By Zachary Wagner, Jeremy Barofsky, and Neeraj Sood

Transcription

1 Global doi: /hlthaff HEALTH AFFAIRS 34, NO. 6 (2015): Project HOPE The People-to-People Health Foundation, Inc. By Zachary Wagner, Jeremy Barofsky, and Neeraj Sood PEPFAR Funding Associated With An Increase In Employment Among Males in Ten Sub-Saharan African Countries Zachary Wagner (zwagner@ berkeley.edu) is a doctoral student in health economics at the University of California, Berkeley. Jeremy Barofsky is the Okun- Model Fellow at the Brookings Institution, in Washington, D.C. Neeraj Sood is an associate professor of health economics and director of research at the Leonard D. Schaeffer Center for Health Policy and Economics at the University of Southern California, in Los Angeles. ABSTRACT The President s Emergency Plan for AIDS Relief (PEPFAR) has provided billions of US tax dollars to expand HIV treatment, care, and prevention programs in sub-saharan Africa. This investment has generated significant health gains, but much less is known about PEPFAR s population-level economic effects. We used a difference-indifferences approach to compare employment trends between ten countries that received a large amount of PEPFAR funding (focus countries) and eleven countries that received little or no funding (control countries). We found that PEPFAR was associated with a 13 percent differential increase in employment among males in focus countries, compared to control countries. However, we observed no change in employment among females. In addition, we found that increasing PEPFAR per capita funding by $100 was associated with a 9.1-percentagepoint increase in employment among males. This rise in employment generates economic benefits equal to half of PEPFAR s cost. These findings suggest that PEPFAR s economic impact should be taken into account when making aid allocation decisions. As of 2010, HIV/AIDS was the leading cause of death and disability among working-age adults in sub- Saharan Africa. 1 Along with its devastating health consequences, the epidemic has had severe economic effects. By some estimates, it has reduced average economic growth rates in sub-saharan Africa by 2 4 percent. 2 To combat the HIV/AIDs burden, the international community nearly tripled its total assistance for health in the 2000s. 3 The largest component of the increased funding came from the US President s Emergency Plan for AIDS Relief (PEPFAR). 3 PEPFAR was established in 2003 to provide funding for AIDS treatment, care, and prevention in countries devastated by the epidemic. From 2003 to 2013 Congress authorized $54 billion to fund programs in developing nations, mostly in sub-saharan Africa. 4 The bulk of PEPFAR assistance was aimed at increasing access to antiretroviral therapy (ART), and by 2013 the program had provided treatment to 6.7 million people infected with HIV. 5,6 The expansion of PEPFAR since 2003 has produced significant health returns, in the form of increased life expectancy 7 and decreased mortality. 8,9 However, most evaluations of PEPFAR have failed to measure its economic effect, although such effects are potentially important. Consequently, the economic impact of the provision of ART through PEPFAR is often overlooked in cost-effectiveness analyses and decisions about aid budgets. Understanding the effect of PEPFAR on economic outcomes is important for US policy makers for several reasons. First, after PEPFAR was reauthorized in 2009, the US government required PEPFAR to transition from providing an emergency response to strengthening the 946 Health Affairs June :6

2 capacity of partner countries and promoting sustainable country-level health programs. 10 Positive economic impacts of PEPFAR would help in the effort to make countries capable of sustainably financing their own health systems. Second, Africa represents a small but rapidly growing US export market, with US exports to sub-saharan Africa increasing 250 percent in the past decade. 11 This means that economic improvements in Africa can benefit the US economy indirectly. Third, amid financial crisis and budget austerity in the United States, spending on aid for health and HIV treatment has stagnated. 12 Given this budgetary environment, understanding the magnitude of PEPFAR s impact beyond health benefits is imperative for the efficient allocation of scarce aid resources. PEPFAR constitutes the largest global health initiative focused on a single disease ever undertaken. 13(p13) Nonetheless, there are no empirical evaluations of its economic impact at the population level. Previous evaluations of PEPFAR have synthesized results from previous microlevel studies; 14 investigated PEPFAR s effect on the provision of maternal health services; 15 described operational capacity, management, and financing through qualitative research; 13 or described the program s financial flows. 