DO THE POOR GAIN FROM MANDATED POLITICAL REPRESENTATION?
Theory and Evidence from India
BY ANIRBAN MITRA1 ,
New York University
JOB MARKET PAPER
December 8, 2011
A BSTRACT
Affirmative action for minorities in the form of mandated political representation has
been a cornerstone of redistribution policy in India since the 1950s. The mandate involves
earmarking a fraction of electoral districts where only candidates from minority groups
(Scheduled Castes/Tribes) are permitted to contest. This paper studies the effect of such
mandated representation on redistribution policies. It builds a political-economy model
that predicts that, in situations where voters favor candidates from their own group, such
affirmative action results in lower transfers to the poor. Redistribution in reserved districts is shown to favor the middle-income group at the expense of the poor leading to a
rise in within-(minority) group inequality. Household level data collected by the National
Sample Survey Organization (NSSO) during 1987-88 combined with data on mandated
representation for Scheduled Castes (SCs) in the Indian parliament are used to examine the empirical validity of the model’s predictions. The paper finds evidence of lower
implementation of poverty-alleviation programs in districts reserved for SC Members of
Parliament as well as an increase in Scheduled Caste inequality. These findings suggest
that mandated political representation at the federal level has harmed the interests of the
poorer sections of the Scheduled Castes and has widened inequality within the Scheduled
Castes group.
JEL codes: D72, D78, O20
Keywords: Affirmative action, income distribution, political economy, targeting
1I
am deeply indebted to Debraj Ray for his insightful comments, support and encouragement. I would
like to thank Rajeev Dehejia, Oeindrila Dube, Raquel Fernández, Natalija Novta, Sarmistha Pal, Rohini
Pande, Rajiv Sethi, Shabana Singh, Adam Storeygard, Shing-Yi Wang and seminar participants at the Royal
Economic Society Annual conference 2011, participants at the APET workshop PED 2011 (Bangkok) and
participants at EconCon workshop 2011 for their comments and suggestions. I would also like to express
my gratitude to Rohini Somanathan for sharing relevant data from the 1971 Census of India. All existing
errors are solely mine.
1
1
Introduction
Serious concerns exist, especially in developing nations, about whether ethnic minorities
have any voice in the formulation of national policies. Additionally, certain minorities
may be economically and politically disadvantaged because of historical reasons. For
example, blacks in the US are a minority who exhibit lower levels of educational attainments, as compared to whites. This acts as an impediment to their wielding significant
influence on policies as they are likely to be under-represented in public offices. Similarly,
in India, certain minority groups called the Scheduled Castes (SCs) and the Scheduled
Tribes (STs) have had a long history of discrimination.
Since the 1950s, India has implemented wide-ranging affirmative action programs (widely
referred to as “reservation”) for improving the conditions of the SC/ST groups. An important component of these programs has been mandated representation in the legislature. The mandate involves earmarking a fraction of electoral districts (at different levels
of government) where only Scheduled Caste (SC)/ Tribe (ST) candidates are permitted
to contest. A natural question that arises is the following. How has such political reservation affected the conditions of the minorities living in these reserved districts? There have been
several studies (discussed later) that confirm that such political reservation benefits the
minority group in the aggregate. However, the question of who gains and who loses within
the minority group has not been studied before. This question is especially relevant when
the minority group — while economically disadvantaged — exhibits a fair degree of heterogeneity in terms of income. In fact, an issue (concerning affirmative action) which has
received much attention in India lately is the “creamy layer” argument. Proponents of
this argument claim that the benefits of affirmative action are effectively cornered by a
small, relatively wealthy section of the minority group (the “cream”); thus, affirmative
action only benefits the minority “cream”. Could mandated political representation be
aggravating the “creamy layer” problem?
This paper attempts to answer questions of this nature using both theory and empirical analysis. The model developed here generates two main predictions. First, political
reservation results in lower transfers to the poor when voters favor candidates from their
own group. Also, redistribution in reserved districts favors middle-income groups at the
expense of the poor. Given that the minority is economically disadvantaged, a large section of them are poor and relatively few are rich or even middle-income. This favoring
of middle-income groups at the expense of the poor leads to a rise in within-minority
group inequality (promoting a “creamy layer”). So the second prediction is that reservation leads to greater within-minority inequality. These predictions are then put to the test
using household level data from India collected by the National Sample Survey Organization (NSSO) and data on mandated representation in the Indian parliament.
The theory builds on the following intuition from previous models of redistributive politics like Lindbeck and Weibull (1987) and Dixit and Londregan (1996). When parties
try to maximize their expected vote shares by promising transfers to different groups, in
equilibrium the group with the least partisan bias (the “swing” group) is most favored by
2
both parties. Thus, both parties compete strongly for groups which are non-partisan or
swing. The model developed here involves a simple two-stage game. In the first stage, in
any electoral district the two parties choose their respective candidates from either group
– majority or minority. Prior to reservation, the candidates could (potentially) come from
any group; once reservation is introduced, all candidates must come from the minority
group. In the second stage of the game, the fielded candidates make budget-balanced
offers to different income groups.
Clearly, if the group (‘caste’ in the Indian context) identities of the fielded candidates have
no effect on a voter’s ideological bias then introducing such political reservation would
not affect the equilibrium redistribution. However, it is reasonable to assume that ceteris
paribus, every voter feels a positive bias for a candidate from her own group. In fact,
for India there exists abundant anecdotal evidence on voting behavior along caste lines.
2 The key point is that the swing group in a reserved district (which by mandate has
same-caste candidates) must come from a higher income group than the corresponding
swing group in a mixed-caste candidate district, in equilibrium. Political reservation —
by eliminating such mixed-caste candidate cases — results in penalizing lower-income
groups and increasing within-minority inequality.
This paper also presents empirical verification of the theoretical predictions using data
from India. Given that every Member of Parliament (MP) is supposed to commission
“developmental works” in his constituency (electoral district) and that India has implemented several employment based poverty reduction programs, constituency-wise differences between the implementation of these programs provides useful information.
These programs are the initiative of the central government (seated in Parliament) and
so the MPs have a prominent role in shaping and implementing them. One implication
of the model could be the lower implementation of poverty-reduction programs — something the poor clearly care about — in reserved parliamentary constituencies. 3
Using household level data collected by the NSSO (43rd round, conducted in 1987-88),
this paper provides evidence of lower implementation of the Public Works programs (the
blanket term for all employment-based poverty alleviation programs) in constituencies
reserved for SC Members of Parliament. Specifically, the impact of reservation on a household’s probability of participation in Public Works programs is negative and significant,
after controlling for a wide range of household characteristics and relevant district characteristics. This effect is robust to various specifications or when limiting the sample to
various sub-groups, like the poor, or the Scheduled Caste households alone, etc. Given
the wide-spread poverty among the Scheduled Castes, this finding implies that reservation hurts them disproportionately. Moreover, there is evidence that in reserved districts
2 Banerjee and Pande (2007) offer a formal model to study some implications (like the effect on legislator
quality) of this phenomenon.
3 This paper studies the implications of reservations for SCs alone; implications of reservations for other
groups like the Scheduled Tribes (STs) are not explored here. The primary reason for exclusively focusing
on SCs is that the socio-economic characteristics of SCs are quite distinct from those of STs on at least two
counts: (i) STs are not as well politically organized as SCs, so political reservation has had limited impact
on their political standing, unlike the SCs who are a political force to reckon with today. (ii) STs tend to live
in rural areas separate from other groups while SCs are spread more evenly in the geographical sense.
3
higher income households are more likely to get benefits from Public Works. This is indeed suggestive of “creamy layer” promotion in reserved constituencies.
Additionally, district regressions reveal evidence of greater within-SC inequality in reserved districts. This effect persists for different measures of within-group disparities
(gini, Inter-Quartile Range, etc.). This is also true when looking at SC-inequality relative to overall inequality. With regards to the “creamy layer” issue, this suggests that
mandated political representation may well be widening disparities within these disadvantaged minorities. Not only is this worrying from the angle of poverty-reduction but it
also serves to alienate the bulk of the minority community by creating a sense of betrayal
in them.4
This paper is part of the broad (primarily empirical) literature which studies the impact
of political reservation – at some level of government – on economic outcomes. There
have been several studies on the impact of reservation on the provision of publicly provided goods at local levels of government like village councils (see for e.g., Besley et al
(2004), Munshi and Rosenzweig (2008), Bardhan, Mookherjee and Parra Torrado (2010)).
Chattopadhyay and Duflo (2004a, 2004b) use political reservations for women in India to
study the impact of women’s leadership on policy decisions. Pande (2003) finds that political reservation for Scheduled Castes in the state legislature has resulted in observable
rise in targeted transfers towards these groups.
In terms of studying the redistributive outcomes of political reservation, there has been little work — either theoretical or empirical — and this is precisely the gap this paper seeks
to close. The only other paper — to the best of the author’s knowledge — which studies
the impact of political reservation on any sub-group of the population is Chin and Prakash
(2011). They estimate the impact of political reservation in the state-level legislature on
overall state-level poverty.5 In terms of the focus on group diversity and redistribution,
this paper is similar to Fernández and Levy (2008). They study the relationship between
redistribution and taste diversity using a model with endogenous platforms involving redistribution and targeted public goods, and find a non-monotonic relationship. However,
there are important differences between their paper and this one.6 They do not deal with
mandated political representation but focus on how redistribution changes with group
diversity.
The remainder of the paper is organized as follows. Section 2 contains the theory while
Section 3 describes the data and empirical strategy. Section 4 presents the empirical findings and Section 5 concludes. Details on the institutional background of political reservation in India and the dataset used here can be found in the Appendix (Section 6).
4
Anecdotal evidence suggest that the roots of Maoist insurgencies in India lie in the struggle over property rights– the discord between the landed elite and the landless/marginally landed farmers. An erosion of
faith in government policies apparently designed to alleviate misery may well fuel such tensions in society.
5 They find that, at the state-level, ST reservation reduces poverty while SC reservation has no such
impact.
6 They have endogenous parties unlike the two-party framework in the current paper. The election
process in their paper is more in the spirit of the “citizen-candidate” model while the current paper is more
in the Downsian tradition.
4
2
The Model
Society is populated by a unit mass of individuals and every individual is characterized
by two features. One is the individual’s income, denoted by w, and the other is the individual’s ethnicity (for our purpose, ‘caste’), denoted by e. Let the distribution of incomes
in society be represented by the cdf G with support on [0, w] where w is positive and
“large”.
Every individual i belongs to a caste : high (h) or low (l).7 Hence for every individual, e is
either h or l. Take any income level w. Let π (w) denote the proportion of w-earners who
are low-caste. Also, let π (w) be continuous in w. In society, the low-caste individuals
form a minority. Hence,
Zw
0
1
π (w) dG (w) < .
2
Also, the low-caste voters are economically disadvantaged; their numbers tend to dwindle as w increases. To capture this aspect, we assume that there is some threshold income
level, call it w, beyond which π (w) is weakly decreasing in w with π (w) < 1/2.
A balanced-budget redistribution — or simply redistribution for short — is a real-valued,
continuous function z defined on income, where z(w) is the transfer to each individual
earning w, and:
Rw
(i) z satisfies the budget constraint z(w) dG (w) = 0.
0
(ii) Every individual’s net consumption, denoted by c(w) ≡ w + z(w) for any individual
earning w, is non-negative.
There are two political parties, denoted by R and P, where R is viewed as pro-rich, and P
as pro-poor, in a sense that we make precise below. Each party fields a candidate of either
caste who proposes a particular redistribution. However, the constituency in question
may be reserved for low-caste candidates. In this case, each party is constrained to field
a low-caste candidate. Note, the entire game proceeds in two stages — in the first stage,
the parties choose their respective candidates concurrently while in the second stage, the
candidates make simultaneous offers of redistribution.8
An individual’s preferences over candidates (and their proposed policies) are described
as follows. First, individual i exhibits a bias αi , positive or negative, for party P. The
corresponding payoff from R is normalized to be zero, so αi is really a difference. It is
assumed that this bias has two main components, one that stems from i’s emotive affiliation with party P, and the other which arises from i’s association with the caste identity
of party P’s candidate. In other words, the net bias is composed of a “class bias” (which is
7 The SCs are often referred to as ‘dalits’ or ‘low castes’ while non-SCs are called ‘upper castes’ or ‘high
castes’ in the literature. Here, we continue the use of ‘high/low’ without being pejorative in any sense.
8 The second stage game is also referred to as the “expected-plurality game” in the subsequent text.
5
income-specific) and a “caste bias”.
Specifically, we assume that individual i feels an affiliation t(wi ) with party P, which is
continuous and naturally decreasing in w. As for the “caste bias”, assume that the voter
feels some degree of association, denoted by s > 0, for a candidate of the same caste as
the voter. Note, when both parties field candidates from the same caste, this association
factor effectively cancels out. Combining, we write
α ( wi , ei ) = t ( wi ) + s i ,
where wi is the individual i’s income, ei is i’s caste and si takes on the values s, −s, or
0, depending on the castes of the two candidates. In particular, when P0 s (R0 s) candidate is from the same caste group as voter i while R0 s (P0 s) candidate is from the other
caste group, then the net caste bias for voter i, which is si , equals s (−s). For same caste
candidates, si = 0.
