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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: pahmed@ucla.edu.
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
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