Socio-Economic Antecedents of Transnational Terrorism: Exploring the Correlation

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1 Soco-Economc Antecedents of Transnatonal Terrorsm: Explorng the Correlaton Levan Elbakdze Research Assstant Professor Department of Agrcultural Economcs Texas A&M Unversty, College Staton Yanhong Jn Assstant Professor Department of Agrcultural Economcs Texas A&M Unversty, College Staton Ths research was supported n part through the Department of Homeland Securty Natonal Center for Foregn Anmal and Zoonotc Dsease Defense at Texas A&M Unversty. The conclusons are those of the author and not necessarly the sponsor. The authors share the senorty of authorshp.

2 Soco-Economc Antecedents of Transnatonal Terrorsm: Explorng the Correlaton Abstract Polces related to thwartng transnatonal terrorsm have been at the forefront of poltcal and socal debates. In ths paper we emprcally examne the mpacts of soco-economc condtons on the probablty and frequency of partcpaton n transnatonal terrorsm events. We use count data analyss technques n combnaton wth the newly combned annual data on transnatonal terrorsm and soco-economc varables from 1980 to We fnd strong correlatons between economc condtons and probablty and frequency of partcpaton n terrorsm events. Specfcally, one of the key fndngs s a non-lnear relatonshp between per capta ncome and partcpaton n transnatonal terrorsm. The results suggest that extreme poverty may preclude the opportuntes to partcpate n terrorsm acts whle relatve allevaton of poverty levels may provde margnal resources to partcpate n terrorsm acts and materalze accumulated hatred. Smlarly, educaton has a non-monotonc effect on the partcpaton n terrorsm acts,.e., mprovng labor force educaton from prmary to secondary level may ncrease frequences of transnatonal terrorsm. On the other hand, mprovng the labor force educaton from secondary to tertary level may decrease the frequences of transnatonal terrorsm events. The results also ndcate that economc freedom, openness to trade, ncome equty, and relgon play a sgnfcant role n the probablty and frequency of transnatonal terrorsm events. JEL classfcaton: D74, F51, O15

3 Soco-Economc Antecedents of Transnatonal Terrorsm: Explorng the Correlaton Terrorsm has become a promnent nternatonal problem over the past half century. Snce September 11, 2001 polces related to thwartng terrorsm have been at the forefront of nternatonal polcy arena. Approaches such as mltary operatons, nternatonal sanctons, fnancal ad, educaton assstance, and varous retalatory actons have been mplemented to varous degrees. The most desrable approach s clearly one that s based on preventatve actons rather than response actons. A good understandng of envronment conducve to terrorst actvtes s crtcal for the success of the counter terrorsm campagn focusng on preventatve actons. Usng count data analyss based on a newly combned data of transnatonal terrorsm and soco-economc varables, we nvestgate socal, economc, and poltcal factors that may nfluence the envronment conducve to terrorst actvtes and, thus, provde nsghts on possble preventatve counter terrorsm strateges. To acheve ths goal, we created a unque data set from varous sources, ncludng the chronologcal data on transnatonal terrorsm events, the World Bank Database, CIA World Factbook, and some natonal censuses. We were able to ncorporate varables such as measures of ncome dstrbuton (per capta GDP, GINI ndex, poverty ndcators), unemployment, educaton, lteracy, relgon, openness to trade, and economc freedom. We emprcally nvestgate the possblty of a nonlnear relatonshp between partcpaton n terrorsm actvtes and ncome. Our hypothess s that gven poltcal and socal nstablty of developng countres, small mprovement n economc condtons from extreme poverty may provde just enough resources to materalze accumulated hatred for 1

4 the developed world. In other words, although n the long run sustaned suffcent economc development may reduce the ncentve to engage n nternatonal terrorsm, n the short run ntal economc development may ncrease number of terrorst events. We also nvestgate whether there s a smlar non-lnear relatonshp between terrorsm and educaton. We hypothesze that hgh levels of educaton may deter partcpaton n terrorst actons, whle lmted level of educaton may have postve effect on partcpaton n terrorst actons. Furthermore, the analyss also examnes the mpact of trade, relgon, and economc freedom on transnatonal terrorsm by ncorporatng correspondng measures. The rest of ths paper s organzed as follows. A lterature revew on transnatonal terrorsm s provded n the followng secton. We dscuss the data n secton three and present a revew on count data estmaton models n secton four. The estmaton results are provded and dscussed n secton fve and concludng remarks and polcy mplcatons are gven n the last secton. Lterature Revew Analyss of strateges to advance counter terrorsm objectves has been the subject of several prevous studes. Lee (1988) argues that some level of cooperatve multcountry retalaton aganst terrorsts may be desrable and nvestgates the obstacles to cooperatve retalaton. Atknson, Sandler and Tschrhart (1987) examne terrorst ncdents as barganng stuatons between government offcals and terrorsts. Usng data on transnatonal terrorsm they study the effects of barganng costs on the length and outcome of ncdents and the effects of bluffng on terrorsts payoff. Enders, Sandler and Cauley (1990) and Sandler, Enders and Lapan (1991) conclude that securty measures lke metal detectors, are effectve n preventng partcular types of terrorst events but not 2

