Mapping Class and Electoral Participation in India from 1996 to 2019
| Published date | 01 December 2023 |
| DOI | http://doi.org/10.1177/23210230231206649 |
| Author | Divya Vaid |
| Date | 01 December 2023 |
Mapping Class and Electoral
Participation in India from
1996 to 2019
Divya Vaid
Abstract
This article studies the relation between class and electoral participation. While the relation between
political participation and many demographic variables such as caste, gender, age and location has been
well researched in India, the same is not the case for the relation between class and electoral partici-
pation. Multiple measures of class (income, asset-wealth, occupation and education) are explored and
conceptualized in this article, following which these measures of class are operationalized using the
National Election Study datasets covering a twenty-three-year period (1996–2019). Each of these meas-
ures is used to trace the relation of class with two outcomes of electoral participation (turnout and
party vote share) over time. Disaggregation by gender, locality and caste is provided. Finally, regression
analysis to study the impact of these variables on turnout and vote share reveals the complexity of class.
We find a complex picture of turnout and party choice with variation across different class measures.
More significantly, variations in results raise questions about the usefulness of existing class indices.
Further, we find that the type of measure being used affects different outcomes differently. For turnout,
income and wealth seem to be better predictors, and for party vote share, subjective class is a better
fit, whereas asset-wealth displays opposite patterns to income and subjective class in some instances.
Keywords
political participation, social class, income, assets, education, occupation
Introduction
Election research, including the analysis of patterns of electoral participation, has had a fairly established
history in the Indian context, with the use of Election Commission data and the National Election Studies
(NES) datasets providing a benchmark for rigorous national-level comparative research (Kothari, 1973;
Original Article
Studies in Indian Politics
11(2) 225–257, 2023
© 2023 Lokniti, Centre for the
Study of Developing Societies
Article reuse guidelines:
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DOI: 10.1177/23210230231206649
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1 Centre for the Study of Social Systems, School of Social Sciences, Jawaharlal Nehru University, New Delhi, India
Corresponding author:
Divya Vaid, Centre for the Study of Social Systems, School of Social Sciences, Jawaharlal Nehru University, New
Delhi 110067, India.
E-mail: divya.vaid.09@gmail.com
226 Studies in Indian Politics 11(2)
Kumar, 2009; Yadav & Palshikar, 2008).2 Much research has focused on the patterns of political
participation by different groups and communities. In particular, patterns of voting by gender (Deshpande,
2009), caste, tribe and religion (Alam, 2009; Kothari, 1973; Vaid, 2009; Yadav, 2000) have been studied.
Theses have been proposed on the resurgence of political participation in India at the margins, or the
‘second democratic upsurge’ (Yadav, 2000), along with critiques of this argument (Palshikar & Kumar,
2004). In all of this, the focus on class has been limited (see the following section for a discussion of the
exceptions; see Fernandes and Heller [2006] on the middle class, and also Sheth [1999], and Ahuja and
Chhibber [2012]).
A possible reason for fewer studies on class in India is its secondary position to caste in the academic
discussion and sometimes a conflation with it (Vaid, 2018). Another reason could be issues around the
measurement of class. While variables such as caste may have their complexities, there are direct ways
in which surveys have captured them. For class, however, there are a variety of definitions and measures
that may preclude a truly comprehensive or comparative study over time.
Given the range of definitions and measures of class, this article has a twofold aim. The first is to
compare and contrast available indicators of class and their relation to two components of electoral
participation: turnout and party vote share.3 The second aim is to analyse the relative impact of these
measures on turnout and vote share. Together, this allows us to study the concept of class, its
operationalisation and its impact on electoral participation, and whether there is any evidence of class-
based voting in India.
This article uses the NES post-poll data for elections held between 1996 and 2019. Since there has
been variation in the measures of class used to study political engagement, this article provides a
comparison across four ‘objective’ measures of class used previously as part of class indices: asset-
wealth, income, occupation and education. To this we add a discussion of ‘subjective class’, or class
self-identification. Using these disaggregated measures of class rather than one combined index allows
for a comprehensive comparison of key patterns of electoral participation over the years. Further,
studying the impact of each of these measures of class on turnout and party choice in the 2019 general
election helps focus on the comparisons across measures of class for one year in detail.
We find a complex picture of turnout and party choice with variation across different class measures.
