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Do Human Resources Run Economic Growth? Empirical Evidence from Pakistan
Bilal Alam
a
, Muhammad Niamat Ullah
b
a
Department of Economics, Institute of Social Sciences,Gomal University Dera Ismail Khan, Pakistan
Email: bilal.alam868@gmail.com
b
Department of Economics, Institute of Social Sciences, Gomal University Dera Ismail Khan, Pakistan
Email: nematbabar@gu.edu.pk
ARTICLE DETAILS ABSTRACT
History:
Accepted 30 April 2021
Available Online June 2021
This study analyzes the role of human capital in economic growth using
data from 1981 to 2017. The data were taken from the World Bank
(WDI) and the Economic Survey of Pakistan (Various Issues). It was
scrutinized for stationarity of variables through ADF and an appropriate
time series econometric technique of ARDL is applied for empirical
analysis. The results suggest that both proxies of human capital,
education, and health have positive impacts on the economic
development of Pakistan. The study findings also suggest that
government machinery may divert enough resources for the
improvement of education and health services to accumulate human
capital for achieving the desired goal of higher growth and development.
© 2021 The authors. Published by SPCRD Global Publishing. This is an
open access article under the Creative Commons Attribution-
NonCommercial 4.0
Keywords:
Education Index; Health Index;
Human Capital
JEL Classification:
P36, I18, J24
DOI: 10.47067/reads.v7i2.351
Corresponding author’s email address: bilal.alam868@gmail.com
1. Introduction
Human Capital (HC) is a wider concept (Goode 1959, Mincer 1958, Becker 1961 and Schultz 1961)
which has all the indispensable ingredients of society, culture, politics, and economics (Harbison and
Myers, 1964). For economists, it is an important determinant of growth. The various theoretical
discussion has been made on the possible role of human capital in economic growth e.g. growth
theories human capital. Two main theories highlighted the role of human capital in economic growth
i.e. exogenous theory of Solow (1956) and the Endogenous Theory of Romer (1989, 1990, & 1994) and
Lucas (1988). In exogenous growth theory, technology is taken as an exogenous factor and the role of
labor and capital is defined in this framework while Endogenous theories of growth postulated that
human and physical capitals are the prime factors of growth. Various researchers confirm the role of
human capital in the development and growth of a country (Sequeira 2007; Wilson & Briscoe 2004;
Abbas 2000).
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Most of the empirical studies used education and health as a proxy of human capital in their
studies and claimed that it is the real proxy that captures the true picture of human capital. Both
developed and developing countries spend a handsome amount of their budget on health and education
with a belief that good health and a well-educated labor force are more productive than ill-health and
less educated ones. For developing countries, it is a bit difficult to cope with the challenges of education
and health for all slogans. It is observed that in less developed nations wealthier people enjoy good
health and education facilities compared to poor masses of the country that’s why both the drop out and
mortality rates are higher than the developed world e.g. North American, European and Japanese
children have the capability of getting 12 years of education (on average) as compared to the African or
South Asian countries who spend less than 4 years in school (UNDP 1998).
Since the inception of Pakistan, the government paved great attention to developing human
capital. Zulfiqar Ali Bhutto government put extra efforts to enhance education through nationalization
policy along with improving the manpower in Pakistan by sending it to develop world specifically Golf
countries. In the current scenario, Pakistan ranked 164
th
(in a total of 195 countries) in respect of
investment in health and education, the two very important determinants of human capital. The
position of Pakistan was improved from 166 to 164 from 1990 to 2016. These statistics suggest that we
are very lag behind the developed world as far as human capital development is concerned. To achieve
higher economic benefits we need to develop our human capital and this study is a timely struggle to
evaluate the role of human capital in the economic development of Pakistan.
The rest of the paper is organized as follows; after the brief introduction, there is a discussion on
relevant literature which is followed by data and methods. Results and discussion are carried after a
methodology. The conclusion is presented in the last section of this study.
2. Literature Review
Although there is a huge discussion on the role of human capital in economic growth but to
make the discussion easy and to come out with some conclusion, the current study emphasized the
most relevant studies.
