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Socio-Economic Antecedents of Transnational Terrorism: Exploring the Correlation
Levan Elbakidze Research Assistant Professor
Department of Agricultural Economics Texas A&M University, College Station
Elbakidze@ag.tamu.edu
Yanhong Jin Assistant Professor
Department of Agricultural Economics Texas A&M University, College Station
yjin@ag.tamu.edu
This research was supported in part through the Department of Homeland Security National Center for Foreign Animal and Zoonotic Disease Defense at Texas A&M University. The conclusions are those of the author and not necessarily the sponsor.
The authors share the seniority of authorship.
Socio-Economic Antecedents of Transnational Terrorism:
Exploring the Correlation
Abstract
Policies related to thwarting transnational terrorism have been at the forefront of political
and social debates. In this paper we empirically examine the impacts of socio-economic
conditions on the probability and frequency of participation in transnational terrorism
events. We use count data analysis techniques in combination with the newly combined
annual data on transnational terrorism and socio-economic variables from 1980 to 2000.
We find strong correlations between economic conditions and probability and frequency of
participation in terrorism events. Specifically, one of the key findings is a non-linear
relationship between per capita income and participation in transnational terrorism. The
results suggest that extreme poverty may preclude the opportunities to participate in
terrorism acts while relative alleviation of poverty levels may provide marginal resources
to participate in terrorism acts and materialize accumulated hatred. Similarly, education
has a non-monotonic effect on the participation in terrorism acts, i.e., improving labor
force education from primary to secondary level may increase frequencies of transnational
terrorism. On the other hand, improving the labor force education from secondary to
tertiary level may decrease the frequencies of transnational terrorism events. The results
also indicate that economic freedom, openness to trade, income equity, and religion play a
significant role in the probability and frequency of transnational terrorism events.
JEL classification: D74, F51, O15
1
Socio-Economic Antecedents of Transnational Terrorism: Exploring the
Correlation
Terrorism has become a prominent international problem over the past half century.
Since September 11, 2001 policies related to thwarting terrorism have been at the forefront
of international policy arena. Approaches such as military operations, international
sanctions, financial aid, education assistance, and various retaliatory actions have been
implemented to various degrees. The most desirable approach is clearly one that is based
on preventative actions rather than response actions. A good understanding of
environment conducive to terrorist activities is critical for the success of the counter
terrorism campaign focusing on preventative actions. Using count data analysis based on a
newly combined data of transnational terrorism and socio-economic variables, we
investigate social, economic, and political factors that may influence the environment
conducive to terrorist activities and, thus, provide insights on possible preventative counter
terrorism strategies. To achieve this goal, we created a unique data set from various
sources, including the chronological data on transnational terrorism events, the World
Bank Database, CIA World Factbook, and some national censuses. We were able to
incorporate variables such as measures of income distribution (per capita GDP, GINI
index, poverty indicators), unemployment, education, literacy, religion, openness to trade,
and economic freedom.
We empirically investigate the possibility of a nonlinear relationship between
participation in terrorism activities and income. Our hypothesis is that given political and
social instability of developing countries, small improvement in economic conditions from
extreme poverty may provide just enough resources to materialize accumulated hatred for
2
the developed world. In other words, although in the long run sustained sufficient
economic development may reduce the incentive to engage in international terrorism, in
the short run initial economic development may increase number of terrorist events. We
also investigate whether there is a similar non-linear relationship between terrorism and
education. We hypothesize that high levels of education may deter participation in terrorist
actions, while limited level of education may have positive effect on participation in
terrorist actions. Furthermore, the analysis also examines the impact of trade, religion, and
economic freedom on transnational terrorism by incorporating corresponding measures.
The rest of this paper is organized as follows. A literature review on transnational
terrorism is provided in the following section. We discuss the data in section three and
present a review on count data estimation models in section four. The estimation results
are provided and discussed in section five and concluding remarks and policy implications
are given in the last section.
Literature Review
Analysis of strategies to advance counter terrorism objectives has been the subject
of several previous studies. Lee (1988) argues that some level of cooperative multi-
country retaliation against terrorists may be desirable and investigates the obstacles to
cooperative retaliation. Atkinson, Sandler and Tschirhart (1987) examine terrorist
incidents as bargaining situations between government officials and terrorists. Using data
on transnational terrorism they study the effects of bargaining costs on the length and
outcome of incidents and the effects of bluffing on terrorists’ payoff. Enders, Sandler and
Cauley (1990) and Sandler, Enders and Lapan (1991) conclude that security measures like
metal detectors, are effective in preventing particular types of terrorist events but not
3
effective in reducing overall number of terrorism incidents. This finding suggests that the
terrorists respond to the security measures such as installation of metal detectors by
substituting their efforts to less protected targets. These two studies also find that
international conventions and retaliation are ineffective in the long run. They conclude
that government ought to fight terrorism using policies and technologies designed to thwart
all forms of terrorist activities, for example by eliminating sources of financial support,
which would be immune to the substitution phenomenon. Our study contributes to this
conclusion by investigating social, economic, and political factors that may
encourage/foster or discourage/thwart terrorist activities. Identifying the factors
contributing to terrorist activities may aid in designing the policies which would limit
individual tendencies for participation in all forms of terrorist activities.
