Running head: DAMILOLA R. ODUNUGA (2019)
1
Effect of Human Capital, Population Growth and Other Relevant Economic
Conditions on Economics Growth
Damilola R. Odunuga
Northeastern University
Commerce and Economic Development
8th December 2019
Abstract
Human capital and technology as being viewed as a driver for economic growth in many developed
countries. This study aims at providing the joint effect of human capital, population growth, technological
advancement, income inequality on economic growth and theoretically testing the correlational
relationships between all these factors. The assumption that human capital, population growth and other
economic factor contributes to economic growth is based on the classical and neo-classical theories. This
study finds that there is a joint significant relationship among all this factors and economic growth,
investment in human capital and technological advancement tends to increase economic growth while
increased population growth and income inequality has the tendency to reduce economic growth.
Keywords: Economic growth, human capital, population growth, income inequality, technology,
correlation, economic model.
DAMILOLA R. ODUNUGA (2019)
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Effect of Human Capital, Population Growth and Other Relevant Economic Conditions on
Economics Growth
Chapter 1
The increase in the production of economic goods and services during a particular period of time
refers to economic growth. The aggregate economic growth depends on some economic factors for
instance human capital, population growth. Recent studies on the impact of intellectual skills in
promoting economic growth gives clarification for the questionable impact of human capital on growth.
Human capital refers to the aggregate measure of ability, skills, and social attribute of individuals which
enables them to contribute to economic growth. Human capital is one of the factors that leads to increase
or decrease of a country’s economy, along with economic conditions like population, education, inflation,
unemployment which has led to the question: what is the impact of human capital, population growth and
other economic factor on economic growth?
A categorized gradual way to deal with the problem, for instance, concentrating on some
variables that determines the factors affecting economic growth may reveal insightful explicit angle,
however the multiple collaboration between different measures prevent such a unique assiduity (Slaus, &
Jacobs, 2011). When any of the dimensions of economic condition is viewed as though it were discrete
and autonomous from its impact on economy, then problems could intensify. For this reason, it is
important to know the effects of some economic factors on economic growth, the correlation between
human capital, population growth has it relates to economic growth or if the correlation could cause an
increase in economic growth.
The combination of work done by Adam Smith, David Ricardo and Robert Malthus called the
classical theory of economic growth states that every country’s economy has a steady state GDP and any
divergence from that steady state is only temporary and will return in the long run. This depends on the
concept that population increases when GDP increases. Therefore, because of the increased demand on
limited resources from a larger population, the increase in population has an adverse effect on GDP.
DAMILOLA R. ODUNUGA (2019)
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However, the Neo-Classical theory is the combination of two economist T.W. Swan and Robert Solow
which is now known as Solow-Swan growth in model. The theory is centered around three elements that
affect economics growth which are labor, capital and technological advancement. The theory states that
technological advancement is the only driver of economic growth. According to the theory, labor and
capital should be adjusted once technological advancement has been made, and further explains that if all
countries uses the same technology, then standard of living will be the same.
Whether or not the economic conditions highlighted has any form of effect on economic growth
or technological improvement is the only factor necessary for the economy, the identifiable circumstance
of the concept of economic growth is needed to determine the predictions of whether increased
investment in human capital combined with population growth, education, labor and any other relevant
conditions have any effect on economic growth. The answer to this question depends largely on the
demographics of human capital, population growth, education, labor force participation rate, population
growth rate and GDP growth rate.
In terms of economic growth, this literature will focus primarily on measurement and investment
in human capital, skill acquisition (both transferable and non-transferable skill), life expectancy, mortality
rate as the main determinant of that has effect on economic growth. Two major problem associated with
the classical and neo-classical theory is the conclusion that technological advancement which cannot be
modeled is the only factor necessary for continued economic growth and the other is that there is no real
life or empirical evidence to support the claim for identifying technology as a factor of economic growth.
