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The Effects of India’s Gender Quota in
Local Government on Rates of Reporting Rapes of Women from Scheduled Castes
and Tribes
Nadia Kale Advisor: Professor Anna Harvey
New York University Politics Honors Thesis
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Abstract
This research project asks whether increased female representation in India increases rates of reporting rapes of women from scheduled castes and tribes. The impact of female representation on the incidence of violence against women has yet to be extensively explored, due to the nonrandom assignment of female representation across electoral districts. In India, however, the Panchayati Raj Act of 1993 introduced a quota system in local levels of government, mandating that one third of seats be reserved for women. For several idiosyncratic reasons, the Act was implemented in different states at different times, creating increases in women’s representation that were as-‐if random. One recent study looked at the gender quota’s impact on crimes committed against all women and found an increase in rates of reporting (Iyer 2012). However, this study did not account for caste and socioeconomic distinctions that may influence which women are empowered to report. In exploring the impact of mandated increases in female representation on rates of reporting rapes of India’s most marginalized women, this project finds that while gender quotas have a positive impact on rates of reporting amongst all women, the same does not hold true for women from scheduled castes and tribes. Acknowledgement I am extremely grateful to Professor Harvey and Hannah Simpson for the time they dedicated to teaching me about quantitative methods and for their guidance in helping me write my thesis. What I have learned this year surpassed my academic goals and expectations and I hope to continue incorporating quantitative analysis into my future studies!
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Statement of Research Question
My project asks whether increased female representation in India reduces
violence against women from scheduled castes and tribes. The impact of female
representation on the incidence of violence against women has yet to be extensively
explored, due to the nonrandom assignment of female representation across
electoral districts. In India, however, the Panchayati Raj Act of 1993 introduced a
quota system in local levels of government, mandating that one third of seats be
reserved for women. For several idiosyncratic reasons, the Act was implemented in
different states at different times, creating increases in women’s representation that
were as-‐if random. One recent study looked at the gender quota’s impact on crimes
against women and found an increase in rates of reporting all types of crimes
committed against women (Iyer 2012). However, this study did not account for class
distinctions that may influence which women are empowered to report crimes
committed against them. My project will explore the impact of the increased female
representation mandated by the Panchayati Raj Act on rates of reported rapes of
women from scheduled castes and tribes. This project will allow me to assess the
impact of increased female representation on India's most marginalized women.
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Literature Review
Rape is one of many violent crimes perpetrated against women by both
known and unknown attackers, though the former is far more likely. In a telephone
survey conducted from 1995 to 1996 on a total of 8,000 women in the District of
Columbia, Kruttschnitt and Macmillan found that over three-‐quarters (78%) of
attackers in violent crimes against women are known to their female victims
(“Patterns of Violence Against Women: Risk Factors and Consequences” (2005)).
Most studies of rape and other violent crimes against women thus focus on factors
motivating victims’ intimate partners to commit such crimes.
Reported rates of violent crimes against women are the product of two kinds
of factors: those responsible for the actual crimes, and those responsible for the
rates of reporting these crimes. Most studies focus on the former set of factors.
However, a few studies have looked at factors motivating reporting of violent crimes
against women.
A theory often cited in explaining causes of violence against women is a
community or region’s poverty level and/or relative level of development. Among
the first quantitative analyses of the relationship between poverty and violence
against women was Miles-‐Doan’s article, “Violence Between Spouses and Intimates:
Does Neighborhood Context Matter?” (1998). In this study, the dependent variable
analyzed is reported incidence of domestic violence, which includes a number of
acts of aggression, such as rape. Using law enforcement data from 1992 and the
1990 census data from one county in Florida with exceptionally high death rates
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due to violence, Miles-‐Doan found that within Duval county, “neighborhoods with a
high concentration of residents living in poverty [and] unemployed males…have
drastically higher rates” of domestic violence than neighborhoods with
comparatively lower concentrations (Miles-‐Doan, 1998; p. 637). The causal
explanation that Miles-‐Doan presents to explain this observed neighborhood effect
is that one’s geographical location influences the networks that one operates within
and will consequently affect “prospects for employment, for public services, for
educational advancement… and much more” (Miles-‐Doan, 1998; p. 626). In cases
where prospects are low, the assumption is that there will be higher rates of
domestic violence, because unemployment, a lack of public services and resources,
and low levels of education are all risk factors associated with both domestic
violence, and violence against women more generally (Campbell, 2005; Kruttschnitt,
2006).
Despite Miles-‐Doan’s findings in favor of neighborhood effect theories, there
is no way to thoroughly distinguish whether the observed results are truly due to a
neighborhood effect, which asserts that it is the poverty and underdeveloped nature
of a specific community that motivates a higher number of domestic violence cases.
The reason that no concrete conclusions can be determined is because the
conditions of each neighborhood are non-‐random, which presents a selection
problem. Without randomization of economic conditions, it is not possible to
accurately deduce what effect a neighborhood’s level of development or affluence
has on rates of domestic violence, because extenuating factors that may influence
neighborhood conditions may also be exogenous variables that affect levels of
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domestic violence. One possibility is that a historically high concentration of families
with issues of domestic violence could affect a neighborhood’s economic conditions,
which would imply reverse causality.
Another investigation of the relationship between economic conditions and
domestic violence is Aizer’s article, “The Gender Wage Gap and Domestic Violence”
(2010). Aizer’s study uses an instrumental variable design, exploiting “exogenous
changes in the demand for labor in female-‐dominated industries” to estimate the
effects of a decreasing male-‐female wage gap on domestic violence (Aizer, 2010;
p.1). Aizer’s measure of the female-‐male wage gap is constructed to reflect a
particular county’s proportions of male and female workers in a given industry in
that county. This is then indexed by the statewide wage for that industry, which
Aizer argues makes the measure of the wage gap exogenous, because of the fact that
she is using state-‐wide wages averaged across all industries, as opposed to using
county-‐specific wages, which would not be random when observing those counties.
The instrumental variable used in Aizer’s investigation is derived from the same
strategy of indexing county-‐specific proportions of workers in a given industry by
statewide growth in that industry. Again, the argument is that this variable is
exogenous to county-‐specific conditions, because Aizer uses statewide growth in an
industry, as opposed to county-‐specific growth.
Aizer ultimately finds that over a span of fifteen years, from 1995-‐2010,
violence against women declined as employment and earnings amongst women
increased. More specifically, Aizer concludes that a decline in the wage gap
witnessed over the same time period can explain 9 percent of the reduction in
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violence against women (Aizer, 2010; pp. 18). As with Miles-‐Doan’s article, Aizer’s
dependent variable is rates of domestic violence, which includes intimate partner
rape. The problem with Aizer’s analysis, however, is that the proportion of workers
in a given industry could be dependent on a number of variables that are not
accounted for in this research design. One possibility is that in areas where there is
more domestic violence, one might observe a higher proportion of women working
in service jobs, which are historically considered part of a lower wage female-‐
dominated industry.
Further exploring the association between economic development and
violence against women, Hackett’s article, “Domestic Violence Against Women:
Statistical Analysis of Crimes Across India” (2011), uses the National Crime Records
Bureau of India’s “Crimes against Women” data to analyze how a state’s level of
development impacts certain types of crime rates against women. Using
multivariate linear regressions involving a number of development indicator
variables, Hackett looks specifically at dowry deaths and cruelty (wife abuse) to
analyze potential causal effects. Both dowry deaths and cruelty are forms of
intimate partner violence perpetrated against female victims by members of their
immediate family. Although not limited to sexual violence, cruelty as a form of
intimate partner violence accounts for rapes committed against married women by
their partners and other family members.
The independent variables that Hackett employs, each of which groups
together a number of variables within them, are human development, gender-‐
equality development, and urban development. Here, Hackett uses a factor analysis
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approach, taking a number of state indicators, such as female literacy rate, child sex
ratio, percentage of population with electricity, and female employment, and
weighting them into three groups, with each group representing a specific type of
development. Results indicate that states with higher rates of urbanization, health,
and education have lower rates of dowry death and cruelty. Further, in regards to
the gender-‐equality development factor, Hackett found that the less developed a
state is in terms of gender-‐equality, the higher the incidence of dowry deaths and
cruelty.
One problem with Hackett’s study is that the independent variables
identified using the factor analysis approach are nonrandom across the Indian
states being analyzed, which creates a causal inference problem. Any number of
extenuating factors could impact one of Hackett’s three independent variables, as
well as cruelty and dowry death rates.
