Contexts of Tyranny:
Religious Majorities and Electoral Outcomes in Contemporary India
1
Diogo Lemos
George Washington University
[email protected]
June 2016
I. Introduction
Does the size of an ethnic majority shape electoral outcomes? Can an
increase in the size of an ethnic majority promote the electoral success of an
ethnic party? In order to address these questions, this paper examines the
contextual effect of religious demography on the electoral performance of a party
espousing a religious nationalist ideology. Specifically, I analyze whether
electoral support for the world's largest religious nationalist party, the Hindu
nationalist Bharatiya Janata Party (BJP), increases with the size of Hindu
population in urban constituencies in India. The results presented below suggest a
strong link between ethnic demography and electoral outcomes. The analysis
shows that not only are Hindu-majority constituencies more likely to vote for the
BJP but also that this likelihood increases with the size of the Hindus majority in
a constituency. This finding is robust to additional covariates, as well as control
for unobserved variation at the municipal, state and national constituency level.
From a policy perspective, questions concerning ethnic party success are
timely. In India, the BJP has achieved considerable electoral success in urban
constituencies. According to the latest electoral results, the BJP has won 40
percent of municipal seats, 24 percent of state assembly seats and 56 percent of
1
This is a draft version. Please do not cite without the author's permission.
1
parliamentary seats in seven of India's largest cities (Table 1). A study of this
2
success thus provides insight into the political trajectory of India - the world's
largest democracy. Beyond India, ethnic political parties flourish in established or
consolidating democracies such as Canada, Spain, Finland, Israel, Sri Lanka,
South Africa and Bolivia. Their success warrants our attention because the
politicization of ethnic categories is often said to threaten the survival of
democratic institutions (see Dahl 1971; Lijphart 1977; Horowitz 1993; Chandra
2005). From this viewpoint, then, research on ethnic parties has important
practical implications for the survival and stability of democratic regimes.
MUNICIPAL ELECTIONS
CITY
ASSEMBLY ELECTIONS
NATIONAL
ELECTIONS
STATE
Total seats
BJP seats
(%)
Total seats
BJP seats
(%)
Total seats
BJP
seats
(%)
Delhi
Delhi
272
138 (51%)
70
3 (4.2%)
7
7
(100%)
Chennai
Tamil Nadu
200
0
16
0
3
0
Bangalore
Karnataka
198
111 (56%)
27
12 (44%)
4
3 (75%)
Hyderabad3
Andhra
Pradesh
150
5 (3%)
15
4 (27.7%)
2
1 (50%)
Ahmadabad
Gujarat
192
151 (79%)
15
13 (87%)
2
2
(100%)
Kolkata
West Bengal
141
7 (5%)
17
0
4
0
Surat
Gujarat
114
98 (86%)
9
9 (100%)
1
1
(100%)
Total
-
1267
510 (40%)
169
41 (24%)
25
14
(56%)
Table 1: Performance of the BJP in the last elections in India's seven metro cities
2
The relatively smaller share of the BJP in state assembly seats can be explained in terms of Delhi's large share of
assembly seats in the sample (70 out of 169) and the landslide victory of the Aam Aadmi Party (AAP) in the 2015 Delhi
Legislative Assembly election. The AAP won 67 out of Delhi's 70 state assembly seats. Still, the BJP was the only other
party to win seats in these elections (3 seats). Moreover, in the previous Legislative Elections, in 2013, the BJP won the
largest number of seats in Delhi (23).
3
Muslims constitute 41.17 percent of Hyderabad's population - the largest city-level Muslim population share among the
seven cities studied. This accounts for the success of the All India Majlis-e-Ittehadul Muslimeen (AIMIM), a party
dedicated to protecting and advancing the rights of the Muslim community in India. The AIMIM won 43 seats in the last
municipal elections and 7 seats in the last assembly elections.
2
The research reported here also contributes to theoretical debates on
contextual effects in three important ways. First, since previous works on the
effect of context on party performance have largely focused on a single minority,
the applicability of these theories to a multiethnic context remains limited. This
study seeks to remedy this shortcoming by focusing on the size of the ethnic
majority. Second, this investigation emphasizes the role of instrumental political
considerations in accounting for the relationship between ethnic context and
electoral performance. Underlying this argument is a simple, yet well-established,
assumption: strategic voters will support the party that offers them the best chance
of becoming part of a winning coalition (Riker 1962; Bates 1974; Chandra 2004;
Posner 2005). Therefore, as the size of the ethnic majority increases so does
electoral support for parties championing the interests of that ethnic majority. In
practice, this might give rise to a situation in which a majority uses its numerical
dominance to exclude and oppress minorities. This is the phenomenon that
previous political theories such as Tocqueville and Mill described as 'the tyranny
of the majority.' Lastly, existing studies on contextual effects remain largely
limited to studies of the advanced industrial democracies of North America and
Western Europe. In this contribution, I provide the first systematic analysis of the
relationship between the size of Hindu electorate and the performance of the
Hindu nationalist BJP in India.