The variables listed above to determine the effect of some economic conditions on economic
growth solely depends on the factors identified by the classical and neo-classical theories excluding
factors like natural resources, physical capital, poor health, political instability and law, institutional
framework and income inequality. However, with the use of cross-country data derived from secondary
sources to determine the relationship between economic growth and economic drivers.
The purpose of this study is to find the correlation between human capital, population growth,
income inequality, technological advancement and economic growth. Section II of this study review prior
DAMILOLA R. ODUNUGA (2019)
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studies of economic growth as a dependent variable on human capital by establishing a difference and
similarities from past efforts. Section III present the model used to measure the relationship between
economic growth, population growth, human capital and describe the data set and analysis of some
factors that may predict the correlation effects and causation. Section IV gives the summary, conclusions
and recommendations for further study.
Literature Review
Economic Growth and Human Capital
An enormous group of literature has been created analyzing the role of human capital in deciding
the level growth of GDP per capita during the last few decades (Goldin, 2016). In endogenous growth
models, economic growth can progress uncertainly on the grounds that the profits on interest in (both
physical and) human capital goods do not really decrease over time. Overflows of intelligence across
producers and external advantages from enhancement of human capital are a piece of this procedure since
they balance inclinations to diminishing returns. Gaining aptitudes and intelligence is a method for capital
formation by postponing utilization with the aim of maximizing future revenue (Teixeira & Queiros,
2016).
Several empirical studies that placed various level of education as variable of human capital for
economic growth found that there is a positive correlation between each variable and economic growth.
According to Becker’s theory of human capital, conventional form of education demonstrate investment
in human capital which includes the productive skill developed, incorporated and reserved in human
themselves. Research revolved around examining the level to which income could be connected to
educational accomplishment in the early days of human capital theory. Education train people to develop
various skills ranging from analytic skills such as analyzing data, observing, interpreting to developing
effective habit like dependability, motivation, etc.
Classical economics would in general view the labor market in absolutely quantitative terms,
while on the contrary, human capital theory prese nted a qualitative angle. In order for the quality of the
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workforce to be improved, education and training were viewed as the most significant (Gillies, 2014).
There are two methods to measure human capital using the indicator-based approach, the quantitative
approach could be in form of educational attainment level while the qualitative approach can be in form
of the quality of work performed by well-educated or well-trained individuals. Human capital additionally
influences economic performance by implication, most prominently through its relationships with
institutions. Human capital aggregation adds to molding productive policies, not so much violence but
rather more political stability (Lipset, 1960; Glaeser et al., 2004) and, in this way, encourages economic
growth.
Despite the immense significance of human capital aggregation, the difference in economic
growth throughout countries needs to be tracked to structural change and the intricacy implicit in their
effective structures. In fact, few studies have shown that the effective structure of an economy and
particularly its elements that is, structural change develop as a significant determinant of economic
growth (Montobbio, 2002; Saviotti & Frenken, 2008; Silva & Teixeira, 2011).
Economic Growth and Population Growth
Population growth has continually been addressed for many economic variables ever since
Thomas Malthus proposed in 1798 that population could be decreased by hunger and diseases. The initial
economic theory of population growth goes back to the classical economists, particularly Malthus and
Ricardo. The decline in agricultural sector due to the fixed supply of productive land while labor supplied
experienced a sharp diminishing return was the major concern of the classical economist. Recently,
neoclassical models concentrate to a great extent on savings and capital accumulation. Solow (1956)
explains how population growth can have negative short-run impact on economic growth.
The reasoning been that there is limitation of resources for any national population and that
population growth which prompt higher population density may at last outperform the conveying limit of
a given region and subsequently would lead to lower standard of living and even starvation and death.
Additionally, there have been many discussions of what the best population size of a given country should
be. Despite the fact that, this discussion has been inconclusive, majority of researchers comprehended that
DAMILOLA R. ODUNUGA (2019)
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one-dimensional focus on total population size makes no sense and the most important is the composition
of the population. Composition of population includes age and sex. Changes in age structure of a
population has significant effect for society and the economy in many respects.
