefore adopt BD’s hypothesis and create a Policy Index of conditions under which AIDS relief is possible , and an Interaction Term of AID-aid and Policy. Health Economics literature theorizes that AIDS-aid is more likely to work in countries with ‘good governance,’ strong health systems, and gender equality, and these factors help develop an appropriate Policy Index for this study.
Selection Bias
In addition to the conditions of recipient nations, the bias of donors also plays a role in the ultimate efficacy of aid. How is AIDS-aid allocated and what is the motivation behind donor behavior? Alesina and Dollar (2000) show that aid is driven primarily by strategic interests, a view supported by Mascarenhas and Sandler (2006) who claim aid is not given altruistically to those in most need, but rather bureaucratically, according to donor interests. PEPFAR programs are openly criticized for giving in this way, as they are accused of funding scientifically ineffective programs, like promotion of abstinence to appease conservative funders, which often means restricting access to birth control and other sexual health services to those in need. Ulterior motives are surely at play when AIDS-aid is given to programs that are scientifically evidenced to be ineffective.
Vreeland’s (2004) findings that nations who align their UN votes with those of the US get greater access to IMF funds suggests there is indeed a selection bias in the aid world, and that perhaps rich nations give for policy concessions instead of based on pure need. Even aside from pursuit of policy concessions to satiate rich nations, Easterly (2005) suggests that AIDS donors may not even consider AIDS relief a desired outcome of donations. He suggests that bureaucratic hierarchies of any large international organization perpetuate giving without requiring results, and that top-down efforts have bad incentives built in because high-level agents are not held accountable when their programs fail, as they remain employed as long as poverty persists. Indeed studies show that PEPFAR disbursements are marred with allegations of bureaucratic delays, resulting in insufficient and untimely disbursements , and the Global Fund shares a record of low disbursement rates and has publicly disclosed in-country experiences that recipient governments were often unable to spend money due to delayed use of resources and reliance on unreliable actors outside the government to implement the grant.
BDM’s selectorate theory (2003) suggests regime type and donor desires for policy concessions as potentially relevant variables for a selection equation if donors do give for reasons other than altruism. If leaders can ensure their political survival by providing a small amount of private goods to their core winning coalition, the chance of receiving aid increases as a country’s winning coalition size decreases, relative to the greater body of all citizens eligible to be part of the elite winning coalition. This occurs because donors can obtain desired policy concessions more cheaply from leaders who will keep and not actually disburse aid as national public goods.
This paper fills an existing gap in aid effectiveness literature that leaves unanswered the effects of specific HIV/AIDS-aid, and explores new measures of aid efficacy, substituting HIV prevalence rates as a dependant variable instead of traditional measures of economic growth, while controlling for selection bias.
4. Research Design
Theory
Based on the findings of Burnside and Dollar (BD) in 2000, and specified by the case-study evidence of countries presently receiving AIDS-aid, I theorize that AIDS-aid will be effective only in countries with “good” policy environments. In the work of three main donors from 1998 – 2003, AIDS-aid has affected the HIV rates in both positive and negative directions. Figure 1 illustrates that while a cluster of decreased rates may be attributed to small amounts of aid, a seemingly equal amount of increases could also be attributed to the provision of aid as well. AIDS-aid appears sufficient but not necessary for a decrease in HIV by the recipient nation.
I adapt the BD model of the effect of aid on growth to suggest a decrease in HIV prevalence is possible but only when the recipient nation upholds a certain institutional and political environment. I modify BD’s Policy Index (P’) and create an interaction term of aid times policy (A*P’) relevant to the AIDS pandemic, to confront the often-overlooked endogeneity issue: does aid create good policy (and ultimately, decreased HIV rates) or does good policy attract donors to give? The interaction term makes it possible to consider the influence of the Policy Index (P’) when measuring aid (A) efficacy on HIV rates (Y):
Δ Y it = a + b (A it × P’ it) + c (A it)+ d (P’ it) +ε it
The effect of aid on the change in HIV rates therefore depends on the Policy Index:
δY it / δ A it = b P’ it + ε it
Although BD do not test for a selection bias in who receives aid, I conduct a treatment effects test because of abundant research warning that aid efficacy is based on both the donor and recipient behavior. In addition to the theories of Mascarenhas and BDM, Bourguignon and Sundberg (2007) emphasize that donor behavior influences national policymakers and Smith (2005) reveals the inadequacies of international organizations like the World Bank, including their failure to institute political reform as a precedent to developmental aid. Milner (2005) echoes these concerns and outlines the realistic costs inflicted on recipient nations involved in funding from international institutions. Their criticisms – for example the use of inputs and not outputs as measurement tools, giving further insight into the logic of corruption (Smith) - suggest that countries may be better off or may have remained at the same level of development without the foreign aid from these organizations.
Much of my theory supports Easterly’s (2002) contention that top-down giving is inherently deficient, both because it prioritizes low-return items like ‘glossy reports’ over legitimate evaluations, and also because it burdens scarce administrative skills of aid-receiving nations. Even more specifically, Bueno de Mesquita and Root (2002) suggest that earmarking money– in this case, for AIDS - causes the problem of recipient nations having no incentive to eliminate AIDS, because if money will consistently be given for AIDS relief, it can consistently be stolen.
The Treatment Effects model to check for selection bias in the distribution of aid legitimates the use of a Policy Index in the performance equation, for if aid is indeed given according to need (to countries with highest HIV prevalence rates) the hypothesis that a Policy Index could determine aid efficacy becomes null due to a selection bias.
Lastly, this study is built to cover the issue of AIDS financing broadly, unlike the more specific efforts of groups like The Center for Global Development (CDG), a team of development experts including Birdsall, Levine, Roodman, Easterly and Radelet who collect research on development, including effectiveness of aid. Their new “HIV/AIDS Monitor” research often features issue- and country-specific case studies on AIDS-aid efficacy, but Bourguignon and Sundberg (2007) warn that such close up examination can make it difficult to establish a counterfactual to findings and leave unanswered major debates over whether or not AIDS-aid in general has any effect on systematically and predictably decreasing HIV rates. Therefore, this study includes a broader base of all countries who have recorded HIV rates by UNAIDS, and who have received aid from one of the three main sources, considering relevant control variables and the possibility of a selection bias.
