Effect of Corruption on Natural Disaster Vulnerability
Effects of Conflict on Growth
1. A Macro-Analysis of Conflict on Growth
John McCallie
Zohaib Anwar
Byungchul Yea
Introduction and Literature Review
This study aims to see if conflict has a negative impact on the economic growth of a
country in the short term, and what factors of conflict seem to contribute most to it. This was
spurred by the seemingly increasing occurrence of civil conflict and uprisings, particularly in the
Middle East and Eastern European regions (i.e. Arab Spring, Ukraine, ISIS). In particular, we
consulted a working paper by Collier and Duponchel which looked at Sierra Leone and how
violent conflict that occurred between 1991 to 2002 affected firms in the regions fighting
occurred (2010). By looking at region level data consisting of four districts, they analyzed a total
of 668 firms, sized small to large, to see how varying degrees of conflict affected their growth
and output (measured using a variation on the Cobb-Douglass production function), as well as
their recovery speeds. Of the variety of discoveries they made, the most applicable for the
purpose of our study is that the firms became smaller due to conflict, and the more severe the
conflict, the more severe the decrease, mainly attributable to a decrease in human capital and
productivity (2010, p. 84). Of note, and possibly important for the results we obtain, is that when
peace returned to those districts that were harshly affected by conflict, those firms experience
higher growth (2010, p. 84).
Another paper came to similar conclusions in their analysis of Rwanda during and 6 years
after the 1994 genocide, using the same types of production function analysis as Collier and
Duponchel (Serneels and Verpoorten, 2013, p. 555), with a focus on how conflict affect human
capital and productivity. By focusing on the performance of rural areas and agriculture, which
comprise most of Rwanda’s economic activity, they take a micro approach to see how economies
2. react to conflict, and how they recover (2013, p. 561). Their findings are parallel to those found
in the previous discussed study. Areas that experienced the most intense conflict suffered a
worse decrease in consumption. The type of conflict (civil war versus genocide) also had
different effects on recovery, depending on the sector and the preexisting infrastructure and
“richness” of the inhabitants (p. 580). Overall, both papers support the intuition that conflict of
violent nature (at least in these specific countries) impact not only the amount of decreased
production, but the growth following the time of conflict. These effects could also apply to other
countries in general, not just firm-level studies in smaller, underdeveloped countries. Capturing a
generalized, world-based view on how countries, specifically their growth, can be affected by
conflict would be invaluable to understanding the overarching effects of civil uprisings and
different levels of violent conflict.
When dealing with conflict, it has become apparent that terrorism itself has a unique
impact on the performance of countries. A paper by Khan and Ruiz not only detail how
devastation cause by terrorism has increased in Syria and Iraq (particularly by groups like ISIS)
since 2004 and into 2013, but that there is a noticeable, significant decrease in economic output
(or economic desgrowth, as they label it) coinciding with the rising rate of attacks and
devastation (2015, p. 12-15).
These papers provide an in-depth understanding of specific moments in specific places,
but what this paper tackles is a more holistic view of the world, incorporating data from 2004 to
2009 for 126 countries. Essentially, we use these papers as our basis, and generally extend the
model to the international scale, to see the wholesome effects on macro indicators such as GDP.
We predict that conflict, in any form will have a negative effect on a countries economic growth.
We will be estimating our model using ordinary least squares estimation methods to test this, and
3. our general findings will show that while the labor and capital growths a significantly positive,
our conflict variables are not significant, meaning our model may not be good enough.
Theoretical Model
Our model will be adopting a production function approach, particularly a Solow growth
function. We will therefore be focusing on the growth of capital, the growth of labor, and how it
impacts the growth of economic output. The functional form will be a Cobb Douglass function
for output such that:
Y = A1-aKaL1-a
Where Y is economic output, K is physical capital, L is amount of labor employed, A is
productivity, and a, 1-a are measures of the returns to scale (assumed to be constant in this
function). According to this model, economic output relies on a combination of capital and labor,
which grow at varying rates, dictated by a, the elasticity of output with regards to capital.
