1

Violence and Conflict Negotiation: Exploring the Roles of Geography and Culture

Sanjeev Kumar1

University of Memphis

Abstract

Given the levels of poverty and economic development, India has not experienced a level of

interpersonal and inter-group violence that have marked others comparable countries. However,

there are large regional variations in the incidence of violence in India. In this paper, the main

variable of interest is the percentage of a district area under rice cultivation. The labor intensive

rice cultivation is more skill-oriented than the non-rice crops like wheat and corn. In the absence

of the modern mechanized agricultural methods, it requires more cooperation from neighbors. I

hypothesize that these features of the rice-growing areas would lead to a lower level of violence.

In a linear regression framework, after factoring in the issues pertaining to selection on both

observables as well as unobservables, the empirical results do support the hypothesis of the

moderating influence of the rice-area variable.

JEL: K00, N30, O17, O21, P5, Q00, Z10

Keywords: Murder, Violence, Identity, Rice, Geography, Biology, Social Capital, Riot, Violence

against Women

1 I am especially grateful to Daniel Millmet, Isaac Mbiti, Thomas Osang, and Karla Hoff for their valuable critiques.

I am also grateful to Dilip Mukherjee for sharing his field-level experience working in the rice-growing areas in

India. I also wish to thank Mehtabul Azam and Vikrant Bhakta for helpful comments. Any remaining errors remain

mine. Correspondence address: Department of Economics, University of Memphis, Memphis, TN 38152-3370

Email: skumar7@memphis.edu.

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1 Introduction

The neglected dimension of the quality of life ---Freedom from Violence---has become a

matter of central concern the world over, especially with the spread of terrorism. In the aftermath

of `The Great Recession', politics is looking to redefine societal goals (Gertner 2010).

Understandably, a systematic thinking about the central problem of violence is quite relevant in

such endeavor. Violence remains a significant stumbling block in the road to a better future.

Having a better understanding of its predicates would help in containing the ubiquitous threat

that it poses. It certainly would also be of help in achieving a better allocation of the scarce

resources towards the violence-mitigation public policies.

Given the levels of poverty and economic development, India has a low level of violence.

However, there are large regional variations in the incidence of violence. In this paper, I

investigate the causal effect of one particular variable, the percentage area of a district under

rice-cultivation, on violence. The differential nature of institutions necessitated by the

geographical and climatic features of the rice-growing areas is investigated to come in terms with

the lower incidence of violence found there. As discussed by Dixit (2009), a state-led policy to

provide public good, like Freedom from Violence, may unravel prosocial preferences that might

in its absence be a leading cause for a low level violence. Thus, it is relevant to have a better

understanding of the causes and correlates of the existence of public goods in a region like---

Freedom from Violence---in order to contain the cost of its provision. In particular, by various

estimates rice is the most labor and skill intensive crop. That may have played role in creating

norms and behaviors that reduce the level of interpersonal and inter-group violence.

I examine various mediating factors that can explain the low level of violence found in the

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rice-growing areas. In a linear regression framework, after factoring in the issues pertaining to

selection on both observables as well as unobservables, the empirical results are unambiguous in

their support of this hypothesis. The historical and continued dependence on rice cultivation,

thus, can be treated as a causal factor that, historically as well as at the present, may have helped

in the management of conflict negotiation in India, at both individual-level and community-level.

The main hypothesis of this paper is that the area traditionally under rice cultivation is an

independent variable of principal interest. I will present several possible explanations for the

basic mechanism at work. An individual living in a rice growing area may evolutionarily be

better at restraining violent behaviors.2 Given the recent findings about the role of self-control in

determining educational outcomes, one can posit that the ability to restrain violent tendencies

reflect the presence of a general ability for self-control. Controlling for literacy, contrary to one's

expectation, does not take away the independent effect of the rice-area variable on violence. The

finding becomes starker with the observation that individuals are usually of shorter stature and

weigh less in a rice growing area.3 A recent working paper by Bodenhorn et al. (2010) suggests

that a shorter stature makes people more prone to crime. An additional channel might be the

prevalence of marriage at an early age. Studies suggest that adolescents are more prone to

violence. Moderate to chronic offenders demonstrate a tendency to enter into criminal activity

between ages of 15 and 19 (Bodenhorn et al. 2010). Given that marriage at an early age is more

common in the rice-growing areas, one can argue that this norm might be putting a break on

youths' propensity for violence. However, controlling for this channel does not take away the

independent effect of the rice area variable.

2 It would be interesting to investigate if one finds more neural connections the prefrontal cortex for people living in

the rice growing area for many generation; thus, making it easier for them control their impulses. 3 National Family Health Survey (2005-2006) is used to get this information.

4

Additionally, the labor intensive nature of rice cultivation, historically, has given rise to the

institution of labor-exchange systems in many regions of the world. Such institution may have

facilitated the building of more stable prosocial preferences, thus creating a context for a lower

propensity for violence. I examine if the presence of more social capital contributes in lowering

the incidences of violence in the rice-growing areas.

The remainder of the paper is organized as follows: Section 2 talks about previous literature.

Section 3 talks about the differential nature of rice cultivation and institutions and norms that

have accompanied it. Section 4 presents the identification strategies. Section 5 describes data

used for the analyses. Section 6 presents the results, while section 7 does sensitivity analyses.

Section 8 discusses the results with some concluding thoughts.

2 Literature Review

In their critical evaluation of what economists have learned about the determination of

crime in the last 40 years, Dills et al. (2008, p. 1) write, "...[Economists] know little about the

empirically relevant determinants of crime". They find that factors that find supports in the US

data deemed not very relevant when taken to the international data. Much of the empirical

researches examine deterrence---an idea that policies can reduce crime by raising the expected

cost of committing crime. In the economics literature, we are not used to thinking about violence

or crime in terms of culture, identity, and prosocial preferences. In their recent work of Fehr and

Hoff (2011) and Akerlof and Kranton (2010) explore the role that culture and identity play in

influencing taste and preferences. However, there is not much research talking about the effect of

something like identity and prosocial preferences---and both being determined by culture and

geography---on capital offence like homicide.

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Research on violent crime in India is a rarefied field of inquiry. Dreze and Khera (2000),

similar in nature to the analyses presented in this paper, discuss the causal role of gender-bias,

captured by an adverse sex-ratio at the district level in influencing violence in India. Their results

find supports in the analysis carried out in this paper too: an adverse sex-ratio makes a district

more violent. Banerjee et al. (2001) talk about the impact that the social heterogeneity has on the

crime rate in India. They find that a higher level heterogeneity is associated with a higher crime

rate. Banerjee and Iyer (2005) talk about the role of the history, which they capture by the land-

tenurial system in India in shaping the nature of conflict. They suggest that the areas under a

feudal-like land tenurial system, which principally are the rice-growing areas, are more

conducive for insurgencies. Such observations make it more intriguing that I find a lower level of

violence in the rice-growing area.

One can create a Prisoner's Dilemma-like game with recourse to violence being a

dominant strategy. With repeated interactions and in the presence of individually internalized

social norms of cooperation or non-recourse to violence, a more efficient of the equilibria---

involving non-recourse to violence---can be sustained. Akerlof and Kranton (2010) indeed talk

about limits placed by society on people's identity can be crucial determinants in people's lives.

Internalized norms and identity of being a more restrained individual ensure self-enforcing

conducts, and if this is situated `historically' in a way that individuals in the rice-growing may be

expected to have internalized such conduct, it becomes part of culture of a community. Then it

provides with loci upon which individuals response to violence inducing circumstances are put to

work (Das Gupta 2009). Such an explanation is similar in spirit to the broken window theory4 of

4 Existence of more broken windows creates an impression that the behaviors leading to it are acceptable, and hence

creates an atmosphere and incentive for similar behaviors.

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criminal behaviors.

3 Rice Cultivation and Its Accompaniments

Rice-cultivation in India goes back at least 4,000 years. And it continues to be the most

important crop in India (Mbiti 2008). For the states in India around 3rd

B.C., rice-cultivation was

a source of revenue during the time of scarcity. The state would release its stock of rice to pull

down prices and in its wake create revenue for the state exchequer (Basham 1957).

In Sanskrit, there is a word for a ‘rich man’ that is ‘Bahuvrihi’: one who has much rice.

