Geography, Strategic Ambition, and
the Duration of Civil Conflict
Halvard Buhaug, Scott Gates, and Päivi Lujala
Centre for the Study of Civil War, PRIO &
Norwegian University of Science and Technology
September 8, 2005
Abstract: Both the control of territory possessing natural resources used to finance
armed conflict and the distances an army must travel to project force affect how a
civil war is fought and who will prevail. In this paper, a model based on a contest
success function designed to explicitly account for distances is employed to model the
ability to project force and sustain conflict. The strategic ambitions of the rebel group
will determine whether the conflict is focused on territorial secession or conquest of
the government. These goals, in turn, affect where the war is fought, how it is fought,
and the likelihood of one of the parties succeeding militarily. Using both Cox
regression and parametric survival analyses, specific propositions regarding the
duration of conflicts derived from the formal model are analyzed. Using a precisely
dated armed civil conflict duration data (Gates & Strand, 2004), which is based on the
Uppsala Armed Conflict dataset (Eriksson & Wallensteen, 2004), and using data
regarding the location of conflict (Buhaug & Gates, 2002) and natural resources
(Buhaug & Lujala, 2004) we are able to assess the role geography has on the duration
of armed civil conflict, especially in terms of the location of the conflict zone and
location of lootable natural resources. We are also able to differentiate armed civil
conflicts according to the strategic ambitions of the rebels.
Paper prepared for presentation at the “Mapping the Complexity of Civil Wars” International
Conference, September 15-17, 2005, Zurich, Switzerland. A previous version of the paper
was presented at the 2005 APSA Annual meeting, September 1-4, 2005, Washington, D.C.
This project has in part been funded by the Research Council of Norway, which has provided
the core grant for the establishment and operation of the Centre for the Study of Civil War at
the International Peace Research Institute, Oslo (PRIO). This paper is also part of the
“Polarization and Conflict” Project (CIT-2-CT-2005-506084) funded by the European
Commission-DG Research Sixth Framework Programme. The paper reflects only the authors’
views and the Commission is not liable for any use that may be made of the information
contained therein. We thank these organizations for their support. We also thank Aysegul
Aydin for her valuable comments and assistance.
Knowledge about the potential impact of geography on conflicts is probably as old as
the art of war. Geographers, as well as theorists of international relations have for
long claimed climate, topography and location to be important determinants of state
behavior (Sprout 1963). In an assessment of previous work on geography and war,
Diehl (1991) discusses three theoretical frameworks of particular influence: Sprout &
Sprout (1965) with the notion of ‘environmental possibilism’; Boulding (1962) with
the loss-of-strength gradient; and Starr (1978) with the concepts of opportunity and
willingness. All three emphasize physical distance as the crucial geographic factor
affecting the risk of conflict.
The theoretical link between geographic proximity and conflict has resulted in
the development of the Correlates of War project’s contiguity dataset (Gochman
1991) as well as Gleditsch & Ward’s (2001) minimum distance dataset. But while the
pioneering works by the Sprouts, Boulding and Starr still are considered highly
influential - and later empirical studies have indeed revealed some convincing
findings - they interpret geography merely as a concept of contiguity and distance.
Accordingly, the geography concept acts more as a proxy of interstate interaction
opportunities than measuring the impact of physical, geographic attributes of
conflicting countries. Moreover, international borders and interstate distance as
analytical concepts are mainly relevant when dealing with international conflicts.
To the extent that geography and conflict have been linked in other and more
fashionable ways, they have been subject to either of two approaches. The first one
deals with micro-level analyses of battlefield effectiveness, typically from a military
point of view. In this respect, issues like weapon and soldier performance in varying
topographic and climatic conditions, and how to exploit geographic advantages
1
(ranging from hills and weather to tidal water) are central concerns, sometimes
illustrated by certain well-selected historical battles (see Collins 1998). On the other
hand, we have system-level discussions of geopolitics and structures of the (post-)
Cold War. Here, spheres of ideological influence and strategies of nuclear deterrence
are central themes (cf. Pepper & Jenkins (1985) for a discussion of Cold-War
geopolitics). Neither of these approaches is suitable for a cross-national study for the
entire post-WW II period; the first is inappropriately detailed, the latter allows little
variation between cases.
Diehl (1991) points us in the right direction with his work on the geography of
interstate war. His work emphasizes the importance of territory in war. The
geographic aspects of territory significantly shape the incentives for going to war.
Defending or conquering some land area may be valuable. A given territory may
hold strategic or valuable resources (loot). The population residing there may
maintain special significance (due to factors of ethnicity, religion, etc.). The
geography of the land may also exhibit strategically desirable characteristics. Rough
terrain, for example, may offer good defensive positions for an army. Or controlling
a mountain pass or a sea passage (i.e. a straight) may offer strategic advantages with
regard to transportation and movement of troops. Territory also may possess certain
non-tangible qualities – symbolic value, identity & cohesion – all of which may play
a critical role in recruitment and allegiance to an army. All aspects of territory as
described here are relevant to either civil war or interstate war. The difference occurs
with regard to theory and the way these characteristics are operationalized in large-N
quantitative analyses.
Gates & Buhaug’s (2002) “The Geography of Civil War” demonstrates the
endogenous relationship between the location and the scope of civil conflict. This
2
work also features the special role national boundaries play in limiting a government’s
ability to project military force beyond its juridical boundaries. Rebel groups often
position themselves near national borders, retreating into neighboring countries’
territory if need be. Buhaug & Gates’s (2002) three-stage-least-squares analysis is
static cross-sectional. In this paper we extend this analysis to consider the dynamic
aspects of civil war.
In this paper we feature the strategic ambitions of the rebel group vis-à-vis the
government, geography (as it relates to natural resource wealth, population, and
terrain), and the duration of civil wars. Whether a rebel group seeks to control
government or to secede will significantly affect the manner in which they deploy
their troops and engage the government in conflict. Geographic considerations will
also affect the strategy of both the government and the rebel group. Together these
strategic choices affect the dynamics and duration of conflict, critically affecting the
decision to continue to fight or to lay down arms and pursue a peaceful solution to the
conflict.
Geography and the Dynamics of Civil War
Few have analyzed the relationship between geography and the dynamics of civil war.
One of the very few works that have analyzed the role of distance in the context of
civil war is Gates (2002). In this theoretical paper on the microfoundations of
rebellion, Gates identifies three factors determining military success and shaping rebel
recruitment: geography, ideology and ethnicity. His central themes are how
geography interacts with ethnicity and ideology, and how this interaction affects the
distance between rebels and government, and how distance enables a rebel group to
3
expand. In this paper, we explicitly draw on the contest success function that Gates
developed to model the dynamics of civil war.