Coale & Hoover (1958), establish the effect of labor supply in which high fertility rates results in
lower volume of people in the labor force due to higher dependency rate. As a result of this effect, GDP
per capita will decrease but output per worker may not necessarily reduce. Studies relating to population
growth unambiguously underlined that there is negative effect mainly in countries with lower level of
development and in countries with unwarranted policies. Recently, economist continue to focus more on
nuanced theories and measures of population growth, especially the demographic transitions while
decrease in mortality are trailed by decrease in fertility rates, in any case just with a considerable slack.
Other Relevant Economic Conditions
Formation of human capital or potentially research & development activities are typically
demonstrated as being subject to increased returns or even more precisely, a lower bound on diminishing
returns to capital. Endogenous growth models can be broadly divided into two groups: first, growth is
achieved through investment in either physical or human capital while the subsequent group highlights
the impact of technological advancement which is created as an economic goods and as one of the
relevant factors that affect economic growth along with income inequality, political instability and
government policies. Despite the fact that our general understanding of growth theory has increase the
significance of technological investment by analyzing the consequences of the attribute of technology as
information, international innovation overflows are rarely demonstrated in the growth literature (Lai,
Peng & Bao, 2006).
The digital divide between the rich or educated and low-income or uneducated families has huge
consequences for children in low income districts as insufficient approach to technology can hinder
learning of tech abilities needed to succeed in today’s economy. This digital divide is known as
technological gap. Research has shown that there is a non-linear relation between technological gap and
economic growth, however other factors listed above mostly have a negative correlation with economic
DAMILOLA R. ODUNUGA (2019)
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growth. Although, less developed countries try technological impersonation by bringing in products or
potentially attracting multinational ventures, its technology absorption capacity will be vitally constrained
for its learning effects (Lai, Peng & Bao, 2006). Therefore, countries with greater share of technology
along with changes in productive structures will in general observe higher economic growth.
Income inequality is another economic condition that affects or is affected by growth or stages of
development according to Kuznets (1955). Recent endogenous growth studies have emerged to research
how slow capital accumulation and growth is caused by disparity in income distribution, focusing on
three major channels. First, it is demonstrated that inequality could create socio-political insecurity that
subvert incentives to save and invest. Secondly, it is usually discussed that socio-political insecurity
brought about by income inequality would create strain to government to build income redistribution that
decreases financial motivations, consequently hindering capital accumulation and economic growth
(Hung Mo, 2003).
The last channel works through the aggregate of human capital. In the event that borrowing is
difficult and exorbitant, the poor are therefore denied investment in human capital. Further understanding
of this research problem may be achieved by reviewing more literature in respect of the effect of human
capital and population growth on economic growth.
Summary, this literature suggests that human capital and some other economic condition have
positive impact on economic growth therefore investment in human capital have the potential to increase
growth. However, population growth has adverse effect on economic growth along with economic
conditions like changes in age structure and increase in fertility rate. There have been studies to show the
effect on human capital on economic growth and effect of population on economic growth but to my
knowledge, there has been no studies to show the effects of these variables together including other
economic factors regarding economic growth.
DAMILOLA R. ODUNUGA (2019)
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Chapter 3
Research Methodology and Design
This section discusses the design and methodology employed to analyze the research problem.
The research assumed that there existed differentiation among each country’s human development index,
population growth rate and other economic factors so as to determine the correlation of each variable with
economic growth. Although, human capital can be viewed from a qualitative angle however, for this
study quantitative analysis of empirical research will be employed in order to carry out the correlational
study.
H1: There is no relationship between human capital and economic growth
H2: There is no relationship between population growth and economic growth
H3: There is no relationship between income inequality and economic growth
H4: There is no relationship between technological advancement and economic growth
The hypotheses above each shows the null hypothesis for each variable, therefore the
alternative of each hypothesis will be H1: X ≠ 0.
