Engineering
Journal of Economics and International Finance Vol. 3(6), pp. 407-417, June 2011
Available online at http://www.academicjournals.org/JEIF
ISSN 2006-9812 ©2011 Academic Journals
Full Length Resear
ch Paper
Fiscal policy: Its impact on economic growth in Nigeria
1970 to 2006
Ogbole F. Ogbole1, Sonny N. Amadi2* and Isaac D. Essi3
1Department of Accountancy, The Federal Polytechnic, Damaturu, Yobe State, Nigeria.
2Department of Banking and Finance, Rivers State University of Science and Technology, Port Harcourt, Nigeria.
3Department of Mathematics/Computer Science, Rivers State University of Science and Technology,
Port Harcourt, Nigeria.
Accepted 16 February, 2011
This study involves comparative analysis of the impact of fiscal policy on economic growth in Nigeria
during regulation and deregulation periods. Econometric analysis of time series data from Central Bank
of Nigeria was conducted. Results obtained showed that there is a difference in the effectiveness of
fiscal policy in stimulating economic growth during and after regulation periods. The impact was
marginally higher (only N140 million or 14% contribution to GDP) during deregulation, than in the
regulation period. We recommend appropriate policy mix, prudent public spending, setting of
achievable fiscal policy targets and diversification of the nation’s economic base, among others.
Key words: Fiscal policy, regulation, deregulation, economic growth.
INTRODUCTION
The Nigerian economy has been plagued with several
challenges over the years. Researchers have identified
some of these challenges as: gross mismanagement/
misappropriation of public funds, (Okemini and Uranta,
2008), corruption and ineffective economic policies
(Gbosi, 2007); lack of integration of macroeconomic
plans and the absence of harmonization and coordination
of fiscal policies (Onoh, 2007); inappropriate and
ineffective policies (Anyanwu, 2007). Imprudent public
spending and weak sectoral linkages and other socioeconomic
maladies constitute the bane of rapid economic
growth and development (Amadi et al., 2006). It is
evident that one of Nigeria’s greatest problems today is
the inability to efficiently manage her enormous human
and material endowment.
In spite of many, and frequently changing, fiscal,
monetary and other macro-economic policies, Nigeria
has not been able to harness her economic potentials for
rapid economic development (Ogbole, 2010). These
policies span through two broad periods, which can be
classified as “regulation” and “deregulation”.
Our main focus is the differential in fiscal policy
*Corresponding author. E-mail: watiamadii@yahoo.com.
effectiveness in promoting economic growth in the two
broad periods.
Our main predictor/explanatory variable is fiscal policy.
We use Federal Government spending as proxy for fiscal
policy. Based on the foregoing, we hypothesize that the
effectiveness (or impact) of fiscal policy on economic
growth is not different between the two periods under
investigation. Our time frame is 37 years (from 1970 to
2006). The period of regulation is considered to be
between 1970 and 1985, while that of deregulation is
from 1986 too 2006.
THEORETICAL/CONCEPTUAL FRAMEWORK AND
REVIEW OF RELATED LITERATURE
The earliest organized school of macroeconomic thought
is the “classical” school. The classical economists were
proponents of the price mechanism (market system)
which assumes a smooth functioning market where there
is effective resource allocation (Ekanem and Iyoha, 1999)
and a guarantee to economic freedom to all and sundry,
with built-in flexibility that excludes the need for conscious
government planning and intervention. It however
has certain limitations and inefficiencies resulting in a
condition referred to as “market failure”. The market
408 J. Econ. Int. Financ.
failed to achieve a satisfactory level of welfare for the
society by providing an equitable or fair distribution of
income and wealth, or all of these (Ogiji, 2004). The
1930s Great Depression was a confirmation of the reality
of the failure of the market economy which led to the
evolution of Keynesian economics. Keynes submitted
that the lingering unemployment and economic
depression were a result of failure on the part of the
government to control the economy through appropriate
economic policies (Iyoha et al., 2003). Consequently,
Keynes proposed the concept of government intervention
in the economy through the use of macroeconomic
policies such as fiscal and monetary policies. Fiscal
policy deals with government deliberate actions in
spending money and levying taxes with a view to
influencing macro-economic variables in a desired
direction. This includes sustainable economic growth,
high employment creation and low inflation (Microsoft
Corporation, 2004). Thus, fiscal policy aims at stabilizing
the economy. Increases in government spending or a
reduction in taxes tend to pull the economy out of a
recession; while reduced spending or increased taxes
slow down a boom (Dornbusch and Fischer, 1990).