Although the resource curse has become a dominant theme in the analysis of
the onset of civil war, only a few authors have theoretically modeled the role of
natural resource rents in the dynamic context of civil conflict.1 Gates (2002) models
how rents from natural resources can be used to recruit rebel soldiers and how
revenues from natural resources are distributed in a rebel organization to deter
defection and to ensure compliance. Skaperdas (2002) models warlordism in a region
where state control is weak. He shows that rents accruing from wealth generated from
minerals, drugs, etc. lead to more intense competition between the warlords and
thereby more soldiers are recruited in the various fighting groups. The Skaperdas
model concludes that potential warlords should prefer an “armed peace” to an open
conflict due to the destruction costs caused by warfare. However, due to incomplete
information or long-term benefits of destroying the enemy (enemies) at once,
negotiated settlement is not reached.
On the other hand, the rebels might not be the only side taking advantage of
access to lootable resources, governments too often engage in appropriative activities.
Indeed, conflict may be preferred to peace by one or both groups involved in a
conflict. Addison et al. (2001) show that protracted low-intensity warfare may be
beneficial for all fighting parties including the government since it provides economic
opportunities that are not available during peacetime. They argue that groups that
have both access to weapons and the opportunity to form armed groups to loot natural
1
Snyder and Bhavnani (2005) develop a model to explain why lootable resources are linked to the
onset of civil wars in some cases and not in others, but they do not explain the role of lootable
resources during conflict.
4
resources or other rents (such as foreign aid), may prefer conflict to peace. Although
conflict is detrimental to society at large and is extremely costly to the general
citizenry, the participating armed groups may experience higher total revenues/utility
during conflict than during peace.
According to Addison et al., conflicts centered around and motivated by
natural resource looting are characterized by low intensity warfare to maximize profits
and minimize costs. Furthermore, in some cases looting may only be possible during
the conflict since peace would increase the competition from other groups to extract
the resources. Therefore, to exploit the wartime opportunities the fighting groups have
an incentive to continue the conflict instead of pursuing peace. The low commitment
and few incentives to restore the peace are likely to result in prolonged conflict
duration.
Modeling Geography and the Dynamics of Civil War
This section presents a model of military conflict between a rebel group and
governmental forces. The model developed follows from Gates (2002) and follows
from a general class of contest success functions first developed by Tullock (1980)
and applied to conflict by Hirshleifer (1989; 2000) and others. An axiomatic
derivation of a general class of contest success functions is provided by Skaperdas
(1996). As applied to military conflict, the contest success function relates to the
relative capabilities of two competing sides of a conflict, such that:
1)
π (Kl,Kg) = f(Kl) / [f(Kl) + f(Kg)],
where f(Kl) is a non-negative, increasing function of military capabilities, and π is the
probability of military success.
.
5
Military capability,εl, depends on some unspecified combination of troop size,
military budget, etc. As such, military capability can be represented as, Kl(xa; εl, xl),
which basically can be interpreted to mean that the capability of a rebel group is a
function of the distance to the site of conflict, xa, and the rebel headquarters, xl, and
the military effectiveness of the rebel army, εl. The closer one is to one's own base
compared to the enemy's distance from its base, the higher one's own probability of
victory even if one is fielding a smaller force. Geographical distance thus affects the
ability to project force. This is similar to Gates’ (2002) model of a contest success
function to model recruitment and geographical distances in the context of principalagent relationships.2
Formalizing Kl(⋅ ), military capability is assumed to take the following form:
2)
Kl(xa; εl, xl) = a + ln(εl) – (xa - xl)2 + ηl,
where ηl is a stochastic element and a is a constant. Since there are so many
unforeseen or even unseen factors that determine victory or defeat, military success is
assumed to be stochastic in nature.
By setting the respective capabilities of the two armies against one another,
their relative strengths can be compared such that: Kl(xa; εl, xl) = a + ln(εl) – (xa - xl)2
+ ηl,= a + ln(εg) – (xa - xg)2 + ηg,= Kg(xa; εg, xg). For the rebel group to have an
advantage in terms of capability, Kl(xa; εl, xl) > Kg(xa; εg, xg), such that: ln(εl) – (xa xl)2 - ln(εg)+ (xa - xg)2 > ηg -ηl. To obtain the success function we follow Gates
(2002) and utilize a subclass of the contest success function, the logit success function
2
The model presented here does not consider the organizational factors that Gates
(2002) features through his principal-agent model.
6
(Hirshleifer 1989; 2000), such that the cumulative density function of the difference
between the two stochastic elements, F(ηg -ηl) is assumed to be logistic:
3)
F (η g − η l ) =
e
(η g −ηl )
1+ e
(η g −ηl )
Thus, the probability of success is expressed in this logistic fashion, directly
accounting for the geographic location of the two sides; such that the probability of
success, πl, depends on the proximity of xa and xl with respect to xg (the location of the
government’s forces). More specifically: 3
εl
4)
πl =
e
εg
− ( 2 x a − x l − x g )( x l − x g )
ε
+ l
.
εg
This functional form of a conflict success function allows us to explicitly incorporate
distance into our analysis.4 The closer xg and xl are together, the flatter the function of
the probability of success and the more the ratio of military effectiveness alone
matters. If xl = xg, such that rebel headquarters were located in the country’s capital,
equation 7 will be the same as the generalized success function, πl = f(εl) / [f(εl) +
f(εg)], since the distance term equals zero, making e0 = 1.
Where a rebel group chooses to locate is a product of a strategic choice.
Locating near a population that serves as a recruitment base is one such strategic
3
Please note that there was a typographical error in Gates (2002); the minus sign in the exponent in the
denominator was missing.
4
See Butler, Gates, and Leiby (2005) for another functional form of a contest success function that
directly accounts for geographical distance. Their contest success function takes on a ratio format
rather than a logistic form as developed here.
7
decision. A group may also choose to focus their activities near a lootable resource in
order to control this valuable territory. Groups also often base themselves near an
international border, which offers an opportunity to retreat to a “safe haven” in
another country. A particular region’s terrain may also offer a defensive advantage for
a rebel group, leading them to establish a base camp there. All of these factors shape
the decision of location. Each factor, in turn, affects the military capability of the rebel
army relative to the government. In terms of our contest success function, these
factors affect military effectiveness, εl.
The relative distance to the combat zone will be determined by the location
decision (shaped by the factors discussed above) and the strategic ambitions of the
group. If the group’s aim is to secede, the group is more likely to be in a favorable
position vis-à-vis the government (presuming that the seceding province is relatively
remote from the capital – as it typically is). If the group aims to capture the apparatus
of the state, it will aim at the capital, and in general this will give the government the
advantage with regard to distance. These distance considerations modeled in our
contest success function exhibit a conceptual similarity to Boulding’s loss-of-strength
gradient (1962).
Contest success function models are particularly useful for explaining why
belligerents in an armed civil conflict continue to fight and do not negotiate a
bargained settlement (even when a bargain seems to be mutually advantageous).5 In
this regard they serve as a useful model for understanding why some civil wars last
longer than others. To empirically analyze this question we employ duration analysis.
5
Butler, Gates, and Leiby (2005) focus their paper on this aspect of contest success functions.
