Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
Delta Region Amnesty, Military Spending and Economic Growth:
Evidence from Nigeria
Kevin Odulukwe Onwuka and Joseph Tchokote
The paper investigates the relationship between military spending and economic growth,
incorporating Delta region amnesty. We examine the time property of data using AugmentedDickey Fuller (ADF) and Phillips-Perron (PP) tests, and Johansen and Jesulius cointegration
procedure to derive the long run coefficients. Also the Fully modified Phillips- Hansen least
square was employed to test the robustness of the long run coefficients. The results indicate
that the delta amnesty is growth enhancing in terms of economic growth and investment, while
military spending negatively affects economic growth and investment. Investment is statistically
marginal to support a meaningful economic growth as it negatively affects economic growth.
This is as result of increase in military spending, made possible by increase in militancy in
Niger Delta region and the Nigeria’s involvement in peace keeping operations in West African
sub-region. Findings of this study imply that peace is required for economic growth and
development. The more the militia’s activity continues the higher the military spending will be
and this further leads to misallocation of resources and thus low economic growth.
Keywords: Delta Region, Amnesty, Military Spending, Economic Growth, Nigeria
JEL Classification: O40,
1. Introduction
Over the two decades, Delta region has been witnessing continuous militant actions against oil
companies and government infrastructural facilities. With the political tension, associated with
high levels of militancy, and military spending, the Nigeria’s long run economic growth
performance is likely to be detracted. In an insecure atmosphere, so the argument goes, a
country must devote a disproportionate share of its endowment of scarce economic resources
to unproductive military spending. Peace is critical to economic growth. In the absence of peace,
meaningful economic activities that would spur economic growth would not take place.
Consequently, the confidence of foreign investors to invest or locate their production subsidiary
into the economy or commit their financial resources either in the money or capital market is
eroded for fear of vandalization of the capital equipments or loss of lives or expropriation of their
investments.
The long standing Delta crisis triggered series of macroeconomic variables amongst which are
high government expenditures and vulnerability of government revenues to external shocks. As
a result government spending devoted to military could be very high. While there have been
persistent conflicts, with internal conflict being a major concern for the developing world and a
few major international conflicts, the major pressure to increase military spending have not
been the result of obvious strategic needs, but of internal pressures by vested interests.
Smaldone (2006) observed African states invest in defence at low levels by global standards,
(see Table 1, appendix) and their defence burdens correspond to political, security, and
economic realities. Security conditions are the main drivers of military spending,
____________________________________________________________________________
Kevin Odulukwe Onwuka, Department of Economics & Development Studies, Faculty of Humanities and Social
Sciences, Federal University Ndufu-Alike Ikwo, P. M. B 1010 Abakaliki,
Ebonyi State,
Email:
odulukwe@yahoo.com, Tel: + 2347060838736
Joseph Tchokote, Department of Economics, Faculty of Social Sciences, Nnamdi Azikiwe, University, Awka, P. M.
B. 5025 Awka, Anambra State, Nigeria, Email: yountchokote@yahoo.com, +234 806 2492 647
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Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
which in turn produces a complex mix of socio-economic effects. Military spending is an important issue
for developing countries. It is an expenditure by governments that has influence beyond the resources it
takes up, especially when it leads to or facilitates conflicts. While most countries need some level of
security to deal with internal and external threats, there are opportunity costs as the money could be used
for other purposes that might improve the pace of development. For example, increase in military spending
may reduce the total stock of resources that is available for alternative domestic use such as investment in
productive capital, education and market-oriented technological innovation. Also high spending on the
military may aggravate distortions that reduce the efficiency of resource allocation, thereby lowering total
factor productivity (TFP) (Knight, Loayza and Villanueva, 1996). With the rapid growing population,
high poverty incidence and the vulnerability of revenues to external shocks, sustained per capita
economic growth is a challenge. There is an increasing awareness among policymakers of the need to
promote a macroeconomic environment that would be conducive to private investment and economic
growth. In recognition of this need, the Federal government of Nigeria (FGN) considered a policy option
of granting amnesty to militants of Delta region. This might reduce the colossal military spending to
achieve peace with force, releasing funds for more productive economic activities. Maintaining peace and
security and reallocating expenditures to productive areas are important factors for coping with this
challenge. That is if amnesty turns out to be empirically significant then, a sustained military spending
cuts would become feasible as a result. Improved internal security should yield a peace dividend in the
form of higher long-run levels of capacity output.
