thesis_the politics of riots_(pankaj verma)

THE POLITICS OF RIOTS IN INDIA:

DOES TIMING OF ELECTION MATTER?∗

Pankaj VERMAPublic Policy and Development

Paris School of Economics

November 15, 2012

Abstract

This paper demonstrates that the intensity of communal riots is in-fluenced by the timing of state assembly elections in India. The politicalcompetition at the state level has significant and positive effect on riot in-tensity. However, having a majority of Members of Parliament (MP) fromthe ruling political party in the state decreases the riot intensity. This alsoholds for states ruled by the Bhartiya Janta Party (BJP) – the right wingpolitical party in India. This paper draws on a unique panel dataset of 16major Indian states on state elections, for the period of 1960-2008. Usingthe variation in riot intensity across states for the same period, I find thatriots occur 3 years before a scheduled election, and political competitionincreases riot intensity by 2 pp. The riot intensity decreases by 28% ifthe ruling party has the majority of MPs in the state. I fail to observeany significant relationship between BJP and riot intensity. This paperpresents the use of factor analysis to construct an index of riot intensity,and challenges a pre-existing notion that right wing political parties ini-tiate communal tensions for political use.

Keywords: Riots, Elections, Political CompetitionJEL Classifications: H11, J15, N45

∗This is a modified version of the dissertation submitted to the Paris School of Economicsin August 2011

1 Introduction

There are compelling evidences that political conflicts intensifies as election areclose. This follow a cycle where timing of a scheduled election influences increasein riots (Krishna C. Vadlamannati, 2008). Riots seemingly occur orthogonal topolitical competition but blatant use of the former by politicians to reap polit-ical gains points towards possible use of riots to secure their vote banks. Anincumbent government and the opposition parties could indulge in widespreadrioting to exploit exisitng communal fault lines to polarize voters and securetheir votes. The intense political competition could shape the size, directionand time of communal violence. An elite dominated ethnic political party mayinduce riots as the most effective methods to mobalize those target group ofvoters who are the brink of voting for the main opposition party. In order toeffectively do it, the party could use ethnic wedge issues to increase, thoughfor a short period, the salience of ethnic or religious issues that will favor theirparty (Steven Wilkinson, 2004)1

Theoretical model on political business cycle explains that there are many waysin which today’s political choices affect future well-being (Nordhaus, 1974)2.Politicians manipulate economic process in anticipation of close election yearsto woo their vote banks. This essentially follow a cycle which exhibits close lagswith elections (Tufte, 1978). Governments usually shift to more visible formof public spending during election years (Clemence Vergne, 2006 and Besley& Burgess, 2002) for political ‘capture’ thereby testifying in favor of existenceof political budget cycle (Shawn Cole, 2007). The motivation behind politicalbudget cycle lie in the desire to polarize voters and consolidate their vote banks.What else, apart from increase in spending, honest development work, soundeconomic policies and transparent governance, could a government do to achievetheir desired political result; to win next election?

While there are many studies that validates the political business cycle hy-pothesis, and few on political violence cycle, very few has explored the roleof political competition in shaping the intensity of riots. Substancial body ofliteraure has struggled with designing an index of riot intensity and explain itwith richer variables of political competition. This paper uses factor analysisapproach to prepare an index of riot and employs series of political competitionmeasures to explain the determinants of riots in India.

In this study, I exploit variation in riot intensity across Indian states over alarge period to, first, empirically show that a political violence cycle exist, andalso quantify the ‘preferred’ timing to riot. The presence of an ideal time to riothas eluded many researchers3. An incumbent government, like all other political

1Votes and Violence: Electoral Competition and Ethnic Riots in India, Cambrdige Uni-versity Press, 2004

2The Political Business Cycle, Review of Economic Studies, 19743Paul Brass (2006), Peter Mayer (2010), Steven Wilkinson (2004)

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parties, have prior information of elections which is constitutionally scheduled .For reasons mentioned above, they might make plans to manipulate their votersthrough various mechanism, riots being one of many. I show that riots followa cycle over scheduled elections. It significantly increases 3 years4 before thenext election which quantifies the search for a ‘preferred’ timing of riot. PeterMayer (2010) in his study has suggested ideal time to be between 16 and 24months from an election. The reason why 3 years before a scheduled electioncould be the most ‘preferred’ timing for riot is subject to interpretation. Like, itmay sound reasonable to assume that a riot will only be beneficial to a politicalparty if it occurs immediately before an election when nationalistic sentimentsare soaring. Steven Wilkinson, while explaining effect of political competitionon political violence in India, sheds light on amount of time it could take fora political party to ‘prepare’ for the next election. Paul Brass (1996, 2002)suggests that communal riots ‘specialist’ exists. But, riots immediately beforean election may not be desired by all political party. The political structurein India, which is witnessing an increasing trend in coalition government, couldaffect the power structure within the ruling class. Riots, based on social lines,polarizes target voters but could adversely affect other vote groups and secularcoalition partners who do not approve of widespread rioting. Therefore, use ofriot to fan nationalist sentiment while still having enough time to surgically ‘fix’spillovers seems most appropriate a political strategy.