8
But before turning to our analysis, we review previous attempts to model the duration
of civil conflict.
Empirical Studies of Geography and the Duration of Civil War
There are strong theoretical arguments that rough terrain favors rebels, making them
harder to detect and defeat by government forces. Among the first empirical studies to
include variables on terrain, Fearon & Laitin’s (1999) unpublished paper uses a
simple dummy variable of mountain-based rebel groups in Eastern European
countries. In a more recent paper (2003), they use a more refined measure – identical
to the one used by Collier & Hoeffler (2004) – that gives the percentage of the
country covered by mountainous terrain. These papers only consider the association
between mountain and risk of civil war, though. Within the context of duration of
conflict, Collier, Hoeffler & Söderbom (2004) include proxies for both mountainous
and forested terrain. They find that the length of internal conflicts is positively
associated with the amount of forest cover in the country whereas the share of
mountainous terrain is irrelevant. In contrast, Buhaug & Lujala’s (2005) comparison
between country- and conflict-specific measures of terrain shows that forest is
consistently negatively related to duration of intrastate conflict while mountains
produce inconclusive findings. The generally ambiguous relationship between terrain
and civil war is further corroborated by DeRouen & Sobek’s (2004) investigation of
civil war outcome. Their analysis suggests that mountains are favorable to rebels
while forest cover reduces the likelihood of rebel victory.
The potentially adverse consequences of natural resource abundance on
political stability have received much attention in the civil war literature in recent
years, largely inspired by contributions in Economics, including Berdal & Malone
9
(2000) and Collier & Hoeffler (1998, 2004) and the introduction of the greedgrievance typology. Among the empirical studies of civil war dynamics, Collier,
Hoeffler & Söderbom find little evidence that resource wealth affects the estimated
duration (contrary to their finding regarding onset of conflict, see Collier & Hoeffler
2004). This contrasts Ross (2004a), whose evaluation of the underlying causalities
between resource abundance and civil war in 13 selected cases indicates that
abundance makes a conflict more likely as well as leading to longer duration. Further,
Ross (2004b) shows that lootable natural resources in particular have a tendency to
lengthen conflicts since they appear to benefit the rebel groups more than the
government, and since they are associated with lower moral in armies that have
opportunity to exploit them. Fearon’s (2004) duration analysis also suggests that
valuable contrabands, such as cocaine, opium, and gemstones, increase the length of
the war, though the robustness of this judgment is questionable due to very limited
resource data. Finally, Buhaug & Lujala find that gemstones contribute to lengthening
a conflict, but only when they are located in the conflict zone.
Another aspect of geography that has been tested in relation to civil war
duration is size of the conflict-ridden country, measured as country area (BalchLindsay & Enterline 2000), population size (Collier, Hoeffler & Söderbom; Elbadawi
& Sambanis 2000; Fearon 2004), or both (Buhaug & Lujala). Again, the findings
appear to be mixed across studies. While the Collier, Hoeffler & Söderbom piece
indicates a positive relationship between size and duration, the other papers find less
evidence of a systematic co-variation.
Finally, some studies have considered the relative location of the rebellion.
Buhaug & Gates (2002), for example, show that conflicts with a larger geographic
scope last longer on average (although there is certainly some degree of endogeneity
10
in that relationship). There is also ample evidence that more remote conflicts are
harder to put to an end than conflicts located in the center of the state (Buhaug &
Lujala). Moreover, Balch-Lindsay & Enterline report that separatist conflicts – which
presumably occur mostly in peripheral areas – last longer, and a similar behavior is
uncovered for the so-called ‘sons-of-the-soil’ conflicts (Fearon).
Other Possible Determinants of Conflict Duration
Most armed intrastate conflicts occur in failed states. A recent report of the World
Bank concludes that the factor most consistently linked to civil war is poverty (Collier
et al. 2003). Large-scale domestic violence is also largely a characteristic of nondemocracies. The empirical relationship (if any) between quality of economic and
political institutions and duration of conflict is substantially weaker. Collier, Hoeffler
& Söderbom’s (2004) analysis gives strong indications of an inverse link between
development and duration, and shows a strong positive association between inequality
(which is correlated with political institutions) and duration of civil war, even though
a separate measure of democracy fails to be significant. In contrast, Fearon (2004)
finds a weak but insignificant positive effect of GDP per capita (and democracy) on
civil war duration, and DeRouen & Sobek (2004) find an even stronger positive effect
of democracy and wealth on expected conflict duration.
Second, several empirical studies suggest a positive (but possibly non-linear)
relationship between ethnic diversity and duration. Collier, Hoeffler & Söderbom,
DeRouen & Sobek, Elbadawi & Sambanis (2000), and Regan (2002) all report a
positive effect of ethnic fractionalization, and Fearon’s (2004) positive estimate for
the ‘sons-of-the-soil’ dummy provides additional, if indirect, support.
11
Most studies that distinguish between various forms of civil conflict find that
type matters. Balch-Lindsay & Enterline (2000), for example, conclude that among
the internal factors the type of conflict has the largest impact, where separatism is
associated with considerably longer civil wars. Fearon, too, finds that coups and
revolutions are shorter on average than conflicts over issues of self-determination.
The relevance of type of the ambition of the rebel group is further supported by
Buhaug & Lujala and DeRouen & Sobek.
Finally, Balch-Lindsay & Enterline’s study suggests that international aspects
are just as important as the domestic environment in determining the length of civil
wars, where support by foreign actors in favor of either side increases the duration of
the conflict. This finding counters their prediction since biased interventions are
expected to alter the balance of power in favor of the supported party, thus increasing
the probability of rapid military success. Regan (2002) reconfirms these findings by
showing that both economic and military interventions increase the expected duration
of civil conflict (see also Elbadawi & Sambanis 2000). In contrast, Collier, Hoeffler &
Söderbom find no effect of interventions, regardless of type or direction.
Shortcomings of Previous Studies
As is evident from the short review above, most factors theorized to influence the
duration of civil war have produced mixed results. To some extent, this is because
different investigations use different sources of conflict data. Whether one chooses to
study large-scale conflicts only (typically relying on the Correlates of War project’s
requirement of at least 1,000 deaths in total) or decides to investigate a more
comprehensive sample of conflicts (for example all conflicts with at least 25 annual
battle-deaths) may ultimately have a substantial impact on one’s findings. Similarly,
12
some conflict data come with precise start and end dates while others only denote the
first and final year in which the death threshold was reached. Different conflict
datasets also treat lulls between periods of fighting differently. Moreover, the quality
of the conflict data is likely to influence the research design. More finely grained data
facilitate sophisticated statistical estimation techniques, which may affect the end
product of the empirical analysis. See Gates & Strand (2005) for more on this subject
matter.
