renewable vs. non-renewable resources as … and st. marie apsa...renewable vs. non-renewable...

Renewable vs. Non-Renewable Resources as Causes

of Conflict in Sub-Saharan Africa, a Time-series

Analysis: 1970-2000

Samuel S. Stanton, Jr.

Grove City College

[email protected]

Joseph J. St. Marie

University of Southern Mississippi

[email protected]

Prepared for presentation at the American Political Science Association Annual

Meeting 2008, Boston, MA.

PLEASE DO NOT CITE WITHOUT PERMISSION OF THE AUTHORS

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Renewable vs. Non-Renewable Resources as Causes of Conflict in

Sub-Saharan Africa, a Time-series Analysis: 1970-2000

Truly the “Heart of Darkness” appears to remain a “fire zone. Where

poets speak their heart then bleed for it.”1 Over the past thirty-five years, non-

African developing countries experienced a decrease, while African countries

experienced an increase in the outbreak of civil war (Collier and Hoeffler, 2002).

While Africa and Asia are the primary locations of civil war today and were the

primary locations of civil war during the Cold War, this work focuses on Sub-

Saharan Africa. We choose Africa because of the growing publicity African

conflicts receive and because of the general focus on Africa and Asia that

emerges in the literature on civil war.

Civil War

The current primary arguments about civil war consider the importance of

greed vs. grievance in explaining human behavior (Collier 1999, 2002, 2007,

Fearon and Laitin 2003, Oechslin 2006). Greed vs. grievance has been further

amended to consider feasibility (economic viability) and motivation (socio-

political inequality), with the caveat that greed promotes feasibility of civil war

and grievance is more motivational. This paper examines both economic

feasibility and socio-political inequality perspectives as greed vs. grievance in the

examination of civil war in Sub-Saharan Africa. Creating a nexus between the

literature on resource wars and renewable natural resource scarcities, this

research analyzes the affects certain resources (both renewable and non-

renewable) have on the occurrence, duration, and magnitude of civil wars in sub-

Saharan Africa.

This paper examines oil, diamonds and forested land2 as non-renewable

resources that feed the greed and feasibility hypotheses. The paper examines

freshwater and arable land, as renewable natural resource causes of civil war.

Renewable natural resources are sources of inequality that lead to grievance and

motivation for conflict.

The importance of continued study of civil war falls into three areas. First,

while conflict appears to be subsiding in the world, civil wars do continue and

according to Bennett and Stam (1996) civil wars last about last about seven (7)

1 Borrowed from Robert Conrad and from the lyrics of “One Tree Hill” by U2.

2 We are well aware that trees are a renewable natural resource. However, the demand for forestry

products, particularly derived from old growth hardwoods, leads us to accept arguments that forestry

products are part of the resources governments and rebels covet control over for monetary reasons.

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years, while international conflicts last only about eleven (11) months on average.

Secondly, civil wars are fought between parties that share and if both survive,

will continue to share the same territorial limitations (as created by international

borders) after the conflict concludes. Finally, civil war can be viewed as a

conflict where defeat can mean the end of the existence of one or more parties to

the war, making compromise in the settlement of the conflict difficult at best

(Licklider, 1995).

The primary question we seek to answer in this paper is: whether greed or

grievance natural resources provide a better causal explanation for civil war in Sub-

Saharan Africa. We examine the years 1970 to 2000 utilizing data from the

Correlates of War, Civil War (Singer and Small 1994, updated by Collier, et al.

2007) using cross-sectional time-series models to test the impact of the greed vs.

grievance hypotheses on the occurrence, duration and magnitude of civil wars in

sub-Saharan Africa. Preliminary indications find a stronger case is made for

grievance and motivation hypotheses in the explanation of civil wars in Sub-

Saharan Africa.

To answer this intriguing question we divide the paper into six sections.

The first and second examine civil war and its causes. The Greed versus

Grievance hypotheses are introduced and explained. The third section outlines

the hypotheses we use in testing our models. The fourth section introduces the

data and explains the variables used in the analysis. The fifth section presents

the results of the statistical analysis. Finally, we will offer some conclusions

about the greed vs. grievance arguments, the veracity and quality of our own

effort, and the direction for future research on civil war in Sub-Saharan Africa.

The Causes of Civil War

We must first establish the definitions of “greed” and “grievance” before

looking at what exact factors are considered in testing these general ideas as

causes of civil war. Greed quite simply is the desire to accumulate more of

something to yourself or to the group you represent. In most cases of greed the

desired object has monetary value. Accumulating a large amount of something

of monetary value is perceived and in many cases translated into political power.

Simply put, wealth begets wealth. Grievance refers to a person or group of

people who are socially or politically differentiated from others, perhaps another

ethnic or religious group. The reality or the perception on the part of the

aggrieved group is that this differentiation, be it artificial or real, is negative

toward them. This is akin to an in-group/out-group situation where benefits

flow to the in-group but not he out-group creating hostility. An example of

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extreme grievance would be a constitutional provision that prohibited members

of a certain group within the population from all forms of political participation

in the state. This group would definitely be aggrieved.

The Greed Hypothesis

At the center of the greed v. grievance debate is the work of Paul Collier,

who along with Anke Hoeffler and others have authored several works in the

last decade arguing that greed is the primary cause of civil war based on study of

income growth, plunder of natural resources, financial ability to maintain

military activity.3 Greed by all apprearance allows rebel movements to maintain

viability (Collier 1999, 2007; Collier and Hoeffler 1999; Humphreys 2002).