For the Solow model, we are adapting the growth accounting model outline in Romer’s
book on macroeconomics, in which the growth of output can be written as such:
ΔY= αK*ΔK+αL*ΔL+R
Where Y, K, and L hold the same meaning, α is the elasticity of output with relation to capital or
labor, and R being the residual change in output not accounted for by changes in capital or labor
(2012, pg. 30). We believe conflict fits into this residual, and that is what makes up some of the
rest of the factors that change output growth.
As mentioned in the previous sections, the main purpose of this regression is to examine
the effects of conflict on the growth of GDP, hence we take the previous conclusions on capital
and labor as given. We modify the aforementioned formula to allow for the effects of conflict:
4. Δy= b_1*Δk+b_2*Δl+b_3*conflict
In this model, which is the model we use, conflict is a vector of variables denoting the level of
conflict present in a country which include terrorism activity, displacement, and violent episodes,
and b_3 , is a vector of coefficients for those variables and can be seen as one of the components
affecting productivity. Therefore, we will be regressing economic output growth, the dependent
variable, against our measure of capital growth, labor growth, and presence of conflict
(terrorism, displacement, violent episodes)—the independent variables.
There are a couple of distinct differences between our model and the models previously
mentioned. First off, both Collier and Duponchel’s study, as well as Serneels and Verpooten’s,
approach this from a micro-analytic view, studying one country over time. Mainly, this is an
effort to give more descriptive power to how conflict affects a country’s economic well-being.
Here, we are comparing multiple different countries of varying economic status and cultural
make-up, to see if there are any broad judgements that can be made about the impact of conflict
in a macro sense. Alas, there are some shortcomings to this, namely that the predictive power of
doing a multi-national study can be very low, as there are a wide variety of other effects that may
be overlooked in each particular country, such as the type of conflict, who is fighting it, and the
length of the conflict. We attempt to mitigate some of this by analyzing our conflict variable in a
variety of ways, so as to more accurately represent the differences, but we acknowledge this is a
shortcoming of the model. Secondly, both the conflict models that serve as a basis for this study
ran IV regressions as well as OLS regressions. They cite conflict being related to other economic
factors, which cause higher intensity in some areas and use distance from borders and capitals as
instrumental variables to remove that problem (Serneels and Verpoorten, 2013, p. 557; Corneels
and Duponchel, 2010, p. 74). Due to our model taking a macro-economic view, and a cross-
5. sectional one at that (rather than a micro-focused time series that Serneels et al conducted), we
did not think that there would be the same kind of endogenous issue, and therefore we will not
present an IV regression. This is also due to the high difficulty of finding a comparable
“distance” measure for our conflict variable, considering we deal with countries all over the
world and the intensity and prevalence of terrorism being caused by reasons not simplified to
instruments such as distance from a capital or border. In other words, we could not find enough
of a reason to think our model would have conflict highly correlated with the error in any sort of
predictable or obvious way.
As stated in the introduction, conflict has been shown to have undeniable negative effects
on productivity and growth, but what those effects are is not always clear. Serneels and
Verpooten’s own research found contradictory answers to how countries behave post-conflict,
and also doubt the virility of claims made by cross-sectional analysis (2013, p. 556-557).
Therefore, we want to test these doubts, and see, by creating our own model, if there is any
validity to a cross-sectional study on conflict on a country-wide level.
Another major difference is our inclusion of terrorism activity, of which we borrowed
from Khan and Ruiz (2015). In preparation for our study, we notice that research of this type
tends to look over this factor of conflict, or at least paid no special attention to it as its own
unique impact. This is understandable, since we see, at least nowadays, terrorism as an
increasingly common tactic in warfare and civil unrest. In an effort to strengthen our model, we
believe it is a good idea to separate out that variable and see if it significantly impacted growth,
as Kahn and Ruiz found.