Contrary to Hackett’s findings, Johnson proposes that improved
socioeconomic conditions might instead lead to increases in violence against
women. In his article, “Rape and Gender Conflict in a Patriarchal State” (2014),
Johnson examines the empirical relationship between female socioeconomic and
political power and rape rates in Kansas. He hypothesizes that as women begin to
progress towards equality, both economically and politically, men react to increased
competition within their community cohort by trying to thwart such advancement
and asserting their dominance and superiority. Johnson’s results suggest that there
is a positive and strong correlation between county rape rates and female
sociopolitical power. According to Johnson’s findings, controlling for overall violent
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crime rates and specific county characteristics, an increase in the number of female-‐
headed households, female-‐owned businesses, and female politicians and police
officers motivated an increase in rapes across all Kansas counties.
However, Johnson’s causal story is inconclusive. Because the increase in
female sociopolitical power, which Johnson observes through a number of variables,
is non-‐random across the counties within Kansas, no accurate conclusions can be
drawn as to how increased socioeconomic status for women affects rape rates
across the state. Moreover, Johnson does not further investigate the possibility that
the increase in rates is actually an increase in reporting of crimes committed against
women, which in itself could be a result of increased socioeconomic and political
status of women in the historically patriarchal state of Kansas. Unlike Aizer and
Hackett’s articles, where the argument is that an increase in status for women,
whether in levels of bargaining power or equality, leads to a consequent decrease in
rates of violence against women, Johnson’s article does not consider the
psychological effects of elevated status on women. Thus, his assumption of backlash
needs to be further explored.
Similar to Johnson’s findings, Iyer et al’s article, “The Power of Political Voice:
Women’s Political Representation and Crime in India” (2012) finds that increased
political representation for women in local government increases rates of violence
against women across states in India. Despite these results, however, Iyer et al
conclude, after further analysis, that the observed increases in rates of violence
against women are actually increases in rates of reporting, which suggests a much
more positive effect of rising status for women in patriarchal societies.
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In order to investigate the effects of increased political representation for
women on rates of violence against women, Iyer et al take advantage of India’s as-‐if
random gender quota, which eliminates the problems of non-‐randomization that
Johnson faced in his study. India’s gender quota was mandated for all 17 of India’s
major states in the 1993 amendment to the Panchayati Raj Act. This presents an
opportunity for an as-‐if random analysis, because the reservation of seats was
mandated at the federal level, so the sudden increase in female political
representatives is consistent across states and cannot be attributed to variation in
state conditions, which otherwise might affect each state’s rates of violence against
women. Further, the variation in dates of implementation of the reservations for
women across states addresses potential endogeneity of the passage of the
Panchayati Raj Act itself. Unlike other policy implementations used as treatments
that may have been implemented due to a specific incident occurring at a particular
time or in a particular state, the Panchayati Raj’s 73rd amendment was passed and
implemented over a span of years with no potential confounding variable that both
led to its initiation and will also impact crime rates against women.
Comparing state-‐level crime rates pre and post reservations for women, Iyer
et al gauge the impact of increased female political representation while controlling
for a number of factors, such as literacy rates, per capita incomes, male-‐female ratio,
level of urbanization, and size of police force. Iyer et al’s independent variable was
reservations for women, and their dependent variable was overall rates of violence
against all women across Indian states.
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Results from these regressions indicate that “political representation for
women is associated with a large and significant increase in the documented crimes
against women” (Iyer, 2012; p.176). There are two possible causal mechanisms to
explain these relationships. The first is backlash theory, which assumes that the
increase in crimes against women is a result of a rise in hostility towards women
due to their rising status, or that in the presence of increased representation for
women, there is a decrease in overall law and order. The second potential causal
mechanism is that the observed increase in documented crimes is actually an
increase in rates of reporting. This hypothesis assumes that with an increase in
female representation, more women feel empowered to come forward and report
violent crimes committed against them. In this case, increased levels of confidence
that a victim’s claims will be handled responsibly and that potential backlash from
reporting is no longer a threat are possible explanations.
In order to support the latter causal mechanism and disprove backlash
theory, Iyer et al needed to prove that the rise in reporting of violence against
women was not an increase in actual crimes against women and crime rates overall.
First they ran the same regressions run for gender quotas and gender violence, but
instead analyzed the impact of the gender quotas on overall crime rates. The results
of these regressions indicate that only crimes specifically relating to women
increased, which suggests that increased female representation has no negative
effect on overall levels of law and order. Instead, these results show that effects of
increased female representation are gender specific.
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To further distinguish between incidence of reporting and incidence of actual
crimes, the authors first identified categories of crime where incidence of
underreporting would be lower, such as murder. The authors then looked at the
effects of increased female representation on murder rates, both overall and
specifically of women, finding that there was no increase in murder rates post
reservation implementation. By doing the same with other crime categories bound
to go less underreported, such as suicide, the authors concluded that the increased
rates were isolated to crimes typically underreported, such as rape and harassment,
which supports the argument that reporting rates increased, not actual crime rates.
In a second analysis of the impact of increased political representation for
women, Iyer et al looked specifically at the reservation of Pradhan (chief person)
seats for women at the district level. The purpose of this second analysis was to
investigate at what level increased representation for women is most effective in
increasing rates of reporting violence against women. Using district level data from
ten states across India, Iyer et al again utilized the Panchayati Raj Act amendment,
but this time took advantage of a different aspect of the stipulated reservations for
women. Perhaps more soundly randomized than their first experiment, Pradhan
seats are reserved for women so that each election cycle, one-‐third of a state’s
districts implement reservations for a woman Pradhan. Every five years, a different
group representing a predetermined and randomly selected one-‐third of a state’s
districts will be required to reserve their seat based on a rotating system. To carry
out this analysis, Iyer et al obtained data from the election commission of ten states
in India and ran regressions that looked at the effects of a district having a female
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Pradhan on overall crime rates against women. Only ten states of the original
seventeen examined were used in this second analysis, because the authors were
not able to obtain the necessary data from seven states’ election commissions.
Nonetheless, the authors had a significantly larger number of observations to work
with due to the district-‐level analysis. Despite this, Iyer et al did not find the same
similarly robust results as those found in their initial analysis. What this indicates is
that at the higher level, the effects of female representatives are diminished.
According to Iyer et al, the causal story behind the diminishing effectiveness
of female representatives at higher levels is that proximity matters in terms of
female representatives increasing confidence amongst female constituents to report
violence against women. Iyer et al’s finding supports the supposition that higher-‐
level political figures will not influence a woman’s likelihood to report crimes,
because a lack of proximity or immediate jurisdiction regarding a case may hinder a
higher-‐level female representative’s ability to positively exert influence.
Iyer et al’s paper also looks at scheduled castes and tribes (SC/STs) and the
impacts of reservations for these two minority groups on the reporting of crimes
specifically targeted at their communities. What the authors find is consistent with
their results regarding gender quotas; reporting of SC/ST specific crimes increase
with an increase in representation by SC/STs. However, the authors do not examine
the effects of gender quotas on violent crimes reported by women from SC/STs,
which results in a failure to account for class distinctions that may impact rates of
reporting. Without fully analyzing the effects of reservations for women on arguably
the most marginalized group in India-‐ women from scheduled castes and tribes-‐
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conclusions cannot be drawn about the effectiveness of quota systems in creating
access to political voice for the most underrepresented people. Further, when
investigating issues of violence against women, especially in a caste-‐conscious
county like India, it is imperative to account for these social disparities.
Causal Model
Based on their results, Iyer et al (2012) conclude that the increase in
reported crimes in the presence of increased female political representation is a
result of a rising willingness amongst women to report. This increased willingness
to report is a result of political empowerment, which Iyer et al argue is spurred by
the identity of politicians, in this case the fact that they are female. According to this
causal story, violence perpetrated against women often goes underreported,
because victims do not feel confident that their claims will be handled responsibly
or that further humiliation or aggression will not ensue. However, with the
reservation of one third of local government seats for women, Iyer et al suggest that
this increased female representation and the establishment of a political voice lead
to feelings of empowerment and rising levels of confidence.
An article by Beaman et al, titled “Female Leadership Raises Aspirations and
Educational Attainment for Girls: A Policy Experiment in India,” supports this claim
(Beaman, 2012). Also using India’s gender quota, Beaman et al investigate the
effects of increased female leadership in the form of reserved council chief
(Pradhan) seats for women on the aspirations of girls. Similar to Iyer et al’s second
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analysis at the district level, Beaman et al take advantage of the Panchayati Raj’s
stipulation that one third of a state’s districts have their Pradhan seat be reserved
for women based on a rotating system. Using this as-‐if random selection, the authors
are able to identify a causal relationship between the election of female leaders and
the aspirations of adolescent girls and their levels of educational attainment.
