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This study assesses the importance of oil in the development of the Nigerian economy in a multivariate VAR model over the period 1960-2009. Empirical evidence shows that the five subsectors are cointegrated and that the oil can cause other non oil sectors to grow. However, oil had adverse effect on the manufacturing sector. Granger causality test finds bidirectional causality between oil and manufacturing, oil and building & construction, manufacturing and building & construction, manufacturing and trade & services, and agriculture and building & construction. It also confirms unidirectional causality from manufacturing to agriculture and trade & services to oil. No causality was found between agriculture and oil, likewise between trade & services and building & construction. The paper recommends appropriate regulatory and pricing reforms in the oil sector to integrate it into the economy and reverse the negative impact of oil on the manufacturing sub sector.
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www.ccsenet.org/jsd Journal of Sustainable Development Vol. 5, No. 4; April 2012
Published by Canadian Center of Science and Education 165
How Important is Oil in Nigeria’s Economic Growth?
Anthony Enisan Akinlo
Department of Economics, Obafemi Awolowo University, Ile-Ife, Nigeria
Tel: 803-370-0756 E-mail: aakinlo@oauife.edu.ng
Received: February 8, 2012 Accepted: March 12, 2012 Published: April 1, 2012
doi:10.5539/jsd.v5n4p165 URL: http://dx.doi.org/10.5539/jsd.v5n4p165
Abstract
This study assesses the importance of oil in the development of the Nigerian economy in a multivariate VAR
model over the period 1960-2009. Empirical evidence shows that the five subsectors are cointegrated and that
the oil can cause other non oil sectors to grow. However, oil had adverse effect on the manufacturing sector.
Granger causality test finds bidirectional causality between oil and manufacturing, oil and building &
construction, manufacturing and building & construction, manufacturing and trade & services, and agriculture
and building & construction. It also confirms unidirectional causality from manufacturing to agriculture and
trade & services to oil. No causality was found between agriculture and oil, likewise between trade & services
and building & construction. The paper recommends appropriate regulatory and pricing reforms in the oil sector
to integrate it into the economy and reverse the negative impact of oil on the manufacturing sub sector.
Keywords: VAR model, Causality, Oil, Impulse response functions, Development, Nigeria
1. Introduction
In the last decades of the 20th century, there was a counter-intuitive relationship between natural resources
abundance and economic development (Auty, 2001). Developing countries with abundant natural resources
underperformed compared with those that are deficient in natural resources (Ranis, 1991; Lal & Myint, 1996;
Sachs & Warner, 1995, 1999; Auty, 2001). Specifically, the per capita incomes of the resource poor countries
increased at rates two or three times faster than those of the resource abundant countries. The growth rate equally
widened significantly since 1970s. The apparent paradox between natural resource abundance and economic
growth as well as development has led to increasing research works into the so called resource curse (note 1)
phenomenon.
Nigeria is a natural resource abundant country. In particular, over the past fifty years, the country’s oil subsector
has grown phenomenally. Both production and exports have increased enormously since commercial production
in 1958. For example, crude oil production increased from 395.7 million barrels in 1970 to 776.01 million
barrels in 1998. The Figure increased to 919.3 million barrels in 2006. The Figure however decreased to 777.5
million barrels in 2009. In the same way, crude oil exports increased from 139.5 million barrels in 1966 to 807.7
million barrels in 1979. The volume of crude oil exports dropped to 390.5 million barrels in 1987 but increased
to 675.3 million barrels in 1998. The trend continued for most years after 2000. In the same way, oil revenue
increased from N166.6 million in 1970 to N 1,591,675.00 million and N6,530,430.00 million in 2000 and 2008
respectively.
The huge revenues from oil, of course, presented net wealth and thus provided opportunity for increased
expenditure and investment; however, the huge revenues complicated macroeconomic management and also
made the economy highly oil dependent. Asides, in spite of the huge rents from oil, the economy still grapples
with many problems including high and rising unemployment rate, declining manufacturing production, high and
rising level of poverty and poor infrastructural development (note 2). The dismal performance of the Nigerian
economy in the face of huge rents from oil has rekindled interest on the importance of oil in the growth and
development process in Nigerian. Hence, the objective of the paper is to examine the contribution of the oil
sector to the Nigerian economy over the period 1960-2009. Specifically, we examine whether or not
cointegrating relationship exists between oil and other non oil sub sectors; and determine the direction of
causality between the various sectors of the Nigerian economy.
The rest of paper is structured as follows. First, the second section provides a very brief summary of the
theoretical perspectives on resource abundance and economic growth. The third section looks at the contribution
of the oil sector to the Nigerian economy over the study period. The methodology adopted in the paper is
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discussed briefly in section 4. The results of the estimation are presented in section 5. The concluding section
contains the summary of major findings and offers some policy prescriptions to integrate the oil sector into the
national economy with a view to ensuring that it positively impacts the non oil sectors.
2. Theoretical Perspectives on Resource Abundance and Growth
This section provides a succinct summary of the theoretical literature on the nature of the relationship between
resource abundance and development. The purpose is to first present a theoretical account of the malign and
benign perspectives on the issue of resource abundance and economic progress. Then, the various channels
through which oil may impact growth and development follow each perspective. However, the literature here
should not be seen as a complete survey (note 3).
2.1 The Benign Perspective: Natural Resource Abundance Beneficial to Growth
The conventional wisdom before the late 80s was that natural resources had positive effect on development
(Rosser, 2006). This view was shared by many development theorists and neoliberal economists until the
resurgence of new view in the 80s that claimed that natural resource abundant was not a blessing to the
developing countries. The basic argument of the benign perspective is that natural resource endowments would
assist the developing countries to transit from the stage of underdevelopment to that of industrial ‘take-off’, as
obtained in such countries as Britain, the United States and Australia.
Essentially, the various channels through which abundance of natural resources like oil sector could contribute to
the economies of the oil producers have been identified in the literature. One, the huge revenues from oil enables
the governments of the oil producing countries to spend and invest massively without recourse to taxation.
Revenues from oil, if properly utilized, could serve as a “big push” for development. This channel is especially
important for developing countries where paucity of capital often constitutes a major hindrance to growth and
development. Moreover, the huge foreign exchange earnings from oil exports, apart from being used for
importing raw material, intermediate and capital goods for production in the non oil sectors, could equally assist
in boosting the foreign reserves of the oil exporting countries. The accumulation of foreign reserves can be seen
as collateral which the oil producing economies can use in attracting foreign investment (Dooley et al., 2004).
Moreover, such holding can be seen as a costly self-insurance strategy to smoothen vulnerability impacts of
domestic and foreign shocks and to intervene in the foreign exchange market.
Oil sector can also contribute to development in the oil rich economies through provision of intermediate inputs
to the rest of the economy. These intermediate inputs include crude oil, gas and liquid feed stocks, as well as oil
and gas into the refining, petrochemical and electricity and energy intensive industries respectively (Al-Moneef,
2006). This channel is critical to growth and development in the developing countries. For instance, many
outputs of the petrochemical industries are crucial to the development of the manufacturing industries.
Likewise, provision of electricity and other basic utilities at favourable prices is of considerable importance in
the process of growing and nurturing the service and manufacturing sub sectors.
Growth and development in the oil rich economies could be enhanced through the market contribution from oil.
The market contribution relates to the demand by oil sector for various inputs of goods and services provided by
local sources. Generally, as a result of oil production, refining and distribution, there is tendency for oil
sector-related services to spring up. These oil sector-related services will not only provide opportunity for
employment but also serve as sources of earnings for the operators.
Asides from the market contribution, the foreign investment (FDI) effect is very important. Oil activity often
leads to inflow of foreign resources such as FDI and portfolio investment. Indeed, the bulk of FDI into majority
of the countries that export oil are concentrated in the oil sector. The various channels through which FDI
impacts growth and development in the recipient countries have been extensively discussed in the literature.
Specifically, FDI inflows to developing countries not only help in increasing their stock of capital but may also
assist in boosting labour productivity and incomes in the host country. Consequently, the levels of output,
employment creation, and potential tax revenues are enhanced in the host countries (Ramirez, 2006) (note 4).
