religion differences in fertility and mortality in india

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Religion and Death in India

Sonia Bhalotra (University of Bristol, UK) and Arthur van Soest (RAND and University of Tilburg)

First Draft; to be updated before June.

Abstract

Muslim children in India face lower mortality risks than Hindu children. This is surprising because one would have expected just the opposite: Muslims have, on average, lower socio-economic status, higher fertility, shorter birth-spacing, relatively low status of women, and are a minority group in India that, in principle, might live in areas that have relatively poor public provision. Although higher fertility amongst Muslims as compared with Hindus has excited considerable political and academic attention in India, higher mortality amongst Hindus has gone largely noticed. This paper investigates the seeming puzzle. We find that the largest part of the Muslim advantage stems from where in India they live, in which state, and whether rural or urban. Since fertility is higher amongst Muslims, their children tend to be born a bit later which, given trend improvements in health technology and services, contributes to their survival advantage. But, overall, only about 50% of the religion differential can be explained by differences in characteristics. Using a supplementary data set, we suggest that further explanation may be sought in considering religion-differences in maternal health and breastfeeding behaviour.

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Religion and Death in India

Sonia Bhalotra and Arthur van Soest

1. Introduction

In his keynote address at the annual conference of the European Society of

Population Economics in 2005, Timothy Guinnane argued that religion explains much

of the variation across Europe in the level and the timing of the onset of the fertility

transition; in particular, religion effects seem to dominate the effects of women’s

education. The extent to which culture effects are compositional is identified as a

“remaining puzzle”. He states that “this may be an uncomfortable fact for

economists”, but “religion really matters”. Although there is a recent surge of interest

amongst economists in ethnicity effects, especially in education (e.g. Fryer and Levitt

2004, Wilson et al 2005), there remains limited research on religion effects in

economics, and especially so in the area of health.

The analysis in this paper is motivated by the observation that Muslim children

in India face lower mortality risk than Hindu children. This is puzzling because

knowledge of religion-differences in the predictors of mortality risk suggests that the

opposite should be the case. In particular, Muslims have, on average, lower socio-

economic status, higher fertility and shorter birth intervals, and they are thought to

accord a lower status to women within the household.1 Moreover, as they are a

minority group in India, we might imagine that the areas in which they are

concentrated receive relatively poor public services. Although higher fertility amongst

Muslims as compared with Hindus has excited considerable political and academic

attention in India, higher mortality amongst Hindus has gone largely noticed. This

paper investigates this seeming puzzle, quantifying the extent to which the religion-

differential in childhood mortality risk can be explained by differences in observed

characteristics between the two communities. In this way, it estimates the size of the

(residual) “religion effect”. For instance, we may find that a part of the observed

survival advantage associated with being Muslim as opposed to Hindu is

compositional and can be captured by, for example, moving from one region to

1 There is considerable evidence that, where mothers have greater power in the household or the community, children benefit in terms of their health and education (e.g. Maitra 2004, Thomas 1990).

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another, but that the rest can only be captured by adopting Muslim practices. The

latter would be described as the religion effect. It describes how, for given inputs, the

outcome is altered by agency, where, in this case, agency refers to the parents’

membership of a particular community. Agency is relevant to understanding how the

effectiveness of public resources may depend upon their distribution, in this case,

across religious communities.

The agency of women has attracted attention in the context of public policy in

both rich and poor countries. While most of the evidence pertains to women’s agency

within the household (e.g. Lundberg, Pollak and Wales 1997), there is also some

evidence of agency effects at the community or the state level (Edlund and Pande

2001, Chattopadhyay and Duflo 2004). Why does female agency matter? There are no

conclusive results here but the evidence suggests that women have different

preferences from men, they are more likely to spend household resources on food

rather than tobacco (e.g. Hoddinott and Haddad 1997), and more likely to spend

village resources on water rather than roads (Chattopadhyay and Duflo 2004). Agency

associated with religion raises a different set of questions but the idea is similar. If

Muslim parents are indeed better agents than Hindu parents for purposes of child

health, then the natural question of interest is why. Can we identify what practices

Muslims follow that, in principle, Hindus can emulate, such as early or more

sustained breastfeeding? Or are there inalienable characteristics of Muslim parents

such as stronger preferences for child health, possibly different by the gender of the

child?

To guide design and interpretation of the study, it is useful to consider essential

features of childhood death in developing countries. Most child deaths occur in the

first four weeks of life (the neonatal period), after which the risk of death tends to

decline continuously. Neonatal risk factors tend to be “biological”, the role of

environment and care rising as the child ages (e.g. Wolpin 1997). In the womb and at

birth, boys tend to be more vulnerable than girls, as a result of which childhood death

rates for boys tend to be higher in societies that do not exhibit any preference for

children of a particular gender. India is not such a society; there are numerous

accounts of son-preference amongst Indians (e.g. Arnold et al 19xx). Although girls

are, by nature, born with a survival advantage, this is seen to be eroded with age and,

for India as a whole, the data we use suggest that the risk of death for girls begins to

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exceed that for boys by the age of one. The girl disadvantage has been observed to

increase with birth-order (e.g. DasGupta 1987), consistent with the notion that parents

the world over like to have at least one boy and one girl. It is the arrival of more and

more girls that appears, in India, to reduce the “value” of the further girls to their

parents. So, individual mortality risk varies with child age, gender and birth-order.

But does the difference in mortality risk between Hindus and Muslims within India

also vary along these dimensions? We find that it does.

The religion differential in mortality risk persists from birth till at least age five,

but the percentage (or relative) differential shrinks, especially after the age of one

(Figure 1). This suggests a plausible role for factors like maternal health, antenatal

care and delivery complications that influence risk earlier on. In particular, it is

possible that the factors that matter more as the child ages (e.g. sex-preference or

access to health clinics) are actually more favourable amongst Hindus, and that the

Hindu-disadvantage apparent in under-5 mortality rates is entirely on account of their

experiencing higher infant (i.e. under-1) mortality rates. The tendency for Hindus to

experience “excess” child mortality (i.e. greater than experienced by Muslims) is

greater amongst girls, and especially girls of higher birth-order. We therefore estimate

models of mortality risk by age, birth-order and religion.

The main results are as follows. On average, only about 50% of the religion

differential can be explained by characteristics, this percentage being greater for

higher-order births. For instance, for first-order births, the risk of under-5 death is

11.9% for Hindus and 10.0% for Muslims, a total differential of 1.9%-points.

Although some of the Hindu disadvantage is explained by their membership of

scheduled castes and tribes (low castes in India), mortality amongst high-caste Hindus

is also higher than amongst Muslims (a differential of 0.83%-points for first-borns).

The largest part of the Muslim advantage stems form where in India they live, in

which state, and whether rural or urban. Since fertility is higher amongst Muslims,

their children tend to be born a bit later which, given trend improvements in health

technology and services, contributes to their survival advantage. These factors

overwhelm the characteristics that predict an advantage for Hindus, namely their

higher levels of parental education, and their longer birth-intervals.

Lower mortality amongst Muslims in India has been noted by Shariff (1995),

Kulkarni (1996) and Bhat and Zavier (2005) The only multivariate analysis that we

are aware of appears in our earlier work (Bhalotra and van Soest (2004). Using data

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from the state of Uttar Pradesh, where 17% of the population is Muslim, we find that

Muslim-status is associated with a 1.6%-point reduction in neonatal mortality. This is

a remarkably large religion effect, given that it obtains after controlling for birth-

spacing (and fertility), the survival status of the previous sibling, maternal age at birth,

education of the mother and father, caste, the gender and birth-order of the child, and

the child’s cohort.

Public awareness of the religion differential in mortality is limited and there

appears to be no previous research that has set out to explain it. In contrast, higher

fertility amongst Muslims as compared with Hindus has been the subject of recent

academic research (see Bhat and Zavier 2005 for an excellent survey), sparked by

political controversy and concerns that the Muslim population in India is growing

“too fast”. Census data show that, in 1951, Muslims constituted 9.9% of the

population of India and, in 2001, this percentage had risen to 13.4%. Over the same

period, the percentage of Hindus had declined from 85% to 80.5%. Higher fertility of

Muslims as compared with Hindus has been noted since before the partition of India

and the creation of Pakistan (e.g. Davis 1951). This differential has widened over time

and it is estimated that, in the late 1990s, the total fertility rate amongst Muslims was

29% higher than amongst Hindus. Lower infant and child mortality amongst Muslims

as compared with Hindus is also not a recent phenomenon (see Bhat and Zavier

2005).2 In the late 1990s, it was 23% lower but, as discussed in section 2 below, the

mortality differential appears to have narrowed over time. As there is considerable

evidence of mortality and fertility differentials being positively correlated over time

and across households, we may have expected a positive fertility differential between

two communities to be associated with a positive mortality differential. We find the

opposite. Our analysis shows that, while higher fertility amongst Muslims does tend

to raise mortality risk amongst their children, this effect is relatively small, and

outweighed by other factors.

2 See their Table 6, p.389, which reports the relevant means from the National Sample Surveys of 1963/4 and 1965/6, the Sample Registration Survey of 1979, Census 1981 and 1991, and the National Family Health Surveys (NFHS) of 1991/2 and 1998/9. In this paper, we use the NFHS survey for 1998/9, which contains information on births and child deaths over a span of 39 years. While data on mortality from surveys such as the NFHS can be subject to large sampling errors, SRS and Census data are not likely to suffer this problem. The religion difference investigated here therefore seems real.

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2. Data The data are drawn from the second round of the National Family Health

Survey of India conducted in 1998/9 (see IIPS and ORC Macro 2000). This

interviewed ever-married women aged 15-49 in 1998-99 and obtained from them

complete fertility histories, including number of live births, birth intervals, and the

time and incidence of child deaths. The survey also contains information on relevant

individual, household and community characteristics.

Births in the sample occur during 1961-1999 and we use all of this

information, which provides not only the advantage of efficiency in estimation but

also a relatively long panel of children per mother. This allows us, in our one equation

model, to examine mortality risk by birth-order, with large samples available for

higher-order births. In the extended model that endogenises fertility, this has even

greater advantages (see section 4 below).

The data used in this paper are for 18 Indian states that, together, account for

more than 95% of India’s population. A list of the states with descriptive statistics

including their weight in the sample and the percentage of Hindus and Muslims in

each state is in Table xx. Not every state has enough Muslims to merit a state-wise

analysis, so we pool data from across the states and include state fixed effects. Our

objective is to address the broad question of how Muslims and Hindus in India as a

whole compare.

We drop the 11.05% of births in the survey that occur in households of

religions other than Muslim or Hindu. Amongst Muslims 1.35% of live births are

multiple (twin, triplet etc) and amongst Hindus the corresponding figure is 1.29%.

Rather than drop just these children, we drop all mothers who have a multiple birth,

which involves dropping 3.6% of children and 7.3% of mothers in the total sample.

The sample used has 202250 live births of 60622 mothers. It is standard practice in

the demographic literature to restrict the analysis to singletons as death risks are many

times higher for multiple births and, although relatively rare, they can skew the

statistics.