Using survey data at the village level, the authors compare villages that have
never had seats reserved for women with villages assigned female leaders for two
election cycles. Beaman et al find that the aspirations of adolescent girls for
themselves, as well as the aspirations held by their parents for them, rise as
exposure to female council chiefs increases. Further, as the number of times a
district has had a female Pradhan as a result of reservations increases, the education
gap between boys and girls and time spent by girls doing household chores
decreases. The authors conclude that in leadership roles, women have a positive
effect on aspirations and educational attainment for girls in two ways. Firstly, as a
Pradhan, women are able to influence the implementation of policies that improve
access to education and other means necessary to succeed. Secondly, as female
leaders in the public eye, women Pradhans become role models, representing
possibilities of success and influence.
This second line of reasoning is particularly relevant to the question posed in
this thesis. While implementation of the gender quotas should have no significant
effect on actual policies related to crime, because Panchayat level politicians have no
control over such policies or the police force, the reporting of crimes committed
against women is arguably dependent on female confidence in the justice system.
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Women in positions of power can inspire this confidence. So, whether in terms of
women constituents’ confidence or their aspirations, the argument presented by
both Iyer et al and Beaman et al is that increased female representation is positively
correlated with feelings of empowerment.
Despite Iyer et al’s convincing causal story, however, the effects of
reservations on women from scheduled castes and tribes may not yield the same
significant and positive results. Because caste in India is an important determinant
of identity that divides interests, it is imperative that it be accounted for when
analyzing the effects of reservations for women. Given historical and still currently
relevant social disparities within India, I do not expect to find a positive causal
relationship between reservations for women and rates of rapes committed against
women from scheduled castes and tribes. The causal model behind this is informed
by Clots-‐Figueras’ and Bardhan et al’s articles, both of which conclude that the
effects of representation for women are not equally distributed across classes and
communities of varying status.
In Bardhan et al’s article, “Impact of Political Reservations in West Bengal
Local Governments on Anti-‐Poverty Targeting” (2010), the authors use India’s
gender quota to analyze the impacts of women as policy makers in West Bengal
using survey data from 16 agricultural districts within the state. Bardhan et al look
specifically at outcomes for anti-‐poverty targeting and analyze how reservations for
women impact programs aimed at women, as well as how they impact initiatives for
scheduled castes and tribes. The authors also do the same for SC/ST reservations in
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an effort to see if elected politicians’ policy decisions cater solely to their own
disadvantaged group or are all encompassing.
To avoid potential heterogeneity of the impact of reservations, the authors
controlled for a number of village characteristics, such as levels of land inequality
and demographic share of SC/ST groups. The main finding with regard to
reservations for women is that there is no associated improvement in any aspect of
anti-‐poverty targeting. Furthermore, the authors find that reservations for women
have a negative impact on intra-‐village anti-‐poverty targeting to SC/ST groups. On
the other hand, results pertaining to reservations for SC/STs differ drastically; the
authors found that there are significant positive effects of SC/ST Pradhan (council
chief) reservations in terms of village level benefits, as well as for targeting to
female-‐headed households. These results suggest that there are major distinctions
amongst the different benefactors of quotas, specifically in terms of reservations for
women. These findings speak to the importance of accounting for class distinctions,
such as caste and tribe affiliations, when investigating how increased representation
for women affects incidence of reporting violence against women. Bardhan et al’s
results indicate that, just as it has been shown that anti-‐poverty targeting to
scheduled castes and tribes will not be improved in the presence of increased
female representation, it cannot be assumed that increased representation for
women will improve rates of reporting violence amongst women from scheduled
castes and tribes.
Another study that supports this reasoning is Clots-‐Figueras’ article, “Women
In Politics: Evidence from the Indian States” (2011). This paper looks at the effects
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of female representation on policy, expenditure, and the allocation of public goods
by using panel data for the 16 main states in India from 1968-‐2000 and a
regression-‐discontinuity design to identify the causal effect of female
representation, in both SC/ST and general seats. Beyond finding that politicians’
gender does affect policy, Clots-‐Figueras also finds that social position, namely a
politician’s caste, should also be accounted for when analyzing effects on policy.
One example of the differences in policy choices between women politicians
filling seats reserved for SC/STs and those filling general seats is in how the two
groups invest in education. While both general female politicians and those filling
seats reserved for SC/STs favor higher education levels in their policy making,
SC/ST female politicians invest more in primary education, whereas general women
politicians invest more in middle and secondary education. This is indicative of
class-‐conscious policy choices, because for general female politicians, who most
often represent the “upper castes [and] educated middle classes, higher education is
something more accessible and more important” (Raman, 1999).
For SC/ST female legislators, however, Clots-‐Figueras finds that increasing
the quality of and access to basic primary education is far more important. The
results also indicate that SC/ST female legislators favor more investments in beds at
hospitals, pro-‐poor redistributive policies, and “women-‐friendly” laws. Female
legislators from higher castes, however, have no impact on “women-‐friendly” laws,
reduce social expenditure, and oppose land reform, which along with investment in
higher education, indicates that there are distinct differences based on class that
impact the policy decisions of female legislators. Given Bardhan et al and Clots-‐
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Figueras’ findings, then, the expectation is that women from SC/STs will not be
affected in the same ways as women of higher social status when it comes to
increased female political representation. Thus, women from SC/STs will not feel
empowered or more confident as a result of an increase in female political
representation, and will not come forward to report violence committed against
them at the same rate as women of higher status.
Problems with Causal Inference
The problem with making inferences about the effects of increased female
representation, or any representation by any group, is that generally this
representation is not random. Most often, political representation at any level is the
product of a number of factors that have contributed to electing a particular
representative. In such cases, conclusions about the effects of these representatives
cannot be deduced, because any number of endogenous factors could be affecting
both the presence of the representative and whatever outcome is being observed.
When investigating the effects of a policy change or law, in this case an amendment
that required reservations of council seats for women, it is necessary to prove that
the implemented policy was randomly assigned across the sample being observed.
Using reservations for women as an example, had the reservation policy not
been imposed across all 17 of the major states in India, observing the effects of
increases in female representation in only states that chose to implement
reservations would not permit causal inference. This is because states that
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implemented reservations for women might have a number of other conditions that
might positively affect rates of reporting violence against women. Conversely, states
that were unwilling to increase representation for women might have a number of
other women-‐unfriendly laws, which might deter women from reporting violence
committed against them.
A number of authors have used India’s gender quota as an as-‐if random
independent variable in order to investigate the effects of increased political
representation on various outcomes. One of the first articles to exploit the as-‐if
random assignment of India’s gender quota was Chattopadhyay and Duflo’s
“Women as Policy Makers: Evidence From a Randomized Policy Experiment in
India” (2004). In their investigation, the authors assess the effects of both the one-‐
third reservation of all council seats as well as the rotating reservation of one-‐third
of Pradhan positions for women. The purpose of their investigation is to analyze the
impacts of women’s leadership on policy making using detailed survey data on
investments in local public goods in a number of villages within two districts in
West Bengal. What the authors find, when looking at both the effects of women
council seat holders and women Pradhans, is that reservations do in fact affect
policy choices. The authors conclude that the policy decisions reflect gender
preferences, with women choosing to “invest more in infrastructure directly
relevant to the needs of their own gender” (Chattopadhyay, 2004; p. 1409).
In Iyer et al’s (2012) article, which this thesis uses as a model, the authors
argue that they are “able to address endogeneity issues by taking advantage of a
unique, countrywide policy experiment in India” (Iyer, 2012; p. 166) In reference to
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India’s implementation of reservations for women, Iyer et al argue that because the
policy was “implemented at varying dates across Indian states,” it is possible to
construct a differences-‐in-‐differences design using a randomized independent
variable.
While the varying dates of implementation across states addresses the
potential endogeneity of the passage of the Panchayati Raj amendment itself, it is
possible that this variation in timing was driven by state-‐level factors that could be
associated with rates of reporting of violent crimes against women. Iyer et al
present three explanations for this variation, which is reported in Table 1.
First, several states already had some level of reservation for women in their
electoral systems prior to the 1993 amendment. The states the authors reference
are Kerala, Karnataka, and Maharashtra.