Empirically, few studies have been have provided results in support of the benign perspective on the impact of
natural resources on economic growth and development. Some of these studies not only reported that resource
abundance had positive impact on growth and development but also found that resource dependence had no
adverse impact on growth (note 5).
2.2 The Malign Perspective: Natural Resource Abundance Not a Blessing
Sequel to the poor performance of most oil–rich countries in the 80s, the idea that natural resource abundance
was a blessing to development was jettisoned by scholars. Critics argued that natural resource abundance is
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harmful to growth. Extensive literature exists on the various channels through which natural resources,
especially oil, harms growth. The major transmission mechanisms include Dutch disease, volatility argument and
inefficiency in resource allocation argument.
The Dutch disease simply says that an exogenous unexpected increase in foreign exchange revenues from natural
resources, arising from increase prices or output, will precipitate a real exchange rate appreciation and thus a
drop in output and employment in the non resource traded good sector, often manufacturing (note 6). The
volatility argument is anchored on the fact that revenues from natural resources especially oil are very volatile,
as they are driven by sharp and significant fluctuations in prices over relatively short periods of time.
Consequently, in the face of fluctuating revenues, governments in the oil rich countries often find it extremely
difficult to pursue a prudent fiscal policy. In addition, there is the general apprehension that windfall revenues
arising from unanticipated high export prices would be used for consumption rather than being invested or at
best invested on wasteful projects.
Moreover, emphasis is placed on the political economy considerations in explaining the nature of the
relationship between natural resource abundance and economic growth. This view contends that large windfalls
from the resource tends to generate and promote rent-seeking activities that involve corruption, voracity and civil
conflict (note 7). Several empirical studies have confirmed the natural resource curse hypothesis.
Some other reasons why resource-rich countries might suffer resource curse are reduced returns to human
investments, precipitated by natural resource exploitation (Gylfason, 2001a; b) and poor economic management
that leads to inefficient resource allocation (Rosser, 2006).
All in all, while there are strong theoretical grounds to suspect a broad correspondence between natural resource
abundance especially oil and low growth, the nature of the linkage is neither direct nor simple. Empirical
literature has not provided conclusive answer to whether abundant natural resource is a curse or blessing (note 8).
Even among studies that claimed the curse of natural resources actually exist, there is no agreement on what
exactly drives the curse of the natural resources and on how it exactly plays out. This explains why further
research should be focused on the causal link between natural resource abundance and growth in the resource
rich economies.
3. A Review of the Role of Oil in Nigeria
The extractive sector in the Nigerian economy is large and extensive, with oil playing a dominant role. With
nearly 37.2 billion barrels in reserves and 2.13% of global production, Nigeria has the world’s tenth largest
proven reserves (3.1% of global reserves), and is among the top 10 oil producers. Since the discovery and
production of oil Nigeria in 1958, the subsector has continued to play a major and dominant role in the Nigerian
economy. In terms of output production and product contribution, oil witnessed steady progress throughout the
period under consideration. Crude oil production increased from 1.9 million barrels in 1958 to 152.4 barrels in
1966 (see Table 1). Production of oil declined sharply in 1967 and 1968 as a result of the civil war. However,
production increased from 395.7 million barrels in 1970 to 660.1 and 845.5 million barrels in 1975 and 1979
respectively. The increase in production witnessed during this period was precipitated by Middle East crisis and
the 1973/74 oil embargo which caused a sharp reduction in world oil supply. The increased oil prices that the
crisis generated helped to boost local oil production in the country. However, this was short-lived as the early
80s witnessed a glut in the international crude oil market owing to over-supply, which culminated in sharp drop
in prices and eventual reduction in the production quotas by OPEC member countries.
Consequently, oil production in Nigeria dropped from 760.1 million barrels in 1980 to 535.9 and 383.3 million
barrels in 1986 and 1987 respectively (note 9). The situation improved in the 90s as crude oil output rose from
383.3 million barrels in 1987 to 711.3, 742.3 and 772.9 million barrels in 1992, 1996 and 1998 respectively. The
trend continued between the year 2000 and 2009. The cumulative crude oil production for the country increased
from 20,575,881 million barrels in 2000 to 27,052, 0677 million barrels in the 2009. In general, crude oil
production witnessed appreciable increase over the period under study.
The contribution of a product or sector to the national economy can be measured by its size in the GDP. The
contribution of oil to the GDP in Nigeria increased steadily over the study period. As evidenced in Table 1 below,
oil accounted for just 3.43 per cent of the GDP in 1965. The share of oil in the GDP increased from 9.27 per cent
in 1970 to 19.37 per cent in 1975. The Figure increased to 38.87 per cent in 2005. The share of oil in the GDP
decreased marginally to 37.44 per cent in 2009 (note 10). Two main reasons can be offered for the increasing
share of oil in GDP. The first is the discovery of oil in large quantity since early 70s which led to massive oil
production and export. The huge revenues from oil led to massive rural urban migration and the neglect of
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agriculture. The second reason is the natural tendency for share of agricultural sector to fall while non
agricultural sector increases as the economy develops. This trend is as dictated by Engel’s law (note 11).
Aside from the increased share of oil in GDP between 1960 and 2009, total oil exports increased phenomenally
over the period. As shown in Table1, total oil exports in 1961 was N23.1 million. The amount increased to N13,
632.1 million in 1980. Oil exports value decelerated in the early 80s to reach a trough of N11, 223.7 million in
1985 owing to the general economic depression in the industrial nations and increased efficiency in energy use
among other factors. However, the value of oil export assumed an upward trend in the 1990s. It increased from
N106,623.5 million in 1990 to N1,920,900.4 million, N7,140,578.9 million and N8,543,261.2 million in 2000,
2005 and 2009 respectively.
As Table1 shows, the share of oil export in total exports in Nigeria increased from mere 6.65 per cent in 1961 to
97.03 per cent and 98.53 per cent in 1990 and 2005 respectively. The Figure decreased marginally to 96.73 per
cent in 2009 following the deliberate government efforts at promoting non oil exports in the country. The high
dependence on oil exports clearly shows that Nigeria is a monocultural economy (note 12). The country’s
non-oil exports were remarkably small. The share of non oil exports in the total exports was consistently less
than 5 per cent over the period 1980-2009. No doubt, this has had a lot of dire consequences on the growth and
development of the economy. If the export sector had been more diversified, the impact of the fluctuations in the
international petroleum markets on the economy would have been minimal.
Sequel to high dependence on oil exports, the share of oil revenue in federally collected revenue increased
phenomenally over the study period. It increased from 26.3 per cent in 1970 to 85.8 per cent in 2005. The
percentage dropped to 78.7 per cent in 2009 reflecting the increasing emphasis by government on non oil exports
since mid 2000. The major implication of high government dependence on oil revenues was the fluctuation of
government revenues in reflection of the value of oil exports in the country. This actually explains why
government expenditure increased when the economy experienced boom and dropped when the economy
slumped. In other word, government’s fiscal policy became procyclical. Hence, government spending tends to
exacerbate the ripples of oil shocks on the economy.
Another major area through which oil industry contributes to the Nigerian economy is in the attraction of FDI.
Nigeria has attracted a lot of FDI particularly into the oil sector over the years and given the huge and bright
potentials of this sector, it is likely that more new investments and reinvestments will be attracted. Several other
channels through which oil has contributed to the Nigerian economy include provision of cheap and readily
available source of energy, boosting of the foreign reserves and provision of employment (Odularo, 2008).
However, critics have contended that the direct effect of oil sector activity on non oil growth in Nigeria is rather
limited. It is argued that the oil sector being an enclave sector has very little linkages with the other sectors in the
economy. The oil sector does not offer much opportunity for employment in Nigeria because it is more capital
than labor intensive industry. This is the reason why the significant expansion of the sector over the years has not
led to a similar increase in job creation. Indeed at present, total level of employment in the Nigerian oil industry
(including employment by ancillary firms) as a percentage of total modern sectors’ employment in the country
currently stands at 1.3 per cent. This is extremely low when viewed in terms of the size of the sector. Moreover,
oil-related outflows including imports of capital equipments, income repatriated to foreign investors and
amortization of FDI liabilities have been highly substantial in the country.
