RENEWABLE NATURAL RESOURCES AND ETHNIC
CONFLICT:
MECHANISMS THAT MATTER
Samuel S. Stanton Jr.
Grove City College
SSStanton@gcc.edu
About the Author
Samuel S. Stanton, Jr. is currently an assistant professor of Humanities and Political Science at Grove City
College. His research is focused primarily on conflict processes and particularly on the relation of
environmental resource scarcities and conflict. His teaching focuses on general international relations,
African and Asian politics, and research methods. He received his PhD from Texas Tech University in 2004.
Abstract
Renewable natural resource scarcities do have an affect on levels of ethnic conflict. The resource scarcities
interact with or cause social changes that often lead to higher levels of conflict. We call these social changes
mechanisms. They are the means by which renewable natural resource scarcities transfer their impetus into
conflict behavior. Which resource scarcities interact with which mechanisms to lead to higher levels of ethnic
conflict? Based on the study of Minorities at Risk from 1985 to 1998, we conclude that migration,
mobilization, repression, and secessionist interests are the mechanisms that matter. When certain renewable
natural resource scarcities interact with these mechanisms there is often an increase in the level of conflict,
but not all renewable natural resource scarcities interact equally with all of the mechanisms studied.
ENVIRONMENTAL SCARCITY AND ETHNIC CONFLICT
Ethnic conflict has been discussed in many contexts, and many explanations for its
occurrence have been offered. Explanations have ranged from lack of democratization to the
overflow of ethnic violence from neighboring states. The form of governmental and societal
institutions has been cited as a cause of ethnic conflict. Inequality in economic well-being has been
used to explain ethnic conflict. Ancient hatreds are often cited as a cause of ethnic violence.
Literature on environmental security has begun to link resource scarcity to ethnic conflict. It is in
this latter area this work seeks to make a contribution. We seek to bridge these literatures in this
study by highlighting the nexus of each in our study of natural resource scarcity and ethnic conflict.
This work is also a response to critics of the study of environmental scarcity as a source of
ethnic conflict. Gleditsch (1998) offers a nine point critique of the literature on armed conflict and
the environment. The critique ranges from theoretical (ill-defined concepts and polemic exercises)
to the practical (failure to include important variables and cases selected on the dependent variable).
Deudney (1990) and Haas (2002) also criticize the inclusion of environmental issues as factors in
security studies based on theoretical and empirical issues. Two problems we seek to overcome in
this paper are 1)inadequacy of the micro foundations for understanding ethnic conflict, and 2) the
research designs used by those who seek to stretch the micro foundations are to date are misspecified
THEORIES OF ETHNIC CONFLICT
Calling a conflict an ethnic conflict covers a wide-range of factors. But ethnic conflict (in
this work) is a conflict between groups identifying themselves in terms of their ethnicity or between
a group and the state. The terms minority and ethnic are interchangeable. Gurr (1985) and
Saideman, Lanoue, Campenni and Stanton (2002) use these terms in an interchangeable way,
because most minority groups in the world today are minorities based on their ethnicity.
Before addressing theories, a concern regarding collective action requires notice; the
assumption of groups acting as rational unitary actors. We choose to accept two arguments for the
case of individuals joining in cooperative action. One, individuals join collective action when they
believe group action will benefit them more than they will lose by joining the effort (Finkel and
Muller, 1998), particularly where success is contingent on group treatment (Horowitz, 1985). Two,
Lichbach (1994) offers persuasive arguments that collective action problems are regularly solved
and we must move beyond them to address important questions. This latter belief echoes a similar
position found in Saideman, Lanoue, Campenni, and Stanton (2002).
ETHNIC COMPETITION THEORY
Olzak (1992) offers an ecological theory of ethnic conflict. The basis of the theory is
competition causes conflict. James (2002) refers to competition as the moral equivalent of war.
Competition is an embedded structure in humans and affects the actions of individuals. When
translated into group settings we see similarities to sports teams athletic contests. The struggle
becomes “us vs. them”, a struggle for glory, reputation, and prestige. Competition is so ingrained it
cannot be rooted out of the behavioral patterns of people. As James (2002) notes, our ancestors bred
it into us (2002, 146). Competition for resources and position are fuel for a greater dilemma. Any
gain made by a group will elicit a response from at least one other group in society, decreasing
stability and increasing the likelihood of the security dilemma.
ETHNIC SECURITY DILEMMA THEORY
Snyder and Jervis (1999) explain security dilemmas as causes of intrastate conflict by
showing how one or both sides to the conflict is less secure because of the actions taken by one side
to increase their security. The security dilemma affects the way in which collective security
measures are taken to intervene and alleviate the conflict. The security dilemma leads to predatory
behavior among elites in groups in society, a point reflected in Uvin’s (1999) study showing
predatory behavior among elites in Rwanda.
Posen (1993) argues that the collapse of governments makes the security dilemma more
acute. Assuming security is the first concern of all groups located in the state, insecurity is a grave
concern. What makes one side more secure makes another side feel less secure, necessitating an
increase in security measures, which makes the original group less secure. The actions that made the
first group secure have made it less secure and the reaction of other groups to the first group led to a
response from the original group which made them all less secure. The ethnic security dilemma is
the result of one group trying to control the state or gain a disparate advantage socially or
economically, causing all groups to be less secure.
ENVIRONMENTAL SECURITY
This work uses environmental security as a framework for understanding the role of
renewable natural resource scarcity as a source of ethnic conflict. We believe, given historical
circumstances, scarce natural resources are a cause of war. While many have argued environmental
resources are a source of conflict, few have included the study of renewable natural resources and
those that have included renewable natural resources have undertaken flawed studies.
The relevance of understanding the linkage between environmental degradation/scarcity and
ethnic conflict becomes obvious when considering the nature of conflict in the last half of the 20th
century. Gleditsch (1996) notes that from about 1975 to the early 1990s armed conflicts have
outstripped the growth of new states (1996, 293). Gurr (2000) shows violent ethnic conflicts have
ebbed in the later half of the 1990s, but looking at the Minorities at Risk data shows nonviolent
confrontation is growing.1 What this does not indicate is a decrease in lost lives. As noted by
Vamik Volkan (1997) “Hundreds of thousands of lives have been lost in ethnic and related largegroup conflicts during this decade (1986-1996),” (Volkan 1997, 16, italics added).
The answer to the question of whether environmental scarcity can cause ethnic conflict is
based on the findings of Baechler (1998) and Homer-Dixon (1993, 1994, 1999). However, their
research relies on conflict having occurred. We answer to question without choosing cases based on
the dependent variable.
Environmental security is divided into categories ranging from protection of the environment
from human destruction to environmental degradation as a source of violent conflict (Baechler
1998). We accept that human activity is destructive of the natural environment, as most human
activity is destructive (did you walk on the grass today?). We accept arguments that natural disasters
are destructive of human life (have you ever lived through a hurricane or tornado?). However, two
categories are important for understanding the relationship between environmental scarcity and
ethnic conflict: environmental degradation and resource depletion. When either of these categories
of environmental security issues is present the result is resource scarcity and it is this factor that
leads to increased ethnic conflict.