5 effectve n reducng overall number of terrorsm ncdents. Ths fndng suggests that the terrorsts respond to the securty measures such as nstallaton of metal detectors by substtutng ther efforts to less protected targets. These two studes also fnd that nternatonal conventons and retalaton are neffectve n the long run. They conclude that government ought to fght terrorsm usng polces and technologes desgned to thwart all forms of terrorst actvtes, for example by elmnatng sources of fnancal support, whch would be mmune to the substtuton phenomenon. Our study contrbutes to ths concluson by nvestgatng socal, economc, and poltcal factors that may encourage/foster or dscourage/thwart terrorst actvtes. Identfyng the factors contrbutng to terrorst actvtes may ad n desgnng the polces whch would lmt ndvdual tendences for partcpaton n all forms of terrorst actvtes. Many studes have addressed motves and drvers of terrorsm and of socal conflcts n general, whch range from poltcal, socoeconomc and relgous to personal reasons. For example, Hess and Orphandes (2001) show that n the presence of reelecton motve, the frequency of war s greater followng recessons than followng economc growth. Enders and Sandler (2000) show that although frequency of nternatonal terrorsm has dramatcally decreased, terrorst ncdents n the post cold war perod have become more hazardous. They attrbute ths ncrease n the severty of terrorst attacks to the growth of relgous terrorsm. Blomberg and Hess (2002) conclude that nternal conflct, external conflct, and the state of the economy are not ndependent of one-another. Economc recessons can ncrease the probabltes of nternal and external conflcts and vsa versa. Blomberg, Hess and Weerapana (2004) fnd that economc recessons, represented by negatve per capta GDP growth, could ncrease the probablty of terrorst 3

6 actvtes n democratc hgh-ncome countres. They argue that durng economc recessons n hgh-ncome countres groups that are unhappy wth current soco-economc status quo, but are unable to nfluence poltcal and nsttutonal stuaton, resort to terrorst actvtes to ncrease ther voce n the economy. L and Schaub (2004) study the effects of economc globalzaton on the frequency of transnatonal terrorst ncdents wthn a country s borders. They fnd that trade, foregn drect nvestment, and portfolo of nvestment of a country have no drect postve effect on the number of terrorst events ntated wthn the country. However, economc development of a country and ts tradng partners has a negatve effect on the number of nternatonal terrorst ncdents wthn a country. Therefore, f trade and foregn drect nvestment promote economc development, then these varables must ndrectly reduce transnatonal terrorsm. L (2005) shows that democratc partcpaton and economc development measured by GDP per capta reduces transnatonal terrorsm whle government constrants ncrease the number of terrorst ncdents. Alesna et al. (1996) fnd that to some extent low economc growth measured by GDP per capta could lead to government turnovers through coups. Stern (2000) attrbutes nvolvement n terrorst acts to lack of adequate educaton. She reports that relgous schools n Pakstan encourage ther graduates, who lack practcal educaton, to fulfll ther sprtual oblgatons by fghtng aganst Hndus n Kashmr and other adversares. Fearon and Latn (2003) analyze 127 cvl wars between 1945 and 1999 and conclude that poverty has a sgnfcant postve effect on volent domestc conflcts because t ads nsurgents n recrutment. Muller and Selgson (1990), who studed 85 developng countres durng , and London and Robnson (1989), who analyzed 51 developng countres durng , show that ncome nequalty s a sgnfcant 4

7 predctor of poltcal volence. Wulf, Hames, and Longstaff (2003) propose that possble reasons why some developng countres mght be supportng terrorsm nclude deologcal dfferences, past operatons and polces of the developed countres, and unfavorable socoeconomc condtons. They argue that through the efforts to mprove the qualty of lfe for ndvduals n the developng countres, the hatred for the developed countres may subsde. The opnon about sgnfcance of socoeconomc factors n provdng favorable envronment for terrorsm s not unanmous. Abade (2006) uses a dataset on the severty of country-level terrorst rsk and fnds no sgnfcant relatonshp between rsk of terrorsm and economc varables. However, he detects a sgnfcant non-lnear relatonshp between poltcal freedom and terrorsm rsk. Specfcally, countres wth ntermedate levels of poltcal freedom are more prone to terrorsm than countres wth hgh or low levels of poltcal freedom. Pazza (2006), usng multple regresson analyss on terrorst ncdents and casualtes n 96 countres from 1986 to 2002, fnds no sgnfcant relatonshp between terrorsm and any of the economc development ndcators such as human development ndex, ncome equty, per capta GDP growth, nflaton, unemployment, and calores per capta. Krueger and Maleckova (2003) explore statstcal relatonshp between nvolvement n terrorsm events and educaton, occupaton and economc actvty. Based on a survey conducted n the West Bank and Gaza Strp they argue that occupaton, poverty, and lack of educaton do not seem to affect the lkelhood that an ndvdual wll engage n terrorst actvty. Usng cross country regresson analyss, they also fnd that there s generally no statstcally sgnfcant relatonshp between GDP per capta and number of terrorst events ntated from each country. However, ther results, whch are 5

