maritime illegal oil trading and unemployment in nigeria ... vol13... · officers of the nigeria...

JORIND 13(2) December, 2015. ISSN 1596-8303. www.transcampus.org/journal; www.ajol.info/journals/jorind

MARITIME ILLEGAL OIL TRADING AND UNEMPLOYMENT IN NIGERIA

Ikpechukwu Njoku andChijioke Akpudo

Department of Transport Management Technology, Federal University of Technology, Akure *E-mail of the author: [email protected]+234-803-664-5117

Abstract

This study investigates the relationship between maritime illegal oil trading and unemployment in Nigeria

from 1995-2012. By employing the E-views econometric software, the unit root, co-integration and

Granger causality tests were carried out on the secondary data set, to make it agreeable to the

application of the vector autoregressive (VAR) modeling of the ordinary least square multiple regression.

It was revealed that the volume of oil theft as an explanatory variable met the a priori expectation with its

negative coefficient, but together with the one-year lagged variables of the dependent variable, was

statistically significant in terms of contributions to the dependent variable. Generally, the study draws a

conclusion that maritime illegal oil trading has contributed significantly to the level of unemployment in

Nigeria. The work suggests that sophisticated modern technology should be employed in the fight against

oil theft and that the law enforcement agencies should live up to this challenge.

Keywords:Illegal oil trading,unemployment, cointegration,vector autoregressive

Introduction

Crude oil, which is indisputably the lifeblood of

modern economy, has now become the most

essential commodity in the world. Thus, no nation

today can survive without oil. This prompted Smil

(2008) to describe it as the ―lifeblood of modern

world‖, adding that, ―without oil, there would be

no globalization, no plastic, little transport, and a

worldwide landscape that few would recognize‖.

Yergin (2008) also calls it ―the world‘s most

important resource‖.

The Nigerian economy is dependent on the

exploitation of crude oil and the nation‘s future is

very much tied to this commodity (Okere, 2013).

In fact, oil and gas resources account for over 90%

of Nigerian export and foreign exchange earnings

and over 70% of total Nigerian revenue

(Ekuerhare, 2002). This prompted Wilson (2012)

to state that the increase or otherwise in crude oil

production affects directly the revenue base and

development of the Nigerian state. Oil is now the

mainstay of Nigeria‘s economy. Unfortunately, the

same resource is being savagely stolen in copious

quantities on daily basis (Adeboboye, 2013).

The upsurge of oil theft in the maritime domain of

Nigeria in recent times is very alarming. Currently,

Nigeria is losing over 300,000 barrels of crude oil

per day (bpd) to oil theft, pipeline vandalism and

related criminal vices in the oil sector (Akpan

2013; Olusola, 2013; Odemwingie and Nda-Isaiah,

2013; Okere, 2013). In spite of the efforts of the

Federal government to curtail the situation by

increasing its security spending in recent years and

devoting millions of naira annually to hire private

security firms as well as equipping men and

officers of the Nigeria Security and Civil Defence

Corps (NSCDC), incessant destruction of pipelines

and other oil facilities as well as trade in stolen oil

by criminal cartels with international connections

have continued unabated (Ugwuanyi, 2013;

Mernyi, 2014). This indicates that the huge

investments of public funds on the safety of oil

facilities have not yielded the desired results. In

effect, the Nigerian economy is in a precarious

situation. She is facing an economic emergency

unprecedented among the oil producers of the

world. Something urgent needs to be done to

reverse the ugly trend. For instance, Nigeria has

been tagged the most country plagued by oil theft

among her contemporaries of Indonesia, Russia,

Iraq and Mexico. Statistics of oil theft among these

major oil-producing countries shows that Nigeria is

losing as much as 400,000bpd which is equivalent

to losses of US$1.7billion a month (Dalby, 2014).

This is a colossal loss compared to a total theft of

5,000 to 10,000bpd and just 2,000 to 3,000bpd in

Mexico and Indonesia respectively (Dalby, 2014).

Thus, oil theft or illegal oil trading in the Nigeria‘s

maritime domain poses a challenge that threatens

the very foundation of the oil industry and by

extension the Nigerian economy (Garuba, 2012).