More significantly the variations in results raise questions about the usefulness of existing class
indexes. Further, we find that the type of measure being used affects different outcomes differently.
For turnout, income and wealth seem to be better predictors, and for party vote share, subjective class
is a better fit, while asset-wealth displays opposite patterns to income and subjective class in some
instances.
The following section of the article discusses the key literature on class and macro-patterns of
electoral participation, followed by a discussion on data and measures used; this is followed by a
section on turnout and vote-share patterns by objective and subjective classes; the article then
compares these patterns by caste/community and class measures; the penultimate section compares
the relative significance of various measures of class on electoral outcomes, and the last section
concludes.
2 Election research is a broad area. As this article focuses on tracing patterns in turnout and vote share, it does not engage with the
ethnographic literature on other election related issues (see Banerjee, 2014).
3 A study of related components such as interest in politics and participation in political activities is outside the scope of the present
study. However, these are discussed briey in relation to voting and vote share later in the article.
Vaid 227
Literature Review
A review of the literature highlights the ways in which class is conceptualised and operationalised in
electoral research and the variation in the findings. Kumar (2009) using the NES data had commented on
the ‘slight increase in turnout among middle class voters’ following the 2009 Lok Sabha elections and
concluded that ‘this was a classic instance of the democratic upsurge levelling off’ (p. 50). For his study,
he used the NES classification of economic class: upper, middle and lower. His classification codes
information on monthly household income and household assets (Lokniti Team, 2009).
For the 2014 NES data, Sridharan (2014) used a ‘composite class index consisting of a combination
of economic (income and ownership of selected durable assets, in particular the type of house) and
sociological criteria (that is, occupation and occupational level), with assets and income adjusted for
rural and urban locations …’ (p. 72). This classification divides the sample into upper middle, middle,
lower and poor classes. Further, using the 2019 NES data, Sridharan (2020) used the NES class index of
‘income, house type, occupations and occupation level’ (p. 52). The details of the index, whether it uses
weights, or is simply additive, or what the ‘occupation level’ implies are not provided. The author finds
that the measures of class are ‘not comparable’ (p. 52) across 2014 and 2019. Using the NES class
scheme, he divides the sample into poor, lower, middle and rich and looks at turnout, party choice and a
range of political opinion variables age-wise, by rural–urban location and by caste. He finds that ‘the
degree of class polarisation in party preferences seems muted’ (p. 58), though he finds a pro-BJP swing
among the lower classes (across groups with the exception of the Muslims).
Jaffrelot (2015) using the NES data provides more detail for 2014. He shows that the NES class
variable is a combination of ‘occupation, type of housing, selected household assets and income’ (p. 34),
providing details of each of these, including an index comprising housing type, occupation and assets
and an income index. His classification also divides the sample into poor, lower, middle and rich. Jeffrelot
comments on the change in the ‘reverse correlation between the socio-economic status and electoral
participation’ with an increase in the participation of the middle and upper-middle classes in 2014
(p. 20). He finds that ‘class has become a more influential factor and has significantly contributed
to Modi’s success’ (p. 34).
As we see, different measures of class using the same database have been used across the years. While
these measures have been described by the authors as an ‘objective class index’, the technical classification
seems to be based on ‘predetermined criteria’ (Jaffrelot, 2015, p. 34) that might involve some subjective
decisions. For example, why place a particular occupation in one class and not another (since occupation
is difficult to code hierarchically unlike income, for instance)? Further, conflating measures that comprise
household-level information (income and assets) with individual-level information (occupation) seems
conceptually and empirically problematic, especially since household resources are seldom equally
distributed within the family (Vaid, 2018). How would we then discuss women’s political participation
in this context? These questions particularly underline the confusions caused due to inadequate
engagement with conceptualising class, prior to operationalising it (Vaid, 2018). What precisely is one
measuring is an important question that needs addressing.
Further, the use of varying definitions of what comprises class, along with differing asset information
across the years (discussed later), as well as a fairly subjective understanding of who is considered poor,
middle or upper in terms of income, creates the additional problem of ‘class’ not being comparative over
time. Looking at individual variables that comprise class separately might allow for some more consistent
over-time comparisons.
While this article provides a small step forward towards establishing patterns of electoral participation
in India by including class in its various complexities, there are limitations of such a study. In addition
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