8 based on a data collected from West Bank and Gaza Strp, may have overlooked the fact that majorty of relatvely educated people n poor regons are probably not educated n a smlar manner as ther counter fellows from developed countres. The approprateness of local educatonal categores as ndcators of general educaton level s questonable because some schools may be delberately teachng the students to become supporters of extremst movements (Stern 2000). Such results may also overshadow the fact that well pad and educated ndvduals n poor countres may be better nformed about relatve qualty of lfe and foregn polces of rch countres than uneducated poor ctzens. Resultng sense of relatve deprvaton may encourage engagement n nternatonal terrorsm as the last and smplest resort to make dfference n ther countres. Data The chronologcal data on transnatonal terrorsm events was obtaned from Dr. Edward Mckolus (Vnyard Software Inc.). The data ncludes records of terrorsm ncdents ncludng date, ncdent s country of orgn, locaton of ncdent, up to three natonaltes of vctms, and up to three natonaltes of perpetrators. Detaled descrpton of ths data set, enttled Internatonal Terrorsm: Attrbutes of Terrorst Events (ITERATE), s avalable n Sandler and Enders (2004), Enders and Sandler (1993), and Mckolus et al. (1993). Consstent wth the transnatonal terrorsm dataset used n ths study, we assume that transnatonal terrorsm s a premedtated threatened or actual use of force or volence to attan a poltcal goal through fear, coercon, or ntmdaton and when ts ramfcatons transcend natonal boundares through the natonalty of the perpetrators and/or human or nsttutonal vctms, locaton of the ncdent, or mechancs of ts resoluton (Mckolus et al. 1989). 6

9 Based on the chronologcal data on transnatonal terrorsm ncdents, our dependent varable s constructed as annual count of terrorsm events n whch ctzens of a partcular country were documented as perpetrators. For example, the annual count of partcpaton n transnatonal terrorsm events for Phlppnes n 2000 s seven, whch means that the Phlppne natonals were documented as perpetrators for seven transnatonal terrorsm ncdents n The orgnal chronologcal data documents up to three natonaltes of perpetrators for each transnatonal terrorsm ncdent. For example, on March 15, 1982 three Salvadorans, two Ncaraguans, one Chlean and others were arrested for ntendng to kdnap an undentfed Amercan dplomat (Mckolus et al. 1989). Ths ncdent ncreases the annual count of transnatonal terrorsm events for Salvador, Ncaragua, and Chle by one each. We use the terms ncdents and counts to refer to terrorsm events and to partcpaton n terrorsm events by varous natonaltes respectvely. The natonaltes of perpetrators nvolved n some of the documented ncdents were unknown because ether the perpetrators or ther natonaltes were not traceable. Table 1 shows that our sample of countres accounts for 33% of total documented ncdents and 49% of the documented ncdents for whch at least one natonalty of the perpetrators was known. Further more, our sample accounts for 51% total counts whch represent perpetrators wth known natonaltes only, and 20% of total counts whch represent perpetrators wth known as well as unknown natonaltes. In our sample, only 71 out of the total 2748 terrorsm counts correspond to terrorsm ncdents wth perpetrators from more than one country. The table also shows that the 90s had fewer terrorsm ncdents and counts than 80s. We ntroduce the followng socal, economc, and poltcal varables to nvestgate whether and how these factors affect the lkelhood and the expected frequences of 7

10 partcpaton n transnatonal terrorsm ncdents. The economc varables nclude GDP per capta (measured n terms of constant 2000 US$), percent of populaton lvng on less than one dollar a day, percent of populaton lvng on one to two dollars a day, GINI ndex that measures ncome equty among households on a scale of zero (perfect equalty) to one (absolute nequalty), unemployment rate, openness to trade, whch s the share of total mports and exports relatve to GDP, and ndex of economc freedom, whch was obtaned from the hertage foundaton (Hertege Foundaton, 2006). The ndex of economc freedom s measured on a scale of one to fve, where one denotes an economc envronment or set of polces that are most conducve to economc freedom, whle a score of fve denotes a set of polces that are least conducve to economc freedom. World Bank s estmates of percentages of populaton lvng on less than one and two dollars per day were used to calculate the percent of populaton lvng on one to two dollars a day. We are aware that the World Bank s estmates of percent of populaton lvng on less than one or two dollars a day have been crtczed for consstency and approprateness as descrptors of nternatonal poverty (Wade 2004). However, we use these estmates as best avalable ndcators. Educaton varables are represented by percentage of populaton, ages 15 and older, who can read and wrte, and percentage of labor force wth the hghest acheved educaton beng prmary, secondary, and tertary levels. The relgous varables are represented by percentage of populaton who practce Chrstanty, Islam, Hndusm, Buddhsm, other relgons, and no relgon at all. The socoeconomc data, except for the ndex of economc freedom, obtaned from the Hertage Foundaton, and relgous measures, obtaned from CIA s World Factbook, were collected from the World Bank data base. We also rely on natonal statstcs servces 8

11 of varous countres to obtan the values for socoeconomc varables, whch have mssng values n the World Bank data base. When some of the hstorcal estmates were not avalable we used hstorcally closest avalable data estmates to fll n the mssng observatons. For example, the earlest avalable economc freedom ndex from 1990s was used as a proxy for the economc freedom ndex for all of 1980s and early 1990s. Typcally, for most countres, the estmates of percent of populaton lvng on less than one or two dollars per day are avalable for one partcular year. These estmates are assumed to be best avalable approxmatons for the remanng years. GINI ndex, educaton varables, and relgon varables are also extrapolated n a smlar manner. Matchng the terrorsm events data wth the soco-economc data, we are able to construct a data set consstng of 1413 observatons by country and year from 1980 to 2000 wth the annual terrorsm partcpaton counts for 77 countres and correspondng soco-economc varables. Notce that reduced tme horzons were used for some of the countres because unstable contemporary poltcal and economc condtons dd not allow extrapolaton of avalable estmates. For example, the economc data for the Republc of Georga at the World Bank database became avalable n Due to hghly unstable socopoltcal stuaton n Georga n the 80s and begnnng of the 90s extrapolaton of data from 1994 to prevous years was not possble. Therefore, the observatons of the Republc of Georga n our dataset start from The frequency of terrorsm counts, shown n the thrd column of Table 2, reveals small number of counts and excessve number of zeros for partcpatons n terrorsm attacks. Observatons wth at most fve counts of partcpaton account for 91%. The phenomenon of excess zeros may be a concern because 60% of the sample has a 9

