Economic Dependency and HIV/AIDS Prevalence in the Developing World: a Comparative, Longitudinal Analysis*

Economic Dependency and HIV/AIDS Prevalence in the Developing World: A Comparative, Longitudinal Analysis*

Gary Maynard, Ohio State University Corinne Ong, Lee Kwan Yew Center for Innovative Cities

The HIV/AIDS pandemic has plagued global society for over three decades. While breakthroughs in antiretroviral treatments (ART) have proven effective in suppressing the virus and HIV/AIDS intervention outreach have widened, epidemic control remains unevenly achieved among countries. At least 95 percent of HIV/AIDS sufferers originate from developing countries. Dependency theory suggests that developing countries’ reli- ance on debt, trade, and foreign investments pose negative effects on their populations’ health. Guided by dependency theory’s propositions, this cross-national study assesses whether increasing dependence on trade, debt, and foreign direct investment potentially increases adult HIV prevalence in developing countries from 1989 to 2012. Using a sample of over 80 nations, we perform a two-way fixed-effects OLS regression to evalu- ate the impact of increasing debt, trade, and foreign investment on adult HIV prevalence. Total debt, short-term debt, external debt, and GDP were found to increase HIV prevalence. The findings for debt support dependency theory’s predictions concerning the ramifications of global economic inequality on HIV/AIDS prevalence.

Introduction In June 2001, the WHO ofﬁcially declared HIV/AIDS to be a global epidemic (World Health Organization [WHO] 2015). By the close of 2014, an estimated 36.9 million people have been diagnosed as living with HIV; between 2000 and 2014, 25.3 million deaths have resulted from HIV/AIDS (UNAIDS 2014). Recently, there has been a general decline in HIV/AIDS prevalence rates globally, mostly attributable to advances in antiretroviral therapy (ART), as well as intensifying local and international efforts at prevention. For instance, new HIV infections have decreased by about 35 percent between 2000 and 2014, while AIDS-related deaths have fallen by 42 percent between 2004 and 2014 (UNAIDS 2014). Despite the above statistics, millions continue to be afﬂicted by this debili- tating disease. Further, while rates of new HIV infections have declined on a global scale, countries in certain world regions are experiencing higher number of HIV infections compared to before. Since 2001, HIV/AIDS infection rates

Sociological Inquiry, Vol. 86, No. 2, May 2016, 189–215 © 2016 Alpha Kappa Delta: The International Sociology Honor Society DOI: 10.1111/soin.12105 190 GARY MAYNARD AND CORINNE ONG grew by more than 50 percent and 20 percent in Middle-East/North African and East Asian countries, respectively. Because HIV/AIDS is a contagious disease and is affected by social forces, managing its prevalence holds wide-stand- ing implications for present and future global health. The Joint United Countries Program of HIV/AIDS (UNAIDS, 2013) has emphasized the need for “global solidarity” and “shared responsibility” in battling the epidemic. Delays in halting the epidemic have the effect of defeating viral suppression, especially where HIV sufferers are left untreated and undiagnosed in the wider community. Also, commensurate with managing a protracted epidemic like HIV/AIDS is its financial burden. In 2012, $18.9 billion from domestic and international sources have been expended to fund HIV/AIDS initiatives for low- and middle-income countries (UNAIDS 2013). Increased HIV/AIDS prevalence in highly effected regions and corresponding attempts to remediate its effects can only be expected to be increasingly cost-consuming. Specific to the focus of this study, HIV/AIDS has deep socioeconomic impacts for developing countries. Presently, nine out of ten persons with HIV are between 15 and 39 years of age—the age strata at which individuals are expected to possess the greatest (economically) productive potential (Interna- tional Labor Organization [ILO] 2014). At the national level, the loss of skilled workers, such as farmers, bankers, teachers, technology specialists, and healthcare workers, to HIV/AIDS translates into major deficits for the capital stock, economic growth, and agricultural productivity of developing countries (Asenso-Okyere et al. 2010; Bell, Devarajan, and Gersbak 2003; Cheru 2002; Haacker 2004; ILO 2014; Poku 2002; Shircliff and Shandra 2011). On the level of households, adult deaths due to HIV/AIDS create single-parent households, or households left with orphaned children (UNAIDS 2013). The loss of a productive household member to the disease also implies the attrition of an income earner. Moreover, in the course of securing medical treatment for a family member afflicted with the disease, households are often compelled to deplete limited savings, borrow money, accumulate major debts, and/or leave the workforce to tend to relatives stricken with HIV/AIDS (Asenso-Okyere et al. 2010; Poku 2002; Whyte et al. 2004). To deal with the impoverishment resulting from income losses and household debts described above, affected families often resort to a range of survival techniques: children are frequently withdrawn from schools to seek work that accrues income; adult women and men may migrate to urban areas to seek waged employment. Last but not least, rural to urban migration renders non-infected populations, especially women, susceptible to the risk of acquiring HIV/AIDS infections. UNAIDS (2012) statistics indicate that up to 50 percent of HIV sufferers worldwide are women; women aged between 15 and 24 also experience twice the propensity to be bearers of HIV infections. These trends indicate how failure to address the HIV ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 191 epidemic could simultaneously perpetuate the condition of gender inequality in gender-stratified societies (Austin and Noble 2014; Burroway 2012; Noble and Austin 2014; UNAIDS, 2012). Objectives of the Study In light of the continued impacts of HIV/AIDS in developing countries that have been explicated above, the objective of this study is to examine the macro-sociological factors that contribute to the HIV epidemic in developing countries. Being aware of the forces that drive this epidemic could create possi- bilities for intervention in the political-economic arena, in a way that supports mainstream epidemiological approaches. The study focuses on the contributions of debt, trade, and foreign investment to HIV/AIDS prevalence, by applying dependency and world-systems theories’ conceptualizations. In recent years, sociologists have sought to test the assumptions of dependency and world-systems theories using cross-national data. Economic dependency has been opera- tionalized by indicators, such as the extent of debt, trade, and foreign direct investments, experienced by developing countries. From this approach, scholars have examined how economic dependency perpetuates underdevelopment, which is indicated by conditions such as environmental quality (Jorgenson 2007; Jorgenson, Dick, and Shandra 2011; Shandra, Shor, and London 2008); infant or under-five child mortality (Shandra, Shandra, and London 2011; Shan- dra et al. 2005; Shen and Williamson 2001; Shircliff and Shandra 2011); and tuberculosis prevalence (Maynard, Shircliff, and Restivo 2012). These studies generally affirm that economic dependency negatively affects social and environmental well-beings in developing countries. Dependency and world-systems theories thus offer relevant and useful perspective for making sense of the causes and symptoms of underdevelopment, where the latter includes HIV/ AIDS. Our study also serves to extend the methodological approaches conven- tionally used to assess debt, trade, and FDI dependencies, and their effects on HIV/AIDS prevalence. We do so by first, factoring in multiple proxies of economic dependency, namely a country’s intensity of trade, debt, and foreign investments. We use this approach because of inconsistent findings among studies that have sought to associate population health with economic dependency. Some studies have validated the negative outcomes of debt dependency on population health (Shandra, Shandra, and London 2011; Shen and Wil- liamson 2001); others, however, have not obtained similar evidence (McIntosh and Thomas 2004; Shircliff and Shandra 2011). Moreover, no known study has specifically inquired into the effects of trade, debt, and foreign investment dependency, on HIV prevalence in developing countries. Studies accomplished to date primarily examine how world polity institutions (Noble and Austin 192 GARY MAYNARD AND CORINNE ONG