The primary argument of the greed hypothesis is that groups challenge

the state for control of resources of monetary value. The resources primarily fall

into what would be considered non-renewable natural resources. Non-

renewable natural resources are those resources that take over one human

generation to replenish themselves. Most non-renewable natural resources

require hundreds of years to regenerate and require great human effort to

retrieve. Among the resources that are considered to be non-renewable would

be oil, natural gas, diamonds, gold and silver, gemstones, and minerals. One

renewable natural resource is usually accepted as a greed resource—forests.

Forests provide a readily accessible source of both income and cover. While our

paper is about renewable vs. non-renewable resources, we will allow forested

land to act as a greed resource rather than a grievance resource.

Most of the recent research has tested the greed hypothesis. Greed has

been tested primarily in terms of commodity exports and done in either 5 year

periods or country years (Collier and Hoeffler 2002, DeRouen and Sobek 2004,

Fearon 2005). It has also been tested using income and economic growth (Collier

and Hoeffler 2002, Fearon 2005). If studied in 5 year periods, exports and

economic growth show a statistically significant relationship with civil war onset

(Collier and Hoeffler 2002, Fearon 2005), but not when studied in country year

format (Fearon 2005). In DeRouen and Sobek (2004) exports showed statistical

significance in relation to multiple forms of civil war termination. Fearon (2004)

includes contraband (illegal drugs, illegal trade in precious gems and minerals)

in his study and finds that contraband was statistically significant in relation to

civil war duration. Hegre etal (2001) include a variable called “development” in

3 Normally this citation would be given in text, but length leads to this footnote. Collier (1999, 2000, 2001,

2007); Collier and Hoeffler (2002, 2004), Collier, Hoeffler and Söderbom (2006); Collier, Hoeffler and

Rohner (2007); Collier and Sambanis (2002).

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their model, which measures per capita energy consumption (2001, 37) yet did

not find it to be related to outbreak of civil war. This is important to note for

greed arguments, as control of energy production resources is pootentially a

great source of wealth. Lujala et al (2005) finds the presence and production of

both primary and secondary diamonds are statistically significant in relation to

civil war onset between 1945 and 1999. DeRouen and Sobek (2004) included a

variable measuring forested land and found this to statistically significant in

relation to multiple forms of civil war termination.

The Grievance Hypothesis

Grievance has also been tested repeatedly. Grievance is tested primarily

in terms of political form, linguistic differences, and religious differences

(DeRouen and Sobek 2004, Fearon 2004, 2005, Hegre etal 2001). The U-shaped

curve has been repeatedly demonstrated in these studies, showing conflict more

likely in transitionary states found in the lower scores of autocracy and

democracy than in either highly authoritarian or highly democratic states.

Interestingly ethnic fractionalization measured by ethnic linguistic

fractionalization (ELF) has not show itself to be highly related to onset or

duration of civil war, except when measured in five year periods (Fearon 2005).

Our work looks primarily are resources as the elements of greed and

grievance. Ross (2004) offers four (4) conclusions about resources and civil war.

1) oil dependence is linked to civil war onset, but not duration;

2) gemstones, opium, coca, and cannabis are not linked to onset, but

lengthen existing wars, and timber’s role is untested;

3) no statistical evidence links agricultural commodities to initiation or

duration of civil war;

4) claims that primary commodities are associated with the onset of civil

war are not very robust (Ross 2004, 352).

Ross is correct in stating that statistical evidence does not exist about agricultural

commodities as a cause of civil war. This does not mean that agriculture per se

has not been considered as an element of conflict. In studying the effects of

resource degradation on conflict behavior, Porter (1995) Baechler (1998), Homer-

Dixon (1994, 1999), and Klare (2001) each point out the negative affects of

resource degradation on the welfare of a society. Baechler and Homer-Dixon

particularly note the causal relation of resource degradation to conflict behavior.

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These scholars have also focused on renewable natural resources as

sources of conflict. Homer-Dixon (1999) refers to the social consequences of

renewable natural resource scarcity and how this may lead to conflict. Of the

renewable natural resources considered by scholars studying the relationship

with conflict behavior, freshwater, arable land, and forested land are those most

often considered to have a strong relationship with conflict behavior (Baechler

1998, Deudney 1990, Hauge and Ellingsen 1993, Porter 1995)

Renewable natural resources in relation to conflict behavior are most

appropriately viewed as grievance mechanisms. The scarcity of a natural

resource can affect all people in society equally, but scarcity can also be

structural, that is caused by the policy of a state. When scarcity is structural it

has the potential to be used against opponents of the state. An example of this is

a policy that makes ownership of water rights illegal for a certain group in a

society. This group is now deprived of one of the necessary elements of life.

This sort of deprivation is a grievance, desire to have access to a required

element of human life is not greed, it is a desire to survive.

Based on the literature we find there is a categorical difference between

the resources that are greed based (sources of monetary wealth and income gain)

versus grievance resources (those resources necessary to support life). This

resource dichotomy provides an interesting means of studying civil war by

testing both the grievance hypotheses regarding renewable natural resources and

civil conflict and the greed hypotheses of civil war in determining which is a

better explanation for civil war in Sub-Saharan Africa, where both greed

resources and grievance resources are present (often both in abundance in the

same country).

We believe that renewable natural resources are a stronger causal

explanation for civil violence based on the literature of environmental security

and the wide divergence of findings regarding greed resources and civil war.

However, in this work we test greed and grievance equally since there is no

existing research that shows either greed or grievance to be beyond a shadow of

a doubt the stronger cause of civil war.

Hypotheses

The dependent variable in this paper is civil war in sub-Saharan Africa.