In the cases of both Kerala and Maharashtra, Iyer et al’s argument in favor of
the as-‐if random nature of their reservations is that both states proactively
implemented reservations in 1991 and 1992 respectively, because the 73rd
amendment, which was introduced in national parliament that year, was considered
imminent. Arguably, the reasoning behind their initiation of reservations for women
was not a bias towards woman-‐friendly laws that might impact rates of reporting
violence against women, but was instead motivated by convenience and a statewide
understanding that the amendment was going to be passed.
Similarly, Karnataka implemented reservations for women before the
passing of the 73rd amendment. However, in Karnataka’s case, this introduction of
gender quotas came in 1987, a significant amount of time before the amendment
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was enacted in 1993. Here, the problem of bias is more significant, because the
reservation was substantial, with 25 percent of local Panchayat seats allotted to
women. The fact that Karnataka also reserved 30 percent of government jobs for
women serves to highlight how the varying implementation dates may not
represent randomization (Raman, 1999). Karnataka’s significant steps to increase
the political voice of women and implement woman-‐friendly policies could also
mean that other aspects of the state’s policies encourage higher rates of reporting
crime against women.
Iyer et al account for these individual state discrepancies using state fixed
effects, which are “included in all [their] regressions [to] capture time invariant
characteristics across states, such as the presence of a prescheduled local
government election” (Iyer, 2012; p. 170).
The second explanation behind variation in differences in the timing of the
implementation of the 1993 amendment is that some states chose to challenge
portions of the Panchayati Raj act with lawsuits. The authors argue that these
lawsuits can be “regarded as reasonably exogenous factors in causing the delay,”
because none of the lawsuits related specifically to objections to reservations for
women (Iyer, 2012; p. 171). Bihar is one state that chose to file a lawsuit, which
specifically challenged reservations for Other Backwards Classes. The distinction
between Scheduled Castes and Other Backwards Classes (OBCs) is that the latter
represent lower classes that are educationally and socially disadvantaged, while
Scheduled Castes represent dalits, who are considered part of India’s untouchable
caste, making them the most marginalized and vulnerable group in the country.
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Because this lawsuit was against reservations for OBCs, it does not present a
challenge to the as-‐if random nature of Bihar’s implementation of gender quotas.
Had the lawsuit been against the reservations for women, however, this would
suggest a state’s bias against woman-‐friendly policies, which could potentially affect
rates of reporting violence against women.
The final reason presented to explain variation in dates is that some states
had to delay elections due to budgetary constraints. Assam, for example, which had
elections in 1992 and therefore should have implemented the reservations in
elections by 1997, did not do so until 2001. In order to substantiate the inclusion of
states with such budgetary constraints, Iyer et al argue that their “main results are
robust to the exclusion of any specific state,” which implies that regardless of these
aforementioned challenges to randomization, results are consistent when all states
are included in analysis and when some states are dropped (Iyer, 2012; 171).
Ultimately, despite the fact that the explanations for premature or late
implementation of the gender quota could be considered nonrandom, Iyer et al
account for these variations by using fixed effects. Further, through investigating the
specific causes associated with each state, the argument as to why the gender quota
remains as-‐if random becomes stronger, because in all but one case-‐ Karnataka-‐ the
explanation for the variation does not present a bias in favor of or against increased
female representation.
In their second analysis, which looks only at reservation of Pradhan seats for
women, Iyer et al’s argument for randomization is made more compelling, both by
the way that this specific reservation is administered, and because a number of
24
other political scientists have also used the Pradhan reservations as the randomized
independent variable in their respective papers, thus supporting Iyer et al’s case.
To implement reservations of the Pradhan (council chief) position for women,
districts are randomly distributed into three groups, with each group of districts
reserving one third of Pradhan seats for women each election cycle. The group of
districts is selected by a rotating cycle, so that every five years, one of the groups
will have districts with female Pradhans, while the other two groups will not
implement reservations. Here, fewer problems of endogeneity are presented,
because of the completely random way that a state’s districts are divided into
groups, and the rotating cycle used to decide which group will have to implement
reservations for women at any given time.
Research Design
As previously discussed, the problem with trying to analyze the effects of
increased representation on a given outcome is that most often, an increase in
representation by a particular group is the result of extenuating factors. As
demonstrated in a number of previously cited papers (Chattopadhyay et al, 2004;
Bardhan et al, 2008; Iyer et al, 2011; Beaman et al, 2012), India’s gender quotas
present a solution by providing an opportunity to study the as-‐if random
assignment of increases in female representation across states in India to observe a
number of outcomes.
25
Much like Chattopadhyay and Duflo and Iyer et al’s research designs, this
thesis will incorporate two aspects of Panchayati Raj reservations for women,
looking at both general council seats and Pradhan positions in two separate
analyses. The first set of regressions will look specifically at the reservation of
general council seats to assess the impact of increased female political
representation on rates of violence against women from scheduled castes and
tribes. Here, regressions like those performed by Iyer et al will be used to compare
rates of violence against all women and women from scheduled castes and tribes.
The second set of regressions will look specifically at the reservation of Pradhan
seats for women to gauge whether there is an effect of a female council chief on
reported rates of violence committed against women from SC/STs.
The treatment in this study is the reservation of local government seats for
women, and the analysis will be of rape rates, comparing rates before and after
implementation of the treatment.
Testable Hypotheses
State-‐Level Analysis
Hypothesis 1: An increase in the number of women holding Panchayat council seats
will not lead to an increase in the rate of reported rapes committed against women
from scheduled castes and tribes.
26
District-‐Level Analysis
Hypothesis 2: An increase in the number of women holding Pradhan (district
chairwoman) seats will not lead to an increase in the rate of reported rapes
committed against women from scheduled castes and tribes.
In this paper, the independent variable will be increased representation for
women, which will be represented by the implementation of India’s gender quota.
However, the actual measurement of increased representation for women will differ
between the two analyses. In the first analysis, the independent variable will be date
of implementation of the Panchayati Raj Act, which resulted in the introduction of
one third of all council seats being reserved for women. In the second analysis, the
independent variable will be reservation of Pradhan seats for women, which will not
be represented by a single implementation date, but will instead be a dummy
variable that represents whether a district had a reservation for women within a
given year.
For both analyses, the dependent variables will be rates of rapes committed
against women from scheduled castes and tribes respectively. Overall rape rates
against all women will also be used as a dependent variable in order to compare the
effects of increased representation for women on all women as opposed to women
from marginalized sections of Indian society. The expectation is that while there will
be an observed increase in rape rates committed against all women, the same
increase will not be found when comparing rape rates of women from scheduled
castes and tribes.
27
In order to account for other factors that may affect the dependent variables,
the following controls will be utilized: overall literacy rates, female literacy rates,
male-‐female ratio, whether a region is rural or urban, the proportion of a state’s
population working in farming, state GDP per capita, strength of police as measured
by number of police per 1000 people, and presence of a female chief executive. The
purpose of including this range of variables is to control for economic, political, and
social development factors that could impact crime statistics, specifically rape rates,
within a state. Finally, to account for unmeasured state specific factors and state
specific time trends, fixed effects will be used, along with an interaction term for
each state and year observation.
Description of Data
India’s National Crime Records Bureau does not specifically document crimes
committed against women from scheduled castes and tribes. However, it does
report rapes of members of scheduled castes and tribes. Assuming that the vast
majority of these reported rapes are of female victims, rape rates plausibly
represent crimes committed against women from scheduled castes and tribes. It is
important to note that despite NCRB beginning to make available data on rapes
committed against women from scheduled castes and tribes in 1992, many states
are not consistent in their reporting of violence against these marginalized groups.
Thus, there are a number of states missing data for rapes committed against women
from scheduled castes or tribes in various years between 1992 and 2007.
28
The limitations in data available for violent crimes committed against women
from scheduled castes and tribes also eliminate the possibility of distinguishing
between actual rape rates and rates of reporting rapes. In Iyer et al (2012),
distinguishing between increases in actual crime rates as opposed to an increase in
rates of reporting required a large number of crime variables specifically pertaining
to women. With this data, they ran regressions on different types of crimes to
compare which types of crimes-‐ those more likely versus those less likely to go
underreported-‐ saw an increase in rates after the implementation of reservations
for women. However, Iyer et al found that rape is a type of crime more likely to go
underreported, and is one for which they observed significant increases after the
implementation of reservations for women. The assumption for this thesis is thus
that fluctuations in rape rates represent fluctuations in rates of reporting, as
opposed to actual increases or decreases in the number of rapes committed.
