Causal Effects of Schooling on Health Belief⇤
Prabal K. De†
December 20, 2014
Abstract
Health beliefs, or individual perceptions on the effects of health actions and behavior have
been shown to be an important driver of health behaviors and outcomes. Unfortunately, very
little research exists on the determinants of health beliefs. Though higher educational attainment in terms of formal schooling should in principle lead to better health beliefs, the relationship is confounded by omitted variables like motivation that may positively affect both
education and health knowledge acquisition. In this paper I try to tackle two problems. I use
data from India, a country characterized by tremendous variations in upper secondary educational achievements that allows me to see the effects of an extra year of upper secondary
schooling on health beliefs. Additionally, I use arguably exogenous variations in age at menarche combined with poor sanitation facilities in schools in India to create a new instrument for
schooling to control for the endogeneity. My results show that while education improves certain health beliefs such as reproductive health beliefs and AIDS awareness, it has no impact
on other types of health belief like environmental health among ever-married women of age
15-49.
Keywords: Health belief, Education, Instrumental Variable
JEL Codes: : I12; J16; O12
⇤I
am grateful to Punit Arora, Marta Bengoa, Maria Binz-Scharf, Kevin Foster, Kameshwari Shankar and seminar
participants at the New York State Economic Association Annual Meeting for many helpful comments. All remaining
errors are mine.
† Department of Economics and Business, The City College of New York and The Graduate Center. 160 Convent
Avenue, New York, NY 10031, Phone:+1-212-6506208, Email:pde@ccny.cuny.edu
1
1
Introduction
Health beliefs are defined as individual perceptions on how various health actions would increase
or decrease the likelihood of health outcomes, with respect to both personal and public health. For
example, whether or not a person is aware of the existence a global disease like AIDS and the ways
it gets transmitted may affect his various health behaviors, influencing both individual and communal health outcome. Even though policy makers are ultimately interested in health outcomes,
learning about health beliefs and changing such beliefs may be equally important. Question is:
What determines health beliefs? Insights from human capital theory and health production function suggests that educational attainment should lead to better health beliefs. This paper offers new
insights in answering that question showing that in societies where both upper secondary education
and beliefs on diseases are incomplete, schooling may play an important role in improving correct
health beliefs.1
That educational attainment (completed schooling) is positively correlated with better health
outcomes is well documented.2 However, there is no single channel through which the benefits of
schooling works (Braveman et al., 2011). First, higher education may increase the probability of
finding a job leading to higher income and consequently affording good healthcare. Second, education may positively affect both personal traits and social support. Third, education, particularly
in countries where primary education is not universal and illiteracy is still pervasive, may lead to
better health belief.
I investigate the third link in the context of India, a middle-income country that ranks 163
among 223 countries in the world in terms of life expectancy at birth.3 Though there is a rich and
1 The
recent (and ongoing at the time of writing this paper) Ebola crisis shows that this issue is not restricted to
poor countries in Asia and Africa. For relatively new diseases, public health belief formation takes time and has
far-reaching implications for health outcomes.
2 For example, in the United States, the average difference in (male) life expectancy between a college graduate
and a high school dropout is 13 years. National Center for Health Statistics, Health, United States, 2011: With Special
Feature on Socioeconomic Status and Health (2012): figure 32. Globally, countries with higher schooling attainment
also typically have higher life expectancy.
3 “India.” CIA World Factbook
2
growing body of research connecting health belief to health outcomes, there is little research on the
determinants of health belief. Role of information and acquiring such information is emphasized in
standard economic models as a key to finding the optimal economic behavior. Therefore, we want
to know how individuals can obtain such information. Intuitively, education is a straightforward
candidate variable - more schooling may help individuals learn more about diseases, their causes
and effects. However, the role of education, typically captured by years of schooling, in promoting
correct health belief is not obvious. UNESCO statistics suggest that more than 700 million of
the world’s adult population are still not literate; two thirds of them women. It will be incorrect
to assume that they have no source of information to form health beliefs. Public information
campaigns work simultaneously with expansions of schooling. On the other hand, there are no
obvious reasons why more formally educated individuals are likely to form correct beliefs on all
dimensions of health.4 Moreover, schooling may affect one set of beliefs, but not others. For
example, sex education in schools can enhance attending students’ belief on reproductive health
and awareness on AIDS, but those students’ belief on environmental health may remain unchanged.
To summarize, that more schooling may lead to better health beliefs is a testable hypothesis. This
paper formally tests such a hypothesis and tries to establish the causal role that years of schooling
plays in generating better health awareness and belief.
Identifying the effects of education on outcome variables is a challenge, because educational
choices are often endogenous. Even after controlling for many observed variables, there may
remain omitted variables such as motivation, drive and other unobservable personal characteristics
that may influence both education choice and the outcome variables. For example, a motivated
(motivation being the unobserved variable) person may try to acquire both health knowledge and
education more. So it may be the case that the personal trait is driving the result, not education per
se. In this paper I have used the timing of the onset of menarche as an instrumental variable (IV) for
Ib. https://www.cia.gov/library/publications/the-world-factbook/rankorder/2102rank.html
4 India had the third-largest number of people living with HIV in the world at the end of 2013, according to the UN
AIDS program, and it accounts for more than half of all AIDS-related deaths in the Asia-Pacific.
3
schooling. Though age at menarche has been used an instrumental variable in some other contexts,
to my knowledge this is the first time it is being used as an instrument for schooling. In India,
this instrument is relevant due to the unfortunate reality of school sanitation infrastructure in many
parts of the country, particularly in rural areas. According to a 2008 report submitted to the Indian
Parliament, more than 466,000 elementary schools did not have toilet facilities. However, lack
of toilets in school is particularly problematic for young girls in their recent years after puberty.
Attending school becomes more costly for them due to lack of safety and privacy. This leads to
missing school and eventually dropout.
Though I will discuss the data and econometric methods in details later in the paper, it is instructive to illustrate my basic instrumental variable strategy at the outset. Figure 1 plots average
years of schooling against the range of ages at which first menstrual cycle occurred for a of group
15-49 years old women. It shows that average schooling increases moderately with age at menarche up to age 12, the global average, but beyond it, average year of schooling increases more
sharply with later onset of puberty. Additionally, it shows that (unsurprisingly) there is a big gap
between urban and rural schooling attainment, though the age-at-menarche-education relationship
holds true for both urban and rural areas. This graph comes with the caveat that the relationship is
likely to be more complex, as other variables may also drive education rendering the age at menarche an insignificant predictor. However, we will see that my instrument is indeed relevant in an
econometric sense.
