Party systems, political cleavages and electoral volatility in India: A state-wise analysis,...

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Electoral Studies 24 (2005) 177–199

Party systems, political cleavages andelectoral volatility in India

A state-wise analysis, 1998–1999

Oliver Heath

Department of Government, University of Essex, Wivenhoe Park, Colchester CO4 35Q, UK

Abstract

One of the defining characteristics of India in the 1990s has been high level of electoral

volatility at the national level. However, this aggregate picture masks competing dynamics atthe state level. Different states show markedly different patterns. Using survey data from theCentre for the Study of Developing Societies, this paper shows that volatility can be explained

by three inter-linked factors: the party system format, the politicisation of social cleavages,and the extent to which these cleavages are polarised. The combined impact of these factorshas a strong impact on volatility, both when measured in terms of vote-switching betweenparties and vote-switching between alliances.

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Keywords: Electoral volatility; Party system format; Political cleavages

1. Introduction

One of the defining features of India in the 1990s has been political instability.From 1989 to 1999 there were five general elections shared between three differentgovernment formations. Four of these five elections produced hung parliaments, andthe resulting government formations at the centre were often made up of numerousand disparate coalition partners. In 1999 the Bharatiya Janata Party (BJP) ledNational Democratic Alliance (NDA) was voted into power, and the BJP became thefirst incumbent government to win since Rajiv Gandhi’s Congress in 1984. However,the internal composition of the BJP’s ally partners changed between the elections of1998 and 1999, and despite the fact that there was only a year between elections, the

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178 O. Heath / Electoral Studies 24 (2005) 177–199

election of 1999 still caused a number of upheavals and turnarounds. As Yadav andKumar (1999) illustrate, an analysis of seat retention shows that of the 537 seats, onwhich their analysis of the 1999 results was based, only 281 seats were retained by thesame party that won in 1998. Moreover, despite increasing its’ seat tally from 265 to296, the BJP and its allies were only able to hold on to 157 of the seats they held in1998. The Congress and its allies, the main opposition formation, retained only 53 of161 seats. Even in an election in which apparently little changed overall, there was stillmajor churning taking place beneath the surface. These figures indicate, as Yadav andKumar (1999) observe, ‘‘that Indian elections continue to produce greater upheavalsthan elections in most other democracies’’.1

This paper examines the factors that account for the high level of observed electoralvolatility in India, and in doing so shows why some states in India are relatively stablewhile others are extremely volatile. I argue that differences between levels of volatilitycanbe explainedby three inter-linked factors: theparty system format, thepoliticisationof social cleavages, and the degree of polarisation. These factors are not only able toexplain volatility; they are also associated with the prevalence of unstable allianceformations in some states, which in turn is a further contributing factor to electoralvolatility. The paper comprises four broad sections. The first section focuses on theparty system format of the states and shows thatmultiparty systems tend to have higherlevels of volatility than two-party systems. The second section examines how socialcleavages are politicised and the extent to which they are reflected in the structure ofparty competition. I show thatparty systems that are cleavagebasedhave lower levels ofvolatility than party systems that are based on ‘catch-all’ politics.Moreover those partysystems that reflect social cleavages have lower levels of volatility than those partysystems that do not. The third section examines the impact of party induced volatility,and shows that volatility is higher in states with weak electoral alliances that changefrom election to election. The final section examines volatility at the individual level anduses logistic regression to model the impact of party systems and political cleavages atthe state level, controlling for individual-level attributes and party induced volatility. Ishow that controlling for individual-level characteristics, such as party identification,people are more likely to change the party that they vote for if they live in state witha multiparty system with weak political cleavages. Moreover, I show that states of thistype are more likely to be characterised by unstable alliances, which further contributeto levels of volatility. This also holds for switching between alliance formations.

Studies on volatility have tended to focus on change over time (see Bartolini andMair, 1990; Pedersen, 1979; Dalton et al., 1984; Korasteleva, 2000; Crewe andDenver, 1985; Heath et al., 1991) and broadly speaking they fall into two groups.Firstly, studies which focus on old democracies in Western Europe or North Americaand examine ‘dealignment’. And secondly studies which focus on new democracies inEast Europe or Latin America and examine ‘stabilisation’ (see Korasteleva, 2000;Toole, 2000; Mainwaring and Scully, 1995). Whereas the emphasis of these studieshas been on change, the emphasis of this paper is on difference. I analyse why someStates are more volatile than others. In doing so the objectives are two-fold; firstly, to

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179O. Heath / Electoral Studies 24 (2005) 177–199

examine the determinants of electoral volatility in India, and secondly to illustratehow party systems and political cleavages influence political choice.

2. Data and methods

Electoral volatility can be thought of in two different ways. On the one hand it canrefer to the changedin absolute termsdin the aggregate vote for party i between twoconsecutive elections (Bartolini andMair, 1990: p. 20). Following this definition it canbe measured at the level of individual parties, groups of parties and at the level of theparty system as a whole. On the other hand volatility can refer to individual votingshifts, such as whether a person changes the party that they vote for between twoconsecutive elections.2 Aggregate, or net, volatility is a system property. Although it isoften taken as a proxy for individual voting shifts, the two are not necessarily connectedand a change in one can occur without an equivalent change in the other. It is thuspossible to have aggregate volatility without any individual volatilitydand of course,vice versa.3 Whereas net volatility is a measure of temporal change in party support,overall (or total) volatility is a measure of voter constancy (Field, 1994: p. 151). Thispaper focuses on voter constancy.Despite the high level of net volatility in India it is thechurning beneath the surface that makes elections so unpredictable. This iscompounded by the fact that alliance formations tend to be quite unstable, changingfrom one election to the next. The main focus of this paper is therefore volatility at theparty level.However, in the final section volatility at the alliance level is also considered.

To explore the Indian experience of electoral volatility, I use the National ElectionStudy (NES) of 1999 that was conducted by the Centre for the Study of DevelopingSocieties (CSDS).4 Table 1 shows the included and excluded respondents for each of

2 Turnout may also be incorporated into the story of how the party system/cleavage structure affects

political choice, and analysis not presented here shows that the findings in this paper are also applicable to

switching between voting and not-voting. However, for a number of reasons turnout is not considered

here. Firstly, on theoretical grounds, the political implications of variation in levels of turnout are

considered to be rather different to the political implication of variation in levels of partisan support.