Many studies have addressed motives and drivers of terrorism and of social
conflicts in general, which range from political, socioeconomic and religious to personal
reasons. For example, Hess and Orphanides (2001) show that in the presence of reelection
motive, the frequency of war is greater following recessions than following economic
growth. Enders and Sandler (2000) show that although frequency of international
terrorism has dramatically decreased, terrorist incidents in the post cold war period have
become more hazardous. They attribute this increase in the severity of terrorist attacks to
the growth of religious terrorism. Blomberg and Hess (2002) conclude that internal
conflict, external conflict, and the state of the economy are not independent of one-another.
Economic recessions can increase the probabilities of internal and external conflicts and
visa versa. Blomberg, Hess and Weerapana (2004) find that economic recessions,
represented by negative per capita GDP growth, could increase the probability of terrorist
4
activities in democratic high-income countries. They argue that during economic
recessions in high-income countries groups that are unhappy with current socio-economic
status quo, but are unable to influence political and institutional situation, resort to terrorist
activities to increase their voice in the economy. Li and Schaub (2004) study the effects of
economic globalization on the frequency of transnational terrorist incidents within a
country’s borders. They find that trade, foreign direct investment, and portfolio of
investment of a country have no direct positive effect on the number of terrorist events
initiated within the country. However, economic development of a country and its trading
partners has a negative effect on the number of international terrorist incidents within a
country. Therefore, if trade and foreign direct investment promote economic development,
then these variables must indirectly reduce transnational terrorism. Li (2005) shows that
democratic participation and economic development measured by GDP per capita reduces
transnational terrorism while government constraints increase the number of terrorist
incidents. Alesina et al. (1996) find that to some extent low economic growth measured by
GDP per capita could lead to government turnovers through coups. Stern (2000) attributes
involvement in terrorist acts to lack of adequate education. She reports that religious
schools in Pakistan encourage their graduates, who lack practical education, to fulfill their
“spiritual obligations” by fighting against Hindus in Kashmir and other adversaries.
Fearon and Laitin (2003) analyze 127 civil wars between 1945 and 1999 and conclude that
poverty has a significant positive effect on violent domestic conflicts because it aids
insurgents in recruitment. Muller and Seligson (1990), who studied 85 developing
countries during 1973-1977, and London and Robinson (1989), who analyzed 51
developing countries during 1968-1972, show that income inequality is a significant
5
predictor of political violence. Wulf, Hames, and Longstaff (2003) propose that possible
reasons why some developing countries might be supporting terrorism include ideological
differences, past operations and policies of the developed countries, and unfavorable
socioeconomic conditions. They argue that through the efforts to improve the quality of
life for individuals in the developing countries, the hatred for the developed countries may
subside.
The opinion about significance of socioeconomic factors in providing favorable
environment for terrorism is not unanimous. Abadie (2006) uses a dataset on the severity
of country-level terrorist risk and finds no significant relationship between risk of terrorism
and economic variables. However, he detects a significant non-linear relationship between
political freedom and terrorism risk. Specifically, countries with intermediate levels of
political freedom are more prone to terrorism than countries with high or low levels of
political freedom. Piazza (2006), using multiple regression analysis on terrorist incidents
and casualties in 96 countries from 1986 to 2002, finds no significant relationship between
terrorism and any of the economic development indicators such as human development
index, income equity, per capita GDP growth, inflation, unemployment, and calories per
capita. Krueger and Maleckova (2003) explore statistical relationship between
involvement in terrorism events and education, occupation and economic activity. Based
on a survey conducted in the West Bank and Gaza Strip they argue that occupation,
poverty, and lack of education do not seem to affect the likelihood that an individual will
engage in terrorist activity. Using cross country regression analysis, they also find that
there is generally no statistically significant relationship between GDP per capita and
number of terrorist events initiated from each country. However, their results, which are
6
based on a data collected from West Bank and Gaza Strip, may have overlooked the fact
that majority of relatively “educated” people in poor regions are probably not educated in a
similar manner as their counter fellows from developed countries. The appropriateness of
local educational categories as indicators of general education level is questionable
because some schools may be deliberately teaching the students to become supporters of
extremist movements (Stern 2000). Such results may also overshadow the fact that well
paid and educated individuals in poor countries may be better informed about relative
quality of life and foreign policies of rich countries than uneducated poor citizens.
Resulting sense of relative deprivation may encourage engagement in international
terrorism as the last and simplest resort to make difference in their countries.
Data
The chronological data on transnational terrorism events was obtained from Dr.
Edward Mickolus (Vinyard Software Inc.). The data includes records of terrorism
incidents including date, incident’s country of origin, location of incident, up to three
nationalities of victims, and up to three nationalities of perpetrators. Detailed description
of this data set, entitled International Terrorism: Attributes of Terrorist Events (ITERATE),
is available in Sandler and Enders (2004), Enders and Sandler (1993), and Mickolus et al.
(1993). Consistent with the transnational terrorism dataset used in this study, we assume
that transnational terrorism is a “premeditated threatened or actual use of force or violence
to attain a political goal through fear, coercion, or intimidation” and when its ramifications
transcend national boundaries through the nationality of the perpetrators and/or human or
institutional victims, location of the incident, or mechanics of its resolution (Mickolus et
al. 1989).
