Global COE Hi-Stat Discussion Paper Series 286

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

More Political Parties, More Redistribution?
Empirical Evidence from State Governments in India

Hi-Stat Discussion Paper

Yuko Mori

March 2013

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

More Political Parties, More Redistribution?
Empirical Evidence from State Governments in India 1

Yuko Mori 2
January 2013

Abstract
This paper uses panel data accumulated from 1972 to 1997 to investigate the effect ofpolitical systems on the public expenditure of state governments in India. We focus on
the decline of the Indian National Congress, which was the dominant party until the mid
1960s, and the development of regional parties (RPs) that are based on specific religious
or caste groups. The effects of changes in party systems are carefully distinguished fromthose in voter preference, which affect party systems and government policy. We findthat the decline of a dominant party increases development expenditure, which suggeststhat little political competition is bad for development. By contrast, an increase in seats
occupied by RPs decreases development expenditure and increases non-development
expenditure. This suggests that, in a pluralistic society, the diversity of political parties
does not contribute to an economic development and a reduction in poverty.

1

The author wishes to express her appreciation to Takashi Kurosaki, Daiji Kawaguchi, Norihito
Sakamoto, and Koji Yamazaki for their helpful comments and suggestions. She is also thankful forthe useful comments made by Yutaka Arimoto, Katsuo Kogure, Shinkichi Taniguchi, and theparticipants of the Development Economics Seminar. This work was supported by the Japan Society
for the Promotion of Science (JSPS) Grant-in-Aid for JSPS Fellows Grant Number 246375.
2 JSPS Research Fellow. Email: k121012z@r.hit-u.ac.jp.
11. Introduction
The political party, which lies at the core of democratic politics, primarily articulates
and aggregates the interests of groups. Through political parties, supporters’ views canbe expressed as part of the governing process. 3 However, in many ethnic or religiously
diverse societies, economically and socially backward groups often are unable toorganize political parties, especially in the early stages of democracy. They are, therefore,
underrepresented politically. Since in a representative democracy political parties play asignificant role in giving diverse groups a voice in policy-making, the multiparty system
may be quite useful in fragmented societies (Lipset and Rokkan, 1967). On the other
hand, it is possible that multiparty systems may increase conflicts between politicalparties, making the government inefficient. For example, larger numbers of politicalparties supply the transfers to specific groups rather than diffusing public goods among
everybody (Fernandez and Levy, 2008; Lizzeri and Persico, 2005). To consider whatparty systems are effective in reducing poverty, especially in developing countries withethnically or religiously diverse populations, this paper investigates the effects on
redistribution of the transformation of India’s political system from a one-party to a
multiparty system. 43 Dalton and Wattenberg (2000) describe the functions of a political party in detail.
4 A considerable number of studies, especially in comparative politics, have been conducted on how
2Recently, there has been a renewed interest among economists regarding theimpact of political parties. According to the traditional theory, in which candidates only
commit to moderate policies and care about winning elections, political decisions reflect
the electorate’s preferences (Downs, 1957). In this framework, the impact of partisanshipon policy outcomes is limited since the median voter preference is decisive; therefore,
political parties do not matter. However, if complete policy commitment is absent, theaffiliation of legislators matters in policy outcomes (Besley and Coate, 1997; Osborne
and Slivinski, 1996). 5 In this case, increasing a group’s political representation will
increase its influence on policy. Several studies empirically estimate the causal effect ofidentity and preference of politicians on policy decisions; most of these papers focus on
the U.S. (Levit, 2006; Lee et al., 2004; Rehavi, 2007; Ferreira and Gyourko, 2009). 6 A
few previous studies have been published with respect to politics in India. Notably,
Bardhan and Mokerhjee (2010), who focused on West Bengal, showed that politicalparty was not a decisive factor in land redistribution policy. In another study concernedwith Indian politics, Clots-Figueras (2011) concluded that the gender of state legislators
affects state policy. Few studies investigate the significance of the political party undermultiparty systems work. Although this paper does not examine the above two hypotheses directly, it
contributes to an understanding of the functions of multiparty systems.
5 Munshi and Rosenzweig (2010) show that social groups can discipline their members to overcome
policy commitment problems by investigating the role of castes in local elections in India.
6 Most of them showed that the legislator’s affiliation influences outcomes. The exception is
Ferreira and Gyourko (2009), who examined mayoral elections in the U.S.
3

