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ISSN 1450-2267 Vol. 58 No 3 September, 2019, pp.164-177 http://www.europeanjournalofsocialsciences.com/

Effect of Government Oil Revenue on Agricultural Sector Growth: Evidence from Nigerian Economy

Nwanne Ndubuisi C

6955 N. Durango Dr #1105, Las Vegas NV 89149, United States of America

Eze, Onyekachi Richard Department of Banking and Finance Ebonyi State University, Abakaliki-Nigeria

Abstract

This study investigated the effect of government oil revenue on the growth of agricultural sector in Nigeria. The study adopted the ex-post facto research design and regression analysis as methodology. Descriptive statistics and graphs were also used to complement the regression result. The result from the study found that there is significant effect of government oil revenue on the growth of gricultural sector in Nigeria while that there is insignificant effect of government budget deficit on the performance of real sector in Nigeria with a bias to agricultural sector in Nigeria. The implication of the study is that government financing budget deficit through domestic means crowd out private investment especially the agricultural sector and thereby reduces its contributions to the growth of the economy. The study recommended that government of Nigeria should adopt a consistent oil policy measures that can entrench budget discipline, transparency and accountability aimed at raising levels of living, higher incomes, the provision of more jobs, better education, and greater attention to cultural and human values, all of which will serve not only to enhance material well-being but also to generate greater individual and national self-esteem by ensuring steady government oil revenue.

Keywords: Oil Revenue, Agriculture, Government Oil, Financing, Growth

1. Introduction

Prior to the discovery of oil in Nigeria, agricultural sector was the main stay of Nigeria economy, contributing about 95% to her foreign exchange earnings, generating over 60% of her employment capacity and approximately 56% to her gross domestic earnings (World Bank, 2013). The major exportable crops were cocoa, palm products, cotton, ground nut, timber and rubber, with these products contributing most of Nigeria’s export, Agriculture was the leading growth sector of the Nigerian economy while oil export was very poor. Infact, available literature on the Nigerian economy has it that Nigeria was primarily an agrarian economy, whose revenue generation was based on agriculture;

statistics from the federal Bureau of statistic indicates that between 1958 and 1969, the contribution of petroleum (GDP) at current factor was just 0.007 percent. While agriculture formed the mainstay of the country’s economy accounting for higher percentage of Gross Domestic Product (GDP) (Nweze and Grey, 2016).

The oil boom of the 1970s led to Nigeria's neglect of its strong agricultural and light manufacturing bases in favour of an unhealthy dependence on crude oil. In 2002 oil and gas exports

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accounted for more than 98% of export earnings and about 83% of federal government revenue. In 2011, fuel exports were 89 per cent of all merchandise exports. New oil wealth, the concurrent decline of other economic sectors, and a lurch toward a statist economic model fueled massive migration to the cities and led to increasingly widespread poverty, especially in rural areas. A collapse of basic infrastructure and social services since the early 1980s accompanied this trend. By 2002 Nigerian’s per capita income had plunged to about one – quarter of its mid – 1970s high, below the level at independence. Along with the endemic malaise of Nigeria’s non – oil sectors, the economy continues to witness massive growth of “informal sector” economic activities (Igberaese, 2013).

Most economists have always asserted that there is a strong connection between oil revenue and economic growth focusing on agricultural sector growth, as compared to the connectedness between monetary policies and growth. This idea has been thought to originate from various channels such as the negative effect of distortive tax on the performance of the economy (Tanzi and Zee, 1997). Studies have revealed that any policy changes that led to an increase in economic incidence and deadweight loss distort economic growth (Karran, 1985, Easterly, 1994, Kneller., 1999). The supply side hypothesis has supported the inverse relationship between tax rates and economic growth. Firstly, increases in the tax rate causing a rise in tax revenue lead to a significant negative impact on economic growth. Second, the relationship between tax revenue and economic growth showed a positive association between the two, that is, any significant increase in tax income will have a positive impact on economic growth. A possible reason is that an increase in tax revenue will boost the economy and prospective economic development. The tests on the relationship between the tax revenue growth and economic growth have been extensively performed especially in developed countries. The results show that economic development was the strongest determinant of tax growth. For instance Easterly (1994) has shown how the distortion in tax structure affects the growth rate. Similarly Kneller (1999) found evidence on how tax can negatively affect the growth rate. in contrast, it was found that a rise in income tax could lead to an increase in economic growth if the time preference is endogenously determined (Chang, 1999). It was further assumed that the government collects income tax revenue and transforms it into a productive public expenditure that has an effect on the economic growth. Most studies have examined how tax may encourage or discourage the long term economic growth rate (Padovano and Galli, 2002, Koch 2005, Lee and Gordon, 2005).