Political violence may be a desperate attempt by the loosing party to ‘shape’the next election (Peter Mayer, 2010). However, opposition party doesn’t haveany profound influence on state machinery and therefore any riot instigated byit could be vehemently crushed by the ruling party. However, if the oppositionparty has a major share in state assembly and has only recently been in power,it could affect political violence significantly. This leads us to the second ques-tion to explore the effect of political competition on riot intensity in a state.Increased political competition, as also mentioned by Steven Wilkinson, doesintensify riot in a state. We present that political competition in a state is notuniform which affects riot intensity. We suggest that intensity of riot is lowerat pockets where ruling party has a MP. It is argued that the effect of rightwinged political party on riot is positive and significant. I show that presence ofBhartiya Janta Party (BJP)5 doesn’t affect the intensity of riot. However, BJPbeing in power and having maximum number Member of Parliament (MP) inthe state significantly decreases the intensity of riot.

The remaining of the paper is organized as follows: section 2 explains the elec-toral system in India. Section 3 briefly explains the relationship between Stateand Centre6. This relationship, for example, plays important role since Centre

4Conversely 2 years after last election. An election in India occurs every 5 years5Formely known as Jan Sangh party, it has close relations with nationalist hindu organi-

zation. BJP is known for its hardline hindu rhetoric6Federal Government of India

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has overriding power when it comes to national security7. Section 4 explainsthe historical perspective of communal violence in India. In section 5, I describethe data and methodology adopted to form various variables. In section 6, Ipresent the empirical strategy adopted for the study. In section 7, I conductpanel regression with state fixed effects to present results for various hypothesis.In the last, section 8, I conclude.

2 History of communal riots in India

Communalism is fundamentally a political and socio-economic phenomenon, andnot a religious one (Engineer, 2006). Most riots today are pre-planned, moreoften than not, communal riots are planned to serve a political purpose andhence a political party is inevitably involved. Communal riots have become adistinct feature of communalism in India. An event is identified as a communalriot if (a) there is violence, and (b) two or more communally identified groupsconfront each other or members of the other group at some point during theviolence (Varshney 2002)8. The reason for such a clash could be superficial andtrivial, though intense political struggle to control and access to power and re-source is the latent motivation deep inside it (Rajeshwari 2002). Though Indiahave had riots during British rule, communal tension and riots only began tooccur in late 19th century (Chandra 1984)9. They got intensified only by theperiod 1947-67, the initial time period covered in my study.

Communal riots from 1960s until 1980 follow a particular pattern which washighly concentrated to urban centers which were major industrial and tradingcenters. Since local economy was largely based on single occupation like textileor trading, places having considerable Muslim population whose political andeconomic interests conflicted with Hindus erupted in communal riots. This wasa period of relative calm political climate with Congress being the single largestparty ruling almost everywhere. The emergence of political conflict, startedwith the birth of Bhartiya Janta Party, changed the political power balanceand hence communal riot pattern. BJP aligned itself with hardline hindutva10

ideology and the issue of temple-mosque11 got politicized. This helped BJPconsolidate its vote bank riding high on Hindu nationalist sentiments. Thisevent was significant enough to bring out religious fault lines in Indian societyand has, ever since, seen an increase in communal tensions and riots. As politi-cization of religion took a turn, it was not only limited to Hindu or Muslims but

7During Gujarat riots 2002, BJP was in power in state and in coalition at centre. Thecentral government repeatedly dismissed reports that riots in Gujarat was planned and was acase of national security even when scores of muslims were massacred

8Ashtosh Varshney, Ethnic Violence and civic life, 20029Communalism in Modern India, Bipin Chandra, 1984

10Right ideology which is based on Hindu nation state and reinstating Hinduism as nationalreligion

11Usually called as mandir-masjid issue, it intensified after a historic mosque in Ayodhyawas demolished by right-winged Hindu fanatic group in 1991

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engulfed Christians12, Sikhs13 and other minority groups (Rajeshwari 2004)14.

Figure 1: Average Riot Intensity for 16 Indian States. Period: 1960-2008

India is divided into 28 states and 7 union territories. Unfortunately datais available only for 16 major Indian states. Figure:1 shows average riot inten-sity for period period 1960-2008 across 16 states. Intensity of riot highly variesacross the states. As evident from Figure 1, states with heterogenous and largepopulation density, like Uttar Pradesh & Maharashtra, exhibits high intensitywhile more homogenous and smaller states like Kerala and Tamil Nadu show lowriot intensity. Riots in India, though mostly directed against Muslims, are notconfined to religion itself but includes division based on caste. Wilinson (2004)explains that political violence in India exploits existing fault lines, however, aspark is needed.