Yet not all military spending is unambiguously counterproductive, or even unproductive, in an economic
sense. It is often argued, for example, that expenditure on military training in developing countries may
contribute to improving the educational level and discipline of the labour force and may act as a
stabilizing influence in the society (Knight, Loayza and Villanueva, 1996). Likewise, it has been argued
(see, Thompson, 1974) that military expenditure can be economically productive to the extent that it
enhances the state of national security and improves the enforcement of property rights, thereby
encouraging private investment and growth. Capital expenditure on the military can also have productive
uses: many developing countries still benefit from extensive transport networks that were originally
constructed for military purposes. These outer-examples suggest that the question of whether, and to
what extent, military spending is economically unproductive cannot be resolved by recourse to anecdotal
evidence and historical generalizations, but instead requires rigorous theoretical and empirical analysis.
Cross-section growth regressions have been used to assess the relationship between the military spending
and long run economic growth. The evidence emerged was mixed and subject to criticism due to the use
of inappropriate empirical techniques. For example Benoit (1973, 1978) used spearman rank order
correlation and regression to show that military spending positively affects economic growth in a sample
of 44 LDCs (Least Developing Countries) between 1950 and 1965. Other studies found a negative effect
of defence spending either directly (Fiani et al, 1984); Lim, 1983) or indirectly through their negative
impact on saving (Deger and Smith, 1985), investment (Deger and Sen, 1983) or exports (Rothschild,
1977). Biswas and Ram (1986) found no consistent statistically significant connection between military
spending and economic growth. Another problem with cross-country growth regressions is that they do
not capture the dynamics of the relationship between these two variables and disregard country specific
factors. Typical cross-section regressions provide no insights into the direction of causality but rather
focus on associating military spending and a host of variables with economic growth. Aware of the
pitfalls in the cross-section analysis, Dakurah el al. (2001) used cointegration and error correction models
to study the causal relationship between the military burden and economic growth for 62 countries and
found no common causal relationship between military spending and growth among the countries. This
study, as well, disregards country specific factors. Recently some empirical studies have begun testing for
the direction of causality by using time series data and applying Granger causality tests. These studies
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Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
focused mainly on developed countries where long term series data are available (Ahsan et al., 1989;
Bharat et al., 2000; and Ghali, 1998).
The objective of this paper is to investigate the on the relationship between military spending and
economic growth, incorporating Delta region amnesty. The Delta region peace deal is expected to reduce
military expenditure of the Federal government of Nigeria and release funds for productive economic
activities and thus promotes economic growth. Military spending is growth retarding because of its
adverse impact on capital formation and resource allocation. Substantial long term peace dividend in the
form of capacity output per capita may result from lower military spending. Conventional wisdom
suggests that reducing military spending may improve a country's economic growth and might lead to
modernisation of military or security outfits. While there have been attempts to investigate military
spending and economic growth to the best of our knowledge there have been no investigation of causal
relationship between military spending and economic growth in Nigeria incorporating Delta region
amnesty. However, if there is, the cross-section analysis has the tendency to underweight the country
specific factors. This study intends to fill this gap and contributes to economic literature on peace. They
can be both positive and negative but are usually not pronounced, although the negative effects tend to be
wider and deeper in Africa and most severe in countries experiencing legitimacy/security crisis and
economic/budgetary constraints. Thus, there is still no consensus view on the subject. Dunne and Uye
(2009) in a survey of 102 studies on the economic effects of military spending, show that almost 39% of
the cross country studies and 35% of the case studies find a negative effect of military spending on
growth, with only around 20% finding positive for both types of studies
The trend in military spending in Nigeria is depicted in Figure 1. There is continuous increase in military
spending in Nigeria since 1988. The rise might be attributed to militancy in Delta region and other
domestic and external crisis in which the Nigeria played a greater role. The military expenditure in 2010
is about 292 billion naira (see Table 1, appendix)
Military Expenditures
350.0
300.0
250.0
(Bilion Nira)
200.0
Military
Expenditures
150.0
100.0
50.0
0.0
1985
1990
1995
2000
2005
2010
2015
Year
This work is organised as follows. First section is the introduction which highlights the effects of
militancy on the economic performance. The second section is the literature review which situates
amnesty into economic theory, and brings out the theoretical link between military spending and
economic growth. While the third section is the model specification and method of analysis or estimation,
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Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
the fourth section deals with the discussion of the results and policy implications. The fifth section draws
the conclusion.
2. Literature Review
2.1 Amnesty in Economic theory
There are two strands of literature to view amnesty in economic theory. First, amnesty can be viewed as a
transfer payment or social security payment. The transfer or social security payment is not as a result of
work done but is offered to aid jobless members of the society pending the time they can secure a job or
to aid poor ones in order to curtail social unrest in the society.
Another strand of theory views amnesty as a trade for peace. That is what you must offer to have peace.
In this case peace is regarded as a commodity of trade which individuals can buy. The higher the price
you offer the more is peace. The problem with this strand of theory is that once amnesty package or
revenue from peace has exhausted the social unrest commences again. Thus for peace to be maintained,
government will continue to offer amnesty package or buy peace. The amnesty package or peace revenue
goes to only a set of individuals formatting unrest. The money meant to provide amenities for the general
public goes to individuals called militia. For conflict resolution person, amnesty can be viewed as an
instrument for peace. The higher the content of amnesty is the better the chance for peace to reign.