Category One, Environmental Degradation as a source of Acute Conflict
Günther Baechler (1998) outlines five ways environmental degradation is related to violent
conflict. First, transformation of the society-nature relationship can cause conflict (1998, 36).
Second, environmental degradation can serve as a trigger for conflict where other tensions already
exist (1998, 36). Third, environmental degradation increases the call for self-determination in
groups discriminated against by resource controlling governments (1998, 36). Fourth,
environmental concerns can serve as indirect channels of sociopolitical cleavages (1998, 36-37).
Finally, transformation of the environment can serve as a catalyst when natural or man-made
environmental events exacerbate on-going resource conflicts (1998, 37).
Category Two, Resource Depletion Causing Deterioration of Human Security
Resource depletion is a cause of deteriorating human security. Lonergan points out human
security problems include economic loss, population migration, problems of food security, and
problems for political security (2000, 69). Porter points out environmental degradation has a
negative impact on the wellness of a society (1995, 218). The logic of this line of reasoning is
environmental disruption causes social change detrimental to the well being of many people in a
state.
What are the stakes in the failure of policy to recognize the threats to security caused by
environmental issues? Homer-Dixon (1999) points out environmental scarcities have social effects
leading to violent conflict (1999, 7, 82, 89, 105). While political and economic factors are still the
most pressing issues of national security, environmental factors add to social stress and the
likelihood of conflict both exogenous to and concurrent with these other issues. However, the
ultimate stake is the ability of humans to exist on the earth. Environmental factors impact on
security because of scarcity. But which scarcities matter?
Freshwater seems to be a predominant environmental cause of conflict. The nature of this
resource is easily discernable, as only three (3) percent of the world’s water is freshwater and a great
portion of this water is locked up in the polar icecaps and glaciers. Of the water that is available,
over half is already being appropriated for human use (Klare 2001, 19).
Another major area of concern noted by environmental studies is deforestation.
Deforestation is the loss of forests and biodiversity caused by human practices. Most of these
1
Charting the ebb of ethnic rebellion between 1985 and 1998 shows a declining trend in violent conflict. Using the same states and
the same data set, the MAR, there is a noticeable increasing trend toward protest behavior among ethnic groups.
human practices are related to economic activities such as logging and subsistence farming. One
argument is that deforestation leads to scarcity and environmental degradation due to loss of the
oxygen producing species found in rainforests. Homer-Dixon (1993, 1994), Meyers (1989), and
Porter (1995) all mention this as an aspect of environmental security, where security is the protection
of the environment.
Degradation of arable land is a key factor in environmental issues related to security. Loss of
arable land due to overuse is a common problem in those countries where agriculture has been
turned into a cash crop industry. The loss of soil fertility and the loss of vegetation necessary to
protect soil from erosion— through deforestation—are the key issues.
Environmental scarcity has a social effect that can lead to violent conflict. Both HomerDixon (1994a, 1999) and Gurr (1985) recognize this point. While, Gurr is more concerned about
economic scarcities than environmental scarcities, Homer-Dixon makes the case that:
Scarcity is often caused by a severe imbalance in the distribution of wealth and power that
results in some groups in a society getting disproportionately large slices of the resource pie,
whereas, others get slices that are too small to sustain their livelihoods. (1999: 15)
To address the concerns of critics of this area of research we use a large-N analysis of the
relationship between resource scarcity and ethnic conflict. The study answers claims that linking
resource scarcity to ethnic conflict is not empirically upheld (Haas 2002) or useful for understanding
national security (Deudney 1990). It also addresses many of the concerns noted by Gleditsch
(1998).
Rather than accepting the argument that empirical support is not available for resource
scarcity as a source of ethnic conflict, we look at how scarcity can translate itself into conflict. This
analysis is conducted using hypotheses about the relationship between resource scarcities and the
mechanisms that translate scarcity into conflict.
HYPOTEHSES, DATA, and MODELS
Three sets of models will be generated in looking at the questions of this work necessitating
three sets of hypotheses. The first set of hypotheses will be discussed prior to examining the data
and the models. The other two sets of hypotheses will be examined prior to looking at the models
generated to test them.
Scarcity and Ethnic Conflict
H1: LOSS OF ARABLE LAND INCREASES THE LIKELIHOOD OF ETHNIC CONFLICT.
H2: DEFORESTATION INCREASES THE LIKELIHOOD OF ETHNIC CONLICT.
H3: FRESH WATER SCARCITY INCREASES THE LIKELIHOOD OF ETHNIC CONFLICT.
Loss of arable land can be due to poor husbandry of the resource, through increased salinity
of the soil, or through governmental decisions. No matter how loss of arable land occurs, it will
instigate social problems due to competition for the resource. Competition will fuel the security
dilemma and a point will be reached at which a group or groups in the society are prepared to engage
in a conflict that may be a losing proposition, but appears as the most rational option or the only
option.
Deforestation leads to loss of species and degradation of the ecosystem’s ability to reproduce
oxygen, necessary to sustain life on earth. For some groups economic well-being is attached to a
livelihood that requires forest resources. Deforestation is a source of increased ethnic conflict where
resource competition concerns are manifested. This results from lower societal standing due to lost
economic opportunity. Being cut off from the general economic wellness of the state by group
positioning in society is a source of social frustration that drives competition.
Scarcity of water resources might lead to attempts by the state to control the resource
allocation. Assuming self-serving rational behavior, a state that is dominated by one group will
appear to be trying to strengthen its own position. When the behavior of the dominant group(s)
becomes apparent to other groups, they will feel their security threatened and at some point likely
engage in conflict behavior.
Using a large N study is necessary to generalize about the hypothesized relationships
discussed above. Many case studies have been conducted in this area. However, the only noticeable
attempt at Large N analysis was conducted by Hauge and Ellingsen (1998) and was handicapped by
lack of data. A Large N study is also an answer the critics. Haas (2002) says of claims that there is
a relationship between environmental security and conflict, “Empirically none of these claims is
upheld,” (2002, 6).
The Minorities at Risk (MAR) data serves as the basic dataset for the analysis.2 The MAR is
modified for use in this project by gathering year specific data for environmental factors in a given
country. We have aggregated this data into group/country/year as the unit of analysis. If a group is
2
The MAR data is available at: http://www.bsos.umd.edu/cidcm/mar/ with the updates now available through 2003 for most groups.
considered at-risk in a country during any of the years observed the group has as many cases in the
dataset as there are years of existence for the state in which the group is observed. This generated
3428 cases.
Concepts and Measures
We use two measures for the dependent variable ethnic conflict: protest and rebellion. As
argued in Saideman, Lanoue, Campenni, and Stanton (2002), protest and rebellion are different
phenomena. Protest represents non-violent conflictual behavior measured on a scale of 0 (none
reported) to 5 (demonstration involving over 100,000 people). Rebellion, a measure of violent
conflict behavior, is measured on a scale of 0 (none reported) to 7 (protracted civil war). Both of
these scores are measured by their highest value for a group in a single calendar year.
