Ecological Losses are Harming Women: A Structural Analysis of Female HIV Prevalence and Life Expectancy in Less Developed Countries

Ecological Losses are Harming Women:A Structural Analysis of Female HIV

Prevalence and Life Expectancy in LessDeveloped Countries

Laura McKinney1 and Kelly Austin2

1Tulane University and 2Lehigh University

A B S T R A C T

Increased inequality in life expectancies across nations due to the advent of the HIVpandemic requires rigorous investigation of gender inequalities, as women now dispropor-tionally represent the majority of global HIV cases. While empirical examinations of wom-en’s status on HIV prevalence and life expectancy have amassed, one under-explored areaof concern is the influence of environmental decline. We fill this gap by integrating ecofemi-nist perspectives to inform our analysis of the direct and indirect effects of ecological losseson female health outcomes in a structural equation model of 136 less developed nations.We find that ecological losses reduce women’s longevity via increased HIV rates, hunger,and diminished health resources. Conclusions point to the importance of ecological condi-tions and the efficacy of incorporating ecofeminist frameworks to explain global health andgender inequalities.

K E Y W O R D S : HIV/AIDS; life expectancy; ecofeminism; environment; gender inequality.

The twentieth century brought major worldwide improvements in numerous measures of health, aseconomic development and enhanced access to medical and sanitation interventions advanced thehealth and well-being of individuals (Soares 2007). Countering such widespread gains, current re-search charts the reemergence of global inequalities surrounding measures of health, including life ex-pectancy (e.g., Neumayer 2004; Riley 2005). HIV/AIDS is a leading factor contributing to healthdeclines in poor nations, where over 95 percent of the 33.2 million individuals infected with HIV re-side (WHO 2013). The spread of HIV/AIDS has been especially detrimental to women in poor na-tions and, in fact, represents the leading cause of death among women of reproductive age (WHO2013).

The number of women infected with HIV has increased dramatically in recent years—youngwomen in less developed nations are about twice as likely as men to become newly infected withHIV (WHO 2013). The factors leading to enhanced likelihood of contraction are complex, but oftencenter on gender-based inequalities that limit their socioeconomic status, access to health resources,

The authors thank the reviewers and editors for their invaluable comments and suggestions. Any errors that remain are entirely theirown. Direct correspondence to: Laura McKinney, Tulane University, 220 Newcomb Hall, New Orleans, LA 70118. [email protected].

VC The Author 2015. Published by Oxford University Press on behalf of the Society for the Study of Social Problems.For permissions, please e-mail: [email protected]

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and safe sex practices that would otherwise help prevent infection (Austin and Noble 2014;Burroway 2012; Heimer 2007; Krishnan et al. 2008). Impoverishment takes an obvious toll on health,and its effect on the spread of HIV is no exception. The deleterious combination of gender-basedinequalities, poverty, malnutrition, lack of education, and inadequate health resources poses acutethreats to the well-being of women in the less developed world (Krishnan et al. 2008); indeed, thesefactors are interconnected dimensions of strife that co-occur and exacerbate one another in ways thatseverely compromise the health and longevity of women in poor nations (Shen and Williamson1999; Williamson and Boehmer 1997). What has been underexplored in the literature is the influenceof environmental factors on women’s health outcomes. Although some studies have begun to link se-lect environmental factors to increased HIV transmission (e.g., Hunter, Reid-Heresko, and Dickinson2011), this is especially relevant to women for a number of reasons.

Women’s health is particularly imperiled by ecological losses1 that disrupt flows of the vital re-sources women are typically charged with providing to the household that are wholly or partially de-rived from the natural environment (Oglethorpe and Gelman 2008). Indeed, women supply the bulkof food, water, and other basic necessities for family members; as resource scarcity complicates thesetasks the health and well-being of the family is jeopardized and women themselves become increas-ingly vulnerable to disease (Barnett and Whiteside 2002; Krishnan et al. 2008; Oglethorpe andGelman 2008; Stillwaggon 2006). Environmental declines undoubtedly constrain food productionand malnutrition potentiates susceptibility to many infectious diseases (Scrimshaw 2003; Scrimshawand SanGiovanni 1997), including HIV/AIDS (Beisel 1996; Stillwaggon 2006). Additionally, there isaccumulating evidence that high rates of HIV are found in areas with extensive contact with contami-nated water. This is particularly harmful to women as they are more likely to encounter contaminatedwater in the course of their daily lives and, as a result, experience urogenital inflammation that is arisk factor for HIV infection (Downs et al. 2011; Kjetland et al. 2006).

Women’s needs for such basic provisions are characteristically subsidiary to men’s (Santow 1995),making them disproportionately vulnerable to malnutrition and associated declines in immunitywhen food and water are scarce. In some cases, severe hunger may increase the likelihood of riskysexual behavior and HIV transmission among women who resign to trading sex for needed householdresources (e.g., Heimer 2007; Mojola 2011). Thus, there are many mechanisms that link environ-mental degradation and women’s health; we focus our efforts on uncovering the connections amongthe environmental, social, and economic dimensions that are causal determinants of women’s healthin less developed locales.

Despite its importance, ecosystem depletion is an under-explored source of death and diseaseamong women in comparative analyses, which is the gap we fill. To do so, we draw on ecofeministperspectives that theorize the deep connection between women and nature that heightens women’svulnerability to ecological degradation. We wed ecofeminist positions with other macro-comparativeapproaches to inform our empirical analysis of the connections among environmental losses, malnu-trition, and female HIV prevalence, which directly and indirectly influence each other and theoutcome—women’s life expectancy. We predict that the women’s health measures are also signifi-cantly impacted in direct and indirect ways by additional factors, such as women’s status, availabilityof health resources, and level of economic development. We begin by discussing the gendered natureof HIV and associated impacts on longevity, followed by an elaboration of the ecofeministapproaches that are of key interest. To anticipate, we find support for the basic premise that environ-mental destruction is a central explanation of declines in health among women, including the spreadof HIV and reductions in life span.

1. We use terms such as ecological losses, ecological destruction, environmental degradation, environmental decline, and biocapac-ity losses interchangeably throughout the article to refer to reductions in the vitality, quality, and functioning of ecosystems,broadly defined, that are consequential to subsistence, health, and vulnerability, in general.

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D E V E L O P M E N T , H E A L T H , A N D G E N D E R E D I N E Q U A L I T I E SComparative research emphasizes that premature death and rates of infectious disease are pro-nounced in poorer nations due to limited economic development, minimal education, poor health-care services, inadequate provisions for sanitation and water, gender inequalities, and insufficientaccess to contraceptives (Austin and McKinney 2012; Austin and Noble 2014; Bates et al. 2004;Burroway 2010, 2012; Heimer 2007; McIntosh and Thomas 2004; Medalia and Chang 2011;Neumayer 2004; Riley 2005; Shen and Williamson 1999; Soares 2007). Much research has begun tofocus on the relevance of women’s status2 and basic health services in predicting health outcomes, in-cluding HIV prevalence and life expectancy (Austin and Noble 2014; Burroway 2012; Medalia andChang 2011; Shen and Williamson 1997; 1999; 2001; Wickrama and Lorenz 2002). Indeed, the sta-tus of women is an influential aspect to consider as women generally use their increased social powerto address concerns that improve their health (Holvoet 2005; Shen and Williamson 1999; Wickramaand Lorenz 2002; Williamson and Boehmer 1997).

