Education and Armed Conflict Prediction∗
Håvard Hegre
Department of Political Science, University of Oslo,
Centre for the Study of Civil War, PRIO
Joachim Carlsen
Østfold University College, Centre for the Study of Civil War, PRIO
Håvard Strand
Centre for the Study of Civil War, PRIO
Henrik Urdal
Centre for the Study of Civil War, PRIO
Contact: [email protected]
February 9, 2009
Abstract
The paper predicts changes in global and regional incidences of armed
conflict for the 2008–2048 period. The predictions are based on a dynamic
multinomial logit model estimation on a 1970–2007 cross-sectional dataset
of changes between no armed conflict, minor conflict, and major conflict.
Core explanatory variables in the estimation model are population size,
infant mortality rates, demographic composition, neighborhood characteristics, and education levels. Predictions are obtained through simulating
the behavior of the conflict variable implied by the estimates from this
model, given two sets of projections of the explanatory variables. For
the demographic variables, we use projections from the UN World Population Prospects. For education, we use a projection of education data
developed by the International Institute for Applied Systems Analysis.
The education projection covers the the 2005–2048 period, and is ageand gender-specific. The education data originate from national censuses
and Demographic Health Surveys (DHS) and has been constructed by
performing demographic back-projections.
∗ The research was funded by the Norwegian Research Council grant no. 163115/V10.
Thanks to Håvard Nygård for excellent research assistance and Naima Mouhleb for valuable
comments to the paper.
1
1
Motivation
This paper predicts the future development in the incidence of internal armed
conflict up to 2048. We define armed conflict as is done in Harbom and Wallensteen (2008), as an armed conflict between a government and an organized
opposition group that causes at least 25 battle-related deaths during a calendar
year.
We base our predictions on a statistical model estimated on data for the
1970–2007 period, using data on education levels and demographic characteristics in addition to information on previous conflicts and conflicts in the neighborhood. We have selected these explanatory variables because the UN has
made available predictions for these variables for the years 2008–2048.
We show that the incidence of minor conflict is likely to decrease further in
the future if the UN predictions come through, but that the incidence of major
conflicts (more than 1000 battle-related deaths per year) will remain stable. We
present predictions for five different scenarios that differ in their projections for
the demographic and educational variables. In the most pessimistic scenario,
where population growth and infant mortality rates are high, the incidence of
conflict will remain constant. We also note, however, that the uncertainty of
these predictions is considerable.
Why is it worthwhile to make uncertain predictions of a phenomenon like
internal armed conflict? Although it is certain that armed conflict happen
more frequently in large, poor countries like India than in small, rich countries
like Luxembourg, statistical models are certainly far from capturing all the
idiosyncratic factors that combine to lead a country into severe domestic conflict.
We believe this exercise has several potential advantages.
First, an ability to predict conflicts before they happen is useful to help
prevent conflicts and avoid much human suffering. We predict a 50% probability
that Tanzania has a conflict in 2015. If – and there is a very big if – the UN
could be reasonably certain that this was a good prediction, the UN could take
early steps to prevent this conflict.
2
Second, even though country-level predictions are uncertain, we believe it is
possible to generate quite accurate predictions when aggregated to regional and
global levels. We show that the different scenarios yield quite different levels of
predicted global incidence of conflict. They show that the implementation of
policies that help increase education levels and reduce poverty (as measured by
infant mortality rates) do have an impact on global conflict levels. Predictions
of the sort we develop here can help assessing the benefits of such policies in
terms of conflict reduction.
Finally, predictive ability is a useful way to evaluate the quality of the empirical models used by scholars that are primarily interested in showing that
certain causal mechanisms work to facilitate or prevent conflicts. One thing is
that our simulations indicate the amount of uncertainty in such models. Another is that the complete effect of interventions such as official development
assistance that succeeds in increasing education is not restricted to the change
in the risk of conflict onset the year after. This risk reduction also transmits
into neighboring countries, since education reduces the risk that these countries
experience a destabilizing conflict in the neighborhood.
2
2.1
The research body underlying this work
Education
The most comprehensive study of education and armed conflict has been done
by Thyne (2006). Using Fearon and Laitin (2003) model as the baseline, Thyne
studied the impact of several educational variables on civil war onset. While
the strongest effects were found for primary enrollment and secondary male enrollment, he also found pacifying effects of alternative measures like education
expenditure as share of GDP, and of literacy. The level of tertiary education,
however, appears to be unrelated to conflict onset. The pacifying effect of education has previously been identified in other studies. Paul Collier and colleagues
have found that secondary male enrollment is associated both with lower risk of
3
outbreak of civil war (Collier and Hoeffler, 2004), and with shorter wars (Collier, Hoeffler and Söderbom, 2004). Barakat and Urdal (2008) replicate Thyne’s
findings using secondary male attainment rates, and low-intensity armed conflict
onset data. Hence, there appears to be a consensus in the empirical literature
that higher education levels reduce conflict risks. While Collier and Hoeffler
(2004) finds education to be an alternative measure of level of development,
both Thyne (2006) and Barakat and Urdal (2008) demonstrate that education
may have a pacifying effect even when controlling for level of income.
2.2
Youth bulges
Early cross-national studies addressing the relationship between young age structure and armed conflict include Mesquida and Wiener (1996) and Esty and
Unger (1998). Mesquida and Wiener (1996) found that large youth cohorts,
measured as the ratio between 15–29 year old males and males of 30 years and
above, were associated with higher intensity levels (manifested in the number of
conflict related deaths) in intrastate and interstate conflicts. The State Failure
Task Force Group (Esty and Unger, 1998)) found some effect of youth bulges
(15–29/15+) on ethnic conflict. Two prominent quantitative civil war studies,
Collier and Hoeffler (2004)) and Fearon and Laitin (2003), report to have initially included youth bulges as one among a high number of variables. Both
studies failed to find effects of youth bulges on civil war and middle-intensity
conflict and report these results only in passing. Cincotta and Anastasion (2003)
and Urdal (2006) have reported increasing risks of armed conflict onset associated with youth bulges (defined as those aged 15-29 and 15-24 respectively
compared to the total adult population of 15 years and above). Further, Urdal
(2006) shows that the youth bulge measure used by Collier and Hoeffler (2004)
and Fearon and Laitin (2003), 15–24/total population, fail to capture the theoretical concept of youth bulges due to the inclusion of the youngest cohorts
(0–14) in the denominator. An emerging consensus is that youth bulges appear
to matter for low-intensity conflict, but not for high-intensity civil war.