Basham (1954, p. 194) mentions an interesting anecdote, "The Greek travelers were most

impressed by the fertility of India's soil and the energy and ability of her cultivators. The Greeks

found it a great source of wonder that India produced two crops a year. In the wetter parts of the

land the two crops might even grow without irrigation, while in the plains a summer crop of rice

would grow during the monsoon and a second irrigation crop in the dry season." This makes it

clear the year round requirement of labor in rice cultivation that is continuing for millennia.

Working in a rice-field is ten to twenty times more labor intensive than working in an

equivalent size corn or wheat field. The annual work load of a wet rice farmer in Asia is around

3,000 hours a year (Gladwell 2009).5 It is critical to understand the cultivation process of rice, in

contrast to other crops, to elucidate the process giving rise to the emergence of prosocial

preferences, and the ability for better self-control or restraints. Bardhan (1974) and Mbiti (2008)

discuss extensively about the difference between the rice and wheat cultivation process. An

International Rice Research Institute reports that some processes particular to the cultivation of

rice require on average 30 person-days of labor per hectare, thus leading to the general difficulty

5 Talking about the culture built around rice-cultivation in the case of China, anthropologist Goncalo Santos

says, "Rice is life. If you want to be anyone in this part of China, you would have to have rice. It made the

world go around" (see in Gladwell, 2009).

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faced by farmers in hiring enough labor during this critical phase of cultivation (IRRI, 2003).

Such scarcity of labor has given rise to labor exchange system like chuai kan labor exchange

system in Thailand (Hara 2002).6 Furthermore, historically in contrast to the communities

dependent on animal husbandry, the farmers in the rice-growing areas would never worry about

losing their crops to thieves at night. Furthermore, given the labor requirement, their survival

was crucially dependent on the cooperation of others in the community. These factors provide

the rationale for investing in the creation of prosocial preferences through development of social

and cultural narratives. It is not surprising, therefore, to find stories and narratives putting a

heavy emphasis on the need to maintain a non-violent outlook in the traditional rice-growing

areas in India.

Figure 1: Labor-intensive rice cultivation in the Mekong Delta region, Vietnam7; Figure 2: Rice cultivation

has always been based on community work (an advertisement on the Golden Rice Network)8; Figure 3: Rice

cultivation in Niger Delta9

In contrast to the group-based rice-cultivation captured in the photographs displayed here

from different part of the rice-growing areas in the world, most of the photographs for other

crops that one can find on the internet rarely display a group of labor together at work in the

6 http://www.ide.go.jp/English/Publish/Periodicals/De/pdf/81_04_02.pdf

7 http://www.terragalleria.com/images/vietnam/viet8078.jpeg

8 www.goldenrice.org/image/who_together.jpg

9 http://www.wetwin.net/about_casestudy.html

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cultivation process.

The major Rice producing areas in India had for a long time been under the landlord

tenurial system until it was abolished immediately after India became independent (Banerjee and

Iyer 2003). The main explanatory variable under consideration: the percentage of a district area

under rice cultivation has not changed significantly over years. Two factors can be argued to

have played significant roles in changing the area under rice cultivation in India, (i) The Green

Revolution, which largely was in the wheat-growing area in the western and north India, and it

mostly worked at enhancing the yield, and (ii) Urbanization. Most of the rice growing area is still

rain-fed (Mbiti 2008). Urbanization is taking place at a snail's pace in India, and is largely

happening around the same cities increasing their densities. India has one of the lowest levels of

urbanization among countries of its size.

Another aspect about the rice-growing area, mentioned by Deaton and Dreze (2008), is

that the average reported hunger in the rice-belt of India is higher than the national average.

Adult men are of shorter stature and less heavy in the states with the rice growing area than those

from the non-rice growing areas. Keeping these accompaniments in mind regarding the rice-

growing areas, the potential interpretation of the main finding of this paper that individuals seem

to have a much better restraint on their violent behaviors becomes quite dramatic.

4 Identification

4.1 Empirics

The economic model of crime can be adapted to illustrate the economics of violence. The

basic economic model of crime was originally developed by Becker (1968) and extended by

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Issac Ehrlich.10

The empirical approach adopted in this paper is to use the area under rice

cultivation as a ‘treatment’ meted out to individuals that lowers their propensity for violent

behavior. The ideal way would be to compare a rice-growing area with the same area without

rice cultivation. In the presence of missing counterfactuals, a number of methods are used to

create a meaningful comparison district (Millimet 2009; Jha 2008).

At first, a base model is estimated with a parsimonious set of covariates. This approach,

though, it suffers from obvious deficiencies, can still shed considerable light on the empirically

relevant covariates. Any instrumented or multifactorial analysis has to be particularly strong to

overcome the raw correlation (Dills et al. 2008). Thereafter, a full specification model with an

expanded set of covariates is put in operation. The sensitivity of the estimates to the inclusion of

observables would hint to a probable and significant role that unobservables might also be

playing in driving the uncovered relationship (Altonji et al. 2005). These specifications then are

compared to Dreze and Khera (2000) specification for the illustration purpose.11

The following econometric specification is used here:12

ds2ds1dsdsds Z+X TY (7)

dsY is the number of violent acts per million committed in district d nested in state s.13

Since

districts are clustered at the state level, and given that the law and order issues largely fall under

10

see online version for more elaborate discussion on this 11

Using the same model as the one used in Dreze and Khera (2000) would amount to specification bias. Many

factors, that they found insignificant, may overtime have found ways to influence violent behaviors. Their model

also does not control for any possibility of heteroskedasticity, or the state fixed effects. 12

The estimates from the box-cox model reject the logarithmic specification of the dependent variable used in the

literature. In the information based test, linktest, the estimated value of the test statistic is significant and small (see,

Cameron and Trevedi 2009). 13

We also carried out estimation with the absolute number of murder to bring the saliency of murder to the fore, and

the broad conclusions continue to hold.

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the state jurisdiction, I also control for district invariant state fixed effects through the following

specification:

)(Z+X TY ds2ds1dsdsds s (8)

Xds consists of the variables which includes the district level average of individual

characteristics: literacy, standard of living, proportion of schedule castes and tribes, religion, and

unemployment level. The variables included as characteristics of districts (Zds) are sex-ratio,

population, indicator for left-wing insurgency, measure for inequality, urbanization,

undernutrition, diversity, presence of government infrastructure, and social custom14

to marry

girls below 18 years of age. Our main coefficient of interest is β for variable Tds, which is the

percentage of a district area under rice cultivation. It can be considered as a pre-determined

variable owing to the fact that intensive rice cultivation has been going on in India for last at

least 4,000 years, largely driven by the geographical features suited for rice cultivation.

Estimates of β might not reflect the true impact of idiosyncratic features of rice-growing areas

for the following reasons: (i) There could be measurement error in the variable, Tds; and (ii) there

could be omitted variables correlated both with the district area under rice-cultivation and with

the outcome variable. I have included the variables whose omission can potentially bias the

estimates. To tackle the measurement error problem, I also check whether the results remain

robust with only a binary measure for district area under rice cultivation.15

14

This is a remnant of slowly receding tradition of child-marriage system in India. 15

In future work, I will use a method suggested by Lewbel (1997) as a means to address both measurement error

and endogeneity issue. This method generates instruments using higher moments of the observable data.

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5 Data

Crime data, reporting various crime statistics for all Indian districts, comes from the

Government of India publication, Crime in India by National Crime Records Bureau (NCRB).

Data on the rice area comes from the Rice Research Institute, Patna, Bihar, India. The variable

used in the analysis, is the average of rice areas for four years from 2001 to 2004. I use data on

437 districts in the 17 states constituting 95% of India's population. The same set of states that

are included in the Dreze and Khera (2000) paper. Data on the volumes (in tonnes) for cash crops

and for fruits and vegetables produce, which is used for conducting the falsification tests, comes

from The Ministry of Agriculture (1998-1999).

I use data on Murder, Rape, and Riot to capture violence. Murder data is a more reliable

among these three. The two other dependent variables are used for the exploratory purpose to see

if the violence restraining feature of the rice-growing areas also holds for (i) violence against

women, and (ii) for group-based violence like riots. The dependent variables are the number of

homicides, reported cases of rape, and registered riot cases per million in the year 1998. The size

of population is also used as a covariate to capture the effect of population growth on the

violence.16

To capture the effect of inequality, the Gini Coefficient is calculated using the Consumer

Expenditure Survey (1998) from the 55th round of the National Sample Survey (NSS).17

To

capture the effects of non-criminal income opportunity, unemployment data at the district level is

used from the NSS 55th

round of the Employment and Unemployment Survey (1998).