7
Based on the chronological data on transnational terrorism incidents, our dependent
variable is constructed as annual count of terrorism events in which citizens of a particular
country were documented as perpetrators. For example, the annual count of participation
in transnational terrorism events for Philippines in 2000 is seven, which means that the
Philippine nationals were documented as perpetrators for seven transnational terrorism
incidents in 2000. The original chronological data documents up to three nationalities of
perpetrators for each transnational terrorism incident. For example, on March 15, 1982
three Salvadorans, two Nicaraguans, one Chilean and others were arrested for intending to
kidnap an unidentified American diplomat (Mickolus et al. 1989). This incident increases
the annual count of transnational terrorism events for Salvador, Nicaragua, and Chile by
one each. We use the terms “incidents” and “counts” to refer to terrorism events and to
participation in terrorism events by various nationalities respectively. The nationalities of
perpetrators involved in some of the documented incidents were unknown because either
the perpetrators or their nationalities were not traceable. Table 1 shows that our sample of
countries accounts for 33% of total documented incidents and 49% of the documented
incidents for which at least one nationality of the perpetrators was known. Further more,
our sample accounts for 51% total counts which represent perpetrators with known
nationalities only, and 20% of total counts which represent perpetrators with known as
well as unknown nationalities. In our sample, only 71 out of the total 2748 terrorism
counts correspond to terrorism incidents with perpetrators from more than one country.
The table also shows that the 90s had fewer terrorism incidents and counts than 80s.
We introduce the following social, economic, and political variables to investigate
whether and how these factors affect the likelihood and the expected frequencies of
8
participation in transnational terrorism incidents. The economic variables include GDP per
capita (measured in terms of constant 2000 US$), percent of population living on less than
one dollar a day, percent of population living on one to two dollars a day, GINI index that
measures income equity among households on a scale of zero (perfect equality) to one
(absolute inequality), unemployment rate, openness to trade, which is the share of total
imports and exports relative to GDP, and index of economic freedom, which was obtained
from the heritage foundation (Heritege Foundation, 2006). The index of economic
freedom is measured on a scale of one to five, where one denotes an economic
environment or set of policies that are most conducive to economic freedom, while a score
of five denotes a set of policies that are least conducive to economic freedom. World
Bank’s estimates of percentages of population living on less than one and two dollars per
day were used to calculate the percent of population living on one to two dollars a day.
We are aware that the World Bank’s estimates of percent of population living on less than
one or two dollars a day have been criticized for consistency and appropriateness as
descriptors of international poverty (Wade 2004). However, we use these estimates as best
available indicators. Education variables are represented by percentage of population, ages
15 and older, who can read and write, and percentage of labor force with the highest
achieved education being primary, secondary, and tertiary levels. The religious variables
are represented by percentage of population who practice Christianity, Islam, Hinduism,
Buddhism, other religions, and no religion at all.
The socioeconomic data, except for the index of economic freedom, obtained from
the Heritage Foundation, and religious measures, obtained from CIA’s World Factbook,
were collected from the World Bank data base. We also rely on national statistics services
9
of various countries to obtain the values for socioeconomic variables, which have missing
values in the World Bank data base. When some of the historical estimates were not
available we used historically closest available data estimates to fill in the missing
observations. For example, the earliest available economic freedom index from 1990s was
used as a proxy for the economic freedom index for all of 1980s and early 1990s.
Typically, for most countries, the estimates of percent of population living on less than one
or two dollars per day are available for one particular year. These estimates are assumed to
be best available approximations for the remaining years. GINI index, education variables,
and religion variables are also extrapolated in a similar manner. Matching the terrorism
events data with the socio-economic data, we are able to construct a data set consisting of
1413 observations by country and year from 1980 to 2000 with the annual terrorism
participation counts for 77 countries and corresponding socio-economic variables. Notice
that reduced time horizons were used for some of the countries because unstable
contemporary political and economic conditions did not allow extrapolation of available
estimates. For example, the economic data for the Republic of Georgia at the World Bank
database became available in 1994. Due to highly unstable sociopolitical situation in
Georgia in the 80s and beginning of the 90s extrapolation of data from 1994 to previous
years was not possible. Therefore, the observations of the Republic of Georgia in our
dataset start from 1994. The frequency of terrorism counts, shown in the third column of
Table 2, reveals small number of counts and excessive number of zeros for participations
in terrorism attacks. Observations with at most five counts of participation account for
91%. The phenomenon of excess zeros may be a concern because 60% of the sample has a
10
zero count of terrorism attacks. This issue is addressed in the methodology section. Table
3 presents the summary statistics of the socioeconomic variables.
Review of Methodology
We utilize count data analysis to investigate the impacts of socio-economic conditions on
participation in transnational terrorist activities. Estimations are based on the pooled data
as well as the panel data analysis. This section presents a review of count data analysis
focusing on the particular pooled data models used in this study.
Poisson regression model is widely used in count data analysis (Cameron and
Trivedi, 1998). In a basic Poisson regression model with a logarithm link function, the
number of events y for individual i has a Poisson distribution with a conditional mean iλ
depending on the i’s characteristics, xi:
(1) ( ) ( ) ( )βλ xxyEx iiiii exp| == ,
where β is a vector of unknown coefficients associated with the covariate vector xi . For
the convenience of notation, we drop xi in ( )xiiλ and useλ i for the rest of this paper. The
probability density function of y given x is
(2) ( ) ( )!
exp|
y
yxyf
i
iiii
iλλ−= .
A property of the Poisson distribution is that its variance is equal to its mean. However, it
is very common to have variance larger than mean, i.e. over-dispersion, in count data
analysis (Cameron and Trivedi, 1998; Winkelmann and Zimmermann, 1995). Over-
dispersion can be caused by unobserved heterogeneity among individuals and/or by excess
zeros in the dependent variable. When over-dispersion is an issue, the estimates based on
the Poisson regression will be inefficient (Cameron and Trivedi, 1998).