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3. Descriptive Statistics This section describes the religion-differential in mortality and the way in which it

varies with child age and birth-order. It also docuents a narrowing of this differential

over time. It then describes the explanatory variables used in the analysis.

The Dependent Variable

The sample analysed in this paper contains 86.9% Hindus and 13.1%

Muslims. Across the births in the data, which span the period 1961-99, average under-

5 mortality is 11.6% amongst Hindus and 9.4% amongst Muslims. So religion creates

a raw mortality differential of 2.2%-points, or 23.4% (Appendix Table 1). The

religion differential in infant and neonatal mortality is 1.5%-points and 1.0%-points

respectively (also see Figure 1). (These are big differentials relative, for example, to

the average annual rates of decline in mortality reported below). To allow the

parameters of the child health production function that is estimated to vary with child

age, the analysis is conducted for each of the neonatal (first month of life), infant (first

year of life) and under-5 (first five years of life) mortality rates.3 The difficulty with

estimating mortality risk year by year is that mortality risk in age 1-2 is conditional

upon survival to age 1.

The religion differential varies non-linearly with birth-order, tending to narrow

for second-births and then become larger for third and higher-order births than it is for

first-births. This pattern is evident in each of the three age-groups considered, though

most marked in the under-5 group (Table 3). Separate equations will be estimated in

this paper for first, second, third and higher-order births.

Over the 38-year period spanned by the data, under-5 mortality declined at an

average rate of 0.37%-points p.a. Separating the sample by religion, we find that the

average annual rate of decline in percent-points was 0.38 amongst Hindus and 0.30

amongst Muslims, indicates a gradual erosion of the Muslim advantage over time in

absolute terms. This is confirmed by comparing mortality rates for both religions for

the first and last decade of the sample-data. For births occuring during 1961-70,

Muslim children show a survival advantage of 1.45, 2.5 and 3.2 %-points in the

3 It has been estimated that the survival advantage exhibited by Muslims persists into adulthood, with Muslims enjoying the greatest advantage in survival probabilities in the age range 15-34 (see Bhat and Zavier 2005). As these authors remark, this phenomenon is under-researched. In this paper, the focus is entirely on childhood.

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neonatal, infant and under5 age-groups respectively. The corresponding figures for

1990-99 are 0.92, 1.25 and 1.61%-points.

The religion differential has widened in relative terms. Consider the under-5

mortality rate. In the 1960s, this was 21.4% amongst Hindus and 18.2% amongst

Muslims, implying a differential of 17.6%. In the 1990s, the Hindu and Muslim rates

were 9.03% and 7.42%, which imply a differential of 21.7%.

The Independent Variables

These include the gender of the child, the birth-year of the child, the age of the

mother at the birth of the child, rural/urban location of the household, an indicator for

whether the household belongs to one of the “backward” castes, and a set of variables

indicating the educational level of the mother and of the father.4 All-India means by

religion are in Tables 4 and Figures 4-8. The corresponding state-specific means are

in Appendix Tables x. The sex ratio at birth is similar between the two communities.

Hindu mothers and fathers tend to be more educated and the religion gap in education

widens with the level of education. On its own, education would predict a Hindu-

advantage in survival. However, other characteristics favour survival amongst Muslim

children. These include their being born a bit later in time (which follows from higher

fertility amongst Muslims), their being born to slightly older mothers on average (this

is again a function of fertility, but will vary by birth-order in our analysis), their being

less likely to live in rural areas, their being less likely to belong to a low-caste, and

their being concentrated in low-mortality states. The analysis to follow will indicate

the magnitude and significance of these tendencies.

4. The Econometric Model The estimated equation is a reduced-form probit for mortality,

4 There is strictly no role for income or wealth in a health production function since incomes and prices determine the level of inputs (e.g. Rosenzweig and Schultz 1983). However, it is common to estimate a hybrid model that represents the reduced form of the technological relation denoted by the health production function and the budget equation (see Strauss and Thomas 1995). Data on household wealth are available only at the time of the survey and since children in the sample are born over a long span of time, these data are not used. Socioeconomic status is represented by the education of the mother and father of the child, together with ethnicity and religion. It is worth noting that the level of per capita income or expenditure (a common measure of income) depends on fertility which, in turn, is endogenous to mortality. For this reason, it would probably be undesirable to include this variable even if it were available for the entire sample of children.

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(1) Mij

* =Φ( xi , xij,; θm) + umij ;

Mij=1 if Mij*>0 and Mij=0 if Mij

*<0

Mij denotes an indicator variable with value 1 if child j in family i dies before a

specified age and 0 otherwise. For reasons discussed in section 3, we use three

alternative specifications of the dependent variable, with the cut-off age being one

month (neonatal), one year (infant) and five years (under-5) respectively. The vectors

xi and xij are exogenous explanatory variables that vary over children (xij, j=1,…,n)

and that do not vary over children (xi); these were described in section 3. The u term

denotes iid errors, and θ is a vector of parameters to be estimated. Equation (1) is

estimated for eight sub-samples: Hindu and Muslim children, of birth-orders one, two,

three, and four or higher. As we use information on all live births, we have the first-

born child of every mother, and first-borns constitute 30% of all births. Birth-orders

two, three and higher comprise 25%, 18% and 27% of the sample respectively.

Estimating the model by birth-order avoids having to introduce family-level

unobserved heterogeneity, and make the assumption that this is uncorrelated with the

included covariates.

Previous research on mortality that uses retrospective histories has tended to

left-truncate the data at ten or fifteen years prior to the survey date (e.g. Bhargava

2003, Bolstad and Manda 2001, Madise and Diamond 1995, Guo 1993, Curtis, et al

1993). One reason is that it is thought that measurement error in recollection of dates

of birth and death is likely to be greater the further back in time the event occurred.

We assume/expect that any recall error is similar across the religious communities.5 A

second reason that demographic research favours left-truncating the available birth-

history data is that they get more scarce the further back one goes in time, and will

contain a disproportionately large number of children born to young mothers. Thus, in

1998, the survey data will include births to mothers as young as 15 and as old as 49.

However, for 1968, the data will contain births to mothers who were 15 in 1968 and

45 in 1998, but it will exclude births to mothers who were 25 in 1968 because those

5 This issue was nevertheless examined in the following way for infant deaths. Since the data reveal age-heaping at six-month intervals, the dependent variable was defined in terms of death before or at 12 months of age, and the results compared with those obtained when the dependent variable refers to death strictly before the age of 12 months. There were no important differences (Arulampalam and Bhalotra 2004).

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mothers are 55 in 1998 and too old to have been interviewed. To allow for this

triangularity in the data structure, we condition on mother’s age at birth.

Allowing Endogenous Fertility

A potential quibble with the specification (1) is that, if fertility and birth-

spacing are endogenous to mortality, then the age of the mother at the birth of the

child, the year of birth of the child and birth-order are endogenous variables (see

Rosenzweig and Wolpin (1995) for an essentially similar argument).

In an earlier paper that focused on neonatal mortality, we addressed this

problem by estimating mortality, birth-spacing and the probability of having another

child jointly using simulated maximum likelihood and exploiting the panel nature of

the data (Bhalotra and van Soest 2004). Mothers constitute the cross-sectional

dimension of the data. As mothers are observed repeatedly, in relation to every birth,

birth-order creates the time dimension of the panel. The model is dynamic in that it

includes “lagged mortality” (i.e. mortality of the preceding sibling), so that the effects

of mortality on birth-spacing and of birth-spacing on mortality are identified by

timing, avoiding the need to impose any arbitrary exclusion restrictions. An equation

for the probability of having a further child was introduced to allow for right-

censoring of the birth-interval data and it was identified using information on female

sterilization.6 A family-level random effect was introduced in every equation to

account for unobservables common to siblings, and these unobserved heterogeneity

terms were allowed to be correlated across equations. Children of different birth-order

were pooled, and birth-order included as a covariate. A reduced-form equation for

mortality of the first-born child was estimated together with the three main equations

to avoid the initial conditions problem that otherwise plagues dynamic models. An

advantage of this model is that endogeneity of not only the preceding birth-interval

but also of maternal age at birth, year of birth of child and birth-order, is taken

account of.

In this paper, we are not primarily interested in modelling the effects of birth-

spacing (or fertility) on mortality because, as discussed in section 1, we observe

higher death risks amongst Hindu children despite the lower fertility and shorter birth-

intervals of Hindus. Nevertheless, to investigate the importance of allowing for these

6 This the most common form of contraception in India and very difficult to reverse.

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effects, and for the endogeneity of other fertility-related variables (maternal age, birth

year, birth-order), we estimated the four-equation model for neonatal mortality

described in our earlier paper, separately for Hindus and Muslims. We also

conducted, for this extended model, the decomposition exercise that we describe in

section x below. Any interesting insights into the central question of why Hindus have

higher mortality than Muslims that is yielded by moving to the extended model are

reported in discussion of the results (full results available on request). However, these

differences are, overall, not impressive. So, having investigated endogeneity and

concluded that it does not change our story about religion differences, we present the

simple model in equation (1) as the preferred model.

A further advantage of taking the single-equation approach here is that it can

be estimated for infant and under-5 mortality. This is relevant to our current project

because, as observed in section 3, the religion differential appears to increase with

child age in the range 0-5. It is not straightforward to incorporate these wider periods

of exposure to death risk into the extended model because, as explained above, it

relies upon the natural sequencing of the birth-spacing and neonatal mortality

processes for identification (details in Bhalotra and van Soest 2004).7

5. Results The estimation results are in Tables 5-7, where marginal effect are presented

for each of the Hindu and the Muslim subsamples for under-5, infant and neonatal

mortality respectively. The discussion of effects of covariates below is, of course,

conditional on the other covariates in the model.

The difference in mortality risk faced by girls as opposed to boys varies with

religion and birth-order. Two fairly general patterns emerge in this respect. First,

across both communities and in every age-group, any excess risk faced by girls is

increasing in birth-order. This result cannot be explained in terms of increasing age 7 To repeat, the neonatal survival status of the preceding sibling (Mij-1) is necessarily revealed before the birth of the index child. So, given controls for (correlated) unobserved heterogeneity, Mij-1 is predetermined and its effects can be interpreted as causal. If neonatal is replaced with infant survival status, this does not always work. The index child could be born eight months after the preceding sibling and the preceding sibling could die between the eighth and twelfth month of her life, which is after the birth of the index child. In this case, Mij-1 is not predetermined. Replacing neonatal with under-5 is even harder. In our earlier work, where the focus was on the bi-causal relation of mortality and birth-spacing, the choice of the neonatal period was not so restrictive since mortality risk is more closely tied to birth-spacing in the neonatal period than later.

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of the mother at birth of the index child because it is obtained conditional upon

maternal age. It could nevertheless reflect biological factors such as maternal

depletion, which we would expect increaes with birth order, or else preferences in

which parents obtain diminishing returns from daughters at a more rapid rate than

from sons.8 Second, for under-5 and infant mortality, any excess risk faced by girls

(i.e. excess over the risk faced by boys) is smaller in Muslim households, whatever the

birth-order.9 It is noteworthy that this is not the case for neonatal mortality, for which

the effect of gender is not very different by religion. Girls face lower death risks in

every subsample, and these risks are significantly lower upto the third birth-order

amongst Hindus and upto the second birth-order amongst Muslims. This age-pattern

of the gender-effect suggests that the advantage that girls enjoy in Muslim households

may have more to do with postnatal care than with more biological variables like

birth-spacing. This is because neonatal death tends to be dominated by biological

causes (e.g. Wolpin 1997).