Unemployment is defined as number of people in the age group (15-59) actively looking for

employment. Data on the Female-Male ratio comes from the Census of India (2001).

16

India adds population approximately equal to population of Australia each year. It is important to see what kind of

stress it putting on the society, and if it gets reflected in a higher or lower incidence of violence there. 17

‘ineqdeco’ command available through users created command.

12

Undernutrition defined as the proportion of children in the age-group 0-72 months being 3-

standard deviation below the internationally accepted benchmark for the weight-for-age ratio;

data for which comes from the Reproductive and Child Health (RCH) survey conducted by the

World Bank in year 2002-2004. Data on female literacy and total literacy come from the Census

of India (2001). Diversity is defined as the ELF measure:

r

2

iri s-1 d

sir is the proportion of individuals belonging to religion r. I again have used the Census of India

(2001) to create this district level index.18

The 5th wave of the World Value Survey (2005) is used to see if one can rationalize the

interpretation of the rice-growing area variable facilitating more prosocial behaviors at the

country level.19

This survey is carried out in 57 countries in the world. Data for only 52 countries

are used with 8 largest rice producing countries. In the WVS survey, a question regarding the

level of trust is used to see if indeed one can find some difference between the traditional rice

growing countries versus non-rice growing countries.

To evaluate how rice-growing areas fare on the various standard measures of social capital, I

use a nationally representative data set, the India Human Development Survey (IHDS), 2005

collected by the University of Maryland and the National Council of Applied Economic

Research. This data is compiled after surveying 40,000 households across India. The variables of

interest were, (i) percentage of households in a district reporting being part of various social,

political, and economic organizations, (ii) their confidence in different social, political, and

18

Loosely defined, ELF measures the probability that two randomly selected persons in a country will not belong to

the same ethno-linguistic group. Mauro (1995) first introduced ELF to economics literature as a tool for measuring

ethnic diversity. 19

http://www.wvsevsdb.com/wvs/WVSAnalizeStudy.jsp

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economic institutions, and (iii) three measures of trust, capturing general and specific context for

social trust.

437 districts in the 17 states constitute 95% of India's population. Murder rate ranges from 2.8

per million to 131 per million. Table 1 gives state-level means of the independent variables. The

state level means of the murder rates for India's major states range from 15 in Kerala to 53 in

Jharkhand.

The district area under rice cultivation (RiceArea) ranges from almost negligent (0.06%) in the

case of Rajasthan to 17.3 percent in the case of Punjab. In the traditional rice producing states of

Bihar, West Bengal, Orissa, Uttar Pradesh, and Tamil Nadu, the percentage area under rice

cultivation at the district level is positive in almost all of their districts, see Table 2. These five

states continue to be the major suppliers of rice even today.

In Table 3, the observable characteristics of the traditional and high rice producing districts are

compared to the low rice producing districts. It is clear that the districts with a significant area

under rice cultivation are more densely populated; are largely in the northern part of India; are

poorer, less urban, and are more religiously diverse. Rice dependent districts also have a higher

share of Muslims in their population: 12% of population is classified as Muslims in the high rice

producing districts, while only 10% are in other districts.20

An extreme form of malnutrition is also less prevalent in the rice-growing area. Also, there is a

negative correlation between the rice-area variable and the proportion of households with low

standard of living, a variable which captures the extent of poverty (see Table 3). The rice-

growing areas also have their girls marry below the age of 18; 33% of all marriages in the sample

have brides less than 18 years of age, and the proportion is highest among the traditional rice-belt

20

It possibly is capturing more than three century of Muslim rule in India, which primarily was dependent on the

tax-revenue from the rice-producing areas of India.

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(52% in Bihar and 48% in West Bengal). One can surmise that the tradition of an early marriage

is a contributory factor in the low level of violence in rice-growing area. It seems that bearing

responsibility at an early age does make individuals less prone to violence.21

Furthermore, a

lower level of publically-funded infrastructure in the rice-growing areas suggests a general

absence of the modern governance infrastructure, thus leaving space for a continued and

entrenched role of the traditional institutions.

I use many strategies to control and check for the potential sources of endogeneity. Ideally, one

should use a district level panel data to control for any time-fixed effects, or have some exclusion

restrictions; but it is hard to find the variables appropriate for the exclusion restriction. In the

absence of these options, the following specifications are used, and the error correlation across

districts within a state is controlled for in all specifications:22

i) District invariant state-level fixed effects specification is used.

ii) Exclusion of those states affected by the Green Revolution, to minimize other district level

characteristics affecting the rice area under cultivation.

iii) To check the robustness of the specifications, other variables capturing violent behaviors

like `attempt-to-murder' and `kidnapping' are used as the right hand side variables to provide

further controls for the unobservables.

iv) Biprobit model is used to check the robustness of the effect to a different level of selection

on unobservables (Altonji et al. 2005).23

21

According to UNICEF's "State of the World's Children-2009" report, 47% of India's women aged 20-24 were

married before the legal age of 18, with 56% in rural areas. The report also showed that 40% of the world's child

marriages occur in India. 22

Coefficients were also estimated using data just for the biggest state in India, Uttar Pradesh (UP), to minimize the

impact of state-level heterogeneity. 23

For the variable capturing violence, a dummy variable is created for all the districts above the 50th percentile

taking value 1. For the rice-area variable, a dummy variable takes the value 1 for all the districts above the 75th

percentile.

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However, given a low level of mechanization and with the predominance of rain-fed agriculture

in the rice producing area, the selection on unobservables should play a small role.

6. Results

6.1 Baseline

The baseline results, using the full sample, are presented in Table 4. The specifications,

displayed in Column (1-3) in Panel I, include the estimates of the effects of the rice-area variable

on all three measures of violence without controlling for any other covariates. Columns (4-6)

capture the effects of the sex-ratio in a district on all three measures of violence. In these

parsimonious specifications, the rice-area variable has the expected negative effect; for both the

murder rate and the rape rate though the effects are significant only at the 10% level of

significance. The estimate has the expected sign for the riot rate, though it is statistically

insignificant.

The sex-ratio in a district has the expected effects on the murder rate---a finding in

consonance with Dreze and Khera (2000). However, it has an insignificant effect on the other

two variables capturing the incidence of violence. In the case of the riot rate, a more favorable

sex-ratio, rather than reducing ethnic violence, seems to have a negative effect.

In Panel II, where other covariates are controlled for, the estimated coefficients on the

rice-area variable are qualitatively similar to the ones shown in Panel I, for both the murder rate

and riot rate. The estimates, however, are less pronounced in the case of violence against women.

In Panel III, where no covariates are included but the effects of the state level unobservables are

controlled for, the rice-area variable has a more pronounced effect in the case of the murder rate-

--suggesting that the state-level unobservables play a significant mediating role. In the case of

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violence against women, controlling for the state level fixed effects makes the effect of the rice-

area variable smaller. The effect of the sex-ratio variable, after controlling for the state-fixed

effect, has the expected sign and is also statistically significant in most specifications.

In some specifications, a dummy variable is created, which switches on for the states

suffering from the left-wing extremist violence directed against both the regional and the federal

state. This variable also captures the resource swamping effect in which prevalence of the Maoist

Insurgency dilutes the justice system resources. Table 6 shows the estimates of this variable to

illustrate the effect of insurgency on interpersonal, gender, and on the inter-group violence.

Naxalite affected areas have higher murder rate; but have a positive effect on violence against

women; and seems to restrain the propensity for the group-based violence too, but the last two

effects are statistically not significant.