12Riots against Christians in state of Orissa in 2005 by right-winged Hindu group13Anti-Sikh riots after assassination of Indira Gandhi in 198414Communal Riots in India: A chronology 1947-2003, IPCS Research Paper 2004

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Figure 2: Riots in Contemporary India

Figure:2 show few of the major riots in modern India. The spike in late 1960swas largely due to sudden outburst of Hindu immigrants from East Pakistan15

who were fleeing anti-Hindu violence back home. Anti-Sikh riots erupted inmany parts of India after assassination of Indira Gandhi16 in 1984 by her Sikhbodyguards. It was followed by 1991 riots in Bombay after demolition of ahistoric mosque in Uttar Pradesh and Gujarat riots in 2002. Gujarat riot is anexample of how an incident could be quickly converted in to a communal riot.It may not be unusual for a political party to throw its weight behind (thoughimplicitly) the riot. Riot fans nationalist sentiments usually at the cost of thecommunity against whom hatred is channeled. This helps the political partysegregate and consolidate their vote bank. Testimonies made by many witnessesshowed how rioters had electoral list in handy17 to pin-point a family belongingto a particular religion. Civil police where ordered to ‘restrain’ while it took aweek to ask Indian army out of their barracks18 only to be ordered stand-by.Inflammatory speeches made by fanatic Hindu religious leaders with politicalconnections19 immediately before outbreak of riot testifies the hypothesis of

15Post Independence know as Bangladesh, after its independence from West Pakistan (Mod-ern Pakistan now)

16The then Prime Minister of India17Eye-witness report to news channels, mentioned at rediff.com/news/godhra01.htm18State Governments are needed to lodge a request to Union for army assistance during

civil unorder19Documentary movie, The Final Solution by Rakesh Sharma. This movie is banned for

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political hand behind riots and use of ‘riot specialists’ as mentioned by PaulBrass (1996).

3 Data & Methodology

I use a panel data that covers 16 major Indian states for the period 1960-2008.I use state variation in riot intensity to identify if timings of election mattersand how political competition shapes it. The panel includes detailed electionresults of Lok Sabha & Vidhan Sabha and riots.

(a) Riot Intensity

Studies on riots, so far, use different measure of riots. While Collins & Margo(2003), while preparing riot index, give equal weights to death, arrests and in-jured; Mayer (2010) and Vadlamannati (2008) use riot data from National CrimeRecord Bureau (NCRB). Wilkinson (2004), though collecting a very comprehen-sive data on political violence in India, use number of death and occurrence ofriots. We use factor analysis approach to prepare an index of riot.

It is imperative to have a clear and accurate proxy of the real riot intensity.Riot data available with NCRB are reported cases which fall under the def-inition of riot under Code of Criminal Procedure in India (CrPc). But riotsreported in the crime statistics by NCRB can’t be used as a valid proxy since itinduces endogeneity. If we assume that state plays any role in defining a commu-nal violence, it would be misleading to treat crime records (riot cases recordedby a state machinery20) registered by a state machinary to be true indicator ofthe intensity itself. Riot incident recorded in NCRB data are registered casesand hence a response which is basically a result of political pressure either tooverestimate or underestimate a communal violence deliberately. Therefore, weuse different data sources based on newspapers clips, online news archive, jour-nals, and use factor analysis approach to make single riot-intensity indicator.We use Wilkinson & Varshney21 data set for period 1960-1995 which capturesnumber of death, injured, arrests and duration of riots. Due to unavailabilityof data for period 1996-2004, we, use NCRB data on riots, dacoity and theft toget an imperfect index of riot. Data for period 2005-2008 is based on records onnumber of death, count of incidents and number of injured from various news-papers, journals and recorded events from online news archive. Using factoranalysis approach for each of these 3 period and purging it together, we get anindex of riot for period 1960-2008.

Figure:3 shows variations in both indicators. griots is the growth rate ofcases registered as riots at NCRB. gfactor is the growth rate of riot intensity

public screening in India20NCRB collates crime statistics from state crime record bureau21Varshney-Wilkinson Dataset on Hindu-Muslim Violence in India, 1950-1995, Version 2

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Figure 3: Variation in NCRB and Factor-analysis Riot index

index generated through factor analysis. As is evident, the latter variable showsmore variation as compared to former. For example, in 1992, immediately afterBabri Mosque demolition, India was engulfed with widespread rioting. This wasfollowed by serial bomb blasts and riots in Bombay in 1993. The NCRB datashows no response to the increased religious tension and riots while riot-intensityindex captures a scenario which is closer to the reality.

Since NCRB reported cases are response to riots (which may be influencedby politics), we will observe significant positive correlation between riots andelections cycle. If we assume that riot figured in NCRB is a response to state’sintent to deal with riots, any increase in ‘riots’ cases during election year is evi-dent since state could take up proactive cases against rioters either (a) to showthey are responsive to crime (b) arrest or file charges against political opponentsor the field-support groups, in order to minimize any nuisance during election(c) preventive arrests. If a state is instrumental in instigating riots but tendsto show taking preventive steps before election, any theory of political violencecycle that shows jump in riots during scheduled election year will be biasedupwards.

(b) Election Data & Variables

General and State elections are organized every 5 years unless otherwise dis-solved. Data on election results is collected from STICERD based at the Lon-don School of Economics and Political Science. It covers detailed election results

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like year, state, name of Chief Minister’s22 party & number of seat won and re-maining party wise number of seats won. I use the number of seats won toidentify the largest opposition party and rest. Unfortunately data is only avail-able for the period until 2000. I use Election Commission of India statisticalreports for Assembly and General election for period 2001-2008 to constructsimilar variables. I use election years to construct dummies for distance from(to) last (next) election. Variables fidist and bidist, where i ranges from 1-5,measures distance to next state election and from last election respectively. Forexample, if elections happened in year 2000 and next scheduled election is in2005, the dummy f3dist takes the value 1 if year is 2003 i.e. f3dist is a dummyfor year when it is 3 years before the next scheduled state election. Similarly,we construct dummy for each years which measures distance back and forth anelection. We use data on election results to construct political variables thatmeasures political competition in a State.

