EDUCATION AND GENDER BIAS IN THE SEX RATIO AT
BIRTH: EVIDENCE FROM INDIA*
REBECA A. ECHÁVARRI AND ROBERTO EZCURRA
This article investigates the possible existence of a nonlinear link between female disadvantage
in natality and education. To this end, we devise a theoretical model based on the key role of social
interaction in explaining people’s acquisition of preferences, which justifies the existence of a nonmonotonic relationship between female disadvantage in natality and education. The empirical validity
of the proposed model is examined for the case of India, using district-level data. In this context, our
econometric analysis pays particular attention to the role of spatial dependence to avoid any potential
problems of misspecification. The results confirm that the relationship between the sex ratio at birth
and education in India follows an inverted U-shape. This finding is robust to the inclusion of additional
explanatory variables in the analysis, and to the choice of the spatial weight matrix used to quantify
the spatial interdependence between the sample districts.
ccess to prenatal sex-detection technologies in areas of the world with rooted son
A
preference has attracted a great deal of attention over recent years. This type of technology
enables a person to control family sex-composition by practicing sex-selective abortions.
Accordingly, the spread of these technologies might help to explain why atypically high
numbers of male births (relative to the number of female births) have become habitual in
various countries. The question was first brought up by the work of Johansson and Nygren
(1991) and Zeng et al. (1993) in the case of China. Since then, gender bias in natality has
been well documented for other countries, such as Korea (Park and Cho 1995) and India
(Arnold, Kishor, and Roy 2002; Sudha and Irudaya Rajan 1999).
When there is no access to prenatal sex-detection technology, preferring boys over
girls leads parents to focus on an ideal number of sons. That is, regardless of the number
of daughters in the family, fertility is completed as soon as the couple has their ideal number of sons (Arnold et al. 2002; Clark 2000). For the aggregate demographic outcomes,
this behavior causes inflated fertility ratios, although it does not give rise to female disadvantage at birth. By contrast, controlling family sex-composition through selective abortions decreases the relative number of female births. Therefore, female disadvantage in
natality is more likely to arise in those areas of the world that combine the existence of a
system of values that give priority to sons over daughters and the availability of prenatal
sex-detection technologies.
Education plays a key role in reducing the magnitude of gender inequality (Clark 2000;
Murthi, Guio, and Drèze 1995). Nevertheless, various works to date that have examined the
relationship between education and female disadvantage in natality in countries characterized by strong son preference have produced inconclusive results. (See the extensive debate
that arose from Das Gupta’s [1987] work for the case of India, and the simulation results
*Rebeca A. Echávarri, Universidad Pública de Navarra, Department of Economics, Campus de Arrosadia,
31006, Pamplona (Spain); e-mail: rebeca.echavarri@unavarra.es. Roberto Ezcurra, Department of Economics,
Universidad Pública de Navarra. We thank two anonymous referees, Jorge Alcalde-Unzu, Ritxar Arlegi, Debopam
Battacharya, Samuel Bowles, Jose Enrique Galdón, Jorge Nieto, and Sudha Shreeniwas for helpful comments and
suggestions on an earlier draft of this article. We also thank Jean Drèze for his encouragement in this research
project during a stay of the first author at Delhi School of Economics. Part of this research was carried out in the
Department of Foundations of Economic Analysis II at Universidad del País Vasco. The usual disclaimer applies.
Financial support from the Spanish Government (CICYT: SEC 2006-11510; ECO 2008-05072-C02-02/ECON;
ECO 2009-10818/ECON; ECO 2009-12836/ECON) is gratefully acknowledged.
Demography, Volume 47-Number 1, February 2010: 249–268
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Demography, Volume 47-Number 1, February 2010
by Kim [2005] for Korea.) Bearing this in mind, this article aims to delve more deeply into
the analysis of the theoretical mechanisms linking education and gender bias in natality.
In that respect, education affects a person’s “freedom and power to act” and “freedom and
power to question and reassess the prevailing norms and values” (see Drèze and Sen [2002]
and Dee [2004] for a similar discussion). In particular, abundant evidence shows that educational gains mean that people are more likely to develop values that give no particular
priority to either sex (Bhat and Zavier 2003; Clark 2000). Accordingly, education may be
considered an instrument of preference change, which would be supported by the enhancement of freedoms and power to question and reassess the prevailing son preference. Based
on efficiency considerations, however, various authors have pointed out that educated
parents’ behavior is biased toward the use of sex-selective abortion technologies (Bose and
Trent 2005; Das Gupta 1987). This suggests that education can be interpreted alternatively
as an instrument of technological-constraint-change because it increases individuals’ freedom and power to access to prenatal sex-detection technologies.
The two effects described above work in opposite directions (Bhat 2002; Clark 2000).
Hence, to determine the final impact of education on gender bias in natality, we need to
know the magnitude of both effects, which may depend in the final instance on the level of
education registered by the society. This suggests the possible presence of a nonmonotonic
relationship between education and female disadvantage at birth. Nevertheless, as far as we
are aware, this issue has not been considered in any of the works that have so far examined
the influence of education on gender bias in natality; see the aforementioned studies. This
article fills that gap by presenting for the first time a theoretical model that justifies the
existence of a nonlinear relationship between education and female disadvantage at birth.
The model is inspired by the literature on cultural transmission, such that particular attention is paid to social interaction and its role in explaining cultural change (Bowles 1998,
2004; Boyd and Richerson 1985).
Using district-level data, we examine the empirical validity of the proposed model
for India. Numerous studies have highlighted the influence of cultural and social factors
on demographic behavior in India (see the pioneering work by Dyson and Moore [1983]
and more recent findings by Rahman and Rao [2004]). This suggests that the geographical
location of the various districts may play a relevant role in explaining the spatial distribution of female disadvantage in natality in India. Bearing this in mind, and to investigate in
greater detail the importance of spatial effects in this context, we use spatial econometric
techniques in our empirical analysis. Most previous work on demographic outcomes in
India has tended to ignore the potential relevance of spatial effects,1 which may affect the
validity of the results (Anselin 2001).
THE MODEL
In this section, we describe a model that provides various insights into the relationship between education and female disadvantage in natality. Our main assumptions are grounded
in empirical evidence on the question under study and the literature on cultural transmission. To start, we consider a population of heterosexual couples in which the husband and
wife have identical preferences regarding the sex of their offspring. In turn, each couple
lives a single period and then is replaced by their offspring. For simplicity, we assume that
population in each generation is of size n, which allows us to eliminate the influence of
changes in the population size over time.
A couple modifies the sex of their future child when the following two conditions
are satisfied: (1) they have access to the technology that enables them to determine the
child’s sex, (2) and they have values (i.e., beliefs and preferences) that lead them to use
1. For an exception, see Murthi et al. (1995), Dharmalingam and Morgan (2004), or Chakraborty and Sinha
(2006).
Education and Gender Bias in the Sex Ratio at Birth
251
this technology. As mentioned in the introduction of this article, a wide range of empirical
studies describe education as one of the principal predictors of how likely a person is to
satisfy each of these conditions. Remember that education is thought of as an instrument
that enables access to prenatal sex-detection (Bhat 2002; Das Gupta 1987) but also as an
instrument that advocates values that give no priority to any particular gender (Bhat and
Zavier 2003; Clark 2000).
We measure education as an exogenous shock that turns a randomly selected portion of
the population into educated individuals. In other words, the shock divides the n individuals
into two groups: educated and noneducated. Let ne denote the size of the educated group
and n0 the size of the noneducated group, where n ≡ ne + n0. Thus, the size of the educational
shock is measured by the share of educated population ne / n. We extend our analysis to the
study of infinitesimal increases in education at the end of this section (Result 3).
Prevalence of Son Preference
Regarding the offspring’s gender, an individual either has no preference for any particular
sex (unbiased preferences) or has a biased preference toward one particular sex. A nontrivial problem in this context is that biased preferences all run in the same direction,
which in turn captures the idea that a portion of the information available to an individual
determining his or her preferences suggests that gender does in fact matter: for instance,
payoffs associated with raising sons are greater than those associated with raising daughters in many areas of the world (see, e.g., the study by Rosenzweig and Schultz [1982] on
rural India).
Without loss of generality, the label female is given to a person with the disadvantageous gender and son preference refers to the biased preferences. In contrast, unbiased
preferences are supported by a different body of information, leading people to welcome
sons and daughters equally. The idea that several different bodies of information might exist
simultaneously means that in acquiring son preference, cultural reasons may be differentiated from alternative ones, such as the payoffs associated with results (Dyson and Moore
1983; Rahman and Rao 2004).
To simulate the effect of education on preferences, we assume that education determines the mechanism through which people acquire preferences. Specifically, we consider
that while noneducated people typically acquire their parent’s preferences, education provides individuals with the ability to question and reassess their parents’ preferences. An
appealing aspect of this assumption is that education may be said to increase the probability
that a person will reevaluate his or her values, preferences, and beliefs in light of a larger
informational set (Dee 2004).
We go on to impose some structure on the effects of people gaining the ability to question and reassess their parent’s attitudes toward preferences. At this point, it is worth raising
the importance of social interaction. A wide range of theoretical and empirical analyses
from different disciplines document a relationship between the aggregated outcomes of
individual behavior and the individual behavior itself as a response to the importance of
social interaction (Bowles 1998). With that relationship in mind, our model relies on the
following nonstandard but highly intuitive assumption: if the size of the educational shock
is small enough, people learn from current evidence that replicating values is a best response (Boyd and Richerson 1985; Castelló-Climent 2008).