We find that the birth-order effect is different for the two communities,

implying that religion and birth-order interact in producing differential girl-boy

mortality. Consider the marginal effects of gender for under-5 mortality. First-born

girls are significantly less likely to die in both communities, though the marginal

effect is about twice as large among Muslims (-0.023 as compared with –0.012). In

the Muslim group, girls of higher birth-orders are no more or less likely to die than

boys. In the Hindu group, girls of birth-order three and higher are significantly more

likely to die than boys. The effect is substantial, being 0.010 percentage points at

birth-order three and 0.023 at higher birth-orders. In the case of infant mortality, the

female advantage at birth persists until birth-order two for both communities, and the

religion-differential is smaller. For first-borns, for example, it is –0.024 for Muslims

and –0.017 for Hindus. In no subsample do girls face a higher risk of death than boys.

The marginal effect of gender on neonatal mortality is of a similar magnitude. For

first-borns, it is –0.020 amongst Muslims and –0.016 amongst Hindus.

For every subsample considered, mortality risk is U-shaped in mother’s age at

birth, and the differences across subsamples are, in general, not significant. The

8 This is consistent with DasGupta (1990) who, based upon data from a region in the state of Punjab, argued that “discrimination” was concentrated amongst later-born girls. 9 This is consistent with a similar finding reported for the case of infant mortality in Barooah and Iyer (2005), using another Indian dataset.

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turning points are reported in the Tables. The finding of a U-shaped relation is

common in the demography of developing countries. Although the coefficients on the

birth year of the child and its square are not individually significant in any of the

subsamples, they are jointly significant at 1% in every case, indicating that mortality

has been declining with time. The per annum rate of decline at the mean year is

reported in the Tables.

As discussed in section x, there are low-castes amongst Hindus, members of

which have been historically disadvantaged and, despite some positive discrimination,

continue to exhibit worse outcomes. This is confirmed by our finding that, for all

birth-orders, scheduled caste children suffer higher mortality risk. The marginal

effect is higher (0.02) for second and fourth or higher-order children than for first

and third borns (0.01). This somewhat odd pattern is repeated in the (smaller)

marginal effects associated with belonging to a scheduled tribe or to other backward

castes, but it is not clear what explains it. The effect of low-caste-membership on

mortality risk is weaker at younger ages. For infant and neonatal mortality, the effects

of SC and OBC membership are of similar size, the effect of ST-status being small

and mostly insignificant. The latter is somewhat surprising given that scheduled tribes

tend to live in geographically more isolated areas and to have weaker access to public

goods than other social groups (e.g. Banerjee and Somanathan 2005).

Consider the variables describing the level of education attained by each

parent where, in both cases, the omitted category refers to no education. The effect of

parental education on survival tends to increase with child age, consistent with an

increasing role for “nurture”. It is striking that a given level of maternal education has

much larger survival-raising effects amongst Hindus than amongst Muslims,

especially in the under-5 group. In this group, the effects of father’s education are

rather more similar between the religions. At low levels of maternal education, the

marginal effect tends to be larger at higher birth-orders. In most cases, the marginal

effect of a given level of education of the father is smaller than that flowing from the

same level of education of the mother, which is a result familiar from other studies of

the effects of parental education on health or educational outcomes for children in

developing countries.

In all but one of the eight samples for which results are reported in Table 3,

rural-residence is associated with higher under-5 mortality risk, and this added risk

tends to be greater in the Muslim community. In general, it does not vary with birth-

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order, except that it is smaller for fourth and higher birth-orders in the infant and

under-5 groups. The marginal effects which, for first-borns, are of the order of 0.01

for Hindus and 0.02 for Muslims, do not change very much with child age. This

suggests that the rural effect probably denotes access to antental services, delivery and

neonatal care, such as iron-supplementation, tetanus for the pregnant mother, the

presence of a trained midwife at birth, or effective mangement of early complications.

The state effects are jointly significant in all subsamples but they are larger

and more significant for Hindus. The omitted state is Andhra Pradesh (see Table x). It

is only for the state of Rajasthan and at higher birth-orders that the state effects appear

significant for Muslims.

6. Decomposition of the Mortality Differential (this section is to be completed and revised) The methods in this section

follow Fairlie (2006), who suggests an extension of the Oaxaca-Blinder procedures

commonly used in linear models to the case of non-linear models such as the probit or

the logit.

A decomposition of the observed differences in mortality risk between the

samples is presented in Tables x4-x13. Refer Table x4 which contains predicted

probabilities of mortality by birth order. Column 1 gives the average predicted

probability of neonatal mortality for Hindus. It takes the average of every variable

over the sample of Hindus and uses the parameter estimates obtained for the Hindu

sample (and reported in Table x1). Column 4 reports the average predicted probability

for Muslims. For every birth order, average predictions are much smaller in column 4

than in column 1, in line with the raw data. But these differences are on account of

both differences in characteristics (values of X) and differences in parameters (the

estimated coefficients). The rest of this section attempts to decompose the total

difference into these two categories.

Columns 2 and 3 of Table x4 report the "counterfactuals." Column 2 gives the

predictions for Hindus using the parameter estimates for Muslims. Similarly, column

3 gives the predictions for the Muslim sample using the parameters for Hindus. So,

comparing columns 1 and 2, we see that if Hindus retained their average

characteristics but had the parameters associated with Muslims, then the survival

chances of their children would improve. A similar result obtains comparing columns

15

3 and 4 for Muslims. These comparisons establish that the Hindu-Muslim difference

in mortality risk is not entirely explained by differences in characteristics. If, instead,

we compare columns 1 and 3 (or, instead, columns 2 and 4), we see that the religion

difference is not entirely explained by parameter-differences either. So, both matter,

but what is their relative explanatory power? The data that answer this question are in

Table x5.

For the second child for example, 0.23 percentage-points of the total mortality

difference of 0.81 percentage-points, a bit more than a fourth, is explained by

differences in the distribution of the explanatory variables in the Hindu and Muslim

samples. This is the difference between columns 1 and 3 in Table 1, that is,

parameters are kept constant at the Hindu estimates. The remaining 0.59 percentage

points is due to differences in the parameter estimates for Hindus and Muslims (the

difference between columns 3 and 4 in Table 1). For the fifth child, the total religion

differential in neonatal mortality is much larger, and a much larger share of it is

driven by sample composition differences. In total, combining all birth orders,

somewhat less than half of the differential is explained by differences in

characteristics (or sample composition) between the two communities.

Table x6 provides a more detailed decomposition. This comes with the health

warning that the decomposition of parameter-differences by variable seems to depend

strongly on the way in which variables are normalized, which benchmark categories

are used to construct dummies, etc., and the results seem hard to interpret So,

although these results are presented for completeness in the right-hand panel of Table

x6, we will not discuss them. Instead, we focus on the contribution of particular sets

of variables to the role of observed characteristics in explaining the religion

differential in mortality.

For illustration, we present the detailed decomposition only for the fifth child.

(Results for other birth orders are available from the authors on request but would

take too much space to present here). As is clear from Table 2, the total differential

explained by characteristics for the fifth child is the sample composition is 0.64

percentage-points. Most of this difference (0.45 percentage-points) is explained by

state dummies. Muslims are better represented in the more developed states where

neonatal mortality is relatively low (Kerala, Assam, West Bengal; see Table xx which

shows percent of Muslims in each state). Similarly, Hindus are more likely than

Muslims to live in rural areas, by virtue of which, the estimates suggest that we

16

should expect Hindu mortality risk to be higher by 0.11 percentage-points. A further

0.22 percentage points of the religion-differential is explained by the fact that the

Hindu group includes scheduled and other backward castes which, as noted in the

previous section, experience higher mortality. Another important contributor to the

excess risk faced by Hindus is the fact that lagged mortality affects current morality

and is lower for Muslims. This persistence effect alone produces a religion-difference

in mortality risk of 0.14 percentage-points. As a result of higher total fertility amongst

Muslims, their children are born somewhat later than the Hindu children, on average.

This explains about 0.09 percentage-points of the mortality difference because of an

overall negative trend in neonatal mortality. Differences between the communities in

the proportion of girls in the sample, and in the age of the mother at the birth of the

index child contribute marginally to excess mortality amongst Hindus, the

contribution of each being of the order of 0.01 percentage-points.

Some of the observed differences in characteristics between the religious

communities predict lower mortality amongst Hindus. In this sense, they make a

negative contribution to explaining excess mortality amongst Hindus. For example,

educational attainment amongst fathers and mothers is higher in the Hindu than in the

Muslim community. Our estimates show that this would predict neonatal mortality

amongst Hindus to be 0.30 (mothers 0.14, fathers 0.16) percentage-points lower than

that amongst Muslims, other things equal. In other words, the distribution of parental

education in the sample contributes -0.30%-points to explaining higher mortality rates

amongst Hindus. A similar counteracting effect is found for the length of the birth

interval. Since birth intervals are shorter for Muslims than for Hindus, the estimates

imply that Muslims would have more neonatal mortality if other factors were kept

constant. The predicted effect is about -0.10 percentage-points.

Results from Extended Model

As discussed in section 1, Muslims have higher fertility than Hindus. A

seeming paradox is that this would have led us to expect higher childhood mortality

amongst Muslims, when in fact we find the converse. To what extent do our estimates

contribute to understanding/resolving this seeming paradox?

Higher fertility amongst Muslims is reflected in the specification for mortality

(equation 1) through a number of (associated) variables: shorter preceding birth

intervals, calendar year of birth, younger age of mother at first birth, and birth order of

17

child. A further association arises from the permitted correlation between the family-

specific unobserved heterogeneity terms in the mortality, birth-spacing and “fertility”

equations (equations 1-3). As we have seen, shorter birth-intervals amongst Muslims

would lead to neonatal mortality in this group being 0.10 percentage-points higher, a

result that underlines the paradox. However, Muslim children being born later (in

calendar time) on average, and the distribution of age at birth of Muslim mothers

lying to the left of the distribution for Hindu mothers contribute 0.09 and 0.01

percentage-points respectively to the finding of higher mortality amongst Hindu

children. As we have presented results by birth-order, the contribution of birth-order

to the religion differential in mortality is not explicit in the decomposition. However,

the birth order terms in both equations in Table x1 are insignificant, consistent with

Figure xx which shows that Hindu’s have higher neonatal mortality than Muslims at

every birth-order. Table x5 shows that the contribution of sample composition to the

total religion differential tends to increase with birth-order [might this follow from

there simply being more Muslim children at higher birth-orders because of higher

fertility? I should think about this later]. Looking at the correlations of the unobserved

heterogeneity terms across equations 1-3 (Table x3’), we find that Hindu families

with higher frailty also have a tendency towards longer birth intervals, while there is

no such relation for Muslims. However, amongst Muslims, but not Hindus, families

with higher frailty tend to be those with lower fecundity. [Estimates for both religions

reveal a negative association of the unobserved heterogeneity terms in the birth-

spacing and fertility equations.] Thus, while fertility and mortality interact in a

number of ways, some of which predict a positive association of these variables, the

religion differential in mortality seems to arise largely from factors that are unrelated

to the fertility differential.