The independent variables are based on assessments made by the World Resource Institute,
the World Watch Institute, the US Central Intelligence Agency, the US Department of State,
Keesing’s World Record, and the United Nations. From these sources we took information about
the reliability of freshwater resources, the amount of forested land, and the amount of arable land for
each country involved in this study for each year of the study.
If freshwater pollution/potability was a concern a country was given a score of 1 for that
year; otherwise it was scored as 0. Of the 3428 cases, 1928 of the cases exhibited fresh water
scarcity. This represents 56.4% of the cases. Deforestation is the percentage of forested land lost in
a given year compared to the previous year. The variable for arable land is measured in the same
manner as deforestation.
The environmental variables that we consider do not exist ceteris paribus. Gleditsch (1998)
pointed out often environmental security research overlooks important factors such as type of
government and the economy. Among the other factors accounted for in this study are population
growth of the ethnic group, population concentration, level of autocracy/democracy of the state, and
change in per capita gross domestic product.
Population growth measures change in the ethnic group population within a state in
thousands (a change noted as 100 equals a real change of 100,000) from year one to year two.
Baechler (1998), Homer-Dixon (1999), and Klare (2001) all consider population to be an important
factor in understanding resource conflicts.
Group concentration is also used as a control variable. This variable is taken directly from
the MAR and measures how spatially concentrated a minority population is within the confines of
state borders. Szayna (2000) argues that higher levels of group concentration make it likely that
potential conflict becomes actual conflict.
We include change in gross domestic product per capita to control for economic effects on
ethnic conflict. The literature on economic impacts on state development and ethnic conflict uses
this factor to account for quasi-modernization arguments that insist that conflict behavior is related
to economic development and class inequality. GDP per capita is measured in U.S. dollars held
constant to 1985 values supplied by the World Bank
We also consider autocracy/democracy levels. It ranges from a prominent explanation to a
prominent control; I employ it as the latter. It is measured on a scale of –10 to 10, with –10 being
completely autocratic and 10 being completely democratic.3 The type of system in the state,
whether it is autocratic or democratic is considered in the context of conflict by Gleditsch (1996),
and Dixon, Mueller and Seligson (1993), and many others.
While we employ data from a 15 year time period (1985-1998), our first model is an ordered
logistical regression (ordered logit). As noted, many of the states for which data is gathered did not
exist for the entire time frame and the dependent variables are ordinal. Thus, we start the analysis
with a logistic model; this does not fully account for the temporal framework because of serial
correlation problems in the data that can only be corrected with the use of a time-series model.
Temporal problems are created because the independent variables do vary over time.
Recognizing this fact, we also utilize a time-series regression. Panel corrected standard
errors are used because each of the country/groups is not present in each year of the study. This is
done to account for mathematical artifacts that occur in time-series cross-sectional data when the
dependent variable is measured in a binary or ordinal manner. Beck and Katz (1995) note that
temporal and spatial issues involved in time-series cross-section data make the use of ordinary least
squares problematic.
3
This data is developed from the Polity IV data available at: www.cidcm.umd.edu/inscr/polity/ this project is managed by Monty Marshall, Keith
Jaggers and Ted Robert Gurr.
First Model Set: Scarcity and Conflict
The modeling is done in two parts: protest and rebellion. Both models measure the
likelihood of an increase in the level of conflict given the presence of the independent and
controlling variables. In the logistical model the coefficients indicate direction and strength of the
relationship. However, by standardizing the coefficients in logistic regression through a logarithmic
formula, we can make substantive statements about the percentage chance of a change in the
dependent variable caused by a one standard deviation increase or decrease in the value of the
independent variable.
Table 1 reports the results for ordered logistical regression models of the different forms of
conflict behavior. Overall, the only coefficient that is not statistically significant in relation to either
protest or rebellion is deforestation. All other independent variables had a significant impact on at
least one form of conflict behavior. Apparently protest behavior is less influenced by renewable
natural resource scarcity than rebellion behavior. The resource scarcity coefficients are all positive,
but not always significant. The results show support for hypothesized relations regarding arable land
and freshwater.
The control variables show mixed results. Change in GDP per capita is not significant in
relation to rebellion, but is in relation to protest. Population growth is not significantly related to
either form of conflict behavior. Spatial concentration of a group does appear to influence conflict
behavior, but regime type only appears to affect protest behavior. Change in GDP Per Capita, and
regime type are not statistically significant as causes of rebellion behavior. This would indicate that
a group that is determined to take forceful action against the state does not care what type of
government atmosphere exists. This also indicates that rebellion is not driven by the pocketbook.
We find some interesting substantive results. If the value of freshwater scarcity increases by
one SD from its mean value of “none reported” there is a 94.6% chance that the value of protest
would increase one unit of measurement.
The likelihood that protest will increase one unit is over
100% when change in GDP per capita, regime type or group spatial concentration increase by one
SD. If protest occurred in the previous year, there is over a 1000% chance that protest this year will
increase one unit if the previous year’s protest were to increase by one SD.
If we look at rebellion there are similarities where variables are statistically significant.
Freshwater scarcity increase of one SD will increase the likelihood of a one-unit increase in rebellion
by 89.5%. Increasing loss of arable land one SD increases the likelihood of a one-unit increase in
rebellion by 93.7%. Increasing the rebellion in the previous year one SD increases the likelihood of
rebellion this year increasing one-unit by 2650.9%.
As noted earlier, a second run on the data is performed utilizing panel corrected standard
error time-series cross-section regression. The purpose of a second model is to ensure that proper
care is taken in accounting for temporal effects. The results of the time series models for protest and
rebellion are reported in Table 2.
The major difference with the logistic model is that for rebellion none of the independent
variables is statistically significant. Regime type is negatively related to rebellion here, which
allows us to notice what is not seen but existed in the logistic model regarding this relationship. In
most ways the models give us similar results.
In the regression model we observe that freshwater scarcity, when present, leads to a .1229
increase in the level of protest or a .2241 increase in the level of rebellion. However, we do not
know what this means because protest and rebellion are ordinal scale without fractional breakdown
between whole number values. Overall, the use of the panel corrected standard error cross-sectional
time-series regressions differs from the understanding of protest and rebellion behavior gained in the
first logistic models in the relationship of loss of arable land and freshwater scarcity with rebellion.
But, the sizes and directions of coefficients remain similar in both sets of models.
Second Model Set: The Mechanisms that Matter
What would bring ethnic groups into competition with the state or with other ethnic groups in
a society? What factors would exacerbate existing competition for position in a society?
If
conflict is to occur what must first happen within the ethnic group? What things happen
engendering insecurity in a group and how related to environmental issues?
These are the
questions arising based on the finding of a simple relationship between renewable natural resource
scarcity and ethnic conflict. These questions are addressed by seeking to find the factors that
channel renewable natural resource scarcity into ethnic conflict. Homer-Dixon calls them the social
effects of environmental scarcities (1999) Baechler calls them mechanisms (1998). We call them the
“mechanisms that matter,” and they are fourfold: migration, mobilization, repression and
separatism.
