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CUNY GRADUATE CENTER PH.D PROGRAM IN ECONOMICS
WORKING PAPER SERIES
Political Inclusivity and the Aspirations of Young Constituents:
Identifying the Effects of a National Empowerment Policy
Stephen D. O’Connell
Working Paper 4
Ph.D. Program in Economics
CUNY Graduate Center
365 Fifth Avenue
New York, NY 10016
November 2014
I thank several colleagues for valuable comments, including Anjali Adukia, Onur
Altindag, Karna Basu, Jonathan Conning, Mike Grossman, David Jaeger, Ted Joyce,
Bill Kerr, Suleyman Taspinar, Wim Vijverberg, and participants at NEUDC 2014.
Support for this work was kindly provided by the CUNY Doctoral Student Research
Grant program. The analyses and conclusions contained herein are my own and not of
any institution with which I am or may be associated. Replication files can be accessed
on my website.
© 2014 by Stephen D. O’Connell. All rights reserved. Short sections of text, not to
exceed two paragraphs, may be quoted without explicit permission provided that full
credit, including © notice, is given to the source.
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Political Inclusivity and the Aspirations of Young Constituents:
Identifying the Effects of a National Empowerment Policy
Stephen Daniel O'Connell
November 2014
JEL No: D13, H11, I21, I22, I24, I25, J16, O10, O12
ABSTRACT
Using two dimensions of exogenous geographic variation in exposure to 1992 reforms
that introduced seat quotas for women in local government in India, I find a one
percentage-point increase in the school enrollment rate of young women for each
additional year of exposure to women leaders. This effect is sizeable given a mean level
of exposure of 11 years and pre-policy enrollment rates averaging 80 percent. The use
of a border discontinuity identification strategy with nationally representative survey data
greatly extends the generalizability of earlier studies. I also show that effects are nonlinear in cumulative exposure, appearing only several years after the introduction of
reservations. School quality and schooling infrastructure do not appear to be a potential
mechanism for the effect to occur; the strongest evidence is that local women leaders
changed the educational aspirations of young women and possibly enhanced the
support of young women in school. To this end, I provide novel empirical evidence
suggesting the media to be one channel for the “role-model effect” to be transmitted.
Stephen Daniel O'Connell
Ph.D. Program in Economics
Graduate Center, CUNY
365 Fifth Avenue
New York, NY 10016
soconnell@gc.cuny.edu
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Many countries enact policies to proactively rectify historical inequities. Mandated seat or
candidate quotas in electoral politics are a common incarnation of such efforts, having been
legislated in over 90 countries as of 2014.1 Given the popularity of such policies, it is important
to thoroughly understand the many dimensions of effects that a changed leadership composition
may have on the well-being of constituents; a persistent question is whether policy aimed at
enhancing individuals’ equality of opportunity in the social or political sphere can lead to
distinctly economic improvements for members of newly-empowered groups. In this paper I
examine the introduction and implementation of a national policy in India reserving elected seats
in local government for women, and the relationship between exposure to women leaders and
young women’s educational enrollment.
Credibly estimating policy effects for such a situation poses a number of challenges.
Beyond likely endogeneity in policy adoption, educational decisions of women in developing
countries are made amidst a complex web of factors that may be slow to change, including longheld traditions and cultural beliefs. This means that the effect of exposure to leaders on
constituents’ human capital investment may be difficult to detect in the short term depending on
the mechanism by which leaders affect the experience of constituents.
To estimate long-term effects on young women’s enrollment I exploit two features of the
Indian experience in seat quota implementation that provide exogenous variation in exposure to
women leaders across areas over a span of 15 years. I find a substantial increase in enrollment
rates of young women corresponding to exogenously-determined exposure to the quota system
on the order of one percentage-point increase in the female school enrollment rate for each
additional year of cumulative exposure to mandated quotas, with no evidence of any effect on the
enrollment of young men. These magnitudes are substantial given a national mean cumulative
exposure of 11 years by 2007 and pre-policy female enrollment rates of 80 percent.
One aspect of this work confirms and extends the generalizability of the analysis by
Beaman et al. (2012), who found that an additional ten years of exposure to women leaders
increases girls’ enrollment by approximately ten percentage points among children in a sample
1
Author’s calculations based on data from The Quota Project, available at www.quotaproject.org. Accessed
September 2014.
2
of 495 villages in West Bengal. 2 While I find similar effects, the difference in the scope of this
analysis is a substantial contribution given India’s size and many dimensions of heterogeneity.
The second major contribution of this work is a set of empirical investigations of
potential causal mechanisms. A substantial literature shows that leaders supply public goods and
services that are preferred by members of their identity group, suggesting that if preferences for
education are stronger among women then the introduction of women leaders should increase the
supply of educational infrastructure or services. Using detailed administrative data on districtwise school infrastructure, I show that there is no evidence of increases in either the extensive or
intensive margin of schooling infrastructure related to higher levels of exposure to seat quotas.
Leaders may also have a direct, if less tangible, effect on the preferences and aspirations
of constituents of the same identity group by serving as role models. I assess the reasons claimed
for rural women of school-going age dropping out of school, finding longer exposure to seat
quotas to be associated with a reduction in the share of female dropouts claiming a lack of
personal interest in school or unspecified personal difficulties as their reason for dropping out.
This suggests the reforms changed girls’ educational interests and aspirations, and possibly
changed the schooling environment to be more conducive to the participation of young women.
Overall, there is strong evidence that a policy aimed at promoting the political involvement of
adult women had a distinct role in closing the educational attainment gender gap seen in younger
generations, and this was possibly effected by women leaders serving as “role models” for
younger women.
Finally, even if aspirations appear to be linking seat quotas to educational enrollment, it is
yet unexplored in the literature precisely how this mechanism operates: how are leaders salient to
constituents? Using a comprehensive database of news articles assembled from archives of the
Times of India, I provide novel empirical evidence that highlights the role of the media in
making women leaders salient to their constituencies—suggesting one potential channel that
political empowerment operates to affect educational decisions of children.
2
There are more than 40,000 villages in West Bengal, which itself is one of the 35 states and union territories of
India.
3
I.
India’s 73rd Constitutional Amendment Act
Quotas for women and other historically marginalized groups in elected bodies were first
implemented in India at the national level with the 73rd and 74th Amendment to the Indian
Constitution. These pieces of legislation, passed in 1992, gave national support to the
formalization and implementation of an historical, decentralized governance structure known as
the panchayat (or, more formally, Panchayati Raj Institutions).3 The 73rd Constitutional
Amendment Act instituted a three-tiered system of local government at the village, sub-district
(block), and district levels across rural areas of the country, while the 74th Constitutional
Amendment Act instituted a revised local governance structure in municipalities.4 The
Amendments were intended to provide large-scale devolution and decentralization of powers to
the local bodies, stipulated that members of the local governance bodies were to be elected at
five-year intervals, and provided for one-third of all seats at each governance level to be filled by
women and a proportionate seat share reserved for representatives from historically marginalized
social groups (Scheduled Castes/Scheduled Tribes).
The 73rd Amendment stipulated that states had the responsibility to adjust or amend local
elections to comply with the provisions of the Amendments, and all states amended existing laws
or passed new laws to be compliant within one year. Compliant elections were eventually held in
nearly all jurisdictions, and there is considerable variation in the timing of these first elections
across states. This timing is plausibly exogenous due to state authorities waiting for the term of
existing governing bodies to expire. That is, most state governments waited for the term of office
3
Traditionally, panchayats operated at the village level and consisted of a small number of individuals chosen by a
village to oversee various local affairs. Panchayats were not standardized in their structures, organization,
operations, or responsibilities, nor were they necessarily elected bodies. By the mid-20th century, the panchayat
system was widely recognized to embody “concealed forms of social prejudice, oppression and exploitation that
were firmly rooted in local power structures” (GOI 2008). In the latter half of the 20th century there was support for
the revival of a reformed panchayat system and by 1989 there was strong support at the national level to give
constitutional status to a broad panchayat system, leading to the 73rd and 74th Constitutional Amendment Acts
being enacted in 1993.
4
The governance structures provided for in the 73rd Amendment have come to be known as the “Panchayati Raj”.
This terminology specifically refers to rural governance, as urban bodies have a different name, function differently,
and the implementation timing of the 74th Amendment was different from the 73rd Amendment. Responsibilities of
the Panchayat include administration of state transfer programs, planning and implementation of schemes for
economic development, establishment and administration of local public goods such as educational and medical
facilities, oversight of local infrastructure (water, sewage, roads, etc.) and the monitoring of civil servants (Duflo
2005).
4
of incumbent local officials to expire before conducting fresh elections in compliance with the
provisions of the reform.5 To the extent that data exist for earlier periods, there was only minimal
involvement of women in local elected bodies prior to this reform (Mathew 2000). Figure 1
depicts the variation in state-level differences in cumulative exposure to the policy as of 2007,
where darkly-shaded states correspond to those states with longer (earlier) cumulative exposure
to (implementation of) reservations. Madhya Pradesh, in the center of the country, is reflected as
having longer exposure due to its early implementation whereas its neighboring Chhattisgarh
was one of the last states to implement the provisions of the 73rd Amendment in 2005.