7. Discussion We find that a large part of the religion differential is unexplained by the

covariates in the estimated model. Even under the category of variation explained by

included characteristics, a large part of the variation is attributed to state fixed effects,

which are basically unobservables. What might be going on that we have not

captured?

18

There may be differences in cultural practices between Hindus and Muslims

that explain the observed differences in mortality. Thus, Hindus have a ritual of

offering the first-milk (colostrum) to the earth, despite scientific evidence that

colostrum is rich in antibodies that prevent recurrent infections. Another possibility is

that there are differences in diet between the two communities that might impact on

child health and survival; in particular, Muslims are more likely to eat meat

That Hindus are relatively less careful about their daughter’s health than are

Muslims is potentially consistent with the greater role of dowry amongst Hindus, and

the importance they attach to having sons perform religious rites. However, this

hypothesis has yet to be carefully investigated.

The wider set of questions put to women who had had a recent birth include

some questions that might provide insight into the puzzling fact of higher mortality

rates amongst Hindu as compared with Muslim children. Figure 5 shows the number

of hours from the time of birth to the first-breastfeed reported by women. The entire

curve suggests that Muslims put the baby to the breast sooner. This is especially

marked for the important first-hour following birth, when the nutrient-rich colostrum

milk is available to the child. Thus 16.9% of Muslim women breastfeed immediately

on birth, compared with 14.48% of Hindu women. Notice how small these fractions

are overall.

Another statistic that appears to shed light on the religion difference in

mortality relates to maternal health. Taking the conventional definition of under-

nourishment as indicated by a body mass index (BMI) smaller than 18.5, 36% of

Hindu women were undernourished at the time of the survey, compared with 31.4%

of Muslim women. Although there is no marked overall difference in socio-economic

status between the religious groups (see Bhat 2005), the higher BMI of Muslim

women may be a reflection of their diet (they are more likely to eat meat and so

possibly to have a more protein-rich diet at a similar SES-status), or else of their

phenotype (Indian Muslims originated North of the Indian subcontinent, where the

racial stock is typically taller and of bigger build). We have confirmed in our data that

low-BMI has a significant and large risk-increasing effect on neonatal (and later)

mortality. Indeed, we find that not only are fewer Muslim women undernourished but

the effect of low BMI on death risk seems to be smaller amongst Muslims.

An advantage of comparing two religious groups residing within the same

country is that there is less unaccounted heterogeneity. As we are drawing inferences

19

about religion here, it may be instructive to compare Muslims with Hindus in other

contexts, such as Pakistan or Indonesia. If we were to find that Muslim children

experience lower mortality than Hindu children in other countries too, we would have

greater reason to belief that there are “cultural” reasons behind the religion effect.

However, if we were to find instead that there is no survival advantage amongst

Muslim children, then we cannot reject the role of “culture” because culture may

operate very differently for Muslims in different countries. It has, for instance, been

recognised in discussion of Muslim-Hindu fertility differences, that this is the case

(e.g. Bhat and Zavier 2005)

8. Conclusions Why do Hindu children face higher mortality risks than Muslim children in India?

To the extent that the religion-difference is attributable to observed characteristics, it

is overwhelmingly the geographical distribution of Muslims that confers a survival

advantage upon their children, with further contributions in this direction flowing

from persistence-effects and from the fact that Muslim children tend to be born later

in time, an artefact of higher Muslim fertility. These factors swamp the effects of birth

interval length and parental education that, on their own, predict higher mortality

amongst Muslim children.

To the extent that the religion-difference can be attributed to parameter

differences, it seems that, for a given maternal age at birth, the deleterious effect on

mortality is much larger amongst Hindus than amongst Muslims. This is consistent

with our suggestion that under-nourishment is less prevalent amongst Muslim

mothers. Another parameter difference that contributes to higher mortality amongst

Hindus is that the mortality-reducing effect of mother’s education is smaller amongst

Hindus. However this is small relative to the maternal-age effect, which is large

enough that it overwhelms a number of the counteracting influences evident in our

results.

On average, and for first to fourth children in the family, observed characteristics

explain less than half of the total religion differential. In the illustrated case of the

fifth child, characteristics explain 0.64 percentage-points, and parameter differences

explain 0.54 percentage-points. The residual “religion” effect will, of course, shrink

as the set of observable covariates available expands. In work in progress, we

20

investigate with a richer dataset, the inclusion of indicators of maternal health, and

maternal behaviours such as breastfeeding and hygiene into the model.

21

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Table 1 Geographic Dispersion of Hindus & Muslims in India

State name Number of observations

% India’s population from this

state

%Muslims in state

population

%Of all Muslims

that live in each state

andhra pradesh 9088 4.47 0.09 3.09 assam 8498 4.18 0.32 10.11 bihar 20211 9.94 0.17 12.82 gujarat 9943 4.89 0.07 2.66 haryana 7422 3.65 0.06 1.60 himachal pradesh 6955 3.42 0.04 0.94 karnataka 10373 5.10 0.15 5.65 kerala 4869 2.40 0.44 8.03 madhya pradesh 20134 9.91 0.07 5.40 maharashtra 11077 5.45 0.17 6.96 orissa 11115 5.47 0.03 1.10 punjab 3220 1.58 0.07 0.84 rajasthan 19772 9.73 0.10 7.64 tamil nadu 9499 4.67 0.07 2.63 uttar pradesh 9835 4.84 0.25 9.14 west bengal 26844 13.21 0.16 15.89 new delhi 5845 2.88 0.11 2.41 north-east 8548 4.21 0.10 3.14 ALL INDIA 203248 100.00 0.13 100.00

Notes: Authors’ calculations from NFHS2 data

24

Table 2 Mortality Rates by Religion, Age and Gender

Mortality rates Muslim Hindu High-caste

Hindus Muslim vs Hindu (% points)

Muslim vs hc-Hindu (% points)

Males Neonatal 0.047 0.056 0.054 -0.90 -0.69Infant 0.071 0.085 0.080 -1.40 -0.88Under-5 0.093 0.111 0.102 -1.80 -0.90Females Neonatal 0.037 0.047 0.045 -1.00 -0.80Infant 0.062 0.079 0.075 -1.70 -1.25Under-5 0.091 0.116 0.106 -2.50 -1.50Female vs male (% points)

Neonatal -1.01 -0.96 -0.89 Infant -0.85 -0.65 -0.53 Under-5 -0.22 0.50 0.40 Notes: Excluding scheduled caste and scheduled tribes from amongst Hindus generates the category defined as high-caste (hc) Hindus. Negative signs in the last two columns indicate that Muslims have a survival advantage over Hindus in both gender groups and in each of the three age groups, although the advantage is greater amongst girls and increasing in child age. The last three rows show that females have a survival advantage over males until the age of 1 year in both communities. After that, the female advantage is gradually eroded and, in the case of Hindus, reversed.

25

Table 3 Religion Differences in Mortality by Birth-Order

Hindu, Child-1

Muslim, Child-1

Hindu, Child-2

Muslim, Child-2

Hindu, Child-3

Muslim, Child-3

Hindu, Child>=

4

Muslim, Child>=

4 mean of neonatal 0.064 0.053 0.045 0.040 0.042 0.033 0.053 0.041 t-test 3.53 (0.00) 1.96 (0.05) 3.04 (0.00) 4.60 (0.00) mean of infant 0.093 0.079 0.075 0.064 0.071 0.052 0.088 0.069 t-test 3.65 (0.00) 2.91 (0.00) 4.77 (0.00) 5.89 (0.00) mean of under5 0.119 0.100 0.104 0.286 0.309 0.272 0.337 0.098 t-test 4.42 (0.00) 3.40 (0.00) 5.49 (0.00) 8.54 (0.00)

Notes: p-values in brackets

26

Table 4 Means of Variables by Religion

Hindu Muslim t-test birth year of child 85.882 86.558 -13.895 proportion female 0.479 0.477 0.388 age ma at birth -12.146 prop rural 0.738 0.603 45.768 caste: proportion SC 0.216 0.000 85.421 proportion ST 0.106 0.000 55.965 proportion OBC 0.335 0.255 25.989 mother’s education no education 0.619 0.648 -9.177 less than primary 0.090 0.123 -17.459 primary 0.072 0.067 3.148 incomplete secondary 0.120 0.108 5.483 complete secondary & higher 0.099 0.053 23.953 father’s education no education 0.316 0.392 -24.854 less than primary 0.112 0.148 -16.957 primary 0.094 0.087 3.609 incomplete secondary 0.211 0.199 4.831 complete secondary 0.119 0.096 10.653 higher 0.148 0.078 30.849 number of obs 175745 26505

Notes: Col. 3 reports a t-test of the null that the means by religion (col.1 & col. 2) are equal. The p-values associated with the t-test is 0.00 in every row except proportion female. SC= scheduled caste, ST= scheduled tribe, OBC= other backward caste.

27

Table 5 Under-5 Mortality

Probits by Religion and Birth-Order

1 2 3 4 5 6 7 8 Hindu,

Child-1 Muslim, Child-1

Hindu, Child-2

Muslim, Child-2

Hindu, Child-3

Muslim, Child-3

Hindu, Child>=4

Muslim, Child>=4

1 if female -0.012** -0.023** 0.004 -0.001 0.010** 0.004 0.023** 0.009 [0.003] [0.007] [0.003] [0.007] [0.003] [0.007] [0.003] [0.006]

age ma at birth of child x10 -0.320** -0.252** -0.356** -0.446** -0.297** -0.248** -0.268** -0.145** [0.003] [0.008] [0.003] [0.007] [0.004] [0.008] [0.003] [0.005]

square of age ma x100 0.066** 0.054** 0.065** 0.087** 0.049** 0.041* 0.044** 0.026** [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

child's birth year x10 -0.013 -0.03 0.004 -0.016 0.026 0.009 0.03 -0.074 [0.031] [0.080] [0.037] [0.095] [0.052] [0.117] [0.063] [0.121]

square of birth year x100 -0.001 0 -0.002 -0.001 -0.003 -0.002 -0.004 0.002 [0.002] [0.005] [0.002] [0.006] [0.003] [0.007] [0.004] [0.007]

1 if currently rural resident 0.015** 0.020* 0.013** 0.012 0.016** 0.022* 0.019** 0.032** [0.003] [0.008] [0.003] [0.009] [0.004] [0.009] [0.004] [0.007]

scheduled caste 0.012** 0.020** 0.011* 0.019** [0.004] [0.004] [0.005] [0.005]

scheduled tribe -0.001 0.014* 0.009 0.013* [0.005] [0.005] [0.006] [0.006]

other backward caste 0.007 0.007 0.009* -0.005 0.005 0.012 0.011* 0.017* [0.004] [0.009] [0.004] [0.009] [0.004] [0.011] [0.004] [0.008]

ma incomplete primary -0.005 -0.005 -0.009* -0.016 -0.013* -0.022* -0.014* -0.043** [0.005] [0.011] [0.005] [0.011] [0.006] [0.011] [0.006] [0.008]