Ali et al (2016) examined the role of human capital in Bangladesh's economy for the sample
period from 1981 to 2014. By applying Johansen Cointegration they found that the accumulation of
human capital has a significant and positive effect on Bangladesh's economy. Khan et al. (2016)
analyzed the selected South Asian countries i.e. Srilanka, Pakistan, Bangladesh, and India. In the time
frame of 1971 to 2013, they found that human capital (which was proxy by education and health) boost
the growth of these sample countries.
Guptha et al (2017) also analyzed the same hypothesis for the Indian economy taking 1991 to
2105 as a sample period of the study. Study results specify that both human capital and technology have
a positive and significant impact on India's economic growth. Hassan (2017) examined male and female
health capital's effect on economic growth. Study findings showed that in the long run, both variables
have a significant and positive effect on the nation’s growth on the other hand both the variables have a
negative and insignificant impact on the growth process.
Hakooma and Seshamani (2017) investigated the underlying hypothesis for the Zambian
economy. They found that there are long-run influence of human capital on the economic development
of Zambia.
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Osoba and Tella (2017) examined interactive effect between economic development and human
capital accumulation in Nigeria. They have taken education and health spending variables as
independent variables whilst GDP has taken as a dependent variable. To examine the data fully
modified ordinary least square has been applied to calculate the findings. Study results specify that on
both sides of dependent and independent the variables have a positive and significant relationship and
also indicate interactive effect between education and health variables.
Ogundari and Awokuse (2018) suggested that health and education have been normally applied
to estimate Human Capital and similarly vast literature has been estimated the relationship between
Human Capital and the growth process. Thus both education and health capital are being considered
the major elements for the formation of Human Capital and it is the human capital that contributes
very much to enhance the economic growth of any country.
Khan and Chaudhry (2019) estimated that the formation of human capital plays a critical role in
enhancing economic growth and create employment for individuals in any nation. A fundamental
objective of the literature is to calculate the impact of human capital on economic growth and
employment in developing countries. For this purpose life expectancy and education are taken as
proxies for Human Capital by utilizing panel data ranging from 1996 to 2018. Results of this analysis
found out that life expectancy and education have a positive and significant effect on real GDP and
employment in developing countries.
Maku et al (2019) checked the impact of human capital accumulation on Nigerian economy’s
using ARDL to estimate the short-run and long-run impact of HC on the selected country's economic
growth. This analysis found that in the short run human capital and economic growth have a negative
and insignificant relationship while in the long run it is found the presence of a significant and positive
relationship between the two in the case of Nigeria.
It is concluded from the mentioned literature that health and education are the major elements
for improving the individual’s life. For any technological development, education performs a critical
role it is experienced that those countries have made progress in technology that could be able to
improve their education. Similarly, technological development makes capable the labor force to raise
their productivity level and it raises their remuneration. Higher-income individuals make them capable
to improve their health status. It is a natural fact that healthy individuals are more efficient and active
as compare to under nourish they proved themselves very productive for their country's economic
development. The main aim of this analysis is to check the impact of human capital in terms of male
education and male health on the economic performance of Pakistan's economy using the time series
data from 1981 to 2017.
3. Materials and Methods
3.1. Model Specification
Previous empirical literature used various models to analyze the role of human capital in
economic growth. This study followed Knowles and Owen (1995) who augmented the Mankiew et al.
(1992) growth, model. The specific form of the model is;
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Here in this model, growth is represented by gross domestic product, MEDU is used for male
education which is one of the proxies of human capital, MLF is male labor force, FCF is fixed capital
formation and MLE is male life expectancy. The specific econometric form of the model is as following;
lnGDPt = α + β1tMEDUt + β3MLFt + β2FCFt + β4MLEt + Ɛt--------------3.2
Here ε α and β are the error term, constant term and slope coefficient respectively and “t” is
time subscript.