We can now write individual i’s bias as
α i = α ( w i , e i ) + ei ,
where the extra term ei is just mean-zero noise. The individual sees the realization of
ei before she votes; the politicians do not; more on this below. We assume that ei is independently and identically distributed across individuals, with a symmetric, unimodal
density f (and corresponding cdf F) that has its support on the entire real line.
To this non-pecuniary bias αi we add the economic benefit from a proposed redistribution
to arrive at the overall payoff. Recall x (w) is the transfer to each individual earning w.
We write the economic benefit to an individual earning w from redistribution x as
m(x, w) ≡ u(w + x (w)),
where u is a standard utility function with u0 > 0, u00 < 0 and u0 (0) = ∞.
Say party P’s candidate proposes x, and R’s candidate proposes y. An individual i earning w, with bias αi will vote for party P’s candidate if
m(x, w) + αi > m(y, w),
will vote for R’s candidate if the opposite inequality hold, and will be indifferent in case
of equality.
From the perspective of the party, an individual’s vote is stochastic. The probability that
she will vote for party P’s candidate is given by
pi ≡ 1 − F (mi (y, w) − mi (x, w) − α(wi , ei )) .
R
The expected plurality for party P is proportional to pi ,9 and this is what party P seeks
i
to maximize — and party R minimize — through the appropriate choice of policy in the
second-stage, conditional on candidate choice in the prior stage.
9 To
R
R
be precise, the expected plurality is given by [ pi − (1 − pi )] = [2pi − 1].
i
6
i
Let γ denote the candidate caste configuration — or simply configuration for short — in
the following manner:
γ = (casteP , casteR )
where the first argument refers to P-candidate’s caste and the second refers to party Rcandidate’s caste. The redistribution profile (x, y) proposed by P and R, respectively, in
equilibrium will depend on γ. We will make this dependence explicit by indexing all
relevant parameters by γ. Therefore, for any given γ, an equilibrium of the expectedplurality game is defined by a redistribution pair (x, y) which constitute mutual best responses from the perspective of the two parties. Note, that this expected-plurality game
is, by its nature, a zero-sum game.
It will be useful to define the term
σ(γ, w) ≡ π (w) f (α(w, l )) + (1 − π (w)) f (α(w, h))
This term is important for subsequent analysis and hence requires some interpretation.
First, fix a candidate configuration γ. Now consider a situation in which for some level
of income, say w, the values α(w, l ) and α(w, h) are “small” in magnitude. This basically
means that the group characterized by income level w (group w henceforth, for brevity),
does not exhibit much partisan bias ex-ante. Given that the density f is unimodal and
symmetric around 0, such lack of strong bias indicates that σ (γ, w) will exhibit a high
value. Alternatively, if the values α(w, l ) and α(w, h) are “large” in magnitude — suggesting that the members of group w feel strongly about one party vis-a-vis the other
party — then σ (γ, w) will exhibit a low value for this group w. In this sense, one can
think of σ (γ, w) as representing the “average swing propensity” of group w. It captures
the extent to which the members of a group may be willing to switch loyalties.
From the perspective of the parties, the groups with high “swing” propensity assume
importance as they are ex-ante more responsive to transfers. The following proposition
makes this explicit.
P ROPOSITION 1. For any given candidate configuration γ, there is at most one equilibrium of this
expected-plurality game. In that equilibrium, there is a unique redistribution scheme xγ offered by
both parties, with the property that
σ (γ, w)[u0 (w + x (w))] = λγ
for every w in [0, w] and some λγ > 0.
Moreover, w + x (w) varies positively with σ (γ, w).
Proof. The arguments presented here closely parallel those in Theorem 1 of Lindbeck and
Weibull (1987).
7
Let d(w) represent the utility differential m(y, w) − m(x, w). Rewrite
R
pi as
i
Zw
[π (w)[1 − F (d(w) − α(w, l ))] + (1 − π (w))[1 − F (d(w) − α(w, h))]] dG (w)
0
This can be re-written as
Zw
[V ( x (w), y(w))] dG (w)
(1)
0
where V ( x (w), y(w)) ≡ π (w)[1 − F (d(w) − α(w, l ))] + (1 − π (w))[1 − F (d(w) − α(w, h))]
Party P seeks to maximize the integral in (1) by a choice of redistribution x while party
R seeks to minimize the same by the choice of redistribution y. Suppose (x, y) is an
equilibrium of this expected-plurality game.
Pick any w in [0, w]. For this group w,
Vx(w) ≡
∂V
= [π (w) f (d(w) − α(w, l )) + (1 − π (w)) f (d(w) − α(w, h))]u0 (w + x (w)).
∂x (w)
The above expression can be interpreted as the per-capita marginal gain in expected votes
for party P from group w w.r.t. a marginal shift in x (w).
Two observations are in order with respect to Vx(w) :
(a) This term is continuous in w. This, in turn, implies that the value of Vx(w) must be the
same for every w in [0, w]. Suppose not. Let w1 and w2 be two distinct income levels such
that
Vx(w1 ) > Vx(w2 )
(2)
Focus on a tiny neighborhood δ1 around w1 and a similarly small neighborhood δ2 around
w2 so that Vx(w) for any w in the δ1 - neighborhood exceeds Vx(w) for every w in the δ2 neighborhood. A marginal decrease in x (w) in the interval (w2 − δ2 , w2 + δ2 ) accompanied by a marginal increase in x (w) in the interval (w1 − δ1 , w1 + δ1 ) while respecting the
redistribution budget constraint improves the expected plurality for party P, contradicting that (x, y) is an equilibrium. So, it must be that
Vx(w1 ) = Vx(w2 )
for any w1 , w2 ∈ [0, w].
(b) The expression Vx(w) must be positive given that both f and u0 are positive everywhere
on the real line.
Hence, we can write the following:
[π (w) f (d(w) − α(w, l )) + (1 − π (w)) f (d(w) − α(w, h))]u0 (w + x (w)) = λ
8
(3)
for every w ∈ [0, w] and some λ > 0.
Now consider
∂V
.
∂y(w)
Analogous arguments apply in this case and hence we can claim
[π (w) f (d(w) − α(w, l )) + (1 − π (w)) f (d(w) − α(w, h))]u0 (w + y(w)) = µ
(4)
for every w ∈ [0, w] and some µ > 0.
Comparing equations (3) and (4) for any group w yields:
λ
u0 (w + x (w))
=
= constant
u0 (w + y(w))
µ
(5)
This implies that in any equilibrium x = y given the strict concavity of u and that both
x and y are balanced-budget redistributions. Suppose not. Assume that for some w1 ,
w.l.o.g. x (w1 ) > y(w1 ). By the above equation, this implies x (w) > y(w) for every w in
violation of the budget-balance constraint. Thus, d(w) = 0 for every group w. Imputing
this in equation (3) and using the symmetry of f around 0, we get:
σ (γ, w)[u0 (w + x (w))] = λγ
(6)
for every group w.
Equation (6) guarantees that w + x (w) varies positively with σ(γ, w) given the strict concavity of u.
The same equation can be utilized to show that there is at most one equilibrium. Suppose
that both (x, λ) and (x0 , λ0 ) satisfy (6). If λ = λ0 then x = x0 by the strict concavity of u.
Alternatively if λ < λ0 then x > x0 for the same reason. However, this implies that both x
and x0 cannot be balanced-budget redistributions. Hence it must be that (x, λ) = (x0 , λ0 ).
Note, the proposition above does not guarantee the existence of an equilibrium of the
expected-plurality game, given a caste configuration γ. Conditional on existence, the
equilibrium is unique and symmetric in terms of redistributions proposed by the two parties. However, existence can be guaranteed by simply assuming
— like Lindbeck and
R
Weibull (1987) do — that party P’s objective function, namely pi , is concave in x for any
i
given y and convex in y for any given x. In particular, assuming
sup f 0 (t) / f (t) ≤ inf u00 ( x ) /(u0 ( x ))2
R
guarantees that this condition on pi is met.
i
Recall the “class bias” t(w) felt by an individual in group w. We have that t(w) is continuous and (weakly) decreasing in w. We will assume the following:
t(w) ≥ s and t(w) < 0.
9
By the continuity of t, there will be some w ∈ (w, w), call it w∗ , such that t(w∗ ) = 0.10 One
can think of this group w∗ as the “middle-income” group which feels equally class-wise
affiliated to either party.
Proposition 1 provides a clue as to which group w will be most favored in terms of final
per-capita consumption c(w) by both parties, in equilibrium. It is the group with the
highest value of σ (γ, w) — the “swing” group. For candidate configuration γ, denote this
group by wγ .
We now examine, one by one, what the equilibrium redistribution policies look like — in
terms of which groups are favored at the expense of others — for the different possible
choices of candidate configuration γ in the first stage. We start with the configuration
(l, h), i.e., party P fields a low-caste candidate while party R fields a high-caste candidate.
P ROPOSITION 2. Consider an unreserved constituency with γ = (l, h). Here group wγ (swing
group) is poorer than the “middle-income” group w∗ , i.e., wγ < w∗ .
Proof. Note, γ = (l, h) implies that every l-voter associates positively with P0 s candidate
while every h-voter associates positively with R0 s candidate. Hence, α(w, l ) = t(w) + s
and α(w, h) = t(w) − s.
The derivative of σ (γ, w) w.r.t. w when evaulated at w∗ is the following:
f 0 (s)t0 (w∗ )[2π (w∗ ) − 1].
This term is negative since π (w∗ ) < 1/2 and both f 0 (s) and t0 (w∗ ) are negative. So,
wγ 6 = w∗ .
Suppose wγ > w∗ . Hence, t(wγ ) ≤ 0 and
σ(γ, wγ ) = π (wγ ) f (t(wγ ) + s) + (1 − π (wγ )) f (t(wγ ) − s).
(7)
It must be that s + t(wγ ) ≥ 0. Suppose not. Consider ŵ such that t(ŵ) + s = 0. Clearly,
ŵ < wγ since t is decreasing in w.
So,
σ(γ, ŵ) = π (ŵ) f (0) + (1 − π (ŵ)) f (t(ŵ) − s).
Also
σ (γ, ŵ) ≥ π (wγ ) f (0) + (1 − π (wγ )) f (t(ŵ) − s) > σ(γ, wγ ).
(8)
where the first inequality comes from π (ŵ) ≥ π (wγ ). The second inequality follows from
the unimodality and symmetry of f around 0 and by observing that
|t(ŵ) − s| = 2s < |t(wγ ) − s|.
10 If t is weakly decreasing then there could be an income interval where the value of t is 0.
The distinction
between a unique w∗ versus an interval makes no significant difference to the results that follow. So for ease
of exposition, we will proceed as if w∗ is unique.
10
Hence, it must be that s + t(wγ ) ≥ 0.
Now, corresponding to wγ , one can always find a group w̃ ∈ [w, w∗ ) such that t(w̃) =
−t(wγ ) > 0. This is possible since t(w) ≥ s by assumption. For this group w̃,
σ(γ, w̃) = π (w̃) f (t(w̃) + s) + (1 − π (w̃)) f (s − t(w̃))
(9)
Now compare equations (7) and (9). Since t(w̃) = −t(wγ ) (by construction) and
1 − π (w̃) > 1/2 > π (wγ ),
it must be that σ (γ, w̃) > σ (γ, wγ ). This leads to a contradiction which establishes the
proposition.
The next proposition considers the case in which both parties field candidates from the
same caste. Given this configuration, we have si = 0 for every voter i. Thus, the casteaffiliation component of every voter’s bias loses relevance.
P ROPOSITION 3. In a constituency in which both parties field candidates from the same caste, the
“middle-income” group becomes the “swing” group in equilibrium. Therefore, wγ = w∗ in such
a constituency.
Proof. In a constituency where both parties field candidates from the same caste, for any
group w
σ(γ, w) = π (w) f (t(w)) + (1 − π (w)) f (t(w)) = f (t(w)).
Clearly, the above is maximized at w = w∗ given that f is unimodal and symmetric
around 0 and that t(w∗ ) = 0.
An immediate corollary of Proposition 3 is that in a reserved constituency where both parties are
constrained to field low-caste candidates, the swing group is always the “middle-income” group.
2.1
Candidate choice by parties
So far the discussion has focussed on equilibrium redistribution policies and identity of
“swing” groups given a candidate configuration. However, as mentioned before, parties are
free to choose their candidates in unreserved constituencies in the first stage of the game.
In fact, parties will choose candidates strategically to maximize their respective payoffs
in any unreserved constituency. In principle, there could be equilibria in which one or
both political parties play mixed strategies, i.e., randomize between fielding a low-caste
candidate and a high-caste candidate. We restrict attention to only those equilibria in
which each political party fields a candidate rather than randomizing since this seems
more natural in our setting.