28

ma complete primary -0.028** -0.007 -0.013* -0.018 -0.016** -0.027* -0.015* -0.016 [0.005] [0.014] [0.005] [0.014] [0.006] [0.013] [0.007] [0.012]

ma incomplete secondary -0.025** -0.029** -0.025** -0.002 -0.018** -0.038** -0.020** -0.016 [0.004] [0.011] [0.004] [0.013] [0.006] [0.011] [0.007] [0.013]

ma sec or higher -0.050** -0.035* -0.030** 0.004 -0.033** -0.016 -0.034** -0.053** [0.005] [0.014] [0.005] [0.020] [0.007] [0.019] [0.011] [0.017]

pa incomplete primary 0.001 -0.017 -0.002 -0.008 0.004 0.006 -0.004 -0.029** [0.005] [0.010] [0.005] [0.011] [0.006] [0.012] [0.005] [0.008]

pa complete primary -0.001 -0.025* -0.013** -0.014 -0.008 -0.004 -0.017** -0.020* [0.005] [0.011] [0.005] [0.012] [0.006] [0.014] [0.005] [0.009]

pa incomplete secondary -0.017** -0.023* -0.022** -0.015 -0.017** -0.004 -0.017** -0.035** [0.004] [0.009] [0.004] [0.010] [0.004] [0.011] [0.004] [0.007]

pa complete secondary -0.025** -0.025* -0.033** -0.027* -0.028** -0.002 -0.034** -0.035** [0.004] [0.011] [0.004] [0.011] [0.005] [0.015] [0.005] [0.009]

pa higher than secondary -0.031** -0.030* -0.028** -0.01 -0.032** -0.01 -0.049** -0.02 [0.005] [0.012] [0.005] [0.014] [0.006] [0.016] [0.006] [0.013]

assam -0.032** -0.003 -0.015 0.011 -0.022* 0.019 -0.034** 0.102* [0.008] [0.024] [0.009] [0.026] [0.011] [0.032] [0.011] [0.042]

bihar 0.005 0.022 0.011 0.032 0.011 0.018 0.020* 0.081* [0.008] [0.027] [0.008] [0.029] [0.010] [0.031] [0.010] [0.037]

gujarat 0.030** 0.049 0.035** 0.052 0.02 0.07 0.011 0.160** [0.010] [0.040] [0.010] [0.041] [0.012] [0.053] [0.012] [0.062]

haryana 0.002 0.046 0.011 0.044 0 -0.024 -0.002 0.076 [0.009] [0.047] [0.010] [0.048] [0.012] [0.031] [0.012] [0.050]

himachal pradesh -0.025** -0.021 -0.020* -0.034 -0.029** -0.015 -0.042** -0.036 [0.008] [0.035] [0.008] [0.032] [0.010] [0.042] [0.011] [0.048]

karnataka 0.007 0.007 -0.009 0.003 0.01 0.032 -0.003 0.075

29

[0.008] [0.027] [0.008] [0.027] [0.011] [0.037] [0.011] [0.042] kerala -0.041** -0.006 -0.036** -0.033 -0.033* -0.033 -0.062** 0.082

[0.010] [0.025] [0.010] [0.019] [0.015] [0.021] [0.019] [0.044] madhya pradesh 0.076** 0.051 0.048** 0.036 0.055** 0.049 0.050** 0.180**

[0.010] [0.036] [0.009] [0.034] [0.011] [0.041] [0.011] [0.053] maharashtra 0.01 -0.03 -0.004 0.003 -0.024* 0.015 -0.014 0.089

[0.009] [0.021] [0.008] [0.026] [0.009] [0.033] [0.011] [0.046] orissa 0.033** 0.134* 0.036** 0.066 0.035** 0.132 0.028* 0.133*

[0.009] [0.065] [0.010] [0.058] [0.012] [0.080] [0.012] [0.067] punjab -0.012 0.04 0.018 -0.023 0.008 -0.037 -0.017 0.077

[0.012] [0.056] [0.014] [0.040] [0.016] [0.034] [0.016] [0.068] rajasthan 0.048** 0.076* 0.046** 0.055 0.044** 0.07 0.034** 0.133**

[0.009] [0.037] [0.009] [0.035] [0.011] [0.043] [0.011] [0.045] tamil nadu 0 0.008 0.005 0.03 0.003 0.011 -0.008 0.112

[0.008] [0.033] [0.009] [0.038] [0.011] [0.040] [0.012] [0.060] west bengal 0.001 0.032 -0.009 0.006 -0.004 0.028 -0.001 0.075

[0.009] [0.029] [0.009] [0.026] [0.011] [0.034] [0.012] [0.039] uttar pradesh 0.060** 0.084* 0.054** 0.04 0.066** 0.051 0.070** 0.137**

[0.009] [0.034] [0.009] [0.029] [0.012] [0.036] [0.011] [0.040] new delhi 0.033** -0.023 0.006 0.004 -0.001 0.024 -0.004 0.120*

[0.012] [0.028] [0.011] [0.034] [0.014] [0.043] [0.015] [0.056] north-east -0.001 0.071 -0.004 0.053 0.002 0.031 -0.019 0.189**

[0.009] [0.041] [0.009] [0.040] [0.012] [0.042] [0.011] [0.059] F tests: (state dummies) 410.97 78.23 265.61 34.33 238.65 33.80 323.22 62.94 (0.000) (0.000) (0.000) -(0.008) (0.000) -(0.009) (0.000) (0.000) (child's birth year quadratic) 286.18 27.68 217.37 46.68 191.31 13.22 222.40 42.83

30

(0.000) (0.000) (0.000) (0.000) (0.000) -(0.001) (0.000) (0.000) (maternal age quadratic) 249.58 19.28 334.15 57.03 181.22 26.50 93.68 8.02 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -(0.018) (low castes) 12.60 0.58 24.14 0.27 5.83 1.35 16.40 4.76 -(0.006) -(0.447) (0.000) -(0.603) -(0.120) -(0.245) -(0.001) -(0.029) (ma education) 99.77 8.03 37.93 3.26 22.51 10.60 17.99 23.19 (0.000) -(0.091) (0.000) -(0.515) (0.000) -(0.031) -(0.001) (0.000) (pa education) 62.75 10.83 73.51 5.54 46.99 0.99 77.51 28.65

(0.000) -(0.055) (0.000) -(0.353) (0.000) -(0.964) (0.000) (0.000) rate of decline p.a. -1.733 -0.030 -3.435 -1.736 -5.133 -3.430 -6.848 3.365 turning point for age-ma 24.402 23.420 27.260 25.516 30.065 30.259 30.533 28.248 mean of under5 0.119 0.100 0.104 0.286 0.309 0.272 0.337 0.098 Observations 53792 6830 45298 5810 32204 4548 44451 9317

P-values in brackets; Standard errors in square brackets; * significant at 5%; ** significant at 1%

31

Table 6 Infant Mortality

Probits by Religion and Birth-Order

1 2 3 4 5 6 7 8 Hindu,


Hindu, Child-2

Muslim, Child-2

Hindu, Child-3

Muslim, Child-3

Hindu, Child>=4

Muslim, Child>=4

1 if female -0.017** -0.024** -0.005* -0.014* -0.002 0.001 0.005 0.002 [0.002] [0.006] [0.002] [0.006] [0.003] [0.006] [0.003] [0.005]

age ma at birth of child x10 -0.262** -0.200** -0.276** -0.381** -0.205** -0.189** -0.184** -0.090* [0.002] [0.007] [0.002] [0.006] [0.003] [0.006] [0.003] [0.005]

square of age ma x100 0.054** 0.043* 0.052** 0.075** 0.034** 0.034** 0.030** 0.016* [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

child's birth year x10 -0.013 0.029 -0.032 -0.067 -0.058 -0.02 -0.011 -0.175 [0.028] [0.073] [0.032] [0.080] [0.043] [0.093] [0.053] [0.100]

square of birth year x100 0 -0.003 0.001 0.003 0.002 0 -0.001 0.009 [0.002] [0.004] [0.002] [0.005] [0.003] [0.005] [0.003] [0.006]

1 if currently rural resident 0.010** 0.021** 0.010** 0.006 0.010** 0.015* 0.009* 0.029** [0.003] [0.007] [0.003] [0.007] [0.004] [0.007] [0.004] [0.006]

scheduled caste 0.007* 0.010** -0.003 0.010* [0.004] [0.004] [0.004] [0.004]

scheduled tribe -0.004 0 -0.003 0.003 [0.004] [0.004] [0.005] [0.005]

other backward caste 0.006 0.005 0.008* -0.009 0.001 0.008 0.006 0.014* [0.003] [0.008] [0.003] [0.008] [0.004] [0.009] [0.004] [0.007]

32

ma incomplete primary -0.003 0 0.002 -0.017* -0.009* -0.013 -0.004 -0.035** [0.004] [0.010] [0.004] [0.009] [0.005] [0.009] [0.005] [0.006]

ma complete primary -0.020** 0.006 -0.008 -0.015 -0.009 -0.017 -0.012* -0.005 [0.004] [0.014] [0.004] [0.011] [0.005] [0.010] [0.006] [0.011]

ma incomplete secondary -0.016** -0.009 -0.016** 0 -0.011* -0.023* -0.007 -0.012 [0.004] [0.011] [0.004] [0.011] [0.005] [0.009] [0.006] [0.011]

ma sec or higher -0.039** -0.018 -0.017** -0.008 -0.018** -0.01 -0.020* -0.026 [0.004] [0.014] [0.005] [0.014] [0.006] [0.014] [0.009] [0.016]

pa incomplete primary 0.003 -0.029** 0 -0.001 0.005 0.002 -0.003 -0.015* [0.004] [0.008] [0.004] [0.009] [0.005] [0.010] [0.004] [0.007]

pa complete primary 0.006 -0.020* -0.009* -0.006 0 0.006 -0.008 -0.005 [0.005] [0.010] [0.004] [0.011] [0.005] [0.013] [0.004] [0.009]

pa incomplete secondary -0.003 -0.021** -0.013** -0.003 -0.005 0.002 -0.009* -0.019** [0.003] [0.008] [0.003] [0.009] [0.004] [0.009] [0.004] [0.006]

pa complete secondary -0.013** -0.019 -0.016** -0.013 -0.018** 0.006 -0.017** -0.024** [0.004] [0.010] [0.004] [0.010] [0.004] [0.013] [0.005] [0.008]

pa higher than secondary -0.015** -0.026* -0.016** 0.013 -0.015** 0.014 -0.026** -0.008 [0.004] [0.011] [0.004] [0.015] [0.005] [0.016] [0.005] [0.011]

assam -0.033** -0.003 -0.004 0.014 -0.019* 0.023 -0.027** 0.065 [0.007] [0.021] [0.008] [0.023] [0.008] [0.031] [0.008] [0.034]

bihar -0.007 0.011 0 0.015 -0.008 0.015 -0.004 0.038 [0.006] [0.023] [0.006] [0.023] [0.007] [0.027] [0.008] [0.027]

gujarat 0.017* 0.028 0.028** 0.049 0.002 0.071 -0.004 0.110* [0.008] [0.033] [0.009] [0.037] [0.009] [0.053] [0.009] [0.052]

haryana -0.008 0.013 0.009 0.057 -0.016* -0.008 -0.01 0.027 [0.008] [0.036] [0.009] [0.048] [0.008] [0.031] [0.009] [0.035]

himachal pradesh -0.021** -0.041 -0.023** -0.014 -0.029** 0.018 -0.029** -0.021

33

[0.007] [0.022] [0.007] [0.031] [0.007] [0.048] [0.009] [0.039] karnataka -0.003 -0.003 -0.01 0.003 -0.006 0.058 -0.017* 0.027

