Energy is key ingredient in achieving sustained growth of every nation in which Nigeria is involved. Energy resources have become a means of industrial development and since every nation in the globe wants to be developed, energy therefore becomes a topical issue at international level. Crude oil is one of the main sources of energy for the productive sectors of Nigerian economy and the entire industrial world. Holistically, oil is referred to as the engine of modern economy and its prices are usually quoted from international market in US dollars per barrel. The increasing spate of differences in oil prices poses a great challenge to policy makers all over the global world. Since the major oil shock of 1973, there have been fluctuations in the world prices of oil. By nature, oil-exporting countries are extremely susceptible to oil price changes given the relative importance of the oil sector in production and exports, and uncertainty in the world oil markets. However, most of the studies empirically related to the effects of oil price fluctuations on macroeconomic variables are devoted to oil-importing economies, with little attention being paid to exporting countries [1].

Oil is of immense usefulness in every modern economy today. Oil is the basic material for a wide gamut of products such as lubricants, asphalt, tars, tires, solvents, plastic, foams, bubble gums, deodorants, crayons, etc. There is assertion that the amount of oil and derived products that an economy consumes depends on numerous factors, such as the level of Gross Domestic Product (GDP), the structure of the economy’s industrial sector, the availability of choices among fuels that permit substitution, and the level of technological progress. 

Nigeria, the most populous black nation situated in western Africa has crude oil as her dominant source of revenue. Consequently, Nigeria became increasingly dependent on oil revenue, which in the last few decades has experienced shocks in its price per barrel and production. With oil revenue as the bed rock of the Nigerian economy, shocks in oil prices are definitely an area of great concern to economists in order to predict the effects of a drastic change-increase or decrease in oil price, on the Nigerian economy as a whole. Oil shock can be described as an abruption, unexpected change in oil price or production. This study, however, focuses on the effect of oil price shocks on industrial output in Nigeria.

Oil as an essential product not only plays a significant role in Nigerian economy as the largest contributor in term of total government revenue but also as the major contributor in her exports composition. It accounted for about 82.1 % of total government revenue during the oil boom in 1974. The share of oil revenue in total government revenue still remains substantial or evidence by the attainment of 85.6 percent and 86.1% in 2007 and 2008 respectively. Thus, persistent oil shocks could have severe macroeconomic implication like fluctuation in the GDP which may induce challenges with respect to policy making [2].

Oil prices fluctuation have influenced the real sector of the economy via both demand and supply side. The demand side effects operate through consumption and investment decision and it is indirectly affected because of its positive association with disposable income. In the mid1970s, the downturn in the industrial production would probably occur at any event and the oil price increase served to deepen a recession that was already on the way. The determinants of oil supply depend on both fixed and variable factors. Fixed factor is the total amount of oil that can be found on earth while variable factors include prices of oil, infrastructure and technology. Rise in oil prices affect supply of goods and services because they make it more costly for firms to produce goods since energy and capital are complements which imply that to run machine, one needs energy. If energy becomes more expensive, firm may have to purchase new energy efficient machine if they want to maintain profit. The profit of firms stunk with less fuel-efficient machines suffers, and so they may invest less in capital and labour. These various investment factors decelerate the economy’s rate of industrial output. However, oil price decreases do not boost industrial output growth as well. This is because, although firms find it cheaper to produce goods when the price of oil falls, which will encourage increased production. But the reallocation effect still shows growth as resource move across sectors in response to lower oil prices. On net basis, all of these factors may wash out, so that the effect of a decrease in the price of oil is just about zero.

Industrialization is regarded as a veritable channel of attaining the lofty and desirable national goals of improved quality of life for the citizenry. Governments, especially in developing countries, see industrialization as a way of transforming their economies [3]. Industrial growth by all intents and purposes is seen as an antidote for economic growth and development. If transformation will take place and the trend of poverty is to be alleviated, rapid industrialization in the African sub-region is an agenda to be pursued. Evidences abound of a fairly strong relationship between economic development and industrial process. Economic growth and development needs structural transformation from low to high productive economic activities. Industrialization is a key factor in the development process. High, rapid and sustained economic growth and development is strongly related to it. However, despite the efforts by previous governments in Nigeria at promoting trade and industrial development, and its effects of macroeconomic factors on the industrial sector shows that efforts by policy makers are not working out as expected and the desired industrial development which is capable of boosting national income per capital, prove foreign exchange earnings, secure full employment and expand the market for local raw materials has not been achieved in Nigeria. The trend of industrial sector performance in Nigeria since independence has been rather poor till date without substantial improvement. 