Migration
Two arguments are useful in explaining how migration can cause ethnic conflict due to
renewable natural resource scarcity. One, when groups migrate, it increases the competition for
resource usage in the new location. Migration due to resource depletion leads to increased tensions
between the group that migrates and the people who already inhabit the area. Two, migration might
also be forced upon a group by the state if the state determines that controlling the resources in the
territory the group occupies is necessary for the “good” of the state. This causes conflict between
the group and the state over the resource and property rights.
Mobilization
Groups that are not politically mobilized are not likely to engage in any form of group
activity. There is a great deal written about mobilization and the problems of collective action.
Individuals must be mobilized to enter into a group effort and there are many problems associated
with getting an individual to move from free-rider status to active participant. Usually
entrepreneurial leadership is required and the leadership makes use of other factors to mobilize the
group’s members and lead them in certain group activities. When considering conflict, it is
incumbent upon leadership to find definitive reasons for action, as minority groups that are not
privileged are at a disadvantage in terms of resources available for the struggle4.
Repression
Ethnic conflict behavior is often a response to the policy or practice of political, legal,
economic, or social discrimination, as against the members of a minority group. Included in
understanding this type of behavior is the response to such things as slavery and apartheid. This side
of ethnic conflict is, in the developing world, often the legacy of colonial imperialism. However, the
question of the relationship of resource scarcity and conflict remains the same for this form of
behavior—will lack of resources, or lack of access to the resources, be a source for sentiment to
grow in the group or be used by the group’s leaders? This form of behavior is the response to
repression.
4
An interesting discussion of the problem of mobilizing a group to action by leadership is found in Lichbach (1995) and is hinted at in Lemarchand
(1995) in discussing the case of Burundi.
Separatism
Separatism has two forms. The first is a disposition to withdraw from something, as in
secession. In the case of ethnic groups the something that they desire to withdraw from is
membership in a state. In relation to renewable natural resources, the question would be whether or
not lack of or lack of access to these resources would be cause for separatist sentiment in a group, or
a usable cause for group leaders to champion separatism in the group. The second is the desire to
rejoin another political entity, which is irredentism. Here the question regarding renewable natural
resources would be much the same, is the wealth of the other entity desirable or is the lack of
resources in the current state bad enough to fuel the irredentist desire?
The question that becomes important at this point is what types of renewable natural resource
scarcity will trigger which mechanism? To recap the previous sections, the mechanisms are
migration, mobilization, separatism, and repression. Taking these one at a time we examine which
resources will trigger the mechanism.
Migration is often triggered by water scarcity and arable land scarcity. In the greater
understanding of the scientific world it is necessary to have forests. Forests are the major producer
of oxygen on planet earth. However, trees are not required for the production of food, while water
and arable land are required. Water and food are needed for physical survival of humans. With this
in mind, it is more difficult to argue that loss of forested land will trigger migration away from an
area, by either choice or force. Therefore, both water and arable land scarcity will be triggers of
migration hence ethnic conflict. Fresh water is necessary to human survival for both primary use
(drinking) and secondary uses (irrigation, fish stocks). If irrigation is needed it means that the land
is arable, or able to support crops. Crops mean both physical and fiscal security and survival are
possible.
Mobilization will result from physical and fiscal concerns for the survival of the group. This
being the case, deforestation, fresh water scarcity, and loss of arable land will be triggers for this
mechanism. For the same reasons that they will trigger migration, water and arable land will trigger
mobilization. This is important to the understanding of ethnic conflict where state policy rather than
true scarcity is the root of resource scarcity.
Repression is almost always the result of state policy. The question is which resource’s
scarcity will be damaging enough to lead to state policy that is repressive of one or more ethnic
groups in a state? Here the answer is loss of forested land and loss of arable land. Repression can be
economically driven policy employed by ruling groups in societies. The amount of repression that
can be explained by renewable natural resource scarcity is then limited to the resources that carry
economic concern and loss of arable land will be important and loss of forested land should have
some effect.
Gauging the triggering power of renewable natural resources on separatism is perhaps easier
in some respects than gauging its effect on other mechanisms. After all, who does not want more
resources at their disposal? For the irredentist who desires to join with another state or reclaim a
state, the issue of resources is crucial. Also, it can directly influence secession sentiments where
physical security is an issue.
With these ideas fresh in hand we offer the following hypotheses about the relationships
between renewable natural resource scarcity and mechanisms that trigger ethnic conflict. Each of
the mechanisms requires several hypotheses, not all of the scarcities have an effect on all of the
mechanisms, and some of the mechanisms must be considered as interacting with scarcities to
trigger other mechanisms. After discussing the hypotheses we turn to a discussion of new controls
that are included in these models. We label these hypotheses as M1 through M11, because they
pertain to testing the relationship between resource scarcities and the mechanisms. Four Models will
be generated based on these hypotheses, one for each of the mechanisms.
M1: An increase in loss of arable land leads to increased migration.
M2: Freshwater scarcity leads to increased migration.
M3: Increased deforestation leads to increased mobilization.
M4: Increased loss of arable land leads to increased mobilization.
M5: Freshwater scarcity leads to increased mobilization.
M6: Increased deforestation leads to increased repression.
M7: Increased loss of arable land leads to increased repression.
M8: Freshwater scarcity leads to increased repression.
M9: Increased deforestation leads to increased separatist sentiment.
M10: Increased loss of arable land leads to increased separatist sentiment.
M11: Freshwater scarcity leads to increased separatist sentiment
Concepts and Measures
Variables for migration, mobilization, repression, and separatist sentiment are taken from the
MAR and serve as the dependent variables in these models5. The independent variables remain the
same as they were for examining the relationship of scarcities with conflict. The control variables
remain the same, with the addition that some of the dependent variables also are control variables for
5
More correctly these variables were formulated using the MARGene program available through the web site maintained by the Minorities at Risk
Project. http://www.cidcm.umd.edu/inscr/mar/margene.htm.
other dependent variables. For instance, repression is a good argument for why mobilization might
occur and migration is a sure source of mobilization. So, in each model, the three dependent
variables from the other three models will be considered as a control.
Table 3 shows the findings of the models. In this analysis the modeling is done in the
logistic regression format. The results give mixed results for the relationship between the
mechanisms and the independent variables. For instance, fresh water scarcity is of marginal
significance in relation to repression and separatism, but highly significant to mobilization and
migration. The high level of significance in the relationship between water scarcity and mobilization
and migration is expected and in keeping with the arguments made in this work. However, the data
indicate little support for the hypothesis (M11) about the relation of fresh water scarcity to separatist
sentiment, because the direction of the relationship found in the data is in the opposite direction than
hypothesized.
There is no statistical relationship in the data between loss of arable land and mobilization or
repression and separatism. But, as hypothesized there does appear to be a relationship between loss
of arable land and migration. Regarding loss of forested land we offered two hypotheses, while it is
hypothesized that increased deforestation will increase the magnitude of the mechanisms for
mobilization, repression, and separatism, the data for repression shows no statistical relationship to
deforestation and the relationship between deforestation and mobilization is marginally significant.