Please insert Figure 1 Here
I examine a variety of women’s health belief - questions regarding reproductive health, neonatal
care and AIDS. The belief system is not trivial. For example, more than 40% of the married women
surveyed in 2004 answered that they had not heard about AIDS. I find that educational attainment
does play a positive and significant role in enhancing health belief. This is true after correcting for
the endogeneity of education and controlling for a variety of candidate explanatory variables such
4
as religion, caste, urbanity and poverty.
The main finding is that higher educational achievement generally improves some particular
forms of health belief among ever-married women in India. Such improvements are observed
in reproductive health belief and AIDS awareness. More schooling, however, does not seem to
increase awareness on indoor pollution or men’s health.
I hope to expand the existing literature on the relationship between education, health belief and
health outcome. The first contribution is to document that education has a causal role to play in
enhancing health belief. Secondly, my instrumentation strategy shows that infrastructure such as
clean, safe toilets may be as important as teachers and school materials in promoting education for
girls. Together, they have implications for both global education and health policy.
The rest of the paper is structured as follows. I start with discussion of health belief model
and its implications. Next I turn to exploring the link between school sanitation and education in
India and elsewhere. Section 4 discusses the survey data, sample selection and estimation strategy.
The following section presents and analyzes my main results. Section 6 and 7 present sensitivity analysis and main caveats respectively. The final section concludes with discussion of policy
implications for my findings.
2
Importance of Health Beliefs and Role of Education
According to the traditional health production model, individuals choose and accumulate health
inputs like diet, exercise and care to produce healthy days that are both consumption and investment
good (Grossman, 1972). Education or formal schooling has been used as an input to production of
both health behavior and health outcome. Cutler and Lleras-Muney (2008) provides an excellent
account of the role of education in promoting health behavior in the United States. Elsewhere,
Jurges et al. (2011) show how expansion of education changed health behavior in West Germany.
Alsan and Cutler (2013) found that exogenous expansions in girls’ schooling led to a reduction in
5
their unsafe sexual behavior. However, not all studies have found a positive and significant effect
of education on health behavior and outcome. In a recent paper Braakmann (2011) found in the
U.K. in the context of expansion of schooling in certain parts of the country no effect of education
on various health related measures nor an effect on health related behavior, e.g., smoking, drinking
or eating various types of food.
Though traditional health production function does not formally include health beliefs, “Health
Belief Models”, popular in the public health literature may help us understand the role health beliefs play. Health Belief Model (HBM) is a conceptual formulation to understand a wide array of
both affirmative and negative individual actions. Rosenstock (1974) argued that for an individual
to ‘behave’ appropriately to avoid a disease, she needs to believe (i) that she is susceptible to the
disease, (ii) that the disease is likely to affect her life, and (iii) that taking certain actions can help
moderate the severity of the disease, should it happen. Meta-studies on empirical evidence, which
have been updated periodically, have largely found support for the model (Carpenter, 2010; Jones
2013). Therefore, correct belief leads to choice of better inputs leading to more productive output.
Though formal schooling has been the most commonly used variable as a determinant of health
outcomes despite its limitations like not controlling for the quality of schools and informal education, it has been never been to test as an explanatory variable for health beliefs. The determinants
of health beliefs and relevant policy implications for HBM have large been restricted to the design
of health communications, not formal schooling. In particular, the link between variations in primary and upper-primary schooling and health belief was not vigorously pursued, perhaps because
the developed world had already achieved universal primary education at the time of development
of HBM.
A small literature does emphasize the importance of individual and parental health belief in
explaining better health outcome and behavior (but does not deal with the determinants of health
belief as I do). The evidence is mixed here too. Miller (2011) shows how better maternal belief
was correlated with better health outcome for their children in Kenya. Kan and Tsai (2004) find
6
a link between individuals’ belief concerning the health risks of obesity and their tendency to be
obese in Taiwan. Contrarily, analyzing parents’ response to their children’s weight report card,
Prina and Royer (2014) found no evidence that better parental belief leads to lowering of incidence
of obesity. A subset of the research brings education, health belief and health behavior together
such as Kenkel(1991) and and Hsieh et al. (1991) in the context of smoking in the US. However,
the link is between knowledge and behavior with education being a mediating factor.
3
Sanitation and School Education
Sanitation facilities (or lack thereof) have been getting attention among the highest policy circles
in India.
5.
This is both unsurprising and encouraging as the overall state of toilet facilities in
India has remained poor despite the country achieving phenomenal economic growth since 1991.
Unfortunately, obtaining reliable data both on general sanitation facilities and facilities in school is
difficult. Official data has largely (and often deliberately) underestimated the lack of infrastructure,
as evident from the discrepancies in numbers that emerge from various surveys 6
In my sample, I find general patterns that (i) toilets are missing from many schools, and (ii) not
all schools with toilets have separate provisions for boys and girls. This is reported in Table 1.
Please insert Table 1 Here
We can see that the problem of lack of toilet is particularly severe in government schools where
almost 40% of the schools do not have a toilet. Even when a school does have toilet facilities, it
5 Recently,
in his address to the nation on India?s Independence Day, Prime Minister Mr. Narendra Modi has
called for construction of toilets across schools of India as part of his “Swachh Bharat Mission (SBM)” ( Clean India)
campaign. The government also promptly published a draft
6 Since the general lack of safe sanitation facilities in India is indisputable, I do not belabor
on this point.
Dean Spears maintains an excellent website on various sanitation issues in India
http://riceinstitute.org/wordpress/author/dean-spears/. Interested readers are encouraged to consult it for further details.
7
may not necessarily have separate ones for boys and girls.7
The link between lack of sanitation and girl student drop out is a global phenomenon. According to UNICEF, one in ten school girls in Africa miss classes or drop out completely due to their
period. Similar reports can be found in Asian countries like Bangladesh and Nepal. In India, both
enrollment and completion rates for girl students have persistently been loIr than boys, particularly
at the upper primary (6th to 8th grade) and secondary levels. For example, in 2002-2003, the year
preceding the survey that I are using, 93% of the girls in the relevant age Ire enrolled in primary
schools as opposed to 97% of the boys. [WDI, 2014]. However the wedge becomes 9% (65.3%
for boys as opposed to 56.2% for girls) for the upper primary enrollments.
8
This means that girls
drop out at a faster rate than boys. Though variables like parental gender preference, gender-biased
employment market and cultural phenomena like early marriage may explain a part of the gap, it is
increasingly understood that for young girls, menstruation creates an obstacle to schooling, leading
girls to drop out of school once they reach puberty due to lack of adequate school based sanitation
facilities and privacy.