Secondly, the dynamics of switching between voting and non-voting and switching between one party and

another are somewhat different, and incorporating them together may cloud the issue somewhat. For

example, research in the UK suggests that reasons for periodic (rather than habitual) non-voting are

generally due to short-term factors, such as illness, being away on business etc. (see Swaddle and Heath,

1989), which are rather different to factors that can plausibly account for vote-switching.3 This can happen due to changes in the electoral register, either through new additions or people dying.4 I would like to thank Yogendra Yadav from the CSDS for giving me permission to use this data. The

survey adheres to strict sampling methods of probability proportionate to size, and is nationally

representative of the electorate as a whole. The 1999 survey was conducted in 432 sampling points in 108

parliamentary constituencies across 20 states and union territories in India. The total issued sample was

15,000 and the response rate was 60.7%, which was more or less constant across the states. A total of 9114

electors were interviewed in their homes in the week after each phase of polling. The last round of the survey

was completed onOctober 5, the day before the counting began.The social composition of the respondents in

NES99 is as follows: 49.3% women, 18.3% Scheduled Castes, 7.9% Scheduled Tribes, 10.3% Muslims,

77.8%rural voters, 41%uneducated voters and6.7%graduates.There is thus anover-representationof rural

voters and an under-representation ofMuslim voters. In terms of the reported party vote shares, the sample

percentages (actual results in brackets) are: the BJP and its allies 38.8% (40.8%), the Congress and its allies

36.7% (33.8%), the Left 7.6% (8.0%) and the Bahujan Samaj Party (BSP) 3.1% (4.2%).


the states that are used in the following analysis. The smallest state included isHaryana, which has a sample size of 170. Several states and Union Territories havebeen excluded from the analysis due to their small sample sizes. Excludedrespondents are all those who did not vote, and all those who said that they didvote, but could not remember for which party.

Volatility at the party level is measured as the proportion of people who voted inboth the 1998 and 1999 national elections and changed the party that they votedfor.5 The measure for this is quite straightforward, respondents who vote for thesame party in both 1998 and 1999 are stable, and respondents who changed the partythat they voted for are volatile. In the last section vote-switching between alliances isalso considered. This is measured as the proportion of people who voted for a partyin 1999 that was not allied to the party they voted for in 1998. If they vote fora member of the same alliance they are stable and if they vote for a different alliancethey are volatile.6 Volatility is measured at the individual level and at the state level(calculated as the percentage of respondents who changed their vote).

Table 1

Excluded respondents

Stable Volatile Excluded Total

Andhra Pradesh 615 199 34 848

Assam 160 62 39 261Bihar 312 332 237 881Gujarat 283 70 129 482

Haryana 69 70 31 170Karnataka 258 155 61 474Kerala 326 38 17 381

Madhya Pradesh 465 26 72 563Maharashtra 558 319 153 1030Orissa 212 106 47 365

Punjab 85 81 63 229Rajasthan 443 69 62 574Tamil Nadu 230 403 88 721

Uttar Pradesh 778 291 241 1310West Bengal 591 123 111 825

All 5385 2344 1385 9114

5 Ideally, the preferred method for investigation would be to use panel data. Although the 1998 and

1999 surveys are based on a partial panel its’ sample size is small, and so in order to have a sample large

enough to carry out state-wide analysis this paper uses a recall question asked in 1999 about which party

the respondent voted for in 1998. The fact that the time elapsed between the two elections was only 1 year

means that we can have more confidence in the reliability of the results than if the time period was longer.

Himmelweit et al. (1978) show that although the marginal distribution for recall vote may be inaccurate,

the relationship between recall and present vote is not greatly affected.6 Many of the state-level alliances changed between 1998 and 1999 which makes exact comparisons

difficult. We take the party voted for in 1998 as the respondent’s chosen party, and consider a vote for any

of that party’s alliance partners in 1999 as a stable vote. However, this needs to be treated with a degree of

caution since it is also possible that the party voted for in 1998 was a vote for the ally of the preferred party

and not the preferred party itself.


The level of electoral volatility is much higher in some states than it is in others (seeFig. 1). Madhya Pradesh has the lowest level of volatility, with just 5% of the peoplewho voted in both 1998 and 1999 changing the party that they voted for. By contrast, inTamil Nadu 64% of the voters changed party. It is clear that there is a considerableamount of electoral turmoil taking place beneath the surface, and that this is not evenlydistributed across the different states in India. The aim of this paper is to explore whataccounts for this variation. Why are some states more volatile than others?

2.1. Party system format and volatility

Following Duverger’s law, two-party systems are thought to be more stable thanmultiparty systems, as over time the latter will converge to the former. This sectionexamines the impact of different types of party system on levels of volatility. Domultiparty systems have higher levels of volatility than two-party systems?

There are three main ways in which party systems are generally classified. Firstly,following Pedersen (1983), party systems can be defined simply in terms of thenumber of parties that contest each election. Secondly, following Bartolini and Mair(1990), they can be defined in terms of the number of ‘significant’ parties, such as thenumber that gain over 2% of the total vote share, and thirdly, following Taageperaand Shugart (1989), they can be defined in terms of the number of ‘effective’ parties,which weights political parties according to their respective vote share. WhereasPedersen’s approach includes parties that have little bearing on the reality of politicalcompetition, and the cut-off point in Bartolini and Mair’s approach is somewhatarbitrarydwhy not 5% of the vote share, or 10%dthe effective number of partiesprovides a practical way of describing the number of parties in a system.

The index, to a certain extent, is an abstract quantification. The value signifies thehypothetical number of equal sized parties that would produce an equivalent degreeof fractionalisation in the political system.7 The index for the effective number ofparties is closely related to Rae’s index of fractionalisation and can be expressed asNv ¼ 1=Sp2v, where Nv is the number of effective parties based on vote share, and pvis the proportion of votes that each party secured. There is no need to draw a cut-offmark to determine which parties should be included in the calculation since thesmaller a party’s vote share is, the smaller its impact is on the number of effectiveparties. Therefore all parties that contested the election in 1999 and were classified bythe Indian Election Commission have been included in the following calculations.8

