Working Paper No. 516 'Unfinished Business': …...Working Paper No. 516 'Unfinished Business':...

Working Paper No. 516

'Unfinished Business': Historic Complementarities, Political

Competition and Ethnic Violence in Gujarat

by

Saumitra Jha

Februrary 2014

Stanford University John A. and Cynthia Fry Gunn Building

366 Galvez Street | Stanford, CA | 94305-6015

‘Unfinished Business’: Historic Complementarities,Political Competition and Ethnic Violence in Gujarat

Saumitra Jha⇤

February 2014

Abstract

I examine how the historical legacies of inter-ethnic complementarity and compe-tition interact with contemporary electoral competition in shaping patterns of ethnicviolence. Using local comparisons within Gujarat, a single Indian state known for bothits non-violent local traditions and for widespread ethnic pogroms in 2002, I provideevidence that where political competition was focused upon towns where ethnic groupshave historically competed, there was a rise in the propensity for ethnic rioting andincreased electoral support for the incumbent party complicit in the violence. However,where political competition was focused in towns that historically enjoyed inter-ethniccomplementarities, there were fewer ethnic riots, and these towns also voted againstthe incumbent. These historic legacies proved to be important predictors of the iden-tity of the winner even in very close electoral races. I argue that these results reflectthe role local inter-ethnic economic relations can play in altering the nature and thebenefits of political campaigns that encourage ethnic violence.

JEL codes: N25, O18, Z12, F10Keywords: Trade, Institutions, Political Polarization, Elections, Culture, Religion,

Cities, Ethnic Conflict, Social Norms, Peace

1 Introduction

On February 27th, 2002, a carriage of the Sabarmati Express carrying Hindu activists caught

fire at Godhra railway station in the western Indian state of Gujarat. At least 58 people

⇤Stanford Graduate School of Business. Address: [email protected], 655 Knight Management Cen-ter, Stanford CA94305. I owe much thanks to Ken Arrow, Susan Athey, Prashant Bharadwaj, Avner Greif,Dan Hungerman, Lakshmi Iyer, Kimuli Kasara, David Laitin, Jessica Leino, Asim Khwaja, Atif Mian, Apra-jit Mahajan, Ken Shotts, Steven Wilkinson and seminar participants at AALIMS, Berkeley, Chicago, Clio,Davis, the Erasmus University Rotterdam, Harvard, the Hebrew University of Jerusalem, Northwestern,Stanford, LiCEP, LSE, MIT, Princeton, the NBER Conference on Religion and Culture, NEUDC, NYU,Rochester, UBC, USC, UWO, the all-UC economic history group and the World Bank for useful commentsand suggestions and to the SIEPR, the Harvard Academy, and CSDP and the Niehaus Center at Princetonfor support.

1

were burnt alive. The burning of the Sabarmati Express precipitated weeks of violent ethnic

rioting between Hindus and Muslims throughout the state that claimed at least a thousand

lives between February and the end of April, 2002, and forced at least 98,000 people into

refugee camps. Many of the riots appeared to have been planned in advance, targeting local

ethnic minorities, mainly Muslims.

The complicity of the ruling Bharatiya Janata Party (BJP) state government, an orga-

nization with strong historic ties to Hindu nationalists (Hansen, 1999), was suspected, as

accounts of pogroms led by local members of the legislative assembly (MLAs) and close ad-

visors to the Chief Minister rapidly surfaced.1 The ruling party dissolved the 187 seat state

assembly in July 2002, ten months early, and in the subsequent state elections, the BJP vote

share rose by an average 4.84 percentage points, gaining them 10 seats. One high-ranking

police o�cial, who chose to be anonymous, told the author in 2007 that the Gujarat violence

was ‘unfinished business’ from India’s long history of inter-ethnic relations. He claimed that

many in the government believed that by allowing the violence to happen once, it would not

happen again.

The incentives for political figures to encourage mobilization on ethnic lines in order to

swing close elections has been observed in settings around the world, including Africa (eg

Eifert, Miguel and Posner, 2010), the United States (Olzak, 1992) and South Asia (Blakeslee,

2013, Wilkinson, 2004, Brass, 1997). Ethnic heterogeneity and polarization has also been as-

sociated with reduced public goods provision, lowered growth and increased civil conflict (eg

Easterly and Levine, 1997, Alesina and La Ferrara, 2005, Montalvo and Reynal-Querol, 2005).

And in the many developing country environments where ethnic parties favor their own with

patronage (eg Chandra, 2007, Banerjee, Iyer and Somanathan, 2008), ethnic cleansing may

itself act as a response to anticipated inter-ethnic electoral competition (Jha and Wilkinson,

2012).

Yet, much less is known about the conditions under which poor ethnically diverse societies

remain peaceful even in the presence of strong political incentives for violence. In this paper,

I examine how di↵erent types of “unfinished business” in the form of historical legacies of

inter-ethnic complementarity and competition interact with electoral incentives in generating

ethnic violence. I focus on local comparisons of towns within districts of the western Indian

state of Gujarat. Gujarat provides a useful environment to study these questions as it could

easily fit within a square of 340 miles and enjoys recorded historical interactions between

1In 2012, the BJP MLA and state minister Maya Kodnani was ultimately convicted for her role in themassacre of Muslims in Naroda Patiya and sentenced to 28 years in jail. Testimony collected by the People’sUnion for Democratic Rights (2002) also implicated local BJP MLAs in Delol, Sanjeli and Visnagar. At thesame time, while Muslim-majority areas were placed under strict curfew, state law enforcement appears tohave shown an asymmetric unwillingness to intervene during mob violence led by Hindus (People’s Unionfor Democratic Rights, 2002).

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Hindus and Muslims going back at least to the eighth century, but despite being one of

India’s wealthiest states and harbouring remarkable traditions of peaceful coexistence and

non-violent political mobilisation, towns throughout Gujarat have also become notorious for

terrible ethnic pogroms. The wave of ethnic rioting between February-April 2002, among

South Asia’s worst since the Partition, was also almost entirely confined within Gujarat’s

borders (Figure 1). In towns in other states, ethnic violence occurred but rarely escalated

or exhibited contagion (see Appendix Figure 4).

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Godhra Riots Feb-April 2002, QuintilesE Violence But No Riot

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c8 Godhra

* Medieval Ports

) Medieval Muslim City

Town Population 1991( > 50000

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Road NetworkNational Highway

Expressway

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0 70 14035 Miles

Figure 1: Patterns of Ethnic Riots and Attempted Violence in Gujarat,Feb-April2002

I draw upon work by Jha (2013b) who describes how overseas trade in the Indian Ocean

was coordinated by Muslim pilgrimages, particularly the Hajj, inducing a thousand years of

inter-ethnic complementarities between Hindu producers and Muslim traders at small inden-

tations in the coast that formed ‘natural harbours’ on the medieval coastline. I complement

Jha’s sub-continent wide study of riots over a 150 year period by restricting the sample ge-

ographically and culturally to nearby towns drawn from a single state, and temporally to a

single two month period of ethnic riots between February and April 2002. Using a novel set

of data on Gujarati towns spanning its medieval, colonial and modern electoral history, I find

3

that despite being larger and more ethnically diverse, Gujarati medieval ports experienced

30% fewer days of rioting than otherwise similar towns during the riots between February

and April, 2002.

I next go beyond this work to study the interaction between local institutions and electoral

competition. I find that riots are more likely when close political races are concentrated

in towns which were founded by Muslims, housed Muslim mints or political capitals, or

faced a longer history of Muslim political rule– factors which, I argue, generated historical

incentives for inter-ethnic competition. Thus, ethnic riots appear to be encouraged when

political incentives for organizing violence intersect with inter-economic competition. I show,

in contrast, that a historic legacy of inter-ethnic trade reduces the duration of riots the most

in towns that housed competitive races. Furthermore, and unlike otherwise similar towns

where the ruling party enjoyed vote gains in the elections that followed the violence, medieval

port constituencies exhibit a vote swing of around six percentage points against that party

following the 2002 pogroms. These swings were pivotal enough to shape the outcome of close

races, leading to a loss to the ruling party of three seats.

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riotsdat_9E Violence but no Riot

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Figure 2: Ruling Party (BJP) vote change, 1998-2002Relative to other towns where the BJP gained vote share following the riots, medieval ports swang against

the BJP and became more competitive following the 2002 elections.

4

I interpret these results as reflecting the fundamentally conditional nature of the rela-

tionship between electoral competition and ethnic violence. In environments where groups

compete economically, close electoral competition may provide enhanced incentives to local

politicians to exacerbate pre-existing inter-ethnic tension that can readily lead to local vi-

olence. However, the presence of inter-ethnic complementarity or institutional mechanisms

that support inter-ethnic cooperation appears to have a stronger mitigating e↵ect where the

incentives for political violence are also otherwise stronger.

Beyond the papers already mentioned, this paper builds upon well-established literatures

in economics and political science, including those examining the determinants and the

e↵ects of close elections, the e↵ects of religious intensity and ethnic identification on voting,

and the corresponding incentives by political figures to foster ethnic mobilization. Esteban

and Ray (2007) present a model where elites from ethnic majorities benefit from providing

resources for poor members of their group to riot against minorities to avoid class conflict.

This resonates with Glaeser (2005)’s analysis of how incentives for politicians to send hate

messages in the US South increased with calls for redistribution. More generally, Canes-

Wrone, Herron and Shotts (2001) show that in competitive elections, local politicians have

greater incentives to ‘pander’ to voters’ sentiments by publicly advocating popular policies

that they personally know will not be socially beneficial.2

In the South Asian context, Brass (1997) describes how politicians in old Indian cities

develop ‘institutionalized’ riot systems to mobilize their base on ethnic lines.3 Wilkinson

(2004) provides state-level evidence that states with a larger number of “e↵ective parties”

competing– settings where Muslims are more likely to be electorally pivotal– are less likely to

exhibit electoral violence. At the same time, Wilkinson and Haid (2009) and Dhattiwala and

Biggs (2012), also looking at constituencies in Gujarat in 2002, find a correlation between

close races in the state legislature elections in 1998 and the subsequent propensity for riots.