The broad objective of this study is to examine the relationship between oil price fluctuations and industrial output in Nigeria while the specific objective of this study is to analyse the shock transmission between oil price fluctuations and industrial output in Nigeria. The rest of the paper is organized as follows: section two is on literature review. This is followed by the research methods and discussion of results in section three and four respectively. Section five concludes the paper.                

Literature Review

A number of empirical works on the relationship between oil price fluctuations and industrial output has been carried out using different estimation approaches. By looking at the channel of transmission of oil price shocks to the larger economy, many researchers have argued the links of fluctuations in oil prices to industrial performance.

Review of Empirical Literature 

Kilian [4] proposed that movement in crude oil prices could be decomposed into the following three components  crude oil supply shocks; shocks to the global demand for all industrial commodities; and demand shocks specific to the global crude oil market. He tested this hypothesis with the help of a structural vector auto regression of the global crude oil market which consisted of the real price of oil, the percent change in global crude oil production, and an index of real economic activity. His results showed that shocks to aggregate demand had persistent and significant effects on economic activities and oil prices while precautionary demand shocks caused an immediate and persistent rise in oil prices. Interestingly, his results showed that supply shocks only resulted in a small increase in the real price of oil. Hamilton [5], however, challenges Kilian’s assertion that precautionary demand supersedes supply shocks and backs it up by showing that U.S. inventories of crude oil and petroleum products (measured as percentage of global production) were going down, not up, during the sharpest movements in oil prices. If precautionary demand shocks have a significant effect on oil prices, one would expect that inventory levels would rise following an event expected to disrupt oil supply. Hamilton’s finding suggests that there may be more to oil price shocks, whether they occur due to shifts in supply or precautionary demand, which will only be revealed with future movements in oil prices.

Rodríguez [6], analyzed the response of manufacturing industrial output to an oil price shock in the four EMU countries namely, France, Germany, Italy and Spain, the US, and the UK the latter is the only net oil exporting country. The study employed panel data for this analysis and the results show that oil price increase lowers the level of aggregate manufacturing output in all countries under study, although the pattern of response differs somewhat across countries.

Sadorsky investigated the dynamic interaction between oil price and other economic variables using an unrestricted VAR with US data on industrial production, interest rate of a 3-month T-bill, oil price (measured using the producer price index for fuels), real stock returns (calculated using the difference between the continuously compounded returns on the S and P-500, and inflation measured using the consumer price index). The data are monthly from 1947.1 to 1996.4. After unit root and cointegration tests, he runs an unrestricted VAR with ordering of interest rates, real oil price, industrial production and real stock returns. For oil price changes he uses the growth rate of real oil price and oil price volatility which is calculated by a GARCH (11). He finds that oil price changes and oil price volatility have a significantly negative impact on real stock returns. He also finds that industrial production and interest rates respond positively to real stock returns shocks. According to him, the response of the stock market to oil price shocks is asymmetric. When he uses asymmetric oil price shocks (positive oil price changes and negative oil price changes), positive shocks explain more forecast error of variance in real stock returns, industrial production and interest rates than negative shocks during the full sample period. For the post-1986 period, positive and negative oil price shocks explain almost the Mordi and Adebiyi (Asymmetric Effects of Oil Prices on Output and Prices) which was the same fraction of forecast error variance of real stock returns, while in the pre-1986 period positive oil price shocks contribute more to the forecast error variance in real stock returns than negative oil price shocks. 