Further, a lot of empirical studies (Asagunla and Agbede (2018); Joseph, Micheal and Stella (2016); Arikpo, Ogar and Ojong (2017); Nwoba and Abah (2017); Musa, Sunusi and Sabiu (2016);

Nweze and Grey (2016); Ubesie (2016); Osinowo (2015); Raymond and Adigwe (2015) and Peter (2015)) have found inconsistent result on the impact of oil revenue on economy which include agricultural sector growth. Based on this premise, the study is thus designed to investigate the effect of government oil revenue on agricultural sector growth in Nigeria.

This study aims at achieving the following objectives:

1. To determine the short-run effect of government oil revenue on agricultural sector growth in Nigeria.

2. Review of Related Literature 2.1. Concept of Revenue

Total federal collected revenue will be used. According to Keynes (1936), change in tax revenue will have negative impact on growth. Increase in tax revenue could be in two folds: either through increasing the rate of payment by existing tax payers or expanding the number of tax payers. The former will reduce disposable income, savings, investment and aggregate demand, thus having negative effect on real sector growth. It is expected to have negative impact on real sector growth. The main purpose of tax is to raise revenue to meet government expenditure and to redistribute wealth and management of the economy (Ola, 2001; Jhingan, 2004; Bhartia, 2009).

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Tax structure varies around the globe with the prime motive of attaining maximum revenue with minimum distortion. Different countries have different philosophies about taxation and have different methods for collection; in the same manner countries have different uses of their revenue which affect the growth differently and as a result their growth rates are different. Atkinson (1995);

Castles & Dawrick (1990) and Agell (1997), all argued that the different uses of total government tax revenue expenditure affect growth differently and a similar argument applies to the way the tax revenue should be raised.

Tax structure varies around the globe with the prime motive of attaining maximum revenue with minimum distortion. Different countries have different philosophies about taxation and have different methods for collection; in the same manner countries have different uses of their revenue which affect the growth differently and as a result their growth rates are different.

Solow (1956), a pioneer theory in this regard, namely the neo-classical growth model concluded that taxes do not affect the steady-state growth rate. This implies that although tax policies are distortionary has no impact on long term economic growth rates and total factor productivity. On the other hand, endogenous growth theory by Romer (1986), emphasises factors such as 'spillover' effects and 'learning by doing' by which firm specific decisions to invest in capital and individual investment in human capital, can yield positive external effects that benefits the rest of the economy. In this model government spending induced growth and tax revenue policies can have a long run sustainable and permanent growth.

Total federal collected oil revenue were used. According to Keynes (1936), change in government revenue will have negative impact on growth. Increase in oil revenue could be in two folds: either through increasing the rate of payment by existing tax payers or expanding the number of tax payers (CBN, 2016). The justification for using government oil revenue is because non-oil revenue is quite smaller. Government oil revenue is more significant in terms of the percentage to total revenue.

Also, when we combine government oil and non-oil revenue which is the total revenue, we discovered that it does not show any meaningful or significant result in terms of stationarity. So we decided to divide them into two considering the fact that government oil revenue is more significant for the achievement of government economic programmes and we decided to use government oil revenue which is more reliable and stationary (Nwanne, 2018).