Similarly, Pathania and Tandon (2011) take a sample of riots in India between 1989 and 1995,

and find that the killing was more intense where the BJP won in contested national (Lok

Sabha) elections between 1989-91. Blakeslee (2013) finds that the BJP was more likely to

direct the ‘Rath Yatra’– a procession originating in Gujarat aimed at religious mobilization–

at marginal constituencies that subsequently experienced greater rioting. Varshney (2002)

describes how towns with ‘civic engagement’- the development of organizations and social

norms conducive to peace- also reveal a lack of violence. This paper complements and

2Close races and religiosity also appear to a↵ect mobilisation. Cox and Munger (1989) show that higherexpenditures on mobilization e↵orts occur in close races in the US Congress. At the same time, Gerber,Gruber and Hungerman (2010) show that shocks that lower the costs of engaging in religious practice raisevoter turnout.

3See also Jha (2013a) who describes how electoral incentives were a likely precipitant of riots againstTata’s Nano plant in West Bengal.

5

unifies these works by establishing the exacerbating role of political competition on violence

when it intersects with inter-ethnic economic competition but the mitigated incentives for

violence when political competition occurs among communities that enjoy traditions of ethnic

tolerance.

This paper also builds upon and contributes to an important literature that exploits

local geographical comparisons and finds that historical exposure to di↵erent political in-

stitutional environments, particularly empires, past incidents of violence or propensities

for inter-ethnic trade, can have lasting e↵ects on traits as diverse as financial develop-

ment (Guiso, Sapienza and Zingales, 2008, Grosjean, 2012), trust in bureaucracy (Becker,

Boeckh, Hainz and Woessmann, 2012) contemporary attitudes towards religion, voting and

democracy (Grosfeld and Zhuravskaya, 2012), and di↵erences in ethnic assimilation, hatred,

tolerance and trust (Voigtlander and Voth, 2011, Nunn and Wantchekon, forthcoming, Jha,

2008b, 2013b, Diaz-Cayeros and Jha, 2013). This paper adds to these works by showing

how modern electoral incentives can strengthen or undermine the e↵ects of such historical

cultural and institutional legacies.

This paper also suggests a direction for additional nuance to an important series of papers

that exploits proposed quasi-random variation in close races to identify the e↵ects of winner

identities. Based as they are upon such local comparisons, these studies have naturally been

ahistorical for the most part. Yet, just as the outcome of close races in Gujarat can be

predicted by local economic incentives for inter-ethnic violence that interact with political

campaign platforms, the measured e↵ects of slight victories in developing countries may

depend and interact in important ways with pre-existing patterns of ethnic polarization and

historical institutions.

I begin by providing a simple theoretical framework and some historical context that

together motivate the empirical approach. I next introduce the data and provide the main

empirical results before discussing avenues for further research.

2 Theoretical Framework

A simple theoretical framework can help illuminate the relationships between economic com-

plementarity and competition and the incentives for ethnic violence that emerge both in the

presence and absence of political competition. First let us consider an environment where

there are just two types of agents: local and non-local (please see Jha (2008a) for details

of the formal theory).4 Non-locals di↵er from locals only in that they have better outside

4This initial discussion of the framework draws significantly upon Jha (2013b).

6

options.5 Suppose that individuals from either group have the following choices every period:

to stay or leave town, to produce a good for exchange, and to attack any other agent that

they encounter. Attacks are destructive, but may be useful for seizing the victim’s property

and to deter or punish the victim’s actions.“Strong” individuals may exist who are more

likely to prevail in a violent attack against weaker opponents.

In the environment above, an important condition that favours ‘peaceful co-existence’–

an equilibrium with a mixed population of locals and non-locals, full production, no out-

migration and no violence– is that non-locals and locals produce complementary goods or

services. To see this, consider first the alternative: that locals and non-locals provide sub-

stitute goods and thus are competitors. Then, with repeated interactions, a strong local will

have an incentive to attack weak non-locals, as this allows that local not only to seize the

non-local’s property but also to encourage non-locals to leave, reducing the future competi-

tion the local faces. In fact, non-local competitors provide more attractive targets of violence

than weak locals, as local competitors are harder to encourage to leave due to their lower

outside options. Thus rather than class violence, societies where local and non-local groups

compete are likely to exhibit greater ethnic violence.

In contrast, when ethnic groups provide complementary goods or services to one another,

then the incentive to attack non-locals falls over long time horizons. If non-locals leave if

attacked, locals will face reduced supply and higher future relative prices for goods that only

non-locals can provide. Not only may there be incentives for reduced violence, there will

also be incentives to invest in further complementary organizations and ‘institutions’ that

may further reduce these incentives (Milgrom, Qian and Roberts, 1991, Jha, 2013b).

It is interesting to consider how incentives that emerge from local ethnic complemen-

tarities or competition then interact with political incentives. Suppose there are two types

of political agents. A state-level incumbent ‘ruler’ cares about winning majority support

among di↵erent constituencies, and can adopt a state-wide policy platform that he or she

will ’crackdown’ or be ‘complicit’ against ethnic violence should any occur. Local politicians

care about winning their local political contest. Suppose that state-level crackdowns on vi-

olence raise the expected costs of violence by civilians. Note that in a constituency where

ethnic groups produce substitutes, the ethnic majority (which is likely to include the median

voter) prefers ethnic violence to be cheap and thus the state-level ruler to be ‘complicit’.

This is because lowering the costs of violence makes it a more credible strategy both as a

means to deter entry and in-migration and to encourage out-migration by minorities that

reduces subsequent economic competition.

5This is arguably a reasonable assumption for groups who originally immigrated from elsewhere or whosegroup membership confers superior access to trade, migration or other outside opportunities than for non-immigrants whose networks, endowments and information may also be concentrated locally.

7

In contrast, in a constituency where ethnic groups produce complements, the majority

is more likely to prefer the state-level ruler to ‘crackdown’ on ethnic violence. A ‘cracking

down’ platform will reduce the likelihood of minority exit. If resident minorities are more

likely to enjoy a costly-to-replicate source of complementarity, having the resultant cheaper

minority products should provide greater benefit for members of the majority. Thus both

ethnic minorities and the majority are more likely to prefer the crackdown platform when

they provide complements to one another.

Now consider the incentives due to local electoral preferences. Suppose that ex ante

electoral preferences are shaped by historical inter-ethnic relationships and by other endowed

constituency characteristics. Suppose that there is also an exogenous component to electoral

preferences that can lead to shocks that also change the relative competitiveness of elections.

This assumption is consistent with an important literature that documents the role played

by factors beyond politicians’ control in shaping incumbent electoral support, such as wins

in football games, natural disasters and lotteries (eg Huber, Hill and Lenz, 2012, Healy,

Malhotra and Mo, 2010). Suppose that local politicians, having observed previous election

results and pre-election polls, invest resources in their campaigns– i.e. in mobilizing their

base and reducing the mobilization of the opposition. One form of such an investment

lies in organizational capacity that overcomes the costs of collective action and facilitates

group-level violence (Brass, 2003, Wilkinson, 2004).

In models with this simple setup, a robust and intuitive finding is that both local incum-

bent and challenger politicians will tend to devote more resources to the most competitive

elections as these are likely to yield the greatest return (in terms of seats) for every dollar

of campaign resources.6 Furthermore, the state-level incumbent, ceteris paribus, will also

choose the state-wide ethnic violence platform that puts greater weight on the preferences

of the median voter in electorally competitive constituencies.

Incentives for both local politicians and individuals in close races to invest resources to

mobilize and be mobilized in ethnic riots, however, will again di↵er according to the nature

of economic competition and complementarity. Local politicians in close races are likely to

invest in mobilizing ethnic violence most where it is likely to be most e↵ective–i.e. where such

mobilization coincides with pre-existing ethnic competition, and less where individuals have

greater incentives to check ethnic violence– i.e. where there is ethnic complementarity. In this

way, close races should magnify the di↵erences in violence and subsequent voting behaviours

in societies where groups compete economically and where they provided complements.

It is useful to summarise the empirical implications of this simple discussion. First, the

6See eg. Erikson and Palfrey (2000) and Grimmer, Hersh, Feinstein and Carpenter (2011). Note alsothat campaign resource investments by challengers and incumbents are strategic complements in this setup.

8

adoption of a state-wide platform complicit with ethnic violence should naturally raise the

incidence of violence throughout the state, but should exhibit less contagion beyond the

state’s borders. Second, the ethnic violence that results from a ‘complicit’ platform should

be accentuated in places where ethnic groups compete economically for pre-determined rea-

sons, and be lower in places where ethnic groups provide complements. Third, electoral

competition should have magnified e↵ects on violence when it occurs in constituencies with

historical conditions that favour inter-ethnic competition and reduced e↵ects on violence

when it occurs in constituencies that favour inter-ethnic complementarity. Fourth, voters in

constituencies with economic competition should respond favourably a state-wide ‘complicit’

platform, while voters in constituencies with economic complementarity should oppose it.

Fifth, di↵erences in the e↵ectiveness of state and local political campaigns based upon eth-

nic polarization in environments with ethnic complementarities and competition should lead

there to be systematic di↵erences in the outcomes even of close political races.

My empirical strategy uses the geographical focuses of medieval incentives for inter-ethnic

economic competition and inter-ethnic economic complementarity (as well as organizations

that reinforced inter-ethnic complementarity and trust) as pre-determined proxies for the

presence or absence of such relationships in contemporary towns. I compare the incidence

of rioting between February and April 2002 and changes in voting behaviour in the subse-

quent elections in towns where Hindus and Muslims enjoyed robust complementarities due

to Muslim pilgrimages to geographically proximate towns within the same district where

Hindus and Muslims were likely to compete for medieval political patronage and to other

towns that were otherwise similar in their geographical and medieval characteristics. My

identification of the e↵ects of a medieval overseas trade legacy rests on the assumption that

the incentives that led some geographically proximate towns to be focuses of trade to the

Middle East in the medieval period– such as their location at medieval-era natural harbours

that subsequently silted up or ceased to trade– are otherwise irrelevant for contemporary

inter-ethnic relations. I control for other initial geographical dimensions through which the

selection of historical port locations may have created local advantages, as well as show the

irrelevance of contemporary port activity as a useful placebo comparison. I also compare

these towns to towns that were focuses of Muslim political patronage historically. These

towns also emerged in the medieval period, were also historically attractive for Muslims, but

the incentives for inter-ethnic interaction were historically that of inter-ethnic competition

rather than complementarity. A little context may help clarify why such historical incentives

existed and why they may have persisted.