11
Suppose that parties can only field their members as candidates and any citizen can potentially join any political party. However, party R by it’s very nature attracts richer individuals, as compared to party P.11 Hence, the relatively wealthier section of society
will fill the ranks of R. This means that the proportion of low-caste members in party R is
strictly lower than in P (since low-caste citizens are on average poorer than their high-caste
counterparts). This, in turn, implies that whenever P fields a high-caste candidate party
R can always afford to do the same. 12
The discussion above suggests that it is relatively easier for party P to field a low-caste
candidate than for party R. To capture this notion, we introduce the following parameters.
For j = P, R let c j be the cost of fielding a low-caste candidate by party j where the cost of
fielding a high-caste candidate is normalized to 0 for both parties.13 We assume that
cR > cP .
Party R being predominantly a high-caste populated party owing to its pro-rich identity
suitably justifies the above assumption on costs.14
Hence, the payoff to party j is party j’s expected plurality net of cost c j . Therefore,
Payoff to party j = π j (γ) − c j .
where π j (γ) represents the expected plurality of party j under configuration γ.
This sets the ground for identifying the set of candidate configurations that can be observed in equilibrium in any unreserved constituency. The following proposition is a
step in that direction.
P ROPOSITION 4. In any unreserved constituency, the candidate configuration γ = (h, l ) will
not be observed in equilibrium.
Proof. Suppose γ = (h, l ) is the equilibrium choice in an unreserved constituency. Since
party R is fielding a low-caste candidate, it must be
π R (h, l ) − c R ≥ π R (h, h).
(10)
Now suppose that party P deviates to fielding a low-caste candidate. Given that γ = (h, l )
is the equilibrium choice, such a deviation should not be profitable for party P. Hence,
π P (l, l ) − c P − π P (h, l ) ≤ 0.
11 Recall,
(11)
R is pro-rich which can be interpreted as being “pro-business”, more “right-wing”, etc.
fact, each party has many more members than the number of seats to be filled — so every party has
several candidates vying for a ticket for any given constituency.
13 There is a large pool of high-caste members in both parties who are eligible for candidacy. So it is
equally costly for either party to field a high-caste candidate.
14 It could be that c and/or c < 0 while maintaining c > c . Thus, we do not rule out the interpretation
P
R
R
P
that a party may have more say/better control over a low-caste candidate than a high-caste one simply
because of the former’s minority status in the party (and society) hierarchy.
12 In
12
However,
π P (l, l ) − c P − π P (h, l ) = π P (l, l ) − c P + π R (h, l ) = π P (h, h) − c P + π R (h, l )
(12)
where the last equality follows from Proposition 3. However,
π P (h, h) − c P + π R (h, l ) ≥ π P (h, h) + π R (h, h) + c R − c P = c R − c P > 0
where the first inequality follows from the relation in (10). Therefore,
π P (l, l ) − c P − π P (h, l ) > 0.
This contradicts the relation in (11) and thus establishes the proposition.
Proposition 4 rules out the possibility of the configuration (h, l ) in an unreserved constituency. However one may ask — in the context of an unreserved constituency — if
(h, h) is the only possible candidate configuration in equilibrium. After all, if the candidate configuration (l, h) is like-wise ruled out then political reservation would not affect
the equilibrium redistribution policy at all. However, it is easy to show that the model
does not rule out the configuration (l, h) in an unreserved constituency. To see why, note
the following:
Consider the configurationR (h, h). In this configuration, si = 0 for every voter i. Here the
payoff to P is π P (h, h) = pi since the cost of fielding a high-caste candidate has been
i
normalized to zero. Now,
Z
pi =
i
Zw
[π (w)[1 − F (−α(w, l ))] + (1 − π (w))[1 − F (−α(w, h))]] dG (w)
0
Rw
which simplifies to [1 − F (−t(w))] dG (w) since α(w, h) = α(w, l ) = t(w).
0
Note that under the configuration (l, h), we have α(w, l ) = t(w) + s and α(w, h) = t(w) −
s. Hence,
π P (l, h) =
Zw
[π (w)[1 − F (−t(w) − s)] + (1 − π (w))[1 − F (−t(w) + s)]] dG (w)
0
So under the configuration (l, h), the payoff to party P is
π P (l, h) − c P =
Zw
[π (w)[1 − F (−t(w) − s)] + (1 − π (w))[1 − F (−t(w) + s)]] dG (w) − c P
0
13
Without imposing further restrictions of the distribution of voter biases and the income
distribution G, there is no way of telling which payoff is larger, π P (h, h) or π P (l, h) − c P ,
since
1 − F (−t(w) − s) > 1 − F (−t(w)) > 1 − F (−t(w) + s)
for every w ∈ [0, w].
Intuitively, in switching from (h, h) to (l, h), party P potentially gains the support of some
low-caste voters while it potentially loses some high-caste supporters. Hence, the payoffs
to party P from making a switch is ambiguous. It depends on the distribution of the
biases among the voters as well as the income distribution in society. Thus, one cannot
assert that in any equilibrium of an unreserved constituency both parties must be fielding highcaste candidates.
The above discussion makes it clear that in an unreserved constituency the only possible configurations in equilibrium are (h, h), (l, l ) and (l, h). The first two configurations
yield identical redistributions in equilibrium (see Proposition 3 and subsequent discussion) while the last configuration produces a redistribution policy which favors poorer
groups as compared to the “middle-income” group (by Proposition 2). In the aggregate,
reservation appears to bias policy against the poor and in favor of middle-income groups.
Additionally, given that the Scheduled Castes are concentrated at the lower end of the
income scale, reservation is serving to exacerbate the inequities within them. Thus, reservation could be aggravating the “creamy layer” problem.
Thus, the theory sets up an empirical test of the nature of redistributive policies pursued
by the elected representatives. We now turn to the empirical verification of these findings.
3
Empirical Analysis
The theoretical model in the previous section provides the following empirically testable
predictions.
[A] Reservation of seats for the Scheduled Castes disfavors the poorer sections of society as transfers
to them are reduced.
[B] Given that the Scheduled Castes are poorer on average, mandated political reservation hurts
most SC members (who are poor) and favors some relatively wealthier SCs. Thus, reservation
would result in increased inequality among the SCs.
In India, every Member of Parliament (MP) obtains funds from the central government for
expenditure on local-area development projects in their respective constituencies. Each
MP has the choice to suggest to the District Collector (head of the district administration)
14
for, works to the tune of 20 million Indian Rupees per year to be taken up in his constituency.15 Ideally one would look at the decomposition of the entire budgetary spending
by every Member of Parliament (in every constituency) in order to assess which income
groups are being favored at the expense of others and how this pattern changes as one
moves from a reserved constituency to one without reservation.
However the data on MP spending which is publicly available, does not allow such a fine
decomposition. The annual reports on MP spending inform us about the total number of
“works” (i.e. local-area small-scale developmental projects) the MP has commissioned
during the past year; there is no information on which types of projects were implemented. So it is not possible to discern which (income) groups are benefiting at the cost
of others.
Further, these reports decompose the aggregate MP spending by sector only at the level of
the states — no finer. The different sectors on which MPs typically allocate their funds (i.e.
they commission “works” in these sectors) are “Roadways, Paths and Bridges”, “Electricity facilities”, “Irrigation”, “Education”, “Health and Public Welfare” and “Sanitation and
Public Health”. So there is information on how much is spent on each of these sectors by
state. However this information is not particularly useful in answering questions about
changes in expenditure on the poorer sections of society as response to reservation of
constituencies.
Another route one could potentially take is by mapping the levels of different public
goods (available from the census) to constituencies and then checking if reservation affects the pattern of public goods provision. In fact, Banerjee and Somanathan (2007) perform an exercise of this nature although their main question is different from the one examined in this paper.16 However, this method of mapping public goods to constituencies
is not very suitable for this paper. It is often not possible to unambiguously demarcate
most public goods as specifically being preferred by any particular income group. In
some cases, the same public good could be either “pro-poor” or “pro-rich” depending on
its precise location.17 Therefore looking at public goods does not cleanly identify losers
and gainers between different income groups.
So what is required is hard information on some aspect of MP spending which the poor
clearly care about and then check if such kind of spending varies with SC reservation.
Given that the MP is supposed to commission “works” in different sectors and that India has implemented several employment-based poverty reduction programs, one could
look at the constituency-wise differences between the implementation of these programs.
15
20 million Indian Rupees is approximately equal to 0.45 million US dollars.
They look at the location of public goods between 1971 and 1991 in about 500 parliamentary constituencies in rural India to assess the relative importance of social structures as regards the allocation of
public resources over the period.
17 For e.g., if all the road-construction happens in such areas so that large wealthy farmers can access the
market more easily, then more roads do not necessarily benefit poor farmers. On the other hand, it could
be that roads are developed in a way that the transportation costs for all are reduced and perhaps the poor
do gain more from this.
16
15
These programs are the initiative of the central government (seated in Parliament) and so
the MPs have a prominent role in shaping and implementing them. So prediction [A] is
checked in the following manner: the implementation of nationwide poverty-reduction
programs in reserved constituencies is compared with that in unreserved constituencies while
controlling for various factors. Any differences between the degree of implementation —
after controlling for relevant factors — would be a useful pointer to the effect reservation
has on transfers to the poor.
For prediction [B], measures of inequality of per-capita expenditure of SC households are
constructed at the district-level and matched with the reservation status of the district.
The next subsection describes the details of the empirical strategy employed.
3.1
Empirical strategy
In order to identify the effect of reservation on redistributive outcomes, one has to understand the process by which electoral districts are selected for reservation. The choice
of the constituencies (within a state) that are reserved for SCs is guided by the following considerations. The Delimitation Commission stipulates that constituencies for SCs
are to be distributed in different parts of the state and seats are to be reserved for SCs in
those constituencies where the percentage of their population to the total population is
comparatively large.18 Also, there was a freeze on rotation since 1977, fixing the identity
of reserved and unreserved constituencies all the way till 2009 (details contained in the
Appendix).
Given the criteria specified for selection of constituencies, it is easy to check if the Delimitation Commission had indeed adhered to the guidelines. Data from the 1971 census
is used to check if there are systematic differences — aside from the differences in SC
population proportion — between those districts subsequently selected for reservation
and those which were not. Table 1 contains some relevant summary statistics of districts
based on the 1971 cenus data. Panel A contains data on 296 districts out of which 68
contained at least one reserved constituency.19 Panel B contains data on 157 districts (out
of this 296) where each of these districts correspond to a single constituency and viceversa. A quick look at the columns indicating differences in district-level means between
reserved and non-reserved districts reveals the following. In both cases (panels A and B),
SC population proportion is significantly larger in reserved districts. Other differences
— apart from Electricity which measures any power facilities available in the district —
do not seem to persist when one looks across both panels. Thus, overall the evidence is
re-assuring. Conditional on SC population proportion, the selection process appears to
have been fairly random.
There is another issue. There are 542 constituencies spread over 338 districts. Although
18 The
Delimitation Commission is a national-level commission whose orders have the force of law, and
cannot be questioned in court.
19 Districts are typically larger than constituencies. More on this later.
16
the household survey data used here (from the National Sample Survey (NSSO)) is fairly
extensive in terms of household characteristics, it does not permit identification of the
household all the way down to the constituency; district is as far as one can go. So,
a dummy variable for reservation at the district level is used here. This variable takes
the value one when any constituency within the district is reserved; it takes the value
of 0 otherwise. The household-level analysis regarding the question of participation in
poverty-reduction programs is carried out using household data from all 338 districts.
Out of these 338 districts, there are 182 cases where every district corresponds to a single constituency and vice-versa. The household analysis — regarding participation in
poverty-reduction programs — is also conducted with this subset as robustness check.
The paper then looks at the empirical verification of [A] which is about the reduced implementation of poverty-reduction programs in reserved constituencies. As briefly mentioned before, India has implemented a large number of poverty alleviation programs
which are employement-based. “Public Works” is about employing poor households for
building public infrastructure especially in the rural parts of the country.20 They were
designed to address the issue of reduction of poverty via employment of poor household
members. At the same time, these programs aid in the creation of basic infrastructure
like roads, dams, bridges, canals, schools, etc. In fact, the wages for these employment
programs are set at a suitable level which are expected to attract only the poor.
The 43rd round of NSS used here contains information regarding the participation of the
households in these employment-based poverty-reduction programs. To be precise, the
following question was put to every (head of) household:
“Did any member of the household work for at least 60 days on public works during the last 365
days?”
It is possible to identify the district a household belongs to, in the particular NSS round
used here (i.e. the 43rd round). Given that the reservation data are available from the Election Commission of India Reports, the following question is posed: “Does the reservation
status of a district to which a household belongs, have any impact on the household’s
participation in Public Works?”. In other words, is a household more or less likely to
participate in these programs if it comes from a district which is reserved for SCs? Recall,
the household survey is from 1987-88 while the freeze on reservation has been in place
since 1977. So the reservation status of the constituency has been unchanged for at least a
decade.
The paper then turns to the prediction regarding the impact of reservation on within-SC
inequality. Here, however, it is no longer possible to continue with all 338 districts (containing 542 constituencies within them) as SC-inequality within a constituency inside a
district may very well be different from SC-inequality within the district overall. Hence,
here the sample is restricted to those 182 cases where constituencies and districts are synonymous. Table 4 compares both sets — ‘single-constituency’ districts (182 in number)
20
The National Rural Employment Programme, Rural Landless Employment Guarantee Programme
(RLEGP), Minimum Needs Programme (MNP), and other schemes aim to provide employment in construction of roads, dams, bunds, digging of ponds, etc. All of these come under this “Public Works” approach.
