More Inequality, More Killings: The Maoist Insurgency in Nepal
Mani Nepal
Tribhuvan University and SANDEE
Alok K Bohara
University of New Mexico
Kishore Gawande
Texas A&M University
__________________________________
Mani Nepal is Associate Professor of Economics at Central Department of Economics,
Tribhuvan University, and Senior Environmental Economist at SANDEE, PO Box 8975, EPC
1056, Lalitpur, Nepal ([email protected]). Alok K Bohara is Professor of Economics
at University of New Mexico, MSC 05 3060, Albuquerque, NM 87131-0001, USA
([email protected]). Kishore Gawande (the corresponding author) is Professor and Roy and
Helen Ryu Chair of Economics and Government at Bush School of Government and Public
Service, Texas A&M University, College Station, TX 77843-4220 ([email protected]).
We thank the editor and three anonymous referees for insightful comments that have improved
the paper. Conflict data were gathered from Informal Sector Service Center (INSEC), a not-forprofit human rights organization that publishes reports of incidences of human rights violation in
Nepal. Data and code for replication of all results are available at: http://kishoregawande.net/
1
Abstract
The hypothesis of inequality as the source of violent conflict is investigated empirically in the
context of killings by Nepalese Maoists in their People's War against their government during
1996-2003. The dependent variable is the total number of people killed during that period by
Maoist rebels in each of 3,857 villages. Inequality is measured by the Gini, the Esteban-Ray
polarization index, and four other between-groups indexes. Using models with district fixedeffects, and instrumenting for endogeneity of the inequality measures, we find strong evidence
that greater inequality escalated killings by Maoists. Poverty did not necessarily increase
violence. Education moderated the effect of inequality on killing, while predominance of farmers
and of Nepali speakers exacerbated it. We find evidence that more killings occurred in populous
villages, lending support to the idea that violence was directed at expanding the Maoist franchise
by demonstrating that opposition to the monarchy and elites in power was possible to achieve.
Keywords: Economic Polarization; Vertical and Horizontal Inequality; Killings; Instrumental
Variables; Negative Binomial; Fixed Effects
2
More Inequality, More Killings: The Maoist Insurgency in Nepal
This paper studies the relationship between conflict and inequality. While it is not the first study
to do so, its focus on inequality as a source of conflict has been debated vigorously.1 The two
most widely cited studies of internal conflict, Fearon and Laitin (2003) and Collier and Hoeffler
(2004), dismiss inequality as a factor behind political violence. While sociologists, political
scientists, and economists have contributed a rich literature in their attempts to answer the
question of whether inequality causes conflict, the literature remains ambiguous about this
relationship.2
We believe this is because much of this literature has inappropriately explored conflict by
means of cross-country regressions. The role of inequality has no clear theoretical basis in that
aggregate setting. Our theory will argue that a pre-condition for violent civil conflict is for
groups – especially repressed groups – to overcome the costly collective action problem of
organizing for revolt against the state’s machinery. Such collective action problems are resolved
locally, and local revolts must survive and proliferate before they become a revolution. The
sensible unit of analysis is a local unit, where mobilizing for collective action is most possible.
The relationship between inequality and conflict is likely to be seen clearly at that micro level,
not at the country level.
A serious empirical problem with searching for determinants of conflict in a crosscountry setting is that the institutional heterogeneity across those units of observations is vast.
Such heterogeneity is unobserved, making quantitative inference about the relationship between
conflict and inequality difficult, if not impossible. Our study uses a within-country sample,
1
Cross-country studies of conflict and inequality pervaded the early literature since the seminal paper by
Sigelman and Simpson (1977). Attempts to reassess this relationship have been made, among others, by
MacCulloch (2004), Mueller (1985), Mueller and Seligson (1987), Selbin (2002), Wang et al. (1993),
Weede (1986, 1987), and Williams and Timberlake (1984).
2
See Lichbach’s (1989) survey.
3
making it better suited to credibly quantifying the inequality-conflict relationship because
institutional heterogeneity is largely controlled in the sample. Further, policy recommendations
are meaningful when they are placed within an institutional context, or more precisely, within the
context of the absence of institutions. Our study is unique in this regard.
The motivation for studying the inequality-conflict relationship is clear. If inequality is
causally related with violence, then policies directed at reducing inequality will also reduce
violence. That objective motivates this paper. The setting of our study is the Maoist rebellion in
Nepal, which has claimed over ten thousand lives and displaced twenty times as many persons
since it began in 1996. We use the regional variation across 3,857 villages in Nepal to explore
the association between conflict and inequality. Exploiting intra-country variation across
villages to address the association between inequality and conflict sidesteps the tremendous
heterogeneity that confronts users of cross-country data, due to a variety of cross-cultural norms,
institutions, and unique historical settings. Our village-level sample is more homogeneous with
respect to such unobservables.
This study differs from the current empirical literature in a number of respects. First, the
hypotheses are motivated by theory, and the empirical models test them. The theory borrows
from separate but well-developed literatures, and emphasizes three facts: the entrapment of
society in an equilibrium characterized by deep economic inequality that worsens over
generations, creating an economically polarized underclass; occurrence of events that expose
weaknesses in the opposition and that act like triggers to bind the underclass, preparing them for
forcible redistribution; the mechanism by which anarchy sustains as it transitions society into a
new equilibrium. The theory produces a testable hypothesis about inequality as the driving force
behind violent redistribution.
Second, our study explores inequality using measures that go beyond the popular Gini
index. We measure economic polarization as proposed in Esteban and Ray (1994, 1999), which
4
emphasizes horizontal inequality, or inequality between groups. We also investigate whether
poverty (distinct from inequality) causes violence. Third, since our dependent variable is
measured as count data -- the number of deaths inflicted by Maoist forces in each Nepalese
village between 1996 and 2003, we use a count-data model to econometrically analyze the
determinants of Maoist killings. Fourth, we wish to go beyond associations, to the causal
relationship implied by theory. We account for the joint determination of violence and
inequality, by using carefully selected instrumental variables to purge the inequality variables of
endogeneity bias. Two distinct sets of instruments are used to demonstrate robustness causal
inference. Fifth, even though our sub-national sample suffers less unobserved heterogeneity than
do cross-country data, we employ district fixed-effects in our count data models.
We are aware of recent studies of Maoist violence in Nepal by Acharya (2007), Do and
Iyer (2009), Macours (2006, 2011) and Murshed and Gates (2005). The data and methods in our
study are distinctly different from the data used in those. To begin with, we assemble villagelevel data on Maoist killings while their studies are at the more aggregate district level.
Collective action is more easily mobilized at the local rather than at the district level, as our
theory will argue, and hence the village is the relevant unit of observation. We use fixed-effects
to account for district-level heterogeneity, while none of these studies employ fixed effects. We
instrument inequality variables for endogeneity and demonstrate that our instruments are indeed
up to the task of allowing causal inference. The Murshed-Gates study’s finding of an association
between the Gini and violence is therefore established to be a causal relationship, deserving
attention from policymakers.3 Our new finding is that inter-group economic inequality strongly
causes Maoist violence. Some of our other results complement the results in these studies, while
others contradict them. In contrast to Do and Iyer’s finding of no association between ethnic
polarization and Maoist violence, we find a strong positive association of Maoist killings with
3
Neither the Murshed-Gates nor the Acharya studies take into account endogeneity of regressors.
5
being primarily Nepali-speaking.4 Acharya (2007) finds politics (number of parliamentary seats
won by the precursor to the Maoist party) to be associated with violence. We add to this the
finding that more populous villages experience greater violence. A reason for this is a Maoist
calculation to expand its franchise and voting base by showing weaker sections of society that
organized violence can be a very effective instrument of opposition to the elites. Macours (2006,
2011) finds increased land inequality to be a source of relative deprivation, motivating recruiting
drives of Maoist through mass abductions of Nepali youth. Our finding of an association
between consumption inequality and Maoist killings complements her finding. Taken together,
these studies and ours provide solid evidence on variables associated with the origin and
escalation of Maoist violence. The variety in the data, methods and issues addressed in these
studies and ours provides a collection of findings on the Maoist problem in Nepal that should be
useful to policymakers and the international community in targeting the source of the problem.
We begin with the historical and institutional context within which we carry out this
study. We then suggest a theory of inequality as a cause of conflict, which leads to testable
hypotheses. An estimating model is laid out, and our village-level Nepal data are described in
detail. The theoretical hypotheses are then tested, and a causal relationship between economic
polarization and conflict established. We also explore new hypotheses about Maoist strategy and
influences, and conclude.
Background
Although Nepal is a small country, the lessons from its attempts at economic and political
emergence are anything but small. The Nepal experience provides a classic setting for
understanding why other emerging countries have hesitated to experiment with political
4
The government recognizes a large number of languages, among them Nepali, Maithili, Bhojpuri,
Tharu, Tamang, Newari, Hindi, Gurung, Limbu. Nepali, the primary language of about 50% of the
population, is the official language.
6
liberalizations. Never since its unification in 1768 has Nepal experienced such a violent division
within its own rank and file. Ganguly and Shoup (2005) provide an account of experiments with
democracy, their failure to improve the average citizen’s livelihood, and the rise of the Maoists.
The birth of democracy in Nepal can be traced to the 1950s when the prominent freedom
fighter B. P. Koirala made his group’s pro-democracy stance vocally and actively felt. They
succeeded in pushing the monarchy, at the time led by King Mahendra, into making political
parties legal and allowing elections. An overwhelming electoral victory in 1959 brought
Koirala’s party the Nepali Congress (NC) to power, only to see the monarchy wrest it back and
ban political parties. Mahendra’s son Birendra assumed the monarchy in 1972. In the face of a
people’s revolt – the “jana andolan” -- in 1990 he lifted the ban on political parties. The andolan
was a watershed: by enabling key figures of the Maoist movement, heretofore a banned
underground movement, to legitimately participate politically it initiated a multi-party system.