To explore these questions, I draw on original data made available by egovernment and transparency initiatives by the Election Commission of India
(ECI) at the municipal, state and national constituency-level. The regional case
studies include seven of India's largest cities: Delhi, Kolkata, Bangalore,
Ahmedabad, Surat, Chennai and Hyderabad. This data provides scalable and
4
fine-grained estimates of religious demography at constituency-level in each of
these seven cities for a quantitative analysis of the relationship between ethnic
majority size and ethnic party support.
4
India's largest city - Mumbai - has been excluded from this paper since the researcher is still collecting data on it.
However, it will be included in subsequent iterations of this paper.
3
In the sections that follow, I first outline the theoretical foundations of the
main hypothesis advanced here. I then provide background into the Hindu
nationalist BJP as well as the effects of demography on electoral outcomes in
India. In the fourth section, I lay out a series of hypotheses based on the foregoing
discussion. In the fifth section, I present the case studies for this study and an
original dataset constructed for this analysis that takes advantage of recent
advances in big data analytics and open-data initiatives by the Election
Commission of India (ECI). In the seventh section, I discuss the methodology
employed to this test the hypotheses. In the eighth section, I present empirical
results of the analysis of the relationship between demographic context and
electoral outcomes in India's seven largest cities. Finally, I conclude with a
discussion of the implications of these results for the broad fields of studies on
electoral competition, ethnic conflict and democratic stability.
II. Argument
My argument draws on the established intuition that local ethnic context
shapes interethnic relations. At the core of arguments emphasizing local
contextual influences is the claim that individuals not only respond to their own
internal motivations but are also influenced by the demographic, social, economic
and cultural characteristics of their residential area. Longitudinal (Levin &
Sidanius 2003), experimental (Habyarimana et al. 2007), meta-analytic (Tropp
and Pettigrew 2005), and geo-coded datasets (Putnam 2007) provide convincing
evidence of a causal relation between ethnic context and ethnic conflict.
For the most part, however, this body of work focuses on one specific
minority, such as the African Americans in the US (Fossett and Kiecolt 1989;
Taylor 1998; Welch et al. 2001), Asians in the UK (Fieldhouse and Cutts 2008)
and non-Western residents in Sweden (Valdez 2014). These works have
illuminated undoubtedly important aspects of the effects of context on electoral
outcomes, but the applicability of these theories to a multiethnic context remains
limited. Specifically, conclusions based on the size of one specific minority
4
cannot be extended to the society-level since there may be other (possibly
significant) minorities in a given context. This problem is particularly acute in
multiethnic societies such as India where several ethnic minorities coexist not just
alongside each other (e.g., Muslim, Christian, Sikh, Buddhist, Jain and Parsi) but
also crosscutting each other across multiple dimensions (e.g. caste, tribe, class,
region, language and gender). Moreover, the few studies that consider multiethnic
contexts (see Cummings and Lambert 1997; Hood and Morris 1998; Oliver and
Wong 2003) focus on the advanced industrial democracies of North America and
Western Europe.
This paper seeks to remedy this shortcoming by focusing on the opposite
side of the equation: the size of the majority community. Specifically, I examine
how the proportion of ethnic majority in a constituency shapes electoral support
for a party championing the interests of that ethnic majority. I hypothesize that as
the size of the ethnic majority increases so does support for a matching ethnic
party. To explain this effect, I argue that voters have strategic incentives to
support an ethnic party as the size of ethnic majority increases in an electoral
constituency. This proposition follows the widely accepted assumption that
individuals will be driven to activate those ethnic categories in their repertoire that
put them in a winning coalition (Riker 1962; Bates 1974; Chandra 2004; Posner
2005). The most effective way of doing so is by comparing the size of the identity
categories in the ethnic structure ('counting heads') and then selecting the ethnic
category that offers the most usefully sized coalition (Chandra 2004).
In theory, the optimal size of an ethnic category both for strategically
thinking politicians and voters is that which contains the fewest members with
whom the spoils of power will have to be shared - the minimum winning coalition.
In practice, however, parties seeking to mobilize larger ethnic categories are often
more successful in electoral competition (Chandra 2004). The existing literature
offers three main explanations for this.
5
First, de Mesquita et al. (2003) argue that the size of a winning coalition
results not only from the institutional features of the party system but also from its
social characteristics. Drawing on this insight, Thachil and Teitelbaum (2015)
argue that the size of the winning coalition of an ethnic party depends on the
breadth of the identity they seek to activate. Parties mobilizing a larger ethnic core
will thus effectively expand the size of winning coalitions. Second, voters often
face a great deal of uncertainty concerning the size of ethnic categories in an
electoral context (Chandra 2004). One way of reducing this uncertainty is by
increasing the visibility of an ethnic category in electoral competition. Previous
research suggests that territorial concentration - and therefore ethnic homogeneity
at the local level - is a particularly effective means of raising the visibility of an
ethnic category (Hale 2004; Bates 1974). Finally, politicians may want to stick to
larger ethnic categories even though they could form smaller viable coalitions.
