EDUCATION AND ECONOMIC GROWTH
: A PANNEL DATA ANALYSIS OF FOUR ASIAN COUNTRIES
REHMAT ULLAH 01-112201-061
DILAWAR ZAMAN 01-112201-017
MOEEZ ZAMAN 01-112201-034
ABSTRACT:
Education is one of the important factors of economic growth. The
objective of this study is to examine whether improvement in
educational sector which can be measured from enrollment rate
secondary and enrollment rate higher secondary has positive impact on
improvement of economic growth which can be measured with GDP and
whether different level of human capital has different contribution
toward the growth of 4 countries in Asia. The sample of 4 Asian countries
is taken during the time period 2000 to 2020 using panel data.
Keywords: Economic growth; Education; Panel data;
1. INTRODUCTION:
The study of human capital has been a longstanding interest in economic
theory, but its significance grew significantly after the Second World
War. This shift occurred as it became evident that traditional factors of
production, such as labor and capital, could not fully account for the
economic growth observed. Consequently, economists, including Solow
(1957), Schultz (1961), Becker (1964), and others, expanded their focus
beyond viewing investment in human capital as merely an important
aspect of production formation.
Education, across its different levels, is widely recognized as a highly
effective method for fostering the genuine development of human
capital. It serves as a fundamental component of sustainable
development and plays a vital role in enhancing human well-being.
According to Schultz (1961), education not only constitutes a significant
form of investment in human capital but is, in fact, the embodiment of
human capital itself. This assertion is based on the understanding that
education largely accounts for the variations and transformations
observed in gross national income, making it a crucial factor in driving
economic progress and prosperity.
2. Literature Review
2.1 Economic Growth
The economic growth of four Asian countries has been assessed using
real GDP as a measure. This metric has been adopted by researchers such
as Abbas and Foreman-Peck (2007), Chaudhary, Iqbal, and Gillani (2009),
Islam et al. (2007), and Jin (2009). Real GDP represents the market value
of all final goods and services produced within a nation's geographical
boundaries in a given year. It excludes the income generated by
resources in foreign countries but incorporates the earnings of
foreigners working within the economy.
2.2 Education
Obtaining or acquiring general knowledge, learning fundamental skills
like mathematics and geography, and developing a basic understanding
of various subjects such as history, natural sciences, social sciences, and
art are all activities related to education. Additionally, education aims to
enhance reasoning abilities, judgmental thinking, and prepare
individuals or others intellectually for adulthood. Researchers like
Loening (2005) have utilized proxies such as school enrollment, primary
(% gross), school enrollment, secondary (% gross), and school
enrollment, tertiary (% gross) to represent primary, secondary, and
tertiary education respectively. This approach, which calculates the
school education enrollment ratio by dividing the total enrollment of
children aged 5-15 by the population within that age group, has been
employed by scholars such as Hassan and Ahmed (2008).
3.3 Physical Capital
In economics, physical capital or just capital is a factor of production (or
input into the process of production), consisting of machinery, buildings,
computers, and the like. In economic theory, physical capital is one of
the three primary factors of production, also known as inputs in the
production function. In this study, Gross fixed capital formation (GFCF) is
proxy for Physical capital.
GFCF, which stands for Gross Fixed Capital Formation, is an important
macroeconomic concept utilized in official national accounts systems
such as the United Nations System of National Accounts (UNSNA),
National Income and Product Accounts (NIPA), and the European System
of Accounts (ESA). Its origins can be traced back to Simon Kuznets'
studies on capital formation in the 1930s conducted at the National
Bureau of Economic Research (NBER), and standardized measures for
GFCF were established in the 1950s.
In statistical terms, GFCF quantifies the value of new or existing fixed
assets acquired by the business sector, governments, and "pure"
households (excluding their unincorporated enterprises), while also
considering the disposal of fixed assets. As a component of expenditure
in gross domestic product (GDP), GFCF provides insights into the
proportion of newly generated value in the economy that is invested
rather than consumed.
2.4 Labor Force
The labor force, also referred to as the workforce or labor force in the
United States, represents the pool of individuals engaged in
employment. While it typically refers to those employed within a specific
company or industry, it can also encompass a geographical area such as
a city, state, or country. Within a company, the term "Workforce in
Place" may be used to denote its value. The labor force of a country
comprises both employed individuals and those who are unemployed. In
this particular study, the labor force is approximated by the labor force
participation rate. The labor force participation rate (LABR) is a measure
that compares the size of the labor force to the total population within a
specific age range. Typically, it excludes employers or management
positions and may specifically refer to individuals engaged in manual
labor. Additionally, the term can encompass all individuals who are
available and actively seeking work.