Globally, women are especially vulnerable to many health problems that lead to early death, in-cluding HIV most notably, as well as complications in pregnancy and childbirth (Shen andWilliamson 1999), respiratory infections, malaria, certain cancers, and a number of other conditions.Theories of gender inequality provide clear insights into such dynamics, as a wide body of literaturehighlights the harmful consequences of inequalities in decision making and control of or access to re-sources for women (e.g., Clark and Peck 2012; Smith 2002; Turmen 2003). In particular, women inless developed nations face barriers to many educational and health resources, including schools andcontraceptives (Burroway 2012; Heimer 2007; Shen and Williamson 1999; Smith 2002).

Women’s participation in schooling is one of the most important cross-national predictors of awide variety of health outcomes in less developed nations (Shen and Williamson 1997; 1999; 2001;Williamson and Boehmer 1997), including HIV prevalence (Burroway 2010; Clark and Peck 2012;Shircliff and Shandra 2011). Enhanced educational attainment among women reduces gender in-equality, as schooling can advance women’s economic standing and autonomy, as well as provide sub-stantive knowledge on disease transmission, reproductive concerns, and other areas related to healthand well-being (e.g., Burroway 2010; Shen and Williamson 1997; 1999; 2001; Soares 2007;Vandemoortele and Delamonica 2002; Wickrama and Lorenz 2002). For instance, educated and fi-nancially independent women have more influence in household negotiations, allowing them to exer-cise greater control over the use of contraceptives (e.g., Smith 2002; Wickrama and Lorenz 2002).Indeed, enhanced use of fertility-reducing contraceptives directly improves women’s health by lessen-ing the chances of maternal death (Shen and Williamson 1999). Moreover, reduced fertility suggestsa declining importance of child-rearing and increased capacity of women to work outside the homeand improve their socioeconomic standing by attending school or engaging in formal employment(e.g., Heimer 2007; Wickrama and Lorenz 2002).

The strong focus on the status of women as a key predictor of health outcomes fits with criticalanalyses that emphasize that non-economic factors improve life expectancy and other measures ofphysical well-being more so than prima facie economic growth (e.g., Brady, Kaya, and Beckfield2007; Franz and FitzRoy 2006; Soares 2007). A growing body of research demonstrates that GDP iscomparatively less influential on health outcomes than factors such as women’s education, the avail-ability of health-care personnel, clean water, or sanitation (Brady et al. 2007; Soares 2007), or thatGDP operates indirectly through other measures to influence health (e.g., Noble and Austin 2014).By extension, we expect women’s health to improve when economic development is properly chan-neled to providing public health resources. Additionally, women must be able to access and utilize

2. The status of women is a multidimensional concept that spans legal, economic, political, educational, social, and health realms,among others (Williamson and Boehmer 1997:306). While acknowledging the conceptual diversity of women’s status, we analyt-ically define women’s status by educational attainment and two measures of reproductive autonomy—low fertility and contra-ceptive use. We adhere to this operationalization based on its relevance to health outcomes, though we note alternative measuresabound and might usefully be applied in future efforts.

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these resources; women’s status likely mediates the relationship between health resources and wom-en’s health outcomes. This leads us to consider the following hypotheses (see Figure 1):

(H1) Economic development is linked to improved health resources, including the availability of medi-cal providers and access to water and sanitation.

(H2) Health resources enhance the status of women (measured by school enrollment, low fertility, andcontraceptive use), which, in turn, conditions HIV prevalence and premature death among women.

In addition to the social and economic dimensions of development explored above, recent evi-dence also suggests that ecological factors deserve increased consideration, especially in the contextof health and gender inequalities (see, for example, Hunter et al. 2011; Mojola 2011; Oglethorpe andGelman 2008). We now consider environment and health linkages, with a continued emphasis onthe significance of gender in shaping these relationships.

G E N D E R , H E A L T H , A N D T H E E N V I R O N M E N THealth experts and practitioners are beginning to place greater importance on the role of the environ-ment in perpetuating disease and death among vulnerable populations. The World HealthOrganization (WHO) (2006) estimates that roughly one-quarter of all healthy life year losses andpremature mortality, globally, have environmental origins, but caution these are extremely conserva-tive figures. This is particularly problematic for individuals in less developed nations, as sociologicalresearch supports that environmental degradation is amplified in those locales due to the global divi-sion of labor that concentrates extractive and highly destructive production processes in peripheralareas.3 Expectedly, the WHO (2006) confirms that differences across income groups are stark withindividuals in less developed areas losing healthy life years and succumbing to disease at rates 15times and 120 times greater, respectively, than their developed counterparts. To be sure, the interac-tion of environmental hazards (e.g., clean water scarcity) with the lack of health-care interventions(e.g., mass drug administration) in poorer nations compounds these divides.

Quantitative macro-comparative analyses corroborate these claims by showing, for instance, thatwater pollution contributes to infant mortality in less developed countries (Jorgenson 2009) and ac-cess to clean water and sanitation reduce child mortality in sub-Saharan African (Shandra, Shandra,and London 2011). Taken together, this research emphasizes that features of the natural and built en-vironment condition health. However, these assessments fail to account for the gender dynamicsthat, as we shall show, are important aspects of environment—human health linkages. To illustrate,water pollutants4 are especially critical to women whose traditional household responsibilities in-crease their reliance on and exposure to water. Similarly, women are more likely to be affected by in-adequate domestic health provisions that represent built environment conditions, including water

Economic Development

Health Resources

Women’s Status

Female Death and Disease

Figure 1. Conceptual Mapping of Hypotheses 1 and 2

3. Though a thorough explanation of these dynamics is beyond the scope of our study, we refer readers to Bunker (1985) and re-cent review by Rudel, Roberts, and Carmin (2011).

4. Although water pollution in advanced nations typically emanates from industrial waste, fertilizer use, pesticide applications, andthe like, water pollutants in less developed areas are more accurately characterized as parasitic water contamination and associ-ated infections that result from contact with infested water. To be sure, the bulk of contact with contaminated water springsfrom basic domestic activities such as washing clothes, fishing, and gathering water.




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and sanitation access; in the absence of basic health infrastructure, women evidence higher fertilityrates, less contraceptive use, and less educational attainment (e.g., Heimer 2007; Krishnan et al.2008; Wickrama and Lorenz 2002).

The links connecting women’s status, health, and the environment comprise the theoretical andempirical focus of this article. Building on prior cross-national research that confirms the harmful ef-fects of pollution and inadequate access to water and sanitation on health outcomes (Jorgenson2009; Shandra et al. 2011), we incorporate explicit emphasis on the gender dynamics that augmentthe adversities experienced by women. We also expand the analytic focus on select environmental fac-tors (e.g., water pollution) to a more encompassing measure of ecological losses that captures multi-ple dimensions of environmental decline that are of direct relevance to the health and well-being ofwomen. We draw on ecofeminist theorizations to develop a framework for advancing claims that en-vironmental losses are especially problematic for women, to which we now turn.