According to Ferree (2012), this is so because there is no guarantee that the new
coalition will not face challenges from alternative new coalitions. Given the costs
of mobilizing a new category as well as its uncertain results, political elites may
prefer to stick to a larger than minimum winning coalition. Furthermore,
Horowitz (1998) argues that it is also more rewarding for politicians to make
ethnic appeals to homogeneous electorates than to craft appeals to heterogeneous
electorates. He then concludes: "There are, in short, bottom-up, reasons for
ethnically based parties to exist once constituencies are homogeneous" (Horowitz,
1998: 29).
III. Background: The BJP in India's Electoral System
The remainder of this paper uses empirical evidence from India to
examine and test the contextual effect of religious majority size on the electoral
performance of a party championing the interests of that ethnic majority.
Specifically, I examine the effect of Hindu population proportion at the
constituency level on the BJP's electoral support. In this section, I provide a brief
introduction into the BJP's electoral profile as well as the relationship between
religious demography and electoral outcomes in India.
6
Following the dominant literature on the Indian party system, I describe
the BJP as an ethnic party (Hasan 2002; Sridharan and DeSouza 2006; Jaffrelot
2009; Thachil and Teitelbaum 2015). In making this conceptual choice, I
5
emphasize the BJP's core ideological commitment to Hindu nationalism - an
ideology that contends that India is the homeland of the Hindus, whereas religious
minorities are outsiders who must adhere to national 'Hindutva' ('Hinduness')
culture - as well as its electoral support. An ethnic party is thus understood here
6
as a party that ideologically champions the cause of one particular ethnic category
or set of categories to the exclusion of others and that receives most of its
electoral support from that ethnic category.
The BJP's electoral basis has historically been the Hindu elites (i.e., the
upper classes and the upper castes). Since this elite constitutes a minority in most
electoral circles in India, this support basis poses a severe challenge to the BJP's
electoral prospects, prompting earlier scholars to argue that it would never form a
viable electoral alternative to the dominant, catch-all Indian National Congress
(INC) (Brass 1993). Yet, despite these pessimistic prognoses, in recent years the
BJP has been able to make significant inroads with other social categories.
According to the National Election Survey (NES) collected by the Center for the
Study of Developing Societies (CSDS), in the 2009 national legislative elections,
the BJP won 16 percent of votes among the poor, 19 percent among the lower
classes, 22 percent among the middle classes and 25 percent among the upper
classes. Accordingly, in the 2014 national legislative elections, the BJP won 24
percent among the poor, 31 percent among the lower classes, 32.2 percent among
the middle classes and 38 percent among the upper classes. This suggests that
while support for the BJP does increase with socio-economic level, the party is
now able to draw support across class categories.
5
For example, Sridharan and DeSouza also use the term 'ethnic party' to described the BJP: "The BJP can perhaps be better
defined as an ethnic party, in this case, the promoter of a religion and religious culture defined Hindu ethnicity and
nationalism, something much more narrower than a broad encompassing multi-ethnic Indian nationalism" (Sridharan and
DeSouza, 2006: 19).
6
Recent work shows that a range of indicators, including but not restricted to a party's explicit appeals, explicit issue
positions and the composition of its electoral support can be used to define it as an ethnic party (Chandra 2011).
7
The same cannot be said about the religious communities in India.
According to CSDS data, the BJP won only 3.7% among Muslims and 12.7
among other minorities in the 2009 national elections. While the BJP's vote share
among Muslims and other minorities rose in the 2014 elections (to 8 and 18.75
percent, respectively), this still represents a meager proportion of the party's
votes. In sum, we may conclude that the BJP, a party ideology committed to
Hindu nationalism, draws its electoral support overwhelmingly from its target
ethnic community: Hindus. As the CSDS data makes clear, this does not mean
that the BJP attract the votes of all, or even a majority, of Hindus. Nor does it
mean that religious categories are internally homogeneous: they are fragmented
along the lines of caste, sect, language, region and community. Rather, the point
highlighted here is that there is a strong relationship between the religious
background of voters and electoral support for the BJP. In the words of Ashutosh
Varshney: "The BJP is a Hindu-nationalist party and its guiding ideology is
deeply distrusted by India's minorities, especially Muslims, who make up 13.4
percent of the country's population" (Varshey 2014: 34). This sets the context for
an examination of the relationship between religious demography and the BJP's
electoral performance.
Previous authors have highlighted the role of constituency demography in
shaping electoral outcomes in India (see for example, Chandra 2005; Kumar
2013). Yet, the literature remains profoundly divided over the direction of this
relationship. This division mirrors the debate between proponents of the 'threat'
hypothesis (Blalock 1957; Fossett and Kiecolt 1989; Huckfeldt and Kohfeld
1989) and proponents of the 'contact' hypothesis (Allport 1954; Pettigrew 1998;
Tropp and Pettigrew 2005). One group of authors contends that a decrease in the
7
number of Hindus in a constituency, and accordingly an increase in the number of
minorities, is likely to heighten Hindu-Muslim tensions (see Shakir 1983;
7
In broad lines, proponents of the 'threat' hypothesis contend that the presence of a sizeable ethnic minority (therefore, a
smaller ethnic majority) threatens the majority's social, economic and political position, resulting in interethnic prejudice
and conflict. In contrast, proponents of the 'contact' hypothesis predict that the presence of a sizeable ethnic minority
improves intergroup relations. In its classic version, the contact hypothesis contends that contact with members of other
ethnic categories provide individuals information about out-groups that can counteract pre-existing negative stereotypes
about them and increase ethnic tolerance more generally. Subsequent work has offered alternative explanations for the link
between contact and interethnic peace.