12 zero count of terrorsm attacks. Ths ssue s addressed n the methodology secton. Table 3 presents the summary statstcs of the socoeconomc varables. Revew of Methodology We utlze count data analyss to nvestgate the mpacts of soco-economc condtons on partcpaton n transnatonal terrorst actvtes. Estmatons are based on the pooled data as well as the panel data analyss. Ths secton presents a revew of count data analyss focusng on the partcular pooled data models used n ths study. Posson regresson model s wdely used n count data analyss (Cameron and Trved, 1998). In a basc Posson regresson model wth a logarthm lnk functon, the number of events y for ndvdual has a Posson dstrbuton wth a condtonal mean λ dependng on the s characterstcs, x : (1) ( ) E( y ) = exp( β) λ x = x x, where β s a vector of unknown coeffcents assocated wth the covarate vector x. For the convenence of notaton, we drop x n ( ) probablty densty functon of y gven x s (2) f( y ) x ( ) exp y = λ λ. y! λ x and useλ for the rest of ths paper. The A property of the Posson dstrbuton s that ts varance s equal to ts mean. However, t s very common to have varance larger than mean,.e. over-dsperson, n count data analyss (Cameron and Trved, 1998; Wnkelmann and Zmmermann, 1995). Overdsperson can be caused by unobserved heterogenety among ndvduals and/or by excess zeros n the dependent varable. When over-dsperson s an ssue, the estmates based on the Posson regresson wll be neffcent (Cameron and Trved, 1998). 10

13 Negatve bnomal (NB) regresson models have been suggested to deal wth the ssue of possble unobserved heterogenety. The NB model adds an error term ε, to the condtonal mean of the Posson dstrbuton to model the unobserved heterogenety, (3) E ( y ) = exp( + ) x x ε. β where exp ( ε ) s normally assumed to follow a gamma dstrbuton wth mean one and varance α. The probablty densty functon of y gven x now becomes 1/ α Γ( y (4) ( ) + 1/ α) 1 λ f y x = y Γ α! (1/ ) 1+ αλ 1 / α+ λ The condtonal mean and varance of y under the NB model are E = and (5-1) ( y x) λ (5-2) ( ) (1 ) VAR y x = λ + αλ. Equatons (5-1) and (5-2) suggest thatα, varance of gamma dstrbuton, ndcates the degree of over-dsperson. As α becomes larger, the dstrbuton wll be more dspersed. As α gets close to zero, the NB model converges to the Posson model. The Posson and NB models are nested, and a statstcal rejecton of the null hypothess of α = 0 wll favor the NB over the Posson model. y. The second possble source of over-dsperson could be excessve number of zeros for the dependent varable, whch s a concern n ths study because almost 60% of the sample has zero count of events (see Table 2). The tradtonal Posson and NB models do not account for excess zeros and thus can produce based estmates. Zero-nflated regresson models, such as a zero-nflated Posson (ZIP) model or zero-nflated NB (ZINB) 11

14 model, are warranted. 1 Zero-nflated count data models have been used to nvestgate determnants of hgh-rsk heterosexual behavor (Helbron, 1994), speces ecologcal abundance (Welsh et al. 1996), accdent frequences n roadway sectons (Shankar, Mlton, and Mannerng. 1997), cares preventon n dental epdemology (Bohnng et al.1999), detecton of specfc process equpment problems (L et al. 1999), evaluaton of occupatonal safety nterventons (Carrvck, Lee, and Yau, 2003), young drvers motor vehcle crashes (Lee et al. 2002), the ncdence of sudden nfant death syndrome (Dalrymple, Hudson, and Ford, 2003), clam frequences n general automoble nsurance (Yp and Yau, 2005), consumpton of beverages (Mullahy, 1986), and so forth. Lambert (1992) frst ntroduced ZIP model (6) y = 0 y ~ Posson λ wth probablty - π = ( ) wth probablty 1 ( y 0,1,2,...) π The probablty of havng an extra zero whch s not subject to the Posson dstrbuton, π, s assumed to have a logt functon 2, (7) exp π = 1+ exp ( zγ) ( γ) z, where z s a vector of observable covarates and γ s a vector of coeffcents assocated wth z. The mean and varance of y n the ZIP model are E = (1 ) and (8-1) ( y x) π λ VAR y x = λ π + λ π. (8-2) ( ) ( 1 )(1 ) 1 There are other zero-nflated count data models, ncludng zero-nflated generalzed Posson (ZIGP) (Angers and Bswas, 2003) and zero-nflated double Posson (ZIDP) (Gurmu,1998) models. Yp and Yau (2005) appled four zero-nflated models, ZIP, ZINB, ZIGP, ZIDP to accommodate excess zeros when analyzng the clam frequency data n general nsurance. 2 The unobserved probablty π s generated as a logstc or probt functon of observable covarates to ensure nonnegatvty. The choce between logt and probt s usually unmportant snce the two functons are close to each other and they usually gve very smlar results (Cheung, 2002). 12