Government interventions in economic activities are
basically in the form of controls of selected areas/sectors
of the economy. These controls differ, and depend on the
specific needs or purpose the government desires to
achieve. Samuelson and Nordhaus (1998), distinguished
between two forms of regulation, namely:
(i) Economic regulation (involving control of prices, entry
and exit conditions, regulation of pubic utilities, such as
transportation and media organizations, regulation of the
financial sector operations.
(ii) Social regulation (aimed at protecting the health and
safety of workers at work place, the environment, and
protection of consumer rights. our focus is on economic
regulation.
Aregbeyen (2007), Ekpo (1994), Amin (1998), Devarajan
et al. (1996), Fuente (1997), Kneller et al. (1999) and
Bose et al. (2003), established positive relationship
between fiscal policy (public spending) and economic
growth. Bose et al. (2003) in Aregbeyen (2007) found that
the share of government capital expenditures in the gross
domestic product is positively and significantly correlated
with economic growth, while the growth effect of current
expenditure is insignificant. Aregbeyen (2007) believed
that although government expenditures were necessary
for economic growth, yet the impact of such expenditures
on the economy is of primary importance. He concluded
that the key to rapid economic growth constituted capital
and public investment expenditure and that increased
government budget deficits do not automatically
guarantee rapid economic growth.
According to Adeoye (2006),
“The debate on the effectiveness of fiscal policy as a tool
for promoting growth and development remains
inconclusive, given the conflicting results of current
studies”
He opined that while the studies of Thornton (1990), Lin
and Liu (2000), indicated a net positive effect, those of
Baily (1980) and Feldstein (1980) indicated a negative
net effect. Also according to Saunders (2006), empirical
studies carried out on the US economy by Anderson and
Jordan (1968), Hafer (1982), Saunders (1995); and on
the UK economy by Saunders (2006), did not give
empirical support to the effectiveness of fiscal policy in
economic stabilization.
The empirical studies cited above, relating to fiscal
policy and economic growth in Nigeria, left some gaps.
No studies have, so far, focused on the effectiveness of
this policy measure in stimulating economic growth in this
country during regulation and deregulation periods. This
is the gap our study intends to fill. Our time frame is 1970
to 2006. The study variables are gross domestic product
(dependent variable) and Government expenditure,
(independent variable). Also capital inflow, export and
private investment are included in our model as check
variables.
METHODOLOGY AND DATA ANALYSIS
The study adopts a comparative approach. Comparative analysis
was made of the effectiveness of fiscal policy in stimulating economic
growth under each of the regulation and deregulation periods of
the Nigerian economy. The analysis involves stationarity test, cointegration
test, and ordinary least squares (OLS) regression.
We used secondary data sourced mainly from the Central Bank
of Nigeria (CBN). Our computational device is the E-view software
(version 4.1). Among the tests conducted are t- test, to ascertain
the significance of regression coefficients (Gujartati, 2003), F-test,
for the overall significance of our model (a test of goodness of fit of
the model) (Patterson and Okafor, 2007); (R2) Coefficient of
determination (Gujarati, 2003) which gives the proportion of the
variation in Y explained by the variables X2, X3 etc. jointly;
stationarity test to ascertain the stationarity conditions of the series.
For this purpose, the Augmented Dickey-Fuller (ADF) test is very
widely used, and was used in our study. For co-integration, the
Johansen’s test was conducted to test for the long-run or
equilibrium relationship between the time series (Omotor and
Gbosi, 2007; Gujarati, 2003).
MODEL SPECIFICATION
To establish the relationship between economic growth and fiscal
policy variables, we adopted a growth model which is in line with
that applied by Adeoye (2006). We have however made some
adaptations to suit our study. Dorrance (1966) in Habeeb (1994)
proposed a relationship between economic growth and inflation. He
asserted that,
“it might be suggested that inflation discourages development and
mild inflation encourages it, after a point the depressive effect of
inflation offsets the stimulating effect of monetary expansion”.