However, where peace negotiation fails or amnesty package could not be reached, the next alternative
available to government may be to use force to impose peace. In this case the military spending will rise.
In either case, public revenue is diverted to unproductive ventures that will not create job opportunities
but rather enrich individuals at the expense of the public.
2. 2. Theoretical link between military spending and Economic growth
The relationship between military spending and output growth is complicated by the fact that it has both
short-run and long-run components which may act in opposite directions. In the short-run as with
increases in other types of government expenditures, a rise in military spending on final goods and
services may increase aggregate domestic demand, thereby exerting a short-run simulative Keynesian
impact on growth rate by inducing a rise in capacity utilization. That is it raises the growth of current
output relative to that capacity output. In this respect defence spending enhances aggregate demand by
increasing purchasing power and produces positive spin-off effect. DeGrasse (1993) argues that defence
spending generates contract awards which generate jobs and increase purchasing power of workers. The
increased purchasing power will lead to more demand. Thus, through this process of increasing aggregate
demand and employment, defence spending helps economic growth. On the other hand, Deger (1986)
points out that in the less-developing countries (LDCs), military may help in creating a socioeconomic
structure conducive to growth. In this aspect, military may engage in research and development, provide
technical skills, educational training and create an infrastructure necessary for economic development.
However, these short-run simulative effects do not necessarily lead to higher levels of capital formation
and capacity output (Knight, Loayza, and Villanueva, 1996). Indeed, over the longer term increases in
military spending are likely to exert a negative effect on capacity output.
Clearly, in developing countries military spending, conflict, economic capacity (education, governance,
institutions, natural resources) all interact to influence growth. Indeed, many poor countries, even those
with civil wars, spend relatively little on the military. In particular many African countries have low
military burdens, but there are other obstacles to growth (Collier, 2007). The theoretical work has
allowed the identification of a number of channels through which military spending can impact on the
economy, through labour, capital, technology, external relations, socio political effects, debt, conflicts etc
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Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
(see Dunne and Uye, 2009). There are two channels by which a sustained increase in military expenditure
might be expected to depress a country's secular growth performance. The first channel results from the
likelihood that, other things being equal, a rise in military spending exerts a negative impact on the rate
of investment in (public and private) productive fixed capital. This occurs because of well-known
crowding-out effects: an increase in military spending must be financed either by raising current taxes or
by borrowing (future taxes). In either case, it will lower the expected after-tax return on productive fixed
capital, while simultaneously reducing the flow of (domestic plus foreign) savings that is available to
finance productive fixed capital formation in the domestic economy (see Deger, 1986; Deger and Smith,
1983). This channel is likely to be particularly important in the case of net-debtor developing countries.
Since such countries are faced with external financing constraints, a rise in military spending to the extent
that it is not associated with larger net capital inflows to finance a higher external current account deficit
can be expected to crowd out capital investment and/or private consumption (see Hewitt, 1992). In the
context of developing countries, Hewitt contends that the justification for military expenditures must be
from national security grounds, since the economic benefits are limited. Though, internal conflicts is a
major concern for the developing world, the major pressure to increase military spending have not been
the result of obvious strategic needs, but of internal pressures by vested interests.
A second channel by which military expenditures may affect the growth path of capacity output is
through their direct impact on the efficiency of resource allocation. Since military spending is not
governed by market processes, it tends to create distortions in relative prices that result in a dead-weight
loss to total productive capacity. In addition, it may exert negative externalities on capacity output. There
are several ways in which these inefficiencies directly affect the growth rate. First, a higher deadweight
loss to domestic production results from either an increase in contemporaneous taxes or heavier
borrowing to finance higher military spending; borrowing from the banking system often leads to higher
inflation, which distorts resource allocation. Second, research and development activities may
concentrate on military progress at the expense of technological advances in economically-productive
areas. Third, policies implemented to support a military program are often detrimental to efficient
resource allocation and market growth: examples are trade restrictions, nationalization of military
equipment producers, military procurement preferences for certain firms and industries, and compulsory
military service. Finally, rent-seeking activities grow around the military because of its non-competitive
allocation of resources. In this way, over and above their depressing effect on the level of investment,
military expenditures may exert a direct adverse impact on the economy's productive efficiency.
These considerations suggest that the net effect of a rise in military expenditure on a country's growth
rate and its steady state level of capacity output are likely to be negative. Therefore, one would expect to
find evidence of this negative impact in longer-run economic data both across countries and over time.