Our assumption implies that the share of education matters in explaining the effects of
education. Recent studies have tested this assumption. In her study explaining the effects
of education on the sustainability of democracy, Castelló-Climent (2008) showed that the
share of education matters more than average schooling years. In our scenario, the intuitive
idea underlying this assumption might be that breaking with family values (or otherwise
regarding groups, such as friends) implies facing social costs, which decrease with the
shock size (which is to say, “enough people might do the same as one does”); eventually,
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educated people become encouraged to break with family values, hence switching to unbiased preferences.
Moreover, the idea of relating the acquisition of behavioral traits and the prevalence
of the traits in the society is reminiscent of arguments explaining behavior in terms of
conformism (see the pioneering work by Boyd and Richerson 1985). Thus, let ρ– be the
portion of the population with son preference before the educational shock, ρe be the portion
of the population in the educated group that develops son preference, and ρ0 be the same
portion in the noneducated group. Moreover, in line with the conformism models (see the
aforementioned literature), let k ∈ [0,1] be a parameter capturing the change benchmark by
individuals. Then, we assume that
ne
ρe 1 ρ– if n $ k ,
(1a)
ρe = ρ− otherwise.
(1b)
Expressions (1a) and (1b) together capture the idea that education might act as instrument of preference change. Furthermore, it reflects that the group—rather than the
individual—triggers such a change. The structure imposed by (1a) and (1b) does not imply
that a person has to develop an identity as a member of a group (the educated group). In
order to break with prevailing values, the person has to be aware that a sufficient percentage
of “others” might change in a certain direction—in this case, in the direction of unbiased
preferences. Because our concern here is to provide as simple an illustrative model as possible, we compare the benchmark k with the educational shock (the portion of educated
population). Similar intuitive approaches might well be abstracted from models that compare k with more elaborated functions.
Moreover, only educated people may question and reassess their parents’ preferences,
ρ0 = ρ–. Thus, expressions (1a) and (1b) together mean that ρe ≤ ρ0 = ρ–. The latter implies
a weak negative relationship between education and son preference, which is not an overly
demanding constraint. A great deal of empirical evidence would support a strict negative
relationship between education and son preference (Bhat and Zavier 2003; Clark 2000).
We opt to consider that invariant preferences might exist. In contexts of rooted female disadvantage, it is quite possible that education alone does not ensure that people are encouraged to break with prevailing norms and values (see Drèze and Sen [2002] for an in-depth
discussion of natality inequality and agency issues).
Access to Technologies
As already stated, our model also captures the role played by education as an instrument that
increases the probability of access to prenatal sex-detection technologies. Thus, let σ– ∈ (0,1)
be the portion of the population with access to prenatal sex-detection technologies before the
educational shock, while σ e and σ 0 denote the share of the population with access in each
corresponding group. The expression σ – ∉ {0,1} is for mathematical purposes alone and
does not imply any change in meaning of theoretical results. We assume that σe > σ 0 = σ –.
This assumption implies a strictly positive relationship between education and access
to technology. Education is generally considered a key instrument in the enhancement
of freedom and power to achieve opportunities that are open to all (Drèze and Sen 1989,
2002). When accounting for the nature of this technology, specifically in the context of
female disadvantage in natality, education is presented as an instrument of technologicalconstraint change (Bose and Trent 2005; Das Gupta 1987).
Theoretical Results
Our concern here is to describe the conditions under which the probability of female
disadvantage in natality (represented as changes in the proportion of sons born from one
Education and Gender Bias in the Sex Ratio at Birth
253
generation to the next) is larger (or smaller) than this probability conditional on being
noneducated conditional on being educated versus noneducated. For a sufficiently large
population, the result of multiplying ρe and σe can be taken as the probability that an educated parent will act to switch an offspring’s gender. The same probability, but conditional
on being a noneducated individual, is obtained by multiplying ρ0 and σ0. Hence, our model
shows the following:
ne
Result 1. If n 1 k , then educated people are more likely than noneducated people to
cause female disadvantage in natality.
ne
Proof. The proof of Result 1 is straightforward. If n 1 k , then ρe = ρ0. Indeed, we get
ρ– = ρ0 by definition, and ρe = ρ– by expression (1b). Meanwhile, by definition, σ e > σ – = σ 0.
As a consequence, the probability that an educated person controls family sex-composition
is larger than the probability that a noneducated person does: ρeσe = ρ0σ e > ρ0σ 0.
Result 1 explains why, at certain points when the share of education increases in a
place, one may obtain the counterintuitive finding that female disadvantage in natality
and education positively correlate (Bhat 2002; Das Gupta 1987). Nevertheless, Result 1
says nothing if the spread of education surpasses the benchmark given by k. According
to our model, once education triggers preference change, any increment in education is
associated with two effects of different sign: ρe < ρ0, while σ e > σ 0. Thus, one might find
that the aforementioned positive relationship turns negative, a phenomenon supported by
a large body of the literature (Bhat and Zavier 2003;
Clark 2000). Let ρeMIN be the share of
ne
educated people who have a son preference if n $ k . This value is, indeed, the minimum
taken by this variable.
ne
Result 2. Consider that n $ k holds true. Educated
people are less likely to cause
tρeMIN
female disadvantage in natality if and only if d 2
, where d = ρ– – ρeMIN , and t = σe – σ–.
σ0
ne
Proof. The proof of Result 2 is straightforward. By expression (1a), if n $ k , then
e
ρe = ρMIN . Thus, ρeσe < ρ–σ – ⇔ (ρ– – d)(σ – + t) < ρ–σ– ⇔ ρ–t – σ –d – td < 0. Ite is convenient
tρ
to rewrite [ρ–t – σ –d – td] as [tρeMIN − σ– d ]. Thus, tρeMIN − σ– d > 0 ⇔ d 2 MIN
. Recall that
σ0 e
e
t
ρ
n
ρ– = ρ0 and σ – = σ 0 by definition. Thus, given n $ k , ρeσ e < ρ0σ 0 ⇔ d 2 MIN
.
σ0
Result 2 concludes that if education triggers preference change, this preference
change decreases the probability that educated people will cause female disadvantage in
natality if and only if there is a large enough decrease in the share of the educated population that has son preference. Here, the decrease in son preference will be large enough
if it compensates the fact that by gaining education, educated people who still have son
preferences have more chance to act in accordance with their preferences than the noneducated population.
At this point, it is interesting to characterize the effect of infinitesimal increases of
education on female disadvantage in natality. To do so, we proceed as follows. Let ε be an
infinitesimal increase in the share of educated population (which might be interpreted as a
second educational shock). This leads to the following result.
tρe + ε
Result 3. Assume that d 2 MIN
holds true. Then for the whole society, the relationσ0
ship between female disadvantage in natality and education has the following pattern.
ne + ε
(a) If n < k , the increment of education in ε is associated with more female disadvantage ine natality.
n ne + ε
(b) If n , n > k , the increment of education in ε is associated with reduced levels of
female disadvantage
in natality.
ne ne + ε
(c) If, n , n . k , female disadvantage in natality remains invariant despite the
spread of education.
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Demography, Volume 47-Number 1, February 2010
Proof. Patterns (a) and (b) follow from n ≡ ne + n0. Thus, an increment in the educated
population in ∆ne = ε implies a decrease of the same size in the noneducated population
∆n0 = –ε. These population sizes are expressed in the notation as n ≡ ne + ε + n0 – ε.
The latter makes the proof of (a) straightforward. As the condition in Result 1 holds,
∆ne = ε means the increment of the population that is more likely to switch its offspring’s
gender, and hence the increments in female disadvantage in natality.
ne
As far as (b) is concerned, as n $ k is upheld, by expression (1a) we arrive at
tρe
+ε
ρe + ε = ρeMIN
< ρ– . Meanwhile, by definition, ρ0 = ρ–. Thus, the condition d 2 MIN
, by
σ0
Result 2, implies that the probability that an educated person will switch an offspring’s
gender is smaller than the probability that a noneducated person will do so. Thus, for the
whole population, ∆ne = ε means the increment of the population that is less likely to switch
its offspring’s gender, and hence the decrease in female disadvantage in natality. Moreover,
it is straightforward to show (c). From (a) and (b), we learn that infinitesimal changes close
to k reflect the slope change in the relationship between education and female disadvantage
in natality.
Result 3 implies an inverted V-shaped link between education and female disadvantage in natality provided that k ∈ (0, 1). The change in the slope arises as soon as the size
e
of educated population equals the benchmark k: that is, nn = k . Our model includes the
linear relationship between the two variables—commonly assumed by this literature—as
a particular case. If parameter k takes value zero, by gaining education, a person switches
to unbiased preferences regardless what the others do. In this case, according to Result 3
(b), we find a negative relationship between education and female disadvantage in natality.
In contrast, parameter k may take a value of 1. It implies invariant preferences over educational groups, given that education opens access to sex-selective technology, and according
to Result 3 (a), education has a positive impact on the relative number of male births. Note
tρe
that the latter case is also implied by dropping the condition d 2 MIN
.