Guidi [7] examined the economic effects of oil prices shocks on the UK manufacturing and services sector using Vector Auto-regressive Model. The study found that increases in oil price caused decreases in manufacturing output, while decreases in oil prices did not increase manufacturing output as much as in case of increases in oil price. Tang and Zhang [8] in a study of the short and long-term effects of oil shocks on the Chinese economy identified six transmission channels. Namely: Supply-side shock effect, focusing on the direct impact on output due to the change in marginal producing costs caused by oil-price shock; wealth transfer effect, emphasizing on the different marginal consumption rate of petrodollar and that of ordinary trade surplus; inflation effect ,analyzing the relationship between domestic inflation and oil prices; real balance effect, investigating the change in money demand and monetary policy; sector adjustment effect, estimating the adjustment cost of industrial structure, which is mainly used to explain the asymmetry in oil-price shock impact; and unexpected effect, focusing on the uncertainty over oil.

Alper and Torul [9] made use of structural VAR Model to examine the effects of oil prices on the manufacturing sector in Turkey. The study found that increases in oil prices did not had significant effect on the whole manufacturing sector, but it affected some sub-sectors adversely.

Mehrara and Sarem examined the effects of oil price shocks on industrial production in three oil-exporting countries, namely Iran, Saudi Arabia and Indonesia using annual data for the period 1970–2005. First, the Gregory and Hansen cointegration technique, allowing for the presence of potential structural breaks in data, is applied to empirically examine the long-run co-movement between oil price and output. Second, we test whether different measures of oil price shocks, including non-linear or asymmetric ones, Granger-cause output. The results indicate a strong causality from oil price shocks to output growth for Iran and Saudi Arabia. Moreover, the oil prices–output relationship in these two countries appears more significant when asymmetric specifications are used to model the relationship between variables. In the case of Indonesia, however, none of the oil proxies have any significant effect on output both in the short and long run. The results confirm the relatively successful experience of countries such as Indonesia in the diversification of the real sector to minimise the harmful effects of oil booms and busts.

Aye, Dadam, Gupta and Mamba [10] investigated the effect of oil price uncertainty on the South African manufacturing production using monthly observations covering the period 1974:02 to 2012:12. In addition, we quantify the responses of manufacturing production to positive and negative oil price shocks. We examine the dynamic relationship using a bivariate GARCH-in-mean VAR simultaneously estimated with a full information maximum likelihood technique. The conditional standard deviation of the forecast of the growth of US crude oil imported acquisition cost by refiners is used as a measure of oil price uncertainty. Our results show that oil price uncertainty negatively and significantly impacts on South Africa’s manufacturing production. We also find that the responses of manufacturing production to positive and negative shocks are asymmetric.

Akinlo A.E. [11] 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 co-integrated 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, building and construction, trade and services, and agriculture. It also confirms negative impact of oil on the manufacturing sub sector.

Ojapinwa and Ejumedia [12] examined the industrial impact of oil price shocks in Nigeria from 1970-2009, the econometric approaches adopted in the paper is the VAR impulse response. This study came out with empirical evidence that would help in understanding the impact of oil price shocks on aggregate industrial output in Nigeria while also considering other variables like Exchange rate, Inflation unemployment and Money Supply. The study came to the conclusion that oil price, inflation and exchange rate has the potentials of causing significant changes in industrial output in Nigeria, while it was also revealed that industrial output was not significantly determined by money supply. 

Mordi and Adebiyi employed a structural VAR model in which the asymmetric impact of oil shocks on output and price is analyzed in a unifying model. The model is applied to Nigeria using monthly data spanning 1999:01 to 2008:12 and the empirical results show that the impact of oil price shocks on output and prices that is asymmetric in nature; the impact of oil price decrease is significantly greater than oil price increase. Also, from the variance decompositions, oil price changes play a significant role in determining the variance decompositions of output and prices. The implication is that any policy that is aimed at moving the economy forward must focus on price stability in which changes in oil price play a significant role.

Adeniyi [13] carried out a pioneer attempt at introducing threshold effects to the linkage between oil price shocks and output growth in Nigeria. The study adopted the regime dependent multivariate threshold autoregressive model, together with the characteristic impulse response functions and forecast error variance decomposition using quarterly data from 1985 to 2008. The results of the study show that oil price shocks do not account for a significant portion of observed movements in macroeconomic aggregates. This according to Adeniyi [13] implied the enclave nature of Nigeria’s oil sector with weak linkages. Therefore, the need to spend revenue productively is imperative if favorable effect on real output growth is envisioned [13]. 