However, it is obviously difficult to disentangle empirically the potential positive short-run effect of the
demand stimulus associated with an increase in military spending from the depressing effect of high
military spending on the longer-run growth path of capacity output, particularly if the estimation work
fails to exploit both the time-series and cross-section dimensions of the data. The striking ambiguity of
past econometric results in the face of strong anecdotal evidence on the long-run economic benefits of
lowering military expenditure suggests that weaknesses in the econometric techniques used to test these
hypotheses may be a problem.
Thus, it is not surprising that a number of past attempts to subject the relation between military spending
and growth to empirical testing (Benoit, 1973, 1978 and Frederiksen and Looney, 1982) seem to have
uncovered empirical support for the thesis that military expenditures were not detrimental to growth.
Benoit (1978), using data for 44 developing countries over 1950-65, finds a positive association between
military spending and growth of civilian per capita output. In contrast, Rothschild (1977) on the basis of
rank correlations on growth, exports, and military spending for 14 OECD countries during 1956-69,
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Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
concludes that higher military spending is associated with lower exports and lower economic growth.
Deger and Smith (1983) find that the direct impact of military expenditures on growth is positive, while
the effect on savings is negative; in their view the net impact of military expenditures on growth is
negative because the negative indirect effect on savings outweighs the positive direct impact. Biswas and
Ram (1986) conclude that military expenditures neither help nor hinder economic growth. Aschauer
(1989) finds that government expenditure on infrastructure in the United States has a positive effect on
growth, while military capital expenditures have virtually no impact. Using data for 71 countries over the
period 1969-89, Landau (1993) concludes that military expenditure is not associated with lower rates of
economic growth, capital formation, or government social and infrastructure spending. Some other
studies have obtained a negative, but weak empirical relationship between military spending and
economic growth. Habibullah, Law and Dayang-Afizzah (2008) used error correction panel model to
study the causal relationship between defence spending and economic growth in Asia countries, and
found the two variables are independent. That is there is no long-run relationship between them. In the
next section we specify a model and suggest a technique of estimation that are both intended to address
the limitations of past empirical research on this relationship. Lebovic and Ishaq (1987) indicate that
higher military spending has suppressed economic growth in the Middle East region even when
alternative measures of the military burden are used.
3. Model Specification, Data and Estimation Technique
In model specification we follow Knight, Loayza and Villanueva, (1996) to specify the rate of economic
growth which is based on Mankiw, Romer and Weil (1992) version of Solow-Swan model. It is derived
by linearizing the transition path of output per capita around its steady-state level (see Knight, Loayza
and Villanueva, 1993). The resulting equation specifies output growth as a function of initial output and
variables that condition for the economy's steady state. The conditioning variables that we include are the
ratio of investment to GDP, the rate of population growth, a proxy for the degree of openness of the
economy to international trade (i.e. an index of the degree of restrictiveness of its system of tariff and
non-tariff barriers to international trade). We incorporate the possible effects of the Delta region amnesty
(a dummy variable) and the military spending on the growth per capita capacity output (ratio of military
spending to GDP). In accordance with the Solow-Swan model we assume that the conditioning variables
are exogenous with respect to output growth; in particular, the ratio of military expenditures to GDP is
assumed to be unaffected by the rate of output growth but rather the militancy.
Equation (1) specifies the per capita output growth rate, y t
where y t represents the natural logarithm of output per capita:
yt   0  1 ln( nt  g   )   2 ln skt   3 ln f t
  4 ln sht   5 ln mt   6 dmt  et
(1)
where ln indicates a natural logarithm, t represents the time period, n is the population growth rate, g is
the technological growth rate,  is the rate of depreciation of stock of physical capital and g   is
assumed to be equal 0.05, sk is the ratio of physical capital investment to GDP, m is the ratio of military
expenditures to GDP, sh is a proxy for the ratio of human capital investment to GDP, f is a proxy for the
degree of restrictiveness of the economy’s international trade system, dm is the proxy for the amnesty in
delta region and e is a white noise error term.
In order to allow for the indirect effect of military spending and amnesty in delta region on growth via its
impact on productive investment, we specify the second equation by extending the model of Knight,
Loayza and Villanueva, (1993) which specifies the ratio of investment in fixed capital as a function of the
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Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
rate of investment in human capital ( sh ), the restrictiveness of the trade system ( f ), the delta region
amnesty ( dm ) and the military spending ratio ( m ). The investment equation is:
ln skt   0  1 ln( nt  g   )   2 ln f t
  3 ln sht   4 ln mt   5 ln dmt  et
(2)
3.1. Data
As the paper examines only a country case, we use the annual data on growth of per capita GDP, the ratio
of military spending, investment in human capital, ratio of investment, trade restrictive index over the
period of 1970- 2008. The delta amnesty is captured by use of dummy variable which takes the value of
one (1) for the years of militancy and zero otherwise. The investment in human capital is proxied by
expenditure in education and the data were collected from CBN bulletin, 2009. The investment is proxied
by national savings and the data were collected from CBN bulletin 2009. Other data were collected from
the World Development Indicator (WDI) of the World Bank, 2009.