The substantive results of the model show us that there is a statistically significant but
modest relationship between loss of arable land and migration and fresh water scarcity and
migration. What is most interesting about the substantive results regarding loss of forested land is
that loss of one percent of forested land is highly significant in relationship to but modest in
magnitude with the level of separatist sentiment. This would indicate that as deforestation increases
it will be likely that separatist sentiment will exist in the at risk minority group. What is also
confirmed is the relationship between deforestation and mobilization, which is also statistically
significant and modest in magnitude.
Water scarcity/pollution as an issue is statistically significant to all of the mechanisms. The
magnitude is greater than any of the other renewable natural resource scarcities. If water
Migration is measured on a 6-point ordinal scale ranging from none (1) to state compulsory (6), with categories for economic hardship migration and
resource deprivation migration. Mobilization is measured by creating a composite score of mobilization activity the score ranges from 0, meaning
mobilization is not used by any of the groups, to 3, meaning there is a high level of mobilization. Repression is variable that measures economic,
social, and political advantage and disadvantage. Scores range from negative two (-2) to 12. The higher the positive number, the greater the amount of
scarcity/pollution becomes an issue, we expect an increase in mobilization activity by the three
largest ethno-political organizations representing groups that are at-risk in a society. Mass migration
of a group would require first that the group mobilize. This indicates that concern for the well-being
of the group is tied to the most important resource known to man—freshwater.
A big surprising finding is that water scarcity actually has a negative impact on separatism.
But we should be cognizant of the fact that when water is a scarcity issue the group in question
would have a scarcity issue whether it secedes or not. And as scarcities are generally regional,
whether the group joins another state or not the scarcity will still exist.
The relationships indicated by the control variables is very unsurprising, with one notable
exception. The spatial concentration of a group is negatively related to mobilization. This indicates
based on the data collected for this study that a concentrated group will be harder to mobilize. This
finding contradicts current wisdom and much literature on the subject and thus bears further
consideration. Of course, it may be a matter of the population size of the group as a whole that bears
on mobilization and not whether or not the group is concentrated in regional pockets in the state in
question.
Overall, the findings in the mechanism models are mixed. Six (6) hypotheses are upheld
through findings that are statistically significant. However, one hypothesis is nullified by a
statistically insignificant finding. This means that 5 hypotheses are not upheld. But there is also the
unwritten hypothesis that there would be no relationship between loss of forested land and migration
that is also upheld. Two hypotheses about migration and two hypotheses about mobilization are
upheld, and one each for repression and separatism. The mixture of results does not diminish the
relationship found between resource scarcities and ethnic conflict, but creates questions about
mechanisms actually being transfer points whereby renewable natural resource scarcities have a
causal effect on conflict.
The findings do indicate support for competition theory and for security dilemma theory.
Competition among groups requires mobilization, which shows likelihood of increase where forested
land is lost and where freshwater scarcity exists. One primary source of competition is an influx of
new population into an area, and migration is seen to increase in likelihood when loss of arable land
occurs or when freshwater scarcity occurs.
disadvantage a group has in the state where they exist at the time of observation. Separatism is dichotomized for use in this discussion as not present
(0) and present (1).
What we do not see yet, is a direct connection between conflict and mechanisms or between
renewable natural resource scarcity and conflict. By creating interactive terms created between the
resource scarcities in question and the mechanisms, we can test the effect of renewable natural
resource scarcity on ethnic conflict.
Third Model Set: Scarcity, Mechanisms And Conflict
We now turn now to the generation of third set of findings. The question here is whether a
relationship exists where scarcities interacting with mechanisms are connected to conflict behavior.
For these models rebellion and protest (as defined earlier) are the dependent variables. The
independent variables will be created using interactive terms between the resource scarcities and the
mechanisms, where a statistically significant relationship was found to exist.
This set of models and findings represent the crux of the statistical analysis of the
relationship between renewable natural resource scarcity and ethnic conflict. Prior research shows a
relationship between the scarcities and conflict and a relationship between the mechanisms and
conflict. However, there is little research in the connection between scarcity-induced mechanisms or
scarcity enhanced mechanisms and ethnic conflict.
The independent variables used in these models are interactive terms showing relationships
between resource scarcities and mechanisms, based on the last set of models. There are a total of six
(6) new variables generated, one for each renewable natural resource scarcity and mechanism
combinations found to have a statistically significant relationship. For freshwater scarcity/pollution
four (4) variables are created (as it was found statistically significant in relation to all four
mechanisms). For loss of arable land and for deforestation, two interactive variables are created
based on previous findings.
For loss of arable land in relation to the mechanisms we turn to consideration of whether or
not there was a 1% or greater loss in a given state in a given year. This dichotomization of loss of
arable land allows an interaction in keeping with the water scarcity interactive variables. A
minimum threshold is created utilizing the dichotomous variable for loss of arable land. While we
appreciate the fact that if there is a sudden loss of 50% of arable land it would enhance issues such as
migration and mobilization, we should also realize that most loss is on the magnitude of one or two
percent, if any, in any given year. In fact, in the data out of 3373 cases 3108 cases show loss of land
of two percent or less.
For loss of forested land we also dichotomize the phenomenon. In 3091 of 3364 cases the
amount of forest lost in a given country in a given year is one percent or less. In fact there is 10% or
greater loss of forested land in only 18 cases. Of these 18 cases there are 7 cases where the data
supplied is finally updated after years of neglect on the part of governments and researchers and
there is no real way to be convinced that the actual loss occurred in that year or was spread out over
several previous years, when no data or faulty data was made available.
Hypotheses
The hypotheses for this section are based on hypotheses tested in the previous sections. As
these hypotheses are for interactive variables they will be designated as IM1…IM2…IM3…IMx.
Because of previous findings the hypotheses do not all suggest increased conflict behavior.
IM1:
IM2:
IM3:
IM4:
IM5:
IM6:
The interaction of migration and loss of arable land will increase ethnic conflict
The interaction of deforestation and mobilization will increase ethnic conflict
The interaction of deforestation and separatism will increase ethnic conflict
The interaction of migration and water scarcity/pollution will increase ethnic conflict
The interaction of mobilization and water scarcity/pollution will increase
ethnic conflict
The interaction of repression and water scarcity/pollution will increase ethnic
conflict
Loss of arable land is statistically significant only in relation to migration. Loss of arable land was
found to be positive in direction with all of the mechanisms except repression. However, while the literature
on migration and mobilization both indicate that loss of arable land is a relevant issue; no relationship was
found in the data used in this work.
The same is true of the relationship of arable land and repression. Similarly, loss of forested land had
mixed results in relation to the mechanisms. There was a statistically significant relationship with
mobilization and with separatism. Counter to conventional wisdom regarding loss of forested land is the lack
of significant relationship with migration. Recent studies on the relationship of forest resources to
insurgencies and ethnic conflict seem in keeping with the finding of a statistically significant relationship with
separatism and loss of forested land.
