In
attempt to determine the effect of human
capital development on economic growth in Nigeria, it is necessary to develop a
model to justify the correlation that exists between the variable. In this
regard, a multiple regression model is this developed to determine the effect
of human capital development on economic growth.
MODEL SPECIFICATION
In
this study, hypothesis has been stated with the view of examining the
relationship between human capital
development with Economic growth in Nigeria. In capturing the study, these
variables were used as proxy.
Thus, the model is represented in the
functional for. It is shown as below:
RGDP = f(GEH, GEE) ------------------ 3.1
Where
RGDP
= Gross Domestic Product (dependent
variable)
GEH = Government Expenditure on Health
(independent
variable).
GEE = Government Expenditure on Education
(independent variable).
In a linear
function, it is represented as follows:
RGDP = β0 + β1
GEH +
β2GEE + ut
---------------- 3.2
Where
β0 = Constant
term
β1 = Regression
coefficient of GEH
β2 = Regression
coefficient of GEE
ut = Error
term
MODEL EVALUATION
At this
level of research, using a time series data, the researchers estimates the
model with Ordinary Least square Method is preferred to other as it is best
linear unbiased estimator, minimum
variance, zero mean value of the random term, etc (Gujarati, 2004).
The tests
that will be considered in this study include: coefficient of multiple
determination (R2), standard error test (S.E.), T-test, F-test, and
Durbin Watson statistics.
Coefficient of
Determination (R2):
It is used to measure the proportion of variations in the dependent variable,
which is explained by the explanatory variables. The higher the (R2) the
greater the proportion of the variation in the independent variables.
Standard
Error test: It is used to test
for reliability of the coefficient estimates.
Decision Rule:
If S.E < ½ b1, reject
the null hypothesis and conclude that the coefficient estimate of parameter is statistically
significant. Otherwise accept the null hypothesis.
T-test:
It is used to test for the statistical significance of the individual estimated
parameters. In this research, T-test is chosen because the population variance
is unknown and the sample size is less than 30.
Decision:
If T-cal > T-tab, reject the null
hypothesis and conclude that the regression coefficient is statistically significant.
Otherwise accept the null hypothesis.
F-Test:
This is used to test for the joint influence of the explanatory variables on
the dependent variable.
Decision rule:
If F-cal > F-tab, reject the null hypothesis
and conclude that the regression plan is statistically significant. Otherwise
accept the null hypothesis
Durbin Watson
statistics: It is used to test for the presence
of autocorrelation (serial correlation).
Decision rule:
If the computed Durbin Watson
statistics is less than the tabulated value of the lower limit, there is evidence
of positive first order serial correlation. If it is greater than the upper
limit there is no evidence of positive first order serial correlation. However,
if it lies between the lower and upper limit, there is inconclusive evidence regarding
the presence or absence of positive first order serial correlation.
SOURCE OF DATA
The data for this research project is
obtained from the following sources:
i.
Central
bank of Nigeria statistical bulletin for various years.
ii.
Central
Bank of Nigeria Annual Account for various years.
iii.
Central
Bank of Nigeria Economic and financial Review for various years.
REFERENCES
Central Bank of Nigeria Statistical
Bulletin, volume 18, Dec., 2008
Gujarati,
D.N. (2004). Theory of Economics United State Military Academy west point, Mc Graw-Hill
inc Book co. Singapore
Koutsoyiannis, A. (2001), Theory of Econometrics: Pal Grave
Houndmills, Basingstoke,
Hamshire RG21 6xs and 175 fifth Avenue, New York, NY10010.