4
2.3
Infant mortality
Infant mortality has been promoted as an alternative measure of level of development (Goldstone, 2001), capturing a broader set of developmental factors
than the standard measure of income levels (GDP per capita). Esty and Unger
(1998) found very strong effects of infant mortality for state failure and conflict,
and Urdal (2005) found high infant mortality rates to be strongly associated
with an increased risk of armed conflict onset. Abouharb and Kimball (2007)
have assembled the most complete dataset of infant mortality rates dating back
to 1816 for all states in the international system. Replicating Urdal (2005) they
found a similarly strong predictive ability of infant mortality on conflict onset.
Generally, infant mortality appears to perform very similar to other measures
of general development.
2.4
Population
Almost all cross-national empirical studies find that populous countries have
more internal conflicts. A country at the size of Nigeria has an estimated risk
that is about 3 times higher than for a country the size of Liberia.1 The increase
in the risk of conflict does not increase proportionally with population, however.
On the contrary, the per-capita risk of civil war onset decreases with country
size.2 The typical study finds that a 1% increase in population leads to a 0.3%
increase in risk of conflict onset.
Studies of the duration of civil war find little evidence that the size of the
country’s population affects how long it lasts (Fearon, 2004, Collier, Hoeffler and
Söderbom, 2004, Buhaug and Lujala, 2005). Whether the severity of conflict
is roughly proportional to a country’s population is contested – Lacina (2006)
does not find this, but Gleditsch, Hegre and Strand (2009) do. Even the results
1 This is based on an estimate of 0.3 which is typical for cross-national logistic regression
models with a log population variable. A host of studies obtain estimates for log population
larger than 0 but smaller than 1 (Collier and Hoeffler, 2004, Collier and Rohner, 2008, de Soysa,
2002, Elbadawi and Sambanis, 2002, Fearon and Laitin, 2003, Gleditsch and Ruggeri, 2007,
Gleditsch, Hegre and Strand, 2009, Thyne, 2006, Urdal, 2005; 2006).
2 This is also noted by Collier and Hoeffler (2004).
5
in the latter study imply that the per-capita risk of being killed in battle is
much higher in small countries than in large ones.
Studies of the location of conflict within countries is also somewhat indeterminate on the effect of population concentrations. Hegre and Raleigh (N.d.)
find in a very fine-grained analysis that conflict events in a sample of African
countries tend to be most frequent in populous locations – rebel groups and governments tend to target each other in valuable locations such as cities. Buhaug
and Rød (2006), focusing on the more general area of operation, find that this
area tends to be located in relatively sparsely populated regions. Buhaug (2006)
also find that geographically large countries are more likely to have territorial
or secessionist conflicts, but size does not affect the risk of conflicts over government.
2.5
Development to supplement discussion of education
and IMR
[THIS SECTION IS UNFINISHED]
Collier and Hoeffler (2004); Collier et al. (2003)
A low average per-capita income is among the factors that almost all scholars find to be associated with a high risk of the onset of conflict (Hegre and
Sambanis, 2006), and GDP per capita is included in virtually all studies of the
risk of armed conflict onset. These empirical studies find a negative relationship
between GDP per capita and the risk of civil war onset.3
The variable explains a considerable amount of variation in conflict frequency
between countries. The typical estimate4 implies that the predicted risk of
conflict is about five times higher in the poorest countries in Africa as in the
richest.5 The risk in the most well-off African countries is three times higher
3 Major examples using a variety of time-frames and measures of armed conflict or rebellion
are: Collier and Hoeffler (2004), de Soysa (2002), Elbadawi and Sambanis (2002), Fearon and
Laitin (2003), ?), Henderson and Singer (2000), ?), Urdal (2006). Some of these studies use
PPP GDP per capita, others market-price GDP per capita.
4 Estimates vary from −0.2 to −1.2. The average estimate in Hegre and Sambanis (2006)
is −0.5, which is the basis for this calculation.
5 Hegre and Sambanis (2006) carry out a systematic sensitivity analysis of 86 variables used
6
again than that of the richest countries in the world.6
Some studies have employed other indicators of development that are highly
correlated with GDP per capita, and obtain similar results. Urdal (2005) finds
a positive relationship between infant mortality rates and the risk of conflict
onset, and Hegre et al. (2001) a negative estimate for energy consumption per
capita. Hegre, Gissinger and Gleditsch (2003) and Thyne (2006) obtain negative
estimates for various measures of education levels. Thyne (2006) even identifies
a conflict-reducing effect of education controlling for GDP per capita, despite
the high correlation between the two variables.
Although there is a strong relationship between income and the risk of conflict onset, there is no robust relationship between income and the duration
of civil wars. Fearon (2004) find no such relationship. Collier, Hoeffler and
Söderbom (2004) and DeRouen and Sobek (2004) even find richer countries to
have longer civil wars.
Although higher income does not translate into shorter wars, there is some
evidence that income leads to less lethal wars. Lacina (2006) and Gleditsch,
Hegre and Strand (2009) find a 1% increase in income to reduce the per-capita
severity of the internal war by about 0.15%.
2.6
Former conflicts
The third variable that almost invariably is included in country-level studies of
civil war is whether a country has had a previous conflict. Results are somewhat
inconclusive. It is clear that conflicts that end have a discouragingly high risk
of recurrence. Collier and Hegre (2008) estimate the risk of conflict reversal to
be around 40% during the first post-conflict decade, and Elbadawi and Milante
in civil war studies. Their study is modeled after Sala-i-Martin (1997). They run about 4
million different models in which GDP per capita, population size, and time since previous
war are always included. The 4 million models include all reasonable combinations of the
remaining 83 variables. Results for a variable are regarded as robust if they are significant on
average over the models they feature in.
6 Risk here is in terms of odds of conflict, or the probability that a conflict starts in a
year divided by the probability that it does not. The odds of conflict is five times higher in
countries with GDP per capita around 120 US$ (e.g., Burundi and Ethiopia) than in countries
at 3000 US$ (Gabon, Botswana, and South Africa)
7
(2008) find an even higher rate using a more inclusive definition of conflict.
Country-level studies that control for other risk factors, on the other hand, do
not always find recent conflicts to increase the estimated risk of new onsets
over and beyond. These studies, however, to some extent mask the fact that
conflicts have a strong adverse effects on the major risk factors included in these
models. In particular, conflicts have a catastrophic impact on average income.