Several studies (Asagunla and Agbede (2018); Joseph, Micheal and Stella (2016); Arikpo, Ogar and Ojong (2017); Nwoba and Abah (2017); Musa, Sunusi and Sabiu (2016); Nweze and Grey (2016);

Ubesie (2016); Osinowo (2015); Raymond and Adigwe (2015) and Peter (2015); Ibeh, (2013)) had investigated the relationship between oil revenue and Nigerian economic activities using different methods of statistical analysis and discovered that, there is a long run relationship between oil revenue and economic growth in Nigeria. However, it could be say that the findings of those studies were based on the data far back from 20014 and geographical area which create gap for the present study.

2.2. Concept of Agricultural Sector Growth

Agriculture is the mainstay of the Nigerian economy, directly contributing 60 percent of the GDP annually in the 1960s. The sector accounts for 70 percent of Nigerian total exports and provides more than 70 percent of informal employment in the rural areas. Therefore, the agricultural sector is not only the driver of Nigerian economy but also the means of livelihood for the majority of Nigerian people (Lawal, 1997). Agricultural sector development suffered total neglect as a result of the oil boom in the 1970s and this neglect continues to the extent that Nigerian agricultural sector cannot afford to produce what her citizenry will consume not to talk about exporting of the produce. The present-day problem of Nigerian economy is as a result of total neglect of the agricultural sector and increased dependence on a mono-cultural economy based on oil. The contribution of agriculture to Nigeria’s GDP now lies within 5% (Olagbaju and Falola, 1996). In contrast to the economy as a whole, for full year 2016, real GDP in agriculture grew by 4.11%, and this growth rate was higher than that recorded in 2015 of 3.72%. The contribution of the Agricultural sector as real sector of the economy to overall GDP in real

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terms was 25.49% in the quarter under review, higher than its share of 24.18% in the corresponding year (NBS, 2017).

Noko (2017) stated that agriculture is estimated to be the largest contributor to the non-oil foreign exchange. According to him a strong agricultural sector is essential to economy development both in its own rights and to simulate and support the growth of industries. Economy growth has gone hand in hand with agricultural progress; stagflation in agriculture is the principal explanation for poor economy performance, while rising agricultural activities has been the most concomitant of successful industrialization (Ukeje 1999). The labour-intensive character of the sector reduces its contribution to the GDP; Noko lamented, and still maintained that agricultural exports are a major earner of foreign exchange in Nigeria. Agriculture can be further broken into crop production, livestock, forestry and fishing. This is as a result of increase in oil revenue and its impact on agricultural sector growth through economic growth in Nigeria.

2.4. Empirical Review

Asagunla and Agbede (2018) examine the contribution of the oil revenue to Nigerian output growth for the period of 1981 to 2014. Using Beghebo and Atima model with little modification, the study employed the fully modified ordinary least squared method (FMOLS) to examine the relationship. The study therefore discovered that oil revenue does not have short run impact on the economic activities of Nigeria. However, the long run impact of this policy gave a sterling story, as it was revealed that the persistence rise in oil revenue will ultimately lead to future economic growth of the country. Sandra, Alar, Eduardo and Giselle (2017) applied a comprehensive tax-benefit incidence analysis to estimate the distributional effects of fiscal policy in Chile in 2013. It was found out that there is an overall positive effect of fiscal interventions on poverty and inequality.

Victor and Roman (2017) analysed the effects of fiscal policies upon agriculture and industry in Ukraine, with the SVAR model using quarterly data for the 2001–2016 period. The results indicate a positive effect of the government spending on both agricultural production and industrial output, while an increase in the government revenue is of the same expansionary impact for the latter only. Among other results, there was a weak negative short-lived spillover from agriculture to industry, with no causality running on the reverse. As agricultural production in Ukraine is associated with a higher level of government spending in the short run, a direction of causality seems to be just the opposite for industrial output. Both agriculture and industry bring about higher budget revenues in the short run, but for the latter this effect is lagged and more persistent. Controlling for fiscal policy effects, the nominal (real) exchange rate depreciation seems to be expansionary for industrial output. For agriculture, a nominal exchange rate depreciation is restrictionary in the short run, with an expansionary effect in the long run (however, this result is not supported in specification with the real exchange rate).