Once the provisions of the reform were implemented, one-third of seats were reserved for
women at any level of the governance hierarchy; for single-seat leadership positions,
reservations were assigned randomly across areas (whether at the village or district level) and
election cycles. This feature of rotating leadership assignment has been used to assess the effects
of women leaders in a number of previous studies (Beaman et al. 2012 and Iyer et al. 2012,
among others). Using data from Iyer et al. (2012), Figure 2 shows the variation in cumulative
exposure to district chairperson reservations as of 2007 for the states where this information is
available (hereafter referred to as the “ten-state sample”) in a manner similar to Figure 1, with
those districts receiving more (less) exposure to women leaders more (less) heavily shaded. 6
Literature examining aspects of the Panchayati Raj and its effect on economic and social
outcomes can be roughly grouped into three major strands. The first investigates how decisions
of the electorate are influenced by a changed composition. Using state-level variation in India
5
In other cases, implementation timing varied less exogenously. Several states chose to incorporate provisions
regarding political reservations for women prior to when the constitutional amendment was to come into effect.
Andhra Pradesh provided for 22 to 25 per cent reservations for women in the Andhra Pradesh Gram Panchayats Act,
1964 (GOI 2008), while Karnataka introduced a similar level of reservation for women in 1985. Both Kerala and
West Bengal restructured their institutions of local government in anticipation of the passing of the 73rd Act (in
1991 and 1992, respectively) although elections implementing these reservations were not held until after
enforcement in 1993. Bihar was prevented from implementation due to legal issues regarding certain provisions of
the Amendments (Iyer et al. 2012). States comprised of primarily tribal populations (Meghalaya, Mizoram and
Nagaland) were explicitly excluded from the purview of the Amendments (GOI 2008). Jammu - Kashmir introduced
reservations at a level consistent with the Amendments via state-level legislation in 1997, although the election of
panchayats implementing the reservations provided for under its own Act has not yet taken place (GOI 2008).
Jharkhand has similarly never held reserved elections. Iyer et al. (2012) and Duflo (2010) provide further details on
the implementation, structure and other aspects of the Panchayati Raj.
6
Comprehensive information on seat reservations is not readily accessible from any public sources or records. For
their study, the authors of Iyer et al. (2012) collected data from diverse sources, including filing Right to Information
Act requests for the history of district leader reservation assignments since the implementation of the 73rd
Amendment. Some states did not respond to these requests, resulting in the ten-state sample shown in Figure 2.
5
over four decades, Pande (2003) identifies how the mandated reservations of legislative positions
for minority members of Scheduled Caste/Scheduled Tribe (SC/ST) individuals increased the
redistribution of resources towards these groups, demonstrating enhanced policy influence.
Besley et al. (2004) found that reservation of a leadership position for SC/ST individuals
increased access among SC/ST households to infrastructure and government services.
Chattopadhyay and Duflo (2004a) use information on the location of public goods to show that
when an area has leadership positions reserved for SC/ST individuals, the share of public goods
going to that group is significantly higher, while Chattopadhyay and Duflo (2004b) use villagelevel variation in political reservations for women to predict the type of public goods provided in
265 reserved and unreserved villages in West Bengal and Rajasthan, finding that leaders invest
more in infrastructure that is directly relevant to the needs of their own gender. Overall, the
group identity of leaders has shown to matter in the type of public goods provided under the
purview of the governing body, and this has been established in various contexts not limited to
the Indian case (e.g., Powley 2007, Washington 2008). Duflo (2005) provides an assessment of
the case for political reservations for women and other historically-underrepresented groups, and,
using evidence from India, concludes that reservations incur a significant reallocation of public
goods toward the preferred allocation of the previously politically-underrepresented groups.
Pande and Ford (2011) provide a recent comprehensive review of the literature on gender quotas.
A second literature assesses how the character and actions of the new electorate may
influence those groups with which they interact. Topalova and Duflo (2004) find that women
leaders in India are less likely to take bribes than their male counterparts. Iyer et al. (2012) use
plausibly-exogenous state-level variation in the timing of the policy implementation to
investigate the effects of political representation on crime against women, finding significant
evidence that political empowerment resulted in greater reporting of crimes against women.
Ghani et al. (2014), following this methodology, quantify the link between the timing of
reservations and changes in women engaged in India’s manufacturing sector as workers and
entrepreneurs and find no evidence that overall employment of women in manufacturing
increased after the reforms, but strong evidence for an increase in the creation of women-owned
establishments in the unorganized manufacturing sector.
6
The third strand of literature characterizes how leaders from newly-empowered groups
may change the perceptions of their group, potentially affecting an array of social and economic
outcomes. Hoff and Stiglitz (2010) develop a conceptual framework to show how changes in
power, technology and contacts with the outside world matter, especially because they can lead
to changes in ideology. Beaman et al. (2009) show how perceptions of women improve once
men are exposed to women in leadership roles, providing substantial evidence in support of the
model of attitudes and bias implicit in Hoff and Stiglitz (2010).7
Earlier work on the relationship between political empowerment and education uses
cross-sectional variation to assess longer-term impacts of politicians on educational outcomes.
Clots-Figueras (2012) uses a regression discontinuity design based on closely-won elections
between female and male candidates to show that the gender of politicians affects the educational
levels of individuals who grow up in the districts where the politicians are elected. The author
finds that the election of women politicians did increase primary school completion and that this
effect was primarily found in urban and not rural areas, but that the effect was not statistically
different for men versus women. Among a sample of households in 495 villages in West Bengal,
Beaman et al (2012) examine the effects of randomly-rotated exposure to women in village-level
leadership positions to identify effects on aspirations and educational outcomes for girls, with
differences in exposure across districts of up to 15 years (three election cycles). Compared to
villages that never had chairperson seats reserved for women, the gender gap in aspirations
closed by 25% in parents and 32% in adolescents in villages assigned to a female leader for two
election cycles (approximately ten years). The gender gap in adolescent educational attainment is
erased in these villages, and girls spend less time on household chores. The authors find no
evidence of changes in young women’s labor market opportunities, attributing the impact of
women leaders to a “role model” effect.
In this paper I follow the above-mentioned studies in exploiting exogenous crosssectional geographic variation in exposure to leaders to identify longer-term effects of seat
quotas on education. As schooling decisions may be slow to change due to the inertia of the
7
Azam and Kingdon (2012) characterize for a recent period the extent and patterns of sex bias in education in India.
Notably, they find that pro-male gender bias exists in the primary school age group and increases with age. The
authors also find that the extent of pro-male gender bias in educational expenditure is substantially greater in rural
than in urban areas.
7
factors that determine them, including tradition and social/cultural beliefs, the link between the
composition of local political representatives and youth schooling decisions may be more
circuitous than that between the former and governance/economic factors directly under the
purview of the governance bodies. Using summary data from two rounds of the Indian National
Sample Survey Office’s household “Participation and Expenditure in Education” survey, Table 1
shows the distribution of self-reported reasons for non-enrollment cited by rural Indian women
dropouts of school-going age. Among 15 possible explanations for dropping out, more than 2/3rds
of respondents cite one of four reasons (as of 1995-96): that they are not personally interested in
studies, that they experienced an inability to cope with the schooling environment or failure, that
their parents were not interested in their studies, or general household financial constraints.
While self-reported reasons for not attending school may be more likely to convey proximate
rather than ultimate causes, they are nonetheless informative as to the role in India of culture and
tradition and the general schooling environment in determining girls’ education. That is, the main
constraints to girls’ education in this context may be less accessible to policy interventions than
commonly-cited economic factors such as infrastructure/schooling provision, household duties,
or participation in income-generating activities and child labor.
II.
Data: Reservations Implementation and School Enrollment
Data on the timing of exposure to seat quotas comes from several publications documenting the
implementation and progress of the 73rd Constitutional Amendment (Mathew 1995, 2000; GOI
2008, Iyer et al. 2012). India’s National Sample Survey Organisation “Socio-Economic
Survey—Schedule 10: Employment and Unemployment” provides data on enrollment.8 The NSS
data come from a survey of a representative sample of households across all Indian states and
union territories approximately every five years, with the household sampling frame drawn from
the most recent population census and stratified within the rural and urban areas in each district. 9
The primary analyses focus on data from thick rounds in 1987-88 (prior to the policy change)
and 2007-08.10 11
8
Hereafter referred to as “NSS data”.
These are sometimes referred to as the NSS “thick” rounds.
10
Hereafter referred to only by the initial year for simplicity.
9
8
India’s Employment/Unemployment schedule is akin to many household-level labor
force surveys administered worldwide. Respondent households provide individual-level details
on demographics, employment, income and consumption particulars for all household members.
The analysis exploits variation in the “usual principal activity” field, which among other
activities indicates whether the individual was currently “attending [an] educational institution”.
Beyond restriction to the estimation sample of school-age children in rural areas of the country,
minimal data cleaning procedures were required. All geographic definitions have been made
forwards- and backwards-consistent over time to account for changes in administrative
boundaries and the bifurcation of three states in 2001.