[0.007] [0.023] [0.007] [0.022] [0.008] [0.043] [0.008] [0.029] kerala -0.035** -0.01 -0.027** -0.02 -0.038** -0.024 -0.059** 0.044

[0.008] [0.020] [0.008] [0.017] [0.009] [0.017] [0.012] [0.034] madhya pradesh 0.044** 0.051 0.027** 0.021 0.013 0.051 0.013 0.098*

[0.008] [0.033] [0.008] [0.028] [0.008] [0.041] [0.008] [0.041] maharashtra -0.003 -0.031 -0.006 0.002 -0.026** 0.025 -0.019* 0.035

[0.007] [0.017] [0.007] [0.022] [0.006] [0.033] [0.009] [0.033] orissa 0.023** 0.126* 0.034** 0.064 0.019* 0.149 0.011 0.058

[0.008] [0.062] [0.009] [0.053] [0.009] [0.086] [0.009] [0.051] punjab -0.015 0.047 0.012 0.002 -0.001 -0.008 -0.006 0.054

[0.010] [0.053] [0.012] [0.042] [0.012] [0.039] [0.013] [0.057] rajasthan 0.025** 0.046 0.032** 0.019 0.016 0.049 0.012 0.064

[0.008] [0.030] [0.008] [0.026] [0.008] [0.039] [0.008] [0.033] tamil nadu -0.008 -0.006 0.002 0.015 -0.009 -0.02 -0.014 0.075

[0.007] [0.026] [0.007] [0.031] [0.008] [0.022] [0.009] [0.049] west bengal -0.001 0.017 -0.006 -0.003 -0.005 0.032 -0.001 0.042

[0.008] [0.025] [0.007] [0.020] [0.009] [0.033] [0.010] [0.030] uttar pradesh 0.039** 0.055 0.039** 0.026 0.026** 0.045 0.036** 0.079*

[0.008] [0.029] [0.008] [0.024] [0.008] [0.034] [0.009] [0.031] new delhi 0.011 -0.011 0.006 0.003 -0.012 0.044 -0.009 0.044

[0.010] [0.026] [0.010] [0.028] [0.010] [0.046] [0.011] [0.040] north-east -0.012 0.043 -0.005 0.036 -0.009 0.023 -0.018* 0.134**

[0.007] [0.035] [0.008] [0.034] [0.008] [0.037] [0.008] [0.051] F tests: (state dummies) 291.14 62.77 209.98 21.9 137.94 31.91 186.99 47.91 (0.000) (0.000) (0.000) (0.189) (0.000) (0.015) (0.000) (0.000)

34

(child's birth year quadratic) 126.3 14.07 89.86 16.68 94.4 11.17 89.86 27.48 (0.000) (0.001) (0.000) (0.000) (0.000) (0.004) (0.000) (0.000) (maternal age quadratic) 196.66 14.82 244.96 58.26 122.58 14.59 61.58 4.42 (0.000) (0.001) (0.000) (0.000) (0.000) (0.001) (0.000) (0.110) (low castes) 9.16 0.3 11.8 1.1 1.29 0.92 6.55 4.58 (0.027) (0.585) (0.008) (0.295) (0.733) (0.339) (0.088) (0.032) (ma education) 71.65 2.32 22.14 4.83 11.22 6.08 7.98 19.39 (0.000) (0.678) (0.000) (0.305) (0.024) (0.194) (0.092) (0.001) (pa education) 22.52 15.52 29.82 3.53 22.14 1.15 28.62 13.14

(0.000) (0.008) (0.000) (0.619) (0.000) (0.949) (0.000) (0.022) rate of decline p.a. -0.013 -5.13 1.688 5.092 3.381 -0.02 -1.731 15.301 turning point for age-ma 24.165 23.339 26.746 25.428 29.964 27.711 30.482 28.234 mean of infant 0.093 0.079 0.075 0.064 0.071 0.052 0.088 0.069 Observations 53792 6830 45298 5810 32204 4548 44451 9317


35

Table 7 Neonatal Mortality Probits by Religion and Birth-Order

1 2 3 4 5 6 7 8 Hindu,


Hindu, Child-2

Muslim, Child-2

Hindu, Child-3

Muslim, Child-3

Hindu, Child>=4

Muslim, Child>=4

1 if female -0.016** -0.020** -0.010** -0.007 -0.007** -0.004 -0.002 -0.005 [0.002] [0.005] [0.002] [0.005] [0.002] [0.005] [0.002] [0.004]

age ma at birth of child x10 -0.191** -0.133* -0.187** -0.227** -0.159** -0.092** -0.117** -0.070* [0.002] [0.005] [0.002] [0.004] [0.002] [0.006] [0.002] [0.003]

square of age ma x100 0.040** 0.031* 0.036** 0.045** 0.027** 0.014** 0.019** 0.013* [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

child's birth year x10 0.003 -0.043 -0.033 -0.049 -0.044 -0.045 -0.008 -0.191* [0.023] [0.058] [0.024] [0.062] [0.032] [0.076] [0.041] [0.075]

square of birth year x100 -0.001 0.002 0.002 0.002 0.002 0.002 0 0.010* [0.001] [0.003] [0.001] [0.004] [0.002] [0.004] [0.002] [0.004]

1 if currently rural resident 0.006* 0.021** 0.006* 0.015** 0.006* 0.017** 0.007* 0.017** [0.003] [0.006] [0.002] [0.006] [0.003] [0.006] [0.003] [0.004]

scheduled caste 0.003 0.005 -0.001 0.008* [0.003] [0.003] [0.003] [0.003]

scheduled tribe -0.001 0.001 -0.001 0.004 [0.004] [0.003] [0.004] [0.004]

other backward caste 0.004 0.004 0.007** 0.002 0.004 0.002 0.009** 0.007 [0.003] [0.007] [0.003] [0.007] [0.003] [0.007] [0.003] [0.005]

ma incomplete primary -0.001 -0.002 0.002 -0.008 -0.009** -0.005 -0.001 -0.021** [0.004] [0.008] [0.003] [0.007] [0.003] [0.008] [0.004] [0.005]

36

ma complete primary -0.010** 0.007 -0.005 -0.014 -0.007 -0.007 -0.007 0 [0.004] [0.011] [0.003] [0.008] [0.004] [0.010] [0.004] [0.009]

ma incomplete secondary -0.006 -0.007 -0.007* -0.007 -0.004 -0.009 -0.001 -0.004 [0.003] [0.009] [0.003] [0.008] [0.004] [0.008] [0.005] [0.009]

ma sec or higher -0.024** -0.007 -0.009* -0.015 -0.009 0.012 -0.014* -0.017 [0.004] [0.012] [0.004] [0.009] [0.005] [0.017] [0.007] [0.012]

pa incomplete primary 0.003 -0.019** 0.003 0.003 0.005 0 -0.004 -0.004 [0.004] [0.006] [0.003] [0.008] [0.004] [0.008] [0.003] [0.006]

pa complete primary 0.004 -0.01 -0.002 -0.005 -0.004 0.013 -0.003 0.005 [0.004] [0.008] [0.003] [0.009] [0.004] [0.012] [0.003] [0.007]

pa incomplete secondary 0 -0.012 -0.005 -0.001 -0.002 -0.001 -0.004 -0.010* [0.003] [0.006] [0.003] [0.007] [0.003] [0.007] [0.003] [0.005]

pa complete secondary -0.005 -0.011 -0.007* -0.004 -0.009** -0.009 -0.006 -0.016** [0.004] [0.008] [0.003] [0.009] [0.004] [0.009] [0.004] [0.006]

pa higher than secondary -0.007 -0.016 -0.006 0.027 -0.003 0.007 -0.013** 0.004 [0.004] [0.009] [0.003] [0.014] [0.004] [0.013] [0.004] [0.010]

assam -0.024** -0.02 -0.011* -0.002 -0.01 -0.004 -0.021** 0.033 [0.005] [0.012] [0.005] [0.015] [0.006] [0.016] [0.006] [0.024]

bihar -0.002 -0.001 -0.003 -0.006 -0.003 0 -0.005 0.019 [0.005] [0.016] [0.005] [0.014] [0.005] [0.017] [0.006] [0.020]

gujarat 0.012 0.007 0.009 0.054 0.004 0.028 -0.01 0.061 [0.007] [0.022] [0.006] [0.036] [0.007] [0.033] [0.006] [0.040]

haryana -0.009 -0.016 -0.002 0.021 -0.008 -0.016** 0.001 [0.006] [0.018] [0.006] [0.032] [0.006] [0.006] [0.022]

himachal pradesh -0.021** -0.035** -0.021** -0.016 -0.022** -0.020** -0.004 [0.005] [0.011] [0.004] [0.020] [0.005] [0.007] [0.033]

karnataka 0 -0.01 -0.011** 0.004 -0.006 0.025 -0.008 0.017

37

[0.006] [0.015] [0.004] [0.018] [0.006] [0.027] [0.006] [0.022] kerala -0.022** -0.015 -0.016** -0.011 -0.016* -0.024** -0.041** 0.011

[0.007] [0.013] [0.006] [0.013] [0.008] [0.008] [0.007] [0.021] madhya pradesh 0.030** 0.032 0.012* 0.009 0.006 0.018 0.005 0.043

[0.007] [0.025] [0.006] [0.021] [0.006] [0.025] [0.006] [0.028] maharashtra -0.005 -0.026* -0.005 0.017 -0.012* 0.012 -0.012 0.017

[0.006] [0.011] [0.005] [0.022] [0.005] [0.022] [0.007] [0.023] orissa 0.013* 0.035 0.013* -0.017 0.014 0.021 0.002 0

[0.007] [0.039] [0.006] [0.019] [0.007] [0.040] [0.007] [0.027] punjab -0.01 0.029 -0.006 0.016 -0.004 -0.002 -0.005

[0.008] [0.040] [0.008] [0.039] [0.009] [0.031] [0.010] rajasthan 0.014* 0 0.011 0.009 0.005 0.012 0.011 0.036

[0.006] [0.017] [0.006] [0.019] [0.006] [0.022] [0.007] [0.025] tamil nadu -0.004 -0.016 -0.002 0.004 -0.004 -0.007 0.033

[0.006] [0.016] [0.005] [0.022] [0.006] [0.007] [0.034] west bengal 0 0.001 -0.010* -0.008 -0.006 0.007 -0.003 0.017