In other words there exists a critical rate of inflation beyond which
growth declines. Habeeb (1994) used inflation as an explanatory
variable for growth in his analysis and we have adopted this
variable in our model also. In the empirical work of Adewuyi (2002),
Ogbole et al. 409
Figure 1. Shows the trend of GDP in the period under review. The variable exhibits a generally rising trend, but
fluctuating in the mid 1980s.
an empirical relationship between volume of export and real capital
flows and rate of growth was established. We have also adopted
this in our model (Appendix 1).
We specified a GDP model and included a dummy variable
(DUM) in it, having values of zero (0) for the period of regulation
and one (1) for the period of deregulation. The magnitude of the
coefficient of the Dummy variable in the model was used to
determine the extent of difference in the effectiveness of fiscal
policy in stimulating economic growth in these two periods. The
probability value (P-value) of DUM was compared with alpha (a ,
0.05) to determine the statistical significance of the difference.
The functional relation of our model,
GDP = f (GE, PI, IFR, CIF, X)
is specified in the regression form below as:
GDP = a0 + a1GE + a2PI + a3IFR + a4 CIF + a5X + a6 Dum + U1
The log-log form is:
Log GDP= a0 + a1 logGE + a2 logPI + a3logIFR + a4 logCIF + a5logX
+ a6 DUM + U1
where:
GE = government expenditure, PI = private investment, IFR =
inflation rate, DUM = Dummy variable, CIF = capital inflow, U1=
random error term, X = export.
A priori expectation (a1, a2, a4, a5, > 0; a3, <0).
DATA ANALYSES AND PRESENTATION OF
RESULTS
Descriptive analysis
We begin with the descriptive analysis of the data in
respect of GDP for the period under review, using line
graphs as shown in Figure 1. Figure 1 shows the trend of
GDP in the period under review. The variable exhibits a
generally rising trend, but fluctuating in the mid 1980s.
Econometric analyses
Stationarity test
ADF test was conducted to ascertain whether the
variables in the model are stationary. The result shows
that the variables were all stationary (Tables 1-6). This
means that in the short run, the variables were stable.
For all the variables, the ADF test statistics were less
than the critical values at 5% level of significance.
410 J. Econ. Int. Financ.
Table 1. Stationarity test on GDP [Lag Length = 9].
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -8.794874 0.0000
Test critical values: 1% level
5% level
10% level
*MacKinnon ( 1996) one-sided p-values
-4.356068
-3.595026
-3.233456
Augmented Dickey-Fuller test equation [Dependent variable= D(GDP,2)]
Variable Coefficient Prob.*
D(GDP(-1))
D(GDP(-1), 2)
D(GDP(-2), 2)
D(GDP(-3), 2)
D(GDP(-4), 2)
D(GDP(-5), 2)
D(GDP(-6), 2)
D(GDP(-7), 2)
D(GDP(-8), 2)
D(GDP(-9), 2)
C
@TREND( 1970)
-3.981706
2.427958
2.164355
2.009223
1.632851
1.303761
1.254300
1.059893
0.713409
0.238700
-19.44328
1.170185
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0001
0.0003
0.0395
0.0000
0.0000
Source: Stationarity test results from analysis using Eviews 5.
Table 2. Stationarity test on GE [Lag Length = 7].
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -5.621173 0.0005
Test critical values: 1% level -4.323979
5% level -3.580623
10% level -3.225334
*MacKinnon ( 1996) one-sided p-values
Augmented Dickey-Fuller test equation [Dependent variable= D(GDP,2)]
Variable Coefficient Prob.*
D(GE(-1))
D(GE(-1), 2)
D(GE(-2), 2)
D(GE(-3), 2)
D(GE(-4), 2)
D(GE(-5), 2)
D(GE(-6), 2)
D(GE(-7), 2)
C
@TREND( 1970)
-3.439898
1.951477
1.726858
1.729414
3.769748
4.127456
5.618496
6.921690
-69.92590
5.317542
0.0000
0.0085
0.0200
0.0176
0.0004
0.0003
0.0001
0.0000
0.1431
0.0633
Source: Stationarity Test Results From Analysis Using Eviews 5.