This addition of terrorism activity also supports the inclusion of other measures of
conflict that might help explain how conflict affects a country in the general sense. Rather than
6. look just at conflict intensity, the forced displacement of individuals can affect the ability of a
country to maintain growth, as people are constantly moving around and unable to work. This
would not necessarily be captured by the labor variable, as internal displacement can lead to
fluctuations in growth without affecting the labor pool available in a country.
According to classical economic theory, an increase in the growth of capital or labor will
cause an increase in the growth of output (at a decreasing rate, but that is not relevant for our
current study). These serve mainly as control variables, as these should explain most of the
fluctuation in economic output growth. Based on the findings in the previous literature, theory
suggests conflict impedes in the ability for a country to operate, and that growth is significantly
reduced during and after conflict. Even though we have excluded productivity explicitly from
our calculations, we assume conflict can affect productivity, and therefore fits into the residual
outlined by Romer above. As Collier and Duponchel illustrate, conflict can be seen to have a
negative effect on human capital, specifically, training provided to employees (2010, pg. 84).
Hence, we see conflict as a portion of the effect of productivity on GDP growth, along with
human capital (not included in our model again due to the lack of good measures of global data),
and other variables that affect output.
To summarize we do not expect there to be any deviation from the general Solow Growth
Model, that the growth of economic output is positively affected by the growth of capital, and
labor. We further posit that the presence of conflict in a country (specifically terrorism activity,
displacement, and violent episodes), will have a negative effect on the growth rate of economic
output or GDP through an effect on productivity.
7. Empirical Model and Data
Our empirical model will take the following form:
y= b_1+ b_2*k+b_3*l+b_4*GTI+b_5*displacement+b_6*acttotal+e
In words, this states the regression of the growth of economic output or GDP, y, on a constant
term, b_1, the growth rate of gross capital formation, k, the growth rate of total labor in a
country, l, the global terrorism index, GTI, the number of refugees originating from a country
summed with the internally displaced population, displacement, and major episodes of political
violence, acttotal, along with an error term, e. All data for these variables is derived for 126
countries, to study the effects of this model in 2008. The countries are chosen by availability of
data, with observations for each variable that has information, meaning there could be an element
of selection bias. This is unavoidable due to lack of data completeness.
The growth of economic output, is measured as the annual percent growth of GDP, in the
year 2008, measured in constant 2005 U.S. dollars (World Bank, 2015). Percent growth of GDP
is defined as “sum of gross value added by all resident producers in the economy plus any
product taxes and minus any subsidies not included in the value of the products.” This series is
normally used as a determinant of economic growth of a country. The data is present on the site
for 248 countries and conglomerated measures such as the growth of GDP for North America.
This was decreased to data for 126 countries to account for missing observations in other
variables.
The growth rate of gross capital formation, is measured as the annual percent growth of
gross capital formation in 2008, based on constant 2005 U.S. dollars (World Bank, 2015). Gross
capital formation is defined as consisting “of outlays on additions to the fixed assets of the
economy plus net changes in the level of inventories.”. This data series is used as a stand-in for
8. the growth rate of physical capital in the theoretical model, as there is no actual series that
measures specifically physical capital for every country, though this data series includes similar
figures. The original data series again has observations for 248 countries and conglomerated
measures. This was decreased to data for 126 countries to again account for missing observations
in other variables.
The growth rate of total labor, is measured by one modified data series. They are the
series for total labor force of each country, in 2008, and 2009, defined as “Total labor force
comprises people ages 15 and older who meet the International Labor Organization definition of
the economically active population,” (World Bank, 2015). They are measured in observational
units of number of people. These are then modified using a simple growth formula, to get the
growth rate as a percent of Labor in 2008, the formula being:
(Labor_2009-Labor_2008)/Labor_2008
This measure gives the percent growth of labor in 2008. There was originally data for 219
countries and conglomerated measures, for both measures of Labor in 2008 and 2009. This was
then decreased to data for 126 countries to again account for missing observations in other
variables, and transformed in the growth measure for the same number of countries.