8
Rajgopal 1987). These arguments are typically grounded on variations of the
'security dilemma': as the size of minorities increases so does the perception of the
threat posed by those minorities to the majority community (Posen 1993). In fact,
leaders of Hindu nationalist organizations in India are often heard voicing these
fears, particularly regarding the size of Muslim minority, in the public sphere. For
instance, following the 2002 Gujarat riots, the current Prime Minister of India,
Narendra Modi, used the expression "hum paanch, hamare pachees" ('we are five
and we will have 25 offspring') to rouse fears about the demographic growth of
the Muslim community. He then equated the relief camps for the Muslim victims
of the riots to 'baby-producing centers' (Outlook 2002). More recently, Sakshi
Maharaj, a BJP member of Lok Sabha, exhorted Hindu women to produce four
children each as a means to protect Hindu religion from those allegedly having
"four wives and 40 children", another reference to the size of the Muslim
community (Times of India 2015).
Another group scholars argue that a decrease in the number of Hindus in a
constituency, and accordingly an increase in the number of minorities, is likely to
assuage ethnic tensions. In regards to electoral outcomes, we can identify two
versions of this argument. Some scholars indicate that India's first-past-the-post
electoral system invites politicians to seek the support of minorities in ethnically
heterogeneous contexts as a way to form winning coalitions (Wilkinson 2004;
Dhattiwala and Biggs 2012). According to this view, heterogeneous
constituencies are thus more likely to elect candidates from non-ethnic or
multiethnic parties. Another version of this argument contends that there is a
positive relationship between the presence of sizeable 'vote banks' and the success
of parties championing a matching ethnic category. This argument rests on the
notion that elites of a particular dominant caste in a constituency are able to
deliver the whole set of votes from that community ('vote banks') to a party or
candidate in exchange for goods and services (see for example, Breeding 2011;
Jaffrelot 2011; Thapar 2013). According to this view, dominant castes (e.g.,
9
Yadavs in Bihar, Patels in Gujarat or Lingayats in Karnataka) are able to tilt
elections to their advantage through the coordination efforts of a middleman.
While vote banks have lost influence in Indian politics in recent years,
their demographic calculus remains central to electoral competition. Most
significantly, Chandra (2004) argues that parties and voters in India form a
'reasonable expectation' about the likely outcome of an election by counting the
heads of members of their 'own' ethnic categories in a constituency. Severe
information constraints about the distribution of state resources are sufficient to
sustain this tendency towards ethnic categorization: "voters expect co-ethnic elites
to favour them in the distribution of benefits, and elites expect voters to favor
them in the distribution of votes" (Chandra 2004: 47). For example, recent
scholarship shows that Muslim voters are more likely to vote for Muslim
candidates but only when they have a realistic chance to win - i.e., when they
have sufficient demographic strength to elect a co-ethnic (Susewind and
Dhattiwalla 2014; Heath et al. 2015).
However, this literature focuses only on the behavior of one minority Muslims - and, hence, does not enable us to reach conclusions about the voting
behavior of Hindus. Likewise, previous authors demonstrate how the lower castes
in northern India have relied on their demography strength to become increasingly
assertive in the political stage (Jaffrelot 2002). These works illuminated the
dynamics underlying the link between demographic strength and ethnic party
support but they do not provide us the tools to examine the effects of religious
heterogeneity on the BJP's electoral perspective. Applying the same calculus to
the religious majority in India - Hindus - presents us with the question of whether
it uses its numerical strength to tilt the political system to its advantage.
Ultimately, then, the question addressed by this paper can be formulated in the
following terms: can an increase in the size of the Hindu population in an
electoral constituency also incentivize higher levels of Hindu assertiveness?
10
IV. Hypotheses
This section presents a series of hypothesis based on the foregoing
discussion on ethnic context at the constituency level and BJP electoral support. I
employ two measures for the dependent variable - BJP electoral success:
percentage of votes (continuous variable) and a one dummy variable (1 = Win and
0 = Loose). The first measure helps us understand the relationship between the
Hindu population and electoral outcomes in more general terms and the second
tests whether this relationship translates into actual seats won by the Hindu rightwing party. In order to test the main proposition advanced by this paper, I thus
formulated two hypotheses:
H1. The size of the Hindu population in an electoral constituency
has a positive and significant effect on the BJP's share of votes.
H2. The size of the Hindu population has a positive and significant
effect on the election of BJP candidates.
In order to test the alternative proposition that the majority feels
threatened in constituencies where it is smaller or in parity with other religious
minorities, I formulated the following two hypotheses:
H3. The size of the Hindu population in an electoral constituency
has a negative and significant effect on support for the BJP.
H4. Support for the BJP increases as the size of minorities and
Hindus reach parity in a constituency.
