




































Economy 
ISSN: 2313-8181 
Vol. 1, No. 2, 68-78, 2014 
www.asianonlinejournals.com/index.php/Economy 

 

 

* Corresponding Author 

 

 

68 

 

Economic Deprivation and Terrorism: Further Empirical 

Evidence from Nigeria 
 

Emmanuel Okokondem Okon
1*

 
 
1
Department of Economics, Kogi State University, Anyigba, Kogi State, Nigeria 

 

Abstract 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
This work is licensed under a Creative Commons Attribution 3.0 License 

Asian Online Journal Publishing Group 

 

 

Contents 
1. Introduction ........................................................................................................................................................... 69 

2. Nigeria: Economy and Terrorism Overview ....................................................................................................... 69 

3. Literature Review.................................................................................................................................................. 70 

4. Specification of Model for Analysis ..................................................................................................................... 71 

5. Empirical Outcome and Discussion ..................................................................................................................... 72 

6. Summary of Findings, Conclusion and Policy Implication ................................................................................ 73 

Appendix .................................................................................................................................................................... 75 

References .................................................................................................................................................................. 77 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

This paper examined the question whether economic deprivation leads to terrorism in Nigeria. The study 

covered the period 1970 to 2012. It employed the econometric methodology of vector error correction 

model and testing the results using stationarity test and co-integration.  The Ordinary Least Square (OLS) 

estimation method was used as an essential component of the estimation techniques. The results show that 

government expenditure has a significant inverse relationship with terrorism while the degree of openness 

of the economy, GDP per capita,  interest rate and macroeconomic policy index have positive 

relationships with the occurrence of terrorism  both in the long run and short run. Some key policy 

implication of these results are that the rapid economic growth experienced by Nigeria should be made to 

show improvements in social welfare and macroeconomic policy inconsistencies should be minimized. 

Similarly, policy reversalsshould be properly checked for both short and long run effects on the economy. 

 
     Keywords: Terrorism economic, Deprivation, Nigeria. 

http://creativecommons.org/licenses/by/3.0/


Economy, 2014, 1(2): 68-78 

 

 

 

 

69 

 

1. Introduction 
Nigeria is a country abundantly blessed with natural resources. For the past fifty years precisely, the country‘s oil 

subsector has tremendously grown. Since commercial production in 1958, oil production and exports have both 

increased tremendously. For example, crude oil production increased from 395.7 million barrels in 1970 to 776.01 

million barrels in 1998 (Akinlo, 2012). Actually, 919.3 million barrels increase was experienced in the year 

2006.According to Akinlo (2012) the figure declined in 2009 to 777.5 million barrels. Similarly, exports of crude oil 

rose from 139.5 million barrels in 1966 to 807.7 million barrels in 1979 (Akinlo, 2012). There was a quantity drop of 

390.5 million barrels of crude oil exports in 1987 thereafter an increase of 675.3 million barrels in 1998 (Akinlo, 

2012). Beyond 2000, this trend persistent. Likewise, oil revenue rose from N166.6 million in the year 1970 to N 

1,591,675.00 million and N6,530,430.00 million in the year 2000 and 2008 respectively (Akinlo, 2012). 

Expenditure and investment increase because of the net wealth provided by huge oil revenues; macroeconomic 

management complication and oil dependent of the economy as a result of the huge revenue. The economy still 

grapples with many challenges (for example, high and rising unemployment rate, manufacturing production decline, 

high and rising level of poverty; insufficient and poor infrastructural development) despite the huge oil rents.  The 

dismal performance of the Nigerian economy in the face of increasing domestic terrorism sparks up the question 

whether economic deprivation leads to terrorism? 

Zumve et al. (2013) summarized in their discourse that economic deprivation, marginalization, frustration, and 

desperation experienced by the greater population of Nigerians is the underlying cause of terrorism in contemporary 

Nigeria. This views lack strong empirical basis even as it plausibly seems. The objective of this paper is to 

empirically investigate whether economic deprivation over the period 1970-2012 leads to terrorism in Nigeria. 

Specifically, the paper examine whether or not cointegrating relationship exists between economic variables such as  

GDP per capita, inflation rate,  trade openness, government total expenditure, interest rate, macroeconomic policy 

index and terrorism. 

The rest sections of this article are structured in this manner. First, Section 2 of this paper provides the trend and 

dynamics of terrorism and performance of Nigerian economy. A review of related literature is done in Section 3. 

Specification of model, data source and technique of analysis are addressed in Section 4. Section 5shows the 

outcome of regression estimation. The concluding section contains the summary of major findings and offers some 

policy prescriptions with a view to mitigating terrorism in Nigeria. 

 

2. Nigeria: Economy and Terrorism Overview 
2.1. Trends and Dynamics of Terrorism in Nigeria 

The historical trends and dynamics of terrorism are older than Nigeria itself. Terrorism predates Nigerian history 

as one nation. During the pre-colonial Nigeria Oyeniyi (2007) noted that the secret societies were involved in acts of 

terrorism like killing, looting, armed robbery, assassination, kidnapping, perversion of justice and so on. Most of 

them were also used by the local chiefs as agents of terror to eliminate opponents and threaten oppositions. In so 

doing, acts amounting to harassment, intimidation, violence, fear and general insecurity, characteristics of terrorism 

were established by the local chiefs. Consequently, it was easy for the colonial masters to collaborate with the local 

chiefs in further committing acts of terrorism on the people (Chinwokwu, 2012). 