7 In reality there are many different constellations that are capable of producing an identical value for

Nv. For example, as Taagepera and Shugart (1989) illustrate, the following vote shares of 34%, 33%, 33%

and 45%, 29%, 21%, 5% and 50%, 17%, 17%, 16% all produce the same value of Nv ¼ 3:0. In this

respect, the index is not able to tell us anything about the actual distribution of votes amongst the parties,

or how many parties share what proportion of the vote. It does not look at the internal composition of the

contest, but at the type of contest itself. What it gives us is an approximation to the kind of constellation

that would produce a similar result. In this sense then, the index for the number of effective parties is best

interpreted as a simile for the actual fractionalisation that exists in the real world. However, as a rule of

thumb the number of effective parties is approximately equal to the number of parties that obtain 10% or

more of the vote.8 Indian Election Commission. http://www.eci.gov.in.

http://www.eci.gov.in


The only states where a slightly different method is used are West Bengal and Kerala.Both states are quite curious cases in India as they are bastions of communism andtheir political competition is not organized between parties, but between fronts. Thekey point is that fronts are ideologically similar and are very stable; parties from thesame front do not stand against each other. This is in stark contrast to the party‘alliances’ which are found in other states. These are often made up of parties thathave little in common ideologically and their composition often changes from oneelection to the other.9

The states with the lowest number of effective parties are Gujarat (Nv ¼ 2:07),Rajasthan (Nv ¼ 2:34) and Madhya Pradesh (Nv ¼ 2:42), which are characterised byentrenched two-party competition between the BJP and Congress (see Table 2). The

AND

HR

A PR

ADES

H

ASSA

M

BIH

AR

GU

JAR

AT

HAR

YAN

A

KAR

NAT

AKA

KER

ALA

MAD

HYA

PR

ADES

H

MAH

ARAS

HTR

A

OR

ISSA

PUN

JAB

RAJ

ASTH

AN

TAM

IL N

ADU

UTT

AR P

RAD

ESH

WES

T BE

NG

AL

0.00

0.20

0.40

0.60To

tal v

olat

ility

Fig. 1. Total volatility by state, 1998–1999 (with 95% confidence intervals).

9 In Kerala, there are two dominant frontsdthe United Democratic Front, which consists of Congress,

the Muslim League and the Kerala Congress, and the Left Democratic front, which consists of the

Communist Party India (CPI), Communist Party Marxist (CPM), Revolutionary Socialist Party (RSP)

and Kerala Congress Marxist. In West Bengal, the Left Front is composed of four main partiesdthe

CPM, CPI, RSP and Forward Bloc. In both states therefore it is not appropriate to calculate the index

based on individual party vote share, but instead to base it on the vote share of the fronts.


states with the highest number of effective parties are Tamil Nadu (Nv ¼ 6:48) andBihar (Nv ¼ 5:31) which are both characterised by complicated multiparty alliancesbetween varying groups of parties.

People who live in states with a high number of effective parties tend to be morevolatile in their voting habits than people who live in states with a low number ofeffective parties. The relationship between the effective number of parties andvolatility is strong and positive (Fig. 2). The slope of the line is 11.14, indicating thatfor every increase in the number of effective parties the percentage of vote switchersincreases by 11 percentage points. The relationship is linear and there is no sign ofa threshold of relevance. Multiparty systems are more volatile than two-partysystems. The R2 of 0.499 is very high and tells us that the party system formataccounts for 50% of the variance that is observed in the levels of volatility betweenthe states.

These findings are consistent with Bartolini and Mair (1990) and Pedersen (1983).Pedersen’s study of electoral volatility in Western Europe between 1948 and 1975found a clear and positive relationship between the number of parties and levels ofvolatility, with an overall correlation of 0.396. Pedersen (1983: p. 52) also founda strong positive relationship between format change and volatility, with high levelsof electoral interchange occurring in cases where the number of parties declined aswell as in those where the number increased. However, the R2 is considerably higherthan that which either of them found. Possibly, this could suggest the format of theparty system has more impact on volatility at the individual level than it does at theaggregate level. This could be because the more parties there are the less attachmentvoters have to one particular party and/or the less they can distinguish one partyfrom another (due to the increased costs of processing lots of different policyplatforms or ideologies etc.). In this instance vote-switching might become more

Table 2

Number of effective parties by State

State Effective parties

Andhra Pradesh 2.84

Assam 3.82

Bihar 5.31

Gujarat 2.07

Haryana 3.43

Karnataka 3.23

Kerala 2.70

Madhya Pradesh 2.42

Maharashtra 4.76

Orissa 3.26

Punjab 4.07

Rajasthan 2.34

Tamil Nadu 6.48

Uttar Pradesh 4.83

West Bengal 3.16


arbitrary, and therefore it would be more likely to cancel itself out at the aggregatelevel. However, it could also just be that the relationship is stronger in India than wasfound in Western Europe.

2.2. Political cleavages and volatility

This section examines the impact of social cleavages on levels of volatility. I arguethat states where the political contest is structured along cleavage lines tend to havelower levels of volatility than states which are not. Moreover, the stronger thecleavagedthe more polarised the contest is along these linesdthe lower the level ofvolatility. Finally I show that party systems which reflect these political cleavagestend to be less volatile than party systems which do not, and this is most evident instates with multiparty systems such as Uttar Pradesh.

Social cleavages are commonly thought to act as forces which both shape andcondition electoral behaviour, their relative strength or hold being indicated by theextent to which they afford a degree of elasticity of electoral choice (Bartolini andMair, 1990: p. 212). Social cleavages thus stabilise political behaviour, and thestronger they are the less frequent are the exchange of votes across them. During the1960s it was a widely held view among political scientists that European partysystems were inherently stable structures whichdwith a few exceptionsdreflectedthe societal cleavage structures of the past (Pedersen, 1983: p. 195). Unlike WesternEurope however, the situation in India is not so straightforward.

The concept of a social cleavage is relatively ambiguous, and many authors havesought to qualify the concept, and refer to cleavages as either political or cultural,structural or non-structural, institutionalised or non-institutionalised, and so on.

R2 = 0.4992y = 11.14x - 9.07

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

1.00 2.00 3.00 4.00 5.00 6.00 7.00

Effective number of parties

Tot

al V

olat

ility

Haryana Punjab

Bihar

Tamil Nadu

Karnataka Maharastra

Orissa

AssamUttar Pradesh

Andhra Pradesh

Gujarat West BengalRajasthan

Kerala

Madhya Pradesh

Fig. 2. Total volatility and effective number of parties.