2014; Shircliff and Shandra 2011), or other macro-level factors such as economic growth, political stability, or religion (McIntosh and Thomas 2004) explain HIV prevalence. We also employ a two-way fixed-effects model to account for the role of change over time in regard to the relationship between debt, FDI, and international trade, on HIV adult prevalence rates. Previous studies, such as that by Shircliff and Shandra (2011), employed a 15-year lag between the independent and dependent variables and did not employ a fixed- effects or first-difference model (see also Noble and Austin 2014). This rather long time lag and lack of difference scores in the model could potentially affect the reliability of findings. We also incorporate a technique similar to Jorgenson (2007 and 2009), using data from every year available instead of at 5-year intervals. The Impacts of Debt, Foreign Investment, and Trade Dependency on HIV/ AIDS Prevalence1 Debt Dependency and its Effects on HIV/AIDS Prevalence. Dependency theory offers critical insights into macro-level, political-economic factors that could explain the prevalence of HIV/AIDS epidemic in developing countries. Debt has been identified by scholars to be a fundamental factor in limiting developing countries’ ability to manage the HIV/ AIDS epidemic effectively (Cheru 2002; Poku 2002). The successive debt crises experienced by mostly developing countries, generally began in the 1970s, reoccurred in the late 1980s and late 1990s, and more recently, between 2008 and 2010. These successive debt crises are speculated to have arisen from multiple factors, including major fluctuations in global oil prices, the dismantling of the Soviet Union, the spread of IMF financing, the recent housing and financial crisis, and global depreciations in the value of primary goods (Cheru 2002; Frank 1967; Labonte and Schrecker 2007; Shandra, Shandra, and London 2011). As dependency theorists posit, debt is a symptom and a catalyst of countries’ underdevelopment, spawned by one’s involvement in the global economy. Dependency theory, first forwarded by Andre Gunder Frank (1967), supports the view that current global economic arrangements between developed and developing countries are inherently unequal. The state of economic and social underdevelopment, as experienced by developing countries, is believed to be attributable to colonialism and re-hashed in contemporary forms of capitalism. Frank speaks of export-oriented trade, manufacturing, and foreign investments, as the means by which developing countries have been co-opted into the world economy. However, the profits and resources accruing from this global economic arrangement have largely flowed to the developed world (or “metropoles”). Developing countries, on the other hand, are left to manage the ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 193 brunt of capitalism, such as when world prices of primary goods decline over time and deindustrialization results. Extreme levels of debt and inflation are among the outcomes of these unequal global transactions that spawn the underdevelopment of developing countries. World-systems theory parallels dependency theory in expounding on the pattern of economic and political inequality that have historically existed between core and semi-periphery/ periphery nations (Chase-Dunn 2013; Emmanuel 1972). World-systems theorists show how capitalist transactions (e.g., wage suppression, capital flight; emphasis on foreign investments) between core and many semi-periphery/periphery nations have proven disadvantageous to the latter economies, posing challenges for developing countries to finance effective social and environmental policies and infrastructures that could resolve local health crises (Chase- Dunn 2013; Chase-Dunn and Grimes 1995). Debt specifically relates to the prevalence of the HIV/AIDS epidemic in two main ways. First, it creates budget deficits in the healthcare sector and sectors, such as education, that bear upon the efficacy of HIV/AIDS interventions (Barnett and Blackwell 2004; Shircliff and Shandra 2011). Second, huge debts accrued from the phase of economic instability since the 1970s have driven developing countries to seek loans from major multilateral lenders, such as the International Financial Institutions (IFIs), in a bid to rescue their economies.2 Subsequent dependency theorists have expounded that poor countries’ indebtedness is yet furthered by loan conditionalities (or structural adjustment policies —SAPs). These conditionalities have been largely imposed by transnational institutions, mostly represented by capitalists from the developed world (Peet 2003; Shen and Williamson 2001). Conformity to SAPs is deemed problematic because it subjects indebted countries to a major reorganization of economies, so as to promote privatization, trade, and FDI liberalization. These macroeconomic policies, in turn, have negative ramifications for HIV/AIDS epidemic control. Qualitative studies performed on countries badly afflicted by the HIV/ AIDS epidemic have revealed the repercussions of public health funding reces- sions on the epidemic (Van Der Geest et al. 2000; Foley 2009). Funding losses have driven local health centers in Senegal to closure (Foley 2009); it has also reduced medical professionals’ renumeration, welfare benefits, morale, and training, in places such as South Africa (Foster 2005), Zimbabwe (Bassett, Bijl- makers, and Sanders 1997; Korir and Kioko 2009); Tanzania (CEGAA 2009), and Zambia (Van Der Geest et al. 2000; Collins and Rau 2001). These affect populations’ access to fundamental health services and the quality of services, both of which bear upon the detection and treatment of HIV/AIDS infections. Proponents of SAPs and neoliberal macroeconomic policies, in general, often defend cutbacks in government spending and states’ devolution of health ser- 194 GARY MAYNARD AND CORINNE ONG vice provisions to the private sector. Such schemes are claimed to relieve states of the burden of expanding healthcare services, and in improving the afford- ability of health care in the long-run. However, multiple empirical sources have contradicted these neoliberal assumptions. Studies (e.g., Collins and Rau 2001; Whyte et al. 2004; Castro 2005; Hanson et al. 2006) exemplify how user-fee impositions by private health providers have had the effect of inhibiting treatment-seeking behaviors, especially among impoverished individuals. In particular, implementing user fees for consulting private health providers have deterred many impoverished individuals from seeking preventive treatment (e.g., procuring condoms), or securing initial and follow-up antiretroviral (ARV) treatments for those prediagnosed with HIV/AIDS (Whyte et al. 2004; Castro 2005; Baker 2010). In Uganda, for instance, individuals who reside in particular districts not entitled to research/treatment programs that offered free access to ARVs cannot afford ARVs sold by private providers. As a result, many of these individuals have turned to informal, illegal recourses for procuring ARVs or alternative medications, believed to treat the condition (Whyte et al. 2004). This is a worrying trend in that many vulnerable populations unable to access affordable or free HIV/AIDS treatments are consequently jeopardizing their health by consuming drugs without adequate medical super- vision. As more people in the population lack knowledge about their infection status, the more likely it is that they will infect their intimate partners and those who come in contact with their blood (e.g., emergency medical technicians). While non-governmental organizations (NGOs) have sought to counteract the crisis by providing free or low-cost healthcare services and preventive outreach to vulnerable populations, these interventions have not been sustainable. Issues of service fragmentation and financial resource limitations experienced by voluntary organizations have narrowed their abilities to suppress an epidemic as widespread as HIV/AIDS (Pfeiffer and Chapman 2010; Shircliff and Shandra 2011). As Pfeiffer and Chapman (2010) assert, comprehensive inter- sectorial commitment, oversight and funding, are needed to overcome the HIV/ AIDS epidemic, granting how the disease has culminated in developing countries. Realistically, epidemic control is best attained through statewide efforts and not a disparate “patchwork” of private interventions (Pfeiffer and Chapman 2010:158). Trade Dependency and its Effects on HIV/AIDS Prevalence Many resource-rich developing countries have been transformed into extractive economies. These include agricultural-intensive countries that spe- cialize in commercial crop (e.g., coffee, tobacco, cotton) production and export (Jorgenson 2007; Jorgenson, Dick, and Shandra 2011; McMichael 2001). Transforming economies to suit global consumption demands is consistent with ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 195 the strategy of trade liberalization and comparative advantage. This is driven by the express goal of improving countries’ economies by tapping on its resource base, such as minerals for extraction, or labor availability to support low-cost manufacturing. However, Cheru (2002) and Barnett and Blackwell (2004) report that countries prioritizing crop production for export have inad- vertently reduced the quantity of crops grown and distributed for local consumption, ironically