11
Negative binomial (NB) regression models have been suggested to deal with the
issue of possible unobserved heterogeneity. The NB model adds an error term ε , to the
conditional mean of the Poisson distribution to model the unobserved heterogeneity,
(3) ( ) ( )εβ iiii xxyE += exp| .
where ( )ε iexp is normally assumed to follow a gamma distribution with mean one and
variance α . The probability density function of y given x now becomes
(4) ( )
++Γ
+Γ=
λα
λ
λααα
α
i
i
i
i
i
iii
y
y
yxyf
/11
1
)/1(!
)/1(|
/1
.
The conditional mean and variance of yiunder the NB model are
(5-1) ( ) λ iii xyE =| and
(5-2) ( ) )1(| λαλ iiii xyVAR += .
Equations (5-1) and (5-2) suggest thatα , variance of gamma distribution, indicates the
degree of over-dispersion. As α becomes larger, the distribution will be more dispersed.
As α gets close to zero, the NB model converges to the Poisson model. The Poisson and
NB models are nested, and a statistical rejection of the null hypothesis of 0=α will favor
the NB over the Poisson model.
The second possible source of over-dispersion could be excessive number of zeros
for the dependent variable, which is a concern in this study because almost 60% of the
sample has zero count of events (see Table 2). The traditional Poisson and NB models do
not account for excess zeros and thus can produce biased estimates. Zero-inflated
regression models, such as a zero-inflated Poisson (ZIP) model or zero-inflated NB (ZINB)
12
model, are warranted.1 Zero-inflated count data models have been used to investigate
determinants of high-risk heterosexual behavior (Heilbron, 1994), species’ ecological
abundance (Welsh et al. 1996), accident frequencies in roadway sections (Shankar, Milton,
and Mannering. 1997), caries prevention in dental epidemiology (Bohninig et al.1999),
detection of specific process equipment problems (Li et al. 1999), evaluation of
occupational safety interventions (Carrivick, Lee, and Yau, 2003), young drivers’ motor
vehicle crashes (Lee et al. 2002), the incidence of sudden infant death syndrome
(Dalrymple, Hudson, and Ford, 2003), claim frequencies in general automobile insurance
(Yip and Yau, 2005), consumption of beverages (Mullahy, 1986), and so forth.
Lambert (1992) first introduced ZIP model
(6) ( ) 0,1,2,...)( 1y probabilit with Poisson~
yprobabilit with 0
i =
=
yπ-y
π y
iii
ii
λ
The probability of having an extra zero which is not subject to the Poisson distribution, π i ,
is assumed to have a logit function 2,
(7) ( )( )γγ
πz
z
i
ii
−+−
=exp1
exp,
where z i is a vector of observable covariates and γ is a vector of coefficients associated
with z i . The mean and variance of y i in the ZIP model are
(8-1) ( ) λπ iiii xyE )1(| −= and
(8-2) ( ) ( ) )1(1| πλπλ iiiiii xyVAR +−= .
1 There are other zero-inflated count data models, including zero-inflated generalized Poisson (ZIGP) (Angers and Biswas, 2003) and zero-inflated double Poisson (ZIDP) (Gurmu,1998) models. Yip and Yau (2005) applied four zero-inflated models, ZIP, ZINB, ZIGP, ZIDP to accommodate excess zeros when analyzing the claim frequency data in general insurance. 2 The unobserved probability π i is generated as a logistic or probit function of observable covariates to
ensure nonnegativity. The choice between logit and probit is usually unimportant since the two functions are close to each other and they usually give very similar results (Cheung, 2002).
13
Equations (8-1) and (8-2) show that ππ
i
i
−1indicates the degree of over-dispersion. As
π i approaches zero, the ZIP model merges into a Poisson model.
Similarly, we can construct a ZINB model having a logit link function. The mean
and variance of yiunder the ZINB model are
(9-1) ( ) λπ iiii xyE )1(| −= and
(9-2) ( ) ( ) ( )( )απλπλ ++−= iiiiii xyVAR 11| .
Equations (9-1) and (9-2) show thatπ
απi
i
−+
1 reflects the degree of over-dispersion in the
ZINB models, which accounts for over-dispersion from both excessive zeros and
unobservable heterogeneity.
The Poisson and ZIP models are not nested, and neither are the NB and ZINB
models. Vuong (1989) proposed a likelihood ratio test for non-nested models, and Greene
(1994) adapted the technique for the cases of ZIP versus Poisson, and ZINB versus NB
models. The test statistic is
(13) s
mNZ
m
= ,
where m and sm are the mean and standard deviation of mi and N is the number of
observations. mi is defined as ( )( )xyp
xypm
ii
iii
|
|ln
2
1)
)
= where ( )xyp ii |1
) and ( )xyp ii |2
) are the
predicted probability of the two competing models. Asymptotically, Z has a standard
normal distribution, with large positive values (>1.96) favoring the zero-inflated model and
with large negative values (<-1.96) favoring the nonzero-inflated model at a 5%
significance level.