Ecofeminist perspectives offer that women are more deeply connected to, affected by, and con-cerned about the environment than men (Mies and Shiva 1993; Terry 2009; Warren 1990).Ecofeminist scholars posit that patriarchy and capital accumulation are twin aspects of the currenteconomic regime that yield numerous consequences for women and the environment; thus womenare deeply connected to nature by virtue of shared structures of domination. Additionally, the tradi-tional household duties and responsibilities accorded to women result in their heightened vulnerabil-ity to ecological losses (Mies and Shiva 1993; Mies 1998; Rocheleau, Thomas-Slayter, and Wangari1996). As one example, the productivity of women in less developed nations relies near exclusivelyon subsistence farming (Boserup 1970; Dunaway 2001; Rocheleau et al. 1996); thus declines in soilfertility and supplies of clean water compromise their ability to provide for themselves and the house-hold (Masanjala 2007).

We focus on women in less developed countries who are theorized to be particularly affected byenvironmental degradation due to the division of labor and associated gender norms that positionthem as collectors and providers of household resources. As women endeavor to fulfill their house-hold duties in light of resource scarcity, they must travel longer distances over increasingly dangerousterrain to secure food, fuel, and fiber (Dunaway and Macabuac 2007; Mies 1998; Mies and Shiva1993). Resource constraints that shift formerly inconsequential tasks, such as walking to a nearbysource to draw water, to hours-long (or even days-long) searches are not only physically strenuous,thus directly impacting women’s health, but also place restrictive demands on women’s time that limitopportunities for educational and economic pursuits that would otherwise improve their status.Specifically, research shows that women who attend school and engage in cottage industries do sowithout reductions in other responsibilities, such as those to the household (King and Hill 1993). Ineffect, long searches for resources restrict the time that girls and women can spend on education toget a job and the ability to work the job itself. Taken together, women disproportionately suffer assearches for household inputs become onerous, physically intense, and prolonged, the latter of whichcan ultimately render unfeasible the educational and economic pursuits that, as treated above, arepowerful avenues for advancing women’s status and health.

Environmental declines that disrupt the supply of food and clean water also weaken the physicalcapacity of individuals to fight against disease, which is especially consequential to women given theirtendency to “eat last” (Santow 1995). The loss of fertile croplands, grazing lands, and vital fishinggrounds severely constricts the ability of households to obtain the macro- (e.g., protein and calories)and micro-nutrients (e.g., iron, zinc, and vitamins) that are critical defenses in resisting disease andstaving off infection (Beisel 1996; Stillwaggon 2006). Quite simply, severely undernourished individ-uals lack the basic “first-line” defenses of a healthy immune system.5 Moreover, as supplies of cleanwater become increasingly scarce, women are more likely to come into contact with and, ultimately,

5. This is part of the evidence that constitutes what Stillwaggon (2006) refers to as the “ecology of poverty” that is an overlookeddimension of disease. Her thesis, substantiated by a meta-analysis of existing studies and her own experiences navigating health




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resort to using water that is infested with worms and parasites that compromise overall health by in-tensifying susceptibility to and progression of life-threatening infections (Stillwaggon 2006). In par-ticular, epidemiological studies confirm that human exposure to water infected by Schistosomahematobium—the predominant cause of female urogenital schistosomiasis (FUS) that manifests ingenital lesions—is a major risk factor among women who have and contract HIV (Downs et al.2011; Kjetland et al. 2006).

Resource scarcity reduces the prospects for women to generate income from handicrafts and othercottage industries that rely heavily on natural resource inputs (Dunaway 2001). The additional con-striction to earning money posed by ecological decline worsens women’s health insofar as they be-come entrenched in poverty, which is clearly consequential given the general view that poverty is amajor culprit in perpetuating HIV transmission.6 The likelihood of contracting the virus is spikedamong impoverished individuals due to their overall poorer health profiles, greater susceptibility todisease (particularly when coupled with malnutrition; see, for example, Beisel 1996; Stillwaggon2006), limited knowledge on preventing transmission (Tladi 2006), and tendency to engage in riskysexual health behaviors (Dunkle et al. 2004). As resource scarcity diminishes women’s options toearn wages, they are propelled into increasingly precarious positions and often sacrifice their ownhealth to fend for others who depend on them, as elaborated below.

Ecological declines coupled with the exclusion from socially acceptable means to earn moneyprovoke some women to resort to prostitution and trading sex for household resources they areotherwise unable to obtain (e.g., Heimer 2007; Masanjala 2007).7 For instance, the documented fish-for-sex trades in sub-Saharan Africa are indicted as perpetuating HIV transmission among women incoastal communities who have no other means of feeding themselves or their families (Mojola 2011).Importantly, risky sex practices are pronounced among women who encounter threats of starvation(Oyefara 2007), and hunger is often amplified among women in less developed locales (Santow1995). Ecological losses complicate food access in a variety of direct and indirect ways that collec-tively perpetuate unsafe sex behaviors among women who face profoundly desperate circumstancesof abject poverty and severe hunger that contribute to their material and physical inability to stave offinfections. Women are, in effect, robbed of safe options for acquiring food for themselves and theirfamilies.

In sum, there are a variety of mechanisms by which environmental declines constitute greaterhealth risks among women. In this view, resource scarcity bears a wide range of deleterious effects onthe health of women due to traditional gender roles that dictate that women take care of the house-hold, including key tasks of finding clean water and food. The relationship between the environmentand health outcomes is likely cyclical and intergenerational in ways that exacerbate and compoundthe strife experienced by each successive generation.

While practitioners and scholars alike have begun to trace certain health deficiencies to select envi-ronmental features (Jorgenson 2009; Shandra et al. 2011), other environment-human health linkageshave been omitted entirely from existing assessments. In fact, the WHO (2006) candidly admits that

systems in less developed countries, posits that ecological factors are more instrumental in perpetuating HIV infections than be-havioral (e.g., sex practices) modes of transmission.

6. We note, however, that emerging evidence gleaned from Demographic and Health Surveys (DHS) in select African nationsquestions this basic logic (see, for example, Fox 2010; Mishra et al. 2007; Parkhurst 2010). These findings point to higher risk ofHIV infection among men and women in the wealthiest income quartiles who are speculated to have the resources to supportmultiple partners and a desire to accrue material assets from multiple partners, respectively. We are hopeful future research onthe individual-level determinants of HIV risk explores the environmental factors treated here to discern if they are subject to thesame dynamics across wealth gradients.

7. This should not suggest that risky sex is the only means for acquiring HIV. Some research suggests that much of women’s HIVrisk is in the context of marriage; therefore it is the sexual behavior of their husband that puts them at risk. This is especially thecase when husbands are engaged in migratory labor that often necessitates long absences from home. Importantly, reduced pro-ductivity of the land and the political economy of natural resource extraction in general are critical factors pushing rural peopleto seek wages in urban areas (Udoh 2013) where HIV prevalence is characteristically higher (Mabala 2006), thus the health ofthe environment is implicated yet again as a key determinant of women’s health.




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“the disease burden associated with changed, damaged, or depleted ecosystems in general [is] notquantified” (p. 5). It follows, then, that death and disease resulting from ecological decline are likelyfar more pervasive than indicated by current authoritative sources. Given the theoretical and practicalimpetus for linking ecological destruction to various facets surrounding women’s health, we examinethe direct and indirect effects of environmental losses on women’s life expectancy via the spread ofHIV, malnutrition, and diminished availability of domestic health resources. In particular, we test thefollowing (see Figure 2):

(H3) Ecological losses are tied to declines in health resources that would otherwise boost the status of women.

(H4) Ecological losses contribute to severe hunger that, in turn, leads to greater HIV and associateddeclines in life expectancy among women.

