Globalization, Distance and Disease:
Spatial Health Disparities in Rural India
Anirudh Krishna
Professor of Public Policy and Political Science
Duke University
212 Sanford School
Durham, NC 27708-0245, USA
(919) 613-7337
ak30@duke.edu
and
Kripa Ananthpur
Assistant Professor
Madras Institute of Development Studies
79, 2nd Main Road,Gandhinagar, Adyar
Chennai – 600020, India
+91 44 2441 1574(Ext 329)
kripa@mids.ac.in
Abstract
More than 50 percent of the Indian population lives in villages that are located more than
five kilometers from the nearest town. This half of India is more likely to experience illnesses
of different kinds and simultaneously less likely to get qualified medical treatment. The
incidence of premature deaths, infant and child mortality, and malnutrition are all
significantly higher within villages located further from towns. In consequence, such villagers
are more susceptible than others to being overcome by the medical poverty trap. Poverty has
increased within villages located more than five kilometers from towns, even as the national
economy was surging ahead. Globalization privileges cities, disadvantaging locations at
greater distances from towns. Public policy is required to compensate. Efforts to limit spatial
inequalities must take precedence in future health policies.
Keywords: spatial health disparities, globalization, rural India, distance from town
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1. Introduction: Investigating Spatial Inequality
Spatial inequalities have widened during the period of post-liberalization economic growth in
India. Globalization produces effects that privilege cities. Prior analyses have shown how
spatial inequalities in India have become more pronounced in relation to per capita incomes
and household assets. Cities, together with a small group of villages located close to cities,
have acquired greater economic potential, moving further ahead. Villages located at greater
distances from towns have fallen further behind.1
This article examines spatial inequalities in the realm of health. It is not clear that disparities
in income will automatically find reflection in similar inequalities in health. At least insofar as
government-run medical services and incentives are concerned, the effects of public policy
should be to minimize, rather than reinforce, inequalities of different kinds; that is, after all, a
guiding objective of public provision. And yet, as the evidence advanced below
demonstrates, as you go deeper into rural areas, health outcomes become progressively
worse. Simultaneously, qualified care becomes harder to access.
A vast majority of Indians continues to live in rural areas, with this share falling only
marginally, from 71 to 69 percent, over the decade prior to the Indian census of 2011. It
would be foolhardy to expect that this share will fall to Western proportions at any time
within the foreseeable future.
Policies intended to serve rural India will be required for a long time – and such policies
need to be designed bearing in mind the growing importance of different degrees of “ruralness.” Villages in India can be segmented according to their distance from the nearest town:
22 percent of the rural population lives within 5 kilometers from the nearest town,
constituting an inner belt of villagers; 28 percent are situated between 5 and 10 kilometers of
a town; while the remaining 50 percent of the rural population lives more than 10 kilometers
from the nearest town.2 Thus, a total of 78 percent of the rural population – amounting to
more than 50 percent of all Indians – lives in settlements that are located 5 or more kilometers
from the nearest town.
The results presented below show that it is among this half of the Indian population that
multiple health disparities are clearly visible. In general, the more rustic one’s existence – the
further one lives from towns – the greater are the odds of disease, malnourishment,
weakness, and premature death.
Section 2 reviews the general proposition concerning how spatial disparities within nations
have arisen together with advancing globalization. Section 3 interrogates the available
national data, uncovering evidence of significant spatial inequalities in relation to a variety of
health outcomes. In an effort to understand better the processes giving rise to such
inequalities and how these effects are experienced among different households, Section 4
probes primary data collected by the authors in one rural part of India. Section 5 concludes
2
by bringing together supply-driven and demand-based explanations for rising spatial
inequalities, offering suggestions for policy reform.
2. Globalization, Geography and Growth
Alongside advancing globalization, economic opportunity has become concentrated within
cities, especially larger ones. Sassen (2001: 3) notes how a “combination of spatial dispersion
and global integration has created a new strategic role for major cities… [which] now
function in four new ways: first, as highly concentrated command points in the organization
of the economy; second, as key locations for finance and for specialized service firms, which
have replaced manufacturing as the leading economic sectors; third, as sites of production…;
and fourth, as markets for the products and innovations produced. These changes in the
functioning of cities have had a massive impact… Cities concentrate control over vast
resources.”3 Another commentator has similarly noted how economic activity in the era of
globalization has become concentrated within city-based “clusters of highly specialized skills
and knowledge, institutions, rivals, related businesses, and sophisticated customers in a
particular nation or region. Proximity in geographic, cultural, and institutional terms allows
special access, special relationships, better information, powerful incentives, and other
advantages in productivity and productivity growth that are difficult to tap from a distance”
(Porter 2000: 32; emphasis added).
The effects of living at a distance from a city or town are experienced in terms of differences
in economic opportunity. While larger cities advance economically, remote rural
communities lag behind.
Remarking upon the “spiky” nature of current-day economic growth, Florida (2008: 19)
notes how “the tallest spikes – the cities and regions [concentrated around cities] – are
growing ever higher, while the valleys… mostly languish.” Such spatial clustering of
economic opportunity has become acute within many parts of the developing world. China’s
remarkable economic growth, for example, is “a result of only a handful of…spiky centers
such as Shanghai, Shenzhen, and Beijing, each of which is a world apart from its vast
impoverished rural areas… In 2006, average household incomes in urban China were two
and a half times those in rural areas [where]…17 percent of China’s population lives on less
than a dollar a day, almost half lives on less than two dollars a day… The prospects for
bridging these gaps are weak… But all that pales in comparison with the growing pains felt
by India’s poor. India’s growing economic spikes – city regions such as Bangalore,
Hyderabad, Mumbai, and parts of New Delhi – are also pulling away from the rest of that
crowded country” (Florida 2008: 35-36).