6.2. Controlling for State-level Unobservables

The violence restraining effect of the rice-area variable becomes more pronounced after

controlling for the state-level district invariant unobservables along with all other covariates. The

estimated effect increases from -0.533 in the case of the murder rate, to -0.918 and remains

statistically significant at the 1% level of significance. In the case of the other two variables, the

estimates are not very encouraging. In the case of violence against women, the estimate becomes

positive and insignificant, suggesting a crucial role of unobservables in driving the result found

in the parsimonious specifications. In the case of ethnic or inter-group violence, the estimated

coefficient continues to be of the expected sign but now smaller and statistically significant at the

5% level of significance. It is illustrative to compare the effects of the sex-ratio variable with

those from Dreze and Khera (2000): the estimated coefficient (-0.11) is quite close to the ones

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reported in their paper. However, the effect of a favorable sex-ratio is much smaller (see, Col (1)

in Table 5) in comparison with the effect of the rice-area variable, (-0.918). It is reassuring to

find consistency in the estimated effects of sex-ratio spanning almost a decade and half. Such

consistency supports the optimism of both primatologists and some social scientists about a

positive role that a more balanced gender relation may have on the social health.

One of the stark findings is the positive effect of the institution or norm of early marriage

on violence. The estimated coefficient is -0.237, and it is significant at the 5% level of

significance.24

It seems that the institution of early marriage is helping society to restrain the

young adults' homicidal tendencies. However, it is not effective when it comes to the rape and

riot rate in a district. Though the coefficient continues to be negative in the case of violence

against women, it is positive when it comes to its effect on riot, though both coefficients are not

statistically significant.

The deterrence capacity of the state at a district level is proxied by the availability of

public infrastructure, which is defined by more than 60% of villages in a district having a

sufficient health infrastructure. This variable has the expected negative and significant coefficient

in the case of both murder rate and riot rate, but a positive and close to zero and statistically

insignificant estimated coefficient in the case of the rape rate.

7. Robustness Check

7.1 Controlling for Measurement Error

To control for the possible error in measurement in the main variable of interest, the rice-

24

Data is not available on the proportion of boys getting married at an early age. However, it would not be

farfetched to assume that an early marriage of a girl can act as a proxy for an early marriage of a male-child in a

district.

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area variable; a dummy variable, RiceDummy75, is defined, which switches on for the value

above 75th quantile. These are districts which have historically and predominantly been the rice-

growing districts of the North East and of the South Indian state of Tamil Nadu.

The results are reported in Col.(2), Col.(4), and Col.(6) of Table 5. The estimates of the

coefficient on the rice area variable are very large and statistically significant for both the murder

rate and riot rate. For violence against women, the effect is of the expected sign but not

statistically significant at the conventional levels.

7.2 Non Green Revolution States

One can argue that the estimates of the effect of the rice-area variable would be corrupted

by the introduction of the Green Revolution in some of the Indian states. The states where the

Green Revolution was introduced: Punjab, Haryana, Andhra Pradesh, and Tamil Nadu, are

excluded from the sample, and the estimates are reported in Col.(1) Col.(3), and Col.(5) of Table

6. The results are qualitatively similar in all specifications, including the ones where a dummy

for the rice area variable is used to control of the potential measurement error.

7.3 Bivariate Probit Model25

Biprobit model is used to check the nature of selection of districts into rice cultivation (Altonji

et al. 2005). For the murder rate, a dummy variable is created which switches on for all the

districts above the median of the distribution. For the rice-area variable, a dummy variable is

created which switches on for all the districts above the 75th quantile. Given the low level of

25

The optimization exercise went through smoothly for the main dependent variable of interest in this paper, the

murder rate. For other measures of violence, the rape rate and the riot rate, the maximum likelihood did not

converge. Thus, this method could not be used for those two cases

19

mechanization and the dependence on the rain-fed agriculture, one would expect that the

selection on unobservables would play a small role in the case of the rice-area variable.

If the unobservables that make a district more likely to continue with its dependence on rice-

cultivation, also makes it less prone to violence, then controlling for a negative selection by

constraining ρ to a negative value, the coefficient on the rice area dummy, though continue to

show negative effect, loses its statistical significance with ρ<-0.1. Thus, allowing for a modest

amount of negative selection explains away a statistically significant effect of the rice-area

variable on violence (see Table 7). However, if the experience with the naxalite violence directed

to the state and the federal government machineries is considered, it is quite pervasive in the rice

producing area, and thus, one can argue for a positive selection into the treatment regime.26

When I fix ρ at positive level to control for positive selection, the effect of the rice-area variable

gets more pronounced.

7.4 Controls for Unobservables27

In order to control for the effects of unobserved variables, in two separate specifications I

have included: Attempt-to-Murder rate and Kidnapping rate variables. The results are reported in

Table 8. This provides another round of robustness checks to see if the estimates are driven by

unobservables. The unobservables that affect the three variables that capture violence would

26

The Naxalites or left-wing insurgents in India can be found in the rice-cultivation area. There stated goal is to

throw the state by resorting to violence. I am in the process of investigating the role that the presence of cooperative

infrastructure might have played in bringing down the transaction cost of mobilizing people to stage violent protest

against the state. 27

To rationalize the use of this estimation strategy to control for the unobservables, a simulation study was

conducted by regressing the outcome variables on an endogeneous regressor along with a variable correlated with

the outcome variable on the right hand side. The bias on the endogenous regressor showed monotonically (inverse)

relation with the extent of correlation between the outcome variables and the variable correlated with the outcome

variable.

20

affect these two variables too, thus putting these variables on the right hand side can potentially

control for the district specific unobservables that could not be controlled via the state level fixed

effects. The estimated coefficients of the rice-area variable continue to be qualitatively similar

for the murder rate and riot rate, but flip sign in the case of the rape rate. The estimates in the

case of the murder rate changes from -0.918 to -0.748, continues to be significant at 1% level of

significance, in the model with the Attempt-to-Murder rate on the right hand side. The estimate

on the rice-area variable changes to -.884 with marginally better precision; when the Kidnapping

rate is used as a variable on the right hand side.

The estimates for the others variables, discussed in the baseline section, do not get

changed in any qualitatively significant manner.

7.5 Falsification Test

It would be reassuring to show that the crops other than rice do not have the moderating

influence on violence. First, data from IHDS (2005)28

is used to create variables on the district

areas under both wheat and rice cultivation (data on only 237 districts could be used for this

analysis). When the new rice area variable is used at the place of the old rice-area variable, I find

that the effect on the murder rate continues to be negative, but not statistically significant.

However, when the wheat-area variable is used the effect on the murder rate is positive, but again

not statistically significant at the conventional level of significance (see in online appendix Table

no. 1, 2, and 3).29

28

IHDS collected data on the cultivated area under various crops for each household. From this, a district level

measure of total cultivated area is calculated. Then, the total area under rice and wheat crops is calculated for each

district to get the percentage district-area under both of these crops. The correlation coefficient between the rice-area

variable used earlier, which was available for 437 districts, and this new variable for 237 districts is 0.42. 29

The correlation coefficient between the new rice-area and wheat-area variable is -0.30: indicating somewhat lack

of overlap between the rice area and wheat area.

21

When district level dummy variables30

for the volumes of cash-crops31

and fruits and

vegetables are separately used at the place of the rice area variable, they do show positive effects

on the murder rate, but the estimates are not statistically significant (see online Table 4 and Table

5). This shows that there indeed is something special about the rice-growing areas that make

individuals there less prone to violence.

8 Discussions

8.1 Is Social Capital the Main Mediating Channel?

Theoretically, one of the most convincing accounts for a lower level of interpersonal and

inter-group violence in the rice growing areas is the presence of more social capital. Data on the

variable capturing information on the level of trust from the World Value Survey 2005-2008

(WVS), when categorized on the basis on rice-growing versus non-rice growing countries

provides an interesting pattern. Rice growing countries are found to be quite different from the

non-rice growing areas for the question such as:32

"I now want to ask you how much you trust various groups of people: Using the

responses on this card, could you tell me how much you trust... your neighborhood". With these

options as a possible answer: (1) Trust completely; (2) Trust a little; (3) Not trust very much; (4)

Not trust at all. Lower percentages of the respondents from the traditional rice growing areas

chose (3) and (4) as their response, see Table 9.