Core Hypothesis and Methodology
As HIV/AIDS-aid increases, I expect HIV rates to decrease but only under specific circumstances of good institutional and policy environments. I use OLS panel regressions to relate HIV rate as a dependent variable to aid, institutional and policy variables by country-period units.
The model will test the impact of aid (A), a calculated Policy Index (P’), an interaction term of aid and policy (AxP’) and an index of independent control variables (j(x)) on HIV rates in specific country (i)-time period (t) units:
ΔY it = a + b (A it × P it) + c (A it) + d (P it) + Σ e(j(x) it) +ε it
I separate the AIDS-aid by donor and run the same regression, measuring World Bank and Global Fund contributions separately, as the two donors differ in their methods of distribution and separation of the aid sources provide insight into what drives current trends of giving.
World Bank: ΔY it = a + b (A (WB) it × P it) + c (A (WB) it) + d (P it) + Σ e( j(x) it) +ε it
Global Fund: ΔY it = a + b (A (GF) it × P it) + c (A (GF) it) + d (P it) + Σ e( j(x) it) +ε it
I hypothesize that b > 0, or that an increase in AIDS-aid in countries with specific policy conditions results in decreased rates of HIV. A reminder that Policy Index is always a negative value, so a decrease in HIV associated with more AIDS-Aid in the Interaction term requires b to be positive. My null hypothesis states that AIDS-aid has no impact on HIV rates, even in countries with specific policy conditions, therefore b=0 and c=0. For the two source-specific models the null and alternative hypotheses remain the same.
The Treatment Effects model measures the binary variable of aid giving as a driver of a selection bias, and is more appropriate for this study than the traditional Heckman selection model, which involves missing dependent variables. In the Treatment Effects model, the continuous variable AIDS-aid from the OLS regression becomes an endogenously chosen binary term, so I expand my performance equation dataset to 500 observations, including all nations that have reported national surveillance data by the UNAIDS, not just aid recipients, from 1999-2005. The effect of aid of AIDS-aid on HIV rates becomes conditional on receiving AIDS-aid, and therefore requires a selection equation and an outcome equation. The selection equation uses one set of independent variables that determine how aid is distributed, unique from the variables that determined the change in HIV rate. The outcome equation then predicts the effect of having undergone ‘treatment’, or having received aid, on the change in HIV rate considering the effect of independent variables from the previous performance equation. I hypothesize that there is a selection bias in determining who receives AIDS-aid. The null hypothesis I attempt to reject is that the error terms in the selection and outcome equations are uncorrelated, or that there is no selection bias in the giving of AIDS-aid.
Data Description
Restricted by the availability of HIV prevalence data, the performance equation study includes 103 countries that experienced recorded changes in HIV rate during three periods: (1) 1999-2001, (2) 2001-2003, and (3) 2003-2005. Units of analysis are country-periods that measure how much the aid received at the beginning of a 2-year period affected the change in HIV rate over those two years. A decrease in HIV rate suggests effective aid while an increase in HIV rate suggests ineffective aid.
I deliberately extracted national HIV prevalence rates from four separate sources, the UNAIDS 2006 Report on the Global AIDS Epidemic (2005 rate), UNAIDS 2004 Report on the Global AIDS Epidemic (2003 rate), Report on the Global HIV/AIDS Epidemic 2002 (2001 rate), and the Common Database of the UN Statistics Division (1999 rate). New estimates are annually reported and figures for previous years are revised, however I use rates from the year in which they were published to control if the decision to give aid was based at all on the recipient nation’s HIV rate. All independent control variables are matched to the three periods of AIDS-aid.
Bearce and Tyrone (2007) emphasize that foreign aid with a lag is considered a more effective measurement of aid efficacy, so AIDS-aid is measured by the amount a nation receives over a 2-year period, lagged behind periods of HIV change one-period to account for the time it takes for aid to be spent on programs and services. Periods of aid correspond with periods of HIV change as (1) 1998-1999, (2) 2000-2001, and (3) 2002-2003. PEPFAR funding is included only in the selection equation because it begins in 2004 and HIV rates for 2007 are yet unpublished, so there are no appropriately lagged periods against which to match the impact of PEPFAR funds in the performance equation. Because PEPFAR funds were excluded I have distinguished the 15 focus countries with a dummy variable.
Three variables are used to construct the Policy Index, and are adopted largely from academic theory and policy recommendations on the efficacy of aid. Corruption is considered an immense obstacle to effective use of foreign aid, and Kurtz and Shrank (2005) emphasize that institutional quality may be even more important to effective healthcare provision than expenditure variables. Smith (2006) suggests that skimming funds off foreign aid for personal use is a rational decision for leaders of countries with a small coalition of people who determine their political survival, because foreign aid is ‘unearned revenue’ that will not cause the same economic damage that results from suppression of public goods. In Ethiopia, Liberia and Malawi, AIDS-aid has more than doubled the national health budget in recent years, creating immense pressure to spend large amounts at the risk of not receiving more, and therefore “recipient nations will find ways to absorb the funds, whether legally or illegally.” In Chad, both the World Bank and the Global Fund suspended their AIDS specific funding in 2006 over concerns that the government was managing funds with a deliberate lack of transparency. In Kenya, 1.4 million people were in need of ARVs in 2004, and it is estimated that the amount of foreign funds donated to the National AIDS and STD Control Programme were enough to provide anti-retrovirals to 200,000. By Nov. 2004, only 24,000 Kenyans were reported to be on ARVs. Lastly in Malawi, a recent report revealed healthcare workers demanding sexual, monetary and material favors in return for medical treatment, giving no care or sub-standard care to those who refused. Control of Corruption is one of six dimensions of governance measured by the World Bank and I have adapted the -2.5 to 2.5 scale to a 0.5 to 5.5 scale. Higher scores indicate greater control of corruption so I expect higher scores to correlate with greater decreases in HIV.
The second Policy Index variable measures the state of national health systems. Health systems – including service delivery, health workforce, physical infrastructure, insurance systems, and regulation and licensing of pharmaceuticals – are necessary for AIDS-aid to have any effect. Health expenditure can measure the government’s intended commitment to improving public health systems, and indicates if there is sufficient capacity to support a national public health approach to the pandemic. I measure infrastructure capacity as Public Health Expenditure as a Percent of GDP (Health Exp. % GDP), using World Health Organization data from years 2000, 2002, and 2004. I expect that higher percentages of public health expenditure will correlate with larger decreases in HIV.