multiparty systems, however. 7 This paper will thus use data from 16 major states inIndia to investigate the variety of party systems. Our focus on India will be useful incomparing the performance of the party systems since the situation of political party andgovernment policy differs greatly among states.
The party systems of state government in India dramatically changed between
the mid 1960s and the 1990s. From the first parliamentary election held in 1951 to themid 1960s, the Indian National Congress (INC) enjoyed a prominent place in nationaland state legislatures. After the mid 1960s, however, the INC gradually lost its politicalpower because of its failed policies on poverty and corruption. Non-INC parties formed
following the electoral decline of the INC. While some of these parties, such as theJanata Party (JP) and the Bharatiya Janata Party (BJP), exert nationwide influence, most
non-INC parties are founded on castes, religions, and regions, and so exercise limited
political power. This paper examines how the decline of the INC and the developmentof regional parties (RPs) affect state government policy.
The variable used in this paper for government policy is public expenditure.
Great attention has been focused on the variation in state or district performance on

7 Chhibber and Nooruddin (2004) investigated the effect of the increasing number of politicalparties on state government policy. It should be noted, however, that they were unable to estimate thecausal effect of political parties on state government policy.
4

economic growth and poverty reduction in India. 8 Some studies highlight thesignificant roles of development expenditure. A prominent work by Banerjee and Iyer
(2005) shows that variants in the legacy of different land tenure systems in India have
led to sustained differences in economic performance in agricultural investment,
productivity, and health and education investment. They also suggest that the landlord
system intensified resentment between landlords and the masses, which leads to theinefficient governments since gaining independence. These authors used a simple
exercise to suggest that their empirical results are partly explained by the differences indevelopment expenditure. We investigate the part of the above mechanism by
estimating the effect of political parties on public expenditure.
We empirically investigate the effects of party systems (i.e., number of seats
occupied by non-INC parties). To eliminate state heterogeneities, we use the fixed effect(FE) model, which controls dummy variables for each state. Under the FE model, thecoefficients of party systems are identified using changes of the party systems withineach state. The main concern is the existence of omitted variable bias, which hascorrelated party systems and affects the policy. For example, when conflict amonggroups is increasing, each group has a strong incentive to organize a political party; the8 See, for example, Datt and Ravallion (1998), Banerjee et al. (2005), and Iyer (2004).
5

preference of each group is also changed to restrain the distribution toward other groups.
Therefore, to estimate the effect of party systems, we have to rule out the omitted
variable bias. To do so, we will use the results of close elections to provide us with
instrument variables. This strategy is based on Clots-Figueras (2011) and Rehavi(2007). 9 Since the results of close elections are considered almost random, we also will
use the fraction of seats that are won by non-INC parties in close elections against the
INC to provide instrumental variables to the fraction of seats won by non-INC parties.
We find that in states where the number of non-INC parties has been increasing,
the share of development and educational expenditure is increasing under both the FE
and the instrument variable (IV) model. This result suggests that a dominant-party
system is inefficient for development due to a lack of political competition. On the other
hand, an increase in seats occupied by RPs has decreased the development expenditureand increased the nondevelopment expenditure, which implies that multiparty systems
are inefficient. This result echoes the view of previous studies that show the drawback
to a large number of parties (Fernandez and Levy, 2008; Lizzeri and Persico, 2005).
The remainder of this paper is structured as follows: Section 2 explores theelection system and summarizes the history of political parties in India. Section 3
9 Both Rehavi (2007) and Clots-Figueras (2011) show that the legislator’s gender affects statepolicy. While Rehavi (2007) uses data from U.S. state governments, Clots-Figueras (2011) uses datafrom state governments in India.
6

discusses empirical strategies. Section 4 gives the results of this paper. Section 5
presents the conclusions.

