SUMMARY OF
FINDINGS
This
research work sought to examine the impact of government expenditure on
education on Nigeria’s economic growth from 1980 to 2008. Government expenditure
on education was captured using Recurrent Government Expenditure on Education
(RGEE) and Capital Government Expenditure on Education (CGEE).
On
the application of advanced statistical techniques employed, the following
information surfaced;
(i) The entire regression plane is
statistically significant. This means that the joint influence of the
explanatory variables (RGEE and CGEE) on the dependent
variable (RGDP) is
statistically significant;
(ii)
Government
expenditure on education has significant impact on economic growth of Nigeria.
(iii)The computed coefficient of
determination shows that 86.99% of the total variations in the dependent
variable (RGDP) is influenced by the variation in the explanatory variables
namely Recurrent Government Expenditure on Education (RGEE) and Capital
Government Expenditure on Education (CGEE).
(iv)
The
total variation of 13.01% in the dependent variable is attributable to the
influence of other factors not included in the regression model.
(v)
There is evidence of first-order
serial correlation (autocorrelation). It implies that there are other variables
which are not captured in the model.
CONCLUSION
In any country, there can be no
meaningful economic growth without adequate funding of the education sector by
the government. In funding the education sector, there will be human capital
development. In human capital development, education is essential. Education is
concerned with the cultivation of “the whole person” including intellectual,
character and psychomotor development. It is the human resources of any nation,
rather than its physical capital and material resources, which ultimately
determine the character and pace of its economic and social development.
The
fiscal operations and developments of Nigeria revealed that federal government
expenditure on education is categorized under the social and community services
sector (Orubu, 1989). The importance of education is reminiscent in its role as
a means of understanding, controlling, altering and redesigning of human
environment (CBN, 2000). Education has a link with economic growth. As once
remarked by Ola (1998: 14) “If you see any economy that is not doing well, find
out what is spent on education”. Thus, this research work analyses and
considers government expenditure on education and its implications on the
Nigeria’s economic growth.
POLICY RECOMMENDATIONS
In the light of the researcher’s findings, the
following recommendations are presented;
·
In the bid
to achieve economic growth, the allocation from the federal account to the
local and state could be increased to allow them to increase expenditures in
the education sector.
·
There is
need to solve the endemic problems of fiscal federalism in particular the
so-called vertical and horizontal fiscal imbalances should be tackled. There should be balance between financial
responsibilities and financial resources at each level of government; federal,
state and local.
·
The search
for appropriate mechanisms and formulae for minimizing the imbalances, which
has been a problem, should be upheld.
·
Government
expenditure on education should be channeled to human capital development where
in the course of education in learning institutions, acquisition of skills will
be enhanced.
·
Salaries of
teachers and lectures should as well be increased as this will induce better
teaching and lecturing which will contribute to the growth of the economy in
the long run and minimize the incessant industrial action embarked by Teachers
and Lecturers almost every academic year.
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APPENDIX THREE
REGRESSION RESULTS
|
Dependent Variable: INF
|
||||
|
Method: Least Squares
|
||||
|
Date: 02/09/11 Time: 10:05
|
||||
|
Sample: 1980 2008
|
||||
|
Included observations: 29
|
||||
|
Variable
|
Coefficient
|
Std. Error
|
t-Statistic
|
Prob.
|
|
C
|
24.93029
|
3.701561
|
6.735075
|
0.0000
|
|
BD
|
1.15E-05
|
5.64E-06
|
2.043721
|
0.0508
|
|
R-squared
|
0.133971
|
Mean dependent var
|
21.26552
|
|
|
Adjusted R-squared
|
0.101896
|
S.D. dependent var
|
18.40100
|
|
|
S.E. of regression
|
17.43832
|
Akaike info criterion
|
8.621689
|
|
|
Sum squared resid
|
8210.564
|
Schwarz criterion
|
8.715985
|
|
|
Log likelihood
|
-123.0145
|
F-statistic
|
4.176794
|
|
|
Durbin-Watson stat
|
0.983580
|
Prob(F-statistic)
|
0.050849
|
|
|
Dependent Variable: GDP
|
||||
|
Method: Least Squares
|
||||
|
Date: 02/09/11 Time: 10:14
|
||||
|
Sample: 1980 2008
|
||||
|
Included observations: 29
|
||||
|
Variable
|
Coefficient
|
Std. Error
|
t-Statistic
|
Prob.