If corruption is controlled and health is a priority, the programs that address AIDS still must be strategically devised to address those who are both most at risk and most able to decrease transmission: women. In a wider study on public spending of health, Pritchett and Filmer (1999) find that child mortality has far less to do with money spent, and more to do with factors like level of female education. Women in sub-Saharan Africa make up 61% of HIV infections and Dr. Farmer et al (1996) suggest that this discrepancy goes beyond the fact that women are anatomically twice as susceptible to infection than men through heterosexual intercourse. They suggest that women are endangered due to the very political powerlessness embedded within institutions worldwide, and are necessarily at greater risk for greater HIV infection when left without a voice. A measure of Women’s Political Rights comes from the Cingranelli-Richards (CIRI) Human Rights Indicators Database, and includes suffrage, the right to run for and hold political office, and to petition government officials. Scores estimate women’s political rights as not legally guaranteed (1), legally guaranteed but severely prohibited in practice (2), legally guaranteed and moderately prohibited (3), or legally and practically guaranteed (4). I expect a negative relationship in which more rights for women leads to decrease in HIV.
The Policy Index is constructed by weighting each of the three variables according to their coefficient in an initial regression of independent variables (excluding aid) on the change in HIV Rate (Table 5, Regression 3):
Policy Index = -.253(Control of Corruption) - 2.71(Health Exp. As % of GDP) - .166(Women’s Pol. Rights)
The remaining independent variables control for conditions that could decrease the HIV rate other than AIDS-aid. Women’s Economics Rights measures women’s accessibility into the labor force, including equal pay, right to gain employment without male relative consent, and job security. Because economic rights also include rights to work at night, to work in occupations classified as dangerous, and to work in the military and police force, the variable measure says less about freedom from and more about exposure to sexual harassment or violation, or opportunities for HIV to be transmitted. The scale ranges from economic rights for women (0), to nearly all economic rights are guaranteed and enforced by law (4). I also measure the condition of national HIV Surveillance Systems constructed from US Census Bureau’s HIV/AIDS Surveillance Database, the Euro HIV Database, and countries’ national HIV surveillance reports made available to WHO/UNAIDS. The quality of systems is scored according to the level of surveillance implementation: poor (1), partial (2), or full (3), and I expect high surveillance scores to correlate with decreases in HIV.
Based on Bueno de Mesquita’s conclusion that leaders of small winning coalitions will give less public goods I have included Freedom of Speech. This value fits BDM and Downs’ (2005) definition of a ‘coordination good,’, or a good that leaders are especially weary of offering because its provision empowers citizens in ways that increase potential for regime overthrow. This ranking measures freedoms of speech and press, from complete government censorship (0) to none (3). I expect that as freedoms increase, HIV rates will decrease, because the freedom is indicative of a government prepared to give public goods of even the riskiest sort to their populace.
I also include a measure of successful Directly Observed Therapy, Short Course (DOTS) Tuberculosis treatments. Because the DOTS treatment requires sponsorship and commitment from the national government, trained and dedicated medical personnel, access to functioning hospital or clinic and laboratory facilities, and availability of pharmaceutical drugs, and a standardized recording and reporting system, the scores indicate the quality of and access to health care, and gives insight into actual health outcomes. Considering that TB is the top cause of death for people with HIV/AIDS, I suggest that an increase in treatment success will equal an increase in HIV prevalence as more HIV positive people will live longer. GDP and GDP Per Capita are included to test contentions that more wealth creates longer personal time horizons and encourages safer behavior that will diminish the spread of HIV.
Multiplying the Policy Index by the amount of aid received creates the Interaction Term, and it is important to note that all values of Policy Index and of the Interaction Term are negative, where values farthest away from zero are considered the “good” policies. I therefore expect both coefficients to be significantly positive in order to support the hypothesis that aid will decrease HIV rates in countries with “good” policies.
However even in countries with incredibly strong controls on corruption, women’s political rights, and commitment to public health, the behavior of donors may still overwhelm and determine the efficacy of aid. BDM et al and Mascarenhas influence the unique set of independent variables required for the selection equation. The variables consider a donor’s desire for policy concessions with terrorist nations, control over natural resources, ease of dealing with new regimes and with autocrats, and rewarding good behavior on human rights. I include the baseline HIV rate to capture the potential that donors might give altruistically.
Terror Rating is taken from the Political Terror Scale, which averages all available annual scores from Amnesty International and State Department reports on a 1-5 scale from lowest to highest human insecurity. Energy Production is the Energy Information Administration measure of World Primary Energy Production in quadrillions of British Thermal Units. Regime Type, taken from the Polity IV dataset, uses the Polity score of difference between democracy and autocracy scores, with -10 representing a strong autocratic regime and 10 representing a strong democratic regime. Regime Stability, also from the Polity IV dataset, measures the number of years since the most recent regime change. Oil production is a measure of national annual oil production, and the Physical Humans Rights score represents an additive index of measures of instances of torture, extrajudicial killing, political imprisonment, and disappearance, ranging from no government respect for freedom from these punishments (1) to full government respect of rights (9).
Based on the findings of Burnside and Dollar (BD) in 2000, and specified by the case-study evidence of countries presently receiving AIDS-aid, I theorize that AIDS-aid will be effective only in countries with “good” policy environments. In the work of three main donors from 1998 – 2003, AIDS-aid has affected the HIV rates in both positive and negative directions. Figure 1 illustrates that while a cluster of decreased rates may be attributed to small amounts of aid, a seemingly equal amount of increases could also be attributed to the provision of aid as well. AIDS-aid appears sufficient but not necessary for a decrease in HIV by the recipient nation.