Analysts examining the rise of inequality in India have noted how income differentials are
widening between urban areas (which still account for no more than 30 percent of the
country’s population), and the vast rural countryside (Deaton and Dreze 2002; Dev and Ravi
3
2007; and Sen and Himanshu 2004). The biggest Indian towns have the largest
concentrations of assets. In towns with populations of more than five million (home to six
percent of the Indian population), 24 percent of all households possessed cars in 2005, 82
percent had color TVs, 64 percent had refrigerators, and 54 percent had mobile phones. The
corresponding percentages in towns with fewer than 50,000 people were 7 percent, 51
percent, 26 percent, and 21 percent. In rural India, these percentages were lower still,
respectively, 3 percent, 24 percent, 8 percent, and 7 percent – less than half the
corresponding proportions within the smallest towns.
The potential for upward mobility is significantly implicated with geography. How well you
do depends to a considerable extent upon where you happen to live. A close observer of
these trends concludes that “despite all the hype about ‘the death of distance’ and the ‘flat
world,’ where you live matters more than ever” (Moretti 2012).
These observations are borne out by recent trends in India. No matter what one’s level of
education or training, earnings are higher if one lives within a large town compared to a
small town and in a small town compared to a rural village. Individuals who have only a
primary education earned up to 68 percent more by living in a metro city (one that has more
than five million people) compared to a smaller town (with fewer than 500,000 people).
Among people with college degrees, the corresponding income differential is smaller though
still substantial: 38 percent (Shukla 2010).
Spatial economic differences have intensified over time in India. During the period 19932005, for example, when India’s economy grew rapidly, the largest cities experienced the
largest average income gains. Smaller towns also gained but not by as much.
Beyond towns, the benefits from economic growth were radially dissipated: Inflationadjusted per capita incomes grew in villages located within five kilometers of towns. But
outside this inner circle of villages, inflation-adjusted per capita incomes have fallen, with the
deepest reductions occurring in villages located at greater distances from towns - which had,
to begin with, lower per capita incomes. To make matters worse, the poorest income groups
within such, more remote, villages have suffered the largest cuts in purchasing power;
evidence of widening income inequalities simultaneously along both spatial and
socioeconomic dimensions (Krishna and Bajpai 2011). Concurrently, poverty has grown. In
villages located between 5 and 10 kilometers from towns, the percentage of households
below the official poverty line increased from 35.8 percent to 41.4 percent, a gain of 5.6
percentage points over this 12-year period, widely regarded as a period of unprecedented
high-speed growth. In villages located more than 10 kilometers from towns, the increase in
poverty was even larger: 6.2 percentage points.
That half of the Indian population which lives more than 5 kilometers from the nearest town
is thus faced with a grimmer set of prospects. In the upward direction its movement is
restricted; for many, their already bad situations are becoming worse.
4
One would hope and expect that in the realm of health care, at least, such disparities would
be minimal or actively reduced. Bad health and medical expenses can bankrupt families. A
slew of recent studies show how the largest numbers of people fall into poverty and remain
poor on account of ill health and high health care costs (Krishna 2010; Whitehead, et al.
2001; Xu, et al. 2001). As many as 3.7 percent of the entire Indian population falls below the
poverty line each year on account of unaffordable medical expenses, often incurring
unbearable burdens of debt (EQUITAP 2005; Garg and Karan 2005).
Spatial disparities in health, therefore, need to be carefully examined. If the burden of disease
were higher and access to qualified care simultaneously lower among villagers located more
than 5 kilometers from towns, then they would be cumulatively disadvantaged. Coupled with
the lower chances that they have, compared to villages located closer to cities, of gaining
higher incomes and accumulating assets, the existence of a greater danger of descent, of
falling into poverty, would tend to make spatial inequalities wider still, difficult to surmount
without sustained external assistance.
Both parts of the analysis presented below help demonstrate that such, indeed, is the case.
We look first at the national picture, focusing on the supply side of the explanation. Section
4 considers a micro-level view, helping understand better some demand-related aspects of
disparate outcomes and health-seeking behaviors.
3. Spatial Health Disparities in National Context.
Prior examinations of health disparities in India have identified a number of factors,
significantly associated with diverse outcomes and behaviors among different population
segments. Analysts have examined differences arising on account of caste and wealth,
finding significantly poorer outcomes among scheduled castes (SCs) and scheduled tribes
(STs)4 and between richer and poorer Indians (Balarajan, Selvaraj, and Subramanian 2011;
Gaudin and Yazbeck 2006; Mohindra, Haddad, and Narayana 2006; Subramanian, et al.
2004). Differences between men and women have also been found to be salient, particularly
when seen alongside socioeconomic inequalities (Iyer, Sen and George 2007; Iyer, Sen, and
Östlin 2008). Substantial regional differences – across states of India – have been uncovered
(Pande and Yazbeck 2003); and gaps between rural and urban areas found to be persistent
and large (Baru, et al. 2010; Duggal 2005).
For reasons examined in the previous section, it is important additionally to examine
differences arising within rural areas, particularly among villages located close to towns and
others situated more remotely. Only one previous study has examined spatial differences of
this kind. An examination of data collected in the early 1990s, during the initial phase of
globalization-driven economic growth in India, found that “inequality in health indicators is
very high… both infant and child mortality rates increase sharply [among villages located at
5
greater distances from the nearest town] … short-term morbidity also shows a positive
relationship with distance” (Kundu, Pradhan, and Subramanian 2002: 5042-3).