3.2. Estimation Technique
3.2.1. Unit root Test
Since the annual data available in our study ranges from 1970 to 2008 (39 observations) robust estimates
is not possible with classical time series econometrics. This presupposes that we examine the time
properties of the data. The time series properties of interest include the stationarity and cointegration. For
stationarity we utilize the Augmented Dick –Fuller (ADF) and Phillips-Perron (PP) tests.
3.2.2. Cointegration Analysis
To conduct cointegration relationship between economic growth and other variables we employ the
maximum-likelihood cointegration test due to Johansen and Juselius (1990). The Johansen procedure
provides more robust results than other methods when there are more than two variables (Gonzalo, 1994).
The Johansen approach sets up the nonstationary time series (Y ) as a vector autoregression (VAR):
k 1
Y   0   i Yt i  Yt k   t
3
i 1
where i   (1  1  ....   i ) for i  1,...., k  1
 i  (1  1  ...   k )
Yt is a vector of p variables,  0 is a constant and  t is a vector of Gaussian random variables.  and 
represent coefficient matrices,  is the difference operator and k denotes the lag length.
The model is estimated by resgressing the Yt matrix against the lagged differences of Yt and Yt  k . The
number of cointegrating vectors is determined by the rank ( (r ) of  , which indicates the number of
cointegrating vectors. If  is of full rank or r  p no cointegration is present as all series are themselves
stationary. On the hand, if  is a null matrix or r  0 then no long relationship is present as equation (3)
is the usual VAR model in the first differences. In the case when 0  r  p , then there exists one or more
cointegrating relationships among the variables. The  matrix can be decomposed as     , where
the elements of the  matrix are the adjustment coefficients and the  matrix contains the cointegrating
vectors. The procedure uses two likelihood ratio statistics to test for cointegration vectors, namely the
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Proceedings of 11th International Business and Social Science Research Conference
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trace and maximum eigenvalue (  max) statistics. The trace test for the null of r or fewer cointegrating
vectors (versus more than) r is
p
TRr   T  ln(1  ˆi )
4
i  r 1
This is function of the squared canonical correlations (ˆi ) between the first difference and the levels of
the variables, having factored out the dynamic and deterministic factors. The (ˆ ) are the solutions to a
i
certain eigenvalue problem. An alternative maximal eigenvalue test statistic of the null that there is r
cointegrating vectors against the alternative that there are r  1 is
  max r  TRr  TRr 1
5
Both test statistics can be compared with their respective critical values provided by Osterwald-Lenum
(1992).
4. Results and discussions
4.1. Unit root test
For testing the number of cointegrating vectors, the Johansen procedure requires that all variables are not
integrated of the order more than one, but the procedure can handle both I (1) and a mixture of I (1) and I
(0). To verify the univariate properties of the variables concerned, we rely on two-unit root testing
procedure - Augmented Dick-Fuller test (ADF) and Phillips-Perron (PP). In applying these tests, the
optimal lag structure is determined using Akaike Information criteria (AIC) and the Schwartz Bayesian
Criteria (SBC). The optimal lag length is one. The results are reported in the Table 2. Overwhelmingly,
the results indicate that all the variables are integrated of order one, except the variable (n  g   ) that is
not stationary in the level and at the first difference in the PP test. For consistency, we rely on the
statistics from ADF test.
Table 2: Unit root Test results
ADF
Variables
ln gdp
ln sk
ln sh
ln m
ln(n  0.05)
ln i sec
5% Critical value
Level
First Difference
Without Trend
-0.63741
With Trend
-0.63928
Without Trend
-3.6912
With Trend
-4.0531
-0.20879
-2.0076
-3.7768
-3.7192
-1.5366
-3.2941
-5.0763
-5.1243
-1.4543
-2.3217
-4.4716
-4.4356
-4.7671
-8.7154
-6.4216
-7.4956
-2.56402
-2.7953
-5.0840
-5.0126
-3.5348
-2.9446
-3.5386
-2.9422
Phillips-Perron (PP)
ln gdp
ln sk
ln sh
ln m
ln(n  0.05)
ln i sec
-1.261824
-1.186716
-5.831152
-6.030680
-0.491172
-1.849212
-6.163160
-6.066276
-0.719308
-3.403930
-11.78133
-17.61595
-1.160080
-2.378565
-6.877007
-6.794809
-1.809537
-2.584041
-2.202559
-2.466817
-3.060788
-3.518188
-8.597789
-8.470444
5% Critical values
-2.941145
-3.533083
-2.943427
-3.536601
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Proceedings of 11th International Business and Social Science Research Conference
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4.2. Cointegration Analysis
Knowing that variables are integrated of order not more than one (at first difference) we proceed to
determine the number of cointegration relations in the eight dimensional vectors. To conduct this test we
employ Johansen and Juselius maximum likelihood test. For multivariate cointegration analysis, a lag
length is necessary for each of the VAR systems. We employed the Akaike Information criteria (AIC)
and the Schwartz Bayesian Criteria (SBC) that is commonly used in the literature to determine the
optimal lag length. The SBC criterion suggests a lag of one period but AIC implies longer lag length in
the VAR model. To avoid over-parametization and under-parametization we choose lag length of two.