4 Other economic evaluations of the impact of ART have investigated effects on HIV patients through the use of modeled effects only 16 or were specific to a single nation. 17 This study is the first to measure the impact of PEPFAR on employment outcomes throughout sub-saharan Africa. It is also the first to explore the effect of any ART program on economic outcomes at the population level. We used a difference-in-differences approach to measure changes in employment between a group of ten countries in sub-saharan Africa that received a very large amount of PEPFAR funding (our focus countries) and a group of eleven countries in sub-saharan Africa that received little or no PEPFAR funding (our control countries). This empirical strategy is consistent with previous work that found large reductions in mortality as a result of PEPFAR program implementation. 8,9 The study results show the extent to which ART in particular and health aid in general can foster economic improvements within nations that receive PEPFAR support. The Impact Of HIV Treatment On Population Outcomes PEPFAR s effect on population-level economic outcomes is ambiguous. On the one hand, there are several reasons why PEPFAR could improve economic outcomes. First, ART has been shown to create economic benefits for HIV-positive people and their families by increasing their productivity in the workforce. 18,19 Second, improved health and productivity for HIV-positive people could also have spillover effects for HIV-negative people through increased consumption, reduced time caring for HIVpositive people, and delayed household end-oflife expenses. There is also evidence that benefits to HIV-positive adults may be passed on to children in the form of better nutrition and increased schooling, which could improve economic outcomes of future generations Third, ART reduces the infectiousness of HIVpositive people, which is likely to decrease new HIV incidence. 25,26 Fourth, the extension in life expectancy produced by the introduction of ART raises incentives for people to invest in their own human capital and increase savings, since they are more likely to live long enough to reap the benefits. 27 On the other hand, there are some reasons why ART expansion may negatively affect economic outcomes at the population level. First, the increase in life expectancy produced by ART (approximately eleven years) 7 has great value. However, if these life expectancy gains increase the share of the population living in an unproductive state (that is, too sick to work), ART may negatively affect per capita economic growth and increase the national dependency ratio (that is, the ratio of workers to nonworkers in a society). Second, if there is chronic unemployment and jobs are limited, the increase in the labor supply produced by longer life expectancy and reduced morbidity could raise unemployment and drive down wages. 28,29 Study Data And Methods Data And Sample Initially, fifteen countries were selected as PEPFAR focus countries those that have received the vast majority of PEPFAR funding. 4 US government criteria for focus country selection were loosely based on HIV prevalence, government capacity, country income, and willingness to partner with the United States. However, these criteria explain country selection only partially. For example, some countries with relatively low HIV prevalence were selected (such as Ethiopia, with a 2 percent HIV prevalence rate in 2004), whereas others with very high prevalence, democratic governance capacity, and low income were not (such as Malawi, with a 15 percent HIV prevalence rate in 2004). This highlights the fact that although country selection by the US government for PEPFAR June :6 Health Affairs 947

3 Global funding was not random, the selection process was not necessarily associated with differential trends in health and employment outcomes an important assumption of our analysis. For this analysis, all countries in sub-saharan Africa with a US Agency for International Development (USAID) Demographic and Health Survey wave before 2004 (the pre period) and a survey wave in 2004 or later (the post period) were included. There were twenty-one such countries ten PEPFAR focus countries (Côte d Ivoire, Ethiopia, Kenya, Mozambique, Namibia, Nigeria, Rwanda, Tanzania, Uganda, and Zambia) and eleven control countries (Benin, Burkina Faso, Cameroon, Gabon, Ghana, Guinea, Madagascar, Malawi, Mali, Niger, and Zimbabwe). Collectively, there were over 750,000 respondents to the survey waves, all of whom were men ages or women ages (for a list of survey years by country, see online Appendix Exhibit A1). 30 Thus, the analysis excluded five PEPFAR focus countries: Botswana, Guyana, and South Africa, all in sub- Saharan Africa, and Haiti and Vietnam. Demographic and Health Surveys are nationally representative household surveys conducted roughly every five years across more than seventy low- and middle-income countries. 