leading to local food shortages. Even as food supplies can supposedly be imported at competitive rates, currency devaluation and the abol- ishment of government food subsidies—factors arising from unequal trade terms and pressures to deregulate economies—offset these benefits by inflating food prices3 (Barnett and Blackwell 2004; Frank 1967). Each of the above situ- ations renders most imported food items unaffordable to the poor. Lack of access to food then engenders malnutrition and under-nutrition (Maynard, Shir- cliff, and Restivo 2012; Shandra, Shandra, and London 2011; Shen and Wil- liamson 2001). A critical implication of nutritional deficits in regard to HIV/ AIDS infections is that it compromises one’s immunity and the body’s recuper- ative potential: The regeneration of skin cells and mucous membranes can affect individuals’ susceptibility to sexually transmitted diseases (STDs), partic- ularly HIV/AIDS (Poku 2002). In addition, local subsistence farmers, disadvantaged by an economy that is increasingly vested in crop exports and foreign food imports, are displaced of their original livelihoods (Labonte and Schrecker 2007). Many subsistence farmers and their families have had to turn to wage labor in commercial planta- tions or urban cities and towns to support livelihoods (Barnett and Blackwell 2004). However, workers’ absences from home over long periods of time and their migration into urban cities heightens their risk of contracting HIV and developing AIDS. This may be attributed to the increased opportunities for sex- ual activity for migrant men and women, as well as female engagement in sex work (Austin and Noble 2014; Burroway 2012; McIntosh and Thomas 2004; Poku 2002; Yasar 2010). Foreign Direct Investment Dependency and its Effects on HIV/AIDS Prevalence Trade liberalization supports foreign direct investments (FDI) funded by multinational corporations (MNCs). This is because low-cost imports benefit foreign corporations or industries that require imported materials for production; on many occasions, export-oriented industries are owned or are sub- sidiaries of MNCs (Ross and Trachte 1990). Generating revenue for debt repayment by attracting direct foreign investments, and thus capital flows into a country, is considered desirable in neoliberal agendas. Many developing countries have assumed the roles of primary and secondary producers in the global 196 GARY MAYNARD AND CORINNE ONG economy, either because they possess the raw resources that can be procured and processed on-site, or that they offer much lower labor costs than developed countries. However, the attraction of FDIs into developing countries has shown to come at a price. First, in competing for a limited number of foreign investment contracts globally, many developing countries have sought to shrink labor costs (i.e., workers’ wages) and even challenged or outlawed labor union formations. This has led to a global depression in mostly blue-collared workers’ wages and a deterioration in worker benefits (e.g., health insurance) and workplace conditions (Grown 2006; Labonte and Schrecker 2007; International Cen- ter for Research on Women [ICRW] 2009; Yasar 2010). Thus, notwithstanding the emergence of jobs and individual income accrued from foreign investments, workers are simultaneously exposed to the risks of unemployment, poverty, minimal health benefits, and suboptimal living standards. These conditions can emaciate one’s ability to buffer against serious health afflictions, such as HIV/ AIDS (Poku 2002; Yasar 2010). Recent research corroborates that many foreign firms have practiced what is known as “capital flight” in developing countries. This situation occurs when foreign investors disinvest in host economies by channeling assets abroad (Labonte and Schrecker 2007; Baker 2010). Such moves are detrimental to host economies, many of which already experience much economic instability. Countries such as Mexico, Argentina, Thailand, Brazil, and those in sub- Saharan Africa have suffered from currency devaluations and economic shocks, as a backlash of such capital flights (Labonte and Schrecker 2007). Currency devaluations are problematic as they ratchet up the prices of imported food products, basic goods, and medicines, for local consumers, and trigger local and regional economic crises. In developing countries where large populations already experience economic hardship, increased prices of imported products further constrain their ability to access these necessities. Economic crises also tend to compound the state of poverty and social inequality in a country. Over- all, the lack of access to basic essentials and poverty clearly jeopardizes vulnerable populations’ ability to ward against or seek treatment for infectious diseases, including HIV/AIDS (Barnett and Blackwell 2004; Labonte and Schrecker 2007; Maynard, Shircliff, and Restivo 2012). Data Sources and Methods To test the above hypotheses, we use cross-national data from the World Bank’s World Development Indicators (WDI) database. The only variable for which data are not obtained from the WDI is level of democracy. Data on this variable for all developing countries come from the Freedom House. We only use data from developing countries since we are interested in examining the impact of dependency on adult HIV prevalence rates in developing nations. All ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 197 of the variables used in the models, except for gross capital formation and the level of democracy, are logged to correct for their positive skewness. With the increased availability of panel data at the cross-national level, we use the dependent and independent variables in this study for every year available between 1989 and 2012. Given that the HIV pandemic occurred in 1981, this time frame captures data that we believe is suitable, given the purpose of this study. The sampling strategy of including data from every available year was also adopted by Jorgenson (2009); such a strategy increases the number of cases and years that can be subject to analysis, which increases the study’s reliability. It also permits researchers to account for more dynamic year-to-year changes in the data, as opposed to analyses of data at 5-year intervals. Although many cross- national studies utilize data at 5-year intervals (see for an example Shandra, Shandra, and London 2012), due to the lack of yearly data for particular variables, or to more approximate a sample versus a population, this strategy consequently reduces the total number of nations and years available for analysis.4 Previous cross-national studies have also tended to use a 5-, 10-, and/or even a 15-year lag between the independent and dependent variables (see e.g., Shircliff and Shandra 2011; Maynard, Shircliff, and Restivo 2012; Shandra 2003). Such strategies are motivated by the logic of making causal inferences, whereby one draws data for predictors that are antecedent to the predicted variable. However, a lag of 5 or more years may involve too long a time period to capture changes imposed by certain variables on dependent variables. For instance, conditions such as trade, debt, and investment can fluctuate drastically on a yearly basis and have relatively quick impacts, depending on the condition of the global economy. The most recent economic crisis (2008–2010) and subsequent economic recovery show very clearly that yearly changes can be profound and thus bear immediate impact for countries’ economic and social conditions, including implications for public health. For the above reasons, we choose to employ a 1- year lag between the dependent and independent variables. Deriving data for lower, lower-middle, and middle-income countries from 1989 to 2012, we obtain a maximum sample of 87 nations with an average of 13.1 time periods per nation and a total N of 1141 for the sample of observations (i.e., including number of nations and time periods).5 The data is unbalanced, but not severely and even though Babones (2013) suggests that mis-specification and unbalanced data bases can cause issues with interpretation the desire to maximize the number of nations and years overrides these concerns since the data set is unbalanced, but only slightly. The inclusion of many more cases is expected to enhance the external validity of findings in the study. In order to examine the relationship between the various measures of debt, trade, and investment dependency on adult HIV rates, we employ a two-way fixed-effects model using the xtreg function in Stata 12. In this type of model, 198 GARY MAYNARD AND CORINNE ONG changes over time within each nation are examined. Fixed-effects models control for unobserved heterogeneity bias by accounting for time-invariant predictors. As cross-national data for more nations and time periods are increasingly made available to researchers, such analyses have been more prevalent in cross-national research (see e.g., Brady, Kaya, and Beckfield 2007; Jorgenson 2009; Shandra, Shandra, and London 2012). Fixed-effects models are expected to offer more rigorous tests of the (temporal) dynamics of change within nations than conventional, static semi-difference, or first-difference models (Firebaugh and Beck 1994; Halaby 2004). Fixed-effects models also tend to approximate experimental conditions (Jorgenson 2009). The formula for the fixed-effects model is as follows:

yit ¼ a þ B1xit1 þ B2xit2 þþBkxitk þ ui þ wt þ eit;

where i = each nation in the analysis, t = each year in the analysis, yit = dependent variable for each nation at each year, a = the constant, Bk = coefficients for each independent variables, xitk = independent variables for each nation at each year, ui = country-specific disturbance terms that are constant over time, wt = period-specific disturbance terms that are constant across all countries, and eit = disturbance terms specific to each nation at each year used. In addition, we conduct regression diagnostics to determine whether multicollinearity, outliers, and heteroskedasticity affect the models. The variance inflation factor (VIF) scores for each variable in our models do not exceed a value of 5 and can thus conclude that the analysis does not experience a problem with multicollinearity (Tabachnick and Fidell 2001). In calculating Cook’s D, we find that none of the values exceed an absolute value of 1. Hence, no cases are removed. We also employ Breusch-Pagan’s tests for all our models to determine whether heteroskedasticity might be a problem. Having detected signs of heteroskedasticity in the models (i.e., p values for Breusch-Pagan <.05 for a one-tailed test), we used robust standard errors for all models to correct for this problem. Dependent Variable Adult (15–49) HIV Prevalence Rate Percent of the Total Population. The dependent variable used in this study is adult HIV prevalence rate for individuals aged 15–49, as a percent of the total population. We use this variable because its data are the most updated and consistently collected over the past 30 years, making it a very reliable measure for our purposes of assessing HIV prevalence over time. Data for this variable are drawn from the years 1990–2012 to give a one-year lag from the independent ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 199 variables. It is collected by UNAIDS yearly and made publicly available by the World Bank’s World Development Indicators (WDI) database. Independent Variables Debt Measures. Debt has been attributed to be a critical factor reducing countries’ ability to manage health exigencies, especially epidemics such as HIV/AIDS that require integrated (intersectoral) interventions (Cheru 2002; Pfeiffer and Chapman 2010; Poku 2002; UNAIDS 2014). The variable is measured from 1989 to 2011, which is lagged a year from the dependent variable. From dependency theory’s perspective, we contend that increasing debt (i.e., total debt, multinational debt, external debt, short-term debt) will lead to increasing levels of HIV/AIDS prevalence, all else equal. We extend this reasoning to all four indicators of debt (described below) employed in this analysis. We also include a variety of debt measures to disaggregate the different types of debt that comprise total debt. This allows us to account for the multiple types of debt that exist and thus to provide a more nuanced analysis of debt’s effect on HIV prevalence. Total debt as a percent of gross national income (GNI). This variable measures the total debt of a nation as a percent of the GNI of each nation. Multinational debt as a percent of public and publicly guaranteed debt service. This variable measures the percent of publicly guaranteed debt service that goes toward paying principle and interest payments to the World Bank, regional development banks, and other multilateral agencies. External debt as a percent of GNI. This variable measures external debt, which is all debt owed to non-residents of each country, including the use of IMF credits and short-term debt, as a percent of the GNI of each nation. Short-term debt as a percent of external debt. This variable measures the level of short-term debt as a percent of external debt.

Trade and Investment Variables. Total trade as a percent of gross domestic product (GDP). This variable measures total international trade as a percent of GDP for each nation in the sample. This variable is included to operationalize the impact of trade dependency on adult HIV rates. We expect to find countries participating more intensively in trade, to experience increasing adult HIV prevalence rates. Foreign direct investment inflows as a percent of GDP. This variable measures the level of investment from foreign corporate entities. We expect to find a positive relationship between the presence of FDIs (as indicated through FDI inflows) in a country and adult HIV prevalence rates, ceteris paribus. 200 GARY MAYNARD AND CORINNE ONG

Internal Development Variables. GDP per capita (PPP). This variable indicates the level of wealth in a nation, as converted by the purchasing price parity (PPP) measure. PPP currency conversions adjust for differing currency and inflation rates between countries. GDP per capita is included in this analysis to control for overall increases in the level of relative wealth in a country, which should increase governments’ ability to fund interventions across all levels of the treatment cascade (UNAIDS 2013).6 This measure has also been included in related studies but has yielded inconsistent findings (see e.g., Austin and Noble 2014 and Shircliff and Shandra’s 2011 findings on GDP per capita). We reason that countries with increasing levels of GDP encounter decreasing levels of adult HIV prevalence, net of all other factors. Gross capital formation as a percent of GDP. This variable measures the level of gross capital formation as a percent of GDP in each country. It is incorporated in this analysis to control for the effect of domestic spending in capital development (e.g., land improvement, equipment purchases, and the construction of roads, railways, schools, offices, and hospitals), on epidemic control and thus HIV prevalence rates. We posit that increasing levels of gross capital formation have the effect of reducing levels of adult HIV prevalence, net of all other variables. Secondary school gross enrollment rate. In order to control for the level of educational attainment in a country and its potential impact on adult HIV prevalence rates, we include a measure of gross secondary school enrollment. Countries whose populations have increasing levels of educational attainment should experience decreasing levels of adult HIV rates, controlling for all other variables (Barnett and Blackwell 2004; Burroway 2010). Level of democracy. Data for this variable are drawn from the Freedom House’s Freedom of the World Political/Civil Rights index (Freedom House 2014). In this index, assessed scores for levels of political and civil rights in each country were added and averaged. The original index is reverse-coded in this analysis to facilitate interpretation. Following the recode, higher values on the index reflect higher levels of democracy and vice versa, for lower levels of democracy. We conjecture that countries with increasing levels of democracy should observe decreasing levels of adult HIV rates, independent of all other variables. Total health expenditures as percent of GDP. This variable measures the total amount of money spent on health expenditures in each country as a percent of GDP. We expect that total health spending should be negatively correlated with adult HIV prevalence—ceteris paribus. Physicians per capita. The variable measures the per capita rate of physicians per 1000 people. This variable has been used as an alternative measure of healthcare access in developing nations. ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 201

Results In Table 1, we present the results of the fixed-effects estimates of adult HIV prevalence (summary statistics and bivariate correlations are presented in Appendix 1)6. In each equation, we include the following set of variables: level of democracy, gross capital formation, GDP per capita, FDI inflows, and the measure of total trade as a percent of GDP. In equations 1.1–1.9, total secondary school enrollment is included to estimate the general impact of education on adult HIV prevalence. As mentioned, diverse measures of debt and their respective effects on HIV/AIDS prevalence rates are estimated in selected models. Thus, in equations 1.1, 1.2, and 1.9 total debt as a percent of GNI is used, while in equations 1.3 and 1.4 multilateral debt as a percent of total debt is included. Next, external debt as a percent of GNI is used in equations 1.5 and 1.6 and short-term debt in equations 1.7 and 1.8. Finally, total health expenditure is not included in odd-numbered equations to maintain a high number of observations since there are already large amounts of missing data for this variable; further, there is a lack of data for this variable between 1989 and 1995, which is a crucial time period for this analysis (i.e., the post-Cold War era and the emergence of HIV/AIDS in human populations). Due to these limitations, we only include this variable in select models. Finally, for comparison we include an alternative measure of the impact of health spending in the form of physicians per 1000 in equation 1.9. This variable is included to test alternative specifications, but the results remain very similar to those using the total health expenditure measure so equation 1.9 acts as a control for any issue with mis-specification of models. The foregoing variables are organized into different equations for a couple of reasons. One, as a single measure may be prone to measurement error, using multiple measures of a variable helps reduce the incidence of such errors. Thus, if a variety of indicators representing a common concept is significant in a common predicted direction, one’s confidence in the contribution of the predictor to the dependent variable is strengthened. Two, the approach of disaggregat- ing the various indicators of debt in separate models is one way to mitigate the problem of multicollinearity, an approach used by many scholars in the field (see e.g., Jorgenson 2007; Shandra, Shandra, and London 2011; Shen and Williamson 2001). We begin by discussing targeted predictors in the study which display significant effects on adult HIV prevalence rates. Total debt and short-term debt are significant and their coefficients, positively correlated with adult HIV rates in all equations where they have been included. Short-term debt, in particular, exerts a highly significant effect on HIV prevalence rates at the .001 level of significance. This may be due in part to the fact that short-term debt needs to 0 AYMYADADCRNEONG CORINNE AND MAYNARD GARY 202