Table 2 shows trends in enrollment rates for various gender, age and social groups over
the period of study based on the NSS data. Since 1987 there has been close to 100 percent
enrollment in lower-primary school cohorts, motivating the restriction to individuals aged nine to
17 for whom enrollment may have been affected by the policy change. Among non-SC/ST
population there has been substantial progress in enrollment rates in all age cohorts between
1987 and 2007, with the 9-11 and 12-14 age brackets for young men improving from 91 and 78
percent to 98 and 93 percent enrollment, respectively. In all age groups, women lag in initial and
ending levels, although increases in enrollment rates were greater. In panel C, I calculate the
educational enrollment gap. In the youngest school-age cohort there is almost no gender gap in
any of the years, while among older age cohorts the gender gap has decreased substantially from
14 percentage points in 1987 to almost zero by 2007 in the 9-11 age group, and from 28 percent
to 10 percent in the 12-14 age group over the same period. The SC/ST group, having started at
lower enrollment levels and with a wider gender gap, saw faster increases in enrollment rates and
reduction in the gender gap, although by 2007 still not reaching full parity with same-age peers.
III.
Within-State Effects of Exposure to District Chairperson Reservation
Table 3 contains summary statistics of the cross-district variation in cumulative exposure to
district chairperson reservations as of 2007 by state (as shown in Figure 2). The mean level of
exposure across states is relatively similar, but there is substantial variation within-state in years
11
After 2006-7 several states chose to increase seat quotas for women to a minimum of 50 percent. I thus restrict my
analysis to the 2007-08 wave due to incomparability of the policy measure and its exogeneity across states.
9
of exposure, reflecting the nature of the chairperson reservation assignmentsNote, however, that
some states (for example, Haryana and Rajasthan) have little variation in exposure across
districts while other states (Gujarat, AP, West Bengal, for example) have district exposure
ranging from zero to 10 or 11 years.
Table 4 investigates baseline (1987) characteristics of districts in this state sample and
how these characteristics differed by their eventual (2007) cumulative number of years of
chairperson reservation. I use the cutoff of five years of cumulative exposure to delineate those
districts which had a woman chairperson for more than one average election cycle. Overall, there
is strong balance between the districts that eventually had high versus low levels of exposure,
with pre-policy enrollment rates across the two district groups not statistically distinguishable
among other groups. These differences also do not change once removing state fixed effects
from both the outcomes and the regressor (Panel B).12 Alternative tests regressing the continuous
exposure measure on local enrollment rates and population characteristics at the district level
yield similar results. 13
Table 5 contains coefficients from an estimation of the following cross-sectional equation
which relates exposure to chairperson reservations to an enrollment indicator, estimated
separately for gender and social subgroups:
, ,
=
, ,
+
∗
,
+
+
, ,
is an enrollment indicator for individual i, in district j, state s,
of state fixed effects, and
,
(1)
a vector
defined in Panel A as the number of years that
district j had the chairperson seat reserved for a woman as of 2007. The identifying assumption
of
, ,
|
,
= 0 is achieved if the seat quotas were assigned over time in a
random manner, as supported by Table 4.
Columns 1 and 2 of Table 5 present the coefficients of separate estimations of enrollment
status for two social groups of women, while Columns 3 and 4 test for similar effects among
corresponding groups of young men as a comparison. Across Columns 1 and 2, there are only
differences in enrollment rates related to the policy for SC/ST women, with a coefficient of
12
The only notable exception to strong balance being that high exposure districts have higher enrollment rates for
non-SC/ST men and have a slightly lower SC/ST population share.
13
F-tests of district-level covariates in predicting eventual exposure across districts yield test statistics ranging from
1.35 to 1.51, consistent with the findings of Table 4.
10
0.014 indicating that, within state, SC/ST women of schooling age had a 1.4 percentage-point
higher enrollment rate for each additional year of chairperson reservations. Among either groups
of young men there is no commensurate or offsetting effect.
In Panel B, the policy exposure measure is made discrete to highlight how the reservation
effects are concentrated among the districts that were in the upper tail of exposure to chairperson
reservations (i.e., those that had experienced closer to two election cycles of chairperson
reservations, relative to those that had less than 3 years of exposure). In this case, effects are
concentrated clearly among the same group (SC/ST women), implying that individuals in
districts having been exposed to nearly two election cycles of reservations have a 16 percentagepoint higher enrollment rate relative to individuals in districts having the chairperson seat
reserved for less than three years.
Figure 4 further highlights this pattern of increasing effect after one election cycle by
plotting the regression coefficients from four separate regressions similar to the specifications in
Table 5, Panel B where the policy measure has been further discretized to a vector of indicators
for two-year increments of exposure. While there no clear pattern for the other groups, there is an
obvious upward trend in the effect apparent from the sample of SC/ST women, lending empirical
support for the supposition that the link between political empowerment and educational
attitudes and investment is unlikely to be discernible within a short timespan after a policy
change.
IV.
Identifying Effects Using Border Discontinuities
The following section briefly motivates and derives an estimating equation which takes
advantage of discontinuities in the level of exposure across neighboring districts lying across
state borders. Consider a set of administrative districts (indexed by j) within larger geographic
areas (indexed by k, not necessarily states). An equation for enrollment rates across districts can
be expressed as the following linear function of some policy exposure (a general version of
equation (1)):
,
=
+
∗
,
+
+
,
(2)
11
captures the cumulative years of exposure to the policy, which varies across
,
districts j. The area-specific error term
contains unobservables related to factors which are
common in the area, while the district-specific disturbance term
,
captures factors determining
enrollments which may be distinct across municipalities within the area but is exogenous to the
policy exposure.
The identifying assumption is that
,
and
,
are uncorrelated;
although Table 4 presents supporting evidence, it may still be the case that area-level factors
such as tradition, culture, or social norms influence unobservables related to the policy across
areas, perhaps via de facto rather than de jure empowerment. The estimation of equation (1)
addresses this concern if
varies by state due to administrative factors or social and cultural
norms differentiated by state. If the variance in
instead is comprised of unobserved
determinants which do not particularly correspond to administrative boundaries, another method
of purging
becomes necessary.
One solution is to find areas similar in factors related to such unobservables described
above, but with differing levels of policy exposure.14 In such a case,
will contain
unobservables which correspond to similarities within a given area k based on the redefinition
above. To purge the equation of
, I difference equation (2) (for clarity and ease of exposition, I
have dropped the individual subscripts for district and instead indexed the first and second
district in a pair), yielding (3):
,
=
−
+
∗(
,
,
−
,
)+(
,
−
,
)
(3)
A set of areas which could be used for such an exercise are adjacent districts lying on
either side of a state border, most likely to satisfy the principles of having similar unobserved
factors but with differing levels of policy exposure such that
,
,
≠
. This strategy thus exploits district-level differences in exposure to the
policy reform to identify causal effects using cross-sectional variation across districts, having
purged the equation of both area-specific and state-specific unobservables.
14
Similar identification strategies have been implemented in many contexts, recently by Michalopoulos and
Papaioannou (2014), Ghani et al. (2014), Jofre-Monseny (2012), Duranton et al. (2011), Naidu (2011), Dube et al.
(2010), and earlier by Holmes (1998).
12
The data comprise the unique set of neighboring district pairs lying across state borders,
with districts ordered within the pair alphabetically by state.15 I use 2007 enrollment rates and
additionally control for two vectors of state fixed effects (θ
,
and
,
) and the 1987 enrollment
differential (before the policy change) as a proxy for the differenced district-specific error and
time-invariant differences across districts, which also yields efficiency gains. The main regressor
is calculated as the difference in cumulative exposure to reservations as of 2007 (i.e., [2007 –
implementation year, district 1] – [2007 – implementation year, district 2]). This yields the
following estimating equation, where a positive coefficient relates longer relative exposure to the
reform to higher relative enrollment rates in 2007:
+
,
)+
,
+
+(
,
,
,
−
∗(
−
,
,
=
, 1987
+
,
∗(
−
−
,
, 1987
,
)
(4)
)
The identifying assumption is now in terms of differences:
,
−
,
|
,
−
,
= 0. Table 6 contains results of
the estimation of equation (4). Note that the sample size is quite small due to the restriction of
the analysis to border district pairs available in the ten-state sample. Panel A indicates that, using
the cross-border strategy, there is a similar (although imprecisely estimated) effect of an
additional year of exposure to chairperson reservations of 0.12 (0.14). The effect becomes
marginally statistically significant when discretizing the measure in Panel B where among
district pair in which the differential in exposure was greater than four years, SC/ST women in
the district with higher exposure had a nearly 14 percentage point higher enrollment rate than
those in the district with lower exposure.16 Given that most districts already have some level of
exposure, these point estimates are comparable both conceptually and in magnitude to those in
15
A single district may have multiple neighbors, and thus may appear multiple times in the data (each instance with
a different neighboring district). In the estimations, two-way clustering is implemented to correct for error term
correlation in two dimensions. See Appendix A for details on the construction of the data used for the border
analysis.
16
There is less variation in differences in exposure across border district pairs than there was across districts in total
cumulative exposure: only a small number of district pairs (less than five percent) have a difference in exposure
greater than five years; for this reason, the discretized policy regressor in Panel B is both conceptually and
numerically different from that in Panel B of Table 5. The indicator for “large difference” captures approximately 25
percent of the district pair sample. The conclusions of the analysis are not sensitive to increasing or decreasing these
cutoffs by one year. Note that for the estimations, in cases where the ordering of districts yields a negative net
difference in exposure the indicator is multiplied by -1.