Oil theft is carried out at different levels and

quantities; hence there are various methods in

which oil theft operations are carried out in the

Niger Delta. The most popular method for stealing

the crude oil is to puncture the pipeline conveying

the product from one point to the other and tap it at

the point where it had been punctured (Adegbite,

2013). Asuni (2009); Katsouris and Sayne (2013)

mailto:[email protected]


opine that there are three operational methods of

oil theft in the Niger Delta. These are: (1) a minor

and small-scale pilfering of condensate and

petroleum product destined for local market; (2)

direct hacking into pipelines or tapping with a hose

from wellhead through practical removal of the

‗Christmas tree‘and (3) excess lifting of crude oil

beyond the licensed amount and using forged bills

of lading. While the first is less significant in that it

is conducted by local people who hide under the

cover of violence in the Niger Delta region, the

second category brings more technical

sophistication into the business with the stolen

product placed in small barges and taken straight

into the sea where it is loaded into larger barges

(mother ships) in return for money and weapons

used to fuel violence. The last category speaks

solely about a spoilt system facilitated by official

corruption in that it involves the use of forged bills

of lading, ―issued by a carrier to a shipper, listing

and acknowledging receipt of goods for transport

and specifying terms of delivery.‖

There are various factors engendering the

persistent thriving oil theft activities in the

Nigeria‘s maritime domain. Adegbite (2013) states

that there are many perceived reasons for engaging

in crude oil theft. The reasons which vary from the

mundane to the absurd include (a) poverty; (b)

greed; (c) lack of respect for national economic

survival; (d) get rich quick syndrome; (e) lack of

gainful employment ; (f) exploiting the loopholes

in the criminal justice system to circumvent the

law ; (g) evolving culture of impunity from the

wrong perception that some people are above the

law; (h) weak institutional structure to checkmate

criminals; (i) malice; and (j) bad governance

(corruption, incompetency), just to mention a few.

Igbuku (2014) also identifies some of the

underlying causes of this scourge to include

poverty, community-industry expectation

mismatch, corruption, unemployment, ineffective

law enforcement and poor governance. He adds

that high unemployment, for instance has created a

huge population of idle young people who are

easily lured to oil related crimes. These crimes in

turn are reinforced in the absence of clear deterrent

measures, arising from the non-prosecution of

alleged perpetrator.

The Niger Delta youths are not adequately engaged

in the production activities in the region. Human

Rights Watch (2004) and Ejibunu (2007:16) attest

to the fact that the number of unemployed youths

is increasing in the region (among whom are the

university graduates), despite the numerous oil

multinationals in the region. Most worrisome is the

fact that some of the oil multinationals operating in

the region hire the services of their high manpower

personnel from outside the region, while the few

that are hired from the region are forcefully

disengaged, thereby increasing the number of

unemployed youths. The unemployed youths

among whom are the disengaged staff of the oil

multinationals with technical knowledge on how to

manipulate the oil facilities, resort to pipeline

vandalism and oil theft as a means of engaging

themselves economically and providing means of

livelihood. This heightens the oil theft activities in

the region.

As noted by Brock (2012), due to years of neglect,

marginalization and underdevelopment of the

Niger Delta by the Federal Government and the

Multinational Oil Companies (MNCs) operating in

the region, rings of organizes criminal groups,

called ―oil bunkerers‖ in our local parlance, has

evolved in the creeks and along our territorial

waters, who specializes in stealing, illegal refining

and transporting crude oil to the international black

market. Similarly, Vidal (2013) states that some

Niger Delta communities freely admit their role in

the theft of oil but blame continuing poverty and

pollution of their farmlands fishing waters for their

actions. ―The government and oil companies are

collecting our oil and we don‘t have jobs or money

so we have to collect the oil and refine our own‖,

says a man in the village of Bolo near where an

illegal refinery was set up. Apparently, due to

joblessness and poverty, the Niger Delta youths see

oil theft or illegal oil trading as a legitimate

business.

Oil theft has been identified as the biggest threat to

Nigeria‘s economy. Its socio-economic impact

includes environmental degradation, loss of

economic activities for the communities, loss of

revenues to the government resulting in inadequate

funding for development initiatives, increased

criminality in Niger Delta region, lack of security

due to illegal activities and infiltration of

international collaborator and bad image for the

country (Duru, 2013; Okere, 2013). Consequently,

crude oil theft has led to declaration of force

majeure and the money shared by the three tiers of

government in Nigeria in 2013 and 2014 was

erratic. In the first quarter of 2013 alone, Nigeria

lost about N191 billion ($1.23 billion) due to drop

in crude oil production, arising from incessant

crude oil theft and vandalism along the major


pipelines within the Niger Delta, (NNPC, 2013).