σ0
EMPIRICAL ANALYSIS: THE CASE OF INDIA
The theoretical model introduced in the preceding discussion suggests a possible relationship between female disadvantage in natality and education that follows a nonlinear pattern
(see Result 3). In this section, we aim to test empirically the validity of our model for the
case of India. Research has been conducted in past decades concerning the links between
education and demographic outcomes in that country (e.g., Clark 2000; Das Gupta 1987;
Murthi et al. 1995), the results of which has failed to achieve a consensus. However, as
far as we are aware, no study to date has formally examined the existence of a nonlinear
relationship between education and female disadvantage in natality.
To measure the degree of female disadvantage in natality, we employ the sex ratio
at birth, defined throughout this article as the ratio of male to female children born in a
specific period. In most human populations, more boys than girls are born as a result of a
biological phenomenon (Waldron 1985). In an analysis based on different countries with
complete and reliable data, Visaria (1971) found that in the absence of intervention, the
number of male births per 100 female births ranged between 103 and 107. Nevertheless,
during the past two decades, studies have shown anomalously high sex ratios at birth in
Asian countries characterized by a long-standing tradition of son preference, such as China
or Korea (e.g., Johansson and Nygren 1991; Park and Cho 1995). In these areas, couples
are increasingly succeeding in avoiding the birth of girls while ensuring the birth of boys.
This raises the possibility that prenatal sex-selection techniques are substituting in these
countries for postnatal methods used traditionally to determine the family composition
(Goodkind 1996, 1999).
Education and Gender Bias in the Sex Ratio at Birth
255
The historical preference for male children in India is well documented in the literature
(e.g., Bhat and Zavier 2003; Rosenzweig and Schultz 1982), which suggests that this country is an interesting case study in this context. However, it is difficult to obtain the Indian
sex ratio at birth because of incomplete national vital registration data (Griffiths, Matthews,
and Hinde 2000; Swamy 1995). Three data sources can be employed by researchers—the
Indian census, the Sample Registration System (SRS), and the National Family Health
Survey (NFHS)—each of which raises different problems (Bhat 1995; Swamy 1995), such
as underenumeration of children (particularly female children), low quality of age reporting, and the presence of sampling errors. In view of these issues, caution must be exercised
when comparing the sex ratios at birth obtained from different data sources. Nevertheless,
it is interesting to note that the findings of various studies based on data drawn from these
three data sources suggest that the Indian sex ratio at birth has tended to increase since the
beginning of the 1980s (Arnold et al. 2002; Sudha and Irudaya Rajan 1999). This increasing trend has been particularly relevant in several states located in North-Northwest India,
a region traditionally characterized by strong female disadvantage (Miller 1981). Specifically, the most recent data provided by the SRS show that Punjab and Haryana were the
two states with the highest sex ratios at birth in India during the period 2004–2006, with
values of 124 and 119 boys born per 100 girls, respectively. These figures contrast with
the values below the national average (112) observed in several states situated in the South
and the East, which confirms the importance of regional differences in the sex ratio at birth
across the Indian states.
According to Sudha and Irudaya Rajan (1999), female disadvantage in natality was
generally greater in urban areas at the beginning of the 1980s. Nevertheless, the empirical
evidence provided by these authors showed that the number of rural zones with anomalously high sex ratios at birth increased considerably throughout the following 10 years.2
As Sudha and Irudaya Rajan (1999) mentioned, this pattern is consistent with the process of
spread of a medical technological innovation. Taking into account that there is no reason to
attribute the observed trend to an increase in the underenumeration of girls over time, this
result suggests the rising employment of prenatal sex-selection techniques and sex-selective
abortions in India (Arnold et al. 2002).
Data
The empirical analysis carried out in this section is based on district-level data taken from
the 1991 Indian census (Registrar General of India 1991). Specifically, our sample consists
of 377 districts for which detailed statistical information is available. These districts belong
to the states of Andhra Pradesh, Assam, Bihar, Gujarat, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh,
and West Bengal, which together accounted for about 90% of India’s population in 1991.
Although the census of India does not publish the sex ratio at birth at the district
level, it includes the necessary information to estimate this statistic, applying a reversesurvival technique (e.g. Sudha and Irudaya Rajan 1999; United Nations 1983). The specific
procedure used to obtain the sex ratio at birth is based on the idea that the population for
age group (0–x) is made up of the survivors from the births that were recorded during the
current and the past x years. In particular, from the sex ratio at a certain age cohort, it is
possible to obtain the sex ratio at birth in the past x years, given that separate estimates exist for males and females on the probability of dying from birth to each age in the relevant
cohort. In our calculations, we use the most recent estimates of the probability of dying
from birth to various ages (1, 2, 3, and 5 years) provided by the Registrar General of India
(1997). These estimates are based on census questions on the number of children ever born
2. Despite this evolution, according to the SRS data for the period 2004–2006, the sex ratio at birth is still
greater in urban zones (114) than in rural areas (111).
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Demography, Volume 47-Number 1, February 2010
and the number of children surviving.3 Because these probabilities were not available for
the 2001 Indian census at the time of this writing, we use the 1991 census in our analysis.
This limitation is not particularly important in this context because, as pointed out by Sudha
and Irudaya Rajan (1999), prenatal sex-selection techniques had already been introduced
in India by 1991 (Arnold et al. 2002). Despite this fact, in the early 1990s, there were still
relatively important differences in the access to these techniques, especially in rural areas
(Sudha and Irudaya Rajan 1999). This situation is perfectly compatible with the framework
outlined in the theoretical model described in the previous section. In any event, as mentioned earlier, the aim of this section is exclusively to test empirically the validity of our
theoretical model. Accordingly, we are not interested in explaining the evolution of the sex
ratio at birth over time in India.4 The model described in the preceding section does not
consider the existence of time effects. Our model is based on the idea that social interaction—not time—is the key factor to understand the relationship between education and
female disadvantage in natality.
District-level data, rather than other potential alternatives, were chosen for various
reasons. In particular, the district is the basic administrative unit in India and, moreover,
the smallest level at which territorially disaggregated information on demographic features
is available. Accordingly, the use of district-level data enables us to maximize the number
of observations employed in the econometric analysis. This issue is particularly important
to justify the adequacy of the methodological approach applied in this section. In fact, the
reduced sample size derived from a state-level analysis is clearly inadequate to allow us to
employ the various statistical techniques used in our study. Additionally, using state-level
data means that existing differences in the sex ratio at birth within the various states are
not taken into account, leading to a relatively important loss of information. In particular,
according to our estimates, within-state variation explains about 50% of total dispersion
in the sex ratio at birth in India, which is consistent with the information provided by, for
example, Murthi et al. (1995) and Dharmalingan and Morgan (2004). All these arguments
reinforce the need to employ the census data in this context, given that this is the only
source that can support a district-level analysis. Furthermore, there are numerous studies
on demographic outcomes in India based on this level of territorial disaggregation (e.g.
Bhattacharya 2006; Murthi et al. 1995; Rosenzweig and Schultz 1982), which will facilitate
any future comparisons of our findings with those previously obtained by other authors.
Econometric Analysis
In view of the implications arising from the theoretical model described in the previous section, our empirical research begins with a preliminary analysis on the possible nonlinearity
of the link between the sex ratio at birth and education in India. To this end, the level of
education of the population of the sample districts is measured by the literacy rate,5 which
is a widely used variable in the literature because of the lack of other alternative indicators
at this level of spatial disaggregation (Bhattacharya 2006; Murthi et al. 1995). The literacy
rates of the sample districts differ considerably, which confirms the importance of regional
disparities in this context.
In the first step of our study to investigate the shape of the relationship between female
disadvantage in natality and literacy in India, we seek to impose as little structure on the
functional form as possible. A nonparametric approach is advisable in this context because
3. As a test of robustness, we compared our estimates with those obtained by Sudha and Irudaya Rajan (1999),
using a slightly different estimation technique. The results, however, were similar in both cases. Further details
are available upon request.
4. For further details in that respect, see Sudha and Irudaya Rajan (1999), Clark (2000), Arnold et al. (2002),
or Bhat (2002).
5. Literacy is defined in the census of India as the ability to read and write with understanding in any
language.
Education and Gender Bias in the Sex Ratio at Birth
Figure 1.
257
Estimated Sex Ratio at Birth and Literacy Rate in India: Locally Weighted Scatterplot
Smoothing
1.07
Sex Ratio at Birth
1.06
1.05
1.04
Bandwidth = 0.5
Bandwidth = 0.7
1.03
Bandwidth = 0.9
1.02
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Literacy Rate
such techniques do not require any prior specification of a particular functional form to
capture the relationship between the two variables under analysis. The methodology we
employ is locally weighted scatterplot smoothing (lowess).6
Figure 1 displays the fitted curves obtained when this smoothing method is applied
to our sample. The weighting scheme employed in the estimates is based on the tricube
weighting function proposed by Cleveland (1979). Accordingly, decreasing weights are attached to observations that are further away from the observation in question. The amount
of smoothing depends directly on the number of observations that are used in each regression (the so-called bandwidth). For this reason, we repeat the estimates using different
bandwidths in each case. The information provided by Figure 1 suggests that the empirical
relationship between the sex ratio at birth and the literacy rate in the sample districts of
India is clearly nonmonotonic. Moreover, it appears to follow an inverted U-shape, which
is in principle consistent with the theoretical arguments laid down in the preceding section.
In fact, this conclusion is robust to the bandwidth employed to obtain the estimates.