Accordingly, Collier et al. (2003, ch. 4) show that there is a considerable ‘conflict
trap’ tendency. They estimate that about half of post-conflict countries return
to conflict. A third of the post-conflict countries succeed in keeping the peace
beyond the first 10 years, but enter a category of countries that they classify
as ‘marginalized countries at peace’ (roughly the same class of countries as
the ‘bottom billion’ countries; cf. Collier, 2008.). This group of countries is
characterized by low incomes sluggish growth, and has a markedly higher risk
of conflict than other countries. Only one sixth of post-conflict countries end
up in the group of ‘successful developers’ and succeed in drastically reduce the
danger of renewed conflict (Collier et al., 2003: 109).
2.7
Neighborhoods
An inspection of the global map of conflicts in Figure 1 is revealing. The armed
conflicts in 2007 (as in all years after WW II) are clustered in some geographical
regions. This clustering is partly due to the fact that factors such as poverty
that have been shown to increase the risk of conflict also have strong tendencies
to cluster. In addition, several studies also show that conflicts tend to spill
over borders when controlling for the presence of these factors (Gleditsch and
Ward, 2000, Hegre and Sambanis, 2006, Collier et al., 2003). There are several explanations of this tendency of conflict spillover (Salehyan and Gleditsch,
2006). Several studies point to the importance of ethnic groups involved in
conflicts that have kins in neighboring countries, especially in conjunction with
significant refugee flows. Others point to how the detrimental economic effects
of conflicts spill over into neighboring countries (Murdoch and Sandler, 2004),
8
Figure 1: Map of conflicts ongoing in 2007
Source: Harbom and Wallensteen (2008)
thereby exacerbating the factors that contribute to facilitating conflicts.
Since our aim is to predict, we are best served by a spatial lag of conflict
as our measure of neighborhood effects. In our estimated models, we rely on
observed levels of conflict in the direct neighborhood of each country. In our
simulation models, we update these variables based on the results from the
simulation itself.
3
The simulation
Table 1 cross-tabulates the conflict level observed in all countries in the 1970–
2007 period with the conflict level these countries had the year before. The
three conflict levels reported in the Uppsala/PRIO conflict data set (Harbom
and Wallensteen, 2008) are ‘no conflict’ or less than 25 battle-related deaths
reported in a year; ‘minor conflict’ or between 25 and 999 battle-related deaths
9
Table 1: Transition probability matrix: Conflict at t vs. at t − 1,
(Conflict level at t)
Conflict at t-1
No conflict Minor conflict Major conflict
No conflict
3323 (0.969)
98 (0.029)
9 (0.003)
Minor conflict
85 (0.180)
348 (0.736)
40 (0.085)
Major conflict
15 (0.113)
35 (0.263)
83 (0.624)
Observations
3423
481
132
1970–2007
Total
3430 (1.000)
437 (1.000)
133 (1.000)
4036
Row proportions in parentheses
per year; and ‘major conflict’ which occurs when more than 1000 battle-related
deaths per year are reported. The row proportions are given in parentheses.
0.969 or 97.9% of the countries that had no conflict in year t − 1 did not have
conflict in year t. 2.9% of them transitioned into minor conflict, and 0.3%
into major conflict. Table 1 also shows that 73.6% of the countries with minor
conflict continued to have minor conflict the year after.
One may estimate the transition matrix using a multinomial logit model
with the conflict level at t as the outcome variable, and the level at t − 1 as a
set of dummy variables. This type of model is often referred to as a ‘dynamic
multinomial model’.7 The multinomial model (see Greene (1997, 914–917);
StataCorp 2005: 210–211) for the three outcomes (j = 0 : ‘no conflict’, j = 1 :
‘minor conflict’, j = 2 : ‘major conflict’) is then
exβ
p(Yi = j) = P2
k=0
j
exβ k
(1)
To identify the model, we set ‘no conflict’ as the base outcome. The probabilities of the three outcomes are then given by:
j
exβ
p(Yi = j) = P2
xβ k
k=0 e
(2)
The β estimates also has a direct interpretation in terms of relative probabilities:
7 Przeworski et al. (2000), for instance, refer to their related model as a dynamic probit
model.
10
0
p (Y = 1)
= eβ1 xi
p (Y = 0)
(3)
0
p(Y = 2)
= eβ2 xi
p(Y = 0)
(4)
and:
The estimates β1 reported below, then, are interpreted as the impact of the
explanatory variable on the probability of being in ‘minor conflict’ relative to
‘no conflict’. The β2 estimates approximate the probability of ‘major conflict’
relative to ‘no conflict’.
If we enter only the state at t − 1 as explanatory variable(s), the predicted
probabilities from estimating this model are identical to those reported in Table
1. The purpose of formulating this as a multinomial logit model, however, is to
be able to account for a set of explanatory variables, discussed in section 4.
[IN A DEFINTIION AND DISCUSSION OF THE UNDERLUYING TRANS
PROB MATRIX]
3.1
Simulation setup
The general setup of the simulation procedure is the following as shown in figure
2 and described below:
1. Specify and estimate the underlying statistical model
2. Make assumptions about the distribution of values for all explanatory variables X for the first year of simulation and about future changes to these.
In this paper, we base the simulations on UN projections for education
and demographic variables
3. Draw a realization of the coefficients of the multinomial logit model based
on the estimated coefficients and the variance-covariance matrix for the
estimates
11
Figure 2: Simulation flow chart
4. Calculate the probabilities of transition between levels for all countries for
the first year, based on the realized coefficients and the projected values
for X.
5. Randomly draw whether a country experiences conflict, based on the estimated probabilities
6. Update the values for the explanatory variables. A number of these variables, most notably those measuring historical experience of conflict and
the neighborhood conflict variables, are contingent upon the outcome of
step 3 and 4.
7. Repeat (4)–(6) for several years, e.g. for 2008–2047, and record the simulated outcome.
8. Repeat (4)–(7) a number of times to even out the impact of individual
realizations of the probability distributions.
9. Repeat (3)–(8) a number of times to even out the impact of individual
realizations of the multinomial logit coefficients.
12
The updating of the explanatory variables allows the procedure to take into
account complex relationships between the explanatory variables. For instance,
Gleditsch (2007) finds that conflicts in one country increases the risk of conflict
in the neighboring countries. This can be captured by including a variable
‘neighbor in war’ in the statistical model. If the simulation procedure draws an
onset of a new conflict in a country for a given year, this will be reflected as a
change in the ‘neighbor in war’ variable for its neighbors and therefore affect
their subsequent probability of experiencing conflict. Likewise, the impact of a
previous conflict on the likelihood of renewed conflict can be captured. Only
simulation techniques can allow researchers to take the full impact of these
complexities into account.