Empirically, there are conflicting claims on the effects of menstruation, lack of sanitary products, lack of toilets and girls? education. While Oster and Thornton (2011) do not find any effects
of randomly distributing modern menstruation products to adolescent girls in Nepal, Adukia(2013)
shows that inadequate school sanitation does hinders educational attainment in India. However, the
results may be consistent as even though girls are equipped with sanitary products, lack of toilets,
particularly separate toilets, in school may still prevent girls from attending school. Moreover,
in many parts of Indian society, particularly in rural India, menstruation is often associated with
taboos and ostracization. Since my identification strategy relies on girls achieving menarche and
not either access to sanitary products or lack of toilets directly, it is consistent with multiple chan7 Since
school information is provided in a different module in the survey, we cannot separate out rural and urban
schools.
8 Note that one should use these numbers with caution. These are enrollment rates, while the actual attendance
rates are likely to be much more less.
8
nels being at work simultaneously to make the probability of a girl dropping out of school at
menarche higher.
4
Empirical Analysis
In this section I use survey data from India to provide evidence on the impacts of education on
health belief and belief.
4.1
Data and Sample Construction
My data comes from the India Human Development Survey (IHDS), which is a nationally representative survey of 41,554 households located in 384 (out of 593 districts identified in 2001
Indian Census) districts in 33 states and union territories of India. The primary sampling unit is
household(s) living in the same residence. The survey has a panel component, but since most
of the variables of my interest are found only on the 2005 sample (and the panel component is
much smaller), I restrict my analysis to cross-section data. It has two modules on individual and
household data. The household module contains information on location including urban vs. rural
location, family background such as religion and castes within religion and economic status. The
second module was administered to 33,510 ever-married women aged 15-49. This module has
detailed health and education related questions.
IHDS is particularly suitable for my purposes, because not only does it ask very detailed demographic, economic and health questions, but it also contains some rare questions on health
outcome, health belief, marriage practices and decision-making including the timing of menarche,
age at first marriage, self-rated health and outcome of all pregnancies that it asks each ever-married
woman. It also has both primary and constructed variables on religion and castes - categories that
have historically played an immensely important role in Indian societies. These variables allow us
to control for many competing hypotheses on the determinants of health belief.
9
To construct my sample, I first consider all ever-married women aged 15-49. Then I check for
missing values and outliers. Having eliminated them, I are left with 33400 ever-married women in
the relevant age range with roughly 64% living in rural areas. Table 2 reports mean and standard
deviations of some key variables across rural and urban areas.
For my identification strategy, I have included ever-married women who have at least one year
of schooling. This is the only sample selection I have made. For girls who were never enrolled in
school, as is the case for many children in India, dropping out of school is irrelevant. Therefore,
my results need to be interpreted as the effects of additional schooling on health belief conditional
on the student getting enrolled.
4.2
Selection of Variables
IHDS contains a survey module called ‘Health Beliefs’ where all ever-married women ages 15-49
are asked about their opinion on a set of health-related questions such as - “Is it harmful to drink
1-2 glasses of milk every day during pregnancy?” or “Have you ever heard of an illness called
AIDS?” my dependent variables come from this module where I have retained three questions on
maternal health, neonatal health and AIDS.
My independent variables belong to three categories: demographic, economic and locational,
apart from my main variable of interest - number of years of schooling completed.
9
Among
demographic variables, I control for age and age-squared. I control for both religion and caste of
a woman. The survey categorizes five religions - Hindu (majority), Muslim, Christian, Sikh and
Jain. There is a complex caste system among Hindus that is a vestige of thousands of years of
social development. I follow the categorization in the survey. I use Brahmins as the base category
as historically Brahmins have been the most privileged class in terms of social status and political
power. There are other high castes like merchant class. Then there are numerous “lower” castes
9 Unfortunately,
parental education is not available for most women.
10
categorized as “Scheduled Castes”, “Scheduled Tribes” and “Other Backward Classes”. “Dalits”
constitutes the lowest rung of the caste hierarchy. Generally, these groups have historically suffered
economic and social discrimination. Finally, there are tribal groups that often deviate from the
mainstream socio-cultural-political architecture of Hindu casteism.
4.3
10
Descriptive Statistics
Table 2 presents some relevant descriptive statistics. Though my subsequent analysis does not use
all the variables, it is instructive to analyze these descriptive statistics to examine some national
patterns to better understand my results. Columns (1), (2) and (3) show the means of the variables
used in the study and some other relevant variables for the whole sample and by urbanity status
respectively. I focus on urbanity because educational opportunities are very different in urban areas. I also see that for all the variables, average values in urban locations are significantly different
from rural locations. The first variable reports the proportion of ever-married women aged 15-49
who report that their general health is “very good” as opposed to good and worse. Only less than
20% of the women interviewed deemed their health “very good” indicating a generally low level
of self-rated health.
11
Rows 2-4 report averages for my three main dependent variables. Though
statistically women residing in urban locations are different from those living in rural locations for
all three variables, the difference is remarkable with respect to the variable that captures the answer
to the question “Have you heard of a disease called AIDS?”. Overall, more than 40% of the respondents have reported to have not heard about AIDS. The proportion is more than 50% for rural
women. This goes on to show that there is a large scope for enhancing health belief and awareness.
There is also a big difference in the literacy rate between rural and urban location, attesting to the
fact that rural India was still lacking in educational opportunities, at least at the time of the survey
10 An
elaborate discussion of caste system is beyond the scope of this paper.
self-rated health is not perfect as an indicator. However, the proportion is drastically loIr than, say,
proportion of Americans who report self-rated health as “very good?. The comparable number for the U.S., according
to the nationally representative National Health Interview Survey, is 62%.
11 Arguably,
11
in 2003.
Please insert Table 2 Here
4.4
Econometric Modeling
In this section I try to estimate the effects of additional schooling on health belief. I estimate
regressions of the form
Yi = b0 + b1 ⇤ Ei + Xi ⇤ b02 + ei ,
(1)
where Yi is the dependent variable and represents one of the three health belief variables that I
use - (i) belief of maternal health (ii) Neonatal health and (iii) AIDS awareness for the ith woman. It
is a binary variable that takes value equals unity when the woman has the correct belief/awareness
and zero otherwise. Ei represents years of schooling conditional on having enrolled in school,
and Xi represents the following set of controls: age, age-squared, employment status (binary with
employed equalling unity), urban location (binary with urban location equalling unity), indicator
for individual belonging to below poverty line (binary with below poverty line equalling one) and
religion and caste indicators with Hindu Brahmin being the base category. Finally, ei is the residual
error term.