This study identifies a conflict as a civil war if it includes organized military

action, at least 1,000 battle deaths in a given year, at least five percent of the

deaths which were inflicted by the weaker party, and the national government at

the time was actively involved, as defined by Singer and Small (1994). These

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figures will be used from the original Correlates of War (COW) dataset developed

by Singer and Small, as well as the updated Armed Conflict Dataset by Nils Petter

Gleditsch (2002). Our study also considers two important aspects of civil war—

duration, the temporal length of the conflict, and magnitude, the number of

deaths caused by the conflict. With this in mind we offer the following

hypotheses:

Greed based hypotheses:

H1: Presence of greed resource production will cause an increase in the

likelihood of civil war onset


duration of civil war


magnitude of civil war

Grievance based Hypotheses:

H4: Renewable natural resource scarcity will cause an increase in the likelihood

of civil war onset

H5: Renewable natural resource scarcity will cause an increase in the duration of

civil war

H6: Renewable natural resource scarcity will cause an increase in the magnitude

of civil war

We do not discount greed as a potential cause of civil war, even though our

sympathies based on extant literature appear to support grievance. Instead we

propose through our hypotheses to test each with equal rigor. We now turn to a

discussion of the data that will allow us to operationalize concepts found in the

literature and hypotheses and finally to the models used in testing this data.

Data and Variables

Our data is grouped in a country-year format. We examine 46 countries in

continental Sub-Saharan Africa from 1970-2000. The yearly panels are not equal,

as not all countries in Sub-Saharan Africa were independent in every year

covered in this study. The data also does not include the smaller, island-states of

Sub-Saharan Africa, nor does it include Lesotho or Swaziland. The choice to not

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include these states is based on the large amount of missing data for these

countries. The dataset as complied has 1207 cases.

The dependent variable will test is civil war. We want to test the viability

of greed, measured as natural resources, in relations to three aspects of civil

war—onset, duration, and magnitude. This requires 3 models utilizing 2

different techniques (OLS and Logit regression) for measuring the statistical

relationship.

The first and fourth hypotheses (likelihood of civil war) use a

dichotomous variable, with “0” indicating a lack of civil war and “1” indicating

the occurrence of a civil war. This data was gathered from Nils Petter Gleditsch’s

updated version of the Correlates of War data (2004), originally compiled by

Singer and Small (1994).4 The Correlates of War data is the premier study and

analysis of civil wars. The data shows 202 cases where civil war occurred. In the

vast majority of cases (1511) no civil war occurred. This is of little surprise, as

violent conflict of any type is a rare occurrence.

The second and fifth hypotheses use duration of civil war as the

dependent variable, ranging from “0-12” with “0” indicating a lack of civil war

occurring in that year and increasing to “12” indicating that a civil war

composed all twelve months of that year, using the original COW data for the

years 1960 – 1997 and the Uppsala Conflict Data Program and International

Peace Research Institute, Oslo UCDP/PRIO Armed Conflict Dataset for the years

1998 – 2000 (2002).5 The mean value of duration is .91, indicating that in all 1713

cases an average civil war lasted less than one month. If we exclude the cases

where no civil war occurred in the country-year observed, however, the mean

value of duration is 9.48. This indicates that when a civil war did occur in a

given country in a given year, the average duration of an actual event was

approximately nine and one-half months.

Onset and Duration have been tested repeatedly in the study of civil war.

Magnitude, however, has received less coverage. In part this is due to the

difficulty in measuring magnitude. Is the magnitude of civil war to be construed

simply as the number of battle deaths in a calendar year or is it the total of all

deaths? We must recognize that the magnitude of conflict could be measured in

both military deaths and total deaths. Azam (2002) points out in a limited look at

civilian vs. military deaths in African civil wars, that the differences are often

staggering. For instance in the Angolan Civil War there were 21,000 military

deaths over 16 years (1975-1991) and 320,000 civilian deaths in the Sudan

4 Civil war occurrence data is available at: http://new.prio.no/CSCW-Datasets/

5 Duration of civil war data available at: http://correlatesofwar.org/datasets.htm

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between 1983 and 1990 there were 10,000 military deaths and 500,000 civilian

deaths (Azam 2002, 132).

Civil war magnitude despite being discussed has not often been tested.

Thus, we propose a test. We test magnatude with the best and most complete

data available which is battle deaths per year. Our two remaining hypotheses

use a scalar dependent variable, measuring the magnitude of each civil war

using the number of battle deaths per year, from 1960 – 1997. This data was

gathered from Bethany Lacina’s Battle Deaths Dataset 1946 – 2005 provided by the

Centre for the Study of Civil War and the International Peace Research Institute,

Oslo (2006).6 While not a perfect measure, the numbers are still sobering to

consider. The mean value of magnitude for all the cases in our data, measured as

battle deaths per year, was 553.60 or roughly 554 battle deaths per year. If we

limit this to actual observed cases of civil war occurrence, the mean value for

magnitude is 5357.79. This indicates that when a civil war did occur in a given

country in a given year, the average number of battle deaths was nearly 5358 per

year.

Independent Variables

Our independent variables fall into two categories, those we believe are

associated with greed and those that are associated with grievance. We address

greed first, and then grievance. Certainly the list for each in not exhaustive, but

given data limitations these variables are the best candidates to conduct our tests.

Moreover, many have been used extensively before in published work

establishing their theoretical significance.

Greed Variables

Oil Producing State This variable measures whether a state was an oil

producing state in the year of observation. 397 of the 1207 cases in our data were

oil producing states in the given year of observation.

One-Third of Export GDP from Fuels Because oil as a resource is a greed

resource, it is necessary to test whether greed comes from larger share of GDP

from the production of oil. This measure is taken from Fearon and Laitin (2003).

119 of 1207 cases were countries in which fuels accounted for one-third or more

of export GDP in the given year.

6 Magnitude of civil war data available at: http://new.prio.no/CSCW-Datasets/

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Diamond Producing State Diamonds have been argued to be one of the

primary sources of funding for civil war and much of the atrocities in Africa.