17
and ‘non-single constituency’ districts (156 in number) — in terms of relevant district
characteristics. There are two main differences. First, in the complete sample (338 districts with 542 constituencies), the proportion of reserved constituencies is higher –79
reserved out of 542 – whereas in the restricted sample (182 constituencies) only 23 are
reserved. This is reflected in the (statistically significant) difference of means for Reserved
dummy. Secondly, the mean district population is higher in the complete sample. This
is reflected in the (statistically significant) difference of means for Population. This makes
sense because every constituency is supposed to represent a similar mass of people and
so more populous districts were split up into two or more constituencies.
3.2
Empirical specification:
3.2.1
Household-level specification
First comes the issue of how the reservation status of a household’s constituency may
affect the household’s participation in poverty-reduction projects. To capture the relationship, a logit model of the usual form is employed.21
P( yh = 1| Xh ) = G ( Xh β h ) = G ( β o + β 1 xh1 + ... + β K xhK )
where G (z) ≡ exp(z)/[1 + exp(z)].
So yh = 1 if a household h has participated in public works for at least 60 days during the
last 365 days and yh = 0 otherwise.
Suitable survey weights have been employed for all the reported regressions keeping the
NSSO survey design in mind. Although the unit of analysis is a household, the explanatory variable of interest, i.e. reservation status of the household’s constituency, varies at the
level of the constituency and not at the level of the household. Therefore any variable
pertaining to reservation takes the same value for all households within the same constituency.
Recall, that the chief variable of interest — a dummy called “Reserved” — takes the value
1 for all households which belong to a district within which at least one constituency is
reserved; otherwise, this dummy variable takes the value 0. So for any household from a
district which has no SC reserved constituencies within it, this variable takes the value 0.
Several controls are available from the NSSO dataset and they have been suitably employed. Table 2 contains a brief summary of the relevant household-level variables. In
this table, the households have been split into groups to facilitate comparisons. The first
set (Panel A) contains information on all households in the complete sample (327 districts). The second set of households – described in panel B – come from those districts
which do not contain any reserved constituencies. The third set of households (Panel C)
21
The same analyses have been conducted using a probit model. The results are extremely similar.
18
come from those districts which contain at least one reserved constituency. A quick look
at SC dummy (the dummy variable for whether a household is SC or not) in the column
of differences, reveals that the probability that a given household belongs to a Scheduled
Caste is higher for households coming from reserved districts than for non-reserved ones.
Other household characteristics can be similarly compared.
Household characteristics which influences the household’s choice of involvement in
Public Works and is possibly correlated with the district’s reservation status, needs to
be controlled for. Typically factors like the household per-capita monthly expenditure,
household size, the level of education in the household, proxies for the wealth of the
household and the primary occupation of the household have the potential to influence
the household’s choice of participation in Public Works. These are roughly “demand side”
considerations which come into play when examining the outcome of a household’s participation. Hence, some of the important controls are (the log of) household per-capita
monthly expenditure, household size, a dummy indicating whether the household belongs to a Scheduled Caste or not, a dummy indicating whether the household is a Hindu
household or not, a dummy for the household’s education level (whether any household member has completed primary school or not) and a dummy indicating whether
the household resides in an urban area or not.
Potentially, there are some “supply side” considerations too — it might happen that
in some districts poverty-reduction programs are far less implemented than in others.
Hence, a typical household may be excluded from participation because there are very
few public works in the district. Clearly that is some property of the district per se which
is not directly related to the participation decision of the household. The key question is
whether or not the reservation status of a district has such a “supply side” impact — and if so,
then in which direction.
Other controls which have been used can be broadly grouped into two types — (i) proxies for the wealth of the household and (ii) variables for the primary occupation of the
household.22
In the first category, the main controls are a dummy for whether the household owns
a home or not, whether the dwelling is built with bricks/stones and cement or with
mud,clay and thatched roof (“pucca”,“semi-pucca” or “kutcha”)23 , whether the dwelling
is an apartment/ house or a shack/shanty (“chawl/bustee”).
22
There is clearly a fairly high degree of correlation between a household’s educational attainments,
wealth and primary occupation. What is noteworthy is that the main findings persist even in those specifications which have potentially severe muticollinearity issues.
23 A pucca house is one, which has walls and roof made of the following material. Wall material: Burnt
bricks, stones (packed with lime or cement), cement concrete, timber, etc. Roof Material: Tiles, GCI (Galvanised Corrugated Iron) sheets, asbestos cement sheet, RBC,(Reinforced Brick Concrete), RCC ( Reinforced
Cement Concrete) and timber etc.
The walls and/or roof of which are made of material other than those mentioned above, such as un-burnt
bricks, bamboos, mud, grass, reeds, thatch, loosely packed stones, etc. are treated as kutcha house.
A house that has fixed walls made up of pucca material but roof is made up of the material other than
those used for pucca house is a semi-pucca house.
19
The variables in the second category include dummies for whether the household is selfemployed rural (or urban), whether the household provides labor in rural areas, whether
the household is an urban regular wage/salary-earning one or whether the household
provided “casual labor” in urban areas. In all regression tables the dwelling-related
wealth proxies are collectively refered to as “Home dummies” while the occupationrelated dummies are called “Employ dummies”.
3.2.2
District-level specification
In order to address the relationship between the reservation status of a district and the
inequality among SCs therein, a standard linear model of the following form is employed:
yd = α + βXd + ρZd + ε d
where yd is some measure of inequality — in terms of monthly per capita expenditure
— among SC households in district d. Xd contains the chief explanatory variable which
pertains to the reservation status of the district and Zd contains district-level controls and
ε d is the district level error term.
The chief explanatory variable is the “Reserved” dummy as before. Here, however, we
restrict the sample to only those districts which have only one constituency within them.
So we drop all districts which have more than one constituency within them.24 This leaves
us with 182 cases where district and constituency mean the same thing.
The Interquartile range (normalized by the mean), the ratio of upper to lower quartiles,
and the Gini coefficient have been used to measure inequality. For measuring poverty
among SCs at the district-level, two of the most widely-used indices have been employed.
One is the Headcount Ratio which simply represents the percentage of households in the
district which are below the poverty line. The other is the Poverty Gap Ratio which is the
mean distance separating the population from the poverty line (with the non-poor being
given a distance of zero), expressed as a percentage of the poverty line. 25
At this point it is important to re-iterate the procedure by which a constituency’s reservation status is determined, as this would influence the choice of controls for the regressions.
For every state, the allocation of the total number of seats for SCs are made on the basis
of proportion of SCs in the state concerned to that of the total population. However, there
remains the question of which seats to pick for SC reservation within the state. This is
done by the Delimitation Commission and is solely based on two criteria: (a) the pop24 Inclusion of districts with multiple constituencies is problemmatic in this case since SC-inequality rising (falling) within a constituency does not necessarily imply that the district level SC-inequality is rising
(falling); and vice-versa.
25 The poverty lines used here are from the Planning Commission of India which is the public body
entrusted with the task of providing the official estimates of poverty for the nation and for the individual
states. The poverty lines are expressed in Rs. monthly per-capita for 1987-88. For every state in India, there
are two such poverty lines — one for the urban households and other for the rural households.
20
ulation percentage of Scheduled Castes in the constituency concerned and (b) that the
constituencies for SCs are to be distributed in different parts of the state.
Any factor which potentially affects the reservation status (reserved or not reserved) and
has a direct effect on the outcome of interest (SC monthly per-capita expenditure or SC
inequality) must be controlled for. Given the procedure of SC reservation, the choice of
a constituency within a state for reservation is partly random and partly determined by
the SC population proportion in the constituency. The randomness comes from the fact
that one could pick several different sets of seats for SC reservation keeping in mind the
consideration about geographical dispersion of these seats.
The different controls used for the various regressions are the (log of) population % of
SCs which is the (log of) percentage of SC households in the district in 1987-88, the (log
of) district population which is the (log of) total households in the district over the total
households in all districts in 1987-88 expressed as a percentage, the log of average percapita monthly expenditure of all households in the district to control for the general level
of prosperity of the district in 1987-88. State dummies are used in certain specifications in
order to capture any possible state fixed effects.
Other important controls include certain district level characteristics from 1971 Census of
India. Recall, the Delimitation Commission which selected the set of reserved districts in
1972 did so on the basis of the 1971 census. Additionally, the freeze on rotation in 1977
clearly implies that the reservation status of districts in 1987-88 were determined solely
by that Delimitation Commission’s orders.
Table 3 contains a brief summary of the single-constituency districts which are used for
the district-level regressions. Here again, the districts are decomposed into groups —
all, non-reserved and reserved — to facilitate groupwise comparisons of district characteristics. Notably, SC population (%) is much higher in reserved districts in relation to
non-reserved districts. Also, one can see that SC-inequality is higher in reserved areas.
Other variables like SC per-capita expenditure, Average Per-capita expenditure and SC
poverty do not exhibit any significant differences.
4
4.1
Regression Results
Household-level regressions
The key question is whether or not the reservation status of a household’s constituency
has any direct effect on the household’s participation in Public Works projects.
Table 5 contains the basic results. The first six columns report logit regressions, column
7 presents a probit regression while OLS is reported for column 8. Recall, the unit of
analysis is a household. The dependent variable is a dummy, Public Works, which takes
the value 1 if the household reports participation in the program, and takes the value 0
21
otherwise. In all eight columns, the coefficient on Reserved dummy is highly significant
and negative — this means that a household from a reserved district is less likely to have
participated in public works projects, while controlling for other factors.
Larger households are more likely to participate as is shown by the significant and positive coefficient on the household size variable (denoted by HH size). This is fairly intuitive
as larger households have more members who can participate. Also most large households typically include children and people of advanced years — this puts more pressure
on the earning members of the household (“more mouths to feed” argument).
The negative coefficient on per-capita expenditure (Pce) in every specification indicates
that poorer households are more likely to have worked in these programs. This strongly
suggests that the beneficiaries of these programs were clearly the non-rich. Also, the negative and significant coefficient on the Urban dummy (indicating whether the household
comes from an urban area or not) suggests that these programs were primarily based
in rural areas; more on this later. The negative coefficient on Education (a dummy for
whether any household member has completed primary schooling or not) suggests that
more disadvantaged households with lower levels of education were participating in
these programs.
Although, the district level poverty figures (Headcount Ratio) are similar for reserved
and non-reserved districts (see Table 2), the analysis is repeated while explicitly controlling for district-level poverty. After all, if reservation is a mere proxy for poverty then
replacing the reservation variable by a district-level poverty measure in the regressions
would deliver a similar coefficient on the poverty variable. Table 6 reports such regression results. In fact, columns 1, 3, 5 and 7 in Table 6 has a measure of district-level poverty
(the Headcount ratio) in place of Reserved dummy.26
In each of the four columns (i.e. columns 1, 3, 5 and 7), the coefficient on Poverty is not
significant even at the 10% level. Secondly, district-level poverty measures were included
as an additional control (see columns 2, 4, 6 and 8 in Table 6). In this case, the coefficient
on Reserved dummy is negative and significant as in the previous table (Table 5), while the
coefficient on Poverty is not significant even at the 10% level.
Table 7 reports similar regressions where the total sample of households is cut along different lines. The sample is restricted, in turns, to SC households (columns 1-2), rural
households (columns 3-4), urban households (columns 5-6) and households from only
single-constituency districts (columns 7-8). OLS regressions are reported in this table and
all subsequent ones. This is mainly because of two reasons: (i) the OLS results are substantively similar to logit or probit regressions and (ii) the interpretation of coefficients
(especially those on interaction terms) is much more straightforward and intuitive with
OLS.
In Table 7, the dependent variable is still the dummy variable indicating the household’s
26 An
alternative measure of poverty was used as well — namely, the Poverty Gap ratio. However, the
results obtained with Poverty Gap ratio are extremely similar to those using the Headcount Ratio. Hence,
only one set is reported.
22
participation in Public Works programs. Here, once again the coefficient on Reserved
dummy is negative in all specifications. However, it is no longer significant for columns
5-8. For columns 5-6 (Urban households), this is potentially due to the fact that Public
Works projects were primarily implemented in rural areas. In columns 7-8, the loss of
significance is most likely due to the large drop in the number of districts (182 from 338
in total; and 23 from 79 in terms of reserved constituencies) since the variation in Reserved
dummy comes at the district level. In fact, the results in Table 8 lend further support to
this idea.
For Table 8, an additional measure of reservation has been constructed, namely Reserved
time. Reserved time is simply the sum of the total number of years of SC reservation for all
constituencies within a district divided by the number of constituencies within a district.
Therefore,
Reserved time =
1
number of years c has been reserved
n c∑
∈d
where c denotes a constituency in district d and n is the total number of constituencies
within d. This measure of reservation goes further than incorporating whether a particular district was subjected to any SC reservation or not; the attempt is to capture the extent
of it in terms of duration. 27
The findings in Table 8 re-iterate what has been observed so far.28 More reservation means
lower chance of a houshehold’s participation in Public Works projects.