However, several reasons suggest that this finding should be treated with some caution.
For example, the nature of the analysis carried out thus far does not allow us to establish
a causal link between literacy rate and sex ratio at birth. Likewise, it is very likely that the
degree of female disadvantage in natality registered in the sample districts does not depend
exclusively on their literacy rates. This suggests that additional explanatory variables
should be included in the analysis. Finally, and in relation to the latter, the lowess method
used prevents any attempt to control for spatial specific factors relating, for example, to
6. See Goodall (1990) for a detailed technical description of this method of nonparametric analysis.
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Demography, Volume 47-Number 1, February 2010
social or cultural features, the importance of which has been repeatedly stressed in the
Indian context by numerous authors (Dyson and Moore 1983; Rahman and Rao 2004). In
view of these issues, and taking into account that Figure 1 provides strong support for the
existence of a quadratic relationship between the sex ratio at birth and literacy rate, we shall
now consider the estimation of the following parametric model:
SRBi = α + βLITi + δLIT2i + φ Xi + νi,
(2)
where SRBi is the sex ratio at birth of district i, LITi is the literacy rate, Xi is a vector of
variables that control for other factors that are assumed to influence the dependent variable,
and νi is the corresponding disturbance term.
Before discussing several technical issues in relation to the estimation of the regression in Eq. (2), we describe the series of variables that make up the vector X (see Table 1
for further details). Although the choice of the selected variables is well grounded in the
literature on the subject, it ultimately depends on the availability of reliable statistical data
for the level of spatial disaggregation on which the study is focused.
We begin by considering the role played in this framework by female labor force participation, measured as the percentage of main workers in a district’s female population. A main
worker is defined in the Indian census as a person who worked for 183 days or more in the
Table 1.
Variable Definitions and Sample Descriptive Statistics: India 1991
Variable
Definition
Sex Ratio at Birth
Number of male births per 100 female births
Mean
SD
105.17
0.032
Literacy Rate
Percentage of population that is literate
40.91
0.134
Female Literacy Rate
Percentage of female population that is literate
29.83
0.154
Male Literacy Rate
Percentage of male population that is literate
51.18
0.122
Female Labor Force
Percentage of female population categorized as
main workers
16.90
0.106
Rural Population
Percentage of population that lives in rural areas
79.67
0.140
Medical Facilities
Percentage of villages that has a medical facility
38.52
0.309
Scheduled Castes
Percentage of population that belongs to a
scheduled caste
16.35
0.075
Percentage of population that belongs to a
schedule tribe
10.04
0.163
Agricultural Workers
Percentage of main workers categorized as
agricultural workers
24.51
0.129
Poverty Index
Regional incidence of poverty measured in the
interval [0,1]
0.518
0.255
Dummy variable = 1 for districts in Bihar, Orissa, and
West Bengal
0.188
0.391
Dummy variable = 1 for districts in Himachal Pradesh,
Punjab, and Rajasthan
0.135
0.342
Dummy variable = 1 for districts in Andhra Pradesh,
Karnataka, Kerala, and Tamil Nadu
0.201
0.402
Scheduled Tribes
East
Northwest
South
Sources: Registrar General of India (1998) for medical facilities; Planning Commission of India (2003) for the poverty index.
The remaining variables are calculated from the 1991 Indian census (Registrar General of India 1991, 1997).
Education and Gender Bias in the Sex Ratio at Birth
259
preceding year. The literature has paid increasing attention to the link between demographic
outcomes and a factor that is referred to as “women’s agency” (Sen 1984) or “women’s
autonomy” (Dyson and Moore 1983), understood as women’s power to exercise choice in
their actions regardless of the constraints imposed by social structures. Female labor force
participation is a relevant factor in this context (Bhattacharya 2006; Rahman and Rao 2004).
Nevertheless, it is difficult to determine beforehand the possible effect of this variable on
the sex ratio at birth. On the one hand, women who work outside the home increase family
income. Accordingly, a high level of female labor force participation may enhance the value
attached to females, thus reducing son preference (Dyson and Moore 1983; Rosenzweig
and Schultz 1982). On the other hand, outside employment means greater social interaction,
which may favor wider access to prenatal sex-detection technologies. These effects work in
opposite directions. Empirical research is therefore key to understanding the nature of the
relationship between female labor force participation and our dependent variable.
In addition, it is worth investigating the extent to which the link between the sex ratio
at birth and education is the same in rural and urban zones of India. In fact, a wide range
of empirical evidence documents that fewer girls than boys are born in Indian urban areas
than in rural zones, which might have to do with the existence of greater access opportunities to sex-selective abortion technologies in urban areas than in rural ones (Sudha and
Irudaya Rajan 1999). In the light of these considerations, we calculate the proportion of
the population in each district that lives in rural areas and include this variable among the
regressors in our empirical model.
Furthermore, we take into account the role of family planning services in this context.
The Indian Government has a long tradition of promoting family planning programs at
the national level (Bose and Trent 2005). This may be relevant in our framework because
districts that have traditionally offered professional advice on family planning decisions are
likely to be home to a higher portion of the population that is prepared to use prenatal sexselection techniques. Accordingly, the availability of medical facilities can exert a direct
effect on the sex ratio at birth through the provision of family planning services. For this
reason, the share of villages in a district with access to medical facilities is included in the
set of control variables that are used to explain the observed differences in the sex ratio at
birth in our sample.
Additionally, the demographic features of Indian districts may depend on the relative
importance of the less-advantaged social groups because different social norms may affect the behavior of people in these groups. Bearing this in mind, we calculate the share
of a district’s population that integrates two minority social groups: scheduled castes and
scheduled tribes. This allows us to illustrate possible contrasts in this context between these
two social categories and other sections of the population, which is of particular interest in
the Indian case (Luke and Munshi 2007; Mitra 2008). The scheduled castes, which make
up about 16% of the country’s population, include various Hindu groups belonging to the
lowest scale in the caste hierarchy. Despite the fact that discrimination on the basis of caste
is illegal according to the Indian constitution, members of this social group suffer from
discrimination in large parts of the country. In turn, the scheduled tribes include the majority of tribal and indigenous communities living in India.
Furthermore, because the demographic features of the Indian districts may depend on
their level of economic development and modernization, we should control our estimations
for these factors. This is not an easy task at this level of territorial disaggregation, since
district-specific indicators of income or expenditure are not available in India. To overcome this important limitation, we use the share of agricultural workers among all main
workers in a district as a proxy for its level of economic development. The relevance of
the agricultural sector in terms of output and employment decreases as advances are made
in the economic development process because of the shift of productive resources toward
manufacturing activities and services (Kuznets 1966). According to this argument, it may
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Demography, Volume 47-Number 1, February 2010
reasonably be supposed that the districts with greater percentages of agricultural workers
tend to register lower levels of economic development. Two of the above-mentioned control
variables—the share of rural population and the degree of access to medical facilities—can
be also used to approximate the level of economic development and the degree of modernization of the various districts object of analysis (Bhattacharya 2006).
Another issue of interest in this context is the potential link between poverty and the sex
ratio at birth (Edlund 1999). Because the Indian census does not gather data on the degree
of poverty registered within the sample districts, we employ in our analysis the poverty
estimates provided by the Planning Commission of India (2003) and based on the 55th round
of the National Sample Survey (NSS). The use of these data is not problem free. In particular,
the sample size in the NSS for many districts is very small, which makes it impossible to
estimate reliable poverty indicators at this level of territorial disaggregation. Therefore, a
different territorial unit is used as a reference. Specifically, the poverty estimates employed
in our analysis refer to the different regions defined by the NSS according to socioeconomic
and agroclimatic criteria. These regions are intermediate spatial units between the district
and the state. Accordingly, the use of these poverty estimates in our empirical model implies
some loss of information because it involves the implicit assumption that intraregional
differences in poverty are relatively small. In addition, the inclusion of this additional control
variable obliges us to reduce the sample size to 330 districts because of the lack of data.
In the light of these considerations, we estimate an alternative version of the regression in
Eq. (2) that includes the poverty indicator in order to compare the results with those obtained
from the original sample.
Finally, we include three regional dummy variables to identify possible regional patterns and reduce the potential impact on the results of the omitted-variable bias: East for
districts in Bihar, Orissa, and West Bengal; Northwest for districts in Himachal Pradesh,
Punjab, and Rajasthan; and South for districts in Andhra Pradesh, Karnataka, Kerala, and
Tamil Nadu. The control region is integrated by the districts in the remaining states. These
three regional dummy variables have been defined by taking our estimates of the sex ratio
at birth into account, as well as the findings of the literature on gender bias in India (e.g.
Bose and Trent 2005; Dyson and Moore 1983).
Table 2 presents the main results of our empirical study. As shown in the first two
columns of the table, we begin by estimating Model (2) by ordinary least squares (OLS).