Finally, I included in the model a number of covariates to test both for the
accuracy of the models, given previous literature on the relationship between
these variables and electoral politics, and their influence on BJP vote share
relative to the main independent variables: Hindu and minority population. In
regards to gender, previous research points out that women are less likely to
support the BJP (Chhibber & Verma 2015). I thus predict that gender will have a
negative effect on BJP success. Likewise, much previous work provides
convincing evidence that lower status groups are less likely to support the BJP
11
(Jaffrelot 1998; Kumar 2013). I thus expect that two constitutionally recognize
disadvantage categories - the Scheduled Castes (SC) and the Scheduled Tribes
(ST) - to have a negative impact on support for the BJP. A similar dynamic
characterizes the relationship between the poor and this party (Thachil 2011;
Harris 2005; Falcao 2009;). Yet, in recent years, there is evidence of a slight
change in this pattern with more among the poor now supporting the BJP
(Jaffrelot and Kumar 2015). I thus expect socio-economic status to have only a
weak to negative effect on BJP support. Finally, the popularity of the Hindu right
among India's youth, particularly the urban youth, has been a much touted topic in
recent years. For example, Kumar argues that the electoral success of the BJP in
the early 1990s can be credited to its strong support amongst the urban poor
(2013: 51). Therefore, in order to test whether the young are more likely to vote, I
collected data on the average age of voters and created an aggregate variable for
age for each constituency.
V. Case Selection and Data
In order to test these hypotheses, I examine the effect of ethnic context on
the BJP's electoral performance in seven of India's largest cities: Delhi, Kolkata,
Bangalore, Ahmadabad, Surat, Chennai and Hyderabad (Figure 1).
These seven cities are particularly well suited to examine these
hypotheses. First, these seven cities comprehend a large number of municipal,
state and national constituencies (Table 1). Second, these seven cities vary in
three factors that have been previously associated with ethnic party success,
namely social service provision (Thachil 2011), the occurrence of pre-electoral
riots (Wilkinson 2004; Dhattiwala and Biggs 2012) and civic ties (Varshney
2002; Chidambaram 2012). Similarly, these seven cities cover the wide expanse
of the Indian territory. This case selection seeks to establish the internal validity
of the hypothesis advanced while also maximizing the potential for generating
external validity. Finally, Indian cities are increasingly central to the country's
12
political development. This trend is likely to accentuate as India's urban
population is set to surpass its rural population by 2039 (Census of India 2011).
Figure 1: Case Studies
To test the foregoing hypotheses, I draw on an original dataset that
includes both estimates of religious demography at constituency-level as well as
the latest municipal, state and national electoral results within the boundaries of
these seven cities. Municipal corporations are divided into numerous wards and
municipal councilors are elected from wards; therefore, this geographic unit has
both administrative and political saliency. In general, municipal wards fit neatly
into state assembly constituencies and, in turn, state constituencies fit into
national parliamentary constituencies. This enables us to aggregate demographic
variables from the bottom up in order to test the relationship between ethnic
context and ethnic party support at the three different levels of government. Data
on elections is available online on the website of the ECI.
In regards to the demographic variables, studies exploring the nexus
between a constituency's religious composition and electoral outcomes have long
13
been plagued by a scarcity of reliable and publicly available demographic data for
smaller geographic units in India. The Census of India, the single largest source of
demographic data in India, does not publish ward-level religious figures. The
Census releases only national, state, district and town-level on population by
religious community. Since these units rarely meet constituency boundaries, the
data published by the Census is of little use to test the relationship between
religious demography and electoral outcomes.
Other sources of data on religious demography are either too large or too
small to produce generalizable insights about electoral constituencies. Data
collected by the CSDS - the most reliable source of political surveys in India allows only for statewide inference given its limited sample size and overall
design at constituency level. For example, the 2014 National Election Study
(NES) sampled their respondents from only 56 booths in 14 assembly segments
across 12 of 26 parliamentary constituencies in Gujarat. In turn, though numerous
surveys have been undertaken in these seven cities, they focus on only one or two
localities within these cities. For instance, Partners for Urban Knowledge Action
and Research (PUKAR) has undertaken an extensive socio-economic survey of
Dharavi - one of the oldest slums in Mumbai - that included questions about
religious identity. However, issues of survey timing, sample size and even
wording complicate the task of cross-tabulating this survey with other data.
Fortunately, as a result of e-government and transparency initiative, the
Election Commission of India (ECI) has published online the electoral rolls for
Indian voters. The rolls contain information about voters' names, age, polling
booth, ward (i.e., municipal constituency), assembly and parliamentary
constituency. This constitutes a breakthrough for studies of religious demography
in India because the religious connotation of names in the 'electoral rolls' can be
exploited as a source of data for individual religious background. They thus
enable us to create estimates of religious demography at the constituency level. In
the past, several studies have extracted names from electoral rolls by hand for this
14
purpose (Field et al. 2008; Galonnier 2012; Jaffrelot and Kumar 2009). However,
this is a taxing and time-consuming task that involves the collaboration of several
local experts.