I adapt the BD model of the effect of aid on growth to suggest a decrease in HIV prevalence is possible but only when the recipient nation upholds a certain institutional and political environment. I modify BD’s Policy Index (P’) and create an interaction term of aid times policy (A*P’) relevant to the AIDS pandemic, to confront the often-overlooked endogeneity issue: does aid create good policy (and ultimately, decreased HIV rates) or does good policy attract donors to give? The interaction term makes it possible to consider the influence of the Policy Index (P’) when measuring aid (A) efficacy on HIV rates (Y):
Δ Y it = a + b (A it × P’ it) + c (A it)+ d (P’ it) +ε it
The effect of aid on the change in HIV rates therefore depends on the Policy Index:
δY it / δ A it = b P’ it + ε it
Although BD do not test for a selection bias in who receives aid, I conduct a treatment effects test because of abundant research warning that aid efficacy is based on both the donor and recipient behavior. In addition to the theories of Mascarenhas and BDM, Bourguignon and Sundberg (2007) emphasize that donor behavior influences national policymakers and Smith (2005) reveals the inadequacies of international organizations like the World Bank, including their failure to institute political reform as a precedent to developmental aid. Milner (2005) echoes these concerns and outlines the realistic costs inflicted on recipient nations involved in funding from international institutions. Their criticisms – for example the use of inputs and not outputs as measurement tools, giving further insight into the logic of corruption (Smith) - suggest that countries may be better off or may have remained at the same level of development without the foreign aid from these organizations.
Much of my theory supports Easterly’s (2002) contention that top-down giving is inherently deficient, both because it prioritizes low-return items like ‘glossy reports’ over legitimate evaluations, and also because it burdens scarce administrative skills of aid-receiving nations. Even more specifically, Bueno de Mesquita and Root (2002) suggest that earmarking money– in this case, for AIDS - causes the problem of recipient nations having no incentive to eliminate AIDS, because if money will consistently be given for AIDS relief, it can consistently be stolen.
The Treatment Effects model to check for selection bias in the distribution of aid legitimates the use of a Policy Index in the performance equation, for if aid is indeed given according to need (to countries with highest HIV prevalence rates) the hypothesis that a Policy Index could determine aid efficacy becomes null due to a selection bias.
Lastly, this study is built to cover the issue of AIDS financing broadly, unlike the more specific efforts of groups like The Center for Global Development (CDG), a team of development experts including Birdsall, Levine, Roodman, Easterly and Radelet who collect research on development, including effectiveness of aid. Their new “HIV/AIDS Monitor” research often features issue- and country-specific case studies on AIDS-aid efficacy, but Bourguignon and Sundberg (2007) warn that such close up examination can make it difficult to establish a counterfactual to findings and leave unanswered major debates over whether or not AIDS-aid in general has any effect on systematically and predictably decreasing HIV rates. Therefore, this study includes a broader base of all countries who have recorded HIV rates by UNAIDS, and who have received aid from one of the three main sources, considering relevant control variables and the possibility of a selection bias.
Core Hypothesis and Methodology
As HIV/AIDS-aid increases, I expect HIV rates to decrease but only under specific circumstances of good institutional and policy environments. I use OLS panel regressions to relate HIV rate as a dependent variable to aid, institutional and policy variables by country-period units.
The model will test the impact of aid (A), a calculated Policy Index (P’), an interaction term of aid and policy (AxP’) and an index of independent control variables (j(x)) on HIV rates in specific country (i)-time period (t) units:
ΔY it = a + b (A it × P it) + c (A it) + d (P it) + Σ e(j(x) it) +ε it
I separate the AIDS-aid by donor and run the same regression, measuring World Bank and Global Fund contributions separately, as the two donors differ in their methods of distribution and separation of the aid sources provide insight into what drives current trends of giving.
World Bank: ΔY it = a + b (A (WB) it × P it) + c (A (WB) it) + d (P it) + Σ e( j(x) it) +ε it
Global Fund: ΔY it = a + b (A (GF) it × P it) + c (A (GF) it) + d (P it) + Σ e( j(x) it) +ε it
I hypothesize that b > 0, or that an increase in AIDS-aid in countries with specific policy conditions results in decreased rates of HIV. A reminder that Policy Index is always a negative value, so a decrease in HIV associated with more AIDS-Aid in the Interaction term requires b to be positive. My null hypothesis states that AIDS-aid has no impact on HIV rates, even in countries with specific policy conditions, therefore b=0 and c=0. For the two source-specific models the null and alternative hypotheses remain the same.
The Treatment Effects model measures the binary variable of aid giving as a driver of a selection bias, and is more appropriate for this study than the traditional Heckman selection model, which involves missing dependent variables. In the Treatment Effects model, the continuous variable AIDS-aid from the OLS regression becomes an endogenously chosen binary term, so I expand my performance equation dataset to 500 observations, including all nations that have reported national surveillance data by the UNAIDS, not just aid recipients, from 1999-2005. The effect of aid of AIDS-aid on HIV rates becomes conditional on receiving AIDS-aid, and therefore requires a selection equation and an outcome equation. The selection equation uses one set of independent variables that determine how aid is distributed, unique from the variables that determined the change in HIV rate. The outcome equation then predicts the effect of having undergone ‘treatment’, or having received aid, on the change in HIV rate considering the effect of independent variables from the previous performance equation. I hypothesize that there is a selection bias in determining who receives AIDS-aid. The null hypothesis I attempt to reject is that the error terms in the selection and outcome equations are uncorrelated, or that there is no selection bias in the giving of AIDS-aid.
Data Description
Restricted by the availability of HIV prevalence data, the performance equation study includes 103 countries that experienced recorded changes in HIV rate during three periods: (1) 1999-2001, (2) 2001-2003, and (3) 2003-2005. Units of analysis are country-periods that measure how much the aid received at the beginning of a 2-year period affected the change in HIV rate over those two years. A decrease in HIV rate suggests effective aid while an increase in HIV rate suggests ineffective aid.
I deliberately extracted national HIV prevalence rates from four separate sources, the UNAIDS 2006 Report on the Global AIDS Epidemic (2005 rate), UNAIDS 2004 Report on the Global AIDS Epidemic (2003 rate), Report on the Global HIV/AIDS Epidemic 2002 (2001 rate), and the Common Database of the UN Statistics Division (1999 rate). New estimates are annually reported and figures for previous years are revised, however I use rates from the year in which they were published to control if the decision to give aid was based at all on the recipient nation’s HIV rate. All independent control variables are matched to the three periods of AIDS-aid.