The democratic reforms implemented under the 1990 constitution were illusory to the
majority. They failed to address fundamental problems facing Nepalese citizens – great
inequality and widespread poverty were reflected in high infant mortality, lack of basic amenities
like power and clean water, and importantly, a palpable rural-urban divide. These shortcomings
were dire in the countryside. Upper-caste Hindu-led parties pursued interests distant from the
median voter. The median voter was illiterate and had stronger ties to his ethnic community than
to the nation. The conditions were ripe for a violent revolt pitting Maoists against the monarchy
and its supporting elites.
In 1991 the communist United People’s Front (UPF) was the third largest political party
in the lower house of Parliament. In 1994, a militant faction broke away from the UPF parent,
ran a parallel party, boycotted the mid-term elections, and threatened to start a violent campaign.
On February 13, 1996 the splinter party started the People’s War. It began with simultaneous
attacks on three remotely stationed police outposts, a bank branch, a soft-drinks bottling plant, a
7
liquor factory, and a private house. A guerilla strategy of establishing bases in the rural and
remote areas fared the insurgents well in redistributing captured land from absentee landlords to
the locals, who could farm the land under a system of cooperatives. Police brutalization of
Maoist sympathizers triggered widespread support of the rural population to the Maoist cause.
What started as an isolated incident in 1996 triggered a devastating ten-year conflict that claimed
more than 13,000 lives and displaced more than 200,000 people.
Nepal’s vacillating transition may be viewed usefully through the lens of Bueno de
Mesquita et al.’s (2003) selectorate theory. Bueno de Mesquita and Smith (2009) posit that
regimes of resource-poor countries like Nepal are more likely to share power than are regimes in
resource-rich countries. True to that prediction, Nepal’s monarchy acceded to Koirala’s nascent
movement that formed the Nepal Congress party. The monarchy then co-opted NC party elites
into the selectorate, expanding the grabbing hands of the selectorate, but not the franchise. Once
Maoist party leaders understood the NC elites were compromised by the monarchy, their options
narrowed down to either seizing power or backing down (perhaps for generations). Emboldened
by the experience of the andolan, a militant splinter-group threw down the glove.
Did Maoist leaders of this group expect the spark they lit in 1996 to ignite a decade-long
war? Intended, probably; expected, probably not. The festering generations-long cleavages had
created the preconditions for revolt in each village. A rural “third estate”, not unlike that in prerevolution France, existed.5 However, it is hard to imagine the decade-long War was centrally
coordinated. More likely, once cracks and weaknesses in the system became exposed,
individuals and close-knit groups independently updated their personal beliefs. Villages with
5
Thapa and Sijapati (2003, Ch. 3) reveal the governance of this estate by a shadow Maoist government
that had developed it from the grassroots since 1949. Its structure was motivated by Mao’s “three-in-one”
principle: the Party comprised 40% of the membership, an army 20%, and front organizations the
remaining 40%. Simkhada and Oliva (2005) document other sources on the Maoist organization.
8
cohesive groups joined the 1996 call to arms. As their numbers swelled, the rebellion spread to
other villages and the guerilla movement sustained.6
A move by parliament to abolish the monarchy, initiated in 1996, came into force in
2008. The problem with this nascent democracy is that underlying problems remain, with the
Maoist movement never far from taking up arms to solve the problems they perceive to persist.
There exists an uneasy peace after the signing of the Comprehensive Peace Accord between the
Nepalese government and the Maoists on November 21, 2006. The likelihood that another
outbreak catapults out of control over an extended period remains, unless its underlying causes
remain unresolved.
Theory and Hypotheses
Economic inequality as the primary driver of conflict is at the crux of theories advanced by Boix
(2003) and Acemoglu and Robinson (2005) about how political regimes evolve. Nepal in many
ways exemplifies the Boix-Acemoglu-Robinson mechanics. Their logic of the emergence of
democracy is this. In democracies, the poor are able to impose higher taxes on the rich, and
therefore redistribute to themselves a higher share of GDP than they can in non-democracies. As
a result, the poor are pro-democracy, while the elites are strongly opposed it. In nondemocracies, excluding the poor from political power poses the threat of revolution. To prevent
this becoming reality, the elite redistribute just sufficiently to prevent revolution. But since such
redistribution is not guaranteed into the future, the prevention of revolution is temporary.
Permanently circumventing the threat of revolution requires that the elites currently in power
credibly commit to sufficient future redistributions. This is accomplished by a cooperative
regime change in which, for example, voting rights are granted. The birth of democracy,
6
Gurung (2003) and Mahat (2005) describe the Maoist People’s War in Nepal in detail. A complete
bibliography of writings on this topic by Nepali authors is summarized in Simkhada and Oliva (2005).
9
however, does not make redistribution permanent, since elites have the incentive and ability to
take power by force, for example by coup d’etat. On their part, the poor correctly understand
that agreeing to a low level of future taxation makes democracy more palatable to elites, and
prevents them from reverting to autocracy. If the commitment to low taxes is not credible about
future levels of taxation, when taxes become high coups become likely.
That is why inequality matters.7 Unequal societies fluctuate in and out of democracy
because they are unable to resolve the conflict between the elites and the poor about the
acceptable level of redistribution through taxation. When the poor are in power, they enforce a
high level of redistribution, to which the circumstances it seizing power back.
The threat of anarchy or revolution in the Boix-Acemoglu-Robinson mechanism often
remains latent. But, what circumstances compel anarchy to materialize? This central question
motivates us. We find answers in well-developed, but separate, literatures. The three main
ingredients in our theory are, in sequence:

Long-lived inequality

How the collective action problem is overcome

Determinants of a sustained anarchy
We take these, in turn.
Long-lived inequality
Sociologists like Gurr (1970) have attributed “relative deprivation” to long-lived inequality. It is
a feeling capable of uniting the underclass for rebellion if they are given the means rebel. Why
inequality persists in many countries but erodes in others, is a central theme of dynastic models
of intergenerational utility (see e.g. Piketty 2000). Low intergenerational mobility rates are the
7
Higher optimal taxes in more unequal societies are predicted with welfare maximizing governments in
Sandmo (1976), and with the median voter determining the optimal tax in Meltzer and Richard (1981).
10
prime cause of persistent inequality in these models.8 Among the reasons advanced for why
intergenerational mobility rates are especially low in developing countries, Banerjee and
Newman’s (1993) theory is popular. They attribute it to the unavailability of credit to the poor
who are unable to furnish collateral against their borrowing. Banerjee and Newman’s model
produces “mobility traps”. A large mass of poor workers produces a low initial wage
equilibrium (credit constraints ensure their non-participation in capital markets). This persists
over generations as the large demand for capital produces even higher interest rates, preventing
the poor from borrowing and participating in wealth creation through capital markets. The rich
can and do participate in high yielding projects requiring large investments. As they grow richer,
the dual economy becomes increasingly unequal and the economic cleavage deepens.
In a number of poor countries, the initial distribution of land ownership has perpetuated
low mobility traps through this mechanism.9 Behrman et al. (2001) show that unequal access to
8
A low correlation between the earnings of a generation and its subsequent one– the intergenerational
earnings correlation (IGEC) – is evidence of low intergenerational mobility. Mulligan (1997) finds an
IGEC of 0.50 in U.S. data, implying that if parents are five times richer (than their cohort average), their
children will be 2.2 times richer, and grandchildren only 50% richer. The correlation is closer to 0.70 in
many developing countries (Dunn 2007). Strikingly, Dunn (2007) finds that in Brazil the IGEC of sons
aged 40 with fathers aged 50 is almost 1, indicating no intergenerational mobility!
9
Sociologists Granovetter and Tilly (1988) advanced a view in which five actors – capitalists, workers,
organizations, households, and government – “contend over the rewards of labor in the three arenas of
employment status, jobs, and labor market primarily by attempting to influence the process of ranking and
sorting” (Granovetter and Tilly, p. 180). The relative bargaining strengths of these actors determine their
ranking and sorting in the labor market. The result is an equilibrium income inequality, consumption
inequality, and wealth inequality. The threat points of these actors in the bargaining game are determined
by their ability to solve internal collective action problems, as they seek to exert the influence during the
sorting and ranking process. Ebbs and tides in these threat points are therefore the source of historical
changes in inequality. For example, landlords’ monopsony power gives rise to rural inequality, which
persists and worsens as landlords unite and strengthen their position. But, if property rights are not
enforceable publicly or privately, villagers could organize and take land by force.
11
educational opportunities is a source of low mobility traps: in Brazil, the likelihood of a whitecollar occupation is 2.6 times higher if one’s father had a similar occupation than if he had a
blue-collar occupation. The numbers are 3.5 for Mexico, 2.8 for Peru, but only 1.5 for the
United States. It is probably worse in Nepal: while the share of agriculture in the national
income is only one-third, 75% of the Nepalese population is engaged in this sector. Land
ownership is highly unequal in agriculture, especially in districts with the most cultivable land
(Macours 2006, Thapa and Sijapati 2003).
Collective action
While redistribution as a consequence of inequality has been featured in recent models,10 violent
redistribution requires a distinctly different calculus. How the problem of collective action, that
leads collective violence, is solved is central to this calculus. Overcoming the collective action
problem requires a personal assessment by each potential participant, of the probability of
success and failure if they took up arms, and its rewards or consequences. The personal
assessment depends crucially on whether other individuals participate as well. Otherwise
inequality produces a dormant force, perhaps a social movement, but not a militant revolt.