Equation (1) can be estimated with a Probit model assuming normality of the error distribution. The estimated coefficient corresponding to the education variable would then show us the
association between schooling and health belief. I will report this estimate as the baseline results
for most of my estimations. However, for the reasons discussed in the introduction, Ei is likely to
be endogenous, making my Probit estimate biased and inconsistent, and as a result, I cannot make
causal interpretation of the role of education in health belief.
my identifying assumption is that ages at menarche is largely exogenously determined and
combined with the poor infrastructure in many schools in India, girls drop out after they reach
12
puberty. Consequently, girls having later puberty will have higher years of schooling on average.
To capture this, I have created a variable called “late menarche” that assumes value of one if the
respondent has reported her age at menarche to be 13 or higher, the global average and zero otherwise.
12
my instrumental variable strategy requires that age at menarche can explain variations in
education even after controlling for other explanatory variables included in Xi above. Therefore,
I estimate the following equation (2) along with equation (1) above, where Zi is a binary variable
that takes value one is the reported age at menarche is 13 or higher and zero otherwise.
Ei = a0 + a1 ⇤ Zi + Xi ⇤ a02 + ui ,
(2)
Since all of my dependent variables are binary in nature (owing partly to the way the survey
questions were designed) and my endogenous regressor is continuous, I estimate the system of
equation (1) and (2) by the maximum likelihood method. Finally, robust standard errors are used
in the analysis to correct for possible heteroscedasticity. I discuss alternative methods of estimating
(1) and (2) and corresponding results in the robustness section.
4.5
Instrumental Validity
An instrumental variable strategy has to pass a two-part validity test. First, it has to be relevant in
the sense that the instrument should be able to explain variations in the endogenous variables even
after controlling for all other explanatory variables. Second, it has to be exogenous and excluded
in the sense that its effects on the dependent variable in equation (1) should transmit through the
endogenous variable and not independently. I argue that women experiencing late menarche had
higher schooling and age at menarche being biologically determined are orthogonal to the omitted
variables that may affect education and health beliefs.13
12 I
also perform the same analysis with a continuous measure of age at menarche. However, I a binary variable is
less prone to recall error.
13 As far as my instrumental variable strategy is concerned, using age at menarche as an instrumental variable per se
is not my innovation, though I am not aware of any work that used the variable for instrumenting education. The most
13
As the pattern in Figure 1 illustrates, the first requirement seems to be satisfied in the IHDS
data. However, I need to control for the other explanatory variables to see if my instrument is
relevant.
14
Table 3 reports the results from various first stage regressions. Column (1) does not
include any controls and column (2) includes full set of controls. Column (3) includes district
fixed effects allowing for the possibility that various district-level variables may drive educational
outcomes of its residents. In all three specifications, the first stage is strong. The relevant variable,
the indicator for late menarche, is positively and significantly associated with schooling. I also
have very high values of F-stat assuaging any concern for weak instruments. Some notable aspects
of the first stage results are the positive association between education and urbanity and negative
association between education and poverty, as expected. Also, except for Christians, every other
religious-caste group has loIr educational attainment than the base group Brahmins, who have
historically been the most educated among all castes. Only counterintuitive result is the negative
correlation between wage employment and education. The clue to this paradox can be found
in the descriptive statistics table, where I can see that urban area has a higher wage labor force
participation for the sample, which consists of ever married women. A large part of wage income
comes from agricultural wage work that does not necessarily require formal schooling.
Additionally, age at menarche has to be excluded from the main relationship between education health belief.instruments need to be orthogonal to the error term in the reduced form equation.
Genetic factors are thought to be the strongest determinants of age at menarche. Research into
the external determinants of age at menarche faces the same challenge of identifying the causal
thorough use of this variable as an instrument has been made by Field and Ambrus (2008), though they instrument
age at marriage with age at menarche. However, some of their other arguments are also relevant for my analysis, and
I shall refer to them in section 4.5. In other studies, Rios-Neto and Miranda-Ribeiro (2009) use age at menarche as
an instrument for sexual debut in Brazil. Mukhopadhyay and Crouse (2014) use age at menarche of a sibling as the
instrument for BMI of a female respondent. Therefore, the particular strategy I use in this paper is novel.
14 To be sure, these results correspond to the estimation of (1) and (2) with or without controls for the first dependent
variable “pregnancy belief”. As I will find out when I discuss my results, my estimation sample varies slightly depending on the outcome variable in use (due to non-reporting issues). Since the differences in the number of observations
in are small, no qualitative difference can be found across these three specifications in terms of first stage regressions.
I am reporting three columns instead of nine to avoid clutter.
14
effects of those variables. Since controlled field experiments are difficult to carry out in this field,
some studies have either adopted ingenious methods in observational studies, while some have
used laboratory experiments. In one remarkable study involving 1,283 pairs of monozygotic and
dizygotic twins, Kaprio et al. (1995) found that the correlation between ages at menarche among
monozygotic twins was three times more than their counterpart dizygotic twins confirming the
importance of genetic factors. Laboratory experiments, however, show that a number of external
factors such that extreme weather, physical stress, exposure to lead or plastic, peer-group sex composition and extreme changes in diets like malnutrition or obesity may delay or hasten puberty
(Field and Ambrus, 2008). Among these, the last variable is of the most concern to us.
15 India,
despite experiencing steady economic growth since 1991, still suffers from the problem of malnutrition. According to the UNISEF, one in three malnourished children in the world lives in India.
There two reasons why this is not a big concern for our strategy. First, malnutrition is strongly
correlated with poverty. There has not been any widespread and sustained food crisis or famine in
India since 1955, the earliest year a 49 year old woman could have been born who was interviewed
in 2004. Therefore, lack of access to food could only have been caused by lack of income. Second, in order to have any influence on the age at menarche biologically, malnutrition has to affect
hormone-releasing agents. Laboratory experimental studies show that the form of malnutrition has
to be extreme to precipitate a hormonal change that would alter the genetically determined age at
menarche.
16
There is a well-established medical literature that argues that age at menarche is
largely genetically determined. Though some recent studies like Dossus et al. (2012) find positive
correlations between age at menarche and variables like income, urbanity and lifestyle, there is no
conclusive evidence yet to defy the natural variations in the age of puberty.
15 India
17
is a tropical country with very few remote areas having extreme temperature. Very few adolescent girls
perform hard labor. Lead and plastic pollutions are a concern only in urban settings and controlled for in our analysis.
Finally, there is a priori reason to believe that some of these women had extraordinary peer group sex composition.