Table 1 Fixed-Effects Estimates for Adult HIV Prevalence Rates (1990–2012)

Independent Variables 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 (1989–2011) Total Debt as % of GNI .105* .072* .110* .061 .040 .052 (.046) (.036) (.047) Multilateral Debt as % of .034 .008 Total Debt .021 .004 (.051) (.038) External Debt as % of GNI .167** .082 .082 .037 (.064) (.052) Short-term Debt as a % of .087*** .044*** External Debt .078 .039 (.023) (.013) Total Trade % of GDP .136 .090 .127 .075 .116 .059 .151 .131 .054 .047 .030 .044 .024 .041 .018 .053 .045 .016 (.192) (.149) (.177) (.142) (.197) (.158) (.158) (.134) (.215) Table 1 (continued)

FDI Inﬂows as % of GDP .022 .004 .024 .006 .021 .006 .027 .005 .030 .026 .004 .028 .005 .025 .005 .030 .005 .025 (.018) (.012) (.019) (.012) (.018) (.012) (.021) (.012) (.034) GDP per capita (PPP) .059 .454* .094 .462* .150 .508** .042 .356* .130 .045 .255 .002 .270 .024 .292 .099 .197 .018 203 PREVALENCE HIV/AIDS AND DEPENDENCY ECONOMIC (.245) (.212) (.245) (.207) (.241) (.214) (.239) (.203) (.237) Gross Capital Formation .004 .004 .005 .003 .004 .003 .005 .003 .0001 .028 .025 .029 .023 .025 .022 .034 .019 .0002 (.004) (.002) (.004) (.002) (.004) (.002) (.004) (.002) (.006) Level of Democracy .021 .011 .017 .011 .027 .014 .032 .015 .029 Higher numbers = more .030 .012 .024 .013 .036 .016 .046 .019 .034 freedom (.039) (.035) (.038) (.035) (.037) (.035) (.035) (.035) (.045) Secondary School .035 .178 .043 .252* .042 .184 .085 .225* .169 Enrollment Gross rate .040 .118 .063 .143 .037 .122 .074 .128 .117 (.208) (.143) (.213) (.150) (.224) (.150) (.202) (.142) (.169) Total Health Expenditures .168 .173 .178 .093 % of GDP .045 .047 .048 .026 (.118) (.119) (.114) (.110) Physician per 1000 .318* .243 (.138) 0 AYMYADADCRNEONG CORINNE AND MAYNARD GARY 204

Table 1 (continued)

Constant .528 3.337 .543 3.049 1.922 4.096* .748 2.132 .629 (2.476) (2.046) (2.443) (1.982) (2.453) (2.025) (2.292) (1.844) (2.612) Number of Nations 87 85 87 85 87 85 87 85 80 Nations-Years 1141 884 1142 885 1142 886 1111 851 416 Average time periods per 13.1 10.4 13.1 10.4 13.1 10.4 12.8 10.0 5.2 nation Adjusted R-Squared within .3191 .1797 .2989 .1679 .3232 .1758 .3443 .2025 .3972 Adjusted R-Squared .0402 .0261 .0045 .0070 .0410 .0236 .0114 .0068 .1698 between Adjusted R-Squared .0004 .0239 .0115 .0051 .0012 .0206 .0392 .0038 .2263 Overall Hausman Chi-squared 47.40 168.65 52.37 172.54 46.81 108.57 46.30 127.15 32.35 value

The first number is the unstandardized coefficient, the second number is the standardized coefficient, and the robust standard errors are in parentheses. *<.05; **<.01; and ***<.001 for a two-tailed test. ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 205 be repaid sooner, thus placing more immediate pressure on governments to tighten national budgets on social spending. As hypothesized, this is likely to limit the efficacy of HIV interventions. Moreover, short-term debts also tend to carry higher interest rates. Hence, total debt payments become higher, ren- dering debt defaults more likely and compounding indebtedness. External debt is also significant and positively correlated with adult HIV prevalence rates in one of the two equations. These findings generally lend support to our hypotheses that a greater degree of indebtedness is associated with higher adult HIV prevalence rates. GDP per capita is significant and positively correlated with adult HIV prevalence rates in four of nine equations. These results are consistent with the findings obtained in Shircliff and Shandra’s (2011) study. Finally, in equation 1.4 and 1.8, secondary school enrollment is significant and negatively correlated with adult HIV rates. This verifies our hypothesis and those of past studies (e.g., Burroway 2010; Shircliff and Shandra 2011) that education can help improve public awareness of HIV transmissions and serve the purpose of epidemic control. We conclude this section by discussing the non-significant findings of this analysis. First, total trade and FDI are not significant across all equations, which contradict our hypothesis on the effects of trade dependency.7 It may be that trade liberalization has an indirect, rather than direct effect, on adult HIV rates. As suggested, trade liberalization impels countries to remove trade tariffs, which reduces the revenue countries can amass from trade; it may be that the ramifications of revenue reductions on adult HIV prevalence are mediated by more direct factors such as government cutbacks on vital social sectors such as health and education (Labonte and Schrecker 2007; Shen and Williamson 2001). Second, despite the significant findings for total, external, and short- term debt as discussed in the above, multilateral debt did not attain significance in any of the equations. This is a somewhat surprising finding since past studies have shown that multilateral debt negatively affects countries’ ability to cope with health crises (e.g., Shandra, Shandra, and London 2011). One possible explanation for this phenomenon is that multilateral debt includes both short-term and long-term debt (World Bank 2014). Thus, multilateral debt which consists of long-term debt may not have a compara- ble effect on HIV prevalence as short-term debt. This is notwithstanding the fact that the 2005 Multilateral Debt Relief Initiative (MDRI) could also have offset the ramification of debt on epidemic control among highly impoverished countries. This finding reveals the importance of accounting for diverse debt measures which might intervene with HIV prevalence control to a different extent. 206 GARY MAYNARD AND CORINNE ONG