Additionally, I control for various measures of social capital used in the literature using

IHDS data set from India. In most cases, the rice area variable continues to exert an independent 30 Both dummy variables switch on for the districts above the 75th quantile of their respective distribution. 31

Major cash-crops in India are: Jute & Cotton, Coconut & Arecanut, Sugarcane, Tea, Coffee, Tobacco,

Spices and Fruits & Potato. 32

http://www.wvsevsdb.com/wvs/WVSAnalizeIndex.jsp

22

moderating influence on violence, although the effect is somewhat less pronounced. This

suggests that the lower incidence of violence in the rice-growing area indeed is mediated through

social capital variables. Table 11 and Table 12 present the results from the fixed-effect

regressions with all the measures of social capital, including three variables measuring different

aspects of trust. These trust variables measure the subjective attitudes toward informal contacts

rather than specific institutions. Table 12 contains the results without these three indicators of

trust-level in a community, as there has been found some inconsistencies in these measures by

some researchers (see, Vanneman et al. 2006).33

Many measures of social capital have the expected moderating influence on violence,

though the variable capturing the associational social capital is more important than the variables

capturing confidence in social, political, and economic institutions.34

Thus, it seems that the available social capital indicators may not be the channel through

which the rice-area variable affects violence. Or, maybe they are not able to capture a more

internalized facet of prosocial preferences, or the non-cognitive ability, like the ability for a

better self-control.

8.3 Biology: A Potential Mediating Factor

Might it be the case that, after all, it is the biology that is one of the crucial mediating

factors making individuals in the rice growing area less prone to violence? In spite of the

discredited theory of genetic disposition toward crime and violence, the literature is coming back

to evaluate the role that human biology and physiology play in creating incentive for committing

33

www.bsos.umd.edu/socy/vanneman/papers/VannemanND06.pdf 34

It should though be noted that only 247 districts could be used for the analysis. Reorganization of states

and districts prevented the full merger of the data with the data utilized for main body of the research.

23

crime and perpetrating violence. Height is shown to affect the propensity for crime in the US;

also, a higher body-weight is shown to have a crime restraining feature. The interpretation of

these mediating biological features is provided through conceptualizing crime as an alternative

labor market. The adverse biological features lower the return to labor in the regular labor

market, thus push individuals toward committing crime (Bodenhorn et al. 2010). In the rice-

growing areas, men are both of a shorter stature and weight less, see Table 10. Given such

observations, it becomes quite striking that one still finds relatively lower level of violence in the

rice-growing areas.

Additionally, another potential mediating factor may be neurobiological. Individuals in

the rice-growing area, given their year-around involvement with skill-intensive labor

requirement, may be neurobiologically better equipped at exercising self-restraints. Bray (1994)

suggests: "In contrast to western agriculture, rice farmers improved their yield by becoming

smarter, by being better managers of their time, and by making better choices: “If you are willing

to weed a bit more diligently, and become more adept at fertilizing, and spend a bit more time

monitoring water levels, and do a better job keeping the clay pan absolutely level, and make use

of every square inch of your rice paddy, you will harvest a bigger crop””. The people who grow

rice have always worked harder than almost any other kind of farmer. Some estimates put the

annual workload of a wet-rice farmer in Asia at 3,000 hours a year (Gladwell 2008). Given that

many instances of homicides are impulsive, individuals in a rice growing area may be better at

controlling their impulses.35

The extent to which such non-cognitive trait gets reflected in a

higher literacy level in a district, the control for the level of literacy still does not make the rice-

area variable any less effective.

35

A detailed report, on the incidences of homicide, in Delhi suggests that 15% of murders are done at the

heat of moments. see, http://timesofindia.indiatimes.com/articleshow/msid-3692804,prtpage-1.cms

24

A more concrete physiological/biological channel that is shown to play roles in violence

is the deficiencies in the crucial micronutrients like zinc and iron.36

The variable, the percentage

of stunted children, captures the extent of malnutrition in a district which proxies for potential

micronutrient deficiencies, is included to investigate this route. The coefficient estimates in the

murder rate equation is positive and statistically significant in some specifications; however, its

inclusion does not affect the rice-area variable in any significant manner, see Table 8.

8.3 Violence and Identity

In their pursuit to bring the role of identity in the fold of economics, Akerlof and Kranton

(2010b, p. 1) write, "Who people are and how they think of themselves is key to the decisions

that they make". Given that the importance of violence in the development of a society, and the

centrality of identity in our decision-making, identity is explored as one of the channels that can

explain violence. The persistent conflict in Africa and other places instigated by the formation of

artificial boundaries or states, and their roles in perpetuation of ethnic conflict to some extent can

be ascribed to the role of identity in violence (Alesina et al. 2009). The WHO's report mentioned

above also makes a very striking observation on the heterogeneous nature of violence across

different areas. While comparing the homicide versus suicide rates, it reports: "In the African

Region and the Region of the Americas, homicide rates are nearly three times greater than

suicide rates. However, in the South-East Asia and European Regions, suicide rates are more

than double homicide rates, and in the Western Pacific Region, suicide rates are nearly six times

greater than homicide rates".37

Strikingly, the areas categorized by the WHO as the Western

36

In a study on the British and the US prisoners, there is found a positive correlation between zinc deficiency

and the elevated level of copper on violent behaviors. 37

http://www.who.int/violence_injury_prevention/violence/world_report/en/summary_en.pdf

25

Pacific Region, has under it the areas that have traditionally been dependent on the labor

intensive rice-agriculture. It seems that the inhabitants of the rice-growing areas have less

exclusionary sense of self; thus, leading them to be less violent towards others.

In the empirical analyses, I have controlled for some of the factors involved in identity-

formation. One particular possibility is that the rice-growing areas may have a more entrenched

caste-system, thus making individuals there adapt to their social status, and therefore leading to a

lower number of social and personal infractions. I control for such mediating effect by using the

percentages of Schedule Castes and Schedule Tribe in a district in the right hand side. The effect

of the rice-area variable remains intact, see Table 8.

Yet another factor, which has been highlighted in the literature relating to identity, is the

higher incidence of violent crime in the urban areas. Glaeser and Sacerdote (1999) suggest that

there is more crime in the cities because cities offer levels of anonymity not found in the rural

areas. However, in the Indian context, there is no significant effect of urbanization on the murder

rate. In fact, the coefficient estimates are negative in many specifications; and its inclusion has

no significant effect on the estimates of the rice-area variable.

8.4 Violence and Culture

Brugger et al. (2009) and Fehr and Hoff (2011) suggest that there has been a keen interest

in understanding the role of culture in shaping the preference. The tremendous spatial variation

in beliefs and values across different states and regions in India provides an ideal set-up to

explore the extent to which society affects the taste for violence.38

Gladwell (2009, p.175) writes,

"Cultural legacies are powerful forces. They have deep roots and long lives. They persist,

38

The saliency of culture was brought out in a study of the higher murder rate found in the American South

during the late 19th and ealrly 20th century by Cohen and Nisbett (1996). Their striking finding was that the

patterns laid down in the English borderlands centuries were continuing to affect behaviors centuries later.

26

generations after generations, virtually intact, even as the economic and social and demographic

conditions that spawned them have vanished, and they play such a role in directing attitudes and

behavior that we cannot make sense of our world without them."39

Owing to its reliance on fertile land and a sustained dependence and saliency of rice-

cultivation---an important facilitating factor in the formation of prosocial behaviors and cultural

norms that strengthen it---India may have acquired the requisite space and ingredients for the

non-violent means of conflict resolutions for millennia. In other parts of the world, primarily in

the poor countries of Africa and Central Asia, such sustained dependence on the skill and labor

intensive agriculture has not been the case (Gladwell 2009). For countries in the "Medium

Human Development" category, India relatively has a low murder rate (Dreze and Khera 2000).

Dill et al. (2008) reports that the homicide rate per million is 71 in the US; 16.2 for the OECD

countries; 20 for China; and 299 for the Russian Federation for the year 2001. While the

corresponding figure for India stands at 36. In a global survey, commissioned by Human Security

Centre to Ipsos-Reid in year 2005, for 11 countries around the world: Brazil, Canada, France,

India, Japan, Russia South Africa, Thailand, Turkey, the UK and the US, it came with a striking

finding about India. Among all these countries, India turned out to be not only the least violent

society, people there also had least actual experiences of violence, see Table 13.

Rice-cultivation seems to be an important reason for a more empathetic and prosocial

preference, an identity woven around restraint, and a neurological ability to control impulses in

India. Furthermore, all these seem to have been further refined and codified in the stories and

39

For illustrative and convincing treatment of the idea that culture legacies cast a long historical shadow, see

Gladwell (2009). Sen (2006) shows dismay at the defiant conclusion that the world has seemed to reach that

culture matters. Probing further, he suggests that culture seems to matter more than it should because of our

tendency to simplify the multifaceted identity that creates, or makes an individual into an easy target for

violence. He does not discuss in much details what are the material factors underpinning violence. This paper

discusses one of the potential material channels through which culture might affect the violence behaviors.