Although any citizen of India could potentially work on Public Works projects, it was
designed in a way so that it attracted only the poor households.29 Viewed in this light,
one could simply use a binary variable — whether the household is poor or not — rather
than the household’s average per-capita expenditure as a relevant explanatory variable.
Hence, in columns 1-4 of Table 9, Pce is replaced by a dummy Poor HH dummy which
simply captures whether the houshold lies below the poverty line or not. In columns 5-8
of the same table, the sample is restricted to such poor households. In all columns of
Table 9, the coefficient on Reserved dummy is negative and highly significant, re-iterating
the previous findings.
To get at the issue of whether in reserved districts there is some favoring of higher income
groups in the allottment of benefits from Public Works, a different set of regressions were
run. The results are reported in Table 10. Here, the variable Reserved dummy is interacted
with the (log of the) household’s per-capita expenditure to yield Reserved dummy X Pce.
Interestingly, while the overall marginal effect of the Reservation dummy is negative, the
27 That
said, the baseline specification is still the Reserved dummy as the duration of reservation inherently
involves a notion of persistence which may not be particularly relevant for programs like ‘Public Works’.
However, it is re-assuring that both measures of reservation – dummy and time – yield similar results.
28 The correlation between Reserved dummy and Reserved time is positive and large (around 0.92). Although
prior to 1977 there was rotation in reservation status (based on population figures from the most prior
census), this high correlation suggests that there was not a large amount of change in the identity of reserved
districts over the decades.
29 The wages were set at a level which would be attractive to poor households alone.
23
coefficient on Reserved dummy X Pce is positive and significant.30 This suggests that higher
income groups got favored in reserved districts even though the overall implementation
of Public Works was lower. This is quite suggestive of “creamy layer” promotion by
reservation.
All these results strongly suggest that Public Works projects have been less implemented
in those districts which have witnessed SC reservation; unless one entertains the notion
that households from the reserved areas were employed lesser as most of the public works
in these reserved areas were capital-intensive relative to those implemented in unreserved
areas. Not only is such a notion baseless, but this also goes against the whole idea of
public works programs.
4.2
District-level regressions
Table 11 contains the results for the regressions where the dependent variable is some
measure of inequality among the SC households in a district. The idea is to capture the
dispersion in incomes within the Scheduled Caste group. In particular, the focus is on
measures which capture the divergence in incomes between the relatively well-off SCs
(the “creamy layer”) and the poorer ones. Hence, the baseline measure is the Interquartile
range normalized by the mean expenditure of SCs (referred to as IQR for brevity). Other
measures are used as well to check the robustness of the relationship.
Specifically, in columns 1–3 the Interquartile range (normalized by the mean SC exp.) is
used, in columns 4–6 the ratio of the upper quartile to the lower one (i.e. Q3 /Q1 ) is used
while the Gini coefficient is used in columns 7–9. Note, Reserved dummy is significant in
these regressions. Moreover, the coefficient on Reserved dummy is always positive. The
effect is robust to adding various controls and it is persistent across different measures of
inequality. Overall, this suggests that reservation did in fact increase inequality among
the SC group.
In Table 12 are regressions where the dependent variable is a measure of SC inequality
relative to the overall district inequality. The three different measures of dispersion discussed above are used here as well. In particular, the dependent variable in columns 1-3
is the ratio of the IQR computed for only SC households in the district to the IQR comIQR SC HHs
puted using all households in the district, i.e.. IQR
All HHs ; the other dependent variables
are defined analgously. Defining relative inequality in this way makes it clear how much
inequality among SCs changed relative to inequality among all households as a result of
reservation. The results are substantively similar to those in Table 11.
30 The
mean of log Pce is around 5.202, so the marginal effect of Reserved dummy is still negative.
24
4.3
4.3.1
Concerns
Alternative explanations
A general concern could be that the lower implementation of the public works projects in
reserved areas could be the result of some mechanism different from the one posited here,
i.e. not necessarily through the channel of electoral competition. Some such alternative
theories are discussed here.
One plausible alternative theory could be one involving inexperience. One could argue
that the low-caste leaders have had lesser training and so are unable to implement policies efficiently. However, over time one would expect this problem would go away and
hence one should see similar policy outcomes for leaders of any caste. But the empirical
results obtained (see Table 8) reveal a negative relationship between Reserved Time and implementation of public works projects — this goes against the spirit of the “inexperience”
theory.
Another story could be the following. Suppose there is taste-based discrimination by
individuals that the MP needs to interact with (for e.g., bureaucrats) to get certain things
done. If these people are unwilling to comply with the low-caste politician, then one
would observe the same predictions: less “Public Works” in reserved districts.
There are two things which go against such an alternative story. First, in the Indian context, there is abundant anecdotal evidence about how bureaucrats get transferred when
they fail to do the politician’s bidding. So the reins of power really lie in the MP’s hands.
Also, if the MP is concerned about re-election or even reputation, he will ensure that the
people under him do as directed. Secondly and perhaps more importantly, such a story
does not really make the case for the benefits of “Public Works” to go to less-deserving
wealthier households. This is what one observes in Table 10; see, in particular, the positive
and significant coefficient on the interaction term Reserved dum.X Pce.
The positive relationship between reservation and SC inequality in the district regressions
raises the following concern: perhaps the rise in SC inequality due to reservation is just a
reflection of the fact that the some of the poor SCs are actually benefitting from reservation and moving out of poverty; it this rise in their expenditures that seems to exacerbate
SC inequality. But this would suggest a positive relationship between SC per-capita expenditures and reservation; something which is not observed in Table 13 (see columns
1–3). Nonetheless, to settle this question, one needs to study the effect of reservation on
poverty among the SC households and check if it mirrors the relation between reservation
and SC inequality.
Columns 4–9 in Table 13 contains regressions where the dependent variable is poverty
among SC households as measured by the Headcount Ratio and by the Poverty Gap Ratio. The regressions reveal that there is no significant association between SC poverty and
the reservation in the district. In other words, reservation does not affect SC inequality
and SC poverty in the same manner.
25
Even if one admits the possibility that reservation did benefit some of the poorest SC
households (something which might not show up in the Headcount numbers or even
perhaps the Poverty Gap measures), this does not fit in with the SC inequality results. A
slight betterment of the poorest SCs should bring the expenditures of the poor SC households closer to each other. Hence, without any increase in expenditures of the non-poor
SC households there should be a decrease — not increase — in within-SC inequality. Thus,
the evidence of increased SC inequality that was found earlier could not have been a byproduct of the SC poverty dynamics.
4.3.2
Reverse Causality issues
There is the possibility of feedback in the case of the household regressions — participation in Public Works could in turn affect income and hence consumption expenditure.
However, in such a case one would expect that greater participation leads to betterment of
income and hence the correlation between expenditure in 1987-88 and prior participation
in public works should be positive from this feedback effect.
What is observed from the regressions in Tables 5 – 8 is that the coefficient on household per-capita expenditure (Pce) is mostly negative. For Table 9, the coefficient on the
Poor HH dummy is positive and significant in most specifications. So one is lead to infer
the following — even if such a feedback exists it is not of sufficient magnitude to overturn the effect of poorer households tending to participate more in “Public Works”. Had
the coefficient been ambiguous or insignificant then one could perhaps argue that it because the feedback effect annuls the effect of the inverse relation between expenditure
and participation. But the fact that a significant and negative coefficient is observed on
Pce in most specifications implies that the beneficiaries of public works schemes were the
poorer households.
5
Conclusion
This paper presents some novel findings on the impact of political reservation for minorities on their economic conditions. The theory developed here exploits the “swing voter”
idea from previous models of electoral competition (like Lindbeck and Weibull (1987) and
Dixit and Londregan (1996)) to generate two main empirically testable predictions. First,
political reservation harms poorer sections of society as long as voters prefer candidates
from their own group (‘caste’ in the Indian context). Alternatively, if such group-wise
association is absent then reservation has no effect on redistribution. Secondly, when the
minority group is economically disadvantaged, reservation increases within-minority inequality.
The empirical validity of the model’s predictions are examined by combining data on
household level consumer expenditure collected by the National Sample Survey Organization (NSSO) during 1987-88 with the data on reservation of constituencies in the Indian
26
parliament. India has – over several decades – implemented a large number of poverty
alleviation programs which are based on providing poor households with some employment. The large-scale household surveys conducted quinquennially by NSSO contain
information regarding the participation of the households in these “Public Works” programs. To check whether reservation actually reduces transfers to the poor or not, this
paper studies the relation between reservation of a district and the implementation of
Public Works therein.
The regression results indicate that Public Works projects are less implemented in those
districts which have witnessed SC reservation. Specifically, a household’s probability of
participation in Public Works program is lower in a reserved district. This effect is robust to the inclusion of different controls (household-level and district-level), to various
specifications or when limiting the sample to various sub-groups, like the poor, or the
Scheduled Caste households alone, etc. Moreover, there is evidence that in reserved districts higher income households are more likely to get benefits from Public Works. The
analysis conducted at the district level suggests that reservation of a district for SCs leads
to an increase in within-SC disparities in the district. A rise in within-group inequality per
se due to reservation may not be undesirable from society’s view. However, if the rise in
inequality comes at a cost to the poor then the welfare effects of reservation may actually
be negative.
On the whole, the predictions of the model are fairly borne out suggesting a grim possibility — political empowerment of a minority group via mandated representation may
be accompanied by creation of greater inequities among them and may hurt the more
economically vulnerable sections of society when voting behavior is influenced by ethnic biases. The results obtained here shed new light on the “creamy layer” issue — the
concern that the benefits of affirmative action primarily go to the wealthier (and hence
less-deserving) members of the disadvantaged minorities.
These findings suggest another direction of reseach. Pehaps the level of government at
which one enacts reservation matters for redistributive outcomes. In fact, several papers
(for example, Chattopadhyay and Duflo (2004a), Besley, Pande, Rahman and Rao (2004),
etc.) have shown that affirmative action for the minorities at the level of village bodies or
even district bodies lead to better targeting of these groups. So perhaps a more decentralized approach to affirmative action may be beneficial rather than reservation of seats at
the national level.
Finally, the rise in Scheduled Caste political participation has been tremendous and it has
no doubt been fuelled by reservation of seats for the Scheduled Castes in Parliament. It
would be interesting to look at the effect of greater Scheduled Caste political participation
on Scheduled Caste inequality, poverty and public goods provision. Of course, rise in
Scheduled Caste political participation could be in turn affected by these same variables.
A better understanding of the forces at play between higher political participation and
economic backwardness could definitely inform policy-making, especially in developing
nations.
27
6
Appendix
6.1
Institutional Background
The economic backwardness of the Scheduled Castes is a direct fallout of the caste system
which has been an integral part of the Indian society for centuries. Scheduled castes form
roughly 16% of the Indian population. Caste membership is hereditary and is largely
reflective of the person’s traditional occupation choice. In particular, members of the
scheduled castes were traditionally assigned low-paying, labor-intensive tasks. In fact,
they were denied education and barred from high-paying occupations. Besides, there
were restrictions on their asset ownership.
In light of this history, the Constitution of India (which came into force in 1950) mandates political reservation in favor of scheduled castes (SCs) and scheduled tribes (STs)
in every state and national election.31 Moreover, there are clear directives for the state
governments to use public policy to improve the economic conditions of these groups.
A brief word about the Indian political system is in order. The Indian Parliament is bicameral in nature. However, the Lok Sabha is the popularly elected House and is de facto
more powerful than the other House (Rajya Sabha). The popularly elected Members of
Parliament (MP) enjoy a five-year term after which fresh Lok Sabha elections are held.
There were 518 (Lok Sabha) constituencies in 1971. This went up to 542 after a Delimitation order in 1976 and then to 543 in 1991.
Population is the basis of allocation of seats of the Lok Sabha. As far as possible, every
State gets representation in the Lok Sabha in proportion to its population as per census
figures. Hence, larger and more populous states have more seats in the Lok Sabha as compared to their smaller and sparsely-populated counterparts. For example, Uttar Pradesh
(a north Indian state) with a population of over 166 million has 80 Lok Sabha seats while
the state of Nagaland with a population of less than 2 million has only one Lok Sabha
seat.
Allocation of seats for Scheduled Castes and Tribes in the Lok Sabha are made on the
basis of proportion of Scheduled Castes and Tribes in the State concerned to that of the
total population (provision contained in Article 330 of the Constitution of India). Thus,
a state which has a relatively larger proportion of SCs will have a larger fraction of its
total Lok Sabha seats reserved for SC candidates. For example, Uttar Pradesh which has
relatively large proportion of SCs has 17 out 80 seats reserved for SCs. On the other hand,
31 Aside
from political reservation, the Indian constitution mandates reservation of jobs (through job
quotas) in all State and Central departments and government institutions. In 1950, the constitution laid
down 15% and 7.5% of vacancies to government aided educational institutes and for jobs in the government/public sector, as reserved quota for the SC and ST candidates respectively for a period of five years,
after which the situation was to be reviewed. This period was routinely extended by the following governments and the Indian Parliament, and no revisions were undertaken. Later, reservations were introduced
for other sections as well. The Supreme Court ruling that reservations cannot exceed 50% (which it judged
would violate equal access guaranteed by the Constitution) has put a cap on reservations.