The OLS estimator is based on the assumption that the error terms are independently and
identically distributed. However, a simple inspection of the estimated sex ratio at birth
values suggests that this variable is not randomly distributed across space in India. This
finding points to the possible presence of spatial dependence in our sample,7 which would
affect the properties of the OLS estimator. To investigate this issue more deeply, we calculate five tests for spatial dependence from the OLS residuals: the Moran’s I test (Cliff
and Ord 1972), the Lagrange multiplier tests for the spatial error model (LMERR), and the
spatial lag model (LMLAG) proposed by Burridge (1980) and Anselin (1988a), plus their
robust versions (R-LMERR and R-LMLAG; Anselin et al. 1996). Before performing these
tests, we must specify a spatial weight matrix to capture the degree of interdependence
between each pair of districts i and j. Within this framework, it is important to stress that
the spatial weights should be exogenous to the model to avoid the identification problems
raised by Manski (1993). For this reason, we use pure geographical distance, which in
itself is strictly exogenous. Specifically, the spatial weight matrix we use, W, is based on
the inverse geographical distance between the centroids of the sample districts. This matrix
is row-standardized, which means that relative—not absolute—distance is what matters.
In addition, as is usual in the literature, when defining W, we consider a cutoff parameter
7. Spatial dependence can be defined as the coincidence of value similarity with locational similarity (Anselin
2001).
Education and Gender Bias in the Sex Ratio at Birth
Table 2.
261
Estimated Sex Ratio at Birth: Results of the Regression Analysis
Variable
OLS
OLS
ML
ML
Constant
1.045***
(0.016)
0.187***
(0.046)
–0.170***
(0.049)
–0.063***
(0.015)
–0.046***
(0.009)
0.010*
(0.005)
–0.003
(0.017)
–0.033***
(0.007)
0.051***
(0.010)
0.289***
(0.088)
0.184***
(0.042)
–0.165***
(0.044)
–0.060***
(0.012)
–0.032***
(0.008)
0.002
(0.004)
–0.025
(0.015)
–0.031***
(0.007)
0.051***
(0.009)
–0.028***
(0.004)
0.042***
(0.005)
–0.017***
(0.004)
1.000***
(0.016)
0.267***
(0.043)
–0.242***
(0.045)
–0.088***
(0.015)
–0.007
(0.010)
0.001
(0.005)
–0.016
(0.017)
–0.038***
(0.008)
0.080***
(0.021)
0.027***
(0.005)
–0.028***
(0.003)
0.035***
(0.005)
–0.025***
(0.004)
–0.014***
(0.003)
0.023***
(0.005)
–0.011***
(0.003)
0.713***
(0.082)
0.381***
(0.106)
0.233***
(0.042)
–0.211***
(0.043)
–0.058***
(0.016)
−0.005
(0.009)
0.001
(0.004)
–0.024
(0.016)
–0.038***
(0.007)
0.072***
(0.020)
0.018**
(0.006)
–0.012**
(0.004)
0.020***
(0.005)
–0.017***
(0.004)
0.593***
(0.100)
918.835
838.476
947.140
854.696
–1,866.279–
–1,679.393–
Literacy Rate
Literacy Rate, Squared
Female Labor Force
Rural Population
Medical Facilities
Scheduled Castes
Scheduled Tribes
Agricultural Workers
Poverty Index
East
Northwest
South
Spatial Lag of SRB (λ)
Log-Likelihood
Akaike’s Information Criterion
–1,813.670–
–1,650.952–
Moran’s I
12.177***
8.070***
LMERR
51.955***
17.019***
R-LMERR
10.251***
LMLAG
81.354***
41.327***
R-LMLAG
39.650***
25.578***
1.270
Wald Test for λ = 0
74.693***
34.860***
LM Test for λ = 0
81.354***
41.327***
Number of Observations
377
330
377
330
Notes: The dependent variable in all cases is the sex ratio at birth, defined as the ratio of male to female births.
Robust standard errors are in parentheses (White 1980, 1982).
*p < .05; **p < .01; ***p < .001
above which spatial interactions are assumed to be negligible. In our analysis the critical
cutoff is determined by the first quartile of the distance distribution.8
8. To check the robustness of our findings, we considered different cutoff parameters. The results were in all
cases very similar to those described in this article. Further details are available upon request.
262
Demography, Volume 47-Number 1, February 2010
According to the information provided by Table 2, the results of the various tests for
spatial dependence lead to the rejection of the null hypothesis of absence of residual spatial
dependence, despite the fact that Model (2) includes different regional dummies. This may
result from the influence of unobserved cultural and social factors on the sex ratio at birth
(Murthi et al. 1995; Rahman and Rao 2004). To decide the most appropriate specification in
this context, we follow the classical approach adopted in the spatial econometric literature.
Specifically, given that in all cases, the values of the LMLAG and R-LMLAG tests are
greater than those for the LMERR and R-LMERR tests, we selected the spatial lag model
as the best specification in this context (Anselin and Rey 1991). Accordingly, the spatial lag
of the dependent variable, WSRBi, must be included in the list of regressors. Consequently,
we should estimate the following model:
SRBi = α +βLITi + δLITi2 + φ Xi + λWSRBi + νi,
(3)
where λ is the spatial autoregressive parameter.
Nevertheless, the estimation of Model (4) by OLS is inconsistent because of simultaneity induced by the spatial lag. Maximum likelihood (ML) estimators have been proposed to
provide consistent estimates (Anselin 1988b).9 In view of this, the third and fourth columns
in Table 2 show the ML estimates of the spatial lag Model (3). Following the suggestion of
White (1982), reported standard errors come from the heteroskedasticity-consistent estimator of the covariance matrix of the ML parameters.
The different measures of goodness-of-fit included in Table 2 (the value of the maximized log likelihood and the Akaike’s information criterion) reveal that the spatial lag
model estimated by ML provides an increased explanatory power. As can be observed, the
spatial autoregressive parameter is significant and positive in all cases. In fact, the internal
coherence of the spatial lag model is strengthened if we take into account the results of the
Wald test and the Lagrange multiplier test for λ = 0. All this clearly shows that the sex ratio
at birth in the neighboring districts has a positive effect when one attempts to explain the
variability of the dependent variable.
As stated earlier, the main objective of our empirical analysis is to investigate the
relationship between female disadvantage in natality and education. In this respect, the
estimated coefficients from the spatial lag model yield interesting results. Table 2 shows
that the coefficients of the literacy rate and the square of the literacy rate are in all cases
statistically significant. Specifically, their signs show the presence of an inverted U-shaped
link between the sex ratio at birth and education in the Indian case, thus confirming the
preliminary evidence provided by the lowess method. Therefore, as the share of the literate
population increases, the sex ratio at birth tends at first to increase. Nevertheless, this relationship does not continue indefinitely, and beyond levels of literacy situated at around 55%
of the total population, our analysis reveals a negative correlation between education and
the sex ratio at birth. When we weigh the relevance of this finding, our results are consistent
with the conclusions derived from the theoretical model discussed in the previous section.
Table 2 also provides information on the role played by the remaining explanatory
variables when explaining the variation of the sex ratio at birth across the Indian districts.
The analysis indicates that higher levels of female labor force participation decrease the sex
ratio at birth, confirming the relevance of women’s agency in reducing female disadvantage
in India (Bhattacharya 2006). This finding is indeed obtained after we control for the level
of economic development and the degree of poverty within the sample districts. Similarly,
earlier studies have identified the positive impact of female labor force participation on the
reduction of the extent of gender bias in child survival in India (e.g., Murthi et al. 1995).
9. For further details on the inclusion of spatial effects in econometric modeling, see the literature review
in Anselin (2001).
Education and Gender Bias in the Sex Ratio at Birth
263
Additionally, our estimates indicate that the proportion of the population that is rural is
negatively correlated with the dependent variable. This suggests that female disadvantage at
birth seems to be lower in rural areas, where access to prenatal sex-detection technologies
is generally more difficult than in urban zones (Bose and Trent 2005; Sudha and Irudaya
Rajan 1999). Nevertheless, this result should be treated with caution, given that the coefficient associated with the degree of urbanization is not statistically significant when the
poverty indicator is included in the list of regressors (fourth column in Table 2).
Another issue of interest is that according to our estimates, the availability of medical
facilities has no effect on female disadvantage at birth. When interpreting this result, remember that efficiency in the functioning of health services is at least as important as their
availability (Murthi et al. 1995). In fact, many studies have highlighted the poor functioning
of public health services in India (e.g., Das and Hammer 2007).
Furthermore, from among the variables included in the analysis to control for the
role played by specific social groups, only the proportion of the total population that is in
scheduled tribes is statistically significant. Therefore, the share of scheduled castes has no
influence on the sex ratio at birth. This is not particularly surprising given that the magnitude of the differences in the gender relations between the scheduled castes and the rest of
the population have narrowed considerably in past decades (Luke and Munshi 2007). The
situation is different with the proportion of scheduled tribes, however. Our estimates show
that relatively high values of this variable lead to a decline in the sex ratio at birth. In fact,
the effect still holds after we control for female labor force participation, which tends to
be greater among this social group than in the population as a whole. This finding suggests
that tribal societies have specific features that contribute to reduce discrimination against
females at birth (Mitra 2008). Although further research is required to study this issue more
deeply, particular attention should be paid to the characteristics of kinship systems and
property rights in this social group (Murthi et al. 1995).
A higher proportion of agricultural workers is positively correlated with our dependent
variable. According to this result, the process of structural change that characterizes the
advances in the development process leads to a decline in female disadvantage at birth. The
degree of poverty registered within the sample districts has a positive impact on the sex
ratio at birth, which suggests the possibility that gender bias in natality may be stronger
among the less-privileged social classes. Nevertheless, the different problems raised by the
inclusion of the poverty indicator in the list of regressors should not be overlooked, for
which reason further studies are required to confirm this finding.