Daily crude oil production during the period

fluctuated between 2.1 and 2.3 million barrels per

day (mbpd) compared with the projected estimate

of 2.48mbpd. Expectedly, the fall between actual

production and forecast in first quarter of 2013

resulted in a drop in crude oil revenue of about

$1.23 billion (N191 billion) that should have

accrued to the Federation Account (Mernyi, 2014).

Also, due to the loss of oil revenue to the oil

thieves, Nigeria can no longer export crude oil

above two million barrels per day as opposed to

budgetary provision of 2.5mbpd (Olateju, 2013).

Nigeria is no longer selling enough crude oil to

meet budgetary provisions.

The government is failing to meet some of its

obligations and domestic debt is rising rapidly. For

instance, the country targeted 2.53mbpd

production, according to its financial plans for the

year 2013, a projection it failed to meet due to oil

theft. Ogbeifun (2014) noted that the negative

impact of vandalism and crude oil theft include the

destruction of aquatic and farmlands, economic

sabotage which explains the shortfall of Nigeria‘s

2014 budget from $29.3 billion in 2013 to $23.3

billion in 2014 and divestments by some

International Oil Companies (IOCs) with attendant

job losses thereby compounding the

unemployment situation in Nigeria. The colossal

loss of revenue to oil theft was succinctly captured

by Gaskia (2013).

Attacks on oil production facilities have led to

several shutdowns and declaration of force majeure

by the IOCs, ultimately resulting in loss of revenue

to the oil companies as well as the government

(Alohan, 2013). The activities of oil thieves in the

Niger Delta has led to several shut-ins and shut-

downs of pipelines and crude oil production

respectively by the IOCs and thus resulted in

decline in production capacity as well as loses of

revenues to the companies. Force majeure is a

legal clause that allows a company to walk away

from a supply contract – owing to theft and

sabotage. The IOCs operating in Nigeria are

counting heavy losses as surge in crude oil theft

and supply disruption have negatively impacted on

their earnings (Asu, 2013). For instance in

September 23, 2013 Shell Petroleum Development

Corporation (SPDC) had to close its trans-Niger

pipeline, which carries 150,000bpd because of

leaks due to theft, less than a week after it had been

reopened. In the process of oil theft, pipelines are

vandalized and oil is spilled. Consequently, there

will be urgent need by the oil companies to repair

the pipelines and clean up of oil spills in the

environment and this involves huge capital

expenditure and it invariably leads to lose of

revenues to the oil companies. The money that

could have been spent on other areas of oil

exploration and production are (now) used for

pipeline repair, maintenance and cleaning oil spills

(Alawode & Ogunleye, 2013). The SPDC has

consistently declared force majeure on its

operations between 2009, 2010, 2011, 2012 and

2013. This was due to the activities of oil thieves

who had damage its pipeline thus disrupting

production. Nigeria Agip Oil Company (NAOC) in

September 2013 declared a force majeure

regarding crude oil lifting at the Brass Terminal

and suspended its activities in Bayelsa State,

following the intensification of oil theft activities

and the vandalism of the 10-inch Kwale-Akri-

Nembe-Brass oil delivery line. In April, 2013, the

SPDC; shut-down the 150,000bpd Nembe Creek

oil pipeline due to the urgent need to clear away

illegal connections (Alohan, 2013).

Another explosion and fire at a crude theft point on

the SPDC‘s facility at Bodo West in Ogoniland

also forced the company to shut the Trans Niger

Pipeline (TNP), in June 2013, deferring some

150,000bpd. The SPDC shut down again Trans

Niger pipeline that produces 150,000bpd on July

16, 2013 due to leakage from vandalism by oil

theft. Shell shut down again the 150,000bpd at the

Trans Niger pipeline on 16th September, 2013

barely a week after it reopened the facility.

Another shut-in occurred on Wednesday

September 18, 2013 following reports of a leaking

crude theft point at Bodo west in Ogoniland. The

SPDC declared force majeure on Bonny light

exports on October 10, 2013 due to increase crude

oil theft resulting in 300,000 barrels shut in from

two key pipelines - Trans Niger pipeline (TNP) at

B-Dere, Nonwa-Tai and Bodo west (Olusola,

2013; Bello, 2013). The frequent illegal tapping of

pipelines is often very crude and causes frequent

pipeline leaks. This forces oil companies to

shutdown production while crucial repairs are

conducted (Sun, 2013). A total of 189 crude theft

points were repaired on the Trans Niger pipeline

(TNP) and Nembe Creek Trunkline (NCTL)

between January and September 2013 due to oil

theft (Bello, 2013). The objectives of this study

were to establish a relationship between maritime

illegal oil trading and unemployment and to raise

prediction models on the relationship between the


maritime illegal oil trading and unemployment in

Nigeria.