28
the state of Gujarat with relatively lower proportion of SCs has only 2 out of 26 seats
reserved for SCs.
The choice of the constituencies (within a state) that will be reserved for SCs is guided by
the following considerations. The Delimitation Commission stipulates that constituencies
for SCs are to be distributed in different parts of the state and seats are to be reserved for
SCs in those constituencies where the percentage of their population to the total population is comparatively large.
The rise in political participation among the Scheduled Castes is, to a large extent, due
to reservation of constituencies for them. In the 1980s, the Scheduled Castes were able to
establish a successful caste-based party. This significantly enhanced their representation
in national politics.
6.2
Data
For information on the reservation status of the Parliamentary constituencies in India,
the sources are the Statistical Reports on General Elections available from the Election
Commission of India. As per the provisions of the Indian Constitution, readjustment and
delimitation of constituencies was to take place after every decennial census. Accordingly, Delimitation Commissions were constituted in 1952, 1963 and 1972. However, the
Parliament by passing the 42nd Constitutional Amendment Act (1976) imposed a moratorium on the number of seats allocated and the territorial boundaries of constituencies as
determined by Delimitation of Parliamentary and Assembly Constituencies Order 1976
until the publication of the population figures of 2001 Census. Accordingly, the Fourth
Delimitation Commission was set up under the Delimitation Act 2002 after a gap of as
many as 30 years. This means that there has been no change in the identity or the number of reserved SC seats after the 1977 Lok Sabha elections. So whichever seats were reserved in 1977
remained so right through till 2001 and no other seats were reserved between 1977 and
2001.
In India, large scale household surveys are conducted quinquennially as part of the National Sample Surveys (NSS). The survey rounds cover the entire nation and capture
monthly expenditure incurred by the sample household for the purpose of domestic consumption.32 The recall period used is 30 days, i.e., the surveyed households are asked
to provide information on consumption expenditure incurred over the past 30 days. For
this analysis, the 43rd round of NSS is extensively used for information on household
monthly per capita expenditure. Data for this round was collected during July 1987- June
1988. There is data on monthly per capita expenditure at the household level for over 300
districts in India – these districts span 18 major states in India and account for over 95%
of the Indian population.
These large scale NSS rounds also have information regarding the surveyed households’
32
Unfortunately, in the case of India there are no available sources of income data on a nationwide scale.
Hence, expenditure is the closest one can get.
29
participation in the nationwide poverty-reduction programs. In fact, most of the poverty
reduction programs gathered momentum in the late 1970s/early 1980s33 This makes the
43rd NSSO round particularly suitable for the task at hand.
Data on various district-level characteristics from the census of India conducted in 1971
has also been used. This is primarily to facilitate comparison — along various dimensions
— of districts which underwent reservation in 1977 with those which did not.
33 See
for e.g. Banerjee and Somanathan (2007) for details on “Garibi Hatao” (translates into “Eradicate
Poverty”) programs during this era.
30
31
68
-9.039
(1.161)***
0.001
(0.110)
-0.246
(0.082)***
-0.017
(0.015)
-163.270
(48.554)***
-140.191
(87.375)*
-5.804
(10.361)
113.227
(90.135)
-0.018
(0.015)
-0.704
(0.112)***
Diff.(NR–R)
136
14.751
(7.828)
6.634
(0.752)
13.798
(0.533)
0.646
(0.117)
237.140
(269.523)
902.229
(536.748)
102.246
(70.226)
1,114.911
(599.377)
0.566
(0.109)
Mean
Non-R
[B]
21
24.475
(8.180)
6.395
(0.671)
13.649
(0.637)
0.672
(0.088)
350.410
(523.703)
855.864
(663.022)
85.524
(71.706)
1,036.695
(618.923)
0.594
(0.108)
Mean
R
-9.724
(1.846)***
0.239
(0.174)
0.149
(0.128)
-0.026
(0.027)
-113.270
(73.644)*
46.365
(130.046)
16.723
(16.510)
78.216
(141.130)
-0.028
(0.026)
Diff.(NR–R)
NOTES: Panel A pertains to data from 296 districts (out of the total 338) and panel B pertains to data from 157 districts (out of 182)
where each district has just one constituency. All variables (except Total seats) have been constructed using data from the 1971 census.
Standard errors in parentheses for the columns indicating differences; standard deviation in parentheses for all other columns. T-test
used for comparing differences. *significant at 10% **significant at 5% ***significant at 1%
228
Observations
Mean
23.012
(10.674)
6.761
(0.665)
14.202
(0.591)
0.674
(0.089)
450.908
(458.984)
1,144.085
(782.837)
118.268
(81.120)
1,084.219
(524.248)
0.599
(0.096)
2.091
(1.126)
Mean
R
13.974
(7.608)
Population density (log) 6.762
(0.827)
Population (log)
13.956
(0.596)
Gini (landholdings)
0.657
(0.114)
Electricity
287.638
(312.651)
Educational facilities
1,003.894
(580.541)
Medical facilities
112.465
(73.074)
Rainfall
1,197.446
(685.577)
Turnout 1971
0.581
(0.110)
Total seats
1.387
(0.768)
SC population (%)
Variables
Non-R
[A]
Table 1: D ISTRICT- LEVEL SUMMARY STATISTICS FROM 1971: Comparing reserved with non-reserved districts.
Table 2: H OUSEHOLD - LEVEL SUMMARY STATISTICS : Reserved vs non-reserved districts
[A]
[B]
[C]
All
Mean
Non-Reserved
Mean
Reserved
Mean
Difference
NR – R
0.05
(0.21)
Per-capita exp. (in Rs.)
196.41
(207.49)
SC dummy
0.18
(0.39)
Urban dummy
0.24
(0.43)
Hindu dummy
0.84
(0.37)
HH size
5.00
(2.60)
Primary Educ. dummy
0.36
(0.48)
Homeowner dummy
0.82
(0.38)
Dwelling type (1 or 2)
1.81
(0.39)
Dwelling structure (1, 2 or 3)
1.87
(0.82)
Self-Employ. Rural dummy
0.38
(0.49)
Laborer Rural
0.31
(0.46)
Self-Employ. Urban dummy
0.08
(0.27)
Wage/Salary earner dummy
0.11
(0.31)
Casual Laborer dummy
0.03
(0.17)
Poor HH dummy (Y=1, N=0)
0.33
(0.47)
Headcount Ratio (district)
0.43
(0 .21)
Observations
108,312
0.05
(0.22)
195.83
(191.82)
0.16
(0.37)
0.24
(0.43)
0.84
(0.37)
5.02
(2.57)
0.36
(0.48)
0.82
(0.38)
1.80
(0.40)
1.89
(0.82)
0.38
(0.49)
0.31
(0.46)
0.08
(0.27)
0.11
(0.31)
0.03
(0.18)
0.34
(0.47)
0.43
(0.22)
78,211
0.04
(0.19)
197.77
(240.55)
0.23
(0.42)
0.24
(0.43)
0.85
(0.36)
5.03
(2.63)
0.35
(0.48)
0.83
(0.37)
1.83
(0.38)
1.84
(0.83)
0.38
(0.49)
0.31
(0.46)
0.09
(0.28)
0.11
(0.31)
0.03
(0.17)
0.33
(0.47)
0.43
(0.18)
30,101
0.02
(0.00)***
-1.94
(1.59)
-0.07
(0.00)***
0.00
(0.00)
-0.01
(0.00)**
-0.00
(0.02)
0.00
(0.00)
-0.01
(0.00)***
-0.03
(0.00)***
0.05
(0.01)***
0.00
(0.00)
0.00
(0.00)
-0.01
(0.00)***
0.00
(0.00)
0.00
(0.00)***
0.00
(0.00)
-0.01
(0.03)
Variables (Household)
Public Works dummy
NOTES: Panel A pertains to households from all districts, while Panels B and C present
household characteristics from non-reserved (250) and reserved districts (77), respectively (a
‘reserved’ district contains at least one reserved constituency). # Observations for Dwelling type
and Dwelling structure are 107,909 (panel A), 77,918 (panel B) and 29,991(panel C). T-test used for
32the columns indicating differences; standard
differences. Standard errors in parentheses for
deviation in parentheses for all others. *significant at 10% **significant at 5% ***significant at 1%.
Table 3: D ISTRICT- LEVEL SUMMARY STATISTICS FROM 1987-88: Comparing reserved with
non-reserved districts (for single-constituency districts).
[1]
[2]
[3]
All
Non-R
R
Mean
Mean
Mean
Diff. (NR–R)
0.475
(0.180)
Q3/Q1 (SC)
1.747
(0.408)
Gini SC
0.239
(0.076)
IQR (overall)
0.520
(0.089)
Q3/Q1 (overall)
1.862
(0.223)
Gini (overall)
0.295
(0.052)
SC per-capita exp. (in Rs.)
158.749
(43.850)
Average per-capita exp. (in Rs.) 191.265
(50.985)
SC population (%)
18.565
(9.644)
Population
0.203
(0.086)
Headcount Ratio SC
0.420
(0.227)
Poverty Gap Ratio SC
0.102
(0.073)
Reserved dummy
0.126
(0.333)
0.464
(0.184)
1.729
(0.415)
0.237
(0.079)
0.517
(0.090)
1.859
(0.229)
0.296
(0.053)
158.876
(44.920)
190.934
(49.954)
17.424
(9.099)
0.205
(0.085)
0.422
(0.229)
0.102
(0.072)
0.547
(0.135)
1.866
(0.347)
0.253
(0.057)
0.543
(0.075)
1.883
(0.179)
0.288
(0.041)
157.868
(36.436)
193.547
(58.809)
26.452
(9.807)
0.191
(0.098)
0.403
(0.210)
0.104
(0.084)
-0.083
(0.040)**
-0.137
(0.091)*
-0.016
(0.017)
-0.027
(0.020)*
-0.024
(0.050)
0.008
(0.012)
1.008
(9.809)
-2.613
( 11.404)
-9.028
(2.050)***
0.014
(0.019)
0.019
(0.051)
-0.002
(0.016)
159
23
Variables (district)
IQR (SC)
Observations
182
[4]
NOTES: The sample of districts is restricted to those which correspond to a single constituency.
All variables except Reserved dummy have been constructed using data from the NSSO round 43
(1987-88). Standard errors in parentheses for the columns indicating differences; standard
deviation in parentheses for all other columns. T-test used for comparing differences. *significant
at 10% **significant at 5% ***significant at 1%
33
Table 4: D ISTRICT- LEVEL SUMMARY
‘Non-Single constituency’ districts.
Variables (district)
STATISTICS :
Comparing ‘Single-constituency’ with
[A]
[B]
Single
Non-Single
Difference
N
Mean
N
Mean
S–NonS
IQR (SC)
182
145
Q3/Q1 (SC)
182
Gini SC
182
IQR (overall)
182
Q3/Q1 (overall)
182
Gini (overall)
182
SC per-capita exp. (in Rs.)
182
Per-capita exp. (in Rs.)
182
SC population (%)
182
Population
182
Reserved dummy
182
0.475
(0.180)
1.747
(0.408)
0.239
(0.076)
0.520
(0.089)
1.862
(0.223)
0.295
(0.052)
158.749
(43.850)
191.265
(50.985)
18.565
(9.644)
0.203
(0.086)
0.126
(0.333)
0.469
(0.134)
1.727
(0.309)
0.248
(0.068)
0.528
(0.099)
1.895
(0.267)
0.303
(0.057)
155.750
(39.527)
191.869
(54.656)
17.400
(8.599)
0.374
(0.179)
0.346
(0.477)
0.006
(0.018)
0.020
(0.041)
-0.009
(0.008)
-0.008
(0.010)
-0.033
(0.027)
-0.008
(0.006)
2.998
(4.573)
-0.605
(5.751)
1.165
(1.001)
-0.171
(0.015)***
-0.220
(0.044)***
145
145
145
145
145
156
156
156
156
156
NOTES: Panel A corresponds to the 182 districts where each district has just one constituency
while Panel B has data from the remaining 156 districts. Standard errors in parentheses for the
columns indicating differences; standard deviation in parentheses for all others. T-test used for
comparing differences. *significant at 10% **significant at 5% ***significant at 1%
34
35
Logit
Logit
[4]
Logit
[5]
Logit
[6]
Probit
[7]
OLS
[8]
108,309
0.024
No
108,309
0.026
No
108,309
0.026
No
108,309
0.027
No
107,819
0.029
No
107,819
0.041
Yes
107,819
0.041
Yes
107,819
0.015
Yes
-0.387*** -0.385*** -0.386*** -0.386*** -0.386*** -0.376*** -0.166*** -0.016***
(0.120)
(0.120)
(0.120)
(0.120)
(0.120)
(0.121)
(0.054)
(0.005)
-0.314*** -0.313*** -0.295*** -0.256*** -0.186**
-0.048
-0.020
-0.002
(0.076)
(0.076)
(0.078)
(0.080)
(0.080)
(0.074)
(0.033)
(0.003)
0.090
0.053
0.062
0.046
0.014
-0.130*
-0.056
-0.007*
(0.070)
(0.071)
(0.070)
(0.070)
(0.071)
(0.076)
(0.035)
(0.004)
-0.975*** -0.957*** -0.959*** -0.919*** -0.855*** -0.557*** -0.228*** -0.011**
(0.104)
(0.104)
(0.104)
(0.102)
(0.102)
(0.212)
(0.088)
(0.005)
0.356*** 0.361*** 0.367*** 0.373*** 0.392*** 0.176*** 0.016***
(0.091)
(0.091)
(0.091)
(0.089)
(0.089)
(0.039)
(0.003)
0.016*
0.019** 0.026*** 0.048*** 0.022*** 0.002***
(0.009)
(0.009)
(0.009)
(0.009)
(0.004)
(0.000)
-0.171*** -0.125**
-0.060
-0.025
-0.003
(0.057)
(0.059)
(0.058)
(0.026)
(0.002)
No
No
No
No
Yes
Yes
Yes
Yes
Logit
Logit
[3]
NOTES: Dependent variable is Public Works. Robust standard errors (clustered by district) in parentheses. *significant at 10%
**significant at 5% ***significant at 1%
Observations
Pseudo/Adj. R2
Employ dum.