Ogbole et al. 411
Table 3. Stationarity Test on PI [Lag Length = 0].
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -8.327312 0.0000
Test critical values: 1% level -4.252879
5% level -3.548490
10% level -3.207094
*MacKinnon ( 1996) one-sided p-values
Augmented Dickey-Fuller test equation [Dependent variable = D(PI(-1))]
Variable Coefficient Prob.*
D(PI(-1))
C
@TREND( 1970)
-1.570663
-32.67746
2.621397
0.0000
0.2204
0.0442
Source: Stationarity test results from analysis using Eviews 5.
Table 4. Stationarity test on IFR [Lag Length =1].
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -5.740390 0.0002
Test critical values: 1% level
5% level
10% level
*MacKinnon ( 1996) one-sided p-values
-4.252879
-3.548490
-3.207094
Augmented Dickey-Fuller Test Equation [ Dependent Variable= D(IFR, 2)]
Variable Coefficient Prob.*
D(IFR(-1))
D(IFR(-1),2)
C
@TREND( 1970)
-1.380810
0.358867
3.770448
-0.185102
0.0000
0.0419
0.5362
0.5076
Source: Stationarity Test Results From Analysis Using Eviews 5.
Table 5. Stationarity test on CIF [Lag Length = 1].
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -8.013040 0.0000
Test critical values: 1% level -4.252879
5% level -3.548490
10% level -3.207094
*MacKinnon ( 1996) one-sided p-values
Variable Coefficient Prob.*
D(CIF(-1)) -1.380810 0.0000
D(CIF(-1),2) 0.358867 0.0131
C 3.770448 0.8938
@TREND( 1970) -0.185102 0.5030
Source: Stationarity Test Results From Analysis Using Eviews 5.
412 J. Econ. Int. Financ.
Table 6. Stationarity test on X [Lag Length = 1].
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -7.635053 0.0000
Test critical values: 1% level
5% level
10% level
*MacKinnon ( 1996) one-sided p-values
-4.262735
-3.552973
-3.209642
Augmented Dickey-Fuller test equation [Dependent variable: D(X, 2)]
Variable Coefficient Prob.*
D(X(-1))
D(X(-1),2)
C
@TREND( 1970)
-1.693006
0.585860
-147.8760
11.45073
0.0000
0.0006
0.2820
0.0761
Source: Stationarity test results from analysis using Eviews 5.
Table 7. Johansen cointegration test on the series GDP, GE, PI, IFR, CIF, X using lag interval of 1 to 2.
Unrestricted cointegration rank test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical value
MHM(1999)
p-value
4 0.235797 13.45149 15.49471 0.09993
Trace test indicates hypothesis of 4 cointegrating equations at the 0.05 level is accepted.
Unrestricted cointegration rank test (Maximum-Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical value
MHM(1999)
p-value
4 0.235797 9.143356 14.26460 0.2744
Max-eigenvalue test indicates hypothesis of 4 cointegrating equations at the 0.05 level is accepted.
Source: Cointegration test results from analysis using Eviews 7.1.
Co-integration test
Johansen’s co-integration test results (Table 7) show that
in the model there is long-run relationship between the
GDP variables; hence they could be used for the
intended analysis. The long run relationship is indicated
by a number of co-integrating equations (CEs) shown by
the trace test ranging from three (3) to five (5) cointegrating
equations (Table 7).
OLS Estimation
GDP = f (GE, PI, IFR, CIF, X)
The above functional relation is expressed in the
regression form below:
GDP = a0 + a1GE + a2PI + a3IFR + a4 CIF + a5X + a6 Dum
+ U1 (1)
where:
GE = government expenditure, PI = private investment,
IFR = inflation rate, DUM =Dummy variable, CIF = capital
inflow, U1 = random error term, X = Export.
A priori expectation (a1, a2, a4, a5, > 0; a3, <0).
The log-log form is:
!
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! "*+ '( (,'(,-
(2)
The log-log form of the GDP model was used because it
showed better values for R2 and adjusted R2 (See Table
8a). The result shows that the model is of good fit,
judging from the value of the R2 (0.89). This means that
approximately 89% of changes in GDP are explained by
changes in the explanatory variables. The overall model
Ogbole et al. 413
Table 8a. GDP Regression equation (Log-Form): Results of Estimation.
