A dynamic analysis of the relationship among human development, exports and economic growth in Pakistan.
Afzal, Muhammad ; Butt, A. Rauf ; Ur Rehman, Hafeez 等
This study investigates the econometrically empirical evidence of
both the short-run and long-run interrelationships among human
development, exports and economic growth in an ARDL framework for
Pakistan. This study also examines causal linkages among the said
variables by applying the Augmented Granger Causality test of
Toda-Yamamoto (1995). By using data on Pakistan's real GDP, real
exports and Human Development Index (HDI) for the period 1970-71 to
2008-09, three models have been estimated. The results show
cointegration between economic growth, physical capital, real exports
and human development when human development is taken as dependent
variables. Furthermore, unidirectional Granger causality running from
real GDP to real exports has been found in Bivariate, Trivariate and
Tetravariate causality framework. The inclusion of HDI as a measure of
human development reduces the physical capital share in real GDP whereas
it improves the robustness of the regression model. Real GDP seems to
provide resources to improve human development in only the long-run
while human capital accumulation does not seem to accelerate real GDP
both in the short-run and the long-run. The empirical results of the
study do not support 'export-led growth hypothesis' and human
capital-based endogenous growth theory in case of Pakistan, however, it
does support 'growth-driven exports hypothesis' in case of
Pakistan.
JEL classification: O11
Keywords: Human Development, Exports, Economic Growth, ARDL,
Causality
I. INTRODUCTION
Human Development (HD), being the ultimate objective of each and
every human activity, plays a vital role in producing high skilled
manpower that leads to economic growth and hence economic development.
"HD denotes both the processes of widening people's choices
and level of their achieved well being" [UNDP (1990), p.10]. HD is
the enlargement of people's choices to live more prosperous lives.
Economists consider HD as one of the most important ingredients of
economic growth. Two periods regarding growth theories are very
important in economic literature. In the first period, i.e. in late
1950s and 1960s, physical capital (PC) was given too much role in
explaining economic growth but long run economic growth can be explained
only by assuming an exogenous technological progress. In the second
period, i.e., late 1980s and early 1990s, economic growth models were
extended by inclusion of human capital (HC) and thereby endogenous
growth theories emerged [Romar (1986, 1987, 1990); Lucas (1988);
Grossman and Helpman (1991); Rebelo (1991)]. Human capital is endogenous
here and growth rate may continue to rise because returns on investments
in human capital do not necessarily exhibit diminishing marginal
returns. Human capital accumulations as an endogenous factor proved to
be the main contributor in explaining sustainable long run economic
growth. There are two main approaches through which human capital is
likely to affect the long-run economic growth. The first approach known
as 'Lucasian' [Lucas (1988)] incorporates human capital into
growth model as one of the factor of production. The second approach
called 'Romerian' [Romer (1990)] depends upon the idea that
human capital promotes technological advancement. According to Romerian
approach, high level of human capital results in more innovation and
more efficiency of the work force that, in turn, leads to more growth in
aggregate income. This paper utilises Lucasian approach. While
explaining endogenous growth theory, Lucas (1988), Romer (1990) and
Grossman and Helpman (1991) have argued that either human capital or
trade is main source of economic growth. Exports, being the important
part of trade, are considered as important ingredient of progress and
prosperity of both developed and developing nations.
A number of studies in literature are available that have examined
the 'export- led economic growth hypothesis and 'growth-driven
exports hypothesis' [e.g. Shan and Sun (1998); Ahmad (2001); and
see Afzal, Rehman and Rehman (2008), for reference of more recent
studies). There also exists a vast literature on the linkage between
human capital and economic growth. Economic growth and hence economic
development cannot be sustained unless and until preceded by
improvements in HD. If HC is a prerequisite for sustainable economic
growth, the government as well as private funding must be allocated in
such a way that help move a nation above a threshold level of HD.
'Export-led growth hypothesis' postulates that exports
actively lead to economic growth in the following manner. Firstly,
export promotion incentives and schemes directly encourage the exporters
to produce more exportables. This, in turn, leads to specialisation and
to get fruits of the economies of scale and country's comparative
advantage. Secondly, increased exports may help the country to import
high value inputs, products and technologies that, further, may have a
positive impact on economy's overall productive capacity.
'Growth-driven export hypothesis' postulates that growth leads
to exports. Economic growth itself promotes trade flows. It also leads
to specialisation and creates comparative advantage in a certain areas
that further facilitates exports. So there mayor may not exit a
bidirectional linkages between economic growth and exports.