15 Equatons (8-1) and (8-2) show that π ndcates the degree of over-dsperson. As 1 π π approaches zero, the ZIP model merges nto a Posson model. Smlarly, we can construct a ZINB model havng a logt lnk functon. The mean and varance of y under the ZINB model are E = (1 ) and (9-1) ( y x) π λ (9-2) VAR( ) ( 1 )( 1+ ( α) ) = λ π λ π +. y x π + α Equatons (9-1) and (9-2) show that reflects the degree of over-dsperson n the 1 π ZINB models, whch accounts for over-dsperson from both excessve zeros and unobservable heterogenety. The Posson and ZIP models are not nested, and nether are the NB and ZINB models. Vuong (1989) proposed a lkelhood rato test for non-nested models, and Greene (1994) adapted the technque for the cases of ZIP versus Posson, and ZINB versus NB models. The test statstc s (13) Z N m =, s m where m and s m are the mean and standard devaton of m and N s the number of observatons. m s defned as m ) p ( y x) ln 1 ) = ) where p1( y x) p ( y ) ) and p2( y x) 2 x are the predcted probablty of the two competng models. Asymptotcally, Z has a standard normal dstrbuton, wth large postve values (>1.96) favorng the zero-nflated model and wth large negatve values (<-1.96) favorng the nonzero-nflated model at a 5% sgnfcance level. 13

16 Based on statstcal tests on the null hypothess α = 0 for over-dsperson for nested models and the Vuong test for non-nested models, we are able to test for model specfcaton among the Posson, NB, ZIP, and ZINB models. As shown n Fgure 1, f the Vuong test favors the ZINB model over the NB model, a statstcal test on α = 0 s conducted to contrast ZINB versus ZIP. If α = 0 s rejected, ZINB s the most approprate specfcaton, and both ndvdual heterogenety and excess zeros contrbute to the overdsperson. Otherwse, ZIP model s compared to Posson model by usng the Voung test. If ZIP s the most approprate specfcaton, then only excessve zeros account for overdsperson. Otherwse no over dsperson s present and Posson s favored. On the other hand, f the Vuong test favors the NB model, we wll test f the heterogenety parameter α s sgnfcantly dfferent from zero to contrast NB vs. Posson. A rejecton of α = 0 suggests that the NB model s most approprate specfcaton and heterogenety accounts for over-dsperson. Otherwse, the Posson and ZIP are compared. Estmaton Results and Polcy Dscussons Table 4 presents the estmaton results of the Posson, NB, ZIP, and ZINB regresson models. Both ZIP and ZINB nclude a logt regresson followed by Posson for ZIP or NB for ZINB. Followng the procedure outlned n fgure 1, after estmatng the ZINB model, two statstcal tests are conducted: (1) the Vuong test favors the ZINB model over the standard NB model (Vuong-statstc=8.26 and p-value=0); and (2) the Student-t test rejected the null hypothess that α = 0 (t-statstc=20.17 and p-value=0). Hence, the over-dsperson stll exsts even after controllng for excess zeros. Therefore, based on the procedure outlned n Fgure 1, ZINB model s most approprate specfcaton for our data among these four count data models. 14

17 Besdes the Posson, NB, ZIP and ZINB models, we also estmate a two-part model (Hu, Sung, and Keeler, 1995; Wasserman, Mannng, Newhouse, and Wnkler, 1991) and a selecton model (Heckman, 1979; Greene, 1999) as part of pooled data analyss. Both the two-part model and the selecton models sequentally estmate the equaton for the partcpaton decson and the equaton for the frequency of events. The partcpaton decson s usually specfed by a logt or probt model, and the second estmaton s truncated at zero and only focuses on observatons wth postve counts. The two-part model assumes that the partcpaton and frequences of events are dsjonted. Whereas, the selecton model ntegrates the estmaton of these two equatons by ncorporatng Mlls rato, whch s calculated after estmaton of the bnary partcpaton equaton, nto the estmaton of event frequences n the Posson or NB to control for selecton bas (Greene 1994). In ths study both two-part and selecton models use NB estmaton appled to observatons wth postve counts only. The results show that the Mlls rato varable s not statstcally sgnfcant (p-value=0.4 for the null hypothess that the coeffcent of the Mlls rato equals to zero). Thus, the two-part model s a better specfcaton than the selecton model. Therefore, n Table 4 we present results of the two-part model to contrast wth other count data estmatons. We also nvestgate the fxed-effects and random-effect NB models on the panel data to explore the possblty of contemporaneous correlaton. Gel et al. (1997) obtan smlar results when usng the pooled and panel NB models to nvestgate determnants of hosptal trps n Germany. However, Hausman, Hall, and Grlches (1984) fnd large dfferences between the cross-secton and panel models when nvestgatng the patents R&D relatonshp. In our study, the comparson of actual and predcted event counts n 15