The presence of blood diamonds requires that a state be a diamond producing

state. Borrowing from the World Development Indicators we use a dichotomous

variable that measures whether a country was diamond producing. This is not

the best measure possible, which would be volume or dollar amount per year of

diamond production. However, secrecy within that industry makes such

numbers difficult to find and unreliable when found. 440 of 1207 cases were

diamond producing countries in a given year in our dataset.

SXP (Interpolated) Lacking a quality measure for the years 1970-2000 for

actual oil and diamond production we use interpolated sxp (Fearon 2005) a

measure of the percentage of GDP represented by primary commodity exports.

Primary commodities include minerals, ores, metals, and unrefined oil. The

interpolation is necessary because this indicator is only available through the

World Bank in five year measurement points. The mean value of SXP was .1761,

indicating that for the average country in a given year of the data the portion of

export GDP from primary commodities was almost 18%. The highest level of

SXP was 56%. The standard error of the mean is .0039.

Percentage of Lost Forested Land This measure gives the actual amount

of forested land lost in a country in a given year from the amount of forested

land present in the previous year. We use this measure to test for economic gain

from timbering, which if reports in many news sources are to be believed, is a

major source of deforestation. This percentage is based on forested land

estimates found in CIA, Keesing’s World Record, United Nations, and World

Development Indicator data. The mean percentage of lost forested land was

.2216% with a standard error of .0298. For most countries the sources used

indicate the loss of no forested land (1096 of 1207 cases have loss of 0%). At the

extreme end of the spectrum one country reported in one year the loss of 20% of

forested land.

For each of the above variables we also have created one and two year

lagged variables. It is entirely likely that desire to control wealth generated by

these resources is built over time as those outside and inside government see the

wealth generated by this resource in previous years and determine to take

control of the resource by force in the current year.

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Grievance Variables

Percentage of Arable Land Lost This measures the percentage of arable

land lost from the previous calendar year. The measurement is based on the

same data sources as forested land. The belief is that land is a grievance resource

given that all people must eat. Conversely it is not greed that makes people

desirous of arable land when most people are involved in subsistence

agriculture—as is the case in Sub-Saharan Africa—it is merely survival. The

average loss of arable land was .2264% with a standard error of .0338. 1103 cases

show no loss of arable land, the extreme case showed loss of 24% of arable land.

Where loss occurred the norms were 1% (47 cases) and 2% (26 cases).

Freshwater Scarcity Accurate data for the cubic feet of freshwater

available in a country is not readily available for most of the world, and

particularly not for much of Sub-Saharan Africa. Simply put, people require

water to live. Not having access to fresh, potable water is a grievance. We turn

to the question of freshwater scarcity and pollution as an environmental concern.

In the CIA country data, Keesing’s country data, and the United Nations country

data, there are a sections that mention environmental concerns. If that section

mentions freshwater scarcity and/or pollution as an environmental concern in at

least two of the three sources, we coded that as a 1 (freshwater scarcity/pollution

present), if this was not listed as a concern in at least two of the sources, we

coded it as 0 (freshwater scarcity/pollution not present). 711 cases showed

freshwater scarcity/pollution was an issue of concern in the country in that year.

495 cases showed no concern for this issue in the country in the given year.

As with greed variable, those things leading to grievance may take time to

fully cause the population or the state to take violent action. A drought may last

only a year before policy is enacted that protects farmers from its ill effects, or

after two years no action may have been taken to protect the farmers and they

may decide it is necessary to take up arms against the state for the purpose of

protecting their lives. Thus, with these variables we also include lagged

variables of one and two years that are tested in our models.

Control Variables

Our independent variables do not exist in a vacuum. Other factors must

be considered as cause and correlates of civil war. We consider four factors that

have been used for a variety of reasons with a variety of results in previous

studies of civil war.

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Polity This variable is the squared value of the standard polity score

generated by adding the Polity IV autocracy and democracy values. It is squared

because this allows us to see the U-shaped relation of governmental type with

respect to conflict. Those states that are highly autocratic and those that are

highly democratic are less likely to experience civil war. This has been

confirmed in many studies.

Gross Domestic Product Per Capita This allows us to test the general

economic health of a state. It is based on the measure of GDP in millions of U.S.

dollars. This is also a proxy measure in some studies for strength of the state and

relates the ability of the state to avoid violent attempts to harm or destroy the

state.

Ethnic Fractionalization We use the ethnic fractionalization scores as

found in Fearon and Laitin (2003). This allows us to control for the amount of

ethnic divisiveness in a country being a causal factor in organizing for and

engaging in civil violence.

Mountainous Terrain This variable measures the estimated percentage of

mountainous terrain in a country in a given year. This measure is taken from

Fearon and Laitin (2003). It is argued that rebel groups are more likely to form

and actively challenge the government to a greater extent when mountainous

terrain exists for the rebels to return to for protection from government action.

This argument is made more poignant by the use of terrain by terrorists and

tribesmen in the Afghanistan and Pakistan border regions.

Lagged Dependent Variables We also include lags of the dependent

variables in the models. It is always likely, and hardly ever found not

statistically significant, that civil war in the previous year (or duration, or

magnitude) is related to civil war in the current year.

Statistical Analysis

We employ a series of models that are in two formats. As civil war onset

is dichotomous, we use logit models to test the independent variables in relation

to onset. Both civil war duration and civil war magnitude are interval-ratio level

variables and allow us the use regression modeling. Our data are time-series and

cross sectional, however, the year panels are not equal for every country (since,

for instance, Angola was not independent until 1975 there are 26 cases for

Angola and since Nigeria existed independently in 1970 there are 31 cases for

Nigeria). To correct for this we use a generalized model that employs panel

corrected standard errors. The panel corrected standard error time series

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regression model creates robust standard errors for the models enhancing the

quality of the results.