In relation to water scarcity, only separatism was found without a statistically significant relationship.
This is because when water is scarce all of a society is more interested in survival than in questions pertaining
to the survivability of group identity. With regards to migration, mobilization and repression there were
statistically significant and positively related relationships with water scarcity/pollution.
We utilize several models in this section. The reasoning for the generation of several models is that
not all of the mechanisms interact with all of the scarcities. In order to account for the fact that not all
scarcities were significant in relation to all mechanisms, we use scarcity area specific modeling. This means
three (3) sets of models are run, one considering each of the renewable natural resource scarcities considered
throughout this work: 1) loss of arable land; 2) deforestation; and 3) water scarcity/pollution.
As these models include interactive terms it is important to remember that the number of variables
will increase. To avoid omitted variable bias it is necessary to include the interactive term
(scarcity*mechanism) and both the scarcity variable and the mechanism variable. Having used the dummy
variable for loss or arable land, deforestation, and water scarcity/pollution allows us to measure three things
simultaneously. One, the effect of the mechanism on conflict behavior in the absence of the scarcity (where
the value of the scarcity is zero the value of the interactive term is also zero, the effect on conflict behavior
will be that of the mechanism) is measured. Two, the effect of scarcity in the absence of the mechanism is
measured (where the mechanism is zero and the scarcity has value). Finally, the effect of the interaction on
conflict behavior is also measured when both the scarcity and the mechanism that it is interacted with have
value. This allows us to see whether it is the scarcity or the mechanism that is having greater causal impact
on the conflict behavior.
Loss of Arable Land
One hypothesis is tested in this area, IM1. Here previous research has shown that the loss of arable
land has a significant relationship with migration. The independent variables considered are the interactive
term, migration, and loss of arable land. There are four models two for protest and two for rebellion as the
dependent ethnic conflict behavior. Two of the models, are cross-sectional time-series regressions with panel
corrected standard errors. Two of the models are ordered logistical regressions. The time-series regressions
are used here like they were in previous chapters to check how temporal frames might change the outcomes of
the causal interactions.
Table 4 shows the results of these four models. The LR Chi-Squared for the logit models is 2467.15
(prob .000) for protest and 2794.66 (prob .000). The R-squared for the time-series regressions is .5857 for
protest and .7994 for rebellion; the Wald Chi-squared values are 824.76 (prob .000) for protest and 4105.95
(prob .000) for rebellion. Wald’s test and Bayesian indicator tests show the models to be well specified.
The results of the models show a lack of support for the hypothesis that loss of arable land interacting
with migration will lead to increased ethnic conflict. For neither protest nor rebellion does the interaction
show any statistical significance. In fact, only in relation to protest does migration show any statistical
significance, and loss of arable land shows no significance in relation to either protest or rebellion in this
model.
The control variables show considerable statistical significance in relationship to both protest and
rebellion. Between the logistic and time-series models change in GDP per capita varies in statistical
significance, showing no significance to either protest or rebellion in the time-series model. One interesting
finding is that population growth has a negative relationship with protest in the logistic model and no
relationship with protest at all in the time-series model. Using the central limits theorem, this relationship
shows a 109% likelihood of decreasing protest by one-unit for each SD increase in population growth and an
increase in protest when population decreases one SD.
Loss of Forested Land
Two hypotheses are tested in this area, mobilization and separatism interacting with loss of forested
land leading to increased ethnic conflict. The independent variables that are considered in the models are loss
of forested land, mobilization, separatism, loss of forested land interacting with mobilization, and loss of
forested land interacted with separatism. The control variables are the same ones used throughout this work,
accounting for population, economic well-being, state regime type, spatial concentration of the group, and
where appropriate to the model, the lag of the dependent variable.
Table 5 details the findings of the models dealing with deforestation. For the logit models the LR
Chi-Square values are 2392.1 for protest and 2566.44 for rebellion the probability for both is .0000. For the
cross-sectional time-series regressions the R-square values are .3024 for protest and .7918 for rebellion, the
Wald Chi-square values both have a probability of .0000 and the value for protest is 1508.47 and for rebellion
2906.03.
The interaction of deforestation and mobilization was found to be significant to the explanation of
protest, but not rebellion. The interaction of deforestation and separatism was only found to be significant in
explaining rebellion. While it was statistically significant only in the cross-sectional time-series model,
deforestation and separatism were marginally outside significance in the logistic regression model. Were it
statistically significant, deforestation interacting with scarcity increasing by one SD would increase the
likelihood of rebellion increasing one-unit by 57.1%. Yet, mobilization was found to be significant in relation
to conflict behavior, by itself, in all of the models, and separatism was found to have not significant
relationship with conflict behavior in any model.
Population growth is unrelated to either protest or rebellion in this model. Change in GDP per capita
is relevant to protest behavior. The spatial concentration of the group is important to both protest and
rebellion, a group that is more concentrated in a geographical region of the state is likely to engage in protest
or rebellion.
Regime type is significant to protest according to both the logit and the cross-sectional time-series
regression models. This finding makes intuitive sense, as the score for regime type goes up, it represents a
more open and democratic state, in such a state protest is tolerated. The interesting finding is that in the logit
model the same relationship exists for regime type’s relationship with rebellion. This indicates that as a state
becomes more democratic or open that there is an increase in the likelihood of conflict behavior. But this
relationship is not also found in the regression model.
Water Scarcity/Pollution
In this area three of the interactions are considered: 1) migration; 2) mobilization, and 3) repression.
All interactions were all found to have statistically significant relationships with water scarcity. For the
modeling done in this section this means seven independent variables must be considered: migration
interacted with water scarcity; mobilization interacted with water scarcity; repression interacted with water
scarcity; migration, mobilization; repression; and, water scarcity. The same control variables are considered
in this model as in the other models.
Table 6 shows the findings for the models dealing with fresh water scarcity issues. The obvious
indication is the dichotomy of conflict behavior. All of the interactive terms are statistically significant in at
least one model for rebellion. None of the interactive terms is statistically significant for any of the protest
models.
Of interest in these findings is that where freshwater scarcity interacts with the appropriate
mechanism, the result is an increase in the relationship with and likelihood of violent ethnic conflict.
Only
water scarcity interacting with migration is not found statistically significant in both models of ethnic
rebellion. Just as interesting is, in the logistic model for protest water scarcity, has a statistically significant
relationship with protest behavior.
Among the control variables, growth of the population has little significance in the models of conflict
behavior. Change in GDP per capita has significant explanatory presence in considering protest, but not
rebellion. Regime type and group concentration both have statistical significance in explaining protest and
rebellion.
Inclusive Findings
In the case of loss of arable land, the findings in this chapter do not support the idea that loss of arable
land is a source of ethnic conflict. In the case of loss of forested land, deforestation and mobilization are
related to protest while deforestation and separatism is related to rebellion (in one model). However, it
appears that mobilization is the driving force in the relationship with protest. But, neither separatism nor loss
of forested land by itself seems to drive the interactive variable’s relationship with rebellion. In the case of
water scarcity/pollution it is evident that water scarcity/pollution drives the relationship found between the
interactive terms and rebellion—where water scarcity is significant and none of the mechanisms are
significant. The relationship between the mechanisms and conflict behavior is significant in relationship to
protest—where none of the interactive terms are significant and water scarcity is significant in one model.