Methodology

This section deals with how data and the

information used in the work had been gathered

and analysed. It also deals with research design,

method of data collection and types of information

generated.The study covered the period from 1995-

2012.

Research design

This study is designed to empirically investigate

the illegal oil trading in the Nigeria maritime

domain. By employing the E-views econometric

software, the study made use of the unit root,

granger causality and co-integration tests in order

to basically produce the regression model thus,

corresponding to the core interest area of the study

namely, the relationship between the illegal oil

trading and unemployment level in Nigeria. This

relationship describes model 7.

Sources of data

As aforementioned, this study is designed to

empirically investigate the illegal oil trading in the

Nigerian maritime domain with respect to

unemployment covering the period of 1995-2012.

Only secondary data were used in the analysis and

were derived from the publications of the Central

Bank of Nigeria, National Planning Office,

National Bureau for Statistics,Nigeria National

Petroleum Corporation (NNPC) and Nigerian

Maritime Administration and Safety Agency

(NIMASSA).

Data analysis methods

The data set were analyzed by using two

approaches namely; the Descriptive Statistics and

Inferential Statistics.While the inferential statistics

were employed to analysis the formulated

hypothesis, other objectives of the study were

however, actualized with the use of descriptive

statistics.

Test of hypothesis

The hypothesis formulated was tested with a linear

regression model with ordinary least square

properties. Hence, a multiple regression approach

was adopted. The analysis involved model

specification and testing of the hypothesis. For the

hypothesis, we made the unemployment level

(UNRATE) the dependent variable.

Test statistics

The time series data for the period, 1995-2012,

were fitted into the linear function. This was to

enable us predict the level of the dependent

variable (UNRATE) that can be achieved given

known levels of the illegal oil trading explanatory

variables. The test statistics therefore, includes the

Coefficient of Correlation (R), Coefficient of

Determination (R2), the analysis of variance

(ANOVA/F-ratio) and the t-distribution (t-test).

While the ANOVA/F-test establishes the

significance or otherwise, of the model as a whole,

the coefficient of correlation seeks to test the

strength or magnitude of the relationship between

the dependent variable and the component of

illegal oil trading as explanatory variable. The t-

test seeks to test the extent of contribution or level

of significance of the illegal oil trading explanatory

variable to the dependent variable.

Test of the model significance

The first test carried out under the hypothesis

testing was a test of the model significance. This

seeks to test for the significance of the model as a

whole. There are two ways to accomplish this; the

analysis of variance or the coefficient of

determination, R2.

The Analysis of Variance approach

This statistical tool aims at splitting the variations

of a variable, for example, in the hypothesis, the

regressand (UNRATE) with its component parts,

variations in the dependent variable, that are

accounted for by the explanatory variables (illegal

oil trading variables), the regressors, that is, the

different sources of growth in the UNRATE as

produced by the illegal oil trading components; are

called the Explained Variations. Other sources not

thus explained are due to random or chance

factors. These are estimates of the population

disturbance variable ‗u‘ and are represented by ‗e‘,

otherwise referred to as the Residuals or error

term.


Table 1: A Hypothetical ANOVA Table

Source of

Variation

Sum of Squares Degree

of

Freedo

m

Mean Square

Error

F-Statistic

Regressi

on ( ) K-1

MSESS

MS RSS

Residual ∑( )

N-k

F-Tabulated

Total

Variation ∑( ) (

)

N-1

Decision:

if Fcal>Ftab reject

Ho and Accept Ha

For the hypothesis, the regression equation is designated as follows;

For example, Rearranging equation 1, we have;

( )

( )

Summing both sides of equation 3 we get;

∑

∑( )

In the Regression, ∑

(estimate of the

population disturbance), is given by ∑

otherwise called the Residual Sum of Squares

(RSS) ∑ ( ) is the sum

of squares of the deviation of the unemployment

rate( ) variables from their mean. While

the explained sum of squares ( ) is gotten with

the formula, ( ) ( ) Where;

Therefore,

The Coefficient of Determination, R2 approach

Another way to test for the model significance is

through the coefficient of determination (R2). The

R2 is calculated from the regression and it gives the

proportion of the total variation in the dependent

variable, actual unemployment ratethat is

explained by the independent variables, here the

various illegal oil trading components. R2, from

the sample is a statistical estimate of the

population, e2, (row squared).