Another cause for deviations across studies is poor operationalizations of key
explanatory variables. One of the most controversial measures in this regard is Sachs
& Warner’s (1995) indicator of a country’s dependence on primary commodity
exports, which is used by e.g. Collier and colleagues. Among other things, this
measure is not exogenous to the unit of observation: conflict. The denominator in the
expression, GDP, is almost without exceptions negatively affected by civil conflicts,
thus inflating the values of the resource proxy. This problem is normally addressed by
applying time lags to the regressor, but many civil wars are preceded by significant
internal unrest or previous armed conflicts (spirals of violence).6 Accordingly, a
country in conflict may have a high ratio of primary commodity export to GDP less
because of abundance of natural resources than due to a collapse in the national
economy. A second problem is that this statistic does not discriminate between
different resource types -- some of which we have no reason to expect to be linked to
rebellion. Abundance of natural resources should not per se be viewed as a conflict-
6
54% of the civil wars in Collier & Hoeffler’s sample (2001) occur in countries that have experienced
previous conflicts since 1945, a figure that undoubtedly will be much larger if one also includes armed
intrastate conflicts below the 1,000 fatalities threshold.
13
promoting factor – it depends on the type and availability of the commodity. See
Fearon (2005) for a more extended discussion of this.
This leads to what we view as a fundamental flaw with several contributions to
the literature, namely the procedure to use aggregated country-level measures of
features that in reality have substantial sub-national variation. Civil wars are by
definition sub-national event and they rarely span throughout the territories of the
conflict-ridden countries. More typically, rebel groups operate in specific areas of the
country – often along the peripheral edges – while other parts of the country may be
virtually unaffected by the conflict. Several theories about civil war also speak of
local conditions. For example, the rough terrain proposition maintains that rebel
groups are favored by inaccessible land that may provide shelter from government
forces. If this is true and the rebels are aware of the advantages that e.g. mountains
constitute, we would expect these groups to operate from such terrain. Yet, empirical
tests of this proposition almost exclusively use country-level aggregates. However,
unless we can assume that country statistics are representative of the conflict zones, it
is not clear to us how to interpret the results. The same argument goes for natural
resources. When analyzing the prospects for peace in a resource-rich, war-torn
country, we would probably want to know whether the valuable resources are located
within the conflict zone. If not, they are less likely to play a prominent role in the
conflict. As the first large-N study to handle this problem, Buhaug & Gates (2002)
used a preliminary version of the Armed Conflicts Dataset (Gleditsch et al. 2002)7 as
well as maps of the location of natural resources to construct a resource dummy that
indicated whether or not there were lootable natural resources in the conflict zone at
7
The Armed Conflicts Dataset includes information on the location of the conflicts; see Buhaug &
Gates (2002).
14
the time of the conflict. With the aid of Geographic Information Systems, the work on
collecting geo-referenced resource data has expanded considerably (see Lujala,
Gleditsch & Gilmore 2005). Systematic comparison between country-level variables
of terrain, population, and resources and more appropriate measures at the scale of the
conflict shows significant differences (Buhaug & Lujala 2005). This suggests that the
traditional aggregated variables may be poor indicators of the real thing, be it
resources, terrain, population density, or other features that are likely to vary across
space. Our paper is a continuation along this line, where the emphasis is placed on
local conditions and the relation between the rebel group and the center of state
power.
Hypotheses
Remote regions are more difficult to control by the government. Hence, Boulding’s
(1962) “loss-of-strength gradient” may be applicable for explaining the duration of
civil conflicts. Governments often face significant logistical obstacles when involved
in a conflict in distant areas. These include physical barriers for transportation of
troops and equipment (such as mountains, lack of transport network), higher costs
associated with longer distance, and limited knowledge of the local environment. In
sum, we predict a positive linkage between the relative location of the conflict and its
duration.
H 1. Conflicts located further from the capital city last longer.
Physical geography such as forested and mountainous terrain provides the
setting where the conflict takes place. As the geography varies from country to
country, the fighting groups face different advantages and limitations determined by
15
the geography. A rebel group that has knowledge of terrain and knows how to benefit
from it is better equipped to succeed on the battlefield. A group that can retreat to
areas where it is protected from the enemy can more easily regroup, rearm, and train,
and is therefore able to continue fighting for prolonged periods. Generally it is argued
that mountain areas and forested terrain are beneficial for the rebels since rough
terrain is hard to access and provides protection against aerial detection. Similarly,
rebels that operate along the international boundaries may avoid government forces by
seeking refuge behind the border.
H 1. Conflicts located in mountainous areas last longer.
H 2. Conflicts located in forested areas last longer.
H 4. Conflicts located at the international border last longer.
Extraction of many resources requires significant investment in technology
and/or in skilled workforce. In addition, some resources require special transport
facilities while others have low value-to-weight ratio, which make transportation
costly and smuggling more difficult. These types of resources can be called nonlootable, since the revenues from the resources are not available to rebels during
conflict. The latent value of non-lootable resource may, however, trigger conflicts by
serving as a motivation for groups to take control of these resources either by ousting
the present government or by secession. Lootable resources, in contrast, can be
exploited during conflict by the rebel forces whereby the associated revenues
contribute to financing the ongoing conflict. Classification of natural resources into
lootable and non-lootable resources allows us to estimate more precisely the
16
circumstances a rebel group can profit from natural resources – be it during the
conflict or after the peace is restored.
Lootable resources provide a source to finance an ongoing conflict. Therefore,
a conflict in which one side or more can benefit from resource exploitation, prolonged
conflict is financially viable. Lootable resources also make rebel movements more
independent from outside forces since they do not depend on foreign support to
finance the fighting. This leaves less leverage for foreign institutions to influence
rebel movements’ decisions to enter into peace negotiations and eventually establish
peace. As Addison, Le Billon & Murshed (2001) argue, a conflict situation may be
preferred by one or more fighting groups over the peace since the conflict may not
decrease the overall revenues accruing to participants. Thus, a conflict can provide
economic opportunities that otherwise would not be present and consequently the
fighting factions have an incentive to continue the fighting instead of pursuing peace.
H 5. Conflicts located in areas with lootable resources last longer.
Data and Research Design
The conflicts under study are taken from the Armed Conflicts 1946-2001 dataset,
version 2.0 (Gleditsch et al. 2002). In this dataset, each conflict may have several
observations, due to varying severity levels and change of combatants. Further, a
conflict is only coded for years during which it reached the minimum severity level of
25 battle-deaths, resulting in two or more units if the threshold is not met for all years
during the temporal span of the conflict. Obviously, we cannot treat observations as
unique conflicts if they in fact compose one civil war. Consequently, we merged units
17
that had identical ID codes, incompatibility, location, main actors, and were separated
by less than 24 months of ‘peace’.8
Applying GIS to Generate Spatial Variables
In order to generate data that are able to incorporate spatial attributes, we relied on
desktop geographical information systems (GIS). A GIS tool, such as ArcView or
ArcInfo, combines digital maps with relational databases that have statistical
functions, and enables the researcher to map and analyze variables that have a spatial
dimension. Several of our variables were generated this way, mapping the distribution
of specific regressors and intersecting the resulting theme layers with the conflict
map. In order to pursue this task we first needed spatial information on the relevant
variables. For the geographic distribution of conflicts, we mapped the units in the
Armed Conflicts in accordance with the latitude, longitude, and radius variables (c.f.