Luckily, recent developments in computer technology enabled us to
systematize this task. Most specifically, an open source command script
('pdftotext') converts the PDFs into text. Once the information is converted into
text, another script can then sort the relevant data into tables that can be readily
used for analysis. The following step involves matching the names extracted from
the electoral to a religious community. Recently, Susewind (2014) developed an
open-source computer algorithm that probabilistically matches the names of
Indian voters to a reference list extracted from the website indiachildnames.com.8
This algorithm outputs the most plausible categorization and all potential
alternatives as well as a certainty index that allows for flexible accuracy
thresholds. Accuracy tests demonstrated that the algorithm’s positive predictive
value (i.e., the rate of accurate positive matches) stood at 95% while its negative
predictive value (i.e., the rate of accurate negative matches) stood at 99%.
Overall, 5% of names could not be classified and were discarded (Susewind 2014:
9). The algorithm also possesses the advantage of scalability that enables
researchers to examine voter lists across a vast number of electoral constituencies.
In addition to these fine-grained estimates of religious demography, this
paper also looks at four other social indicators - gender, caste, socio-economic
status and age (Table 2). To measure gender, I collected aggregated data from the
2011 Census of India on the percentage of females per municipal 'ward'. In
regards to caste, this project combines Census data on scheduled caste (SC) and
scheduled tribe (ST) population. There are two main reasons for this. One, the
number of ST individuals is very low in many cities at the ward level. In addition,
as both SCs and STs have been the most excluded and discriminated groups, they
have been afforded similar constitutional rights in the form of affirmative action
8
A database that links roughly 23,000 names to gender and the religious categories Hindu, Muslim, Sikh, Christian, Jain,
Parsi, and Buddhist.
15
policies. To measure socio-economic status, the Census does not include a
specific question on income or consumption. Instead, this project uses data on
male illiteracy as a blunt measure of socio-economic status, as done by previous
studies (Vithayathil & Singh 2012; Kumar 2013). Indeed, due to female exclusion
from schooling among many middle and upper class households, female literacy
fails to correlate strongly with socio-economic status in the Indian context.
Finally, I calculate the average of age of each constituency using the data included
in the electoral rolls. Table 2 presents the demographic aggregate indicators for
these seven cities.
CITY
POPULATION
(2011)
HINDUS
(%)
MUSLIM
(%)
SC/ST
(%)
FEMALES
(%)
MALE
ILLITERACY
(%)
MEAN
AGE
Delhi
11,007,835
81.7
12.9
15.9
46.5
9
40
Chennai
4,646,732
81.3
9.4
16.9
49.7
7.9
43.6
Bangalore
8,425,970
79.4
13.4
13.2
48.5
8.9
40.7
Hyderabad
6,809,970
70
27
4.9
48.9
14.4
38.7
Ahmadabad
5,577,940
83
13.8
11.9
47.3
9.6
41.2
Kolkata
4,496,694
74.7
23.3
4.5
38.4
7.7
45.3
Surat
4,461,026
87
7
5.3
43.1
8.8
39.3
India
-
80.5
13.4
25.2
44.4
17.8
25
Table 2: Hindus, Muslims, SC/ST, Females and Male Illiteracy in India's Seven Metro Cities
VI. Methods
This paper explores the effect of an increase in the size of the ethnic
majority (the Hindus) upon the electoral performance of parties championing a
matching ethnic category (the BJP). In a statistical analysis delineating these
effects on electoral outcomes, it is necessary to first carefully distinguish between
two broad categories of analysis: contextual effects and ecological inference. As
Katz and King (1999) remind us, these categories are analytically and
methodological distinct. Research questions about the relationships among
aggregate variables pertain to the field contextual effects, whereas research
16
questions about the characteristics of individuals who make up aggregate electoral
data require a model of ecological inference. For example, a study of the effect on
the vote for more liberal parties of having a college town is a contextual effect. In
contrast, using ecological data to study whether college students are more likely
than others to vote for liberal political parties requires an ecological inference
model (Katz & King 1999: 16).
The research question explored here falls within the purview of contextual
effects analysis. This is so because the aim here is not establish how demography
shapes individual voting preference (for answering this question would require
survey data) but how demography affects electoral outcomes. In other words,
while the crux of the argument is that Hindus will vote more for the BJP as their
demographic weight increases, this does not preclude that members of other
religious categories may also vote for such parties as the size of Hindus in a
constituency increases. This point is important to clarify the methodology
employed by this study.
To test whether support for the BJP increases with the size of the Hindu
population in a constituency, I employed a multilevel linear mixed effects model
(LMM). Multilevel models share the notion that individual observations are
grouped into higher levels of analysis by the design of the data. Multilevel
modeling thus represents an optimal strategy for addressing the question under
study. In the present case, there are three levels of analysis corresponding to three
levels of government: municipal, state and national. Mixed models are
characterized for containing both fixed and random effects. The fixed effects are
analogous to standard regression coefficients and are estimated directly. The
random effects are not directly estimated but are summarized in terms of their
estimated variances and covariances.