31 For our main analysis, survey waves from the years are included. In addition, we used the World Bank s World Development Indicators to understand baseline differences in national characteristics that the survey does not measure but that may affect employment trends in both focus and control countries. 27 To estimate the effect of receiving more compared to less PEPFAR funding, we also used data compiled by the Center for Global Development. 4 Outcome Measures The main outcome variable used in this analysis was generated from a Demographic and Health Survey question that asked respondents if they had done any work (aside from household work) in the previous twelve months. This does not include nonmarket labor such as domestic duties and subsistence agriculture activities. We created a binary variable that was set at 1 if respondents answered yes to this question and 0 if they answered no. Statistical Methods CHANGE IN EMPLOYMENT RATES: We used a difference-in-differences model to compare the differential change in employment rates between PEPFAR focus and control countries before and after the implementation of the PEPFAR program. We controlled for time and countrylevel fixed effects. We also weighted our regressions by each nation s contribution to the population of sub- Saharan Africa represented in our sample of twenty-one focus and control nations. This estimation strategy is identical to that used in the analysis by Eran Bendavid and coauthors of PEPFAR s effect on all-cause adult mortality. 9 We estimated a linear probability model to identify the differential impact of PEPFAR on employment between focus and control countries. We ran each analysis separately for males and females, since the two groups have different employment and HIV transmission characteristics and thus may be affected differently by PEPFAR programs. Standard errors were clustered at the country level in all analyses. The fundamental assumption underlying this method is that control countries would exhibit the same employment trends that focus nations would have experienced, absent PEPFAR program implementation. Although this assumption could not be formally tested, an analysis of employment trends prior to 2004 helps shed light on how focus- and control-country trends compare. We investigated pre-pepfar employment rates in the two groups of countries, using all countries in the sample that had at least two survey waves prior to 2004 (the seven focus countries and six control countries indicated with # in Appendix Exhibit A1). 30 BASELINE DISEASE BURDEN AND CUMULA- TIVE FUNDING: To further isolate the effect of PEPFAR on employment, we investigated how PEPFAR focus countries with higher baseline HIV burden or higher levels of PEPFAR funding were affected, compared to countries that had lower baseline burden or received less funding, respectively. In a dose-response relationship, we expect that if the employment changes we observed were driven by PEPFAR, countries with a higher HIV disease burden at baseline would benefit more from PEPFAR s introduction. Similarly, countries that received more per capita funding should have larger employment gains (for analytical details, see the Appendix). 30 A finding that countries with higher prevalence and higher funding experienced greater employment increases would add confidence that PEPFAR, and not some unobservable confounder, was likely to be causally linked to employment changes. Limitations This work is limited in a number of ways. First, it is possible that PEPFAR selection criteria were correlated with employment outcomes in some unobserved time-variant way. Although employment trends in the two groups of countries prior to PEPFAR were similar, we further isolated the PEFPAR effect by assessing whether countries with higher baseline HIV prevalence benefited more from PEPFAR funding, compared to countries with lower baseline 948 Health Affairs June :6

4 HIV prevalence. Second, the measure we used for employment does not capture key employment characteristics such as hours worked and wages received. This information would add richness to the analysis, but it is not available in the Demographic and Health Survey data. Third, HIV testing data are available for only a subset of survey respondents in a subset of countries and years. Therefore, we could not estimate changes in prevalence for the full set of countries, nor could we use these data to estimate heterogeneity by HIV status. Study Results Descriptive Statistics Households in PEPFAR focus countries that were sampled in the Demographic and Health Survey had a lower employment rate than households in control countries in the previous twelve months for both males and females and for the youngest age cohort, people ages (Exhibit 1). PEPFAR focus countries also tended to have larger populations, to have a lower gross domestic product per capita, and to have a slightly lower life expectancy in 2003, compared to control countries. PEPFAR focus countries also had a slightly higher baseline HIV prevalence rate. Mortality Our findings confirmed the results of Eran Bendavid and Jayanta Bhattacharya 8 and showed that after PEPFAR was implemented, there was a decrease in HIV-related mortality among the focus countries used in our analysis, relative to that mortality in the control countries (see Appendix Exhibit A13). 