18 Table 2 ndcates that the panel estmaton may not ft the data as well as the pooled data estmaton. Hence, we only provde the estmaton results based on the pooled data n Table 4. Table 4 summarzes estmaton results of fve pooled data models, ncludng the Posson, NB, ZIP, ZINB, and the two-part models. The varables that tend to show up as statstcally sgnfcant n all these fve models are GINI ndex, populaton rato lvng on one to two dollars a day, unemployment rate, openness to trade, and economc freedom ndex. The sgns of these varables are consstent wth our expectatons. Rate of unemployment has a postve effect on the probablty and the frequences of partcpaton n terrorsm events. Openness to trade has a negatve effect on the probablty and the frequency of partcpaton n terrorsm acts. Thus, as a country becomes more globally ntegrated, as measured by the rato of the volume of ts trade and GDP, the lkelhood and frequency of t s ctzens partcpaton n transnatonal terrorsm decreases. Ths result dffers from L and Schaub (2004) who found no statstcally sgnfcant drect relatonshp between trade and terrorsm acts wthn the countres borders. However, L and Schaub (2004) propose possble ndrect effect of trade through ts postve effect on economc development, whch s shown to have a negatve effect on terrorsm. Economc freedom ndex n our results dsplays a postve effect on probablty as well as frequency of partcpatng n terrorsm acts. Ths concdes wth the expectaton that decrease n economc freedom has a postve effect on overall nvolvement n transnatonal terrorsm. Prevous lterature provdes mxed conclusons on the effects of per capta ncome on terrorsm. The noton that poverty breeds terrorsm and poltcal volence s consstent wth some studes, ncludng Alesna et al. (1996), L (2005), Fearon and Latn (2003), 16

19 Wulf, Hames and Longstaff (2003) who fnd that GDP per capta has a negatve effect on terrorsm. On the other hand, Pazza (2006), Abade (2006), and Krueger and Maleckova (2003) fnd no evdence that poverty affects terrorsm. We ntroduce GDP per capta and ts square term as ndependent varables and fnd a non-lnear effect of GDP per capta on the frequences of partcpaton n transnatonal terrorsm n all fve models. Specfcally, the concave form mpled by the estmated coeffcents suggests that an ncrease of GDP per captal ncreases (decreases) frequences of partcpaton n terrorsm when a country s at a relatvely lower (hgher) level of per capta ncome. Though these results correspond to our hypothess regardng frequences of partcpaton, they are less conclusve about the probablty of partcpaton because the coeffcents of GDP and GDP 2 for probabltes of partcpaton are not statstcally sgnfcant. However, the estmated coeffcents assocated wth poverty ndcators - percentage of populaton lvng on less than one dollar a day and percentage of populaton lvng on one to two dollars a day suggest smlar results for probabltes of partcpaton n transnatonal terrorsm. Specfcally, results reveal that ncreasng the proporton of populaton lvng on less than one dollar per day decreases, whle ncreasng the proporton of populaton lvng on one to two dollars per day ncreases the lkelhood of partcpaton n terrorsm acts. The results also show that both, the proporton of populaton lvng on less than one dollar a day and proporton of populaton lvng on one to two dollars per day, have postve effects on frequency of partcpaton n transnatonal terrorsm. These fndngs suggest that margnal mprovements from the extreme poverty may enable the dsadvantaged to materalze ther hatred for the socetes whch they deem responsble for, or contrbutng to, ther mpovershed lvng condtons, and/or whch they vew as threats to ther culture. Ths 17

20 result s consstent wth Peter Bernholz s (2004) argument of ncrease n supreme value based terrorsm as a result of ncreased resource avalablty. Furthermore, our results also show that ncorporaton of ncome equty s mportant for explanng partcpaton n transnatonal terrorsm acts. As expected, the GINI ndex has a postve sgn -- the hgher the ncome nequty the hgher the lkelhood of partcpaton n terrorsm events and the greater the frequency of partcpaton n terrorsm attacks. Among the educaton measures, lteracy has no statstcally sgnfcant effect on probablty of partcpaton n terrorsm event, except n the ZINB model, but has statstcally sgnfcant and postve effects on the frequences of partcpaton n terrorsm events. Prmary and tertary educaton varables have no statstcally sgnfcant effect on lkelhood of partcpaton, except n the ZINB model. However, both have negatve effect on frequency of partcpaton n terrorsm. The results on secondary educaton are mxed across models. To better understand the effects of educaton on the probabltes and frequences of terrorsm partcpaton, we conduct the followng exercse. Let β j denote the coeffcents of the varables representng percentage of labor force wth the hghest acheved educaton beng prmary (j=p), secondary (j=s), and tertary levels (j=t). The mprovement of educaton, correspondng to movng 1% of labor force from prmary educaton level, as hghest acheved, to the secondary educaton level, ncreases the condtonal frequences by ( β p + β s )exp(xβ) n the Posson and NB models and by ( β p + β s )exp(xβ)(1-π ) n the ZIP and ZINB models. Smlarly, movng 1% of labor force from secondary educaton level to tertary level wll ncrease the condtonal frequences by ( β s + β t )exp(xβ) n the Posson and NB models and by ( β s + β t )exp(xβ)(1-π ) n the ZIP and 18

21 ZINB models. The Student-t tests show that β p + β s s statstcally sgnfcant and greater than zero n the Posson and ZIP models, and β s + β t s statstcally sgnfcant and less than zero n the Posson, ZIP, ZINB, and two part models, at 1% sgnfcant level. Ths suggests that mprovng educaton from prmary to secondary level ncreases, whle mprovng educaton level from secondary to tertary level decreases the frequences of partcpaton n terrorsm events. These results are robust across dfferent estmaton models on the pooled data except the NB model where they are not sgnfcant at 10% sgnfcance level. Hence, the results suggest that lmted educaton may ncrease the frequency of partcpaton n transnatonal terrorsm, whle advanced educaton levels may deter the partcpaton frequency. The estmaton results show that percentage of populaton who practce organzed relgons lke Chrstanty, Hndusm, Buddhsm, and Islam, relatve to no relgon, has sgnfcant and postve effect on the frequences of partcpaton n transnatonal terrorsm events across all these fve models. These varables have a sgnfcant postve mpact on the probablty of terrorsm partcpaton n the two-part model. These results mply that an ncrease n the proporton of populaton who practce any of the consdered relgon categores seems to ncrease the frequency of terrorsm attacks relatve to populaton practcng no relgon at all. Decade dummy has a sgnfcant, negatve effect on probablty as well as frequency of partcpaton n transnatonal terrorsm, except for ZINB model where the effect on the probablty of partcpaton s not statstcally sgnfcant. These results suggest that the 1990s relatve to the 1980s had lower probablty and frequency of partcpaton n terrorsm events. 19