Nwoba and Abah (2017) examined the impact of crude oil revenue on the growth of the Nigerian economy between (1960-2010). Hence the specific objectives are to ascertain the extent of economic growth impacted by the oil proceeds and multinational oil companies in Nigeria. And also to establish the long run relationship between crude oil proceeds and Gross domestic product (GDP). The findings revealed the extent of economic growth impacted by the oil industries was significant based on the ordinary least square (OLS) regression analysis result where the calculated F-Statistics of (212.1293) is greater the tabulated F-statistics of (5.35147). The study also found the long run positive relationship between oil revenue and gross domestic product.

Nweze and Grey (2016) examined oil revenue and economic growth in Nigeria between 1981 to 2014. Secondary data on gross domestic product (GDP), used as a proxy for economic growth; oil revenue (OREV), and government expenditure (GEXP) which represented the explanatory variables were sourced mainly from CBN publications. In the course of empirical investigation, various advanced econometric techniques like Augmented Dickey Fuller Unit Root Test, Johansen Cointegration Test and Error Correction Mechanism (ECM) were employed and the result reveals among others: That all the variables ware all stationary at first difference, meaning that the variables

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were not integrated of the same order justifying cointegration and error correction mechanism test. The cointegration result indicated that there is long run relationship among the variables with three cointegrating equation(s). The result of the error correction mechanism (ECM) test indicates that all the variables except lag of government expenditure exerted significant impact on economic growth in Nigeria. However, all the variables exhibited their expected sign in the shortrun but exhibited negative relationship with economic growth in the longrun except for government expenditure, which has positive relationship with economic growth both in the longrun and shortrun. The study concluded that Government should use the revenue generated from petroleum to invest in other domestic sectors such as Agriculture and manufacturing sector in order to expand the revenue source of the economy and further increase the revenue base of the economy.

Ogunmakin, Adebayo and Dada (2014) examine the economic development and oil revenue in Nigeria. In doing this, regression analysis was carried out using SPSS. The result revealed the overdependence of Nigeria economy on oil revenue. Thus, this paper recommends policies and functional institutions to checkmate the poor transparency in the management of oil revenue that robbed the people of their potential benefits and economy diversification that will lead to improvement in revenue generation via other sources in the economy.

Musa, Sunusi and Sabiu (2016) Using a multivariate regression analysis, this study aims at critically analysing the impact of oil revenue on the Nigerian economy. Findings indicate a high insignificance in establishing the flow of oil revenue into the key economic sectors in Nigeria.

Ibeh (2013) investigate the impact of the oil industry on the economic growth performance of Nigeria. In the process of the research, the ordinary least square (OLS) regression technique was employed. Considering the impact of time on changes in economic variables, the analysis was carried out using the simple regression method in which Gross Domestic Product (GDP), proxy for economic growth was used as the dependent variable, while the oil Revenue (OREV) and time appeared as repressor’s. A two-tailed test of 5% significant levels were conducted indicating that the two explanatory variables did not have any significant impact on growth performance of the Nigerian economy within the same period.

2.5. Theoretical Framework

Based on the nature of this study, the theoretical framework adopted were Keynesian Theory.

2.5.1. The Keynesian Economic Theory

Keynesian Economic Theory was developed by British Economist John Maynard Keynes (1936). This theory believes that active government intervention in the market place through fiscal policy responsibility was the only method for ensuring growth and stability in agricultural sector growth of the economy. This can be achieved through ensuring efficiency in resources allocation, regulation of markets, stabilization of the economy and harmonization of social conflicts.

Assumptions: Keynes economic theory states that in the short run, agricultural sector growth through economic growth is strongly influenced by total spending in the economy. Keynesian theory states that public expenditures such as funds from oil revenue can contribute positively to economic growth by increasing government consumption through increase in employment, profitability and investment. The theory also states that government can reverse economic downturns by borrowing money from the private sector and returning the money to private sector through various spending.