30 Employment We observed that pre-pepfar employment trends were similar for males and for females across the two groups of countries, when we considered only those that had at least two pre-2004 survey waves (Exhibit 2). This supports the validity of the parallel trends assumption inherent in our difference-in-differences model. In addition, we found that employment trends among males rose more sharply in focus countries than in control countries after PEPFAR program initiation. In contrast, we observed a slight decline in female employment in focus countries compared to that in control countries. Exhibit 3 presents our main regression results. The effect of PEPFAR on the probability of employment in the previous twelve months represents the change in the employment rate after PEPFAR initiation for focus countries relative to control countries (that is, the difference-indifferences coefficient). Focus nations experienced a differential increase in employment among males of all ages (15 54) equal to 9.9 percentage points (13 percent; 95% confidence interval: 3.7, 22.1). Younger males exhibited the biggest employment gains, with the employment rate for those ages in focus countries increasing by 16.5 percentage points compared to the same age group in control countries. For females, we found no significant change in employment following PEPFAR initiation, when we looked at all ages combined. However, we observed negative point estimates and significant changes for the youngest three age cohorts. This suggests that female employment reductions coincided with PEPFAR initiation for these age groups. The coefficient for all female age groups combined constitutes a 5.6-percentagepoint decline in employment, which was relatively stable by age group in the focus countries. With results for both males and females combined, we found the effect to be an insignificant 2.7-percentage-point increase in employment (Appendix Exhibit A12). 30 The results were nearly identical when we estimated the marginal effects from a probit model (Appendix Exhibits A17 and A18). 30 We also looked at results separately for rural and urban residents and by occupation type (manual labor, agriculture, domestic services, and sales or professional services; Appendix Exhibits A14 A16). 30 The effect of the PEPFAR program for males was about 40 percent larger in rural areas than in urban areas; for females, it was more negative in rural areas than in urban areas. For males, most of the benefit in employment came in the agriculture and sales or professional services sectors. For females, there was Exhibit 1 Summary Statistics For Selected Countries In Sub-Sarahan Africa, By Funding Received From The President s Emergency Plan For AIDS Relief (PEPFAR) Control countries PEPFAR countries Employment rate ( ) Ages % 65.7% Ages Males Females World Bank development indicator (2003) Average HIV prevalence 5.2% 7.8% Average GDP (millions) $6,090 $16,400 Average population (thousands) 12,200 36,100 Average GDP per capita $ $ Average life expectancy (years) SOURCE Authors analysis of data from the US Agency for International Development s Demographic and Health Survey and of the World Bank s World Development Indicators. NOTES Employment rates are for The ten focus countries received a total of $54 billion of PEPFAR funding from 2004 to The eleven control countries received little or no PEPFAR funding in the same period. Thecountriesinthetwogroupsarelistedinthetext.EmploymentratesarefromtheDemographic and Health Survey and are from the same sample used in our main analysis. GDP is gross domestic product. June :6 Health Affairs 949

5 Global Exhibit 2 Employment Trends For Males And Females Over Time In Selected Countries In Sub-Saharan Africa SOURCE Authors analysis of data from the US Agency for International Development (USAID) Demographic and Health Survey. NOTES The two pre-2004 waves of the Demographic and Health Survey are the two most recent before 2004, when the President s Emergency Plan for AIDS Relief (PEPFAR) was implemented. The post-2004 wave combines all surveys after The exhibit presents data for all countries in the sample that had at least two survey waves prior to 2004 seven focus countries (Côte d Ivoire, Kenya, Mozambique, Nigeria, Tanzania, Uganda, and Zambia) and six control countries (Benin, Burkina Faso, Cameroon, Ghana, Madagascar, and