Dependent variable: LGDP
Variable Coefficient Prob.*
C
LGE
LPI
LIFR
LCIF
LX
DUM
3.918911
0.131468
-0.127102
-0.020253
-0.013331
0.117253
0.141159
0.0000
0.0793
0.0159
0.3852
0.6552
0.0096
0.067
R-squared
Adjusted R-squared
DW Statistic
0.889229
0.867074
0.826452
AIC
SC
Prob(F-statistic)
-1.622870
-1.318101
0.000000
Source: Regression results from analysis using Eviews 5
is also significant with the probability value (P-value,
0.00) of the F-statistic being less than a (0.05).
We observe that R2 >DW, (Table 8a), a possible
explanation could be that the static regression
specification is spurious. This is the view of Granger and
Newbold (1974) and Gujarati (2004). However, in multiple
regression situation, R2 >DW, could be due to
multicollinearity. How do we know this? Simply observe
that R2 is high and significant but the t values for all or
most of the coefficients are not significant. Following this
situation, we also apply vector error correction (VEC).
The specifications used are as follows:
1 2 3 4 5 1 D(GDP) = a GDP(-1) + a IFR(-1) + a GE + a PI + a DUMMY + u 3
(3)
1 2 3 4 5 2 D(IFR)=bGDP(-1) +b IFR(-1) +b GE +b PI + b DUMMY +u 4
(4)
The VEC Equations (3) and (4) are estimated by system
estimation. The system method used here is seemingly
unrelated regression.
Though, this time R2 is low in both equations ( R2 =
0.09 and 0.30 respectively), it is lower than their
corresponding DW values of 1.86 and 1.73 respectively
(see Table 8b).
DISCUSSION OF FINDINGS
Results of our GDP model estimation are in (Table 8a).
The effect of government expenditure on gross domestic
product is not significant (P-value, 0.079>a ). This could
be largely due to misappropriation of public funds and
corruption that have resulted in channeling public funds
to non-productive areas rather than investing in productive
ventures, (such as infrastructure and other growth
promoting activities). Billions of dollars unaccounted for
but claimed to have been spent on the power sector is a
glaring example.
The proportion of public funds channeled to investment
in infrastructure is usually less than those spent on
consumption expenditure. The coefficient is positive
(0.13) which agrees with our a priori expectation. This
shows that if the quality of government expenditure is
improved upon by directing it to productive channels, it
would, ceteris paribus, stimulate economic growth as
confirmed by Aregbeyen (2007) in his study of forty
African countries, including Nigeria.
The effect of private investment (PI) on gross domestic
product is significant, with a p-value (0.016) approximately,
less thana . The negative sign of the coefficient
(-0.13) does not agree with a priori expectation. This
could be because the lack of steady power supply, good
roads and other basic infrastructure that the government
failed to adequately provide, may have undermined the
potentials of the Nigerian private sector. It could also
mean that government expenditure had a crowding-out
effect on private investment. However, the fact that
private investment is significant shows that it has a great
potential to enhance economic growth, provided that the
government creates the enabling environment. The effect
of inflation rate on gross domestic product is not
significant (p-value, 0.3852 >a ). This may be due to the
fact that Nigeria is not a producer nation as she relies
more on imported goods. The major export commodity is
crude oil, which continues to be produced in spite of high
inflation rate. The negative coefficient (-0.020) of inflation
rate agrees with a priori expectation as inflation, beyond
certain limits adversely affects productivity as we see in
Nigeria. Capital inflow exerted non significant, (p-value,
0.6552 < a ) and negative (-0.01), effect on gross
414 J. Econ. Int. Financ.
Table 8b. Vec for gdp & ifr with ge, pi & dummy as exogenous variables using system
estimation.
System: SYS02
Estimation Method: Seemingly Unrelated Regression
Date: 01/28/11 Time: 11:03
Sample: 1971 2006
Included observations: 36
Total system (balanced) observations 72
Linear estimation after one-step weighting matrix
Coefficient S td. Error t -Statistic P rob.
