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Women's Access to Maternity Care During Structural Adjustment in Egypt and India: Does Minority Status Matter? Patricia Ahmed (University of California, Los Angeles) * * Please direct all correspondence to Patricia Ahmed, 264 Haines Hall, Los Angeles, CA, 90095. E-mail: [email protected]. Abstract Following the collapse of the Soviet Union, many developing countries previously oriented towards Moscow, liberalized their economies via structural adjustment programs, ostensibly to generate economic growth and improve national living standards. While the efficacy of these programs sparks lively debates in the development literature, not enough attention is paid to women, who may be especially vulnerable during market transitions. The normative neo-liberal perspective on development suggests that free markets promote economic growth that will improve women’s living standards. In contrast, more critical gender approaches posit that free markets trap women in menial jobs and restrict their access to vital services, such as healthcare. Structural adjustment mandated cut-backs in social services may exacerbate the situation further. Preliminary inquires into how women are faring using the above frameworks yield mixed results. These divergent findings suggest that that marketization’s effect on gender may be mediated by other social markers, such as minority status. Yet, surprisingly few empirical works explicitly examine structural adjustment’s impact on “doubly disadvantaged” minority women. In this article, I examine how minority women are faring in terms of one important quality of life indicator, access to maternity care, in two developing countries currently undergoing structural adjustment, Egypt and India. I estimate marketization’s impact on three groups of minority women: Egyptian Coptic Christian, Indian Muslim and Indian Scheduled Caste women’s access to maternity care, operationalized in terms of access to prenatal care and institutitional delivery of babies with logistic regression models, using data from Demographic and Health Surveys fielded in both countries. The results indicate that while minority women are generally less likely to have had access to maternity care, net of socioeconomic and regional marketization controls, this effect is most pronounced among Indian Scheduled Caste women. 2 In 1989, many countries in Central Eastern Europe underwent a sweeping series of political and economic changes known as the market transition. A short time later, another market transition commenced. Many developing countries – previously oriented towards Moscow -- liberalized their economies, under World Bank and IMF pressure, through structural adjustment programs.1 Many economists applauded these shifts, insisting that market-driven economic growth improves living standards in “Third World” nations, in keeping with neoclassical economic theory (see for example Bhagwati 1993; Bhoothalingham 1992; Dessus and Suwa-Eisenmann 1998; El Din 1998; Harik 1997; Lal 2000: 5). A critical literature, however, challenges these claims (Clark 2000; Dreze and Sen 1995; Ganguly-Scrase and Scrase 2001; Jogdand 2000a; Kienle 1998; Sen 1996a; 1996b; Taylor 2001; Taylor and Berg 2001). It asserts that structural adjustment decreases national living standards, as a result of increasing unemployment, rising inflation, and decreasing social spending that often occur in adjusting economies (Bibars 2001: 1; Jogdand 2000a; Kemal 2000: 71-72; Nayar 2000: 39; Swaminathan 2000). Some speculate that healthcare access, in particular, may be severely compromised as a result of structural adjustment-mandated privatization of social services (Bangser 2002: 262-263; Gezairy 2001: 36; Mc Michael and Beaglehole 2003: 10; Sanders et al. 2003: 141; Sen, Iyer and George 2002: 281-312). Women may find it particularly difficult to obtain needed health services (Blumberg 1995: 10; Fustukian, Sethi and Zwi 2002: 213; Standing 2002: 349, 351), especially in the areas of family planning and maternity care (Bangser 2002: 265; McMichael and Beaglehole 2003: 10). Yet, there is dearth of systematic, empirical analyses of how women fare following economic liberalization (Chua, Bhavani and Foran 2000: 821; Ganguly-Scrase and Scrase 2001: 1 Owoh (1995: 181), for example, notes that by late 1993, 35 of 46 African countries implemented structural adjustment programs. 3 146-148; Moghadam 1995: 18; Standing 2002: 364 Vecchio and Roy 1998: 10). Existing works often derive data from geographically circumscribed case studies or anecdotal sources, which while insightful, often yield inconsistent findings (Standing 2002: 364). Some works suggest that women bear the brunt of structural adjustment (Ghosh 1996: 115-120; Hill 2001: 124; Scott 1996: 80; Vecchio and Roy 1998: 5-7), while other studies indicate that women in developing countries benefit from economic liberalization (Feldman 1992: 116-127; Rock 2001: 225; Uswatte-Aratachi 2001: 81-83), or in some instances break even (Creevey 2002: 105). These contradictory findings suggest that structural adjustment’s effects on gender outcomes may be context- specific (Chua, Bhavani and Foran 2000: 836), or that gender may interact with other social markers in adjusting economies, such as race or class, yielding group-specific effects, especially in the area of health care access (Sen, George and Ostlin 2002: 7; Standing 2002: 364). I suggest that ethnoreligious affiliation serves as one such marker. Given that many developing countries have sizeable ethnoreligious minority populations,2 which often are subjected to various degrees of discrimination and/or persecution, questions concerning structural adjustment’s impact on minority women’s well-being are undeniably salient. Yet, surprisingly there appear to be relatively few works examining how “doubly disadvantaged” (Marcova 1995: 93) women fare during market transitions. Unnithan-Kumar (1999: 621), for example, laments a lack of research into the reproductive health issues of Indian Muslim women. Sen, Iyer and George (2002: 283-284) likewise note a lack of corresponding data on Scheduled Caste3 women in India. The latter may find their employment opportunities, and consequently, 2 3 Muslims, for example, comprise 12.5% of India’s 1 billion+ population (Census of India: 2003). The Indian census designation for members of the 66 officially recognized sudra ( so-called “untouchable”) castes. 