The probability of whether a town will have a Hindu-Muslim riot is highlyrelated to its level of electoral competition, even once we control for demo-graphic balance or its past record of violence (Wilkinson, 2004). States with aclose electoral race are considerably more likely to have a riot than states withuncompetitive race. It is, therefore, imperative to have accurate measure of elec-toral competition. Given the political structure in a state, numerous regional& national political parties and fractionalized electorates, political competitioncould be difficult to observe.

A political party who wins majority of seats during legislative assembly electionis invited to form the government in a State. The 2nd largest party forms theopposition. The competition will be intense if the margin between these twoparty is small. An absolute difference in number of seats between ruling andopposition party could be an index for political competition. The variable ‘Dist.b/w Ruling-Opposition’ measures distance between ruling and opposition in thestate. Struggle for power could intensify if opposition party was in power inrecent years. If so, opposition will try their best to grab power during nextelection. Therefore, variable ‘Last time Opp in power?’ measures number ofyears since opposition was last in power in the State. Additionally, share ofboth ruling and opposition party in the state assembly could be used as controlfor size of political parties. Variables ‘Share of Chief party’ and ‘Share of op-position’ are share of ruling and opposition party respectively.

Political parties in a state, apart from MLAs, also have MPs since most ofthem field candidates for both State Assembly and General elections. There-fore, political competition could be less intense at places where a MP from thesame political party is elected. The variable ‘Dummy:Ruling=Major MP’ is adummy if the same political party has majority of MP in a state. Das(2004)argues that disbursement of fund from the centre is mostly a political deci-

22Chief Minister is the head of the council of Ministers in a state

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sion. If same political party rules the Centre and State, it has higher chancesof getting more fund as opposed to other parties. The variable ‘Same party instate=Delhi?’ is a dummy if the political party at centre and state is the same.

I construct a riot severity dummy, ‘Dummy Riot Severeity’, which takes value1 if riot intensity is above the mean and 0 otherwise. Since I used NCRB data,for period 1996-2003, to construct riot index, a dummy ‘NCRB’ is introducedto control for it. Also, I use my own collection of riot data for period 2004-2008 for which I introduce a dummy ‘OWN’ to control for the period. Thereare few elections that were scheduled early. I control for those elections thatdidn’t occur in traditional 5 years time period. The dummy ‘Dummy Unsc Elec’controls for unscheduled elections.

4 Empirical Strategy

An panel regression with state fixed effect model:

Rit = α0 + α1Rit−1 + α2Pit + α3Tit + α4Xit + α5Si + εit (1)

Where i=State and t=time in years. Rit is the riot intensity for state i intime t ; α0 is the intercept for the equation. The main variable of interest is thepolitical variables Pit which is measures of political competition. The variableTit is the time distance to/from the next/last state election. The vector Xit

includes controls that could affect the intensity of riot and state famine expen-diture. Si is state dummies to control for time invariant state effects. The modelexploits the variation (in riots and expenditure) in States over time. We makea locality assumption that riots, and more so political violence in India, areinstigated locally, based on local incidents and therefore any state correlationwill be low. And εit is the error term in the model.

Introduction of lagged dependent variable could induce serial correlation. Though,any estimates under the presence of serial correlation remain unbiased but stan-dard errors and test statistics are not valid, even asymptotically. Given theseries follows an AR(1) process, I take first difference (FD) in order to eliminatetime invariant state fixed effect from the model. The model take the followingspecification:

4Rit = α14Rit−1 + α24Pit + α34Tit + α44Xit +4εit (2)

The model is in first difference but presence of Rit−1 mean εit−1 is still insystem which is correlated with the error term i.e. Cov(εit− 1, εit) 6= 0. Thismakes the lagged-difference dependent variable endogenous. In order to elim-inate this endogeneity problem, 4Rit−1 needs to be instrumented. Anderson& Hsiao (1981) proposes that Yit−2 or 4Yit−2 could be used as a valid inter-nal instrument. It makes sense since introduction of Yit−2 in the system willmake Cov(εit− 1, εit− 2) = 0 since series is autoregressive of order 1. The

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model, now, takes the following specification where estimator is Arellano-Bonddifference GMM estimator

4Rit = α1Rit−2 + α24Pit + α34Tit + α44Xit +4εit (3)

5 Results

In this section, I try to probe compelling questions on timing of riots withrespect to schedule elections. I also explain that political competition or powerstruggle in a state has significant effect on the shape and size of the riot. Ialso show that the presence of right wing political party has not effect on theintensity of riot.

(a) When do Riots happen?