Bivariate causality framework between economic growth and exports
excludes some other most relevant economic and non-economic variables
(such as financial development, macroeconomic stability, energy
resources, trade openness, debt, imports, expenditures on R&D,
investment share in GDP, FDI, exchange rate, political stability, labour
and labour productivity etc.) that may have significant impacts on the
two main variables being studied. In spite of a clear conceptual link
among HC, economic growth and exports, there exist a few empirical
studies like Chuang (2000) for Taiwan and Narayan and Smyth (2004) for
China that have examined the causal linkage among economic growth, HD
and exports in a multivariate framework. There is hardly any study on
Pakistan that examines the linkages among human capital, economic growth
and exports. The present study is an attempt to examine both the SR and
LR dynamic analysis of the relationship and causality among economic
growth, HD and exports using Pakistan's data.
The main objectives of this study are:
* To empirically examine both the short-run and long-run dynamic
relationships among economic growth, human development and exports in
Pakistan.
* To examine the validity of human capital based endogenous growth
theory, growth driven export and export-led growth hypotheses.
* To check the causal link among the variables being studied.
This study confines to Pakistan's economy on the dynamic
relationships and causal nexus among economic growth, human development
and exports. The HDI that is used as a composite measure of HD has been
improving since 1970-71. The estimated HDI was 0.24 or 24 percent in
1970-71. This number increased to 0.34, 0.44, 0.51 and 0.56 in 1980-81,
1990-91, 2000-01 and 2008-09, respectively. This means that the HD has
improved more than double from 1970-71 to 2008-09 in Pakistan. The
average annual increase in HDI remained at 2.25 percent from 1970-71 to
2008-09 that needs to be further improved in the coming years to cope
with the requirement of latest technology used in Production. The growth
of real exports in Pakistan has also been much rapid. It increased from
Rs 74000 millions in 1970-71 to Rs 329086.1 millions and Rs 871956.9
millions in 1990-91 and 2007-08, respectively. The per annum average
increase in real exports has been 6.89 percent from 1970-71 to 2007-08.
The average annual export to GDP ratio has been below 10 percent from
1970-71 to 1989-90 in Pakistan. It fluctuated between 10 percent in
1990-91 to 13 percent in 2008-09. The average annual increase in
Pakistan's real GDP has remained 5.25 percent from 1970-71 to
2008-09.
The remaining study is organised as under: Review of literature is
presented in Section II. Section III includes specification of model,
data sources and methodology. Empirical results are discussed in Section
IV. Conclusion and recommendations have been given in Section V.
II. REVIEW OF LITERATURE
Many empirical studies exist in literature that have examined the
linkage between exports and economic growth either by using correlation
analysis of by using a bivariate causality analysis. Testing causality
in a bivariate framework may not be very well free of specification
bias. An important variable or variables may be missing or omitted in a
bivariate causality case. Empirical studies on 'Export-led economic
growth hypothesis' have supported mixed results in a bivariate
causality framework. Empirical support for the validity of
'export-led growth hypothesis' in both developing and
developed countries was found considerably weak in recent era when
analysed by using cointigration and augmented Granger causality analysis
rather than earlier correlation based or simple causality analysis. A
few empirical studies also exist that have included other relevant
variables (e.g. financial development, trade openness, debt, imports,
expenditures on R&D, share of investment in GDP, FDI, energy,
exchange rate, labour stock and capital stock, etc.) for causality
analysis and try to exert their influence on exports and economic
growth. Afzal, Rehman, and Rehman (2008) tested the causality among
economic growth, external debt servicing and exports in a bivariate and
trivariate framework for Pakistan by applying Toda-Yamamoto Augmented
Granger Causality analysis and found no support to 'export-led
growth hypothesis'. Their study further supported the
'growth-driven export hypothesis'. The principal findings of
the study by Shan and Sun (1998) do not support the validity of
'export-led growth hypothesis'. Awokuse (2003) tested the
credibility of 'export-led growth hypothesis' for Canada and
found it to be valid. Applying ARDL approach to cointegration and
Toda-Yamamoto non-causality test, Omisakin (2009) found support for
'export-led growth hypothesis' for Nigeria. A comprehensive
list of the studies that directly or indirectly have empirically
examined the causality between economic growth and exports is given by
Jung and Marshal (1985) for 37 developing countries and found one-way
causality running from exports to growth for four countries only, Chow
(1987) found causality running from exports to growth for only one
country out of eight newly industrialised countries, Al-Yousif (1997)
for Arab Gulf countries, Thornton (1996, 1997) for Mexico and Europe,
Awokuse (2005) for Korea, Xu (1996) and Riezman, et al. (1996) for set
of countries including Korea, Hong Kong and Taiwan, Bahmani-Oskooee, et
al. (1991) for 20 countries, Kwan and Cotsomitis (1991) for China, Marin
(1992) for industrialised countries, Shan and Sun (1998) for China,
Hetemi and Manuchehr (2000) for Nordic economies, Ahmed and Kwan (1991)
for 47 African countries and found no causality running from exports to
growth, Lee and Pan (2000) for East Asian countries, Graves, et al.