27

epics. These---Dawkinsian ‘memes’40

---facilitate the process of social inheritance and culture

that seem to have helped India to meaningfully address the role of violence in the formation of

institutions.41,42

The open access society is just six-decades old in India. Yet, in a sharp contrast to the

social and political unrest in its neighborhood, India has experienced a well-functioning

democracy in most years of its modern polity. The openness, accessibility, and economic growth

of India may be because of the violence mitigation role played by its culture, which in turn can

be explained by the geography of the area. Thus, if culture does causally affects the propensity

for the violent behavior, then policies should be directed at creating such cultural capital for

individuals to utilize.

39. The British scientist Richard Dawkins coined the word `meme' to discuss the idea of spread of ideas and cultural

practices through writing, speech, gestures, rituals or other imitable phenomena, see en.wikipedia.org/wiki/Meme.

40. Akerlof and Shiller (2009) talk about important roles that narratives and stories play both at an individual and

societal level. 41. As mentioned in the introduction, success of a society depends on the stories it creates about itself. The hard-

earned success of democratic polity in India is one such story (Vajpeyi 2009).

28

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31

Table 1: Summary Statistic (Full Sample)

Variables Mean

Std.

Dev. Min Max

Murder Rate 35.79 19.00 2.84 130.73

Attempt-to-Murder 30.55 26.62 0.00 237.79

Total Kidnapping 19.77 18.23 0.00 139.24

Rice Area 4.31 5.47 0.00 28.00

Naxalite 0.34 0.47 0.00 1.00

South 0.30 0.46 0.00 1.00

Malnutrition 19.38 9.19 1.30 71.70

Anemia 43.49 15.32 2.40 84.10

Total SC (%) 17.24 7.32 1.45 50.13

Total ST (%) 9.41 15.40 0.00 78.56

Pop Density 534.36 1197.81 13.00 24231.00

Inequality (Gini) 25.31 5.46 12.56 51.41

FLP 0.33 0.13 0.06 0.66

Unemployment Rate 2.45 2.80 0.00 16.69

Literacy 53.62 12.30 0.00 85.75

Sex-Ratio 941.16 59.40 821.00 1147.00

Low St. Living (%) 48.46 19.20 3.20 91.50

Urbanization 22.58 15.50 1.20 100.00

Diversity (Religious) 24.13 14.66 0.27 69.41

Public Infrastructure 32.48 29.26 0.00 100.00

Islam 10.55 10.37 0.00 68.45

Population (in Mill.) 2.03 1.29 0.22 9.64 Note: 437 districts in the 17 states are used in the final data analysis. Data on violence come

from Crime in India (1998). Information on demographic characteristics comes from the

Census of India (2001), 55th round of the National Sample Survey (1998).

32

Table 2: Distribution of Area under Rice

across Sample States

State Name Mean S.D Min Max

Andhra Pradesh 4.442 4.625 0.719 19.215

Bihar 5.601 2.753 1.082 14.491

Chhattisgarh 3.592 2.105 1.084 9.290

Gujarat 1.213 1.891 0.000 5.774

Haryana 7.135 7.163 0.000 23.542

Jharkhand 2.602 1.240 0.807 4.747

Karnataka 1.807 1.580 0.005 5.268

Kerala 1.783 1.772 0.195 5.812

Madhya Pradesh 0.491 0.685 0.000 3.203

Maharashtra 0.94 1.524 0.000 5.391

Orissa 4.003 2.402 0.820 9.529

Punjab 17.71 7.045 5.226 28.000

Rajasthan 0.069 0.153 0.000 0.590

Tamil Nadu 5.551 4.833 0.000 17.829

Uttar Pradesh 5.121 3.676 0.023 14.470

Uttaranchal 1.428 2.638 0.191 10.096

West Bengal 16.385 6.578 1.835 26.482

Total 4.318 5.478 0.000 28.000

Note: Data is from the Rice Research Institute, Patna, Bihar. Bold rows are the

traditional rice growing area; largely rain-fed; ones in italics are the

Green-Revolution states

33

Table 3: Comparison of Low Rice Producing vs.

High Rice Producing Districts VARIABLES Low Rice Area High Rice Area Total

Sex-Ratio 944.366 931.839 941.156

Literacy 53.799 53.1 53.619

Urbanization 23.203 20.767 22.579

Poverty 49.574 45.222 48.458

SC 15.765 21.533 17.243

ST 11.722 2.702 9.41

Rice Area 1.75 11.745 4.312

Population (in

mill.) 1.894 2.414 2.027

Malnutrition 20.189 17.023 19.378

Naxalite 0.317 0.411 0.341

Diversity 22.801 27.986 24.13

Inequality 25.305 25.32 25.309

Unemployment 2.405 2.567 2.447

Public

Infrastructure 32.738 31.719 32.477

Early

Marriage 33.057 34.184 33.346

Islam 9.917 12.386 10.549

Note: High Rice Area is defined the district with area under rice cultivation above 75

th quantile

34

Table 4: OLS Estimates (1) (2) (3) (4) (5) (6) VARIABLES Murder Rape Riot Murder Rape Riot

Panel I RiceArea -0.533‡ -0.622‡ -5.064 (0.253) (0.314) (3.201) Sex-ratio -0.101† 0.000500 0.0595 (0.0426) (0.0235) (0.233) Panel II RiceArea -0.797* -0.252‡ -5.235† (0.220) (0.127) (2.160) Sex-ratio -0.109* -0.0205 0.238 (0.0348) (0.0172) (0.169) Panel III RiceArea -1.195† -0.0214 -1.107‡ (0.525) (0.136) (0.621) Sex-ratio -0.110† -0.00461 -0.229* (0.0512) (0.0126) (0.0699) N 437 437 437 437 437 437

Note: Panel I uses no covariates except for the rice area variable. Panel II controls for all the covariates except the state fixed effects. Panel III uses the rice variables along with the state fixed effects. Cluster-Robust standard errors in parentheses; * p<0.01, † p<0.05, ‡ p<0.1.

35

Table 5: Fixed Effects Models (1) (2) (3) (4) (5) (6) VARIABLES Murder Murder Rape Rape Riot Riot

RiceArea -0.918* 0.023 -1.263† (0.160) (0.102) (0.557) RiceDummy75 -8.952* -0.976 -10.909† (0.844) (0.869) (3.919) Sex-Ratio -0.118* -0.114* -0.015 -0.013 -0.259‡ -0.256 (0.030) (0.029) (0.009) (0.008) (0.145) (0.147) Malnutrition 0.300 0.306‡ -0.024 -0.029 -0.626 -0.611 (0.179) (0.171) (0.072) (0.072) (1.008) (0.996) Diversity 0.150 0.157 0.087 0.090 -1.528 -1.523 (0.120) (0.123) (0.062) (0.063) (0.912) (0.917) Islam -0.189 -0.222 0.060 0.061 1.647 1.602 (0.136) (0.133) (0.113) (0.114) (1.355) (1.348) Inequality (Gini) 0.125 0.201 0.073 0.074 -1.288 -1.188 (0.171) (0.161) (0.076) (0.076) (1.321) (1.323) Unemployment -0.917* -0.963* -0.525‡ -0.541‡ 0.137 0.094 (0.287) (0.296) (0.266) (0.265) (1.304) (1.323) Public Infrastructure

-0.114‡ -0.121‡ 0.008 0.005 -0.474‡ -0.480‡

(0.060) (0.060) (0.037) (0.038) (0.235) (0.228) Early-Marriage -0.273† -0.259† -0.096 -0.093 0.793 0.808 (0.124) (0.119) (0.086) (0.086) (0.781) (0.781) Literacy -0.222 -0.239 0.071 0.075 1.196 1.168 (0.165) (0.165) (0.049) (0.051) (1.517) (1.508) Urbanization -0.080 -0.075 0.024 0.019 -0.422 -0.410 (0.080) (0.079) (0.047) (0.047) (0.388) (0.376) Poverty -0.357† -0.350* 0.104‡ 0.101 -0.965‡ -0.952‡ (0.124) (0.118) (0.059) (0.059) (0.467) (0.465) SC (%) 0.130 0.113 0.118‡ 0.120‡ 1.273‡ 1.249‡ (0.156) (0.164) (0.057) (0.058) (0.686) (0.674) ST (%) 0.319† 0.302‡ 0.178 0.175 1.788 1.768 (0.150) (0.149) (0.108) (0.108) (1.632) (1.633) Population (in mill.) -1.376 -1.515 -0.362 -0.317 1.681 1.441 (1.131) (1.161) (0.550) (0.553) (2.055) (2.178) Constant 183.583* 177.343* 17.385 16.233 343.799* 336.751* (34.377) (33.197) (11.136) (10.852) (104.781) (106.509) N 437 437 437 437 437 437 R-squared 0.300 0.298 0.081 0.082 0.105 0.104 Number of state 17 17 17 17 17 17

Note: Cluster-Robust standard errors in parentheses; * p<0.01, † p<0.05, ‡ p<0.1; the state fixed effects are controlled for in all the columns. Other district level variables have done good jobs of capturing the effects of the state-level unobservables.