“Horizontal” or inter-group inequality is beginning to be seen as the more powerful
driver of violent conflict than “vertical” inequality among individuals (Stewart 2002). Our
theory provides a simple explanation: horizontal inequality solves the problem of collective
action naturally. Common identity – tribal, religious, or caste – bonds each group (Akerlof and
10
Alesina and Rodrik (1994), Milante (2004), Perorri (1993), and Persson and Tabellini (1994) are
prominent examples. However, violent-conflict-as-redistribution is fundamentally different from
economic redistribution featured in these papers, mainly because they assume a set of rules and norms
within which redistribution occurs (e.g. bribing officials as in Bardhan (1997)). Violent-conflict-asredistribution requires the absence of those institutions (Easterly 2001) or a breakdown of those rules and
norms (Keefer and Knack 2002).
12
Kranton 2000). Inequality separates these groups, and more inequality polarizes them. The
greater their polarization, the greater is the potential for violent inter-group conflict.
Transitions to democracy provide numerous opportunistic triggers that can spur violent
collective action. By exposing weakness in the state’s institutional muscle used for suppressing
discontent, such transitions trigger collective movements. Cracks in the status quo create a oncein-many-generations opportunity for the repressed to take control of institutions for
redistribution. The 1990 andolan was such an event for the Maoists because it exposed
deficiencies in the monarchy’s control over the state’s resources. As the rural classes updated
their chances of success, they grew restive in villages across the country.
Violence through sustained anarchy
Once revolts start, they can be short-lived or they can be long and indecisive. What determines
whether revolts are put down or become drawn out? Revolts can transition a stable
(authoritarian) system to another stable system or a similar one. What stable system lies at the
end? Hirshleifer’s (1995) theory answers these questions.11 Two groups (Maoists (M) and
Government (G)) divide their available resources between productive efforts EM and EG that
produce income, and fighting efforts FM and FG that are used to forcibly acquire resources or
protect from such acquisition. The relative amount of fighting resources FM /FG determines the
relative proportion pM/pG in which the total country’s resources get shared, but via
“decisiveness” parameters mM and mG. If both m’s are low, they favor entrenchment in which
defense wins, while high m’s produce large payoffs to fighting effort and yield rapid results. In
winner-take-all systems, therefore, m is high for at least one group, leading that group to expend
a high F and win. In pre-1959 Nepal, high mG prevailed in the winner-take-all monarchy. By
forcing the monarchy to recognize the NC party, Koirala’s movement effectively lowered mG.
11
Grossman and Kim (1995) refine the model further to situations where property rights are weak.
13
That is, the same fighting effort by the monarchy now brought fewer rewards. The 1990 andolan
lowered mG even further as a third (Maoist) party was allowed to emerge.
The first prediction from Hirshleifer’s model (1995 p. 33) is that sufficiently low m’s sustain an
anarchic system: the two groups embark upon a prolonged and indecisive conflict. It is a fairly
accurate theoretical portrayal of Nepal’s People’s War.12
To sum up, long-lived inequality polarized Nepal’s society into two groups, breeding
animus that fueled Hirshleifer’s anarchy mechanism. Before the polarized underclass in Nepal
became united at the national level, they were more closely bonded within their villages. The
collective action problem was more easily resolved at that micro level. The trigger that set in
motion Maoist killings was a combination of the andolan that exposed vulnerability in the
opposition, and the breakdown of power-sharing negotiations with the elites in the nascent
democracy. Knowledge that the elites had failed to stamp out the first (isolated) wave of Maoist
violence in 1996 only affirmed the weakness of the elites. These incidents lowered private
assessments of mG sufficiently to galvanize revolt in more villages, beginning a long period of
killings across Nepal’s villages in the People’s War. We state our main hypotheses, as:
H1: Greater inequality causes more killings.
A second prediction from the model (Hirshleifer 1995, p. 33) is that unless a minimum
equilibrium income ensues, anarchy breaks down into an order in which the richer party
dominates. Thus, where poverty levels are high, we should see no conflict, for it is not viable.
Sen (1992, Ch. 7) stresses the view of poverty is a deprivation condition beyond the income
space, as a failure of basic capabilities. Failure to possess minimum acceptable capabilities only
reinforces Hirshleifer’s prediction. Our second hypothesis is thus:
12
In the model, anarchy can theoretically break down in multiple ways. In Nepal, the Maoists eventually
won representation as their resource base experienced a surge in numbers. The War’s denouement is not
our focus in this paper. The fact that it was a prolonged, violent anarchy is more relevant to us.
`
14
H2: Poverty prevents people from engaging in conflict due to a destitution constraint.
Thus, poverty is associated with no killings or fewer killings.
Hypothesis H2 is relevant to the greed versus grievance debate initiated by Collier and Hoeffler
(2004) and Fearon and Laitin (2003). They find opportunities for rebellion to be the greater
force behind civil wars, not inequality. Our results contradict their findings – grievance is the
major drivers of civil conflict, and the dimension of grievance that is important for civil conflict
is inequality. In Murshed and Tadjoeddin’s (2009) view, violent conflict is fundamentally due to
the breakdown of institutions. Poverty and growth failure due to this breakdown simply
accelerates the degradation of the social contract. While we agree, we add that unequal societies
are much more likely to violently react to the degradation.
Data and Variables
The hypotheses motivate empirically explaining violent conflict in Nepalese villages as:
KILLINGS = f (INEQUALITY, POVERTY),
[1]
Greater inequality is hypothesized to cause more killings. The sign on POVERTY is predicted to
be zero or negative; villages are simply too poor to mount an organized force and are bound by a
destitution constraint. However, it could be positive if poverty is a cause espoused by those able
and willing to seek redistribution by force on behalf of those in poverty. Whether it is the former
or the latter case should be of interest in understanding Maoist strategy.
Nepal is administratively divided into 75 districts, with each district subdivided into over
3900 village development committees (VDCs) or villages. Our sample covers over 95% of the
Nepalese villages. The finer village-level detail distinguishes our study from the Do-Iyer,
Acharya, and Murshed-Gates studies. The dependent variable KILLINGS is the total number of
persons killed in each village by Maoists during the period 1996-2003. The conflict data are
drawn from annual reports of the Informal Sector Services Center (INSEC), a non-profit national
15
human rights organization. The annual reports contain details such as the date of each event that
resulted in human casualties, the circumstances surrounding the event, and the number of deaths.
Based on these reports, the casualty data are summed for the eight-year period. Since time-series
data on inequality, polarization, and poverty are non-existent at the village-level, we use the
village cross-section to make inferences. Heterogeneity at the district level is controlled using
fixed effects.13 Since the period over which the dependent variable is measured leads other
variables, it reduces (but does not eliminate) concerns about endogeneity.
We use two main measures of economic inequality, the Gini and a polarization measure.
The Gini measures vertical inequality, while the polarization measure quantifies horizontal
inequality. The construction of these measures requires consumption expenditures at the
household level, covering all villages in Nepal. We combine two sources of information in order
to obtain household consumption expenditures. The 2001 Nepal Population Census administered
a long form to one in every eight households in Nepal, covering over 500,000 households.
While the long form elicited information on a number of household and community
characteristics it did not ask about income or expenditures. Household consumption data,
however, are accurately available for a more limited number of households from the World
Bank’s Living Standard Measurement Survey for Nepal conducted in 1995-96 jointly with
Nepal’s Central Bureau of Statistics (CBS), to which we refer as the Nepal Living Standard
Survey (NLSS). The NLSS collected information on household income and expenditures plus
socio-economic and demographic characteristics from a national sample of 3,373 rural and urban
households selected from 274 communities, using a probability-proportional-to-size (PPS)
sampling plan. Together with an NLSS community-level survey, that collected information
about characteristics of the communities in which these households resided, we have a rich set of
variables that explain household expenditures. From the 3,373-houshold NLSS data, we
13
The 75 districts are listed in a Supplementary Information section available from the authors.
16
estimate a model predicting expenditures using covariates common to both the NLSS and the
census.14 This estimated model is used to predict consumption expenditures for the half million
census households. These predicted expenditures are used to construct our inequality and
poverty measures.
The Gini index for each village is computed using a variant of Deaton (2000, p. 139).15
Esteban and Ray’s (1999) concept of polarization is designed to measure rivalry among
homogeneous groups (the Gini is a special case of their polarization index, were the group size is
one). Three characteristics must be reflected in the polarization measure in the Nepal context: (i)
in each village, the measure must partition the distribution of consumption spending into more
than one group cluster, and preferably not too many; (ii) there must be a high degree of intragroup homogeneity as measured by a large mass within each partition; and (iii) there must be a
high degree of inter-group heterogeneity, as measured by significant distances between the
partitions. Satisfying these conditions yields a measure that may not necessarily be correlated
with the Gini since it seeks to measure potential hostility or antagonism among groups, and
therefore captures a horizontal dimension of inequality different than the Gini. In our case, the
Esteban-Ray economic polarization measure maps the distribution of consumption spending by
households in a village into a value for the village. The higher this value, the greater is the
degree of polarization within the village. The polarization index for a village is:
14
Covariates include socioeconomic and demographic characteristics, for example, ethnicity, housing
characteristics, location, sources of lights, whether there is electricity, and source of water. The model
fits the NLSS sample well -- the R2 is 0.502. A detailed appendix with the estimated model and data
sources is available from the authors. This micro-level estimation method for predicting consumption is
developed in the small area statistics literature (see e.g. Ghosh and Rao 1994). Elbers, Lanjouw and
Lanjouw (2003) describe the theory behind this technique.