13
Panel B of Table 5, which found that within states, high exposure districts (8-11 years) had a 16
percentage-point higher enrollment rate compared to unexposed districts; compared naïvely to
the coefficient on the “medium” exposure groups of 3-7 years (0.031) this difference is of similar
magnitude to the estimates in Table 6. In this estimation there is also an effect among non-SC/ST
women in both panels following the magnitude and pattern of increasing effect with higher
exposure differential, which is plausibly driven by the purging of area-specific unobservables in
addition to state fixed-effects in this specification. 17
Such correspondence of the empirical results from an internally valid identification
strategy to those results derived from the border discontinuity strategy is only meaningful if the
average treatment effect of the two populations is expected to be similar. In Appendix Table 1 I
test for differences in observable characteristics (in terms of enrollment rates and population
distributions) between the interior and border districts. That these populations are not observably
different in terms of starting enrollment rates supports the argument that the average treatment
effect should be similar across the two samples.18
V.
Border Discontinuity with State-Level Exposure Differences
In order to use state-level implementation as exogenous identifying variation, I first look
for evidence that the implementation timing across states was related to pre-period enrollment
rate levels or trends. Figure 3 shows separately the initial state-level enrollment rates for the four
gender/social groups in 1987 plotted against the order in which states implemented the
reservations, supporting that claim that the reform implementation was exogenous to pre-period
enrollment rate levels. The line in each panel is virtually flat, indicating no detectable
relationship between pre-implementation enrollment rates and the subsequent timing of
implementation across states. Table 7 provides empirical support for parallel time trends in
enrollment rates in the ten years preceding the policy implementation by directly estimating the
17
A small sample such as that available for the specifications in Table 6 warrants substantive concerns over outliers.
The conclusions from Table 6 are robust to 5% Winsorization of the dependent variable or the inclusion of an
additional vector of covariates as controls (differences in the 1991 district-level values of the sex ratio, the SC/ST
population share, and the rural female literacy rate). These results are available from the author upon request.
18
The border districts do have a slightly higher population share of SC/ST individuals than interior districts, which
would suggest a stronger treatment effect among those groups due to higher mandated seat shares corresponding to a
higher population share. The expected direction of this difference is roughly borne out in the empirical estimates.
14
enrollment status of individuals using data from the earliest available survey year (1983-84)
through the year of the reform passage (1993-94) with a linear trend term and an interaction of
this trend with the state-level year of implementation. State fixed effects absorb the main effect
of differential adoption years by state, and state-clustered standard errors correct for serial
correlation within states. A negative coefficient on the interaction term would indicate if states
that implemented the policy earlier also had more quickly-rising enrollment rates prior to the
policy change. I find no evidence of this for any of the subgroups that are the focus of the
analysis.
Table 8 provides summary statistics of the absolute differences across border district
pairs. 19 Table 9 contains the results of the estimation of equation (4) where coefficient
magnitudes can be read directly as the increase in the enrollment rate percentage points per
additional differential year of exposure.20 The results parallel those obtained in cross-border
analysis for non-SC/ST women: an additional year of exposure to the overall Panchayat system
results in a nearly one percentage point increase (0.009) in the enrollment rate. For SC/ST
women, however, there are no effects using this national sample.21
The difference in estimated effects for SC/ST is due to the changed sample: Panel B
restricts the estimation sample to the border district pairs form the ten-state sample, showing in
effects among SC/ST women consistent with those found previously. 22 Panel A shows a
nationally- representative effect: for each additional differential year of exposure, non-SC/ST
women in the more exposed district saw a nearly one percentage point increase in enrollment
rates. Panel C adopts an additional strategy which instruments for the policy exposure
differential based on an “expected” differential calculated from the expected implementation
19
There are different observation counts across measures due to some cells containing zero values in the
denominator; in general, these district pairs would be jointly very small in size and thus less important in weighted
regressions. In the estimations, district pairs missing values in any regressor are implicitly dropped.
20
Two things are slightly different in this specification: standard errors are clustered by border (allowing for
correlation across district pairs sitting on the same state border) and the two vectors of state fixed effects have been
excluded due to the policy measure being a function of state-level implementation.
21
All regressions are unweighted and are robust to weighting by the natural log of the population of the combined
district pair in 1987. Results are also robust to Winsorization of the dependent variable at both 1 and 5 percent.
22
It is unclear why this might be the case. In Appendix Table 2, I compare characteristics of areas falling in the tenstate sample versus those outside the sample, with minimal observable differences across the two groups. The
difference in results between the two samples suggests parameter heterogeneity across states and that arbitrary
geographic restrictions of the analysis led to externally invalid estimates even when the subsample is not observably
different in a way that would yield priors regarding such parameter heterogeneity.
15
timing based on election cycles prior to the passage of the 73rd Amendment. To construct this
expected differential, I used state-wise information on local governance elections prior to the
1993 reforms found in Mathew (2000).23 While the magnitude of the effect is somewhat
moderated, the direction and pattern of effects is still consistent with Panel A.2425 All estimates
are additionally robust to the inclusion of a vector of the district pair differential in female
literacy rates, sex ratios and the SC/ST population share for rural areas based on the 1991
Population Census. Fixed-effects specifications yield results nearly identical to those in Table 9,
and Monte Carlo simulations based on placebo variation in state implementation timing indicate
the magnitude and significance of the point estimates are unlikely to have arisen by chance (see
Appendix Table 3 for details).
In unreported estimations, I discretize the policy measure into groups of low, moderate
and high levels of exposure differential and confirm that the largest effect magnitude is found
among the same groups among district pairs having the largest exposure differentials. Figure 5
plots a semi-parametric estimation of treatment effects by disaggregated spans of exposure
differentials. While the pattern is not quite as clear as Figure 4, there is again an upward-trending
effect magnitude for non-SC/ST women.
23
While not directly available for every state, I am able to construct the expected implementation year for a majority
of states based on election cycle lengths and pre-1993 election years; the majority of these expected
implementations are the same or very close to the actual effective implementation years. For states for which it is
not possible to construct this information I use the actual effective implementation date. This suggests some
mechanical violation of the exogeneity restriction and that caution should be taken in the interpretation of the IV
estimates, which are provided merely as a robustness check rather than as a preferred strategy.
24
In Appendix B, I look for similar results for either the chairperson or state-level timing effects in a well-identified
differences-in-differences framework estimating short-run policy effects. This approach has been used by several
studies in the literature. Perhaps unsurprisingly, there are no discernible effects of either level of policy variation on
enrollment rates.
25
These results appear to contrast with those of Clots-Figueras (2012). In particular, Clots-Figueras (2012) found
that women leaders increased educational enrollment in urban but not rural areas, and among both boys and girls,
and this effect occurred through greater provision of schooling infrastructure. In my paper, I find effects for girls
only, and rule out schooling provision as a mechanism for this to occur. It may not be reasonable, however, to
compare the two studies directly: Clots-Figueras (2012) studies a substantially earlier time period in India when
enrollment rates were substantially lower and the electoral context was absent any seat quota system. For this
reason, incentives regarding the provision of public goods, as well as constituents’ perception of women leaders, are
likely to be different in the two contexts. Although the current paper studies effects of women leaders in different
levels of government than those examined by Beaman et al. (2012), there is a correspondence in the magnitude and
pattern of the effect of women leaders on girls’ educational enrollment.
16
VI.
Discussion and Investigation of Causal Mechanisms
Various mechanisms likely contribute to the circuitous link between political and economic
empowerment, including infrastructure and public goods provision (Chattopadhyay and Duflo
(2004a, b), gendered allocation of household resources and womens’ bargaining power (Schultz
2001) and/or aspirations (Beaman et al 2012). In the following section, I investigate and discuss
these hypotheses as potential channels by which educational effects of a changed local leadership
composition may arise.
Labor market effects on bargaining and returns to education
Labor market dynamics making women’s work more valuable may affect educational decisions
of young women in two ways: via increased incomes of adult women changing relative
intrahousehold bargaining power, and by increasing the returns to education for women. A long
literature has looked at gender-differentiated effects of household income as well as the role of
female earnings and bargaining power on educational attainment.26 Two recent studies provide
strong evidence that seat quotas in India affected labor market opportunities for adult women,
plausibly altering girls’ education decisions by either of the two channels above. For the private
sector, Ghani et al. (2014) find strong evidence that political reservations caused an increase in
women-owned establishments created in the unorganized manufacturing sector, particularly in
traditional and household-based industries. In the public sector, longer cumulative exposure to
women leaders increased the share of government-provided employment given to women at the
introduction of a national rural employment guarantee scheme over the period of 2005-2008
(Ghani et al. 2013). These studies jointly suggest labor market returns as one channel by which
women leaders affect educational outcomes of young women.27
26
Edmonds and Pavcnik (2002) use a panel dataset of Vietnamese households to show that exogenous changes in
household income though rice prices cause a reduction in child labor, and these reductions are largest for girls of
secondary-school age and correspond to a commensurate increase in school attendance for the group; for female
bargaining power, de Carvalho Filho (2008) finds a pronounced effect of social security benefits on female child
labor when benefits go to female household members.