Bartolini and Mair argue that this approach fails to distinguish between what is, forexample, a political cleavage on the one hand and other concepts, such as politicalopposition or political division on the other. According to Lipset and Rokkan(1967), the central components of a cleavage are two-fold. Firstly, it refers toa social-structural group with a certain degree of closure, and secondly, the membersof the group in question must share a common set of values and beliefs. A cleavageis, therefore, a dividing line between two groups. This has most commonly beenexpressed in terms of a simple dichotomy. Lipset and Rokkan (1967) identify fourtypes of dichotomous cleavage; centre/periphery, church/state, land/industry andowner/worker, and more recently, scholars such as Inglehart (1990) have attemptedto introduce a new value cleavage, which is characterised by a dichotomy betweenmaterialist and post materialist values. However, the case of India does not seem tofall quite so easily into these simple bifurcations. For example, caste and religionboth stratify Indian society along many different and sometimes competing lines.

In the European context, countries differ considerably according to the characterand intensity of the cleavages that form the basis for political conflict and politicalorganisation (Daalder, 1990: p. 87), and the same could also be said of Indian states.Cleavages based on caste, religion, class, centre/periphery, rural/urban and others alldivide political conflict to varying degrees in various states. Ideally the extent towhich each of these is mobilised within the structure of party competition should beexamined. However, space prevents such an investigation in this analysis. For thetime being I therefore focus on the caste–religion cleavage. This is certainly the mostwidespread cleavage in terms of political mobilisation and political conflict, althoughit is stronger in some states than it is in others.10 To measure caste–community in thispaper I employ the CSDS’s six-fold classification.11 This pays special attention to thesocial blocs explicitly politicised by the Mandalisation of politics (see, for example,Corbridge and Harris, 2001).

Measuring social cleavages is somewhat problematic, and indicators of socialdiversity have often been used as proxies in its place. This technique is unsatisfactorythough, as it fails to get to grips with the central issue of how salient the cleavages areand the extent to which they are embedded in the structure of party politics. In thispaper I propose an alternative method of measuring social cleavages which focuses

10 There is a huge literature on the role of caste and religion in Indian politics. See for example, Gupta

(2000), Kaviraj (1997) and Kothari (1970) for particularly insightful analysis. For a recent ethnographic

illustration of the use of caste as a tool for political mobilisation, see Michelutti (2004).11 Broadly speaking, the CSDS classification groups the different jatis together into four social blocs,

groups the Muslims together into one group, and groups all the other smaller religions into a residual

group. For example, the upper caste jatis, such as Brahmins, Banias and Rajputs are banded together into

one ‘upper caste’ category. Similarly, the shudra jatis, such as Yadavs, Jats and Kurmis are banded

together to form the Other Backward Class (OBC) category. The ex-untouchable jatis, such as the

Chamars, are banded together to form the Scheduled Castes (SC) and the aboriginals are banded together

to form Scheduled Tribes (ST); the OBCs, SCs, and STs all have official ‘reservation status’, which

provides quotas in government jobs, schools and universities for their members. Outside of the Hindu

caste system, the index for caste–community also includes Muslims, and Other Religions, which are

specifically Sikh in Punjab and Christian in Kerala. In the other states, however, this is just a residual

category of all non-Hindu and non-Muslim religions.


on the extent to which the caste–community cleavage is politicised by politicalparties. Are states that are characterised by caste-based politics less volatile thanstates that are characterised by catch-all politics? I examine the extent to which thesecleavages are reflected in the structure of party competition. Is volatility higher instates where parties compete against each other for the same sections of society thanin states where they compete for different sections of society?

Many political parties explicitly mobilise their supporters along caste–communitylines and this is reflected in their social bases (see for example Heath and Yadav,1999 and Heath, 1999). To measure the extent to which the caste–communitycleavage is politicised by political parties I examine the degree to which their supportbase is concentrated within particular communities and classify them according towhether they are predominantly upper caste, predominantly Scheduled Caste,Muslim etc., or predominantly ‘catch-all’, respectively. I argue that volatility is likelyto be lower in states that are characterised by competition across different cleavagelines (such as upper caste vs. Dalit) than in states where the parties tend not todifferentiate between these groups.

In order to do this I use cluster analysis. Cluster analysis allows relativelyhomogenous groups of parties to be identified on the basis of the caste–communitycharacteristics of their supporters.12 The number of clusters in each state equates tothe number of politically mobilised cleavage groups. For example, if the caste–community groups vote for the same parties in the same proportion, then thenumber of cleavage groups in a state is one. This represents cleavage-less, or ‘catch-all’ politics. However, if each of the six groups behaves in markedly different ways interms of the parties that they voted for then the number of cleavage groups is six. Asituation approaching this scenario can be observed in Uttar Pradesh, where partycompetition is basically structured along three caste–community lines between fourmajor parties, with Scheduled Castes voting for the BSP, OBCs (in particular

12 To do this a merged data file is created that combines party voted for in 1998 and 1999. This

ameliorates the problem of parties splitting and disappearing, and provides a stable base on which to judge

each party’s social profile. The caste–community index is turned into six dummy variables of upper caste/

other, OBC/other etc. Having done this the data file is aggregated using party voted for as the ‘break’

variable and each of the dichotomous caste–community variables as the ‘aggregate’ variables (using SPSS

terminology). Each party therefore represents a case and for each case the mean score for each of the

caste–community variables is calculated. Hierarchical cluster analysis iteratively matches pairs of cases in

order of proximity. The parties are clustered according to the similarity of their scores across all the

community variables. The coefficient value provides a measure of how much each pair of cases matches

each other. The number of clusters is determined by looking at the point when the coefficient score jumps.

This process is repeated for each of the states. The minimum number of clusters that the parties can be

grouped into is two, and the maximum number of clusters is N� 1. However, in some states there is not an

obvious pattern among the coefficient values. This can make the cut-off mark for the number of clusters

more open to question, and judgement about the political situation in the state is therefore needed to make

a decision. There are several reasons why the parties in a state might not easily fall into clusters. The first

reason might be that the parties are fairly heterogeneous in terms of their social bases, and are therefore

not easily grouped into homogenous clusters. This could be because each party has a very distinct social

profile, as in the case of Uttar Pradesh. Or, it could be the reverse, in that there is little to distinguish one

party from another. Whereas the first reason is probably a result of a deeply polarised state with many

parties, the latter is probably a result of a relatively unpolarised state with many parties.


Yadavs) and Muslims voting for the SP and to a lesser extent the Congress, andupper castes voting for the BJP. Thus in states where there are two clusters (cleavagegroups), political competition can be seen to operate across one cleavage line.