36

Table 6: Fixed Effects Models without Green Revolution States (1) (2) (3) (4) (5) (6) VARIABLES Murder Murder Rape Rape Riot Riot

RiceArea -1.181* -0.129 -0.537 (0.232) (0.130) (0.819) RiceDummy75 -9.696* -2.066‡ -8.399 (1.286) (1.081) (5.964) Sex-Ratio -0.118* -0.118* -0.011 -0.009 -0.303‡ -0.297 (0.031) (0.032) (0.009) (0.009) (0.167) (0.169) Malnutrition 0.272 0.275 -0.044 -0.048 -0.719 -0.736 (0.210) (0.208) (0.086) (0.086) (1.210) (1.196) Diversity 0.136 0.135 0.105 0.108 -1.929 -1.920 (0.163) (0.160) (0.081) (0.082) (1.174) (1.181) Islam -0.208 -0.247 0.064 0.062 1.815 1.804 (0.158) (0.147) (0.129) (0.130) (1.649) (1.633) Inequality (Gini) 0.103 0.148 0.067 0.074 -2.172 -2.141 (0.251) (0.231) (0.103) (0.102) (1.587) (1.577) Unemployment -0.955* -1.009* -0.629‡ -0.641‡ 0.349 0.298 (0.303) (0.295) (0.310) (0.311) (1.714) (1.707) Public Infrastructure -0.086 -0.090 0.029 0.025 -0.760‡ -0.773† (0.065) (0.067) (0.061) (0.061) (0.360) (0.354) Early-Marriage -0.286† -0.284† -0.112 -0.108 0.744 0.759 (0.125) (0.122) (0.098) (0.097) (0.875) (0.867) Literacy -0.219 -0.247 0.101 0.102 1.223 1.223 (0.200) (0.200) (0.061) (0.062) (1.667) (1.652) Urbanization -0.014 -0.003 0.059 0.056 -0.165 -0.174 (0.081) (0.082) (0.052) (0.052) (0.481) (0.467) Poverty -0.333† -0.328† 0.151‡ 0.151‡ -0.987 -0.989 (0.153) (0.143) (0.070) (0.070) (0.644) (0.640) SC (%) 0.095 0.096 0.140‡ 0.138‡ 1.213 1.207 (0.201) (0.216) (0.070) (0.074) (0.818) (0.829) ST (%) 0.324‡ 0.317‡ 0.171 0.168 1.941 1.929 (0.162) (0.161) (0.113) (0.112) (1.795) (1.797) Population (in mill.) -1.338 -1.558 -0.405 -0.404 1.306 1.308 (1.331) (1.343) (0.658) (0.649) (2.235) (2.314) Constant 183.978* 182.893* 10.603 9.077 420.656* 414.589† (39.876) (41.101) (13.279) (13.133) (137.449) (139.31) N 352 352 352 352 352 352 R-squared 0.326 0.326 0.092 0.094 0.121 0.122 Number of states 13 13 13 13 13 13

Note: Cluster-Robust standard errors in parentheses; * p<0.01, † p<0.05, ‡ p<0.1; The Green-Revolution states (Punjab, Haryana, Andhra Pradesh, and Tamil Nadu) may have biased the estimates. Estimates are very similar to the estimates from Table 15.

37

Table 7: Sensitivity Analysis: Bivariate Probit Results with Different

Assumptions Concerning Correlation Among the Disturbances

Correlation of the Disturbances

Unconstrained ρ ρ= - 0.3 ρ= - 0.2 ρ = - 0.1 ρ = 0 ρ = 0.1 ρ= 0.2 ρ= 0.3

Murder Rate

0.047 (5.150) -0.166 -0.333 -0.497* -0.657 * -0.814* -0.969* -1.12*

* p<0.01, † p<0.05, ‡ p<0.1; ρ=0 denotes an univariate probit regression; ρhat shows the estimated correlation between the unobservables in the violence equation (ε) and the unobservables affecting the probability of the percentage of a district area under rice cultivation (ν).

38

Table 8: Fixed Effects Models with Proxy Variables to Control for

Unobservables

(1) (2) (3) (4) (5) (6) VARIABLES Murder Murder Rape Rape Riot Riot

Rice Area -0.748* -0.884* 0.086 0.051 -1.068‡ -1.105 (0.187) (0.137) (0.107) (0.096) (0.556) (0.676) Sex-Ratio -0.087* -0.106* -0.003 -0.005 -0.224 -0.206 (0.019) (0.026) (0.010) (0.008) (0.149) (0.185) Malnutrition 0.216‡ 0.271 -0.055 -0.047 -0.722 -0.759 (0.112) (0.162) (0.061) (0.056) (0.938) (0.907) Diversity 0.126 0.157 0.078 0.093‡ -1.557 -1.497‡ (0.103) (0.121) (0.063) (0.051) (0.909) (0.765) Islam -0.171 -0.250 0.067 0.010 1.668 1.365 (0.106) (0.146) (0.124) (0.110) (1.348) (1.287) Inequality (Gini) 0.104 0.064 0.065 0.022 -1.312 -1.571 (0.121) (0.176) (0.075) (0.081) (1.322) (1.330) Unemployment -0.972* -0.822† -0.545‡ -0.446 0.073 0.580 (0.279) (0.284) (0.274) (0.269) (1.365) (1.515) Public Infrastructure -0.111† -0.094 0.009 0.025 -0.471‡ -0.384‡ (0.048) (0.063) (0.034) (0.039) (0.239) (0.196) Early-Marriage -0.220† -0.289† -0.076 -0.109 0.854 0.721 (0.080) (0.113) (0.087) (0.087) (0.753) (0.603) Literacy -0.217 -0.314‡ 0.073 -0.006 1.201 0.767 (0.145) (0.169) (0.052) (0.040) (1.459) (1.423) Urbanization -0.140† -0.114 0.001 -0.004 -0.492 -0.581 (0.061) (0.075) (0.037) (0.038) (0.399) (0.481) Poverty -0.260† -0.317† 0.140‡ 0.138‡ -0.852‡ -0.775 (0.107) (0.127) (0.069) (0.070) (0.481) (0.480) SC (%) 0.072 0.114 0.097‡ 0.106‡ 1.207‡ 1.203‡ (0.123) (0.145) (0.051) (0.059) (0.615) (0.679) ST (%) 0.302† 0.260 0.171‡ 0.129 1.768 1.514 (0.129) (0.150) (0.090) (0.085) (1.587) (1.434) Population (in mill.) -0.265 -0.966 0.053 -0.020 2.961 3.592 (0.741) (0.960) (0.442) (0.420) (1.951) (2.161) Attempt To Murder Rate 0.341* 0.127† 0.393† (0.084) (0.047) (0.153) Kidnapping Rate 0.360‡ 0.300* 1.678* (0.173) (0.036) (0.111) Constant 139.738* 171.475* 0.997 7.295 293.238† 287.384‡ (24.807) (32.050) (13.771) (11.485) (115.178) (144.624) N 437 437 437 437 437 437 R-squared 0.494 0.375 0.150 0.214 0.119 0.194 Number of state 17 17 17 17 17 17

Note: Cluster-Robust standard errors in parentheses; * p<0.01, † p<0.05, ‡ p<0.1; Estimates are very similar to the estimates from Table 15

39

Table 9: Response to the Survey Question---Do You Trust Your

Neighborhood?