The value of R2 ranges between 0 and 1;

-0.0 - - - - -1.00 Inverse or negative variation

0.00 - - - - 0.29 Highly insignificant, positive

0.30 - - - - 0.49 Insignificant, positive

0.70 - - - - 1.00 Highly significant, positive

In setting up the test, the following hypothesis is

tested:

HO:ρ2 = O i.e., the regressors, the growth in

the illegal oil trading components,

or sources of growth in the

unemployment rate, in a given

year have no significant

relationship with the actual growth

of the unemployment rate for that

year.

HAρ2 > O (One-tailed test of significance) i.e.,

at least, there is a significant

relationship between one of the

independent variables and the

actual growth of the

unemployment rate.

Decision rule

If F-ratio calculated is greater than the F-ratio

tabulated or theoretical F, at alpha () – level of

significance, and (K-1), (N-K), degrees of


freedom, then we Reject Ho; and Accept Ha, and

thus state that there is some truth in the estimated

model (i.e. the regression model is significant since

the regressors significantly account for the

variation in the dependent variable (UNRATEt).

( ) ⁄

⁄

Test of significance of the explanatory variables,

t-Test

Having established the significance of the

estimated model, as a whole, the next step that

followed was the test of the various regressors in

bringing about this result. This is carried out

through the test on the estimated parameters of the

regressors. The test-statistics or student t-test is

calculated as follows;

( )

Where;

βk = Estimate of the population parameters for the

regressors (i.e. illegal oil trading components)

Se(βk) = Standard error of the estimate

Decision rule

|

( )|

level of

significance, we Reject H0 and Accept HA: to

conclude that the variable belongs significantly to

the model.

Specification of model The actual dependent variable (unemployment

rate) figures for the period 1995 – 2012, herein

represented by the symbols, UNRATEt was

regressed on the various components of illegal oil

trading components figures for the corresponding

period. These components of illegal oil trading are

hereby represented as follows:

VASt = Total value of stolen oil in

year t;

VOSt = Total volume of stolen oil in

year t;

The dependent variable, however, is as specified:

UNRATEt = Level of Nigeria unemployment

in year t;

Data estimation

Here, we note that the data set was estimated by

carrying out the following tests; unit root, co-

integration and Granger causality tests. While the

unit root test sought to test for the stationarity of

the data set not to produce spurious results, the

informational content of the model were confirmed

by the use of the co-integration test which helped

to establish the nature of the model, whether short-

or long-run relationships existed among the

variables of the model. Finally, with the granger

causality test, the direction of the effects was thus

established.

Data presentation and analysis

As aforementioned, the data set for our estimation

was generated from the records and websites of the

NIMASSA, the CBN and various publications

from other related agencies, comprising of Nigeria

data set on volume of oil theft, value of oil theft,

gross domestic product, the level of

unemploymentandgross domestic per capita for the

period, 1995-2012.


Table 2: Volume of Oil Theft, Value of Oil Theft, GDP & Unemployment Rate

S/N YEAR VOS VAS GDP UNRATE GDPC

1 1995 229565000 91.76 1933211.6 1.9 256

2 1996 230031800 111.74 2702719.1 2.8 313

3 1997 257947000 107.56 2801972.6 3.4 314

4 1998 249207600 76.26 2708430.9 3.5 272

5 1999 257791600 105.13 3194015 17.5 288

6 2000 242350000 357.68 4582127.3 13.1 660

7 2001 337322415 821.7 4725086 13.6 679

8 2002 390463495 1079.1 6912381.3 12.6 682

9 2003 237250000 786.6 8487031.6 14.8 676

10 2004 193450000 812.8 11411067 13.4 727

11 2005 156950000 1161.6 14572239 11.9 783

12 2006 255500000 2240.6 18564595 13.9 804

13 2007 255500000 2304 20657318 12.7 832

14 2008 292000000 4056 24296329 14.9 862

15 2009 694925910 6655.5 24794239 19.7 889

16 2010 283078530 3525 33984754 21.1 926

17 2011 386091290 6975 37543655 23.9 973

18 2012 179514150 3239.5 39650864 22 1016

Source: NIMASA, CBN, various years

Data estimation

In this section, our objective is to establish the

stationarity of the entire data set employed in the

estimation. When a particular data set is found to

be stationary, it then suffices that the data set can

be relied upon for the estimation, having

eliminated the possibility of spurious results.