We have developed a simulation framework written as a C plug-in to Stata
(with an interface in Stata’s ado language). The general framework is complemented with individual, specific simulations written in C#.
4
4.1
Data
Education Data
Education data originate from a new dataset compiled by researchers at the International Institute for Applied Systems Analysis (Lutz and Sanderson, 2007),
providing historical estimates for 120 countries for the 1970-2000 period. The
dataset is based on individual-level educational attainment data from recent Demographic Health Surveys and national censuses. Historical estimates are constructed by five-year age groups and sex using demographic multi-state methods
for back projections, and taking into account gender and education-specific differences in mortality. The dataset measures educational attainment using definitions and categories that are consistent over countries and time, representing
a vast improvement over previous education data. The four categories of educational attainment are consistent with the International Standard Classification
of Education (ISCED) categories: No education (no formal education); Primary
13
(those with uncompleted primary to uncompleted lower secondary, ISCED 1);
Secondary (those with completed lower secondary to uncompleted first level of
tertiary, ISCED 2, 3, and 4); Tertiary (those with at least completed first level
of tertiary, ISCED 5 and 6).
For this study we follow Barakat and Urdal (2008) and employ a measure of
male secondary education, defined as the proportion of males aged 20-24 years
with secondary or higher education of all males aged 20-24.
A recent addition to Lutz and Sanderson (2007) is a projection scenario for
educational attainment until 2050 (Samir et al., 2008). In addition to the General Trend Scenario, assuming a general, incremental increase in educational
attainment globally, we have constructed two alternative educational scenarios.
The low education scenario is based on an assumption of no improvement in
relative educational attainment, i.e. that the share of young men receiving secondary education is constant. The high education scenario assumes a trajectory
that is 5% above the baseline trend scenario, converging towards 100% of young
men aged 20-24 having secondary education.
4.2
Demographic Data
The demographic variables originate from the World Population Prospects 2006
(UN, 2007) produced by the United Nations Population Division. This is the
most authoritative global population dataset, covering all states in the international system between 1950 and 2005 and providing projections for the 2005–
2050 period.
Three key demographic indicators are used in this study. Total population is
defined as the de facto population in a country as of 1 July of the year indicated,
and expressed in thousands. The measure has been log-transformed following an
expectation of a declining marginal effect on conflict risk of increasing population
size. Infant mortality is defined as the probability of dying between birth and
exact age 1 year, expressed as the number of infant deaths per 1000 live births.
Youth bulges are measured as the percentage of the population aged 15–24
14
years of all adults aged 15 years and above. Age-specific population numbers
are provided by the UN (2007) for five-year groups of de facto population,
measured in thousands.
Only one scenario exists for each indicator for the 1950–2005 period. Estimates are based on a range of different historical sources, including population
censuses, demographic and health surveys, and population registers. The UN
estimates are revised biannually, and each new revision incorporates all new and
relevant information about past demographic trends. Consequently, historical
estimates may change between revisions.
Several different demographic projection scenarios are provided by the UN
Population Division. For each country, the starting point is the 2005 mid-year
population estimate. In order to project the population until 2050, assumptions
have to be made about future trends in fertility, mortality, and international
migration. Projecting such trends necessarily involves considerable uncertainty.
For this project, we use the three main scenarios from the population projections. These differ from each other exclusively as a result of different assumptions regarding future fertility trajectories. In the medium scenario, total
fertility rates for all countries are assumed to converge towards 1.85 children
per woman assumed to follow a path similar to historical experiences of fertility
decline. However, not all countries are projected to reach the level of 1.85 by
the end of the period. Under the high fertility scenario, fertility is projected to
remain 0.5 children higher than the medium projection (i.e. converge towards
2.35), while in the low fertility scenario, fertility is assumed to be 0.5 children lower than the medium scenario (converging towards 1.35). For all three
scenarios, mortality is assumed to follow models for change in life expectancy
developed by the UN Population Division, with increasingly smaller gains at
higher levels of life expectancy. The international migration assumptions are
based on past international migration estimates as well as considerations of
states’ migration policies with regards to future international migration.
The three main projection scenarios are used to construct three different scenarios for total population and for youth bulges. For countries that currently
15
(2005) experience high fertility levels, the three different fertility trajectories
lead to significant differences in population size estimates by 2050. For the
youth bulge measure, the three scenarios yield identical estimates until 2024
since the relevant youth cohorts were already born by 2005. Beyond 2025, the
different fertility assumptions lead to significant variation in the youth bulge
projections for many countries. For infant mortality, the UN Population Division (UN, 2007) only provides one projection scenario. However, given the
considerable fluctuation in infant mortality associated with different economic,
social, and political conditions, it is plausible to expect significant uncertainty
associated with future trends. For the purpose of this analysis, we have constructed one high and one low infant mortality scenario. For the high infant
mortality scenario, we assume a correction in the infant mortality rate identical
to a 0.5% increase for each successive year compared to the UN projected baseline. For the low infant mortality scenario, we conversely assume a downward
correction in the infant mortality of 0.5% per year compared to the UN projected baseline. This implies that absolute variation will be greatest in countries
with high levels of infant mortality, and that over 40 years the correction will
be approximately 20% higher and 20% lower than the baseline UN projection
for our high and low infant mortality scenarios respectively.
4.3
Conflict data
We utilize the 2008 update of the UCDP/PRIO Armed Conflict Dataset Harbom
and Wallensteen (2008), Gleditsch et al. (2002), which records conflicts at two
levels. Minor conflicts are those that pass the 25 battle-related deaths threshold
but have less than 1000 deaths in a year. Major conflicts are those conflicts that
pass the 1000BRD threshold . We only look at internal armed conflicts, and
we only include the countries whose governments are included in the primary
conflict dyad (i.e., we exclude other countries that intervene in the internal
conflict).
16
Number
1
2
3
4
5
6
7
8
9
10
11
4.4
Table 2: List of regions
Region Name
North America
Central America and the Caribbean
South America
Western, Northern, and Southern Europe
Eastern Europe
Western Asia and North Africa
Western Africa
East, Middle, and Southern Africa
South-central Asia
Eastern and South-eastern Asia
Oceania
Neighborhood and region data
We define the neighborhood of a country A as all n countries [B1 ...Bn ] that
share a contiguous border with A, as defined by Gleditsch and Ward (2000).
The spatial lag of conflict is a dummy variable measuring whether there is
conflict in the neighborhood or not. Since the dependent variable is nominal,
we construct two spatial lags, one for minor conflicts and one for major conflicts.