(H5) Ecological losses are directly linked to the female HIV burden insofar as women are more likelyto engage in risky sexual behavior to meet household needs.

D A T A A N D M E T H O D S

SampleOur sample includes all less developed nations for which data are reported for female life expectancyin 2012 (World Bank 2013). We restrict out sample to less developed nations, as the predictors ofhealth, especially HIV, tend to differ across developed and less developed nations.8 As previouslymentioned, issues of poor health, gender inequality, and dependence on local environmental re-sources are also more pronounced in less developed nations, making it appropriate to focus on poornations for the topics explored. Table 1 lists all countries included in the analysis alongside values forfemale HIV percent and life expectancy measures used.

Analytic StrategyTo test the theoretical positions outlined above, we construct a structural equation model (SEM).SEM is particularly useful in this context based on its ability to model direct and indirect effects.Other features that make it a favorable estimation technique include its ability to create composite in-dices, model error, and provide model fit statistics that enable the researcher to judge the fit of themodel as a whole to the data provided, and make adjustments based on this information (Bollen1989). The latter point is particularly helpful in deriving theoretically best-fitted models, given the rel-atively nascent state of integrating various dimensions from the theories treated above. Another

Ecological Losses

Health Resources

Women’s Status

Female Death and Disease

Hunger

Figure 2. Conceptual Mapping of Hypotheses 3 through 5

8. We follow prior researchers in defining less developed nations as those in the lower three quartiles of the World Bank IncomeClassification, which is based on GDP per capita, for the year 2012.




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Table 1. Reported Female Life Expectancy and Percent of Women with HIV for NationsIncluded in the Analysis

Country Female LifeExpectancy

FemaleHIV Percent


FemaleHIV Percent

Afghanistan 61.35 Latvia 78.80 .21Albania 80.30 Lebanon 81.77 .05Algeria 72.38 .03 Lesotho 48.46 15.78Angola 52.56 1.15 Liberia 60.81 1.00Argentina 79.61 .18 Libya 76.96Armenia 77.77 .07 Lithuania 79.30 .03Azerbaijan 73.73 .05 Macedonia, FYR 77.21Bangladesh 70.66 .00 Madagascar 65.32 .07Belarus 76.90 .16 Malawi 54.23 6.45Belize 76.56 1.73 Malaysia 77.07 .08Benin 60.36 .69 Maldives 78.27 .06Bhutan 67.79 .15 Mali 54.03 .59Bolivia 68.86 .07 Mauritania 62.74 .23Bosnia & Herzegovina 78.59 Mauritius 76.97 .39Botswana 46.04 17.43 Mexico 79.36 .10Brazil 77.05 Micronesia, Fed. Sts. 69.64Bulgaria 77.80 .03 Moldova 72.55 .27Burkina Faso 56.03 .74 Mongolia 71.21 .01Burundi 54.96 1.99 Montenegro 76.90Cambodia 73.80 .48 Morocco 72.21 .05Cameroon 55.24 3.18 Mozambique 50.48 6.34Cape Verde 78.20 Myanmar 66.87 .31Central African Rep. 50.66 3.09 Namibia 66.15 8.61Chad 51.09 1.94 Nepal 68.68 .15Chile 82.27 .14 Nicaragua 77.28 .07China 76.37 .04 Niger 57.65 .37Colombia 77.34 .21 Nigeria 52.02 2.22Comoros 61.83 .03 Pakistan 67.17 .03Congo, Dem. Rep. 51.08 Panama 80.15 .35Congo, Rep. 59.19 2.00 Papua New Guinea 64.32 .55Costa Rica 81.84 .12 Paraguay 74.36 .12Cote d’Ivoire 50.86 2.42 Peru 76.98 .12Cuba 80.98 .04 Philippines 71.90 .01Djibouti 62.40 1.81 Romania 78.20 .04Dominican Republic 76.25 .65 Rwanda 64.52 1.63Ecuador 78.89 .15 Samoa 75.94Egypt, Arab Rep. 73.09 .01 Sao Tome & Principe 68.01El Salvador 76.67 .34 Senegal 64.51 .50Equatorial Guinea 53.56 3.33 Serbia 77.30 .03Eritrea 64.11 .47 Seychelles 77.40Ethiopia 63.79 Sierra Leone 45.27 .98Fiji 72.61 .05 Solomon Islands 68.69Gabon 63.75 3.30 Somalia 56.00 .32

(continued)




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feature of SEM is its utilization of maximum likelihood estimates (MLE) that calculate pathway coef-ficients on the basis of all available data points; when cases are missing information on select variablesthose cases are dropped from those pathway estimations but retained for others when the data areavailable. Thus, SEM allows us to maximize our sample of nations by retaining cases that might bemissing data on one or two variables included in the model.9

Table 1. Continued


FemaleHIV Percent


FemaleHIV Percent

Gambia, The 59.71 1.18 South Africa 57.20 13.00Georgia 77.54 .06 South Sudan 55.09Ghana 61.73 1.17 Sudan 63.49 .81Grenada 75.00 Suriname 73.89 .42Guatemala 74.94 .28 Swaziland 48.16 16.74Guinea 56.38 .77 Syrian Arab Republic 77.60Guinea-Bissau 55.37 1.54 Tajikistan 70.53 .07Guyana 68.52 .73 Tanzania 61.31 3.34Haiti 64.18 1.36 Thailand 77.44 .62Honduras 75.66 .32 Timor-Leste 68.05Hungary 78.70 .02 Togo 56.64 2.15India 67.74 .15 Tonga 75.33Indonesia 72.47 .07 Tunisia 76.70 .02Iran, Islamic Rep. 75.43 .03 Turkey 78.09 .00Iraq 72.78 Turkmenistan 69.46Israel 83.60 .06 Uganda 59.05 3.72Jamaica 75.71 .73 Ukraine 75.88 .69Jordan 75.26 Uruguay 80.32 .18Kazakhstan 73.79 .09 Uzbekistan 71.39 .06Kenya 62.12 3.81 Vanuatu 73.16Kiribati 71.10 Venezuela, RB 77.38Korea, Rep. 84.40 .01 Vietnam 80.27 .19Kosovo 72.30 Yemen, Rep. 64.08Kyrgyz Republic 73.70 .10 Zambia 57.30Lao PDR 68.72 .11 Zimbabwe 56.46 9.48

Note: N¼ 136

9. Although there were some missing data points, the level of missing data for each measure is relatively low, and some measuressuch as female life expectancy, GDP per capita, and fertility rates, had no missing data. Furthermore, there appeared to be no pat-tern to the missing values that would bias results. Utilizing the strengths of the SEM technique, we use full maximum likelihoodmissing value routine. Maximum likelihood missing value estimation is not an imputation procedure. Instead, the likelihood forthe entire sample is created by summing the likelihoods for each case, using whatever information each case has available. Thismeans that each country contributes the maximum amount of information possible to the estimation (Arbuckle 1996; Endersand Bandalos 2001). The estimates are consistent and efficient under the condition that the data are missing at random (MAR).This is an easier condition to meet than missing completely at random (MCAR), which is required for methods of listwise dele-tion. Analyses that compare missing data methods consistently find that the full maximum likelihood missing value routine is su-perior to methods of listwise deletion, pairwise deletion, and imputation procedures in terms of parameter estimate bias,parameter estimate efficiency, convergence failures, and model fit (e.g., Enders and Bandalos 2001). Furthermore, we also con-ducted the analyses using listwise deletion, as this is the most common strategy used in comparative research, and achieved con-sistent substantive results (results not presented but available upon request). This demonstrates that the results presented hereare not driven by the missing data method used or the sample size. However, in the listwise-deleted analyses, the sample size wasgreatly reduced (N< 50) and such a strategy produces estimates that are statistically consistent but not efficient (Arbuckle 1996;