The global terrorism index is measured by the annual data series released by the Institute
for Economics and Peace, as a five year weighted average (they release the averages as part the
index) of the effects of the level of terrorist activity and damage present in a country in 2008. It
is defined as “a comprehensive study which accounts for the direct and indirect impact of
terrorism in 162 countries in terms of its effect on lives lost, injuries, property damage and the
psychological aftereffects of terrorism,” (Institute for Economics and Peace, 2015). The variable
is measured from zero to ten, with ten being the highest level and impact of terrorism, and zero
9. being the least, and nonexistence of it. The observation unit is a unit of intensity of terrorist
activities, as defined by the Institute. The data is again decreased to data for 126 countries, to
account for missing data in other variables.
Displacement is measured as the annual series for “the number of refugees (times 1000)
originating in the Named Country, at the end of the Designated Year,” deemed “source” by this
paper, along with another series for “the number of internally displaced persons (times 1000) in
the Named country at the end of the year,” deemed “idp,” by this paper (Center for Systemic
Peace, 2014). Observational units for both of these series is one thousand people. Both of these
series are added together for the years 2004, 2005, 2006, 2007, and 2008. After adding the series
together, the observational unit is still thousands of people. Then the values are merged through a
simple average to give our variable for “displacement:”
Displacement=(displaced_2004+displaced_2005+displaced_2006+displaced_2007+displaced_2008)/5
Where displaced followed by the year is the number sum of the two series idp, and source for
that year. The observational unit for this final transformation is still in thousands of people. The
data is present for 161 countries in all forms. This is decreased to data for 126 countries to
account for missing data in other variables.
Violence is measured as the series for the summation of the magnitudes of all societal
and interstate violence for a country. Magnitudes are measured from a scale of one to ten, with
zero being the lowest magnitude with no violent episodes, and ten being the highest magnitude.
Societal violence in a country includes the magnitudes of ethnic warfare, and ethnic violence,
civil war and civil violence, and interstate violence including international violence and
international warfare (Center for Systemic Peace, 2014). The summations of the magnitudes of
all these make up the series we use for each year which is given by Systemic Peace itself, as the
variable “acttotal.” The observational unit for this measurement is a unit of the magnitude for the
10. level of violence in a country, with again zero being no episodes of violence, and any increase
indicating the presence of some form of violent episodes. We take a simple of average of this
series in the years 2004, 2005, 2006, 2007, and 2008, to get our variable for violence named
“acttotal,” in our regression and by Systemic Peace:
acttotal= (acttotal2004+acttotal2005+acttotal2006+acttotal2007+acttotal2008)/5
Where acttotal followed by the year is the magnitude of the variable in the data series released
by Systemic Peace for that year. The data for each of these variables and transformations is
present for 159 countries, which is then decreased to data for 126 countries to account for
missing data in other variables.
We considered using other forms of this model. We wanted to include human capital as
another additive term in the model as another proxy for effects on productivity, but unfortunately
we did not have access to any complete data series for the 126 countries we tested for. We also
considered using foreign direct investment in place of gross capital formation, to determine the
growth rate of capital, but discovered the growth rate of capital formation is more-so aligned
with the growth rate of physical capital. Finally, we considered using a log-log functional form,
but since growth rate data information for the variables was directly available it seemed like a
more direct and more easily manipulated approach. Also, in alignment with the fact that the
growth of productivity directly affects the growth of GDP, we considered placing the conflict
variables in growth form, but concluded that we would follow the approach of the
aforementioned papers, and treat the conflict variables in level forms, seeing as how there is not
much yearly growth in conflict variables.
The descriptive statistics and correlation matrix have been placed at the end of the paper
in Table 1 and Table 2.