In the 20 years since these data were collected, this stream of explanation has not been
followed up. While scholars have investigated diverse aspects of the relationship between
globalization and health care,5 finding both positive and negative features, the spatial
dimension of health disparities has not attracted further examination within India.
In other developing countries, researchers have looked at the effects of distance to nearest
health facility, concluding variously how distance, so measured, does or does not correlate
with diverse health outcomes and disparate care-seeking behaviors. A study conducted in the
late-1980s in Ghana found, for example, that distance was an important deterring factor in
seeking institutional health care; the cost of care was less important in comparison to
distance (Lavy and Germain 1994). A study undertaken in rural parts of one state of Nigeria
came to a similar conclusion (Awoyemi, Obayelu and Opaluwa 2011). Other studies have,
however, arrived at the opposite conclusion, finding that distance makes either no or
relatively little impact (Acharya and Cleland 2000; Kesterton, et al. 2010; and Moisi, et al.
2010).
It is timely and important, therefore, to examine the recent evidence for India, considering
whether and how in the phase of advancing globalization spatial health disparities have
become larger or less significant. We present below the results from analyses undertaken
using recent nationally-representative data sets.
District-Level Household and Facility Surveys (DLHS) were launched by the Government
of India in 1996-1997 to provide important indicators on maternal and child health. These
surveys were conducted by the Mumbai-based Indian Institute for Population Sciences
(IIPS) and published by the Ministry of Health and Family Welfare of the Government of
India. The second such survey (DLHS-2) was conducted in 2002-2004. A total of 620,000
households were interviewed from 593 districts and 26 states in India using a systematic,
multi-stage stratified sampling design. DLHS-3, the third in this series of surveys, was
conducted from 2007 to 2008. It provides information related to 720,320 households from
28 States and 6 Union Territories of India. A total of 78 percent of the surveyed households
(559, 663 households in all) lived in rural areas, and we focused upon this part of the DLHS3 sample, also consulting data from DLHS-2.
We looked, first, at patterns of institutional delivery in national context, paralleling the
micro-level examinations presented in the next section. Table 1 presents these figures at the
national level.
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Table 1 about here -
These results show that villages located at greater distances from towns have consistently
lower proportions of institutional deliveries. Among inhabitants of villages located within
6
two kilometers of towns, nearly 45 percent of all deliveries were conducted within
institutions, but in the group of villages located more than 10 kilometers from towns, a
much smaller proportion (33 percent) of deliveries were conducted within institutions of any
kind. The proportion of institutional deliveries does not, however, provide the only
indication that villages located at greater distances have health behaviors and outcomes of
different kinds.
Distance to town matters across a broad spectrum. People in villages located further from
towns are significantly more likely to have cholera, malaria, and other diseases. Health
problems during pregnancy are more frequent in such villages, resulting in significantly
higher infant and child mortality. More children below the age of 5 years are severely or
moderately malnourished within these more remote villages.
Table 2 reports a sample of these results, reporting statistically significant differences
between villages located within 5 kilometers and others located further away from towns.
The 5-kilometer dividing line, separating inner-belt villages from more distant ones, remains
salient across analyses considering a variety of outcomes, not only in the realm of health but
as well in relation to per capita incomes, asset holdings, and educational trends, as prior
analyses, discussed above, have also consistently found.
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Table 2 about here -
The 50 percent of India that lives beyond a radius of 5 kilometers from the nearest town
faces much greater odds of disease, malnourishment, weakness, and premature death. Health
problems experienced during pregnancy occur in greater frequency. Mortality and morbidity
among mothers and newborns are higher in further-away villages. Among adults as well, the
likelihood of illnesses is higher within villages located at greater distances from towns.
Malnutrition, a problem across a large swathe of the Indian population (Chatterjee 2007;
Deaton and Dreze 2009), is more prevalent within more distant villages. The body-mass
index (BMI) of children below the age of 5 is lower; many more children are mildly to
severely malnourished – and these disparities have grown larger with the passage of time
(Table 3).
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Table 3 about here -
In 1993-94, the percentage of mildly-to-severely malnourished children was not widely
different among villages at different distance ranges, being, in fact, somewhat higher in
villages within 5 kilometers of towns (81 percent) and somewhat lower in villages at the
greatest distances, more than 10 kilometers, from towns (78.4 percent). But over the ensuing
12-year period, this difference reversed itself and grew larger. The incidence of
malnourishment fell in villages within 5 kilometers of towns, but in the more remote villages
(more than 10 kilometers away) the incidence of malnourishment went up, becoming a little
7
over 80 percent in 2004-05, which is the highest proportion among villages of the three
distance-related groups.
How does one explain these reinforcing distance-related disparities? It seems likely that both
supply-related and demand-based factors are to account. We will look at aspects of healthseeking behaviors in the next section. Meanwhile, let us look at some recent trends in the
supply of health care, examining particularly the locations of hospitals, dispensaries, and
clinics in both the public and private sectors, each of which has grown at a rapid clip –
particularly in rural locations closer to cities.
With the advent of globalization, towns have acquired greater importance, as discussed
above, and governments, national and state, have followed where markets have led,
preferentially locating public infrastructure, including medical facilities, within urban and
close-to-urban rural locations. Our analysis of data from the Indian census of 2001 showed
that while more than two-thirds of all villages within five km of towns have been provided
with paved roads, fewer than half of villages beyond 20 km from towns were similarly
endowed through public provision. The corresponding proportions for electric power
supply are 85 percent and 64 percent.