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Our research design utilizes a time-ordered dependent variable, where the dependent variable ismeasured in time after the independent variables. This is a common strategy used in cross-sectionalmacro-comparative research in order to help adhere to conditions of causality, where causes mustprecede effects in time (e.g., Austin and McKinney 2012; Burroway 2010, 2012; Shircliff and Shandra2011).10 Also, substantively, it is likely that the effects of environmental decline on women’s healthaccumulate over time, or carry over across generations. For example, soil depletion from deforesta-tion that inhibits food production may take years to manifest. Thus, we measure biocapacity lossesover a fairly significant period of time (from 1971-2001), and the final outcome of average female lifeexpectancy is measured later in 2012. We measure HIV in 2009, as there can be several years betweenHIV detection and death.11 The predictors of female HIV percent, including hunger, public health re-sources, and women’s status, are measured two years prior in 2007, and GDP is measured in 2005, asthe effects of economic growth on public health resources and women’s status may take severalmonths to be evidenced in cross-national statistics.

Variables Included in the AnalysisFemale life expectancy is our ultimate dependent variable and indicates the number of years a femalenewborn can expect to live, on the basis of prevailing mortality trends (World Bank 2013). We focuson female life expectancy for a few reasons. First, ecofeminist theories emphasize the adversitiesposed to women by environmental destruction. As life expectancy represents a common measure ofoverall physical quality of life, it follows that female life expectancy is remarkably relevant for testingsuch propositions. Second, women (especially young women) in less developed countries are mostvulnerable to contracting HIV, which shortens life span and represents the leading cause of prema-ture death among women of reproductive age (WHO 2013). Third, prior research shows that genderinequality and HIV prevalence have uniform effects on life expectancy for men and women in less de-veloped locales (see, for example, Medalia and Chang 2011); thus we expect the findings presentedhere to evidence similar patterns if other measures of life expectancy (e.g., total or male) were substi-tuted in the analysis. In an alternative specification we considered the ratio of female to male life ex-pectancy as the outcome, which evidenced consistent substantive relationships as those uncoveredhere though female HIV prevalence is a comparatively stronger predictor in the model presentedbelow.

Female HIV represents a key variable in our analysis, given the expected influence on female lifeexpectancy and the postulation that environmental declines reduce resistance to disease, includingHIV/AIDS (Stillwaggon 2006). Data on the number of female HIV cases come from the UNAIDSReport on the Global AIDS Epidemic (UNAIDS 2011). Female HIV percent is constructed by divid-ing the number of HIV-infected women aged 15 to 49 by the total number women aged 15 to 49,then multiplying that quotient by 100 to form a percent. This variable was log transformed to reducethe influence of extreme outliers.

Depth of hunger measures the average caloric amount that food-deprived people lack in terms of di-etary energy. Each nation’s food deficit, in kilocalories per person per day, is determined by compar-ing the average amount of dietary energy that undernourished people get from the foods they eatwith the minimum customary amount of dietary energy needed to maintain body weight and

Byrne 2009; Enders and Bandalos 2001). We thus prefer to report models that utilize as much information as possible and pro-duce consistent and efficient estimates under the less-restrictive assumption of MAR.

10. Causal assertions and inferences are implicit in SEMs (see, for example, Bollen 1989:40; Pearl 2009:135-8). Future effortsmight apply longitudinal data and companion analytic techniques to confirm causality for the relationships tested here.

11. On average there is around a six to ten year incubation period between HIV contraction and death. However, the average timefrom HIV detection to death varies widely, and is often much shorter than this as many people in developing nations do notget tested for HIV until they are far along in the incubation period. Also, because some people can live with HIV for a consider-able amount of time before death, a cross-sectional statistic of prevalence measured at 2009 includes cases that have had HIVfor several years prior (if the HIV measure was an incidence measure, capturing new HIV cases, then a longer time lag betweenHIV and life expectancy would be necessary).




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undertake light activity (World Bank 2013). We include this variable to assess the hypothesis thatbiocapacity losses punctuate hunger, which is especially consequential to women’s health. We usethis measure rather than other indicators of food insecurity,12 precisely because the severity of hungerand starvation most closely captures the dire circumstances that increase susceptibility to disease(Beisel 1996; Stillwaggon 2006) and the likelihood of engaging in risky transactional sex behaviors(Mojala 2011; Oyefara 2007).

Prior analyses confirm that multiple aspects of women’s status are central to improving women’shealth, including reducing HIV and increasing life expectancy (e.g., Austin and Noble 2014; Bradyet al. 2007; Medalia and Chang 2011; Shircliff and Shandra 2011; Wickrama and Lorenz 2002). Weinclude three commonly used indicators to estimate the effects of women’s status: fertility rate, con-traceptive use, and female schooling, which numerous studies demonstrate are among the most im-portant predictors of general and female health outcomes (e.g., Brady et al. 2007; Shen andWilliamson 1997, 1999, 2001; Wickrama and Lorenz 2002; Williamson and Boehmer 1997). Fertilityrate indicates the number of children an average woman would have if current age-specific fertilityrates remained constant during her reproductive years (World Bank 2013). In this analysis, we re-verse code this variable to construct a measure of low fertility rate, so that higher values indicate rela-tive improvements in the status of women. We also include the percent of women using contraceptivesto represent the percentage of women aged 15 to 49 who are practicing, or whose sexual partners arepracticing, any form of contraception (World Bank 2013). Although only some forms of contracep-tives protect against HIV transmission, use of any contraceptive method suggests increased power ofwomen in negotiating their reproductive rights, another crucial factor of women’s status. Lastly, weinclude female secondary school enrollment to measure gross enrollment ratio, where the ratio of totalenrollment for females, regardless of age, is divided by the population of the age group that officiallycorresponds to secondary level education (World Bank 2013). Female participation in schooling islinked to myriad improvements in the status of women, as educated women are better positioned toearn money, access health-care resources, and evidence greater autonomy (e.g., Burroway 2010,2012).

General domestic health resources represent important control variables in the analysis. We in-clude number of health providers to estimate the number of doctors, nurses, and midwives per 1,000people, which includes generalist and specialist medical personnel (World Bank 2013). Access to san-itation and clean water are additional public health factors that can also be considered features of thebuilt environment, thus having central relevance to biocapacity losses and health.13 Access to improvedsanitation indicates the percent of the population with at least adequate access to disposal facilitiesthat can effectively prevent human, animal, and insect contact with excreta, including flush systems(to piped sewer system, septic tank, or pit latrine), ventilated improved pit latrines, pit latrines withslab, or composting toilets (World Bank 2013). Access to clean water designates the percentage of thepopulation using an improved drinking water source ranging from piped water located inside theuser’s dwelling, plot, or yard to other improved water sources, such as public taps or standpipes, tubewells, or boreholes, protected dug wells, protected springs, and rainwater collection.