3. Data and Methodology
3.1 Data
The information regarding education levels, specifically school
enrollment in secondary, and tertiary levels, as well as labor force
participation rates and gross fixed capital formation, has been collected
from official sources such as the World Bank, and UNESCO Institute for
Statistics (UIS). These organizations have published the data on their
respective official websites.
Dependent Variable
Gross Domestic Product (GDP): GDP is used as a proxy for economic
growth. This metric represents the total value of goods and services
produced within a country's borders over a specific time period. It is used
to measure the economic performance and growth of a nation.
Independent Variable 1
School Enrollment in Secondary: Education is used as a proxy for school
enrollment. This refers to the number of students enrolled in secondary
education, typically covering ages between 12 and 18. It reflects the level
of participation and access to secondary schooling within a given
population.
Independent Variable 2
School Enrollment in Tertiary: This indicator measures the number of
students enrolled in tertiary education, including universities, colleges,
and vocational institutions. It provides insights into the higher education
landscape and the proportion of individuals pursuing advanced studies.
Control Variable
⚫ Labor Force Participation Rate: This rate indicates the proportion of
the working-age population that is either employed or actively
seeking employment. It helps assess the level of economic
engagement and workforce utilization within a country.
⚫ Gross Fixed Capital Formation: This metric represents the total
investment in physical assets, such as buildings, machinery, and
infrastructure, within an economy during a specific period. It serves
as an indicator of investment activity and capital accumulation.
⚫ Inflation: Inflation refers to the rate at which the general level of
prices for goods and services rises, eroding the purchasing power of
a currency. It is typically expressed as an annual percentage increase
and is an important economic indicator that reflects the overall price
stability within an economy.
Variables
Economic
Growth
Education
Proxies
Real Gross
Domestic
Product
School
enrollment,
secondary
gross)
Notation
GDP
SEC
(%
School
enrollment
teritary (% gross)
Labor Force
Physical capita
Labor force
participation
rate
Gross fixed
capital
formation
Inflation
HIG
LABR
GFCF
CPI
3.2 Methodology
GDP i,t= β() + βSEC1i,t + βHIG2i,t + βLABR3i,t + βGFCF4i,t βCPI5i,t + eit
GDP:
Gross Development Product
SEC:
school enrollment in secondary
HIG:
school enrollment in tertiary
LABR:
labor force participation rate
GFCF:
gross fix capital formation
CPI:
Inflation
At first, a descriptive statistic is to be taken. Correlation among variables
was then examined, and a correlation matrix is presented below. Then
Ordinary least squares (OLS) regression model among variables was
checked. As the data taken is panel data so in this case, appropriate
economics techniques will be used to estimate the panel data model, we
will use a panel regression technique in which first random effects results
would be checked and then fixed effects and to choose between these
two a Hausman Test will be conducted to get the desired results. If the
p-value is statistically significant we will go with fixed effects regression
model, and in case if p-value is not statistically significant we will go with
random effects regression model.
4. Results
4.1. Descriptive Statistics
The results are summarized in the table below.
Table 2. Summary of Descriptive Statistics
Variable Obs
Mean
GDP
SEC
4.599019 2.46845 -7.48405 8.858868
68.16482 13.20381 44.87198 88.91017
84
81
Std. Dev. Min
Max
HIG
LABR
GFCF
CPI
81
84
84
84
22.85345
62.13398
6.707123
5.219027
11.23448
5.624016
6.073719
3.061235
5.63134
50.187
-14.4164
-1.1387
46.7621
70.608
26.41948
13.10867
4.2. Correlation Matrix Between Model Variables
The results are summarized in the Table 3 below.
Table 3. Correlation Matrix
GDP
SEC
HIG
LABR
GFCF
CPI
GDP
SEC
HIG
LABR
1
0.066
-0.0299
0.0485
0.6576*
0.1743
1
0.9263*
0.4391*
-0.2755*
-0.4462*
1
0.4419* 1
-0.3345* -0.1175 1
-0.4927* -0.2227* 0.1703
4.3. Ordinary least squares (OLS) Regression Model:
Model Summary:
Number
of obs
F(5, 74)
Prob > F
Rsquared
Adj Rsquared
Root
MSE
=
80
=
=
=
15.44
0
0.5105
=
0.4774
=
1.7455
GFCF
CPI
1
GDP
Coef.