Models 1 through 4, reported in Table 1 are for the logit models with civil

war onset as the dependent variable.7 Model 1 includes only the current year

independent variables. Model 2 employs the one year lag independent variables.

Model 3 employs the two year lag independent variables and model 4 includes

the current year and both one and two year lag variables.8

Model 1 includes 1165 country-year observations. The Log Ratio Χ2 is

580.96 with a probability of .0000 and a Pseudo R2 of .5935.9 Of the independent

variables tested, only freshwater scarcity was found statistically significant and

then only at the .10 level. The control findings were consistent with findings in

previous studies indicating that rebel groups consider having terrain that is

favorable for hiding and defense against superior armed forces a plus when

initiating a civil war and that civil wars generally will not occur in highly

democratic or highly autocratic states.


580.21 with a probability of .0000 and a Pseudo R2 of .5932. None of the

independent variables tested showed statistical significance in relation to civil

war onset. As with model 1 the control variables showed consistency with

previous studies of civil war onset. The preliminary indication of this model is

that what occurred one year prior to the civil war onset is not correlated or causal

to the event.


565.31 with a probability of .0000 and a Pseudo R2 of .5944. In the same pattern

as model 2, the independent variables showed not statistically significant

relationships with civil war. The control variables remained consistent in

significance and in the indicated direction of the relationships. The indication of

this model is that resource production and issues from 2 years prior to the event

are not correlated with the onset of civil war.


566.78 with a probability of .0000 and a Pseudo R2 of .5964. As with the first

three models the independent variables show no statistical significance in

7 Tables are located in Appendix 1.

8 1 and 2 year lags for fuel production greater than 33% of export GDP and diamond production were

dropped due to collinearity with the initial variable from which they were derived. Likewise, sxp lagged 1

year was also dropped due to collinearity with sxp. 9 We do not report the Pseudo R-sq. here as a means of showing fit or explained variance, as this cannot be

done with this statistic. The Pseudo R-Sq used in our models is a count Pseudo R-Sq. which ranges 0-1, in

each of our four logit models the Pseudo R-sq. of the final version of the model is greater than .5 and a

larger value than any of the other iterations of the model we considered.

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relation to civil war and the control variables are in keeping with the findings of

existing scholarship.

Models 5 through 8 are the time-series regressions with civil war duration

as the dependent variable with the independent variables done in the same

manner as in models 1 through 4. These results are shown in Table 2.10

Model 5, looks at measurements taken in the particular year in relation to

duration in that year and includes 1164 observations. The R2 for this model is

.6873, meaning nearly 69% of variance in civil war duration as measured

(number of months in this year that the conflict lasted) is explained by the

included independent variables. The Wald Χ2 for the model is 741.80 with

probability .0000 indicating a well specified model.

Oil producing states and having fuel exports greater than 33% of export

GDP were both statistically significant in relation to civil war duration. Being an

oil producing state was negative in its direction and 33% or greater export was

positive in its direction. Among the grievance variables freshwater scarcity was

statistically significant but was negative in its direction. GDP Per Capita was

significant and negative in direction. Civil war onset, civil war magnitude, and

the duration of civil war in the previous calendar year were all statistically

significant in relation to civil war duration in the given country in the given year.

Interestingly civil war onset is negatively related to civil war duration in this

model.

If each value of each independent variable is held at its value of central

tendency, the value of Civil War Duration would equal -2.36 months. In short, it

appears civil war is so rare even in one of the world’s most civil war prone areas

that duration is non-existent.11

Model 6, which uses one year lags of the independent variables in relation

to duration in that year, includes 1162 observations. The R2 for this model is





Oil production and fuel exports greater than 33% were both statistically

significant in model 6, with oil production still being negative in direction. No

other independent variables were statistically significant. GDP Per Capita and

Civil War Onset showed negative relationship with duration, and Civil War

Magnitude and the lag of Civil War Duration were both positive and statistically

significant in their relationships with civil war duration. When all of the

10

See fn 8. 11

Equations for models 5-12 are expressed in Appendix 2.

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statistically significant variables were held at their measure of central tendency,

the value of civil war duration in model 6 is 7.7690 months. This indicates that

with duration past events do have a greater impact than current events.

Model 7, using two year lags of the independent variables, includes 1121

observations. The R2 for this model is .6888, meaning nearly 69% of variance in

civil war duration as measured (number of months in this year that the conflict

lasted) is explained by the included independent variables. The Wald Χ2 for the

model is 752.98 with probability .0000 indicating a well specified model.

The same variables that were significant in model 6 are significant in

model 7. The difference being here they are 2 year lags instead of one year lags.

The directions of the indicated relations remain the same. When all of the

independent variables are held at their measures of central tendency, the value of

civil war duration is 8.4063 months adding more credence to the finding that

events in previous years has greater impact on civil war duration than the events

of the current year.

Model 8 uses current year values plus the 1 and 2 year lag values of the

independent variables and includes 1119 observations. The R2 for this model is





The same variables that were significant in the earlier models remained

significant in this model and maintain the same direction of the relationship.

Freshwater scarcity did not reenter the equation in this model, an interesting

finding that will be discussed in the conclusions. When all of the independent

variables are held at their measures of central tendency, the value of civil war

duration is 8.5082 months adding more credence to the finding that events in

previous years has greater impact on civil war duration than the events of the

current year.

Table 3 records the results for models using civil war magnitude as the

dependent variable. As with the other models we test current and lead year

variables separately and then in a combined model.12

Model 9 uses current year values for the independent variables and has

1163 observations. The R2 for this model is .7020, meaning slightly more than

70% of variance in civil war magnitude is explained by the included independent

variables. The Wald Χ2 for the model is 694.84 with probability .0000 indicating a

well specified model.

