RESEARCH DESIGN AND METHODOLOGY
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. The methodology used is simpler linear regression method .
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.