16 Discussion in paragraph has mostly been based on Field and Ambrus (2008) who perform a similar analysis in
Bangladesh, a country that was formerly a part of India under British rule and share many characteristics like weather,
economy and culture making such arguments relevant here also.
17 Field and Ambrus (2008) offer a comprehensive discussion of the role of genetic factors in the determination of
15
Please insert Table 3 Here
My main concern is whether age at menarche directly affects health belief. One can reasonably
argue that girls having early maturation may also start learning about reproductive health sooner.
Hence they are more likely to report higher incidence of correct belief. If this is the case, then I
would see a positive association between health belief and early onset of menarche (which would
translate into a negative association between health belief and the variable I are using in this paper
- late menarche).
This is not the case as Table 4 shows. Late onset of menarche is associated positively and
significantly with health belief suggesting that there is no prima facie evidence of early menarche
directly leading to better health belief.
Please insert Table 4 Here
5
Main Results
The section presents empirical results on the impact of schooling on health belief. The results
for estimating equation (1), and the system of equations (1) and (2) are reported in Tables 5A5C, when the dependent variables are pregnancy health belief, neonatal health belief and AIDS
awareness respectively. There are four columns in each of them. Columns (1) and (3) provide
single equation Probit model estimates (marginal effects) from equations of form (1). Columns (2)
and (4) report results from joint estimation of (1) and (2) by maximum likelihood methods. The
columns are also organized to separate estimates without controls (columns (1) and (2)) from with
controls (columns (3) and (4)).
age at menarche including references that date before 2008. They also explain how some of the laboratory findings
in developed country are not relevant for country like Bangladesh. Since they belong to the subcontinent (and indeed
part of the same nation for many years) such arguments are valid in this case also. I do not repeat those arguments
here.
16
I start with the estimates from single equation Probit models as they provide the baseline indication of possible correlation between the dependent and the main explanatory variable. Without
any controls and correction for endogeneity, there is a statistically significant positive effect of education on health belief for all three dependent variables as columns (1) and rows (1) show in tables
3A-3C. The results indicate that an additional year of schooling by itself can lead to an increase
in the probability of having relevant health belief by .02 percent point to 4 percent points. However, when I introduce controls, the average return to one extra year of education is not significant
for pregnancy health belief according to Probit estimates. Though for the other two dependent
variables schooling remains positive and significant even after introducing controls.
Please insert Table 5A-5C Here
Looking at columns (2) and (4), my instrumental variable estimates suggest a strong and positive effect of schooling on health beliefs. The effects are substantial: even after controlling for
several variables, an extra year of schooling for an average woman is likely to increase correct
health belief by 22%. That estimators that do not correct for endogeneity underestimate the effects
of educational attainment is shown elsewhere in the literature (Card, 1999, 2001; Griliches, 1977).
Though my dependent variables are unconventional (not income or wages), similar logic can explain the potential underestimated values of Probit estimates. If motivation and/or intelligence
enhance health belief, as is intuitive, and if they are omitted from the regression, they are likely to
bias the Probit estimates downward. Similar effects are obtained for the other two dependent variables under consideration. In both of these cases, educational attainment is positive and significant
without controlling for endogeneity, but instrumental variable estimates are larger in magnitude.
Interestingly, the results suggest that it is not generally the case that rural and poor women have
worse health beliefs for the first two variables that deal with reproductive health. However, as far
as AIDS awareness is concerned, the effects are reversed. The conclusion from this section is that
while educational attainment matters, the exact nature of such influence in interaction with other
17
variables is not homogenous. Depending on the nature of the question concerned, I get divergent
patterns. I will elaborate on this point in section 7.
6
Robustness
In this section I perform two sensitivity analyses to check if my results are robust to alternative
specification and estimation methods. First, instead of my baseline maximum likelihood estimation, I use linear two-stage estimation. While the former guarantees that the predicted values of
the (binary) dependent variable will lie between zero and one, it depends on the assumption of
joint normality of error terms. This may be a tenuous assumption in practice. An alternative is to
estimate the model using linear 2SLS method. According to Angrist (1991), there exist conditions
under which linear instrumental variables will consistently estimate average treatment effects. Results from linear 2SLS are presented in Panel A of Table 6. Comparing the coefficient estimate
for the dependent variable pregnancy health belief with my baseline estimate (Table 5A, row (1),
Column (4)), I are assured that not only are the estimates qualitatively same, they are also quantitatively similar. Similar patterns can be found for the other two dependent variables also.
Please insert Table 6 Here
Second, I include district fixed effects in my estimates. Districts are possibly the most important
administrative units in India. There are 630 districts belonging to 27 states. Districts vary widely in
terms of infrastructure like road and schools even within a state. Therefore, there may be observed
and unobserved variables at the district level that may drive the educational achievements of its
residents. Inclusion of district fixed effects imposes greater burden on other explanatory variables
as my outcome variables can potentially be explained mostly by location. However, as Panel B of
Table 4 shows, the effects of education remains significant, though the magnitude is diminished
after controlling for the district fixed effects.
18
7
Caveats - Schooling does not improve all health belief
The health belief questions that I analyzed so far are rather personal in nature for ever-married
women of 15-49 years. The same survey module also contains questions that are not related to reproductive or sexual health. It asks (i) question on indoor air pollution (Is smoke from a wood/dung
burning traditional oven good for health, harmful for health or do you think it doesn’t really matter?); and (ii) question on male sterilization (Do men become physically weak even months after
sterilization?). This section examines if higher educational attainment leads to better awareness
on these issues. The brief answer is the negative.
Please insert Table 7 Here
The results are presented in Table 7. For brevity I have presented only the maximum likelihood
IV estimates, my baseline model. The first row shows that in the case of indoor pollution, schooling
has no significant on an individual woman’s belief on indoor pollution caused by the smoke from
a wood or dung-burning oven. For the second variable, educational attainment actually has a
negative and significant effect on correct belief. Interestingly, for the other control variables, the
pattern of significance is as expected - urban women know better, women living below poverty line
know worse (though not significantly in one case) and every other caste-religious group (except
Christians) know worse than Brahmins, the historically learned caste.
The health effects of indoor pollution in developing countries has received attention from social scientists only recently (Balakrisnan et al., 2013; Gall et al., 2013; Hanna, 2014). Though
the ill-effects of using indoor ovens is well-documented, the reason as to why a large section of
world population is using such technology is not clear. The latest explanation comes from Kishore
(2013), who argues that in India, women’s position in the household is a determinant of the use
of traditional oven. My analysis provides a simple explanation - despite the international spread
in awareness, a large section of the poor in the world are still in the dark on the negative effects
19
of indoor air pollution. Unfortunately, the results suggest that formal schooling does not play any
role in promoting such awareness also. This is not unexpected, as environmental issues were not
included in school curriculum in India before 2005, the year of the survey.