11. Estimation Method, Empirical Results, Tests and Inference
The empirical model that we test is:
y= b_1+ b_2*k+b_3*l+b_4*GTI+b_5*displacement+b_6*acttotal+e
We regress the growth of GDP (y) on the growth of capital and labor (k, l), a global terrorism
index for each country (GTI), displacement for each country, and occurrence of episodes of
violence that a country goes through (acttotal). We run this regression using ordinary least
squares estimation. This method of estimation allows us to observe the validity of a linear
relationship between the dependent and independent variables as depicted in the empirical
formula. The method estimates the constant, and coefficient terms in the regression (b_1, b_2,
b_3, b_4, b_5, b_6), by minimizing the sum of the squared residuals, e, which is the difference
between the linearly predicted values for the dependent variable and the actual values of the
variable.
These constant and coefficient terms are meant to be estimators of the true population
linear parameters. In order for these estimators to provide reliable information about the true
population linearized parameters several assumptions about the regression must hold, which we
will discuss later on. Essentially, these assumptions allow us to assume the estimators are
unbiased, which means their expected value is the true population parameter, and more-so
efficient than other estimators, meaning they have minimum variance out of all estimators, as
well consistent, which means the estimators will converge to the true population parameter in a
probability limit. With this in mind we present the following results from the regression in the
following table:
12. Table 3
Regression of
y on:
Coefficient
estimate
Standard
Error
t-
value
p-
value
Constant=b_1 2.678272 0.3684141 7.27 0.000
k 0.1264808 0.0148999 8.49 0.000
l 0.3695125 0.1064811 3.47 0.001
GTI -0.0427738 0.1406877 -0.30 0.762
displacement 0.0010149 0.0005249 1.93 0.056
acttotal -0.1983113 0.2873884 -0.69 0.491
Table 4
Statistics from the regression Values
Number of observations (n) 126
R2 0.4569
Adjusted R2 0.4342
F-statistic 20.19
F-statistic p-value 0.0000
SSE 739.270022
Breusch-Pagan Chi2 with df=1 0.49
Breusch-Pagan p-value 0.4839
White’s test Chi2 with df=20 22.63
White’s test p-value 0.3071
Ramsey RESET test t-statistic -1.61
Ramsey RESET test p-value 0.111
The information in table 3 tells us about the coefficients and constant estimates of the regression.
The coefficient tell us that the growth of capital formation is positively related to the growth rate
of GDP. On average it says that for every unit increase in the percent of growth in capital, there
is a 0.1264808% increase in the percentage growth of GDP. This estimate is significantly
different from 0, as the p-value tells us that the probability that there is a value greater than
0.1264808 is basically 0, hence it is well below the level of significance (which tells us at what
13. level we would fail to reject the fact that the coefficient is significantly different from 0—the null
hypothesis) of 5%. This means that the coefficient is a valid estimate of the population
parameter. This is the result we expected—growth of capital to be positively related to the
growth of GDP. A similar conclusion can be drawn for the coefficient for the percentage growth
of labor in a country. This also turns out to be statistically significant (different from 0) as its p-
value is well below 5%, hence on average when there is a one unit increase the percentage
growth of labor, the percentage growth of GDP increases by 0.3695125% (holding everything
else constant). The constant term is also statistically significant, hence it tells us the intercept
point for the linearization of the regression—when everything else is 0, on average the
percentage growth of GDP will be 2.678%.
Unfortunately, the rest of the parameter coefficients are not statistically significant as all of
their p-values are greater than 5%, which tells us we cannot say these estimates are statistically
different than 0, based on a 95% level of confidence. The value for GTI is negative suggesting
on average, a unit increase the terrorism index, would lead to a subsequent decrease in the
growth rate of GDP of 0.043%. This is the sign we predicted for the coefficient, as with
increased conflict—we expected a decrease in GDP—but since the coefficient is not significant
we cannot expect this to hold. A similar conclusion can be drawn about the coefficient for
acttotal, or the variable measuring episodes of violence in a country—a 1 unit increase in the
magnitude of those violent episodes, would on average lead to a decrease of 0.198% in the
growth rate of GDP. This is again the sign we expected, it is again not significant. The estimate
for the coefficient of displacement, though technically not significant at the 5% level, would be
at a 10% level since the p-value is 5.6%, hence hinting that the value is somewhat believable.