We include GDP per capita as a measure of economic development, which is an essential controlin cross-national analyses. We expect GDP per capita to influence many key variables, directly and

12. Another widely available measure of food insecurity is prevalence of undernourishment, which was tested in alternative modelsbut failed to reach statistical significance. While this measure is an important dimension of food access, we opt for the intensityof deprivation among those who lack adequate access to food given the potentiating effects of starvation or extreme hunger onmorbidity and mortality (Beisel 1996; Stillwaggon 2006). We refer readers to Food and Agriculture Organization (FAO)(2010) for data calculation details.

13. Although some studies use health expenditures to capture health resources or services, health spending estimates are influencedby the use of expensive medical equipment, high costs of intensive care for older individuals with chronic conditions, and a vari-ety of other factors. Trained health-care workers and access to sanitation and water are favored measures of the health resourcesthat are central to preventing disease and extending life expectancy.




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indirectly, in line with the theoretically derived hypotheses treated above. GDP per capita is the totalmarket value of all final goods and services produced in a country in a given year, equal to total con-sumer, investment, and government spending, divided by the midyear population. It is converted intocurrent international dollars using purchasing power parity (PPP) rates, which provides a standardmeasure allowing for cross-country comparisons of real price levels (World Bank 2013). We per-formed a log transformation of this variable to reduce the influence of extreme outliers.

Biocapacity loss is a key independent variable in this analysis, measured as the percent change indomestic ecological resources for the years 1971-2001.14 This variable, taken from the GlobalFootprint Network (2010), quantifies reductions in the amount of biologically productive resourcesavailable to individuals in a nation and is comprised of stocks of grazing land, cropland, forestland,and fishing grounds. While prior scholarship employs select measures of natural capital losses (e.g.,deforestation used by Hunter et al. 2011), we prefer this measure given the comprehensive assess-ment of diverse indicators of environmental destruction that closely depicts the hypotheses we test.Another point of departure is that our measure is a per capita measure, which provides a meaningfulbasis for comparison that illuminates the severity of ecological crises and resource declines as distrib-uted across the population. Biocapacity loss is taken as an exogenous variable, co-varied with ourother exogenous term, and specified as indirectly influencing the life span of women via effects on do-mestic health resources, hunger, and female HIV percent. Expectations based on the literature re-viewed above are that losses diminish domestic health resources, worsen hunger, and exacerbate HIVprevalence among women, which bring associated declines in life expectancy.15

A N A L Y S I S A N D D I S C U S S I O N O F R E S U L T STable 2 displays the correlation matrix and descriptive statistics for the variables used in the analyses;all bivariate correlations are significant at the p< .05 level or better. The magnitude of the relation-ships among the variables demonstrates that many of the predictor variables are highly correlated,such as the indicators of women’s status (fertility, contraceptives, female schooling) and health re-sources (health-care providers, sanitation, clean water). This further warrants the use of SEM givenits superior handling of intercorrelated independent variables through the creation of latent con-structs and direct and indirect pathways that circumvent the tendency to bias coefficient estimates(e.g., Bollen 1989; Byrne 2009).16

A preliminary step was to validate empirically whether health resources and women’s status repre-sent distinguishable components. Drawing on prior scholarship, we expect these are distinct as theformer concerns health provision availability across all individuals, while women’s status indicatorscapture women’s access to and benefit from use of such resources. To test this, we performed a con-firmatory factor analysis with two separate factors: health resources (indicated by health-care pro-viders and access to sanitation and clean water) and women’s status (specified by low fertility,contraceptive use, and female schooling) and analyzed the overall and component measures of fit.We compared this to an alternative model where all six indicators loaded on a single factor.17 By em-pirical standards, we find evidence at both the component and overall model levels to support our

14. This time period is chosen based on the generally accepted view that “time lags of several decades” (Wackernagel et al.2004:271) exist between the ecological changes and social impacts that constitute our analytic focus. We derive this value by di-viding biocapacity in 1971 by the change in biocapacity from 1971-2001: (T2-T1)/T1. We then multiplied by negative one(*-1) to ease interpretation of results such that larger values indicate greater losses.

15. We tested additional measures such as Exports as a Percent of GDP and Multinational Corporate Penetration. None of theseindicators significantly impacted female HIV or life expectancy, thus they were removed from the model and are not includedas a theme of the article.

16. Before conducting the SEM analysis, we performed basic regression diagnostics (e.g., residual plots, Breuch-Pagan Test, WhiteTest, Cook’s D) using Stata 13 of sets of regression models predicting both female HIV and life expectancy. No problems withinfluential cases or heteroscedasticity were evidenced. While endogeneity is also a concern, we rely on theories and prior evi-dence to include the predictors most relevant to female health outcomes identified in the literature.

17. Results available upon request.




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predictions that the two-factor model is superior, which resonates with our substantive and theoreti-cal interpretations of prior development and gender stratification literatures.

Figure 3 presents the final SEM results of female life expectancy as directly and indirectly condi-tioned by the various environmental, social, and economic factors outlined above. We derive our finalSEM through an iterative process that begins by testing all theoretically and substantively informedpaths proceeded by successive steps of eliminating non-significant relationship(s) and model re-estimation to achieve maximal parsimony and optimize model fit (based on statistical significance ofcoefficients and fit statistics), as is standard practice in the SEM tradition (Byrne 2009). To establishthat the inclusion of non-significant paths did not substantively change the results presented in theparsimonious model (Figure 3), we append Figure A1, which provides a more fully specified modelfor inspection. A close comparison of the model fit statistics, treated below, clearly suggests that theparsimonious model is superior, and thus we focus our discussion of results on Figure 3.

Before interpreting our findings, we note the model fit statistics indicate an excellent fit of themodel to the data. Specifically, in accordance with empirical standards, the chi-square test statistic isnon-significant (v2¼ 46.61; df¼ 38; p¼ .159); the values of the incremental fit index (.992),Tucker-Lewis index (.985), and Confirmatory Fit index (.991) all exceed .90; and the root meansquared error of approximation (RMSEA) value (.041) is below the suggested threshold of .05(Bollen 1989; Byrne 2009). Together, the overall fit indices demonstrate that the model presentedhas excellent fit to the data and permit interpretation of the pathway coefficients, which appear asstandardized regression coefficients and are all statistically significant at the .05 level or better.

The results presented in Figure 3 demonstrate that biocapacity loss represents an important un-derlying factor that contributes both directly and indirectly to health declines, especially amongwomen, in less developed nations. Specifically, we find that biocapacity losses are directly associatedwith reductions in the availability of domestic health resources (�.29), such as sanitation and cleanwater, intensification of hunger (.21), and increases in HIV rates among women (.26). We also findthat the depth of hunger increases the level of female HIV (.26) across less developed nations. Theseresults suggest that the effects of resource declines on women operate in ways that fully support eco-feminist positions that emphasize that women bear the brunt of environmental declines; we extendthis view to include the increased likelihood of contracting life-threatening infections and associatedreductions in life expectancy.