15
More detail on the Gini, Esteban-Ray polarization, and poverty measures are in Supplementary
Information section available from the authors.
17
Li
POLARIZATION ( )  K 
i 1
Nj

j 1
1
i
 j | yi  y j |,
[2]
where | yi  y j | is the size of absolute difference in the consumption expenditure of households i
and j,  i is the ith household’s proportional weight16 and Lk is the number of households
sampled from kth village. K is a positive constant. α measures the intensity of group
identification, termed the “degree of polarization sensitivity” by Esteban and Ray, and shown to
range between 0 and 1.6. The Gini index is the special case of [2] with α = 0. The larger is α,
the greater is the departure of the inequality measure from the Gini. Using the kernel estimation
method of Duclos, Esteban and Ray (2004), we construct an Esteban-Ray polarization measure
at the village level based on α=1. The measure is termed POLARIZATION(α=1). Finally,
poverty is measured using the Foster-Greer-Thorbecke (FGT) poverty-gap index for the year
1995-96 to calculate the percentage of households in a village below the poverty line.
While the idea of horizontal inequality (HI) (Østby 2008; Stewart 2002) is captured in the
Esteban-Ray polarization measure (Murshed and Tadjoeddin, 2009) , it is possible that divisions
based explicitly on religion, language and caste partition society into those that are upwardly
mobile and those that aren’t. Were Maoist killings driven by inequality across such partitions?
To see whether this was so, we construct four HI measures. Three of these are based on the
measure used by Cederman et al. (2010):
HI (g) = (ln (yg / ))2,
where yg is the per capita income of group g (in the village), and
is average per capita income
over the sample of villages. We construct this measure for three traditionally underprivileged
groups g: (i) lower caste (non- Brahmin or non-Chhetri), (ii) non-Nepali speaking, and (iii) nonHindu. The fourth HI measure, based on Mancini (2008), partitions based on ethnicity:
16
Since the census sample includes only one in eight households, we construct the household’s
proportional weight using information about population size of each village and the size of the censussampled households.
18
GCOV
1
where R is the number of ethnic groups in a village,17
villages,
is overall mean of income across all
is the mean income for ethnic group r, and pr is group r’s population share.
Methodology
Villages are likely to share the characteristics of districts to which they belong – ethnic
composition, political representation, culture, and socio-economic backgrounds are likely to be
common to villages within a district. In order to account for unobserved heterogeneity we
include district-level fixed effects for the seventy-five districts.18
Since the dependent variable KILLINGS is measured as count data, an appropriate
econometric specification is the Negative Binomial (NB) model. It is preferred over the Poisson
model for over-dispersed count data (Greene 2007), which characterizes our sample.19 The
conditional mean function (the “link” function) of the NB model is log linear, that is, the
conditional mean of KILLINGS, E(KILLINGS), is modeled as log-linear in the regressors. The
models we specify below are therefore specified in terms of this link function. It also makes
clear how we admit fixed effects into the NB model, an issue that the methods literature has
debated and only recently agreed on this solution.20
17
Seven ethnicities are used to partition the population of each village: Brahmin/Chhetri, Tamagurali
(Tamang, Magar, Gurung, Rai, and Limbu), Dakasa (Damai, Kami, Sarki), Newar, Muslim, Terai Caste
(lower “untouchable” caste from Terai), and Other Caste.
18
Including fixed effects for the 75 districts disables the use of variables that are otherwise interesting but
can only be measured at the district level, for example government handouts and group networks.
19
Empirical tests preferring NB over the Poisson model for our data are available from the authors. The
killings data are also skewed, with a number of villages experiencing zero or few killings, and some
villages experiencing killings in large numbers. We have performed a number of robustness checks on
our results, including zero-inflated Poisson models.
20
Estimating NB panel models using the “xtnbreg” command in Stata does not account for fixed effects
in the usual sense. Rather, as in Hausman, Hall and Griliches (1984) the fixed effects enter the model via
19
The baseline model of killings includes an inequality measure and district fixed effects:
ln[E(KILLINGSvd )] = β1 INEQUALITYvd + β2 POVERTYvd + ud + evd ,
[3]
where KILLINGSvd is the number of deaths inflicted by Maoists in village v belonging to district
d. In (3), ln[E(KILLINGS)] is the log of expected killings. ud contains the 75 district fixedeffects. evd is a random unobserved error that we assume to be identically and independently
distributed across observations, conditional on the regressor and fixed effects. The issue variable
for testing Hypothesis 1, INEQUALITY, is measured as GINI, POLARIZATION(α=1), and the
four HI measures. The issue variable for testing Hypothesis 2 is POVERTY.
Next, since KILLINGS are likely to be higher in more populous villages, we include the
log of population as a regressor. There are two reasons for this. Do and Iyer, for example, scale
their measure of killings by population. Since we use count data model we do not need to scale
KILLINGS, and by including (log of) population as a regressor we do not need to constrain its
coefficient to one, as implied by the Do-Iyer scaling.
ln[E(KILLINGSvd )] = β1 INEQUALITYvd + β2 POVERTYvd
+ β3 ln(POPULATIONvd) + ud + ev.
[4]
There is another potentially important reason to include population. Strategically, it made sense
for Maoists to widen their support base with those who felt powerless or disenfranchised. This
could be achieved either by making pre-campaign promises of redistribution once the Maoists
won at the polls, or via more extreme measures designed to demonstrate to the weaker segments
their power against the elite. The former strategy was unlikely to deliver support and votes as a
cynical public would simply receive pre-campaign promises as cheap talk. The Maoist calculus
was one of expanding their franchise through planned acts of violence that would demonstrate to
the dispersion parameter, not the conditional mean function, i.e. the NB link function (Greene 2007).
Allison and Waterman’s (2002) simulations show that explicit fixed-effects dummies as regressors in the
usual negative binomial model (“nbreg” in Stata) adequately account for the heterogeneity across units
(here, villages). We estimate our NB models using true fixed effects.
20
the long-subdued masses that the elite’s hold over resources and institutions was tenuous and
temporary. The use of violence as a strategy to consolidate support was best achieved by
demonstrating their power and potency against elites in the most populous villages. If the
Maoists were simply a band of pillagers, it made more sense to rob villages which were less
populous and therefore weaker. A positive coefficient on ln(POPULATION) would affirm the
idea that Maoist killings were strategically directed at expanding their real membership, which
would serve them well later at the polls.
Ignoring other relevant variables causes omitted variables bias in the parameter estimates.
From a policy perspective, we may also be ignoring important variables that may help alleviate
the problem that inequality poses for violence. Fortunately, we have available village-level
variables from 2001 that minimize the omitted variables problem and also allow us to explore
new hypotheses. These variables are: average years of schooling (EDUCATION), average
months of employment (EMPLOYMENT), percent farmers in the village (FARMER), whether
primary language is Nepali (NEPALI), and whether the village is primarily rural (RURAL). Our
extended fixed-effects model include all these variables in the vector Xvd :
ln[E(KILLINGSvd )] = β1 INEQUALITYvd + β2 POVERTYvd
+ β3 ln(POPULATIONvd) + Xvd B + ud + evd .
[5]
Endogeneity
A major concern, not treated in the literature with the gravity it deserves, is the endogeneity of
the focus variables, here inequality. Miguel, Satyanath and Sergenti (2004) draw attention to the
fact that most studies of conflict wrongly presume that the main variable of interest causes
violent conflict without even testing for the variable’s exogeneity. It is likely that shocks to the
error term, for instance due to a sudden outbreak of violence in a region, are correlated with
similar movements in the inequality variables GINI and POLARIZATION. If, for example,
21
there is significant out-migration of wealthy landlords or high-income families in response to
sudden outbursts of violence, then the two inequality measures are negatively correlated with the
error term, and their coefficient estimates are biased downward.
We instrument for the endogenity of GINI and POLARIZATION(α=1) using three
village-level instrumental variables (IV) from the 2001 census that we believe to be uncorrelated
with shocks to the error term. These are: percent households operating agricultural land
(%WithAGLAND), percent of households in which women own land (%WithFEMALELAND),
and the average number of big head livestock owned by women (BIGLIVESTOCK). To qualify
as justifiably excluded instruments, these variables must not be themselves capable of logically
explaining variation in KILLINGS. From the 1995/96 and 2003/04 Nepal Living Standard
Surveys, Macours (2006) finds that land ownership was stable between those two years in Nepal,
and hence household’s land access is largely exogenous. From the 2001 Nepal Demographic
and Health Survey, Allendorf (2007) finds that women’s land rights empowered women and
benefitted young children’s health. Thus, women who owned land were likely to be empowered
to have the final say in household decisions. If these instruments belonged directly in the
regression, Allendorf’s findings should imply that %WithFEMALELAND and
BIGLIVESTOCK negatively associate with Maoist killings – villages in which women were
deprived of land and assets would be ripe for revolt. 21 We find the opposite to be the case.
Whether households operate land, and in which households women own land and
livestock, are determined by traditions and cultures specific to villages: Macours (2006) finds
land markets in Nepal to be thin, and changes in land ownership to be determined by inheritance
or intra-family transfers. We do not have any reason to think that these long-held customs
should be affected, systematically or otherwise, by shocks to KILLINGS. They are thus
21
Ideologically, Mao had himself championed gender equality since the beginning of his regime. The
1950 Marriage Law in China abolished the feudal marriage system, and the 1954 Constitution guaranteed
women equal rights with men in all spheres of life (Mackerras et al. 1998).