4 their access to medical care, dwindling owing to privatization.4 Members of Scheduled Castes find it difficult to secure private-sector employment, owing to discriminatory hiring practices (Teltumbe 2000: 120). In this paper, I expand upon the above scholarship by examining the relationship between structural adjustment and minority women’s access to maternity care in two developing countries currently undergoing structural adjustment, Egypt and India. I selected Egypt and India as cases partly because they exhibit important similarities: both were former British colonies; both adopted quasi-socialist post-colonial economic development models; both underwent structural adjustment in 1991; both implemented “gradualist” (as opposed to “shock therapy”) reforms; both have sizeable minority populations; and both witnessed resurgent religious fundamentalism following marketization. Yet, they also display some striking differences. The Egyptian state, under Nasser, implemented sweeping socialist reforms, including guaranteed employment schemes; consequently prior to liberalization, unemployment rates were very low (Hansen 1991: 177-182). The wide scope of these reforms greatly improved the living standards of many Egyptians (Nagi 2001: 102-103). Muslim women, in particular, initially benefited from many of these reforms (Bibars 2001: 15-16; Hatem 1992: 190-191). However, following structural adjustment, the state eliminated many programs geared towards women, capitulating both to the demands of the “Washington Twins” (Pieper and Taylor 1998: 41)-- the IMF and the World Bank (Clark 2000: 160, 162), and of militant Islamists, who threaten Egypt’s political stability (Bibars 2001: 15-16; Hatem 1992: 190-191). The Indian state, however, gradually implemented less comprehensive social programs, directed toward improving the living standards of the rural poor and members of certain underprivileged minorities prior to 4 The Indian government reserves a percentage of public employment positions for members of Scheduled Castes; structural adjustment mandated privatization of state-held enterprises has led to the contraction of the Indian public 5 liberalization (Basu 2000: 92; Jogdand 2000b: 4; Ravillion 2001: 280-282). Thus, they appear to have had limited impact on women’s living standards. The Indian government made few changes to its existing social safety net following structural adjustment. While there are preliminary indications that marketization may have been more socially devastating in Egypt than India (Clark 2000: 160, 162; Dreze and Sen 1995; Hansen, 1998: 148-149; 1998: 302-303; Hinnebusch 2001: 123-124; Kienle 1998: 228; Rao and Dutt 2001; Taylor 2001; Taylor and Berg 2001), there are few systematic comparative analyses, which investigate whether these findings hold more generally for minority women. I focus on the outcomes of “doubly-disadvantaged” Egyptian Coptic Christian women and Indian Muslim and Scheduled Caste women, because they constitute politically and/or economically vulnerable populations in their respective milieu (Ibrahim et al 1996: 16; Lateef 1998: 260; Thorat 2000: 217), and thus, I speculate may be especially susceptible to diminished healthcare access during structural adjustment. I concentrate on maternity care for three reasons. First, it is strongly correlated with maternal mortality and morbidity, both important indicators of women’s well being in developing countries (Unnithan-Kumar 2002: 387). Second, the promotion of women’s reproductive health is likewise considered essential for “sustained development” in these milieus (United Nations 1995: 8-9). Third, access to maternity care is an internationally sanctioned reproductive right (United Nations 1995: 13). Two main perspectives inform existing studies of structural adjustment and women’s access to maternity care: the neoliberal and gender and development approaches. Each has important empirical implications for Egyptian and Indian minority women. I briefly review each theoretical framework below. sector (Patankar 2000: 64; Teltumbe 2000: 125). 6 Neoliberal Theory, Structural Adjustment and Minority Women’s Health in Developing Countries Neoliberal theory, the force behind recent market-driven economic reforms in developing countries (Agrawal et al. 1995; Bhagwati 1993; Bhagwati and Srinivasan 1993; Bhoothalingham 1992; Dessus and Suwa-Eisenmann 1998; El Din 1998; El-Erian and Mohieldin 1998: 4; Harik 1997; Lal 2000; 2001), adopts a generally positive stance on structural adjustment’s impact on healthcare access (Uswatte-Aratchi 2001: 81-82). 5 This perspective holds that liberalization stimulates rapid economic growth (typically operationalized in terms of GDP/GNP) (Bhagawati 1993: 25; Bhagwati and Srinivasan 1993; Dutt and Rao 2001; Harik 1997; Lal 1985; 2000; see also critique in Byres 1998), which enhances everyone’s ability to obtain necessary health services (See review in McMichael and Beaglehole 2003: 4), including minority women’s. Neoliberal economists often cite two mechanisms through which these market-driven improvements occur. The first is the neoclassical “trickle-down” effect of wealth generated by increased economic growth (Stewart 2001: 188; see also critiques in Sen 1996a; 1996b and Lal’s (2000:5, 156) implicit rebuttal to Sen). The second is the market-driven expansion of wage labor, even if it only generates menial forms of employment (Lal 1985; 2000). Both mechanisms ultimately increase wages, thus reducing poverty and improving income distribution (Lal 1985: 90-93), which facilitates access to health care (Dutt and Rao: 180). Consequently, neoliberal theory downplays the need for massive state investment in social programs during structural 5 Some neoliberal economists concede that marketization may have social costs, such as increased inequality. However, they view adverse outcomes as temporary setbacks, which will be reversed as the transition to free markets progresses (Harik 1992: 496; 1997: 198). Stewart (2001: 188) ties the likelihood of social costs to the rate of market-driven economic growth: rapid, continual economic growth will offset any adverse changes in income distribution. Stewart (2001: 188) also asserts that growth in GDP per capita, in particular, reduces private income poverty, and thus, is rarely immiserizing. 7 adjustment.6 Indeed, it advocates the privatization of healthcare sector as a means through which developing countries can achieve lasting improvements in national health (Lal 2000: Chapter 7; World Bank 1993).7 Some empirical studies indicate that market-driven growth may positively affect women’s health. Seth (2001: 166), for example, cites Indian studies indicating that employment of women in urban areas improves maternal nutritional status among lower middle class women, and prevents the nutritional deterioration of low-income poverty groups. Other evidences links female wage employment in the Philippines also with improved nutritional status, particularly among poor women (Bisgrove and Popkin 1996; Ostlin 2002: 66). Uswatte-Aratchi (2001: 8182) cites major improvements in healthcare provision in Sri Lanka following structural adjustment, particularly in the area obstetrics and gynecology. Most notably, the national infant mortality rate fell by fifty percent following economic adjustment (Uswatte-Aratchi 2001: 81). Owoh (1995: 188-189) notes that while implementation of user fees following structural adjustment in Nigeria rendered access to maternity care difficult for many poor women, it had the unintended benefit of giving many Nigerian women more autonomy over the birthing process, as private clinics now openly competed with poorly managed state facilities for “clients.” In sum, the neoliberal perspective views structural adjustment’s ultimate impact on national health positively: free markets stimulate economic growth and increase the demand for 6 Deepak Lal (2000: 150), for one, is critical of state investments in social safety nets in developing countries, calling it a form of “nirvana economics”, a “fashionable left ideology”, which in his view lacks “credible justification.” 