12

See fn 8.

15

No grievance variable is significant in model 9, whole only one of the

greed variables is significant—loss of forested land. The direction of the

relationship is negative indicating as timbering activity increased the number of

battle deaths per year decreased. Among the controls used polity-squared,

ethnic fractionalization, civil war onset, civil war duration, and the lag of civil

war magnitude were positively correlated and statistically significant

The findings regarding polity-squared and ethnic fractionalization are

interesting. Both highly autocratic regimes and highly democratic regimes can

expect higher magnitude civil war. Sub-Saharan African countries that have

lower heterogeneity of ethnicity can expect to have civil wars of greater

magnitude. If all of the statistically significant variables are held at their measure

of central tendency the magnitude of civil war on average would be 305.0559 or

roughly 300 battle deaths per year per country in Sub-Saharan Africa.

Model 10 uses 1 year lag values for the independent variables and has





In this model only the same control variables that were significant in

model 9 are significant. None of the greed or grievance variables showed

significance. If all of the statistically significant variables are held at their

measure of central tendency the magnitude of civil war on average would be

459.9097 or roughly 456 battle deaths per year per country in Sub-Saharan Africa.

Model 11 uses 2 year lag values for the independent variables and has





One greed variable, sxp, and one grievance variable, lost arable land, are

statistically significant when we consider a two year lag of their value in relation

to current year civil war magnitude. The value of sxp is negative in directional

relation to magnitude indicating that increased export value of primary

commodities in previous years leads to lower numbers of battle deaths during a

civil war in the current year. The value of lost arable land is positive in

relationship to magnitude. A probable implication of the arable land finding is

that it takes time for grievance caused by land loss to manifest in human action.

The same control variables were significant in this model as in the previous

magnitude models. If all of the statistically significant variables are held at their

16

measure of central tendency the magnitude of civil war on average would be

466.5636 or roughly 467 battle deaths per year per country in Sub-Saharan Africa.

Model 12 uses 1 and 2 year lag values as wells as the current year values

of the independent variables and has 1118 observations. The R2 for this model is

.7057, meaning slightly more than 70% of variance in civil war magnitude is

explained by the included independent variables. The Wald Χ2 for the model is

735.63 with probability .0000 indicating a well specified model.

One greed variable remains statistically significant in this full model. Lost

forested land remained significant and negative in direction with respect to civil

war magnitude. Two grievance variables were statistically significant, the two

year lag of lost arable land and the current year freshwater scarcity. Both of

these are positively correlated with civil war duration. What is indicated by this

combination is that the past loss of arable land couples with current freshwater

scarcity to increase the level of grievance mobilization into armed conflict.

The control variables found significant in models 9-11 remained

significant and in the same direction in model 12. The overall model indicates an

average value of 1117.5872, or roughly 1118 battle deaths per year per country

when the significant variables are held constant at their central tendency.

Conclusions

Based on findings from our models the data we have tested do not

support hypotheses dealing with the onset of civil war from either the greed or

grievance perspective. Our hypotheses regarding civil war duration are both

supported and contradicted in relation to greed and not supported at all for

grievance. The test of greed and grievance is split again with civil war

magnitude with greed not being supported or being directly contradicted and

grievance finding a strong measure of support. These findings are summarized

in Table 4 in relation to the offered hypotheses.

TABLE 4: Summary of Findings in Relation to Hypotheses

Hypothesis Greed Grievance

Civil War Onset +

Civil War

Duration - + -

Civil War

Magnitude - - - + + +

+ is positive correlation with this measure of civil war, - is negative correlation

17

Given the number of variations on each of the variables used, there were

28 chances for greed to have positive or negative relation with each measurement

of civil war used. In our models greed was negatively more often than positively

correlated. There were 12 chances for grievance to be positively or negatively

related to civil war. Grievance was positively related four times and negatively

related one time. The most interesting set of findings is in relation to civil war

magnitude. Here greed causes magnitude to diminish and grievance causes

magnitude to increase. It is in the area of civil war magnitude that we also have

our most robust results in relation to the hypotheses offered.

To be sure, civil war is a difficult subject to explain in great detail. What

we believe we have found are unique processes that shed light on how we look

at intrastate political violence. Civil wars are processes not just brush fires that

start quickly, burn hot and then die out as quickly as they start. On the contrary,

civil wars are processes and as such we have utilized models that take this into

account. We believe that our examination of natural resources and civil war

provides a way to unlock the civil war process on a different level than has been

accomplished in previous studies.

Specifically we find that lost land, lost forests and less fresh water are

indeed important but are not immediately important. This seems to indicate that

immediate needs are not only important but are important in the long-term. For

example, subsistence farmers may be able to survive one year having been

pushed from their land but not two, thus they have a grievance since they

probably have no other form of income or skills to earn a living. They would also

have a grievance if they cannot return to their land.

Conversely the “greed” hypothesis would indicate that the possession of

large amounts of land is an initiating factor in civil war, however this would only

be true in capital intensive agricultural societies where large amounts of land can

be exploited by a handful of people. A counter-argument would be that it is not

the production but the exports of these products that creates the conditions for

greedy people to expropriate them for their own gain. However, the composition

of the greed variable SXP is skewed heavily toward non-renewable products and

non-foodstuffs. Thus our findings on lost arable land and water scarcity as

important components of the conflict process reinforce the grievance hypothesis

since they are both immediate and long-term elements of agricultural

production. As for urban dwellers loss of land is not important but the ability to

purchase food certainly is. They might have grievances if the government cannot

provide access to food; however this is different from greed.