In the state-‐level analysis, my independent variable is coded from state-‐level
data on the date of Panchayati Raj implementation, specifically the date of the first
cycle of elections within each state that adhered to the provisions of the Panchayati
Raj’s 73rd amendment. I use data collected by Iyer et al for their original dataset, in
which the variable is coded as a dummy variable, where 1 indicates the presence of
reservations for women and 0 otherwise. The three dependent variables in my
state-‐level analysis are drawn from state-‐level data on crime rates across India and
were collected by Iyer et al from India’s National Crime Records Bureau. Rape rates
for all women are divided by the entire population (women and men) for each state,
while rape rates for women from scheduled castes and tribes are divided by the
29
respective populations in each state. Population data for all women came from Iyer
et al’s dataset; I obtained the scheduled caste and tribe population data from India’s
1981, 1991, 2001, and 2011 censuses.
Data for control variables came from Iyer et al (2011), and were originally
collected from a number of sources, including India’s 1981, 1991, and 2001
censuses and the Government of India’s Ministry of Statistics and Program
Implementation.
In the state-‐level analysis, the years observed range from 1992-‐2007. In Iyer
et al’s analysis of crimes committed against all women, the years ranged from 1985-‐
2007. However, because rapes committed against women from scheduled castes
and tribes were not recorded until 1992, my analysis’ years of observations are
limited. A summary of all variables used in my state-‐level analysis can be found in
Table 2.
For the district-‐level analysis, district-‐level electoral data on which districts
have had to reserve Pradhan positions for females was used to code the
independent variable. Iyer et al collected this data from the ten state government
websites that made this data readily available. Again, the independent variable is a
dummy, coded as 1 when a district has a female Pradhan, and 0 otherwise. District-‐
level crime data was used to construct the dependent variable. Because Iyer et al
only report district-‐level data on overall crimes against women, I retrieved district-‐
level data on reported rapes of all women and reported rapes of women from
scheduled castes and tribes from India’s National Crime Records Bureau. For this
analysis, the observed years run from 2001 to 2007, as opposed to Iyer et al’s
30
observations, which span from 1992 to 2007. This is a result of limited access to
crime data from the National Crime Records Bureau, which currently only has crime
data available online beginning in 2001. Data for control variables again came from
Iyer et al (2011), and were originally collected from a number of sources, including
India’s 1981, 1991, and 2001 censuses and the Government of India’s Ministry of
Statistics and Program Implementation. Summary statistics for my district-‐level
analysis can be found in Table 3.
Empirical Method & Expected Results
State-‐Level Analysis:
In order to observe the effects of reservations of council seats for women on
rates of reporting rapes against women from scheduled castes and tribes, I will use
the following equation:
In this equation, 𝑅𝑎𝑝𝑒𝑠!" represents the number of rapes in a given state and
year, and 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛!" represents the total population-‐ both men and women-‐ of
the specific group being analyzed in a given state and year. For example, when
looking at rape rates of all women, the denominator will be the entire population
within a state in a given year, whereas for rape rates of women from scheduled
castes, the denominator will be the total population of all scheduled castes within a
state in a given year. This equation will be run three times to determine the effects
ln(𝑅𝑎𝑝𝑒𝑠!"/𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛!")= 𝛼! + 𝛽! + 𝑓𝐷!" + 𝑑’𝑿𝒔𝒕 + 𝜀!"
31
of the gender quota on all women, women from scheduled castes, and women from
scheduled tribes, in relation to their relative populations.
α! represents state fixed effects; 𝛽!represent year fixed effects; 𝐷!" is the
dummy that represents the reservation implementation, equaling 1 if the
reservation for women was implemented in that year or any year after, and 0
otherwise; and 𝑋!" represents a number of state and time varying controls: strength
of police force, a state’s GDP per capita, female-‐male ratio, overall literacy rates,
female literacy rates, urbanization, proportion of state population in farming,
whether a state has a female chief minister, and state time trends.
The purpose of the fixed effects is to account for state or time-‐specific
variation that could impact rape rates across states and over time. To account for
potential spikes in rape rates over time that could be correlated with specific state
conditions, all standard errors are clustered at the state level.
In this state-‐level analysis, the coefficient on my reservation dummy variable
𝐷!" , which indicates the year in which the electoral gender quotas were
implemented in a given state, will signify whether the quota had an effect on the
reported incidence of rapes committed against all women, women from scheduled
castes, and women from scheduled tribes. I expect that the coefficient for all women
will be positive and statistically significant, whereas the coefficients for women from
scheduled castes and tribes will not be significantly different from zero. This would
suggest that increased representation for women does not have a positive impact on
the reporting of rapes against women from scheduled castes and tribes.
32
District-‐Level Analysis:
In order to observe the effects of reservations of district Pradhan seats for
women on rates of reporting rapes against women from scheduled castes and tribes,
the following equation will be used:
This equation is similar to that used in the first analysis and is again modeled
on the equation used in Iyer et al (2012). Here, the dependent variable is the rate of
rapes within a given district during a given year. 𝑎! represents district fixed effects,
while 𝑏! represents year fixed effects. The main independent variable is
ChairPersondt, which is a dummy variable equaling one if the observed district’s
chairperson in a given year is reserved for a woman Pradhan, and zero if not.
Controls are included for female-‐male population ratio, literacy rates, and a district’s
level of urbanization. 𝐷!" is the final control, which accounts for the timing of
Panchayati Raj implementation, and is therefore measured at the state level. As with
my first analysis, this equation will be run three times, for the three dependent
variables: rape rates of all women, rape rates of women from scheduled castes, and
rape rates of women from scheduled tribes. All errors are clustered at the district
level in an effort to account for district-‐level conditions that significantly affect rape
rates.
In terms of expectations, Iyer et al found that having a Pradhan seat reserved
for a woman in a given year has no statistically significant effect on rates of
ln (𝑅𝑎𝑝𝑒𝑠!"/𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛!") = 𝑎!+ 𝑏!+ 𝑔𝐶ℎ𝑎𝑖𝑟𝑃𝑒𝑟𝑠𝑜𝑛!"+ 𝑑’𝑋!" + f𝐷!" + 𝑒!"
33
reporting crimes committed against women. Based on their findings, I expect that
the coefficient on the ChairPerson dummy will not be significantly different from
zero when analyzing the effects on all women, women from scheduled castes, and
women from scheduled tribes.
State-‐Level Analysis Results In the state-‐level analysis, no effect is seen when rape rates for all women are
regressed on the implementation of reservations, without any controls. Similarly, no
increases in rates of reporting among all women are seen when demographic,
political and economic controls are added, or when controls for strength of police
force and female literacy are included. However, when state-‐specific time trends are
controlled for, the implementation of the gender quota is associated with an
approximate 9% increase in reported rapes amongst all women at the 95%
significance level. When all controls are included, an 8.9% increase in reported
rapes is observed at the 95% significance level. These results are reported in Tables
4-‐10.
These are smaller effects than those found by Iyer et al (2012). In their
analysis, with all controls, the implementation of the gender quota was associated
with a 20% increase in reported rapes against all women, significant at the 1% level.
However, the two analyses use different samples; in order to compare the results for
all rapes to those for rapes of women from scheduled castes and tribes, I use fewer
years of observation.
34
Unlike the observed significant increase in rates of reporting rapes amongst
all women after the implementation of the gender quota, the coefficients for the
effect of the gender quota on reported rape rates for women from scheduled castes
and tribes remain statistically insignificant regardless of controls. These findings
suggest that the positive effects of increased female political representation are not
equally distributed across all populations of women. Instead, gender quotas appear
to be ineffective in terms of empowering women from scheduled castes and tribes to
report rapes committed against them.
Figures 1-‐3 depict rape rates for all three populations using two different
colored points for pre and post-‐quota implementation, and plotting predicted rape
rates from regressions of rape rates on year. In Figure 1, which displays rape rates
for all women, the purple dots represent rates of reporting prior to implementation
of the gender quota in individual states. There does not appear to be an upward
sloping pattern over time in these purple dots. Green dots represent rates of
reporting after the implementation of the gender quota in individual states. There
does appear to be an upward sloping pattern in the green dots. This upward sloping
pattern of green dots, and the corresponding upward sloping regression line for all
observations, appear to represent the increasing rates of reporting rapes amongst
all women in the presence of the gender quota.
However, in Figures 2 and 3, which display scheduled caste and tribe rape
rates respectively, there are no upward sloping patterns amongst either the purple
or the green dots. The lack of a clear pattern in the green dots and the
corresponding regression lines for all observations suggest that the gender quota
35
had no effect on rates of reporting rapes amongst women from either scheduled
castes or tribes.