22
exogenous. More importantly, we believe these village-specific institutions play an important
role in explaining the variation in inequality. Formal tests of overidentification restrictions
demonstrating our instruments are justifiably excluded, are carried out in the empirical section.
A second requirement of instruments is that they not be weak, that is, they be strongly
correlated with the inequality variables. Specifically, if the instruments are correlated with other
independent variables – for example RURAL or district fixed effects -- then they may be
incapable of explaining variation in the inequality and poverty conditional on those regressors,
proving to be weak instruments. We report weak instrument diagnostics to indicate when the
instruments are weak and when they are not.
Finally, we emulate Miguel, Satyanath and Sergenti’s identification strategy by using
regional rainfall data to construct a second set of instruments. Our rainfall data are five years
apart for the years 1980, 85, 90, and 95.22 We use it to compute rainfall variance in each region
as a measure of rainfall shocks, which are clearly exogenous to killings. These rare data are,
however, unavailable at the village level, since they are recorded (incompletely) at 281 rainfall
stations scattered across Nepal. We are able to map them into the 75 districts. Thus, rainfall
variance may only be used in models without fixed effects. We report these results as well.
They demonstrate the robustness of our village-level IV results.
Results
Descriptive statistics for all variables used are reported in Table 1. Over the 1996-2003 period,
an average of 0.682 persons per village were killed by Maoists. The Maoists main targets were
the police and army, the most striking example being the February 2002 killing of over 125,
including 76 police officers and 48 soldiers (the then Royal Nepal Army owed loyalty to King
22
Data are collected by Government of Nepal’s Department of Hydrology and Meteorology, from 281
meteorological stations.
23
Gyanendra), in the town of Mangalsen in the mountainous Accham district in Western Nepal
(Popham 2002). The consumption Gini ranges between 0.03 to 0.40, with a sample mean of
0.238. Widespread poverty in the villages (44.7% are below the poverty line) keeps the Gini
from worsening. The population is largely uneducated with 3.608 years of schooling on average
per resident. 34.3% are farmers, and 50.5% speak Nepali as their primary language. Most
villages in the sample are rural. Of note are the within-district standard deviations reported in
the last column. A comparison with the corresponding sample standard deviations indicates
there is considerable within-variation in the data, so that district fixed effects do not wipe out
most, or even much, of the variation. Rainfall variance and inverse variance are measured only
at the district level. When we use them with our village data, we drop the fixed effects.
Uninstrumented Estimates
We begin in Table 2 with OLS results from a log-linear model that include district fixed effects.
The dependent variable is ln(KILLINGSvd +1) so we can take logs where KILLINGS=0. The
conditional mean in the IVNB models – which we adopt – is log-linear in the regressor, and the
results are interpreted similarly. While the purpose of Table 2 is to find a model that best fits the
data, the OLS results are themselves of interest if it is believed that GINI and POLARIZATION
are exogenous. For example, an initial distribution of wealth determined exogenously far back in
history may be principally responsible for current inequality outcomes. While empirically
impossible to prove, theoretical support for the idea that initial conditions perpetuate inequality
was noted in dynastic models of intergenerational utility.
The left part of Table 2 experiments with a sparse specification that includes only the
fixed effects plus poverty (as our hypotheses demand), and another that includes log population,
so that we may explore the idea of Maoist expansion of their franchise. The fixed effects capture
only 15% to 23% of the total variation in ln(KILLINGS), indicating the considerable within-
24
district variation in the data. The OLS estimate of 1.947 on GINI indicates that an increase of
0.04 in GINI (one standard deviation change) causes a 7.7% increase in expected Maoist killings,
or a total of 202 additional deaths over the eight-year period. The estimate of 3.599 on
POLARIZATION(α=1) indicates that a 0.01 increase (one sd change) increases expected Maoist
killings by 3.6% or 95 deaths over the eight-year period. While statistically significant, these
numbers are neither large nor slight. The negative binomial model with fixed effects (NB-FE) is
better suited for explaining the count data.23 The coefficient on GINI is not statistically
significant in the NB-FE model, while the coefficient on POLARIZATION(α=1) is larger than in
the OLS model. The estimate of 11.17 indicates that a 0.01 increase in POLARIZATION(α=1)
escalates expected Maoist killings by 11.2% or 295 over the period.
Despite the attention paid to poverty in the literature, including in the well-known greed
and grievance debate (Collier and Hoeffler 2004; Fearon and Laitin 2003; Murshed and
Tadjoeddin 2009), we find that poverty did not drive Maoist killings in Nepal. Our findings
accord with Hypothesis H1 and H2, affirming inequality, not poverty, drives violence. Further,
horizontal or inter-group inequality (POLARIZATION) is a more compelling force than vertical
or individual inequality (GINI). We reiterate the reason for this, embedded in the second
component of our theory: collective action becomes a less demanding problem if a polarized
underclass closely identifies with many others through a pre-existing bond. This is what
POLARIZATION measures.
The extended models on the right include, in addition, EDUCATION, EMPLOYMENT,
FARMER, ETHNICITY and RURAL. They affirm the above findings: greater horizontal
inequality is associated with more killings, while poverty matters not at all. The new variables
contribute significant explanatory power to the model, and do not confound inference about our
23
The overdispersion parameter α is greater than 1, indicating the data prefer NB over a Poisson model.
25
focus inequality and poverty variables. All subsequent models are therefore based on the
extended models, since the data favor their inclusion by any model selection criteria.
Instrumental Variables (IV) Estimates
Considering inequality to be exogenous, simply because it was determined in the past, would
greatly understate its impact. Just because there were no killings does not imply there was no
animus. Animosity and inequality co-existed through history, and the latent variable, animus,
was always jointly determined with inequality. Since killings were realizations of that latent
variable (once collective action was resolved), killings are correctly modeled as endogenous.
Instrumental variables are therefore needed to disentangle the effect inequality had on killings.
With the available data, we claimed that the village-level instruments %WithAGLAND,
%WithFEMALELAND, BIGLIVESTOCK are justifiably excluded. Two diagnostics back this
argument. Hansen’s chi-squared statistics from the log-linear IV models in Table 3 fail to reject
the null of no correlation between the instruments and the error term. It should be noted that this
test itself relies on the validity of at least some of the instruments. In an auxiliary regression that
includes the three instruments in the extended NB model, %WithFEMALELAND is estimated
with the wrong sign (positive), and BIGLIVESTOCK with a statistically insignificant
coefficient. %WithAGLAND is also estimated with a puzzling negative sign since operators (as
different from owners) of land were basically bonded laborers.24 Clearly, more than one of these
instruments is valid, and the Hansen statistics consequential.
Kleibergen and Paap’s (2006) weak-identification statistic is a generalization of the firststage F-statistic for the joint test that the three instruments all have coefficients equal to zero (see
Appendix Table A1). Staiger and Stock’s (1997) thumb-rule that F > 10 overcomes the weakinstrument problem, is satisfied when instrumenting GINI and POLARIZATION(α=1). Using
24
See Table A2 contained in a Supplementary Information section available from the authors.
26
Stock and Yogo’s (2005) small-sample diagnostics we can make more precise statements. With
our sample size, if Kleibergen-Paap F>14, the bias in the IV estimates is less than 5% of the bias
in the uninstrumented OLS estimates. The large F’s in Table 3 therefore instill confidence in
making causal statements about the impact of these measures on Maoist killings. The bias in the
other horizontal inequality (HI) measures is larger with the village-level instruments, but smaller
with the district-level rainfall instruments.
The IVNB estimates on the inequality variables in Table 3 are much larger, indicating
that the log-linear models vastly understated their impact on Maoist killings. The estimate of
35.29 on GINI indicates that an increase of 0.04 in GINI (a one within-sd change) is associated
with a 141.2% increase in expected Maoist killings, or a total of 3,713 additional deaths over the
eight-year period. The estimate of 123.6 on POLARIZATION(α=1) indicates that a 0.01
increase in that measure escalates expected Maoist killings by 123.6% or 3,251 deaths over the
eight-year period. Our results solidly assert the validity of hypothesis H1 about the dire
consequence of inequality on violence. With district level rainfall instruments (without FE), the
numbers are alarming. An increase of 0.01 in POLARIZATION(α=1) increases expected Maoist
killings by 209.5% or 5,497 deaths during the period.
POVERTY is associated with more killings in the IVNB model with GINI – a one
within-sd increase in POVERTY is associated with 736 additional expected Maoist killings.
However, in models with POLARIZATION(α=1), POVERTY is not statistically significant.
Vertical inequality leaves some variation to be explained by poverty, but horizontal inequality
swamps the impact of poverty.25 Hypothesis 2, that overwhelmingly poor villages hit a
destitution constraint and are incapable of collective violence, thus continues to be supported by
25
Do and Iyer (2009) estimate 23-25 conflict-related deaths due to a 10% increase in the district-level
poverty rate. We find no effect in our village-level data once POLARIZATION(α=1) is included.
27
the IV results. While this may be taken to imply that organized Maoists did not care to feed the
poor by stealing from the rich, we think the data indicate greater ambition.
In all models with POLARIZATION(α=1), the coefficient on ln(POPULATION) is
positive.26 It supports the idea that Maoist killings were strategically directed at expanding their
membership, which would serve them well later at the polls. In that sense, the Maoist movement
in Nepal is distinct from grassroots rebellion in other countries. The fight for control over quasiinstitutions and the possibility of doing it at the polls led to such a calculus (also observed by
Reynal-Querol 2002). Kalyvas (2006) has rejected the hypothesis that violence is most intense
in the most militarily contested areas. We agree. The finding about ln(POPULATION) implies
violence is most intense in the most politically contestable areas. The Maoists desired to become
a competing political party, not a fringe movement of marauders who redistributes by looting
locally. The possibility of political control, in turn made possible by the presence of quasidemocratic institutions, made the Maoists movement potent. At its height, the Maoist movement
ran a parallel government in the villages, replacing dysfunctional government institutions.