Third, gross capital formation which comprises the construction and main- tenance of hospital and public health clinics, as well as total health expenditures, evince no significant effects on adult HIV prevalence. WDI data for gross capital formation currently does not specify the amount of investments allocated in health infrastructures. Hence, increased gross capital formations in a country may not necessarily imply greater investments in public health infrastructures that contribute to epidemic control. These factors could ultimately impinge the quality of HIV/AIDS interventions (Fox et al. 2011). Finally, the level of democracy is not significant in any of the equations. Discussion The main goal of this article is to expand the sociological literature on the impact of economic dependency on HIV/AIDS prevalence in the developing world. Specifically, we test the role of debt, trade, and foreign investment dependency on adult HIV prevalence in developing countries. Debt is argued to be a chief measure of dependency in this study because it greatly curtails a country’s ability to finance HIV/AIDS intervention initiatives and induces local governments to secure capital through foreign loans, thus acquiescing to associated loan impositions. Lack of funding for initiatives, ranging from preventative education about HIV, diagnostic tests, and ARV treatment regimens, are factors known to maintain high levels of HIV/AIDS in developing countries (Baker 2010; UNAIDS 2013). Tied to debt, we also investigated two macroeconomic initiatives that are often included in neoliberal loan conditionalities—FDI and trade liberalization—and their respective effects on HIV prevalence among indebted countries. Overall, we discovered that debt dependency (specifically, short-term, external, and total debt) exerted relatively consistent effects on HIV prevalence in the expected direction. This finding demonstrates that among trade, debt, and FDI dependency, debt tends to have a greater negative impact on countries’ ability to counteract epidemics such as HIV/AIDS in the developing world. First, at least three of the four indicators of debt (total debt, external debt, and short-term debt) produced significant effects on adult HIV rates in this study. Debt dependency is as hypothesized, a significant predictor of increasing adult HIV prevalence. These findings on debt challenge previous findings that debt tends to reduce (Shircliff and Shandra 2011) or bear no significant effect on adult HIV rates (Noble and Austin 2014). This difference in findings may be justified by the unique method of analysis used in this study compared to other studies. For instance, in this inquiry, we account for the role of change over time using fixed-effects model, unlike Shircliff and Shandra (2011), who adopted a 15-year lagged, non-difference model for their analysis. The authors also only encompassed two measures of debt dependency (total debt service ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 207 and a structural adjustment dummy variable), in contrast to this study which employs four different measures of debt. The different conclusions drawn from each study stresses the importance of replicating studies in a similar scope of inquiry (i.e., economic dependency and HIV prevalence), by using more robust methods of analysis and diverse indicators. The study does, however, corroborate Shircliff and Shandra’s (2011) conclusions about the effect of GDP on adult HIV rates. The authors discovered that GDP and economic growth from 1990 correlated positively with adult HIV prevalence rates. A similar relationship between GDP and adult HIV prevalence was noted in this study, indicating that economic development may not have the beneficial effect often presupposed by neoliberal proponents (Peet 2003). The unequal distribution of wealth and income in a country, government cor- ruption, or institutional decisions to allocate budgets in other sectors may offset economic gains that could have been directed toward HIV/AIDS interventions (Fox et al. 2011; Poku 2002). Several policy implications ensue from the findings of this study. First, international creditor institutions should look into allowing debtor countries greater flexibility in repaying debt, such as through gradual or extended debt scheduling. Creditor institutions might also refrain from encouraging countries to cut back on social sector expenditures, especially health and education, for the sole purpose of debt repayment. As this study demonstrates, debt and the implementation of macroeconomic measures to satisfy debt repayment could be detrimental to a country’s response to health crisis, in this case, to the HIV/ AIDS epidemic (McMichael 2001; Peet 2003). Other debt repayment strategies that could negatively directly or indirectly affect the well-beings of populations by increasing their risk of contracting the disease, and which should be carried out with discretion, include currency devaluation, massive privatization of health services, and debt defaults. Second, developing countries burdened with debt repayment could, in the meantime, focus on more cost-effective and preventative measures of decreasing adult HIV rates; broadening educational cam- paigns about how HIV/AIDS is spread and providing free and universal access to HIV testing are some of the critical strategies to contain the epidemic. Finally, we discuss some of the limitations encountered in this study, and recommendations for future research. First, the measure of adult HIV prevalence rate is prone to measurement error since data is collected and reported by individual countries. There is a risk that countries with high levels of HIV/ AIDS would underestimate HIV prevalence rates due to less efficient reporting or sampling systems, or to avoid reporting findings that might present them in a negative light to financial investors and international funders. More reliable and alternative data sources on adult HIV prevalence, such as those directly collected by external or international statistical agencies, are desirable for future 208 GARY MAYNARD AND CORINNE ONG research. Second, there is significant missing data for some predictors, such as democracy, total health expenditure, and secondary school enrollment rates, which were analyzed in this study. Such data losses may influence the statistical significance of findings (Allison 2001). We have sought to mitigate this limitation by including data from every available year instead of at every 5-year interval. With greater data availability in the future, scholars would benefit from replicating this study’s analyses. Third, direct effects models like fixed-effects models adopted in this study do not model indirect relationships between variables. Subsequent studies could consider the use of structural equation modeling (SEM) in modeling complex relationships as that between economic dependency and public health (see arguments by Cornia 2001; Woodward et al. 2001; and Noble and Austin 2014). SEM has been rarely used in cross-national research or sociological research, despite its ability to reflect direct and indirect relationships between social conditions.

ENDNOTES

*Please direct correspondence to Gary Maynard, The Ohio State University, 1885 Neil Ave mall, Sociology Department, Columbus, Ohio, USA. e-mail: [email protected]. 1We relate to the speciﬁc idea of structural adjustment rather than economic globalization in this paper. Underlying this choice is that SAPs represent a set of neoliberal macroeconomic interventions that were designed for and actualized in indebted developing countries. Developing countries in need of debt relief from the IFIs were expected to comply with SAP-endorsed austerity policies to be eligi- ble and made accountable for debt support. Thus, the idea of SAPs underscores the debtor–creditor relationship that has largely inﬂuenced debtor countries’ role in the global economy. 2To ensure that debtor countries were able to meet their debt schedules, the IFIs instituted SAPs that bear the imprint of neoliberal economic propositions formalized in the Washington Con- sensus. Controversy arises as to the extent in which true economic and debt recovery has been mediated by the SAPs, three decades after its initiation (Barnett and Blackwell 2004). 3Currency devaluation in developing countries is supported by SAP proponents to keep developing countries’ exchange rates high, and thus favorable to foreign trade partners and investors (Barnett and Blackwell 2004; Cheru 2002). On the other hand, the abrogation of government- imposed price controls, such as food and consumer subsidies, adheres to the neoliberal principle of “government deregulation.” This implies governments’ non-interference in the market, to allow unfettered competition among industry players. 4Even in using every year from every developing nation available, this study was only able to derive a sample of 87 nations and only an average of 13.1 years—less than half the full number of years used. This constitutes a sample and not a population, which was a criticism leveled at Jorgen- son’s 2009 study. For a true population to exist, data would have to be drawn from all developing countries across all time periods; for various reasons, however, the lack of data for many countries around the world have not made such population-level analyses possible. 5We also ran the same models without a lag and with a 2-year lag. The results are very similar as those presented in this study (i.e., using 1-year lags). These results are available upon request. 6Nations included: Afghanistan, Albania, Algeria, Argentina, Azerbaijan, Bangladesh, Belarus, Belize, Benin, Bolivia, Botswana, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 209

Cameroon, Central African Republic, Chad, China, Colombia, Democratic Republic of the Congo, Republic of the Congo, Costa Rica, Cote d’Ivoire, Djibouti, Dominica, Dominican Republic, Ecua- dor, Egypt, El Salvador, Eritrea, Ethiopia, Fiji, Gabon, The Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Guyana, Honduras, Hungary, India, Indonesia, Iran, Jamaica, Jordan, Kaza- khstan, Kyrgyz Republic, Lao PDR, Lebanon, Lesotho, Liberia, Madagascar, Malawi, Malaysia, Maldives, Mali, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, the Philippines, Poland, Romania, Rwanda, Senegal, Serbia, Sierra Leone, Solomon Islands, South Africa, Sri Lanka, Swaziland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Tunisia, Turkey, Uganda, Ukraine, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, and Zambia. 7We also ran the models using exports and imports as a percentage of GDP and the results were very similar to those presented here. These results are available upon request.