|
|
C
|
246248.5
|
14568.01
|
16.90337
|
0.0000
|
|
BD
|
-0.236436
|
0.022207
|
-10.64694
|
0.0000
|
|
R-squared
|
0.807634
|
Mean dependent var
|
321387.6
|
|
|
Adjusted R-squared
|
0.800509
|
S.D. dependent var
|
153659.2
|
|
|
S.E. of regression
|
68630.95
|
Akaike info criterion
|
25.17735
|
|
|
Sum squared resid
|
1.27E+11
|
Schwarz criterion
|
25.27164
|
|
|
Log likelihood
|
-363.0715
|
F-statistic
|
113.3574
|
|
|
Durbin-Watson stat
|
0.712214
|
Prob(F-statistic)
|
0.000000
|
|
APPENDIX ONE
RAW DATA FOR ANALYSIS
|
YEAR
|
INF
|
BD
|
|
1980
|
9.9
|
265
|
|
1981
|
20.9
|
1876.8
|
|
1982
|
7.7
|
-489.5
|
|
1983
|
23.2
|
872.2
|
|
1984
|
39.6
|
1325.7
|
|
1985
|
5.5
|
2009.3
|
|
1986
|
5.4
|
-3627.9
|
|
1987
|
10.2
|
3361.9
|
|
1988
|
38.3
|
-152.8
|
|
1989
|
40.9
|
12842.1
|
|
1990
|
7.5
|
37834.2
|
|
1991
|
13
|
34407.2
|
|
1992
|
44.5
|
97655.8
|
|
1993
|
57.2
|
1540.5
|
|
1994
|
57
|
41017.6
|
|
1995
|
72.8
|
211219.2
|
|
1996
|
29.3
|
186379.4
|
|
1997
|
8.5
|
162935.8
|
|
1998
|
10
|
-24112.8
|
|
1999
|
6.6
|
1310.2
|
|
2000
|
6.9
|
-510400
|
|
2001
|
18.9
|
-794899
|
|
2002
|
12.9
|
-844899
|
|
2003
|
14
|
-968500
|
|
2004
|
15
|
-1029999
|
|
2005
|
17.9
|
-1262300
|
|
2006
|
8.2
|
-1331900
|
|
2007
|
7.2
|
-1878500
|
|
2008
|
7.7
|
-1363233
|
SOURCE: CBN STATISTICAL BULLETIN
VOLUME 19, 2008
APPENDIX TWO
RAW DATA FOR ANALYSIS
|
YEAR
|
GDP
|
BD
|
|
1980
|
31546.8
|
265
|
|
1981
|
205222.1
|
1876.8
|
|
1982
|
199685.3
|
-489.5
|
|
1983
|
185598.1
|
872.2
|
|
1984
|
183563
|
1325.7
|
|
1985
|
201036.3
|
2009.3
|
|
1986
|
205971.4
|
-3627.9
|
|
1987
|
204806.5
|
3361.9
|
|
1988
|
219875.6
|
-152.8
|
|
1989
|
236729.6
|
12842.1
|
|
1990
|
267550
|
37834.2
|
|
1991
|
265379.1
|
34407.2
|
|
1992
|
271365.5
|
97655.8
|
|
1993
|
274833.3
|
1540.5
|
|
1994
|
275450.6
|
41017.6
|
|
1995
|
281407.4
|
211219.2
|
|
1996
|
293745.4
|
186379.4
|
|
1997
|
302022.5
|
162935.8
|
|
1998
|
310890.1
|
-24112.8
|
|
1999
|
312183.5
|
1310.2
|
|
2000
|
329178.7
|
-510400
|
|
2001
|
356994.3
|
-794899
|
|
2002
|
433203.5
|
-844899
|
|
2003
|
477533
|
-968500
|
|
2004
|
527576
|
-1029999
|
|
2005
|
561931.4
|
-1262300
|
|
2006
|
595821.6
|
-1331900
|
|
2007
|
634251.1
|
-1878500
|
|
2008
|
674889
|
-1363233
|
SOURCE:
CBN STATISTICAL BULLETIN VOLUME 19, 2008