2. Background2.1. History
The INC, which was at the forefront of the struggle for Indian independence, enjoyed aprominent place from the country’s first elections after independence until the mid
1960s. In the first national elections in 1957, the INC received 74.4% of votes and won
most state elections. 10 However, after the national election in 1967, the INC lost its
political power, owing mainly to its failed policies on poverty and corruption and a lackof strong leadership after the rule of Jawaharlal Nehru. Although the INC was restored
to power in the 1971 national elections, a sluggish economy and serious poverty
prompted the electorate to launch a movement against the government in the 1970s. As
a result, the government imposed a nationwide state of emergency from 1975 to 1977.
After the emergency rule was lifted, the INC lost a national election for the first time; italso lost political power in the state elections. This meant that Janata Dal (JD) party
ruled the national legislators in 1977 elections. The JD party was, however, unable to

10 There are many studies on why the INC remained dominant over the years; for example, see
Kothari (1964).
7

maintain its position as the ruling party since it includes various background groups.
After the late of 1980s, the BJP which advocates Hindu Nationalism grew byrepresenting by the upper castes. Since the INC and BJP continue to receive large shareof seats in national elections, India is often classified as a two-party system. However,since 1989, no single party has won a majority of seats; therefore, some classify India asa multiparty system.
The reason that the INC could maintain strong political power for a long timeis that it had been a comprehensive party that draws support from various social
divisions (Kothari, 1964). However, it also has been mainly ruled by upper-caste voters.
Since other voters, especially those from the Other Backward Castes (OBC) held agrievance against the INC, these voters gradually developed their own politicalorganizations. Since the features of these parties are based on specific groups, they holdpolitical power only in individual states. Some examples of state-based parties are theTelugu Desam Party (in Andhra Pradesh), Tamil Maanila Congress (in Tamil Nadu), andDravida Munnetra Kazhagam (in Tamil Nadu). Together with these parties, caste-based
parties have also emerged in parts of India. For instance, in Uttar Pradesh, because of
the electoral competition among the upper castes supporting the BJP, the backward
castes favor the Samaj Party and the scheduled castes (SCs) support the Bahujan Samaj
8Party (BSP) (Brass 1965). Although some of these parties, such as BJP or BSP, havegrown up to national parties, most of these parties have specific religions or castes as
their foundations.
In the following section, we will use election data to provide an overview ofthe shift in party systems in India.

2.3. Overview
In this section, we will use election data to provide an overview of the shift in party
systems in India. The states and union territories of India are divided into constituencies
that elect a single representative in state elections for the lower-house legislative
assembly, or the Vidhan Sabha. State elections are constitutionally scheduled to occur
every 5 years. In some cases, elections are held sooner, primarily due to shifting
political alignments. Since 1989, the minimum voting age in India has been 18 years;
previously, it had been 21 years. While the borders of the various constituencieschanged a few times prior to 1976, they were fixed from 1977 to 2007.
Figure 1 shows the number of political parties with seats in state legislaturesfrom the 1960s to the 1990s. Although this number has grown steadily in most states,
the level of growth varies among states. For example, while the number of parties in
9

Uttar Pradesh and Bihar has increased gradually, that in Andhra Pradesh and Rajasthan
has remained stable. The shares of seats won by the INC in state legislatures are plottedin Figure 2. Although this share has fluctuated over time, it exhibits a downward trendthat points to its demise. However, the imminent demise of the INC differs among states.
For example, while the INC remains strong in the states of Madhya Pradesh andRajasthan, it has been seriously challenged and almost collapsed in the states of West
Bengal and Tamil Nadu.
Two main reasons explain the differences in the decline of the INC and party
fragmentation among the states. The first is occasioned by economic development. For
example, since organizing and sustaining political parties is costly, an improvement in
the economic situation enables people to create political organizations where poverty
had previously prevented this creation. Moreover, higher education levels also affectparty development through an increased awareness of and interest in political affairs.
The second reason arises from conflicts among ethnic, religious, or caste groups. As
Banerjee and Iyer (2005) demonstrate, intensifying resentment between landlords andthe masses promoted the formation of class-based political conflict.
Since economic development and conflict are related, not only are the electors’
preferences shaped but government policy is also affected. Therefore, in this study, we
10will carefully examine the effect of political parties. In the following sections, we will
discuss the empirical strategy.