The coefficient determines that on average, for a one thousand person increase in the number of
14. those displaced within and without a country, it would lead to a 0.001% increase in the growth
rate of GDP. This is not the sign we expected. Displacement, as we saw it, in terms of refugees,
and internal forcibly displaced people, was a sign of conflict, and therefore we believed would
lead to a negative effect on the growth of GDP. Since none of these coefficients end up being
significant at the 5% level, and since the coefficient for displacement is the opposite sign from
what we expected—there are a number of problems that may have occurred.
Before going into the problems the model may have had, an explanation of table 4 is
required. The R2 tells us that our model is somewhat of a good fit. Technically, it says on
average 45.7% of the variation in the percentage growth of GDP is explained by the model. The
adjusted R2, tells a similar story, relaying that 43.4% of the variation is explained by the model--
accounting for the number of variables decreases the amount explained. The F-statistic tests for
if all of the coefficients (excluding the constant term) of the model are simultaneously equal to
0—that is the null hypothesis. We can easily reject this null hypothesis, since the p-value is less
than any level of significance (since the p-value is 0), in favor the alternative that at least one of
the coefficients is not equal to 0.
With that explained, we can move on to the ostensible problems with the model. First,
and foremost, our predictions about the signs and significance of these variables could just have
been wrong. Perhaps, conflict, does not have a negative impact on the growth rate of GDP in any
particular year, but this does not seem likely as evidenced by the findings of previously
mentioned works on the topic. More-so likely is the fact that the measurements we chose as
purveyors of conflict may not efficiently display the level of conflict in a country. Perhaps,
forcibly displaced persons do not imply enduring conflict. Perhaps, the GTI and the number of
violent episodes in a country do not display the level of conflict that other variables may display
15. (such as number of military related deaths perhaps).
Another problem can be that we chose not to have these measures of conflict in growth
form, which is the form productivity would take in the model—hence, since we are using the
measures as potential factors that affect productivity—perhaps they should have been in growth
form as well.
Other problems can come in the form of violations of the assumptions of ordinary least
squares estimates, by our data which could lead biased, inconsistent, and inefficient estimates.
First, we test whether our data adheres to the assumption of homoscedasticity, or constant
variance of the error terms using the Breusch-Pagan test for heteroscedasticity. The null
hypothesis is that there is homoscedasticity. Since the p-value (which equals 0.4389 according to
table 4) is significantly greater than 5% level of significance we fail to reject the null hypothesis
of homoscedasticty. We also use the White Test for heteroscedasticity, with the same null
hypothesis, we again fail to reject null hypothesis of homoscedasticity, with a p-value of 0.3071,
which is much greater than 5%. Hence, based on our evidence we can reject the assumption of
homoscedasticity of this model.
Another, assumption that can be violated is that of multicollinearity, which occurs when
the independent variables move together in systemic ways, which causes difficulties in getting
accurate results. According to the correlation matrix in table 2, and several auxiliary regressions
we ran using only the explanatory variables (for example regressing l on k, acttotal, gti, and
displacement) the R2 from these regressions and correlation matrices never exceeds 0.8, the
benchmark to decide whether multicollinearity distorts the estimates. Although the values for the
correlations between the explanatory variables never exceeds or even comes close to the value
0.8, some come close, so we believe that there still is some possible collinearity between the
16. variables, which could still effect the calculations somewhat. Regardless, there is not enough
evidence to reject the model.
To test if we have the correct functional form, we used the Ramsey-Reset test, which
involves taking the fitted value of the regression, and including them as an explanatory variable
in the original regression. The null hypothesis is that there is no specification error with the
model in terms of its functional form. The p-value ends up being 0.111 for which we fail to reject
the null hypothesis, that there is no specification error, since it significantly greater than the level
of significance- 5%.