27
Beyond bargaining power, expanded labor force activities available to women may change incentives for parents
to invest in daughters. A substantial literature investigates the role of demand-side factors changing the underlying
valuation of education for girls in LDCs. Munshi and Rosenzweig (2006) find that the expansion of the financial
sector and other white collar industries caused enrollment in English language schools to increase for girls (but not
boys). Similarly, Oster and Millet (2010) show that the introduction of a call center in towns in southern India
17
School provision
Chattopadhyay and Duflo (2004a) find that the gender of local leaders affects the public goods
provided in a community, and that women leaders direct public spending to infrastructure that is
more relevant to their own gender. While it is not noted in earlier work whether there is a
difference in preferences for educational infrastructure between male and female leaders, a
conceptual extension of earlier work would suggest differential provision if women leaders
indeed prefer educational infrastructure more so than male leaders. To investigate whether
educational infrastructure provision was caused by longer exposure to the 73rd Amendment
reforms, I use data from India’s “District Report Cards” provided by the District Information
System for Education (DISE). Since 2000-01, this agency has annually compiled district-level
data on over 400 education-related indicators. For the purpose of parsimony, I construct a small
set of variables from the DISE data to allow me to test hypotheses regarding the extensive and
intensive margins of schooling infrastructure, including schools per thousand persons, new
schools established per thousand persons since 1995, teachers per pupil and the share of
classrooms in good condition. I also capture a set of measures related to the experience of girls in
schools, particularly the share of schools that have a separate girls’ lavatory and the share of
teachers who are female. Using the 2007-08 DISE data corresponding to the enrollment figures
analyzed above, Table 10 shows the results of estimating equation (6) as in Table 9 using these
schooling indicators as the outcome (with the same pre-period control measure based on the nonSC/ST female enrollment differential in 1987). Each column in Table 10 shows the coefficient
on the difference in exposure using the dependent variable indicated. For the extensive margin,
the preferred measure (rural schools per thousand rural population) does not appear to be
correlated to a longer exposure gap.28 For the intensive margin, there is also no detectable
difference in schooling infrastructure, either in terms of the share of classrooms in good
generated large increases in school enrolment for both boys and girls. Shastry (2010) finds that the information
technology sector grew more rapidly in areas of India where English is more widely spoken, and that in turn those
areas experienced increased school enrolment. Kochar (2004) showed that urban rates of return influence rural
schooling, particularly amongst households who are most likely to seek urban employment. Jensen (2010a)
highlights the role of perceived returns in the Dominican Republic, while Jensen (2010b) finds that knowledge
(salience) of educational returns increases educational investment among girls. Other salience literature, such as
Nguyen (2008), finds that role model identity matters in the effect of information on updating perceived returns: role
models of similar backgrounds to students have a larger impact on outcomes than role models of dissimilar
backgrounds. In this case, the gender of local leaders may make changing returns to women’s educational
investment more salient, thereby indirectly causing increases in educational enrollment.
28
Results of this analysis are not sensitive to inclusion or exclusion of the 1987 enrollment difference as a control.
18
condition or teacher-to-pupil ratios. Finally, among gender-related factors, there is no
relationship between female-friendly facilities or higher female share of teachers and longer
policy exposure. Overall, there is no evidence of an increase in educational infrastructure
associated with longer exposure to the reform.
Attitudes, social norms and aspirations
Beaman et al. (2012) establish that exposure to women leaders closed the “aspirations gap”
between adolescent boys and girls in a cross-sectional sample of 495 villages in West Bengal,
India. To check whether there is evidence of this finding more broadly, I use state-level
tabulations from official reports based on the NSSO’s “Participation and Expenditure in
Education” (Schedule 25.2) surveys carried out in 1995 and 2007. These surveys contain an
additional module on education which asks individuals of school-going age whose primary
activity is other than attending an educational institution their main reason for having dropped
out. While not a replication of the approach and data used in Beaman et al. (2012), the novel
survey questions allow an investigation of a similar theme with broader scope (but perhaps less
specificity). In Table 1 it is apparent that there is a large reduction in the share of dropouts citing
personal disinterest and “inability to cope”, with most of this share reduction being reallocated to
“parents not interested in studies” and “financial constraints”.29 Given that non-enrollment was
lower in the latter period (Table 2), the increase depicted for these latter two dropout reasons is
only in share and not in absolute levels—suggesting that these factors may be the margin on
which the reduction non-enrollment among rural women occurred. Figures 6 and 7 plot the
reduction in the share of individuals reporting these two reasons for dropping out with the
horizontal axis showing the year of the policy implementation by state. The upward sloping
relationship suggests that states that implemented the policy earlier saw a larger reduction in the
share of individuals reporting those two reasons for dropping out, thus linking reservations to the
reduction in individuals citing those reasons for dropping out. Taken together, this analysis
provides further support for the finding that the reservations affected young girls through a “role
model” effect, changing their educational aspirations (Figure 6), and that local women leaders
may have made the schooling environment more suitable to female attendees (Figure 7).
29
While a more detailed, district-level analysis is desirable, I am limited in my analysis of these data to using
publicly-available summary tables, which are disaggregated only by state, location (rural/urban) and sex.
19
Media as a conduit for salience
The majority of available evidence thus points to aspirations as the primary identifiable
proximate cause of changes in the enrollment behavior of young women in the current context.
However, it is yet unexplored how precisely aspirations are affected by politicians; that is, what
is the conduit by which the introduction of women in politics is made salient to the younger
generation? Among others, one such conduit may be the media. To test whether the media plays
a role in supporting the salience of women leaders, I analyze the web archive of news media
articles available on the Times of India from 2001 to 2007.30
The archive in total contains 560,552 valid links to articles as of August, 2014. Once
downloaded, articles are inspected for geographical references in the initial lines of text based on
a list of 1,200 of India’s largest cities. Of these, 378,439 articles reference a specific geographic
location. The articles are then assigned a reference state based on city information, and
information regarding the timing of elections is matched into the data. I remove from the sample
articles identifiable as coming from sports, business, fashion/style, or international sections of
the paper, leaving over 345,000 articles. After dropping articles about states not part of the
panchayat system (largely referencing Delhi), I am left with 244,462 articles.
I next inspect the entire text of the articles for strings indicating words referring to three
distinct concepts: panchayats or leaders (“panchayat”, “sarpanch”, “mukhiya”, or “pradhan”),
school or education (“educat-“, “school”, “class”, “teach-”, “college”, or “university”) and
women or girls (“women”, “woman”, “female”, or “girl”). While rudimentary, this approach
nonetheless should capture the bulk of articles referring to any of these concepts, with perhaps
the exception of the latter being overly general and likely more noisy by construction. 31 I use the
textual information to construct three separate indicators for articles containing panchayat,
education, or “women” words. I then use a difference-in-differences identification strategy to
assess the change in the incidence of articles containing references to these concepts in state
elections years versus non-election years to associated election years with differential coverage
of these topics relative to non-election years. The estimating equation is:
30
Available at: http://timesofindia.indiatimes.com/archive.cms, accessed May-September 2014.
No other words, stems or combinations have been tested to identify alternative sets of article groups referencing
these concepts.
31
20
, ,
Where
, ,
=
+
∗
,
+
+
+
is an indicator for article i, referencing state s in year t,
there being panchayat elections in state s and year t, and
state and year indicators, respectively.
, ,
and
(5)
, ,
,
an indicator for
the vectors of coefficients on
is the disturbance term, potentially correlated within
states. I estimate this specification via a linear probability model for six outcomes indicators in
, ,
: separately for articles referencing the three topic groups above, as well as articles
referencing three combinations of the topics: women and panchayats, school and panchayats, and
women and education.32
Table 11 contains the estimated coefficients for
for the different outcomes, scaled in
magnitude to be read as “per thousand articles”. Within state and controlling for national yearly
shocks, Column 1 shows an increase of 10 out of every 1,000 articles that reference panchayats
in elections years versus non-election years; this is not particularly surprising: media coverage of
the governance system goes up in election years. Column 2 shows that the share of articles
referencing schools also increases in election years by nearly the same magnitude, while column
3 shows no discernible increase in articles containing the set of words used to capture references
to women (admittedly an imprecise measure). Column 4 looks at the increase in the share of
articles that reference both the panchayat system and women: nearly one-fifth of the increase in
the share of articles referencing panchayats is comprised of articles also referencing women, and
there is a smaller and marginally significant increase in the share of articles referencing both
panchayats and schools. There is no increase in articles referencing both women and schools,
although this may be due to the noise in the former measure.
The magnitudes seem small until the volume of new articles analyzed is considered.
Given nearly 250,000 articles over the sample period, this translates to nearly 100 articles per
day. Given the magnitudes above, this implies an additional article referencing panchayats per
day in election years, and an additional article approximately weekly referencing panchayats and
women, and panchayats and schools, in election years. The above analysis suggests popular
32
It is important to note that this analysis does not attempt to distinguish whether news media changes in response to
the presence of women leaders,or demand from readers for articles on certain topics, or if news media definitively
drives educational outcomes; rather the analysis looks for evidence of this channel as one allowing women leaders to
be salient to the general public.