14
Based on statistical tests on the null hypothesis 0=α for over-dispersion for
nested models and the Vuong test for non-nested models, we are able to test for model
specification among the Poisson, NB, ZIP, and ZINB models. As shown in Figure 1, if the
Vuong test favors the ZINB model over the NB model, a statistical test on 0=α is
conducted to contrast ZINB versus ZIP. If 0=α is rejected, ZINB is the most appropriate
specification, and both individual heterogeneity and excess zeros contribute to the over-
dispersion. Otherwise, ZIP model is compared to Poisson model by using the Voung test.
If ZIP is the most appropriate specification, then only excessive zeros account for over-
dispersion. Otherwise no over dispersion is present and Poisson is favored. On the other
hand, if the Vuong test favors the NB model, we will test if the heterogeneity parameter
α is significantly different from zero to contrast NB vs. Poisson. A rejection of 0=α
suggests that the NB model is most appropriate specification and heterogeneity accounts
for over-dispersion. Otherwise, the Poisson and ZIP are compared.
Estimation Results and Policy Discussions
Table 4 presents the estimation results of the Poisson, NB, ZIP, and ZINB
regression models. Both ZIP and ZINB include a logit regression followed by Poisson for
ZIP or NB for ZINB. Following the procedure outlined in figure 1, after estimating the
ZINB model, two statistical tests are conducted: (1) the Vuong test favors the ZINB model
over the standard NB model (Vuong-statistic=8.26 and p-value=0); and (2) the Student-t
test rejected the null hypothesis that 0=α (t-statistic=20.17 and p-value=0). Hence, the
over-dispersion still exists even after controlling for excess zeros. Therefore, based on the
procedure outlined in Figure 1, ZINB model is most appropriate specification for our data
among these four count data models.
15
Besides the Poisson, NB, ZIP and ZINB models, we also estimate a two-part model
(Hu, Sung, and Keeler, 1995; Wasserman, Manning, Newhouse, and Winkler, 1991) and a
selection model (Heckman, 1979; Greene, 1999) as part of pooled data analysis. Both the
two-part model and the selection models sequentially estimate the equation for the
participation decision and the equation for the frequency of events. The participation
decision is usually specified by a logit or probit model, and the second estimation is
truncated at zero and only focuses on observations with positive counts. The two-part
model assumes that the participation and frequencies of events are disjointed. Whereas,
the selection model integrates the estimation of these two equations by incorporating Mills
ratio, which is calculated after estimation of the binary participation equation, into the
estimation of event frequencies in the Poisson or NB to control for selection bias (Greene
1994). In this study both two-part and selection models use NB estimation applied to
observations with positive counts only. The results show that the Mills ratio variable is not
statistically significant (p-value=0.4 for the null hypothesis that the coefficient of the Mills
ratio equals to zero). Thus, the two-part model is a better specification than the selection
model. Therefore, in Table 4 we present results of the two-part model to contrast with
other count data estimations.
We also investigate the fixed-effects and random-effect NB models on the panel
data to explore the possibility of contemporaneous correlation. Geil et al. (1997) obtain
similar results when using the pooled and panel NB models to investigate determinants of
hospital trips in Germany. However, Hausman, Hall, and Griliches (1984) find large
differences between the cross-section and panel models when investigating the patents—
R&D relationship. In our study, the comparison of actual and predicted event counts in
16
Table 2 indicates that the panel estimation may not fit the data as well as the pooled data
estimation. Hence, we only provide the estimation results based on the pooled data in
Table 4.
Table 4 summarizes estimation results of five pooled data models, including the
Poisson, NB, ZIP, ZINB, and the two-part models. The variables that tend to show up as
statistically significant in all these five models are GINI index, population ratio living on
one to two dollars a day, unemployment rate, openness to trade, and economic freedom
index. The signs of these variables are consistent with our expectations. Rate of
unemployment has a positive effect on the probability and the frequencies of participation
in terrorism events. Openness to trade has a negative effect on the probability and the
frequency of participation in terrorism acts. Thus, as a country becomes more globally
integrated, as measured by the ratio of the volume of its trade and GDP, the likelihood and
frequency of it’s citizens participation in transnational terrorism decreases. This result
differs from Li and Schaub (2004) who found no statistically significant direct relationship
between trade and terrorism acts within the countries’ borders. However, Li and Schaub
(2004) propose possible indirect effect of trade through its positive effect on economic
development, which is shown to have a negative effect on terrorism. Economic freedom
index in our results displays a positive effect on probability as well as frequency of
participating in terrorism acts. This coincides with the expectation that decrease in
economic freedom has a positive effect on overall involvement in transnational terrorism.
Previous literature provides mixed conclusions on the effects of per capita income
on terrorism. The notion that poverty breeds terrorism and political violence is consistent
with some studies, including Alesina et al. (1996), Li (2005), Fearon and Laitin (2003),
17
Wulf, Hames and Longstaff (2003) who find that GDP per capita has a negative effect on
terrorism. On the other hand, Piazza (2006), Abadie (2006), and Krueger and Maleckova
(2003) find no evidence that poverty affects terrorism. We introduce GDP per capita and
its square term as independent variables and find a non-linear effect of GDP per capita on
the frequencies of participation in transnational terrorism in all five models. Specifically,
the concave form implied by the estimated coefficients suggests that an increase of GDP
per capital increases (decreases) frequencies of participation in terrorism when a country is
at a relatively lower (higher) level of per capita income. Though these results correspond
to our hypothesis regarding frequencies of participation, they are less conclusive about the
probability of participation because the coefficients of GDP and GDP2 for probabilities of
participation are not statistically significant. However, the estimated coefficients
associated with poverty indicators - percentage of population living on less than one dollar
a day and percentage of population living on one to two dollars a day – suggest similar
results for probabilities of participation in transnational terrorism. Specifically, results
reveal that increasing the proportion of population living on less than one dollar per day
decreases, while increasing the proportion of population living on one to two dollars per
day increases the likelihood of participation in terrorism acts. The results also show that
both, the proportion of population living on less than one dollar a day and proportion of
population living on one to two dollars per day, have positive effects on frequency of
participation in transnational terrorism. These findings suggest that marginal
improvements from the extreme poverty may enable the disadvantaged to materialize their
hatred for the societies which they deem responsible for, or contributing to, their
impoverished living conditions, and/or which they view as threats to their culture. This
18
result is consistent with Peter Bernholz’s (2004) argument of increase in “supreme value”
based terrorism as a result of increased resource availability.