This theory regards the economy as being inherently unstable and required active government intervention through spending to achieve real sector growth. The relevance of this theory is that Keynesian assigns a low degree of importance to monetary policy and high degree of importance to fiscal policy especially in the area of real sector development.

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3. Research Methodology

This study made use of Ex-post facto research design. Onwumere (2009) states that ex-post facto design is the type of research involving events that has already occurred. The data already exist as no attempt would be made to control or manipulate relevant independent variable. This study covered government oil revenue and its impact on agricultural sector growth in Nigeria for the period under review-1980-2017. The reason for this is because; it was a period when there was increase in government spending with low returns from the agricultural sector in Nigeria. The period has also been considered adequate enough to generate good result from regression analysis which is the method that is relevant in testing the hypotheses formulated for the study. Based on the above, the model for this study were therefore estimated as follows:

AGDP = f (GOR, INFL, EXR, INTR) … (1)

i.e. AGDP = βo + β1GOR + β2INFL + β3EXR + β4INTR +ut. … (2) WHERE;

A/GDP = Agriculture Sector Contribution to AGDP/GDP GOR = Government Oil Revenue

INFL = Inflation Rate EXR = Exchange Rate U = Stochastic error term

4. Result and Discussion 4.1. Descriptive Test

The descriptive statistics of data series gives information about simple statistics such as mean, median, minimum value, maximum value and the distribution of the sample measured by skewness, kurtosis and the Jaque-Bera statistic and it was used in this study to. The descriptive result is presented in table 1.

Table 1: Descriptive Statistics

Parameters AGDP GOR INFL INT EXR

Mean 23.38 73.89 20.09 13.54 82.30

Median 22.88 73.70 12.55 13.25 57.37

Maximum 37.52 88.64 76.76 27.00 35.60

Minimum 14.43 47.44 0.22 6.13 0.55

Std. Dev. 5.07 8.95 18.24 5.19 86.30

Skewness 0.53 -0.79 1.64 1.04 1.12

Kurtosis 3.29 3.79 4.72 4.02 4.23

Jarque-Bera 1.93 4.90 21.71 8.49 10.31

Probability 0.38 0.09 0.00 0.01 0.01

Sum 888.43 2807.72 763.32 51.62 31.23

Sum Sq. Dev. 950.14 2963.43 123.52 97.72 27.20

Observations 38 38 38 38 38

Source: Author’s Computation 2019

Table 1 above reports the overall mean and standard deviation for all the variables involved in this standard regression analysis. The mean of the dependent variables chosen which is agricultural sector contribution to the growth of Nigerian economy (AGDP) is 23.38 which is not all that very low compared to the mean of other independent variables which are the variables of government oil revenue. With the mean value of GOR standing at 73.89 while those of INF, INTR and EXR are 20.09, 13.54 and 82.30 respectively, we can conclude that EXR has the highest mean value, followed by AGDP, INFL, then, INTR. The standard deviation of each variable (AGDP, GOR, EXR, INTR and

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INFL) with 5.07, 8.95, 18.24, 5.19 and 86.30 respectively appears to follow the same hierarchical trend as those of the mean values.

The above result indicates that all the variables displayed a high level of consistency as their mean and median values are perpetually within the maximum and minimum values of these series.

Besides, the standard deviation revealed that actual data in the series are not really different from the mean value. The skewness and kurtosis statistics provide useful information about the symmetry of the probability distribution of various data series as well as the thickness of the tails of these distributions respectively. These two statistics are particularly of great importance since they are of use in the computation of Jarque-Bera statistic, which is used in testing for the normality or asymptotic property of a particular series.

4.2. Correlation Test

Correlation is a term that refers to the strength of a relationship between two variables. Correlation results arising from the study as part of the descriptive analysis were also presented in table 2 below.

Correlation test was used to ascertain the strength and magnitude of the relationship between the dependent and independent variables.