Table 3: Unit Root Test for the Variables Employed

Augmented Dickey-Fuller Unit Root Test

Variable T-statistic Critical value Order of

Integration

Significanc

e

VOS -3.527962 -3.052169 1(0) 5%

VAS -8.649341 -3.920350 1(1) 5%

GDP -3.287799 -3.065585 1(1) 5%

UNRATE -5.271891 -3.920350 1(1) 1%

Source: E-views 6.0 Econometric Package.

Unit root test results

The unit root test was carried out using the

Augmented Dickey Fuller test in order to

determine whether the data set was stationary and

the order of integration. From table 3, it could be

observed that only the volume of oil was stationary

at level. Other variables turned out to be stationary

at first difference. Generally, the data set can be

relied upon for analysis as it shows no evidence of

producing spurious results.

The co-integration results

Having established the stationarity of the data set,

the Johansen co-integration test was the applied,

which adopts no exogenous variables as it is based

on the vector auto regression (VAR) modeling.

Under here, we try to establish the presence of a

short or long-run equilibrium existing between the

variables and hence the various estimated

regression equation results. These results are

presented in Table 4.


Table 4: Co-integration and Test Results

Johanssen Co-integration Test

Mod

el

Number of Co-

integrating

Equations

Nature of

Equilibriu

m

1 Maritime Illegal Oil Trading and Poverty Rate 1 Long-run

2 Maritime Illegal Oil Trading and

Unemployment Rate

Nil Short-run

Source: E views 6.0 Econometric Package.

From Table 4, whereas model 1 shows evidence 1

co-integrating equation which is of long-run

relationships, model 2reveals the presence of short-

run relationship existing between the variables.

The Granger causality results

The result here does show that the pairs of

variables have not, in fact produced significant

causal effects. However, we observe a one-

directional effect from the value of illegal oil

trading to the poverty level.

Hence, more specifically, the level of

unemployment granger causes the per capita

income (GDPC) at 1%.

Test of hypothesis

The Influence of Illegal Oil Trading on

Unemployment in Nigeria

Here, one lead equation is to be estimated and the

hypothesis states as follows:

HO: There is no significant relationship between

the level of Illegal Oil Trading and the level of

Unemployment in Nigeria.

The sub-hypotheses from HO are as follows;

HOa: The value of Illegal Oil Trading has

no significant effect on the

level of Unemployment in

Nigeria.

HOb: The volume of Illegal Oil Trading

has no significant effect on the


Nigeria.

HOc: The one-year lagged variable of

Unemployment has no significant

effect on the Unemployment in

Nigeria.

Table 5: Results of the Global Statistics for the Influence of Illegal Oil Trading on Unemployment

Test-statistic Model

Least Square, With Lag

R-square 0.733

Adjusted R-square 0.671

S.E of Regression 3.556986

Sum of squared residual 164.4779

Log likelihood -43.41324

Durbin-Watson stat 2.412149

Mean depend. Var 13.81176

S.D. depend. Var 6.202105

Akaike info criterion 5.58

Schwarz criterion 5.77

Hannan-Quinn criterion 5.60

F-statistic 11.88151

Prob(F-statistic) 0.000502

NB:*** = significant at 1%; ** = significant at 5%; * = Not significant. F-ratio tabulated DF (3, 14); 1%

= 5.56, 5% = 3.34, t- ratio DF (14); 1% = 2.98, 5% = 2.14.

Source: E-views 6. Statistical Package

Table 5 shows the results of the global statistics as produced under the model 7

Test of model significance – ANOVA

In order to confirm the specification status of our

model, we employ the ANOVA. The aim of using

this method was to split the total variations of a

variable (around its mean) into components which

may be attributed a specific (additive) causes. For

instance, variations in the dependent variable

(UNRATE) that are accounted for by the


explanatory variables (maritime illegal oil trading

variables) - independent variables, that is, the

different sources of growth in the UNRATE as

produced by the maritime illegal oil trading

components. To simplify the analysis we assumed

that there was only one systematic factor

influencing the variable being studied. Any

variation not accounted for by this (explanatory)

factor was assumed to be random or (chance)

variation, due to various random happenings.

Decision rule

Employing the E-views software, we have that F–

ratio calculated (11.88151) > F–ratio tabulated

(5.56, 3.34), at both 1% and 5% levels of

significance respectively. Since F–ratio calculated

is greater than the F–ratio tabulated, we reject Ho

and conclude that there is a significant relationship

between the level of illegal oil trading and

unemployment in Nigeria. The estimated

regression result is presented as follows:

The impact of illegal oil trading on

unemployment in Nigeria (test of

sub-hypotheses)

The sub-hypotheses from HO are as follows;

HOa: The value of Illegal Oil Trading has

no significant effect on the


Nigeria.