In the post colonial era, Abubakar (1997) contends that during the years of the first republic (1960-1966) the 

political system was typified by communal conflicts – such as the Tiv riots, electoral violence epitomized by Western 

regional crisis of 1965, and the Agbakoya uprising. The tensions and terrorist activities that were carried out against 

the Igbo ethnic group during this period eventually led to the fall of the first republic and the rise of the 1966 

pogrom. The 1966 pogrom saw the real Northern hatred, wickedness and unequivocal display of total acrimonious 

acts of terrorism perpetrated against the Igbo and Southern minorities. To completely wipe out the Igbo race was the 

main aim. The actions of the federal government or northern political or military leaders was seen as the worst 

display of terrorist acts against a nation aside from the Hitler‘s Jewish six million gas chamber genocide 

(Chinwokwu, 2012). The display of terrorism has been further demonstrated in all the phases of government in 

Nigeria.  

During the military era, the dynamic nature of terrorism in Nigeria assumed a different political dimension, not 

only in the weaponry, strategy, intensity and scope but also in the choice of targets and mass destructions. From 

1986, when the use of a parcel bomb was introduced and used to kill Dele Giwa during the regime of General 

Ibrahim Babangida, till present day the dynamics, dimensions, intensity and pervasiveness of terrorism assumed an 

unprecedented proportion in the history of Nigeria (Chinwokwu, 2012). Both state and non-state terrorism became 

rampant and alarming with massive human casualties.  

The return of civil rule in 1999 opened up new opportunities for Nigerians to breathe air of freedom from the old 

order of tyranny and terrorism. It is important to state that instead terrorism assumed an alarming proportion with the 

introduction of kidnapping, hostage taking, assassinations, armed robbery, murder and soon (Chukwurah, 2007; 

Okoronkwo, 2007; Sokumbi, 2007). It was so severe and brutish in the Niger Delta and the South – East that oil 

facilities were destroyed, expatriates abandoned their projects and deserted the zones. Many Nigerians called for 

state of emergency to be declared in the zones. Even the state governors of the five South-East approached the 

Presidency, claiming that they lack the resources to contain criminal activities in their states (Adeleye, 2010; 

Chidozie, 2010; Fabiyi, 2010). See Table 1 (in Appendix) for cases of domestic terrorism arising from bomb 

explosions in Nigeria from 1986 to 2012. Also, see images of terrorism in Nigeria below.   

 



Economy, 2014, 1(2): 68-78 

 

 

 

 

70 

 

 
Christmas Day bombings, including one Picture of a Boko Haram fighterat St. Theresa Catholic 

Church in captured by the military authoritiesMadalla, Nigeria.65 people were reported killed 

Source: Goodspeed (2006),CKN Nigeria (2013) 

2.2. Nigeria Economy at a Glance  
Categorically, Nigeria is as an emerging market. It is rapidly reaching middle income status, given its abundant 

supply of natural and human resources, well-developed legal, financial, communications and transport sectors, as 

well as stock exchange. The Nigerian Stock Exchange happens to be the second largest in Africa. As at 2007, in 

terms of GDP(PPP), Nigeria was ranked 37
th
 .Nigeria is the United States' largest trading partner in sub-Saharan 

Africa and supplies a fifth of its oil (11% of oil imports) (Wikipedia, 2009).  

Currently, for U.S. goods, Nigeria is reported to be the 50
th

-largest export market and concerning goods to the 

U.S., Nigeria is said to be the 14
th

-largest exporter. Than any country worldwide, it has the seventh-largest trade 

surplus with the U.S. The United States is the country's largest foreign investor (State.gov., n.d).  The bulk of 

economic activity is centered in four main cities: Lagos, Kaduna, Port Harcourt, and Abuja. Beyond these three 

economic centers, development is marginal (Wikipedia, 2009). 

Many years of military rule, corruption and mismanagement had hindered economic development, previously but 

the restoration of democracy accompanied with economic reforms have successfully put the country  back on the 

path of achieving its full economic potential as one of Africa‘s main economies. As the Economist Intelligence Unit 

and the World Bank reported, the country‘s GDP(PPP) has almost doubled from $170.7 billion in the year 2005 to 

$292.6 billion in the year 2007 (Economist.com., N.d). The GDP per head has jumped from $692 per person in 2006 

to $1,754 per person in 2007 (Economist.com., N.d). 

Nigeria accumulated a huge foreign debt to finance core infrastructural investments during the 1970s oil boom. 

The country struggled to keep up with its loan payments as a result of the fall of oil prices during the 1980 period oil 

glut. It finally defaulted on its principal debt repayments; thus limiting repayment to the interest portion of the loans. 

The size of the debt increased because of arrears and penalty interest accumulated on the unpaid principal. 

Nevertheless, in October 2005, following negotiations the Nigeria authorities and its Paris Club creditors reached an 

agreement such that Nigeria‘s debt was repurchased at a discount of approximately 60%. Part of the country‘s oil 

profits was used to pay the residual 40%, freeing up at least $1.15 billion annually for poverty reduction programs 

(Wikipedia, 2009). In April 2006, history was made when Nigeria become the first African Country to completely 

pay off its debt (estimated $30 billion) owed to the Paris Club. Some macroeconomic indicators in Nigeria from 

2006 to 2010 are show in Table 2. 