22 Conclusons Usng a newly combned data of the transnatonal terrorsm events and socoeconomc data from varous sources, we use count data analyss to nvestgate the mpacts of soco-economc condtons on the probablty and the frequences of partcpaton n transnatonal terrorsm. We are not aware of any publshed studes whch use count data analyss, combned wth the type of data used n ths study, to nvestgate how socoeconomc characterstcs of perpetrators countres of orgn nfluence probablty and frequency of partcpaton n transnatonal terrorsm. The results support our ntal hypothess that per capta ncome has a nonlnear effect on partcpaton n transnatonal terrorsm. Extreme poverty may preclude the opportuntes to partcpate n terrorsm acts and relatve allevaton of poverty levels may provde margnal resources to partcpate n terrorsm acts and materalze accumulated hatred. Ths may be true n the context where terrorsm acts are prmarly organzed and sponsored by ndvduals and/or ndvdual groups rather than by centralzed terrorst organzaton. It also could be true f centralzed terrorst organzaton requres expendtures on the part of terrorst recruts. For example, there maybe personal fnancal costs assocated wth partcpatng n terrorst tranng camps. Ths result suggests that allevaton of poverty, whch one could argue may be a way to deter terrorsm, may lead to ncrease partcpaton n terrorsm n the short run. Therefore, poverty allevaton related polces need to be desgned carefully consderng the possblty of ncreased terrorsm due to ncreased personal ncome. Ths analyss also shows that lmted educaton and hgher educaton may have opposte effects on frequences of partcpaton n transnatonal terrorsm--lmted educaton may ncrease but advanced educaton may decrease the 20

23 frequency of partcpaton n transnatonal terrorsm events. The results also show that openness to trade, equalty of ncome and consumpton dstrbuton, employment opportuntes, and relgon, may have sgnfcant effects on probablty and frequency of partcpaton n transnatonal terrorsm events Overall, the results ndcate that careful plannng s necessary when fghtng terrorsm through such approaches as allevaton of extreme poverty. Though preventatve optons lke economc development and mprovng educaton levels could n the long run deter the tendency of partcpaton n terrorsm actvtes, careful plannng s needed for mplementaton of such polces n the short run because of possble rsk of ncreased tendences for partcpaton n transnatonal terrorsm. The advantage of mplementng carefully desgned preventon polces s that they may reduce the necessty to use response actons such as mltary retalaton and economc sanctons. Moreover, preventatve polces whch are desgned to reduce all forms of terrorsm may be more effcent n the fght aganst terrorsm than the polces whch concentrate on specfc forms of terrorsm, such as ncreased arlne/embassy/varous nfrastructure securty. However, ths should not be nterpreted as a suggeston for substtutng response actons wth any of the preventatve actons whch may be mpled by the results of ths study Fnally, t should be noted that the dataset was constructed usng a lmted amount of best avalable nformaton and nvolved extrapolaton of soco-economc estmates over the perods for whch data was not avalable. As such the data set used n ths study n many cases s an approxmaton of nternatonal soco-economc ndcators rather than actual estmates. Therefore, cauton s warranted for nterpretaton of the results. The fndngs should be nterpreted as no more than a prelmnary support of the dea that 21

24 socoeconomc factors may play a role n encouragng/dscouragng terrorst behavor. Further studes based on ether more complete records or on alternatve approaches, whch would avod relance on observatonal data, are necessary to fully understand the lnkage between socoeconomc factors and partcpaton n transnatonal terrorsm acts. 22

25 Table 1: Number of terrorsm ncdents and events Terrorsm ncdents Wth known natonaltes only Includng unknown natonalty Wth known natonaltes only Terrorsm counts Includng unknown natonaltes a Documented Total 5,504 8,162 5,378 13,947 Durng 80s 3,038 4,651 3,090 9,409 Durng 90s 2,466 3,511 2,288 4,503 Study sample Total 2,677 2,748 Durng 80s 1,493 1,542 Durng 90s 1,184 1,206 a Terrorsm events wth unknown natonaltes of perpetrators nvolve those ncdents for whch the number of partcpatng natonaltes was documented, however the dentty of those natonaltes was not known 23

26 Table 2: Observed and predcted frequences of the annual counts of terrorsm events Event Event Predcted counts range Actual Posson NB ZIP ZINB Twopart Fxedeffect Random effect 0 [0,1) (59.52) (34.32) (41.05) (53.22) (69.64) (75.08) (72.89) (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 Note: Fgures n the parenthess are percentages and fgures above the parenthess are frequences. The actual event counts for each observaton are ntegers. However, the predcted event counts are not necessarly ntegers. Thus, event counts n ntegers for the actual data and event counts are expressed n range for the estmated counts. 24