7 The World Bank (1993), for example, strongly promoted the utility of competition in the delivery of health services in developing milieux asserting that “competition can improve quality and drive down costs, governments should foster competition and diversity in the supply of health services and inputs, particularly drugs, supplies and equipment.” 8 wage labor, both of which facilitate access to healthcare for all members of society, a benefit, which in theory, should be applicable to maternity care for minority women. Gender Approaches, Structural Adjustment, and Minority Women’s Health in Developing Countries Gender analysts tend to take a dimmer view of structural adjustment’s affects on women’s access to maternity care and other essential services than neoliberal economists (Bibars 2001: 20-23; Datta and Kornberg 2002: 5; Dreze and Sen 1995: Chapter 7; Standing 2002: 349 Unnithan-Kumar 1999: Vecchio and Roy 1998: 39). Some gender and development theorists assert that structural adjustment adversely affects women’s access to healthcare in two ways. First, it often mandates cutbacks in vital social services (Bibars 2001: 1; Jogdand 2000a; Kemal 2000: 71-72; Nayar 2000: 39; Swaminathan 2000), including state-subsidized health programs (Bangser 2002: 263; Beaglehole and Bonita 2003: 260; Standing 2002: 352). Family planning and maternity care programs are often hit hard by these cuts (McMichael and Beaglehole 2003: 10), and thus, may adversely affect maternal and infant outcomes (Standing 2002: 349). Second, market-driven expansion of wage-labor promotes the rapid adoption of “flexible labor practices,” i.e., the creation of insecure, low-paying employment positions, typically occupied by women, a global phenomenon known as the “feminization of labor” (Connelly et al 2000: 66; Fustukian, Sethi and Zwi 2002: 213; Mies 1994: 116-117; 1998: 16). The menial jobs held by women in developing countries do not offer health benefits (Momsen 1998: 105-106). Furthermore, the meager wages earned by female workers places them at a material disadvantage relative to men, thus, often putting healthcare out of their financial reach, especially in light of mandated cuts in social services (Sen, George and Ostlin 2002: 8). These setbacks may be even more pronounced for socially disadvantaged groups of women, including minorities (McPake 9 and Mills 2000; Sen, Iyer and George 2002: 281), such as Indian Scheduled Caste women (Deshpande 2000: 174). There is some empirical support for these assertions. Standing (2002: 351) notes that home deliveries appear to be on the rise in a number of adjusting countries, leading to worse maternal and infant outcomes. In Zimbabwe, for example, women limited their antenatal care and began giving birth at home, following the imposition of user fees, as they could no longer afford obstetrical care (Bangser 2002: 265). Other studies associated the implementation of user fees in Nigeria with a 46% decline in hospital deliveries and a 56% increase in maternal deaths (Bangser 2002: 265). Other scholars looking at gender costs to structural adjustment suggest that the likelihood of poor reproductive outcomes may be offset by continued or increased state investment in healthcare programs. However, these funds must be targeted towards women, and in diverse countries like India, tailored to the specific needs of women in various regional settings, if they are to yield desired outcomes (Pachauri 1998: 335-336). Evidence supports a link between targeted state investment in health and improved maternity care. Frankenberg and Thomas (2000: 253) and Frankenberg (1995: 160) find that health programs implemented in Indonesia, geared toward improving Indonesia’s abysmal maternal/infant mortality rates were effective in achieving these ends. Ravindran (1996: 97) likewise finds that an analogous program initiated in the Indian state of Kerala yielded similar results. By extension, minority women should also benefit from such state investment in programs, which target their needs. Thus, in contrast with neoliberal economists, who link marketization with economic growth that improves women’s living standards, a critical gender literature contends that 10 liberalization adversely affects women’s living standards. The feminization of labor, which exacerbates existing economic hardships, and mandated cutbacks in health spending, may greatly impede women’s access to maternity care. Economically disadvantaged populations or groups subject to discrimination, such as minority women, may more acutely feel the burden of these cutbacks. Some of structural adjustment’s adverse effects, however, may be mitigated by targeted state funding in healthcare. Postcolonial Egyptian and Indian Economic Development and Minority Women As indicated previously, I selected Egypt and India as cases because they are similar in key ways (e.g., shared colonial legacies and timing of the latest economic reforms), yet different in others (e.g., certain pre-and post- marketization state social, economic and political factors). I speculate that these differences may contribute to distinct local outcomes with respect to minority women’s living standards in these states. In the following sections, I overview postcolonial developments in these countries, focusing on key areas correlated with maternity care access, such as education, and political, cultural and/or social barriers faced by Egyptian Coptic Christian and Indian Muslim and Scheduled Caste women, which may impede such access. Egyptian Coptic Christian Women Unfortunately, there is dearth of literature detailing how Coptic Christian women have fared in post-colonial Egypt. They plausibly did not do well under Gamal ‘Abd ul-Nasser (see Ayalon (1999) and Ibrahim et al. (1996: 16) for overview of the Copts’ declining living conditions under Nasser). Nasser’s nationalization policies hit the Coptic elite hard; many lost their property and business enterprises (Ayalon 1999; Ibrahim et al 1996: 16). His government discriminated against Christians in filling state and public sector positions and in allocating 11 higher education funds (Ayalon 1999: 55, 58). Given Egypt’s repressive, one-party system, Copts lacked an effective political channel through which to protest these discriminatory practices (Ayalon 1999: 55). Faced with unrelenting discrimination, limited economic opportunities and little political recourse, wealthy Copts emigrated to the United States, Canada, and Australia en masse, leaving the economically vulnerable members of their community behind (Ayalon 1999: 26). Conditions deteriorated even further under the leadership of Anwar al-Sadat, who encouraged Islamic radicalism in the interest of political expediency to the further detriment of the Coptic Christian population (Ayalon 1999: 56, 57; Ibrahim 1996: 18-20; Waterbury 1983: 359-364). Structural adjustment has conceivably aggravated Coptic women’s living situation. Economic liberalization