(1995) for Korea, Onchoke and In (1994) for selected South Pacific
Island Nations, Mah (2005) for China, Hetemi (2003) for Japan, Demirhan,
Erdal, and Akcay (2005) for selected MENA countries, Ahmad (2001),
Kovacic and Djukic (1991) for Yugoslav economy, Jordaan and Eita (2007),
Doganlar and Fisunoglu (1999) for Asian countries, Islam (1998),
Baharumshah and Rashid (1999) for Malaysian economy, Khalid and Cheng
(1997) for Singapore, Din (2004) for five largest economies of South
Asia including Pakistan, Afzal (2006) for Pakistan, Ahmed, et al. (2000)
for South and South-East Asian countries, Wernerheim (2000) for Canada,
Reppas and Christopoulas (2005) for African and Asian countries,
Amoateng and Adu (1996) for African Countries, Hsiao (1987) for newly
Industrialised Asian economies, Ahmad and Harnhirun (1995) for Asian
countries, Chuang (2000) for Taiwan, Narayan and Smyth (2004) for China,
Liu, et al. (1997) for China, Shan and Tian (1998) for Shanghi (China),
Konya (2006) for OECD countries, Shirazi and Manap (2004) for Pakistan
and Afzal, Rehman, and Rehman (2008) for Pakistan.
Doganlar and Fisunoglu (1999) examined the causal linkage for seven
Asian countries including Pakistan and found unidirectional causality
running from economic growth to export growth in Pakistan. Vohra (2001)
investigated linkage between export and economic growth for Pakistan,
Philippines, Malaysia, Thailand and India, and found that exports
positively affected the economic growth. Din (2004) explored the
'export-led growth hypothesis' for five South Asian countries
including Pakistan and found cointegration among exports, imports, and
output for Pakistan. Afzal (2006) found feedback causality between
manufactured exports and GDP. Amoateng and Adt, (1996) and Chen (2007)
supported both the 'growth-driven exports' and
'Export-led economic growth hypotheses' in trivariate and
tetravariate causality analysis respectively.
The linkages between (i) economic growth (EGr) and human capital
(HC), (ii) HC and trade, and (iii) EGr and trade, have been studied and
discussed by Narayan and Smyth (2004). A strong linkage was found
between EGr and HD (Ranis, Stewart and Ramirez, 2000). Narayan and Smith
(2004) tested Granger causality between HC and real income in a
cointegrated VAR processes for China and found unidirectional Granger
causality running from HC to real income in the LR while in SR, one-way
Granger causality running from real income to HC. On one side, EGr
supply the resources to improve HD and on the other side, HD in the form
of improvements in quantity and quality of labour force contributes and
accelerates EGr. Judson (2002) states that even though conventional
wisdom does support a positive correlation between output growth and HC,
the empirical results are mixed, i.e., the positive correlation between
growth and HC has been found exceptionally rather than as a rule. So,
examining the causality between HD and EGr for Pakistan is the need of
hour.
The contribution of EGr to HD is mainly through activities of
households, government, NGOs and other civil society. The same level of
income can contribute differently to HD. This depends upon the
allocation of the income among households, government activities and on
the behaviour and priorities of these sectors and institutions.
Household disposable income directly contributes to the promotion of HD
by making more expenditure on basic food, health and education. Poor
households and female's control over cash income households are
found to make more expenditures out of their income on HD related items
than those with high income group and of male's control over cash
income groups. Poor families and poor households are seen to spend less
on education item of HD.
Birdsall (1985), Behrman and Wolfe (1987a, 1987b), King and Lillard
(1987), Deolalikar (1993) and Alderman, Behrman Khan, Ross and Sabot
(1996a, 1996b) have empirically proved for many countries including
India and Pakistan that family earnings changes exerted a positive
impact on child's schooling. On the other hand, improvements in HD
depend upon government's expenditure on social sector and how much
of the total public expenditure goes to HD related items especially on
basic education and health. On the other hand, NGOs do contribute to HD
by deriving resources from both domestic as well as foreign private and
government donations. The effectiveness of NGOs varies from country to
country. In some regions of the world, their role is just supplementary,
but in other few countries (e.g. BRAC and Grameen Bank in Bangladesh,
The Harambee School in Kenya and Peru's Comedores Populares), NGOs
appeared as a major facto)" in the improvement and enhancement of
HD (Riddell, Robinson, deConinck, Muir and White, 1995). Ghazali
Education Trust (GET), Beaconhouse education system and Zindagi seem to
improve and enhance HD in Pakistan.