Styled to achieve what Mao did in 1930s China, the insurgency in Nepal is similar to Central
American insurgencies and distinct from anti-elite movements in Africa. It is a contest for
control over institutions and political power, not simply wealth.
The ETHNICITY variable (used effectively by Reynal-Querol in a cross-country study of
conflict) captures ethnic polarization in Nepalese society. Nepali is the language spoken by the
traditional ruling class, the Ranas and Shahas. Non-Nepali speaking minorities perceive Nepali
to be a basis for discrimination and domination over their ethnic identity (Bhattachan and
Pyakuryal, 1996). The strong positive coefficients on ethnicity show that identity based on
26
Log population is statistically insignificant in models with GINI due to their near-collinearity – after
partialing out fixed effects, their correlation equals 0.651. In contrast, the partial correlation of
POLARIZATION(α=1) with log population is 0.207.
28
ethnicity may have been the glue that solved the collective action problem in Nepal’s villages.
Our finding about ethnicity stands in contrast with Fearon and Laitin (2003) and Do and Iyer
(2009), neither of whom finds significance for it. This source of polarization is not just relevant,
but long-lived, and takes political will even in economically advanced countries to stamp out.
In Table 4 we explore IVNB models with horizontal inequality measures that are distinct
from POLARIZATION(α=1). Inherent in these measures are two Esteban-Ray-like
mechanisms. Shared language, religion or caste bonds groups of people, and partitions society
based on those bonds. They co-exist so long as they are not polarized. But economic inequality
drives some partitions further from each other until society forms into polarized groups, charged
with animus. Uniting for anarchy and violence then becomes more probable.
With both sets of instruments, the HI measures are all positive, and statistically
significant, with one exception.27 The coefficient of 11.65 on HINEQUALITY(R), measuring
inequality between Brahmins and Chhetris versus others, implies that an increase of 0.17 (one
within-sd) in this variable would increase expected Maoist killings by 198% or 5208 over the 8year period. Other inequality measures show large magnitudes as well: with one within-sd
increases in the caste-based measures HINEQUALITY(C1) and HINEQUALITY(C2), expected
Maoist killings increase by 191% and 76.3%, respectively. The former depicts stark two-group
polarization, while the latter is a more diffuse multi-group measure of polarization. Notably, the
district-level rainfall instruments produce quantitatively similar results.28
The theory is designed to demonstrate the inequality-violence relationship, and is unable
to offer finer hypotheses about what variables might dampen the impact of inequality (beyond
those that exacerbate collective action). Choice of policies to mitigate conflict makes such
27
The village-level variables are weak instruments for these HI measures, but the rainfall instruments are
up to the task. Regardless, the similar estimates across the two sets attest to their robustness.
28
Including the (instrumented) HI measures one-at-a-time in an IVNB model together with
POLARIZATION(α=1) lowers their statistical significance. That is, the latter wins in a horse race.
29
variables important. In the remainder of this section we offer exploratory hypothesis for further
theoretical development. The primary policy finding thus far is that polarization and other forms
of inequality should directly be mitigated. However, inequality takes time to be permanently
reduced, if at all. Might employment and education blunt inequality’s destructive edge?
Table 5 indicates greater education softens the violent impact of inequality on Maoist
killings. In the model with GINI, every additional year of education (per village resident)
reduces ∂ln E(KILLINGS)/∂GINI by 2.145. With ten additional years of education, the impact
of (vertical) inequality can be greatly controlled.29, 30 It is entirely possible that raising the level
of education such that all children pass high school can eliminate the impact of GINI on Maoist
killings. That is in fact what the model with POLARIZATION(α=1) indicates. Twelve total
years of education would reduce ∂ln E(KILLINGS)/∂ POLARIZATION(α=1) to zero, even at
current levels of polarization! Since the interactions provide concrete answers to counterfactuals
(e.g. what if education was increased?), the policy lessons from Table 5 are impossible to ignore.
Quite surprisingly, employment is not a mitigating factor. The quality of employment
leaves much to be desired. Months of employment at low quality jobs means fewer opportunities
to move out of the villages than education provides. Finally, the poverty associated with being a
farmer is manifest in interactions of FARMER with inequality – the combination of FARMER
and inequality always exacerbates killings. Farmers are obviously caught in a mobility-trap, and
the Maoist movement is evidence of their belief that their only way out is through forcible
redistribution. Perhaps educating the rural population can simultaneously afford better urban
jobs in Nepal or across the border, and also relieve overcrowding on rural lands. It is a political
and economic agenda worth pursuing.
29
Including all the interactions in a single model causes collinearity among the variables, and therefore
each interaction is considered separately.
30
The results with the rainfall instruments are much weaker.
30
Conclusion
In seeking a definitive answer to the relationship between inequality and violent conflict, we
empirically investigate killings by Maoists guerillas in Nepal during 1996-2003. The empirical
examination is conducted within the confines of a single country because it does not suffer, as
cross-country studies do, from the problem of vast and uncontrollable heterogeneity across
countries. To a large extent, the within-country setting keeps the heterogeneity problem under
control. We extend the measurement of inequality beyond the traditional individual-based Gini
index to a group-based economic polarization measure due to Esteban and Ray (1994, 1999).
We also construct inter-group horizontal inequality measures, with groups partitioned on the
basis of religion, caste and language. Since the inequality-conflict association likely suffers from
reverse causality and other sources of endogeneity bias, we choose two sets of carefully selected
instrumental variables to solve the endogeneity problem, demonstrating a causal relationship.
Our empirics solve a number of problems in the conflict literature enumerated in Kalyvas (2008),
namely observational equivalence of variables such as poverty, the endogeneity problem, and
omitted variables which are controlled using a number of observed variables plus district fixed
effects.
A theory that lays bare the mechanics of the inequality-violence relationship guides the
empirics. It stresses long-lived inequality as a fundamental cause of polarization which is a
latent force seeking forcible redistribution. The glue for such redistributive collective action is a
bond among the economically repressed, such as a shared religion, caste or language. The
collective action problem is solved when weakness is publicly exposed in the opposition, for
example their inability to suppress revolt.
From a cross-section of 3857 Nepalese villages we find strong evidence that economic
inequality, measured by the Gini or Esteban-Ray polarization, or the identity-based HI measures,
are strongly positively related Maoist killings over the 1996-2003 period. This result is robust
31
across a variety of models, all of which include district-fixed effects to further control for
possible heterogeneity across groups of villages in each district, a number of village-level control
variables (which have themselves been the focus of interest in other studies) such as education,
ethnicity and employment, and different modeling specifications. We also find, contrary to
previous studies of conflict, that poverty did not necessarily influence Maoist killings. One
reason may be that widespread poverty imposed a destitution constraint on a village and disabled
the means to rebel. Another reason may be that poverty without inequality implies a village is
uniformly poor, and there is little to gain through local redistributive activity.
Inequality as a central and resilient source of conflict in Nepal, and its solution, deserves
the full attention of policymakers. Our other findings have implications for policy and for future
studies about what might temper the negative effect of inequality on violence. We find that
education softened the impact of inequality on Maoist killings, but villages with more farmers –a
proxy measure for poorly paid labor – exacerbated the impact of inequality on killings. Like
other measures of horizontal inequality, ethnicity was a strong basis for violence. The Nepali
language, spoken by half the Nepali people, is associated with elites dating back to Nepal’s
rulers from the eighteenth and nineteenth centuries. It therefore serves to ethnically partition
society. We find that villages where a greater proportion speaks Nepali as the primary language
experienced greater killings.
An implication of our results for future studies in the conflict area is that horizontal
inequality measures like Estban-Ray polarization perform better than the traditional Gini in
explaining violence. The polarization measures also claim to better measure non-economic
sources of conflict, and this would be a fitting context in which to test that claim.
The experience of countries that have prevented their inequality from degenerating into
violence may provide a partial policy answer. India, for example, has been witness to violent
conflict between the government and Maoist organizations, but as of now they are local, not
32
national incidents. A hypothesis is that redistribution by the federal government from wealthier
states to poorer ones has been effective in preventing inequality from worsening beyond the limit
at which collective action for violence might occur. Further research into whether this model
might work for Nepal should be rewarding for several reasons. If this hypothesis is valid, it is
not only consistent with our findings but would exemplify the kind of center-state institutions
that Nepal needs in its democratic transition. It would also indicate to emerging countries that
have not seen violence on this scale how to forestall widening inequality and deepening
polarization in their societies, which might threaten their growing prosperity.
Finally, our findings provide foundational support for the theoretical view espoused in
Boix (2003) and Acemoglu and Robinson (2005) that inequality is the fundamental source of
distributive pressures in a society. The Downsian model is a cornerstone of their theories – in a
country with great income inequality, since the median voter strongly favors redistribution from
the rich to the poor, the platform of any elected government will be redistributive. Acemoglu
and Robinson recognize, in addition, that serious commitment problems on the part of both the
elite and the poor prevent polarized countries from agreeing upon the future course of
redistribution since the elite always want less redistribution while the poor always want more.
Highly unequal countries thus vacillate between democracy and non-democracy. A prerequisite
to progressing to a liberal democracy is the moderation of that inequality. Our study empirically
affirms this central link between inequality and the deterioration of a political regime, and
demonstrates violent revolution from below is a means to achieving that redistributive end.