3.2 Data Sources
3.2.1. Data
Secondary data for the period of 1981 to 2017 are collected from World Development Indicators
and different issues of Pakistan Economic Surveys. Where Gross Domestic Product (GDP) is employed
to calculate economic growth. Earlier empirical literature used the same proxy for economic growth
(i.e. Khan 2016; Chaudhry 2009). Male education is calculated by using the education enrollment index.
Its calculation is based on the various stages of schooling levels. The level of education as a proxy of
human capital was also used by previous literature (e.g. Barro & Lee; 2000). Physical Capital is a proxy
by FCF which is also used by Ali et al (2012). Male labor force participation is another important
determinant of human capital which one of the most productive means especially in developing nations.
Following Knowles and Owen (1995) this study used the male life expectancy index as a proxy of human
capital. Literature supports the idea that healthy individuals have a positive and significant influence on
any economic growth as healthy people having higher prospect to acquire higher education, training &
skills which in turn make them capable to work for a longer time to increase their productivity.
3.2.2 Estimation Strategy
This study used the ARDL approach for model estimation developed by Pesaran and Shin (1995; 1998
and 2001) This approach is completed in three different stages where at stage one it is attempted to
find out the cointegration in the model, while in the second and third phase (once the cointegration
established) long and short-run elasticities are calculated. Short Run deviations were estimated through
Error Correction Mechanism (ECM) suggested by Sargan (1964). Some diagnostic tests were also
employed to find the validity of the model. The coefficient stability was tested through CUSUM and
CUSUMQ.
4. Results and Discussion
4.1. Unit Root Analysis
The stationarity of data is tested through the application of the ADF test. Although testing data
for stationarity is not the pre-condition of ARDL but still, it is believed that time-series data most of the
time has time trends and which may cause spuriousness in results if not handled properly (Gujrati;
2003) therefore this study tested the data for stationarity to avoid the misleading conclusion. Results
of stationarity analysis are given in table 4.1.
Table: 4.1 Unit Root test
Variable Level 1
st
Difference Decision
C T + C C T+C
LnGDP -2.25(0.191) -2.67(0.253) -3.46(0.015) -3.50(0.054) I(1)
Medu 0.11(0.962) -2.71(0.237) -5.63(0.000) -5.58(0.000) I(1)
MLF -5.86(0.000) -6.07(0.000) - - I(0)
FCF -4.37(0.001) -4.34(0.007) - - I(0)
LEM -1.82(0.723) -1.87(0.648) -5.84(0.000) -5.95(0.000) I(1)
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Results of stationarity analysis suggested that variables like Male labor Force and Fixed Capital
Formation are stationary at a level while others at first difference. Having a mixed order of integration
leads us to apply the ARDL approach discussed above.
4.2. Long Run Cointegration
The result of long-run cointegration is made based on Wald F statistics. Is suggested by Pesaran
and Shin (1995, 1998, 2001) that for the presence of long-run cointegration the calculated Wald F value
has to be greater than the upper boundaries of tabulated Wald F values at 5% level. Results are
displayed in table 4.2 which suggested that there is long-run cointegration in the model as the
calculated value (6.537) is far upper than the upper boundaries of tabulated values (4.544) at 5% level.
Table 4.2: The Bound Test for Cointegration
Specification Lower
bound
Upper
bound
F-statistic Decision
LNGDP/MEDU, FCF,
MLF, LEM
3.202
4.544
6.537(.031)
Integrated
4.3. Long Run Estimates
Here in this model, it is confirmed the presence of Long Run Cointegration and we proceed
towards estimating the LR calculations. In table 4.3 indicate that all the chosen variables' coefficients
are positive and significant. In our calculation, it is found that an increase of one percent in the life
expectancy of the male population leads to a seventeen percent increase in economic growth. The
outcomes bear with the suggestion of Knowles and Owen (1995) and Barro & Martin (1995) having
discovered a viable and positive relationship between male Life Expectancy and income level of any
nation. The findings of our study imply that healthy individual’s contribution to economic productivity
is higher than unhealthy individuals which leads to raising economic growth.