Besides, it is contended that the high dependence of the country on oil has posed significant challenges to
formulation and implementation of economic policies in the economy. As global price changes are difficult to be
fully addressed by domestic macroeconomic policies; the oil price volatility has induced macroeconomic
challenges in the economy. As an illustration, whenever there is a fall in the price of oil, counter-cyclical fiscal
action is constrained due to lack of other sources of revenue (note 13). Oil price changes often make the
exchange rate volatile thereby encouraging undue short-term capital flows. More often than not, government
action to mitigate this volatility often results in pro-cyclical monetary stance. In general, as a result of the limited
capacity of small non-oil sector in counteracting swings in oil prices in response to policy actions, the
effectiveness of macroeconomic policy is often constrained.
4. Methodology
This paper follows the multivariate cointegration VAR model developed by Johansen (1988) and Johansen &
Juselius (1990; 1992). This approach is adopted as against other possible candidates for several reasons. One, no
a priori assumption of exogeneity of variables is required. Two, vector auto regressive model allows each
variable in the system not only to impact on itself but also on each other without the need to impose a theoretical
structure on the estimates. Moreover, the approach affords us the opportunity of knowing not only how a given
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variable impact on itself but also on others through the use of variance decomposition (VDCs) and impulse
response functions.
As our main goal is to ascertain whether oil and the rest of the economy are cointegrated, we divided the
economy into five broad sectors namely: agriculture (agr), manufacturing (man), building & construction (buc),
oil (oil) and trade & services (tsr) or xt = (oil, agr, man, buc, tsr)'. The VAR model as specified in Johansen and
Juselius (1992) is given as (note 14):
xt =
 xt-i + xt-1+ Ψzt + εt (1)
where ’s (i = 1,……, ρ) are (5x5) matrices for the variables xt-1, xt is a (5x1) column vector of the first
differences of xt; is a (5x5) matrix for the variables xt-1 which is a (5x1) column vector of lagged dependent
variables, zt is a (5xn) matrix containing n deterministic variables for each dependent variable; εt is a (5x1)
column vector of disturbance terms normally distributed with zero means and constant variances. In general, the
time series characteristics of the variables determine whether vector-error corrections model (VECM) or an
unrestricted vector autoregression model (VAR) will be specified. For cointegrated non-stationary variables, the
correct specification is VECM. However, for uncointegrated non-stationary variables, the appropriate
specification is an unrestricted VAR model.
Quarterly time series data of GDP indices of the five sectors over the 1960-2009 are used in setting up the VAR
model. The 200 observations are quite adequate for VAR model. The data were compiled from the Central Bank
of Nigeria (CBN) Statistical Bulletin 2009 edition.
5. Empirical Results
In general, a VAR specification requires the determination of the time series property of the data set.
Consequently, we performed stationarity and cointegration tests on all the variables. All variables were
expressed in natural logarithm so as to minimize the scale effect. To test for stationarity, we employed two main
tests namely; the Augmented Dickey Fuller (Dickey & Fuller, 1979) and KPSS
Kwiatkowski-Phillips-Schmidt-Shin (1992) both with a constant and a deterministic trend. Thus the limitation of
the ADF statistic in deciding whether φ =1 or φ = 0.98, in a model like: Xt = μ + φXt-1 + εt is remedied by the
application of the KPSS statistic simultaneously. The Akaike Information Criterion (AIC) and Schwartz
Criterion (SC) both indicated optimal lag length of four. The results in Table2 clearly reveal that all the variables
are integrated of order one, I (1).
As the variables are stationary at first difference, we therefore tested for cointegration using the Johansen and
Juselius (1990) method (note 15). The results of λ-maximum and the trace tests are as reported in Table3. The
third and the fourth columns report maximum eigenvalue statistics and critical values respectively, while the
fifth and the sixth columns show the trace statistics and its critical values at 95 per cent respectively. The results
in Table 3 show that the null hypothesis of no cointegration relationship can be rejected at the 5 per cent level
using either λ-maximum or trace test statistics. The trace test suggests three cointegrating vectors while the
λ-maximum test suggests one cointegrating vector. This simply means that long run relationship exists amongst
the five sub sector. This result suggests that these five variables (sub-sectors) could not have moved too far away
from each other, thereby displaying a co-movement phenomenon for agriculture, manufacturing, oil, building &
construction and trade & services in Nigeria over this sample period.
The cointegrating vectors (normalized successively on oil, agr, man, buc and tsr) are as shown in Table 4
respectively (note 16). The coefficients of the variables imply the elasticities of the variables, since all the
variables are in logarithms. Some general observations are discernible from the results in Table 4. One, it is
interesting to notice that inter-sectoral relationships can be either negative or positive. The coefficient of oil
variable is positive in all the models except in the manufacturing model. The coefficient of oil variable is
significant in all the models. Two, the coefficient of agriculture is not only positive but also significant in all the
models. Three, negative coefficient of oil variable in the manufacturing model lends credence to the ‘Dutch
disease’ argument (note 17). A positive trend coefficient for building & construction and trade & services
suggest that these two sectors experienced much faster growth than the aggregate growth trend of economy. The
negative trend coefficients for agriculture, manufacturing and oil suggest that these sectors lagged behind the
economy’s aggregate growth rate. The neglect of agriculture and manufacturing by the various governments in
the country over the years is clearly reflected in the results. The negative trend coefficient in oil equation might
possibly reflect the disruptions in the oil production precipitated by the activities of the Niger Delta militants
over the period under consideration (note 18).
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As postulated by Engle and Granger (1987), cointegrated variables must have an error correction representation
whereby an error correction term is incorporated into the model. Essentially, such a formulation helps to
reintroduce the information lost in the process of differencing and thus allowing for long-run equilibrium as well
as short run dynamics. Therefore, a Vector Error Correction Model (VECM) was formed and estimated to
determine between oil, manufacturing, building & construction, agriculture and trade & services. Table 5
presents the estimated results of VECM. Each of the error correction terms is significant except for agriculture.
Moreover, except for manufacturing each of the error terms denoted as ξt-1 are negative. This seems to suggest
that active channels of causality exist among the variables. The summary of the direction of causality is as shown
in Table 6.
The results from Table 6 show that no causality exists between oil and agriculture [agr <> oil]. Also, there is no
evidence of causality between building & construction and trade & services [buc <> tsr]. The results show a
case of unidirectional causality between manufacturing and agriculture [manufacturing => agriculture],
agriculture and trade & services (agr => tsr], and trade & services and oil [tsr => oil]. Finally, a bidirectional
causality was found between manufacturing and trades & services [man <=> tsr], agriculture and building &
construction [agr <=> buc], oil and manufacturing [oil <=> man], building & construction and oil [buc and oil]
and manufacturing and building & construction [man <=> buc]. Essentially, the results from the causality test are
summarized as shown in Figure 1.
As it has been noted in the literature, individual coefficients from error-correction model are difficult to interpret
in the case of the vector auto-regressive model. Consequently, the dynamic properties of the model are analyzed
by examining the impulse response functions (IRFs) and the variance decompositions (VDCs) (note 19). The
impulse response functions trace the dynamic responses to the effect of shock in one variable not only upon itself
but also upon on all other variables. These impulse response functions are plotted in Figure 2. It can be seen
clearly from Figure 2 that the response of agriculture to a one standard deviation (SD) innovation in oil is
slightly positive in the short run with some tendency to decline before assuming constant level in the long run. A
one standard deviation shock to oil shows negative impact on the manufacturing sub sector turn positive in the
2nd period but becomes negative after the third periods. This no doubt provides evidence in support of the
resource curse hypothesis. The response of building & construction to a one standard deviation (SD) innovation
in oil is sharply negative in the short run but the impact turns positive in the medium term before assuming
constant level in the long run. In the same way, the response of trade and services (tsr) to a one SD innovation in
oil is negative and after two periods becomes positive though the increase is not dramatic.