Home dum.
Educ.
HH size
Hindu dum.
Urban dum.
SC dum.
Pce
Reserved dum.
[2]
[1]
Table 5: H OUSEHOLD - LEVEL REGRESSIONS : Reservation and household participation in ‘Public Works’.
36
No
0.008
108,309
Employ dum.
Adjusted R2
Observations
[3]
[4]
[5]
[6]
[7]
[8]
108,309
0.009
No
108,309
0.008
No
108,309
0.009
No
107,819
0.009
No
107,819
0.010
No
107,819
0.014
Yes
107,819
0.016
Yes
0.016
0.015
0.016
0.013
0.014
0.015
0.016
(0.013)
(0.013)
(0.013)
(0.013)
(0.013)
(0.013)
(0.013)
-0.017***
-0.017***
-0.016***
-0.016***
(0.005)
(0.005)
(0.005)
(0.005)
-0.011*** -0.009** -0.009**
-0.006*
-0.006*
-0.001
-0.000
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
(0.003)
(0.003)
0.003
0.001
0.002
-0.001
0.001 -0.008**
-0.007
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
-0.031*** -0.029*** -0.029*** -0.027*** -0.027*** -0.012** -0.011**
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.005)
(0.005)
0.014*** 0.015*** 0.015*** 0.015*** 0.015*** 0.016*** 0.016***
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
0.001*
0.001*
0.001**
0.001**
0.001** 0.002*** 0.002***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.007*** -0.008*** -0.006** -0.005**
-0.003
-0.003
(0.003)
(0.002)
(0.003)
(0.003)
(0.002)
(0.002)
No
No
No
Yes
Yes
Yes
Yes
[2]
NOTES: OLS regressions where the dependent variable is Public Works. The Headcount Ratio (HCR) is used as the measure of
(district-level) poverty. Robust standard errors (clustered by district) in parentheses. *significant at 10% **significant at 5%
***significant at 1%
No
-0.011***
(0.004)
0.002
(0.004)
-0.031***
(0.003)
0.014***
(0.003)
0.001
(0.000)
0.015
(0.013)
Home dum.
Educ.
HH size
Hindu dum.
Urban dum.
SC dum.
Pce
Reserved dum.
Poverty (HCR)
[1]
Table 6: H OUSEHOLD - LEVEL REGRESSIONS : Reservation, district-level poverty and household participation in ‘Public Works’.
37
Rural
[3]
Rural
[4]
Urban
[5]
Urban
[6]
Single
[7]
0.009
16,760
Adjusted R2
Observations
16,760
0.010
Yes
68,236
0.006
No
68,236
0.012
Yes
39,583
0.001
No
39,583
0.002
Yes
43,434
0.010
No
43,434
0.019
Yes
-0.006
(0.011)
0.005
(0.004)
-0.009
(0.006)
-0.021**
(0.008)
0.030***
(0.006)
0.003***
(0.001)
-0.026***
(0.008)
Yes
Single
[8]
NOTES: OLS regressions where the dependent variable is Public Works. SC refers to regressions with only SC households; likewise for
Rural and Urban. Single means that the the sample is restricted to households from only those districts which house a single
constituency (there are 182 such districts). Robust standard errors (clustered by district) in parentheses. *significant at 10%
**significant at 5% ***significant at 1%
No
-0.023*** -0.021*** -0.020***
-0.002
-0.002
-0.007
(0.007)
(0.006)
(0.006)
(0.004)
(0.004)
(0.011)
0.011**
-0.007
0.000
-0.001
-0.001
-0.004
(0.006)
(0.005)
(0.004)
(0.002)
(0.002)
(0.005)
-0.001
-0.009*
0.001
0.000
0.002
(0.004)
(0.005)
(0.003)
(0.003)
(0.006)
-0.041***
-0.003
-0.029***
(0.007)
(0.017)
(0.006)
0.023**
0.024** 0.017*** 0.019*** 0.007*** 0.006*** 0.029***
(0.011)
(0.011)
(0.004)
(0.005)
(0.002)
(0.002)
(0.005)
0.003*** 0.003*** 0.002*** 0.003***
0.001
0.001*
0.002**
(0.001)
(0.001)
(0.001)
(0.001)
(0.000)
(0.000)
(0.001)
-0.010*
-0.007 -0.025*** -0.020***
0.003
0.002 -0.032***
(0.005)
(0.005)
(0.005)
(0.005)
(0.002)
(0.003)
(0.008)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
SC
SC
-0.023***
(0.007)
0.008
(0.005)
[2]
(R OBUSTNESS ): Reservation and household participation in ‘Public Works’ (different
[1]
REGRESSIONS
Employ dum.
Home dum.
Educ.
HH size
Hindu dum.
Urban dum.
SC dum.
Pce
Reserved dum.
Table 7: H OUSEHOLD - LEVEL
samples).
38
SC
[3]
SC
[4]
Rural
[5]
Rural
[6]
Single
[7]
Single
[8]
107,819
Observations
107,819
0.015
Yes
16,760
0.007
No
16,760
0.009
Yes
68,236
0.006
No
68,236
0.011
Yes
43,434
0.011
No
43,434
0.020
Yes
NOTES: OLS regressions where the dependent variable is Public Works. All refers to regressions with all households; likewise for SC
and Rural. Single means that the the sample is restricted to households from only those districts which house a single constituency
(there are 182 such districts). Robust standard errors (clustered by district) in parentheses. *significant at 10% **significant at 5%
***significant at 1%
0.009
No
-0.001*** -0.001*** -0.001** -0.001** -0.001*** -0.001***
-0.001*
-0.001*
(0.000)
(0.000)
(0.001)
(0.001)
(0.000)
(0.000)
(0.000)
(0.000)
-0.007**
-0.002
0.008 0.011**
-0.007
0.001
-0.003
0.005
(0.004)
(0.003)
(0.005)
(0.005)
(0.005)
(0.004)
(0.005)
(0.004)
0.000
-0.007*
-0.001 -0.010**
0.003
-0.008
(0.004)
(0.004)
(0.004)
(0.005)
(0.006)
(0.006)
-0.028*** -0.011** -0.042***
-0.004
-0.029*** -0.020**
(0.003)
(0.005)
(0.007)
(0.017)
(0.006)
(0.008)
0.015*** 0.016***
0.021** 0.021** 0.017*** 0.018*** 0.029*** 0.030***
(0.003)
(0.003)
(0.011)
(0.011)
(0.004)
(0.004)
(0.005)
(0.005)
0.001** 0.002*** 0.003*** 0.003*** 0.002*** 0.003***
0.002** 0.003***
(0.000)
(0.000)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
-0.005**
-0.003
-0.010*
-0.007 -0.025*** -0.020*** -0.032*** -0.026***
(0.003)
(0.002)
(0.005)
(0.005)
(0.005)
(0.005)
(0.008)
(0.008)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
All
All
Adjusted R2
Employ dum.
Home dum.
Educ.
HH size
Hindu dum.
Urban dum.
SC dum.
Pce
Reserved Time
[2]
[1]
Table 8: H OUSEHOLD - LEVEL REGRESSIONS (R OBUSTNESS ): Duration of reservation and household participation in ‘Public Works’.
39
All
[3]
All
[4]
Poor
[5]
Poor
[6]
Poor
[7]
Poor
[8]
0.009
108,309
Adjusted R2
Observations
108,309
0.009
No
107,819
0.010
No
107,819
0.015
Yes
32,618
0.012
No
32,618
0.012
No
32,463
0.012
No
32,463
0.018
Yes
NOTES: OLS regressions where the dependent variable is Public Works. All refers to regressions with all households and Poor means
that the sample is restricted to households which are below the official poverty line. Robust standard errors (clustered by district) in
parentheses. *significant at 10% **significant at 5% ***significant at 1%
No
-0.016*** -0.016*** -0.016*** -0.028*** -0.028*** -0.028*** -0.027***
(0.005)
(0.005)
(0.005)
(0.008)
(0.008)
(0.008)
(0.008)
0.013***
0.010**
0.005
(0.004)
(0.004)
(0.004)
-0.009
-0.008
-0.007
-0.001
(0.012)
(0.012)
(0.012)
(0.012)
0.003
0.002
0.000
-0.007*
-0.006
-0.006
-0.008 -0.015**
(0.003)
(0.004)
(0.004)
(0.004)
(0.006)
(0.006)
(0.006)
(0.007)
-0.036*** -0.033*** -0.030*** -0.012** -0.046*** -0.045*** -0.041***
-0.015
(0.004)
(0.003)
(0.003)
(0.005)
(0.006)
(0.006)
(0.005)
(0.009)
0.015*** 0.015*** 0.016*** 0.016*** 0.017*** 0.017*** 0.018*** 0.019***
(0.004)
(0.004)
(0.003)
(0.003)
(0.006)
(0.006)
(0.006)
(0.006)
0.001*
0.001** 0.001*** 0.002***
0.001**
0.001**
0.002** 0.002***
(0.000)
(0.000)
(0.000)
(0.000)
(0.001)
(0.001)
(0.001)
(0.001)
-0.008*** -0.005**
-0.002
-0.006
-0.004
-0.001
(0.003)
(0.003)
(0.002)
(0.004)
(0.004)
(0.004)
No
No
Yes
Yes
No
No
Yes
Yes
-0.016***
(0.005)
0.014***
(0.004)
All
All
Employ dum.
Home dum.
Educ.
HH size
Hindu dum.
Urban dum.
SC dum.
Pce
Poor HH dum.
Reserved dum.
[2]
[1]
Table 9: H OUSEHOLD - LEVEL REGRESSIONS : Reservation and below-poverty line household participation in ‘Public Works’.
Table 10: H OUSEHOLD - LEVEL REGRESSIONS : Reservation and household participation in
‘Public Works’ (Interaction effects).
[1]
Reserved dum.
Pce
Reserved dum. X Pce
SC dum.
Urban dum.
Hindu dum.
HH size
Educ.
Home dum.
Employ dum.
Adjusted R2
Observations
[2]
[3]
[4]
[5]
[6]
-0.114*** -0.112*** -0.112*** -0.112*** -0.112*** -0.109***
(0.035)
(0.035)
(0.035)
(0.035)
(0.035)
(0.034)
-0.019*** -0.019*** -0.018*** -0.016*** -0.013***
-0.007*
(0.005)
(0.005)
(0.005)
(0.005)
(0.005)
(0.004)
0.019*** 0.019*** 0.019*** 0.019*** 0.019*** 0.018***
(0.006)
(0.006)
(0.006)
(0.006)
(0.006)
(0.006)
0.005
0.003
0.003
0.003
0.001
-0.007
(0.003)
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
-0.032*** -0.031*** -0.031*** -0.029*** -0.027*** -0.012**
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
(0.005)
0.014*** 0.014*** 0.015*** 0.015*** 0.016***
(0.003)
(0.003)
(0.003)
(0.003)
(0.003)
0.001
0.001*
0.001** 0.002***
(0.000)
(0.000)
(0.000)
(0.000)
-0.007*** -0.005**
-0.003
(0.002)
(0.003)
(0.002)
No
No
No
No
Yes
Yes
No
No
No
No
No
Yes
0.009
0.009
0.009
0.009
0.010
0.016
108,309
108,309
108,309
108,309
107,819
107,819
NOTES: OLS regressions where the dependent variable is Public Works. Robust standard errors
(clustered by district) in parentheses. *significant at 10% **significant at 5% ***significant at 1%
40
41
[3]
182
Observations
182
0.086
No
157
0.116
0.215**
(0.084)
0.249***
(0.081)
0.446**
(0.180)
-0.041
(0.074)
-0.570
(0.737)
0.278
(0.342)
-0.228***
(0.088)
No
Q3 /Q1 (SC)
Q3 /Q1 (SC)
0.131*
(0.077)
-0.046
(0.058)
0.447***
(0.171)
[5]
[4]
Gini SC
[7]
Gini SC
[8]
Gini SC
[9]
157
0.202
182
0.099
157
0.120
157
0.184
0.217**
0.018*
0.017*
0.014
(0.103)
(0.010)
(0.010)
(0.018)
0.218*
0.018*
0.030
0.038
(0.127)
(0.011)
(0.020)
(0.027)
0.398** 0.095*** 0.089*** 0.090**
(0.188)
(0.020)
(0.032)
(0.037)
0.061
0.025*** 0.040***
(0.076)
(0.009)
(0.015)
-0.933
-0.128*
-0.132
(0.799)
(0.067)
(0.132)
0.296
0.043
0.048
(0.462)
(0.104)
(0.084)
-0.155
-0.023*
-0.017
(0.104)
(0.012)
(0.022)
Yes
No
No
Yes
Q3 /Q1 (SC)
[6]
NOTES: OLS regressions where the dependent variable is a measure of SC-inequality: Mean-normalized Inter-quartile range (IQR) in
columns 1-3, Q3 /Q1 ratio in columns 4-6 and Gini in columns 7-9. Robust standard errors in parentheses. Standard errors are clustered
by state.*significant at 10% **significant at 5% ***significant at 1%
157
157
0.223
0.084
Adjusted R2
0.125
0.087**
(0.038)
0.108*
(0.058)
0.139*
(0.077)
0.075*
(0.043)
-0.049
(0.037)
0.103
(0.171)
-0.069
(0.051)
Yes
SC IQR SC IQR
[2]
0.082*** 0.093***
(0.028)
(0.031)
Population
-0.003 0.128***
(0.025)
(0.039)
Pce.