[0.006] [0.016] [0.005] [0.014] [0.006] [0.019] [0.008] [0.020] uttar pradesh 0.026** 0.017 0.017** 0.017 0.013* 0.005 0.023** 0.035

[0.007] [0.019] [0.006] [0.019] [0.006] [0.018] [0.007] [0.022] new delhi 0.003 -0.026 -0.013* 0.008 -0.005 0.006 -0.01 0.017

[0.008] [0.014] [0.006] [0.025] [0.008] [0.025] [0.008] [0.027] north-east -0.01 0.023 -0.01 -0.002 -0.005 0 -0.008 0.081*

[0.006] [0.025] [0.005] [0.019] [0.006] [0.020] [0.007] [0.040] F tests: (state dummies) 201.78 45.43 135.09 22.24 68.84 15.89 146.93 26.95 (0.000) (0.000) (0.000) -(0.176) (0.000) -(0.320) (0.000) -(0.042) (child's birth year quadratic) 75.44 9.96 41.38 7.52 60.10 7.98 43.95 30.78 (0.000) -(0.007) (0.000) -(0.023) (0.000) -(0.019) (0.000) (0.000)

38

(maternal age quadratic) 138.56 6.64 167.94 33.10 106.30 11.33 44.36 5.46 (0.000) (0.036) (0.000) (0.000) (0.000) (0.004) (0.000) (0.065) (low castes) 2.69 0.31 9.79 0.07 3.39 0.07 10.86 1.72 -(0.441) -(0.579) -(0.020) -(0.791) -(0.335) -(0.792) (0.013) (0.190) (ma education) 37.47 1.45 10.28 3.84 9.14 2.62 5.13 11.84 (0.000) (0.836) (0.036) (0.428) (0.058) (0.623) (0.274) (0.019) (pa education) 8.11 8.95 10.18 7.95 11.08 3.58 10.12 9.79

(0.150) (0.111) (0.070) (0.159) (0.050) (0.611) (0.072) (0.081) rate of decline p.a. -1.717 3.396 3.406 3.390 3.395 3.394 -0.008 17.004 turning point for age-ma 23.746 21.060 26.183 25.184 29.065 31.956 30.747 27.579 mean of neonatal 0.064 0.053 0.045 0.040 0.042 0.033 0.053 0.041 observations 53792 6830 45298 5810 32204 4303 44451 9248


39

Fig. 1

0.00

0.25

0.50

0.75

1.00

Ris

k of

dea

th

0 20 40 60Age of the Child/ months

Hindu Muslim

Kaplan-Meier survival estimates

40

Fig. 2

41

Fig. 3

42

Fig. 4

0.0

2.0

4.0

6Fr

eque

ncy

dens

ity

60 70 80 90 100Year of birth of child

Hindu Muslim

Year of birth of child religion comparison

Fig. 5

0.0

2.0

4.0

6.0

8.1

Freq

uenc

y de

nsity

10 20 30 40 50Age ma at birth of index child (years)

Hindu Muslim

Age ma at birth of index child

43

Fig. 6

61.88%8.97%

7.21%

12.01%

9.93%

64.81%12.32%

6.68%

10.84%5.35%

Hindu Muslim

No education Incomplete primaryComplete primary Incomplete secendaryComplete secondary and higher

Education of Mother

Fig. 7

31.58%

11.20%9.37%

21.15%

11.88%

14.83%

39.24%

14.79%8.68%

19.85%

9.63%7.81%

Hindu Muslim

No education Incomplete primaryComplete primary Incomplete secendaryComplete secondary Higher

Education of Father

44

Fig. 8

26.23%

73.77%

39.7%

60.3%

Hindu Muslim

Not Rural Rural

Proportion of families in Rural areas

Fig. 9

52.15%47.85%

52.27%47.73%

Hindu Muslim

Male Female

Proportion of females

45

Appendix Table 1 Mortality differences by Religion & Child Age

State name Neonatal Neo-Diff Infant Inf-Diff Under5 Under5-

Diff Hindu Muslim Hindu Muslim Hindu Muslim

andhra pradesh 0.059 0.033 0.026 0.089 0.043 0.046 0.114 0.054 0.060assam 0.032 0.042 0.010 0.053 0.070 0.017 0.068 0.087 0.018bihar 0.049 0.049 0.000 0.075 0.073 0.003 0.112 0.104 0.008gujarat 0.052 0.046 0.005 0.082 0.063 0.020 0.113 0.086 0.027haryana 0.037 0.031 0.007 0.064 0.066 0.002 0.088 0.094 0.006himachal pradesh 0.022 0.015 0.008 0.041 0.029 0.012 0.053 0.036 0.017karnataka 0.047 0.041 0.006 0.073 0.058 0.015 0.105 0.079 0.027kerala 0.022 0.024 0.002 0.030 0.038 0.008 0.039 0.049 0.010madhya pradesh 0.069 0.053 0.016 0.109 0.082 0.027 0.163 0.111 0.052maharashtra 0.040 0.025 0.015 0.061 0.035 0.026 0.083 0.048 0.035orissa 0.062 0.036 0.026 0.099 0.100 0.001 0.128 0.126 0.002punjab 0.033 0.034 0.001 0.056 0.055 0.001 0.071 0.067 0.004rajasthan 0.060 0.047 0.013 0.096 0.077 0.019 0.136 0.123 0.013tamil nadu 0.046 0.028 0.017 0.067 0.050 0.017 0.091 0.071 0.020uttar pradesh 0.072 0.052 0.020 0.114 0.088 0.026 0.157 0.122 0.035west bengal 0.044 0.044 0.000 0.071 0.067 0.004 0.090 0.092 0.002new delhi 0.032 0.025 0.007 0.054 0.043 0.010 0.070 0.059 0.011north-east 0.037 0.056 0.018 0.059 0.086 0.027 0.081 0.114 0.033

ALL INDIA 0.052 0.042 0.010 0.083 0.068 0.015 0.116 0.094 0.022

46

Appendix Table 2 Distribution of children by birth-order for each religion

Child birth-order % for Hindus % for Muslims 1 30.61 25.77 2 25.77 21.92 3 18.32 17.16 4 11.44 12.61 5 6.59 8.66 6 3.67 5.78 7 1.90 3.61 8 0.94 2.21 9 0.43 1.27

10 0.20 0.60 11 0.07 0.28 12 0.02 0.08 13 0.01 0.02 14 0.00 0.01 15 0.00 0.01 16 0.00 0.00

47

Appendix Table 3 Under-5 Mortality Differences by Religion and Birth Order

State name Child-1 diff Child-2 diff Child-3 diff Child>=4 diff

Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim andhra pradesh 0.120 0.065 0.054 0.108 0.071 0.037 0.106 0.049 0.057 0.128 0.032 0.096 assam 0.063 0.079 0.017 0.067 0.089 0.022 0.064 0.078 0.014 0.077 0.095 0.018 bihar 0.116 0.117 0.000 0.108 0.109 0.002 0.101 0.090 0.010 0.128 0.103 0.025 gujarat 0.119 0.095 0.024 0.114 0.100 0.014 0.104 0.094 0.011 0.121 0.112 0.009 haryana 0.089 0.126 0.038 0.083 0.107 0.025 0.079 0.043 0.036 0.104 0.097 0.007 himachal pradesh 0.056 0.050 0.006 0.050 0.030 0.020 0.050 0.039 0.011 0.065 0.020 0.045 karnataka 0.113 0.087 0.026 0.088 0.086 0.003 0.108 0.085 0.023 0.118 0.070 0.048 kerala 0.041 0.058 0.017 0.034 0.037 0.003 0.047 0.025 0.021 0.053 0.072 0.018 madhya pradesh 0.194 0.112 0.081 0.155 0.100 0.055 0.154 0.098 0.056 0.161 0.133 0.028 maharashtra 0.098 0.034 0.063 0.076 0.063 0.013 0.062 0.050 0.012 0.094 0.051 0.043 orissa 0.136 0.183 0.047 0.124 0.133 0.009 0.123 0.143 0.020 0.135 0.088 0.046 punjab 0.061 0.105 0.044 0.075 0.038 0.037 0.072 0.023 0.049 0.085 0.072 0.013 rajasthan 0.150 0.148 0.003 0.136 0.127 0.010 0.130 0.121 0.009 0.139 0.114 0.024 tamil nadu 0.094 0.064 0.031 0.086 0.076 0.010 0.090 0.056 0.033 0.110 0.091 0.019 uttar pradesh 0.094 0.113 0.020 0.077 0.090 0.013 0.088 0.086 0.002 0.114 0.076 0.038 west bengal 0.164 0.163 0.002 0.144 0.117 0.027 0.149 0.107 0.041 0.167 0.128 0.039 new delhi 0.084 0.041 0.043 0.058 0.063 0.005 0.057 0.060 0.004 0.079 0.085 0.006 north-east 0.088 0.137 0.049 0.072 0.112 0.040 0.079 0.076 0.003 0.084 0.117 0.033 ALL INDIA 0.119 0.100 0.018 0.104 0.090 0.014 0.107 0.080 0.026 0.130 0.098 0.032

48

Appendix Table 4 Infant Mortality Differences by Religion and Birth Order


Hindu Muslim Hindu Muslim Muslim Hindu Muslim andhra pradesh 0.100 0.056 0.044 0.078 0.055 0.024 0.084 0.028 0.056 0.096 0.028 0.067 assam 0.048 0.069 0.021 0.055 0.071 0.016 0.047 0.050 0.003 0.056 0.075 0.019 bihar 0.087 0.091 0.005 0.070 0.068 0.002 0.063 0.052 0.012 0.080 0.072 0.008 gujarat 0.094 0.071 0.023 0.084 0.083 0.001 0.070 0.070 0.000 0.080 0.089 0.009 haryana 0.068 0.084 0.016 0.062 0.095 0.033 0.049 0.029 0.020 0.073 0.063 0.010 himachal pradesh 0.050 0.025 0.025 0.032 0.030 0.002 0.034 0.039 0.005 0.050 0.020 0.031 karnataka 0.087 0.066 0.022 0.061 0.064 0.003 0.071 0.074 0.003 0.074 0.044 0.030 kerala 0.036 0.048 0.013 0.026 0.028 0.002 0.026 0.013 0.013 0.027 0.055 0.028 madhya pradesh 0.145 0.101 0.044 0.102 0.070 0.032 0.092 0.071 0.022 0.099 0.088 0.011 maharashtra 0.076 0.025 0.050 0.054 0.050 0.005 0.042 0.035 0.007 0.064 0.030 0.034 orissa 0.112 0.169 0.057 0.097 0.117 0.019 0.091 0.122 0.032 0.094 0.053 0.041 punjab 0.050 0.105 0.056 0.055 0.038 0.017 0.054 0.023 0.030 0.073 0.058 0.015 rajasthan 0.113 0.107 0.006 0.096 0.073 0.024 0.087 0.069 0.018 0.094 0.071 0.023 tamil nadu 0.074 0.047 0.027 0.063 0.051 0.012 0.060 0.016 0.044 0.075 0.077 0.002 uttar pradesh 0.079 0.088 0.009 0.057 0.058 0.001 0.067 0.058 0.009 0.086 0.058 0.029 west bengal 0.132 0.126 0.006 0.106 0.083 0.023 0.098 0.070 0.028 0.116 0.090 0.026 new delhi 0.065 0.041 0.024 0.045 0.049 0.004 0.040 0.052 0.012 0.060 0.047 0.014 north-east 0.067 0.104 0.037 0.052 0.084 0.032 0.055 0.042 0.013 0.061 0.096 0.035 ALL INDIA 0.093 0.079 0.014 0.075 0.064 0.011 0.071 0.052 0.019 0.088 0.069 0.019