The number of clusters describes the cleavage structure in a similar way that thenumber of parties describes the party structure of a state. It does not describe howsalientdor ‘effective’dthe cleavages are. For example, both Andhra Pradesh andRajasthan have just two cleavage groups. However, whereas in Andhra Pradesh89.7% of the votes are polled for one of the blocs, in Rajasthan the votes are moreevenly split between the two blocs, with 45.1% voting for one and 54.9% voting forthe other (see Table 3). To classify both party systems as politicising social cleavagesto the same extent is therefore somewhat misleading. In Andhra Pradesh partycompetition is not meaningfully structured along cleavage lines, and as the vastmajority of voters fall under the same umbrella it is more appropriate to describe thecompetition as based on ‘catch-all’ politics (at least in caste–community terms).However, in Rajasthan there is clear dichotomisation along caste–community linesbetween the different parties.

To describe the cleavage structure in practical terms that more closely reflects thepolitical reality, the number of cleavage groups in a State can be classified in terms oftheir ‘effectiveness’. The ‘effective’ number of cleavage groups can be calculated inthe same way as the effective number of parties, only the unit of analysis is clustersrather than parties. The number of effective cleavage groups can therefore beexpressed as Nc ¼ 1=Sp2b, where Nc is the number of effective cleavage groups, and pbis the proportion of votes that each bloc secured.13 In Andhra Pradesh there are 1.23

Table 3

Frequency distribution of clusters

State No. of

clusters

Cluster 1

(%)

Cluster 2

(%)

Cluster 3

(%)

Cluster 4

(%)

Effective

clusters

Andhra Pradesh 2 10.32 89.68 – – 1.23

Assam 2 31.99 68.01 – – 1.77

Bihar 2 64.73 35.27 – – 1.84

Gujarat 2 42.01 57.99 – – 1.95

Haryana 2 48.24 51.76 – – 2.00

Karnataka 2 42.83 57.17 – – 1.96

Kerala 3 7.74 80.18 12.07 – 1.51

Madhya Pradesh 3 53.82 34.81 11.37 – 2.36

Maharashtra 3 34.95 57.04 8.01 – 2.20

Orissa 3 52.05 43.84 4.11 – 2.15

Punjab 2 48.91 51.09 – – 2.00

Rajasthan 2 45.12 54.88 – – 1.98

Tamil Nadu 4 3.68 18.17 72.47 5.69 1.78

Uttar Pradesh 3 17.25 49.77 32.98 – 2.59

West Bengal 3 22.88 19.15 57.97 – 2.35

13 To carry out this calculation I return to the merged SPSS data file and recode all the parties in each

state according to the cluster that they fall in. Using the frequency distribution of these clusters the

effective number of cleavages can be calculated.


effective cleavage groups which indicates that the caste–community cleavage is notvery important in structuring party competition (see Table 3).

At first glance the number of cleavage groups in a state does not appear to havemuch of a bearing on the level of volatility (see Fig. 3). The R2 of 0.09 suggests thatrelatively little of the variation in volatility between the states can be accounted forby the respective politicisation of social cleavages. However, Andhra Pradesh is anextreme outlier, and if this state is excluded from the analysis then the fit is muchbetter. The direction of the slope indicates that there is a negative relationshipbetween the number of cleavage groups and the level of volatility. States that arecharacterised by cleavage-less or ‘catch-all’ politics tend to be somewhat morevolatile than states where party competition is structured along caste–communitylines.

However, it is not the number of cleavages per se that is important for explainingvolatility, but how these cleavages interact with the party system. Comparison ofFigs. 2 and 3 make for interesting reading. On the one hand states with many partiestend to be more volatile than states with few parties, and on the other hand stateswhere the political contest is divided along cleavage lines tend to be less volatile thanstates which are not. This suggests that it may not only be the number of effectiveparties that is important for determining the level of volatility, but how thesepolitical parties reflect the political cleavages. If there is more than one party thatrepresents a particular cleavage group then the voter has a greater incentive tochange his vote, and vote for one of the other parties that represent/target hiscommunity. Similarly, high expected levels of volatility in multiparty systems may beoff-set if the contests are fought along several caste–community lines, as in UttarPradesh. States with party structures that do not well reflect the cleavage structuresare more prone to volatility than states where the match is good.

y = -14.743x + 59.727

R2 = 0.0898

70.00

60.00

50.00

40.00

30.00

20.00

10.00

0.001.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40 2.60 2.80

Effective number of cleavages

Tot

al V

olat

ility

Uttar Pradesh

Tamil Nadu

Bihar

Haryana

Punjab

Karnataka MaharashtraOrissa

AssamAndhra Pradesh

GujaratWest Bengal

RajasthanKerala

Madhya Pradesh

Fig. 3. Total volatility and effective number of cleavages.


The last aspect of political cleavages to be investigated is the impact of theirstrengthdor relative holddon levels of volatility. The transfer of votes acrosscleavage lines is less frequent in States that are deeply polarised. To examine this Ilook at the relationship between caste–community and the cluster voted for, and usean index of dissimilarity to measure the degree to which political competition ispolarised along caste–community lines. The index of dissimilarity measures thedifference between the observed results and the expected results assuming norelationship between the two variables. By looking at the difference between theobserved percentage of total and expected percentage of total in each cell we cancalculate what percentage of each state’s population would have to change their votein order for there to be no association between caste–community and which clusterthey voted for. The greater the value of this difference the more polarised we can saythe state is. This technique has the advantage over other methods for examining therelationship between two variables as it is neither dependent on sample size nor ondegrees of freedom. We can therefore produce a single comparable measure ofpolarisation for each state. From Table 4 we can see that Uttar Pradesh is the mostpolarised state (56.07) and Tamil Nadu is the least polarised (17.4).