A similar position obtains with respect to the location of health-care facilities. Table 4 shows
how all types of medical facilities, including both more sophisticated and less sophisticated
ones, are more likely to be situated in villages located within 5 kilometers of a town.
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Table 4 about here -
Public as well as private provision has been disproportionately located within an inner belt of
villages. While villages located within 5 kilometers of towns account collectively for less than
one-fourth of the rural population, they nearly three times as likely compared to villagers
further away to have a government or a private hospital and more than twice as likely to
have a PHC or Block PHC. In every respect, despite their much smaller share of the rural
population, inner-belt villages are far better endowed with medical infrastructure.
Hospitals, with their sophisticated equipment and teams of specialists, can reasonably be
expected to be centralized, being better located at centers of concentrated population, within
or close by larger towns. However, the rationale behind locating even the simpler facilities –
public clinics and government dispensaries – more abundantly in villages inside the 5kilometer inner circle seems both inexplicable and counterproductive. However, the chances
of finding a Block PHC or government dispensary are all higher in villages closer to towns,
and lower in villages beyond the 5-kilometer inner circle.
Partly as a result of these supply-side factors, people of further-away villages, who are more
often sick, are less able to obtain treatment close to hand and – because their illnesses
remain untreated for longer – experience worse health outcomes compared to inhabitants of
villages nearer to towns. The prospects of falling into poverty are consequently larger within
8
more remote villages; one reason why poverty has grown within more-distant villages, as we
saw above for the period between 1993 and 2005.
4. Examining care-seeking behaviors and health outcomes in rural Karnataka
Supply-related factors are not all that matter. People’s health-seeking behaviors – factors
influencing their demand for qualified health care – can also make a difference, helping
produce spatial disparities of different kinds.
A small-scale investigation undertaken in two parts of rural Karnataka helped understand
better the effects of distance from town when seen in conjunction with several other factors
– including caste, wealth, gender and education, which, according to previous analyses,
influence people’s demands for qualified health care. Instead of ranging widely across all
types of health outcomes, we selected to focus in these investigations upon a specific subset
– births and deliveries – examining the nature of factors associated with people’s decisions
to select institutional deliveries in preference to deliveries at home.
Two districts, Gulbarga and Raichur, were selected for this part of the inquiry, which count
among the more “backward” districts in Karnataka in relation to various socio-economic
indicators. Both districts are also included within the coverage of the Janani Suraksha Yojana
(JSY), part of the Indian government’s National Rural Health Mission, launched in 2005,
which is intended to boost institutional deliveries through providing cash incentives to
delivering mothers (Campbell and Graham 2006; Lim, et al. 2010). As we will see below,
financial incentives do not suffice to remove the handicaps that are associated with living at
a greater distance.
A combination of qualitative and quantitative methods was utilized for this study. Twelve
villages were selected in the following manner. Villages of the two selected districts were
initially categorized into four distance categories: within 2 kilometers of nearest town; from 2
to 5 kilometers; from 5 to 10 kilometers; and beyond 10 kilometers. Random sampling
helped select 12 villages, four from each of these distance categories. An average of 306
households live within these villages, and average household size is just below 5.5, making
for average village population of 1,700. A census of all households was carried out in each
village. Basic information, related to caste group and relative wealth, especially house type –
kaccha (mud) v. pukka (brick) – and land holdings was collected at the same time.
Households were categorized on the basis of their land holding into four groups: landless,
small and marginal, middle, and large. Random sampling was employed to pick 20 percent of
households from within each of these four categories. A total of 772 households selected in
this manner were interviewed using a pretested questionnaire.
Particularly detailed information was elicited about all pregnancies and births occurring over
the ten-year period preceding the survey. A subset of 47 women (selected from among the
9
list of pregnant women maintained by local health workers) was selected for further detailed
process documentation. Field investigators visited each of these women on a monthly basis,
discussing their ante-natal care (ANC) practices and their interactions with health care
providers of different kinds. Discussions were also held with focus groups, which included
village elders, grama panchayat (elected village council) members, youth leaders, members of
women self-help groups, village health professionals, and NGO workers. Separate interviews
with held with doctors and nurses at block, district, and state capitals and with village-level
health workers.
These results show that during the ten-year period preceding our survey, the 772 households
selected for interviews had a total of 2,261 deliveries,6 of which 45 percent occurred within
institutional settings – 18 percent in a government hospital, 3 percent at a Primary Health
Centre (PHC), and 24 percent in private hospitals.7 The remaining 55 percent of all deliveries
occurred in non-institutional settings: 24 percent at the home of the delivering mother,
another 30 percent at the home of her parents, and a further one percent on the way to a
hospital. Untrained dais (traditional birth attendants) supervised the majority of these noninstitutional deliveries, with relatives or friends attending the remaining part. These
proportions of institutional and non-institutional deliveries are not significantly different
from those observed above for all of rural India (Table 1).
In order to examine household characteristics associated with institutional deliveries, we
created a household-level variable, dividing households into the following mutually-exclusive
categories:
Never Institutional: Households in which all deliveries over the past 10 years took place
outside institutions.
Sometimes Institutional: Household that had at least one institutional delivery and at least
one non-institutional delivery over this ten-year period.
Always Institutional: Household in which all deliveries over the past 10 years took place
within institutional settings.
Table 5 gives the breakdown of households by these categories. Notice how roughly equal
proportions of households fall within the “never institutional,” “sometimes institutional,”
and “always institutional” groups.
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Table 5 about here -
Using these categories, we examined the effects upon demand for qualified health care of
diverse household- and individual-level factors that prior studies have identified. We began
our analysis by looking at diverse indicators of household wealth.
