American Economic & Social Review; Vol. 2, No. 1; 2018 ISSN 2576-1269 E-ISSN 2576-1277 Published by Centre for Research on Islamic Banking & Finance and Business 20 Nigeria: Does Terrorism Spring from Economic Conditions? Emmanuel O. Okon1 1Department of Economics, Kogi State University, Anyigba, Kogi State, Nigeria Correspondence: Department of Economics, Kogi State University, Anyigba, Kogi State, Nigeria, E-mail: tonydom57@yahoo.com. Tel: +2348023275716 Received: January 2, 2018 Accepted: January 8, 2018 Online Published: January 13, 2018 Abstract Over the last half-century, Nigeria has become one of Africa‟s three giants along with Egypt and South Africa, thereby gaining considerable clout on the regional and global arenas. It is Africa‟s largest oil producer and recent finds ensure Nigeria‟s significance in the energy market for the foreseeable future. But the country has an inability or an unwillingness to distribute economic resources and development programs equitably. The primary objective of this paper is to find out whether economic condition leads to domestic terrorism in the country, as the contemporary Nigeria society is engulfed by terrible acts of Terrorism. This paper uses annual data for the time period 1970-2016 and the multivariate regression results suggest that government expenditure hinders terrorism, whereas macroeconomic policies foster it. Possible reasons for the outcomes and the policy implications of the findings were discussed. Keywords: Terrorism, Economic Deprivation, Nigeria. 1. Introduction Nigeria is Africa‟s most populous country and the 9th most populous country in the world. With an estimated population of 150 million, one in every five Africans is a Nigerian (UNICEF Nigeria, 2007).The country has been undergoing explosive population growth and has one of the highest growth and fertility rates in the world. By UN estimates, Nigeria will be one of the countries responsible for most of the world's total population increase by 2050.Nigeria is home to four large ethnic groups: Fulani, Hausa, Igbo and Yoruba and there are as many as 350 languages spoken across the country. The country has a federal system of administration with a Federal Capital Territory (FCT), 36 States and 774 Local Government Areas. Nigeria has one of the fastest growing economies in the world. Petroleum and oil resources play a large role in the Nigerian economy. The country is the 6th largest producer of petroleum in the world; it is the 8th largest exporter and has the 10 th largest proven reserves (UNICEF Nigeria, 2007). While the revenues made from oil provide the largest source of income for Nigeria, the country has become overly-dependent on its oil sector whereas other areas of the economy such as agriculture, palm oil production and coconut processing are in decline. Nigeria possesses a stark dichotomy of wealth and poverty. In spite of the country‟s vast oil wealth, the majority of Nigerians are poor with 71 per cent of the population living on less than one dollar a day and 92 per cent on less than two dollars a day(UNICEF Nigeria, 2007).Although the country is rich in natural resources, its economy cannot yet meet the basic needs of the people. Such disparity between the growth of the GDP and the increasing poverty is indicative of a skewed distribution of Nigeria‟s wealth (UNICEF Nigeria, 2007). Terrorism in Nigeria is going out of hand. Nigerians are generally religious inclined, hence the multiplicity of religious groups in the country, which in itself testifies to the sociopolitical and economic diversity of the Nigerian society (Naijagist, 2012). The main objective of this paper is to find out whether economic deprivation leads to terrorism in Nigeria, consequent on that, suggestions to reduce the incidence of terrorism can be made. Empirical studies investigating the root causes of terrorist incidents generally employ traditional cross sectional analysis, implicitly assuming the same economic, social and political environments for countries under consideration. This is a highly restrictive assumption and may result in heterogeneity bias. Resorting to country studies rather than cross- www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 21 country analysis may overcome such a heterogeneity bias. As such, following the introductory section, Section 2 of this paper provides some stylize facts on the economic situation of Nigeria and level of terrorist activities. A review of related literature is presented in Section 3. The methodology of the study is discussed in Section 4. An econometric analysis is presented in Section 5 while Section 6 presents the summary, conclusions and policy implications. 2. Economy of Nigeria and Terrorism: Facts And Figures 2.1 Overview of the Economy Nigeria‟s GDP (1990 constant price) rose from N9.9 trillion in 2003 to N18.6 trillion in 2007(Anyanwu, 2008). It was the 49th largest economy in the world in 2006 (Anyanwu, 2008). Table 1 shows the structural changes that have taken place since 1960.Overall; there was remarkable improvement in the economy in the nine years period, following the extensive reforms that were carried out by the government. Agricultural and oil production accounted for 65.8 per cent of the GDP in 2007(Anyanwu, 2008). Services, wholesale and retail trade, manufacturing, and building and construction accounted for 16.2, 16.2, 4.0 and 1.7 per cent, respectively (Anyanwu, 2008). The economy could therefore be regarded as agrarian and primary in nature. From the early 1970s, there was a shift from agrarian monoculture to dependence on another primary commodity – petroleum (Also, see Figure 1 for the structure of GDP) Data from the National Bureau of Statistics indicated