9

3 Context

Gujarat is one of India’s wealthiest states, its modern borders demarcated to leave it linguis-

tically homogeneous and made mostly of flat plains and marsh that could fit easily within a

square of 340 miles. The riots that followed the burning of the Sabarmati Express at Godhra,

though initially concentrated near that city, rapidly spread to towns throughout the state

as it became clearer that the state government and police were either complicit or at least

unlikely to intervene. In Porbandar, a medieval port on the western coast, a respondent

mentioned that attempts were made to instigate violence by outsiders, who sent women’s

bangles to the members of the Hindu community as a symbol of their lack of masculinity

in not joining the riots [author’s field observations, 2006-7]. Bangles were delivered to other

communities in the state as well.7

Yet, not every community responded to the bangles with violence. Gujarat shows great

diversity in its inter-ethnic relations, both historically and as historical incentives manifest

themselves in modern political behaviour. With 14% of India’s coastline, Gujarat has housed

centers of commerce trading to the Middle East even before the rise of Islam (Casson, ed,

1989), and has a history of political rule by Muslims that goes back to the 11th century.

Hindus and Muslims within Gujarati towns have long faced incentives to interact and to

develop means to mitigate, and sometimes also propagate, ethnic violence.

I build upon work by Jha (2008b, 2013b), who documents that due to the coordination

of overseas trade throughout Muslim pilgrimage, including the Hajj, which made Mecca the

largest textile market in the world, Muslim seafarers throughout the Indian Ocean – who nat-

urally had preferred access to their own pilgrimage routes– enjoyed exogenous, non-replicable

complementarities with local communities in inter-ethnic trade. Furthermore, because trade

was coordinated by the pilgrimage, it was relatively easy for any Muslim to enter, and intra-

Muslim competition limited the potential for monopsonistic rents. Mutual incentives existed

among local communities in medieval ports to develop systems of norms, beliefs and orga-

nizations that reinforced inter-ethnic trust and tolerance. Jha (2013b) shows that medieval

ports that were the geographical focuses of these historic complementarities between Hindus

and Muslims benefited from lasting reductions in ethnic violence across the towns of the sub-

continent between 1850 and 1995. Jha provides case and household level survey evidence in

support of a number of institutional mechanisms: Muslims in medieval ports have continued

to be specialised in complementary roles coordinated by their religious leadership, show en-

hanced propensities to join religious organizations, show enhanced propensities to vaccinate

their sons against polio– a behavioural measure of trust in the majority community– and

7See, for example, the description of riots in the village of Sanjeli, Dohad district, by the People’s Unionfor Democratic Rights (2002).

10

report less exposure to ethnic conflict in their communities.8

Thus norms, organizations and beliefs of syncretism and ethnic tolerance, inspired in part

by medieval inter-ethnic trade, appear to continue to persist in medieval ports, despite the

decline of international trade, and to have spread in varying degrees throughout the state9.

Years later, when asked about the local response to the bangles used to incite violence

during the 2002 riots, my Porbandari respondent remarked: “This did not work. We didn’t

want [violence], our police and o�cials did not want it. Nobody wanted it.” [author’s field

observations, 2006-7]. Porbandar, despite a reputation for gang violence centered around

caste-based organized crime, remained peaceful during the Hindu-Muslim riots of 2002.

But Gujarat’s history was not just one of inter-ethnic complementarity between Hin-

dus and Muslims due to trade, but also one of considerable ethnic violence and potential

grievance. Though Arabs had conquered neighbouring Sind by amphibious invasion in the

early eighth century, one of the first encounters that contemporary India faced of Muslim

military power was a raid by the Afghan ruler, Mahmud of Ghazni, on the temple city of

Somnath, approximately 70 miles from Porbandar, in 1026. From the time of the chroni-

cler, Alberuni (1030), who accompanied Mahmud, to the modern day, the destruction of the

temple has been seen as a Kosovo Pole-like event in Indian history, one that has polarized

Hindus and Muslims against each other ever since.10

8These patterns have shown spillovers throughout the state. In the non-medieval port town of Santrampur,near Godhra, Muslims in the Sant locality witnessed the arrivals of Hindu mobs of 150 on the 28th of Februaryand 2500 on the 3rd of March, containing many that they recognized from among their neighbours. Whilemost Muslims were able to flee, two were hacked to death by swords while trying to escape, and two mosques,a madrassa and a dargah (a tomb of a local Muslim saint) were destroyed (People’s Union for DemocraticRights, 2002).Yet, approximately one mile from these events, another mixed Hindu-Muslim area housed a temple of the

Pranami sect of Hinduism. The Hindu community o↵ered protection to their Muslim neighbours, who chosenot to flee and were not attacked (Burman, 2005)[pg.106]. The Pranami sect was established in 1651 byMehraj Thakur, the son of the Diwan (Vizier) of Jamnagar, a medieval port in western Gujarat, who hadfollowed the trading and pilgrimage routes to Basra in Iraq and had returned to organize a syncretic sect thatfocused on the essential similarities of Islam and Hinduism and the desirability of social interactions betweenthe two. Later called Mahamati Prannath, the sect he founded gained around 3 million adherents aroundnorthern India (Burman, 2005)[pg.103], among these the mother of Mahatma Gandhi. Gandhi was raisedin the medieval port of Porbandar and Rajkot, and would later get his start as a lawyer for PorbandariMuslim merchants in South Africa. He would draw upon Gujarati Hindu and Jain ideals of ahimsa– ornon-violence– and arguably also the social traditions of inter-ethnic tolerance among Muslims and Hindusin Gujarati medieval ports in crafting his political message of non-violence.

9please see Jha (2013b) for detailed case study comparisons of institutions of tolerance developed byMuslim trading communities, such as the Bohras, in Surat relative to Ahmedabad, as well as discussion ofother medieval ports

10It is not an accident that the BJP leader L.K. Advani, had chosen Somnath to begin his Rath Yatraprocession- aimed at ethnic mobilization across the country- in 1990 (Blakeslee, 2013). Yet, records existthat in 1262, the authorities of the rebuilt Somnath temple made a large-scale land grant of temple lands to aMuslim trader, Nur-ud-din Firuz of Hormuz to settle in the adjacent medieval trading port of Veraval, awareof the commercial taxation and prosperity that a colony of Muslims could bring (Sircar, 1962, Thapar, 2004,Jha, 2013b). This ethnically- mixed community still exists, despite Veraval’s diminished economic prospects

11

In fact, in direct contrast to the robust complementarities visible at medieval trading ports

were the incentives present in towns that were the centres of Muslim political authority, where

Hindus and Muslims often acted as substitutes for one another and competitors for patronage.

Following the Somnath raid, Muslim rule began to spread into India, and eventually to

Gujarat too. With the conquest came control of patronage and land revenue systems, based

upon control of the surplus from India’s mainly agricultural wealth. These patronage systems

were concentrated in towns, like Ahmadabad, which were established by the fiat of the

Muslim rulers themselves, as well as existing cities that became the administrative capitals

of local rulers, such as Palanpur and the medieval port of Cambay (Raychoudhari, 1998).

In medieval Indian kingdoms, political and religious patronage played a very important

role. Though not necessarily members of the royal household themselves, the majority of the

city’s population was often tied by client relations to people who were.11 Once flourishing

cities that lost their roles as political centres rapidly became ghost towns.

Following the Muslim conquest, it is likely that Muslim clients, both converts and immi-

grants, substituted for and competed with Hindu clients for patronage. Though “vertical”

inter-ethnic links existed between Hindu artisans and Muslim patrons, such ties were often

in competition to intra-Muslim patron-client relations. Though the Hindu and Muslim arti-

sans that constituted the majority of the populations of these cities lived side by side, there

was limited incentive for inter-ethnic exchange between these groups. Thus, despite the fact

that, like medieval ports, political centres provided historical incentives for conversion to Is-

lam and enjoyed historical wealth, patronage centres were the historical focus of inter-ethnic

competition rather than inter-ethnic complementarity and exchange. Thus patronage cen-

tres were less likely to develop institutions to support to support such exchange, and instead

were likely to continue to be loci for competition in economic spheres.12

4 Data

To construct the dataset for the analysis, I went through news reports on Gujarat from the

day of the burning of the Sabarmati Express on February 27th until April 15th. News sources

include redi↵.com and the Times of India. These were supplemented from an amicus curiae

brief by the Concerned Citizens Tribunal and a compilation of eyewitness refugee testimonial

by the People’s Union for Democratic Rights (2002), which provided information about less-

as a fishing port in a largely vegetarian state. Yet, Veraval too remained peaceful in 2002.11Thus, when the Mughal Emperor Aurangzeb went South to campaign in the late 17th century, it was

not unusual that four of five of Delhi’s 400,000 residents left with him (Blake, 1991).12Consistent with this interpretation,Field, Levinson, Pande and Visaria (2008) find evidence that violence

within the city of the Muslim capital of Ahmadabad occurred within the context of competition between thedi↵erent ethnic groups over property.

12

widely publicised violence in smaller towns and villages. Following Varshney and Wilkinson

(2004), I coded a riot as occurring in a town if there was evidence of violence by communally-

identifiable “mobs” or other large groups in that town. I also coded a day of “violence” as

having occurred in a town if there was an isolated incident, such as a stabbing, without any

evidence of broader groups being involved.

Following Jha (2008b, 2013b) I identified a town as a medieval trading port if it exhibited

substantive evidence of direct overseas trade, prior to the 18th century and independent of

European involvement. This definition eliminates most river ports and those ports founded

by Europeans. Of the 68 confirmed medieval trading ports in undivided India, 13 are in our

sample of Gujarati towns (Figure 1.)