In line with what prior analyses have also found, higher wealth is positively associated with
decisions to have institutional deliveries. Richer households (those who live in pukka homes)
are nearly twice as likely compared to poorer ones (with kaccha homes) – 60 percent v. 32
10
percent – to always have institutional deliveries. A similar result obtained when we looked
instead at land or asset holdings as alternative indicators of wealth.
Interestingly, households who have added to their asset holdings in the ten years preceding
the survey were also more likely to have institutional deliveries compared to households
whose assets holdings have declined or remained the same, suggesting that as people become
richer the proportion of institutional deliveries should rise. However, the relationship
between wealth and care-seeking is far from determinate. Even among the richest
households in these villages – those who have the largest land holdings, better houses,
televisions, and in a few cases, also motor cars – as many as 31 percent of households are in
the “never institutional” category.
Other factors need to be considered; for instance, higher education should go together with
higher demand for institutional deliveries. Table 6 breaks down these categories – always,
sometimes, and never institutional – in relation to the highest level of education in the
household.
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Table 6 about here -
Notice how the proportion of “never institutional” households is as high as 48 percent among
households where the highest level of education is 1-4 years (and it is nearly 44 percent
among households whose highest education is 5-7 years), but this share falls to 36 percent
among households whose highest education level is 8-10 years (and further to just over 21
percent in households who have pre-university (PUC) qualification or a university degree).
Simultaneously, the proportion of “always institutional” households rises from 15 percent to
53 percent.8
Education is rising rapidly in Indian villages, and the effects of rising education should be
felt in terms of increasing institutional deliveries over time. But even within households
where the highest education level is a university degree, as many as 21 percent fall within the
“never institutional” category, and another 25 percent fall within the “sometimes
institutional” category – indicating that, just as in the case of rising wealth, the effects of
rising education are not assured or predictable in all cases.
Something else matters in addition to education and wealth. We examine below the effects
of caste and religion, looking later at several characteristics considered together.
A variety of caste and religious groups live within these villages, some with relatively few
members. We looked at the major caste categories, each with more than 20 households in all,
considering especially, Scheduled Castes (SCs), Scheduled Tribes (STs), and Muslims. We
also looked at two specific caste groups, Lingayat and Kuruba, who constitute substantial
shares of village populations and have been dominant, numerically and in terms of economic
and political power, within these villages. Examining dominant castes specifically is especially
important, as advised by Srinivas (1987). Table 7 provides these results.
11
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Table 7 about here -
All caste and religious groups exhibit a spread of care-seeking behaviors. Every group has a
significant proportion of “always institutional” households, but every group also has a fair
proportion of “never institutional” households.
The proportion of “always institutional” households is lowest among STs, followed by
Muslims, in line with what investigations undertaken in other parts of India have earlier
revealed (e.g., Subramanian, et al. 2004; Subramanian, Smith, and Subramanyam 2006). As
remarked above, the pattern of institutional deliveries for SC households does not differ a
great deal from the average for all households. Further, the fact that the proportion of
“sometimes institutional” households is high among both Muslims and STs – in fact, higher
among STs than the average for the population – suggests that caste- and religion-specific
norms are not determinative but pliable.
Experiences of discrimination can reduce the motivation for institutional deliveries; service
providers have been known to treat poor villagers, SCs, Muslims, and other disadvantaged
groups with disfavor, presenting attitudes of disdain, and some times, outright rudeness
(Malhotra and Do 2012). Members of the study team heard, and on occasion, witnessed,
examples of such discriminatory behaviors.9
Still, our focus group interviews showed how a belief has taken root that institutional
deliveries are safer and better, and a rising proportion of households prefer to take this road.
Younger households across all social groups have opted for institutional deliveries in much
higher proportions compared to their older counterparts. Compared to deliveries occurring
between 2001 and 2004, a greater proportion of deliveries occurring between 2007 and 2010
took place within institutional settings – and this rise in proportion was experienced by all
social groups.
Despite this overall rising trend in demand for qualified care, physical access continues to
represent a problem. Care-seeking behaviors within villages located at greater distances from
towns are substantially different from those in villages located closer by. On average, 31.7
percent of all households are “always institutional,” i.e., all of their deliveries in the past ten
years have occurred within institutional settings, but in Delari Village, 10 located 4 kilometers
from the nearest town, this proportion is as high as 60 percent, while at the other extreme, in
Rakami Village (15 kilometers from the nearest town), it is only 17 percent.
In general, villages located outside the 5-kilometer inner circle have uniformly lower shares
of institutional deliveries. Figure 1 presents a visual depiction of this relationship.
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Figure 1 about here -
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Villages within the 5-kilometer inner circle have variously high and low percentages of
always-institutional households, on average performing better than villages further away. But
villages located beyond the 5-kilometer radius have consistently lower-than-average shares of
institutional deliveries.
It could be that villages located at greater distances have higher shares of STs and lower
average incomes, thus making the effects of distance nothing more than an artifact of other
and more basic effects. However, even when seen alongside other factors, including caste,
religion, family education, and relative wealth, distance to town continues to make a
significant difference to people’s health-seeking behaviors. Logistic regression was carried
out (coding “always institutional” households as one and all other households as zero).
Separate analyses considering “never institutional” as the response category did not produce
qualitatively different results in terms of which independent variables gained significance.
Table 8 reports these results.11
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Table 8 about here -
This analysis shows that several factors influence the demand for qualified medical care, in
this case, institutional delivery. In multiple specifications of the regression model, however,
distance continued to exert a significant influence.