The variables f and dm are assumed to be exogenous. The other variables are assumed to be endogenous
in the system. The results are presented in Table 3.
The maximum engeinvalue indicates one cointegrating vector while the trace shows five cointegrating
vectors. Akaike Information Criterion (AIC) detects eight cointegrting vectors while Schwarz Bayesian
Criterion (SBC) detects one. Although there is a long-run relationship among jointly endogenous
variables arising from constraints implied by the economic structure on the long run relationship it is not
possible to identify the relation of interest. In general the more number of cointegrating vectors the more
stable will be the system of non-stationary cointegrated variables (Johansen and Juselius, 1990, Dickey et
al., 1991) as indicated by the trace. However the multiple cointegrating vectors lead to the problem of
identifying the single long-run relation of interest. Using the trace statistics we could not identify the long
run relationship of interest. Thus the analysis is based on the maxium engeinealue. The normalised long
run coefficients with r  1 are reported in Table 4. The coefficients are multiplied by minus one.
Table 3: Johansen and Juselius Cointegration results: Dependent Variable: GDP per capita (ln gdp)
  Max
Hypothesis
H0
r 0
r 1
r2
r 3
r4
r 5
r6
r7
HA
r 1
r2
r 3
r4
r 5
r 6
r 7
r 8
r 0
r 1
r2
r 3
r4
r 5
r6
r7
r 1
r2
r 3
r4
r 5
r6
r7
r 8
Statistic
95%Critical Value
90% Critical Value
116.052*
51.1500
48.2300
41.3882
45.6300
42.7000
39.7156
39.8300
36.8400
29.3446
33.6400
31.0200
26.3511
27.4200
24.9900
16.7991
21.1200
19.0200
9.2545
14.8800
12.9800
4.6895
8.0700
6.5000
283.595*
157.8000
152.0100
167.543*
124.6200
119.6800
126.154*
95.8700
91.4000
86.439*
70.4900
66.2300
57.094*
48.8800
45.7000
30.7431
31.5400
28.7800
13.9440
17.8600
15.7500
4.6895
8.0700
6.5000
Trace
Notes: Aterisk (*) indicates statistically significant at the 5% level, (see Osterwald-Lenum, 1992)
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Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
Table 4: Estimates of Restricted cointegrating relations (Dependent Variable: GDP per capita (ln gdp) )
Variables
ln gdpt
Coefficients
Standard error
-1.0000
ln skt
-0.34885*
0.073002
S
ln sht
ln(n  0.05)
ln i sec
ln f
ln m
dm
0.035927
0.037251
NS
-3.9228*
0.98992
S
0.56964*
0.17922
S
-0.41502
0.42140
NS
-0.57113*
0.14554
S
0.54759*
0 .19520
S
Note NS – not significant, S = significant
As can be seen from the Table 4, some of the long run coefficients carry the expected signs except the
ratio of physical investment to GDP. The long run coefficient of the ratio of military spending to GDP
has expected sign (negative) and is significant. This implies that as militias continued the operations, the
military spending increases. This is also depicted in Figure 1. As militia operation continued the military
expenditure rises. The increase in military spending depresses output per capita. This result agrees with
the theory that over the longer term, increases in military spending are likely to exert a negative effect on
capacity output (Knight et al., 1996). On the empirical side the result lends a support to the findings of
Lebovic and Ishaq (1987) that military spending suppressed economic growth. Other things being equal,
a rise in military spending exerts a negative impact on the rate of investment in (public and private)
productive fixed capital. This occurs because of well-known crowding-out effects: an increase in military
spending must be financed either by raising current taxes or by borrowing (future taxes). In either case, it
will lower the expected after-tax return on productive fixed capital, while simultaneously reducing the
flow of (domestic plus foreign) savings that is available to finance productive fixed capital formation in
the domestic economy. Military spending is not governed by market processes and as such it tends to
create distortions in relative prices that result in a dead-weight loss to total productive capacity. In this
case it may exert negative externalities on capacity output.
As evidence shows the coefficient of ratio of investment to GDP is negative and significant. With
militancy the long run effect of investment on output growth is small. This implies that during the period
of militancy new investment is not initiated or investors are not willing to commit their scarce resources
into projects or undertake investments in areas or countries that have political tensions for fear
vandalisation and loss of lives. The amnesty period is positive and significant. The result appears as
expected. At this point we can see that amnesty or peace is necessary for output growth despite the short
period.