Table 2: Correlation Matrix

AGDP GOR INFL INT EXR

AGDP 1.000

GOR 0.122 1.000

INFL -0.093 0.157 1.000

INT 0.387 -0.198 0.198 1.000

EXR 0.401 -0.358 -0.346 0.382 1.000 Source: Author’s Computation 2018

The correlation matrix as seen in the table 2 above is from numbers between -1 and 1. It shows whether variables or paired set of data are related or not. The closer the values are to 1, the more confidence we have that the variables have positive linear correlation and a negative sign implies an inverse correlation. The correlation matrix as seen in the table 2 above is from numbers between -1 and 1. It shows whether variables or paired set of data are related or not. The closer the values are to 1, the more confident we have that the variables have positive linear correlation and a negative sign implies an inverse correlation. The correlation between government oil revenue (GOR) and Agricultural Sector Contribution to GDP (AGDP) with values of 0.1224 indicates a positive and weak correlation between the two variables. In summary, the relationship or correlation or association between dependent and independent variable is generally fair. This is because a correlation of r = 0.9 suggests a strong, positive association between two variables, whereas a correlation of r = -0.2 suggest a weak, negative association. A correlation close to zero suggests no linear association between two continuous variables. This is an indication that government oil revenue have not actually helped the growth of Nigerian economy through contribution of agricultural sector output over the years under study.

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4.3 Graphical Trend

Figure 1: Trend analysis of AGDP (1980-2017)

12 16 20 24 28 32 36 40

1980 1985 1990 1995 2000 2005 2010 2015

AGDP

Source: Author’s computation 2019

Figure 1 shows the graphical trend of agricultural sector contribution to Gross Domestic Product (AGDP) in Nigeria for the period 1980-2017. From 1980 to 1983 there was steady increase but in 1984, there was a slight decrease. From 1985, it picked with 18.26% and continued with minimal fluctuation increase until 2002 where reached the highest peak of 37.52%. The graphical analysis shows that agricultural sector contribution to Gross Domestic Product (AGDP) in Nigeria had the highest contribution of 37.52% in 2002 followed by 34.48% in 2003, and the lowest in 1980 with 14.43 followed by 1981 with 15.5%. From 2008 to 2017, it recorded increase with minimal fluctuation.

Since agricultural sector contribution to Gross Domestic Product (AGDP) in Nigeria has a greater effect on the economy and indicate the level of real sector contribution on economic growth, we can say that agricultural sector contribution to gross domestic product (AGDP) in Nigeria will over the years under study have minimal fluctuation.

Figure 2: Trend analysis of GOR (1980-2017)

40 50 60 70 80 90

1980 1985 1990 1995 2000 2005 2010 2015

GOR

Source: Author’s computation 2019.

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Figure 2 above shows the trend analysis of one of the independent variables (Government Oil Revenue (GOR)) from 1980 to 2017. From Figure 2, it can be seen that the government oil revenue as a percentage of total government collected revenue was on 81.09% in 1980 and continue descending up till 1990 with a minimal fluctuation. By 1991, government oil revenue (GOR) started ascending with 81.86%, this ascension continued till 1992 and in 1993, it descended to 84.09% and continue till 2003 with 80.55% even though it is inconsistence. It increased to 85.57% in 2004 and this trend continued till 2006 where we observed the highest contribution of 88.64. By 2007, it descended to 77.92% and rose again in 2008 with 83.02%. From 2011 to 2015, there was up and down movement from the level of government revenue. In 2009 to 2017, government oil revenue as a percentage to total revenue was going up and down. In summary, we can conclude that over the period of study, government oil revenue (GOR) have been experiencing an increase but with minimal fluctuations in the early years. From this, we observed that the inability of Nigerian government to fully implement their fiscal policies such as government capital and recurrent expenditure was as a result of inconsistence in oil revenue.