Mali). Focus and control countries are defined in Exhibit 1. Percent who worked is the percentage of respondents who answered yes to a Demographic and Health Survey question asking if they had done any work (aside from household work) in the previous twelve months. Exhibit 3 no effect for any of the occupation types. Heterogeneity By Baseline Disease Prevalence And Cumulative Funding We estimated that the PEPFAR effect for men was stronger at higher levels of baseline HIV prevalence, but this result did not quite reach significance (p ¼ 0:106). However, when we analyzed the age The Impact Of The President s Emergency Plan For AIDS Relief (PEPFAR) On Probability Of Employment In The Past Twelve Months, Sub-Saharan Africa Ages (years) Males Impact of PEPFAR *** 0.165** *** * *** No. of observations 228,395 86,787 61,147 43,779 28,796 Females Impact of PEPFAR * * * No. of observations 553, , , ,533 41,463 SOURCE Authors analysis of data from the US Agency for International Development (USAID) Demographic and Health Survey. NOTES Each cell represents a separate regression for the different age groups. The impact of PEPFAR represents the difference-in-differences coefficient (PEPFAR funding multiplied with a binary variable that equals 1 in the post-pepfar era and 0 otherwise) in the regression model. Details are available in the Appendix (see Note 30 in text). This is the difference in changes in employment from before 2004 to after 2004 between PEPFAR and control countries. Standard errors were clustered at the country level. All models included country and year fixed effects and were weighted by country population. *p<0:10 **p<0:05 ***p<0:01 groups separately, the results were significant for three of the four groups: those ages 15 24, 35 44, and (Appendix Exhibits A5 and A7). 30 We found similar results when we ran the same analysis using 2003 HIV-related mortality instead of HIV prevalence to measure disease burden (Appendix Exhibit A8). 30 We also found that higher levels of PEPFAR cumulative per capita funding were associated with greater employment gains (Appendix Exhibits A6 and A7). 30 Specifically, for each $100 per capita increase in PEPFAR cumulative funding between 2004 and 2010, employment among males increased by 9.1 percentage points (p<0:05; Appendix Exhibit A6). 30 Overall, these results help validate the main results in Exhibit 3. Sensitivity Analyses We conducted a series of sensitivity analyses to test the robustness of our results. First, since we could test the parallel trends assumption for only thirteen of our twenty-one countries, we conducted the same analysis from Exhibit 3 on only the thirteen countries (Appendix Exhibit A4). 30 We found that the point estimates were nearly identical to those in Exhibit 3. Next, we tested the sensitivity of our results to the exclusion of each country individually. If the exclusion of one country dramatically changed our estimate, this country might be an outlier. To do this, we excluded each country individually and reran the main analysis from Exhibit 3. We found that exclusion of any one country did not dramatically change our results (Appendix Exhibit A10). 30 Finally, we assessed the change in male employment from before to after PEPFAR initiation for each country separately (Appendix Exhibit A11). 30 We found that eight of the ten focus countries experienced an increase in male employment, with five countries showing increases of over 9 percentage points. Only five of the eleven control countries experienced an increase in male employment, and only one reached an increase of 9 percentage points. This indicates that the increase in male employment following PEPFAR implementation was consistent across countries. Discussion This study is the first to explore the impact of PEPFAR on employment outcomes in sub- Saharan Africa. We found that employment among males increased by 9.9 percentage points (13 percent), with much of this effect occurring among those ages However, female employment did not increase. If anything, we found imprecisely estimated evidence that PEPFAR was associated with a decline in female employment. 950 Health Affairs June :6