Bearce and Tyrone (2007) emphasize that foreign aid with a lag is considered a more effective measurement of aid efficacy, so AIDS-aid is measured by the amount a nation receives over a 2-year period, lagged behind periods of HIV change one-period to account for the time it takes for aid to be spent on programs and services. Periods of aid correspond with periods of HIV change as (1) 1998-1999, (2) 2000-2001, and (3) 2002-2003. PEPFAR funding is included only in the selection equation because it begins in 2004 and HIV rates for 2007 are yet unpublished, so there are no appropriately lagged periods against which to match the impact of PEPFAR funds in the performance equation. Because PEPFAR funds were excluded I have distinguished the 15 focus countries with a dummy variable.
Three variables are used to construct the Policy Index, and are adopted largely from academic theory and policy recommendations on the efficacy of aid. Corruption is considered an immense obstacle to effective use of foreign aid, and Kurtz and Shrank (2005) emphasize that institutional quality may be even more important to effective healthcare provision than expenditure variables. Smith (2006) suggests that skimming funds off foreign aid for personal use is a rational decision for leaders of countries with a small coalition of people who determine their political survival, because foreign aid is ‘unearned revenue’ that will not cause the same economic damage that results from suppression of public goods. In Ethiopia, Liberia and Malawi, AIDS-aid has more than doubled the national health budget in recent years, creating immense pressure to spend large amounts at the risk of not receiving more, and therefore “recipient nations will find ways to absorb the funds, whether legally or illegally.” In Chad, both the World Bank and the Global Fund suspended their AIDS specific funding in 2006 over concerns that the government was managing funds with a deliberate lack of transparency. In Kenya, 1.4 million people were in need of ARVs in 2004, and it is estimated that the amount of foreign funds donated to the National AIDS and STD Control Programme were enough to provide anti-retrovirals to 200,000. By Nov. 2004, only 24,000 Kenyans were reported to be on ARVs. Lastly in Malawi, a recent report revealed healthcare workers demanding sexual, monetary and material favors in return for medical treatment, giving no care or sub-standard care to those who refused. Control of Corruption is one of six dimensions of governance measured by the World Bank and I have adapted the -2.5 to 2.5 scale to a 0.5 to 5.5 scale. Higher scores indicate greater control of corruption so I expect higher scores to correlate with greater decreases in HIV.
The second Policy Index variable measures the state of national health systems. Health systems – including service delivery, health workforce, physical infrastructure, insurance systems, and regulation and licensing of pharmaceuticals – are necessary for AIDS-aid to have any effect. Health expenditure can measure the government’s intended commitment to improving public health systems, and indicates if there is sufficient capacity to support a national public health approach to the pandemic. I measure infrastructure capacity as Public Health Expenditure as a Percent of GDP (Health Exp. % GDP), using World Health Organization data from years 2000, 2002, and 2004. I expect that higher percentages of public health expenditure will correlate with larger decreases in HIV.
If corruption is controlled and health is a priority, the programs that address AIDS still must be strategically devised to address those who are both most at risk and most able to decrease transmission: women. In a wider study on public spending of health, Pritchett and Filmer (1999) find that child mortality has far less to do with money spent, and more to do with factors like level of female education. Women in sub-Saharan Africa make up 61% of HIV infections and Dr. Farmer et al (1996) suggest that this discrepancy goes beyond the fact that women are anatomically twice as susceptible to infection than men through heterosexual intercourse. They suggest that women are endangered due to the very political powerlessness embedded within institutions worldwide, and are necessarily at greater risk for greater HIV infection when left without a voice. A measure of Women’s Political Rights comes from the Cingranelli-Richards (CIRI) Human Rights Indicators Database, and includes suffrage, the right to run for and hold political office, and to petition government officials. Scores estimate women’s political rights as not legally guaranteed (1), legally guaranteed but severely prohibited in practice (2), legally guaranteed and moderately prohibited (3), or legally and practically guaranteed (4). I expect a negative relationship in which more rights for women leads to decrease in HIV.
The Policy Index is constructed by weighting each of the three variables according to their coefficient in an initial regression of independent variables (excluding aid) on the change in HIV Rate (Table 5, Regression 3):
Policy Index = -.253(Control of Corruption) - 2.71(Health Exp. As % of GDP) - .166(Women’s Pol. Rights)
The remaining independent variables control for conditions that could decrease the HIV rate other than AIDS-aid. Women’s Economics Rights measures women’s accessibility into the labor force, including equal pay, right to gain employment without male relative consent, and job security. Because economic rights also include rights to work at night, to work in occupations classified as dangerous, and to work in the military and police force, the variable measure says less about freedom from and more about exposure to sexual harassment or violation, or opportunities for HIV to be transmitted. The scale ranges from economic rights for women (0), to nearly all economic rights are guaranteed and enforced by law (4). I also measure the condition of national HIV Surveillance Systems constructed from US Census Bureau’s HIV/AIDS Surveillance Database, the Euro HIV Database, and countries’ national HIV surveillance reports made available to WHO/UNAIDS. The quality of systems is scored according to the level of surveillance implementation: poor (1), partial (2), or full (3), and I expect high surveillance scores to correlate with decreases in HIV.
Based on Bueno de Mesquita’s conclusion that leaders of small winning coalitions will give less public goods I have included Freedom of Speech. This value fits BDM and Downs’ (2005) definition of a ‘coordination good,’, or a good that leaders are especially weary of offering because its provision empowers citizens in ways that increase potential for regime overthrow. This ranking measures freedoms of speech and press, from complete government censorship (0) to none (3). I expect that as freedoms increase, HIV rates will decrease, because the freedom is indicative of a government prepared to give public goods of even the riskiest sort to their populace.
I also include a measure of successful Directly Observed Therapy, Short Course (DOTS) Tuberculosis treatments. Because the DOTS treatment requires sponsorship and commitment from the national government, trained and dedicated medical personnel, access to functioning hospital or clinic and laboratory facilities, and availability of pharmaceutical drugs, and a standardized recording and reporting system, the scores indicate the quality of and access to health care, and gives insight into actual health outcomes. Considering that TB is the top cause of death for people with HIV/AIDS, I suggest that an increase in treatment success will equal an increase in HIV prevalence as more HIV positive people will live longer. GDP and GDP Per Capita are included to test contentions that more wealth creates longer personal time horizons and encourages safer behavior that will diminish the spread of HIV.