21
media, by making women leaders and issues of education more salient, may play a role in
affecting the aspirations of young women.33
VII.
Conclusion
If external validity has been a persistent and entrenched thorn in the side of the experimental
literature, then development economics surely sits at a particular nexus that exacerbates such
concerns, given the desire for policy advice and the increased use of RCTs yielding causal
estimates for a (typically) limited population. Increased recent attention to publication dynamics
in the social sciences has pushed many to the consensus that the field should seek to expand
upon the generalizability of focused experimental studies, moving Angrist and Pishke’s (2010)
“particular truth” incrementally closer to the whole truth.34
This paper makes one step in such a direction in assessing the relationship between
political inclusion and economic well-being, finding that India’s flagship political empowerment
policy mandating representation quotas for women increased enrollment rates of girls on the
order of one percentage point per year of additional policy exposure. Similar to the magnitude of
effects found by Beaman et al (2012), these findings extend the generalizability of their localized
estimates to the national level. These country-level findings (the first of which I am aware) in
such a heterogeneous country begs further research as to the generalizability of these dynamics
outside India.
I contribute to existing knowledge regarding the link between political empowerment and
economic outcomes in several ways. First, the effects are based on variation in exposure to
women leaders at different levels of government than have been previously investigated in the
context of educational investment, suggesting that the potential of elected leaders to serve as role
models likely pervades the hierarchy of government and extends beyond those leaders serving a
highly localized constituency. Next, semi-parametric estimations provide strong evidence that
33
Some caveats are in order. The Times of India is known to be slightly “center-left” in its reporting. This may skew
the results upward if such leaning increases the responsiveness to reporting on local governance and/or coverage of
gender issues. However, likely dominating such an effect is that the Times of India is a national English-language
daily, which may be less likely to report on local elections and issues relative to regional or local newspapers—thus
likely understating the media’s role in making leaders salient to constituents at the local level.
34
Efforts to encourage direct replications of RCT studies is one example of the field’s recognition of concerns
regarding external validity in the experimental literature.
22
educational enrollment is not responsive to the focal policy intervention in the short run, but
rather takes some time to affect educational outcomes.
I shed light on mechanisms providing the channel though which the effects of leaders on
education are realized. The strongest findings suggest that women leaders increased the
educational aspirations of girls via a “role model” effect, and I provide novel empirical evidence
highlighting the role of the popular media as one potential channel for this role-model effect to
be transmitted. I also rule out other possible channels (e.g. the extensive margin of schooling
provision) that come from a reasonable extension of earlier studies on infrastructure provision.
The empirical strategy undertaken provides a method for assessing the long-term impacts
of mandated seat quotas in a general context and using administrative data, as it takes advantage
of a design naturally arising from constraints likely to be faced if similar policies are enacted at a
national level: staggered implementation arises from asynchronous election timings across
jurisdictions, and randomly subjecting leadership seats to reservation is a fair and accepted
method to implement such provisions for levels of the hierarchy which are comprised of only
one individual. Ideally, future work will further investigate the existence of the relationship
between political and economic empowerment in other countries that have implemented seat or
candidate quotas. As a body of work, such results can guide policymakers as to whether political
reservations may be a viable option to reduce gender or social inequality across dimensions that
are historically difficult to affect directly via typical policy levers.
23
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28
Tables and Figures
Table 1
Share of rural female non-attendees aged 5-24
reporting reason for dropping out
Reason
Child is not interested in studies
Inability to cope or failure
Parents not interested in studies
Financial constraints
Has to attend to other domestic activities
Others
Has to participate in other economic activities
School is not near
Has to look after sibling(s)
Education is not considered useful
Has to work for wage/salary
No tradition in the family/community
School atmosphere unfriendly
Completed the desired level of school
Awaiting admission to next level*
For helping in household enterprise**
Inadequate number of teachers**
Language at school unfamiliar**
Nonavailability of ladies' toilet**
Nonavailability of lady teacher**
Timing of educational institution not suitable**
1995-96
.21
.18
.17
.10
.09
.09
.04
.04
.03
.02
.01
.01
.01
.00
.00
2007-08
.11
.06
.23
.18
.07
.09
.01
.04
.02
.09
.01
.02
.00
.05
.01
.00
.00
.00
.00
.00
Sources: Attending an Educational Institution in India, Its Level, Nature and
Cost, NSS Report No. 439, 52nd Round (July 1995-June 1996); Author's
calculations using NSS Round 64 (2007-08) Schedule 25.2 - Participation and
Expenditure in Education.
Notes: *response available only in Round 52 survey; **response available
only in Round 64 survey.
Table 2
Mean enrollment rates in rural areas
by social group, gender, age and year
Age
Panel A: Male
5 -- 8
9 -- 11
12 -- 14
15 -- 17
1987
1993
non-SC/ST
1999
2004
.98
.91
.78
.50
.99
.94
.85
.55
.99
.97
.88
.61
1.00
.99
.91
.63
.99
.98
.93
.69
.01
.07
.15
.19
.96
.83
.65
.39
.98
.89
.73
.41
.99
.94
.79
.48
.99
.98
.87
.51
.99
.97
.88
.55
.03
.14
.23
.17
Panel B: Female
5 -- 8
9 -- 11
12 -- 14
15 -- 17
.96
.77
.50
.22
.98
.84
.62
.31
.99
.93
.73
.42
1.00
.96
.79
.48
.99
.96
.83
.55
.03
.19
.33
.33
.94
.60
.32
.12
.94
.73
.43
.19
.98
.86
.60
.30
.99
.93
.71
.37
.97
.93
.78
.45
.03
.33
.46
.33
Panel C: Female - Male enrollment gap
5 -- 8
-.02
-.02
.00
9 -- 11
-.14
-.11
-.05
12 -- 14
-.28
-.23
-.15
15 -- 17
-.29
-.24
-.19
.00
-.03
-.12
-.15
.00
-.02
-.10
-.14
.02
.12
.18
.15
-.02
-.23
-.33
-.27
-.04
-.16
-.30
-.22
-.01
-.09
-.19
-.19
-.01
-.05
-.16
-.14
-.02
-.04
-.10
-.10
.01
.19
.23
.16
2007
Change
1987
1993
SC/ST
1999
2004
2007
Change
Notes: Author's calculations using National Sample Survey data (various rounds). Enrollment rates are calculated as the mean across individuals of an indicator
for usual principal activity reported as "attending an educational institution" for the age groups specified. Population estimates are constructed by weighting by
the inverse sampling probability (sample weights) provided with the data.
Table 3
Cross-district variation in years of exposure to female district
chairperson seat reservation, 2007
State
Andhra Pradesh
Bihar
Gujarat
Haryana
Kerala
Maharashtra
Orissa
Punjab
Rajasthan
West Bengal
Mean
4.1
1.0
3.6
4.4
4.3
4.2
4.1
3.1
4.3
3.6
Std. Dev.
2.6
0.9
2.7
0.9
1.8
2.2
1.7
2.9
1.0
2.9
Min.
0
0
0
3
0
0
1
0
3
0
Note: Author's calculations using data from Iyer et al. (2012).
Max.
11
2
10
5
8
8
7
8
5
10
Districts (N )
22
27
16
9
14
28
13
10
26
16
Table 4
Baseline differences in household characteristics across districts, 1987
2007 exposure to Pradhan
reservation
Variable
Panel A: as observed
Enrollment [0/1]: non-SC/ST, female
Enrollment [0/1]: non-SC/ST, male
Enrollment [0/1]: SC/ST, female
Enrollment [0/1]: SC/ST, male
Mean per capita consumption, Rs
Household size
Female [0/1]
SC/ST [0/1]
Low
(≤5 years)
High
(>5 years)
0.510
0.738
0.332
0.611
186.010
6.532
0.458
0.275
0.549
0.727
0.317
0.553
160.816
6.104
0.464
0.258
0.039
-0.012
-0.015
-0.058
-25.194+
-0.428+++
0.006
-0.017
(0.048)
(0.038)
(0.051)
(0.047)
(12.730)
(0.161)
(0.011)
(0.036)
6,807
7,921
2,116
2,498
19,279
19,342
19,342
19,342
0.577
0.755
0.320
0.573
166.606
6.141
0.465
0.263
0.064
0.022
-0.010
-0.026
-14.278
-0.312+
0.006
-0.009
(0.052)
(0.042)
(0.056)
(0.058)
(12.057)
(0.170)
(0.011)
(0.040)
6,807
7,921
2,116
2,498
19,279
19,342
19,342
19,342
High - Low Std. Err.
N
Panel B: residuals after removing state fixed effects
Enrollment [0/1]: non-SC/ST, female
Enrollment [0/1]: non-SC/ST, male
Enrollment [0/1]: SC/ST, female
Enrollment [0/1]: SC/ST, male
Mean per capita consumption, Rs
Household size
Female [0/1]
SC/ST [0/1]
0.513
0.733
0.330
0.599
180.884
6.453
0.459
0.272
Notes: Means and differences estimated by a univariate linear regression of individual characteristics on an indicator for
"high" exposure status (as of 2007) on the 1987 individual-level cross-section. Heteroskedasticity-consistent robust
standard errors clustered by district reported. Sample is comprised of individuals 9-17 years old in rural areas. Panel B
removes state-level differences in both the "high exposure status" indicator and the outcome to account for differing mean
district-level exposure across states arising from state implementation timing. Significance levels indicated by + <.10, ++
< .05, +++ < .01.