 
Table-2. Macroeconomic Indicators (2006 – 2010) 

Year External 

Reserves 

($million) 

Contributions 

to Real GDP 

(%) 

Oil Production Level at 

Constant Prices (N 

billion) 

Oil Sector 

Growth (%) 

Inflation 

Rate (%) 

GDP 

Growth (%) 

2006 42,298.11 21.85 130,193.52 -4.51 8.50 6.03 

2007 51,333.15 19.60 124,285.12 -4.54 6.60 6.45 

2008 53,000.36 17.35 116,594.57 -6.19 15.10 5.98 

2009 42,470.00 16.29 117,121.37 0.45 13.90 6.96 

2010 32,339.25 15.85 122,957.88 4.98 12.70 7.87 

     Source: (National Bureau of Statistics, 2010) 

 

3. Literature Review 
According to Lacquer (1987), terrorism is not peculiar to the present era. The terms ‗terrorism‘ and ‗terrorists‘ 

can be traced to 18
th
 century. Most terrorist events were simply localized, before the 1960 period. It was strictly 

reduced to certain regions or limited to specific geographical area. However, the rapid advances in transportation and 

communication technology associated with globalization have brought about a shift in the nature and scale of the 

terrorist threat (Zumve et al., 2013). 

Gurr (1970) developed the term ‗‗relative deprivation,‘‘ which links economic disparity with the propensity of 

individuals to resort to violent political action. Gurr uses relative deprivation to ‗‗denote the tension that develops 

from a discrepancy between the ‗ought‘ and the ‗is‘ of collective value satisfaction, that disposes men to 

violence‘‘(Piazza, 2006). When a person‘s expectations of economic or political commodities exceed the actual or 

real distribution of those commodities, political unrest (violence) is more likely to happen.  

Gurr‘s work provides a theoretical base for a large number of scholars studying political violence, including 

Huntington (1968), who borrows from the relative deprivation framework to explain the increase in political violence 

witnessed in the United States and in Southeast Asia during the 1960s and 1970s. To briefly note the studies that 

make use of the deprivation model: Muller and Seligson (1990) study of eighty-five developing states between 1973 

http://en.wikipedia.org/wiki/Emerging_markets
http://en.wikipedia.org/wiki/Nigerian_Stock_Exchange
http://en.wikipedia.org/wiki/GDP


Economy, 2014, 1(2): 68-78 

 

 

 

 

71 

 

- 1977 found that income inequality, rather than misdistribution of land, is a (slightly) significant predictor of 

political violence, even when controlling for regime repression and level of national economic development. 

Through an analysis of fifty-one developing countries between 1968 and 1972, London and Robinson (1989) found a 

significant relationship between income inequality and political violence that was mainly mediated by the degree to 

which distribution of wealth in domestic economies had been altered due to penetration by multinational 

corporations. 

In a study on a related topic—civil war and insurgency— Fearon and Laitin (2003) also found socioeconomic 

factors to be significant. From 1945 to 1999, during the study of 127 civil wars, Fearon and Laitin showed that 

poverty comfortably positively predicts violent domestic clashes, in line with general unstable political system, 

rugged terrain, and large size population levels, due to ‗‗bureaucratically and financially poor (weak) states‘‘ and 

encourages insurgents in recruitment. Nevertheless, ethnic or religious diversity within countries was not found to be 

a significant predictor of civil war, as oppose to the assumptions of most scholars. 

The general picture that emerges from the above studies is that economic deprivation leading to terrorism is not 

conclusive. Nonetheless, the literature showing link between economic deprivation and terrorism is not much 

discussed with respect to Nigeria, therefore this present paper is devoted for that purpose. It specifically seeks to 

determine through multiple regression analysis the degree to which economic variables predict terrorism. 

 

4. Specification of Model for Analysis 
It is assumed that the occurrence of domestic terrorism in Nigeria depends on economic conditions such as GDP 

per capita, inflation rate, trade openness, government total expenditure, interest rate and macroeconomic policy index 

and takes the following form: 

TERR = Ƹ0+Ƹ1logGDPC  + Ƹ2 logOPEN +  Ƹ3logINFL + Ƹ4logGOVX + Ƹ5logINTR + Ƹ6POLX +  Ὡt      ……....(1) 

Where: 

 TERR = dummy variable which takes the value of 1 if terrorist attack occurs in a year and 0 if  

otherwise 

 GDPC =  GrossDomestic Product per capita 

 INFL = rate of inflation 

 OPEN= an indicator variable for trade openness 

GOVX = expenditure of government 

 INTR = rateof interest on loan 

POLX= index of economic policy 

Ὡt    = White noise (signifying error term) 

A priori expectation:Ƹ1< 0, Ƹ2< 0, Ƹ3> 0, Ƹ4< 0, Ƹ5>0 , Ƹ6< 0 

 

4.1. Data Source and Technique of Analysis 
To investigate the relationship specified in equation 1,data were sourced from secondary sources much include 

Central Bank publication, relevant textbooks and journals. The study covered the period between 1970 and 2012.The 

choice of the period is due to availability of data. The net effect of the explanatory variables on the dependent 

variable can be captured. This is because the data from this period gives a reasonable degree of freedom that is 

required. The ordinary least square regression analysis was the main analytical tool employed. The method of OLS is 

extensively used in regression analysis primarily because it is initiatively appealing and mathematically much 

simpler than any other econometric technique (Gujarati, 2004). The empirical investigation consists of three main 

steps. 