Std. Err.
SEC
HIG
LABR
GFCF
CPI
_cons
0.083936
-0.03357
0.02369
0.275263
0.146712
-4.40341
0.0399732
0.0492914
0.0393599
0.0345754
0.0732338
2.784692
t
P>t
2.1
-0.68
0.6
7.96
2
-1.58
[95% Conf.
0.039
0.498
0.549
0
0.049
0.118
Interval]
0.0042873
-0.131782
-0.0547364
0.2063704
0.0007908
-9.952031
0.1635842
0.0646485
0.1021164
0.3441564
0.2926341
1.145209
4.4. Panel Regression Analysis:
Random-effects GLS regression
Number of obs
Number of
groups
Obs per group:
min
avg
max
Wald chi2(5)
Prob > chi2
Group variable: CN
R-sq:
within = 0.5057
between = 0.9301
overall = 0.5105
corr(u_i, X) = 0 (assumed)
GDP
SEC
HIG
LABR
GFCF
CPI
_cons
Coef.
Std. Err.
z P>z
2.10
0.083936 0.0399732 0.036
0.68
-0.03357 0.0492914 0.496
0.60
0.02369 0.0393599 0.547
7.96
0.275263 0.0345754 0.000
2.00
0.146712 0.0732338 0.045
1.58
-4.40341 2.784692 0.114
[95% Conf.
=
80
=
4
=
=
=
=
=
19
20
21
77.18
0
Interval]
Column1
0.0055896 0.1622818
-0.1301761 0.0630426
-0.0534541 0.1008341
0.2074968
0.34303
0.0031767 0.2902481
-9.861307
1.054485
sigma_u
0
sigma_e 1.760086
rho
0 (fraction
of
variance
due
to
u_i)
Fixed-effects (within) regression
Number of obs
Number of
groups
Obs per group:
min
avg
max
F(5,71)
Prob > F
Group variable: CN
R-sq:
within = 0.5125
between = 0.9548
overall = 0.4896
corr(u_i, Xb) = -0.4247
GDP
Coef.
Std. Err.
SEC
0.112119 0.0483934
HIG
-0.078 0.0631388
LABR
0.086721 0.1116558
GFCF
0.273504 0.0349871
CPI
0.204947 0.0861819
_cons
-9.51203
8.147467
t P>t
[95% Conf.
2.32
0.023
1.24
0.221
0.78
0.440
7.82
0.000
2.38
0.020
1.17
0.247
Interval]
=
80
=
4
=
=
=
=
=
19
20
21
14.93
0
Column1
0.0156256 0.2086129
-0.2038979 0.0478925
-0.1359143 0.3093563
0.2037414
0.343266
0.033105 0.3767884
-25.75762
6.733557
sigma_u 0.559847
sigma_e 1.760086
rho
0.091879 (fraction
of
variance
due
to
u_i)
4.5. Hausman Test:
hausman fe re
Column1
(b)
fe
(B)2
re
SEC
HIG
LABR
0.112119 0.083936
-0.078 -0.03357
0.086721 0.02369
(b-B)
sqrt(diag(V_b
Difference S.E.
0.028184
-0.04444
0.063031
0.0272775
0.0394572
0.1044883
V_B))
GFCF
CPI
0.273504 0.275263
0.204947 0.146712
-0.00176
0.058234
0.0053515
0.0454325
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from
xtreg
Test: Ho: difference in coefficients not systematic
chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B)
=
1.73
Prob>chi2 =
0.8845
5.Conclusion
In this study, the relation between education and economic growth is
analyzed for the period between 2000 to 2020 in terms of 4 Asian
member countries. Education is represented separately by the concept
of school enrollment in secondary, and school enrollment in tertiary. The
findings indicate the significance of Education on economic growth. The
empirical findings highlight that education, as a proxy for Enrollment rate
secondary and higher education, has a positive and significant impact on
economic growth. As a result, it suggests implementing effective
education, such as enrollment, is essential for promoting economic
growth.
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