3. Empirical Strategy
3.1. Data(a) Government Policy
We used state public expenditure to analyze government policy. For public expenditure,
we used the share of development, non-development, educational, and healthexpenditures.

11

Total

expenditureconsistsofdevelopment

expenditure,

non-development expenditure, and compensation and assignments to local bodies.
Development expenditure has often been examined in previous studies. For example,
according to Datt and Ravallion (1998), it significantly affects poverty reduction.
Banerjee and Iyer (2005) found that the difference in performance between landlord
areas and non-landlord areas is partly due to the difference in development expenditure.
Non-development expenditure is also used for understanding how the composition of abudget is changed. In addition to above broad categories, we used educational andhealth expenditures. Educational and health expenditures are part of development

11 Data are from the Economic Organisation and Public Policy Indian States Data Base.
11expenditure. Educational expenditure includes expenditures that promote education, artand culture, scientific services, and research. Health expenditures include medicine,family planning, public health, sanitation, and water supply. Both types of expenditureare believed to contribute to poverty reduction. Since the budget reclassification thattook place in 1972, budget data from 1962 to 1972 cannot be confidently compared tothe data for later periods for all expenditure categories. We therefore restricted the periodof our study from 1972 to 1997.

(b) Political Parties
In this paper, we distinguish regime differences across states to examine the nature andextent of political organization. Since we are focusing on the fall of the INC and thedevelopment of other parties, we simplified our analysis by classifying parties as “INC”
and “non-INC.” 12 For the first exercise, it is useful to look at the decline of the INC andthe development of the non-INC parties because the non-INC parties have the potential
to change the political complexion in India. Second, we chose RPs from among thenon-INC parties that in this paper are defined as non-national parties. 13 These parties
12 The INC includes the Indian National Congress and the Indian National Congress (I). If we
include the Indian National Congress (U) in the INC, the results in this paper do not change.
13 National parties are defined as parties that are elected to seats in several states. The national
parties include: INC, Indian Congress (Socialist), Indian National Congress (Organization), All India
Indira Congress (Tiwari), BJP (Bahujan Samaj Party, Jan Sangh, and Bharatiya Jan Sangh),
12

represent most of the middle and lower classes, which roughly constitute all Indian
citizens outside of the upper castes and those who played no role in the INC government(Harriss, 1995). These parties, not surprisingly, lean toward redistribution policies.

3.2. Empirical Model
This paper uses panel data for the main 16 states in India during the period 1972 to 1997
to investigate the effect of political parties on government policy. The difficulty in ourempirical strategies lies in estimating the causal effects of political parties. For the firststep, we use FE model to rule out the state specific characteristics which are correlated
both government policies and party systems. Even though we are using the FE model,
the omitted variables, which vary across year and states, also affect the party system andgovernment policy. For example, as we discussed in the previous section, differences in
economic development or conflicts among groups are related to the development ofpolitical parties; these differences can affect government policy by shifting voter
preferences. Since the purpose of this paper is to investigate the significance of politicalparties, their causal effects need to be estimated.
To address this issue, we will use the change in political conditions resulting
Communist Parties (Communist Party of India and Communist Party of India [Marxist]), Janata
Party (JD, JP), Socialist Party, BSP, Swatantra, Republic Party of India, Sanghata Socialist Party,
Lok Dal, Forward Bloc, Praja Socialist Party,and Samajwadi Party.
13

from close elections as exogenous variations. This empirical strategy is based on thework of Clots-Figueras (2011) and Rehavi (2007), who investigated the effects of theincrease in the share of seats won by females in legislature on government policy.
We use the following estimated empirical model to examine the effect of the
non-INC parties share on government policy. The same model can also be used toestimate the effects of RPs:
Yit = α i + β t + γFit + µTCit + X it δ + ε it ,(1)