The Periplus Maris Erythraei (Casson, ed, 1989), an ancient Greek navigator’s handbook

(ca 1st-4th century) provided the locations of a number of pre-Muslim and early Muslim

ports. These records were then supplemented by the accounts of contemporary Muslim,

Christian and Chinese observers, including Chau Jukua (1225), Ibn Ibn Battuta (1355),

Ludovico di Verthema (1503), Duarte Barbosa (1519) and Zayn al-Din al Malibari (1528).

These contemporary narratives were augmented by secondary sources (Yule, ed, 1866, Sub-

rahmanyam, 1990, Chaudhuri, 1995, Chakravarti, 2000). Finally, every town listed in the

Imperial gazetteers of India from 1907 was examined for evidence of contemporary and me-

dieval trade and to trace its foundation and historic role as an administrative, as well as a

trade, center.

Medieval ports were then linked to their geographical location using ArcGIS. This enabled

me to gather data on initial conditions that might shape the initial selection of locations for

medieval ports, including the presence of navigable rivers and the presence of medieval-era

indentations on the coast which may have served as natural harbours.13

Looking at e↵ects over long periods of history raises a separate challenge that deviates

from a canonical experiment: even controlling for initial conditions, towns under study were

subject to di↵erences in external political influences both during and after the treatment

that might also influence subsequent religious relations. Some component of these political

influences– e.g. the expansion of Muslim or European political rule– might have been in

part a result of a desire to occupy regions with active medieval trade. To account for these

political channels, I compare the e↵ect of medieval trade both with and without a rich set

of controls for these political factors, and assess sensitivity of the baseline estimates.

13To categorize medieval era “natural harbours,” I used the US Geological Survey Digital Atlas of SouthAsia 2001 to identify water bodies that were within 10km of the modern Indian coastline, including non-perennial ponds and streams and those without an outlet to the sea. If these water bodies intersected thecoast in the medieval period, they would have produced minor inlets, or sheltered harbours. I define townswithin 10km of those water bodies as having had access to a “medieval natural harbour.” This approachidentifies major irregularities and inlets that are likely to have existed in the medieval period.

13

I also examine the e↵ect of Muslim political rule. If,as I have argued, Muslim political rule

is likely to have rendered intensified historic and persistent ethnic competition for patronage,

then a greater exposure to Muslim political rule might be associated with a greater propensity

for contemporary ethnic mobilization. The presence of state protectors of local minorities

may also have crowded out mutual incentives to develop local institutions of trust in towns

with inter-ethnic complementarity (Jha, 2013b). However, it might also be that longer

exposure to Muslim minority rulers has a beneficial e↵ect by reducing psychological biases,

as in Beaman, Chattopadhyay, Duflo, Pande and Topalova (2009). To derive such a local

measure of the historic stock of Muslim political rule, I used historical maps of the extent of

Muslim sultanates and empires over time, particularly drawing on Schmidt (1999) and the

medieval chronicler Ferishta (1620). Layering these maps over time gives a measure of the

duration every location was under Muslim rule prior to the death of the Emperor Aurangzeb

in 1707. Since, our preferred estimation involves 1981 district level fixed e↵ects, we will

be exploiting the local variation of towns within the same district that acted as patronage

centers due to their genesis as Muslim administrative cities, or had di↵erent extents of Muslim

rule.14 I also add separate controls for the religion of native rulers in the colonial period,

relative to those districts– such as Ahmadabad, Broach, Panch Mahals and Surat– that were

controlled directly by the British.

Another potential reason mooted for group cooperation (eg Wade, 1988) or conflict (eg

Miguel, Satyanath and Sergenti, 2004) is the propensity for natural disasters that require

people to work together or may lead to greater attempts to extract. Economic shocks due

to rainfall (Bohlken and Sergenti, forthcoming), or relative income changes between Hindus

and Muslims have also been suggested as potential precipitants of violence (Mitra and Ray,

2010), while economic shocks may also encourage the adoption of religious behaviours that

provide entry into ‘clubs’ that provide social insurance (Chen, 2010). While the two month

period of the ethnic riots in Gujarat did not exhibit much weather variation, long-term

di↵erences in propensities for such shocks might also be important. Thus I collected data

from the Imperial gazetteers on a number of di↵erent natural disasters from 1850 to 1900,

including droughts, earthquakes, locust infestations, floods and cyclones.

To examine the extent to which past ethnic cleansing may have also driven contemporary

conflict, I use the Partition-era measure of ‘minority outflows’ developed by Bharadwaj,

Khwaja and Mian (2008). It is the reduction of the Muslim population relative to that

expected given local trends in Muslim fertility and local mortality rates of those not targeted

14It is likely, however, that Muslim conquerors– like those that resisted them– contested those with thehighest wealth potential the most. Insofar as wealth decreases ethnic violence (eg Fearon and Laitin, 2003)this is likely to lead to downward bias on using these measures as a proxy for the historical legacy ofinter-ethnic competition.

14

during the time of the Partition. As Jha andWilkinson (2012) argue, this measure is arguably

a good measure of ethnic cleansing, as it gauges how many members of the target ethnic

minority departed, were removed, were forcibly converted as well as were killed during the

Partition period.I also build on Bharadwaj et al. (2008) to examine whether a key potential

source of grievance– the extent to which a district or native state territorial unit15 received

Hindu and Sikh refugees from Pakistan– also may have influenced the extent of contemporary

Hindu-Muslim violence and political polarization.

I merged the town data to the Electoral Commission’s data on each Vidhan Sabha con-

stituency using the spatial point location of the centre of the town. While some urban areas,

particularly for the larger towns, such as Ahmadabad, Surat, Vadodara and Rajkot span

multiple constituencies, the old precincts that are of particular interest largely correspond to

the central constituencies. I supplement this electoral data with data on town-level religious

composition from the 1981 Census and other town-level data on potential factors that might

also influence political behaviour- such as worker participation rates, the caste composition

and literacy rates- from the town directories of the 1991 Census.

Since I argue that medieval trade incentives due to the Hajj induced complementarity

between Hindus and Muslims that later were undermined with increased European inter-

vention, modern ports can act as a useful placebo comparison, one which also allows us to

control for any modern port e↵ects. For this reason, I also gathered data on contemporary

port activity from the Gujarat Maritime Board.

5 Results

Table 1 provides summary statistics for our sample, by status as medieval port. Notice that

apart from being significantly more likely to be located at medieval natural harbours, and to

be coastal towns, medieval ports are statistically indistinguishable to other Gujarati towns

along a range of other initial conditions and subsequent medieval and colonial characteris-

tics, including in their propensity for natural disasters, distance to navigable rivers, history

under Muslim political rule and Partition-era flows. In terms of modern indicators, Gujarati

medieval trading ports seem to have equivalent levels of employment and literacy, though a

higher proportion are active ports. I control for the presence of active modern ports, while

also exploiting these as a useful placebo comparison to see whether it is in fact historical

inter-ethnic complementarities as I argue, or instead, modern trade that might explain the

results. I show that modern port activity appears irrelevant.

15Gujarat was divided into many territorial units prior to Independence, largely due to the many princelystates. Thus data observed at the level of historical territorial units can create within-modern districtvariation that we will exploit.

15

Table 1: Summary statistics, Gujarat riots data, February-April 2002:

2-sided t(Welch)

Mean SD Mean SDElectoral and Violence measures:H-M riot occurred, Feb-Apr 2002 0.180 0.385 0.231 0.439Days of H-M rioting, Feb-Apr 2002 0.413 1.658 0.615 1.193Any H-M violence occurred, Feb-Apr 2002 0.305 0.462 0.308 0.480Vote swing to BJP 1998-2002 3.328 12.523 -0.706 10.807BJP vote %, 2002 49.143 11.144 47.705 7.164BJP vote %, 1998 45.815 10.902 48.411 12.451BJP vote %, 1995 41.990 13.397 41.748 14.828Vote Margin of Victory %, 1998 15.044 10.553 18.889 14.262Vote Margin of Victory %, 2002 14.585 12.007 8.889 8.589 **Distance to Godhra measures:Within 100km of Godhra 0.168 0.375 0.000 0.000 ***Within 200km of Godhra 0.407 0.493 0.308 0.480Within 300km of Godhra 0.623 0.486 0.385 0.506Geographical initial conditions:Town with Medieval Natural Harbour 0.168 0.375 0.769 0.439 ***Coastal town 0.162 0.369 1.000 0.000 ***Log (Dist. to Navigable River) 13.022 0.273 12.964 0.372Natural disasters, 1850-1900 2.060 2.203 1.308 2.250Historical measures:Medieval Muslim City 0.018 0.133 0.231 0.439Centuries Muslim Rule 3.465 1.020 2.998 1.176Town under Hindu ruler 0.635 0.483 0.462 0.519Town under Muslim ruler 0.084 0.278 0.231 0.439% Hindu / Sikh Partition Inflows 1931-51 1.150 1.031 1.160 1.017% Muslim Outflows 1931-51 1.486 1.818 2.462 2.512Demographic measures:Log(population 1991) 10.156 0.989 11.342 1.267 ***Class 1 Town (100,000+) 0.078 0.269 0.385 0.506 *Class 2 Town (50-100,000) 0.162 0.369 0.231 0.439Class 3 Town (25-50,000) 0.287 0.454 0.308 0.480Proportion Muslim 17.283 0.130 24.534 0.142 *Prop. Scheduled Tribe in Town 4.936 8.736 3.433 5.412Prop. Scheduled Caste in Town 7.521 3.813 5.763 1.701 ***Economic measures:Active port in 2001 0.072 0.259 0.692 0.480 ***Worker's participation rate 30.142 7.475 29.001 2.272Literacy rate 72.887 9.570 71.670 6.365

Other Gujarati towns (N=167)

Medieval Ports (N=13)

All measures based upon 1991 values unless otherwise mentioned. Welch t-test allows unequal variances; *significant at 10%; ** 5%; *** 1%

16

Other dimensions in which medieval ports appear di↵erent is that they continue to have a

larger proportion of Muslims than the average Gujarati town (24% compared to 17.3%), and

on average are significantly larger in population than other towns in our sample.16 In the

absence of inter-ethnic complementarity, these factors are also likely to raise the likelihood

of ethnic rioting. Yet, despite these increased potential risks, medieval ports do not appear

a priori statistically di↵erent from non-ports in their probability of experiencing ethnic riots

in 2002.