HOb: The volume of Illegal Oil Trading

has no significant effect on the


Nigeria.

HOc: The one-year lagged variable of

Unemployment has no

significant effect on the

Unemployment in Nigeria.

Having tested the significance of the model, we

took a step further to test the significance of the

illegal oil trading in contributing to the total

variation in the level of unemployment in Nigeria.

This was achieved through the use of the student t–

test (refer to the regression result in Table 6). Also,

in Table 6, only the one-year-lagged variable of

unemployment proved to be significant

contributors to the level of unemployment since

the t-ratio calculated (2.97) > t-ratio critical (2.14)

at 5% level of significance.

Table 6: t- statistic table-UNEMPLOYMENT

NB:*** = significant at 1%; ** = significant at 5%; * = Not significant. T-ratio DF (14); 1% = 2.98,

5% = 2.14.

Source: E-views 6.0 Statistical Package.

Variable X1,Value of Oil

Theft,

VASt

X2,Volume of Oil

Theft,

VOSt-1

X3,One-year Lagged

Variable of

Unemployment,

UNRATEt-1

Test Statistic

Coefficient of the

Variable

0.000960 -1.35E-09 0.570696

Standard Error 0.000723 1.04E-08 0.192271

T-Statistic Calculated 1.327820

NS

0.129581

NS

2.968194 ***

T-Statistic Tabulated

1%

2.98 2.98 2.98

T-Statistic Tabulated

5%

2.14 2.14 2.14

Significance 0.207 0.899 0.011


Resultsanddiscussion Model 7 examined the relationship between illegal

oil trading and the level of unemployment in

Nigeria. This result revealed that a significant

relationship actually exists between the illegal oil

trading and the level of unemployment in Nigeria,

with also only the one-year lagged variable of

unemployment rate being statistically significant

at 5%. In addition, this model, with an R-squared

of 73.3% has shown that the changes in the

explanatory variables taken together, have been

able explain at least, 73% of the total variations in

the dependent variable, unemployment rate, thus,

leaving only about 27% to chance occurrence. The

estimated regression result is presented thus;

Again, model 7 above reveals that, only the

volume of illegal oil trading, met the a priori

expectation, bearing a negative coefficient.

Conclusion

This study essentially focused on the impact of

illegal oil trading on the level of unemployment in

Nigeria covering the period, 1995-2012. Having

this as the objective in mind, study found that a

significant relationship exists between illegal oil

trading and the level of unemployment in Nigeria

and that none of the explanatory variables met the

a priori expectation in terms of the signs of their

coefficients and contribution to the level of

unemployment in Nigeria. Thus, this study has

been able to empirically determine the relationship

between maritime illegal oil trading and

unemployment in Nigeria. Hence, through the

determination of the relationship, prediction model

was produced for predicting the level of

unemployment to attain given that level of illegal

oil trading is known. The model presents ready

working tools for policy makers to essentially

manipulate key variable like the level of

unemployment through effective reductions in the

illegal oil trading in Nigeria and in this lies the

modest contribution of this study. The study

therefore concludes that the volume of illegal oil

trading is high and is in consequence of the level

of unemployment in Nigeria and suggests that

sophisticated modern technology should be

employed in the fight against oil theft and that the

law enforcement agencies should live up this

challenge.

References

Adeboboye, T. (2013): How oil thieves wreak

havoc on Niger Delta communities. The Sun.

August 23.

Adegbite, I. (2013): Climate Change, Perennial

Crude Oil Theft and the Quest for

Sustainable

Development in Nigeria, OIDA International

Journal of Sustainable Development 06:

12.

Akpan, U. (2013): Indigenous Operators Express

Worry as Cost of Oil Theft hits

N15.66n. National

Mirror, November 12.