33
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37
Variable
KILLINGS
ln(KILLINGS)
GINI
POLARIZATION (α=1)
HINEQUALITY(L)
HINEQUALITY(R)
HINEQUALITY(C1)
HINEQUALITY(C2)
POVERTY
EDUCATION
EMPLOYMENT
FARMER
ETHNICITY
RURAL
POPULATION
ln(POPULATION)
Table 1: Descriptive Statistics
Description
Number killed by Maoists (1996-2003)
log KILLINGS
Consumption GINI Index
Polarization Index when α = 1 (see Section 3)
Horizontal Inequality: Language based, 2 groups (Section 3)
Horizontal Inequality: Religion based, 2 groups (Section 3)
Horizontal Inequality: Caste based, 2 groups (Section 3)
Horizontal Inequality: Caste based, 7 groups (Section 3)
% below poverty line
Average years of schooling in each village (VDC)
Mean months of employment in 2001
% farmers
% of people with Nepali as primary language
1 if rural, 0 otherwise
Population
ln(Population)
Mean
sd within-sd
0.682 3.717 3.634
0.244 0.531 0.498
0.238 0.044 0.039
0.151 0.012 0.011
0.224 0.179 0.137
0.179 0.191 0.170
0.176 0.214 0.197
0.357 0.131 0.110
0.447 0.175 0.095
3.608 1.089 0.903
8.379 1.575 1.380
0.343 0.138 0.092
0.505 0.381 0.210
0.985 0.122 0.121
5709 12780 12388
8.372 0.652 0.489
%WithAGLAND
%WithFEMALELAND
BIGLIVESTOCK
RAINVariance
RAINPrecision
% of Households operating agricultural land
% of Households with women-ownership of land
Average number of big head livestock owned by women
Rainfall variance across 1980, 85, 90 and 95
1/RAINVariance
0.868
0.099
0.110
0.124
34.875
0.153
0.079
0.173
0.198
77.731
0.107
0.063
0.155
−
−
Notes:
1. N =3857 villages. Within-variation over 75 districts. Variables with no within-variation are measured at the district level
and replicated at the village level.
2. Data Sources:
a. KILLINGS compiled from Informal Sector Service Center (INSEC): Nepal Human Rights Yearbooks (1996-2004).
b. GINI, POLARIZATION, POVERTY constructed from data obtained from Central
Bureau of Statistics, 1996 Nepal Living Standards Survey (NLSS), and Nepal Population Census 2001.
c. POPULATION, FARMER, EDUCATION, RURAL, MOUNTAIN, HILL, TERAI, ETHNICTY,
EMPLOYMENT, %WithAGLAND, %WithFEMALELAND, BIGLIVESTOCK obtained from Nepal
Population Census 2001, Central Bureau of Statistics, Kathmandu. Variables measured in 2001.
All variables measured at the village level, except RAINVariance, RAINPrecision (at district level and replicated).
Table 2: Specification Search: OLS-FE (Log-linear) and Negative Binomial-FE models of KILLINGS
Dependent variable: ln(KILLINGS +1)
Baseline Models
Extended Models
OLS-FE (Log-linear)
OLS-FE (Log-linear)
NB-FE
NB-FE
GINI
POLARIZATION (α=1)
POVERTY
1.947***
[0.224]
-
−0.415
[0.277]
-
-
-
−1.003
[1.726]
-
3.599*** 1.474**
[0.738]
[0.727]
−0.287*** −0.290*** −0.583*** −0.235*** −1.155**
[0.093]
[0.090]
[0.085]
[0.085]
[0.559]
0.291***
0.265*** 1.208***
[0.021]
[0.017]
[0.118]
-
11.17***
[3.878]
−0.945*
[0.499]
1.103***
[0.102]
-
−0.277
[0.281]
-
-
−0.885
[0.569]
1.146***
[0.127]
−0.0018
[0.064]
−0.0595*
[0.032]
−0.12
[0.552]
0.696***
[0.219]
−0.199
[0.331]
3857
Yes
10.96***
[3.900]
−0.737
[0.548]
1.054***
[0.102]
−0.0068
[0.063]
−0.0598*
[0.032]
0.142
[0.551]
0.696***
[0.219]
−0.151
[0.331]
3857
Yes
-
-
EMPLOYMENT
-
-
-
-
-
-
FARMER
-
-
-
-
-
-
ETHNICITY
-
-
-
-
-
-
RURAL
-
-
-
-
-
-
3857
Yes
3857
Yes
3857
Yes
3857
Yes
3857
Yes
3857
Yes
0.034
0.201
0.081
0.202
0.021
0.229
0.081
0.195
-
-
0.097
0.158
0.098
0.153
-
-
-
-
0.102
3.354***
0.104
3.324***
-
-
EDUCATION
N
75 District Fixed Effects
within-R 2
Fraction FE
pseudo-R 2
a (overdispersion param.)
-
1.202*
[0.726]
0.0246
[0.101]
0.195***
[0.019]
0.0123
[0.011]
−0.005
[0.006]
−0.044
[0.095]
0.107***
[0.040]
−0.576***
[0.074]
3857
Yes
−0.00554
[0.105]
0.212***
[0.022]
0.013
[0.011]
−0.004
[0.006]
−0.076
[0.096]
0.109***
[0.040]
−0.570***
[0.075]
3857
Yes
ln(POPULATION)
−1.225
[1.588]
-
Notes:
1. Standard errors in parentheses. ***, **, and * indicate statistical significance at 1%, 5% and 10%, respecitvely.
0.104
0.105
3.324*** 3.299***
Table 3: IV Log-Linear (2SLS) and IVNB models of KILLINGS
Village Level instruments
District level Instruments
IV Log-linear (FE)
IVNB (FE)
IVNB
GINI
4.029**
[1.640]
POLARIZATION (α=1)
POVERTY
ln(POPULATION)
EDUCATION
EMPLOYMENT
FARMER
ETHNICITY
RURAL
N
75 District Fixed Effects
R 2 (w/ fixed effects)
2
Pseudo-R
K-P Weak Identification
Hansen Overidentification
Hansen p -value
a (overdispersion param.)
35.29***
[9.738]
36.12***
[12.13]
12.97**
123.6***
[5.994]
[37.60]
0.434**
0.043
2.947***
−0.476
[0.186]
[0.113]
[1.030]
[0.566]
0.011
0.156***
−0.525
0.717***
[0.080]
[0.030]
[0.484]
[0.181]
0.008
0.009
−0.0567 −0.0468
[0.011]
[0.011]
[0.067]
[0.067]
−0.005
−0.008 −0.0647** −0.101***
[0.006]
[0.006]
[0.032]
[0.034]
0.152
0.123
1.787**
1.708**
[0.128]
[0.127]
[0.776]
[0.801]
0.0918** 0.102**
0.564** 0.653***
[0.046]
[0.045]
[0.247]
[0.240]
−0.717*** −0.548*** −1.487***
0.003
[0.149]
[0.133]
[0.471]
[0.312]
3857
3857
3857
3857
Yes
Yes
Yes
Yes
209.5***
[75.49]
3.420***
0.692
[0.983]
[0.462]
−0.346
0.409*
[0.427]
[0.214]
−0.065
−0.104
[0.089]
[0.0938]
−0.0957*** −0.161***
[0.037]
[0.0432]
3.404***
4.497***
[0.995]
[1.336]
0.651**
1.683***
[0.323]
[0.267]
−1.253*** −0.0328
[0.396]
[0.500]
3857
3857
No
No
0.177
0.156
-
-
-
-
31.58
0.404
0.817
-
18.15
2.382
0.304
-
0.106
3.281
0.106
3.287
0.0517
4.878
0.0513
4.891
Notes:
1. Standard errors in parentheses. ***, **, and * indicate stat. sig. at 1%, 5% and 10%, resp.
2. Instruments:
(i) Village-level excluded instruments: %WithAGLAND, %WithFEMALELAND, BIGLIVESTOCK.
(ii) District level excluded instruments: RAINVariance, RAINPrecision.
3. Quality of instruments is assessed using:
(i) Weak Instruments: Kleibergen-Paap F-statistic (a more general version of the first-stage
F -statistics for excluded instruments)
(ii) Validity of overidentification restrictions: Hansen's J-test statistic (chi-squared).
Table 4: IV-NB models of KILLINGS with Horizontal Inequality measures
Village Level instruments
District level Instruments
IVNB (FE)
IVNB
HINEQUALITY(L) 7.391
8.126**
[5.035]
[3.557]
HINEQUALITY(R)
11.65**
11.11***
[5.184]
[4.088]
HINEQUALITY(C1)
9.672***
8.087***
[3.706]
[2.985]
HINEQUALITY(C2)
6.944**
10.76**
[2.906]
[4.664]
POVERTY
−8.249 −9.827** −6.990*** −4.979** −4.132* −2.711** −1.004
−2.347
[5.270]
[4.229]
[2.610]
[1.996]
[2.215]
[1.376]
[0.813]
[1.446]
ln(POPULATION) 1.062*** 0.826*** 0.902*** 1.051*** 1.149*** 0.734*** 0.733*** 1.061***
[0.129]
[0.186]
[0.153]
[0.126]
[0.182]
[0.147]
[0.147]
[0.159]
EDUCATION
0.0234
0.0245
−0.012
−0.0373 0.00312 −0.0381
−0.053 −0.0735
[0.069]
[0.066]
[0.066]
[0.067]
[0.086]
[0.086]
[0.088]
[0.090]
EMPLOYMENT
0.0182
0.0621
0.0331
−0.0339
0.029
0.056
−0.007
−0.02
[0.064]
[0.065]
[0.049]
[0.034]
[0.069]
[0.068]
[0.050]
[0.051]
FARMER
0.346
0.799
0.0271
0.806
0.612
0.084
0.831
1.826**
[0.646]
[0.704]
[0.557]
[0.693]
[0.804]
[0.837]
[0.723]
[0.723]
ETHNICITY
0.418 1.204*** 1.373*** 0.762*** 0.592
2.156*** 2.360*** 0.937***
[0.341]
[0.319]
[0.331]
[0.239]
[0.371]
[0.390]
[0.455]
[0.271]
RURAL
1.07
1.086*
0.613
0.951*
0.504
−0.195 −0.785** 0.843
[0.915]
[0.643]
[0.432]
[0.566]
[0.737]
[0.467]
[0.383]
[0.861]
N
3857
3857
3857
3857
3857
3857
3857
3857
75 District FE
Yes
Yes
Yes
Yes
No
No
No
No
2
Pseudo-R
0.105
0.105
0.105
0.105
0.0505
0.0512
0.0512
0.0493
Notes:
1. Standard errors in parentheses. ***, **, and * indicate stat. sig. at 1%, 5% and 10%, resp.
2. Instruments:
(i) Village-level excluded instruments: %WithAGLAND, %WithFEMALELAND, BIGLIVESTOCK.