Multiplying the Policy Index by the amount of aid received creates the Interaction Term, and it is important to note that all values of Policy Index and of the Interaction Term are negative, where values farthest away from zero are considered the “good” policies. I therefore expect both coefficients to be significantly positive in order to support the hypothesis that aid will decrease HIV rates in countries with “good” policies.
However even in countries with incredibly strong controls on corruption, women’s political rights, and commitment to public health, the behavior of donors may still overwhelm and determine the efficacy of aid. BDM et al and Mascarenhas influence the unique set of independent variables required for the selection equation. The variables consider a donor’s desire for policy concessions with terrorist nations, control over natural resources, ease of dealing with new regimes and with autocrats, and rewarding good behavior on human rights. I include the baseline HIV rate to capture the potential that donors might give altruistically.Terror Rating is taken from the Political Terror Scale, which averages all available annual scores from Amnesty International and State Department reports on a 1-5 scale from lowest to highest human insecurity. Energy Production is the Energy Information Administration measure of World Primary Energy Production in quadrillions of British Thermal Units. Regime Type, taken from the Polity IV dataset, uses the Polity score of difference between democracy and autocracy scores, with -10 representing a strong autocratic regime and 10 representing a strong democratic regime. Regime Stability, also from the Polity IV dataset, measures the number of years since the most recent regime change. Oil production is a measure of national annual oil production, and the Physical Humans Rights score represents an additive index of measures of instances of torture, extrajudicial killing, political imprisonment, and disappearance, ranging from no government respect for freedom from these punishments (1) to full government respect of rights (9).
5. Main Results
Performance Equation: When does AIDS-aid work?
My final results do not provide a strong case that “good” policies are necessary for aid efficacy, however they ultimately support Easterly’s contention (2006) that large scale top down aid efforts will not be effective in decreasing the burden of HIV/AIDS.
Table 5 (Regressions 1 and 2) examines the effect of pure AIDS-aid on HIV rates and pure Policy conditions, without any controls. The results demonstrate significance between the Policy Index and HIV rates, and a lack of significance in the relationship between AIDS-aid and HIV rates. The trend accurately reflects reality, for example, in Uganda where despite the decline in HIV prevalence since the 1990s, recent surveillance data shows that HIV rates are presently increasing. The recent increased HIV rate coincides with reports of mismanaged Global Fund aid by Ugandan corrupt government officials in 2006, suggesting that political conditions of the recipient do affect the ability to successfully use allocated AIDS-aid.
However when controls are added, two of the Policy Index variables change signs (Regression 4) and the Index itself loses significance (Regression 5). When the Interaction Term of Aid and Policy is introduced it is significant within World Bank data (Regression 6) but not Global Fund (Regression 7), however it maintains its positive sign, as does the Policy Index, in all regressions in which it is included (Regressions 6-9). Bolded t-scores illustrate that Interaction Term significance is not far off, so the Term suggests that countries with the best policies (which will be large negative numbers) can benefit from a large decrease in HIV rates once aid is given. Most importantly, Aid alone and the Policy alone maintain even less significance, supporting the Easterly contention that neither aid nor policy alone will solve the problems of the poor.
Several factors may be at the root of insignificant coefficients. This study lacks necessary micro-level data, such as the effects of non-governmental and faith-based organizations that have dominated grassroots efforts to address HIV/AIDS, who are not included and may in fact affect the significance. I also use macro country-level data that may not be appropriate to measure the progress of a disease that disproportionately strikes poorer communities and at-risk populations within countries, for which we lack sufficient micro-level data. Missing data and variables may better explain the situation than the macro indicators I have employed. Without good macro measurements it is difficult to establish the effect of large-scale aid. However most importantly, HIV prevalence may be a poor choice for dependent variable in estimating the efficacy of AIDS-aid, as the power of aid and control variables can send the rate in different directions. By definition, a decline in HIV prevalence can mean more AIDS deaths (less people living with HIV) or more effective prevention that leads to less new HIV infection. Similarly, increases in HIV can imply poor health services exist and transmission continues unchecked, or may be due to the results of exceptional health services that prolong the lives of the HIV positive and thus have more living with the disease. I use prevalence as the dependent variable, despite the potential confusion, because the data is available and because it is this broad scale of national prevalence that is most often cited as an indicator of AIDS burden on a nation. A more precise study would have regressed aid, Policy, controls, and the Interaction Term on two dependent variables: the incidence of HIV, or number of new infections reported per year, and the AIDS-attributable mortality per year. Unfortunately this data is not collected in a standard way nor does a methodology exist that has been implemented globally. Social stigma and poor health systems keep new infections from being accurately reported. The convoluted implications of changes in HIV rate may also help explain the signs and significance of values within the Policy Index. While all start off with negative values (Regression 3), only the measure of Women’s Political Rights maintains its negative direction. Health Expenditure may become positive once aid is added because more strongly supported health budgets result in more people living longer with HIV. Women’s Political Rights expectedly remains negative, supporting Farmer et al contentions as well as instinctive assumptions that women as decision makers will address risks to females, including traditional practices of female circumcision, gender disparities, violence, spousal abuse and rape, and mother to child transmission. Logically, women as child-bearers and caretakers have inherently greater power to decrease HIV infection. For example HIV positive mothers can use an ARV called Nevirapine during and after childbirth to prevent transmission of HIV to their child. If women’s political rights increase, more pressure may be placed on programs and politicians to provide these specific medications to the many poor communities still lacking access, and potentially prevent all new infant HIV infections. I expected corruption to maintain its negative sign, as it is so prevalent in the literature as a barrier to aid efficacy. Even the former President of the Global Fund, Richard Feachem, recounted in 2005 an instance of Fund embezzlement, stating, “Can we bring the risk of corruption to zero? No, of course we can't….World Bank money is stolen; USAID [United States Agency for International Development] money is stolen. Anyone in the business of development finance has their money stolen on some occasion in some places, so we cannot make this a zero-risk game.” But once again the unique challenge of HIV/AIDS-aid can convolute what would otherwise seem basic logic. The provision of free life-extending antiretroviral drugs (ARVs) is at the core of many World Bank, Global Fund and PEPFAR initiatives. Since demand consistently outweighs supply of ARVs, it is those who bribe and not those who are legally eligible for free drugs, who receive treatment. A black market for ARVs has emerged, presently estimated by the World Bank to be worth $32 billion, because poor oversight of ARV distribution allows patients to register on multiple programs for free ARVs and sell their excess, and allows corrupt official to siphon drugs out of the supply chain. Akunyili (2006) explains that the market thrives because the anonymity (no program enrollment) circumvents the social stigma and long clinic lines that deter people from accessing available resources. This massive corruption sends HIV rates in both directions. Those who buy illegally miss out on the clinical counseling that explains necessary lifestyle changes to prevent future transmission, so prevalence increases. However the rate may simultaneously decrease because counseling also explains how to properly take drugs, so those who take them improperly may be dying. Also those who are incentivized to sell their drugs to buy other more necessary commodities for their families may die without the medication, also decreasing the HIV rate. The most significant control variables are Women’s Economic Rights and whether or not the Country is a PEPFAR Focus Country. Because Economic Rights is linked to financial wellbeing, and Political Rights is not, it may be the case that more women are able to afford ARVs and therefore live longer and increase the number of people living with HIV. It could also mean more new HIV cases if greater participation in and exposure to the labor force has increased faster than gender bias. Women may simply be more at risk of sexual abuse and HIV transmission. As expected, HIV Surveillance ratings imply that countries with more effective systems for surveying HIV will also help to decrease levels, and that countries with higher HIV rates will be more likely to see bigger increases in HIV than countries with low HIV rates. GDP and GDP per capita are unsurprisingly insignificant. Higher GDPs do lead to longer life spans and result in people being more risk averse than someone with a short lifespan and therefore will be more likely practice safe sexual behavior. However another recent study from Zimbabwe suggests that recent inflation has decreased GDP and GDP Per Capita, making the hiring of prostitutes and maintaining of multiple partners too expensive for many men. This results in less risky sexual behavior and less HIV transmission. Aggregate results are mainly driven by the World Bank aid data, as the Global Fund results were not at all statistically significant, which is most likely due to the small sample set. Alternative Explanation: Selection Analysis After determining policy conditions in the recipient nation that have the potential to affect AIDS-aid efficacy, the Treatment Effects Model determines the potential of a selection bias. Results indicate that there are ulterior motives to aid giving, and that pure aid, without supporting policies, may be ineffective not because of the lacking political capacity, but because donors systematically choose who to give money to, and for purpose other than HIV/AIDS relief. Table 6 shows results of the preliminary logit regression and measures of marginal effects that support the findings of Mascarenhas and BDM: significantly more aid is given to countries with higher terror ratings and high energy production, and aid is not significantly given based on need.
The Treatment Effects model calculates the decision to give AIDS-aid as an endogenous decision, conditional on the independent variables that determine change in HIV. Table 7 includes both equations of the Treatment Effects Model, the Outcome Equation and the Selection Equation, and the two sets of independent variables determine, respectively, the factors that actually influence the decision to give aid, and whether or not a country will receive aid.
The likelihood ratio test reveals a small probability that the chi-squared statistic is greater than the critical value for statistical significance, therefore I can reject the null hypothesis that the error terms in the selection equation and the outcome equation are uncorrelated. I therefore reject the null hypothesis that the independent variables do not determine AIDS contributions and confirm that there is indeed a selection bias in deciding the recipients of AIDS-aid. However we can see that this bias has less to do with the need, the HIV rate, and more to do with potential for policy concessions: terror and energy production. The results support BDM’s (2004) suggestion that aid is given bilaterally to facilitate policy concessions and Mascarenhas and Sandler (2006), who find no evidence of cooperative behavior among donors.
The HIV rate is insignificant as a driver of the decision to give aid, and we can eliminate the possibility of endogeneity, or that AIDS-aid is only given to those with high HIV rates. However, the results do not conclude that AIDS-aid is therefore meaningless. In fact, the significance of the binary AIDS-aid variable in the outcome equation proves that countries who have received aid may have been worse off had they not received any aid, which here correlates with a significant decrease in HIV prevalence.
My final results do not provide a strong case that “good” policies are necessary for aid efficacy, however they ultimately support Easterly’s contention (2006) that large scale top down aid efforts will not be effective in decreasing the burden of HIV/AIDS.
Table 5 (Regressions 1 and 2) examines the effect of pure AIDS-aid on HIV rates and pure Policy conditions, without any controls. The results demonstrate significance between the Policy Index and HIV rates, and a lack of significance in the relationship between AIDS-aid and HIV rates. The trend accurately reflects reality, for example, in Uganda where despite the decline in HIV prevalence since the 1990s, recent surveillance data shows that HIV rates are presently increasing. The recent increased HIV rate coincides with reports of mismanaged Global Fund aid by Ugandan corrupt government officials in 2006, suggesting that political conditions of the recipient do affect the ability to successfully use allocated AIDS-aid.
However when controls are added, two of the Policy Index variables change signs (Regression 4) and the Index itself loses significance (Regression 5). When the Interaction Term of Aid and Policy is introduced it is significant within World Bank data (Regression 6) but not Global Fund (Regression 7), however it maintains its positive sign, as does the Policy Index, in all regressions in which it is included (Regressions 6-9). Bolded t-scores illustrate that Interaction Term significance is not far off, so the Term suggests that countries with the best policies (which will be large negative numbers) can benefit from a large decrease in HIV rates once aid is given. Most importantly, Aid alone and the Policy alone maintain even less significance, supporting the Easterly contention that neither aid nor policy alone will solve the problems of the poor.