These results suggest that the observed increase in rates of reporting rapes
amongst all women in the presence of increased female representation does not
actually apply to women of lower caste and socioeconomic status. This finding is
consistent with the interpretation that increased female representation does not
empower women of lower caste and socioeconomic status to report violence
committed against them in a pattern similar to their higher caste counterparts.
State-‐Level Analysis Robustness
Because India’s gender quota was implemented in 1993 at the federal level,
the consequent reservations for women are thought to not be subject to any state-‐
level biases or conditions that could have affected rates of reporting violence against
women. However, as seen in Table 1, Karnataka, Kerala, Maharashtra, and Orissa all
implemented gender quotas prior to 1993. In order to ensure that the results from
the state-‐level analysis aren’t being affected by any pre-‐existing conditions within
these four states, I ran the same regressions as reported above but excluded
observations from these four states.
The results of this first robustness test, displayed in Table 11 and Figure 4,
are consistent with my initial results. When excluding the states that implemented
reservations for women prior to 1993 and using no controls, a 13.2% increase in
rates of reporting amongst all women is seen at the 95% significance level. When
36
controls for economic, political, and demographic or police strength factors are
added, no significance is seen. However, controlling for state-‐specific time trends
yields an 11.9% increase in rates of reporting for all women at the 1% significance
level. Finally, when all controls are added, the result is an 8.2% increase at the 5%
significance level. For scheduled castes and tribes, there are no significant
coefficients on the gender quota variable, regardless of the controls included. These
results further suggest that, while there was a significant effect of reservations for
women on rates of reporting rapes among all women, gender quotas had no positive
impact on rates of reporting rapes among women from scheduled castes and tribes.
It is, however, important to note that each dependent variable in Table 11
(all rapes, scheduled caste rapes, and scheduled tribe rapes) has a different number
of observations. There are more observations for rapes reported by all women -‐ 221
observations for all women, 183 for SC women, and 121 for ST women-‐, because of
inconsistently documented rape rates for scheduled castes and tribes, which can be
seen in Table 12. As a result, the larger number of observations for all rapes may
have contributed to the statistical significance of the gender quota variable for this
sample.
To address this concern, I conducted an additional robustness test wherein I
ran the same regressions on all rapes as those reported in Table 11, but further
restricted the samples to years for which there were corresponding observations for
scheduled caste rapes and then scheduled tribe rapes. The results, shown in Table
13, reveal that when the analysis of all rapes is limited to states and years wherein
there is data for rapes of women from scheduled castes, there is still a significant
37
increase in rates of reporting all rapes as a function of the implementation of gender
quotas. This significance disappears for an analysis of all rapes in states and years
wherein there is data for scheduled tribe rapes.
These results support the inferences made above about the effect of the
gender quotas on rates of reporting rapes among all women, relative to rates of
reporting rapes among women from scheduled castes. When the analysis is limited
to states and years wherein there is data for rapes of women from scheduled castes,
there is a significant increase in rates of reporting all rapes as a function of the
implementation of gender quotas, but no effect on rates of reporting rapes among
women from scheduled castes. Again, this finding is consistent with the
interpretation that reservations for women are not equally beneficial for the most
marginalized groups of women in India.
However, when the analysis is limited to states and years wherein there is
data for rapes of women from scheduled tribes, there is no effect on rates of
reporting rapes as a function of the implementation of gender quotas either for all
women, or for women from scheduled tribes. This is most likely due to the high level
of missing data on rapes of women from scheduled tribes. This lack of data means
that we cannot draw robust inferences about the relative effects of the gender quota
on rates of reporting rapes across all women and women from scheduled tribes.
This robustness test draws attention to the problem of missing data on
crimes committed against women from scheduled tribes in India. As seen in Table
12, Haryana and Punjab do not report any crimes committed against women from
scheduled tribes because these two states do not have tribal populations. Similarly,
38
the failure of Tamil Nadu and Himachal Pradesh to consistently report crimes
committed against women from scheduled tribes could be because both states have
such small tribal populations. However, in Assam, Kashmir, and West Bengal, the
inconsistently reported scheduled tribe rape rates cannot be attributed to small
tribal populations, because Assam and Kashmir have significant tribal populations,
and all three are also significantly underreporting for scheduled castes.
Some states might not report rape rates because they are less female-‐friendly
or because they lack the resources necessary to ensure properly recorded data.
Table 12 also reports state demographic data that might measure these factors.
However, Assam and Kashmir do not stand out significantly in terms of their female-‐
male ratios, fractions of literate women, or state GDP per capita. In the case of West
Bengal, it is the second poorest state with an average female-‐male ratio and the
lowest overall literacy rates and female literacy rates. These particular
demographics potentially indicate that a lack of funds or resources could contribute
to the state’s inconsistent reporting. Ultimately, though, there are no clear patterns
or parallels indicating why these states only inconsistently report rape data for
women from scheduled castes and tribes.
District-‐Level Analysis Results
The district-‐level analysis looks at how the election of a female chairperson
in one-‐third of a state’s districts affects rates of reporting rapes amongst different
groups of women. As found in Iyer et al’s investigation of reservations for female
39
Pradhans, the effects of female chairpersons on rates of reporting rape against all
women, women from scheduled castes, or women from scheduled tribes are not
significantly different from zero. These results, shown in Tables 14-‐16, imply that at
higher levels of government, the effect of elected female representatives on all
reported rapes diminishes, and there is no longer a difference in the effect of the
gender quota on reported rape rates across these three populations.
One explanation for this finding could be that female representatives with
greater proximity to victims have a greater impact on victims’ feelings of
empowerment, because they are more visible. The visibility of female
representatives encourages more women to come forward with the belief that their
cases will be taken seriously and handled responsibly. So, because female Pradhans
hold a higher seat in local government than the women elected to general Panchayat
seats, the assumption made by female constituents might be that female Pradhans
are less capable of exerting influence at such a local level, whereas the women
elected to general seats can more effectively advocate on behalf of local women. The
fact that no difference is seen amongst women from scheduled castes and tribes is
also consistent with previous findings, because it reiterates the assertion that
women from scheduled castes and tribes are not feeling empowered by increased
female political representation and are not reaping any of the benefits.
40
Conclusion
My thesis addresses whether increased female representation is as effective
at empowering women from marginalized groups in India, as it is at empowering
women from higher status groups.
In my state-‐level analysis, my initial findings are consistent with Iyer et al’s
results, which indicate that increased political representation for women has a
positive and statistically significant impact on rates of reporting crimes committed
against all women. For women from scheduled castes and tribes, however, the same
increase in rates of reporting is not observed. For women from scheduled castes, the
lack of a statistically significant increase in rates of reporting after the introduction
of gender quotas does not appear to be due to missing data. For women from
scheduled tribes, however, the lack of a statistically significant increase in rates of
reporting after the implementation of gender quotas may be simply due to missing
data.
In my district-‐level analysis, as in Iyer et al, the effect of female chief
ministers on the reporting of all rapes is negligible. There is also no effect of female
chief ministers on the reporting of rapes of women from scheduled castes and
tribes. This finding suggests that gender quotas are not effective at empowering
women to report crimes committed against them when female political
representatives are less visible and accessible to constituents.
The findings of this thesis may be relevant to current policy debates. The
Indian Parliament is currently debating the Women’s Reservation Bill, which would
41
further extend India’s gender quota system to parliament. The bill was passed in the
Rajya Sabha (upper house of parliament) in 2010 and is currently awaiting decision
in the Lok Sabha (lower house). While the results reported here speak to the
positive effects that increased political representation for women can have on rates
of reporting violence against all women, they also raise questions about possible
shortcomings of gender quotas. My results suggest that quotas alone may not be
enough to adequately empower and improve conditions for women from scheduled
castes and tribes. Thus, legislators may need to move beyond quotas for women or
for scheduled castes and tribes in order to better address issues plaguing women
from these vulnerable populations.
Finally, perhaps the most important contribution made by my findings is that
there are serious deficiencies regarding the collection of data concerning violence
against women from scheduled castes and tribes in India. A more complete
understanding of the circumstances contributing to the underreporting of this data
is necessary before we can begin to understand rates of reporting violent crimes
committed against women belonging to India’s most marginalized groups. Better
policies need to be implemented in order to ensure that data are documented within
a given state and year and are compiled and reported nationally.