(ii) District level excluded instruments: RAINVariance, RAINPrecision.
Table 5: IVNB(FE) models of Interactions: Village Level Instruments
GINI Interactions
POLARIZATION Interactions
GINI
35.18*** 48.72*** 29.06** 34.69*** 43.10***
[9.844]
[11.06]
[13.43]
[9.775]
[10.31]
GINI × POVERTY
0.558
[7.631]
GINI × EDUCATION
−2.145***
[0.797]
GINI × EMPLOYMENT
0.729
[0.930]
GINI × FARMER
26.07***
[9.746]
GINI × INCOME
−0.260***
[0.0906]
POLARIZATION (α=1)
117.7*** 205.9***
87.41
71.15** 147.3***
[43.75]
[55.65]
[76.25]
[34.84]
[39.20]
POL1 × POVERTY
16.53
[70.73]
POL1 × EDUCATION
−17.27**
[7.938]
POL1 × EMPLOYMENT
4.368
[7.559]
POL1 × FARMER
384.5***
[89.37]
POL1 × INCOME
−0.521**
[0.207]
POVERTY
2.822
3.381*** 2.921*** 3.591*** 2.188**
−2.98
−0.553
−0.476
−0.624 −2.004**
[1.989]
[1.039]
[1.035]
[1.049]
[1.052]
[10.72]
[0.566]
[0.566]
[0.563]
[0.873]
ln(POPULATION)
−0.532
−0.758
−0.511
−0.882*
−0.749 0.713*** 0.684*** 0.718*** 0.502*** 0.677***
[0.489]
[0.481]
[0.489]
[0.486]
[0.491]
[0.180]
[0.175]
[0.182]
[0.172]
[0.180]
EDUCATION
−0.0569 0.450** −0.0546
−0.06
−0.0457 −0.0462 2.567** −0.0462 −0.0619
−0.035
[0.0675]
[0.210]
[0.0683] [0.0680] [0.0680] [0.0670] [1.213] [0.0673] [0.0674] [0.0674]
EMPLOYMENT
−0.0650** −0.0733** −0.232 −0.0661** −0.0660** −0.103*** −0.114*** −0.757 −0.123*** −0.106***
[0.032]
[0.033]
[0.222]
[0.032]
[0.032]
[0.034]
[0.035]
[1.140]
[0.035]
[0.034]
FARMER
1.791** 2.056*** 1.808**
−3.855
1.854** 1.724** 2.008** 1.738** −55.14*** 1.764**
[0.780]
[0.776]
[0.780]
[2.345]
[0.771]
[0.807]
[0.808]
[0.799]
[13.18]
[0.799]
ETHNICITY
0.565** 0.547** 0.564**
0.494*
0.520** 0.656*** 0.620** 0.659*** 0.512** 0.614**
[0.244]
[0.247]
[0.250]
[0.258]
[0.248]
[0.238]
[0.245]
[0.241]
[0.246]
[0.241]
RURAL
−1.501*** −1.979*** −1.470*** −2.153*** −1.715*** −0.0309
−0.271
0.00494 −0.606*
0.0764
[0.499]
[0.487]
[0.476]
[0.491]
[0.467]
[0.335]
[0.322]
[0.312]
[0.314]
[0.313]
Notes: N =3857; District fixed-effects included; Pseudo R 2 approximately 0.107 in all models.
GINI
Included Exogenous variables
Excluded Instruments
%WithAGLAND
Table A1: First Stage regressions for endogenous variables
District level instruments
Village Level Instruments
POL (α=1)
HI(L)
HI(R)
HI(C1)
HI(C2)
GINI
POL (α=1)
HI(L)
HI(R)
HI(C1)
HI(C2)
-0.0502*** -0.0151*** -0.0953** -0.0897** -0.144*** -0.198***
[0.00975]
[0.00294]
[0.0394]
[0.0415]
[0.0496]
[0.0557]
%WithFEMALELAND 0.0533*** 0.00777*** -0.0634*
0.0117
0.0282
-0.0266
[0.0106]
[0.00290]
[0.0328]
[0.0421]
[0.0515]
[0.0317]
BIGLIVESTOCK
-0.00329
-0.00171*
0.0139
0.0311*
0.0287
0.00241
[0.00300] [0.000926]
[0.0103]
[0.0180]
[0.0237]
[0.00749]
RAIN Variance
0.00243
-0.000857 -0.0458***
-0.02
-0.0281
-0.0342***
[0.00301]
[0.00102]
[0.0102]
[0.0136]
[0.0237]
[0.00901]
RAIN Precision
6.20e-05*** 8.87e-06*** 0.000145*** 0.000158*** 0.000215*** 0.000112***
[8.31e-06] [2.75e-06]
[3.10e-05]
[3.65e-05]
[5.41e-05]
[1.92e-05]
POVERTY
-0.0870***
0.0019
1.021*** 0.796*** 0.675***
0.642***
-0.0724*** 0.000814
0.624***
0.323***
0.232***
0.305***
[0.00772]
[0.00268]
[0.0615]
[0.0507]
[0.0608]
[0.0577]
[0.00381]
[0.00126]
[0.0182]
[0.0235]
[0.0251]
[0.0181]
EDUCATION
0.000517
0.000164
-0.00367
-0.0033
-0.000515
0.00392
0.000742
0.000299
-0.00539** -0.000306
0.00143
0.00304
[0.000968] [0.000295] [0.00316] [0.00567] [0.00606]
[0.00372] [0.000658] [0.000210]
[0.00262]
[0.00333]
[0.00375]
[0.00238]
EMPLOYMENT
5.84E-05 0.000275** -0.0113*** -0.0107*** -0.0101*** -0.00493*** -0.000354 0.000266** -0.0165*** -0.0145*** -0.0121*** -0.00780***
[0.000357] [0.000126] [0.00126] [0.00226] [0.00264]
[0.00110] [0.000372] [0.000122]
[0.00138]
[0.00207]
[0.00250]
[0.00107]
FARMER
-0.0290*** -0.00706*** -0.00946
-0.0337
0.0569
-0.0294
-0.0449*** -0.0130***
0.148***
0.152***
0.117***
-0.00116
[0.00623]
[0.00245]
[0.0325]
[0.0441]
[0.0471]
[0.0317]
[0.00530]
[0.00167]
[0.0212]
[0.0268]
[0.0306]
[0.0211]
ETHNICITY
0.00323
0.000623
0.0448*** -0.0409* -0.0658** 0.000348
0.0139*** -0.00264*** 0.0633*** -0.0928***
-0.153***
0.0158***
[0.00293] [0.000779]
[0.0143]
[0.0231]
[0.0279]
[0.0150]
[0.00181] [0.000566]
[0.00700]
[0.00999]
[0.0106]
[0.00565]
RURAL
0.0412*** 4.81E-06
-0.160*** -0.100*** -0.0674** -0.141***
0.0110*
-0.00390** -0.169***
-0.0591**
-0.00828
-0.159***
[0.00751]
[0.00216]
[0.0304]
[0.0308]
[0.0307]
[0.0414]
[0.00660]
[0.00181]
[0.0300]
[0.0256]
[0.0261]
[0.0404]
ln(POPULATION)
0.0453*** 0.00287***
0.0035
0.0227*** 0.0190*
0.0044
0.0373*** 0.00283*** -0.0189*** 0.0242***
0.0333***
-0.00598
[0.00258] [0.000534] [0.00614] [0.00789] [0.00979]
[0.00826]
[0.00143] [0.000354]
[0.00424]
[0.00558]
[0.00637]
[0.00411]
N
3857
3857
3857
3857
3857
3857
3857
3857
3857
3857
3857
3857
R2
0.528
0.075
0.47
0.2
0.113
0.269
0.439
0.099
0.457
0.137
0.113
0.179
District Fixed Effects
YES
YES
YES
YES
YES
YES
NO
NO
NO
NO
NO
NO
First stage F
15.6
12.81
2.93
2.91
4.53
4.34
27.97
6.33
25.96
12
10.26
27.52
Notes:
1. Standard errors in parentheses. ***, **, and * indicate statistical significance at 1%, 5% and 10%, respectively.
2. First stage F is used to test the joint null that %WithAGLAND = %WithFEMALELAND = BIGLIVESTOCK =0 in FE models and RAINVariance=RAINPrecision=0 in non-FE models..