Human development index covers indicators such as education, labor force participation rate and
unemployment rate thus, it is considered the best indicator for human capital. GDP per capita has a close
relationship with the trend in standard of living therefore, it is an indicator for economic growth.
Population growth rate as an indicator for population growth, GINI coefficient as an indicator for income
inequality. Unlike HDI, there has been no standard way of measuring technological advancement, still
many components make up a country’s technological achievement such as creation of technology,
diffusion of recent innovation and diffusion of old innovations.
Therefore, technological achievement index (TAI) will serve as proxy for technology
advancement. This study employed a cross-country data for 2018 with random sampling of 20
observations. The data are derived from a secondary source like World Bank data and United Nations. For
this study, human capital, population growth, technological advancement and income inequality are the
DAMILOLA R. ODUNUGA (2019)
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independent variable while economic growth is the dependent variable. In order to determine the
correlation between the predictor variables and the dependent variable, this study will make use of
multiple linear regression analysis.
Ŷ = B0 + B1x1 + B2x2 + B3x3 + B4x4
Where Ŷ is the predicted value of dependent variable which is GDP per capita
B0 is the value of Y when all independent variables are equal to zero
B1 to B4 are estimated regression coefficient of independent variables
X1 to X2 are independent variables
GDP per capita = B0 + B1(HDI) + B2(Population growth rate) + B3(Gini coefficient) + B4(TAI)
Multiple regression analysis helps determine which variable is important, which variable needs to
be remove and how the variables interact with each thus, eliminating possibility of multicollinearity.
However, its limitation still remains that the cause and effect relationship between the variables still
remain thus, the estimated value of variable made on the basis of regression equation may lead to
misleading results. Multiple regression analysis involves prolonged procedure of calculation and analysis.
Research Analysis
Countries
GDP Per
capita ($)
HDI
Gini
Coefficient
TAI
0.924
0.535
Population
growth rate
(%)
0.619
1.411
USA
Zimbabwe
62,641.015
2,146.996
41.5
43.2
0.733
0.220
Egypt
2,549.139
0.696
2.033
31.8
0.236
South Africa
6,374.015
0.699
1.358
63.0
0.340
Panama
15,575.073
0.789
1.693
50.4
0.321
Croatia
14,869.091
0.831
0.855
30.8
0.391
Argentina
11,652.566
0.825
1.016
42.4
0.381
Brazil
8,920.762
0.759
0.784
51.3
0.311
Finland
49,648.149
0.920
0.178
27.1
0.744
China
9,770.847
0.752
0.456
42.2
0.299
Australia
57,305.299
0.939
1.575
34.7
0.587
France
41,463.644
0.901
0.182
32.7
0.535
DAMILOLA R. ODUNUGA (2019)
Countries
GDP Per
capita ($)
HDI
Japan
39,286.738
Colombia
10
Gini
Coefficient
TAI
0.909
Population
growth rate
(%)
-0.203
32.1
0.698
6,651.291
0.747
1.517
50.8
0.274
Algeria
4,278.85
0.754
2.007
27.6
0.221
Canada
46,210.548
0.926
1.409
34.0
0.589
Indonesia
3,893.596
0.694
1.134
39.5
0.211
Korea
31,362.751
0.903
0.328
31.6
0.666
India
2,015.59
0.640
1.037
35.1
0.201
Poland
15,424.046
0.865
0.01
31.8
0.407
Sources: worldbank.org
Hdr.undp.org
The table above shows the countries selected at random with the value of each variable used for
this analysis. GDP per capita is in US dollars ($), human development index (HDI) value is measured on
a scale between 0 and 1 that is, a country with value closest to 1 has a high level of social and economic
development. Population growth rate is in percentage because it measures the change in can population
during a given period of time. GINI coefficient ranges from 0 to 1 or 0% to 100%, with values closer to 0
or 0% representing equality and value closer to 100% or 1 representing inequality. Technological
advancement index (TAI) is also measured inn scale from 0 to 1, higher value represents higher
technological advancement vis-à-vis.