42
Table 1
Year of Women’s Reservation Implementation
Year of Women's Reservation Implementation
Number of States
State Name
1987 1 Karnataka 1991 1 Kerala 1992 2 Maharashtra, Orissa 1993 1 West Bengal 1994 2 Punjab, Madhya Pradesh 1995 5 Gujarat, Haryana, Rajasthan, Himachal Pradesh, Andhra Pradesh 1996 1 Tamil Nadu 2001 2 Kashmir, Bihar 2002 1 Assam 2006 1 Uttar Pradesh
43
Table 2
Summary Statistics for State-‐level Analysis
Variables Observations Mean SD Min Max All Rapes per 1000 total pop 272 -‐4.27 0.58 -‐5.74 -‐3.01 SC Rapes per 1000 SC pop 247 -‐5.61 1.33 -‐9.9 -‐3.21 ST Rapes per 1000 ST pop 184 -‐5.63 1.27 -‐8.47 -‐2.64 Year women's reservation implemented
391 1996 4.49 1987 2006
Per capita state GDP 391 1.67 0.8 0 4.24 Fraction of population literate 391 0.51 0.12 0.27 0.81 Fraction of literate women 391 0.42 0.14 0.14 0.8 Female-‐male ratio 391 0.94 0.05 0.86 1.07 Population rural 391 0.75 0.09 0.51 0.92 Fraction of state pop. in farming 391 0.18 0.05 0.02 0.29 Police strength (# of police/1000 pop.) 391 1.54 0.89 0.08 5.92 Female Chief Minister 391 0.08 0.27 0 1
Note: All rape rates in logs. Observations range from 1992-‐2007. Each rape rate is per 1000 total population of the respective group. Female chief minister is a dummy that equals 1 if a state has a female chief minister and 0 if male chief minister.
44
Table 3
Summary Statistics for District-‐level Analysis
Variable Observations Mean Std. Dev. Min Max
All Women Rapes 1752 2.95 0.97 0 5.46
SC Rapes 924 0.81 0.72 0 3.09
ST Rapes 501 0.57 0.63 0 2.38
Female Chairperson 1128 0.29 0.42 0 1
Female-‐male Ratio 1128 0.94 0.06 0.85 1.34
Fraction urban 1128 0.23 0.14 0.03 0.82
Fraction of females literate
1128 0.48 0.15 0.17 0.85
Note: All rape values represent total rapes in a given year in a given district. Rapes are in logs and are not divided by population. All variables are at the district level and represent the years 2001-‐2006.
45
Table 4
State-‐level Analysis
No Controls
(1) (2) (3)
VARIABLES
All Rapes (per 1000 total
pop)
Scheduled Caste Rapes
(per 1000 SC pop)
Scheduled Tribe Rapes
(per 1000 ST pop)
Women’s Reservation Implemented 0.049 0.038 -‐0.101
(0.050) (0.118) (0.174)
Constant -‐4.605*** -‐5.743*** -‐5.833***
(0.054) (0.157) (0.222)
Observations 289 247 184 R-‐squared 0.875 0.899 0.854 Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: Regression for 17 major states and years 1992-‐2007. All rape variables are in logs. State and year fixed effects used. Standard errors are in brackets, corrected for clustering at the state level.
46
Table 5
State-‐level Analysis
GDP & Demographic Controls
(1) (2) (3)
VARIABLES
All Rapes (per 1000 total
pop)
Scheduled Caste Rapes
(per 1000 SC pop)
Scheduled Tribe Rapes
(per 1000 ST pop)
Women’s Reservation Implemented 0.040 0.069 0.006
(0.048) (0.114) (0.169)
Female-‐male ratio -‐1.861 -‐7.169 -‐6.846
(3.206) (7.425) (10.278)
Fraction rural -‐3.600*** -‐9.793*** 10.467*
(1.198) (2.735) (5.865)
Fraction of state population literate -‐3.441*** 0.765 1.843
(0.795) (1.906) (2.255)
Fraction of state population in farming -‐3.107 -‐14.244*** -‐30.712***
(1.888) (4.604) (6.080)
Presence of female chief minister -‐0.100* -‐0.060 -‐0.217
(0.057) (0.120) (0.159)
State per capita GDP -‐0.170*** 0.190 0.005
(0.061) (0.136) (0.175)
Constant 2.300 9.203 -‐3.164
(3.262) (7.663) (9.690)
Observations 289 247 184 R-‐squared 0.893 0.914 0.883 Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: Regression for 17 major states and years 1992-‐2007. All rape variables are in logs. State and year fixed effects used. Standard errors are in brackets, corrected for clustering at the state level.
47
Table 6
State-‐level Analysis
Police Controls (1) (2) (3)
VARIABLES
All Rapes (per 1000 total
pop)
Scheduled Caste Rapes
(per 1000 SC pop)
Scheduled Tribe Rapes
(per 1000 ST pop) Women’s Reservation Implemented 0.041 0.072 0.017
(0.049) (0.115) (0.172)
Female-‐male ratio -‐1.865 -‐6.994 -‐7.183
(3.212) (7.460) (10.345)
Fraction rural -‐3.601*** -‐9.817*** 10.261*
(1.201) (2.742) (5.906)
Fraction of state population literate -‐3.463*** 0.680 1.843
(0.824) (1.927) (2.261)
Fraction of state population in farming -‐3.066 -‐14.118*** -‐30.625***
(1.931) (4.630) (6.102)
Presence of female chief minister -‐0.101* -‐0.065 -‐0.211
(0.058) (0.121) (0.161)
State per capita GDP -‐0.171*** 0.190 0.000
(0.062) (0.137) (0.176)
Number of police per 1000 state population -‐0.005 -‐0.047 0.097
(0.052) (0.141) (0.252)
Constant 2.315 9.155 -‐2.815
(3.272) (7.681) (9.760)
Observations 289 247 184 R-‐squared 0.893 0.914 0.883 Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: Regression for 17 major states and years 1992-‐2007. All rape variables are in logs. State and year fixed effects used. Standard errors are in brackets, corrected for clustering at the state level.
48
Table 7
State-‐level Analysis
Female Literacy Controls
(1) (2) (3)
VARIABLES
All Rapes (per 1000 total
pop)
Scheduled Caste Rapes
(per 1000 SC pop)
Scheduled Tribe Rapes
(per 1000 ST pop) Women’s Reservation Implemented 0.044 0.056 0.014
(0.049) (0.114) (0.173)
Female-‐male ratio -‐1.682 -‐8.868 -‐7.782
(3.223) (7.471) (10.678)
Fraction rural -‐2.996** -‐13.282*** 9.467
(1.430) (3.248) (6.796)
Fraction of state population literate -‐8.936 33.671** 8.390
(7.065) (16.955) (27.519)
Fraction of state population in farming -‐3.357* -‐12.384*** -‐30.526***
(1.968) (4.683) (6.135)
Presence of female chief minister -‐0.098* -‐0.085 -‐0.217
(0.058) (0.120) (0.163)
State per capita GDP -‐0.183*** 0.237* -‐0.005
(0.064) (0.138) (0.177)
Number of police per 1000 state population 0.015 -‐0.124 0.098
(0.058) (0.145) (0.253)
Fraction of female population literate 5.203 -‐31.631* -‐6.378
(6.671) (16.152) (26.716)
Constant 2.386 9.727 -‐2.318
(3.275) (7.635) (10.010)
Observations 289 247 184 R-‐squared 0.893 0.915 0.883 Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: Regression for 17 major states and years 1992-‐2007. All rape variables are in logs. State and year fixed effects used. Standard errors are in brackets, corrected for clustering at the state level.
49
Table 8
State-‐level Analysis
State-‐Specific Time Trend Controls
(1) (2) (3)
VARIABLES
All Rapes (per 1000 total
pop)
Scheduled Caste Rapes
(per 1000 SC pop)
Scheduled Tribe Rapes
(per 1000 ST pop)
Women’s Reservation Implemented 0.090** 0.147 -‐0.016
(0.035) (0.126) (0.195)
State x Year Interaction term 0.008 0.023 0.067**
(0.007) (0.025) (0.031)
Constant -‐60.719*** -‐45.041* -‐66.056*
(6.864) (22.980) (34.103)
Observations 289 247 184 R-‐squared 0.957 0.926 0.894 Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: Regression for 17 major states and years 1992-‐2007. All rape variables are in logs. State and year fixed effects used. Standard errors are in brackets, corrected for clustering at the state level.