The results for male sterilization question is not surprising. Though male sterilization as a family planning to tool has been available in India for some time, the awareness level has always been
low. In fact in a recent study Mahapatra et al. (2014) has found that awareness on modern methods of male sterilization is lacking even among community workers interviewed. Sex education in
Indian school is in its infancy and topics of intercourse and reproduction remain largely taboo in
Indian schools, both urban and rural. Therefore, the results that formal schooling does not enhance
knowledge on contraceptive behavior of opposite sex should not come as a surprise.
8
Conclusion and Policy Implications
In this paper I have explored the role schooling attainment plays in forming correct health beliefs
among ever-married women between ages 15-49 in India and find that for these women, higher
schooling significantly leads to better health belief with respect to reproductive health and AIDS
awareness. On the contrary, schooling does not seem to play a role in enhancing belief on indoor
pollution or men’s health. To correct for the potential endogeneity introduced by the schooling
variable, I have used age at menarche as an instrument for schooling noting that in India many girls
leave school after they reach puberty due to poor sanitary conditions in their schools. I find that age
at menarche is a strong predictor of schooling achievement with girls having late menarche going
on to have more years of schooling. Combined with the biological evidence that forces that are
exogenous to my structural relationship determine age at menarche, I argue that I have been able to
establish a causal relationship between education and health belief. This is the main contribution
of the paper.
This relationship is at the confluence of education and health policy. Poor countries often
20
struggle to spread awareness on diseases like AIDS. my results suggest that expansion in schooling,
particularly in rural areas, through better infrastructure can go a long way in aiding such efforts.
Improvements in sanitation can go a long way to combating the problem. In particular, building
toilets in schools enables girls to manage their periods more easily. Fortunately, expansion of
sanitation has been a priority in Indian public policy over the past few years. Hopefully, education
and health outcomes would improver in the near future.
21
References
Adukia, A. (2013). Sanitation and Education. Cambridge, MA: Harvard Graduate School of
Education.
Alsan, M. M., & Cutler, D. M. (2013). Girls’ education and HIV risk: Evidence from
Uganda. Journal of Health Economics, 32(5), 863-872.
Angrist, J. (1991). Instrumental variables estimation of average treatment effects in
econometrics and epidemiology. NBER Working Paper.
Balakrishnan, K., Ramaswamy, P., Sambandam, S., Thangavel, G., Ghosh, S., Johnson, P.,
& Thanasekaraan, V. (2011). Air pollution from household solid fuel combustion in
India: an overview of exposure and health related information to inform health
research priorities. Global health action, 4.
Berger, M. C., & Leigh, J. P. (1989). Schooling, self-selection, and health. Journal of
Human Resources, , 433-455.
Braakmann, N. (2011). The causal relationship between education, health and health related
behaviour: Evidence from a natural experiment in England. Journal of Health
Economics, 30(4), 753-763.
Card, D. (2001). Estimating the return to schooling: progress on some persistent
econometric problems, Econometrica, 69(5): 1127-1160.
Card, D. (1999). The Causal Effect of Education on Earnings, in (O. Ashenfelter and
D. Card, eds). , Handbook of Labour Economics, vol. 3A. Amsterdam: Elsevier
Science, North-Holland, and pp.-1801– 1863.
Carpenter, C. J. (2010). A meta-analysis of the effectiveness of health belief model variables
in predicting behavior. Health communication, 25(8), 661-669.
Cutler, D. M., & Lleras-Muney, A. (2010). Understanding differences in health behaviors by
education. Journal of Health Economics, 29(1), 1-28.
Dossus, L., Kvaskoff, M., Bijon, A., Fervers, B., Boutron-Ruault, M. C., Mesrine, S., &
Clavel-Chapelon, F. (2012). Determinants of age at menarche and time to menstrual
cycle regularity in the French E3N cohort. Annals of epidemiology, 22(10), 723-730.
Field, E., & Ambrus, A. (2008). Early marriage, age of menarche, and female schooling
attainment in Bangladesh. Journal of Political Economy, 116(5), 881-930.
22
Griliches, Z. (1977). Estimating the Returns to Schooling: Some Econometric Problems,
Econometrica, 45: 1-22.
Gall, E. T., Carter, E. M., Matt Earnest, C., & Stephens, B. (2013). Indoor air pollution in
developing countries: research and implementation needs for improvements in global
public health. American journal of public health, 103(4), e67-e72.
Greenstone, M., & Hanna, R. (2011). Environmental regulations, air and water pollution,
and infant mortality in India. American Economic Review, October.
Hsieh, C., Yen, L., Liu, J., & Chyongchiou Jeng Lin. (1996). Smoking, health knowledge,
and anti-smoking campaigns: An empirical study in Taiwan. Journal of Health
Economics, 15(1), 87-104. doi:http://dx.doi.org/10.1016/0167-6296(95)00033-X
Jürges, H., Reinhold, S., & Salm, M. (2011). Does schooling affect health behavior?
Evidence from the educational expansion in Western Germany. Economics of
Education Review, 30(5), 862-872.
Kan, K., & Tsai, W. (2004). Obesity and risk knowledge. Journal of Health Economics,
23(5), 907-934. doi:http://dx.doi.org/10.1016/j.jhealeco.2003.12.006
Kenkel, D. S. (1991). Health behavior, health knowledge, and schooling. Journal of
Political Economy, , 287-305.
Kishore, Avinash. "Essays on Economics of Indoor and Outdoor Air Pollution in India."
Digital Access to Scholarship at Harvard, http://nrs.harvard.edu/urn3:HUL.InstRepos:10403669
Lavy, V., & Zablotsky, A. (2011). Mother's schooling, fertility, and children's education:
Evidence from a natural experiment. National Bureau of Economic Research.
MAHAPATRA, Swati et al. Assessment of knowledge and perception regarding male
sterilization (Non-Scalpel Vasectomy) among community health workers in
Jharkhand, India. Indian Journal of Community Health, [S.l.], v. 26, n. 4, p. 428433, dec. 2014. ISSN 2248-9509.
Miller, E. M. (2011). Maternal health and knowledge and infant health outcomes in the Arial
people of northern Kenya. Social Science & Medicine, 73(8), 1266-1274.
Mukhopadhyay, S. (2014). Causal effects of BMI on wage. Mimeo. Department of
Economics, University of Nevada Reno.