Furthermore, our results also show that incorporation of income equity is important
for explaining participation in transnational terrorism acts. As expected, the GINI index
has a positive sign -- the higher the income inequity the higher the likelihood of
participation in terrorism events and the greater the frequency of participation in terrorism
attacks.
Among the education measures, literacy has no statistically significant effect on
probability of participation in terrorism event, except in the ZINB model, but has
statistically significant and positive effects on the frequencies of participation in terrorism
events. Primary and tertiary education variables have no statistically significant effect on
likelihood of participation, except in the ZINB model. However, both have negative effect
on frequency of participation in terrorism. The results on secondary education are mixed
across models. To better understand the effects of education on the probabilities and
frequencies of terrorism participation, we conduct the following exercise. Let βj denote the
coefficients of the variables representing percentage of labor force with the highest
achieved education being primary (j=p), secondary (j=s), and tertiary levels (j=t). The
improvement of education, corresponding to moving 1% of labor force from primary
education level, as highest achieved, to the secondary education level, increases the
conditional frequencies by (–βp+ βs)exp(xβ) in the Poisson and NB models and by (–βp+
βs)exp(xβ)(1-πi) in the ZIP and ZINB models. Similarly, moving 1% of labor force from
secondary education level to tertiary level will increase the conditional frequencies by (–
βs+ βt)exp(xβ) in the Poisson and NB models and by (–βs+ βt)exp(xβ)(1-πi) in the ZIP and
19
ZINB models. The Student-t tests show that –βp+ βs is statistically significant and greater
than zero in the Poisson and ZIP models, and – βs+ βt is statistically significant and less
than zero in the Poisson, ZIP, ZINB, and two part models, at 1% significant level. This
suggests that improving education from primary to secondary level increases, while
improving education level from secondary to tertiary level decreases the frequencies of
participation in terrorism events. These results are robust across different estimation
models on the pooled data except the NB model where they are not significant at 10%
significance level. Hence, the results suggest that limited education may increase the
frequency of participation in transnational terrorism, while advanced education levels may
deter the participation frequency.
The estimation results show that percentage of population who practice organized
religions like Christianity, Hinduism, Buddhism, and Islam, relative to no religion, has
significant and positive effect on the frequencies of participation in transnational terrorism
events across all these five models. These variables have a significant positive impact on
the probability of terrorism participation in the two-part model. These results imply that an
increase in the proportion of population who practice any of the considered religion
categories seems to increase the frequency of terrorism attacks relative to population
practicing no religion at all.
Decade dummy has a significant, negative effect on probability as well as
frequency of participation in transnational terrorism, except for ZINB model where the
effect on the probability of participation is not statistically significant. These results
suggest that the 1990s relative to the 1980s had lower probability and frequency of
participation in terrorism events.
20
Conclusions
Using a newly combined data of the transnational terrorism events and socio-
economic data from various sources, we use count data analysis to investigate the impacts
of socio-economic conditions on the probability and the frequencies of participation in
transnational terrorism. We are not aware of any published studies which use count data
analysis, combined with the type of data used in this study, to investigate how socio-
economic characteristics of perpetrators’ countries of origin influence probability and
frequency of participation in transnational terrorism.
The results support our initial hypothesis that per capita income has a nonlinear
effect on participation in transnational terrorism. Extreme poverty may preclude the
opportunities to participate in terrorism acts and relative alleviation of poverty levels may
provide marginal resources to participate in terrorism acts and materialize accumulated
hatred. This may be true in the context where terrorism acts are primarily organized and
sponsored by individuals and/or individual groups rather than by centralized terrorist
organization. It also could be true if centralized terrorist organization requires
expenditures on the part of terrorist recruits. For example, there maybe personal financial
costs associated with participating in terrorist training camps. This result suggests that
alleviation of poverty, which one could argue may be a way to deter terrorism, may lead to
increase participation in terrorism in the short run. Therefore, poverty alleviation related
policies need to be designed carefully considering the possibility of increased terrorism
due to increased personal income. This analysis also shows that limited education and
higher education may have opposite effects on frequencies of participation in transnational
terrorism--limited education may increase but advanced education may decrease the
21
frequency of participation in transnational terrorism events. The results also show that
openness to trade, equality of income and consumption distribution, employment
opportunities, and religion, may have significant effects on probability and frequency of
participation in transnational terrorism events
Overall, the results indicate that careful planning is necessary when fighting
terrorism through such approaches as alleviation of extreme poverty. Though preventative
options like economic development and improving education levels could in the long run
deter the tendency of participation in terrorism activities, careful planning is needed for
implementation of such policies in the short run because of possible risk of increased
tendencies for participation in transnational terrorism. The advantage of implementing
carefully designed prevention policies is that they may reduce the necessity to use response
actions such as military retaliation and economic sanctions. Moreover, preventative
policies which are designed to reduce all forms of terrorism may be more efficient in the
fight against terrorism than the policies which concentrate on specific forms of terrorism,
such as increased airline/embassy/various infrastructure security. However, this should not
be interpreted as a suggestion for substituting response actions with any of the preventative
actions which may be implied by the results of this study
Finally, it should be noted that the dataset was constructed using a limited amount
of best available information and involved extrapolation of socio-economic estimates over
the periods for which data was not available. As such the data set used in this study in
many cases is an approximation of international socio-economic indicators rather than
actual estimates. Therefore, caution is warranted for interpretation of the results. The
findings should be interpreted as no more than a preliminary support of the idea that
22
socioeconomic factors may play a role in encouraging/discouraging terrorist behavior.