49

Appendix Table 5 Neonatal Mortality by Religion & Birth Order


Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim andhra pradesh 0.069 0.047 0.022 0.053 0.033 0.021 0.052 0.028 0.024 0.059 0.021 0.038 assam 0.032 0.038 0.005 0.029 0.039 0.009 0.030 0.030 0.001 0.029 0.046 0.017 bihar 0.062 0.070 0.008 0.045 0.036 0.009 0.040 0.035 0.005 0.048 0.044 0.003 gujarat 0.066 0.048 0.019 0.050 0.072 0.022 0.045 0.047 0.002 0.041 0.061 0.020 haryana 0.044 0.042 0.002 0.036 0.060 0.024 0.030 0.000 0.030 0.034 0.029 0.006 himachal 0.029 0.013 0.017 0.016 0.015 0.001 0.015 0.000 0.015 0.028 0.020 0.009 karnataka 0.062 0.045 0.017 0.036 0.043 0.007 0.040 0.056 0.015 0.047 0.032 0.015 kerala 0.028 0.036 0.008 0.020 0.021 0.001 0.021 0.008 0.013 0.011 0.030 0.019 madhya pradesh

0.099 0.080 0.020 0.065 0.043 0.022 0.054 0.047 0.007 0.058 0.050 0.009

maharashtra 0.051 0.016 0.034 0.037 0.039 0.002 0.028 0.029 0.001 0.038 0.020 0.018 orissa 0.075 0.085 0.010 0.059 0.017 0.043 0.056 0.041 0.016 0.053 0.018 0.035 punjab 0.035 0.088 0.052 0.027 0.038 0.011 0.029 0.023 0.005 0.044 0.000 0.044 rajasthan 0.075 0.054 0.021 0.055 0.044 0.011 0.047 0.039 0.008 0.061 0.047 0.014 tamil nadu 0.053 0.030 0.023 0.042 0.030 0.011 0.038 0.000 0.038 0.048 0.049 0.000 uttar pradesh 0.056 0.061 0.005 0.032 0.034 0.001 0.036 0.043 0.007 0.049 0.035 0.013 west bengal 0.090 0.080 0.010 0.065 0.057 0.008 0.057 0.037 0.020 0.074 0.049 0.024 new delhi 0.043 0.018 0.025 0.020 0.035 0.016 0.026 0.026 0.000 0.033 0.028 0.005 north-east 0.044 0.080 0.036 0.030 0.034 0.004 0.032 0.028 0.004 0.040 0.065 0.026 ALL INDIA 0.064 0.053 0.011 0.045 0.040 0.006 0.042 0.033 0.010 0.053 0.041 0.012

50

Appendix Table 6 Means of Independent Variables

State name Prop female births birth year of child Age ma at birth Prop rural SC ST OBC

Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim andhra pradesh 0.484 0.458 84.881 85.056 20.818 22.849 0.774 0.318 0.184 0.000 0.054 0.000 0.505 0.114 assam 0.478 0.471 86.171 87.815 22.663 22.289 0.731 0.936 0.169 0.000 0.267 0.000 0.174 0.007 bihar 0.478 0.478 86.851 87.374 22.778 23.593 0.912 0.869 0.251 0.000 0.074 0.000 0.531 0.575 gujarat 0.480 0.478 85.739 86.201 22.516 22.725 0.646 0.268 0.176 0.000 0.226 0.000 0.251 0.264 haryana 0.458 0.493 86.275 88.221 22.884 24.351 0.739 0.888 0.236 0.000 0.000 0.000 0.222 0.389 himachal pradesh

0.479 0.422 86.029 87.847 22.938 22.671 0.771 0.644 0.241 0.000 0.004 0.000 0.186 0.197

karnataka 0.491 0.468 85.457 86.565 21.303 21.695 0.728 0.502 0.217 0.000 0.077 0.000 0.474 0.011 kerala 0.495 0.471 84.858 85.340 23.364 22.474 0.673 0.814 0.168 0.000 0.019 0.000 0.518 0.459 madhya pradesh

0.478 0.493 86.654 86.293 22.116 22.757 0.793 0.302 0.177 0.000 0.232 0.000 0.407 0.539

maharashtra 0.479 0.486 85.920 87.642 21.526 21.923 0.532 0.125 0.107 0.000 0.110 0.000 0.244 0.045 orissa 0.483 0.478 86.451 87.042 22.377 23.570 0.817 0.385 0.215 0.000 0.187 0.000 0.319 0.065 punjab 0.460 0.365 86.195 86.092 23.524 24.081 0.526 0.244 0.343 0.000 0.000 0.000 0.187 0.734 rajasthan 0.476 0.475 86.955 87.165 22.962 23.883 0.820 0.455 0.204 0.000 0.168 0.000 0.231 0.258 tamil nadu 0.485 0.496 85.581 86.047 22.165 22.477 0.619 0.257 0.272 0.000 0.010 0.000 0.700 0.966 uttar pradesh 0.488 0.477 86.885 87.482 21.790 22.334 0.851 0.617 0.317 0.000 0.057 0.000 0.051 0.005 west bengal 0.471 0.486 85.344 86.519 23.061 23.597 0.540 0.802 0.234 0.000 0.026 0.000 0.317 0.182 new delhi 0.470 0.470 86.658 88.120 23.224 22.988 0.078 0.136 0.261 0.000 0.008 0.000 0.157 0.287 north-east 0.488 0.494 87.206 89.076 23.536 22.685 0.799 0.657 0.190 0.000 0.140 0.000 0.232 0.128

ALL INDIA 0.479 0.477 85.882 86.558 22.494 22.897 0.738 0.603 0.216 0.000 0.106 0.000 0.335 0.255

51

Appendix Table 6 cont. Fathers’ education

State name No education Incomplete

primary Complete primary Incomplete

secondary Complete Secondary

Higher

Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim andhra pradesh 0.479 0.304 0.096 0.078 0.117 0.101 0.126 0.186 0.081 0.152 0.100 0.180 assam 0.295 0.493 0.183 0.199 0.046 0.034 0.259 0.165 0.086 0.056 0.132 0.053 bihar 0.438 0.582 0.063 0.062 0.055 0.046 0.173 0.141 0.147 0.121 0.124 0.048 gujarat 0.274 0.171 0.166 0.178 0.076 0.065 0.235 0.326 0.090 0.138 0.159 0.123 haryana 0.326 0.630 0.041 0.047 0.065 0.094 0.201 0.092 0.217 0.085 0.148 0.052 himachal pradesh 0.164 0.418 0.062 0.036 0.099 0.056 0.235 0.241 0.264 0.165 0.175 0.084 karnataka 0.403 0.410 0.117 0.158 0.064 0.083 0.175 0.194 0.124 0.101 0.117 0.054 kerala 0.058 0.118 0.163 0.276 0.118 0.129 0.343 0.319 0.197 0.112 0.120 0.046 madhya pradesh 0.354 0.255 0.141 0.183 0.137 0.148 0.189 0.191 0.039 0.068 0.140 0.155 maharashtra 0.201 0.190 0.151 0.176 0.055 0.083 0.271 0.323 0.149 0.138 0.174 0.089 orissa 0.332 0.290 0.156 0.198 0.115 0.137 0.228 0.276 0.086 0.065 0.082 0.034 punjab 0.223 0.495 0.037 0.000 0.097 0.095 0.228 0.171 0.241 0.180 0.174 0.059 rajasthan 0.395 0.480 0.075 0.099 0.104 0.082 0.194 0.212 0.097 0.068 0.135 0.059 tamil nadu 0.234 0.109 0.143 0.140 0.148 0.203 0.250 0.254 0.101 0.164 0.125 0.130 uttar pradesh 0.271 0.402 0.214 0.245 0.056 0.072 0.236 0.169 0.080 0.049 0.142 0.064 west bengal 0.296 0.492 0.063 0.086 0.113 0.100 0.206 0.137 0.133 0.096 0.188 0.089 new delhi 0.135 0.334 0.029 0.066 0.069 0.097 0.183 0.200 0.198 0.128 0.385 0.176 north-east 0.244 0.354 0.201 0.229 0.076 0.098 0.268 0.218 0.076 0.044 0.136 0.058

ALL INDIA 0.316 0.392 0.112 0.148 0.094 0.087 0.211 0.199 0.119 0.096 0.148 0.078

52

Appendix Table 6 cont. Mother’s education

State name No education Incomplete

primary Complete primary Incomplete

secondary Secondary or

Higher Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim Hindu Muslim

andhra pradesh 0.685 0.508 0.080 0.108 0.073 0.072 0.096 0.146 0.065 0.166assam 0.497 0.670 0.135 0.166 0.040 0.025 0.223 0.108 0.105 0.032bihar 0.797 0.888 0.037 0.048 0.028 0.023 0.081 0.025 0.057 0.016gujarat 0.582 0.429 0.105 0.194 0.059 0.083 0.135 0.222 0.120 0.072haryana 0.657 0.950 0.029 0.007 0.097 0.024 0.085 0.002 0.131 0.017himachal pradesh 0.402 0.719 0.057 0.016 0.199 0.112 0.151 0.092 0.191 0.060karnataka 0.603 0.635 0.100 0.100 0.038 0.104 0.143 0.109 0.116 0.051kerala 0.093 0.172 0.164 0.294 0.083 0.104 0.313 0.318 0.348 0.112madhya pradesh 0.728 0.618 0.093 0.133 0.067 0.110 0.061 0.069 0.051 0.070maharashtra 0.431 0.370 0.161 0.176 0.043 0.101 0.221 0.231 0.144 0.123orissa 0.599 0.604 0.130 0.147 0.088 0.092 0.131 0.140 0.053 0.017punjab 0.415 0.806 0.033 0.018 0.159 0.063 0.123 0.041 0.271 0.072rajasthan 0.809 0.865 0.038 0.047 0.053 0.039 0.059 0.037 0.041 0.012tamil nadu 0.426 0.269 0.146 0.173 0.125 0.214 0.193 0.234 0.110 0.110uttar pradesh 0.464 0.597 0.191 0.229 0.057 0.044 0.178 0.105 0.110 0.026west bengal 0.737 0.827 0.047 0.031 0.078 0.067 0.066 0.035 0.072 0.040new delhi 0.366 0.713 0.044 0.030 0.100 0.076 0.171 0.087 0.319 0.094north-east 0.482 0.634 0.190 0.195 0.060 0.052 0.179 0.104 0.089 0.015

ALL INDIA 0.619 0.648 0.090 0.123 0.072 0.067 0.120 0.108 0.099 0.053

religion differences in fertility and mortality in india

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