Finally, the three regional dummy variables included in our model are statistically
significant even after we control for the remaining variables, which highlights the presence
of relevant spatial differences in this context. Accordingly, the geographical location of
the various districts plays an important role in explaining the variability of the sex ratio at
birth. Despite some exceptions, the sex ratio at birth is relatively higher in the Northwest
compared with the control region. These areas have a long tradition of social systems in
which exogamous marriages, dowries, and the seclusion of women play an important role
(Dyson and Moore 1983; Rahman and Rao 2004). On the contrary, discrimination against
females at birth is less relevant in the southern states and the rice-cultivating eastern zone
of the country. The south of India has been characterized historically by more liberal social
structures than other parts of the country in relation to marriage customs, kinship, and inheritance patterns (see Dyson and Moore 1983; Rahman and Rao 2004).
The variable we used to capture the level of education of the population living in the
sample districts—the overall literacy rate—does not take into account existing differences
between male and female literacy rates. This issue, however, may be of particular relevance
in this context because of the relatively high degree of gender inequality in access to education in India. For this reason, and to complete our earlier findings, we repeat the estimations using the male and female literacy rates as an alternative to the overall literacy rates
264
Demography, Volume 47-Number 1, February 2010
employed in the preceding analysis. The information provided by Tables 3 and 4 indicates
that the results are in all cases very similar to those we already discussed. The most relevant
issue is that the bell-shaped relationship observed previously between education and the
sex ratio at birth still holds when the male and female literacy rates are employed, thus
confirming our earlier findings with overall literacy rates.
Table 3.
Robustness Analysis 1: The Role of Male Literacy Rate
Variable
OLS
OLS
ML
ML
Constant
1.030***
(0.021)
0.205**
(0.065)
–0.156**
(0.061)
–0.064***
(0.015)
–0.048***
(0.009)
0.009
(0.005)
–0.000
(0.017)
–0.032***
(0.008)
0.053***
(0.011)
0.282**
(0.090)
0.208***
(0.058)
–0.162**
(0.055)
–0.061***
(0.012)
–0.035***
(0.008)
0.001
(0.004)
–0.024
(0.016)
–0.030***
(0.007)
0.052***
(0.009)
–0.029***
(0.004)
0.041***
(0.005)
–0.018***
(0.004)
0.983***
(0.021)
0.282***
(0.060)
–0.219***
(0.055)
–0.086***
(0.016)
–0.014
(0.010)
0.001
(0.004)
–0.013
(0.017)
–0.037***
(0.008)
0.087***
(0.021)
0.030***
(0.006)
–0.026***
(0.004)
0.033***
(0.005)
–0.026***
(0.004)
–0.015***
(0.003)
0.022***
(0.005)
–0.012***
(0.003)
0.705***
(0.083)
0.325**
(0.104)
0.258***
(0.056)
–0.203***
(0.052)
–0.056***
(0.015)
–0.010
(0.009)
0.000
(0.004)
–0.024
(0.016)
–0.036***
(0.007)
0.078***
(0.020)
0.020***
(0.006)
–0.010*
(0.004)
0.019***
(0.005)
–0.018***
(0.004)
0.629***
(0.098)
917.778
–1,811.557–
11.535***
47.029***
8.390**
78.393***
39.755***
833.373
–1,640.746–
9.253***
24.537***
2.945
48.056***
26.464***
945.199
–1,862.398–
851.679
–1,673.359–
71.171***
78.393***
377
41.028***
48.056***
330
Male Literacy Rate
Male Literacy Rate, Squared
Female Labor Force
Rural Population
Medical Facilities
Scheduled Castes
Scheduled Tribes
Agricultural Workers
Poverty Index
East
Northwest
South
Spatial Lag of SRB (λ)
Log-Likelihood
Akaike’s Information Criterion
Moran’s I
LMERR
R-LMERR
LMLAG
R-LMLAG
Wald Test for λ = 0
LM Test for λ = 0
Number of Observations
377
330
Notes: The dependent variable in all cases is the sex ratio at birth, defined as the ratio of male to female births.
Robust standard errors are in parentheses (White 1980, 1982).
*p < .05; **p < .01; ***p < .001
Education and Gender Bias in the Sex Ratio at Birth
Table 4.
265
Robustness Analysis 2: The Role of Female Literacy Rate
Variable
OLS
OLS
ML
ML
Constant
1.071***
(0.012)
0.114***
(0.030)
–0.122***
(0.037)
−0.059***
(0.015)
–0.048***
(0.010)
0.010*
(0.005)
–0.001
(0.017)
–0.036***
(0.007)
0.048***
(0.010)
0.297***
(0.087)
0.118***
(0.027)
–0.123***
(0.034)
–0.056***
(0.012)
–0.032***
(0.009)
0.002
(0.004)
–0.033***
(0.015)
–0.031***
(0.007)
0.048***
(0.009)
–0.028***
(0.004)
0.042***
(0.005)
–0.018***
(0.004)
1.033***
(0.013)
0.183***
(0.028)
–0.194***
(0.035)
–0.086***
(0.015)
–0.007
(0.011)
0.001
(0.005)
–0.013
(0.017)
–0.042***
(0.008)
0.069***
(0.021)
0.026***
(0.006)
–0.029***
(0.003)
0.035***
(0.005)
–0.025***
(0.004)
–0.014***
(0.004)
0.022***
(0.005)
–0.012***
(0.003)
0.727***
(0.082)
0.424***
(0.107)
0.155***
(0.028)
–0.164***
(0.034)
–0.005
(0.016)
–0.005
(0.010)
0.001
(0.004)
–0.021
(0.016)
–0.041***
(0.007)
0.063***
(0.019)
0.017**
(0.006)
–0.013***
(0.004)
0.020***
(0.005)
–0.018***
(0.004)
0.580***
(0.101)
916.605
–1,809.210–
12.888***
57.748***
12.439***
84.304***
38.995***
838.109
–1,650.219–
7.968***
16.136***
1.375***
37.950***
23.189***
945.904
–1,863.808–
853.352
–1,676.703–
79.260***
84.304***
377
32.678***
37.950***
330
Female Literacy Rate
Female Literacy Rate, Squared
Female Labor Force
Rural Population
Medical Facilities
Scheduled Castes
Scheduled Tribes
Agricultural Workers
Poverty Index
East
Northwest
South
Spatial Lag of SRB (λ)
Log-Likelihood
Akaike’s Information Criterion
Moran’s I
LMERR
R-LMERR
LMLAG
R-LMLAG
Wald Test for λ = 0
LM Test for λ = 0
Number of Observations
377
330
Notes: The dependent variable in all cases is the sex ratio at birth, defined as the ratio of male to female births.
Robust standard errors are in parentheses (White 1980, 1982).
*p < .05; **p < .01; ***p < .001
CONCLUDING REMARKS
In this article, we investigate the possible existence of a nonlinear relationship between female disadvantage in natality and education. In a first stage, we devised a theoretical model
that justifies the existence of a nonmonotonic link between both variables. As is usual
in the literature on cultural transmission, our framework stresses the relevance of social
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interaction when explaining cultural change. Specifically, the final impact of education on
gender inequality in natality in our model depends in the final instance on the magnitude
of two effects that work in opposite directions. These two effects are based on the double
role played by education in this context: as an instrument of preference change and as an
instrument of technological-constraint change.
In a second stage, we examined the empirical validity of the proposed model in the
context of India, a country characterized by a long-standing tradition of son preference.
To do so, we estimated the sex ratio at birth of the Indian districts by applying a reverse
survival method. Following the common practice in the literature, we proxied the level of
education of the various districts by their literacy rate. To avoid potential misspecification
problems, our empirical study paid special attention to the role played in this context by
spatial effects, for which reason we employed a methodological approach based on spatial
econometric techniques. This is particularly advisable in the present framework because the
calculations of the various spatial dependence tests indicate the need to include the spatial
lag of the sex ratio at birth in the list of regressors (spatial lag model).
Our estimates reveal that the relationship between the sex ratio at birth and the literacy rate follows an inverted U-shape, which confirms the conclusions derived from the
theoretical model proposed in the article. In fact, this finding is obtained after including
various additional explanatory variables in the analysis such as female labor force participation, the share of rural population, the proportion of villages in a district with access
to medical facilities, the relevance of scheduled castes and scheduled tribes, the share of
agricultural workers, the degree of poverty registered within the sample districts, and regional dummy variables.
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Gender and the Digital Divide in
Latin America∗
Tricia J. Gray, University of Louisville
Jason Gainous, University of Louisville
Kevin M. Wagner, Florida Atlantic University
Objectives. We analyze differences in how men and women in Latin American countries are utilizing
the Internet to identify a possible regional gendered digital divide in Internet use. The extent, degree,
and implications of this gender digital divide are explored across countries with varying degrees
of digital freedom. Methods. We employ a series of random- and fixed-effects models utilizing
individual-level data from the 2010 Latin Barometer merged with country-level data obtained from
the U.N. Gender Inequality Index. Results. Our results suggest that, in general, Latin American
men tend to use the Internet more than women. Men also use more social media and gather political
information more frequently. In addition, Internet use is higher across these categories in countries
with more gender equality. Conclusion. The potential for the Internet to serve as a social and
political equalizing force in Latin America is stymied in part by the gendered digital divide.