Buhaug & Gates 2002 or Gleditsch et al. 2002).
Dependent Variable: Duration of Internal Conflicts
The dependent variable is the duration of internal conflicts. Although the Armed
Conflicts dataset includes start-dates of the conflicts, it does not contain detailed
information on the termination of conflicts. Consequently, we used the Gates &
Strand (2005) dataset, which more precisely dates war start and termination. This
dataset offers a number of advantages. First as with all data that are part of the Armed
8
Deciding where to place the cut-off point between ongoing and new conflicts is not trivial and may
have a substantial impact on the results. Cut-offs less than 24 months seem to be more problematic than
longer cut-offs. Moreover the 24 month criterion possesses a certain prima facie validity. See Gates &
Strand (2005) for a more detailed discussion of coding civil wars involving intermittent fighting.
18
Conflict Database family (PRIO/Uppsala), the battle casualty threshold is much lower
than other datasets – twenty-five as opposed to one thousand. Second, the unit of
analysis is the conflict not the country. The data thereby can differentiate between
several contemporaneous conflicts occurring in the same country. This allows us to
account for unmeasured heterogeneity. Third, the more precise dating of duration in
the Gates-Strand dataset allows us to account for the large number of conflicts that
last less than a month. Indeed, as Gates & Strand show, there are a number of
conflicts, particularly coups that last only days.
Independent Variables
Location and Distance
To control for the relative location of the conflict, we include the natural logarithm of
the distance between the conflict center and the capital, similar to Buhaug & Gates
(2002). We also include a dummy variable to mark off conflict zones that abut the
border of a neighboring state. In addition, since rebel groups that operate from distant
regions may not need to cross the border in order to avoid government forces, and
groups that have access to safe havens in a neighboring country are less dependent on
having peripheral bases in their home country, we add an interaction term between
location and border.
Lootable resources
Diamonds are classified into two types according to the nature of the deposit. Primary
deposits are called kimberlite pipes, which are deep subterranean rock-formations.
Mining of kimberlite diamonds requires high investment in exploration, capitalintensive equipment and underground tunneling and are therefore often mined by
19
large international companies (Smillie et al. 2000). Thus, kimberlite diamonds are not
classified as lootable natural resource. Diamonds in Botswana, for example, come
from kimberlite deposits. Alluvial diamond deposits are eroded diamondiferous
kimberlite that has been carried away by running water. These deposits can be found
in vast areas and can be mined by a single person; the mining method is simple and
only requires removal of topsoil to expose the diamondiferous gravel. This gravel is
centrifuged and washed, and the resulting mix is searched by hand for the diamonds.
The end product has an extremely high value-to-weight ratio and smuggling of
diamonds is easy. Consequently, alluvial diamonds are often referred as the ultimate
loot for armed groups. Diamonds in Sierra Leone, Angola and Congo (Zaire) are
mainly mined from alluvial deposits.
Other gemstones than diamonds such as rubies, sapphires and emeralds occur
both in alluvial and primary deposits. However, the lootability of the two types of
deposit does not differ significantly. Although alluvial gem deposits are extracted
easier and faster, extraction of primary deposits does not require special skills or
equipment. The end product has high value-to-weight ratio and as in the case of
diamonds, they are smuggled easily. Therefore, gems from both alluvial and primary
deposits can be considered lootable
In the empirical analysis below, we include measures of alluvial diamonds,
other gemstones, and narcotics cultivation (coca, opium, cannabis). These variables
are dichotomous and take on the value 1 whenever the given resource is located
within the conflict zone (as defined by the Armed Conflict Dataset) at the time of the
conflict. See Lujala, Gleditsch & Gilmore (2005) for further information on the
resource data.
20
Terrain
The variables for mountainous and forested terrain are based on Buhaug & Lujala
(2005). With the aid of GIS, we calculated the share of the two-dimensional area of
each conflict zone that is covered by the given type of terrain. Geo-referenced data on
mountains were drawn from UNEP (2002) while gridded forest data are from FAO
(2002).
Intervention
Following the findings of e.g. Balch-Lindsay & Enterline (2000) and Regan (2002),
we include measures of third-party interventions. Our indicators are constructed from
the Armed Conflicts dataset, where any foreign state that intervened on the part of the
government or opposition is identified. Only 10.5% or 27 of the 257 civil conflicts in
our sample are coded as internationalized – i.e. included foreign intervention on one
or both sides of the conflict. This figure strongly contrasts those of Elbadawi &
Sambanis (2000) and Regan (2000, 2002), who report interventions in 64.5% and
67% of the cases, respectively.9 The reason is that the Armed Conflicts data only
record events of direct military engagement by third parties whereas Regan
additionally counts economic and indirect military assistance, such as aid,
intelligence, and sanctions.10 Regan also only includes cases of civil war as defined by
the Correlates of War project whereby a civil war is defined by 1000 battle deaths in a
year. The Armed Conflict Dataset uses a lower threshold of 25 annual battle-deaths.
9
Elbadawi & Sambanis and Regan use the same intervention data.
10
Regan (2000: 10) defines intervention as “convention-breaking military and / or economic activities
in the internal affairs of a foreign country … with the aim of affecting the balance of power between
the government and opposition forces.”
21
Territory
This is essentially a dummy-coded version of the incompatibility variable in the
Armed Conflicts dataset, and distinguishes between conflicts over territory
(secessionism) and governance.
Methodology
A standard model to capture the duration dependence of civil conflict is the Weibull
model. In this model, the “hazard” of war termination is either high immediately after
the initiation of conflict, and then decreases at a steady rate, or it starts low and
increases. To test whether the duration dependence of armed civil conflict is
monotonic in this way, we also employ a log-logistic survival function and employ
two variants of the Cox proportional hazards model. We run a Cox model with
clustering and with frailty.
The data for this analysis are structured as multiple-record data with multiple
events with censoring. Given that civil wars frequently occur in the same country at
different points in time, and indeed with the Uppsala data, more than one armed civil
conflict can occur contemporaneously. The data structured in this way allows for
clustering on country COW code, which, in turn, is used in the calculation of the
robust standard errors. A number of wars were still on-going in 2001 (the last year of
our data); we censored to account for this. The multiple-record per subject survival
data structure allows us to account for time varying parameters. This is a big problem
for most other duration analyses of civil war.