Several factors may be at the root of insignificant coefficients. This study lacks necessary micro-level data, such as the effects of non-governmental and faith-based organizations that have dominated grassroots efforts to address HIV/AIDS, who are not included and may in fact affect the significance. I also use macro country-level data that may not be appropriate to measure the progress of a disease that disproportionately strikes poorer communities and at-risk populations within countries, for which we lack sufficient micro-level data. Missing data and variables may better explain the situation than the macro indicators I have employed. Without good macro measurements it is difficult to establish the effect of large-scale aid. However most importantly, HIV prevalence may be a poor choice for dependent variable in estimating the efficacy of AIDS-aid, as the power of aid and control variables can send the rate in different directions. By definition, a decline in HIV prevalence can mean more AIDS deaths (less people living with HIV) or more effective prevention that leads to less new HIV infection. Similarly, increases in HIV can imply poor health services exist and transmission continues unchecked, or may be due to the results of exceptional health services that prolong the lives of the HIV positive and thus have more living with the disease. I use prevalence as the dependent variable, despite the potential confusion, because the data is available and because it is this broad scale of national prevalence that is most often cited as an indicator of AIDS burden on a nation. A more precise study would have regressed aid, Policy, controls, and the Interaction Term on two dependent variables: the incidence of HIV, or number of new infections reported per year, and the AIDS-attributable mortality per year. Unfortunately this data is not collected in a standard way nor does a methodology exist that has been implemented globally. Social stigma and poor health systems keep new infections from being accurately reported. The convoluted implications of changes in HIV rate may also help explain the signs and significance of values within the Policy Index. While all start off with negative values (Regression 3), only the measure of Women’s Political Rights maintains its negative direction. Health Expenditure may become positive once aid is added because more strongly supported health budgets result in more people living longer with HIV. Women’s Political Rights expectedly remains negative, supporting Farmer et al contentions as well as instinctive assumptions that women as decision makers will address risks to females, including traditional practices of female circumcision, gender disparities, violence, spousal abuse and rape, and mother to child transmission. Logically, women as child-bearers and caretakers have inherently greater power to decrease HIV infection. For example HIV positive mothers can use an ARV called Nevirapine during and after childbirth to prevent transmission of HIV to their child. If women’s political rights increase, more pressure may be placed on programs and politicians to provide these specific medications to the many poor communities still lacking access, and potentially prevent all new infant HIV infections. I expected corruption to maintain its negative sign, as it is so prevalent in the literature as a barrier to aid efficacy. Even the former President of the Global Fund, Richard Feachem, recounted in 2005 an instance of Fund embezzlement, stating, “Can we bring the risk of corruption to zero? No, of course we can't….World Bank money is stolen; USAID [United States Agency for International Development] money is stolen. Anyone in the business of development finance has their money stolen on some occasion in some places, so we cannot make this a zero-risk game.” But once again the unique challenge of HIV/AIDS-aid can convolute what would otherwise seem basic logic. The provision of free life-extending antiretroviral drugs (ARVs) is at the core of many World Bank, Global Fund and PEPFAR initiatives. Since demand consistently outweighs supply of ARVs, it is those who bribe and not those who are legally eligible for free drugs, who receive treatment. A black market for ARVs has emerged, presently estimated by the World Bank to be worth $32 billion, because poor oversight of ARV distribution allows patients to register on multiple programs for free ARVs and sell their excess, and allows corrupt official to siphon drugs out of the supply chain. Akunyili (2006) explains that the market thrives because the anonymity (no program enrollment) circumvents the social stigma and long clinic lines that deter people from accessing available resources. This massive corruption sends HIV rates in both directions. Those who buy illegally miss out on the clinical counseling that explains necessary lifestyle changes to prevent future transmission, so prevalence increases. However the rate may simultaneously decrease because counseling also explains how to properly take drugs, so those who take them improperly may be dying. Also those who are incentivized to sell their drugs to buy other more necessary commodities for their families may die without the medication, also decreasing the HIV rate. The most significant control variables are Women’s Economic Rights and whether or not the Country is a PEPFAR Focus Country. Because Economic Rights is linked to financial wellbeing, and Political Rights is not, it may be the case that more women are able to afford ARVs and therefore live longer and increase the number of people living with HIV. It could also mean more new HIV cases if greater participation in and exposure to the labor force has increased faster than gender bias. Women may simply be more at risk of sexual abuse and HIV transmission. As expected, HIV Surveillance ratings imply that countries with more effective systems for surveying HIV will also help to decrease levels, and that countries with higher HIV rates will be more likely to see bigger increases in HIV than countries with low HIV rates. GDP and GDP per capita are unsurprisingly insignificant. Higher GDPs do lead to longer life spans and result in people being more risk averse than someone with a short lifespan and therefore will be more likely practice safe sexual behavior. However another recent study from Zimbabwe suggests that recent inflation has decreased GDP and GDP Per Capita, making the hiring of prostitutes and maintaining of multiple partners too expensive for many men. This results in less risky sexual behavior and less HIV transmission. Aggregate results are mainly driven by the World Bank aid data, as the Global Fund results were not at all statistically significant, which is most likely due to the small sample set. Alternative Explanation: Selection Analysis After determining policy conditions in the recipient nation that have the potential to affect AIDS-aid efficacy, the Treatment Effects Model determines the potential of a selection bias. Results indicate that there are ulterior motives to aid giving, and that pure aid, without supporting policies, may be ineffective not because of the lacking political capacity, but because donors systematically choose who to give money to, and for purpose other than HIV/AIDS relief. Table 6 shows results of the preliminary logit regression and measures of marginal effects that support the findings of Mascarenhas and BDM: significantly more aid is given to countries with higher terror ratings and high energy production, and aid is not significantly given based on need. 
The Treatment Effects model calculates the decision to give AIDS-aid as an endogenous decision, conditional on the independent variables that determine change in HIV. Table 7 includes both equations of the Treatment Effects Model, the Outcome Equation and the Selection Equation, and the two sets of independent variables determine, respectively, the factors that actually influence the decision to give aid, and whether or not a country will receive aid.
The likelihood ratio test reveals a small probability that the chi-squared statistic is greater than the critical value for statistical significance, therefore I can reject the null hypothesis that the error terms in the selection equation and the outcome equation are uncorrelated. I therefore reject the null hypothesis that the independent variables do not determine AIDS contributions and confirm that there is indeed a selection bias in deciding the recipients of AIDS-aid. However we can see that this bias has less to do with the need, the HIV rate, and more to do with potential for policy concessions: terror and energy production. The results support BDM’s (2004) suggestion that aid is given bilaterally to facilitate policy concessions and Mascarenhas and Sandler (2006), who find no evidence of cooperative behavior among donors.The HIV rate is insignificant as a driver of the decision to give aid, and we can eliminate the possibility of endogeneity, or that AIDS-aid is only given to those with high HIV rates. However, the results do not conclude that AIDS-aid is therefore meaningless. In fact, the significance of the binary AIDS-aid variable in the outcome equation proves that countries who have received aid may have been worse off had they not received any aid, which here correlates with a significant decrease in HIV prevalence.
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