The data gathered at each level of government was first merged into a
single file. Observations at the lower level of measurement, i.e., the municipality,
17
that did not match the above two levels of government, i.e., the state and the
national, were dropped from the dataset. The observations dropped correspond to
municipal constituencies that do not fully fit within state constituencies as well as
those state and national constituencies that contain more lower level
constituencies than those included in the municipal sample. After this, the sample
was reduced to 783 cases.
VII. Results
MODELS
DEPENDENT
VARIABLE
Vote Share
Hindu pop.
Dummy
Vote Share
Minority
pop.
Dummy
Hindu
Population
2.6376 ***
(3.479)
0.8484 ***
(1.640)
-
Muslim
Population
INDEPENDENT VARIABLES
Average
Female
Age
SC/ST
Male
Illiteracy
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-2.675064 ***
(3.460)
-0.7471 ***
(1.559)
2.6166 ***
-1.430 ***
0.510
-0.132 *
-0.254 *
(3.842)
(0.220)
(0.312)
(0.071)
(0.146)
Dummy
0.6608 ***
-0.199 ***
0.212 **
-0.0194
-0.072
(1.680)
(0.655)
(0.930)
(0.026)
(0.047)
Vote Share
0.2578
Hindu/
(1.658)
minority
Dummy
0.0208
parity
(0.352)
Vote Share
0.5901***
Hindu pop.
(1.231)
categorical
Dummy
0.1446 ***
(0.302)
Hindu pop.
Vote Share
0.4829 ***
-1.454 ***
0.739**
-0.049
-0.503***
(1.220)
(0.221)
(0.319)
(0.073)
(0.145)
categorical
with
Dummy
0.1018 ***
-0.221 ***
0.1972**
-0.003
-0.114**
covariables
(0.332)
(0.063)
(0.096)
(0.025)
(0.045)
Note: 1. To measure models 5 and 6, I divided the Hindu population into two categorical variables: the first between 0 and
50 and the second between 50 and 99. The results reported here correspond to the change in the second categorical variable
(> 50 and < 99) using the first one (< 50) as the baseline. Standards errors reported in parenthesis. *p < .1, **p < .05, ***p
< .01.
Hindu pop.
with
covariables
Vote Share
Table 3: Ethnic Context and BJP support
I began by examining the relationship between the proportion of Hindus in
a constituency and the BJP's electoral performance. I first employed a simple
linear mixed methods model using the continuous variable, Hindu party vote
share, as the dependent variable. I then used a logistic regression using a dummy
variable to measure ethnic party success: Win = 1, Loose = 0. My primary
18
specifications confirmed my major expectations (Table 3). The size of the Hindu
majority in a constituency has a strong and positive impact on the electoral
success of the Hindu right. More concretely, as the size of the Hindu majority
increases so does percentage support for the BJP and the SS as well as the number
of seats won by these two parties. These results were statistically significant at the
.001 level.
I then tested the alternative hypothesis, i.e., that an increase in the minority
population has a positive effect on the likelihood of success of Hindu right-wing
parties. To measure minority population, I used the percentage of Muslim,
Christian, Sikh and Buddhist sounding names in each constituency resulting from
the extraction of voter names from the electoral rolls. As in the first model, I
employed two measures for the dependent variable: a continuous and a dummy
variable. The results of this analysis were the mirror image of the first model. The
minority population in a constituency has a very significant negative effect on the
chances of success of the BJP. This suggests strong support for the main
hypothesis advanced here, namely that support for the BJP increases with the size
of Hindus in an electoral constituency. In line with the basic propositions of the
contact hypothesis, these results suggest that greater heterogeneity at the
constituency level mitigates support for ethnic parties. Ultimately, this analysis
suggests that Hindus and minorities in India's seven largest cities have
diametrically opposed voting preferences. This confirms the expectations of
previous research into the voting patterns of religious communities in India
(Kumar 2013; Varshney 2014; Susewind & Dhattiwala 2014; Heath et al. 2015).
Model 3 tests the relationship between the size of the Hindu population
and Hindu right-wing party support while including additional covariates. These
are the percentage of females, the average age, the percentage of scheduled castes
(SCs) and scheduled tribes (STs), and the percentage of male illiteracy in a
constituency. In this model, both for the continuous and dummy variables, the
effect of Hindu population on the BJP's electoral performance remains positive
19
and significant at the 0.001 level. In line with the expectations, this model shows
that the percentage of females, SC/STs and male illiteracy in a constituency has a
negative and significant effect on the Hindu right-wing party. This effect is more
significant for the variable females than for SC/STs and male illiteracy. However,
contrary to the expectations, the results of this analysis show that support for the
BJP increases as the average age also increases in a constituency. While this
contradicts dominant views in India about the youth's support for the BJP, it fits
conventional wisdom about the positive relationship between age and support for
right-wing parties in Western Europe and the United States (see for example,
Bessinger 2013; Oesch 2008; Miller & Schofield 2008).
Model 4 tests whether support for the BJP increases as Hindus and
minority communities reach parity. In order to test this hypothesis, I created a
variable for constituencies where Hindus constitute between 40 to 60 percent of
the population and compared these with the other observations in the dataset.
While the relationship between this variable and Hindu party performance is
positive it is not statistically significant both in the mixed effects and the logistic
regression. We can then reject the alternative hypothesis that religious community
parity inflates support for the BJP.