4.4. Akaike Information Criteria Test

Figure 3: Akaike Information Criterion (AIC)

0.9 1.0 1.1 1.2 1.3 1.4

ARDL(3, 4, 4, 4, 4) ARDL(4, 4, 4, 4, 4) ARDL(3, 4, 4, 2, 4) ARDL(3, 4, 4, 0, 4) ARDL(3, 4, 4, 1, 4) ARDL(4, 4, 4, 0, 4) ARDL(3, 4, 4, 3, 4) ARDL(4, 4, 4, 2, 4) ARDL(4, 4, 4, 1, 4) ARDL(4, 4, 4, 3, 4) ARDL(3, 4, 2, 4, 4) ARDL(3, 4, 0, 4, 4) ARDL(3, 4, 3, 4, 4) ARDL(4, 4, 2, 4, 4) ARDL(3, 4, 1, 4, 4) ARDL(4, 4, 4, 3, 2) ARDL(4, 4, 0, 4, 4) ARDL(4, 4, 3, 4, 4) ARDL(3, 4, 0, 3, 4) ARDL(4, 4, 1, 4, 4)

Akaike Information Criteria (top 20 models)

The Akaike Information Criterion (AIC) graph above shows the model selection value for the twenty best estimated models with the lowest criterion value. To achieve parsimony, the model with the least AIC, that is ARDL (3, 4, 4, 4, 4) is selected to determine the error correction and long run models.

To verify whether the residuals from the model are serially uncorrelated, in the estimation view, we proceed to Residual Diagnostics/Serial Correlation LM Test and select the number of lags. In our case, we chose 4. Here's the output

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Table 3: F-Bounds of ARDL Co-integration Test for MGDP

t-Bounds Test Null Hypothesis: No levels relationship

Test Statistic Value Signif. I(0) I(1)

t-statistic -6.534622 10% -1.62 -3.26

5% -1.95 -3.6

2.5% -2.24 -3.89

1% -2.58 -4.23

Source: Author’s computation 2019

The T-statistic value 6.534622 is evidently below the I(0) critical value bound. Our analysis of this series indicates that we fail to reject the null hypothesis that there is no equilibrating relationship.

Since the null hypothesis is that the residuals are serially uncorrelated, the T-statistic p-value of 6.534622 indicates that we will not fail to reject this null. We therefore conclude that the residuals are serially correlated.

4.5. Diagnostic Tests

Normality Test (Null Hypothesis: Standardized Residuals are normally distributed)

Figure 4: Normality Test

0 1 2 3 4 5 6 7

-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

Series: Residuals Sample 1984 2017 Observations 34

Mean 8.35e-05 Median -0.024341 Maximum 0.336809 Minimum -0.376166 Std. Dev. 0.187440 Skewness -0.059967 Kurtosis 2.568951

Jarque-Bera 0.283599 Probability 0.867795

Source: Author’s computation 2019

From the above test, we observed that null hypothesis cannot be rejected since the probability value of the Jarqua-Bera statistics is higher than 1%, 5% and 10%. This implies that the Standardized Residuals from the estimated EC model in ARDL framework is normally distributed, which is consistent with the OLS assumptions.

4.6. Error Correction Mechanism

Co-integration relationship has been established among the variables, and then Error Correction Mechanism was used for this exercise to determine the behavior of Nigerian stock market depth on manufacturing sector performance. With the help of E-view 8.0 package, the Error Correction Model Estimate was run and presented below.

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Table 4: ARDL ECM Result ARDL Error Correction Regression Dependent Variable: D(AGDP) Selected Model: ARDL(4, 4, 4, 3, 4) Case 1: No Constant and No Trend Sample: 1980 2017

Included observations: 34 ECM Regression

Case 1: No Constant and No Trend

Variable Coefficient Std. Error t-Statistic Prob.