has exacerbated militant Islamism in Egypt (Clark 2000: 160; Hinnebusch 2001: 123-124; Kienle 1998: 228; Moghadam 1995: 30) to the further detriment of Egyptian women (Bodman 1998: 15-17; Moghadam 1995: 30), both Muslim and Coptic. Coptic Christian, however, women may be particularly vulnerable to persecution by militant Islamists: most Copts live in impoverished Upper Egypt, where radical Islamism is most intense (Ibrahim et al 1996). Given that most Coptic women remaining in Egypt are poor, the severe cutbacks in the State’s formerly well-funded healthcare system may impede their access to vital services such as maternity care (Ibrahim et al 1996). Indian Muslim Women Muslims remaining in post-partition India were overwhelmingly poor and had limited political or legal recourse (Parikh 1998: 43). Periodic outbreaks of communal violence, coupled with the passage of the Evacuee Property Act of 1950, which deprived Muslims of property rights when family members migrated to Pakistan, drastically undermined the post-colonial 12 living conditions of Indian Muslim women (Lateef 1998: 260). Unlike members of Scheduled Castes, disadvantaged Muslims could not demand compensatory treatment, owing to Nehru's "determinedly secular approach", which discouraged the use of religion as a basis for mobilization (Parikh 1998: 43). As in the case of Coptic Christian women, structural adjustment in India may be exacerbating Muslim women’s political and economic vulnerability. Hindu nationalism, which displays a marked middle-class (and upper caste) “Hindu” bias, that stigmatizes Muslims (Breman 1999; Desai 1999; Hansen 1998a; 1998b; Jaffrelot 1996; Lateef 1998: 267), flourished following economic liberalization. Muslim women living in areas where Hindu nationalist sentiment is especially pronounced, such as Maharashtra, may find their access to subsidized maternity care and other vital services owing to institutional discrimination. For example, Maharashtra's Hindu nationalist ruling BJP-Shiv Sena coalition administers its own social programs, which allegedly disproportionately benefit its largely middle-class Hindu constituency (Hansen 1998: 302-303; Patnaik et al. 1996: 60-61). The life chances of Indian Muslim women, too, are further compromised by militant Islamism, imported from the Middle East via returning migrant workers. Militant Islamists, for example, successfully mobilized around the passage of the 1986 Muslim Women's Act, which binds Muslim women to archaic Hanafi 8 legal codes that severely restrict their rights to alimony and child support, should their husband decide to divorce them, which they may do at will, and until recently, orally. These adverse developments may further hinder Muslim women’s ability to obtain maternity care and other health services during structural adjustment. 8 One of the major traditional legal schools of Islam. 13 Indian Scheduled Caste Women Following independence, a minority of Scheduled Caste women achieved modest economic gains, owing to reservation policies, which had originally been stipulated in the 1932 Poona Pact negotiated between Mahatma Gandhi and B.R. Ambedkar, then leader of an important lower-caste social movement (Parikh 1998: 38). The reservations set aside a number of places in universities and government employment in each Indian state (Parikh 1998: 38). While the established quotas often went unfilled (Parikh 1998:38), the number of government employees from the Scheduled Castes nonetheless increased steadily (Thorat 2000: 216). Poverty rates among Scheduled Caste households also decreased during the pre-1991 structural adjustment period (Thorat 2000: 217), no doubt facilitating better healthcare access for at least some Scheduled Caste women. However, structural adjustment may be reversing some of the modest gains made by Scheduled Caste women, owing to the decline of the public sector (where quotas ensured them and/or their spouses a number of employment positions), the decline of other areas traditionally employing scheduled caste members including the non-farm rural sector and lingering caste discrimination (Thorat 2000: 215). Consequently, they may lack the financial resources necessary to obtain maternity care in the post-adjustment economic environment. In sum, the above sketches indicate that minority women in Egypt and India may be at risk for decreased access to maternity care during structural adjustment. Economic liberalization may have exacerbated the already precarious living conditions of both Egyptian Coptic and Indian Muslim women. Adjustment, too, may be reversing the few gains achieved by Indian Scheduled Caste women following independence. In the following section, I outline the theoretical implications for minority women’s access to maternity care in both milieus. 14 Analyzing Structural Adjustment and Minority Women’s Access to Maternity Care in the Egyptian and Indian Contexts In this paper, I investigate whether minority women, in two developing countries Egypt and India are disproportionately bearing the costs of structural adjustment in terms of decreased access to maternity care services. As noted previously, two influential approaches inform many scholarly inquiries into these relationships: the neoliberal and the gender perspectives. I present the empirical implications of each for Egyptian and Indian cases below. I. Neoliberal Approaches The proponents of the neoliberal perspective hold that structural adjustment driven economic growth improves women’s access to healthcare, though the mechanisms through which this process occurs vary. Many neoliberal theorists attribute market-driven improvements in living standards with a “trickle down effect” of increased GDP per capita (Stewart 2001: 188). If this applies also to minority women, then minority women’s access to maternity care should improve as a function of GDP per capita, net of the control variables. Other neoliberal economists do not directly attribute improved access to healthcare with increased GDP per se, but rather through market-driven expansion of wage labor, both skilled and unskilled, since even menial employment allegedly expands women’s economic capabilities (Lal 1985: 95; 2000: 177). If these benefits, do indeed, extend more generally to minority women, then the data should indicate a positive effect of regional level of wage labor on their access to maternity care. II. Gender Approaches Many gender theorists, in contrast, are wary of structural adjustment programs. In particular, they view the expansion of wage labor as detrimental to women and young girls. Subsequently, they suggest that women’s access to health care will suffer following structural 15 adjustment since the aforementioned expansion of a wage labor economy often forces women into menial jobs. This situation is aggravated by adjustment mandated cuts in state-subsidized health programs in developing countries; often family planning and reproductive health services are especially vulnerable in these respects. Even if states make substantial investments in public healthcare, this may not have a significant impact on women’s access to healthcare, particularly if these funds are not