Similarly, results also show that contribution of male education to economic growth is positive
and significant. Study outcomes suggest that a 1% increase in male education raises economic growth
by fifteen percent and these results are consistent with well-known Barro & Lee (1994) & Barro &
Martin (1995). The reason behind education's positive impact on the economy is that educations
generate a skilled and trained labor force and this type of labor force has a much higher productivity
level as compared to un-skill and untrained individuals. Our model findings come to this conclusion that
by raising the training and skill of the individuals the output per worker is rising and in turn, it leads to
higher economic growth. It is normally believed that the formation of human capital perform a crucial
role in shaping a country's economic growth. So it is concluded from this discussion through education
and quality health facilities human capital of any nation may improve.
In our model, Fixed Capital Formation and Labor Force Participation are controlled variables
that indicate positive and significant association with growth rate. The results displayed in Table 4.2
shows that every 1pc increase in FCF enhances GDP by 16 percent. Khan (2016) & Lucas (1988) also
found similar results and argued that if sufficient resources are redirected for improvement of human
capital resultantly fixed capital would be utilized efficiently. It is the human capital that creates trained
and skilled labor force and FCF efficient utilization required skill and train labor force. Further, the
results also revealed that a 1pc increase in MLF leads to a 10pc rise in GDP which supports the idea of
Khan (2016). In Pakistan male population having dominancy over the female population and due to this
reason the greater part of the employed Labor Force compose of on Male Labor Force and their
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contribution to economic growth is significant. since workers work and earn income against providing
their services by this way they save a part of their income for further investment to raise the
productivity level of a nation.
Table.4.3 Long Run Coefficients
Regressor Coefficient Stand. Errors T-Ratio Prob
LEM .1760 .0742 2.368 [0.054]
MLF .1023 .0436 2.346 [.015]
MEDU .1586 .0628 2.525 [.050]
FCF .1626 .0798 2.037 [.046]
A 10.5506 .054801 192.5261 [.000]
T .010956 .0065252 1.6790 [.104]
R-Squared .92
R-Bar-Squared .88
F-stat. 54.8[.000]
DW-statistic 1.9459
4.4. Short Run Dynamics
Short-run dynamics are not very much different than long-run elasticities. Error Correction
Mechanism (ECM) suggests that any disequilibrium is adjusted with a reasonable speed of adjustment
(41%). Similarly, the values of R square and Adjusted R square show that variations in regress and are
well explained by the regressors.
Table.4.4 Short Run coefficients
Regressor Coefficient Stand. Errors T-Ratio Prob
dLEM .0740 .0499 1.478 [.153]
dMLF .0886 .0396 2.2373 [.081]
dMEDU .053983 .023830 2.264 [.031]
dFCF .0043165 .0013888 3.1081 [.004]
dA 4.4264 1.3037 3.3951 [.002]
t .0045963 .0022486 2.0440 [.050]
ecm(-1) -.41954 .12361 -3.3940 [.002]
4.5. Diagnostic Tests
The outcomes of the Diagnostic test are shown in table 4.5 which suggested that the model is
free of heteroskedasticity and autocorrelation. Similarly, the table also confirmed that error terms are
normality distributed and the model is functionally well structured.
Table 4.5: Diagnostic Tests
Test Statistics LM Version
Serial Correlation 1.009 [.315]
Functional Form .1346 [.714]
Normality 2.113 [.348]
Heteroscedasticity .01606 [.899]
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4.6: Stability Test
Coefficient stability is confirmed through CUSUM and CUSUMQ. The displayed figures suggested
that the coefficient remains in the given range.
Figure 4.1: CUSUM
Figure 4.2: CUSUMQ
5. Conclusion
The possible role of human capital in the economic growth of Pakistan was investigated in this
study. The data for this purpose was taken from Economic Surveys of Pakistan and World Development
Indicators for the period from 1981 to 2017. The growth was measured through GDP and Human capital
as a proxy by using male education and health indexes. The results revealed that human capital played a
positive and significant role in the economic growth of Pakistan. The education and health sectors can
play a pivotal role in generating inclusive economic growth rate for the long run on a sustained basis.
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