Further studies based on either more complete records or on alternative approaches, which
would avoid reliance on observational data, are necessary to fully understand the linkage
between socioeconomic factors and participation in transnational terrorism acts.
23
Table 1: Number of terrorism incidents and events
Terrorism incidents Terrorism counts With known
nationalities only
Including unknown nationality
With known nationalities
only
Including unknown
nationalities a
Documented
Total 5,504 8,162 5,378 13,947
During 80s 3,038 4,651 3,090 9,409
During 90s 2,466 3,511 2,288 4,503
Study sample
Total 2,677 2,748
During 80s 1,493 1,542
During 90s 1,184 1,206
a Terrorism events with unknown nationalities of perpetrators involve those incidents for which the number of participating nationalities was documented, however the identity of those nationalities was not known
24
Table 2: Observed and predicted frequencies of the annual counts of terrorism events
Predicted Event counts
Event range
Actual Poisson NB ZIP ZINB Two-
part Fixed-effect
Random effect
0 [0,1) 841 (59.52)
485 (34.32)
580 (41.05)
752 (53.22)
984 (69.64)
901 (75.08)
1030 (72.89)
143 (10.12)
1 [1, 2) 233 (16.49)
420 (29.72)
374 (26.47)
376 (26.61)
187 (13.23)
10 (0.83)
324 (22.93)
274 (19.39)
2 [2, 3) 92 (6.51)
208 (14.72)
173 (12.24)
152 (10.76)
93 (6.58)
53 (4.42)
55 (3.89)
289 (20.45)
3 [3, 4) 52 (3.68)
129 (9.13)
91 (6.44)
59 (4.18)
52 (3.68)
49 (4.08)
4 (0.28)
193 (13.66)
4 [4, 5) 42 (2.97)
75 (5.31)
41 (2.90)
27 (1.91)
35 (2.48)
34 (2.83)
156
(11.04)
5 [5, 6) 26 (1.84)
45 (3.18)
52 (3.68)
18 (1.27)
29 (2.05)
50 (4.17)
98 (6.94)
6+ [6, ∝ )
127 (8.99)
51 (3.61)
102 (7.22)
29 (2.05)
33 (2.34)
103 (8.58)
260 (18.40)
Total 1413 1413 1413 1413 1413 1200 1413 1413
Note: Figures in the parenthesis are percentages and figures above the parenthesis are frequencies. The actual event counts for each observation are integers. However, the predicted event counts are not necessarily integers. Thus, event counts in integers for the actual data and event counts are expressed in range for the estimated counts.
25
Table 3: Summary statistics of social-economic variables
Variable name Variable definition Mean SD Min. Max.
Income measures
GDP GDP per capita ($1,000) 6.62 8.86 0.07 44.76 GINI GINI index (0=perfectly inequity;
1=perfectly equity) 0.40 0.11 0.19 0.74 PV1 population ratio living under $1 per day 0.11 0.15 0.00 0.71 PV12 population ratio living on $1 to $2 per day 0.15 0.15 0.00 0.57
Education measures
Literacy population ratio who can read and write 0.82 0.20 0.29 1.00 Primary percent of labor force with primary
education as the highest achieved 0.38 0.18 0.03 0.85 Secondary percent of labor force with secondary
education as the highest achieved 0.29 0.19 0.00 0.79 Tertiary percent of labor force with tertiary
education as the highest achieved 0.15 0.11 0.00 0.54
Religion measures
Christian Christian population ratio 0.63 0.37 0.00 1.00 Muslim Muslim population ratio 0.18 0.32 0.00 1.00 Hindu Hindu population ratio 0.02 0.10 0.00 0.81 Buddhist Buddhist population ratio 0.05 0.19 0.00 0.95 Other religion population ratio with other regions 0.07 0.09 0.00 0.66 No religion population ratio with no religions at all 0.06 0.15 0.00 0.94
Other variables
Unemployment unemployment rate 0.09 0.06 0.00 0.36 Openness to trade
(export+import)/GDP) 0.67 0.52 0.09 4.97