Next, we examine the relationship between the strength of the cleavage lines ina state, and the level of volatility. At first glance the level of polarisation in a stateappears to have only a weak impact on volatility (see Fig. 4). The R2 of 0.10 indicatesthat the level of political polarisation can explain little of the observed variationbetween the states. The equation of the regression line is y ¼ 44:77� 0:50x. There isa negative relationship and for every one unit increase in the level of polarisation (ona 0–100 scale) volatility decreases by 0.50 points. States that are deeply polarisedtend to be less volatile than states where party competition is not very polarisedalong caste–community lines. Although this relationship is not very strong it is worthbearing in mind that Uttar Pradesh is somewhat of an outlier. U.P. is unusual in that

Table 4

Polarisation index frequency distribution by state

State Cleavage polarisation

Andhra Pradesh 18.65

Assam 40.17

Bihar 21.81

Gujarat 28.50

Haryana 26.82

Karnataka 22.82

Kerala 31.43

Madhya Pradesh 33.44

Maharashtra 24.81

Orissa 40.52

Punjab 26.65

Rajasthan 33.04

Tamil Nadu 17.39

Uttar Pradesh 56.07

West Bengal 23.38


it has a high number of effective parties but is also extremely polarised. If this state isexcluded from the analysis then the fit is much better.

Overall, states with multiparty systems tend to be more volatile than states withtwo-party systems. However, this is off-set by the extent to which the caste–community cleavage is politicised. States where the structure of party competition isfought along different caste–community lines are less volatile than statescharacterised by catch-all politics, and the more polarised the competition is alongthese lines the lower is the level of volatility. These factors all need to be consideredin conjunction to each other. Levels of volatility are lower in states where the partysystems reflect the political cleavages than in states where they do not. Thus, eventhough Uttar Pradesh has a relatively high number of effective parties, the effect ofthis on levels of volatility is ameliorated by the fact that it also has a high number ofpoliticised cleavages, and that these are reflected in the structure of partycompetition.

3. The combined effect of party system format and political cleavages

So far we have considered only the bivariate impact of each factor on levels ofvolatility. However, to a certain extent the impact of each factor is dependent oneach of the other factors, and it therefore makes more sense to consider their jointimpact rather than to consider each one separately. In this sense they forma hierarchy: the impact of polarisation depends on how it relates to the cleavagestructure; and the impact of the cleavage structure depends on how it relates to theparty structure. Table 5 summarises Figs. 2–4 and shows how levels of volatility vary

R2 = 0.1007

y = -0.5045x + 44.768

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00

Polarisation

Tot

al V

olat

ilit

y

Tamil Nadu

Bihar

Haryana

Punjab

Karnataka

MaharashtraOrissa

AssamUttar PradeshAndhra Pradesh

Gujarat

West Bengal RajasthanKerala

Madhya Pradesh

Fig. 4. Total volatility and polarisation.


across different states according to their respective party systems, politicisation ofsocial cleavages and political polarisation. Each cell shows the average (if there ismore than one state) level of total volatility for the states in question. For states withtwo effective parties, volatility decreases from 20% to 8% as the number of cleavagerises from 1 to 2. Similarly, the level of volatility decreases from 23% to 10% as thelevel of polarisation rises from 20 to 30. For states with four or more effectiveparties, the average level of volatility falls from 47% to 33% as the number ofcleavages increases, and a jump in the polarisation index from 20 to 30 reduces thelevel of volatility by 16 percentage points. Uttar Pradesh has a relatively low level ofvolatility given its number of effective parties, and this seems to be because its’ partysystem reflects its’ cleavage structure. Similarly, levels of volatility are much lower inMaharashtra and Punjab than in Bihar and Tamil Nadu. Although all four states aremultiparty systems, in Bihar and Tamil Nadu there are fewer effective cleavage linesand hence a worse fit between the party structure and the cleavage structure (inparticular there are a large number of ‘OBC parties’).

3.1. Changing alliances formations and party induced volatility

In addition to system level attributes, such as the party system and cleavagestructure, it is also necessary to consider the role of political parties in determiningvolatility. Elections in India are unpredictable partly because alliances arecontinually changing. For example, in Tamil Nadu there were substantial changesin the internal composition of the alliance formations between 1998 and 1999.Whereas in 1998 the election was contested between the AIADMK–BJP alliance, theDMK–TMC alliance and the Congress, in 1999 everything changed and the DMKcontested with the BJP, the AIADMK with Congress, and the TMC with a numberof minor parties. These turnarounds meant that voters in some constituencies did nothave the opportunity to vote for the same party in successive elections because theirconstituency was allocated to a new alliance partner. In some states party turnoverof this type had a substantial impact on levels of total volatility. This was a particular

Table 5

Average level of volatility by effective parties, effective cleavages and polarisation

Effectiveparties

Effectivecleavages

Polarisation

0–29 States 30C States Average

1–2.9 1–1.9 23 AP, GUJ 14 RAJ 202C – 8 KER, MP 8Average 23 10 16

3–3.9 1–1.9 38 KAR 28 ASS 342C 23 HAR, WB 33 ORI 26Average 28 31 29

4C 1–1.9 47 BIH, TN – 47

2C 38 MAH, PUN 27 UP 33Average 43 27 38


problem in Tamil Nadu, Orissa and Punjab. In Tamil Nadu 29% of the voters had tochange the party they voted for because changes in the alliance structure meant thatthe party they voted for in 1998 did not stand in 1999 (Table 6).14

States which saw substantial turnarounds in the composition of the differentalliances witnessed much higher levels of volatility than states which did not (Fig. 5).This type of party turnover thus undoubtedly has a strong impact on levels ofvolatility. However, to a certain extent this party induced volatility is endogenous,and may partly re-describe the patterns we have previously seen. Parties can onlychange alliance partners in multiparty systems. In states with a high number ofeffective parties there is more room for manoeuvre than in states with only a few.Similarly changing alliance partners is a much more attractive proposition for partiesthat share a similar social base than for ones that are markedly different. It is thusalso more likely to take place in states which have a poor fit between the partystructure and the cleavage structure, or in states that are not very polarised. Toexamine the independent effect of state-level party systems and political cleavages itis thus necessary to control for the level of party turnover. This is done in the nextsection.

3.2. The determinants of electoral volatility at the individual and state level

The results presented up until now have been rather descriptive in nature. This isbecause the states are of interest in themselves. However, it is possible to carry out

Table 6

Percentage of voters for whom the party voted for in 1998 did not stand again in 1999

State Party contested

in 1998 and 1999

Party contested

in 1998 but not

1999

N

Andhra Pradesh 97 3 848

Assam 99 1 261

Bihar 92 8 881

Gujarat 94 6 482

Haryana 95 5 170

Karnataka 91 9 474

Kerala 97 3 381

Madhya Pradesh 100 0 563

Maharashtra 97 3 1030

Orissa 85 15 365

Punjab 87 13 229

Rajasthan 100 0 574

Tamil Nadu 71 29 721

Uttar Pradesh 98 2 1310

West Bengal 98 2 825

TOTAL 94 6 9418

14 Party turnover is measured at the constituency level by the proportion of voters for whom the party

they voted for in 1998 did not stand in 1999.


more robust analysis on the determinants of electoral volatility and to see whatimpact party systems and political cleavages have when we control for individual-level characteristics, such as caste–community, education and party identification onthe one hand, and party turnover, that is changes in the composition of the alliancegroupings, on the other hand.