22
Results and Discussion
In order to evaluate the hypothesized relationships, we explored the effect of the
explanatory variables across multiple models with different specifications and
assumptions. Table 1 presents the results for four initial models, using slightly
different estimation techniques. The two first models are estimated through Cox
proportional hazard (with clustered observations and shared frailty, respectively), the
third model reports Weibull accelerated failure-time (AFT) coefficients, while the
final column present estimates from a log-logistic model. The same set of covariates
is applied to all models: two indicators of relative location plus an interaction term,
two dummies for lootable resources in the conflict zone, two rough terrain measures, a
dichotomous intervention variable, an indicator of type of conflict, and a control for
population density. Our proxy for level of economic development – (log) GDP per
capita – is not included to avoid missing observations. The interpretation of the results
differs between the semi-parametric Cox and the fully parametric Weibull and loglogistic models. In the former case, the coefficient indicates the hazard of ‘failure’
(i.e. the conflict ending) within a short time interval (t, t+∆t), given that the unit has
survived until time t. Accordingly, a negatively signed coefficient implies that the
factor contributes to lengthening the conflict. In contrast, negative AFT and loglogistic estimates indicate that the given covariate is negatively associated with the
expected conflict duration. With few exceptions (which we will discuss in more detail
below), the various models produced largely similar results.
All models in Table 1 provide strong evidence that location matters.
Disregarding for now the possibly endogenous relationship, (which is probably not a
huge problem given the crude nature of the location variables), civil wars that occur
on (or beyond) the boundaries of neighbor states last significantly longer than other
23
conflicts. This supports Hypothesis 4 and the notion that neighbor territory provides
rebel sanctuaries, although it is also consistent with the assumption that access to
foreign soil facilitates arms trade and smuggling, which might have an independent
effect on the sustainability of the rebel group. In addition, we find that strong support
for Hypothesis 1 in that conflicts that occur in the periphery is less likely to be
resolved within a short period of time. In part, this might be because a ruling elite may
view events in distant parts of the country less critical to their political survival and
thus allocate fever resources than is needed to strangle the unrest quickly. But we
believe this finding also reflects the inability of governments to project sufficient
force and maintain full authority over peripheral regions, in particular if the rebellion
is supported by the local population. As expected, the interaction term shows that the
joint impact of the two factors is less than their combined individual effects. This
suggests that access to foreign soil may act as a substitute for not having a peripheral
base of operation and vice versa.
Aside from the location of the rebellion, access to some lootable natural
resources seems to affect the dynamics of intrastate conflicts. The dummy
representing secondary diamonds and other precious stones exerts a significant
estimate in all models, and the direction of the effect is in accordance with Hypothesis
5. Civil wars in countries with easily exploitable gems last considerably longer, on
average, than conflicts in less valuable terrain. The variable for drug production,
however, shows no systematic relationship to the duration of conflict. The different
behavior of these variables clearly illustrates the need to differentiate between types
of resources; some resources are likely to have a larger influence on characteristics of
conflict than others, and they may in fact be associated with different forms of
domestic conflict (see Le Billon 2001b).
24
The proxies for rough terrain generally fail to influence the duration of
conflict, and Hypotheses 2 and 3 are thus not supported. This might imply that the socalled rough terrain argument is not applicable to civil wars in general, but it could
also mean that inaccessible bases are most crucial in the early phase of the conflict,
before the rebel group is strong enough to conduct a more open warfare (as indeed
was the case for Castro’s guerrilla war in Cuba, see Pérez-Stable 1999). Therefore, in
the most protracted of conflicts, the balance of power between the government and the
opposition side is close to equal, and rough mountains become less crucial from a
military-strategic point of view.11 In addition, even though our variables give the
proportion of mountains and forest in the conflict zone, rather than in the country as a
whole, we cannot rule out that the lack of support for the hypotheses are due to poor
data.
Table 1 is generally in line with other studies that report a negative effect of
outside intervention on the prospects for peace. Even though the statistical
significance of this finding is sensitive to model specification, the models are
consistent with respect to the direction of the effect. We also see that territorial (or
separatist) conflicts are likely to last longer than conflicts over state authority, but
again, the models differ on the significance of the effect. Finally, only the loglogistics model suggests that densely populated countries are associated with different
conflict dynamics than other countries.
In Table II we include a measure of national wealth (the log GDP per capita in
time, t-1, prior to the conflict onset and a number of interactive terms. The analyses
run in parallel to those in Table I. The findings are largely robust with those reported
11
Yet, there are several cases to the contrary; long-lasting conflicts occur in jungles and mountainous
areas in, e.g. Myanmar.
25
in Table I. The signs and hazards rates change only marginally. The pattern across
estimations is for the most part similar across the estimations reported in Tables I and
II. For the Weibull AFT estimation, however, the statistical significance of
intervention and population density both improve with these estimations. The
interactive terms are also statistically significant at the .05 or borderline (0.05 < p <
0.10). This pattern is somewhat reflected in both the Cox estimations. Wealth itself is
not statistically significant in any of the estimations.
In Table III we compare Weibull AFT estimations with and without outliers.
Figure I plots the outliers. We also identify the specific outlier cases. Whether the
estimations are run with or without the outliers, it makes little difference; further
demonstrating robustness across estimations.
The results demonstrate extremely strong support for the role of geography in
understanding the duration of civil war.
Conclusions
Drawing on the PRIO/Uppsala Armed Conflict dataset for the 1946-2001 period
(Gleditsch et al., 2002), we have identified the location of all battle-zones for all
conflicts in this period and thereby identified the location and geographic extent of the
conflict. Using information regarding these battle-zones we have determined whether
lootable resources were accessible to rebel groups. We hypothesized that access to
lootable resources will allow a rebel group to prolong the conflict, thereby resulting in
longer duration of civil war. We found that alluvial diamonds and gemstones were
strongly associated with longer wars. These lootable resources allowed the rebel
groups associated with their exploitation to sustain the conflict. We did not find that
drugs were associated with longer wars.
26
We also determined whether the conflict zone exhibited geographical factors
that favor a rebel army based in the area. The location of the conflict is strongly
associated with duration. Indeed all variables that deal with relative distance are
statistically and substantively significant. Proximity to an international border also
prolongs civil conflict.
We still have some work to do. In an effort to further explore the capacity of a
rebel army to sustain conflict, we need to further examine the factors that affect rebel
recruitment and retention. We also plan to further examine how the strategic goals of
rebel groups affect the duration of armed conflict. Simply using a simple dummy
variable indicating whether the conflict was secessionist does not provide enough
information about a rebel group’s strategic ambitions with regard to territory. Further
examination of specific factors associated with rebel-held territory should be
especially fruitful.
27
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32
Table I.
Event History Analysis for Civil War Duration data using Base Model (Excluding GDP)
Cofactor Name
Weibull A.F.T
Log-logistic
Estimate (s.e.)
Estimate(s.e.)
Cox (clustering)
Estimate(s.e.)
Cox (frailty)
Estimate(s.e.)
Conflict Zone at Border
Log conflict-capital distance
Confl. Zone*log Conflict-Capital dist.