Table 5
Cumulative effect of chairperson reservation on enrollment rates
within state, cross-district variation
Dependent variable: individual enrollment [0/1]
Sample:
women
non-SC/ST
SC/ST
(1)
(2)
men
non-SC/ST
(3)
SC/ST
(4)
Panel A: Linear
Exposure to chairperson
reservations (years)
0.000
(0.003)
0.014
(0.005)
-0.004
(0.003)
0.006
(0.005)
0.030
0.026
0.013
0.005
Moderate (3-7 years) exposure
to chairperson reservations
-0.000
(0.015)
0.031
(0.025)
-0.018
(0.015)
0.006
(0.024)
High (>7 years) exposure
to chairperson reservations
0.008
(0.028)
0.158
(0.038)
0.002
(0.022)
0.029
(0.041)
adj. R 2
0.030
0.026
0.013
0.004
N
Dependent variable mean
10,170
0.771
4,275
0.702
11,399
0.862
4,994
0.788
adj. R 2
Panel B: Differential effects
Notes: Coefficients estimated by a linear probability model of an individual-level enrollment status indicator regressed on
the level of exposure as of 2007. Panels contain coefficients from separate regressions estimated for the subsample indicated
in the column header. Heteroskedasticity-consistent robust standard errors clustered by district reported in parentheses.
Sample is comprised of individuals 9-17 years old in rural areas and observations are weighted by the provided sampling
weight. All specifications contain an unreported constant term and state fixed effects.
Table 6
Cumulative effect of chairperson reservation on enrollment rates
cross-state border district variation
Dependent variable: district pair enrollment rate differential
Sample:
women
non-SC/ST
SC/ST
(1)
(2)
men
non-SC/ST
(3)
SC/ST
(4)
Panel A: Linear
Difference in exposure to
chairperson reservations (years)
0.013
(0.008)
0.012
(0.014)
-0.007
(0.007)
-0.003
(0.012)
Enrollment differential, 1987
0.069
(0.153)
-0.027
(0.237)
0.117
(0.094)
-0.014
(0.190)
0.233
0.447
0.289
0.231
Moderate difference in exposure to
chairperson reservations (1 - 3 years)
-0.012
(0.031)
0.014
(0.044)
-0.019
(0.018)
0.013
(0.032)
Large difference in exposure to
chairperson reservations (>4 years)
0.096
(0.032)
0.137
(0.085)
-0.029
(0.050)
-0.056
(0.063)
Enrollment differential, 1987
0.119
(0.181)
-0.010
(0.241)
0.117
(0.096)
-0.030
(0.170)
adj. R 2
0.232
0.455
0.281
0.232
N
Dependent variable mean
69
-0.022
69
-0.025
69
-0.010
69
0.004
adj. R 2
Panel B: Differential effects
Notes: Panels present coefficients from unweighted linear regressions of the differential enrollment rate in 2007 on the 2007
difference in policy exposure across district pairs lying across state borders. Columns contain coefficients from separate
regressions estimated for the subsample indicated in the column header. Heteroskedasticity-consistent robust standard errors
using two-way clustering reported in parentheses. Sample is comprised of individuals 9-17 years old in rural areas. All
specifications contain an unreported constant term and two vectors of state fixed effects.
Table 7
Testing for differential trends in pre-policy enrollment rates
Dependent variable: individual enrollment [0/1]
Sample:
Year
Year*73rd CAA implementation year
N
adj. R 2
women
men
non-SC/ST
(1)
0.018
(0.00213)
SC/ST
(2)
0.020
(0.00254)
non-SC/ST
(3)
0.011
(0.00174)
SC/ST
(4)
0.010
(0.00202)
0.00071
(0.00053)
0.00009
(0.00066)
0.00016
(0.00028)
0.001
(0.00044)
65,961
0.289
22,531
0.306
78,718
0.186
27,805
0.202
Notes: Table presents a test of differential pre-period trends estimated via a linear probability model regressing individual-level
enrollment status of 9 to 17 year-olds in rural areas across three pre-intervention survey waves on a linear time trend and the time
trend interacted with the state-level policy implementation year. Heteroskedasticity-consistent robust standard errors clustered by
state reported in parentheses. All specifications include an unreported constant term and vectors of state fixed effects. Estimations
are weighted by the provided sampling weight.
Table 8
Summary statistics, cross-border estimation sample
Mean absolute
difference
Std. Dev.
Min
Max
N
0.14
0.20
0.10
0.14
0.10
0.17
0.12
0.10
0.00
0.00
0.00
0.00
0.52
0.75
0.97
0.52
316
313
316
314
Enrollment rate differential - 1987
non-SC/ST, female
SC/ST, female
non-SC/ST, male
SC/ST, male
0.20
0.21
0.14
0.21
0.14
0.17
0.13
0.16
0.00
0.00
0.00
0.00
0.61
0.91
0.90
1.00
286
281
286
282
Exposure gap, 2007
2.6
3.0
0.0
11.0
316
Variable
Enrollment rate differential - 2007
non-SC/ST, female
SC/ST, female
non-SC/ST, male
SC/ST, male
Combined population of district pair, 1987
545,902
269,790
94,815
1,356,876
286
Notes: Table shows summary statistics calculated across district pairs lying across state borders. District-level enrollment rates calculated as
the count of individuals reporting usual principal activity as "attending an educational institution" divided by the total population for the
sample of 9 to 17 year-olds in rural areas. Mean absolute differences shown.
Table 9
Estimation of school enrollment rates
cross-state border district variation
Dependent variable: district pair enrollment rate differential
Sample:
women
non-SC/ST
SC/ST
(1)
(2)
men
non-SC/ST
(3)
SC/ST
(4)
Panel A: Full sample
Exposure gap, 2007 (years)
0.009
(0.002)
0.002
(0.006)
0.004
(0.003)
0.000
(0.004)
Enrollment differential, 1987
0.194
(0.044)
0.263
(0.082)
0.124
(0.042)
0.099
(0.045)
286
0.145
279
0.068
286
0.064
281
0.015
Exposure gap, 2007 (years)
0.012
(0.010)
0.034
(0.013)
0.003
(0.006)
0.024
(0.006)
Enrollment differential, 1987
0.108
(0.076)
0.260
(0.205)
0.075
(0.045)
-0.017
(0.162)
69
0.062
69
0.168
69
0.007
69
0.139
Exposure gap, 2007 (years)
0.006
(0.002)
0.001
(0.008)
0.004
(0.004)
-0.004
(0.003)
Enrollment differential, 1987
0.198
(0.044)
0.263
(0.080)
0.123
(0.042)
0.102
(0.045)
286
0.141
279
0.068
286
0.064
281
0.006
N
adj. R 2
Panel B: Ten-state sample
N
adj. R 2
Panel C: IV estimates, full sample
N
adj. R 2
Notes: Table presents coefficients from an unweighted linear regression of the differential enrollment rate in
2007 on the 2007 difference in policy exposure among neighboring districts lying across state borders.
Underlying sample is comprised of 9-17 year-olds in rural areas. Enrollment rates are calculated using sample
weights. Heteroskedasticity-consistent robust standard errors clustered by border reported in parentheses. All
specifications include an unreported constant term. Results are robust to discretizing the main policy measure as
in Table 6.
Table 10
Differences in schooling infrastructure and schooling environment
cross-state border district variation
Schools per total
population
(1)
Dependent variable: district pair differential in schooling infrastructure indicator
Schools per
New schools
Share of
Teachers per Share of schools Share teachers
population
established per classrooms in
pupil
with girls'
female
(rural)
000 population
"good"
lavatory
condition
(2)
(3)
(4)
(5)
(6)
(7)
Implementation gap as of 2007 (years)
-0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
0.004
(0.006)
-0.000
(0.000)
0.011
(0.010)
-0.004
(0.004)
Enrollment differential, 1987
-0.001
(0.000)
-0.001
(0.001)
-0.001
(0.000)
-0.041
(0.072)
0.022
(0.006)
0.076
(0.127)
0.218
(0.064)
282
0.035
282
0.049
282
0.053
284
0.009
284
0.180
284
0.027
284
0.125
N
adj. R 2
Notes: Table presents coefficients from an unweighted linear regression of the differential schooling infrastructure measure (indicated by column) in 2007 on the 2007 difference in
policy exposure among neighboring districts lying across state borders. Heteroskedasticity-consistent robust standard errors clustered by border reported in parentheses. All specifications
include an unreported constant term.