that the average inflation rate, year on year, averaged 24.4 per cent for the decade 1980-1989; rising to 30.2 per cent over the following decade, 1990-1990; but fell sharply to 13.1 per cent during 2000-2007(Anyanwu, 2008). Indeed, inflation rate fell persistently from 2004, recording single digit in 2006 and 2007. The deceleration in inflation has been attributed to the fiscal restraint adopted by the Federal Government, tight monetary policy and good agricultural harvest, resulting from good weather conditions and the various agricultural initiatives of the government. See Table 2 for average growth rate of selected macroeconomic indicators for the period. Table 1: Structural Change in GDP, 1960-2007 1960 1970 1981 1990 2000 2003 2004 2005 2006 2007 Agriculture 64.1 47.6 33.6 37.9 42.7 41.0 41.0 41.2 41.7 42.2 Manufacturing 4.8 8.2 5.6 4.5 3.4 3.6 3.7 3.8 3.9 4.0 Crude Oil 0.3 7.1 29.2 30.6 26.0 26.5 25.7 24.2 21.8 19.4 Building & Construction NA NA 4.1 1.6 2.0 1.4 1.4 1.5 1.6 1.7 Wholesale & Retail NA NA 13.9 13.4 13.1 12.6 12.9 13.8 15.0 16.2 Services NA NA 9.8 8.2 11.2 12.3 14.7 15.2 15.7 16.2 NA = Not Available Source: CBN Annual Report, Various Issues Source: Anyanwu(2008). www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 22 Source: Anyanwu(2008). Figure 1: Structure of GDP, 1960-2007 Table 2: Growth Rate of Selected Macroeconomic Indicators Year Growth Rate of Real GDP Inflation (Y-O-Y) Growth Rate of Index of Agric Prod. Capacity Utilization Average (1980-1989) 2.2 24.8 7.2 45.0 Average (1990-1999) 3.0 30.2 4.4 35.1 Average (2000-2007) 6.2 13.0 4.9 46.4 Source: Anyanwu (2008) The Nigerian economy slowed down from 7.4% growth in 2011 to 6.6% in 2012 (AEO, n.d.). The oil sector continues to drive the economy, with average growth of about 8.0%, compared to -0.35% for the non-oil sector (AEO, n.d.). Agriculture and the oil and gas sectors continue to dominate economic activities and Nigeria. The fiscal consolidation stance of the government has helped to contain the fiscal deficit below 3.0% of gross domestic product (GDP)(AEO, n.d.). This, coupled with the tight monetary policy stance of the Central Bank of Nigeria (CBN), helped to keep inflation at around 12.0% in 2012 (AEO, n.d.). The outlook for growth remains positive (see Table 3 for estimation of macroeconomic indicators for 2012 and projections for 2014). Short- and mid-term downside risks include security challenges arising from religious conflict in some states, costs associated with flooding, slower global economic growth (particularly in the United States and China) and the sovereign debt crisis in the euro area. The economic growth has not translated into job creation or poverty alleviation. Unemployment increased from 21% in 2010 to 24% in 2011 because the sectors driving the economic growth are not high job-creating sectors (the oil and gas sector, for example, is a capital intensive “enclave” with very little employment-generating potential)(AEO, n.d.). Table 3: Macroeconomic indicators 2011- 2013 Note: Figures for 2012 are estimates; for 2013 and later are projections. Source: AEO (n.d.) 2.2. Terrorism Nigeria, like many nations in Africa, is not in short supply of groups and associations agitating for one thing or the other. Historically, three waves of such groups are discernible in Nigeria. The first of such groups existed even before colonial rule. They were the age-grades, guild associations and special interest groups performing one function after another in the overall engineering of their respective polities (Oyeniyi, 2013). Examples include Ndinche, Modewa, Aguren, Eso, Akoda and Ilari and so on. The second wave relates to groups, essentially based on kinship affinity, with presence in every part of Nigeria, including the northern region, Fernando Po, and the Gold Macroeconomic indicators Years 2011 2012 2013 2014 Real GDP growth 7.4 6.6 6.7 7.3 Real GDP per capita growth 4.9 4.1 4.2 4.8 CPI inflation 10.9 12 9.7 9.5 Budget balance % GDP -0.1 3.7 4.4 5.7 Current account % GDP 3.2 10.4 11.8 14.6 www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 23 Coast. As Coleman had noted, such groups were formed as people began moving from one area to the other in search of colonial jobs. As ethnic associations, they were based on strong loyalty and obligation to their kinship group, towns or villages. These associations were the „organizational expression of strong persistent feeling of loyalty and obligation to the kinship group, the town or village where the lineage is localized‟. Examples include the Calabar Improvement League, Owerri Divisional Union, Igbira Progressive Union, Urhobo Renascent Convention, Naze Family Meeting, Ngwa Clan Union, Ijo Rivers People‟s League, Ijo Tribe Union, etc (Oyeniyi, 2013). The third wave comprises of groups such as the O‟Odua Peoples‟ Congress(OPC), Arewa Youth Consultative Forum, Movement for the Actualization for the Sovereign State of Biafra, Anambra State Vigilante Service, Abia State Vigilante Service, Imo State Vigilante Service, Niger-Delta Volunteers Force, Ogoni Youth, Ijaw Youth, Bakassi Boys, Egbesu Boys, Onitsha Traders Organization and Mambilla Militia Group (Oyeniyi, 2013).Several factors underlie the growth and development of groups of the third wave. Economic recession of the 1980s, falling commodity prices, OPEC price increases, privatization, economic liberalization, deregulation, currency devaluation, Cold War politics, trade barriers, civil conflict, etc. are some of the notable examples. These myriads of problems reduced government‟s ability to fund welfare projects. The impact of these policies ranged from job cuts, high inflation rates and unemployment to a burgeoning informal sector(Oyeniyi, 