C(1) - 0.002615 0.032686 -0.080004 0.9365
C(2) 0.077228 0.064657 1.194420 0.2369
C(3) 0.004078 0.007498 0.543831 0.5885
C(4) -0.000273 0.009093 -0.029998 0.9762
C(5) -0.185520 2.417236 -0.076749 0.9391
C(6) 0.227126 0.072200 3.145774 0.0025
C(7) -0.557798 0.142823 -3.905530 0.0002
C(8) -0.025512 0.016563 -1.540263 0.1286
C(9) 0.012668 0.020087 0.630675 0.5306
C(10) -8.544309 5.339505 -1.600206 0.1146
Determinant residual covariance 5778.159
Equation: D(GDP) = C(1)*GDP(-1)+C(2)*IFR(-1) +C(3)*GE+C(4)*PI +C(5)
*DUMMY
Observations: 36
R-squared 0.094388 Mean dependent var 2.946667
Adjusted R-squared -0.022465 S.D. dependent var 6.309052
S.E. of regression 6.379524 Sum squared resid 1261.648
Durbin-Watson stat 1.868473
Equation: D(IFR) = C(6)*GDP(-1)+C(7)*IFR(-1) +C(8)*GE+C(9)*PI+C(10)
*DUMMY
Observations: 36
R-squared 0.303643 Mean dependent var -0.155556
Adjusted R-squared 0.213791 S.D. dependent var 15.89283
S.E. of regression 14.09192 Sum squared resid 6156.051
Durbin-Watson stat 1.730517
domestic product. It is not significant probably due to
political and socio-economic instability, coupled with lack
of needed infrastructure. Also, existing capital inflow in
the form of grants and foreign aids are largely mismanaged
rather than channeled to productive activities to
enhance growth. The negative sign is contrary to a priori
expectation as capital inflow is expected to boost GDP
growth.
Export (X) exerted a significant (p-value, 0.0096 <a )
and positive (0.12) impact on GDP. This agrees with a
priori expectation. However, this marginal improvement
can be enhanced by policies that encourage export and
diversification of the economy towards non-oil exports to
enhance GDP growth. The dummy variable included in
the GDP model captures the relative effect of fiscal policy
on GDP during regulation and deregulation periods. The
positive coefficient (0.14) indicates a relatively marginal
increase in gross domestic product in the period of
deregulation than in the period of regulation, but this
difference is not significant (p-value, 0.0670 >a ).
Equilibrium relation exists between GDP and IFR.
SUMMARY OF FINDINGS
Our null hypothesis of no significant difference in
theeffectiveness of fiscal policy on gross domestic
product during regulation and deregulation periods was
not rejected because p-value of 0.067 is great than a
(0.05). However the dummy variable coefficient (0.14)
shows that there is a difference in the extent to which
fiscal policy can stimulate gross domestic product growth
between regulation deregulation periods. Though
equilibrium relation exists between GDP and IFR it is not
so strong because of low R2 .
This weak equilibrium relation may be due to small n,
the sample period.
CONCLUSION
From the results of our analysis and findings, we
conclude that there is a difference in the level of
effectiveness of fiscal policy in stimulating economic
growth in Nigeria. This is only marginally higher (by about
an average of N140 million or 14%) in the period of
deregulation than in the period of regulation. However,
this difference is not statistically significant (Dummy
variable p-value, 0.067 >a ).
Theoretical implication
The marginal and insignificant difference in efficiency of
fiscal policy in both regulation and deregulation periods is
instructive. It shows that both economic periods are good,
and none is bad per se. But the specific needs or
peculiarities of a given country and its specific economic
circumstances and objectives are probably the basic
factors that inform the choice of an economic policy
regime to adopt.
RECOMMENDATIONS/POLICY IMPLICATION
From the foregoing we recommend as follows:
Government fiscal policy should refocus and redirect
government expenditure towards production of goods
and services so as to enhance GDP growth.
This can be achieved by setting specific goals/targets
for each state and for the Federal Government. Attention
should focus on the real sector.
The goals should aim at minimizing, if not completely
eradicating, diversion of public funds to private pockets
and embezzlement.