Aside from IRFs, this study traces the variance decomposition (VDC) of each variable over a ten period. The
VDC provides information about the relative importance of each random (one-standard deviation) shock to the
endogenous variables in the VECM. The summary of the results is as reported in Table 7. The results show that
relatively low proportion of the variance in agriculture shocks is attributable to shocks in oil, and other sub
sectors. The results suggest, that after 10 years, a unitary shock in the other sub sectors explains about 5 per cent
of the accumulated forecast error variance of agriculture. However, a good proportion of the variance in
manufacturing is attributable to shocks in agriculture, and building & construction. In the same way, agriculture
and oil account for shocks in building & construction. A good proportion of the variance in trade & services is
attributable to shock in agriculture, oil and building & construction. Agriculture accounts for shocks in oil. In
general, oil accounts for shocks in building & construction, trade & services, but not nearly as much in
manufacturing. The importance oil shock to agriculture is highly negligible.
6. Conclusions
This paper has examined the role of oil in the development of the Nigerian economy. Several major findings
were obtained from the analysis. First, evidence from the descriptive analysis reported in section 2 suggests that
crude oil production has increased substantially in Nigeria since the discovery of oil in 1958. The share of oil in
the total GDP has increased phenomenally over the period 1958 to 2009. In the same way, oil exports increased
from mere 1.9 million barrels in 1958 to an average of 2012.5 thousand barrels in 2009. Second, the paper
adopted a multivariate cointegration analysis using the VAR model developed in Johansen & Juselius (1990;
1992). The evidence from estimated econometric model suggests that the variables included are stationary at first
differences. Hence, they are integrated of order one. The Johansen cointegration test shows that there is
cointegration and hence, confirmed the existence of long run equilibrium relationship between the variables
included in the model. This means that the economic sectors tend to move together in the long run. However, the
results show that oil negatively affected manufacturing sub sector during the study period.
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The results provide evidence which suggests that there exists bidirectional causality between manufacturing and
oil, building & construction and oil, manufacturing and trade & services, oil and building & construction, and
manufacturing and building & construction. No evidence of causality was found between agriculture and oil as
well as between building and construction and trade and services; a unidirectional causality from trade &
services to oil and manufacturing to agriculture. The reported IRFs and VDCs suggest that oil accounts for
shocks in building & construction, and trade & services, but not nearly as much in manufacturing. Its role in
agriculture is highly negligible. A good proportion of the variances in manufacturing, oil, building &
construction, and trade & services are attributable to shock in agriculture. The importance of building and
construction shocks to trade and services is apparent in contrast to the reverse case where shocks to building and
construction are almost negligible in explaining shocks to trade and services.
From a policy perspective, the finding that the five economic sectors are cointegrated is an indication that
increased oil activity could impact on the other non oil sub sectors. However, to ensure that oil continues to
foster better growth and development there is the need to focus on three major areas. These are sustenance of
increased investment inflows to the oil sector, stimulation of local labour and capital and institution of
appropriate reforms to enhance efficiency and transparency. These reforms will entail institution of appropriate
pricing policy in the oil sub sector and elimination of corruption in the sector. In order to eliminate the current
massive corruption in the sector, there is need to deregulate the sector to allow private initiatives. In the
meantime, the government should ensure that the various refineries are reactivated to produce refined products
for local consumption and export. This will assist in the integration of the oil sub sector into the economy
through increased employment and positive value added.
Moreover, the direct benefits of stronger oil activity would be more in the other non oil subsectors if the negative
impact of oil activity on the manufacturing in the long run is reversed. However, if the manufacturing sub sector
is to benefit positively from oil activity, there is the need to increase investments in the sub sector. Increased
investments in the sub sector will come in form of infrastructural development, education and research.
Increased investment in the manufacturing sub sector will assist in eliminating economies of scale and other
distortions which make loss of manufacturing capacity costly to reverse, in the face of dwindling oil revenues.
Acknowledgements
Author would like to thank the Editor and two anonymous referees for valuable comments and suggestions to
improve the quality of the paper.
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Notes
Note 1. The resource curse argument came to front burner as a result of the influential research of Sachs and
Warner (1995; 1999). They indeed popularized primary resource exports over GDP as a measure of resource
wealth.
Note 2. The Nigerian economy has been grappling with economic, social and political problems since late 70s.
Government attempt at solving these problems led to the implementation of structural adjustment programme in
1986. Several other measures have been implemented since then without much success.
Note 3. Here, we only provided a brief summary of the theoretical relationships between resource abundance and
economic growth and development for two main reasons. The first, is the need to conserve space. The second is
that a number of studies have examined the theoretical relationships between resource abundance and economic
growth. Some of these earlier studies include Corden & Neary (1982); Krugman (1987); Neary & Wijnbergen
(1986); Leite & Weidmann (1999); Collier & Hoeffler (2004) among others.
Note 4. Indeed, several studies have demonstrated empirically the positive impact of FDI on growth in the host
countries. See the works of De Mello Jr. (1997); Huang (2004); Ram & Zhang (2002); and Aitken & Harrison
(1997).
Note 5. Empirical evidence in support of this argument can be found in the works of Brunnschweiler and Bulte
(2008) and Brunnschweiler (2009) among others.
Note 6. However, it is being argued in the literature that this on its own should not generate adverse long run
implications for the entire economy. This is because once the revenues from the resource are diminished or
vanished totally; the economy is expected to re-adjust except there are important non-convexities or
rigiditie/imperfections in the economy. As an illustration, the loss of manufacturing capacity will be very costly
to reverse if the manufacturing sector is subject to economies of scale or learning by doing. (Esfahani et al.,
2009).
Note 7. Details of this arguments can be found in the works of Mauro 1995, Leite & Weidmann (1999); Lane &
Tornell (1996); Tornell & Lane (1999).
Note 8. For example, study by Jerome et al (2006) showed that oil had non significant positive effect on growth
in seven oil producing African countries while Odularo (2008) found that oil did not contribute significantly to
growth in Nigeria between 1970 and 2005. However, study by Brunnswhweiler (2008, 2009) found that oil had
significant positive effects on growth in 27 selected newly independent states of the former Soviet Union (FSU)
and Central and Eastern Europe (CEE). Likewise, Al-Moneef (2006) provided evidence of positive impact of oil
on the growth and development of the Arab countries; oil and non oil exporting countries inclusive.
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Note 9. Other factors that were responsible for the low production levels in the 80s included general economic
depression in the industrial nations, increased efficiency in energy use, rigorous conservation efforts and
successful substitution of oil with other forms of energy in major oil importing countries, as well as the
exploration and development of competing oil fields in North Sea, Alaska and Mexico.
Note 10. Although, the share of oil in the total GDP has increased over the years, the sector has had a
disproportionately low contribution to the GDP and overall economic transformation of the country. Assessing
this disconnect is key to securing long term supplies in Nigeria.
Note 11. Engel’s law states that consumers tend to spend a lower proportion of incremental income on food
product as personal income grows as a result of economic development. Consequently, the share of agriculture in
the GDP will fall, while that of non oil, especially manufacturing will increase.
Note 12. As a matter of fact, of the twenty most oil dependent countries in the world, Nigeria ranked as the
highest. Government efforts at diversifying the economy have not yielded results due mainly to poor economic
management and corruption in the country.
Note 13. This risks could have been somewhat mitigated if the Nigerian government had adopted prudent policy
of judiciously investing or saving the bulk of the oil proceeds. Unfortunately, the reverse was the case as
government, over the years, wasted bulk of the oil proceeds on white elephant projects that dotted the map of the
country.
Note 14. The VAR methodology has been widely adopted in the literature to warrant detailed description here.
Therefore, we only provide a sketch of the technique in this paper. For detailed explanations and reviews of the
technique one can consult the work of Ambler, 1989; Muscatelli & Hurn (1992); Harris (1995) and Renote, Jr.
(2001) among others.