Table 2. Correlation Matrix and Univariate Statistics

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

1. Female life expectancy 1.0002. Female HIV percent (ln) �.647 1.0003. Depth of hunger �.603 .521 1.0004. Fertility rate (reversed) .801 �.522 �.564 1.0005. Contraceptive use .711 �.378 �.348 .762 1.0006. Female secondary schooling .745 �.440 �.544 .831 �.725 1.0007. Health service providers .594 �.341 �.607 .598 .583 .746 1.0008. Access to improved sanitation .768 �.510 �.536 .762 .672 .823 .674 1.0009. Access to clean water .698 �.455 �.552 .777 .654 .787 .612 .771 1.00010. GDP per capita (ln) .672 �.331 �.519 .760 .721 .822 .676 .781 .743 1.00011. Biocapacity losses �.469 .550 .479 �.522 �.391 �.390 �.434 �.398 �.460 �.305 1.000

Mean 68.89 �5.56 112.48 �3.49 44.51 65.62 1.26 60.99 81.09 8.22 .44Standard deviation 9.69 2.01 108.04 1.61 22.96 29.60 1.29 30.01 17.13 1.03 .15Maximum 84.40 �1.16 640.00 �1.21 96.00 112.55 4.68 100.00 100.00 10.17 .63Minimum 45.27 �10.50 .00 �7.59 7.60 4.63 .02 9.10 28.50 5.80 .06




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In addition to biocapacity losses and the depth of hunger, women’s status represents a principalpredictor of female HIV rates and life expectancy. Lowered fertility, increased use of contraceptives,and secondary female education enrollment are tied to reduced female HIV prevalence (�.24) andlonger female life expectancy (.72) across less developed nations. As expected, the percent of womenwith HIV is associated with reductions in female life expectancy across nations (�.26). Overall, thesefindings confirm the key hypotheses under investigation by demonstrating the importance of environ-mental decline, intense hunger, and the status of women in explaining cross-national variation in fe-male HIV rates and life expectancy.

Consistent with our substantive interpretations of prior literature, we also find that health re-sources are strongly associated with women’s status (.97). Yet, as previously mentioned, these repre-sent distinct factors; this suggests that while improved health resources tend to confer advances inwomen’s status, health resources only improve female health outcomes insofar as women are able toutilize them. Put differently, health resources evidence no direct effects on female HIV percent or lifeexpectancy (see Table 2), but indirectly condition women’s health via improvements to basic indica-tors of women’s status. Health resources are also associated with declines in the depth of hunger(�.52), suggesting that individuals in countries with better health services experience less starvation.In contrast to the harmful role of biocapacity losses on health resources, the results confirm thatGDP per capita increases health provisions (.79), where nations with higher levels of economic devel-opment evidence greater access to health-care providers, sanitation, and clean water.

It is important to note that GDP per capita did not have any other direct relationships to alterna-tive predictors in the model. In many ways, this supports current research emphasizing the relativeimportance of social measures in predicting health outcomes, rather than solely focusing on economicdeterminants (e.g., Austin and Noble 2014; Brady et al. 2007; Soares 2007). Additionally, this findingsuggests that economic development only benefits women’s health when channeled to improving

Figure 3. SEM Predicting Female Life Expectancy in Less Developed Nations




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health provisions, and subsequently women’s access to them. Equally as notable is the insignificant ef-fect of GDP per capita on depth of hunger when the pathway is included in the model, though thecoefficient came very close to meeting traditional thresholds of statistical significance. In fact, inclu-sion of this pathway worsened model fit, though all other significant coefficients and substantive in-terpretations remained. Thus, we extend the logic above to apply to the determinants of hunger aswell, in emphasizing the critical role of health provisions (themselves influenced by ecological condi-tions) in improving a wide range of health outcomes.

C O N C L U S I O N S A N D I M P L I C A T I O N SThe analysis presented above imparts a number of theoretical and methodological insights forapproaches to gender, ecology, and health in the less developed world. Primary to this endeavor is ex-panding our understanding of the factors that contribute to the health, well-being, and longevity ofwomen in poor nations. The theories and empirics presented above inform our central conclusionthat environmental losses are strongly associated with women’s health, in direct and indirect ways.This implies that developmental and epidemiological approaches to improving women’s health maybenefit from incorporating environmental dimensions as a key area of concern. Concomitantly, if weare to gain footing in this direction, it is equally as critical that companion methodologies are em-ployed that enable researchers to model the complex theoretical hypotheses—that include direct andindirect effects—they purport to test. In what follows, we elaborate on these conclusions, associatedimplications, and avenues for future research.

Theoretically, we find robust support for ecofeminist propositions and extend the empirical basisof this framework to confirm that women’s health is deeply linked to ecological destruction. In doingso, our results fill an important gap in the ecofeminist literature that is generally lacking in macro-comparative quantitative assessments (for exceptions, see Ergas and York 2012; Norgaard and York2005; Nugent and Shandra 2009), particularly those that uncover the underlying mechanisms thatconnect gender to environmental conditions (Ergas and York 2012:966). The implications includethe efficacy of incorporating ecofeminist frameworks into global perspectives on health, gender in-equality, and the environment; in fact, our results suggest that failing to recognize the interconnectednature of these dimensions could lead to severely underspecified models. Though we focus solely onless developed countries, we advocate future research incorporate ecofeminist frameworks to test keypropositions across developmental contexts.

We also conclude that ecological losses are closely linked to hunger and health provisions in poornations, which are subsequently tied to female HIV and reductions in life expectancy among women.Our analysis enables greater specification of the nexus of linkages among environmental degradation,malnutrition, health resources, and health outcomes across nations. While the negative effect of envi-ronmental decline on subsistence is theorized (see, for example, Masanjala 2007; Oglethorpe andGelman 2008), we provide empirical evidence of such dynamics in poor nations that are especiallyconsequential for women. The ways in which ecological losses condition public health resources is asimilarly theorized and practical position (see, for example, WHO 2006), but one that might benefitfrom greater exploration. Our findings suggest that not only are aspects of the built environment,such as water and sanitation, linked to environmental decline, but that the presence of medicalprofessionals also suffers as a result. We speculate that doctors and physicians might find resource-damaged locales particularly unhospitable and this could be an important factor in inducing a braindrain phenomenon in which trained medical personnel avoid such locations, though in-depth re-search is needed to explore those connections. Thus, we advocate further research be conducted inthese settings to strengthen our understanding of how environmental declines confer losses to do-mestic health resources that go beyond particular aspects of the built environment.

Our findings give support to frameworks that incorporate non-economic factors alongside tradi-tional economic considerations in explaining gender-based health inequalities across nations. The useof an integrative modeling strategy illustrates that some commonly assumed relationships involving




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GDP per capita and health resources are not direct in their impacts on women’s health. Rather, wefind that women’s status and health resources themselves mediate many of the economic and non-economic influences on female HIV and life expectancy. This finding suggests that economic growthneeds to be carefully channeled to improving health resources and that women’s access to those re-sources is fundamental to cultivating companion advances in their health conditions. We modestly in-fer that practitioners and policy makers seeking to address current health crises in poor nations maybegin to consider holistic approaches that account for the synergies among social, economic, and eco-logical dimensions. The analysis presented here provides preliminary empirical support for the myr-iad ways in which social, economic, and environmental conditions directly and indirectly influencewomen’s health, and we advocate future research employ similar tactics to scrutinize applicability toother health dynamics across populations of interest.