The Internet has seen exponentially high growth coupled, in part, with the tremendous
increase in the availability of access points such as smartphones and access speed through
broadband technology. With its rapid growth, penetration and ability to allow any person
to access political information and organize political activities, the Internet has been seen
as a democratizing force in developing countries. Some propose that the Internet offers
the means by which those disadvantaged under the current system can gain influence
and political power. Citizens, political activists, and politicians who in the past lacked the
resources and position to compete politically, can use the Internet to more evenly balance
the political field by creating more opportunities for ideas counter to those sent by, and
through, the existing media power structure (Barber, 2003; Corrado and Firestone, 1996;
Hagen and Mayer, 2000).
However, the ability to access and use the Internet is not evenly distributed (Norris,
2001). Optimistic views are tempered by concern that the Internet can simply be a new
tool that will be studied and eventually captured by the dominant political players. This
less hopeful view suggests that the Internet will be a normalized influence after its potential
is harnessed by existing authorities, rather than the means to open up opportunity for those
outside the prevailing power structure. If so, there likely would be no durable shift in the
basic power balance of the political systems (Bimber and Davis, 2003; Hindman, 2008;
Margolis and Resnick, 2000; Stromer-Galley, 2014; Ward, Gibson, and Lusoli, 2003).
A definitive statement about the Internet as being either transformative or ineffective
across national and cultural borders is likely too simple and overbroad an approach. We
∗
Direct correspondence to Tricia Gray, Department of Political Science, University of Louisville, Louisville,
KY 40292 tricia.gray@louisville.edu. The data are available from the authors for purposes of replication. We
thank the Social Science Quarterly reviewers for helpful comments.
SOCIAL SCIENCE QUARTERLY, Volume 98, Number 1, March 2017
C 2016 by the Southwestern Social Science Association
DOI: 10.1111/ssqu.12270
Gender and the Digital Divide in Latin America
327
propose that this two-dimensional division between equalization and normalization is
likely too imprecise a construct of Internet influence. We suggest that the power of the
Internet to influence a political system will likely vary based on context, including cultural,
political, and social influences within a state. Scholars have already shown that technology
can have an outsized influence in autocratic states where communication and dissemination
platforms are at a premium (Wagner and Gainous, 2013). The effect of technology can
be limited by cultural context that limits or filters the impact of its uses as well (Gainous,
Wagner, and Abbott, 2015).
In this article, we narrow the focus and consider the effectiveness of the Internet as a
means to equalize political participation in the social and political context of Latin America.
In particular, we center on whether gender norms and traditional roles in this region are
likely to be overcome by the new communication and information available through the
Internet. While information communication technologies (ICT) are becoming ubiquitous
in developing nations, there are remaining differences in the platforms for access and quality
of access as well as potential cultural and role differences in the way men and women utilize
the Internet. Previous studies on the digital divide have identified many key factors that
relate to people’s access to digital technology, and gender is almost always an important
factor (Bimber, 2001; Chen and Wellman, 2003; Dixon et al., 2014; Norris, 2001).
However, very little research has looked at this divide in Latin America, and to the best of
our knowledge, no research has explicitly examined the cross-national divide with largeN individual-level data in that region. Our analysis directly addresses this understudied
area.
Before moving to the analysis, we lay our theoretical framework by examining the research
on the digital divide derived mostly from studies of the high-income developed nations
making up the Organization for Economic Cooperation and Development (OECD). We
compare that research with emerging research on developing regions while incorporating
the literature that explicitly addresses the gender divide based on existing theory highlighted
in this review. We expected to identify a clear gender divide in Internet usage, and we also
expected to identify a cross-national pattern where Internet usage is lower in those countries
with less gender equality. If so, the potential for the Internet to serve as an equalizing
force—when it comes to gender equality—is seriously diminished. Our findings support
this hypothesis, and are discussed below.
The Digital Divide and Limits of Technology-Based Change
The effect of the Internet on the political process in developed nations has been significant
and measurable in a number of areas. The Internet has altered in some important ways
campaigning, fundraising, advertising, and even political organization (Bimber, 2003;
Mossberger, Tolbert, and McNeal, 2008; Wagner and Gainous, 2009). Since it is a means
to organize and disseminate political information, the Internet creates a political forum
outside the traditional system that provides opportunities for political actors and citizens to
shape, or even reshape, the system. The new medium requires new management and control
strategies that are still being developed and implemented. With its relatively low cost, as
well as its reach and speed, the Internet has the potential to move toward equalization of
the balance of power between political actors. However, this is contingent on the level of
usage and penetrations as well as the social and political structure of the underlying region.
In theory, the Internet is revolutionary. In practice, the evidence is more mixed (Boulianne, 2009, 2015; Bimber and Copeland, 2013). Cyber optimists contend that ICTs
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help socioeconomic development and strengthen democracy, while cyber pessimists assert that access to the ICTs is an obstacle making the poor poorer, reinforcing the power
of elite, and hindering economic development (Hawkins and Hawkins, 2003). In many
measures in developed nations, the effects of the Internet are significant, but the size is
often smaller than projected, and consistency across election cycles is absent (Bimber and
Copeland, 2013). Ultimately, the questions left unanswered are less about the power of the
technology and increasingly more focused on the context of the region of the state that is
adopting and using the new medium. In developed nations where Internet communication
technologies are increasingly common, scholars have focused more on the political actors
and their ability to capture and harness the technologies so as to maintain a dominant
position. As a result, the more prominent debates—whether the Internet will equalize the
system or be normalized into the prevailing structure—are over (Gainous, Marlowe, and
Wagner, 2013).
In other environments, the implications are different. In a nation with limited media,
or government-controlled media, the Internet can be a new means to circumscribe state
control. As an often difficult to regulate medium, the Internet and social media can be
paradigm-shifting technologies for closed states (Howard, 2011). They can facilitate the
development of opposing political movements and ideologies by removing the barriers to
communication and organization while increasing the visibility of actors and ideas that
contrast with the state-controlled media (McAdam, Tarrow, and Tilly, 2001; Tarrow, 2005).
However, such power presumes a fairly wide and even distribution of access to the Internet
as well as the digital literacy skills necessary for using it.
For many states, the ability of the Internet to provide an alternative forum for political
information is limited by structural restrictions that have a varied impact across social
and demographic groupings (Gainous and Wagner, 2007). Uneven access and limited
ability with the new medium have become known as the digital divide. The concept
was extended to the state level and was understood as the unequal division of access to
technology between the OECD “haves” and the developing world “have nots” (National
Telecommunications and Information Association [NTIA], 1995, 1999; Norris, 2001).
While access has increased in both state-level groups, the underlying access divide between
the developed versus the developing world still exists (Robison and Crenshaw, 2010).
In the early 2000s, OECD states had between 30 percent and 40 percent of their national
populations as Internet users, while the rest of the world still had less than 5 percent use
in their national populations (Guillén and Suárez, 2005; Chen and Wellman, 2003).
Increases in Internet access across the globe have been rapid, but uneven. Penetration levels
remain significantly lower in the developing world. High-income OECD states averaged 67
percent of their populations online compared to 25 percent in Latin America, 16 percent
in the Middle East, and 4 percent in Sub-Saharan Africa (Servon, 2002). The disparities
in the availability of ICT are closing somewhat, but the entire developing world has not
reached the halfway mark of the levels of penetration in North America. Africa is lagging
the farthest behind (Ali, 2011; Robison and Crenshaw, 2010). In the developing regions,
Latin America has the highest ICT density and one of the largest number of users, although
Asia is likely to exceed it in the near future.
Differentials in access to ICTs may have important consequences for the political process.
As ICTs become ubiquitous in politics, the digital divide becomes significant because of
uneven access to political information and government functions via e-government. In
addition, offline citizens will lack the empowering potential of ICTs in political debate
and participation (Bimber, 2001; Barua and Barua, 2012; Chen and Wellman, 2004; Van
Deursen and Van Djik, 2010). However, the problem is more than just a simple bivariate
Gender and the Digital Divide in Latin America
329
measure of physical access. The existence of access to ICTs does not mean that all people can
access them in the same ways or with similar frequencies. Divides between groups persist
between developed and developing states as well as within almost all states (Chen and
Wellman, 2003). Americans might understand access as a 24-hour broadband connection
from home or a smartphone. In the developing world, however, Internet access can mean
a weekly trip to the public library to check your e-mail; it may mean that a person has an
acquaintance who will let him or her use a computer or cell phone; or it may mean only
exposure to traditional media reports of what is trending in social media feeds.
Ultimately, the nature, quality, and context of access can, and do, matter. The variations in
the effect of the Internet across different contexts illustrate the importance of understanding
the conditions under which the new technologies are accessed. The Internet helps to remove
barriers that favor some groups and individuals in the electorate (Barber, 2001), but this
effect may be more prominent in some nations than others (Wagner and Gainous, 2013),
leading to more research on the conditions under which barriers are removed, by what
mechanisms, when they are removed, and how usage differs.