Table 1 Descriptive statistics
Min
1st Qu
Median
Mean
St dev
3rd Qu
Max
GDP per
capita
2016
5850
13261
21602
20348.9
39831
62641
HDI
0.5350
0.7350
0.8070
0.8004
0.1768
0.9045
0.9390
Population
growth rate
-0.2030
0.4240
1.0265
0.9699
0.6632
1.4375
2.0330
Gini
coefficient
27.10
31.80
34.90
38.68
9.4134
42.60
63.00
TAI
0.2010
0.2645
0.3605
0.4183
0.1895
0.5875
0.7440
The table above shows the descriptive statistics of all the variables using the five numbers
summary including median and standard deviation. Table 2 shows the relationship between GDP per
DAMILOLA R. ODUNUGA (2019)
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capita, HDI, Gini coefficient, population growth rate and technological advancement index Pearson
correlation, to determine the effect of each independent variable on the dependent variable and other
independent variables.
Table 2 Correlation coefficient using Pearson
Variables
GDP per capita
HDI
Gini Coefficient
TAI
0.8510
1.0000
-0.483
Population
growth rate
-0.3964
-0.483
1.0000
GDP per capita
HDI
Population growth
rate
1.0000
0.8510
-0.3964
-0.3421
-0.4090
0.2684
0.9278
0.8700
-0.5826
Gini Coefficient
-0.3421
-0.409
0.2684
1.0000
-0.3514
TAI
0.9278
0.8700
-0.5826
-0.3514
1.0000
A positive correlation indicates that the mean of the dependent variable increases as the value of
the independent variable increases and vice versa. The result of the analysis shows a strong correlation
between human capital and economic growth, also between economic growth and technological
advancement. However, there is a negative correlation between population growth and economic growth
and, also between economic growth and income inequality. That is the mean of GDP per capita increases
as the value of HDI increases, same applies to TAI. The analysis also shows the correlation of all the
independent variables to check for possibility of multicollinearity as shown in the scatterplot matrix
below:
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Multicollinearity occurs when there is a high correlation between two or more independent
variables. The graphs show a high correlation between human capital and technological advancement,
however there seems to be correlation between population growth and income inequality, since it is not a
strong correlation then, it is not significant. The independent variables that appear to be to highly
correlated with each will not be used for the regression analysis because there are redundant. In order to
accurately determine the independent variable that must be included in the multiple regression to avoid
multicollinearity issues, the p-value of all the variables must be determined. The table shows the p-value,
t-test, degree of freedom and confidence interval of each independent variables and the dependent
variable.
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Table 3 Pearson Correlation
Variables
GDP per capita
HDI
Gini Coeff
TAI
-
Population
growth rate
-
-
-
GDP pc
t-test
df
p-value
Conf Int
HDI
t-test
df
p-value
6.8775
18
0.0000
Conf int
t-test
0.655, 0.939
-1.8318
-2.339
-
-
-
df
p-value
18
0.0836
18
0.031
-
-
-
Conf int
-0.713, 0.055
-
-
-
t-test
df
p-value
-1.5448
18
0.1398
-0.762,0.051
-1.9018
18
0.0733
1.182
18
0.2525
-
-
Conf int
-0.681, 0.118
-0.721, 0.040
-0.197, 0.635
-
-
t-test
10.553
7.4867
-3.0413
-1.5925
-
df
18
18
18
18
-
p-value
0.0000
0.0000
0.007
0.1287
-
Conf int
0.823, 0.971
0.695, 0.947
-0.815, -0.188
-0.687, 0.107
-
Population
growth rate
Gini coeff
TAI
The table above shows the intersection of the dependent and all independent variable, the p-value
derived shows that there is a multicollinearity problem with two independent variables. HDI and TAI
from the Pearson correlation coefficient in table 2 above shows 0.8700 coefficient which signifies a very