Table 2 examines the determinants of the log-normalized number of days of Hindu-Muslim

rioting during February-April 2002. This measure arguably captures the relative intensity

of ethnic rioting that is more straightforward to observe and verify than other measures

of intensity, such as riot deaths or injuries. I first match medieval ports to other towns

by the initial geographical factors mentioned above that might influence initial medieval

port location (Col 1), then by medieval and colonial historical factors that might influence

contemporary inter-ethnic relations (Col 2). I then compare medieval ports to other towns

with similar modern (but pre- riot) economic and social characteristics, including Muslim

population (Col 3), and compare towns within the same district (Col 4). All regressions

control flexibly for distance to Godhra and town size.

As Table 2 suggests, medieval ports experienced between 18.6% to 29.2% fewer days of

Hindu-Muslim rioting between February and April 2002, comparing both between and within

districts. This e↵ect strengthens when comparing towns with similar political and colonial

histories, modern economic and social characteristics and comparing towns within the same

district. Note that political histories and contemporary ethnic composition also increased the

intensity of the rioting: comparing across districts, towns that spent longer under Muslim

rule had more days of rioting (Cols 2-3), as did towns with a greater share of Muslims in their

population. However, despite having more Muslims that otherwise similar towns, medieval

ports exhibited less rioting. Note also that a number of modern economic indicators , such

as the literacy rate, the worker’s participation rate or our placebo comparison–whether a

town was an active modern port– seem to be irrelevant (Cols 3-4).

Columns 5-8 examine how the e↵ect of historical inter-ethnic incentives di↵er with the

extent of political competition. I use two di↵erent measures of political competition: a

discrete measure– whether the vote margin between the victor and runner up was less than

5% in 1998– and a continuous measure– the logarithm of the vote margin in 1998. Notice that

the average e↵ect of a medieval port legacy strengthens with these controls (Cols 5-8), and

the point estimate of the interaction between medieval port is consistently negative on the

16The relatively small share of scheduled castes in medieval ports can be accounted for by the relativelyhigh share of Muslims– it appears that the proportions of Hindus that are scheduled castes are fairly similarin medieval ports and other towns.

17

Table 2: Regression (OLS): Log. Days of Hindu-Muslim Riots, Feb-April 2002

N=180, Clusters=18 Districts (1) (2) (3) (4) (5) (6) (7) (8)Medieval Port -0.186 -0.208* -0.289** -0.292*** -0.331** -0.310** -0.323*** -0.323***

[0.136] [0.100] [0.110] [0.099] [0.121] [0.110] [0.103] [0.105]Coastal town 0.027 0.05 -0.003 -0.014 0.029 -0.032 -0.002 -0.041

[0.046] [0.050] [0.078] [0.126] [0.076] [0.120] [0.068] [0.109]Log (Dist. to Navigable River) 0.097 0.082 0.288** 0.035 0.283** 0.007 0.324*** 0.022

[0.102] [0.084] [0.106] [0.228] [0.102] [0.284] [0.097] [0.226]Natural disasters, 1850-1900 0.008 0.001 -0.01 -0.007 -0.015 -0.004 -0.014 -0.009

[0.019] [0.021] [0.018] [0.018] [0.017] [0.015] [0.017] [0.018]Medieval Muslim City 0.246 0.237 0.188 0.232 0.218 0.355 0.317

[0.393] [0.389] [0.373] [0.364] [0.359] [0.322] [0.325]Centuries Muslim Rule (to 1707) 0.068* 0.113** 0.064 0.118** 0.074 0.103** 0.06

[0.035] [0.044] [0.052] [0.043] [0.046] [0.045] [0.051]Hindu Colonial Ruler 0.095 0.053 0.144** 0.039 0.149** 0.019 0.103

[0.079] [0.086] [0.066] [0.076] [0.062] [0.076] [0.066]Muslim Colonial Ruler 0.173 0.087 0.240** 0.025 0.189 0.009 0.184*

[0.135] [0.141] [0.108] [0.095] [0.114] [0.119] [0.105]% Partition Majority Inflows (1931-51) 0.021 -0.007 0.03 0.003 0.037 -0.009 0.019

[0.033] [0.032] [0.029] [0.031] [0.029] [0.030] [0.028]% Partition Muslim Outflows (1931-51) -0.029 -0.025 -0.066 -0.027 -0.055 -0.016 -0.044

[0.017] [0.019] [0.041] [0.017] [0.046] [0.016] [0.040]Prop. Scheduled Tribe in Town -0.014*** -0.017** -0.013*** -0.019** -0.013*** -0.018**

[0.004] [0.007] [0.004] [0.007] [0.004] [0.007]Prop. Scheduled Caste in Town 0.002 0.004 0.001 0.002 0.001 0.004

[0.008] [0.008] [0.008] [0.009] [0.008] [0.008]Active port in 2001 0.133 0.128 0.031 0.079 0.041 0.063

[0.145] [0.174] [0.171] [0.211] [0.161] [0.185]Workers Participation Rate 0.003 0.002 0.003 0.002 0.003 0.002

[0.008] [0.008] [0.007] [0.007] [0.008] [0.008]Literacy Rate 0.002 -0.001 0.002 -0.002 0.001 -0.003

[0.005] [0.005] [0.005] [0.005] [0.005] [0.005]Prop. Muslim in Town, 1981 1.158** 0.811 1.133** 0.859* 1.065** 0.806*

[0.513] [0.469] [0.471] [0.463] [0.480] [0.453]Prop. Muslim in Town^2, 1981 -1.232* -1.026 -1.089* -0.988 -1.005 -0.95

[0.625] [0.636] [0.610] [0.633] [0.613] [0.606]Vote Margin <5% 98 -0.112 -0.125**

[0.079] [0.057]Med. Port x Vote Margin <5% -0.473* -0.328

[0.264] [0.263]Med. Muslim City x Vote Margin <5% 1.433** 0.916

[0.538] [0.750]Log Vote Margin 98 -0.011 -0.014

[0.028] [0.026]Med. Port x Log. Vote Margin 98 0.204 0.202

[0.127] [0.126]Med. Muslim City x Log. Vote Margin 98 -0.532*** -0.459**

[0.177] [0.173]Joint-F Med Port vars. 6.70 7.83 6.32 5.85Prob>F 0.01 0.00 0.01 0.01Incremental Controls Geog Colonial Modern Modern Modern Modern Modern Modern1981 District FE N N N Y N Y N YR-squared 0.46 0.49 0.53 0.61 0.55 0.62 0.56 0.62

Notes: Geographical controls include log. distances from navigable rivers, coastal town, NaturalDisasters, log. population (1991), Class of town (I,II,III) and distance to Godhra (100,200,300km).Colonial controls add: Centuries Muslim rule, Muslim-founded or capital, % Partition Hindu/Sikh Inflows and Muslim Outflows. Modern controls add: Proportion SC/ST, Modern active port,Literacy Rate, Worker’s Participation Rate, Proportion Muslim 1981 (and a quadratic term). Allvalues observed in 1991 unless noted. All interactions are demeaned. Robust standard errors inbrackets (clustered at 1981 district): * significant at 10%; ** 5%; *** 1%

18

5% vote margin dummy and positive on the log vote margin measure, both suggesting that

medieval ports that were the focuses of greater political competition exhibited fewer days of

rioting. As the F-tests reveal, the average medieval port e↵ect and the interaction are jointly

significant in all specifications. These e↵ects are consistent with the role of historic inter-

ethnic complementarities and supporting organizations in mitigating incentives for violence.

Columns 5-8 also provide an interesting comparison with medieval Muslim patronage

towns, which as we have argued lacked such incentives and were more likely to be centers

of economic competition. Notice that though medieval Muslim patronage towns do not

appear to reveal significant increases on average in the days of rioting in 2002, and our

political competition measures themselves appear to have weak average e↵ects on violence,

medieval Muslim patronage towns that were the focuses of political competition saw much

greater rioting. This is again consistent with our theoretical discussion: it is where political

competition and inter-ethnic economic competition coincide where ethnic rioting is more

likely.

One concern may be that though the (log. normalised) number of days of rioting in the

two month period captures the relative intensity of ethnic rioting in a relatively observable

manner, such measures may still be susceptible to over-influence by outlier towns where

the rioting was most severe. Table 3 examines the extensive margin– the probability that

any riot occurs in a town during this period. Notice that a medieval trade legacy reduces

the probability of a riot occurring by between 13.1 percentage points and 21.7 percentage

points. The determinants of the extensive margin of riots show a robust consistency with

those of the intensive margin in other dimensions as well. Once again, medieval ports that

experience greater political competition show lowered probabilities of rioting, while medieval

patronage towns that experience greater political competition exhibit greater probabilities

of an ethnic riot occurring. Once again, a history of Muslim rule raises the likelihood that

any riot occurs, and once again, our placebo comparison– modern ports– seem as likely as

any other town to have an ethnic riot.17

Table 4 examines the determinants of the vote share change of the incumbent ruling party,

the BJP, in the elections that took place immediately after the riots. Notice that despite

an average increase of 4.84 percentage points across the state in BJP support following the

violence, medieval ports swung 6.28 percentage points against the BJP in the aftermath

of the riots, comparing towns within the same district (Col 4,6,8). Towns within the same

17Appendix Table 2 examines the determinants of another measure of riot intensity: the log. numberof deaths and injured per capita that occurred between February and April 2002. Though this measure isparticularly susceptible to outliers, such as Ahmadabad, that experienced more than three hundred deathsalone, the sign of both the average e↵ect of a medieval port legacy and the interactions with our politicalcompetition measure are consistent with the patterns above, and in some specifications marginally significant.