This may compel the local, state, and Federal
Government to utilize their funds for the achievement of
set economic goals within specified time periods.
Factors to be considered in setting these goals/ targets
should include the level of human and economic
resources available, allocations from the federation
account, and other factors considered relevant.
Time limits set for the realization of these goals would
encourage commitment, probity, accountability and
transparency by public funds mangers.
Government economic policies should focus on diversification
of the economy to enhance the performance of
the non-oil sector, so as to create more jobs in this
Ogbole et al. 415
sector.
This will be a more effective way of reducing
unemployment and increasing the gross domestic
product.
The non-oil sector in Nigeria has a greater potential for
job creation than the oil sector.
Efforts should be made by government to ensure
appropriate policy mix for harmony and proper
coordination of economic policies.
Fiscal policy should give priority attention to capital and
public investments by making them of higher proportion
in gross government expenditure, thereby creating more
jobs and enhancing the quality of public spending and the
attainment of sustainable growth and development.
Emphasis should be on the development of basic
infrastructure (example. transportation, energy and
communication). Human capital development should be a
priority.
To ensure that all the main objectives of fiscal policy
and their targets are achieved, there is need to redirect
pubic expenditures towards making Nigeria a producer
nation. This ought to be the central focus of fiscal policy
objective.
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APPENDIX. Comprehensive data of variables. (N’BILLION) (IFR in %).
YEAR GDP GE PI IFR CIF X Dummy
1970 54.20 0.904 0.30 13.80 0.30 0.90 1
1971 65.70 1.02 0.40 16.00 0.50 1.30 1
1972 69.30 1.50 0.50 3.20 0.40 1.40 1
1973 73.80 1.60 0.60 5.40 0.60 2.30 1
1974 82.40 2.70 1.00 13.40 0.50 5.80 1
1975 80.00 6.00 1.60 33.90 0.80 4.90 1
1976 88.90 7.80 2.00 21.20 0.50 6.60 1
1977 96.10 8.90 2.30 15.40 0.70 7.60 1
1978 89.00 7.90 2.60 16.60 0.70 6.10 1
1979 91.20 7.40 3.70 11.60 0.70 10.80 1
1980 96.2 15.00 5.20 9.90 0.80 14.20 1
1981 70.40 11.40 5.80 20.90 0.60 11.00 1
1982 70.20 11.40 6.30 7.70 2.20 8.20 1
1983 66.00 11.10 8.10 23.20 1.70 7.50 1
1984 62.50 9.90 9.40 39.60 1.40 9.10 1
1985 68.30 13.10 10.60 5.50 1.40 11.70 1
1986 70.80 16.10 11.50 5.40 4.00 8.90 0
1987 71.20 22.10 15.10 10.20 5.10 30.40 0
1988 77.70 27.90 18.40 38.30 6.20 31.20 0
1989 83.20 41.10 17.80 40.90 4.70 58.00 0
1990 92.20 60.30 23.10 7.500 10.50 109.90 0
1991 94.20 66.70 30.40 13.00 5.60 121.50 0
1992 97.00 93.90 43.40. 44.50 11.70 205.60 0
1993 99.60 136.70 60.90 57.20 42.60 218.80 0
1994 100.90 156.80 76.10 57.00 7.80 206.10 0
1995 103.10 307.20 93.30 72.80 56.00 950.70 0
1996 106.60 283.00 115.40 29.30 5.70 1309.50 0
1997 110.00 428.20 154.10 8.50 10.00 1241.70 0
1998 113.50 487.10 161.90 10.00 32.40 751.90 0
1999 116.70 947.70 241.60 6.60 4.00 1189.00 0
2000 121.20 701.10 343.20 6.90 16.50 1945.70 0
2001 126.30 1019.10 452.00 18.90 5.00 1868.00 0
2002 131.50 1188.70 556.00 12.90 9.00 1750.00 0
2003 136.50 1225.90 655.70 14.00 13.50 3098.20 0
2004 145.40 1384.00 797.50 15.00 20.10 4620.10 0
2005 152.35 1743.20 1317.00 17.90 26.10 6310.30 0
2006 160.28 1942.30 1647.65. 8.20 32.50 7916.30 0
Source: CBN Statistical Bulletin, 2006.
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