23
Oster, E., & Thornton, R. (2011). Menstruation, sanitary products, and school attendance:
Evidence from a randomized evaluation. American Economic Journal: Applied
Economics, 91-100.
Prina, S., & Royer, H. (2014). The importance of parental knowledge: Evidence from weight
report cards in Mexico. Journal of Health Economics, 37(0), 232-247.
doi:http://dx.doi.org/10.1016/j.jhealeco.2014.07.001
Rios-Neto and Miranda-Ribeiro (2009) : Intra- and intergenerational consequences of
teenage childbearing in two Brazilian cities: Exploring the role of age at menarche
and sexual debut. Paper presented at the XXVI IUSSP International Population
Conference, Marrakech, Sep 27- Oct 2, 2009.
Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education &
Behavior, 2(4), 328-335.
Spears, D. (2012). Effects of rural sanitation on infant mortality and human capital: evidence
from India's Total Sanitation Campaign. Mimeo. Princeton University.
WDI (World Development Indicator) Database (2014). World Bank. Washington, DC.
24
Figure 1. Relationship between age at menarche and average schooling.
25
Table&1:&&School&Toilet&Facilities&in&the&Sample&
&
(1)
Variable
School has a toilet (binary; =1 if yes)
Observations
Standard deviations in brackets
Variable
School has separate toilets (binary; =1 if yes),
conditional on school having a toilet and
admitting girls
Observations
Standard deviations in brackets
26
Government Schools
0.6092
(.4881)
2031
(2)
Private
Schools
0.7832
(.4122)
1748
Government Schools
Private
Schools
0.7532
(.4313)
1236
0.7928
(.4054)
1369
Table&2.&Descriptive&Statistics&
!
Variable
Self-rated health very good
Correct belief Pregnancy
Correct belief breast serum
Aware of AIDS
Literate Dummy
Years of Schooling
Employed
Married within same caste
Arranged Marriage
Gold Dowry Common
Cash Dowry Common
No access to electricity
Observations
Standard deviations in brackets
*** Significant at the 1% level
(1)
Total
(2)
Rural
(3)
Urban
0.1556
(.3625)
0.7785
(.4153)
0.7562
(.4294)
0.5877
(.4922)
0.5596
(.4964)
4.6257
(4.8087)
0.2345
(.4237)
0.945
(.228)
0.572
(.4948)
0.7513
(.4323)
0.3991
(.4897)
0.2122
(.4089)
33400
0.1386
(.3455)
0.7663
(.4232)
0.7367
(.4404)
0.4723
(.4992)
0.4616
(.4985)
3.3968
(4.1905)
0.2895
(.4535)
0.9515
(.2147)
0.5811
(.4934)
0.7106
(.4535)
0.414
(.4926)
0.3026
(.4594)
21398
0.1861
(.3892)***
0.7997
(.4003) ***
0.791
(.4066) ***
0.7925
(.4055) ***
0.7342
(.4418) ***
6.8053
(5.056) ***
0.1365
(.3433) ***
0.9333
(.2495) ***
0.5559
(.4969) ***
0.8239
(.381) ***
0.3725
(.4835) ***
0.0518
(.2216) ***
12002
Notes: For all variables the p-value for the t-test between urban and rural is less than 0.01 making the
difference significant at 1% level. The values are not reported to avoid clutter.
27
Table 3: First Stage Results
Dependent Variable - Years of Schooling
Late_menarche
(1)
0.53
(0.051)
Age
Age-squared
Employed
URBAN
POOR
(2)
0.418***
(0.0480)
0.121***
(0.0228)
-0.00264***
(0.000347)
-0.205***
(0.0731)
1.904***
(0.0484)
-1.575***
(0.0650)
(3)
0.415***
(0.0488)
0.115***
(0.0228)
-0.00257***
(0.000346)
-0.169**
(0.0739)
1.955***
(0.0504)
-1.564***
(0.0661)
-0.676***
(0.100)
-1.398***
(0.0959)
-2.170***
(0.105)
-1.663***
(0.145)
-2.265***
(0.116)
-0.425**
(0.174)
0.818***
(0.172)
YES
Caste/Religion Groups:
(Base Group is Brahmin)
Other High caste
District FE
p-value for Fstatistic
NO
-0.610***
(0.0984)
-1.377***
(0.0941)
-2.098***
(0.103)
-1.461***
(0.139)
-2.170***
(0.114)
-0.370**
(0.173)
0.933***
(0.172)
NO
0.00
0.00
0.00
Observations
18,010
18,010
17,851
OBC
Dalit
Adivasi
Muslim
Sikh, Jain
Christian
Notes: Robust Standard Errors are in parentheses
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
The sample size refers to the second stage regressions with the dependent variable “Correct belief on
Pregnancy Health Question”
Please refer to the Appendix for detailed variable descriptions.
28
Table 4: Correlation between late menarche and health belief
late_menarche
N
(1)
(2)
(3)
Pregnancy
Health
Neo-natal Health
Question
AIDS
Awareness
0.136***
(0.0158)
31677
0.183***
(0.0151)
33006
0.0991***
(0.0140)
33144
Notes: Robust Standard Errors are in parentheses. *** Significant at the 1% level, ** Significant at the 5%
level, * Significant at the 10% level
Please refer to the Appendix for detailed variable descriptions.
29
Table 5A: Probit and Instrumental Variable Estimates of the Effects of Education on
Health Belief
Dependent Variable: Correct belief on Pregnancy Health Question
(1)
Probit
(2)
IV
(3)
Probit
(4)
IV
0.00258***
(0.000880)
0.192***
(0.0207)
0.000593
(0.000973)
0.000108
(0.00310)
7.21e-06
(4.67e-05)
0.0201**
(0.00877)
0.0350***
(0.00647)
0.00374
(0.00933)
0.220***
(0.0239)
-0.0263***
(0.00979)
0.000599***
(0.000154)
0.0977***
(0.0274)
-0.325***
(0.0566)
0.355***
(0.0463)
-0.0127
(0.0120)
-0.0159
(0.0116)
-0.0142
(0.0131)
-0.0321*
(0.0179)
-0.0768***
(0.0150)
0.0130
(0.0211)
0.0354*
(0.0196)
18,010
0.104**
(0.0422)
0.272***
(0.0531)
0.432***
(0.0688)
0.254***
(0.0703)
0.306***
(0.0849)
0.106
(0.0697)
-0.0819
(0.0727)
18,010
VARIABLES
Years of Schooling
Age
Age-squared
Employed
URBAN
POOR
Caste/Religion Groups:
(Base Group is Brahmin)
Other High caste
OBC
Dalit
Adivasi
Muslim
Sikh, Jain
Christian
Observations
18,029
18,029
Notes: Robust Standard Errors are in parentheses
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
Coefficients are marginal effects.