REFERENCES

Allison, Paul D. 2001. Missing Data (Vol. 136). Thousand Oaks, CA: Sage. Asenso-Okyere, Kwadwo, Catherine Aragon, Paul Thangata, Kwaw Andam, and Daniel A. Mekonnen. 2010. “HIV and AIDS and Farm Labor Productivity: A Review of Recent Evidence in Africa.” Journal of Development and Agricultural Economics 2(12):406–15. Austin, Kelly F. and Mark D. Noble. 2014. “Measuring Gender Disparity in the HIV Pandemic: A Cross-National Investigation of Female Empowerment, Inequality, and Disease in Less- Developed Nations.” Sociological Inquiry 84(1):102–30. Babones, Salvatore J. 2013. Methods for Quantitative Macro-Comparative Research. Thousand Oaks, CA: Sage. Baker, Peter G. 2010. Framework for Action on Interprofessional Education and Collaborative Practice. Geneva, Switzerland: World Health Organization. Barnett, Tony and Michael Blackwell. 2004. “Structural Adjustments and the Spread of HIV/ AIDS.” A Report to the Christian Aid. Retrieved 21 May, 2014. . Bassett, Mary T., Leon Bijlmakers, and David M. Sanders. 1997. “Professionalism, Patient Satisfaction and Quality of Health Care: Experience During Zimbabwe’s Structural Adjustment Program.” Social Science Medicine 45(12):1845–52. Bell, Clive, Shantayanan Devarajan, and Hans Gersbak. 2003. “The Long-Run Economic Costs of AIDS: Theory and Application to South Africa.” The World Bank Policy Research Working Paper No. 3128. Brady, David, Yunus Kaya, and Jason Beckﬁeld. 2007. “Reassessing the Effect of Economic Growth on Well-Being in Less Developed Countries, 1980–2003.” Studies in Comparative International Development 42:1–35. Burroway, Rebekah. 2010. “Schools Against AIDS: Secondary School Enrollment and Cross- National Disparities in AIDS Death Rates.” Social Problems 57(3):398–420. ———. 2012. “A Cross-National Analysis of Sex-Speciﬁc HIV Prevalence Rates and Women’s Access to Property, Land, and Loans in Developing Countries.” International Journal of Sociology 42(2):47–67. Castro, Arachuo. 2005. “Adherence to Antiretroviral Therapy: Merging the Clinical and Social Course of AIDS.” PLoS Medicine 2(12):1217–21. Chase-Dunn, Christopher. 2013. “Symposium: Five Linked Crises.” Journal of World-Systems Research 19(2):175–80. 210 GARY MAYNARD AND CORINNE ONG

Chase-Dunn, Christopher and Peter Grimes. 1995. “World –Systems Analysis.” Annual Review of Sociology 21:387–417. Cheru, Fantu. 2002. “Debt, Adjustment and the Politics of Effective Response to HIV/AIDS in Africa. Global Health and Governance: HIV/AIDS.” Third World Quarterly 23(2): 299–312. Collins, Joseph and Bill Rau. 2001. AIDS in the Context of Development Geneva: United Nations Research Institute for Social Development. Cornia, Giovanni A. 2001. “Globalization and Health: Results and Options.” Bulletin of the World Health Organization 79:834–41. Emmanuel, Arghiri. 1972. Unequal Exchange: A Study on the Imperialism of Trade NY: The Monthly Review Press. Firebaugh, Glenn and Frank D. Beck. 1994. “Does Economic Growth Benefit the Masses: Growth, Dependence, and Welfare in the Third World.” American Sociological Review 59:631–54. Foley, Ellen. 2009. Your Pocket Is What Cures You: The Politics of Health in Senegal New Brunswick, NJ: Rutgers University Press. Foster, Kira E. 2005. “Clinics, Communities, and Cost Recovery: Primary Health Care and Neoliberalism in Postapartheid South Africa.” Cultural Dynamics 17(3):239–66. Fox, Ashley M., Allison B. Goldberg, Radhika J. Gore, and Till B€arnighausen. 2011. “Conceptual and Methodological Challenges to Measuring Political Commitment to Respond to HIV.” Journal of the International AIDS Society 14(2):1–13. Frank, Andre G. 1967. The Development of Underdevelopment Boston: New England Free Press. Freedom House. 2014. Freedom in the World Comparative and Historical Data (1973–2014) [Datafile]. Retrieved 15 May, 2014. . Grown, Caren 2006. “Trade Liberalization and Reproductive Health: A Framework for Understanding the Linkages.” in Trading Women’s Health and Rights? Trade Liberalization and Reproductive Health in Developing Economies, edited by Caren Grown, Elissa Braunstein and Anju Malhotra. London and New York: Zed Books. Haacker, Markus. 2004. The Impact of HIV/AIDS on Government Finance and Public Services. The Macroeconomics of HIV/AIDS. Washington, DC: International Monetary Fund, 2004 Halaby, Charles. 2004. “Panel Models in Sociological Research: Theory Into Practice.” American Review of Sociology 30:507–44. Hanson, Kara, Ian Hopwood, Chris D. James, and Christina Kirunga. 2006. “To Retain or Remove User Fees? Reflections on the Current Debate in Low- and Middle-Income Countries.” Applied Health Economics and Health Policy 5(3):137–53. International Labor Organization. 2014. “HIV/AIDS and the Workplace.” Retrieved on 21 May, 2014. International Center for Research on Women [ICRW]. 2009. Trade Liberalization and Women’s Reproductive Health: Pathways and Linkages. Retrieved on 21 May, 2014. Jorgenson, Andrew K. 2007. “The Effects of Primary Sector Foreign Investment on Carbon Dioxide Emissions From Agriculture Production in Less-Developed Countries, 1980–99.” International Journal of Comparative Sociology 48(1):29–42. ———. 2009. “Foreign Direct Investment and the Environment, the Mitigating Influence of Institutional and Civil Society Factors, and Relationships Between Industrial Pollution and Human Health: A Panel Study of Less-Developed Countries.” Organization & Environment 22:135–57. Jorgenson, Andrew K., Christopher Dick, and John M. Shandra. 2011. “World Economy, World Society, and Environmental Harms in Less-Developed Countries.” Sociological Inquiry 81:53– 87. ECONOMIC DEPENDENCY AND HIV/AIDS PREVALENCE 211