where Yit represents the government policy of state i in the year t; α i and β t are thestate and year fixed effects. Fit represents the fraction of seats occupied by non-INC
parties; TCit represents the share of close elections, which is defined as the number ofclose elections between new parties and the INC divided by the number of all seats; andX it represents other control variables, including real net state domestic product(NSDP) per capita, rural population share, and fraction of the SCs/STs reservation
seats. 14 Standard errors are clustered at election-state levels. For election year, Fit isconstructed using political conditions prior to the elections, since it takes time for newly
elected legislators to affect policies.
The first-stage regression corresponding to equation (1) is defined as follows:
14 Since 1950, the Constitution of India has introduced an affirmative-action provision to improve
the social and economic conditions of disadvantaged groups, SCs, and STs. This provision
guarantees them political seats in the national legislature, state legislatures, and village councils.
14Fit = α i + β t + κFCit + λTC it + X it δ + υ it ,(2)

where FCit is an instrument variable that represents the fraction of seats won by the
non-INC parties in close elections against the INC. The numerator is, therefore, thenumber of seats won by the non-INC parties in close elections while the denominator isthe number of close elections. The idea behind this identification is that election results
in close elections are considered largely random. 15 This paper defines a close election
as that in which the margins of victory are less than 4.5%. In the following sections, wewill discuss the validity of this identification. Since the frequency of close elections is
an endogenous variable, we control the fraction of close elections among all
constituencies through TCit .
The summary statistics are shown in Table 1. The mean of the share of seats
won by all of the non-INC parties in all seats is around 57%. The share of seats of RPs
is 16% on average. The fractions of close elections for the non-INC parties and RPs are
13% and 4.5%, respectively. Fractions of seats won by the non-INC parties and RPs arearound 50%, which is consistent with the randomness of the results in close elections. It
is important to note that since some observations for non-development, educational, andhealth expenditures are missing, the sample size of regression using non-development

15

and education/health expenditure differs from that using development expenditure.

4. Results
4.1. Identification Validity
In this section, we will discuss the identification validity. Although it is impossible todemonstrate randomness of results in a close election conclusively, we will show thatthe results in close elections cannot be predicted by any state characteristics. To this end,
we will regress the fraction of seats won by the non-INC parties (or RPs) in closeelections against the INC parties (or RPs) on real state domestic product per capita, rural
population, proportion of seats reserved for disadvantaged castes and tribes, proportionof seats won by the non-INC parties (or RPs) in the past, proportion of seats that had
close elections in the past, and turnout by percentage. To provide an additional robustness
check, we are also using newspaper circulation per capita and head count index in ruraland urban areas. We checked the validity of the instruments for the non-INC parties andRPs, the results of which are reported in Table 2. Columns (1) to (3) show the results for
non-INC parties. All of the coefficients are insignificant. The results for RPs, as shownin columns (4) to (9), are also similar to those for non-INC parties. Since we cannot
observe significant correlations between the fraction of seats won by each party group16

and state characters, the randomness of election results in close elections is confirmed.

4.2. Results
The results of the first stage regression are shown in Table 3. Panels (a) to (c)display coefficients of the instrumental variable for non-INC and RP seats, respectively.
The difference between the columns in each panel is due to the number of observations
used in the second stages. The coefficients of fraction of seats won by non-INC parties
in a close election are positive and similar, although, in columns (1) and (2), they arestatistically significant only at 10%. Panel (b) demonstrates results for RPs that estimatethat the fraction of seats won by RPs in a close election are positive and statisticallysignificant around 5%.
The results of the second-stage regression are reported in Table 4. Panel (a)demonstrates the results for non-INC parties. As shown in columns (1) and (3), while
the FE coefficient of non-INC parties for development expenditure is significantly
positive, that for non-development expenditure is significantly negative. On the other
hand, column (2) shows that the IV coefficient for development expenditure is positive
but statistically insignificant. The difference in coefficients between FE and IV meansthat in states where the demand for development expenditure has been increasing, the17number of non-INC parties has also been increasing. Moreover, our finding that the IV
coefficient is not significant suggests that the determinative factor for development
expenditure is not reflective of party but of voter preference. With respect to otherexpenditures, the IV coefficient for educational expenditure, as shown in column (6), is
positive and statistically significant. The reason for this may be that one of the large
non-INC parties is the Communist Party, which heavily favors education, although it isdifficult to check this explanation empirically if we use the geographical bias of thisparty.
Panel (b) shows the results for the RPs. Under the FE, only the coefficient of theRPs for health expenditure is positive and statistically insignificant, which suggests voter
preference is important in promoting health expenditure. The coefficient for development
expenditure, under IV estimate, is negative and statistically significant while that for
non-development expenditure is positive and statistically significant. Non-development
expenditure includes administrative expenditure and salaries for public officers who act
to benefit specific people. The finding of this paper is consistent with the findings of
Fernandez and Levy (2008) and Lizzeri and Persico (2005), who showed that a larger
number of political parties increases the transfer for specific groups and decreases the
public goods that are useful for development or poverty reduction. In the case of18