The human capital has positive but statistically insignificant effect on growth. The rate of population
growth, technological growth, and rate of physical capital depreciation is negative and significant
implying that output growth is enhanced through reduction in population growth and depreciation of
physical capital. One notable result in this study is that of internal security. The long run coefficient of
this variable is positive and significant. This implies that internal security is crucial for output growth and
economic growth as it protects investments and other capital formation project from being vandalised.
Thus, this variable requires the attention of the policy-makers at any given time period. The degree of
restrictiveness of economy to international trade is negative and not significant.
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Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
Next we examined the total impact of military spending and amnesty on the physical investment as
specified in equation (2). First, we look at the long run co-movement of the variables in the equation (2).
As it is the case in equation 1, the variables f and dm are assumed to be exogenous and others are
endogenous. The results of this exercise are reported in Table 5. There is evidence of cointegration
among the variables. The maximum engeinvalue indicates one cointegrating vector while trace detects
three cointegrating vectors.
The long run coefficients are extracted from cointegration analysis and normalised by multiplying minus
one. These coefficients are reported in Table 6. The result of the human capital contradicts the theory
which says that investment is positively related to human capital. Its coefficient is negative though not
significant. Considering the abundant human resources in Nigeria and high unemployment rate, it is
expected that industrial sectors would maximise this resource. However it is quite unfortunate that this
resource appears to be negative in investment equation. Does it mean that labour has not acquired the
required knowledge needed in the industrial sector? Or does it mean that our educational system is faulty?
If this is the case the education curriculum needs to be revisited or redesigned to meet the needs of the
industrial sector. The internal security is positive and significant scoring the importance of internal
security for industrial sector growth. The peace is very critical variable for industrial development.
Table 5: Johansen and Juselius Cointegration results: Dependent Variable: (ln sk )
  Max
Statistic
Hypothesis
H0
r 0
r 1
r2
r 3
r4
r 5
r6
HA
r 1
r2
r 3
r4
r 5
r 6
r 7
r 0
r 1
r2
r 3
r4
r 5
r6
r 1
r2
r 3
r4
r 5
r6
r7
95%Critical Value
90%Critical Value
86.5571*
45.6300
42.7000
37.4087
39.8300
36.8400
28.7241
33.6400
31.0200
26.4794
27.4200
24.9900
9.8578
21.1200
19.0200
7.3099
14.8800
12.9800
0.25164
8.0700
6.5000
196.5885*
124.6200
119.6800
110.0315 *
95.8700
91.4000
72.6228*
70.4900
66.2300
43.8987
48.8800
45.7000
17.4193
31.5400
28.7800
7.5615
17.8600
15.7500
0.25164
8.0700
6.5000
Trace
Notes: Aterisk (*) indicates statistically significant at the 5% level, (see Osterwald-Lenum, 1992)
For fear, the investors would not commit their scare financial resources in an environment with political
tensions or possible vandalization of their equipments. The military spending has a negatively significant
effect on the investment. This implies that in the long run, an increase in military spending will decrease
physical capital investment and thus per capita output. The magnitude of coefficient is high.
11
Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
Table 6: Estimates of Restricted cointegrating relations (Dependent Variable: Ratio of Investment to GDP (ln sk )
Variables
Coefficients
Standard error
ln skt
-1.0000
-
ln sht
ln(n  0.05)
ln ise
ln f
ln m
dm
-0.27087
0.36527
NS
-13.3645*
3.7442
S
4.3847*
1.9714
S
2.5548*
1.1830
S
-3.1139*
2.8399
S
0.73608
1.3928
NS
Note NS – not significant, S = significant
The international trade restrictiveness index ( f ) is has a positive significant effect on physical investment
or industrial sector. This means that opening to world trade or removal of impediments to investments is
quite crucial for industrial sector growth. Any restriction on imports of critical inputs or exports of
industrial output would hamper physical investment in such a way that investment would impact
economic growth negatively. The Fully modified Phillips- Hansen least square is used to re-estimates
long run coefficients of both equations 1 and 2. The results are reported Tables A1 and A2 in Appendix.
The short-run estimates are not impressive as none of the coefficients seem to be significant. As such the
results are not reported but can be made available on request.