0.189*** 0.177**
(0.066)
(0.081)
SC%
0.024
(0.055)
SC% (1971)
-0.031
(0.029)
Gini 71 (landholdings)
0.161
(0.127)
Population 71
-0.100**
(0.039)
State dummies
No
No
Reserved dum.
SC IQR
[1]
Table 11: D ISTRICT- LEVEL REGRESSIONS : Reservation and SC inequality.
42
IQR
IQR
IQR
[3]
182
Observations
157
0.057
157
0.167
182
0.029
No
157
0.037
0.083**
(0.042)
0.033
(0.043)
0.093
(0.101)
0.014
(0.068)
-0.035
(0.039)
0.077
(0.147)
-0.053
(0.046)
No
Q3 /Q1
Q3 /Q1
0.055
(0.042)
-0.057
(0.035)
0.082
(0.083)
[5]
[4]
Gini
[7]
Gini
[8]
Gini
[9]
157
0.137
182
0.010
157
0.086
157
0.065
0.085* 0.081***
0.041
0.037
(0.047)
(0.031)
(0.035) (0.054)
0.004
0.013
-0.059 -0.005
(0.066)
(0.039)
(0.058) (0.083)
0.057
0.084*
0.095
0.047
(0.088)
(0.050)
(0.091) (0.125)
0.053
0.086*** 0.123**
(0.043)
(0.028) (0.050)
-0.052
0.021
0.043
(0.041)
(0.169) (0.393)
0.245
0.078
0.119
(0.227)
(0.235) (0.243)
-0.029
0.034
0.017
(0.052)
(0.034) (0.067)
Yes
No
No
Yes
Q3 /Q1
[6]
Gini SC
Gini All
IQR SC
IQR All
in
columns 1-3,
in columns 4-6 and
in columns 5-8. IQR stands for the Inter-quartile range (normalized by the mean).
Robust standard errors in parentheses. Standard errors are clustered by state.*significant at 10% **significant at 5% ***significant at 1%
Q3 /Q1 SC
Q3 /Q1 All
NOTES: OLS regressions where the dependent variable is a measure of SC-inequality relative to overall district-level inequality :
0.030
Adjusted R2
0.113** 0.127** 0.132*
(0.053) (0.054) (0.069)
Population
-0.036 0.133**
0.074
(0.053) (0.065) (0.107)
Pce.
0.190
0.203
0.145
(0.123) (0.150) (0.130)
SC%
0.079 0.157**
(0.107) (0.076)
SC% (1971)
-0.066 -0.103
(0.057) (0.066)
Gini 71 (landholdings)
0.169
0.379
(0.222) (0.346)
Population 71
-0.123* -0.069
(0.070) (0.086)
State dummies
No
No
Yes
Reserved dum.
[2]
[1]
Table 12: D ISTRICT- LEVEL REGRESSIONS : Reservation and SC inequality relative to overall district inequality.
43
SC pce
SC pce
SC pce
[3]
HCR SC
[4]
HCR SC
[5]
HCR SC
[6]
PGR SC
[7]
PGR SC
[8]
PGR SC
[9]
182
Observations
157
0.694
157
0.715
182
0.497
157
0.504
157
0.614
182
0.391
157
0.455
157
0.579
NOTES: OLS regressions where the dependent variable is log SC per-capita exp. (in the district) for columns 1–3 and a measure of
SC-poverty in the remaining columns (Headcount Ratio in columns 4–6 and Poverty Gap Ratio in columns 7–9). Robust standard
errors in parentheses. Standard errors are clustered by state.*significant at 10% **significant at 5% ***significant at 1%
0.697
Adjusted R2
-0.006
0.020
0.032
-0.015
-0.010
-0.038
0.002
0.006
-0.003
(0.023)
(0.023)
(0.030)
(0.019)
(0.027)
(0.024)
(0.012)
(0.015)
(0.012)
Population
-0.042
-0.058
-0.034
0.027
0.055
0.119**
-0.002
0.011
0.033*
(0.028)
(0.039)
(0.065)
(0.026)
(0.050)
(0.060)
(0.011)
(0.018)
(0.018)
Pce.
0.830*** 0.866*** 0.936*** -0.607*** -0.618*** -0.785*** -0.177*** -0.183*** -0.225***
(0.047)
(0.065)
(0.077)
(0.050)
(0.052)
(0.074)
(0.017)
(0.023)
(0.024)
SC%
-0.007
-0.007
-0.020
0.013
0.003
0.012
(0.019)
(0.032)
(0.038)
(0.036)
(0.010)
(0.009)
SC% (1971)
-0.094
-0.219
0.064
0.266
-0.051
-0.009
(0.208)
(0.271)
(0.259)
(0.259)
(0.083)
(0.090)
Gini 71 (landholdings)
-0.142
-0.034
0.117
0.148
0.081
0.086
(0.126)
(0.140)
(0.176)
(0.160)
(0.060)
(0.056)
Population 71
0.018
-0.000
-0.013
-0.072
-0.012 -0.036**
(0.032)
(0.049)
(0.039)
(0.048)
(0.015)
(0.015)
State dummies
No
No
Yes
No
No
Yes
No
No
Yes
Reserved dum.
[2]
[1]
Table 13: D ISTRICT- LEVEL REGRESSIONS (R OBUSTNESS ): Reservation and SC pce, SC poverty.
References
A RULAMPALAM , W., S. D ASGUPTA , A. D HILLON , AND B. D UTTA (2009): “Electoral goals
and center-state transfers: A theoretical model and empirical evidence from India,”
Journal of Development Economics, 88(1), 103 – 119.
A USTEN -S MITH , D., AND M. WALLERSTEIN (2006): “Redistribution and affirmative action,” Journal of Public Economics, 90(10-11), 1789 – 1823.
B ANERJEE , A., AND R. PANDE (2007): “Parochial Politics: Ethnic Preferences and Politician Corruption,” Harvard University, Kennedy School of Government, typescript.
B ANERJEE , A., AND R. S OMANATHAN (2007): “The political economy of public goods:
Some evidence from India,” Journal of Development Economics, 82(2), 287 – 314.
B ARDHAN , P., AND D. M OOKHERJEE (1999): “Relative Capture at Local and National Levels: An Essay in the Political Economy of Decentralization,” Working paper, Institute
for Economic Development, Boston University, November 1999.
(2000): “Capture and Governance at Local and National Levels,” American Economic Review, 90(2), 135–139.
(2010): “Determinants of Redistributive Politics: An Empirical Analysis of Land
Reforms in West Bengal, India,” American Economic Review, 100(4): 15721600.
B ARDHAN , P., D. M OOKHERJEE , AND M. PARRA T ORRADO (2010): “Impact of Political
Reservations in West Bengal Local Governments on Anti-Poverty Targeting,” Journal of
Globalization and Development: Vol. 1 : Iss. 1, Article 5.
B EAMAN , L., R. C HATTOPADHYAY, E. D UFLO , R. PANDE , AND P. T OPALOVA (2009):
“Powerful Women: Does Exposure Reduce Bias?,” Quarterly Journal of Economics,
124(4), 1497 – 1540.
B ERTRAND , M., R. H ANNA , AND S. M ULLAINATHAN (2008): “Affirmative Action: Evidence from college admissions in India,” (April 2008). NBER Working Paper No.
W13926.
B ESLEY, T., R. PANDE , L. R AHMAN , AND V. R AO (2004): “The Politics of Public Good
Provision: Evidence from Indian Local Governments,” Journal of the European Economic
Association, 2(2/3), 416–426.
B ESLEY, T., R. PANDE , AND V. R AO (2005): “Participatory Democracy in Action: Survey
Evidence from South India,” Journal of the European Economic Association, 3(2/3), 648–
657.
C HATTOPADHYAY, R., AND E. D UFLO (2004a): “Impact of Reservation in Panchayati Raj:
Evidence from a Nationwide Randomised Experiment,” Economic and Political Weekly,
39(9), 979–986.
44
(2004b): “Women as Policy Makers: Evidence from a Randomized Policy Experiment in India,” Econometrica, 72(5), 1409–1443.
C HAUDHURY, P. (2004): “The ’Creamy Layer’: Political Economy of Reservations,” Economic and Political Weekly, 39(20), pp. 1989–1991.
C HIN , A., AND N. P RAKASH (2011): “The redistributive effects of political reservation for
minorities: Evidence from India,” Journal of Development Economics, 96(2), 265 – 277.
D ESHPANDE , A. (2000): “Does Caste Still Define Disparity? A Look at Inequality in Kerala, India,” The American Economic Review, 90(2), 322–325.
D IXIT, A., AND J. L ONDREGAN (1996): “The Determinants of Success of Special Interests
in Redistributive Politics,” The Journal of Politics, 58(4), pp. 1132–1155.
(1998): “Fiscal federalism and redistributive politics,” Journal of Public Economics,
68(2), 153 – 180.
E LECTION C OMMISION OF I NDIA , . (ed.) (1985): Statistical Report on General Elections,
1984 to the Eighth Lok Sabhavol. 1. Election Commission of India.
E LECTION C OMMISION OF I NDIA , . (ed.) (1986): Statistical Report on General Elections,
1985 to the Eighth Lok Sabhavol. 1. Election Commission of India.
F ERN ÁNDEZ , R., AND G. L EVY (2008): “Diversity and redistribution,” Journal of Public
Economics, 92(5-6), 925 – 943.
F OSTER , A., AND M. R OSENZWEIG (2001): “Democratization, Decentralization and the
Distribution of Local Public Goods in a Poor Rural Economy.,” Working Paper, Department of Economics, Brown University.
F OSTER , J. E., AND A. F. S HORROCKS (1991): “Subgroup Consistent Poverty Indices,”
Econometrica, 59(3), 687–709.
G HOSH , S., AND S. PAL (2010): “Poverty, Heterogeneous Elite, and Allocation of Public
Spending: Panel Evidence from the Indian States,” Brunel Economics and Finance Working paper 10-20, London, UK.
G HURYE , G. S. (1969): “Caste and Race in India,” 5th edition, Popular Prakashan, India.
H NATKOVSKA , V., A. L AHIRI , AND S. B. PAUL (2011): “Breaking the Caste Barrier: Intergenerational Mobility in India,” Working paper, May 2011.
L INDBECK , A., AND J. W. W EIBULL (1987): “Balanced-Budget Redistribution as the Outcome of Political Competition,” Public Choice, 52(3), 273–297.
L IZZERI , A., AND N. P ERSICO (2001): “The Provision of Public Goods under Alternative
Electoral Incentives,” The American Economic Review, 91(1), 225–239.
45
M UNSHI , K., AND M. R OSENZWEIG (2008): “The Efficacy of Parochial Politics: Caste,
Commitment, and Competence in Indian Local Governments,” (September 2008).
NBER Working Paper No. W14335.
M YERSON , R. B. (1993): “Incentives to Cultivate Favored Minorities Under Alternative
Electoral Systems,” The American Political Science Review, 87(4), 856–869.
PANDE , R. (2003): “Can Mandated Political Representation Increase Policy Influence for
Disadvantaged Minorities? Theory and Evidence from India,” The American Economic
Review, 93(4), 1132–1151.
R AY, D., AND R. S ETHI (2009): “A Remark on Color-Blind Affirmative Action,” forthcoming, Journal of Public Economic Theory.
S EN , A. K. (1997): “On Economic Inequality,” (enlarged edition with a substantial new
annex co-written with James. E. Foster), Clarendon Press, 1997.
S HORROCKS , A. F. (1980): “The Class of Additively Decomposable Inequality Measures,”
Econometrica, 48(3), 613–625.
46