Methodologically, our results underscore the importance of using statistical techniques that permitthe estimation of complex hypotheses, such as those presented here, that specify a suite of factorsthat influence each other and outcomes of interest in direct and indirect ways. SEM is one such possi-bility for honing the precision of model specification and associated results, which are critical consid-erations for moving a discipline forward. SEM is also particularly useful as a theory generator; in thepresent context this is especially advantageous given the relatively nascent state of theorization andcompanion empirics that connect the status of women to environmental dimensions. We stronglyrecommend greater incorporation of methodological approaches that reflect the ways in which con-current (environmental, economic, social) systems interact to shape the various contours of globalinequality.

Our analysis, like all others, is subject to certain limitations. The data available to researchers areoften deficient in many ways as they reflect the focal needs of international organizations that spear-head international data collection efforts. We feel further empirical precision would be beneficial toanalyze the linkages that connect gender inequality and the environment. For example, a breakdownalong gender lines for the depth of hunger variable we employ represents one such refinement; thatis, a measure of the depth of hunger among women would be preferable to the indicator used here,though we are unaware of existing data in this regard. Similarly, income inequality by gender repre-sents another useful variable for inclusion that is currently unavailable. Should those data becomeavailable, we advocate for further analytic scrutiny of those measures. Additionally, endogeniety is aconcern in any analysis. We endeavor to limit potential bias by carefully constructing theoreticallygrounded models that include a host of economic and social factors identified to be important expla-nations of women’s health outcomes, in addition to the environmental factors that are our chief focus.While not exhaustive in including the universe of potential influences, we hope our findings lay afoundation for future work in the area.

To this end, we offer a few additional avenues that warrant further exploration. While our primaryanalytic focus rests on the effects of environmental degradation on women’s social and health status,there are other possibilities that should also be examined. For example, the logic presented abovethat elaborates the ways in which ecological losses are associated with declines in women’s status andhealth that could, in principle, be extended to female political, legal, and economic achievements.With respect to political realms, for instance, case studies demonstrate how women’s representationin political structures promotes policies that are critical to rectifying issues of gender inequality, in-cluding changes that address gender-based violence and extend landholding rights to women18 (seeBurnet 2011; Devlin and Elgie 2008); we encourage future research to discern if policies enacted alsoimprove women’s health. Beyond legislation, ethnographic research in Rwanda (the world leader inproportion of parliament seats held by women) has uncovered the additional benefits to women per-ceived as emanating from their greater political representation, such as the respect of their kin and

18. Data on gender equality across nations recently developed and released by the United Nations could be helpful in this regard.




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community, public inclusion, autonomy, and educational access (Burnet 2011). It follows that thepositive outcomes associated with political representation might also improve women’s healththrough legislative and indirect channels, which is a possibility that warrants rigorous investigation.

We conclude that efforts to stem the spread of HIV/AIDS in less developed locales may benefitfrom focusing on women and addressing the root causes of their increased vulnerability to death anddisease, which we have shown to be closely linked to environmental conditions. Women’s status isalso deeply tied to reductions in HIV and longer life expectancy for women; thus gains in women’sstatus may offset some of the harmful aspects of environmental decline. Future inquiries might fruit-fully examine the potential relationships between women’s status and treatment of the environment,as the theories and analysis presented suggest improvements in one area has positive, ripple effectson the other.

Focusing on the importance of women to address the HIV pandemic is a practical stance, giventhat women are more efficient transmitters of the virus via sexual intercourse and mother-to-childtransmission (e.g., Heimer 2007); thus, failing to orient discussions around rates of infection amongwomen omits prevailing pathways of transmission. Additionally, debilitating disease among womenthat renders them unable to perform traditional caretaking roles brings wider losses incurred by thehousehold and community. Children who are orphaned as a result of losing their mother to HIV/AIDS are more likely to contract the disease themselves (if they are not infected at birth) due to theabject poverty they are born into that severely limits their options for obtaining provisions to meettheir basic needs (Masanjala 2007; Oglethorpe and Gelman 2008; Stillwaggon 2006; Tladi 2006).Tragically, many are forced into prostitution and transactional sex practices as a result, which furtherexacerbate possibilities for infection (Oglethorpe and Gelman 2008). Our findings suggest that envi-ronmental destruction is closely linked to female HIV and life expectancy, thus curbing ecological de-clines might be advantageous to promoting the sustainability of nations and the people in them.

As an encouraging trend, the administration of anti-retroviral therapies (ARTs) is increasing glob-ally and has recently been scaled up in Africa, though gaps in access to ARTs persist. ARTs are ex-tremely beneficial to HIV-infected people, as they have the potential to prevent mother-to-childtransmission and transmission between partners, while also lengthening lifespan and boosting thehealth of those infected with HIV. Therefore, as ART coverage continues to improve, it is likely thatthe profound effects of HIV on measures such as life expectancy will lessen. However, the effects ofART expansion on longevity or quality of life are likely to be extremely uneven—for example, inNorth Africa only 11 percent of adults living with HIV are receiving ARTs with the global average inART coverage across less-developed nations estimated at 34 percent (UNAIDS 2013:4). The gapsfor HIV-positive children are even worse with only 6 percent of those residing in North Africa receiv-ing ARTs, and an average of 28 percent of pediatric HIV cases across Africa receiving the ART medi-cines they need (UNAIDS 2013). A number of logistical and even biological hurdles to ARTsolutions remain, ranging from issues of treatment eligibility, service gaps, and costs to medicinal re-gime adherence and the evolution of drug resistant strains (DART Trial Team 2010; Stevens, Kaye,and Corrah 2004). Moreover, all of the treatment scenarios assume every person has been tested andis aware of their current status, which is not the case for the majority of individuals in poor nations(UNAIDS 2013). Quite simply, it looks unfavorable at the present moment that ART drugs will beable to keep up with the continued spread of HIV. In addition to scaling up access to ART treat-ments, we offer that taking action to preserve the state of the environment is a pathway with potentialto address the root causes of HIV infection.

Notably, in the current context of global climate change the dynamics treated here are and willcontinue to be of the utmost importance. To the degree that climate change creates environments inwhich populations must adjust to long-term climatic changes (e.g., shorter growing seasons, highermean temperatures) and erratic weather events (e.g., more frequent and severe floods and droughts),we can expect that resource scarcity, declines in productivity, and shifts in production relations arelikely to occur with adverse effects on the health and well-being of individuals. As we have shown,




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environmental losses are especially harmful to women and thus should be taken as a foremost con-cern for climate justice and gender equality advocates.

APPENDIX

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Austin, Kelly F. and Mark D. Noble. 2014. “Measuring Gender Disparity in the HIV Pandemic: A Cross-NationalInvestigation of Female Empowerment, Inequality, and Disease in Less-Developed Nations.” Sociological Inquiry84(1):102–30.

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Less Developed Countries, 1980–2003.” Studies in Comparative International Development 42:1–35.

Figure A1. Saturated SEM Predicting Female Life Expectancy in Less Developed Nations




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Bunker, Stephen G. 1985. Underdeveloping the Amazon: Extraction, Unequal Exchange, and the Failure of the ModernState. Urbana: University of Illinois Press.

Burnet, Jennie E. 2011. “Women Have Found Respect: Gender Quotas, Symbolic Representation, and FemaleEmpowerment in Rwanda.” Politics & Gender 7(3):303–34.

Burroway, Rebekah. 2010. “Schools Against AIDS: Secondary School Enrollment and Cross-National Disparities inAIDS 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.

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Ecological Losses are Harming Women: A Structural Analysis of Female HIV Prevalence and Life Expectancy in Less Developed Countries