5. Conclusion and Policy Implications
The study does suggest that the amnesty granted to Niger Delta militias improves economic growth as
well as investment, as military spending decreases. The military spending does, in fact, negatively affect
the economic growth. It is seen that investment is small to support a meaningful economic growth. This
might be as result of increase in military spending, made possible by increase in militancy in Niger Delta
region and the Nigeria’s involvement in peace keeping operations in West African sub-region. The policy
implication is that the more militia’s activity continues the military spending will rise. Invariably, this
will reduce the funds available for investments, social welfare and other infrastructure like roads, which
will have grater impact on economic growth. Secondly, there will be a great loss of manpower. The
effects of military spending vary by levels of economic poverty or prosperity, political stability,
instability or conflict, and possibly arms production capability (Smaldone, 2006). In general, states with
more resources and political stability experienced more positive effect of military spending whereas
resource constrained, conflicted, and non-arms producing countries experienced more negative effects
including higher external debts. Based on this, we are not surprised on the effects of the military
spending on Nigeria economic growth in which the level of poverty is high; there is some evidence of
political tensions in the country.
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Proceedings of 11th International Business and Social Science Research Conference
8 - 9 January, 2015, Crowne Plaza Hotel, Dubai, UAE. ISBN: 978-1-922069-70-2
Appendix
Table A2: Fully Modified Phillips-Hansen Estimates (Dependent Variable:
Variables
Coefficients
Standard error
yt )
T-Ratio[Prob]
Intercept
2.4209
0.92933
2.6050[0.014]
ln skt
-0.24568
0.050116
-4.9022[0.000]
ln sht
ln(n  0.05)
ln ise
ln f
ln m
dm
0.12122
0.025986
4.6650[0.000]
-2.1589
0.42956
-5.0260[0.000]
-0.11927
0.062807
1.8990[0.067]
0.30975
0.22598
1.3707[0.181]
-0.10570
0.059840
-1.7663[0.088]
-0.14443
0.081961
-1.7622[0.088]
Table A3: Fully Modified Phillips-Hansen Estimates (Dependent Variable: ln skt )
Variables
Coefficients
Standard error
T-Ratio[Prob]
Intercept
-10.3127
0.43577
2.4178
0.042172
-4.2653[0.000
-4.9022[0.000]
-6.9254
0.87929
-7.8762[0.000]
0.22329
0.19687
1.1342[0.265]
ln sht
ln(n  0.05)
ln ise
ln f
ln m
dm
1.6645
0.71241
2.3365[0.026]
-0.77077
0.15087
-5.1088 [0.000]
-0.50027
0.25844
-1.9357 [0.062]
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Proceedings of 11th International Business and Social Science Research Conference
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Table 1: Regional Military Expenditures (billion US$)
World Total
Africa
1988
1990
1992
1995
1998
1999
2000
2003
2004
2005
2008
2009
2010
1441
1339
1108
983
962
980
1017
1172
1237
1288
1446
1540
1559
13.9
14.6
12.0
12.8
13.7
19.0
17.0
18.3
20.5
21.4
25.6
27.1
28.5
North Africa
Sub-Saharan Africa
Nigeria (billion Naira)
3
4
4
4
5.2
4.9
4.9
6.5
7.1
7.3
9.4
10
10.6
10.6
11.1
8.0
8.5
8.5
14.2
12.1
11.8
13.5
14.0
16.2
17.1
17.9
1.2
2.2
3.0
14.0
25.162
45.4
37.49
75.9
85.0
88.5
192.0
224.0
292
Americas
583
547
507
442
409
411
428
537
583
613
692
745
767.7
North America
Central America & the Caribbean
South America
549
520
482
407
374
375
389
498
542
568
637
688
707.3
2.8
3.0
3.5
3.7
4.0
4.2
4.4
4.2
3.9
4.2
5.1
5.5
5.6
31.2
24.4
21.1
31.7
31.7
31.8
35.2
35.1
37.4
41.1
49.4
51.8
54.8
Asia and Oceania
119
128
139
145
156
161
165
193
204
214
258
284
288
-
-
0.9
0.6
0.7
0.7
0.7
1.0
1.2
1.3
2.0
2.0
..
88.4
96.8
107
110
118
120
122
147
153
161
197
218
222
18.3
19.3
18.5
21.5
24.0
26.9
28.0
29.6
33.6
35.2
39.6
43.6
42.6
12.6
12.3
12.6
12.8
13.4
14.0
14.0
15.3
15.9
16.4
19.0
20.4
21.3
Europe
673
579
388
330
320
326
335
351
353
354
378
387
376
Western Europe
Eastern Europe
Central Europe
317
320
306
278
280
286
287
293
293
289
297
306
297
296
233
59.1
32.0
20.9
21.4
28.8
37.2
39.0
43.1
58.5
59.8
59.1
59.5
26.5
23.1
20.2
19.5
19.0
19.1
20.6
20.5
21.4
22.2
21.4
20.5
Middle East
52.3
70.0
62.5
52.7
62.2
62.1
71.3
72.7
77.4
85.2
93
97
98.6
Central Asia
East Asia
South Asia
Oceania
Source: Stockholm International Peace Research Institutes (SIPRI) Military Expenditure Database 2011, http://milexdata.sipri.org.
16