.

Ayadi [14] employed structural Vector Auto-Regressive Model to analyse the Oil Price Fluctuations and the Nigerian Economy between 1980 and 2004.The result of the study showed that changes in oil prices have no impact on industrial production. Olomola investigated the impact of oil price shocks on aggregate economic activity (output, inflation, the real exchange rate and money supply) in Nigeria using quarterly data from 1970 to 2003. The findings revealed that contrary to previous empirical findings, oil price shocks do not affect output and inflation in Nigeria significantly. However, oil price shocks were found to significantly influence the real exchange rate. The author argues that oil price shocks may give rise to wealth effect that appreciates the real exchange rate and may squeeze the tradable sector, giving rise to the Dutch-Disease.

Asaolu and Ilo, analyzed the relationship between oil price and stock returns using co-integration and VECM framework from 1984-2007. Their findings show that Nigeria still experiences the golden rule- “oil up, stock down”, which should only apply to oil-importing countries. This could be traceable to the failure to translate huge foreign earnings from oil into improved industrial sector. Also, the failure to develop the local refinery thereby resulting in importation of refined oil products could also be a good explanation for this.

Methodology

This section discusses theoretical framework, the model specification, a priori expectation, estimating techniques and sources of data.

Theoretical Framework                

The Dutch disease is primarily associated with a natural resource discovery, but it can result from any large increase in foreign currency, including foreign direct investment, foreign aid or a substantial increase in the natural resource prices. The term was coined to describe the adverse impact on the economy of the Netherlands in the 1960s following the discovery of natural gas in the North Sea. It so happened that the new found wealth caused the Dutch Guilder to rise, causing the exports of all non‐oil products to become less competitive on the world market and eventually resulting in a decline in the manufacturing sector. In the 1970s, the same economic condition occurred in Great Britain, when the price of oil quadrupled and whereby it became economically viable to drill for North Sea Oil off the coast of Scotland. By the late 1970s, Britain had become a net exporter of oil; it had previously been a net importer. The Pound soared in value, but the country fell into recession when British workers demanded higher wages and exports became uncompetitive. Nigeria and other oil exporters also suffered catastrophically from Dutch Disease in the 1970s when the oil prices boomed (resulting in a severe contraction in Nigeria’s agriculture, a highly tradable sector).

The theory of Dutch disease is that an increase in revenues from oil will adversely affect the tradables (manufacturing and agriculture) of a nation’s economy by appreciating the local currency, which in turn makes manufacturing and agriculture less competitive. According to Corden and Neary in their analysis of the Dutch disease, the economy can be divided into three sectors: the natural resource sector (in this case oil), the non‐resource tradable sector (in this case agriculture and manufacturing), and the non‐tradable sector (which includes the non‐tradable services and construction). The real exchange rate is defined as the price of non‐tradables (set in the domestic economy) relative to the price of tradables (set in the world market). The Dutch disease broadly has two transmission channels: the spending effect on the one hand, and the resource movement effect on the other.

The Spending Effect 

This comes into play when increased income from the booming natural resource sector stimulates demand and spending by the private and public sectors, leading to higher prices and output in the non‐tradable sectors (non‐tradable services and construction). However, for the non‐natural resource tradable sectors (manufacturing and agriculture), prices are fixed at international levels and profits are squeezed by rising economy‐wide wages, which in combination render tradables less competitive in international markets. Increased demand increasingly meted out of rising imports as imports become cheaper.