D(AGDP(-1)) -0.395531 0.228526 -1.730793 0.1217

D(AGDP(-2)) -0.387880 0.133406 -2.907521 0.0197

D(AGDP(-3)) -0.536512 0.179379 -2.990946 0.0173

D(GOR) 0.035499 0.060232 0.589365 0.5719

D(GOR(-1)) -0.379399 0.076059 -4.988226 0.0011

D(GOR(-2)) -0.473085 0.092312 -5.124876 0.0009

D(GOR(-3)) -0.302700 0.087660 -3.453124 0.0087

INFL 0.014195 0.026092 0.544036 0.6012

INT 0.434367 0.080982 5.363769 0.0007

EXR 8.50E-05 0.006028 0.014102 0.9891

CointEq(-1)* -0.284409 0.058774 -4.839063 0.0013

R-squared 0.932464 Mean dependent var 0.308824

Adjusted R-squared 0.814275 S.D. dependent var 3.066866

S.E. of regression 1.321690 Akaike info criterion 3.648364

Sum squared resid 20.96238 Schwarz criterion 4.636009

Log likelihood -40.02218 Hannan-Quinn criter. 3.985179

Durbin-Watson stat 2.506320

* p-value incompatible with t-Bounds distribution.

Levels Equation

Case 1: No Constant and No Trend

Variable Coefficient Std. Error t-Statistic Prob.

GOR 1.314723 0.664469 2.978607 0.0032

EC = AGDP - (1.3147*GOR ) Source: Author’s computation 2019

As expected, the EC term, here represented as CointEq(-1), is negative with an associated coefficient estimate of −0.284409. This implies that about 28.44% of any movements into disequilibrium are corrected for within one period. Moreover, given the very large t-statistic, namely

−4.839063, we can also conclude that the coefficient is highly significant. The short-run coefficients estimates show the dynamic adjustment of all variables. The short run coefficients for GOR has significant effect on AGDP in lag 1, 2, and 3. In summary, the short run coefficients for GOR is statistically significant at the 5% level. The coefficient of error correction term ecm (-1) estimated at - 0.284409 is highly significant indicating that the agricultural sector contribution to GDP and government oil revenue are cointegrated. The estimated value of the coefficient indicates that about 28.44 percent of the disequilibrium in agricultural sector contribution to GDP is offset by the short run adjustment in the same quarter.

Moreso, the parsimonious model is free of serial correlation going by the value of the Durbin- Watson statistics of 2.51. The coefficient of determination (R-square) which was used to measure the goodness of fit of the estimated model, indicates that the model is reasonably fit in prediction, that is, 93.25percent change in AGDP was due to GOR, EXR, INTR and INFL collectively, while 6.75percent unaccounted variations was captured by the white noise error term. It showed that GOR, EXR, INTR and INFL had strong and significant impact on the AGDP in Nigeria.

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Decision Rules

Decision Rule 1: Accept the alternate hypothesis and reject the null hypothesis if the P-value is less than the chosen level of significance (0.05). It implies that the estimated variable has significant impact on the dependent variable.

From the table 4 above using the coefficient of determination, we observed that the increase in government oil revenue increases the contribution of agricultural sector output in Nigerian economy in short run. This means that government's oil revenue has positive and significant effect on agricultural sector contribution to the growth of Nigerian in the short run. In this case, the null hypothesis was rejected while the alternate hypothesis was accepted with the conclusion that there is significant and negative effect of government oil revenue on the growth of real sector in Nigeria focusing on agriculture sector.

5. Conclusion and Recommendations 5.1. Conclusion

Based on the findings of this study, we conclude: that there is significant effect of government oil revenue on the growth of gricultural sector in Nigeria while that there is insignificant effect of government budget deficit on the performance of real sector in Nigeria with a bias to agricultural sector in Nigeria. The implication of the study is that government financing budget deficit through domestic means crowd out private investment especially the agricultural sector and thereby reduces its contributions to the growth of the economy.

5.2. Recommendations

Based on the findings, the study makes the following recommendations;

1. Government of Nigeria should adopt a consistent oil policy measures that can entrench budget discipline, transparency and accountability aimed at raising levels of living, higher incomes, the provision of more jobs, better education, and greater attention to cultural and human values, all of which will serve not only to enhance material well-being but also to generate greater individual and national self-esteem by ensuring steady government oil revenue.

2. Government of Nigeria should formulate appropriate policy mix that would motivate the firm in the oil sector to enhance improved performance and contribution of the sector.

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References

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