Note 15. The Johansen approach was preferred above the Engle & Granger (1987) method because it is capable
of determining the number of cointegrating vectors for any given number of non-stationary series (of the same
order). Its application is appropriate in the presence of more than two variables, and more importantly, Johansen
(1988) has demonstrated that the likelihood ratio tests in this procedure (unlike the DF, ADF tests) have
well-defined limiting distributions.
Note 16. The normalization of cointegrating vector on the each sector is carried out to be able to determine the
impact of oil variable on the other sectors in the long run.
Note 17. One other possible reason for the negative impact of oil on the manufacturing sector may be the enclave
nature of the oil sub sector. For some years now, the quantity of domestic refining has fallen following the
collapse of the refineries; crude oil is just leaving the country without touching the soil. Moreover, the high tariff
placed by high income countries on processed petroleum products than crude oil to protect their own
manufacturing firms against competition might have inhibited the development of the down stream industries
that add value to petroleum. Finally, the oil boom of the 1970s caused the exchange rate to be overvalued
thereby making Nigerian manufacturing exports less competitive on the international market.
Note 18. Since 1990, the Niger Delta region has engulfed in many political violence. The local ethnic minorities
have been agitating for a greater share of oil revenues. Government has made efforts since independence to
address the demands of the minorities by setting up various commissions and more recently the Amnesty
programme for Niger Delta militants. Yet, the community demands for greater autonomy, and greater control
over oil revenues, have not abated significantly. Local groups continue to obstruct the activities of oil firms, in
order to press their demands.
Note 19. We are aware of the fact that Cholesky decomposition can be affected by the ordering of the variables
and the possibility of correcting it by using generalized decomposition process (GIRF) proposed by Pesaran &
Shin (1998). But, as demonstrated recently by Kim (2009), GIRF may yield quite misleading economic
inferences. According to Kim (2009), the method yields a set of response functions that are based on extreme
identifying assumptions that contradict each other, unless the covariance matrix is diagonal. However, to check
the consistency of our estimates using Cholesky decomposition, we reversed the order of the first and the last
variables and .re-estimated the model. The results obtained were not significantly different from the one reported
here. Hence, we do not find necessary to report them here.
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Published by Canadian Center of Science and Education 175
Table 1. Oil output, exports and revenue in Nigeria, 1960-2009
Year Production
(bm ) Oil Revenue Oil/Total Revenue (%) Oil/GDP (%) Oil Export
(Nm)
Oil Export/
Total Export
(%)
1961 16.80 nil
N
il 0.9 23.1 6.65
1965 150.3 nil
N
il 3.43 136.2 25.37
1970 395.7 166.4 26.3 9.27 509.6 57.54
1975 660.1 4271.5 77.5 19.37 4563.1 92.64
1980 760.1 12353.2 81.1 28.48 13632.1 96.09
1985 507.5 10923.7 72.6 16.75 11223.7 95.76
1990 660.6 71887.1 73.3 37.46 106623.5 97.03
1995 712.3 324547.6 70.6 39.65 927565.3 97.57
2000 797.9 1591675.8 83.5 47.72 1920900.4 98.72
2005 919.3 4762400 85.8 38.87 7140578.9 98.53
2009 759.2 3191938 78.7 37.44 8543261.2 96.73
Source: (a) Central Bank of Nigeria, Statistical bulletin, various years
Table 2. Nigeria: Unit root tests for stationarity with constant and linear trend, 1960-2009
Series ADF KPSS
Level 1s
t
difference Level 1s
t
difference
ln agr(constant) -0.691 -6.221 3.789 0.055
(constant & linear) -2.022 -6.205 0.481 0.054
ln man (constant) -1.463 -4.875 3.594 0.030
(constant & linear) -1.484 -4.979 0.809 0.016
ln oil (constant) -2.309 -6264 3.485 0.374
(constant & linear) -1.159 -6.609 0.947 0.026
ln buc (constant) -1.362 -5.002 3.339 0.111
(constant & linear) -1.586 -5.054 0.762 0.072
ln tsr (constant) -1.031 -5.151 3.719 0.088
(constant & linear) -2.016 -5.163 0.729 0.072
Note:Critical values for ADF are: -3.46, -2.88, and -2.57 (constant only); -4.01, -3.43, and -3.14 (constant and
linear) at 1%, 5% and 10% level of significance respectively. However, the critical values for KPSS test
are:0.739, 0.463 and 0.347 (constant only), 0.216, 0.146 and 0.119 (constant and linear) at 1%, 5% and 10%
level of significance, respectively.
Table 3. Nigeria: Johansen Co-integration Test (with a linear Trend) where is the Number of ointegrating vectors
Null
A
lternative
r
λ
-ma
x
Critical valuesaTrace Critical valuesa
0 1 95.03 40.08 179.58 95.75
<1 2 31.55 33.88 84.55 69.82
<2 3 27.09 27.58 53.00 69.82
<3 4 14.66 21.13 25.90 29.80
<4 5 9.53 14.26 11.25 15.49
<5 6 1.72 3.84 1.72 3.84
Note: Critical values at 95% level
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Table 4. Normalized cointegrating vector; coefficients normalized on agr, man, buc, tsr and oil respectively
ag
r
oil man
uc ts
r
tren
d
-1.000 0.513
(3.20)***
0.276
(1.03)
-0.184
(-0.99)
-0.122
(-0.38)
-3.506
(-6.69)***
man oil ag
r
uc ts
r
tren
d
-1.000 -1.859
(4.11)***
3.625
(3.17)***
0.667
(0.595)
0.442
(0.22)
-12.71
(-7.12***
uc oil ag
r
man ts
r
tren
d
-1.000 2.788
(3.78)***
5.438
(5.15)***
1.50
(0.99)
-0.663
(-0.47)
19.09
(6.68)***
ts
r
oil ag
r
man
uc tren
d
-1.000 4.205
(3.20)***
8.202
(5.20)***
2.26
(1.01)
-1.508
(-1.25)
28.75
(4.14)***
oil ag
r
man
uc ts
r
tren
d
-1.000 1.951
(3.07)***
-0.538
(-1.28)
0.359
(0.61)
0.238
(0.22)
-6.838
(-6.63)***
Note: The t ratios are in parenthesis.