efficiently allocated and/or specifically targeted to reproductive healthcare. Thus, economically challenged minority women may not be able to afford maternity care in this context. If this is the case, the level of wage labor should not show a positive, mediating effect on minority women’s access to maternity care. Methods and Variables Data I use data primarily from Demographic and Health Surveys (DHS) for Egypt and for India (http://www.measuredhs.com). In these analyses, I draw upon the women's survey, administered to all ever-married women, aged 15-49 present in a given household, which collects data on respondents’ background characteristics, access to heath care, childcare and other gender-related issues (DHS 1992: 10). The questionnaires were administered in the appropriate local vernacular. I briefly overview the DHS sampling procedures for Egypt and India below. The 1992 and 1995 Egyptian DHS Surveys The geographical coverage of the Egyptian DHS differed slightly between the two years. Egypt is divided into 26 governorates; the 1992 EDHS excluded the five frontier governorates owing to a lack of resources. However, the impact of this omission was negligible; these areas only account for about 1% of Egypt's total population (DHS 1992: 201). In the 1995 survey, however, the EDHS expanded its coverage to the frontier governorates (DHS 2000: 220). 16 The EDHS selected households for interviews using a three-stage probability sampling strategy (1992:201). For each governorate, a list of towns comprised the initial primary sample frame for urban areas, and a list of villages of constituted the frame for rural areas (202). During the second phase, the EDHS obtained detailed maps of each town and village with over populations over 20,000. These maps were divided in to parts of equal size; one part was selected from each PSU. The EDHS then conducted quick count field operations in both urban and rural PSUs to obtain information about dwelling units. The EDHS next systematically selected two segments from urban areas and one segment from rural areas from each PSU, with probability proportional to the 1986 Egyptian Census population for each survey. During the third stage, the EDHS systematically sampled residences from household lists from these segments. Response rates were around 98% for both survey years. For example, the EDHS selected 11, 304 residences for the 1992 household survey, of which 10,761 were successfully interviewed. In the interviewed households, 9,978 eligible women were found, of whom 99% were interviewed (1992: 212). The 1992 and 1999 Indian DHS Surveys The Indian National Family Health Survey (NFHS) fielded the 1992 and 1999 DHS surveys in India on state-by-state basis (DHS 2000b: 8). The NFHS adopted a uniform sample design each state. First, it selected the rural sample in two stages using the 1991 Census list of villages as the sample frame: the selection of PSUs, which are villages, with probability proportional to population size (PPS), followed by the random selection of households within each PSU (9). The first level of stratification was geographic, with districts being subdivided into contiguous regions. Villages were stratified within regions, using selected variables (including 17 sub regions, village size, and percentage of membership in scheduled castes). The NFHS then systematically sampled villages with probability proportional to the 1991 Census population of the village (8). It linked small villages (5-49 households) to an adjoining village to form PSUs with a minimum of 50 households (8). The NFHS conducted a mapping and household listing operation in each sample area in every state (9). These listings provided the frame for second stage household selection. Large sample villages were segmented, and two segments were selected randomly using the PPS method (9). The NFHS selected households to be interviewed with equal probability from the household list in each area using systematic sampling. Typically, 30 households were targeted for selection in each selected enumeration area. The NFHS implemented a three-stage procedure in urban areas. First, it selected wards with PPS sampling. Second, it randomly selected one census enumeration block (CEB) from each sample ward. In the final stage, it randomly selected households within each sample CEB (8). The NFHS conducted the selection process in urban areas as follows. First, it arranged 1991 Census wards according to districts within districts by the level of female literacy, and a sample of wards was selected systematically with probability proportional to size (10). Next it selected one census enumeration block from each selected ward. As in rural areas, the NFHS initiated a household listing operation in the census enumeration block, which served as the basis of the household selection frame in the third selection stage (10). Once the sampling was completed, the surveys were administered, yielding overall response rates of around 96% (2000b: 11). Individual Level Variables Table 1 shows the means and standard deviations of the variables used in following analyses for India and Egypt. I coded two dependent variables to measure access to maternity care, both 18 of which are strongly correlated with maternal morbidity and mortality in developing countries. Prenatal care is a dichotomous variable, coded 1 for women who indicated they received antenatal care for any pregnancies occurring within the last three years prior to the interview from a certified practitioner9 and 0 if they did not. Institutional delivery is coded 1 for women who delivered babies in a clinical setting (e.g., hospital, maternity clinic) and 0 if for women who delivered elsewhere. In Egypt, minority status is coded 1 one for Christian respondents and 0 for Muslim respondents.10 In India, minority status is coded 1 for Muslims, 2 for members of Scheduled Castes or Tribes and 0 for women who self-identified as non-Scheduled Caste Hindu.11 I control for SES using two variables.12 High educational attainment is coded 1 for women who attended college and 0 for women who did not. High occupational status of spouse is coded one for women whose current/past husbands were white collar/professional employees, and 0 for women who husbands who had lower paying occupations.13 I include four other standard control variables. Rural is a dichotomous variable coded 1 for women living in rural areas, and 0 otherwise. I use this variable since rural residence strongly correlated with limited access to maternity in both India and Egypt. Number of preschool children present in respondent’s household and respondent’s age, both coded as continuous 9 In the case of India, I code women receiving care from homepaths and aryuvedic practioners as 0, since these types of doctors are notorious for practicing allopathic medicine without being licensed to do so, often to the detriment of their patients (Sen, Iyer and George 2002: 296). I also code women receiving care from untrained dais (midwives) as 0, since they often use unclean instruments and rustic techniques that put women at high risk for potentially fatal complications (Unnithan-Kumar 2002). 