The Digital Divide and Latin America
The developing world continues to have stark digital divides globally and domestically
that correlate ICT access and usage with age, gender, socioeconomic status (SES), and
urbanity. In much of Latin America, government policy initiatives have attempted to bridge
the digital divide with expanded Internet access through physical infrastructure such as
community technology centers and public libraries (Inter-American Development Bank
[IDB], 2010; Everett, 1998; Friedman, 2005; Hoffman, 2013; Prado, 2011). Creating
public access points is important for reaching disadvantaged groups that often lack other
readily available means to get online. Public access points and community outreach were
initially a significant means of increasing access for women and ethnic minorities, even
in developed nations such as the United States (Chow, Ellis, and Wise, 1998). There
is evidence that this has held true in Latin America as well. In the early 2000s, Latin
American women’s organizations in Argentina and Mexico combatted the gender digital
divide through “chains of access” such as directly teaching digital literacy skills, sharing
access via community radio and videoconferencing, and distributing electronic newsletters
in printed form at local offices (Friedman, 2005).
Despite these initiatives, Internet penetration levels in Latin America are low compared
to OECD states. Yet, they are high in comparison to other regions in the developing
world. Latin America has an average of 43 percent Internet penetration, compared to
79 percent in North America and approximately 60 percent in both Europe and Australia
(Intel Corporation, 2012). However, the average may be misleading as there is significant
variation within the region. The digital divide within Latin American states and between
them is stark in the patterns of penetration, platforms for access, and connection speeds. In
2011, only five states had more than one-third of their population using ICTs (Colombia,
Chile, Argentina, Colombia, Costa Rica), and even in the wealthiest states, only about 1
in 10 had a broadband connection (Prado, 2011).
However, a focus solely on fixed outlets for Internet access may be insufficient. A more
recent influence on the growth of Internet access is the rapid adoption of portable access
devices such as smartphones (IDB, 2010; Hoffman, 2013). Mobile telephony has been
one of the fastest diffusing technologies in history (Robison and Crenshaw, 2010). Mobile
phones have significant growth potential to expand access to the Internet, especially without
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Social Science Quarterly
the infrastructure costs of fixed access locations. However, as a means of expanding Internet
access, mobile phones have some limitations. There is a gender gap in mobile telephony,
as more men than women use mobile smartphones with Internet capabilities. Mobile
technologies might prove to be a more viable means of online expansion in the future.
Currently, there is also a measurable age gap, as younger people are more likely to use and
understand mobile technologies (Abraham, Morn, and Vollman, 2010; Blair, 2010).
At present, the current divisions have produced an uneven pattern of online access that
is also distinct in measures of usage. The profile of an average ICT user continues to be
predominantly urban, white and male, with higher SES indicators and especially with
English proficiency (Gómez, 2000; Chen and Wellman, 2003; Prado, 2011). Globally,
women make up almost 46 percent of Internet users, and men are slightly more than
54 percent (Abraham, Morn, and Vollman, 2010). Interestingly, in developed nations, the
significant gender gap in Internet use from the 1990s has declined sharply, and in the
United States and France, there are now more women ICT users (Dixon et al., 2014; Intel
Corporation, 2012). Higher income postindustrial societies have a small gender access
divide because of the ubiquitous nature of digital technology in those societies. However,
divides within many of these nations, even if declining, do persist based on age, income,
education, gender, and urbanity (Bimber, 2001; Chen and Wellman, 2003; Norris, 2001).
Similar patterns of a shrinking gender gap in access are seen in emerging middle-income
states in Latin America (e.g., Mexico, Brazil, Chile) (Prado, 2011). In fact, Latin America
has one of the lowest gender digital access gaps at only 10 percent. The gap in Africa is nearly
45 percent, and about 35 percent in South Asia, the Middle East, and Northern Africa
(Intel Corporation, 2012). However, the small gap can be misleading when subregions in
the area are compared. For some regions, the gender divide is substantial and appears more
intractable. Latin America has distinctive gender gaps based on internal development by
region or even internally within various states. The digital divide in the region marginalizes
the rural, poor, and illiterate populations within nations in a way that actually parallels that
gap between the developing and developed states.
More Than Access: Gender and the Internet
A gender lens on the digital divide is not just about a binary measure of access or no access.
Gender differences persist across the nature, quality, and frequency of access. Research on
the digital divide has recently shifted from focusing on inequalities of access to consider
digital literacy and usage (Van Deursen and Van Djik, 2014; Min, 2010; DiMaggio and
Hargittai, 2001). Scholars suggest various dimensions along which divides might exist
at this second level, including technical means such as software, hardware, connectivity;
autonomy of use; geographic region; quality of access; patterns of Internet usage; social
support networks for assistance with ICTs; and digital literacy skills to use ICTs effectively
(Min, 2010; Hoffman, 2013; Van Deursen and Van Djik, 2014).
Further, gender influences the frequency and manner of use. In Latin America, women
are disproportionately more moderate users of the Internet, whereas men are more often
high-frequency users. Differences in both amount and type of Internet usage between
men and women impact the ability of this new medium to equalize political engagement
and participation. Social networking via the Internet generally has a positive effect on
political participation (Bode, 2012; Boulianne, 2015; Gainous and Wagner, 2011, 2014;
Valenzuela, Park, and Kee, 2009). Using the Internet generates politically relevant social
capital disproportionately among the portion of the population that actually participated
Gender and the Digital Divide in Latin America
331
in online social networking (Gainous, Marlowe, and Wagner, 2013). Thus, if men are more
likely to use the Internet and social media, then the benefits, such as increased political
participation, will be uneven across gender.
Beyond frequency of use, there are gender variations as to usage as well. Once online,
women are more likely to spend their time on social aspects of the Internet (Abraham,
Morn, and Vollman, 2010; Intel Corporation, 2012). E-mail has been linked to the growth
in women’s adoption of the Internet, and it continues to be a category where women spend
much of their online time (Abraham, Morn, and Vollman, 2010). A recent ComScore
report declared that “[w]omen are more social the world over” (Abraham, Morn, and
Vollman, 2010:8). Men are more likely than women to access political information in their
social networks (Gainous and Wagner, 2014). Men favor the Internet for the experience it
offers, while women prefer it for the human connection it promotes (Fallows, 2008).
Increased use of online social networking more strongly impacts users who have higher
levels of politically relevant exchanges in their networks (Klofstad, 2007, 2009; McClurg,
2003). The Internet will not affect political participation as significantly if the usage is
unrelated to politics (Gainous, Marlowe, and Wagner, 2013). It might matter less if the
various uses were randomly assigned, but they are measurably distinct across gender lines.
For example, while Twitter has a marginally higher use by women than men, in the
aggregate, the nature of the usage differs (Abraham, Morn, and Vollman, 2010). Men tend
to post their own tweets or engage in more political exchanges than women. This is a pattern
across multiple social media platforms. Studies have found that men are more likely to use
the Internet for gaming, news, and multimedia, while women are more likely to engage
socially, send e-mail and participate in online commerce (Boneva, Kraut, and Frohlich,
2001; Chen and Wellman, 2003; Abraham, Morn, and Vollman, 2010; Drabowicz, 2014;
Intel Corporation, 2012). As a result, gender and group differences in the nature of online
usage may make the benefits of building political capital online uneven.
If men are exchanging more political content, then the impact of the Internet will favor
men over even those women who are online. Rather than creating a benefit for all users, the
advent of online social networking may exacerbate traditional gender cleavages in political
participation because the social capital built by using these sites is not qualitatively equal
across the gender divide. More directly, women face some significant challenges in using
the Internet to maximize their impact on the political system. Women are less likely to get
online, and they use social media less often and for more social than political functions. In
areas with a significant gender gap in access and usage, we expect that women will be less
likely to benefit from online opportunities.
The digital divide may further disadvantage developing states due to the late-adoption
effect that reinforces socioeconomic divisions that exacerbate the digital divide and undermine the democratic potential of the Internet (Min, 2010; Hilbert, 2011; Rogers, 2003).
As the Internet becomes more widespread and new political opportunities available online
increase, including e-government and new social movements, the importance of Internet
skills and political interest will become even more significant for political participation
(Min, 2010). However, we note that being slower to a technology does not mean that
women will not take advantage at some point in time. In truth, considering the opportunities and the relatively low cost, there is great motivation for women and other groups to
move online (Hilbert, 2011). Given the growing ubiquity of ICTs, especially in relation
to politics through access to political information and e-government, we expect some of
the disparity in benefits accruing to men to dissipate with time. Ultimately, should the
context change, the Internet should have an equalizing effect for gender equity, descriptive
representation, political engagement, and participation.
332
Social Science Quarterly
Data and Measurement
The data for this study are obtained from two sources: (1) the 2010 Latin
Barometer and (2) the 2013 U.N. Gender Inequality Index (the data are collected
prior to 2013 so that there is little, if any, change from the time the survey data were
collected). The 2010 Latin Barometer consists of surveys of 22,687 respondents in 18
Latin American countries with sample sizes ranging from 1,000 to 1,204.1 The following
countries are included: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama,
Paraguay, Peru, Uruguay, and Venezuela. These public opinion data were merged with
the U.N. data, creating a nested data set where each respondent from each country, respectively, was assigned a nonunique value ranking gender equality in their country (see
http://hdr.undp.org/en/content/gender-inequality-index-gii for a description of the index).2 We rely on multilevel modeling to address the nesting and assess effects at both
individual and aggregate levels. Our basic strategy is to estimate three multilevel models of
Internet use (general, social media, and political information gathering) across countries,
interpret the individual-level fixed effects (gender and controls),3 and then examine the
pattern in aggregate random effects by classifying the respective model intercepts according
to whether a country has more or less ge...
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