19

Table 3: Regression (OLS): Probability of a Hindu-Muslim Riot, Feb-April 2002

N=180, Clusters=18 Districts (1) (2) (3) (4) (5) (6) (7) (8)Medieval Port -0.131* -0.140** -0.186** -0.168** -0.217** -0.179** -0.214*** -0.191**

[0.075] [0.051] [0.064] [0.063] [0.075] [0.080] [0.073] [0.083]Coastal town 0.024 0.024 0.008 0.008 0.034 -0.005 0.011 -0.011

[0.037] [0.046] [0.054] [0.084] [0.054] [0.083] [0.047] [0.077]Log (Dist. to Navigable River) -0.050 -0.118 0.018 -0.075 0.010 -0.122 0.040 -0.107

[0.071] [0.090] [0.080] [0.287] [0.078] [0.291] [0.065] [0.306]Natural disasters, 1850-1900 0.005 -0.006 -0.014 -0.006 -0.019* -0.005 -0.018 -0.008

[0.014] [0.015] [0.013] [0.018] [0.010] [0.017] [0.011] [0.017]Medieval Muslim City 0.066 0.051 -0.002 0.045 0.020 0.138 0.085

[0.229] [0.229] [0.251] [0.240] [0.253] [0.169] [0.228]Centuries Muslim Rule (to 1707) 0.046 0.072* 0.002 0.077** 0.011 0.067* 0.002

[0.029] [0.036] [0.045] [0.034] [0.038] [0.035] [0.042]Hindu Colonial Ruler -0.022 -0.049 0.037 -0.057 0.048 -0.075 0.012

[0.079] [0.082] [0.057] [0.075] [0.053] [0.076] [0.063]Muslim Colonial Ruler 0.108 0.046 0.207 0.002 0.187 -0.007 0.192

[0.134] [0.145] [0.154] [0.111] [0.148] [0.132] [0.157]% Partition Majority Inflows (1931-51) 0.001 -0.018 0.018 -0.011 0.025 -0.020 0.009

[0.028] [0.028] [0.018] [0.029] [0.022] [0.027] [0.019]% Partition Muslim Outflows (1931-51) -0.019 -0.017 -0.035 -0.018 -0.030 -0.011 -0.021

[0.017] [0.020] [0.043] [0.019] [0.043] [0.019] [0.043]Prop. Scheduled Tribe in Town -0.009** -0.010 -0.009* -0.012* -0.009* -0.011

[0.004] [0.006] [0.004] [0.006] [0.004] [0.007]Prop. Scheduled Caste in Town 0.005 0.010 0.004 0.008 0.005 0.010

[0.008] [0.009] [0.008] [0.009] [0.008] [0.009]Active port in 2001 0.060 0.038 -0.014 0.004 -0.011 -0.013

[0.092] [0.117] [0.114] [0.150] [0.099] [0.126]Workers Participation Rate 0.002 0.002 0.002 0.003 0.002 0.002

[0.007] [0.008] [0.006] [0.007] [0.007] [0.008]Literacy Rate 0.003 0.001 0.003 0.001 0.002 0.000

[0.003] [0.003] [0.003] [0.003] [0.003] [0.003]Prop. Muslim in Town, 1981 0.952* 0.488 0.931* 0.519 0.873 0.473

[0.516] [0.569] [0.500] [0.554] [0.509] [0.563]Prop. Muslim in Town^2, 1981 -0.967 -0.426 -0.854 -0.388 -0.784 -0.354

[0.618] [0.694] [0.602] [0.688] [0.585] [0.667]Vote Margin <5% 98 -0.098 -0.125**

[0.060] [0.052]Med. Port x Vote Margin <5% -0.327* -0.259

[0.188] [0.198]Med. Muslim City x Vote Margin <5% 1.042** 0.625

[0.379] [0.459]Log Vote Margin 98 -0.002 0.000

[0.026] [0.029]Med. Port x Log. Vote Margin 98 0.173* 0.179

[0.094] [0.106]Med. Muslim City x Log. Vote Margin 98 -0.400*** -0.314*



20

Table 4: Regression (OLS): Change in Ruling Party (BJP) Vote %, 1998-2002

N=180, Clusters=18 Districts (1) (2) (3) (4) (5) (6) (7) (8)Medieval Port -1.665 -1.200 -4.508* -6.280** -4.785* -6.365** -4.549* -6.339**

[2.745] [1.740] [2.442] [2.799] [2.350] [2.763] [2.508] [2.722]Medieval Muslim City -1.943 -1.866 -1.544 -2.155 -1.952 -0.777 -1.407

[3.018] [2.659] [3.679] [3.445] [3.828] [3.438] [3.752]Centuries Muslim Rule (to 1707) 1.886 3.669*** 3.860*** 3.394*** 3.424** 3.225*** 2.675**

[1.257] [0.836] [1.179] [0.872] [1.240] [0.887] [1.125]% Partition Muslim Outflows (1931-51) -1.533** -1.462*** -2.171 -1.355** -1.850 -1.244** -1.620

[0.602] [0.487] [1.906] [0.472] [2.012] [0.504] [1.704]Prop. Muslim in Town, 1981 39.174** 26.089 39.996** 26.097 39.438** 24.744*

[18.520] [17.574] [18.666] [16.814] [17.780] [13.826]Prop. Muslim in Town^2, 1981 -48.177 -34.989 -48.752 -37.050 -47.948 -34.613

[28.976] [25.976] [29.579] [26.347] [28.900] [24.194]Vote Margin <5% 98 2.403 6.857**

[3.454] [3.159]Med. Port x Vote Margin <5% -0.154 5.721

[5.133] [5.008]Med. Muslim City x Vote Margin <5% 10.639* -3.483

[5.937] [7.302]Log Vote Margin 98 -1.598 -3.613***


[2.509] [2.067]Med. Muslim City x Log. Vote Margin 98 -4.771** -1.342



21

Table 5: Regression (OLS): BJP Winner, Close Elections Sub-Sample, 2002

Clusters= 16 Districts (1) (2) (3) (4) (5) (6)2002 Vote Margin Within 10% 10% 10% 5% 5% 5%Medieval Port -0.510*** -0.543*** -0.527*** -0.402** -0.426** -0.425**

[0.151] [0.140] [0.118] [0.185] [0.181] [0.184]Medieval Muslim City 0.814*** 0.810*** 0.771***

[0.197] [0.223] [0.243]Log (population), 1991 0.049 0.048 0.042 0.017 0.024 0.022

[0.079] [0.082] [0.079] [0.096] [0.092] [0.096]Centuries Muslim Rule (to 1707) -0.038 -0.027 -0.061 -0.062

[0.084] [0.086] [0.120] [0.126]Prop. Muslim in Town, 1981 0.796 1.060 0.481 0.402

[1.151] [0.991] [1.203] [1.029]Prop. Muslim in Town^2, 1981 -1.145 -1.436 -0.919 -0.823

[1.445] [1.241] [1.438] [1.151]BJP incumbent 1998 0.148 -0.042

[0.180] [0.302]Observations 83 83 83 55 55 55R-squared 0.09 0.1 0.12 0.05 0.07 0.07

Notes: Robust standard errors in brackets (clustered at 1981 district): * significant at 10%; **5%; *** 1%. No Medieval Muslim patronage city fell within the 5% vote margin in 2002.

district that were exposed to greater spells of Muslim rule also appear to have swung towards

the BJP following the violence, consistent with the interpretation that rather than reducing

biases, longer Muslim rule may have generated greater inter-ethnic competition.18

The BJP also appears to have made particular gains in close elections, consistent with a

successfully targeted election campaign (Cols 5-8). Though medieval ports experienced an

average swing against the BJP, and politically competitive medieval ports also experienced

a lowered propensity for ethnic rioting (Tables 2 and 3), the BJP’s losses in vote share

following the riots does not seem to have been greater when medieval ports were more

politically competitive. In contrast, the BJP’s gains appear to have been accentuated in

Muslim medieval patronage cities that had previously been politically competitive. As we

have seen these towns also were both more likely to have experienced an ethnic riot and more

days of rioting (Tables 2 and 3), and are arguably locations where political competition and

inter-ethnic competition coincided.

A related approach is rather than look to the 1998 elections for a measure of subsequent

political competition, we can examine whether the ultimate winner of the 2002 elections was

18Ironically, as Table 4 suggests, the BJP lost vote share in towns in districts which had already experiencedgreater ethnic cleansing of the Muslim population during the Partition of 1947, and but made gains in townsthat had sustained a greater subsequent Muslim population.

22

predictable by historic incentives for inter-ethnic complementarity and competition even in

those races that ended up being very close in 2002. Table 5 presents the probability of a

BJP seat win looking only at those towns in which the winner won by less than 5% (Cols

1-3) and 10% (Cols 4-6).19

In fact, as Table 5 suggests, even in the Gujarat elections that followed the 2002 riots,

there appears to have been no strong incumbency e↵ect for BJP candidates (Cols 3,6). Yet,

even in these close races, the probability that the winner of the 2002 election was from the

BJP was heavily influenced by the nature of historical incentives. Relative to other close

races, the BJP was less likely to win in medieval ports following the riots, and significantly

more likely to win in medieval patronage cities. These results suggest that the outcome of

Gujarat’s closest elections following the riots were not ‘as good as random’. Instead, these

findings appear consistent with the reduced e↵ectiveness of the BJP’s broader campaign

to win close elections by adopting a platform complicit with ethnic violence in towns with

close races that enjoyed inter-ethnic complementarity and the higher dividends of such an

approach in similar towns where ethnic groups have historically competed.