The first step in this analysis involves testing the order of integration of each variable. Among the many 

procedures for the test of order of integration developed by researchers, Augmented Dickey-Fuller (ADF) test 

credited to Dickey and Fuller (1979; 1981), and Phillip-Perron (PP) credited to Phillips (1987) and Phillips and 

Perron (1988) are the most popular ones. Augmented Dickey-Fuller test is based on rejecting a null hypothesis of 

unit root (i.e., series are non-stationary) for the alternative hypotheses of stationarity. The tests are carried with and 

without a deterministic trend (t) for each of the variables (series). The general form of Augmented Dickey-

Fuller(ADF) test is given as: 

∆
yt
 = α

0
 + α

1
y

t-1
 + ∑     α∆yt+ e

t                   
..……………...(2) 

∆yt= α0 + α1ytμ-1 + ∑
 

   
α1∆yt+ δt+ et            ………………..(3) 

Where yis a time series, t is a linear time trend, Δ is the first difference operator, α0 is a constant, n is the 

optimumnumber of lags in the dependent variable and e is the random error term. 

Thedifference between equation (2) and(3) is that the first equation includes just drift. However, the second 

equation includes both drift and linear timetrend pp. 

∆yt= α0 + α1yμ-1 +      et                     ………………..(4) 

Step two of this analysis is testing of the presence or otherwise of cointegration between the variables of the 

same order of integration. This is done by forming a cointegration equation. The notion behind cointegration is that if 

in the long-run, two or more variables move closely together, even though the variables themselves are trended, the 

difference between them is constant. It is wise to see these variables as defining a long-run equilibrium relationship, 

invariably, the difference between them is stationary (Hall and Henry, 1989). A lack of cointegrationimplies that 

such variables do not exhibit long-run relationship (Dickey et al., 1991).The maximum-likelihood test procedure 

established by Juselins and Johansen (1990) and Johansen (1991) is employed. Specifically, if Yt is a vector of n 

stochastic variables, then there exists a p-lag vector auto regression with Gaussian errors of the following form: 

Johansen‘s methodology takes its starting point in the Vector Autoregression (VAR) of order P given by: 

yt= μ + ∆1yt-1 + …+∆p yt-p + εt………………..(5) 



Economy, 2014, 1(2): 68-78 

 

 

 

 

72 

 

Where ytis an n x1 vector of variables (which  are integrated of common denoted (1)order) and the n x1 vector of 

innovations is  εt. 

In another form, the VAR is expressed as follows: 

∆yt= μ + ɳyt-1 +∑
   
   r1∆yt-1 + εt………………..(6) 

whereΠ = ∑     
 
    and ∑   

 
      

To determine the number of co-integration vectors, Johansen (1991) and Juselins and Johansen (1990) suggested 

two statistic test, the first one is the trace test (λ trace). It function is to tests the null hypothesis in order to ensure that 

the number of distinct cointegrating vector is less than or equal to q as opposed to a general unrestricted alternatives 

q = r. The test calculated is as follows: 

λtrace ( r) =  ∑    (     )
 
      

WhereT is refers to the number of observations (usable), and the estimated eigenvalue( from the matrix) are the 

λ1,s . 

The dynamics of output relation is then specified in an error correction model (ECM), incorporating the one 

period lagged residual from the static regression. The error correction model is designed to capture the short-run 

deviations that might have occurred in estimating the long-run co-integrating equation (Engle and Granger, 1987). 

Thus, equation 1 is re-specified as follows to include an error correction term (ECM) 

TERR = Ƹ0+ Ƹ1logGDPC  + Ƹ2 logOPEN +  Ƹ3logINFL + Ƹ4logGOVX + Ƹ5logINTR + Ƹ6POLX +  ECMt-1 +Ὡt                    

………………………………(7) 

 

5. Empirical Outcome and Discussion 
From the Pairswise correlation matrix in Table 3 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 

expenditure (GOVX) and quality of institution (QINS). Other variables exhibited moderately weak correlation in 

general.  

Table 4 in Appendix contains the multivariate regression results of the basic model from equation 1. The results 

indicate that LOG(INFL)is statistically insignificant. This necessitates the dropping of inflation variable from the 

model and hence the parsimonious estimation contained in Table 5 which will be the focus of the discussion. In 

Table 5, 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 quality of institution (QINS), which showed a positive sign instead 

of a negative sign. The improved results as contained in Table 5 show that individually, all the coefficients of the 

variables are statistically significant.  