We define 11 regions as listed in Table 2. The list is a somewhat condensed
version of the UN region definition.8 In the simulations presented here, however, we do not use these regions as predictor variables in our model, as we
seek to generate predictions solely based on the projections for education and
demographic variables. Dummy variables that capture inter-regional heterogeneity will certainly improve the quality of in-sample predictions, since they
help maximizing the explained variance in the observed data. We cannot be certain it improves predictions for the future, however, since it may be untenable
to assume that these heterogeneities persist indefinitely.
4.5
Temporal Dummies
Similar considerations apply to temporal dummies. We could easily fit the
model better to the data at hand by adding yearly fixed effects. While these
8 The
UN list is found at http://www.un.org/depts/dhl/maplib/worldregions.htm.
17
temporal dummy variables would be correlated with several of our explanatory
variables, such as IMR, they would add explanatory power. However, it is not
very helpful to know, trying to predict the level of violence in 2034, that 2034 is
not the year 1976. Furthermore, it is not unproblematic to add a large number
of correlated variables to a non-linear model like the multinomial logit.
On the other hand, there are good reasons to believe that the underlying
transition probability matrix for a country with a given set of characteristics is
not constant over the observed period. At least we know that the end of the
Cold War led to the eruption of an unusually high number of new conflicts, but
at the same time increased the number of conflict terminations (Elbadawi and
Milante, 2008). Moreover, the Cold War saw a steady accumulation of conflicts
whereas the last 15 years have seen an encouraging decline in the number of
ongoing conflicts.
These fluctuations give rise to some difficult model specification issues. Will
the baseline transition probability matrix be the same in the 2008–2048 period
as in 2007, as during the Cold War, or any other period? The most obvious
choice is to use the most recent year as the point of departure. This also
helps us scaling the inital level of our prediction so that the first year simulated
(2008) is not radically different from the last year observed (2007). However, the
estimated transition matrix for a single year is based on only a few transitions
and is probably heavily influenced by individual events.
After an extensive series of trials, we have chosen to estimate our model
with three temporal dummies. The first period is 1970–1986, the second period
is 1987–2001, the third period is 2002–2006, while 2007 constitutes a separate
period by itself. The 2002–2006 period is the reference category in the regression
model, and all simulations are run with the years after 2007 having the value ‘1’
for the 2007 dummy variable. In the model underlying the predictions presented
in Section 6, we also include interactions between the state at t−1 and the 1970–
86 and 1987–2001 dummies in order to model temporal differences in transition
probabilities, not only in the distribution of states at t.
This approach appears to be a reasonable compromise between the need to
18
control for larger time trends, and the need for a scaled intercept in the early
phases of the simulation, while keeping the number of covariates at a fairly
restrictive level. Our choice implies that the transition probability matrix of
the 2002–06 period guides the simulation, but that the initial distribution over
the three conflict levels is the same as in 2007.
5
Arriving at the best model specification
[Also list of which conflict countries that were active in 2007 that we did not
predict correctly, and which non-conflicts we did not predict correctly. Report
country names.]
In order to maximize the predictive power of our model, we ran a splitsample model where we estimated each candidate model on the period 1970–
1999, using our simulation program to obtain predictions for the period 20002007. Due to the reduced temporal span, we ran these tests with only one full
set of temporal period dummy. We entered dummy variables for the period
1970–1986 and the interactions between this variable and the conflict level at
t − 1. In addition, we added a last year dummy for the year 1999 without the
interaction terms. The 1999 dummy was held constant at 1 for the simulation
period. This model implies that the underlying transition probability matrix
for the 1987–1998 period was assumed to govern the transitions for the 2000–07
period, but that the initial distribution of conflict levels was that of 1999.
5.1
Comparing five candidate models
19
20
2.914∗∗∗
3.079∗∗
-0.386
-0.0493
1.389∗∗∗
-0.534
0.214
0.0269
0.407∗∗∗
-1.749∗
1.661∗
2.037
-0.431
-0.469
-7.478∗∗∗
Major conflict equation
Minor Conflictt−1
3.953∗∗∗
Major Conflictt−1
6.290∗∗∗
Temporal dummy 1970–86
0.00981
1999 year dummy
-1.473
1970–86*Minor Conflictt−1
0.685
1970–86*Major Conflictt−1
-0.427
Infant Mortality Rate (ln)
0.527
Youth Bulge (ln)
0.0171
Total Population (ln)
0.0997
Education (ln)
-0.453
Educ.*Minor Conflictt−1
0.979
Educ.*Major Conflictt−1
2.307
Neighboring minor conflict
2.574∗
Neighboring major conflict
2.438∗
Constant
-11.34∗∗∗
Observations
3096
Standard errors in parentheses
∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
Minor conflict equation
Minor Conflictt−1
Major Conflictt−1
Temporal dummy 1970–86
1999 year dummy
1970–86*Minor Conflictt−1
1970–86*Major Conflictt−1
Infant Mortality Rate (ln)
Youth Bulge (ln)
Total Population (ln)
Education (ln)
Educ.*Minor Conflictt−1
Educ.*Major Conflictt−1
Neighboring minor conflict
Neighboring major conflict
Constant
(1.030)
(1.228)
(0.535)
(1.176)
(0.683)
(0.832)
(0.386)
(0.0481)
(0.119)
(1.685)
(1.821)
(2.097)
(1.075)
(1.068)
(2.737)
(1.019)
(1.209)
(0.536)
(1.168)
(0.684)
(0.830
(0.388)
(0.0465)
(0.117)
(1.643)
(1.809)
(2.060)
(2.526)
-9.947∗∗∗
3096
(1.217)
(0.488)
(1.009)
(0.232)
(0.444)
(0.374)
(0.770)
(0.205)
(0.0255)
(0.0624)
(0.688)
(0.823)
(1.731)
3.914∗∗∗
6.170∗∗∗
-0.108
-1.377
0.697
-0.425
0.599
0.0297
0.127
-0.278
0.946
2.586
-7.426∗∗∗
2.902∗∗∗
3.035∗∗
-0.337
-0.0477
1.413∗∗∗
-0.566