50
Table 9
State-‐level Analysis
All Controls
(1) (2) (3)
VARIABLES
All Rapes (per 1000 total
pop)
Scheduled Caste Rapes
(per 1000 SC pop)
Scheduled Tribe Rapes
(per 1000 ST pop)
Women’s Reservation Implemented 0.089** 0.089 0.054
(0.039) (0.141) (0.245)
Female-‐male ratio -‐2.151 151.817 250.298
(28.324) (102.785) (207.277)
Fraction rural 3.212 -‐77.019 -‐51.805
(14.522) (52.553) (148.663)
Fraction of state population literate -‐0.478 2.568 -‐22.614
(5.309) (19.653) (30.089)
Fraction of state population in farming -‐1.125 144.653** -‐17.817
(17.910) (71.331) (98.246)
Presence of female chief minister -‐0.035 0.027 -‐0.520**
(0.041) (0.125) (0.199)
State per capita GDP -‐0.130** 0.041 0.026
(0.052) (0.168) (0.204)
Number of police per 1000 state population -‐0.025 -‐0.390** 0.056
(0.049) (0.173) (0.254)
State x Year Interaction term 0.021 0.767* 0.112
(0.110) (0.422) (0.555)
Constant -‐94.973 -‐1,055.040 -‐92.730
(185.633) (721.823) (1,001.596)
Observations 289 247 184 R-‐squared 0.959 0.930 0.902 Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: Regression for 17 major states and years 1992-‐2007. All rape variables are in logs. State and year fixed effects used. Standard errors are in brackets, corrected for clustering at the state level.
51
Table 10
State-‐level Analysis
Consolidated Table
Observations: All women-‐ 289 | Scheduled Castes-‐ 247 | Scheduled Tribes-‐ 184
Note: Regression for 17 major states and years 1992-‐2007. All rape variables are in logs. State and year fixed effects included in all six regressions. Standard errors are in brackets, corrected for clustering at the state level.
52
Table 11
State-‐level Analysis
Robustness Test
Regressions excluding pre-‐1993 states
Karnataka (’87), Kerala (’91), Maharashtra (’92), Orissa (’92)
Note: Regression for 13 major states and years 1992-‐2007. All rape variables are in logs. State and year fixed effects included in all six regressions. Standard errors are in brackets, corrected for clustering at the state level.
53
Table 12
State-‐level Analysis
Consolidated State-‐level Demographic Table
Note: This table displays the number of years for which each state has available scheduled caste and tribe rape rate data. It also includes demographic data from 1995, because that is the year that the majority of states implemented reservations for women in local levels of government. Female chief minister is a dummy that equals 1 if a state has a female chief minister and 0 if their chief minister is male. Scheduled caste and tribe population data is from the 1991 census.
* SC/ST data was not recorded for Kashmir in the 1991 census. The percentage shown for ST % of population in Kashmir is from the 2011 census and was obtained from the Government of India’s Ministry of Tribal Affairs website.
54
Table 13
State-‐level Analysis
Robustness Test Excluding all pre-‐1993 states & limiting sample size
(1) (2) (3)
VARIABLES
All Rape Observations 1992 -‐2007
All Rapes (limiting analysis to years/states
with corresponding
SC observations)
All Rapes (limiting analysis to years/states with corresponding
ST observations) Women’s Reservation Implemented 0.082** 0.083* 0.036
(0.038) (0.045) (0.063)
Female-‐male ratio -‐42.999 -‐28.107 26.901
(31.537) (40.212) (61.446)
Fraction rural -‐37.369*** -‐59.102*** -‐111.559***
(14.110) (17.560) (40.770)
Fraction of state population literate 21.656*** 19.198** 27.848***
(5.778) (7.553) (10.002)
Fraction of state population in farming -‐30.555 -‐31.362 -‐32.026
(21.096) (29.358) (32.379)
Presence of female chief minister -‐0.016 -‐0.020 -‐0.014
(0.037) (0.038) (0.050)
State per capita GDP -‐0.047 -‐0.021 -‐0.042
(0.049) (0.053) (0.052)
Number of police per 1000 state population -‐0.019 -‐0.016 -‐0.055
(0.045) (0.053) (0.062)
State x Year Interaction term -‐0.484*** -‐0.470** -‐0.644***
(0.147) (0.210) (0.244)
Constant 871.495*** 960.682*** 1,358.913***
(252.220) (367.468) (414.982)
Observations 221 183 121 R-‐squared 0.970 0.973 0.981 Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
55
Table 14
District-‐level Analysis Pradhan = District Chairperson
No Controls
(1) (2) (3)
VARIABLES All Rapes Scheduled Caste
Rapes Scheduled Tribe
Rapes Female Pradhan -‐0.047 -‐0.034 -‐0.073
(0.051) (0.075) (0.126)
Constant 3.110*** 0.912*** 0.755***
(0.032) (0.062) (0.093)
Observations 1,021 635 331 R-‐squared 0.811 0.545 0.562 Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: Regressions are for 188 districts in 10 major states in India and years 2001-‐2006. State and year fixed effects used. Standard errors are in brackets, and are clustered at the district-‐level. All rape variables are in logs.
56
Table 15
District-‐level Analysis
Demographic controls: female literacy, urbanization, and female-‐male ratio
(1) (2) (3)
VARIABLES All Rapes Scheduled Caste
Rapes Scheduled Tribe
Rapes Female Pradhan -‐0.046 -‐0.041 -‐0.083
(0.051) (0.077) (0.131)
Female-‐male ratio -‐1.553 -‐17.806 -‐28.530
(9.317) (13.151) (22.931)
Population urban 3.179 4.378 -‐12.312
(5.185) (8.834) (22.164)
Fraction of female population literate 0.486 3.092 -‐1.608
(2.140) (3.836) (5.733)
Observations 1,021 635 331 R-‐squared 0.812 0.550 0.566 Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: Regressions are for 188 districts in 10 major states in India and years 2001-‐2006. State and year fixed effects used. Standard errors are in brackets, and are clustered at the district-‐level. All rape variables are in logs.
57
Table 16
District-‐level analysis
State Specific Time Trends (interaction term) & Demographic controls:
(1) (2) (3)
VARIABLES All Rapes Scheduled Caste Rapes
Scheduled Tribe Rapes
Female Pradhan -‐0.072 -‐0.048 -‐0.124
(0.044) (0.078) (0.127)
Female-‐male ratio -‐3.837 -‐9.644 -‐44.920*
(9.939) (16.998) (26.284)
Population urban -‐2.512 -‐1.781 -‐10.424
(7.151) (11.678) (23.854)
Fraction of female population literate 4.306 9.541 10.582
(4.165) (5.801) (10.578)
State x Year Interaction Term -‐0.045 -‐0.143* -‐0.070
(0.066) (0.085) (0.167)
Constant -‐12.749 196.300 191.717
(82.392) (119.296) (230.578)
Observations 1,021 635 331 R-‐squared 0.829 0.560 0.588 Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: Regressions are for 188 districts in 10 major states in India and years 2001-‐2006. State and year fixed effects used. Standard errors are in brackets, and are clustered at the district-‐level. All rape variables are in logs.
58
Figure 1
State-level Analysis
Note: this graph reports rape rates for all women from 1992-‐2007. Rape rates are in logs. Purple dots indicate state-‐level logged rape rates prior to implementation of the gender quota, while green dots represent post-‐reservation rape rates. The fitted values line represents a regression of logged rape rates on year for all observations, and suggests that there is a positive and statistically significant increase in rates of reporting rapes amongst all women over time.
59
Figure 2
State-‐level Analysis
Note: this graph reports rape rates for women from scheduled castes from 1992-‐2007. Rape rates are in logs. Purple dots indicate state-‐level logged rape rates prior to implementation of the gender quota, while green dots represent post-‐reservation rape rates. The fitted values line represents a regression of logged rape rates on year for all observations, and suggests that there is no statistically significant change in rates of reporting rapes amongst women from scheduled castes over time.
60
Figure 3
State-‐level Analysis
Note: this graph reports rape rates for women from scheduled tribes from 1992-‐
2007. Rape rates are in logs. Purple dots indicate state-‐level logged rape rates prior to implementation of the gender quota, while green dots represent post-‐reservation rape rates. The fitted values line represents a regression of logged rape rates on year for all observations, and suggests that there is no statistically significant change in rates of reporting rapes amongst women from scheduled tribes over time.
61
Figure 4
State-‐level Analysis
Robustness Test
Excluding Karnataka, Kerala, Maharashtra, & Orissa
Note: this graph reports rape rates for all women from 1993-‐2007 for those states that implemented reservations for women after 1992. Rape rates are in logs. Purple dots indicate state-‐level logged rape rates prior to implementation of the gender quota, while green dots represent post-‐reservation rape rates. The fitted values line represents a regression of logged rape rates on year for all observations, and suggests that there is a positive and statistically significant increase in rates of reporting rapes amongst all women over time in states that implemented reservations after 1992.
62
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