18

In sum, our findings seem to confirm that the grievance hypothesis is a

stronger explanation and predictor for civil war than the greed hypothesis. The

results indicating the importance of the magnitude of civil war is an important

innovation has been shown to be significant especially with renewable resource

variables. This finding indicates that the processes that start civil wars are

complex and have an indeterminate lead time. Further research on the particular

mechanisms, time required for grievances to reach a critical mass and the specific

renewable resources are warranted.

19

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23

APPENDIX 1: TABLES

TABLE 1: CIVIL WAR ONSET Variables Model 1 Model 2 Model 3 Model 4

Oil Producing

State

-.0121

.3926

.0189

.3914

-.0390

.4016

-.0504

.4043

Fuel Production >

33% export GDP

.4848

.5235

.6424

.5407

1 Yr Lag .4950

.5200

2 Yr Lag .6389

.5344

Diamond

Production

.1042

.3617

.0936

.3704

1 Yr Lag .1152

.3604

2 Yr Lag .0883

.3688

SXP .0077

.0482

-.2176

6.7223

1 Yr Lag .0078

.0516

2 Yr Lag .0072

.0394

.2247

6.7223

Lost Forested

Land

-.0115

.1113

-.0200

.1119

1 Yr Lag .0455

.1079

.0462

.1084

2 Yr Lag .0001

.0077

.0001

.0081

Lost Arable

Land

-.0951

.1357

-.1032

.1363

1 Yr Lag .0753

.0886

.0758

.0901

2 Yr Lag .0581

.0977

.0576

.0966

Freshwater

Scarcity

.4462*

.3116

.7619

.9360

1 Yr Lag .3797

.3089

-.6880

1.4008

2 Yr Lag .3593

.3113

.3297

1.1369

GDP Per

Capita

-.00001

.0001

-.00006

.0001

-.00006

.0001

-.00007

.0001

Polity-

Squared

-.0012**

.0004

-.0011**

.0004

-.0012**

.0004

-.0011**

.0003

Ethnic

Fractionalization

.4879

.9154

.4487

.9206

.6404

.9386

.6629

.9444

Mountainous

Terrain

.0153**

.0075

.0149**

.0075

.0130*

.0076

.0128*

.0076

Lag of Civil

War Onset

4.7867***

.2889

4.7919***

.2898

4.7964***

.2931

4.8236***

.3002

Constant -4.2752***

.7836

-4.2683***

.7853

-4.3298***

.8027

-4.3992***

.8092 Top number in each cell is coefficient, bottom number is standard error. *p > .1, ** p > .05, *** p > .001

24

TABLE 2: Civil War Duration

Variables Model 5 Model 6 Model 7 Model 8

Oil Producing

State

-24.3139*

13.7244

-24.2982*

13.71412

-27.5212*

14.1489

-27.4782*

14.1809

Fuel Production

> 33% export

GDP

35.8770**

17.7678

39.9815**

18.6264

1 Yr Lag 36.0669*

17.7716

2 Yr Lag 38.8162**

18.4573

Diamond

Production

4.6366

12.9408

5.9756

13.5416

1 Yr Lag 4.6254

12.9563

2 Yr Lag 5.8398

13.4490

SXP -.0097

.0071

-40.7349

76.3465

1 Yr Lag -.0098

.0072

2 Yr Lag -.0111

.0075

40.7227

76.3469

Lost Forested

Land

-.2089

2.5816

-.3621

2.6831

1 Yr Lag .0011

.0153

1.6265

2.6315

2 Yr Lag -.0043

.0164

-.0039

.0169

Lost Arable

Land

1.2981

2.5138

1.0594

2.5412

1 Yr Lag 2.1399

2.5745

2.0622

2.5842

2 Yr Lag -.2649

2.6771

-.4545

2.6536

Freshwater

Scarcity

-10.021*

6.4143

-6.4333

17.4674

1 Yr Lag -9.9663

6.3621

-2.4785

22.7877

2 Yr Lag -10.3114

6.5658

-2.026

17.6488

25

TABLE 2: Civil War Duration (Continued)


GDP Per

Capita

-.0064*

.0038

-.0063*

.0038

-.0066*

.0039

-.0066*

.0039

Polity-

Squared

-.0099

.0157

-.0101

.01567

-.0119

.0159

-.0122

.0159

Ethnic

Fractionalization

-16.3275

34.2062

-16.5666

34.3471

-15.7704

35.5866

-16.2562

35.4882

Mountainous

Terrain

-.00009

.3007

.0024

.3046

.0022

.3144

-.0108

.3133

Civil War Onset -163.342***

20.5912

-163.5275***

20.6362

-168.9332***

21.4597

-169.6368***

21.5442

Civil War

Magnitude

.0116***

.0018

.0116***

.0018

.0119***

.0018

.0119***

.0018

Lag of Civil War

Duration

.7373***

.0456

.7370***

.0457

.7325***

.0466

.7319***

.0464

Constant 31.1538

29.1407

30.8674

29.1844

31.7913

30.3051

31.8742*

30.1682 Top number in each cell is coefficient, bottom number is standard error. *p > .1, ** p > .05, *** p > .001

26

TABLE 3: Civil War Magnitude


Oil Producing

State

62.4807

102.4037

65.0846

102.1888

104.0218

103.7207

101.0414

103.5762

Fuel Production

> 33% export

GDP

-151.3944

272.989

-198.8436

294.6382

1 Yr Lag -174.4339

273.1208

2 Yr Lag -185.1261

287.7116

Diamond

Production

-60.0832

121.8249

-92.4155

125.3395

1 Yr Lag -71.2676

121.5836

2 Yr Lag -99.0411

125.3938

SXP -.03918

.0627

1121.734

1740.686

1 Yr Lag -.0807

.0617

2 Yr Lag -.1042*

.0634

-1121.778

1740.682

Lost Forested

Land

-101.4269**

48.5619

-119.4112**

50.7262

1 Yr Lag -.1712

.1766

-31.9891

50.5032

2 Yr Lag -.1226

.1962

-.1048

.3270

Lost Arable

Land

-17.3386

34.5299

-32.8543

28.0738

1 Yr Lag -4.5492

34.5164

-9.0277

27.2523

2 Yr Lag 133.2197***

27.822

141.9307***

28.3108

Freshwater

Scarcity

81.6495

103.7291

657.4915**

269.2569

1 Yr Lag 20.5242

104.7026

-412.2042

350.5822

2 Yr Lag -3.4474

110.1319

-223.3321

279.2877

27

TABLE 3: Civil War Magnitude (Continued)