0.186
0.0219
0.390∗∗∗
-1.803∗∗
1.724∗
2.138
Interactions model
(0.370)
(0.0460)
(0.116)
(1.073)
(1.074)
(1.070)
(2.472)
0.444
0.0240
0.106
-0.0307
2.627∗
2.419∗
-11.53∗∗∗
3096
(0.260)
(0.267)
(1.182)
-0.450
-0.515
-7.621∗∗∗
(0.434)
(0.475)
(0.303)
(1.175)
(0.201)
(0.0247)
(0.0627)
(0.621)
0.0736
0.0348
0.425∗∗∗
-1.548∗
4.729∗∗∗
7.079∗∗∗
0.0684
-1.472
(0.184)
(0.361)
(0.186)
(0.475)
4.385∗∗∗
3.725∗∗∗
0.0178
-0.0870
Neighborhood model
Table 3: Multinomial Regression Results
(0.488)
(1.013)
(0.234)
(0.442)
(0.375)
(0.772)
(0.206)
(0.0258)
(0.0639)
(0.686)
(0.825)
(1.735)
(0.271)
(0.274)
(1.233)
Full Model
-10.14∗∗∗
3096
0.508
0.0379
0.129
0.254
4.690∗∗∗
7.078∗∗∗
-0.0392
-1.387
-7.528∗∗∗
0.0416
0.0285
0.405∗∗∗
-1.584∗
4.419∗∗∗
3.719∗∗∗
0.0828
-0.0829
(2.268)
(0.371)
(0.0445)
(0.115)
(1.046)
(0.432
(0.470)
(0.297)
(1.171)
(1.164)
(0.199)
(0.0243)
(0.0612)
(0.621)
(0.183)
(0.360)
(0.183)
(0.477)
Basic model
-5.754∗∗∗
3096
4.983∗∗∗
7.419∗∗∗
-3.476∗∗∗
4.889∗∗∗
4.096∗∗∗
(0.354)
(0.415)
(0.459)
(0.115)
(0.178)
(0.351)
Null model
Table 4: AUCs for five models estimated on data for 1970–1999 and compared
to observed conflicts 2000–2007
Model
Area under the Standard
χ2 df P r > χ2
ROC curve
error statistic
Full model
0.8770
0.0231
Basic model
0.8691
0.0225
0.1342
1
0.7142
Neighborhood
0.8528
0.0247
1.7451
1
0.1865
Interactions
0.9028
0.0188
1.7484
1
0.1861
Null model
0.7718
0.0322 19.7382
1
0.0000
The multinomial logit results for five models are reported in Table 3. The
Null model contains only the lagged depended variable necessary to estimate
the ‘average’ transition probability matrix modeled by the dynamic multinomial model. The Basic model includes the demographic variables, population,
infant mortality rate, education, and the youth bulge variable. The Interaction
model adds interactions of education and the temporal dummy with the lagged
dependent variable, while the Neighborhood model is similar to the basic model
except that it adds the spatial lag of the dependent variable. The Full model
contains both aspects of the interaction and neighborhood models.
We ran 100 simulations for each of these models. We want to identify the
model that yields the predictions that most closely reflect what we actually
observed in 2000–2007. Evaluations of predictions are more straightforward for
dichotomous variables, so we group the cases where we predict either minor
or major conflict into one category and compare with a similarly dichotomous
observed variable. We then summarize all simulated outcomes for each countryyear as the share of simulations where we predict conflict and the share where we
predict no conflict. These predicted shares are in turn paired with the observed
outcomes. As a goodness-of-fit measure we use the area under the Receiver
Operator Curves (AUC) (Fawcett, 2006). The AUC is equal to the probability
that the simulation predicts a randomly chosen positive observed instance higher
than a randomly chosen negative one. The ROC curves for the five models are
presented in Figure 3.
The AUCs for the five models and formal tests of the differences between
21
Figure 3: Receiver Operator Characteris
them are reported in Table 4. Comparing the five models, it is surprisingly
evident that the full model is not the best model. The spatial lags introduced
in the neighborhood model actually worsen the predictive power of that model,
probably by increasing the chance of false positives. As a consequence, the
full model also predicts worse than the interaction model. From this finding
one might suggest that the pattern of incidence of conflict was less spatially
correlated after the cold war than during the cold war.
In Section 6 we present simulations based on the full model despite the results reported in Table 4. This is to some extent due to time constraints in
preparing this conference version of the paper, and partly to other considerations. First, while all models are significantly better than the null model, there
are no significant differences between them in predictive ability. Moreover, it is
conceivable that the relatively short time span (2000–2007) for the comparison
is contributing to the misfit of the neighborhood variables. Finally, we have
only explored five models out of very large set of potential models, and expect
to be able to locate even better models later.
22
5.2
Final model
Table 5 shows the results of estimating the preferred model specification on the
entire dataset from 1970–2007.
The estimate for the education variable in the minor conflict equation is
−1.8, meaning that the risk of being in minor conflict relative to no conflict is
about 0.16, or 84% lower, when all males in the 20–24 age group have secondary
education compared to when no males in the group has such education. A more
moderate comparison between for instance 50% with secondary education and
60% with education decreases the risk by 16%. The estimate for the interaction term between education and ‘minor conflict at t − 1’ is +1.7. Interpreted
together, this means that education does not make any difference for conflict
incidence for countries in which conflicts have already started. High education
levels reduce the risk of conflict onset, but does not affect its duration. The
interaction with ‘major conflict at t − 1’ is of the same magnitude.
In the major conflict equation, the education variables are not statistically
significant, but reflect patterns similar to those for minor conflict. The lack of
statistical significance does not mean that education does not affect the risk of
serious conflict, since the effect of education on minor conflict onset is transmitted into a lower probability that conflicts escalate to the major conflict level.
The estimates for the demographic variables also large correspond to what
obtained in previous studies. The estimate for log population is 0.42 and its
standard error small in the minor conflict equation. We do not obtain estimates
for the Infant Mortality Rate variable that are significantly different from 0.
The education variable seems to pick up most of the relationship between development and armed conflict. The estimates for the youth bulge variable are
not significant in either model.