The Resource Movement Effect 

This takes place when the boom in the natural resource sector (oil) and the non‐tradable sector (non‐tradable services and construction) attract capital and labour away from other parts of the economy. Output declines in the tradable sectors, where prices are fixed at world market levels. Since the natural resources sector (oil) can only absorb a small proportion of the labour force, the biggest proportion of the labour force seeks employment in the non‐tradable sectors. Both the spending and resource movement effects result in a fall in the output share of non natural resource tradables (agriculture and manufacturing) relative to non‐tradables. Consequently, countries that suffer from Dutch disease are expected to experience some or the entire following phenomena:

• A decrease in the price of imports and subsequent increase in demand for imported goods and services

• A rise in the prices of non‐tradables (services and construction) due to increased demand and subsequent resource movement into those sectors (labour and capital); and consequently more production of non‐tradables at the expense of tradables

• Disincentive to invest in tradables (agriculture and manufacturing)

• Export concentration ‐ production of tradables (agriculture and manufacturing) suffer and could get wiped out due to absence of competitiveness; Jobs in manufacturing sector move to lower‐cost countries

• Mixed welfare outcomes especially for people that were originally engaged in production of tradables

• Mixed growth outcomes

However, Standard Model does not assert that resource boom makes country poor but it affirms that real appreciation occurs and there will be more imports of tradable goods which crowding out of domestic tradable commodities mainly manufactures and/or agricultural goods that can enhance production of non-tradables. Much theoretical works have analyzed the benefits and costs of energy discoveries Such as Corden and Sachs and Warner for a survey), but the empirical works of Lama and Medina and Corden discussed the fast growing Australian mining sector on  one side and the lagging manufacturing sectors on the other. Bjornland and Thorsrud adopted traditional Dutch disease theory which built on standard Model by using Bayesian Dynamic Factor Model (BDFM) in analysing and quantifying the linkages between a booming energy sector and sectoral performance in the domestic economy using a structural model, while also allowing for explicit disturbances in real oil prices, world activity and activity in the non-oil sector. The theory emphasises on the number of variables in which industries included in the analysis, this is also the most comprehensive analysis to date of the relationship between energy booms and macroeconomic activity at the industry level in a resource rich economy.

Model Specification         

The model to be used in this study follows the model of Bjornland and Thorsrud with modifications which derived its root from traditional theory of Dutch disease. The model is thus specified below:

IDP=f (COP, CAP, TSC, EXR, PIS, CEX)

Explicitly,

IDP= α₀ +α₁COP +α₂ CAP+α₃TSC +α₄CEX+EXR +u_t

Where

• IDP: Industrial Output
• COP: Crude Oil price
• CAP: Government Capital Expenditure
• TSC: Total sale of crude oil (proxy by crude oil supply)
• CEX: Crude oil Export
• EXR: Exchange Rate
• α₀: intercept
• α₁ - : parameters to be estimated
• u_t: error term

A Priori Expectation

The following are expected to hold concerning the signs of parameters:

There is a direct relationship between industrial output and crude oil price

        A positive relationship is expected between Industrial output and government Capital Expenditure.

        A direct relationship is expected between Industrial output and Total Sale of crude oil

        Industrial output is expected to be a direct relationship with crude oil Export.

        There will be inverse relationship between industrial 0utput and exchange rate.

Estimation Techniques

This study employed both Vector Error Correction Model (VECM) and Structural Vector Autoregressive (SVAR) model. VECM was used to examine the long run relationship between oil price fluctuations and industrial output while Structural Vector Autoregressive Model was used to estimate shock transmission between oil price and industrial output. The Argumented Dickey Fuller (ADF) test is employed to test for the unit root and to determine the stationarity. 

Sources of Data

Secondary data is used for this study. The data like crude oil price, government capital expenditure, total sales of crude oil and crude oil export were sourced from the CBN publications, Central Bank of Nigeria statistical bulletins while Exchange rate and industrial output were sourced from National Bureau of Statistics between 1970 and 2015.

ADF Unit Root Test Result

In the Table 1, the result shows the ADF unit root test result for industrial output, crude oil price, oil sales, oil export, exchange rate and government capital expenditure respectively. The study reveals that all the variables are integrated of order one. The statistical implication of this is that estimation using the variables in level will not follow the standard distribution, and the issue of spuriosity is highly feasible, so there is need to take co integration into account. Based on this, Johansen co integration test is utilized as it is capable of detecting more than one co integrating vector.