This finding adds significant support for security dilemma theory as a source of explanation for the
relationship. After all, the security dilemma is about violent conflict occurring as a result of actions taken to
create security. A key finding is water, so crucial to human life, is the one resource for which fighting will
always be a viable option.
A few things are made clear about the control variables. One, growth in population is marginally
related to ethnic conflict behavior. Two, change in GDP per capita, the measure of economic well-being in
this work, impacts protest, but not rebellion. Regime type continues to be related to protest behavior in the
same way as seen in the earlier models and group concentration continues its relationship with protest and
rebellion in the same ways seen earlier in this work. Table 7 shows the results for the hypotheses offered in
this chapter specified for the type of conflict behavior.
FINDINGS AND CONCLUSIONS
One genuine thread emerges from this statistical analysis of the relationship between renewable
natural resource scarcity and ethnic conflict—there is a relationship, and it is empirically determinable. Some
notable scholars of environmental policy and international environmental politics, and a few environmental
security scholars, have forwarded the idea that studying the relationship between renewable natural resource
scarcity and ethnic conflict is not fruitful and is indeed detrimental to both the study of environmental politics
and ethnic conflict. We find that the relationship does exist and that it can be empirically proven.
In our findings, half of the hypotheses that are offered regarding the relationship between renewable
natural resource scarcity and ethnic conflict are substantiated. Furthermore, several of the substantiated
hypotheses show substantive relationships of a positive nature exists between renewable natural resource
scarcity and ethnic conflict.
When care is taken to observe how renewable natural resource scarcities channel into conflict
behavior, a more discernable picture emerges. Water scarcity and loss of forested land have strong statistical
relationships with two or more channeling mechanisms. Loss of arable land shows a strong relationship with
migration. All of the mechanisms by which renewable natural resource scarcities are argued to impact
conflict behavior are statistically significant with the conflict behaviors. When we interact the mechanisms
with the resource scarcities, these enhanced mechanisms do create a situation where ethnic conflict might
increase in five out of six hypothesized relationships (see Table 6).
Some of the criticism is on target; we cannot hope to explain all acute conflict as a result of renewable
natural resource scarcity. It appears as if loss of arable land, while related to conflict behavior statistically,
does not have a method of forcefully transferring into conflict behavior. The other scarcities, deforestation
and freshwater scarcity, both forcefully translate into ethnic conflict. In fact, the surprising issue of the
findings at this point is that the resource scarcities interaction with causal mechanisms is likely to trigger or
increase open armed conflict (shown as rebellion in the analysis) rather than protest. In the five hypotheses
for which support was found, four are related to rebellion behavior and one to protest behavior. The findings
point to scarcities as tipping points rather than initial causal elements leading to conflict behavior. What this
points to concerning the theoretical framework of this project is that the interactions are more supportive of
security dilemma theory than competition theory.
Competition for a resource can generate conflict, but does not seem to lead to armed conflict.
However, where a dilemma exists, competition over the resource shows a greater propensity toward a positive
relationship with armed conflict behavior. Another way of examining the question of theoretical support is
whether or not it is intuitive that competing for renewable natural resources should lead to armed conflict?
Does it follow that ethnic groups competing for position in a society will engage in armed conflict? The
intuitive answer is no, engaging in armed conflict would lessen the likelihood or endanger the likelihood of
your group advancing in position relative to another group. The payoff for the competition is endangered by
armed conflict. Competition theory supports and is supported by the finding related to protest behavior. The
validity of competition theory is not diminished by the findings related to rebellion behavior, but the findings
do not give more weight to competition theory as the best explanation of the relationship between renewable
natural resource scarcity and ethnic conflict.
Rebellion behavior finds its greatest support from security dilemma theory. Actions taken by a state
to increase its security make it less secure because those actions threaten the security of certain ethnic groups.
Actions taken by ethnic groups to secure themselves make the state or other groups less secure. In either case,
armed conflict is a likely response. This means that the ethnic group is now engaged in armed rebellion
against the state in response to state action or as a result of their own action. The findings in this study
support a greater relationship between renewable natural resource scarcities transmitted through certain
mechanisms being positively related to increased rebellion behavior. Thus the greater findings of this
research are in support of the ethnic security dilemma as the theory explaining the phenomenon and supported
by the phenomenon.
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Table 1 Results of Ordered Logit Models for Protest/ Rebellion
Variables
Protest
Rebellion
.0083
.0376*
Percentage of Lost Arable Land
(.0224)
(.0306)
.0022
.0043
Percentage of Lost Forested Land
(.0187)
(.0242)
.1125*
.2241**
Water Scarcity/
(.0770)
(.1063)
Pollution
.00005*
.00003
Change in GDP Per Capita
(.00002)
(.00003)
-.00003
.000003
Group Population Growth
(.00003)
(.00004)
.0659*
.2684***
Group Spatial Concentration
(.0345)
(.0630)
.027***