[SOMETHING ON TEMPORAL DUMMY ESTIMATES]
23
Table 5: Multinomial Logit Regression Results, Model for Simulation
Coefficient (st. error)
Minor conflict equation
Minor Conflictt−1
3.962∗∗∗
(0.542)
Major Conflictt−1
5.373∗∗∗
(1.499)
1970–86 temporal dummy
1.628∗∗∗
(0.330)
1970–86*Minor Conflictt−1
0.377
(0.452)
1970–86*Major Conflictt−1
–2.872∗
(1.307)
1987–2001 temporal dummy
1.823∗∗∗
(0.311)
1987–2001*Minor Conflictt−1
–0.629
(0.397)
1987–2001*Major Conflictt−1
–1.855
(1.233)
2007 temporal dummy
1.868∗∗∗
(0.542)
Education (ln)
Education*Minor Conflictt−1
Education*Major Conflictt−1
–1.799∗∗
1.700∗
2.129
(0.596)
(0.703)
(1.516)
Infant Mortality Rate (ln)
Youth Bulge (ln)
Total Population (ln)
0.218
0.0308
0.420∗∗∗
(0.179)
(0.0220)
(0.0571)
Neighboring minor conflict
Neighboring major conflict
Constant
–0.0273
–0.0711
–10.08∗∗∗
(0.259)
(0.260)
(1.064)
Major conflict equation
Minor Conflictt−1
Major Conflictt−1
1970–86 temporal dummy
1970–86*Minor Conflictt−1
1970–86*Major Conflictt−1
1987–2001 temporal dummy
1987–2001*Minor Conflictt−1
1987–2001*Major Conflictt−1
2007 temporal dummy
7.403∗∗∗
10.08∗∗∗
2.539∗
–2.625∗
–4.032∗
2.445∗∗
–3.112∗∗
–3.338∗
0.683
(1.468)
(1.883)
(0.993)
(1.253)
(1.629)
(0.883)
(1.151)
(1.513)
(1.252)
Education (ln)
Education*Minor Conflictt−1
Education*Major Conflictt−1
–0.283
0.878
1.978
(1.479)
(1.548)
(1.856)
Infant Mortality Rate (ln)
Youth Bulge (ln)
Total Population (ln)
0.417
0.00410
0.0932
(0.335)
(0.0400)
(0.108)
1.768∗∗
1.668∗
–12.20∗∗∗
(0.675)
(0.670)
(2.306)
Neighboring minor conflict
Neighboring major conflict
Constant
24
∗
∗∗
∗∗∗
p < 0.05,
p < 0.01,
p < 0.001
Figure 4: Observed and simulated proportion of countries in conflict, UN scenario, both conflict levels, all countries, 1960–2048
6
Results
We then ran simulations for the about 120 countries for which we have data
for the education and demographic variables. We ran separate simulations for
five scenarios. In the three first, we use the projections for the demographic
variables that the UN finds most plausible (the projections they refer to as
‘best’, see Section 4.2), but vary the education scenarios. We use the projection
published by the UN (see Section 4.1 as our main scenario, but also explore the
two more optimistic and the more pessimistic scenarios of own construction.
We ran 10,000 simulations for the main scenario, and 100 simulations for the
other four.
25
6.1
6.1.1
Three education scenarios
The UN projection
Figure 4 shows the observed and simulated incidence of conflict. In this figure,
we merge the two conflict levels into one, and plot the proportion of the countries
in our dataset that has an ongoing conflict of either intensity level. The line
to the left of the vertical line present the observed proportion of countries in
conflict. This proportion increased steadily up to 1992 and then declined to the
year 2003, after which the incidence of conflict increased somewhat. In 2007,
about 13% of the countries in the dataset were in conflict.
To the right of the vertical line we see our projections. The solid line is the
proportion of countries in conflict averaged over all simulations. The dashed line
represents the 10th percentile of proportion of conflict in conflict for every year.
The dotted line represents the 90th percentile of proportion of conflicts. That
is, in 80% of our simulations the predicted proportion of countries in conflict
were found between the dashed and dotted lines.
These predictions imply that we are likely to see a continued decrease in the
proportion in conflict. The predicted decrease is particularly swift from 2009 to
about 2012. Thereafter, the prediction is a gradual decrease from about 11% in
conflict to 7% in 2007.
Figure 5 shows similar observations and projections for the proportion of
countries in major conflicts. The observed decline since 1992 is even more
marked than for all conflicts. In 2007, less than 1% (i.e., one country) had a
major conflict. The proportion of countries in conflict was higher in the years
2002–2006, however. Partly since the transition probability matrix underlying
in the 2002–2006 period was chosen as the baseline, the simulation predicts an
increase to a level just above the average level for that period. From about 2010,
the prediction is that the proportion of countries with major conflict remains
constant at just above 3%. The confidence band for this prediction is very wide,
however. In more than 10% of the simulations there are no major conflicts in a
given year. In 10% of them, more than 8% of countries have major conflicts.
26
Figure 5: Observed and simulated proportion of countries in major conflicts,
UN scenario, all countries, 1960–2048
Figure 6: Observed and simulated proportion of countries in minor conflicts,
UN scenario, all countries, 1960–2048
27
Figure 7: Observed and simulated proportion of countries in conflict, UN scenario, minor conflicts, by region, 1960–2048
Figure 6 shows the predicted incidence of minor conflicts. The simulation
predicts a strong decrease in the incidence of such conflicts from about 13% in
2009 to 7% in 2012, and then a continued decline to about 4% in 2048. These
predictions are as expected from Figures 4 and 6. The uncertainty around these
simulations is considerably smaller, however. In 2048, we predict with 80% confidence that between 1 and 6% of the countries have an ongoing minor conflict.
The increased precision is because the number of minor conflicts is much higher
than major conflicts. This is reflected in much smaller standard errors in the
minor conflict equation in Table 5 than in the major conflict equation.9
Figure 7 shows simulated incidence of conflict broken down on individual
regions (see Table 2 for a list of regions). For the two European regions (regions
4 and 5), we predict a continued very low incidence of minor conflict. Also in the
9 There is clearly a potential gain in specifying a more parsimoneous model. In future
iterations of the projects, we will implement the possibility of specifying constraints on the
multinomial logit parameters, and also evaluate whether an ordinal logit model would yield
better predictions.
28
Figure 8: Observed and simulated proportion of countries in conflict, UN scenario, major conflicts, by region, 1960–2048
three American regions (regions 1 (not shown), 2, and 3) the expectation is a
decrease to levels under 5%, in contrast to the 10% incidence recorded for much
of the post-World War II period. The simulation also predicts considerable
reductions in conflict incidence in the three Asian regions (regions 6, 9, and 10).
The highest predicted incidence and the slowest decrease in conflict prevalence
is predicted in the two regions in Africa South of Sahara. In both these regions,
the predicted incidence is about 15% in the coming 20 years. This is roughly
the incidence recorded in these regions in the last decade. After about 2030,
the predicted incidence in both regions is reduced to about 20% in 2048.
Figure 8 presents the same results for major conflicts. Again, Africa South of
Sahara (regions 7 and 8) seem to have the least desirable future. The simulation
predicts an increase in Western Africa to an incidence of about 10% immediately
after 2007, and a continued slight increase thereafter. A similar prediction
applies to the Eastern and Southern part of SS Africa, although the expected
average incidence i somewhat lower. The populous countries of Eastern and
29
Southern Asia are also predicted to have a considerable share of major conflicts.