In the Table 2, the result depicts the Johansen system cointegration test result. It was revealed that, firstly, the null hypotheses of no cointegration are rejected after taking into consideration intercept or a trend or both in the Johansen cointegration test. However, the study strictly follows the results with one Co integrating vector. In essence, we proceed to the VECM (Vector Error Correction Model) in order to capture the true dynamics in the study.

Table 3 above shows the estimated long-run effect of the crude oil price, oil sales, oil export, exchange rate and government capital expenditure on the industrial output in Nigeria. It was shown from the Table 3 that all the variables are significant at the conventional levels. The result shows that crude oil price, oil supply, and government capital expenditure have significant negative relationship with the industrial output in the long-run while oil export and exchange rate has significant positive relationship with the industrial output in the long-run respectively. The implication of this is that an increase in the price of crude oil over time has not considerably contributed to the growth of industrial output in Nigeria. Likewise, increase in the supply of crude oil in the world market within the period reviewed has not equally impacted positively on industrial growth. In the same vein, increase in the government expenditure particularly government capital expenditure which capable of enhancing industrial output has not been in consonance with the expected connotation. This might be attributed to high level of corruption in the country as well as inefficiency of some institutions. However, oil exports exhibited positive impact on industrial output which implies that an increase in oil exports has enhanced industrial output and this may be ascribed to the fact that oil exports is main source of government revenue in Nigeria.

Structural Impulse Responses of Industrial Output

The results of the impulse response in Figure 1 and the forecast error variance decomposition in Table 4 also corroborate the results of the long run dynamics vector error correction model. The response of industrial output from the crude oil price shock showed that industrial output increased in the short run but slightly fell down over time in the long run. The response of the industrial output to shock from oil export is very significant and positive while the response to shock from government capital expenditure is not significant in the long run. Moreover,  the  result  of  variance  decomposition  also revealed that there is evidence that only oil exports that explain a major variation in the industrial sector output. It explains a considerable percentage oil export variation from the year one (40.29%) and continues to increase reaching 55.89% at the end of 12- year period. while other variables explain very little variations in the industrial output.

Table 1: ADF Unit Root Test

ADF @ level
Assumptions
C	t-Stat.	-0.2494	-1.0297	-1.9172	-1.5035	-2.1195	-0.7737
	Prob.	0.9231	0.7328	0.3212	0.5211	0.2385	0.8151
C & T	t-Stat.	-3.6043	-2.2957	-1.7590	-1.9345	-1.2777	-1.4343
	Prob.	0.4281	0.4260	0.7046	0.6170	0.8784	0.8342
No C & T	t-Stat.	1.8774	0.2096	0.2983	0.7061	1.8173	2.3458
	Prob.	0.9838	0.7417	0.7670	0.8636	0.9816	0.9945
ADF @ first difference
C	t-Stat.	-5.4328	-5.9300	-6.3502	-5.9484	-5.2000	-6.3230
	Prob.	0.0001***	0.0000***	0.0000***	0.0000***	0.0001***	0.0000***
C & T	t-Stat.	-5.3418	-5.9148	-6.4984	-7.6874	-5.6076	-6.2780
	Prob.	0.0005***	0.0001***	0.0000***	0.0000***	0.0003***	0.0000***
No C & T	t-Stat.	-4.9723	-5.9862	-6.3968	-5.8482	-4.2156	-2.8192
	Prob.	0.0000***	0.0000***	0.0000***	0.0000***	0.0001***	0.0061***
	Remark	I(1)	I(1)	I(1)	I(1)	I(1)	I(1)

Source: Authors’ computation.

*** p<0.001, ** p<0.01, * p<0.05, C represents constant while T represents a trend

Table 2:  Cointegration Test

*Critical values based on MacKinnon-Haug-Michelis, Selected (0.05 level*) Number of Cointegrating Relations by Model                 