Alawode, J. A. & Ogunleye, O. I. (2011):

―Maintenance, Security, and

Environmental Implications of

Pipeline Damage and Ruptures in the Niger Delta

Region‖ The Pacific Journal of Science

and

Technology. (12) 1.565-573 ISSN 2039-2117

(online) ISSN 2039-9340 (print)

Mediterranean Journal of

Social Sciences MCSER Publishing, Rome-Italy

Vol 6 No 3 S2 May 2015 572

Alohan, J. (2013): Crude Oil Theft: Act of

Terrorism or Lack of Political will?

leadership November 10,

Retrieved December 30, 2013 from

http/leadership.ng/news/10/1/13

Asuni, B. J. (2009): Blood Oil in the Niger Delta

Special Report. Washington; United

States Institute of

Peace.www.usip.org

Brock, J. (2012): Rampant Oil Theft Ravages

Nigeria‘s Delta. Chicago Tribune, June 4, p.67.

Dalby, C. (2014): These are the 5 countries most

plagued by oil theft. Oilprice.com, July

4. Retrieved


August 28, 2014 from

www.thisdaylive.com/articleshttp://busi

ness.financialpost.com/2014/07/04/these

are-the-5-countries-most-plagued-by-oil-theft

Duru, A. E. (2013): Addressing oil theft, illegal

bunkering in Niger Delta. Daily

Independent Retrieved

November 29, 2013 from

www.dailindependent.com/2013/11

Ejibunu, H. T. (2007): Nigeria‘s Niger Delta

Crisis; Root causes of Peacelessness. In

R. H. Tuschi (Ed.)

EPU Research Paper, Issue 07/07.

www.ac.at.fileadm/.../rp/07/07.pdf

Ekuerhare, B. (2002): Sustainable Development

models for the Niger Delta Region. In P.

I. Ozon-Ezor

and U. Ukiwo (Eds.) The Niger Delta

Development Commission: Towards a

Blue Print. Port Harcourt:

Centre for Advanced Social Sciences

Garuba, S. D. (2012): Global Roots of Local Theft:

Niger Delta Oil and the International

Dimension of

Illegal Bunkering Operations in Nigeria.

Unpublished P.hD Seminal Paper at the

Dept. of Political

Science and Public Admin., University of Benin,

Benin City.

Gaskia, J. (2013): Crude Oil theft, Organized

Crime, The Niger Delta Environment

and The National

Economy. Sahara Reporter. August 19

Human Right Watch (2004): The emergence of

arm groups in Rivers State.

ww.hrw.org/background/Africa/Nigeria.

Igbuku, A. (2014) Crude Oil Theft and Illegal

Refining in the Niger Delta. Delta State

Oil and Gas

Stakeholders‘ Conference, Tuesday,

Katsouris, C. & Sayne, A. (2013): Nigeria’s

Criminal Crude: International Options

to Combat the Export

of Stolen Oil. London: Chatham House.

Mernyi, D. (2014): Crude Oil Theft, Pipeline

Vandalism Cross Over To 2014. The

Sun, January 1.

Retrieved February 28, 2014 from

http://sunnewsonline.

com/new/specials/abuja-metro/crude -

oil-theft

pipeline- vandalism-cross-2014

NNPC, Annual Statistical Bulletin, 2011, 2012 and

2013; 10-year domestic product yield,

Table 18.02 for

2011 data

Odemwingie, E & Nda-Isaiah, J. (2013): Nigeria

Loses 400,000 Barrels Daily To Crude

Oil Theft

Sensate. Leadership, November 14.

Ogbeifun, B. (2014): Why Nigeria should not treat

oil theft with kid gloves—Ogbeifun.

Vanguard in

Sweet Crude, January 7. Retrieved on July 15,

2014 from

http://www.vanguardngr.com/2014

/01/nigeria

treat-oil-theft-kid-gloves- ogbeifun

Smil, V. (2008): Oil: A Beginner’s Guide. Oxford:

One world Publications.

Sun, K. (2013): Oil Theft in Nigeria. Retrieved

August 20, 2014 from

http://www.ocnus.net

/artman2/publish /Africa_8/Oil-Theft-in-

Nigeria.shtml

Ugwuanyi, E. (2013): Oil theft: Endless Search,

for Solution the Nation March 26, P.17.

Vidal, J. (2013): €1bn a month: the spiraling cost

of oil theft in Nigeria. The Observer

October 6.

Retrieved November 29, 2013 from

www.observer.com.

Wilson, G. (2012): Militancy in the Niger Delta

Region and its impact on Nigerian State.

International

http://www.thisdaylive.com/articles

http://www.thisdaylive.com/articles

http://business.financialpost.com/2014/07/04/these

http://www.ocnus.net/


Journal of Educational Development. Vol. 2, No.

1, March, pp.55-66.

Yergin, D. (2008): the Prize: The Epic Quest for

Oil Money and Power. New York: Free Press

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