6 We also found that PEPFAR was associated with a larger increase in male employment in countries with a higher pre-pepfar HIV burden and that greater per capita PEPFAR funding coincided with larger increases in male employment. Both of these findings provide confidence that our estimated effect was due to PEPFAR and not to some unobservable confounder. Three results from our analysis merit further discussion. Increased Male Employment, Decreased Female Employment First, an increase in male employment coinciding with a decrease in female employment could be indicative of intrahousehold substitution of labor in households affected by HIV. In other words, females might enter the labor force when male household members contract HIV and become too sick to work. Then, as HIV-positive males initiate treatment as a result of PEPFAR, females a group with generally lower returns to market labor can cease employment. It is also important to note that nonmarket employment (that is, household duties and subsistence agriculture activities) is omitted from the Demographic and Health Survey question we used. This fact could also explain the lack of effect of PEPFAR for females. PEPFAR may have coincided with more domestic productivity for females, which was not captured in this analysis. Largest Employment Gains For Younger Males Second, our finding that younger males exhibited the largest employment gains in PEPFAR focus countries has several possible explanations. One is that younger males exhibited substantially lower baseline employment rates: around 50 percent, compared to almost 90 percent among all other age groups. This implies that older males were constrained in increasing employment, because their employment level was already close to universal male employment. Another possible explanation is that the economic effects of PEPFAR came not only from gains in health, but also from a large economic stimulus created by injecting billions of dollars into these low-income nations. PEPFAR outlays from 2004 to 2011 constituted roughly 6 percent of total GDP across the ten focus countries (for a country breakdown, see Appendix Exhibit A3.1). 30 Although roughly one-third of PEPFAR spending goes toward drug procurement, much of the rest is used to pay for health system support and labor to provide HIV care. 6 For example, PEPFAR explicitly intended to train and employ 140,000 new health care workers in both 2013 and Therefore, PEPFAR also constitutes an investment that provides jobs for low- and mediumskill workers and generates economic stimulus benefits through expansion of the health care system. This stimulus may disproportionately increase employment for younger males, who have more room for employment growth because they are less likely to be employed already. Still another possible explanation is that ART reduces morbidity most for those with advanced disease. This is likely to increase employment the most among older populations, since people in those groups are more likely to be in an advanced disease state. The expansion of PEPFAR also prevents mortality, which could result in keeping older HIV-positive people alive longer but in an unproductive state, since health improvements after ART are neither complete nor immediate. Previous research indicates that it takes four years after ART initiation for an HIV-positive person to reach 90 percent of his or her pre- HIV employment level. 18 Slow employment response and a longer duration in an unproductive state among the older treated population may counteract PEPFAR s morbidity benefits and attenuate the employment benefits observed among older cohorts. This is less likely in younger cohorts, who are less likely to be in an advanced disease state. Large Increase In Male Employment Third, our finding of a 13 percent increase in male employment is rather large, considering that the HIV prevalence rate was only around 7.8 percent in PEPFAR focus nations at baseline. However, when we take into account the fact that PEPFAR funding was a stimulus that constituted 6 percent of the combined GDP of PEPFAR countries, this result is plausible (for a full description of the stimulus effect and calculation, see Appendix Part 3). 30 Using data on the share of the population that benefited from PEPFAR and the expected increase in employment after ART initiation, 19 we were able to estimate the expected health effects of PEPFAR (a 0.6 percent increase). Then, we estimated the stimulus effect by assuming a multiplier effect of and a 1:1.75 relationship between employment growth and GDP. 34 This gave us a 5.5 percent increase in employment as a result of the stimulus. Combining these two estimates produced an expected increase of 6.1 percent, which is well within our confidence interval (95% CI: 3.7, 22.1). However, this suggests that our main estimate may be an upperbound estimate. Contributions To The Literature This article makes two main contributions to the literature. First, to our knowledge, only one other study an unpublished one using data from South Africa 17 has explored the economic effect of ART s expansion at the population level. Con- 9.1 Percentage points For each $100 per capita increase in PEPFAR cumulative funding between 2004 and 2010, employment among males increased by 9.1 percentage points. June :6 Health Affairs 951