Table 3: Long run dynamics Vector Error Correction Model

Source: Authors’ computation, *** p<0.001, ** p<0.01, * p<0.05

Figure 1: Structural Impulse Responses of Industrial Output

Table 4:  Structural Forecast Error Variance Decomposition

Source: Authors computation, 2020

The study examined the relationship between oil price fluctuation and industrial output in Nigeria. The results revealed that there is an existence of co-movement between crude oil price fluctuation and industrial output in Nigeria. The VECM results showed that crude oil price, oil supply, and government capital expenditure have significant negative relationship with the industrial output in the long-run while oil export and exchange rate has significant positive relationship with the industrial output in the long-run respectively. The results of the Vector Autoregressive scheme corroborated the VECM results that the response of the industrial output to shock from oil export is very significant and positive while the response to shock from government capital expenditure is not significant in the long run. The study therefore concludes that increase in the crude oil price and increased growth experienced in the reviewed period was incapable of enhancing industrial output. More so, the huge money sourced mainly from crude oil that government invested on infrastructures has not translated to the growth of industrial output in Nigeria. Based on these findings, this study recommends that the income government is generating from crude oil should be properly channeled into productive projects that can boost the industrial growth of this country. In addition, the proceeds deriving from this crude oil should be effectively managed by the government as well as making industrialization a priority in their budgetary allocation.

1. Lardic, S. et al. “Oil Prices and Economic Activity: An Asymmetric Cointegration Approach.” Energy Economics, vol. 34, no. 4, 2005, pp. 3910–3915. 

2. Olaokum, O.O. “Oil Price Shock Effects on Economies of African Nations.” African Economic Journal, vol. 3, no. 10, 2000, pp. 30–39.

3. Akpan, E.O. “Oil Price Shocks and Nigeria Macroeconomy.” Paper presented at the Annual Conference of CSAE, Economic Development in Africa, 22–24 Mar. 2009, Oxford University.

4. Ayodele, A.I. et al. The Nigerian Economy: Structure and Pattern of Development. JODAD Publishers, 2003.

5. Kilian, L. “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market.” American Economic Review, vol. 99, no. 1, 2009, pp. 53–69.

6. Hamilton, J.D. “Understanding Crude Oil Prices.” Energy Journal, vol. 12, no. 1, 2009, pp. 179–206.

7. Jimenez-Rodriguez, R. “The Impact of Oil Price Shocks: Evidence from the Industries of Six OECD Countries.” Energy Economics, vol. 30, 2009, pp. 3095–3108.

8. Guidi, F. “The Economic Effects of Oil Prices Shocks on UK Manufacturing and Service Sectors.” Munich Personal RePEc Archive (MPRA Papers), 2015.

9. Tang, Y.S. et al. “Oil Price Shocks, Monetary Policy Adjustment and Output Volatility: Empirical Evidence from China.” Journal of Economic Theory and Economic Management, vol. 7, no. 1, 2010, pp. 17–27.

10. Alper, C. et al. “Asymmetric Effects of Oil Prices on the Manufacturing Sector in Turkey.” 29th Annual Conference of MEEA, San Francisco, 2009.

11. Aye, G.C. et al. “Oil Price Uncertainty and Manufacturing Production.” Energy Economics, vol. 43, no. 2, 2012, pp. 41–47.

12. Akinlo, A.E. “How Important Is Oil in Nigeria’s Economic Growth?” Journal of Sustainable Development, vol. 5, no. 4, 2012, pp. 105–114.

13. Ojapinwa, T.V. et al. “The Industrial Impact of Oil Price Shocks in Nigeria.” European Scientific Journal, vol. 8, no. 12, 2012, ISSN: 1857–7881.

14. Adeniyi, O. et al. “Oil Price Shocks and Economic Growth in Nigeria: Are Thresholds Important?” OPEC Energy Review, vol. 35, no. 3, 2010, pp. 308–333.

15. Ayadi, F.O. “Oil Price Fluctuations and the Nigerian Economy.” OPEC Energy Review, vol. 29, no. 3, 2005, pp. 199–217.

16. Mork, K.A. “Oil and Macroeconomy When Prices Go Up and Down: An Extension of Hamilton’s Results.” Journal of Political Economy, vol. 97, no. 3, 1989, pp. 740–744.

17. Mordi, C.N. et al. “The Asymmetric Effects of Oil Price Shocks on Output and Prices in Nigeria Using Structural VAR Model.” Central Bank of Nigeria Economic and Financial Review, vol. 48, no. 1, 2010, pp. 10–18.