2013). More recently, the activities of militant Islamist sects, or at best evolving terrorist groups, in northern Nigeria is now a growing source of security concern to Western capitals. Hitherto driving much of the attention is the ramping up of violent attacks on diverse civilian and military targets in Nigeria by the Jama‟atuAhlissunnahlidda‟awatiwal Jihad, or the Boko Haram, using such violent tactics like placement of improvised explosive devices (IEDs), targeted assassination, drive-by shooting and suicide bombing (see Tables 4, 5, and 6 in Appendix). Images of terrorist attacks in Nigeria Source: Gstatic.com (n.d);Kio-Lawson and MajekodunmiIn (2011) 3. Review of Related Literature Enders and Sandler (1993, 1999 and 2000) define terrorism as the premeditated use or threat to use violence by individuals or subnational groups against noncombatants to obtain political and social objectives through the intimidation of a large audience beyond that of immediate victims. Empirical studies investigating the economic impact of terrorist incidents generally report that terrorist activities affect economic growth through various channels: It may lead to an increase in military expenditures (Eckstein and Tsiddon, 2004); an increase in production and transaction costs (Frey et al. 2007); a decrease in tourism revenues (Enders et al. 1992; Yechiam et al. 2005); a decrease in savings (Fielding 2003) and a decrease in foreign direct investment (Fielding 2004). Thus it is important to reveal the determinants of terrorism in order to be able to draft counter-terrorist measures (Yildirima, Öcalb and Korucuc, 2010). Existing studies trying to investigate the determinants of terrorism agree that terror can originate more easily in economically and politically under-developed countries and / or provinces (Yildirima, Öcalb and Korucuc, 2010). Several studies have already investigated the economic roots of terrorism. Considering the supply side of terrorism, Berrebi (2003) finds that high standards of living and educational levels are positively associated with participation in Hamas and Palestinian Islamic Jihad (PIJ) terrorist activities in Israel. Krueger and Maleckova (2003) …find that the connection between poverty, education and terrorism is indirect, complicated and probably quite weak. A few studies on international terrorism …find that economic development and social welfare policies are important determinants terrorism (Burgoon, 2006; Li and Schaub, 2004; Li 2005). Several cross-country studies have shown that terrorism has no economic roots. Among these studies the most influential ones are Abadie (2004) and Krueger and Laitin (2007). Abadie (2004) shows that terrorist risk is not significantly higher in poor countries when we control for political freedom. The terrorist risk data used by Abadie www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 24 (2004) includes information on the country of occurrence but not on the target countries and on the countries of origins of terrorism. Therefore the data confounds between different types of terrorism (Derin-Gürey, 2009). The subject of this paper is domestic terrorism in Nigeria; the perpetrators, victims, locationand nation match. The violence, in other words, concerns matters within the nation. Yet, literature is not able to say categorically whether economic deprivationsexplain terrorism and empirical testing is largely absent or severely limited. Domestic terrorism is by far the more common phenomenon (Enders, et al. 2011; Feldman and Ruffle, 2008; Kis-Katos, et al. 2011; Merari, 1999; Piazza, 2011; Sanchez-Cuenca and De la Calle, 2009) in Nigeria. Ironically, it is the least empirically studied. The purpose of this paper is to contribute to rectifying this shortcoming. 4. Research Design and Strategy Research design is the structure and strategy for investigating the relationship between the variables of the study. The research design adopted combines the theoretical consideration with empirical observation. It enables us therefore to observe the effects of explanatory variables on the dependent variables 4.1Research Domain This investigation about economic deprivation leading to terrorism was conducted in Nigeria. The data spans the period 1970 to 2016 (46years). The data from this period present a considerable degree of freedom that is necessary to capture the net effect of explanatory variables on the dependent variables. 4.2 Data Sources Secondary data were used for this study. The choice of these secondary sources is based on their authenticity and reliability. The data were obtained from the publication of Central Bank of Nigeria, websites, journals and newspapers. In examining the data, the line graphs for the variables are presented in Figure 2 (see Appendix) and their descriptive statistics are shown in Table 7 in Appendix. The correlation matrix of the variables is shown Table 8 (see Appendix). 4.3 Data Processing Technique To test for stationarity of the data, a general form of Augmented Dickey Fuller (ADF) (Dickey and Fuller 1979, 1981) regression is formed below: ∆yt = β yt-1 + 𝑚 𝑘=0 αi∆yt-i +Φ + ʎt + εt…………..(1) Where ∆y is the first difference of the series, mis the lag length,t is a time trend, εt is a white noise residual. The ADF test is carried out by using the null hypothesisasH0 :α2 = α3 = 0. The lag length used is relatively small to savedegrees of freedom and large enough to avoid the existence of autocorrelation inthe residual. 