Table 5. Nigeria: VEC model estimates
Equation
ag
r
t
mant
oilt
buct
tsr
t
ξt-1 -0.0033
(-0.265)
0.0732
(9.467)
-0.0252
(-4.304)
-0.0107
(-3.663)
-(0.126
(-4.367)
α 0.0287
(1.348)
0.0401
(1.248)
0.0574
(2.304)
0.0258
(2.067)
0.0347
(2.822)
agrt-1 -0.4843
(-5.399)
0.4904
(3.543)
0.0066
(0.063)
-0.0725
(-1.384)
-0.0318
(-0.616)
agrt-2 -0.2781
(-3003)
0.1427
(0.999)
-0.0067
(-0.062)
-0.0353
(-0.653)
-0.0227
(-0.426)
mant-1 -0.0382
(-0.504)
-0.0784
(-0.669)
-0.1430
(-1.613)
0.0605
(1.364)
-0.0076
(-0.173)
mant-2 -0.0739
(-1.508)
0.1940
(2.565)
-0.0507
(-0.886)
0.0978
(3.418)
0.0108
(0.382)
oilt-1 0.0551
(0.684)
0.0665
(0.535)
-0.3469
(-3.687)
-0.0061
(-0.045)
0.0095
(0.109)
oilt-2 0.0288
(0.362)
0.0404
(0.329)
-0.0912
(-0.982)
0.0061
(0.132)
0.0095
(0.207)
buct-1 -0.4919
(-2.269)
-2.4271
(-7.259)
0.4274
(1.689)
0.1872
(1.479)
0.2742
(2.198)
buct-2 -0.0594
(-0.238)
0.5894
(1.531)
0.1419
(0.487)
-0.2099
(-1.441)
-0.1148
(0.799)
tsrt-1 0.7796
(2.902)
0.8786
(2.120)
0.2566
(0.818)
-0.0339
(-0.216)
-0.1592
(-1.029)
tsrt-2 0.2755
(1.348)
-0.5923
(-1.425)
-0.1103
(-0.351)
-0.0236
(-0.149)
-0.0455
(-0.293)
R-
2
0.214 0.871 0.333 0.632 0.510
F-statistics 5.761 118.49 9.71 31.05 19.18
Likelihoo
d
-24.38 -108.07 -54.29 79.51 82.25
AIC 0.377 1.244 0.687 -0.699 -0.727
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Published by Canadian Center of Science and Education 177
Table 6. Nigeria: Block exogeneity wald tests
ag
r
man oil
uc ts
r
Join
t
ag
r
- 16.347* 4.997 7.707* 4.931 25.23*
man 2.699 - 64.243* 214.672* 161.886* 322.63*
oil 0.962 20.934* - 17.761* 13.749* 51.96*
uc 13.031* 120.495* 6.215* - 4.589 90.98*
ts
r
31.642* 20.791* 1.062 0.446 - 127.53*
Note: * denotes statistical significance
Table 7. Nigeria: Variance decompositions
Horizon (yrs) ag
r
man oil
uc ts
r
Shock to agr explained by innovation in:
1 100.00 0.00 0.00 0.00 0.00
5 94.63 1.24 0.63 0.58 2.92
10 94.79 1.23 0.53 0.45 2.99
Shock to man explained by innovation in:
1 13.87 86.13 0.00 0.00 0.00
5 18.08 57.12 6.83 13.71 4.27
10 20.61 47.76 9.71 15.59 6.33
Shock to oil explained by innovation in:
1 13.37 0.26 86.36 0.00 0.00
5 23.99 8.01 66.58 1.38 0.03
10 25.70 9.43 63.74 1.07 0.05
Shock to buc explained by innovation in:
1 10.23 10.44 22.38 56.95 0.00
5 17.76 9.03 20.87 52.18 0.15
10 18.23 6.34 21.51 53.77 0.15
Shock to tsr explained by innovation in:
1 27.06 3.89 17.95 29.38 21.71
5 37.01 6.47 12.96 23.88 19.68
10 38.74 6.63 12.25 23.09 19.29
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178
Legend
Unidirectional
Bidirectional
Nocausality
Oil
Manufacturing
Agriculture
TSR
Buildin
g/
Cons
Figure 1. Summary of the causality tests
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Published by Canadian Center of Science and Education 179
Figure 2. Impulse response analysis in VAR models
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LOG (AG R) to LO G (AG R)
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LOG (AG R) to LO G (MAN)
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LO G(A GR) to LO G( OI L)
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LOG (AG R) to LOG (BUC)
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LO G(A GR) to LO G( TS R)
-.4
-.2
.0
.2
.4
.6
2 4 6 8 10
Response of LOG (MA N) to LO G(A GR)
-.4
-.2
.0
.2
.4
.6
2 4 6 8 10
Response of LOG (MA N) to LO G( MAN)
-.4
-.2
.0
.2
.4
.6
2 4 6 8 10
Response of LO G(M AN) t o LOG (O IL)
-.4
-.2
.0
.2
.4
.6
2 4 6 8 10
Response of LOG (MA N) to LO G( BUC)
-.4
-.2
.0
.2
.4
.6
2 4 6 8 10
Response of LO G(M AN) t o LOG (T SR)
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LOG (O IL) t o LOG (AG R)
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LOG (OI L) to LOG (MA N)
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LOG (OI L) to LOG (O IL)
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LOG (OI L) to LOG (BUC)
-.1
.0
.1
.2
.3
.4
2 4 6 8 10
Response of LOG (O IL) to LO G( TSR)
-.1
.0
.1
.2
2 4 6 8 10
Response of LOG (BUC) t o LOG (A GR)
-.1
.0
.1
.2
2 4 6 8 10
Response of LOG (BUC) t o LOG (M AN)
-.1
.0
.1
.2
2 4 6 8 10
Response of LOG (BUC) t o LOG (O IL)
-.1
.0
.1
.2
2 4 6 8 10
Response of LOG (BUC) t o LOG (BUC)
-.1
.0
.1
.2
2 4 6 8 10
Response of LOG(BUC) to LOG(TSR)
-
.05
.00
.05
.10
.15
2
4
6
8
10
Response of LOG (TS R) to LO G (AG R)
-.05
.00
.05
.10
.15
2
4
6
8
10
Response of LOG (TS R) to LO G (MA N)
-.05
.00
.05
.10
.15
2
4
6
8
10
Response of LOG (TS R) to LO G (OI L)
-.05
.00
.05
.10
.15
2
4
6
8
10
Response of LOG (TS R) to LO G (BUC)
-.05
.00
.05
.10
.15
2
4
6
8
10
Response of LOG(TSR) to LOG(TSR)
Response to Chol esky One S.D. Innovations ± 2 S.E .
... Undoubtedly, since its discovery in 1956, emerged from being merely the supportive economic (Asagunla, 2018) to the principal source of foreign exchange earnings and most viable access to international investment opportunities (Asagunla, 2018). Regrettably, however, the country extreme/over reliance on oil has triggered: structural difficulties for the economy as earnings from oil fluctuate along with market trends (Aigbedion & Iyayi, 2007); acute unemployment rate as the sector could only employ limited number of the population and worst still, only experts (Uzonwanne, 2015); high and rising level of poverty (Akinlo, 2012); and poor infrastructural development which are exacerbated by the neglect of other productive sectors of the economy notably the non-oil revenue-earning sectors (Riti, Gubak & Madina, 2016) where the potentials remain great but largely unexploited (Riti et al., 2016). These adverse consequences of overreliance of the economy on oil has continued to heighten the call and need to diversify the productive base of the economy towards non-oil sectors (Esu & Udonwa, 2015). ...
... Based on the theoretical framework of neoclassical growth model, to empirically examine the interactions among non-oil sectors, economic diversification and growth, this study employs the growth accounting framework as utilized in Odetola & Etumnu (2013) in which aggregate output is determined by the contributions of each sector and dimension of diversification of the economy. Specifically, to model the underlying dynamic interactions among the series the study, takes after the works of Akinlo (2012) and, adopts Vector Autoregression (VAR) modelling framework. This procedure is adopted against other possible methods for numerous reasons. ...
... This procedure is adopted against other possible methods for numerous reasons. First, no a priori assumption of exogeneity of variables is required (Akinlo, 2012). Second, it allows each variable in the system to impact not only on itself but also on other without the need for theoretical structures on the estimates. ...
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Article
The adverse consequences of over-reliance of Nigerian economy on oil has continued to heighten the call and need to diversify the productive base of the economy towards non-oil sectors. To heed the call, successive governments have implemented several economic policies at different periods. However, while the contents of these policies are plausible, the full-anticipated benefits are far from being realized. The disappointing results have been linked to two principal factors: the incoherent implementation of the policies and neglect of the country-specific circumstances. Informed by the need to take cognizance of the country’s peculiar circumstances, the question as to which priority sectors that Nigeria should target for diversification efforts has come up in literature. Empirically, in view of the country’s natural resource abundance, studies have suggested agricultural, industrial and many others non-oil sectors as plausible options for diversifying the economy. In light of this, the study examines the interactions among non-oil sectors, economic diversification and growth in a multivariate VAR model over the period 1960-2019. Empirical results reveal that while an expansion of economic activities into resource-based sectors is a necessary condition for diversification; however, it is not a sufficient condition as the dimension of the diversification matters.
... Contrary to the findings for oil-importing countries, positive oil price shocks have been reported to boost output in oil exporting countries (see for instance Abayomi, Adam and Alumbugu, 2015;Adeniyi et al., 2011;Akinleye and Ekpo, 2013;Akinlo, 2012;Benkhodja, 2014;Bergholt and Larsen, 2016;Bergholt et al., 2017;Bjørnland, 2009;Ferrero and Seneca, 2019;Huseynov and Ahmadov, 2014;Iklaga, 2017;Romero, 2008;Sadeghi, 2017). In a study for Norway, Bergholt et al. (2017) found that a positive oil price shock boosts economic activities with the real GDP growth reaching a peak of 0.6 per cent after about 12 quarters. ...