GDP Per

Capita

-.0022

.0354

.0045

.0358

.0088

.0371

.0065

.0370

Polity-

Squared

.5738**

.2551

.5822**

.2547

.5885**

.2847

.5770**

.2479

Ethnic

Fractionalization

473.9728*

253.1283

486.4529*

258.2397

479.4923*

263.3079

464.7894*

257.9229

Mountainous

Terrain

3.2577

2.5323

3.0793

2.5643

2.5251

2.8199

2.8067

2.8371

Lag Civil War

Magnitude

.6555***

.0534

.6537***

.0535

.6549***

.0545

.6585***

.0540

Civil War Onset 2219.388***

342.6417

2240.864***

343.0118

2253.604***

356.4664

2237.556***

354.2981

Civil War

Duration

1.7949***

.3821

1.7892***

.3808

1.7924***

.3935

1.7812***

.3937

Constant -421.8676**

197.3726

-424.4516**

198.8118

-438.6012**

205.7979

-413.3164**

205.7858 Top number in each cell is coefficient, bottom number is standard error. *p > .1, ** p > .05, *** p > .001

28

APPENDIX 2

FORMULAS FOR REGRESSION LINES

The formulas include only those variables that were found statistically significant

in relation to the dependent variable.

CIVIL WAR DURATION: Models 5 through 8

Model 5: Current Year Value of IVs

Civil War Duration = 31.1538 + βOil Producing State + βFuel Production <33% of

Export GDP + βFreshwater Scarcity/Pollution + βGDP Per Capita + βCivil War

Onset + βCivil War Magnitude + βLag of Civil War Duration

Civil War Duration = 31.1538 – 24.3139(0) + 35.8770(0) -10.021(1) - .0064(1195.29) -

163.342(0) + .0116(641.988) + .7373(-31.5846)

Model 6: 1 Year Lags of IVs


Export GDP + βGDP Per Capita + βCivil War Onset + βCivil War Magnitude +

βLag of Civil War Duration

Civil War Duration = 30.8674 – 24.2982(0) + 36.0669(0) - .0063(1195.29) -

163.5275(0) + .0116(641.988) + .7370(-31.5846)

Model 7: 2 Year Lags of IVs


Export GDP + βGDP Per Capita + βCivil War Onset + βCivil War Magnitude +

βLag of Civil War Duration

Civil War Duration = 31.7913 – 27.5212(0) + 38.8162(0) - .0066(1195.29) -

168.9332(0) + .0119(641.988) + .7325(-31.5846)

Model 8: Current Year, 1 Year Lag, 2 Year Lag IVs

29


Export GDP in current year+ βGDP Per Capita + βCivil War Onset + βCivil War

Magnitude + βLag of Civil War Duration

Civil War Duration = 31.8742 – 27.782(0) + 39.9815(0) - .0066(1195.29) -169.6368(0)

+ .0119(641.988) + .7319(-31.5846)

CIVIL WAR MAGNITUDE: Models 9 though 12

Model 9: Current Year Value of IVs

Civil War Magnitude = -421.8676 + βLost Forested Land + βPolity-squared +

βEthnic Fractionalization + βLag of Civil War Magnitude + βCivil War Onset +

βCivil War Duration

Civil War Magnitude = -421.8676 – 101.4269(.2216) + .5738(44.911) +

473.9728(.7343) + .6555(669.726) + 2219.388(0) - 1.7949(35.3297)

Model 10: 1 Year Lag Values of IVs

Civil War Magnitude = -424.4516 + βPolity-squared + βEthnic Fractionalization +

βLag of Civil War Magnitude + βCivil War Onset + βCivil War Duration

Civil War Magnitude = -424.4516 + .5822(44.911) + 486.4529(.7343) +

.6537(669.726) + 2240.864(0) + 1.7892(35.3297)

Model 11: 2 Year Lag Values of IVs

Civil War Magnitude = -438.6012 - βSXP + βLost Arable Land + βPolity-squared +

βEthnic Fractionalization + βLag of Civil War Magnitude + βCivil War Onset +

βCivil War Duration

Civil War Magnitude = -438.6012 - .1042(30.9045) + 133.2197(.2064) + .5885(44.911)

+ 479.4923(.7343) + .6549(669.726) + 2253.604(0) + 1.7924(35.3297)

Model 12: Current Year, 1 Year Lag, 2 Year Lag IVs

30

Civil War Magnitude = -413.3164 – βLost Forested Land + βLost Arable Land 2

Year Lag + βFreshwater Scarcity + βPolity-squared + βEthnic Fractionalization +

βLag of Civil War Magnitude + βCivil War Onset + βCivil War Duration

Civil War Magnitude = -413.3164 – 119.4112(.2264) + 141.9307(.2064) + 657.4915(1)

+ .5770(44.911) + 464.7894(.7343) + .6585(669.726) + 2237.556(0) + 1.7812(35.3297)

renewable vs. non-renewable resources as … and st. marie apsa...renewable vs. non-renewable...

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