4.4. Technique of Analysis The method of study in this paper is both descriptive and analytical. The descriptive tools consist of the use of table and percentages. The analytical tool used is the ordinary least square regression analysis. The OLS method is based on some assumptions (Gujarati, 2003) which make the OLS estimators to become Blue (Best linear Unbiased Estimator). Some of the short comings of the OLS method include the fact that while some of its assumptions are unrealistic (such as no autocorrelation, homoscedasticity and no multicollinearity); a single model as well cannot fully satisfy all the assumptions at a time. Also, no single test can solve all the problems of this method at a time. Moreover, the OLS method cannot be applied to purely non-linear models such as ones that are non-linear in parameter. As a result of some of these short-comings, we use the OLS method but correct the standard errors for autocorrelation by a Newey-West method. The corrected standard errors are known as HAC (Heteroscedasticity – and autocorrelation-Consistent) standard errors or simply as Newey-West standard errors. To account for serial correlation, autoregressive (AR) term was also introduced. 5. Estimation Model The model was developed to access the effect of economic variables on terrorism in Nigeria between 1970-2016. To achieve robust statistical analysis, potential economic predictors of terrorism such as inflation rate, GDP per capita, trade openness of the economy, government total expenditure, interest rate and policy index were included in the model. The specification is therefore given as: TERR = ƒ(GDPC, OPEN, INFL, GOVX, INTR, POLX) ………..(2) Equation(1) can be operationalised for the purpose of estimation into the following equations: TERR = ɲ0+ɲ1GDPC +ɲ2OPEN + ɲ3INFL +ɲ4GOVX +ɲ5INTR +ɲ6 POLX + ȹt…..(3) Where TERR is a dummy variable which takes the value of 1 if terrorist attack occurs in a year and 0 if otherwise, GDPC is per capita GDP, INFL is inflation rate, OPEN is an indicator variable for trade openness,GOVX is www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 25 government total expenditure, INTR is interest rate, POLX is index of economic policy,ɲ0 is a constant term, ɲ1…ɲ5 are coefficients that will be estimated empirically and ȹis white noise, which is common to all econometric models given that by their nature, they are non-deterministic. Most of the variables in the above equation are transformed into logarithm to facilitate easy estimation. The behavioral assumptions are stated as follows: ɲ1< 0, ɲ2< 0,ɲ3> 0,ɲ4< 0, ɲ5>0 ,ɲ6< 0 Using the number of terrorist attacks per year or the number of victims per incident as dependent variable is quite common in terrorism research as this reflects more accurately the magnitude of terrorism risk. Due to issues of data availability and reliability, the dependent variable used in this paper is the occurrence of terrorism (TERR) and it is based on the chronological data on terrorism incidents in Nigeria. The variable is constructed using binary. It takes the value of 1 if terrorist attack occurs in a year and 0 if otherwise. The information about the underlying distribution of TERR is shown in the kernel density in Figure 3(see Appendix). Concerning the independent variables, it is believed that economic development measured by GDP per capita (GDPC) reduces domestic terrorism. The openness variable measured as exports plus imports divided by GDP (X + M / GDP) is used as proxy for the level of trade between the economy and the rest of the world. The degree of openness (OPEN) is commonly assumed to be a channel of economic growth; it indirectly reduces terrorism within a country. Therefore it is expected to have the same sign of GDP per capita. Inflation (INFL) denotes the average annual change in consumer price index. On one hand, it proxies changes in purchasing power of individuals which can affect the standard of living eventually leading to terrorism. The formulation of sound economic policies ensures the overall stability of the economy. This necessitated the inclusion of policy index (POLX) as an explanatory dummy variable. It represents the different regimes and their policy stance. It takes the value of 0 for military rule and 1 for civilian rule. It is a common belief that government plays a significant role in the development of a country. The implication is that an increase in government expenditure (GOVX) will yield a positive increase in the growth of the economy by increasing the national income, especially when it is injected in development programs (Omoke, 2009) liking providing public (utilities) goods such as roads, communication, power, education and health. This is expected to discourage terrorism. Interest rate (INTR) could increase the probability of terrorist activities through the negative relationship between with investments. That is a fall in investment as a result of high interest could bring about unemployment and poverty which could encourage terrorism. 5.1 Empirical Result and Discussion From the Pairs wise correlation matrix in Table 8 in Appendix, terrorism (TERR) and interest rate showed a highly positive correlation of about 0.71. This is followed by a strongly positive movement between government total expenditure (GOVX) and policy index (POLX). Other variables exhibited moderately weak correlation in general. The results of unit root test are contained in Table 9 (see Appendix). The results show that all the variables are stationary at first difference (d(1)). Table 10 in Appendix contains the multivariate regression results of the basic model from equation 3. The results indicate that In the table, some of the presumptive signs were correct apart from the log of GDP per capita, log of openness of the economy to trade, interest rate (LOG(INTR) and policy index(POLX), which showed a positive sign instead of a negative sign. The results indicate that LOG (INFL)is statistically