strong correlation between the two independent variables with a p-value of 0.0000 is a strong indication
of multicollinearity since the p-value is less than 0.001. Gini coefficient has a higher p-value of 0.1398
and coefficient of -0.3421, therefore it is not statistically significant, also it has 0.2684 correlation with
population growth. This also serves as a confirmation for the scatterplot matrix above. The intersection of
other independent variables as shown above did not show any sign of multicollinearity since the p-values
are greater than 0.001 and a lesser or negative coefficient. Therefore, in other to run the multiple
DAMILOLA R. ODUNUGA (2019)
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regression analysis, one of the independent variables with high linear correlation will not be included in
the analysis. To determine which variable should be removed from the regression analysis, variance
inflation factor must be carried out. Variance inflation factor shows the estimate of how the variance of a
regression coefficient is inflated as a result of multicollinearity in the regression model. It can be
calculated using the following formula:
VIF =
!!#" (#$%)!' #
!#
Where 𝑠()" is the standard deviation of independent variables (x1, x2,…)
n is sample size
SE2 standard error of slope coefficient
S2 Residual
Variables
VIF
HDI
Population growth rate
10.18
1.454
Gini Coefficient
1.149
TAI
4.550
A VIF of 1 means that the variable is not correlated, between 1 and 5 means that the variable is
moderately correlated while VIF greater than 5 is highly correlated. This means that HDI is highly
correlated with an independent variable and will be removed from the regression, that is the regression
analysis will only consider population growth rate, Gini coefficient and TAI. This proposal will not test
hypothesis for human capital since it will be removed from the equation. Therefore, the new multiple
regression equation will be:
GDP per capita = B0 + B1(Population growth rate) + B2(Gini coefficient) + B3(TAI)
Table 4 Regression Analysis using Excel
Regression Statistics
Multiple R
0.9451749
R Square
0.8933557
Adjusted R
Square
0.8733599
DAMILOLA R. ODUNUGA (2019)
Standard Error
Observations
15
7241.4476
20
ANOVA
df
Regression
Residual
Total
Intercept
Population
growth rate
GINI Coefficient
TAI
Significanc
eF
5.31391E08
SS
MS
F
3
7.03E+09
44.677159
16
19
8.39E+08
7.87E+09
23428060
52438564.
3
Coefficients
-29044.523
Standard
Error
10738.26
t Stat
-2.7047700
P-value
0.0156186
Lower 95%
-51808.6177
Upper
95%
-6280.4
6798.1377
-74.490126
3092.813
189.1695
2.1980435
-0.3937744
241.667068
-475.511567
13354.6
326.531
112215.058
11139.73
10.073405
0.0430092
0.6989456
2.48084E08
88599.8777
135830.2
The multiple regression model is:
Ŷ = -29044.5 + 6798.1(population growth rate) – 74.5(Gini coefficient) + 112215(TAI)
The intercept value -29044.5 is the predicted value of Ŷ if, X1, X2 and X3 = 0. The coefficient of
B1 which is 6798.1 is the mean increase in Y per unit increase in X1 holding all other independent variable
constant, also each coefficient represents the additional effect of adding one more variable to the model.
The multiple R shows the correlation coefficient and it ranges from -1 < r < +1, this means value closer to
-1 is negative relationship while values closer to +1 is positive relationship. The value of the multiple R
0.9452 shows a positive relationship between the X variables and the Y variable. The regression statistics
above gives the overall goodness of fit measures: the correlation between by R2 = 0.8933, with adjusted
R2 of 0.8733 this means that 87.33% of the variation of Y around its mean is explained by the
independent variables that is 87.33% of the value fits the model.