educational and health expenditure—as shown in Table 4, panel (b), columns (6) and(8)—we also do not find significant effects of RPs share. This confirms that RPs do notundertake a role in poverty reduction.

5. Conclusion
This paper focused on the decline of the INC and the development of non-INC parties in
its investigation of the importance of political parties in state government performance.
Accordingly, we first found that increasing the number of non-INC parties promotes
development expenditure, which implies that dominant-party systems work inefficiently.
This result suggests that a lack of political competition through political parties is badfor economic development and poverty reduction. By contrast, we found statisticallynegative effects when we examined the share of non-INC parties on development andpositive effects on non-development expenditure. This implies that fragmented politicalparties make governments work inefficiently.
Although this paper contributes to our understanding of the function of politicalparties, our study has limitations. First, the policy variables that we used do not containdetailed information. Future studies should examine the effects of political parties on
the allocation of public goods using more detailed policy information, even for a limited
19

region or period. Second, our classification of political parties is somewhat simplistic.
Given the numerous political parties extant in India, it is difficult to examine politicalsystems in light of the detailed characteristics of each party. Future studies may focus on
specific states to enable collecting the more detailed data necessary to understand theeffect of political systems. Third, this paper did not investigate the development of thepolitical parties. This is important to our understanding of how the mechanism of policy
has changed in the democratic institutions. More analysis on the progressive mechanismsof multiparty systems is thus required.
Despite these limitations, this paper contributes to a better understanding of thefunction of political parties in developing countries. In particular, it helps us to understandthe implications for those political parties based on ethnic or religious diversity where
the development of political organizations would not promote development policies.

20References
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22

Figure 1. The number of parties with seats in the state legislatures.
Assam

Bihar

GujaratHaryana

Jammu & Kashimir

Karnataka

KeralaMadhya Pradesh

Maharashtra

OrissaPunjabRajasthan

Tamil Nadu

Uttar PradeshWest Bengal

1960 1970 1980 1990 2000

1960 1970 1980 1990 2000

1960 1970 1980 1990 2000

1960 1970 1980 1990 2000

10 15
5
0
10 15
5
0

5

10 15

0

Number of Parties

0

5

10 15

Andhra Pradesh

year

Note: We have plotted the number of parties with seats in the state legislatures.

23

Figure 2. The fraction of seats won by the INC in state legislatures.
Assam

Bihar

GujaratHaryana

Jammu & Kashimir

Karnataka

KeralaMadhya Pradesh

Maharashtra

OrissaPunjabRajasthan

Tamil Nadu

Uttar PradeshWest Bengal

0 .2 .4 .6 .8
0 .2 .4 .6 .8
0 .2 .4 .6 .8

Share of INC

0 .2 .4 .6 .8

Andhra Pradesh

1962

2000 1962

2000 1962

year

24

2000 1962

2000

Table 1. Summary Statistics
Variable

Obs.

Mean

Std. Dev.