Table 11
Salience of leaders via news media:
Difference-in-differences estimates using the Times of India web archive (2001-2007)
Election year indicator
N
adj. R 2
Dependent variable: indicator for at least one word in topic set(s) found in article text
Panchayats/
School/
Women/ girls
Women/girls
School/
Women/ girls
leaders
education
and Panchayats/ education and
and school/
leaders
Panchayats/
education
leaders
(1)
(2)
(3)
(4)
(5)
(6)
10.330
9.708
-2.297
1.711
1.033
-0.630
(2.960)
(4.197)
(4.071)
(0.861)
(0.635)
(2.728)
244,462
0.009
244,462
0.011
244,462
0.003
244,462
0.002
244,462
0.003
244,462
0.002
Notes: Table presents coefficients from an unweighted linear probability model regressing an indicator for appearance of word related to the topics in column headers
on an indicator for the state and year the article references holding Panchayat elections. Heteroskedasticity-consistent robust standard errors clustered by state
reported in parentheses. All specifications include separate vectors of state and year fixed effects and an unreported constant term.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Appendix A: Construction of the border dataset
Since borders do not sit at square angles with only a single neighbor on each side, it was
necessary to incorporate (and correct for) districts that have multiple contiguous cross-border
neighbors. In the example below, district Adilabad (Andhra Pradesh) is contiguous with four
districts in Maharashtra.
Map of the Andhra Pradesh - Maharashtra border at District Adilabad, AP
In the border district dataset, this would result in four separate observations, one for each pair
of contiguous neighbors. In estimations, these four observations would be clustered together for
standard error calculations. Given that it may also be the case that the second district (in a given
Appendix
1
pair) could be repeated across entries in a similar way, I use two-way clustering to account for
both dimensions of mechanical nonindependence arising by construction. See Cameron,
Gelbach, and Miller (2011) for details on two-way cluster robust inference.
Appendix
2
Appendix B: Difference-in-differences estimates of the short-term causal effects of
leadership composition on enrollment rates
I restrict the NSS data sample used in the primary analyses to the 9 to 17 age group in rural areas
of the country and adopt the state-level difference-in-differences strategy introduced by Iyer et al
(2012) using an enrollment indicator as the outcome in a linear probability model as specified
below:
Enrollment !,!,!
= !! + !! [!"#"$%&'()*#!!"#$%&'()]!,! + ! !"#$ ! + ! !"#$
!
(1)
+ ![!"#]! + ![!"# ∗ !"#$]!,!
+ ![ln!(!"#$%!"#$%&!!"#!!"#$%")]!,! + !!,!,!
Enrollment !,!,! indicates the enrollment status of individual i, in area s, year t.
[!"#"$%&'()*#!!"#$%&'()]!,! is the policy indicator which takes a value of one if the area had
mandated seat reservations as of year t, Areas is a vector of area fixed effects (depending on the
use of district- or state-level policy variation), Yeart a vector of year dummies, Agei a vector of
cohort dummies, and [Age*Year]i,t a vector of age-by-year fixed effects. !! captures the effect of
the program as identified by exogenous within-area changes in policy exposure status over time.
Appendix Tables 4 and 5 present the coefficient !! estimated across a range of
subsamples and various robustness and sensitivity checks for the ten-state and full sample,
respectively. All estimations are weighted by the provided sampling weight unless otherwise
noted and all estimations report standard errors clustered by state. These estimations show no
discernible short-term effect of the policy on educational outcomes for any sample using either
level of exogenous variation.
Appendix
3
Appendix Tables and Figures
Appendix Table 1
Comparison of state border and interior district enrollment rates
Ten-state sample
Interior
State border
Border Interior
p-value
N
0.527
0.748
0.37
0.635
0.461
0.192
0.478
0.724
0.310
0.624
0.449
0.289
-0.049
-0.025
-0.060
-0.012
-0.012
0.097+++
0.11
0.26
0.10
0.72
0.11
0.00
180
180
180
180
180
180
0.765
0.85
0.747
0.788
0.461
0.251
0.777
0.868
0.708
0.801
0.471
0.340
0.012
0.018
-0.038
0.013
0.010
0.089+++
0.52
0.21
0.19
0.54
0.24
0.00
180
180
177
178
180
180
Enrollment
rates
Panel A: 1987
non-SC/ST, female
non-SC/ST, male
SC/ST, female
SC/ST, male
Female population share
SC/ST population share
Enrollment
rates
Panel B: 2007
non-SC/ST, female
non-SC/ST, male
SC/ST, female
SC/ST, male
Female population share
SC/ST population share
Notes: Sample is comprised of individuals 9-17 years old in rural areas. Statistical significance tested using
two-sample t -tests assuming equal variances. Significance levels are indicated by + <.10, ++ <.05, +++ <.01.
Results are not sensitive to adjusting standard errors for correlation within state and border/interior group.
Appendix Table 2
Ten-state sample compared to rest of country
Ten-state
sample
All others
Ten-state
- others
p-value
N
0.495
0.732
0.331
0.628
0.454
0.255
0.436
0.713
0.301
0.631
0.431
0.273
0.059
0.020
0.030
-0.004
0.023
-0.018
0.34
0.56
0.72
0.93
0.27
0.65
374
374
368
368
374
374
0.773
0.862
0.722
0.796
0.468
0.309
0.804
0.884
0.769
0.826
0.466
0.322
-0.031
-0.022
-0.047+
-0.030
0.002
-0.012
0.27
0.13
0.06
0.16
0.80
0.79
384
384
376
376
384
384
Enrollment
rates
Panel A: 1987
non-SC/ST, female
non-SC/ST, male
SC/ST, female
SC/ST, male
Female population share
SC/ST population share
Enrollment
rates
Panel B: 2007
non-SC/ST, female
non-SC/ST, male
SC/ST, female
SC/ST, male
Female population share
SC/ST population share
Notes: Sample is comprised of individuals 9-17 years old in rural areas. Statistical significance tested using
two-sample t -tests assuming equal variances. Significance levels are indicated by + <.10, ++ <.05, +++ <.01.
Results are not sensitive to adjusting standard errors for correlation within state and border/interior group.
Appendix Table 3
Monte Carlo simulations - effects on non-SC/ST women
Distribution statistics from 1,000 simulations
In sample
(Table 9,
Column 1)
Mean
Coefficient
0.009
0.000022
0.003231
[ -.000178, .000223]
t-statistic
3.821
0.004815
1.185798
[-.068770, .078399]
Standard
Empirical 95%
Deviation Confidence Interval
Notes: Table presents a summary of results from 1,000 Monte Carlo simulations
using placebo state implementation uniformly-distributed over the observed
implementation year range estimated for the specification in Table 9, Column 1.
Appendix Table 4
Estimation of school enrollment status, 9-17 year-olds
Difference-in-differences using district-level chairperson reservations, ten-state sample
Dependent Variable: Enrollment Status [0/1]
Specification:
Baseline
Unweighted
Lower income
quartile
(4)
Upper income
quartile
(5)
Urban areas
(2)
Dropping small
states
(3)
(1)
non-SC/ST, female
-0.006
(0.010)
0.000
(0.007)
-0.006
(0.010)
-0.034
(0.021)
0.008
(0.022)
0.011
(0.013)
non-SC/ST, male
-0.002
(0.007)
-0.003
(0.006)
-0.002
(0.007)
-0.004
(0.015)
-0.013
(0.012)
-0.008
(0.011)
SC/ST, female
0.008
(0.019)
-0.006
(0.017)
0.008
(0.019)
-0.017
(0.026)
0.111
(0.056)
0.010
(0.024)
SC/ST, male
0.006
(0.015)
-0.002
(0.013)
0.006
(0.015)
0.001
(0.021)
0.078
(0.044)
-0.046
(0.031)
Sample:
(6)
Notes: Table presents coefficient on a state-by-year implementation indicator from regressions on individual-level enrollment status estimated separately for the indicated sample
and specification. District clustered standard errors reported in parentheses. All specifications include unreported constant term, state, year, age, and year*age fixed effects.
Estimations are weighted by the inverse sampling probability (sampling weight) provided with the data unless otherwise noted.
Appendix Table 5
Estimation of school enrollment status, 9-17 year olds
Difference-in-differences using state-level implementation timing, full sample
Dependent Variable: Enrollment Status [0/1]
Specification:
(1)
(2)
Dropping small
states
(3)
non-SC/ST, female
-0.022
(0.031)
-0.027
(0.026)
-0.022
(0.032)
-0.005
(0.028)
-0.012
(0.043)
0.008
(0.026)
non-SC/ST, male
-0.038
(0.023)
-0.025
(0.020)
-0.038
(0.023)
-0.042
(0.026)
-0.076
(0.049)
-0.012
(0.009)
SC/ST, female
0.027
(0.033)
0.065
(0.041)
0.027
(0.033)
0.038
(0.031)
0.061
(0.060)
-0.025
(0.026)
SC/ST, male
0.004
(0.024)
0.027
(0.022)
0.004
(0.024)
-0.005
(0.020)
0.002
(0.042)
-0.020
(0.025)
Sample:
Baseline
Unweighted
Lower income
quartile
(4)
Upper income
quartile
(5)
Urban areas
(6)
Notes: Table presents coefficient on a state-by-year reservations indicator from regressions on individual-level enrollment status estimated separately for the indicated sample and
specification. State clustered standard errors reported in parentheses. All specifications include unreported constant term, state, year, age, and year*age fixed effects. Estimations
are weighted by the inverse sampling probability (sampling weight) provided with the data unless otherwise noted.