27 Table 3: Summary statstcs of socal-economc varables Varable name Varable defnton Mean SD Mn. Max. Income measures GDP GDP per capta ($1,000) GINI GINI ndex (0=perfectly nequty; 1=perfectly equty) PV1 populaton rato lvng under $1 per day PV12 populaton rato lvng on $1 to $2 per day Educaton measures Lteracy populaton rato who can read and wrte Prmary Secondary Tertary percent of labor force wth prmary educaton as the hghest acheved percent of labor force wth secondary educaton as the hghest acheved percent of labor force wth tertary educaton as the hghest acheved Relgon measures Chrstan Chrstan populaton rato Muslm Muslm populaton rato Hndu Hndu populaton rato Buddhst Buddhst populaton rato Other relgon populaton rato wth other regons No relgon populaton rato wth no relgons at all Other varables Unemployment unemployment rate Openness to trade Freedom (export+mport)/gdp) economc freedom (1=hghest economc freedom; 5=lowest economc freedom)

28 Table 4: Estmaton results of the Posson, ZB, ZIP, ZINB, and two-part models Independent ZIP ZINB Two-part model varables Posson NB Logt Posson Logt NB Probt NB GDP 0.11 *** (6.92) 0.26 *** (5.11) 0.04 (0.61) 0.06 *** (5.93) (-1.62) 0.28 *** (5.39) 0.10 *** (3.46) 0.11 *** (2.61) GDP square *** (-6.61) *** (-4.27) (1.15) *** (-9.21) 0.34 * (1.66) *** (-5.03) *** (-2.39) *** (-2.69) GINI 4.14 *** (15.08) 8.51 *** (8.30) 2.97 *** (2.94) 3.21 *** (21.90) (-1.45) *** (10.39) 2.34 *** (4.39) 4.75 *** (5.58) PV (1.32) 0.83 (1.20) ** (-2.15) 1.42 *** (10.20) ** (-2.03) 2.64 *** (3.69) * (-1.87) 1.44 ** (2.36) PV *** (6.60) 3.01 ** (3.37) 3.00 *** (3.38) 0.45 *** (2.61) * (1.91) 2.01 ** (2.24) 2.18 *** (4.45) 0.64 (0.89) Lteracy 2.56 *** (10.05) 4.07 *** (5.45) 0.12 (0.14) 3.37 *** (29.70) ** (-2.14) 6.06 *** (8.83) 0.31 (0.66) 3.64 *** (6.33) Prmary ** (-2.35) * (-1.73) 0.29 (0.50) *** (-6.85) *** (2.52) ** (-2.20) 0.08 (0.27) * (-1.22) Secondary 0.61 *** (3.08) ** (-1.91) ** (-2.31) 0.30 *** (3.05) 1.50 (0.23) (-1.40) *** (-1.79) 0.16 (0.32) Tertary (-0.47) ** (-2.42) 0.19 (0.22) (-1.31) ** (2.00) *** (-5.35) 0.63 (1.31) *** (-3.26) Chrstan 1.95 *** (7.44) 1.51 *** (3.48) 0.12 (0.18) 1.83 *** (19.30) (-0.46) 1.43 *** (4.96) 0.65 ** (2.39) 0.92 ** (2.35) Hndu 1.79 *** (5.19) 2.30 *** (3.70) 0.12 (0.14) 2.25 *** (14.43) (-1.49) 3.33 *** (4.72) 0.71 * (1.64) 1.76 *** (3.55) Buddhst 2.84 *** (9.62) 2.67 *** (5.03) 0.51 (0.63) 2.50 *** (24.73) (-1.21) 2.95 *** (7.12) 1.16 *** (3.39) 1.55 *** (3.48) Muslm 3.49 *** (12.98) 3.78 *** (6.73) 0.18 (0.25) 3.22 *** (31.40) (-1.48) 4.27 *** (10.40) 0.93 *** (3.04) 2.46 *** (5.64) Other relgon 3.48 *** (10.60) 4.32 *** (4.69) (-1.57) 4.71 *** (27.89) ** (-2.13) 9.44 *** (10.35) (-0.21) 5.89 *** (6.66) Unemployment rate 4.88 *** (14.65) 5.61 *** (5.00) 2.85 *** (2.51) 4.30 *** (30.89) (1.41) 5.36 *** (5.89) 2.30 *** (3.71) 3.24 *** (3.81) Openness to trade *** (-24.11) *** (-8.33) *** (-3.67) *** (-27.81) * (-1.61) *** (-7.65) *** (-6.08) *** (-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) *** (-7.69) *** (-3.02) *** (-3.54) *** (-13.63) (-0.25) *** (-3.11) *** (-4.32) * (-1.65) Constant *** (-11.93) *** (-6.69) * (-1.73) *** (-17.35) (0.91) *** (-8.04) *** (-4.09) *** (-4.69) Dsperson parameter 3.28 *** (16.47) 2.09 *** (20.17) 0.73 *** (14.14) Vuong Stat OBSs No

29 Loglkelhood Adjusted R Astersks ( *, **, *** ) ndcate 10%, 5%, and 1% sgnfcance levels. Fgures n the parenthess are t-statstcs. 27

30 favor ZINB test on α = 0 reject ZINB vs. ZIP fal to reject favor ZIP ZINB vs. NB Vuong test favor NB Posson vs. NB ZIP vs. Posson fal to reject Vuong test favor Posson test on α = 0 reject Fgure 1: The procedure to check for model specfcaton among the Posson, NB, ZIP and ZINB models 28

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