World Value Survey (2005-2008)

Countries Trust

completely Trust a little Not trust

very much

Not trust at

all Number of Countries

Rice Growing 0.210 0.600 0.170 0.010 8 Non Rice Growing 0.210 0.510 0.210 0.070 44

Note: Traditional Rice-growing countries: South Korea, India, China, Taiwan, Thailand, Indonesia, Vietnam, Malaysia; Non-Rice Growing Countries: Rest of the 57 countries covered in the World Value Survey 2005-2008, some countries relevant data were not available.

Table 10: Height and Weight data from the National Family Health Survey-

III (2005)

Summary Statistics: Mean

Rice Height (cm) Weight (kg)

1 163.8 (6.405) 56.7 (6.54)

0 164.8 (11.43) 58.1 (11.65) Note: UP, Bihar, Assam, WB, Orissa, and TN are the traditional rice-growing states.

40

Table 11: Fixed Effects Models with Social Capital Variables with Trust-

variables (1) (2) (3) (4) (5) (6) VARIABLES Murder Murder Rape Rape Riot Riot

RiceArea -0.549† 0.158 -1.066 (0.248) (0.119) (0.764) RiceDummy75 -5.399‡ -0.638 -6.284 (2.656) (1.355) (12.272) Sex-Ratio -0.093* -0.091* 0.000 0.002 -0.403 -0.403 (0.013) (0.015) (0.014) (0.015) (0.313) (0.315) Malnutrition 0.124 0.139 -0.088 -0.090 -0.854 -0.829 (0.125) (0.124) (0.069) (0.071) (1.042) (1.047) Diversity 0.073 0.081 0.068 0.073 -1.111 -1.110 (0.113) (0.117) (0.044) (0.044) (0.742) (0.761) Islam -0.037 -0.047 -0.081 -0.090 1.231 1.233 (0.114) (0.116) (0.074) (0.074) (0.970) (0.989) Inequality (Gini) 0.172 0.206 -0.088 -0.088 -1.217 -1.169 (0.177) (0.165) (0.110) (0.111) (1.439) (1.429) Unemployment -1.139† -1.112† -0.756‡ -0.818† -0.252 -0.097 (0.491) (0.505) (0.361) (0.362) (1.377) (1.371) Public Infrastructure -0.081 -0.080 -0.028 -0.029 -0.578‡ -0.573‡ (0.061) (0.061) (0.041) (0.042) (0.297) (0.299) Early-Marriage -0.201‡ -0.190 -0.061 -0.057 0.559‡ 0.566‡ (0.113) (0.110) (0.067) (0.065) (0.305) (0.307) Literacy -0.093 -0.091 0.011 0.010 1.316 1.322 (0.085) (0.081) (0.032) (0.031) (1.675) (1.681) Urbanization -0.127 -0.127 0.039 0.028 -0.728 -0.707 (0.091) (0.087) (0.046) (0.050) (0.667) (0.652) Poverty -0.115 -0.116 0.097 0.082 -0.572 -0.545 (0.122) (0.126) (0.069) (0.069) (0.630) (0.618) SC (%) 0.196 0.185 0.127 0.132 2.454† 2.429† (0.183) (0.173) (0.121) (0.120) (0.906) (0.894) ST (%) 0.271† 0.264† 0.147‡ 0.140‡ 2.248 2.251 (0.106) (0.109) (0.070) (0.070) (1.616) (1.635) Population (in mill.) -0.421 -0.527 -0.229 -0.088 3.170 2.753 (1.141) (1.157) (0.400) (0.439) (4.491) (4.333) Constant 109.997* 105.372* 14.927 15.384 503.461 496.155 (19.205) (21.799) (10.434) (10.136) (317.053) (317.370) N 267 267 267 267 267 267 R-squared 0.485 0.483 0.302 0.299 0.238 0.237 Number of states 16 16 16 16 16 16

Note: Cluster-Robust standard errors in parentheses; * p<0.01, † p<0.05, ‡ p<0.1; Estimates are very similar to the estimates from Table 6 and Table 8. The econometric specifications are similar to the ones in Table 8 with the Attempt-to-Murder rate variable on the right hand side.

41

Table 12: Fixed Effects Models With Social Capital (without Trust Variables) (1) (2) (3) (4) (5) (6) VARIABLES Murder Murder Rape Rape Riot Riot

RiceArea -0.598‡ 0.155 -1.298 (0.303) (0.119) (0.868) RiceDummy75 -5.932‡ -0.657 -8.940 (2.900) (1.369) (12.106) Sex-Ratio -0.080* -0.077* 0.000 0.002 -0.397 -0.395 (0.014) (0.015) (0.014) (0.015) (0.291) (0.293) Malnutrition 0.125 0.140 -0.086 -0.090 -0.750 -0.720 (0.131) (0.129) (0.067) (0.071) (0.987) (0.984) Diversity 0.070 0.081 0.067 0.073 -1.151 -1.142 (0.105) (0.109) (0.044) (0.044) (0.757) (0.777) Islam -0.018 -0.031 -0.081 -0.089 1.228 1.221 (0.106) (0.109) (0.075) (0.075) (0.999) (1.020) Inequality (Gini) 0.186 0.227 -0.090 -0.089 -1.253 -1.185 (0.192) (0.180) (0.113) (0.114) (1.390) (1.375) Unemployment -1.192† -1.139† -0.770† -0.826† -0.590 -0.399 (0.465) (0.471) (0.340) (0.340) (1.392) (1.359) Public Infrastructure -0.066 -0.065 -0.027 -0.029 -0.583‡ -0.579‡ (0.062) (0.061) (0.042) (0.044) (0.303) (0.303) Early-Marriage -0.161 -0.148 -0.062 -0.057 0.481 0.494 (0.117) (0.112) (0.058) (0.057) (0.313) (0.320) Literacy -0.102 -0.099 0.011 0.010 1.267 1.273 (0.097) (0.091) (0.031) (0.030) (1.632) (1.638) Urbanization -0.099 -0.099 0.039 0.028 -0.727 -0.707 (0.085) (0.082) (0.043) (0.048) (0.609) (0.595) Poverty -0.127 -0.123 0.094 0.081 -0.679 -0.649 (0.116) (0.119) (0.070) (0.071) (0.587) (0.573) SC (%) 0.164 0.152 0.126 0.132 2.346† 2.313† (0.194) (0.179) (0.121) (0.119) (0.872) (0.847) ST (%) 0.259† 0.250‡ 0.147† 0.140‡ 2.178 2.174 (0.114) (0.117) (0.067) (0.067) (1.561) (1.579) Population (in mill.) -0.682 -0.777 -0.242 -0.090 2.716 2.284 (1.115) (1.126) (0.415) (0.457) (4.016) (3.887) Constant 99.050* 93.144* 15.468 15.581 538.084 527.811 (19.021) (19.409) (12.045) (11.933) (314.916) (314.198) N 267 267 267 267 267 267 R-squared 0.468 0.467 0.302 0.299 0.231 0.230 Number of states 16 16 16 16 16 16

Note: Cluster-Robust standard errors in parentheses; * p<0.01, † p<0.05, ‡ p<0.1; Estimates are very similar to the estimates from Table 6 and Table 8. The econometric specifications are similar to the ones in Table 8 with the Attempt-to-Murder rate variable on the right hand side.

42

Table 13: Expectations of Violence: Experience versus Reality

Country Percentage of respondents who think it likely they will become victims of violence in the next year

Percentage of respondents with a household member who had been violently attacked or threatened in the last five years

Brazil 75 27

Thailand 50 7

South Africa 48 24

France 33 16

Turkey 30 9

United States 17 12

Canada 16 14

Japan 14 4

Russia 13 16

India 10 1

All countries 22 12 Note: Data comes from a global survey, which polled more than 6000 people worldwide, commissioned by Human Security Centre to Ipsos-Reid in year 2005, for 11 countries around the world: Brazil, Canada, France, India, Japan, Russia South Africa, Thailand, Turkey, the UK and the US (Human Security Report (2005), Human Security Centre, University of British Columbia, Canada