Among household-level factors, wealth and education are significant for this analysis. Two
particular social groups (Lingayat and ST) also gain significance. The coefficient for SC is
positive, but it is not statistically significant. 12 Similarly, the coefficient for Muslim is negative
but not statistically significant.
Two separate distance variables are significant. Distance to nearest town is consistently
significant. Distance to paved road makes a separate difference. In substantive terms,
distance to town outweighs distance to paved road. While distance to town varies from two
to 31 kilometers, distance to paved road varies within a much narrower range.
Greater distance from town is reflected at the household level in terms both of higher
transportation costs and sheer physical difficulty of gaining access. Distance to paved road
compounds the difficulty of gaining access.
Qualitative investigations uncovered the main reasons for why distance exerts this nature of
influence. We asked each of more than 700 respondents about the main barriers they have
faced while seeking institutional health care. The largest number, 41 percent, mentioned
“lack of transport” as the most important constraint. A further 21 percent and 26 percent,
respectively, mentioned distance and time taken, making for a total of 88 percent for whom
problems of physical access figured among the top three constraints. In comparison, many
fewer households, 43 percent in all, mentioned costs or quality of service among their top
three constraints, showing how financial incentives can go only a part of the way toward
altering care-seeking behaviors and improving health outcomes.
13
The introduction of cash incentives through the government’s Janani Suraksha Yojana (JSY)
program has reduced the financial disincentive to institutional deliveries. However, our
interviews revealed that considerable obstacles still remain. Although JSY incentives have
been available in these villages since 2005, no more than eight percent of all deliveries had
been assisted either by this program or by any other government program in force.
Admittedly, our inquiries spanned a longer period of time, ranging over the ten years
between 2000 and 2010. A little fewer than half of all 2,261 deliveries occurring over this
ten-year period took place after the coming into force of JSY. Still, eight percent is a very
small proportion – and this proportion is smaller still within villages located outside the 5kilometer inner circle.
In order to probe this issue further, we looked at the cases of those 176 households that had
at least one delivery both before and after the introduction of JSY. How many of these
households increased their institutional delivery percentage after the introduction of JSY,
how many decreased their institutional delivery percentage after the introduction of JSY?
We found that in 64 percent of these households the introduction of JSY did not change the
household’s behavior; in another 26 percent of cases, a household that elected noninstitutional delivery before JSY chose, instead, to have an institutional delivery after 2005;
but in the remaining ten percent of cases, the opposite behavior pattern was discerned:
households that had one or more institutional deliveries before 2005 elected to have one or
more non-institutional deliveries after JSY incentives were put in place. Households in
villages located further from towns were considerably were less likely to alter their careseeking behaviors.
Cash incentives cannot alone resolve the problem of distance. Liabilities associated with
physical access – on account of distance to town – continue to make a separate and
substantial difference.
Further, these investigations revealed how the birth of a child is not the only occasion when
residents of more remote villages suffer handicaps while seeking and obtaining professional
health care. Instances of diseases left untreated were more often cited in villages located
further away. Morbidity and mortality are higher across the board within these further-away
villages – among newborns, infants, children, and adults – as we have saw above while
looking at national results.
5. Conclusion: Distance and Disease
Contrary to what might once have been true, the rustic life is not necessarily healthier. In
fact, the more rustic one’s existence – the further one lives from towns – the greater are the
odds of disease and malnourishment, and the smaller is the incidence of institutional
treatment.
14
In situations where physical access remains a challenge, many families either select to forego
treatment, or they incur huge costs in order to come to the hospital, a decision that is often
made at a very late stage, requiring specialized medical attention, which further raises costs.
Ironically, it is not just medical emergencies; even ordinary events, such the birth of a child,
can drive rural families into a medical poverty trap (Ensor and Cooper 2004; Mavalankar, et
al. 2009; Whitehead, et al. 2001).
On the one hand, people who live in villages more than 5 kilometers from the nearest town
have seen their real per capita incomes drop. On the other hand, their likelihood of having a
health episode (with expenses to boot) is higher. Thus, even as their prospects of moving up
are limited, the risks of moving down, falling into poverty, are larger. Especially in a context,
such as rural India, where out-of-pocket costs account for the vast bulk of health care
expenditure, such overlapping disparities can become progressively hard to surmount,
resulting in impoverishment and immiserization on a large scale. The greater the distance to
town, the higher are these risks.
Not a great deal is being done at present to address spatial disabilities. A few noteworthy
examples exist of interventions by NGOs and private foundations, 13 but these are mainly
spots of light in an otherwise dark landscape, much like a map of India at night.
Unless corrective measures are put in place through public policies, spatial disparities will
likely become worse in years to come. Why have such spatial differences arisen, and how can
they be rectified? We looked above at a range of factors. No simple or mono-causal
explanation will suffice. Several elements must be considered at the same time, including
factors related, respectively, to the demand for and supply of better health care.
On the supply side, the provision of health care facilities has been concentrated
disproportionately within India’s cities and within villages located less than 5 kilometers
distant from towns (Table 4). Residents of more distant villages have to cross higher hurdles
just in order to have their loved ones attended to by a qualified doctor or nurse.