In order to obtain a more detailed picture of how the increase in the size of
the Hindu community affects electoral outcomes, I then create two additional
models that divided the Hindu population into two categorical variables. The first
categorical, to be used as a baseline in this model, contains constituencies where
the Hindu population constitutes between 0 until the 50 percent of the population.
The second variable includes those constituencies where Hindus constitute
between the 50 and 99 percent of the total population in a constituency. These
tests enable me to learn whether a simple majority (30 percent in India's first-pastthe-post electoral system) is sufficient to produce a significant increase in support
for the BJP. Model 5 tests only relationship between the main independent
variable and the dependent variable, whereas Model 6 adds additional covariates.
20
Both models provide strong and significant support for the main hypothesis
advanced by this paper. In regards to the additional covariates, the results are
similar to those of Model 3 for almost all variables. The most salient difference
between these two models is that, in Model 6, the significance in these
relationships as increased for all but for the SC/ST variable.
VIII. Concluding Discussion
Policymakers and scholars examining interethnic relations in diverse
societies have a broad interest in discovering the conditions that mitigate or
intensify ethnic animosity. This paper seeks to contribute to this debate by
examining the contextual effect of religious demography on the electoral
performance of a party espousing a religious nationalist ideology. Specifically, I
analyze whether electoral support for the world's largest religious nationalist
party, the Hindu nationalist Bharatiya Janata Party (BJP), increases with the size
of Hindu population in urban constituencies in India.
The statistical analysis of this dataset provided strong, positive and
significant evidence for the relationship between the demographic size of the
ethnic majority and support for a party championing the interests of an ethnic
majority. More specifically, it showed that as the numbers of the ethnic majority
in a constituency increase so does support for a party championing a matching
ethnic category. This finding is robust to additional covariates, as well as control
for unobserved variation at the municipal, state and national level. In adjudicating
between the threat and contact hypothesis, this paper thus comes on the side of the
proponents of the contact hypothesis: as the demographic proportion of ethnic
minorities increases in a constituency, support for a party that champions the
interest of the ethnic majority decreases. In other words, ethnic heterogeneity at
the constituency level alleviates ethnic tensions. To account for this causal effect,
this paper argues that there is a strong strategic element in voters' calculus.
Following Chandra (2004), I argue that voters count heads from each ethnic
category in their constituency, from which they can guess the relative position of
21
each party if each ethnic category votes along ethnic lines. As described above,
this is the case with religion in India since minorities tend to vote for non-BJP
parties. Voters will then use this information, and vote for the ethnic party that has
the best chance of forming a winning coalition. As revealed by the data, parties
seeking to mobilize larger ethnic categories will be more successful in electoral
competition, helping the BJP do better in constituencies where Hindus constitute
the overwhelming majority.
It is important to highlight here that this conclusion stems from an analysis
of the effects of context at the level of the electoral constituency. Thus, it is
possible that others might find a different relationship between context and
electoral outcomes by exploring alternative units of analysis. On the micro scale,
they might look, for example, at the effects of context on BJP performance at the
residential block or neighborhood; on the macro scale they might examine these
effects at the city, district of state level. Yet, to the extent that electoral
constituencies refer to spatially defined places and they form the foremost arena
of ethnic competition, they provide a firm footing to the conclusions reached by
this investigation.
At the same time, this research lays the foundation for a novel theory of
ethnic party success that emphasizes the role of the demographic characteristic in
electoral outcomes. These conclusions provide important clues about why ethnic
parties are successful in some constituencies, but not in others; and which
mechanisms are likely to be effective in fostering harmony and goodwill. This
account differs from the three main existing explanations for ethnic party electoral
success, namely social service provision (Thachil 2011), the instrumental use of
pre-electoral riots (Wilkinson 2004; Dhattiwala and Biggs 2012) and civic ties
(Varshney 2002; Chidambaram 2012). However, rather than contracting, the
explanation advanced here complements these accounts for the ethnic party
success. Specifically, by helping to heighten the distinction between ethnic
categories, religious homogeneity at the constituency level may facilitate the
22
deployment of each of these strategies. The corollary of all this is that ethnic
parties have a vested interested in inflating the proportions of Hindus within their
constituencies to prop up their chances of success.
Finally, these conclusions also provide insight into the recent electoral
success of Hindu nationalist BJP in India. They suggest that the presence of a
sizeable Hindu majority at the constituency level is likely to improve the BJP's
chances of electoral success. This has important direct implications for the future
of the world's largest democracy. Indirectly, this also contributes to the growing
literature on India's urban transition. Indeed, official estimates suggest that India's
urban population is set to surpass its rural population by 2039 (Census of India
2011). By proposing a link between ethnic unmixing and electoral politics, this
project offers a novel perspective on debates about urbanization in India. More
broadly, the case of communal politics in India has the potential to provide
important lessons for other diverse developing societies. Subsequent work might
thus find it fruitful to explore whether this relationship between ethnic context and
electoral outcomes holds in other fast urbanizing, developing democracies such as
Bolivia, Indonesia and South Africa.
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