Freedom economic freedom (1=highest economic freedom; 5=lowest economic freedom) 3.01 0.64 1.80 4.78
26
Table 4: Estimation results of the Poisson, ZB, ZIP, ZINB, and two-part models
ZIP ZINB Two-part model Independent variables
Poisson
NB Logit Poisson Logit NB Probit NB
GDP 0.11*** (6.92)
0.26*** (5.11)
0.04 (0.61)
0.06*** (5.93)
-4.20 (-1.62)
0.28*** (5.39)
0.10*** (3.46)
0.11*** (2.61)
GDP square -0.003***
(-6.61) -0.006*** (-4.27)
0.003 (1.15)
-0.003***
(-9.21) 0.34* (1.66)
-0.007*** (-5.03)
-0.002*** (-2.39)
-0.003***
(-2.69)
GINI 4.14*** (15.08)
8.51*** (8.30)
2.97*** (2.94)
3.21*** (21.90)
-15.00 (-1.45)
10.04*** (10.39)
2.34*** (4.39)
4.75*** (5.58)
PV1 0.36 (1.32)
0.83 (1.20)
-1.68** (-2.15)
1.42*** (10.20)
-28.37** (-2.03)
2.64*** (3.69)
-0.84* (-1.87)
1.44** (2.36)
PV2 1.80*** (6.60)
3.01** (3.37)
3.00*** (3.38)
0.45*** (2.61)
29.43* (1.91)
2.01** (2.24)
2.18*** (4.45)
0.64 (0.89)
Literacy
2.56*** (10.05)
4.07*** (5.45)
0.12 (0.14)
3.37*** (29.70)
-34.85** (-2.14)
6.06*** (8.83)
0.31 (0.66)
3.64*** (6.33)
Primary -0.44** (-2.35)
-1.02* (-1.73)
0.29 (0.50)
-0.73*** (-6.85)
55.80*** (2.52)
-1.41** (-2.20)
0.08 (0.27)
-0.55* (-1.22)
Secondary 0.61*** (3.08)
-1.25** (-1.91)
-1.59** (-2.31)
0.30*** (3.05)
1.50 (0.23)
-0.88 (-1.40)
-0.66*** (-1.79)
0.16 (0.32)
Tertiary -0.12 (-0.47)
-2.01** (-2.42)
0.19 (0.22)
-0.18 (-1.31)
32.30** (2.00)
-4.32*** (-5.35)
0.63 (1.31)
-2.09*** (-3.26)
Christian
1.95*** (7.44)
1.51*** (3.48)
0.12 (0.18)
1.83*** (19.30)
-6.89 (-0.46)
1.43*** (4.96)
0.65** (2.39)
0.92** (2.35)
Hindu
1.79*** (5.19)
2.30*** (3.70)
0.12 (0.14)
2.25*** (14.43)
-39.48 (-1.49)
3.33*** (4.72)
0.71* (1.64)
1.76*** (3.55)
Buddhist
2.84*** (9.62)
2.67*** (5.03)
0.51 (0.63)
2.50*** (24.73)
-21.88 (-1.21)
2.95*** (7.12)
1.16*** (3.39)
1.55*** (3.48)
Muslim
3.49*** (12.98)
3.78*** (6.73)
0.18 (0.25)
3.22*** (31.40)
-29.57 (-1.48)
4.27*** (10.40)
0.93*** (3.04)
2.46*** (5.64)
Other religion 3.48*** (10.60)
4.32*** (4.69)
-1.74 (-1.57)
4.71*** (27.89)
-49.38** (-2.13)
9.44*** (10.35)
-0.12 (-0.21)
5.89*** (6.66)
Unemploy-ment rate
4.88*** (14.65)
5.61*** (5.00)
2.85*** (2.51)
4.30*** (30.89)
13.37 (1.41)
5.36*** (5.89)
2.30*** (3.71)
3.24*** (3.81)
Openness to trade
-2.25*** (-24.11)
-1.37*** (-8.33)
-0.67*** (-3.67)
-1.28*** (-27.81)
-2.77* (-1.61)
-1.02*** (-7.65)
-0.60*** (-6.08)
-0.67*** (-4.82)
Freedom 0.40*** (5.34)
0.93*** (3.98)
0.46* (1.88)
0.33*** (7.54)
6.56*** (2.17)
1.03*** (4.26)
0.40*** (3.06)
0.53*** (2.74)
Decade (1=1990s)
-0.32*** (-7.69)
-0.36*** (-3.02)
-0.47*** (-3.54)
-0.24*** (-13.63)
-0.14 (-0.25)
-0.36*** (-3.11)
-0.32*** (-4.32)
-0.15* (-1.65)
Constant -6.51*** (-11.93)
-11.29*** (-6.69)
-2.95* (-1.73)
-5.68*** (-17.35)
18.17 (0.91)
-13.93*** (-8.04)
-3.56*** (-4.09)
-6.67*** (-4.69)
Dispersion parameter
3.28*** (16.47)
2.09***
(20.17) 0.73***
(14.14)
Vuong Stat. 6.05 8.26
OBSs No. 1413 1413 1413 1413 1413 572
27
Log-likelihood
-4387 -2139 -3134 -2003 -852 -1440
Adjusted R2 0.18 0.06 0.15 0.16 0.10 0.06
Asterisks (*, **, ***) indicate 10%, 5%, and 1% significance levels. Figures in the
parenthesis are t-statistics.
28
Figure 1: The procedure to check for model specification among the Poisson, NB, ZIP
and ZINB models
Vuong test
test on 0=α
ZINB vs. NB
favor ZINB favor NB
ZINB vs. ZIP Poisson vs. NB
test on 0=α
fail to reject
reject
fail to reject reject Vuong test
favor ZIP favor Poisson
ZIP vs. Poisson
29
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