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Book
This thesis is a collection of three papers aimed at investigating the macroeconomic effects of oil price shocks on resource-rich economies as well as the appropriate policy responses for ameliorating such effects. The first paper begins by examining the implications of physical capital and oil intensity of domestic production for the response of a small open economy to an oil price shock. Building on the work by Ferrero and Seneca (JMCB 2019), we find that the introduction of physical capital amplifies the responses of output and inflation to oil price shocks whereas the effects are attenuated by the oil intensity in domestic production. Also, our results reveal that the added features are important for the response of monetary policy to an oil price shock. Under our model set up, the optimal monetary policy response requires that the central bank keeps an eye not only on output and inflation, but also the exchange rate. These results highlight the need for cautious interpretation of the quantitative impacts of an oil price shock generated based on New Keynesian models of oil producing economies that abstract from capital. Paper 2 studies the role of oil price shocks in driving business cycle fluctuations of an oil-producing emerging economy with an inefficient fuel subsidy regime. Results from our estimated DSGE model for the Nigerian economy show that output fluctuations are driven mainly by oil and monetary policy shocks in the short run. However, oil shocks play a less prominent role in driving inflation dynamics owing partly to the low pass-through effect of international oil price into domestic prices implied by the fuel subsidy regime. While we find the core inflation-based Taylor rule optimal, we demonstrate that the Central Bank of Nigeria (CBN) faces a dilemma of either stabilising output or inflation in the face of an adverse oil price shock. Simulation results show that an “across-the-board” monetary policy strategy does not exist for dealing with an oil price shock in the resource-rich economy; thus, it is important that the CBN is aware of the observed trade-offs. The last paper investigates monetary-fiscal interactions in a resource-rich emerging economy whose fiscal policy is largely driven by resource-related flows. To achieve this, we analyse Nigeria’s experience over the last two decades by developing and estimating a suitable DSGE model. Our results provide convincing evidence of an active monetary and passive fiscal policy over the full sample. Furthermore, we confirm the presence of revenue substitution; a phenomenon that alters the “automatic stabilisers” role of fiscal policy in the resource-rich economy. The 2008/09 global financial crisis did not significantly alter these findings. However, our results are sensitive to (i) the response of fiscal policy to resource-related flows and (ii) the response of monetary policy to exchange rate.
... Additionally, it increased to N1,591.7 billion in 2000 and to N5,545.8 billion in 2018. The economy has become oil-dependent as a result of the high revenue generated by the oil sector, complicating macroeconomic management (Akinlo, 2012). According to Onaolapo et al. (2013), Nigeria's petroleum industry is the country's largest and primary source of GDP, accounting for 70% of government revenue and approximately 95% of foreign exchange earnings. ...
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Article
Economic growth and health expenditure in Nigeria have become major priorities, and there is no doubt that health expenditure in Nigeria has risen over the years. According to studies, revenue and expenditure are important factors that contribute to a country's economic growth. Using pairwise granger causality, the study investigated the relationship between oil revenue, health expenditure, and economic growth. The variables of interest were oil revenue, health expenditures, real gross domestic product, consumer price index, and money supply. The annual time series data from 1980 to 2020 were obtained from the Central Bank of Nigeria (CBN) statistical bulletin, annual reports, and the World Bank Database. According to the findings, there is a bidirectional relationship between total health expenditure and real GDP. However, there is a unidirectional relationship between oil revenue and GDP. Furthermore, there is a unidirectional relationship between oil revenue and health expenditure. The study concluded that oil revenue and health expenditure granger cause Nigerian economic growth. Therefore, the government should make better use of oil revenue, close loopholes, and increase health spending. To increase productivity and economic growth, the government should increase public spending on health.
... The author then argues that the negative long -run impact is due to inefficient debt management. Furthermore, the level of oil dependency is very high in Nigeria (Odularu 2008;Akinlo, 2012), implying that the government will likely not borrow externally and domestically when oil revenue is high and it is impacting growth positively, as the results of this study show. ...
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Article
This paper examines the long-run effects of external and domestic debts on economic growth in Nigeria, using the dynamic OLS cointegration technique and data spanning 1981 to 2017. The results show that none of external and domestic debts enhances growth in Nigeria in the long-run. Although domestic debt tends to have a positive long-run effect on growth, the effect is not statistically significant. On the other hand, external debt has a negative and statistically insignificant long-run effect on growth. However, when government revenues (oil and non-oil revenues) are included in the analysis, only oil revenue has a statistically significant positive effect on economic growth in the long-run, indicating the high importance of oil in Nigeria. These findings imply that the country should reduce the use of debts to finance expenditure and seek to enhance growth with oil revenue maximally without relying unnecessarily on oil.
... on the economy due to heavy reliance on oil revenues. Despite the volatility of the economy to external shocks, government expenditure remains unaffected by such shocks. The study also shows that there was a causality running from oil price shocks to inflation. This positively influence inflation and caused a significant fluctuation in oil prices.Akinlo (2012) employed the multivariate VAR model to empirically evaluate the importance of oil to Nigerian economic growth spanning 1960 to 2009 as well as its cointegration with other sectors namely; Agriculture and Telecommunication and so on. The result indicated that the majority of the sectors are co-integrated, observing that the oil sector ha ...
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Article
Oil has been the backbone of Nigeria's economy since it was found and its pricing has impacted on her macroeconomic indicators, especially the economic growth. The reassessment of the extent of this influence is the focus of this study. Employing the unit root, cointegration test and autoregressive distributed lag estimating (ARDL) technique, the study found evidence for significant positive influence on economic growth; from the result, the GDP growth rate increased as crude oil prices rise and decreased during the time of oil price decline. It was therefore recommended that the export revenue base should be diversified as a means of curtailing dependence on crude oil. This can be achieved through reforms and the revival of the non-oil sector of the economy. Finally, higher revenues from oil sales during boom should be invested in different areas of the economy that will help improve the economic productivity in times of decline in oil prices.
... In specific terms, from the estimated model X, as table 9 reveals, a one-percent increase in non-oil export will bring about a 0.099 and 0.038 percent increase in growth, respectively, in the longrun and short-run. Similar results were also obtained in Akinlo (2012) and Onodugo et al. (2013). In relation to the growing impact of oil export, a careful look at Table 9 revealed that, in all specifications, the elasticity coefficients of oil-export are positive and statistically significant, as anticipated. ...
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Article
The question of whether developing countries should pursue specialization or diversification in export as a driver of sustainable economic growth has been a subject of an intense debate in economic literature. At present, one understanding of the debate, as postulated by Imbs and Wacziarg (2003), is that economies grow through two stages of diversification and concentration as income grows: they initially diversify but re-specialize once a (relatively) high level of income per capita is attained. A U-shaped curve best explains the notion. With Nigeria as a reference country, we employed ARDL procedure and examined the aforementioned exposition over the period 1960-2019. Specifically, the non-monotonic relationship between diversification and growth is examined. In furtherance, we examined the impact of diversification on the effect of non-oil exports on growth. Employing an augmented production-function framework and two distinct measures of diversification, we find, contrary to the Imbs-Wacziarg notion, a monotonic (increasing) relationship between diversification and growth, suggesting that diversification, rather than specialization, continues with growth. Applying a similar framework and five different measures of non-oil exports, we find that the impact of diversification on the effects of agricultural and industrial sectors on growth is higher, as compared to building and construction, wholesale and retail, services sectors.
... Investigation of hydrocarbon polluted soils often involves direct sampling of soils and water (i.e., both surface and underground) from mechanically drilled shallow/deep boreholes or pits and subsequent physical and chemical analysis (Echefu and Akpofure, 2002;Akinlo, 2012). Many boreholes or pits are required for reasonable accurate delineation of both the vertical and the lateral extent of the pollution plume. ...