insignificant. With the exception of inflation variable, all the coefficients of the variables are statistically significant. However, there is serial correlation as indicated by a low Durbin-Watson statistic of1.445299. This necessitates the introduction of autoregressive (AR) term in the model and hence the estimation contained in Table 11 which will be the focus of the discussion. The R 2 0.911515 implies that 91.15 percent of total variation in terrorism is explained by the regression equation. Coincidentally, the goodness of fit of the regression remained high after adjusting for the degrees of freedom as indicated by the adjusted R2(R2=0.893298 or 8933%).The F-statistic 50.04, which is a measure of the joint significance of the explanatory variables, is found to be statistically significant at 1 percent as indicated by the corresponding probability value (0.000000). The Durbin-Watson statistic of 2.19 rules out autocorrelation. From Table 11(see Appendix), it can seen that the coefficients of LOG(INFL), LOG(GDPC), LOG(INTR), LOG(OPEN) are far from being statistically significant. The results show that government expenditure has an adverse effect on terrorism. In Nigeria, government expenditure has been on the rise owing to the huge receipts from production and sales of crude oil, and the increased demand for public (utilities) goods. With a negative and statistically significant coefficient, the result suggests that the increase in government total expenditure reduces terrorism. Unfortunately, the rise in government expenditure has not translated into meaningful growth and development, as Nigeria ranks among the poorest countries in the world (Sevitenyi, 2012). In addition, many Nigerians have continued to wallow in abject poverty, while more than 50 percent live on less than US$2 per day(Sevitenyi,2012).Couple with this, are dilapidated infrastructure (especially roads and power supply) that has led www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 26 to the collapse of many industries, including high level of unemployment and abandoned elephant projects. As such the result should be taken with caution. Policy index variable has a positive and statistically significant relationship with terrorism. From 1960, when the nation gained independence, to 2013, Nigeria experienced about twenty-five years of civilian, as opposed to military rule. The government‟s policy stance in the macro economy shows that there has been considerable fluctuation and that some bad habits e.g., deficit budgeting have been persistent. The implication of the result is that government policy stance ultimately affects the poverty level over the years. Invariably, terrorism in Nigeria is a direct consequence of the people‟s deep dissatisfaction with their government‟s macroeconomic policy. 6. Conclusion and Policy Implication The primary objective of this paper is to find out whether economic condition leads to terrorism in the country, as the contemporary Nigeria society is engulfed by terrible acts of Terrorism. This paper used annual data for the time period 1970-2016 and employed Ordinary Least Square technique. The results suggest that government expenditure hinders terrorism, whereas macroeconomic policies foster it. This study has strong policy implications, suggesting that government should minimize policy inconsistency. Proper implementation and co-ordination of macroeconomic policy objective should be rigorously pursed since implementation of such policy is usually multidimensional and hence calls for effective co-ordination among the various government department, institutions and other relevant sectors. The proportion of government expenditure that goes into capital and recurrent expenditure financing should be increased since these components exert significant negative effect on terrorism. In the same vein, government should restructure its various organs of public administration in order to engender efficiency and effectiveness in service delivery. The infrastructures should be improved upon to aid economic growth. However, like any empirical study, this paper has some weaknesses associated with data. Data are not available on the number of terrorist attacks per year or the number of victims per incident especially in the 1970s and 1980s. As such, the paper was forced to construct a binary for the dependent variable. This very likely could have affected the results of the study. The findings should be interpreted as no more than a preliminary support of the idea that economic factors may play a role in encouraging/discouraging terrorism. While this paper offers some interesting results, further analyses might be wise by considering more advanced technique of analysis and inclusion of other economic, social, political variables. References Abadie, A.(2004). "poverty, political freedom, and the roots of terrorism", National Bureau of Economic Research, Inc., NBER Working Papers: No. 10859. Abimbola, J. O., and Adesote, S. A. (2012).“Domestic terrorism and Boko Haram insurgency in Nigeria, issues and trends: A historical discourse”. Journal of Arts and Contemporary Society, vol. 4. 