Precisely, the coefficient of LOG(GDPC) is found to be statistically significant at 5 percent level as indicated by 

its probability value 0.0244butwrongly signed (positive). This, therefore, implies that 1 percent increase in GDP per 

capita increases terrorism by 24.4 percent. This is not in line with the apriori expectation. The implication is that 

economic development in Nigeria spurs terrorism. The result also indicates that as GDP per capita increases 

terrorism rises, as implied in the positively weak correlation between GDP and TERR in Table 8 (see Appendix).A 

plausible explanation for this result is that Nigeria‘s economy is struggling to leverage the country‘s vast wealth in 

fossil fuels in order to displace the crushing poverty that affects about 57% of its population (DoubleGist.com., 

2013). ‗Resource curse‘ is a phrase which economist used to refer to the coexistence of vast wealth in natural 

resources and extreme personal poverty in developing nations such as Nigeria. Although ‗resource curse‘ is more 

widely understood to mean an abundance of natural resource this fuels official corruption resulting in a violent 

competition for the resource by the citizens of the nation, hence 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 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). In addition to this, are 

deteriorated infrastructure (roads and power supply precisely) which have resulted to the close down of many 

industries, accompanied by high level of unemployment and abandoned gigantic projects. As such the result should 

be taken with caution. 

There is a strong believe that trade openness stimulates economic growth through its effect on global economies 

integration and better markets generation. The positive relationship of LOG(OPEN) suggests that as Nigeria‘s 

economy opens to the world, terrorism is attracted. Openness attracts inflow of foreign direct investment. Some 

foreign nationals having investments in Nigeria have been arrested in connection with terrorist activities. Recently, a 

terror cell and Lebanon-based Hezbollah armory was uncovered in Bompai, Kano state, according to a report by 

NiajaGist.com. (2013). Also, illegal aliens from Chad and other neighboring countries armed with weapons have 

been arrested. They come into Nigeria through the porous borders. 

Regarding savings and investment behavior of households as well as enterprises, real interest rate is a vital 

determinant and also a factor in terms of cyclical development as well as long-term economic growth. The positive 

relationship of interest rate coefficient suggests that it encourages terrorism. This is because of high lending rate of 

loans by commercial banks, which small and medium scale industries could not afford because of their limited 

capital and production base. Thus, the need for the introduction of non-interest banking in Nigeria as stated by the 

Central Bank of Nigeria (CBN) (Chima, 2011). But this plan has generated a lot of controversies as some religious 

groups have argued that the move violates the country's secular constitution and that it may cause division in the 

country. 

Finally, the results in Table 5 indicate statistically significant coefficient on   policy index variable(POLX) 

suggesting a positive relationship with terrorism. From 1960, when the nation gained independence, to 2013, Nigeria 



Economy, 2014, 1(2): 68-78 

 

 

 

 

73 

 

experienced about twenty-five years of civilian, as opposed to military rule. The government‘s policy stance in the 

macro-economy has been considerable fluctuating and 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. 

The 0.805794R
2
 value implies that 80.58 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 R
2
 (0.779550 or 77.96%). The 30.70F-statistic value 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).As a result of the observed 1.41 D.W statistic is low to rule out 

autocorrelation, decision was taken to analyze it further by conducting LM test for autocorrelation up to the first-

order. As depicted in Table 6, the statistic labeled ―Obs*R-squared‖ is the LM test statistic for the null hypothesis of 

no serial correlation. The 0.040551 probability value indicates the presence of some level of serial correlation in the 

residuals. 

Furthermore, there is evidence of autoregressive conditional heteroskedasticity (ARCH) in the residuals as 

shown by the probability value of 0.072136 on the statistic labeled ―Obs*R-squared‖ in Table 7.White‘s 

heteroskedasticity test was also carried out as shown in Table 8.White‘s test statistic is asymptotically distributed as a 

χ
2
with degrees of freedom equal to the number of slope coefficients, excluding the constant, in the test regression 

(five in this case). Thus the critical χ
2
value is calculated as 11.0704976935. Since ―Obs*R-squared‖ value 

of28.62386isgreater than the 5% criticalχ
2
 value of 11.0704976935, the null hypothesis of no heteroskedasticity can 

be rejected. The presence of serial correlation, heteroskedasticity and ARCH indicate the need for further analysis. 

Thus, the unit root test was carried out as shown in Table 9 (see Appendix). The results reported indicate that all the 

variables are non-stationary in their respective levels. By taking first differences, all the variables become stationary 

as the ADF statistic for each time series shows at the 1% significance level with the exception of GDP per capita at 5 

% significance level. In other words, the time series data of all the variables are integrated of order one I(1).  

Given the unit root properties of the variables, the study proceeded to establish whether or not there is a long-run 

cointegrating relationship among the variables by using the Johansen cointegrating test. The trace and maximum 

eigenvalue figures reported are displayed in Table 10 of the Apppendix. The trace test indicates 6cointegrating 

equation(s) at the 5% level while the maximum eigenvalue statistic indicates no cointegration at both 5% and 1% 

levels. According to Juselins and Johansen (1990) as cited in Owoye and Onafowora (2007), the use of trace 

statistics is recommended when the two statistics exhibit conflict. This is because the trace statistics takes into 

consideration all eigenvalues of the smallest figure; it shows addition power than the max eigenvalue statistic. Since 

the trace statistic was accepted that there is cointegration then there is need to further subject the variables to error 

correction test. The error correction term (ECM) is stationary at level and statistically significant at 1% level (see 

Table 10 in Appendix).In addition, the ECM shows a long run relationship between regressors and regressand in the 

model. The absolute value of the coefficient of the error-correction term indicates that about 33 per cent of the 

disequilibrium in the terrorism (TERR)is offset by short-run adjustment in each year. The error correction term is 

correctly signed (negative) (see Table 11 in Appendix).Furthermore; the goodness-of-fit of the estimated model 

indicates that the model is reasonably accurate in prediction.  