The standard error has a value of 7241. 45 which is large than the coefficient of -29044.5 shows
that the coefficient is not different from 0 that is the lower limit of confidence coefficient is less than zero
DAMILOLA R. ODUNUGA (2019)
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which is -51808.62. The coefficient of Gini coefficient has estimated standard error of 189.17, t-statistics
of -0.3938, and p-value of 0.6989 indicates it is statistically insignificant at significance level α = 0.05 as
p > 0.05 that is there is no significant relationship between income inequality and economic growth,
therefore we fail to reject the null hypothesis. The standard error of TAI is 11139.7, t-statistics of 10.073
and p-value of 0.000000024808, is statistically significant at significance level α = 0.05 as p < 0.05. At
this significance level, the null hypothesis is rejected that is there is a relationship between technological
advancement and economic growth. Population growth rate has an estimated standard error of 3092.8, tstatistics of 2.1980 and a p-value of 0.043 which is less than 0.05 significant level, this shows it is
statistically significant as p < 0.05. The null hypothesis is rejected that is there is a relationship between
population growth and economic growth.
The overall F-test statistics from the ANOVA table is 44.677 with a p-value of 0.0000000531391,
H0: B1 = 0, B2 = 0 and B3 = 0 while H1: at least one of B1, B2 and B3 does not equal zero. Since
0.0000000531391 < 0.05, we reject the null hypothesis at a significance level of 0.05. The overall F-test
indicated that all the predictor variable used in the regression are jointly significant therefore, the
correlation between the independent variables and dependent variables is statistically significant.
Chapter 4
Conclusion
In this proposal, the relationship between economic growth and human capital, population growth
and other economic factor is analyzed for 20 countries. This research proposal shows a joint relationship
between human capital, population growth, income inequality, technological advancement and economic
growth, although population growth and income inequality as negative correlation with economic growth.
Previous research highlighted human capital investment and technological advancement as an important
aspect of achieving sustainable economic growth especially human capital based endogenous models.
This research confirms that there is a positive correlation between human capital and economic growth
and, also between technological advancement and economic growth with a correlation as high as 0.8510
DAMILOLA R. ODUNUGA (2019)
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for human capital and 0.9278 for technological advancement. Human skill plays an important role to be
able to adapt to new technologies, this explains the technological gap between the rich or educated and
the poor or uneducated which is why there is a high correlation between human capital and technological
advancement.
As shown by recent empirical study on income inequality and growth issues usually find
substantial negative effect of income inequality on growth. Yet, the exact way in which income inequality
affects growth are still empirically unknown. Some research work argued that income inequality enters
each part of the economy which altogether lessen the effectiveness thereby affecting productivity growth
rate. This research concludes that income inequality has significant negative correlation with economic
growth. A significant part of the inspiration for human capital strategies in developing countries is the
possibility of providing economic development that will raise the level of earnings in these countries. The
focus on mitigating poverty in developing countries relates downrightly to economic development in view
of the acknowledgment that essentially redistributing incomes and assets won't prompt long run answers
for poverty (Hanushek, 2013).
Population growth has negative correlation with economic growth, however correlation unlike
causation tells little about the actual relationship population growth and economic growth. However,
changes in age structure contributes and high level of fertility contributes to reasons why population
growth may have adverse effect on economic growth especially in developing countries. While in
developed countries population growth tends to be low giving rise to age structure with a large proportion
of elderly people in the population. Lower fertility and higher mortality contribute to the negative impact
of population growth on economic growth.
Recommendations
Human capital measurements include education, fertility and mortality rate, life expectancy and
good health therefore investment in education and good health system indirectly become investment in
human capital, since human capital has a positive relationship with economic. Along with investment in
human capital, countries around the world also need to investment in technology. Technology has solved
DAMILOLA R. ODUNUGA (2019)
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a lot of medical problems therefore increasing life expectancy rate for developed countries. For further
study, the emphasis should be on the causal relationship between income inequality, population growth
rate and economic growth. Since the exact cause of the significant negative effect of income inequality is
unknown. In order to concentrate on the causal relation population growth and economic growth, every
aspect of population needs to be estimated to determine the real cause of negative correlation.
DAMILOLA R. ODUNUGA (2019)
19
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