11-29. http://www.cenresinpub.org/pub/Sept%20Edition%202012/JACS/Page%2011-19_842_.pdf AEO (African Economic Outlook) (n.d.).Nigeria-Overview. http://www.africaneconomicoutlook.org/en/countries/west-africa/nigeria/ Anyanwu, C.M (2008). The role of monetary policy in achieving Nigeria‟s Vision 2020 agenda.A paper presented at the Retraining of Middle Level Civil Servants at NIPSS Kuru, near Jos, Plateau State, on July 27 to August 03. http://www.google.com.ng/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CCsQFjAA& url=http%3A%2F%2Fwww.ecolabconsult.com%2Fcomponent%2Fsimpledownload%2F%3Ftask%3Ddow nload%26fileid%3D%252Fdownload%252FMonetary%2BPolicy%2Band%2BVision%2B2020%2Bteamw orkv2.doc&ei=- R7gUY7MEIrQqgH_zoHABg&usg=AFQjCNEDLbDKbKkmJ4t4jAm7MDZNsNYWtw&bvm=bv.487056 08,d.aWM Berrebi, C. (2003). "Evidence about the link between education, poverty and terrorism among Palestinians", Working Papers 856, Princeton University, Department of Economics, Industrial Relations Section. Burgoon B. (2006). "On welfare and terror: Social welfare policies and political economic roots of terrorism", Journal of Conflict Resolution, 4(50), 176-203. Caruso, R., and Schneider, F. (2011).The socio-economic determinants of terrorism and political violence in Western Europe (1994-2007).European Journal of Political Economy,27(1),37–49. Chima, O. (2011). Sanusi: Why Nigerians must accept non-interest banking. http://www.thisdaylive.com/articles/sanusi-why-nigerians-must-accept-non-interest-banking/99500 Derin-Gürey, P. (2009). Does terrorism have economic roots?http://www.imtlucca.it/_kbase/seminar_paper/006519_000245_.pdf www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 27 Dickey, D.A., and Fuller, W.A. (1979). Distributions of the estimators for autoregressive time series with a Unit Root, Journal of the American Statistical Association, 74(1), 427 -431. Dickey, D.A., and Fuller, W.A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Association, 74,(1), 426-435. DoubleGist.com (2013). An overview of Nigerian economy.http://www.doublegist.com/an-overview-of-nigerian- economy/ Enders, W., and Sandler, T. (1993). “The effectiveness of antiterrorism policies: A vectorautoregression- intervention analysis”. American Political Science Review, 87(4), 829-844. Enders, W., and Sandler, T. (1999).“Transnational terrorism in the post–coldwarera”.International Studies Quarterly, 43(1),145-167. Enders, W., and Sandler. T. (2000).“Is transnational terrorism becoming more threatening? A time series investigation”.Journal of Conflict Resolution, 44(3), 307-332. Eckstein, Z., and Tsiddon, D. ( 2004). “Macroeconomic consequences of terror: Theory and the case of Israel”. Journal of monetary economics,51(5), 971–1002. Enders, W., Parise, G. F., and Sandler, T. (1992). “A time-series analysis of transnational terrorism: Trends and cycles”. Defense Economics, 3(4), 305-320. Enders, W., Sandler,T., and Gaibulloev, K. (2011). "Domestic versus transnational terrorism: Data, decomposition, and dynamics." Journal of Peace Research,48 (3), 319-37. Fielding, D. (2003).“Counting the cost of the intifada: Consumption, saving and political instability in Israel”.Public Choice, 116(3-4), 297-312. Fielding, D. (2004). “How does violent conflict affect investment location decisions? Evidence from Israel during the Intifada”.Journal of Peace Research, 41(4), 465-484. Feldman, N. E., and Ruffle, B. J. (2008). "Religious terrorism: A cross-country analysis." In Economics of National Security, 1-32. Samuel NeamanInstitute, Haifa. Forest, J.J.F. (2012). Confronting the terrorism of Boko Haram in Nigeria. JSOU Report 12-5. http://cco.dodlive.mil/files/2012/09/Boko_Haram_JSOU-Report-2012.pdf Gstatic.com (n.d). Images.http://t1.gstatic.com/images?q=tbn:ANd9GcQtWU0HhRE6hHXpKTu- hmb_PjBxdw4HYHjBA583Qfy89j3xFAgg Gujarati, D.N. (2003). Basic Econometrics. New York: McGraw Hill Book Co. Johansen, S., and Juselins, K. (1990). Maximum Likelihood Estimation and Influence on Cointegration with Application to Demand for Money, Oxford Bulletin of Economics and Statistics. Kio-Lawson, T., and MajekodunmiIn, I. (2011). The battle against “the new face of terrorism” in Nigeria: What weapons do we possess? Businessday newspaper.http://www.businessdayonline.com/NG/images/stories/9jaterror_320.jpg Kis-Katos, K., Liebert, H., and Schulze, G.G. (2011)."On the origin of domestic and international terrorism."European Journal of Political Economy, 27 (1),17-36. Krueger, A.B., and Maleckova, J. (2003). "Education, poverty, political violence and terrorism: Is there a causal connection?",Journal of Economic Perspectives, 17(4), 119-144. Krueger, A.B., and Laitin, D.D. (2007). "KtoKogo?: A cross-country study of the origins and targets of terrorism", terrorism, economic development, and political openness edited by Philip Keefer, Norman Loayza, Cambridge University Press. Li, Q. (2005). "Does democracy promote or reduce transnational terrorist incidents?" Journal of Conflict Resolution, 49(2), 278-297. Li, Q., and Schaub, D. (2004)."Economic globalization and transnational terrorism: A pooled time-series analysis", Journal of Conflict Resolution, 48(2), 230-258. Merari, A. (1999). “Terrorism as a strategy of struggle: Past and future”.Terrorism and Political Violence ,11(4), 52- 65. NiajaGist.com (2013). Hezbollah terrorists camp found in Kano state Nigeria; Rocket propelled guns & dangerous ammunition recovered. http://naijagists.com/hezbollah-terrorists-camp-found-in-kano-state-nigeria-rocket- propelled-guns-dangerous-ammunition-recovered/ Omoke P. (2009). “Government expenditure and national income: A causality test forNigeria”.European Journal of Economic and Political Studies, 2(1), 1-11. Onuoha, F. C. (2013). Jama'atuAnsarulMusilimina Fi Biladis Sudan: Nigeria‟s evolving terrorist group. Report Aljazeeral Centre for Studies. http://studies.aljazeera.net/ResourceGallery/media/Documents/2013/3/14/2013314103734423734Nigerias %20Evolving%20Terrorist%20Group.pdf http://www.doublegist.com/an-overview-of-nigerian-economy/ http://www.doublegist.com/an-overview-of-nigerian-economy/ http://studies.aljazeera.net/ResourceGallery/media/Documents/2013/3/14/2013314103734423734Nigerias%20Evolving%20Terrorist%20Group.pdf http://studies.aljazeera.net/ResourceGallery/media/Documents/2013/3/14/2013314103734423734Nigerias%20Evolving%20Terrorist%20Group.pdf www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 28 Onwubiko, E. (2013). Corruption as terrorism.Guardian.http://www.ngrguardiannews.com/index.php?option=com_content&view=article&id=1 17753:corruption-as-terrorism&Itemid=730 Owoye, O. andOnafowora, O. A. (2007). M2 targeting, Money Demand and Real GDP growth in Nigeria: Do rules apply?, Journal of Business and Public Affairs,1(2),1- 20.http://www.scientificjournals.org/journals2007/articles/1229.pdf Oyeniyi, B. O. (2013). Terrorism in Nigeria: Groups, activities, and politics. Academia.edu. http://academia.edu/327799/Terrorism_in_Nigeria_Groups_Activities_and_Politics Piazza, J. A.