Please refer to the Appendix for detailed variable descriptions.
30
Table 5B: Probit and Instrumental Variable Estimates of the Effects of Education on
Health Belief
Dependent Variable: Correct belief on Neo-natal Health Question
VARIABLES
Years of Schooling
(1)
Probit
(2)
IV
(3)
Probit
(4)
IV
0.0148***
(0.000846)
0.274***
(0.00605)
0.0145***
(0.000923)
0.00248
(0.00291)
-2.80e-06
(4.40e-05)
0.0199**
(0.00834)
-0.00377
(0.00609)
0.0397***
(0.00878)
0.306***
(0.00619)
-0.0291***
(0.00837)
0.000731***
(0.000127)
0.0934***
(0.0255)
-0.536***
(0.0233)
0.513***
(0.0251)
-0.0269**
(0.0117)
-0.0126
(0.0111)
-0.0243*
(0.0125)
-0.136***
(0.0183)
-0.0603***
(0.0139)
0.0769***
(0.0174)
0.112***
(0.0151)
18,476
0.130***
(0.0369)
0.385***
(0.0373)
0.557***
(0.0441)
0.227***
(0.0603)
0.534***
(0.0502)
0.263***
(0.0689)
0.0806
(0.0788)
18,476
Age
Age-squared
Employed
URBAN
POOR
Caste/Religion Groups:
(Base Group is Brahmins)
Other High caste
OBC
Dalit
Adivasi
Muslim
Sikh, Jain
Christian
Observations
18,495
18,495
Notes: Robust Standard Errors are in parentheses
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
Please refer to the Appendix for detailed variable descriptions.
31
Table 5C: Probit and Instrumental Variable Estimates of the Effects of Education on
Health Belief
Dependent Variable: Correct belief on AIDS Awareness
(1)
Probit
(2)
IV
(3)
Probit
(4)
IV
0.0460***
(0.000827)
0.295***
(0.0222)
0.0398***
(0.000903)
0.00985***
(0.00260)
-0.000138***
(3.95e-05)
0.00194
(0.00732)
0.114***
(0.00560)
-0.0303***
(0.00739)
0.299***
(0.0293)
0.0215
(0.0133)
-0.000169
(0.000221)
0.0357
(0.0319)
0.187*
(0.110)
0.102
(0.0782)
0.0102
(0.0118)
-0.0142
(0.0113)
-0.0130
(0.0123)
-0.0547***
(0.0158)
-0.0884***
(0.0138)
-0.0143
(0.0208)
0.119***
(0.0163)
18,539
0.134**
(0.0562)
0.150*
(0.0797)
0.256**
(0.105)
0.0208
(0.101)
0.00189
(0.128)
-0.0138
(0.0870)
0.562***
(0.139)
18,539
VARIABLES
Years of Schooling
Age
Age-squared
Employed
URBAN
POOR
Caste/Religion Groups:
(Base Group is Brahmins)
Other High caste
OBC
Dalit
Adivasi
Muslim
Sikh, Jain
Christian
Observations
18,557
18,557
Notes: Robust Standard Errors are in parentheses
*** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level
Please refer to the Appendix for detailed variable descriptions.
32
Table 6: Robustness Tests
PANEL A: 2SLS Estimates
(1)
VARIABLES
Pregnancy
(2)
Neonatal
Years of
Schooling
0.229***
0.306***
(0.0233)
(0.00700)
Observations
17,851
18,456
Standard errors in brackets
*** p<0.01, ** p<0.05, * p<0.1
(3)
AIDS
0.325***
(0.0229)
18,539
PANEL B: Estimates including District Fixed Effects
(1)
(2)
(3)
VARIABLES
Pregnancy
Neonatal
AIDS
Years of
Schooling
0.0943***
0.214***
(0.0179)
(0.0236)
Observations
17,525
17,981
Robust standard errors in brackets
*** p<0.01, ** p<0.05, * p<0.1
0.0872***
(0.0128)
18,042
Notes: Both regressions control for all the variables included in Tables 3A-3C.
33
Table 7: Effects of Education on Alternative health belief questions
VARIABLES
Years of Schooling
Age
Age-squared
Employed
URBAN
POOR
Sterilization
Indoor
Pollution
-0.234***
(0.0198)
0.0427***
(0.00943)
-0.000814***
(0.000145)
-0.0253
(0.0285)
0.588***
(0.0253)
-0.425***
(0.0355)
-0.0151
(0.0493)
0.0127
(0.0125)
-0.000191
(0.000211)
-0.0958***
(0.0317)
0.183**
(0.0925)
-0.123
(0.0828)
-0.149***
(0.0392)
-0.352***
(0.0450)
-0.572***
(0.0517)
-0.201***
(0.0678)
-0.597***
(0.0554)
-0.214***
(0.0680)
0.674***
(0.0844)
13,760
-0.261***
(0.0552)
-0.348***
(0.0794)
-0.209*
(0.114)
-0.511***
(0.0896)
-0.484***
(0.114)
0.170*
(0.102)
0.0419
(0.0970)
18,535
Caste/Religion Groups:
(Base Group is Brahmins)
Other High caste
OBC
Dalit
Adivasi
Muslim
Sikh, Jain
Christian
Observations
Standard errors in brackets
*** p<0.01, ** p<0.05, * p<0.1
Please refer to the Appendix for detailed variable descriptions.
34
Appendix
Dependent Variables:
Correct belief on Pregnancy Health Question:
Survey Question: Is it harmful to drink 1-2 glasses of milk every day during pregnancy?
Correct belief on Neo-natal Health Question:
Survey Question: Do you think that the first thin milk that comes out Good=1 after a baby is born is
good for the baby, harmful for the baby, or it doesn't matter?
Correct belief on AIDS Awareness
Survey Question: Have you ever heard of an illness called AIDS?
Sterilization
Survey Question: Do men become physically weak even months after sterilization?
Indoor Pollution
Survey Question: Is smoke from a wood/dung burning traditional chulha good for health, harmful for
health or do you think it doesn't really matter?
Instrumental Variables:
Late_menarche: = 1 if age at menarche >12, 0 otherwise.
Explanatory Variables:
Age, Age-squared: Continuous varables.
Employed = 1 if wage-employed, 0 otherwise
URBAN = 1 if urban location, 0 otherwise
POOR = 1 if below poverty line, 0 otherwise
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