A second supply-side problem, which quantitative examinations cannot easily uncover, but
which our qualitative investigations in Karnataka helped illustrate, relates to the scant
supervision provided to the health personnel who are deployed by the government at the
village level. A vast army of such “barefoot” or basic health practitioners, including ANMs
and ASHAs,14 paid for by the Indian taxpayer, has been assembled by the Indian
government, and put in place in villages, including the furthest-out ones. The quality of
services provided by these individuals is, however, highly variable. In some villages, ASHAs
and ANMs are highly-valued resources, being regularly available and serving diligently. In
other villages, these employees are hard to find, discriminatory, careless or callous about
their work, and otherwise negligent. Villagers themselves have little control over the health
staffs deployed to serve them. Beyond complaining to higher officials, most often located in
towns, villagers can do little or nothing to check absenteeism, enforce accountability, or
15
insist upon norms of professional behavior.15 Greater distance to towns reduces the
frequency of complaint, lowering one motivation for providing a higher quality of service.
Focus group interviews revealed how instances of uncaring behavior and absenteeism on the
part of health staff occurred more often in villages further from towns; out-of-sight and
often out of mind for higher officials.
These factors – related to the supply of health care – are reinforced by other factors, which
in different ways influence villagers’ care-seeking behaviors. We saw how several factors,
related to household wealth, caste, religion, gender, and education, are implicated with higher
and lower demands for institutional health care.
Distance matters both in addition to these other demand-related factors and as well in
interaction with them. The effects of distance – higher cost, more time taken, more hassle –
acting as deterrents, push downward the demand for qualified health care in further villages.
Further, some other demand-related factors are themselves significantly related with
distance. For instance, the proportion of adults with high school (or higher) education falls
progressively the further one goes from towns, ranging from 48 percent in villages within
two kilometers of towns to 35 percent in villages more than ten kilometers from towns.
Average years of schooling are lowest in more distant rural locations; reading, writing and
computation ability also become progressively lower with increasing distance.16
The relative impacts made by different influences acting together and in combination will
need to be investigated more closely in different parts of India. Understanding the different
ways in which distance influences behaviors and outcomes will help fashion better remedies
against growing spatial disparities of multiple kinds.
Poverty in India cannot be reduced substantially without first making affordable and
accessible quality health care available to all (Gupta and Mitra 2004; Krishna 2010).
Removing spatial disparities is an essential part of getting health care right. In addition to
taking account of disparities arising on account of gender, caste, and wealth, efforts aimed at
improving health equity in rural India must compensate for the effects of distance to town.
Some recent initiatives have helped in this regard, particularly the introduction in the year
2008 of the 108-ambulance service, a public-private partnership. In Karnataka, for example,
each 108-ambulance service is expected to cover a population of 100,000 over distances of
no more than 30 kilometers. It seems important to note, however, that despite the
introduction of the 108-ambulance service only a small proportion of all women who
obtained institutional deliveries were brought to the hospital or clinic in an ambulance. The
majority utilized hired means of transportation, including, most often, tractors, auto
rickshaws, and trucks, and less frequently, buses and cars.
Vastly expanding the network of free or low-cost ambulance services is essential to reduce
the human misery and often ruinous costs associated with greater distance and its
16
consequence, inability or reluctance to seek and obtain institutional health care. Other
measures – e.g., mobile health clinics – can also help.
Enhancing accountability and reducing absenteeism among government health personnel
deployed in PHCs and at the village level is another important step. Lapses in governance as
much as (and probably more than) shortages of money represent the principal obstacles that
remain to be overcome.
***
17
Acknowledgements
Data collection exercises in Karnataka were partly supported by a grant (number OW2: 205)
received from the International Initiative for Impact Evaluation (3ie).We thank, without in
any way implicating, 3ie, and its Director, Howard White, both for the grant and for
comments and advice. We also thank Gregory Schober for help with this part of the data
analysis and Tulsi Patel for assistance with the DLHS data. The usual disclaimers apply.
18
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21
Table 1: Distance to Town and Institutional Delivery: All-India Data
Village distance to nearest town
Place where last delivery was
0-2
2-5
5-10
More
conducted
kilometers kilometers kilometers than 10
kilometers
Hospital
14.63
12.44
11.56
10.77
Dispensary
0.17
0.17
0.17
0.19
UHC/UHPC/UFWC
0.29
0.23
0.27
0.26
CHC/Rural Hospital
5.52
4.87
4.91
4.28
PHC
5.93
6.07
5.11
5.38
Sub Center
0.79
0.86
0.69
0.58
Ayush Hospital/Clinic
0.01
0.03
0.02
0.03
NGO/Trust Clinic
0.34
0.37
0.28
0.24
Private Hospital/Clinic
16.89
17.09
14.96
11.52
Private Ayush Hospital
0.16
0.2
0.17
0.15
All Institutional
44.73
42.33
38.14
33.4
On way to hospital
0.42
0.39
0.5
0.42
At home
50.63
52.93
56.77
61.62
At parents home
3.85
4.02
4.24
4.2
Work place
0.05
0.06
0.05
0.07
Other
0.31
0.29
0.27
0.28
All Non-Institutional
55.26
57.69
61.83
66.59
DLHS data
22
Table 2: Worse Health Indicators in Further Villages of India