Timing of election determines political violence (riots). Riots usually starts asa random incidents but could be quickly used by political parties for their ownbenefits. However, there is always a considerable lag between the exogenousspark and ‘planned’ riots23. A panel regression with state fixed effects formsthe basic specification:

4Rit = δ1Rit−2 + β1−5F1−5it + θ1−5B

1−5it + µit (4)

Where variables F 1−5it are dummies for distance to the next scheduled as-

sembly elections. Coefficients β1−5 captures the effect of those dummies on riotintensity. Similarly, variables B1−5

it are dummies for distance from the last as-sembly election. Therefore, coefficients θ1−5 captures the effect of distance fromlast election on riot intensity. The coefficient δ1 controls for the influence ofpast riot (instrumented by t-2 ) in state i.

Table 1 show that the riots are significantly high 3 years before a scheduledelection. Riots increases by 29.4% three years before a scheduled election whichis significant at 99% level. The coefficients, even when controlled for NCRB &OWN, are significant at 95% level. Conversely, riots increases 21.3% two yearsafter an election and it is significant at 90% level. Since elections happen every5 years, 3 years before an election is basically 2 years after an election. Wemake two measures of distance to add robustness to the evidence that riot cycleexists and follow timing of election.

Figure 5 shows coefficient and p-value of both distance variables. For exam-ple, if an election happens in year 2000 and is scheduled again for year 2005,riots significantly increases in year 2002 which is two years after (or three yearsbefore) the election.

23Wilkinson suggests there is delay of few days, after an incidence, before rioting starts

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Table 1: Timing of Riot

(1) (2) (3) (4)VARIABLES Riot Riot Riot Riot

Riott−2 0.174*** 0.172*** 0.175*** 0.173***(0.0364) (0.0365) (0.0365) (0.0366)

1-yr for next state election 0.0976 0.0709(0.106) (0.114)


3-yr for next state election 0.294*** 0.280**(0.113) (0.120)


5-yr for next state election -0.115 -0.114(0.231) (0.235)

NCRB -0.107 -0.105(0.0934) (0.0934)

OWN -0.103 -0.102(0.137) (0.135)

Dummy Unsc Election -0.0789 -0.0955(0.171) (0.171)

1-yr from last state election 0.136 0.114(0.107) (0.114)

2-yr from last state election 0.213* 0.193(0.110) (0.118)



5-yr from last state election -0.0989 -0.117(0.250) (0.254)

Constant -0.0455 0.00226 -0.0336 0.0145(0.0728) (0.0871) (0.0740) (0.0877)

Observations 746 746 746 746Number of id 16 16 16 16

Notes: Arellano-Bond difference GMM estimator. Source: STICERDand Own calculation. Sample includes year 1960-2008. Dummy for dis-tance back-forth election included. Controlled for Unscheduled Election.Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

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Figure 4: Coefficient and p-value of distance variable: Riot Cycle

This quantifies suggestions made my few authors about an ‘ideal’ time to riot.Peter Mayer in his paper24 suggests that there exits a preferred point in theelectoral cycle for riots to occur. He further suggests that, ideally riots shouldnot occur immediately before a scheduled election but a year or two after anelection seems like the most preferred one in order to achieve the desired effectof the riot. It is interesting to understand why 2 years after an election maybe most preferred by the political parties. Paul Brass in his paper25 explainsthat there are ‘specialists and ‘a network of specialists’ who form a ‘riot sys-tem’. These men are engaged, in normal circumstances, in business, politics andcultural-religious activities but are willing and capable of disseminating rumorsand discourses into local mobilization. He argues that riots are almost alwaysintentional with clear objectives in mind. The arguments, however, shift ourfocus towards meticulous planning and invoking of ‘sleeper cells’ for an explicitpolitical gain.

(b) Political power struggle at state and role of centre

It is very intriguing to understand the political power structure at the state.The electoral system in India provides checks and balance at all level. Thelargest political party with majority seat in state assembly gets invited by theGovernor to form a government. The second largest political party forms themain opposition. In Indian democratic system an opposition plays a very im-portant. They are the biggest check to the state’s policies. But, the fact thatopposition party is not in power makes them oppose almost everything thatthe ruling party suggests to portray a negative picture. The incentive for an

24Are there Political Patterns in Communal Violence in India?, July 201025Theft of an Idol, 1996

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opposition party to be “proactive” is the possibility of getting in power in nearfuture. Therefore, if the opposition party was not in power since long, it showsthey aren’t strong enough in state. Conversely it also means that ruling partyenjoys absolute majority and have no threat from the opposition. In order tounderstand the political struggle at state it is important to ascertain the sizeof opposition. A state with big opposition but long distance from last powermakes it highly unstable with increased risk of political clashes.

Table 2 shows that the absolute difference in seats between Ruling and Op-position party is insignificant. However, as hypothesized, an increase in durationsince opposition party was last in power increases intensity of riot by 0.029%.Although the magnitude is small, it is significant at 95% level. Having a biggeropposition in the house increases riot intensity by 2.1%. As mentioned above,political struggle will be intense when share of opposition is large and it wasonly recently when they were in power. As expected, the interaction term ofduration since last in power and opposition share, oppdist*oppshare, increasesriot intensity by 0.0014%. All results are significant at 95% level. Demographiccontrols like population density has a positive and significantly small effect onriot intensity. This is in line with our thought that states or localities withhigher population density are prone to increased friction.