Table 6: The 20 countries with highest probability of conflict in 2015
GW country no.
750
530
770
510
500
432
436
483
475
710
630
437
501
640
541
439
816
517
771
434
Country
India
Ethiopia
Pakistan
Tanzania
Uganda
Mali
Niger
Chad
Nigeria
China
Iran
Cote D’Ivoire
Kenya
Turkey
Mozambique
Burkina Faso
Vietnam
Rwanda
Bangladesh
Benin
Minor conflict
.416
.584
.307
.356
.317
.337
.208
.188
.168
.188
.188
.228
.178
.099
.198
.149
.188
.119
.149
.099
Share in...
Major conflict
.277
.079
.228
.158
.168
.119
.238
.198
.198
.129
.129
.089
.099
.178
.069
.109
.030
.099
.069
.079
Both conflicts
.693
.663
.535
.515
.485
.455
.445
.386
.366
.317
.317
.317
.277
.277
.267
.257
.218
.218
.218
.178
Table 6 presents the 20 countries with the highest probability of conflict
(both levels) in 2015. Note that the list excludes country for which we do not
have data. Some high-risk countries are among these, e.g. D.R. Congo.
Country-level predictions are highly uncertain, but an inspection of this list
is still interesting. The two top countries are no surprises – two very populous
countries that indeed have had ongoing conflicts for several decades. That
Pakistan makes number 3 on the list is not surprising either, given the estimates
in Table 5. Pakistan is a populous and relatively poor country located in a highrisk neighborhood, and one of the few countries that has recently seen the onset
of new conflicts.
That Tanzania is predicted to have conflict with probability 0.5 in 2015 is
less intuitive, and indicates one weakness with the current model. None of our
models have employed information on events more than one year back in time.
Our model, then, is blind to the fact that Tanzania has avoided conflict of this
30
Figure 9: Observed and simulated proportion of countries in conflict, Higheducation scenario, both conflict levels, all countries, 1960–2048
sort for several decades, despite being located in a very unstable neighborhood.
The short memory of the model is an obvious shortcoming that will be addressed
in future versions of the model. The same may also explain why Iran is on this
list, but not Algeria.
6.2
The high-education and low-education scenarios
Figure 9 shows the predicted global incidence of conflict (both levels) for a
projection of development in education that is more optimistic than the UN
scenario, with a trajectory that is 5% above the UN baseline trend projection.
[EXPAND EXPLANATION HERE]. Figure 10 shows the corresponding simulation for a more pessimistic scenario where education levels remain constant at
the 2007 level in all countries.
These simulations indicate the importance of improvement in education levels and other aspects of poverty reduction. In the more optimistic scenario, the
predicted incidence of conflict is only marginally lower than in the UN baseline.
31
Figure 10: Observed and simulated proportion of countries in conflict, Loweducation scenario, both conflict levels, all countries, 1960–2048
In the pessimistic scenario, the incidence of conflict is predicted to remain at
about the 2006 level of 14% for more than a decade, and then only gradually
decrease to about 12 % in 2048.
6.3
Two combined demography and education scenarios
In Figure 11, we present the predicted incidence of both conflict levels for an
optimistic scenario where we project that education levels are 5% above the
UN trajectory and that the demographic variables follow UN’s low-population
growth scenario. If the countries in the dataset have smaller populations, lower
infant mortality rates and smaller youth bulges, the predicted incidence is even
lower than in Figure 9.
Figure 12 shows the predicted incidence of conflict (both levels) for a scenario
where education levels remain at 2007 levels, and the demographic variables
follow the UN high-population growth scenario. These scenarios yield a much
less optimistic development in conflict incidence. The predicted incidence of
32
Figure 11: Observed and simulated proportion of countries in conflict, Lowpopulation growth and high-education scenario, both conflict levels, all countries, 1960–2048
conflict remains in that case constant at 2006 levels or even increase to 15%
in the 2030s where the largest youth bulges in this scenario are predicted to
increase the risk of conflict.
7
Conclusion
We have specified a model to predict the incidence of minor and major conflict
based on education and demographic variables for which we have observed data
back to 1970s and projections up to 2050. We use out-of-sample predictive ability to help identifying a good model, and simulate the incidence of conflict based
on estimates for this model. Our predictions indicate that the global incidence
of conflict is likely to continue to decrease from the current level, and probably
be reduced to about half the number of conflicts in 2048. The prediction is quite
dependent on the UN projections being accurate, however. A simulation based
33
Figure 12: Observed and simulated proportion of countries in conflict, Highpopulation growth and low-education scenario, both conflict levels, all countries,
1960–2048
on UN’s high-population growth scenario and a more pessimistic projection of
education levels gives a predicted incidence of conflict at the current level.
The current paper reports work in progress. There is considerable improvement potential in implementing the following improvements to the prediction
procedure:
First, it is necessary to supplement the ‘vertical’ split-sample evaluation we
present here with a ‘horizontal’ split of the sample. In this type of evaluation,
we estimate the model on a subset of the countries in the dataset using data
for the entire 1970–2007 period, and use these estimates to predict conflicts for
the remaining subset of countries. This evaluation procedure is likely to yield a
lower rate of correct predictions since the simulated time-span is much longer,
but may give more importance to the neighborhood variables since information
on observed conflicts in neighbors may be available for the conflicts predictions
are generated for.
34
Second, it is necessary to specify a larger set of models than the five models
presented here. We have not evaluated interactive terms between all explanatory
variables and the lagged dependent variable, for instance.
Third, we need to incorporate a model for how more distant events in the
past influence the risk of conflict onset, continuation, and escalation. We have
observed that Tanzania was predicted to have a high probability of conflict given
its location, size, and levels of poverty. We expect the predicted risk to be lower
when taking conflict history before t − 1 into account. Likewise, we expect the
predicted risk of post-conflict countries like Burundi to be considerably higher.
Fourth, we will generate estimates for education and demographic variables
for countries that are not included in the UN (2007) and Samir and Lutz (2008)
datasets in order to generate predictions for all countries. One way to obtain
such estimates is to use a weighted average of the values for neighboring countries, for instance.
Finally, we aim to implement simplifications to the model. Many parameter
estimates in Table 5 are not statistically significant. Such parameters are not
likely to improve predictive ability and probably reduce the precision of the
predictions. We will implement an ability to specify constraints to the simulation
program, and consider the use of ordinal logit models.
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