Korir, Julius and Urbanus Kioko. 2009. Evidence of the Impact of IMF Fiscal and Monetary Policies on the Capacity to Address HIV/AIDS and TB Crises in Kenya. Cape Town, South Africa: Centre for Economic Governance and AIDS in Africa and Results Education Fund [CEGAA]. Labonte, Ronald and Ted Schrecker. 2007. “Globalization and Social Determinants of Health: The Role of the Global Marketplace (Part 2 of 3).” Globalization and Health 3(6):1–17. Maynard, Gary, Eric J. Shircliff, and Michael Restivo. 2012. “IMF Structural Adjustment, Public Health Spending, and Tuberculosis.” International Journal of Sociology 42(2):5–27. McIntosh, William A. and John K. Thomas. 2004. “Economic and Other Societal Determinants of the Prevalence of HIV: A Test of Competing Hypotheses.” The Sociological Quarterly 45 (2):303–24. McMichael, Philip. 2001. Development and Social Change: A Global Perspective Thousand Oaks, CA: Pine Forge Press. Noble, Mark D. and Kelly F. Austin. 2014. “Gendered Dimensions of the HIV Pandemic: A Cross- National Investigation of Women’s International Nongovernmental Organizations, Contraceptive Use, and HIV Prevalence in Less-Developed Nations.” Sociological Forum 29 (1):215–39. Peet, Richard. 2003. Theories of Development. New York: The Guilford Press. Pfeiffer, James and Rachel Chapman. 2010. “Anthropological Perspectives on Structural Adjustment and Public Health.” Annual Review of Anthropology 39:149–65. Poku, Nana K. 2002. “Poverty, Debt, and Africa’ HIV/AIDS Crisis.” International Affairs 78 (3):531–46. Ross, Robert J. and Kent C. Trachte 1990. Global Capitalism: The New Leviathan. Albany, NY: SUNY Press. Shandra, Carrie L., John M. Shandra, and Bruce London. 2012. “The International Monetary Fund, Structural Adjustment, and Infant Mortality: ACross-National Analysis of Sub-Saharan Africa.” Journal of Poverty 16(2):194–219. ———. 2011. “World Bank Structural Adjustment, Water, and Sanitation: A Cross-National Analysis of Child Mortality in Sub-Saharan Africa.” Organization & Environment 24:107–29. Shandra, John M., Bruce London, and John B. Williamson. 2003. “Environmental Degradation, Environmental Sustainability, and Overurbanization in the Developing World: A Quantitative, Cross-National Analysis.” Sociological Perspectives 46(3):309–329. Shandra, John M., Eran Shor, and Bruce London. 2008. “Debt, Structural Adjustment, and Organic Water Pollution: A Cross-National Analysis.” Organization & Environment 21(1):35–55. Shandra, John M., Jenna E. Nobles, Bruce London, and John B. Williamson. 2005. “Multinational Corporations, Democracy, and Child Mortality: A Quantitative, Cross-National Analysis of Developing Countries.” Social Indicators Research 73(2):267–93. Shen, Ce and John B. Williamson. 2001. “Accounting for Cross-National Differences in Infant Mortality Decline (1965–1991) Among Less Developed Countries: Effects of Women’s Status, Economic Dependency, and State Strength.” Social Indicators Research 53(3):257–88. Shircliff, Eric and John M. Shandra. 2011. “Non-Governmental Organizations, Democracy, and HIV Prevalence: A Cross-National Analysis.” Sociological Inquiry 81(2):143–73. Tabachnick, Barbara G. and Linda S. Fidell. 2001. Using Multivariate Statistics, 5th ed. Boston, MA: Allyn and Bacon. UNAIDS. 2012. “Fact Sheet: Women, Girls, Gender Equality and HIV.” Retrieved on 28 Mar, 2014. UNAIDS. 2013. “AIDS by the Numbers.” Retrieved 15 May, 2014. . 212 GARY MAYNARD AND CORINNE ONG

UNAIDS. 2014. “Fact Sheet: 2014 Statistics.” Retrieved Retrieved on 15 May, 2014. . Van Der Geest, Sjaak, Mubiana Macwan’gi, Jolly Kamwanga, Dennis Mulikelela, Arthur Mazimba, and Mundia Mwangelwa. 2000. “User Fees and Drugs: What did the Health Reforms in Zambia Achieve?” Health Policy Plan 15(1):59–65. Whyte, Susan R., Michael A. Whyte, Lotte Meinert, and Betty Kyaddondo. 2004. “Treating AIDS: Dilemmas of Unequal Access in Uganda.” Journal of Social Aspects of HIV/AIDS Research Alliance 1(1):14–26. Woodward, David, Nick Drager, Robert Beaglehole, and Debra Lipson. 2001. “Globalization and Health: A Framework for Analysis and Action.” Bulletin of the World Health Organization 79:875–80. World Bank. 2014. World Development Indicators [Dataﬁle]. Retrieved 15 May, 2014. . World Health Organization [WHO]. 2015. “10 Facts on HIV/AIDS.” Retrieved 21 May, 2015 . Yasar, Yavuz. 2010. “Gender, Development, and Neoliberalism: HIV/AIDS in Cambodia.” Review of Radical Political Economics 42:52. Appendix 1: Bi-variate Correlations and Summary Statistics

Summary Statistics CNMCDPNEC N I/ISPEAEC 213 PREVALENCE HIV/AIDS AND DEPENDENCY ECONOMIC

Variable Obs. Mean Std. Dev. Minimum Maximum

Adult HIV Prevalence Rate (ln) 2415 .349 1.573 2.302 3.339 Total Trade % of GDP (ln) 4418 4.306 .579 1.175 6.331 FDI % of GDP (ln) 4050 .658 1.662 13.551 5.903 GDP per capita PPP (ln) 4369 8.448 1.351 4.422 11.420 Secondary School Enrollment (ln) 3254 4.122 .661 1.452 5.079 Gross Capital Formation 4240 22.885 8.895 2.424 113.577 Level of Democracy (7=higher democracy) 4273 4.449 2.228 1 7 Total Health Expenditure as % of GNI 3353 1.757 .409 .210 3.114 Total Debt as % of GNI (ln) 2824 1.134 1.044 5.639 4.908 Mulit-lateral Debt as % of Total Debt (ln) 2910 3.491 1.017 4.952 4.605 Short Term Debt as % of External Debt (ln) 2866 1.971 1.427 9.210 4.487 External Debt as % of GNI (ln) 2828 3.940 .848 1.433 7.230 Physicians per 1000 (ln) 1891 .0337 1.428 4.961 3.857 1 AYMYADADCRNEONG CORINNE AND MAYNARD GARY 214 Appendix 1: (continued)

Bi-variate Correlations *<.05 confidence level

Variables 12345678910111213

Adult HIV 1.000 Prevalence Rate (ln) Total Trade .0588* 1.000 % of GDP (ln) FDI % of .0779* .4586* 1.000 GDP (ln) GDP per capita .1030* .3151* .2296* 1.000 PPP (ln) Secondary School .3226* .2463* .2480* .7761* 1.000 Enrollment (ln) Gross .0805* .2910* .2382* .1298* .1282* 1.000 Capital Formation Level of Democracy .0134 .1326* .1147* .4856* .4627* .0046 1.000 (7=higher democracy) Total Health .1054* .0017 .0699* .1851* .2442* .0999* .4773* 1.000 Expenditure as % of GNI Total Debt as .0726* .2111* .676* .2671* .1812* .0254 .1784* .2141* 1..000 % of GNI (ln) Appendix 1: (continued)

Bi-variate Correlations *<.05 confidence level

Variables 12345678910111213 CNMCDPNEC N I/ISPEAEC 215 PREVALENCE HIV/AIDS AND DEPENDENCY ECONOMIC Mulit-lateral .1161* .0361 .0246 .2746* .2442* .1154* .1042* .2606* .1503* 1.000 Debt as % of Total Debt (ln) Short Term .0212 .0097 .1237* .2775* .2129* .0135 .0368 .2214* .0283 .1299* 1.000 Debt as % of External Debt (ln) External .0933* .0182 .0243 .3443* .2668* .2206 .0449* .2708* .0677* .4217* .1104 1.000 Debt as % of GNI (ln) Physicians .5240* .1680* .1330* .6940* .8400* .0430 .3990* .2930* .1900* .4110* .5660* .2520* 1.000 per 1000 (ln)