Significant difference in odds
Nature of problem
(Villages > 5 kilometers from town compared to
other villages)
(A) During pregnancy
Convulsions
Visual Disturbance
Malaria
Hypertension
Paleness, giddiness, weakness
Excessive bleeding 6 weeks
pregnancy
Weak or no movement of fetus
Excessive bleeding during delivery
(B) Among newborns
Diarrhea during last two weeks
Pneumonia during last two weeks
Child died
(C) Among general population
Malaria
Cholera
Other communicable diseases
12% greater
16% greater
14% greater
22% greater
8% greater
after
14% greater
9% greater
13% greater
17% greater
23% greater
20% greater
31% greater
28% greater
31% greater
DLHS data
23
Table 3: Proportion of Mildly-to-Severely Malnourished Children 5 years and
younger (BMI<18.4)
(percent of all children)
Distance to
town
<5 kilometers
5 to 10 kilometers
>10 kilometers
1993-94
81.0
81.5
78.4
24
2004-05
74.1
77.2
80.2
NCAER data
Table 4: Availability within villages of different health care facilities
FACILITY
Government hospital
Private hospital
AYUSH health facility
Primary Health Centre (PHC)
Block PHC
Government dispensary
Private clinic
Percentage of villages having this facility
Within 5 kilometers of towns
Beyond 5 kilometers
10.4
3.5
15.4
5.3
18.2
10.5
23.2
11.8
13.4
6.5
15.6
11.1
28.5
17.7
DLHS data
25
Table 5: Household Categories and Institutional Deliveries in Karnataka Villages
Category of household
Never Institutional
Sometimes Institutional
Always Institutional
Total
Number of households
266
261
245
772
26
Share of total
34%
34%
32%
100%
Table 6: Household Education and Institutional Deliveries
Category of Household
Highest Level
of Education
in Household
Illiterate
1-4 years
5-7 years
8-10 years
Pre-university
College degree
Post-graduate
Percent
of total
6%
13%
19%
32%
19%
10%
<1%
Never
Institutional
Sometimes
Institutional
Always
Institutional
33%
48%
44%
36%
21%
22%
0%
38%
37%
30%
33%
40%
25%
50%
29%
15%
27%
31%
39%
53%
50%
27
Total
100%
100%
100%
100%
100%
100%
100%
Table 7: Social Group and Institutional Deliveries
Category of Household
Social Group
Kuruba
Lingayat
Muslim
Scheduled
Caste
Scheduled
Tribe
Other
Average
Share in
village
population
10%
24%
8%
19%
Never
Institutional
Sometimes
Institutional
Always
Institutional
Total
38%
22%
49%
37%
33%
27%
29%
32%
29%
51%
22%
31%
100%
100%
100%
100%
19%
44%
44%
12%
100%
20%
32%
34%
37%
34%
31%
32%
100%
28
Table 8: Factors associated with being an “Always Institutional” household
(results of logistic regression)
Constant
Household characteristics
Kaccha house
Female-headed
Household education
(years)
Lingayat
Kuruba
SC
ST
Muslim
Village characteristics
Coefficient
Std. error
P>|z|
2.433*
1.138
0.033
-0.939*
-0.217
0.187*
0.274
0.308
0.075
0.001
0.480
0.013
0.619*
-0.032
0.103
-0.780*
-0.501
0.240
0.327
0.263
0.329
0.379
0.033
0.921
0.697
0.018
0.186
0.017
0.046
0.016
0.033
1.662
1.151
0.000
0.567
0.470
0.551
Distance to town (km)
-0.041*
Distance to paved road
-0.099*
(km)
Literacy rate
-0.952
Percent landless
-0.832
Population
0.000
N=
768
LR chi-sq.
108
Prob>chi-sq.
0.000
Pseudo R-sq.
0.22
* indicates significance at 5% level or better
29
10
20
30
40
50
60
FIGURE 1: Distance to nearest market town and share of households that always
have institutional deliveries
0
10
20
Distance to nearest town (km)
30
30
NOTES
We use the terms town, city, and urban area interchangeably. In the empirical analysis that follows, we use the official
definition of town employed by India’s government. See http://censusindia.gov.in/2011-provresults/paper2/data_files/India2/1.%20Data%20Highlight.pdf
2 These data were derived from a nationally-representative sample survey – the Indian Human Development Survey –
carried out by the National Council for Applied Economic Research (NCAER) in 2004-05.
3 A remarkably similar argument advanced in the specific context of contemporary urban India is provided by Chatterjee
(2004: 142-47).
4 SCs are former untouchables, while STs are, loosely speaking, India’s indigenous people.
5 See, for instance, Fort, Mercer, and Gish (2004); Hazarika (2010); Labonte and Schrecker (2007); and Kruk (2012).
6 Out of a total of 2,343 recalled pregnancies, as many as 2,216 (94.6%) resulted in live births. Roughly equal percentages
(2.7% each) resulted in still births or were aborted.
7 We coded all deliveries occurring at any of the following locations as institutional deliveries: government hospital or
dispensary; UHC/UFWC; CHC; PHC; SHC; Ayush hospital or clinic; NGO/Trust hospital or clinic; and private
hospital or clinic. The remaining deliveries, occurring at any of the following locations, were classified as noninstitutional: at home, at parents’ home, at work, on the way to a hospital.
8 We separately analyzed these data considering the mother’s education level – and not the highest education level of any
household member. But these results were not qualitatively different.
9 These findings from the case histories that we compiled are not presented here because of lack of space. A separate
report, presenting these cases and the related findings, is available upon request.
10 Actual village names have been disguised to preserve confidentiality.
11 The results reported below were robust to alternative specifications of the regression model and diverse constructions
of the social group variables. Tests for collinearity did not give reason for concern.
12 Zero-one dummy variables were constructed for each of these groups. High-caste Hindus serves as the comparison
category. Apart from Hindus and Muslims no other religions are represented in any significant numbers.
13 For some instructive contemporary examples, see Arora, et al. (2011).
14 ANMs are auxiliary nurse midwives, and ASHAs are accredited social health activists, both deployed in rural areas by
the governments of different Indian states.
15 Whether deepening decentralization will serve as a suitable remedy to these problems is itself a topic worthy of
additional research. See, for instance, Corbridge, et al. (2005) and Manor (2010).
16 Author calculations from nationally-representative data collected by NCAER in 2004-05.
1
31