Min

Max

Fraction of seats won by non-INC parties

432

0.57

0.26

0.17

1

Fraction of seats won by RPs

432

0.16

0.24

0

0.94

432

0.13

0.065

0

0.31

432

0.045

0.057

0

0.27

432

0.49

0.15

0

1

432

0.46

0.24

0

1

Share of development expenditure

432

67.49

5.75

36.29

79.80

Share of non-development expenditure

336

29.60

4.01

17.95

42.44

Share of education expenditure

410

21.24

4.11

8.55

36.63

Share of health expenditure

410

7.74

2.23

2.25

13.87

Real per capita NSDP

432

12.989

5.347

4.813

32.461

Proportion of rural population

432

0.767

0.078

0.583

0.913

Proportion of SC/ST reserved seats

432

0.211

0.078

0.079

0.4

Political variable

Fraction of seats that had close elections between
non-INC parties and INC
Fraction of seats that had close elections between
RPs and non-RPs
Fraction of seats won by non-INC in a closeelection against INC
Fraction of seats won by RPs in a close election
against non-RPs
Policy variable

State characteristics

25

Table 2. Proportion of Seats Won by non-INC Parties and RPs Parties in a Close
Elections and State Characteristics(1)
Variables
Proportion of seats reserved

(2)

(3)

Non-INC parties against
INC

(4)

(5)

(6)

RPs against non-RPs-0.0356 -0.0851-0.0247-1.017-0.919-0.624(0.205)(0.222)(0.251)(0.627)(0.764)(0.826)

Proportion of seats that had-0.283-0.237-0.0402

2.339

2.020

2.929

close elections in the past

(0.470)(0.490)(0.528)(1.474)(1.765)(1.822)

Proportion of seats won by-0.228-0.279-0.306-0.159-0.115-0.259non-INC parties/RPs in(0.140)(0.168)(0.186)(0.339)(0.378)(0.362)0.04300.04850.08900.03870.01930.144(0.191)(0.190)(0.204)(0.421)(0.419)(0.460)-0.342-0.460-0.468

0.515

0.593

0.454

(0.301)(0.340)(0.342)(0.635)(0.687)(0.829)0.600-0.0682

1.353

7.988

9.079

5.473

(3.680)(3.671)(4.341)(7.681)(8.926)(12.75)-0.339-0.355

0.301

0.365

(0.411)(0.425)(0.827)(0.832)for SC/ST

the past
Turnout

Rural population share
Real state domestic productper capita
Newspaper circulation percapitaHead count index (rural)

Head count index (urban)

0.214

0.506

(0.179)(0.365)-0.126-0.627(0.174)(0.428)Observations

96

96

92

95

95

91

R-squared

0.081

0.085

0.107

0.081

0.085

0.096

Note: Dependent variable: proportion of non-INC parties and RPs that won in a close election perstate and election year. Sample is limited to state elections that were close. Robust standard errors
are shown in parentheses.

26

Table 3. First-Stage Regression(a) Dependent Variable: Fraction of seats won by non-INC parties

(1)

(2)

(3)

Dependent Variables in theDevelopment

Nondevelopment

Education or

Second Stages

Expenditure

Expenditure

Health expenditure

Fraction of seats won by non-INC

0.339+

0.307+

0.344*

parties in a close election against(0.185)(0.158)(0.173)-1.076**-1.239**-1.063**(0.299)(0.391)(0.299)INC
Fraction of seats that had closeelections between non-INC parties
and INC
Proportion of rural population

0.388-2.3890.315(1.577)(2.326)(1.603)

Real per capita NSDP

-0.00318-0.00149-0.00288(0.00882)(0.0105)(0.00894)-2.099*-3.065*-2.141*(1.013)(1.193)(1.019)Observations

432

336

410

R-squared

0.569

0.623

0.580

Proportion of SC/ST reserved seats

Note: Robust standard errors clustered at state-election units are reported in parenthesis. +, *, and **denote statistical significance at 10%, 5%, and 1%, respectively. All specifications include state andyear FEs.

27

(b) Dependent Variable: Fraction of seats won by RPs (excluding INC)

(1)

(2)

(3)

Dependent Variables in theDevelopment

Nondevelopment

Education or Health

Second Stages

Expenditure

Expenditure

expenditure

Fraction of seats won by RPs in a

0.0862*0.101*

0.0865*close election against non-RPs(0.0386)(0.0405)(0.0396)

Fraction of seats that had close

1.753**

1.600**

1.738**

elections between RPs and(0.462)(0.449)(0.461)

0.943

1.460

0.835

(1.134)(1.193)(1.159)0.0138*0.0167**0.0136*(0.00569)(0.00586)(0.00551)-0.07530.232-0.0703(0.512)(0.539)(0.516)

Observations

432

330

333

R-squared

0.786

0.801

0.804

non-RPs
Proportion of rural population
Real per capita NSDP

Proportion of SC/ST reserved seats

Note: Robust standard errors clustered at state-election units are reported in parenthesis. +, *, and **denote statistical significance at 10%, 5%, and 1%, respectively. All specifications include state andyear FEs.