7 Global sistent with our results, the author found a population-level increase in male employment after a clinic opened between three and fifteen miles away from a respondent, but no change in employment for females. This study estimated an increase in male employment of 3.3 percentage points a substantially smaller effect than our estimate. This discrepancy in effect size may exist because our analysis covered a larger geographic area and a longer time horizon than the previous study; this longer horizon allowed more time for employment to rebound among ART initiators and for PEPFAR s stimulus to improve the economy overall. 18 However, it is also possible that the effect of PEPFAR was simply smaller in South Africa than in our focus countries, and the exclusion of South Africa in our analysis may have resulted in an overestimate of the general effect. Second, contrary to articles suggesting that increases in life expectancy result in no effect or in decreased per capita productivity, 28,29 our study shows that even though PEPFAR produced significant life expectancy gains, extending life was also associated with substantial economic benefits. Policy Implications This study has three important implications for global health policy. First, in addition to PEPFAR s sizable health benefits, our results show that PEPFAR s population-level economic impact should be taken into account when resource allocation decisions about foreign aid are being made. Combining our estimated increase in male employment (9.9 percentage points) with the male share of the population in each country, and assuming the average wage is equivalent to per capita GDP, we estimate that PEPFAR s economic benefits are equal to 52 percent (95% CI: 15, 89) of its costs. This analysis excludes benefits in the form of orphan care costs averted and delayed end-of-life care, but adding these would only increase PEPFAR s measured benefits. Focusing exclusively on program health benefits without quantifying economic impact means underestimating PEPFAR s effects. Given the ongoing debate on how scarce public health funding should be spent after 2015, a better understanding of the magnitude of these ancillary benefits will help guide policy makers toward making the most efficient use of resources. Second, after reauthorizing PEPFAR in 2009, the US government required PEPFAR to transition from an emergency response program to one that strengthens partner countries health system capacity. 10 Our results suggest that the expansion in employment that coincided with PEPFAR implementation could help countries sustainably finance their own health systems and reduce the need for future aid. However, given the magnitude of the HIV burden and the measured benefits of treatment, any reduction in aid now would serve to roll back the human capital investments that PEPFAR currently allows. Third, even though PEPFAR aid to Africa was conceived of as a humanitarian effort, this work shows that not only does aid provide muchneeded relief to recipient countries, but it also can help stimulate employment growth. Conclusion PEPFAR constitutes one of the largest and most important humanitarian projects ever undertaken by the US government. Although it was designed to provide emergency relief from the AIDS epidemic, we found that PEPFAR also represents an investment in human capacity with substantial economic dividends. This analysis characterizes the magnitude of these dividends in one domain and over a relatively short time span. If the international community maintains its commitment to expand access to antiretroviral therapy, future research will be able to more fully identify the complementary relationship between health improvement and poverty reduction. The results of this study were presented at the 5th Biennial Conference for the American Society of Health Economics at the University of Southern California, in Los Angeles, June Funding for the study was provided by the National Institute on Aging (Grant No. T32-AG000246). The authors thank the audience and the discussantatthe5thbiennial Conference as well as two anonymous reviewers. 952 Health Affairs June :6

8 NOTES 1 Institute for Health Metrics and Evaluation. GBD 2010 heat map [Internet]. Seattle (WA): University of Washington, IHME; c 2013 [cited 2015 Apr 7]. Available from: vizhub.healthdata.org/irank/heat.php 2 Dixon S, McDonald S, Roberts J. The impact of HIV and AIDS on Africa s economic development. BMJ. 2002; 324(7331): Institute for Health Metrics and Evaluation. Financing global health 2012: the end of the golden age? [Internet]. Seattle (WA): University of Washington, IHME; c 2012 [cited 2015 Apr 7]. Available from: files/files/policy_report/2012/ FGH/IHME_FGH2012_FullReport_ MedResolution.pdf 4 Fan V, Silverman R, Duran D, Glassman A. The financial flows of PEPFAR: a profile [Internet]. Washington (DC): Center for Global Development; 2013 Jul [cited 2015 Apr 7]. (CGD Policy Paper No. 027). Available from: 5 ReliefWeb. Shared responsibilitystrengthening results for an AIDSfree generation: latest PEPFAR funding as of January 2014 [Internet]. 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