 

5.1. Robustness Test of the Estimation 
To determine whether the results of the primary variables are robust to the inclusion of other control variables, 

unemployment was introduced. The significance of parameter estimates of the primary variables tend not to fluctuate 

over the sample period but the coefficient of the unemployment variable was statistically insignificant (see Table 12 

in Appendix) at conventional test levels. In order words, the magnitude of the coefficients did not change 

considerably compared with the results in Table I1 and the signs remained the same even after the unemployment 

variable was introduced in the regression.  

The test of equation stability and of estimated values was done using the most used tests of stability: CUSUM 

Tests; CUSUM of Squares Tests; Recursive Coefficients. The cumulative sum of the equation errors in regression is 

what CUSUM test is premised on. Figure 1 shows the cumulative sum of errors together with critical lines of 5%.The 

equation parameters are not considered stable if the whole sum of recursive errors gets outside the two critical lines. 

From the graph CUSUM stays within the 5 per cent critical line, indicating parameter constancy throughout the 

sample period. 

CUSUM of Squares test is similarly calculated and interpreted as CUSUM test, with the difference that instead 

of recursive errors, the recursive doubled errors is used. For the analyzed equation, according to this test, the values 

of the equation are stable for the study period as shown by the CUSUM test staying within the 5% critical line 

(Figure 2, see Appendix).Recursive Coefficients shows the equations figures computed regressively. The figures are 

said to be stable if, together with the improvement of the pattern, their figure is not changed. For calculating 

recursive coefficients we start with the first observation k + 1 where k represents the number of coefficient of the 

regression equation. We proceed similarly until we estimate coefficients for the whole pattern of available data. Then 

recursive coefficients are graphically represented. For the analyzed equation, recursive coefficients are represented in 

Figure 3 (see Appendix).After having analyzed the multiple linear regression model the general conclusion is that it 

is valid. 

 

6. Summary of Findings, Conclusion and Policy Implication  
The paper examined whether economic deprivation leads to terrorism in Nigeria. The study spanned a period of 

1970 through 2012. It employed the econometric methodology of vector error correction model and testing the 

results using stationarity test and co-integration. The Ordinary Least Square (OLS) estimation method was used as an 



Economy, 2014, 1(2): 68-78 

 

 

 

 

74 

 

essential component of the estimation techniques. The findings reveal that government expenditure has a significant 

inverse relationship with terrorism, whereas per capita GDP, the degree of openness of the economy to international 

trade, interest rate and macroeconomic policy index have positive relationships with terrorism both in the long run 

and short run. 

 
Table-1. Cases of Domestic Terrorism arising from Bomb Explosions in Nigeria 1986-2012 

 

 

 

 

 

 

 

 

 

 

                         

Source: Chinwokwu (2012), Ajayi (2012) 

 

Budget surplus in relation to GDP, rate of inflation and trade openness are some of the major indicators of 

macroeconomic policy. The three policy variables are used to construct policy index instead in this paper dummy 

was constructed to reflect government‘s policy stance in the macro economy during the 32 years of rule in the 

country. It takes the value 1 for civilian and 0 for military. Also, due to data limitation on the number of terrorist 

attacks per year or the number of victims per incident through the study period a binary was developed for the 

dependent variable. It was done from a chronology of terrorist attacks over the years.  All this factors could have 

affected the results of the study. As such further studies based on alternative approaches are necessary to shed more 

light on the connection between economic deprivation and terrorism in Nigeria. Nonetheless, the results in this paper 

suggest that economic deprivation encourages the occurrence of terrorism confirming the need for policy efforts in 

mitigating the associated risk. Therefore, the paper suggests that border trade should be monitored. This calls for 

serious and sustained surveillance and supervisory efforts of law enforcement agencies to curb activities of illegal 

aliens and unpatriotic Nigerians who assist them.Since the analysis showed that government total expenditure has 

negative effect on terrorism, more favorable attention in the allocation of government expenditures should be done to 

ensure that capital expenditure and recurrent expenditure are properly managed in a manner that it will raise the 

nation‘s production capacity and accelerate economic growth. As a prerequisite for generating economic growth, 

government must embark on growth-enhancing reforms and be sensitive to the behavior of interest rates in the 

country.  Thus the monetary authority must formulate and implement financial policies that enhance investment-

friendly rate of interest and take into consideration those other factors which negatively affect investment in the 

country. This will definitely enhance policy formulation for development of private sector as a catalyst for general 

economic growth of the country.The rapid economic growth experienced by Nigeria should be made to show 