10 There were 2 respondents who self-identified as “other” in the data. I dropped them for the purposes of these analyses. 11 I originally coded a fourth response category from the Indian data for women indicating Sikh, Buddhist, Christian or Jewish religious affiliation. However, this category did yield any significant results in any of the models, so I dropped it from the analyses. 12 I do not include income, since the DHS does not have good measures of income. However, income is often a poor indicator of socioeconomic status in developing countries (Sen 2001: 173).In Egypt, for example, the state offsets the poor wages of public sector employees with generous subsidies on utilities, housing and luxury items; thus, income is not a good indicator of living conditions in these instances (Harik 1997). 13 I use spouse’s occupation as an SES indicator rather than the respondent’s occupation, given the low formal labor market participation rates of women in developing countries. 19 variables, are added as demographic controls. I include survey year, coded 0 for the earlier survey and 1 for the later survey as a temporal control. Regional Macro Variables The macro-level variables are derived from regional indicators published UNDP National Human Development Reports (1996; 2000).14 Regional GDP per capita is coded as a continuous variable indicating GPD per capita for a given governorate (in Egypt) or state (in India) for a given year. I include this variable since it has important implications for neoliberal theories of economic development and maternity care access in developing countries. Regional ratio of wage labor is calculated as the number of wage earners employed in the private sector over the total number of private+public sector wage earners in a given governorate or state in a given year. I use this variable to gauge the extent to which given region’s labor force is embedded within the market economy. As noted earlier, the literature is divided over how market-driven expansion of wage labor affects women’s access to maternity care. Regional state per capita investment in health is indicating the amount of per capita health funding provided to a given governorate (in Egypt) or state (in India) for a given year. I include this variable, in part, to assess whether state healthcare funds are being efficiently allocated with respect to women’s reproductive health needs. Quantitative Models I will examine the relationship between minority status and access to maternity care using logistic regression models, since the dependent variables are dichotomous. I correct for the clustering of respondents within regions by calculating the standard errors using robust variance analysis (South and Crowder 1999: 120). I run the models separately for each country. 20 Results Minority Women and Access to Prenatal Care Tables 2 and 3 show the odds ratios for women’s access to prenatal care for Egypt and India, respectively. The results indicate that SES has a strong, positive effect on women’s access to antenatal care in general. Highly educated women in Egypt (Table 2, Model 1), for example, are 4.29 times more likely to have had prenatal care than less educated women net of control variables; in India (Table 3, Model 5), highly educated women are almost 12 times more likely to have had prenatal care than less educated women. Women whose spouses enjoy occupational status (Table 2, Model 1 and Table 3, Model 5), too, about twice as likely to have had prenatal care in both countries versus women whose spouses occupy lower occupational niches. The data show, however, that minority status in both Egypt and India is negatively correlated with access to antenatal care, net of socioeconomic status and the other control variables. In Egypt (Table 2, Model 1) Christian women were .810 times less likely than Muslim women to have had any prenatal care. In India (Table 3, Model 5) Muslim women were .779 times less likely and Scheduled Caste women were .585 times less likely than Hindu women to have had prenatal care. Regional GDP per capita (Table 2, Model 2 and Table 3, Model 6), in keeping with neoliberal predictions, was a strong predictor of Egyptian and Indian women’s access to prenatal care in general. Egyptian women living in higher GDP regions were 2.90 times more likely to have obtained prenatal care (Table 2, Model 2); Indian women living in states with high GDP per capita were 4.83 times more likely to have obtained prenatal care, net of the control variables. 14 In some cases, no data was provided for a given region, thus creating some differences in the Ns for models using estimating the effects of the regional level variables. [note to P: Use some of Treiman’s suggestions to correct for this in future runs of the data]. 21 Regional GDP does explain Egyptian Christian women’s access to prenatal care (Table 2, Model 2) and Indian Muslim women’s access to prenatal care (Table 3, Model 6) to a certain extent. It does not, however, affect Scheduled Caste women’s likelihood of having prenatal care, relative to non-Scheduled Caste Hindu women, net of the control variables (Table 3, Model 6). The regional level of wage labor does not significantly affect Egyptian women’s access to prenatal care (Table 2, Model 3). However, it does, however, mediate the likelihood of Christian women’s access to prenatal care. In contrast, the regional level of wage labor in India has a strong, significant effect on women’s access to prenatal care (Table 3, Model 7): women living in high wage labor regions are 1.51 times more likely to have had prenatal care, than women living in regions with lower rates of wage labor participation. [This strong effect can probably be explained by the explosive growth of information technology and other organized sectors employing educated, primarily upper-caste Hindu Indians (Burange 1999: m48; Ghose 1999: 2605-2606; Lakha 1994: 216; Nambiar, Mungekar and Tadas 1999: 417, 423-424), following structural adjustment. Organized sector participation is more likely to be recorded in official labor statistics than informal sector participation]. The regional level of wage labor does, however, explain some of the differences between Muslim and Hindu women’s access to prenatal care (Table 3, Model 7). It however, does not affect Scheduled Caste women in this respect: they were .564 times less likely to have received prenatal care than Hindu women, net of the level of regional wage labor and the other controls. The regional level of state per capita investment in healthcare was not a strong predictor of obtaining prenatal care in either country (Table 2, Model 4 and Table 3, Model 8), though it does explain some of the differences in Indian Muslim and Hindu’s women’s likelihood of having had prenatal care (Table 3, Model 8). [These results, in particular, support some gender 22 theorists’ assertions, that Egypt and India, like many other developing undergoing structural adjustment are not efficiently channeling funds into reproductive healthcare programs]. Minority Women and Deliveries in Clinical Settings Tables 4 and 5 show the odds ratios for the likelihood of institutional deliveries for Egyptian and Indian