(2011)."Poverty, minority economic discrimination, and domestic terrorism."Journal of Peace Research ,48(3),339-53. Sanchez-Cuenca, I., and De la Calle, L. (2009). "Domestic terrorism: The hidden side of political violence." Annual Review of Political Science, 12(1), 31-49. Sevitenyi, L. N. (2012). "Government expenditure and economic growth in Nigeria: An empirical investigation (1961-2009)". The Journal of Economic Analysis, 1(1), 38-51. UNICEF Nigeria (2007).The Nigerian situation.http://www.unicef.org/nigeria/1971_2199.html Yildirima, J., Öcalb, N., and Korucuc, N. (2010).Analysing the determinants of terrorism in Turkey using geographically weighted regression.http://www.ub.edu/sea2009.com/Papers/10.pdf Appendix Table 4: Attacks Blamed on the JAMBS Date Location Target(s) Description Remarks 26 Nov. 2012 Garki, Abuja Headquarters of the Special Anti- Robbery Squad (SARS) Attack and freeing of some inmates in the detention facility of the SARS headquarters JAMB claimed that the attack was in compliance with a Quranic injunction that urged believers to fight for the oppressed andthe feeble. It promised similar attacks against detention centres across the country 19 Dec. 2012 Katsina State Francis Colump Kidnapping of Francis, a French citizen working for the French company Vergnet JAMBS claimed that the reason for kidnapping Colump is the stance of the French government and the French people on Islam, specifically citing France‟s major role in the (planned) intervention in northern Mali http://www.ngrguardiannews.com/index.php?option=com_content&view=article&id=117753:corruption-as-terrorism&Itemid=730 http://www.ngrguardiannews.com/index.php?option=com_content&view=article&id=117753:corruption-as-terrorism&Itemid=730 http://www.ngrguardiannews.com/index.php?option=com_content&view=article&id=117753:corruption-as-terrorism&Itemid=730 www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 29 19 Jan. 2013 Okene, Kogi State Convoy of Mali- bound Nigerian soldiers Ambushing of a truck conveying Mali-bound Nigerian soldiers, resulting in the death of two soldiers and injuring of five others JAMBS claimed it attacked the soldiers because of Nigeria‟s contribution of troops to Mali 17 Feb. 2013 Jamaare (Bauchi state) Seven expatriates working with a Lebanese construction company, Setraco Nig. Ltd Those abducted were four Lebanese, one Briton, a Greek citizen and an Italian JAMBS claimed responsibility for the kidnapping, citing „the transgressions and atrocities done to the religion of Allah by the European countries Source: Onuoha(2013) Table 5: Categories of Militia Groups in the Niger Delta. Private Militia Ethnic Militia Pan-Ethnic Militia Niger Delta People Volunteer Force (NDPVF) The MeinbutusArugbo Freedom Fighter Movement for the Emancipation of the Niger Delta (MEND) Adaka Marines Iduwini Volunteer Force (IVF) The Coalition for Militant Action in the Niger Delta (COMA) Martyrs Brigade Egbesu Boys of Africa The Niger Delta People Salvation Front Niger Delta Volunteers Niger Delta Militant Force Squad (NDMFS) Niger Delta Coastal Guerillas (NDCGS) Source:Forest (2012) Table 6: Major Incidents of Boko Haram Attacks since 2009 up till date . Date Casualties . July 26, 2009 Boko Haram launches mass uprising with attack on a police station in Bauchi,starting a five-day uprising that spread to Maiduguri and elsewhere. September 7, 2010 Boko Haram attacked a prison in Bauchi, killed about five guards and freed over700 inmates, including former sect members. October 11, 2010 Bombing/gun attack on a police stationin Maiduguri destroys the station andinjures three by the group December 24, 2010 The group carried out a bomb attack in Jos killing 8 people. December 28, 2010 Boko Haram claims responsibility for the Christmas Eve bombing in Jos that killed38 people December 31,201 The group attack a Mammy market at Army Mogadishu Barracks, Abuja, 11people died April 1, 2011 The group attacked a police station in Bauchi April 9, 2011 The group attacked a polling center in Maiduguri and bombed it April 20, 2011 A bomb in Maiduguri kills a policeman April 22, 2011 The group attacked a prison in Yola and freed 14 prisoners www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 30 April 24, 2011 Four bombs explode in Maiduguri, killing at least three. May 29, 2011 Bombings of an army barracks in Bauchicity and Maiduguri and led to death of 15people May 31, 2011 Gunmen assassinate Abba AnasIbnUmarGarbai, brother of the ShehuofBorno, in Maiduguri. June 1, 2011 The group killed Sheu of Borno‟s brother, Abba El-kanemi June 7, 2011 A team of gunmen launch parallel attacks with guns and bombs on a church andpolice stations in Maiduguri, killing 5 people. June 16, 2011 Bombing of police headquarters in Abuja, claimed by Boko Haram. Casualty reportsvary. June 26, 2011 Gunmen shoot and bomb a bar in Maiduguri killing about 25 people August 16, 2011 The Bombing of United Nations Office in Abuja, killing over 34 people by thegroup December 25, 2011 Bombing