Date Place State Terrorist Group Casualty 

19/10/1986 Parcel bomb, Lagos Lagos Nil 1 

31/5/1995 Venue of launching of 

family support Ilorin 

Kwara Nil No record 

18/01/96 Durbar Hotel Kaduna Kaduna Nil 1 

19/1/1996 Aminu Kano Airport, Kano Kano Nil No record 

11/4/1996 Ikeja cantonment Lagos Nil No record 

25/4/1996 Airforce base Lagos Nil No record 

14/11/1996 MMIA Lagos Nil 2 

16/12/1996 Col. Marwa convey Lagos Nil No record 

18/12/96 Task force(Lagos state) 

onenvironment (bus) operating 
inLagos 

Lagos Nil No record 

7/1/1997 Military bus at 

Ojuelegba, Lagos 

Lagos Nil No record 

12/2/1997 Military vehicle Fakka 

D608 on Ikorodurd, Lagos 

Lagos Nil No record 

7/5/1997 Nigerian army 25 seater 

bus at Yaba, Lagos 

Lagos Nil No record 

12/5/1997 Eleiyele, Ibadan Oyo Nil No record 

16/5/1997 Onitsha Anambra Nil 5 

6/8/1997 Port Harcourt Rivers Nil 1 

2/9/1997 Col. InuaBawa convey, Akure Ekiti Nil No record 

18/12/1997 Gen. O. Diya in Abuja airport Abuja Nil 1 

22/4/1998 Evan square Lagos Nil 3 

23/4/1998 Ile-Ife Osun Nil 5 

6/1/ 2012 Attack on some Southerners in 
Mubi 

Adamawa Boko haram 13 

 21/1/ 2012 Multiple bomb blasts rocked 

Kano city 

Kano Boko haram Over 185 people killed 

29/1/ 2012 Bombing of a Police Station at 

Naibawa area of Yakatabo 

Kano Boko haram No record 

 8/2/ 2012 Bomb blast rocked Army 

Headquarters  

Kaduna Boko haram No record 

15/2/ 2012 Attack on KotonKarfe Prison 
which 119 prisoners 

were freed 

Kogi Boko haram 1 Warder killed 

19/2/ 2012 Bomb blast near Christ 

Embassy Church, in Suleija 

Niger  Boko haram 5 people injured 

 26/2/ 2012 Bombing of Church of Christ 

in Nigeria, Jos 

Plateau Boko haram 2 people killed and 38 

injured 

11/2/ 2012 Bombing of St. Finbarr‘s 
Catholic Church Rayfield, Jos 

Plateau Boko haram 11 people killed and many 
injured 

29/2/ 2012 Attack on Bayero University Kano Boko haram 16 people killed and many 

injured 

30/2/ 2012 Bomb explosion in Jalingo Taraba Boko haram 11 people killed and several 

others Wounded 



Economy, 2014, 1(2): 68-78 

 

 

 

 

75 

 

improvements in social welfare. As such poverty reduction and job creation must be made to keep pace with 

population growth of the country. This can be made possible when public funds are judiciously utilized. In other 

words, every public fund should be spent to maximize social welfare. A large portion of government income should 

be used for development projects and in import sectors of the economy (i.e., agricultural and manufacturing sectors).  

Finally, the government should ensure that macroeconomic policy inconsistencies are minimized and policy 

reversals are properly checked for both short and long run effects on the economy. 

 

Appendix 
 

Table-3. Correlation Matrix (Pairwise) 

 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  

POLX -0.21  0.47  0.64  0.17  0.29  0.49  1.00 

                                 Source: Researchers‘ computation, 2013, adapted from regression result using E-view 4.1 

 
Table-4. Estimates of Regression 

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(GOVX) -0.113963 0.043641 -2.611361 0.0131 

LOG(OPEN) 0.071738 0.034837 2.059220 0.0468 

LOG(INTR) 0.657786 0.101564 6.476560 0.0000 

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-5. Parsimonious Estimates 

Variable Coefficient Std. Error t-Statistic Prob.   

C -0.665862 0.575589 -1.156834 0.2548 

LOG(GDPC) 0.167195 0.071219 2.347612 0.0244 

LOG(GOVX) -0.108547 0.040482 -2.681342 0.0109 

LOG(OPEN) 0.067505 0.032406 2.083096 0.0442 

LOG(INTR) 0.651310 0.098776 6.593788 0.0000 

POLX 0.229565 0.093079 2.466343 0.0184 

R-squared 0.805794     Mean dependent var 0.767442 

Adjusted R-squared 0.779550     S.D. dependent var 0.427463 

S.E. of regression 0.200703 Akaike info criterion -0.245198 

Sum squared resid 1.490416     Schwarz criterion 0.000551 

Log likelihood 11.27176     F-statistic 30.70394 

Durbin-Watson stat 1.408547 Prob(F-statistic) 0.000000 

                                           Source: Researchers‘ computation, 2013, adapted from regression result using E-view 4.1 

 
Table-6. Serial Correlation LM Test: Breusch-Godfrey 

F-statistic 3.891456     Probability 0.056250 

Obs*R-squared 4.194698     Probability 0.040551 

                                             Source: Computational results using Eviews 4.1 

 
Table-7.ARCH Test 

F-statistic 1.384540     Probability 0.246284 

Obs*R-squared 1.405131     Probability 0.235866 
                                           Source: Computational results using Eviews 4.1 

 

Table-8.White Heteroskedasticity Test 

F-statistic 2.410239     Probability 0.023128 

Obs*R-squared 28.62386     Probability 0.072136 
                                          Source: Computational results using Eviews 4.1 

 
 

 

 

 

 

 

 

 



Economy, 2014, 1(2): 68-78 

 

 

 

 

76 

 

Table-9.Unit Root Test 

 
 

 

 

 
 

 
 
                        Source: Authors‘ computation from computer output. 