During Gujarat Riots, the BJP coalition government at the Centre was ac-cused of taking a soft stand against the Gujarat government because the sameparty was ruling at the time of riots. Therefore, it is imperative to test if havingthe same party at State and Centre could have any positive effect on intensity ofriots. Riots don’t spread everywhere. There are always pockets in a state/regionwhich are prone to riots while other places remain comparatively clam. We don’thave data below state level, however, we could still have cursory explanation ofthe phenomenon. A State has both MPs and MLAs. Though State governmentscomprise of MLAs, it exhibits a correlation with number of MPs affiliated tothe same party elected from the state. Since electoral boundary of MP consistsof several MLA boundaries, we can assume that the presence of an MP from aparty, largely, shows that electorates in that region favor that particular party.Therefore, if a State ‘plans’ a riot, it has no incentive to engaged those thoseplaces which are already favored by electorates. Therefore we expect a minimaleffect of low political competition on riot intensity at places where the rulingparty has its own MP.

Table 3 shows that having same party at both State and Centre doesn’tmatter. The coefficient of the dummy is insignificant. As expected, the dummythat takes value 1 when Ruling Party in the State has majority of MPs too,decreases intensity of riot significantly by 28.8%. In other words, we can say thatthe presence of MPs, who belong to the ruling party, decreases intensity of riots.This result is significant at 95% level. This is in line with our expectation thatthere is no incentive for a ruling party to allow riots in their own constituencywhen political idea is to polarize voters.

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Table 2: Political Competition in State

(1) (2) (3)VARIABLES Riot Riot Riot

Rt−2 0.0910** 0.0786* 0.0791*(0.0428) (0.0429) (0.0429)

3-yr for next state election 0.310*** 0.298** 0.298**(0.119) (0.118) (0.118)

NCRB -0.144 -0.167 -0.121(0.236) (0.236) (0.236)

Population Density 0.00169* 0.00212* 0.00232**(0.00102) (0.00109) (0.00105)

State Domestic Product 2.04e-08 3.45e-08 1.97e-08(3.87e-08) (3.93e-08) (3.86e-08)

Dist. b/w Ruling-Opposition -3.75e-05 0.00271 0.000600(0.00118) (0.00186) (0.00121)

Last time Opp in power 0.000281**(0.000126)

Share of Opposition 0.0210**(0.00930)

oppdist*oppshare 1.36e-05**(5.50e-06)

Constant -0.411 -1.357*** -0.778***(0.254) (0.436) (0.296)

Observations 560 558 558Number of id 16 16 16

Arellano-Bond difference GMM estimator. Source: STICERD andOwn calculation. Time Period 1960-2008. Interaction term: LastTime Opposition was in Power & Size of opposition in State assembly.Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

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Table 3: More Political Competiton: Role of MPs


Rt−2 0.0905** 0.0894** 0.0902** 0.0901**(0.0427) (0.0453) (0.0454) (0.0453)

3-yr for next state election 0.306** 0.334** 0.331** 0.331**(0.119) (0.133) (0.134) (0.134)

NCRB -0.142 -0.157 -0.137 -0.137(0.236) (0.252) (0.252) (0.250)

Population Density 0.00172* 0.00173 0.00228* 0.00228*(0.000994) (0.00118) (0.00122) (0.00121)

State Domestic Product 2.11e-08 1.94e-08 1.31e-08 1.32e-08(3.70e-08) (4.01e-08) (4.03e-08) (3.99e-08)

Same party in state-delhi 0.0316 0.233 0.233(0.110) (0.149) (0.149)

No. of MP ruling Party -0.000939 -5.73e-05(0.00745) (0.00757)

Dummy:Ruling=Major MP -0.288** -0.287**(0.143) (0.142)

Constant -0.439* -0.397 -0.508 -0.509(0.260) (0.341) (0.354) (0.329)


Arellano-Bond difference GMM estimator: Source: STICERD and Own calcu-lation. Sample 1960-2008. State Domestic Product available only until 1997.Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

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(c) Role of right-winged political party

Much has been suggested about the role of right winged political party in com-munal riot. Bhartiya Janta Party is a national party having strong connectionto nationalistic Hindu organization. They are known for hard-line pro-Hindurhetoric. Vadlamannati (2008) show that the presence of BJP positively andsignificantly affect the riot. However, it is never discussed what incentive mightBJP have to make their ‘intentions’ explicit. I introduce number of seats of BJPand INC26 in the state assembly respectively in the model to check if it has anyeffect.

Table 4 shows that absolute strength of BJP and INC doesn’t have any ex-plicit effect on riot intensity. As expected, size of BJP and INC doesn’t seem tohave any positive effect on intensity of riot. The fact that state machinery lieswith the Government and hence the political party in power, size of these twobig national parties will not have any significant effect on riot.

However, things might be different if they are in power themselves. We probethis further in the following table by introducing two dummies. First, we createa dummy bjprule which take a value 1 if BJP is the party ruling the state.

Table 5 show that intensity of riot decreases more when BJP is in powerand has majority of MPs belong to it. The result derives its interpretation fromTable 4 where we showed that riots decreases significantly at places where MPsbelong to the same party. This is consistently significant when BJP is in poweri.e. riots decreases by 82.5% when BJP owns majority of the MPs in the state.The result is significant at 99% level. This phenomenon is inconsistent withINC and insignificant with any other political party in the state.