6 Robustness and Mechanisms

So far we have argued that the e↵ect of medieval ports reflects historical incentives for inter-

ethnic complementarity and the accompanying community organizations that emerged to

support such inter-ethnic exchange. Another possibility is that medieval ports simply just

happened to have less violent individuals or that attempts to instigate violence in this period

just happened to be less common. Table 6, Panel A presents the determinants of whether a

town experienced any ethnic violence (both riots and isolated incidents of attempted violence)

during the two months of the Godhra riots, while Panel B examines the probability a town

experienced attempted violence but no escalation that resulted in riots. Notice that despite

significantly less rioting than otherwise similar towns, medieval ports appear to be as likely

as other towns to experience violent incidents. In fact, medieval ports that were the focuses

of greater political competition appear, if anything, more likely to experience violence that

nevertheless did not escalate into riots (Panel B, Cols 7-8). Rather than stopping any

precipitating events, the major e↵ect of a medieval trade legacy appears to be to reduce the

escalation of religious violence into broader mob confrontation. This is consistent with the

presence of community-level incentives provided by institutional mechanisms that mitigate

19The limitations of the regression discontinuity approach applied to elections are still an open researcharea, with a measurable incumbency advantage appearing in very close US elections (Caughey and Sekhon,2011, Grimmer, 2011) but less evidence for incumbency di↵erences in non-US elections (Eggers, Folke, Fowler,Hainmueller, Hall and Snyder, 2013).

23

shocks to inter-ethnic peace, rather than the presence of inherently peaceful individuals or

the lack of agents provocateur in medieval ports.

If, as I have argued, medieval ports have less support for political platforms complicit

with ethnic violence, then lowered political support for the BJP might also be expected

in the aftermath of the preceding major wave of ethnic violence between 1990 and 1993.

This wave of violence culminated in widespread riots following the destruction of the Babri

Mosque in Ayodhya in Uttar Pradesh in December 1992, with Gujarati towns relatively less

a↵ected (the previous peak of 35 Hindu-Muslim riots in Gujarat in 1990 compares to 105

riots in 2002 (Varshney and Wilkinson, 2004)). Yet, as Table 7(Cols 1-4) suggest, medieval

ports also gave the BJP around 6-7 percentage points less support than otherwise similar

towns in the subsequent 1995 elections.

Another potential concern is that perhaps the vote swing against the BJP visible in

medieval ports in 2002 is simply due to regression to the mean, perhaps due to higher

pre-2002 support for the BJP in medieval ports. As Table 7 (Cols 5-8) reveals, despite

lowered support in 1995, medieval ports were not significantly more likely than towns with

similar geographical, historical or modern characteristics to oppose or support the BJP in

the elections in the 1998 elections that preceded the 2002 violence.20 In contrast, medieval

Muslim patronage centers, which, as we have seen, showed more support for the BJP in close

elections in 2002, were also more likely to support the BJP in 1998. Both of these patterns

appear inconsistent with e↵ects driven by regression to the mean.

The focus of our analysis has been on the e↵ects of historical patterns of inter-ethnic

complementarity and economic competition on the incentives for ethnic riots and the subse-

quent political support of agents of the party seen to be complicit in that riots. A separate

question is whether ethnic violence itself a↵ects political support directly, or both ethnic

violence and political support are chiefly a manifestation of historic inter-ethnic incentives

and other factors. As I have argued, the escalation of precipitating acts of ethnic violence to

full-scale mob riots depends directly on local support and thus is clearly endogenous to local

incentives in the towns involved. Further, it is likely, as the theoretical framework describes,

that political agents will have incentives to target the organization of violence more in areas

where it is more likely to be successful as a political strategy.

However, as Table 6 suggests, the locations of violent events, including isolated acts,

appear less clearly selected. Medieval ports were as likely as other towns to experience such

violence, and there were a number of ‘failed experiments’: inter-ethnic violent acts which

did not escalate into rioting. Insofar as there is a random component to the extent to which

these precipitating events were attempted or occurred in otherwise similar towns matched

20Between 1995 and 1998, Gujarati towns experienced four Hindu-Muslim riots in total.

24

Table 6: Regression (OLS): Ethnic Violence, Feb-April 2002

N=180, Clusters=18 Districts (1) (2) (3) (4) (5) (6) (7) (8)Panel A: Any Ethnic Violence (Including Isolated Incidents)Medieval Port -0.015 -0.048 -0.057 -0.106 -0.073 -0.104 -0.067 -0.088

[0.150] [0.134] [0.134] [0.104] [0.146] [0.114] [0.117] [0.082]Medieval Muslim City 0.088 0.051 0.080 0.069 0.088 0.041 -0.007

[0.242] [0.249] [0.244] [0.251] [0.246] [0.223] [0.153]Vote Margin <5% 98 -0.110 -0.086

[0.073] [0.050]Med. Port x Vote Margin <5% -0.481 -0.137

[0.327] [0.218]Med. Muslim City x Vote Margin <5% 0.583* 0.068

[0.289] [0.312]Log Vote Margin 98 0.058* 0.041

[0.030] [0.025]Med. Port x Log. Vote Margin 98 0.043 -0.096

[0.147] [0.121]Med. Muslim City x Log. Vote Margin 98 0.036 0.312**

[0.165] [0.137]Joint-F Med Port vars. 3.84 1.29 0.62 0.68Prob>F 0.04 0.30 0.55 0.52R-squared 0.40 0.45 0.48 0.66 0.49 0.67 0.49 0.68Panel B: Ethnic Violence that Did Not Escalate to a RiotMedieval Port 0.116 0.092 0.129 0.061 0.144 0.074 0.148 0.104

[0.107] [0.122] [0.132] [0.108] [0.136] [0.107] [0.115] [0.100]Medieval Muslim City 0.023 0.000 0.082 0.023 0.068 -0.097 -0.092

[0.351] [0.382] [0.406] [0.397] [0.410] [0.258] [0.233]Vote Margin <5% 98 -0.012 0.039

[0.050] [0.043]Med. Port x Vote Margin <5% -0.154 0.121

[0.282] [0.206]Med. Muslim City x Vote Margin <5% -0.458 -0.557

[0.355] [0.530]Log Vote Margin 98 0.060** 0.041**

[0.025] [0.017]Med. Port x Log. Vote Margin 98 -0.130 -0.275**

[0.086] [0.098]Med. Muslim City x Log. Vote Margin 98 0.436** 0.626***



25

Table 7: Regression (OLS): Ruling Party (BJP) Vote %, 1995 and 1998

Obs=180, Clusters= 18 Districts (1) (2) (3) (4) (5) (6) (7) (8)BJP vote share in: 1995 1995 1995 1995 1998 1998 1998 1998

Medieval Port -7.692** -6.360** -4.363 -6.095 2.264 0.821 1.507 2.386[2.693] [2.912] [4.389] [4.213] [3.279] [2.927] [3.117] [3.136]

Medieval Muslim City 0.488 -1.58 -2.473 11.349** 9.426* 9.616*[6.870] [6.769] [8.069] [5.366] [5.179] [5.416]

R-squared 0.15 0.21 0.26 0.44 0.22 0.26 0.30 0.43Incremental Controls Geog Colonial Modern Modern Geog Colonial Modern Modern1981 District FE N N N Y N N N Y


along a range of observables, I can examine whether towns that isolated actors may have

‘intended to treat’ with riots show di↵erential voting patterns.

Table 8(Cols 1-5) examines whether a town that exhibited any ethnic violence (including

isolated incidents) showed a greater vote swing towards or away from the ruling party follow-

ing the violence. Notice that there appears to be no residual correlation of violence on the

ruling party’s vote share on average. However, medieval ports that experienced an act of eth-

nic violence actually reduced their support for the BJP by an additional 7 percentage points.

This provides further suggestive evidence that is consistent the framework: attempting eth-

nic violence in medieval ports may have in fact backfired electorally, by instead mobilizing

those groups opposed to ethnic pogroms.

Table 8(Cols 6-10) examines the residual correlation between the number of days of

the ethnic riots that occurred in a town and its support for the ruling party. Note that,

on average, towns with similar pre-independence histories that experienced more extensive

rioting do show increased subsequent support for the ruling party (Cols 6-7). The residual

correlation is weakened by controls for ethnic demography, modern economic factors and

district fixed e↵ects, however, and the interactions with proxies for historical incentives are

insignificant. This may be because of the spillover e↵ects of riots: riots may alter voting

behaviour both where they occur and elsewhere. But the results are also consistent with

both the propensity for riots and subsequent voting being functions of local support that

itself may reflect deeper historical and contemporary economic and political incentives.

26

Tab

le8:

Reg

ression

(OLS):

Changein

RulingParty(B

JP)Vote

%,1998-2002:Violence

Intera

ctions

N=1

80, C

lust

ers=

18 D

istri

cts

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Med

ieva

l Por

t-1

.597

-1.0

62-4

.386

*-6

.318

**-6

.398

**-0

.568

-0.1

83-3

.853

-5.9

85**

-5.9

94**

[2.4

93]

[1.6

95]

[2.5

20]

[2.8

00]

[2.3

65]

[2.5

24]

[1.8

39]

[2.4

80]

[2.6

65]

[2.7

27]

Any

Eth

nic

Vio

lenc

e (in

cl. I

sola

ted)

4.42

62.

852

2.13

7-0

.362

-0.6

22[2

.799

][2

.887

][2

.505

][2

.502

][2

.423

]M

ed. P

ort x

Eth

nic

Vio

lenc

e-6

.982

*[3

.741

]M

ed. M

uslim

Cty

x E

thni

c V

iole

nce

6.27

3[5

.080

]Lo

g R

iot D

ays

5.90

0**

4.87

7*2.

269

1.00

80.

692

[2.4

69]

[2.4

65]

[2.4

46]

[1.8

17]

[1.9

85]

Med

. Por

t x L

og R

iot D

ays

0.43

9[2

.976

]M

ed. M

uslim

Cty

x L

og R

iot D

ays

1.61

6[1

.910

]M

edie

val M

uslim

City

-2.1

95-1

.974

-1.5

15-3

.842

-3.1

42-2

.405

-1.7

33-2

.980

[3.0

15]

[2.8

02]

[3.7

24]

[5.1

79]

[2.6

31]

[2.6

54]

[3.5

89]

[3.6

94]

Join

t-F M

ed P

ort v

ars.

6.18

2.87

Prob

>F0.

010.