                        Note:  * significant at 1%; ** significant at 5%; and ***significant at 10% 

 
Table-10. Unrestricted Cointegration Rank Test 

Hypothesized  Trace 5 Percent 1 Percent 

No. of CE(s) Eigenvalue Statistic Critical Value Critical Value 

None *  0.537180  102.5062  94.15 103.18 

At most 1 *  0.399611  70.91915  68.52  76.07 

At most 2 *  0.371117  50.00190  47.21  54.46 

At most 3 *  0.301093  30.98568  29.68  35.65 

At most 4 *  0.251351  16.29797  15.41  20.04 

At most 5 *  0.102396  4.429068   3.76   6.65 

 *(**) denotes rejection of the hypothesis at the 5%(1%) level 

 Trace test indicates 6 cointegrating equation(s) at the 5% level 

 Trace test indicates no cointegration at the 1% level 

 

Hypothesized  Max-Eigen 5 Percent 1 Percent 

No. of CE(s) Eigenvalue Statistic Critical Value Critical Value 

None  0.537180  31.58708  39.37  45.10 

At most 1  0.399611  20.91725  33.46  38.77 

At most 2  0.371117  19.01622  27.07  32.24 

At most 3  0.301093  14.68771  20.97  25.52 

At most 4  0.251351  11.86890  14.07  18.63 

At most 5 *  0.102396  4.429068   3.76   6.65 

 *(**) denotes rejection of the hypothesis at the 5%(1%) level 

 Max-eigenvalue test indicates no cointegration at both 5% and 1% levels 

                                           Source: Researchers‘ computation, 2013, adapted from regression result using E-view 4.1 

 

Table-11.Error Correction Model Estimates 
Variable Coefficient Std. Error t-Statistic Prob.   

C -0.472115 0.570480 -0.827576 0.4135 

LOG(GDPC) 0.147372 0.072297 2.038430 0.0491 

LOG(GOVX) -0.102344 0.041005 -2.495909 0.0174 

LOG(OPEN) 0.070241 0.033035 2.126235 0.0406 

LOG(INTR) 0.585034 0.102283 5.719735 0.0000 

POLX 0.240609 0.091067 2.642103 0.0122 

ECM(-1) -0.334137 -0.170726 -1.957157 0.0583 

R-squared 0.810114     Mean dependent var 0.785714 

Adjusted R-squared 0.777562     S.D. dependent var 0.415300 

S.E. of regression 0.195869 Akaike info criterion -0.271726 

Sum squared resid 1.342768     Schwarz criterion 0.017886 

Log likelihood 12.70625     F-statistic 24.88679 

Durbin-Watson stat 2.066517 Prob(F-statistic) 0.000000 

                                                 Source: Researchers‘ computation, 2013, adapted from regression resultusing E-view 4.1 

 
Table-12. Test of Robustness of Estimates 

Variable Coefficient Std. Error t-Statistic Prob. 

C -0.476808 0.600756 -0.793680 0.4329 

LOG(GDPC) 0.148157 0.078129 1.896312 0.0664 

LOG(GOVX) -0.102874 0.045392 -2.266368 0.0299 

LOG(OPEN) 0.070518 0.034835 2.024338 0.0508 

LOG(INTR) 0.586184 0.111019 5.280054 0.0000 

LOG(UNEM) 0.002022 0.069331 0.029162 0.9769 

POLX 0.239577 0.098932 2.421626 0.0209 

ECM(-1) -0.332837 0.178862 -1.860858 0.0714 

R-squared 0.810118 Mean dependent var 0.785714 

Adjusted R-squared 0.771025 S.D. dependent var 0.415300 

S.E. of regression 0.198726 Akaike info criterion -0.224132 

Sum squared resid 1.342734 Schwarz criterion 0.106853 

Log likelihood 12.70677 F-statistic 20.72270 

Durbin-Watson stat 2.065951 Prob(F-statistic) 0.000000 

                                                 Source: Researchers‘ computation, 2013, adapted from regression resultusing E-view 4.1 

 

      Augmented Dickey-Fuller 

variables levels 1st difference 2nd difference Lag length Order of integration 

INFL -3.502597 -6.330409*  9 I(1) 

GDPC -2.502918 -3.542123**  9 I(1) 

GOVX 0.954130 6.061500  9 I(1) 

OPEN -2.232916 -7.139956*  9 I(1) 

INTR -1.747155 -10.31167*  9 I(1) 

TERR -1.840175 -6.403124*  9 I(1) 

POLX -2.282445 -7.695598*  9 I(1) 

ECM -4.757667*   9 I(0) 



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77 

 

 
         Figure-1. CUSUM Test                                         Figure-2. CUSUM of Squares Test 

 
                                          Source: Researchers‘ computation, 2013, adapted from regression resultusing E-view 4.1 

 

 

                  Figure-3. Recursive Coefficients Test 
 

                                   Source: Researchers‘ computation, 2013, adapted from regression resultusing E-view 4.1 

 

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-5

0

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10

15

20

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CUSUM 5% Significance

-0.4

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0.4

0.6

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1.0

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Recursive C(5) Estimates ± 2 S.E.

-.1

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.1

.2

.3

.4

.5

.6

.7

.8

1990 1995 2000 2005 2010

Recursive C(6) Estimates ± 2 S.E.

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Views and opinions expressed in this article are the views and opinions of the authors, Economy shall not be responsible or answerable for any loss, 

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