26Indian National Congress is the other major national party and is known for its center-right orientation

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Table 4: Role of Right Winged Political Party

(1) (2) (3)VARIABLES Riot Riot Riot

Rt−2 0.0911** 0.0889** 0.0895**(0.0427) (0.0428) (0.0428)

3-yr for next state election 0.305** 0.313*** 0.310***(0.119) (0.119) (0.119)

NCRB -0.144 -0.124 -0.139(0.237) (0.237) (0.237)

Population Density 0.00168* 0.00185* 0.00181*(0.000993) (0.000999) (0.00101)

State Domestic Product 2.22e-08 -1.43e-09 4.45e-09(3.83e-08) (4.21e-08) (4.17e-08)

No. of INC seat 0.000144(0.00105)

No. of BJP seat 0.00226(0.00213)

Share of BJP 0.00571(0.00624)

Share of INC 0.000589(0.00238)

Constant -0.428 -0.471* -0.491*(0.283) (0.253) (0.296)

Observations 558 558 558Number of id 16 16 16

Arellano-Bond difference GMM estimator. Source: STICERD.Sample 1960-2008. Absoulte and Size of BJP and another ma-jor national party INC included. Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1

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Table 5: Political Competition: Right Winged Political Party


Riott−2 0.144*** 0.142*** 0.142*** 0.142***(0.0412) (0.0411) (0.0411) (0.0411)

3-yr for next state election 0.277** 0.271** 0.269** 0.269**(0.115) (0.115) (0.115) (0.115)

NCRB -0.313** -0.301** -0.325** -0.326**(0.140) (0.139) (0.140) (0.140)

Population Density 0.00252*** 0.00285*** 0.00283*** 0.00286***(0.000639) (0.000680) (0.000679) (0.000687)

Same party in state-delhi 0.185 0.0326 0.316** 0.310*(0.127) (0.109) (0.157) (0.159)

Dummy:Ruling=Major MP -0.271**(0.119)

bjprule -0.356*(0.204)

Last time Opp in power 9.63e-05 0.000148 0.000149(0.000111) (0.000113) (0.000113)

opposite*bjprule -0.657** -0.862*** -0.825***(0.267) (0.279) (0.316)

opposite*incrule -0.410** -0.367(0.162) (0.236)

opposite*other -0.0474(0.177)

Constant -0.537*** -0.783*** -0.746*** -0.744***(0.195) (0.228) (0.228) (0.228)


Arellano-Bond difference GMM estimator. Source: Own Calculation. Sample:1960-2008. bjprule, incrule, other are dummy for BJP, INC or other party inpower respectively. opposite is dummy if ruling party has majority of MP in thestate. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

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6 Conclusion

There are strong theoretical reasons to believe that political parties will manipu-late resources under their control in order to achieve electoral success. There arealso compelling evidence that riots in India follow a cycle which largely followelection. An incumbent government will do everything possible to gain electoralconfidence and embark on next session of power and governance. Whereas op-position party, in anticipation of power, does everything possible to win overcitizens trust and their vote in next election. Intense political competition in aState shapes the magnitude of riots and spending. Yet compelling examples ofthis political competition are rarely explored in depth and documented in theliterature. The first contribution of this study is to employ improved measureof political competition to test their effect on political violence in form of riots.Using a rich index of riot and exploiting political & electoral variations in In-dian States for 49 years, I put forward intriguing results that struggle for powermatters.

The second contribution of this study is to propose a way of creating a riot-index which has not been much explored in the literature. Factor analysismethod exploits all available information to bring out the latent variable thatbest describes the phenomenon. This approach removes any arbitrary decisionto assign weights to variables that partly describe an event. This paper alsoshows that overdependence on NCRB data have potential problems and resultsfrom any empirical study may be biased.

It is worth noting that these results are not inconsistent with previous find-ings on effect of political competition on riots. Results from previous workextensively use data which suffers from endogeneity problem. Although theireffects might be overestimated but sign of coefficients remains the same. Previ-ous empirical work has ignored the need to construct better measure of politicalcompetition and richer riot index. The findings reported here are important, interms of understanding the role of political struggle for power. Political partiesuse riots as a tool for polarizing voters and consolidating their existing votebank. How much should a political party use this tool is determined by thetiming of election and political competition.

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7 Appendix

(a) Riot Index: Factor Analysis

I use principle component method under factor analysis approach to constructan index for riot intensity. This method uses all available information to con-struct an index. For example, available information pertaining to riot in a stateare number of deaths, number of injured, number of arrests, duration of riots, etc.Problem while constructing an index is evident. Collins & Margo (2003)27 facesimilar problem of assigning weights to above factors. They assign equal weightsto each of the variables to construct the index. They, however, acknowledge intheir paper, that the index they made have potential shortcomings. They foreseequestion arising that counts of destructive events do not necessarily correspondto economic damage. Additionally, its a dilemma to decide if 1 death is moreimportant than 10 injured. They argue that equal weight still stands usefulbecause in an event of riots, variables are highly correlated with each other.They conclude that any alternative choice of weights would necessarily be asad-hoc as their choice of equal weights, thus, leaving window open for furtherimprovement in methodology.