28

Table 4. Party Systems and Government Expenditures(a) Fraction of seats won by non-INC parties and Government Expenditure
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Development

Nondevelopment

Education

HealthDependent Variables

Expenditure

Expenditure

Expenditure

expenditure

Method

FE

IV

FE

IV

FE

IV

FE

IV

4.066+

4.888-0.781+-4.835*-1.1487.035+

0.581

9.825

(2.110)(8.557)(0.420)(2.002)(0.851)(3.869)(1.116)(6.560)11.07+

12.11-0.245-5.680+-8.573**

2.386

2.748

15.21

(6.591)(13.70)(1.509)(3.135)(2.843)(5.784)(5.007)(10.09)88.60**

88.09**-5.466-9.281-7.2142.336-54.10-40.79(27.82)(26.23)(9.518)(11.79)(16.71)(23.64)(34.28)(38.73)0.349*

0.351*

0.06790.0719-0.272+-0.273-0.414*-0.427*(0.161)(0.152)(0.0487)(0.0487)(0.158)(0.168)(0.201)(0.202)

Proportion of SC/ST

2.258

4.156

-6.403-18.60*-19.24+

6.146

8.512

37.42

reserved seats(22.09)(28.89)(5.864)(8.979)(10.26)(14.71)(21.40)(27.57)

Observations

432

432

330

330

333

333

336

336

R-squared

0.650

0.649

0.281

0.093

0.343

0.087

0.609

0.462

Fraction of seats won by
non-INC parties
Fraction of seats that had
close elections between
non-INC parties and
INC
Proportion of rural
population
Real per capita NSDP

Note: Robust standard errors clustered at state-election units are reported in parentheses. +, *, and **denote statistical significance at 10%, 5%, and 1%, respectively. All specifications include state andyear FEs. Instrument variables in IV regression are the fraction of seats won by non-INC parties inclose elections.

29

(b) Fraction of seats won by regional parties and government expenditure
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Development

Nondevelopment

Education

HealthDependent Variables

Expenditure

Expenditure

Expenditure

expenditure

Method

FE

IV

FE

IV

FE

IV

FE

Fraction of seats won by

1.411-39.98*2.660

24.21*

1.957-3.2411.459+

4.223(3.299)(19.82)(3.840)(10.09)(1.871)(6.826)(0.873)(3.072)2.360

86.62+

4.909-39.51+

1.450

12.01

-2.377-7.995

RPs and non-RPs(11.73)(52.59)(7.923)(21.86)(5.187)(15.51)(2.407)(6.393)

Proportion of rural

83.40**

137.6*-58.09+-103.8*100.0**

106.3**

5.229

1.887

(26.61)(55.43)(34.32)(40.52)(22.71)(23.49)(7.841)(8.056)0.310+

0.828**-0.497*-0.831**

0.05320.116-0.0795*-0.113*(0.184)(0.283)(0.201)(0.250)(0.106)(0.128)(0.0350)(0.0526)

Proportion of SC/ST-8.722

6.570

4.540

-8.352-4.283-2.511

1.545

0.603

reserved seats(20.31)(21.56)(18.73)(17.58)(13.26)(13.46)(6.571)(5.960)Observations

432

432

336

336

410

410

410

410

R-squared

0.633

0.158

0.617

0.430

0.783

0.769

0.823

0.809

RPs

IV

Fraction of seats that had
close elections between

population
Real per capita NSDP

Note: Robust standard errors clustered at state-election units are reported in parentheses. +, *, and **denote statistical significance at 10%, 5%, and 1%, respectively. All specifications include state andyear FEs. Instrument variables in IV regression are the fraction of seats won by RPs in closeelections.

30