Another violation of the assumptions of the ordinary least square assumptions can come
in the form of endogeneity problem, for which violates the assumption that independent variables
and the error terms are not correlated, which leads to biased and inconsistent estimates. Since, we
do not have an instrument for the variables, we cannot conclude anything about this violation,
and therefore cannot test to see if an IV estimation is more appropriate. But, there does remain a
possibility that the conflict variables could be correlated with the error, like the previous papers
found, and that can be a possible cause of why the results that did not meet our expectations.
If none of these assumptions are violated, then we can state that the problem perhaps
comes from that our variables are not accurate measures of conflict. Another form of the
variables may have been more efficient, or conflict does not actually affect the growth of GDP in
the way the previous papers on the topic purported. This could also mean that macro-focused
cross sectional analysis is not an effective way to measure how conflict affects economies, and
time-series may be more appropriate (since the conflict effects may take a while to surface).
17. Conclusion and Comments about Future Work
This study hypothesized that conflict had a negative impact on a countries ability to grow,
based on previous works findings concluding as such. We took a cross-sectional, macroeconomic
approach to the problem, in which we used OLS estimation methods to see if adding conflict to a
Solow growth model could provide statistically significant measures of the residual effects on output
growth. We found that while our capital and labor measures fit what was expected (a positive
relationship with output), our conflict variables of intensity, terrorism attacks, and forced
displacement did not end up being impactful at a significant level.
We believe the main issue could be our method of estimation. The papers on which we based
this on, and the Solow Growth model which we used as well, are time-series focused, and we were
stuck with looking at one period. While we tried to capture some of the time elements by doing
growth over a year, rather than just looking at each variable in the same year, that may not have been
enough to capture the time effects, if the data is assumed to be appropriate measures of conflict.
More generally, it may be that looking at the world overall may not be the correct approach; much
like most economic properties, every country has internal variations that makes making broad
assumptions about how they operate very ineffectual. Conflict has the same issues; sometimes the
conflict and internal civil unrest occurs because of faltering economic issues in the first place, and we
would not be able to capture this with our model.
Moving forward, we would highly recommend trying to narrow the type of conflict (just
ethnic cleansing, interstate conflict, revolutions) and see if they have different, significant impacts,
instead of looking at conflict in the broadest sense. But, if one positive result was to come out of this,
our study did show that taking a growth approach is a sufficient way to measure the effect we are
looking at; the problem forms while picking the correct residual effects. Hopefully others can use this
and improve on our model, and provide a more illuminating response.
18. References:
Collier, P., & Duponchel, M. (2010). The Economic Legacy of Civil War: Firm Level Evidence
from Sierra Leone. Journal of Conflict Resolution, 57(1), 65-88.
Estrada, M. A. R., & Khan, A. (2015). The effects of terrorism on economic performance: the
case of Islamic State in Iraq and Syria (ISIS). Journal of Conflict Resolution, 59(4), 555-
592.
Romer, David. (2012). Advanced Macroeconomics. McGraw-Hill Irwin. 4th Edition.
Serneels, P., & Verpoorten, M. (2012). The Impact of Armed Conflict on Economic
Performance: Evidence from Rwanda. Center for the Study of African Economics, 1-39.
Working Paper.
Data:
The World Bank. (2015). Sources found at:
GDP Growth (annual %); http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG
Gross Capital Formation (annual % growth);
http://data.worldbank.org/indicator/NE.GDI.TOTL.KD.ZG
Labor force, total; http://data.worldbank.org/indicator/SL.TLF.TOTL.IN
Center for Systemic Peace. (2014). Integrated Network for Societal Conflict Research. Sources
found at:
Forcibly displaced population, 1964-2008; http://www.systemicpeace.org/inscrdata.html
Major episodes of political violence 1964-2014;
http://www.systemicpeace.org/inscrdata.html
Insititute for Economics and Peace. (2015). Vision of Humanity.
http://www.visionofhumanity.org/#/page/our-gti-findings