08In

crem

enta

l Con

trols

Geo

gC

olon

ial

Mod

ern

Mod

ern

Mod

ern

Geo

gC

olon

ial

Mod

ern

Mod

ern

Mod

ern

1981

Dis

trict

FE

NN

NY

YN

NN

YY

R-s

quar

ed0.

360.

400.

480.

610.

620.

380.

420.

480.

620.

62

Notes:Geographicalcontrols

includelog.

distancesfrom

nav

igab

lerivers,coastaltown,NaturalDisasters,log.

pop

ulation

(199

1),Class

oftown(I,II,III)

anddistance

toGod

hra

(100

,200

,300

km).

Colonialcontrols

add:Centuries

Muslim

rule,Muslim

-fou

nded

orcapital,

%Partition

Hindu/SikhInflow

san

dMuslim

Outflow

s.Modern

controls

add:Proportion

SC/S

T,Mod

ernactive

port,

LiteracyRate,

Worker’sParticipationRate,

Proportion

Muslim

1981

(andaqu

adraticterm

).Allvalues

observed

in19

91unless

noted

.Allinteractions

aredem

eaned

.Rob

ust

stan

darderrors

inbrackets(clustered

at19

81district):*sign

ificant

at10

%;**

5%;**

*1%

27

0.0

2.0

4.0

6

20 40 60 80 20 40 60 80

Other Medieval Port Constituencies

1998 2002 2007

x

Figure 3: Change in Ruling Party Vote Share, 1998-2002Relative to other towns where the BJP gained vote share following the riots, medieval ports swang against

the BJP and became more competitive following the 2002 elections.

28

7 Discussion

This paper argues that there exists a fundamentally conditional relationship between political

competition and the nature of inter-ethnic economic incentives for exchange in generating

incentives for ethnic violence. While it is likely that marginal electoral constituencies in

general are likely to be the focus of more intensive political campaign e↵orts than safe seats,

the results suggest that the extent that such intensive campaigns manifest themselves in

ethnic riots can depend importantly upon the legacy of historical incentives for trade between

communities. Where marginal electoral constituencies coincided with or reflect pre-existing

historic inter-ethnic economic competition, as occurred in medieval Muslim patronage cities

in Gujarat from 1998-2002, politicians appear to have gained local incentives to foster ethnic

mobilization and violence. In contrast, where political competition occurred in societies with

historic incentives for inter-ethnic trade and tolerance, such as Gujarat’s medieval ports,

attempts to foment riots appear to have failed. These towns also appear to have sanctioned

the party seen as complicit in the violence at the polls.

The findings suggest that local historical incentives for inter-ethnic exchange and compe-

tition can have important electoral consequences, particularly in environments where ethnic

violence can be used as a campaign strategy. The framework, however, raises a further in-

triguing possibility. State politicians seeking to win marginal constituencies where ethnic

groups compete should emphasize the preferences of the majority in these communities. This

may lead them to adopt platforms complicit with violence state-wide, leading to a ‘conta-

gion’ of ethnic rioting. Yet, the corollary may also be true: state politicians seeking to win

marginal constituencies where ethnic groups enjoy complementary economic relations should

also face incentives to emphasise these voters’ preferences more relative to their more po-

larized constituents, leading potentially to a de-escalation in state-wide platforms of ethnic

hatred. While proper testing of this conjecture would require multiple state-elections, and

is a topic of on-going research, the Gujarat case does provide some corroboration.

Indeed, five years after the Gujarat riots, in 2007, the state prepared itself for another

election. As Figure 3 and Table 1 suggest, the average margin of victory in medieval ports

fell from 18.8% in 1998 to 8.89% in 2002, with 40% of seats in Gujarati medieval ports won

by margins of less than 5%. Although there was an increase in electoral competition on

average, which might be expected to increase the intensity of local campaigning, there was

no surge in electoral rioting in the state and the incidence of riots remained below the levels

of rioting that had occurred even in non-election years prior to the pogrom. Narendra Modi,

the Chief Minister, campaigned explicitly on a platform of economic development, with eth-

29

nic mobilization conspicuously absent from the ruling party platform.21 Dissident elements

in the state ruling party, including alleged stalwarts of the 2002 riots like the then state

home minister, rallied against the party leadership, accusing the Chief Minister of betraying

the Hindu community and no longer being worthy of the honorific “Hindu Hriday Samrat”

(Emperor of Hindu Hearts).22 Instead, observers noted that Modi explicitly reached out in

his election campaign to Muslims from the Memon, Khoja and Bohra communities, each

of whom had their origins in India as medieval traders (Engineer, 2008).23 In a remarkable

reversal, de-emphasizing ethnic mobilizing appears to have helped the BJP win an unprece-

dented third term in 2007 and a fourth term in 2012, with a significant degree of Muslim

support, particularly among trading communities, and even winning in Muslim majority con-

stituencies, without having fielded a single Muslim candidate. Like Uhuru Kenyatta, another

alleged orchestrator of ethnic violence, Narendra Modi has gone from being internationally-

reviled and potentially subject to criminal prosecution for his alleged role in state-sponsored

pogroms to being seen as a credible Prime Ministerial candidate.

Could it be that the anonymous police o�cer in Ahmadabad was right, that the ethnic

violence in 2002, arguably India’s worst since the Partition, was ‘unfinished business’ that

would result in less ethnic tension thereafter? That o�cer’s view, like that of some in

the Jim Crow South (Bleakley and Owens, 2010) was that the minority would ‘learn its

place’, and cease to compete for political patronage or resources. However, it does not

appear that opponents of violent ethnic domination– whether among the ethnic minority

or the majority– ‘learned their place’: towns with a long history of inter-ethnic tolerance

voted strongly against the ruling party in the aftermath of the violence, when such action

might yet have proven dangerous and costly. By doing so, they may have also subsequently

rendered themselves electorally more competitive and thus more influential in shaping state-

wide political platforms on the issue of ethnic mobilization. Electoral competition has long

been seen as a source of ethnic mobilization and violence. Yet, even in highly ethnically-

polarized states, when electoral competition becomes located among communities with local

21In an interview to the Hindu newspaper, when “asked where Muslims figured in his vision of Gujarat, heflared up: I don’t like this thinking. I work for five-and-a-half crore Gujaratis. For me, anyone who lives hereis a Gujarati, and I will not allow politics to come into this.” Vidya Subramaniam, “The Muslim Questionin Gujarat” The Hindu, October 9th, 2007.

22Ibid.23While accounts agree that these groups were viewed as marginal constituencies, accounts di↵er on why

these communities were plausibly persuadable despite the pogroms in which they also were targeted. Engineer(2008), himself a prominent left-wing dissident Bohra, argues that Bohras and other Gujarati Muslim tradingcommunities “are rich traders and they will be as much attracted by the development discourse as uppercaste Hindu Gujaratis.” In contrast, the Bohra activist J.S. Bandukwala claims that: “The Syedna [theBohra’s spiritual leader] will always seek a cordial relation with the party in power. It is in his interests,and as he sees it, in the interests of the community”. See Raheel Dhattiwala “Muslim as BJP supporter inGujarat”, The Hindu, 2013.

30

traditions of tolerance and inter-ethnic trust, such competition may actually generate broader

beneficial spillovers of peace.

31

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c8DD

D

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DD

DDD

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Malur

Murbad

Aligarh

AyodhyaKishangarh

Muddebihal

Godhra

Riots Feb-April 2002, QuintilesE Violence But No Riot

D 1

D 2

D 3

D 4

D 5 - 15

c8 Godhra

* Medieval Ports

) Medieval Muslim City

Town Population 1991( > 50000

( 50001 - 100000

( 100001 - 200000

( 200001 - 500000

( 500001 - 100000000

0 370 740185 Miles

Figure 4: Patterns of Ethnic Riots and Attempted Violence, all-India ,Feb-April2002Ethnic riots were confined almost entirely to Gujarat. In towns in other states, ethnic violence occurred but

rarely escalated or exhibited contagion.

37

Table 9: Regression (OLS): Log Killed and Injured, Feb-April 2002

Obs=180, Clusters=18 Districts (1) (2) (3) (4) (5) (6) (7) (8)Medieval Port -0.116 -0.166 -0.314 -0.358 -0.337 -0.363 -0.353* -0.395*

[0.248] [0.225] [0.206] [0.226] [0.201] [0.217] [0.190] [0.220]Medieval Muslim City 0.684 0.699 0.669 0.721 0.711 0.768 0.749

[0.683] [0.650] [0.724] [0.653] [0.707] [0.686] [0.766]Centuries Muslim Rule (to 1707) 0.039 0.139* 0.071 0.150* 0.077 0.134 0.056

[0.057] [0.076] [0.072] [0.080] [0.066] [0.088] [0.079]Prop. Muslim in Town, 1981 3.039** 2.604** 2.991** 2.604** 2.929** 2.505**

[1.053] [1.014] [1.038] [1.038] [1.056] [1.033]Prop. Muslim in Town^2, 1981 -3.799** -3.499** -3.697** -3.431** -3.611** -3.320*

[1.492] [1.585] [1.516] [1.578] [1.580] [1.628]Vote Margin <5% 98 -0.104 -0.125*

[0.105] [0.066]Med. Port x Vote Margin <5% -0.657 -0.715

[0.540] [0.535]Med. Muslim City x Vote Margin <5% 0.864 0.737

[1.101] [1.513]Log Vote Margin 98 -0.008 -0.033


[0.202] [0.227]Med. Muslim City x Log. Vote Margin 98 -0.319 -0.322

[0.344] [0.354]Joint-F Med Port vars. 2.37 2.7 2.19 2Prob>F 0.12 0.10 0.14 0.17Incremental Controls Geog Colonial Modern Modern Modern Modern Modern Modern1981 District FE N N N Y N Y N YR-squared 0.5 0.53 0.58 0.62 0.58 0.62 0.58 0.63


38

Working Paper No. 516 'Unfinished Business': …...Working Paper No. 516 'Unfinished Business':...

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Transcript of Working Paper No. 516 'Unfinished Business': …...Working Paper No. 516 'Unfinished Business':...