women, respectively. The results indicate a strong, positive SES effect on this outcome net of the other controls for women in general. Highly educated Egyptian mothers (Table 4, Model 9), for example, are 3.16 times more likely to have delivered babies in a clinical setting than less educated mothers, net of control variables; in India (Table 5, Model 13), highly educated mothers were about 7 times more likely to have delivered babies in institutional settings, net of the controls, than less educated mothers. Women whose spouses enjoy high occupational status (Table 4 Model 9 and Table 5, Model 13), too, were about twice as likely to have delivered babies in clinical settings than women whose spouses have lower occupational status. The results indicate, however, that minority mothers in both Egypt and India are less likely to have delivered babies in institutional settings, net of socioeconomic status and the other control variables. In Egypt (Table 4, Model 9) Christian mothers were .799 times less likely than Muslim mothers to have had an institutional delivery. In India (Table 5, Model 13) Muslim women were .702 times less likely and Scheduled Caste women were .438 times less likely than Hindu women to have delivered babies in clinical settings. Regional GDP per capita (Table 4, Model 10 and Table 5, Model 14), once again, was a strong predictor of Egyptian and Indian women’s likelihood of having institutional versus noninstitutional deliveries, in keeping with neoliberal predictions. It also explains some of the difference Egyptian Christian versus Muslim mothers' likelihoods (Table 5, Model 14) and 23 Indian Muslim versus Hindu mothers' likelihoods (Table 5, Model 14) of having delivered babies in clinical settings to a certain extent. It does not, however, impact Scheduled Caste mothers' likelihood of delivery in an institutional versus non-institutional setting (Table 5, Model 14). The regional level of wage labor does not have a significant impact on Egyptian women’s likelihood of institutional deliveries (Table 4, Model 11). It does however, mediate Christian versus Muslim likelihoods of having delivered babies in hospitals or other clinical settings (Table 4, Model 11). In contrast, the regional level of wage labor in India has a very strong, significant effect on women's likelihood of delivering in a clinical versus non-clinical setting (Table 5, Model 15): women living in high wage labor regions are 2.03 times more likely to have delivered babies in hospitals or other medical facilities then women living in regions with lower wage labor regions. [This strong effect, once again, is attributable to the fact that a significant amount of expansion in India’s organized sector has occurred in the area of information technology, and thus, has generated high paying jobs with benefits. It is plausible that middleclass women, in particular, now may have improved likelihood of delivering babies in clinical settings, either directly, through their own employment, or indirectly, through their husbands' lucrative employment formal sector employment]. However, the regional level of wage labor does not significantly mediate Muslim and Scheduled Caste women’s likelihood of accessing prenatal care. Muslim women were .561 times less likely and Scheduled Caste women were .430 times less likely to have delivered babies in medical facilities than Hindu women, net of the level of regional wage labor and the other controls. The regional level of state per capita investment in healthcare was not a significant predictor of the likelihood of an institutional versus non-institutional delivery in either country (Table 4, Model 12 and Table 5, Model 16), though it does mediate both Egyptian Coptic 24 Christian (Table 4, Model 12) and Indian Muslim (Table 5, Model 16) women’s likelihood of having had prenatal care. [These results, once again, point to a need for more carefully targeted state investment in reproductive healthcare in these milieus]. Discussion and Conclusion In the early 1990s, many developing countries implemented structural adjustment programs under World Bank and IMF pressure. The net impact of these programs on national living standards continues to be debated in the literature. Neoliberal economists assert that market-driven economic growth improves quality of life in transitional nations, either through a trickle-down effect of GDP or an expansion of wage labor (Bhagwati 1993; Bhoothalingham 1992; Dessus and Suwa-Eisenmann 1998; El Din 1998; Harik 1997; Lal 2000: 5). Critics, however, assert that structural adjustment adversely impacts the poor, citing increased unemployment rates, rising food costs and social spending cuts that often accompany shifts to free markets (Bibars 2001: 1; Jogdand 2000a; Kemal 2000: 71-72; Nayar 2000: 39; Swaminathan 2000). Gender analysts suggest that women, who often are forced into menial jobs following structural adjustment, may experience diminished access to vital services, such as reproductive healthcare (Bangser 2002: 262-263; Gezairy 2001: 36; Mc Michael and Beaglehole 2003: 10; Sanders et al. 2003: 141; Sen, Iyer and George 2002: 281-312), which is an internationally sanctioned reproductive right (World Bank 1995:13). These adverse effects may be magnified by certain social markers in adjusting economies (Sen, George and Ostlin 2002: 7; Standing 2002: 364). I suggested that ethnoreligious minority status may be one such distinguishing characteristic. To this end, I analyzed the likelihood of maternity care access of three groups of religious minority women: Egyptian Coptic Christians, Indian Muslims and Indian Scheduled Caste 25 women, living in countries currently undergoing structural adjustment. I selected these groups because these women are economically and politically vulnerable. Baseline models indicated that minority women are significantly less likely to have had prenatal care and delivered babies in clinical settings, relative to majority women in these countries, net of important correlates of healthcare access, such as socioeconomic status and rural residence. However, the effect of minority status for Egyptian Christian women and Indian Muslim was offset by the level of regional GDP per capita, which exhibted a strong, significant effect of women’s general likelihood of obtaining prenatal care and having delivered babies in an institutional setting. regional GDP did not have a strong, positive effect on women’s access to maternity care in general, it did have mediating effect on Egyptian Christian and Indian Muslim women’s access to prenatal care. It, however, did not have any impact on Scheduled Caste women’s access to prenatal care. The regional level of wage labor, contra to both the neoliberal approach (which posits a positive effect on women’s healthcare access) and the more critical gender approaches (which posit a negative effect on women’s healthcare access), did not have a significant impact on women’s access to maternity care in Egypt. It did, however, explain some of the effect of Christian status on the likelihood of access prenatal care. It contrast, the regional level of wage had a strong positive effect on maternity