Log GDP per cap. in t-1 prior to conf. onset
Gemstones or sec. dia. prod. in conf. zone
Drug prod. in conf. zone
Log %mountain in conf. zone
Log %forest in conf. zone
Intervention
Population Density
Territorial Conflict
Constant
1.021(.298)***
.680(.121)***
-.744 (.205)***
.765 (.273)***
.348(.378)
-.176(.098)*
-.099(.080)
.734(.423)*
.002 (.002)
.564(.290)*
-3.055(.570)***
1.495(.384)***
.753(.134)***
-.704(.187)***
1.179(.354)***
-.030(.406)
-.085(.124)
-.127 (.085)
1.189(.448)***
.004(.002)**
.666(.346)*
-5.005(.619)***
-.548(.178)***
-.343(.068)***
.384(.117)***
-.469(.156)***
-.234(.241)
.082(.051)
.060(.046)
-.362(.230)
-.001(.001)
-.344(.169)**
-
-.648(.174)***
-.352(.063)***
.390(.103)***
-.481(.211)**
-.125(.247)
.082(.054)
.068(.048)
-.426(.244)*
-.001(.001)
-.316(.194)
-
Shape Parameter (σ)
Variance of the θ (Random Effect)
Log Likelihood
N
.546 (.032)
-529.08101
1483
1.269(.085)
-533.74582
1483
-1016.2741
1483
3.09 (p<.039)
-1014.7289
1483
Note: 1) Robust standard errors are reported. 2) Two tailed hypothesis tests; * = p<.10; ** = p<.05; *** = p<.001.
33
Table II.
Event History Analysis for Civil War Duration Data, using Base Model plus Drug*Mountains, GDP, GDP*Mountains and GDP*Forest variables
Cofactor Name
Weibull A.F.T
Log-logistic
Cox (clustering)
Cox (frailty)
Estimate (s.e.)
Estimate(s.e.)
Estimate(s.e.)
Estimate(s.e.)
Conflict Zone at Border
Log conflict-capital distance
Confl. Zone*log Conflict-Capital dist.
Gemstones or sec. dia. prod. in conf. zone
Drug prod. in conf. zone
Log %mountain in conf. zone
Log %forest in conf. zone
Intervention
Population Density
Territorial Conflict
Log GDP per cap. in t-1 prior to conf. onset
GDP*Mountains
GDP*Forest
Drug*Mountains
Constant
1.032(.288)***
.730(.117)***
-.579 (.180)***
.916 (.269)***
-.183(.512)
-.272(.133)**
-.034(.100)
1.199(.417)***
.004 (.002)**
.288(.269)
-.058 (.159)
.089 (.054)*
-.136(.067)**
1.334 (.806)*
-2.904(1.377)**
1.567(.425)***
.777(.142)***
-.618(.221)***
1.354(.389)***
-.337(.535)
-.110(.152)
-.106 (.123)
1.663(.435)***
.005(.002)***
.540(.326)*
-.024(.201)
.066(.074)
-.099(.083)
.969(.969)
-5.150(1.463)***
-.592(.172)***
-.390(.075)***
.265(.108)**
-.616(.170)***
-.162(.267)
.123(.066)*
.048(.049)
-.658(.233)***
-.002(.001)***
-.164(.170)
.075(.087)
-.037(.030)
.078(.040)*
-1.309(.556)**
-
-.592(.176)***
-.390(.065)***
.266(.111)**
-.616(.214)**
-.162(.323)
.123(.066)*
.048(.048)
-.658(.258)*
-.002(.001)*
-.164(.194)
.075(.084)
-.037(.035)
.078(.045)*
-1.309(.593)**
-
Shape Parameter (σ)
Variance of the θ (Random Effect)
Log Likelihood
N
.569 (.039)
-474.27563
1226
1.248(.098)
-481.79445
1226
-889.3642
1226
1.8e-05 (p<.498)
-889.36419
1226
Note: 1) Robust standard errors are reported. 2) Two tailed hypothesis tests; * = p<.10; ** = p<.05; *** = p<.001.
34
Table III.
Event History Analysis for Civil War Duration Data, using Base Model with Drug*Mountains, GDP, GDP*Mountains and GDP*Forest variables
Cofactor Name
Weibull A.F.T
Weibull A.F.T After Dropping the Outliers
Estimate(s.e.)
Estimate(s.e.)
Conflict Zone at Border
Log conflict-capital distance
Confl. Zone*log Conflict-Capital dist.
Gemstones or sec. dia. prod. in conf. zone
Drug prod. in conf. zone
Log %mountain in conf. zone
Log %forest in conf. zone
Intervention
Population Density
Territorial Conflict
Log GDP per cap. in t-1 prior to conf. onset
GDP*Mountains
GDP*Forest
Drug*Mountains
Constant
1.032(.288)***
.730(.117)***
-.579 (.180)***
.916 (.269)***
-.183(.512)
-.272(.133)**
-.034(.100)
1.199(.417)***
.004 (.002)**
.288(.269)
-.058 (.159)
.089 (.054)*
-.136(.067)**
1.334 (.806)*
-2.904(1.377)**
1.005(.306)***
.810(.132)***
-.644(.179)***
.900(.278)***
-.006(.607)
-.312(.137)**
-.073 (.104)
1.294(.444)***
.004(.002)**
.264(.277)
-.111(.182)
.107(.054)**
-.122(.068)*
1.092(.885)
-2.787(1.594)*
Shape Parameter (σ)
Variance of the θ (Random Effect)
Log Likelihood
N
.569 (.039)
-474.27563
1226
.563(.041)
-469.24901
1219
Note: 1) Robust standard errors are reported. 2) Two tailed hypothesis tests; * = p<.10; ** = p<.05; *** = p<.001.
35
6
Figure I.