of St. Theresa‟s Catholic Church, Madalla, killing over 46 people January 6, 2012 The Sect attacked some southernersinMubi killing about 13 Igbo January 21, 2012 Multiple bomb blast rocked Kano city, claiming over 185 people January 29, 2012 Bombing of Kano Police Station at Naibawa Area of Yakatabo February 8, 2012 Bomb blast rocked Army Headquarters in Kaduna February 15, 2012 KotonKarife Prison, Kogi State was attacked by the sect and about 119prisoners were released and a warder was killed. February 19, 2012 Bomb blast rocked Suleja Niger State near Christ Embassy Church, leaving 5 people seriously injured February 26, 2012 Bombing of Church of Christ in Nigeria, Jos leading to the death of about 2worshippers & about 38 people sustained serious injuries. March 8 2012 An Italian, Franco Lamolinara and a Briton, Christopher McManus, who wereExpatriate Staff of StabilimVisioniConstruction Firm were abdicated in 2011 by a splinter group of Boko Haram and were later killed. March 11, 2012 Bombing of St. Finbarr‟s Catholic Church, Rayfield, Jos resulting in the killing of 11people and several others wounded. April 26 2012 Bombing of three media houses (Thisday Newspaper in Abuja killing 3 &2 securityofficers&injured 13 people; Thisday, the Sun & the Moments newspapers in Kaduna killing 3 persons & injured many others April,29,2012 Attack on Bayero University, Kano, killing 13 Christian Worshippers, a senior non-academic staff & two Professors April 30, 2012 Bomb explosion in Jalingo, claiming 11 persons and several others wounded. Sources: Abimbola and Adesote (2012). Figure 2: Line Graphs of Each of the Series 0 10 20 30 40 50 60 70 80 70 75 80 85 90 95 00 05 10 INFL 0 400 800 1200 1600 2000 2400 70 75 80 85 90 95 00 05 10 GDPC 0 4 8 12 16 20 24 28 70 75 80 85 90 95 00 05 10 INTR 0 1000000 2000000 3000000 4000000 70 75 80 85 90 95 00 05 10 GOVX 0 40 80 120 160 200 70 75 80 85 90 95 00 05 10 OPEN 0.0 0.2 0.4 0.6 0.8 1.0 70 75 80 85 90 95 00 05 10 POLX 0.0 0.2 0.4 0.6 0.8 1.0 70 75 80 85 90 95 00 05 10 TERR www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 31 Figure 3: Kernel Density of TERR Table 7: Descriptive Statistics Source: Researchers‟ computation, 2013, adapted from regression result using E-view 4.1 Table 8: Pairwise Correlation Matrix INFL GDPC GOVX OPEN INTR TERR POLX INFL 1.00 GDPC -0.24 1.00 GOVX -0.22 0.49 1.00 OPEN 0.03 -0.02 0.43 1.00 INTR 0.34 -0.09 0.19 0.35 1.00 TERR 0.20 0.15 0.35 0.36 0.71 1.00 QINS -0.21 0.47 0.64 0.17 0.29 0.49 1.00 0.0 0.4 0.8 1.2 1.6 2.0 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 TERR Kernel Density (Triangular, h = 0.4358) INFL GDPC GOVX OPEN INTR TERR POLX Mean 19.48442 540.9800 769439.9 35.64977 11.20209 0.767442 0.441860 Median 12.90000 368.5400 66584.00 6.140000 12.24000 1.000000 0.000000 Maximum 72.81000 2300.000 3848545. 180.7300 26.00000 1.000000 1.000000 Minimum 1.650000 166.6200 5503.000 0.020000 3.500000 0.000000 0.000000 Std. Dev. 17.32761 493.6716 1214355. 54.47743 5.312399 0.427463 0.502486 Skewness 1.507690 2.689627 1.593320 1.474146 0.368211 -1.266108 0.234146 Kurtosis 4.333762 9.527664 4.176456 3.832437 2.770214 2.603030 1.054825 Jarque-Bera 19.47800 128.1880 20.67354 16.81547 1.066253 11.77072 7.172052 Probability 0.000059 0.000000 0.000032 0.000223 0.586767 0.002780 0.027708 Sum 837.8300 23262.14 33085916 1532.940 481.6900 33.00000 19.00000 Sum Sq. Dev. 12610.33 10235891 6.19E+13 124647.2 1185.307 7.674419 10.60465 Observations 43 43 43 43 43 43 43 Augmented Dickey-Fuller variables levels 1st difference 2nd difference Lag length Order of integration INFL -6.330409* 9 I(1) GDPC -3.542123** 9 I(1) GOVX 6.061500 9 I(1) OPEN -7.139956* 9 I(1) www.cribfb.com/journal/index.php/aesr American Economic & Social Review Vol. 2, No. 1; 2018 32 Source: Researchers‟ computation, 2013, adapted from regression result using E-view 4. Table 9: Unit Root Test Source: Authors‟ Computation from Computer Output. Note: * significant at 1%; ** significant at 5%; and ***significant at 10% Table 10: Regression Estimates I Variable Coefficient Std. Error t-Statistic Prob. C -0.616363 0.598483 -1.029875 0.3099 LOG(INFL) -0.014902 0.041387 -0.360071 0.7209 LOG(GDPC) 0.172628 0.073634 2.344397 0.0247 LOG(INTR) 0.657786 0.101564 6.476560 0.0000 LOG(GOVX) -0.113963 0.043641 -2.611361 0.0131 LOG(OPEN) 0.071738 0.034837 2.059220 0.0468 POLX 0.227890 0.094308 2.416438 0.0209 R-squared 0.806491 Mean dependent var 0.767442 Adjusted R-squared 0.774240 S.D. dependent var 0.427463 S.E. of regression 0.203106 Akaike info criterion -0.202282 Sum squared resid 1.485067 Schwarz criterion 0.084425 Log likelihood 11.34906 F-statistic 25.00635 Durbin-Watson stat 1.445299 Prob(F-statistic) 0.000000 Source: Computational results using Eviews 4.1 Table 11: Regression Estimates II Variable Coefficient Std. Error t-Statistic Prob. C 6.333720 4.832757 1.310581 0.1988 LOG(INFL) -0.004015 0.026225 -0.153081 0.8792 LOG(GDPC) 0.045682 0.061769 0.739568 0.4646 LOG(INTR) 0.008179 0.094853 0.086224 0.9318 LOG(GOVX) -0.303033 0.148141 -2.045578 0.0486 LOG(OPEN) 0.012170 0.028209 0.431424 0.6689 POLX 0.275613 0.072915 3.779929 0.0006 AR(1) 0.976248 0.028413 34.35878 0.0000 R-squared 0.911515 Mean dependent var 0.785714 Adjusted R-squared 0.893298 S.D. dependent var 0.415300 S.E. of regression 0.135659 Akaike info criterion -0.987703 Sum squared resid 0.625714 Schwarz criterion -0.656718 Log likelihood 28.74175 F-statistic 50.03528 Durbin-Watson stat 2.188453 Prob(F-statistic) 0.000000 Inverted AR Roots .98 Source: Researchers‟ computation, 2013, adapted from regression result using E-view 4.1 Copyrights Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/) INTR -10.31167* 9 I(1) TERR -6.403124* 9 I(1) POLX -7.695598* 9 I(1)