Factor analysis is used mostly for data reduction purposes. It is applicableto create indexes with variables that measure similar things. The mathematicunderpinning of this approach lies in the fact that there exits ‘few’ unobserv-able factors that are linearly related to number of variables that we observe.Under certain plausible assumptions, the hypothesized factor model has someimplications, and these implications can be tested against the observations. Forexample, lets say we have data on above mentioned variables i.e. deaths, injuredand arrests. Let Y1, Y2 and Y3 represent variables respectively. Let availabledata contains of n years for a given state. We now assume that there are twounderlying factors, F1 and F2, tentatively and rather loosely defined as inten-tion of a rioter and willingness of the state, respectively. It is assumed thateach Y variable is linearly related to the two factors, as follows:

Y1 = β10 + β11F1 + β12F2 + ε1

Y2 = β20 + β21F1 + β22F2 + ε2 (5)

Y3 = β30 + β31F1 + β32F2 + ε3

Let call parameters, βij , as loadings in factor analysis language such thatβ12 is called the loading of variable Y1 on factor F2. It can be argued that thenumber of deaths and injured case depends heavily on the discretion of riotingpublic while number of arrest is related to state’s willingness to crack on riotersand take effective action. Though these two factors may sound correlated butthis is something we will come back later. We assume these two factors existand F1 is strongly related to variables Y1 and Y2 while loosely related to Y3,

27The Labor Market Effects of the 1960s Riots, HIER, 2003

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and vice-versa for factor F2. This simple model of factor analysis is based ontwo assumptions:

• The error terms εi are independent of one another, and such that E(εi)=0and V ar(εi) = σ2

i

• The unobservable factors Fj are independent of one another and of theerror terms, and are such that E(Fj) = 0 and V ar(Fj) = 1

Factors are assumed to be standardized for mathematic convenience since theyare not observed. Therefore, each observable variable is a linear function ofindependent factors and error terms and can be written as:

Yi = βi0 + βi1F1 + βi2F2 + (1)ei

Solving for variance, we get two parts:

V ar(Yi) = β2i1 + β2

i2 + σ2i

β2i1 and β2

i2 are communality of the variable and are explained by the com-mon factors F1 and F2. The second part, σ2

i is specific variance, is the part ofthe variance of Yi that is not accounted by the common factors. Next steps in-volves calculating covariance of any two observable variables and, subsequently,present a table of variances and covariances in form of matrix. This is calledthe theoretical covariance variance matrix. This matrix is symmetric in natureas follows: ∣∣∣∣∣∣

S21 S12 S13

S21 S22 S23

S31 S32 S23

∣∣∣∣∣∣Thus, S2

1 is the observed variance of Y1, S12 is the observed covariance of Y1and Y2, and so on. If the model’s assumptions are true, we should be able toestimate the loadings βij so that the resulting estimates of the theoretical vari-ances and covariances are close to the observed ones. However, loadings are notunique i.e. there exist an infinite number of sets of values of the βij yielding thesame theoretical variances and covariances. It is thereby imperative to rotate28

the matrix to arrive at the same theoretical and observed figure. Therefore,number of rotations could be infinite large and factor analysis approach usesthis to its advantage.

Principal component method is the most widely used method for determin-ing the first set of loadings. This method seeks values of the loadings that bringthe estimate of the total communality as close as possible to the total of theobserved variances. The covariances are ignored. Larger the communality, more

28There exists infinite number of rotations. STATA carries rotations satisfying certaincriteria. The most widely used of these is varimax criterion. It looks for rotated loading thatmaximize the variance of the squared loadings for each factor. Goal is to make this as largeas possible while minimizing others

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successful the postulated factor model can be said to be in explaining the vari-able. The principal component method determines the values of the βij whichmake the total communality approximate as closely as possible the sum of theobserved variances of the variables. Therefore, the sum of squared loadings onF1,

∑i b

2i1, can be interpreted as the contribution of F1, and that on F2,

∑i b

2i2,

as the contribution of F2 in explaining the sum of the observed variances. Thequestion of two factors i.e. F1 and F2 were hypothesized in advance. In situa-tions, the number of factors involved and their interpretation may not be clear.Statistical package makes this choice easy using scree test29.

Figure 5: Scree Plot in factor analysis

Figure 4 shows a Scree plot which is generated after running factor analysisover 4 variables i.e. death, injured, arrest and duration. Scree shows that thereexists factors that can explain a given proportion of the sum of the variancesof the variables of interest. When the variables are standardized a commondefault is to identify factors whose contribution is greater than 1, which is what Iobserve in my case. This validates Collins & Margo (2003) argument that in caseof riots, variables are highly correlated with each other. This implicitly meansthat when variables are highly correlated with each other, there is increasedchance to observe a single factor that explains most of the variation. It isevident from the graph which shows that only 1 factor explains the most and isabove the default benchmark of 1 and hence an index for riot intensity.

29[scree] command in STATA shows potential number of factors

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thesis_the politics of riots_(pankaj verma)

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