.0119
Regime Type
(.0057)
(.0076)
1.7476***
1.7023***
Lag of Dependent Variable
(.0437)
(.0480)
MODEL 1: N = 3093, LR Chi-Sq. = 2384.80 Probability = .0000, Psuedo R–Sq. = .2602
MODEL 2: N = 3128, LR Chi-Sq. = 2668.92 Probability = .0000, Psuedo R-Sq. = .3921
Standard Errors given in parentheses in cells. Level of Significance: *.10, **.05, ***.01
TABLE 2. Panel Corrected Standard Error Time Series Regression
Variable
Protest
Rebellion
.0032
.0043
Percentage Loss of Arable
(.0151)
(.0100)
Land
.0061
Percentage Loss of Forested .0009
(.0103)
(.0074)
Land
.0456*
.0121
Freshwater Scarcity
(.0427)
(.0314)
.00002**
.000006
Change in GDP Per Capita
(.00001)
(.000003)
-.00001
.000002
Group Population Growth
(.00002)
(.000005)
.0333**
.0322**
Group Concentration
(.0129)
(.0159)
.0107**
-.0046**
Regime Type
(.0039)
(.0024)
.7442***
.8806***
Lag of Dependent Variable
(.0515)
(.0323)
.3228***
.0541**
Constant
(.0737)
(.0312)
Significance for coefficients: * .10, ** .05, ***.001, Std. Errors in parentheses in cells
Model 3: N = 3154, R-Sq. = .1317, Wald Chi-Sq. = 1249.39 Probability = .0000
Model 4: N = 3162, R-Sq. = .0814, Wald Chi-Sq. = 1636.85 Probability =. 0000
Table 3. Results for Ordered Logistical Regressions
Variable
Migration
Mobilization
Repression
Separatism
.05984**
.0197
-.0069
.0145
Loss of Arable
(.0279)
(.0244)
(.0219)
(.0279)
Land
-.0014
.0356*
-.0123
.1031***
Loss of Forested
(.0234)
(.0223)
(.0189)
(.0263)
Land
.2683***
.2681***
.1333*
-.1648*
Water
(.0851)
(.0800)
(.0747)
(.0913)
Scarcity/Pollution
.00003***
.000004
.000001
.00002
Change in GDP
(.000008)
(.000008)
(.000007)
(.00003)
Per Capita
-.0000004
-.000007**
.000002
Population Growth .00002***
(.000004)
(.000003)
(.000003)
(.00003)
-.3472***
-.1594***
.0264
.5204***
Group
(.0324)
(.0386)
(.0363)
(.0426)
Concentration
-.0572***
.0203***
.0625***
-.0254***
Regime Type
(.0070)
(.0065)
(.0060)
(.0068)
NA
.0314
.3212***
.0881***
Migration
(.0219)
(.0198)
(.0254)
.0697**
NA
-.1561***
.3286***
Mobilization
(.0324)
(.0291)
(.0362)
.1827***
-.0656***
NA
-.0217*
Repression
(.0126)
(.0119)
(.0133)
.1560***
.3501***
-.1704***
NA
Separatism
(.0336)
(.0313)
(.0290)
Std. Error in Parentheses, Significance > .1*, .05**, .01***
N=2456, LR ChiSq. = 408.38 (.0000)[migration], 213.15 (.0000)[mobilization], 465.87 (.0000) [repression],
499.55 (.0000)[separatism]
Table 4 Logit/Regression Results for Loss of Arable Land
Variable
Protest Logit
Protest TimeRebellion Logit Rebellion TimeSeries
Series
Regression
Regression
-.00005
.0004
-.0193
-.0047
Loss of Arable
(.0374)
(.0181)
(.0496)
(.0195)
Land,
Migration
-.0056
-.0018
-.0300
-.0042
Loss of Arable
(.0277)
(.0185)
(.0349)
(.0125)
Land
.0259*
.0109*
.0135
.0016
Migration
(.0219)
(.0073)
(.0292)
(.0147)
-.00004*
-.00002
.0000003
.0000003
Growth in
(.00003)
(.00002)
(.00005)
(.000005)
Population
.00005**
.00002**
.00004
.0000006
Change in GDP
(.00002)
(.00001)
(.00003)
(.000003)
Per Capita
.0236***
.0101**
.0128*
-.0047*
Regime Type
(.0059)
(.0039)
(.0079)
(.0025)
.0481
.0241*
.2456***
.0295*
Group
(.0365)
(.0126)
(.0656)
(.0169)
Concentration
1.764***
.7499***
1.706***
.8810***
Lag of
(.0448)
(.0508)
(.0492)
(.0331)
Dependent
Variable
NA
.2897***
NA
.0495
Constant
(.0709)
(.0409)
*p>.1, **p>.05, ***p>.001 (std. deviation in parentheses) N = 2744 (protest) N = 2779 (rebellion)
Table 5 Logit/Regression Results for Loss of Forested Land
Variable
Protest Logit
Protest TimeRebellion Logit Rebellion TimeSeries
Series
Regression
Regression
.0086*
.0017*
-.0137
-.0042
Deforestation,
(.0725)
(.0372)
(.0909)
(.0367)
Mobilization
.0003
.0002
.0009
.0005*
Deforestation,
(.0009)
(.0004)
(.0012)
(.0003)
Separatism
-.0016
-.0017
.0042
-.0041
Loss of Forested
(.0213)
(.0105)
(.0275)
(.0071)
Land
.1101***
.0540**
.1213**
.0368**
Mobilization
(.0322)
(.0187)
(.0449)
(.0134)
.0001
.0001
-.0006
-.00007
Separatism
(.0003)
(.0001)
(.0004)
(.0001)
-.00003
-.00001
-.000001
.000002
Growth of
(.00003)
(.00002)
(.00004)
(.000005)
Population
.00005**
.00002*
.00004
.0000004
Change in GDP
(.00002)
(.00001)
(.00003)
(.000003)
Per Capita
.0227***
.0106**
.0148*
-.0021
Regime Type
(.0060)
(.0039)
(.0082)
(.0025)
.0715**
.0370**
.2812***
.0339**
Group
(.0355)
(.0155)
(.0649)
(.0148)
Concentration
1.750***
.7410***
1.703***
.8664***
Lag of
(.0457)
(.0513)
(.0512)
(.0327)
Dependent
Variable
NA
.2406***
NA
-.0062
Constant
(.0637
(.0309)
*p>.1, **p>.05, ***p>.001, std. deviations in parentheses, N = 2642 (protest) 2676
Table 6 Logit/Regression Results for Fresh Water Scarcity/Pollution
Variable
Protest Logit
Protest TimeRebellion Logit Rebellion TimeSeries
Series
Regression
Regression
.0157
-.0031
.0672
.0258*
Water Scarcity,
(.0481)
(.0177)
(.0679)
(.1067)
Migration
-.0175
.0044
.1559*
.0312*
Water Scarcity,
(.0662)
(.0249)
(.0964)
(.0223)
Mobilization
.0038
.0081
.0678*
.0216**
Water Scarcity,
(.0247)
(.0109)
(.0395)
(.0088)
Repression
.0576*
.0605
1.151**
.1141*
Water Scarcity
(.2133)
(.0848)
(.3379)
(.0734)
-.0215
-.0037
-.0334
.0226
Migration
(.0318)
(.0108)
(.0543)
(.0148)
.1284**
.0552**
.0532
.0208
Mobilization
(.0483)
(.0202)
(.0655)
(.0162)
.0379**
.0138**
-.0063
-.0049
Repression
(.0183)
(.0061)
(.0270)
(.0070)
-.00005*
-.00002
.000005
.0000006
Growth in
(.00003)
(.00002)
(.00005)
(.000005)
Population
.00005**
.00002**
.00001
-.000001
Change in GDP
(.00002)
(.00001)
(.00005)
(.000003)
Per Capita
.0175**
.0080**
.0261**
-.0012
Regime Type
(.0068)
(.0041)
(.0097)
(.0023)
.0613
.0039*
.2621***
.0339**
Group
(.0386)
(.0191)
(.0712)
(.0155)
Concentration
1.724***
.7361***
1.818***
.8707***
Lag of
(.0486)
(.0529)
(.0598)
(.0311)
Dependent
Variable
NA
.2009**
NA
-.0177
Constant
(.0775)
(.0815)
*p>.1, **p>.05, ***p>.001, std. dev. in parentheses, N = 2276 (protest) 2308 (rebellion)
Table 7 Results for Hypotheses IM1 through IM6
Hypothesis
Protest
Migration and Arable Land No Relationship
Increased
Mobilization and
Deforestation
No Relationship
Separatism and
Deforestation
No Relationship
Migration and Water
Scarcity
No Relationship
Mobilization and Water
Scarcity
No Relationship
Repression and Water
Scarcity
Rebellion
No Relationship
No Relationship
Mixed Results, shows
Increase
Mixed Results, shows
Increase
Increased
Increased