Since there are only 15 level 2 units (States) it is not possible to simultaneouslymodel the impact of all the level 2 terms together. For this reason the state-levelparty system and political cleavage factors are summarised in a single volatilityindex. To construct the volatility index each of the factors are standardised witha mean of 0 and a standard deviation of 1. (The effective number of cleavages andpolarisation index are multiplied by minus 1 so that the effects all go in the samedirection). The index treats the impact of each factor hierarchically, as illustrated inTable 5, and gives greatest weight to the effective number of parties; then to theeffective number of cleavages; and then finally polarisation:

Volatility index ¼ 4!Effective partiesC2!Effective cleavagesCPolarisation

Tamil Nadu has the highest score on the volatility index (or put another way it is theState with the greatest volatility potential) and Madhya Pradesh has the lowest scoreon the index (Table 7).

Using logistic regression we take firstly vote-switching between parties (coded 1for switching parties and 0 for staying with the same party) as our outcome (Table8). As our explanatory variables at the Individual level we take age, sex, education,the six-fold measure of caste–community and a measure of party identification.15

y = 1.6115x + 19.177

R2 = 0.5897

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

0.00 5.00 10.00 15.00 20.00 25.00 30.00

Party Turnover

Tot

al V

olat

ilit

y

Tamil Nadu

Madhya Pradesh

KeralaRajasthan

West Bengal GujaratAndhra Pradesh

Uttar PradeshAssam Bihar

OrissaMaharashtra

Karnataka

PunjabHaryana

Fig. 5. Party change and total volatility.

15 Respondents were asked to say if they felt close to any political party. Responses are coded 1 for no

and 0 for yes.


These act as a control. As our explanatory variables at the State level we take theeffective number of parties (Model 2), the effective number of cleavages (Model 3),and the level of polarisation (Model 4) in turn and the combined volatility indexwhich summarises the effect of all three factors (Model 5).16 We also control for thechange in the composition of the different alliances by including the measure of partyturnover (Model 6).

Each of the state-level variables has a significant impact on individuals’ chances ofvote-switching. From the chi square statistic in the bottom row of the table we cansee that when we control for state-level factors there is a very much better fit to thedata than when we control for just individual-level variables. However, a muchbetter fit to the data is given by Model 5, which contains the volatility index for thecombined effect of the state-level variables (Chi2 of 618.7 compared to a Chi2 of116.7 in Model 1). This shows that the effective number of parties, the effectivenumber of cleavages and the level of polarisation in a state are better able to explainthe level of volatility in a state when they are considered together than when they areconsidered separately. Finally, Model 6 controls for party turnover at theconstituency level, and provides by far the best fit to the data (Chi2 equals 804.6).The term is significant and positive (b ¼ 0:03) indicating that party induced volatilitymakes a substantial contribution to overall levels of volatility. Interestingly, it alsohas an impact on the magnitude of the coefficient for the volatility index, whichdecreases from 0.14 in Model 5 to 0.10 in Model 6. This suggests that there is anassociation between party turnover and the volatility index, and that party turnoveris higher in states with a high volatility potential (this is supported by a highlysignificant Pearson correlation between the two variables of 0.43).

Table 7

Volatility index by state

State Volatility index

Andhra Pradesh 1.19

Assam 0.18

Bihar 5.44

Gujarat �4.49

Haryana �0.78

Karnataka �0.88

Kerala �4.43

Madhya Pradesh �5.49

Maharashtra 2.29

Orissa �2.88

Punjab 1.01

Rajasthan �4.21

Tamil Nadu 9.26

Uttar Pradesh �1.41

West Bengal �2.62

16 Since there are only 15 level 2 units (States) it is not possible to simultaneously model the impact of all

the level 2 terms together.


At the individual level, the only factor that does any real work is partyidentification. Across all the models, people who do not feel close to a political partyare consistently and significantly more likely to vote-switch than people who do feelclose to a political party. Interestingly, the magnitude of the effect shows some signof variation. The impact of party identification is somewhat weaker when we controlfor party turnover than when we do not (0.35 in Model 6 compared to 0.41 in Model5). This suggests that there is an association between party identification and partyturnover and that identification tends to be somewhat weaker when voters areunable to reinforce it by voting for their preferred party (this is supported by a highlysignificant Pearson correlation between the two variables of �0.09).

Party turnover at the constituency level has a strong bearing on vote-switchingbetween parties. In this sense some of the churning and turmoil in Indian elections canbe regarded as the result of the explicit strategies of the political parties rather than dueto the structure of the political system or the behaviour of individual voters. However,it is interesting to see how the voters respond to this, and whether they follow theparties’ cues and vote for the alliance partner that is put up in place of their preferredparty, or whether they abandon the party completely and vote for a rival alliance.

Taking vote-switching between alliances as our outcome, Table 9 presents fourmodels. The first model includes only individual-level explanatory variables. Thisprovides a very poor fit to the data (as shown by a Chi2 of 83). The second modelincludes the volatility index, which provides a substantially better fit to the data (asshown by aChi2 of 377). The index is highly significant and has a positive impact on theodds of vote-switching between alliances (b ¼ 0:11). The third model also includes

Table 8

Logistic regression models of vote-switching between parties, indicator contrasts

Volatility (switching between parties)

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Age �0.01 �0.02 �0.02 �0.02 �0.03 �0.03

Male �0.10 �0.07 �0.10 �0.11� �0.08 �0.07

Education 0.03 0.01 0.03 0.02 0.01 0.01

Hindu OBC 0.35�� 0.17�� 0.34�� 0.33�� 0.09 0.00

Dalit 0.27�� 0.12 0.27�� 0.26�� 0.09 �0.02

Adivasi 0.12 0.31�� 0.08 0.07 0.19 0.22

Muslim 0.44�� 0.32�� 0.46�� 0.47�� 0.35 0.36��Other 0.26� 0.19 0.27� 0.27� 0.18 0.17�No PID �0.46�� 0.43�� 0.46�� 0.46�� 0.41�� 0.35��

Effective parties 0.36��Effective cleavages �0.25��Polarisation �0.02��

Volatility index 0.14�� 0.10��Party turnover 0.03��

Constant �0.50 �1.82 0.00 �0.02 �0.43 �0.66

Chi2 116.36 482.22 142.25 172.54 618.70 804.61

�Significant at p! 0:05. ��Significant at p! 0:01. ��Significant at p! 0:001. N ¼ 7893.


a measure of party turnover at the constituency level. This term is significant andpositive (b ¼ 0:01), indicating that parties are not able to transfer all their votes to theiralliance partners. However, the magnitude of the effect is somewhat smaller whenconsidering switching between alliances than when considering switching betweenparties (compare with Model 6 in Table 8). This suggests that many of those whoswitch parties do remain faithful to the alliance and vote for one of the ally partners.However, we can see that the impact of the volatility index does not vary muchbetween predicting switching between parties (b ¼ 0:10) and switching betweenalliances (b ¼ 0:10).

The final model includes a measure of whether the party that the respondent votedfor in 1998 actually contested in 1999 or not. This model provides by far the best fitto the data (Chi2 of 588.9). The term is positive and highly significant (b ¼ 1:37). Themagnitude of the impact is illustrated by transforming the log odds into probabilities(Probability ¼ ðe

Plogit=1Ce

PlogitÞ). The probability of an average man in Tamil

Nadu switching alliances increases from 0.51 if the party he voted for in 1998contests again in 1999, to 0.80 if it does not and an alliance partner is put up in itsplace. This shows that votes do not automatically transfer across alliances from oneparty to another, and changing the internal composition of an alliance at theconstituency level causes considerable turmoil.

4. Conclusion

Indian States display markedly different characteristics to each other, both in termsof the nature of their respective systems and in the degree to which these systems are

Table 9

Logistic regression models of vote-switching between alliances, indicator contrasts

Volatility (switching between alliances)

Model 1 Model 2 Model 3 Model 4

Age �0.02 �0.03 �0.03 �0.03

Male �0.11� �0.09 �0.07 �0.06

Education �0.01 �0.02 �0.02 �0.03

Hindu OBC 0.24�� 0.03 0.01 0.00

Dalit 0.23�� 0.08 0.07 0.05

Adivasi 0.17 0.23� 0.26� 0.26��Muslim 0.39�� 0.31�� 0.32�� 0.32��Other 0.08 0.01 0.00 �0.06

No PID �0.41�� 0.36�� 0.34�� 0.32��

Volatility index 0.11�� 0.10�� 0.09��Party turnover 0.01��Party voted

for in 1998 did not stand

in 1999

1.37��

Constant �0.56 �0.51 �0.67 �0.72

Chi2 83.22 377.41 399.87 588.90

�Significant at p! 0:05. ��Significant at p! 0:01. ��Significant at p! 0:001. N ¼ 7893.


either stable or volatile. Broadly speaking, the two-party systems are relatively stablein terms of electoral volatility. However, even among this group, the states in which theparties adopt ‘catch-all’ mobilisation strategies, such as Andhra Pradesh, are morevolatile than those which are more cleavage based, such as Madhya Pradesh. As thenumber of effective parties in the party system increases, the level of volatility alsotends to increase. However, even though the number of effective parties has a strongimpact on the level of volatility this relationship should not be viewed at the expense ofall else. In particular, part of the key to understanding volatility seems to lie in therelationship between the party structure and the cleavage structure.

The example of Uttar Pradesh illustrates that states with a large number ofeffective parties can be relatively stable in terms of electoral volatility, but only if theparty structure matches the cleavage structure. In Uttar Pradesh each of the threedifferent party blocs displays a high degree of homogeneity, as evidenced by the highlevel of polarisation. This indicates that Uttar Pradesh’s current electoral system isfairly entrenched, and there is thus little reason to suppose that the party system willconverge towards a bi-polar contest over time, as Duverger’s law would predict.However, it would seem that there is more potential for system change in the statesthat have a high number of parties per cleavage and weak alliance formations. Thesestates tend to be extremely volatile, especially when there are a large number ofeffective parties. Where more than one party represents a particular cleavage, itseems plausible that one party may consume its rivals for a particular social group.To a certain extent this has already happened in states such as Rajasthan andGujarat, where the BJP has eaten up its previous alliance partners, and become thesole party of the upper castes. This is compounded by the fact that states with a highvolatility potential also tend to be characterised by competition between unstablealliance formations. This contributes to even greater levels of volatility.

On one level, the findings in this paper bear comparison to studies of the West.For example, Dalton et al. (1984) argued that an apparent increase in volatility in the1960s led to a general period of electoral dealignment, and in a later work Dalton(1988) went on to say that parties could no longer mobilise their supporters becausevoters had become smarter, wiser, and had begun to vote instrumentally on the basisof individual issues rather than blindly on the basis of a loyalty cemented in theiryouth. Without investigating change over time, this paper cannot make strong claimsas to whether the current situation in some states in India represents a general periodor dealignment, realignment, or whether it even precedes alignment in the first place.However, what is clear is that in many states in India there is what can be termed un-alignment. The party system in its current guise is relatively recent, and it seems thatin many states the party system does not reflect the societal cleavages. At this stage itis too early to say whether it is because the parties have not caught up with thecleavages, or whether the cleavages have not caught up with the parties. However, toa certain extent, the causal direction of the relationship is not of interest. The keypoint is that, contrary to the Lipset and Rokkan model of European partycompetition, despite over 50 years of electoral politics the party system in some statesin India is not ‘a relatively stable structure’ and in many states the party system doesnot reflect the social cleavage structure.


Acknowledgements

The survey was designed and coordinated at the national level by Prof. V.B. Singh(Principal Coordinator), Sanjay Kumar (National Coordinator) and YogendraYadav, all from the CSDS. The other members of the central team at the CSDSwere: Himanshu Bhattacharya, Mona Gupta, Sudhir Hilsayan, K.A.Q.A. Hilal,Bhaskar Jha, Angad Kumar, Kanchan Malhotra, Anindya Saha and Chitrali Singh.I would like to extend my thanks to all in the CSDS team and am grateful for theirpermission to use the data. I also thank Vicky Randall, Eric Tanenbaum, DavidSanders and an anonymous referee.

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