Outlier Analysis, using Base Model plus Drug*Mountains, GDP, GDP*Mountains and GDP*Forest variables
cum. Cox-Snell residual
2
4
916
777
1462
585
989
428
1482
1432
575
352
895 616
646 320
62
1176
1189
803
429
1270
929
975
1159
1408
444
681
45729 1104
1443
1069
595
1223 1289
1396434
1434
1474
583988
1040
1425
1327
937
504
960730
807
903 1372 499 869 834
6521414
578
662
520
1394
422
648
873
466
1251
1231
704
1475
419
1139
776
229
1181
1187
1448
427
1333
1459
533546
482
1380
1200
1292
1148
848
283
505
1205
1184
910
1208
559
502
286
1054
1314
1456
1480
1383
1203
1330
1150
839
1350
1235
1360
781
1013
1356
27010871008
1239
1472
1128
1374
1477
697
739
65
1398
810
683
257
904
1484
1300
686
1421
475
1466
553
1072
980
876
1435
1436
918
1417
6921048
21201
467
734
1468
556
1295
1271
483
905
778
1206
1191
1009
1450
246
1070
3
1210
14
522
1481
476
548
909
1445
912
1301
1483
558
184
407
468
1451
1345
907
1376
684
783
469
1444
423
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1344
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1209
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962
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1192
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470
1160
1296
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322
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693
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1177
804
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1194
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247
521
981
1151
1
287
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705
1014
1377
687
917
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40
183
275
323
411
782
963
1190
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248
586
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288
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1457
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31
554
870
1290
836
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896
1328
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1473
85
252
1252
284
682
1105
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484
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142
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840
1418
1426
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15
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547
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576
991
1149
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12
30
1073
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4
63
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185
226
230
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507
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41
688
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249
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412
964
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1042
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1362
474
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1273
1458
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32
555
871
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837
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897
1329
1471
253
285
1253
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1438
1106
143
272
436
485
841
1419
1427
16
914
1386
9
431
425
577
992
1212
1074
1400
5
64
67
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231
259
290
354
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459
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524
535
550
561
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618
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736
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780
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920
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1011
1050
1089
1130
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1162
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1183
1186
1196
1225
1233
1237
1299
1303
1316
1332
1336
1358
1410
1416
1423
1479
733
851
983
1057
1153
508
707
1016
1379
273
689
42
277
325
413
965
1354
250
588
814
1043
1363
879
1243
1274
1465
872
33
74
838
254
898
1254
1439
1107
1348
437
486
842
1420
1428
144
915
1387
10
17
432
426
993
1213
1075
1401
6
68
82
90
187
228
232
260
291
355
447
460
479
525
536
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562
581
598
619
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655
665
700
737
742
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978
1012
1051
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1131
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1238
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1359
1411
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1197
852
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1017
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43
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293
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600
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702
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1133
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1165
1199
1228
1306
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511
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45
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1257
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263
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528
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601
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855
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94
191
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264
295
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623
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856
1062
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513
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1021
47
282
330
418
970
593
819
884
1248
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1279
38
79
1259
1112
442
491
847
22
149
1392
998
1218
1080
1406
95
192
237
265
296
360
452
465
530
541
567
603
624
660
670
747
791
926
945
1095
1136
1147
1168
1309
1322
857
1063
514
1022
713
3
48
331
971
594
820
1369
885
1249
1280
1260
1113
492
150
443
1393
23
999
1219
1081
1407
9
96
193
238
266
297
361
453
531
542
568
604
625
661
671
748
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927
946
1096
1137
1169
1310
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858
1064
515
714
1023
49
332
972
821
1370
886
1250
1281
1261
1114
493
151
24
1000
1082
1220
267
298
362
605
626
749
928
1138
1170
1311
1324
97
194
239
454
532
543
569
672
793
947
1097
859
1065
516
715
1024
50
333
973
1371
822
887
1282
1262
1115
494
152
25
1001
1083
1221
98
195
240
268
299
363
455
544
570
606
627
673
750
794
948
1098
1171
1312
1325
1066
860
517
716
1025
51
334
974
823
888
1283
1116
1263
495
153
26
1002
1222
1084
99
196
241
269
300
364
456
545
571
607
628
674
751
795
949
1099
1172
1313
1326
1067
861
518
717
1026
52
335
824
889
1284
1264
1117
496
27
154
1003
1085
100
197
242
301
365
572
608
629
675
752
796
950
1100
1173
862
1068
519
718
1027
53
336
825
890
1285
1265
1118
497
155
28
1004
1086
101
198
243
302
366
573
609
630
676
753
797
951
1101
1174
863
719
1028
54
337
826
891
1286
1266
1119
498
156
1005
102
199
244
303
367
574
610
631
677
754
798
952
1102
1175
864
720
1029
55
338
827
892
1287
1267
1120
157
245
1006
103
200
304
368
611
632
678
755
799
953
1103
865
721
1030
56
339
828
893
1288
1121
1268
158
1007
104
201
305
369
612
633
679
756
800
954
866
722
1031
57
340
829
894
1269
1122
159
105
202
306
370
613
634
680
757
801
955
867
723
1032
58
341
830
1123
160
106
203
307
371
614
635
758
802
956
868
1033
724
59
342
831
1124
161
107
204
308
372
615
636
759
957
725
1034
60
343
832
1125
162
108
205
309
373
637
760
958
726
1035
61
344
833
1126
163
109
206
310
374
638
761
959
727
1036
345
1127
164
110
207
311
375
639
762
728
1037
346
165
111
208
312
376
640
763
1038
729
347
166
112
209
313
377
641
764
1039
348
167
113
210
314
378
642
765
349
168
114
211
315
379
643
766
350
169
115
212
316
380
644
767
351
170
116
213
317
381
645
768
171
117
214
318
382
769
172
319
383
770
118
215
173
119
216
384
771
174
120
217
385
772
175
121
218
386
773
176
122
219
387
774
177
123
220
388
775
178
124
221
389
179
125
222
390
180
126
223
391
181
127
224
392
182
225
128
393
129
394
130
395
131
396
132
397
133
398
134
399
135
400
136
401
137
402
138
403
139
404
140
405
141
0
1342
0
20
40
_t
Outliers in the Weibull estimation of Base Model
1. Ethiopia 1991
2. France 1962
3. Dominican Republic 1965
4. Uganda 1977
5. Uruguay 1972
6. Egypt 1998
7. Pakistan 1996
60
Table IV.
Efficient Score Residual Analysis for Cox Proportional Hazard Model
Variable | Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------Confbord
1226
-1.09e-18
.1710565
-.7074644
1.201734
lndistx
1226
-3.08e-17
.674641
-3.683614
6.514269
borddist
1226
-6.88e-18
.3667695
-3.695487
2.215432
allgems
1226
7.19e-18
.1442937
-.3827357
.8265945
alldrugs
1226
1.12e-17
.1264603
-.5766388
.9052037
-------------+-------------------------------------------------------Lnmt
1226
1.61e-16
.598164
-2.918337
4.995954
lnfrst
1226
-3.98e-17
.6417699
-2.548883
4.46737
interven
1226
2.61e-18
.1092812
-.9447767
.8769241
popdcntry
1226
-1.36e-15
35.25886
-145.5632
681.6089
terr
1226
1.29e-17
.1794729
-.5339436
.8020931
-------------+-------------------------------------------------------Lgdp96x
1226
-1.42e-16
.3401965
-2.221149
2.445117
gdpmnt
1226
1.46e-16
1.065303
-8.811657
6.552158
gdpfrst
1226
3.33e-17
.8379015
-4.522243
5.486199
drugmnt
1226
1.51e-18
.0669784
-.2524446
.9242502
37
Figure II.
Kaplan-Meier Survival Estimates for Intervention as Estimated in Base Model
0.00
0.25
0.50
0.75
1.00
Kaplan-Meier survival estimates, by interven
0
20
40
60
analysis time
interven = 0
interven = 1
38
Figure III.
Kaplan-Meier Survival Estimates for Territory as Estimated in Base Model
0.00
0.25
0.50
0.75
1.00
Kaplan-Meier survival estimates, by terr
0
20
40
60
analysis time
terr = 0
terr = 1
39