care outcomes in India. It is unclear, however, to what extent this finding supports the neoliberal perspective, since the variable was coded from official labor participation statistics, which may be reflecting the explosive growth of high-paying organized sector jobs in India. India’s informal sector (where most women are likely to menially employed) has also demonstrated rapid post-structural adjustment growth, but its labor 26 participation rates are not likely to be recorded in official statistics.15 Thus, these results may reflect more of an SES effect as opposed to a labor market effect, and therefore, must be interpreted cautiously. Notably, the positive effect of wage labor did not mediate the effects of Muslim and Scheduled Caste status on the likelihood of access to prenatal care or institutional deliveries. The results also showed that regional level of per capita state investment in health is not a significant indicator of maternity care access in general in these countries. Regional differences in health spending do explain some of the effect of Muslim women’s access to maternity care in the Indian case. However, the overall lack of an effect strongly points to the need for both states to more efficiently allocate resources for women’s reproductive healthcare services, particularly for minority women, who are clearly “doubly disadvantaged” in this regard. In conclusion, I stress that these are preliminary findings, which require further empirical investigation. Nonetheless, the results support calls in literature for the need to consider the effects of minority status, and other “social markers” on women’s health outcomes in adjusting economies. 15 In future analyses, I plan to run models with regional labor participation variables coded directly from respondents’ information in the DHS data sets. This should provide a more “balanced” accounting of organized versus informal labor participation for my data. 27 Table 1. Descriptive Statistics of Variables Used in Analyses, Ever-Married Women, 18-49, Egypt, 1992 & 1995 and India, 1992 & 1999 (Ns and STDs in Parentheses). Variable Dependent Variables Egypt India Access to Prenatal Care .427 (13,744) .615 (66,154) Institutional Delivery .289 (13,731) .303 (66, 075) Christian .057 (24,639) -- Muslim -- .111 (180,013) Scheduled Caste -- .205 (180,013) Rural .556 (24,643) .691 (180,080) Hi Educational Attainment .712 (24,643) .060 (180,080) Spouse High Occupational Attainment .826 (22,862) .081 (173,206) Widowed Divorced .072 (24,643) .060 (180, 080) Age of Respondent 32.8 (std: 8.54) (24,643) 31.14 (std: 8.81) (180,080) Number of Preschool Children in Home 1.21 (std: 1.35) (14,779) 1.00 (std: 1.18) (180,080) GDP Per Capita 3,756 (std: 1,250) (14,779) 2,514 (std: 1,120) (176,062) Ratio of Wage Labor .324 (.std: 485) (13,533) .259 (std: .112) (177, 856) Per Capita State Health Investment 13.78 (std: 10.05) (13,533) 1.37 (std: .757) (174, 757) Independent Variables Control Variables Regional Variables 28 Table 2. Odds Ratios from Logistic Regression of Access to Prenatal Care on Selected Independent Variables, Ever-Married Women 18-49, Egypt, 1992 & 1995 (P-Values in Parentheses). Independent Variable Model 1 Model 2 Model 3 Model 4 Christian .821 (.027) .852 (.193) .789 (.121) .776 (.055) High Educational Attainment 4.20 (.000) 4.60 (.000) 4.77 (.000) 4.77 (.000) Spouse High Occupational Status 2.00 (.000) 1.98 (.000) 1.94 (.000) 1.94 (.000) Employed 1.22 (.000) 1.21 (.041) 1.24 (.033) 1.25 (.000) Widowed/Divorced .639 (.003) .567 (.026) .562 (.023) .560 (.021) Rural .343 (.000) .387 (.000) .321 (.000) .332 (.000) Age .984 (.000) .984 (.000) .984 (.000) .984 (.000) Number of Preschool Children Present in Household .765 (.000) .757 (.000) .747 (.000) .747 (.000) 1995 .508 (.000) .764 (.102) .539 (.005) .516 (.000) Regional Level Variable Per Capita GDP 1.00 (.016) Wage Labor .988 (.579) Per Capita State Investment in Health N 1.00 (.000) 12,845 8, 757 8,757 8,757 29 Table 3. Odds Ratios from Logistic Regression of Access to Prenatal Care on Selected Independent Variables, Ever-Married Women 18-49, India, 1992 & 1999 (P-Values in Parentheses). Independent Variable Model 5 Model 6 Model 7 Model 8 Muslim .767 (.000) .880 (.417) .658 (.021) .814 (.228) Scheduled Caste .593 (.000) .634 (.000) .583 (.000) .630 (.000) High Educational Attainment 11.98 (.000) 11.66 (.000) 10.8 (.000) 11.9 (.000) Spouse High Occupational Status 1.89 (.000) 1.92 (.000) 2.02 (.000) 1.76 (.000) Employed .876 (.000) .825 (.044) .868 (.170) .857 (.143) .811 (.003) .781 (.014) .881 (.397) .721 (.001) Rural .239 (.000) .312 (.000) .245 (.000) .241 (.000) Age .958 (.000) .960 (.000) .964 (.000) .954 (.000) Number of Preschool Children Present in Household .912 (.000) .918 (.001) .925 (.000) .916 (.001) 1.75 (.000) 1.53 (.005) 1.96 (.000) 1.89 (.000) Widowed/Divorced 1999 Regional Variables Per Capita GDP 1.00 (.002) Wage Labor 68.2 (.000) Per Capita State Health Spending N 1.43 (.087) 64,078 62,616 63,336 61, 948 30 Table 4. Odds Ratios from Logistic Regression of Access to Institutional Delivery on Selected Independent Variables, Ever-Married Women 18-49, Egypt, 1992 & 1995 (P-Values in Parentheses). Independent Variable Model 9 Model 10 Model 11 Model 12 Christian .708 (.001) .842 (.241) .747 (.054) .726 (.027) High Educational Attainment 3.28 (.000) 2.75 (.000) 2.87 (.000) 2.85 (.000) Spouse High Occupational Status 1.56 (.000) 1.79 (.000) 1.79 (.000) 1.70 (.000) Employed 1.53 (.000) 1.40 (.000) 1.43 (.000) 1.46 (.000) Widowed/Divorced 1.07 (.667) 1.10 (.624) 1.08 (.703) 1.07 (.737) Rural .227 (.000) .278 (.000) .207 (.000) .220 (.000) Age 1.00 (.867) .995 (.387) .995 (.364) .995 (.431) Number of Preschool Children Present in Household .692 (.000) .757 (.000) .709 (.000) .709 (.000) 1995 1.32 (.000) 2.29 (.000) 1.45 (.020) 1.37 (.083) Regional Level Variable Per Capita GDP 1.00 (.000) Wage Labor .979 (.328) Per Capita State Investment in Health N 1.06 (.665) 12,832 8, 744 8,744 8,744 31 Table 5. Odds Ratios from Logistic Regression of Institutional Delivery on Selected Independent Variables, Ever-Married Women 18-49, India, 1992 & 1999 (P-Values in Parentheses). Independent Variable Model 13 Model 14 Model 15 Model 16 Muslim .678 (.000) .735 (.230) .561 (.021) .698 (.161) Scheduled Caste .454 (.000) .470 (.000) .430 (.000) .464 (.000) High Educational Attainment 7.04 (.000) 6.97 (.000) 6.57 (.000) 6.87 (.000) Spouse High Occupational Status 1.72 (.000) 1.75 (.000) 1.90 (.000) 1.64 (.000) Employed .679 (.000) .660 (.000) .665 (.000) .675 (.000) 1.08 (.316) 1.07 (.505) 1.23 (.188) 1.02 (.837) Rural .208 (.000) .255 (.000) .210 (.000) .200 (.000) Age .983 (.000) .984 (.024) .992 (.309) .981 (.014) Number of Preschool Children Present in Household .779 (.000) .781 (.001) .795 (.000) .780 (.000) 1.45 (.000) 1.33 (.030) 1.53 (.001) 1.50 (.001) Widowed/Divorced 1999 Regional Variables Per Capita GDP 1.00 (.088) Wage Labor 171.7 (.000) Per Capita State Health Spending N 1.19 (.255) 64,007 62,550 63, 267 61, 872 32 References Aggarwal, J.C., and N.K. Chowdhry. 1991. Indian Economy: Crisis and Reforms. New Delhi: Shipra. 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