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Article
In this study, Bayesian maximum entropy (BME) was implemented to take advantage of the limited data available for reliable prediction of total hydrocarbon content (THC) in the soil because it allows assimilate of secondary and its uncertainty data. Thirteen (13) physico-chemical soil variables were obtained. Upon the application of multiple regression analysis to the soil variables, electrical conductivity, EC was found as a covariate to the eleven (11) data. Twenty-eight (28) data were then generated by regression model between and EC and their 28 uncertainty data were also derived using probability density function. Therefore, 11 , 28 and 28 error data in the computation of THC soft were integrated at BME technique to produce BME prediction and BME standard deviation maps of THC. The BME predicted THC value in the area ranges from 0 – 16 . The BME prediction map provides the mean variable of the estimation posterior pdf of THC. It shows the spatial variability of THC as the concentration decreases from the highest concentrated zone of THC.
... has been increases in oil price, particularly between 2003, mid-2008 and 2010/2014. On the other hand, starting in 2009, along with major price changes of the early and mid-1980s, 1991, periods after the Asian financial crisis, late-2008 and late-2014 are periods of falling price of oil(Akinlo, 2012). ...
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Thesis
The study investigated how global the oil market factors and their shocks affect exchange rate and economic activity in Nigeria. These factors were expressed in terms of the determinants of oil price movements; namely, changes in global oil production, global real economic activity and speculative behavior of the oil market players. The study examined the spillover effect of these factors on the economy and how their shocks (specific to changes in oil price) are related to exchange rate and economic activity in Nigeria within the period of 1999 to 2016. Analysis of the relationships between global oil market shocks, exchange rate and economic activity were based on secondary data. Monthly data from January, 1999 to August, 2016 were sourced on variables such as world crude oil production, real global aggregate economic activity index, oil price which represented the global oil market factors and were used to generate the shocks series of oil supply shocks, aggregate demand shocks and oil specific demand shocks. In addition, data were sourced on the naira exchange rate and Nigerian stock market capitalization. These sets of data were sourced from the Central Bank of Nigeria (CBN) database, Energy Information Administration (EIA) Database, Federal Reserve Economic Data (FRED) Database and University of Michigan Database. These data were analyzed using both descriptive and econometric technique. Findings from the study revealed that the major drivers of shocks to the price of oil are the speculative activities of oil market players and the real global economic activity which have positive effect on oil price. Meanwhile, changes in oil production was found to be insignificant to oil price. It was revealed that the sharp fall in oil price in 2014 was predominantly as a result of the speculative activities of oil market players. Both global real economic activity and the speculative activities of oil market players affect economic activity positively and exchange rate negatively. Oil supply shocks were revealed have positive effect on the economic activity but insignificant to exchange rate. Aggregate demand shocks have positive effect on economic activity and negative effect on exchange rate. Oil specific demand shocks have positive effect on economic activity and exchange rate. Lastly, exchange rate and economic activity generally are insignificant to each other but individual specific shocks were revealed to affect significantly. Negative adjustment in exchange rate shocks have positive effect as positive economic activity shocks have negative effect on exchange rate. Therefore, the study concluded that oil price movement is important in affecting exchange rate and economic activity in Nigeria as the factors that determine oil price are significant in affect macroeconomic variable. The shocks of these factors that are specific to oil price are as well important in impacting on the economy.
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Article
This study investigated the effect of oil wealth on capital accumulation in Nigeria over the period 1981 to 2017. In addition, the study assessed the nonlinear effect of oil wealth on capital accumulation in Nigeria. These were with a view to examining the uncertainties in the relationships between oil wealth and capital accumulation in Nigeria. Data collected were analysed using Autoregressive Distributed Lag (ARDL) and the Non-Linear Autoregressive Distributed Lag (N-ARDL) econometric techniques. Linear ARDL result indicated that oil wealth had a negative and insignificant relationship with capital accumulation (t=-1.11; p>0.10). Non-linear ARDL results show that both positive (t =-6.69; p<0.01)and negative (t =-5.59; p<0.01) changes in oil wealth significantly affect capital accumulation negatively while only the positive long run sum of capital accumulation affect oil wealth negatively (t =-2.76; p<0.05). Finally, real effective exchange rate had effects on capital accumulation (t =-6.66; p<0.01) and oil wealth (t =-4.66; p<0.01) both in the short run and long run. Globalisation had positive long run and short run effects on capital accumulation (t = 5.56; p<0.01 and t = 4.38; p<0.01) and short run positive effect on oil wealth (t = 2.56; p<0.01). The study therefore, concluded that oil wealth have a Article 228 negative relationship with capital accumulation which aligns with the resource curse argument for Nigeria.
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Conference Paper
This study investigated the effect of oil wealth on capital accumulation in Nigeria over the period 1981 to 2017. In addition, the study assessed the nonlinear effect of oil wealth on capital accumulation in Nigeria. These were with a view to examining the uncertainties in the relationships between oil wealth and capital accumulation in Nigeria. Data collected were analysed using Autoregressive Distributed Lag (ARDL) and the Non-Linear Autoregressive Distributed Lag (N-ARDL) econometric techniques. Linear ARDL result indicated that oil wealth had a negative and insignificant relationship with capital accumulation (t=-1.11; p>0.10). Non-linear ARDL results show that both positive (t =-6.69; p<0.01)and negative (t =-5.59; p<0.01) changes in oil wealth significantly affect capital accumulation negatively while only the positive long run sum of capital accumulation affect oil wealth negatively (t =-2.76; p<0.05). Finally, real effective exchange rate had effects on capital accumulation (t =-6.66; p<0.01) and oil wealth (t =-4.66; p<0.01) both in the short run and long run. Globalisation had positive long run and short run effects on capital accumulation (t = 5.56; p<0.01 and t = 4.38; p<0.01) and short run positive effect on oil wealth (t = 2.56; p<0.01). The study therefore, concluded that oil wealth have a Article 228 negative relationship with capital accumulation which aligns with the resource curse argument for Nigeria.
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Article
This note discusses a pitfall of using the generalized impulse response function (GIRF) in vector autoregressive (VAR) models (Pesaran and Shin, 1998). The GIRF is general because it is invariant to the ordering of the variables in the VAR. The GIRF, in fact, is extreme because it yields a set of response functions that are based on extreme identifying assumptions that contradict each other, unless the covariance matrix is diagonal. With a help of empirical examples, the present note demonstrates that the GIRF may yield quite misleading economic inferences.
Article
Liberalization - a policy shift toward market or marketlike devices - is a reaction to excessive intrusion of governments into national economies. If as economies grow and become more complex, the role of government remains large or even expands, then significant problems can be anticipated. This volume examines the variety of issues that arise as governments in some of the newly industrializing economies of East Asia grapple with this difficult process. There are ten chapters, which are abstracted separately. -from Editors
Article
Adds political elements to the theorizing concerning the development process and thereby supplements the Krueger model. An underlying question is posed: What accounts for the relatively greater success of East Asian NICs such as Taiwan and South Korea when compared to Latin American countries such as Colombia and Mexico? The Ranis model identifies the crucial points in the evolutionary process of development where government policies can be particularly important in determining subsequent outcomes. Though four stages of growth are identified, the model does not depend upon the stages following one another in a mechanistic way. The model suggests that six instruments of policy are of particular importance in setting the policy framework: the interest rate, the growth of the money supply, the foreign exchange rate, levels of tariffs and other trade barriers, tax and subsidy rates, and the wage rate. Section 2 will focus on the set of initial conditions shared by the family of natural-resources-poor countries in contrast to the natural-resource-rich countries in the context of transition growth. Section 3 describes the actual evolutionary performance via different subphases of transitional growth that has been in evidence in the two types of LDCs. Section 4 focuses on the "why' of this divergence, emphasizing especially the importance of the natural-resource endowment for political economy. -from Editors
Book
These contributions bring both theoretical models and case studies to bear on the consequences of natural resource discoveries in developed and developing countries. Whether it is natural gas in the Netherlands, oil in the UK, Norway, or Mexico, or minerals in Australia, these discoveries have been accused of causing several structural problems, which have been given the name ''Dutch Disease.'' Although a sizable literature dealing with various aspects of the Dutch Disease has now developed, this is the first attempt to confront theory with evidence.