High level of HD (in the form of improved health, nutrition and
quality education) can contribute more to EGr. High level of HD affects
the EGr by enhancing people's choices, capabilities, creativity and
hence productivity. Better health and quality labour force education are
the main determinants of exports and output growth. They also help in
the proper and better utilisation of foreign borrowed technology very
effectively. On one hand, quality secondary and tertiary education
proved it to facilitate the acquisition of skills and managerial
capabilities and on other hand, its contribution towards technological
capability and technical change in industries is obvious and very
important. The role of better health and quality education cannot be
overlooked in the exports growth that affects the EGr. So there exists a
positive significant correlation between EGr and exports. Ranis, Stewart
and Ramirez (1997) explored the linkage between HD and EGr for the time
period 1970-92. Their finding implied that, although both EGr and HD
should be promoted jointly, but HD be given sequential priority.
According to Narayan and Smyth (2004), exports can promote HC
accumulation in developing countries through three main channels.
Firstly, exports, being the important component of trade help in
facilitating transmission of technology to developing countries from
developed countries. Transfer of technology is biased in favour of
skilled labour and induces investment in HC [Pissarides (1997)].
Secondly, export is a source of learning by doing. Thirdly, the
diffusion of soft and hard technologies including marketing, production
and management expertise can be promoted by exports which in turn
accelerate the productivity of factors of production such as labour and
capital [Grossman and Helpman (1991); Kim (1998)]. Improvements in HC
can Granger cause exports. Improvements in HC stock can increase the
quality of workforce that, in turn, raises the labour productivity and
accelerates further exports and hence EGr [Chuang (2000)]. Gould and
Ruffin (1995), Hanson and Harison (1995) and Stokey (1996) conducted
studies for different countries and for different time periods and
suggested that HC accumulation was accelerated and promoted by trade and
rice versa.
Expansion in exports can increase growth through a variety of
channels. 'Export- led growth hypothesis' is one of them.
'Export-led growth hypothesis' postulates that exports
expansion is vital to EGr. It raises investment and employment
opportunities, production efficiency, technological advancement, and
hence EGr. On the other way, EGr can also increase exports [see Ahmed
(2001); Afzal, Rehman, and Rehman (2008)]. Jung and Marshall (1985)
found that the internally generated economic growth better explained
exports growth. New trade theories also support growth causing exports
hypothesis [e.g. see Helpman and Krugman (1990)]. It is concluded from
the above discussion that high exports economies also perform well in
their growth rate of GDP. Such type of linkages between EGr and exports
induce the researchers to examine the causality between the two.
Empirical analysis based on bivariate causality framework on both
the hypotheses has provided the diverse results. However, a few studies
have been found in literature that tested causality between the HD and
EGr. The studies that tested the bivariate causality between HD and EGr
include De Meulemuster and Rochat (1995) for six developed countries
including Sweden, UK, Japan, France, Italy and Australia, In and
Doucouliagos (1997) for US, and Asteriou and Agiomirgianakis (2001) for
Greece. All the studies conducted for developed countries provide mix
results about unidirectional and bidirectional causality. Lee and Lee
(1995) utilised secondary school achievement test score as a direct
measures of HC for 17 developed and developing countries including
India, Iran and found that the higher initial HC stock per worker led to
higher economic growth per worker. A few studies have been carried out
for the developing countries. A study conducted for Pakistan by Khan, et
al. (1991) found one-way Granger causality running from literacy to
productivity for Pakistan. Narayan and Smyth (2004) tested temporal
bivariate causality between real income and HC in a co-integrated VAR
processed for China for the time period 1960 to 1999 and found the
evidence of log run Granger causality running from HC to real income
while the short run one way causality running from real income to HC was
observed.
A few studies also exist in literature that has tested the causal
link between exports and EGr by including HD as a third variable in a
multivariate framework. Chuang (2000) tested the casual linkages among
exports, HC and EGr for Taiwan for the period 1952-95. He found the
bidirectional casualty between exports and HC accumulation. HC based
endogenous growth theory and export-led growth hypothesis were found
valid in case of Taiwan. Narayan and Smyth (2004) employed
co-integration and error correction techniques to test the casualty
among real income, real exports and HC stock for China using annual data
over the period 1960 to 1999 and found evidence of co- integration among
real income, real exports and HC when real exports served as dependent
variable and HC and real income are taken as independent variables. They
found (i) the evidence of short run bi-directional Granger causality
between HC and real exports, (ii) unidirectional Granger causality
running from real income to HC and (iii) no evidence of Granger
causality between real exports and real income. Furthermore their
results do not support the 'export-led growth hypothesis'.
In the present study, the validity of 'growth-driven
exports' and 'export-led growth hypotheses' are examined
in case of Pakistan by including HD as a third variable. In addition,
the validity of human-based endogenous growth theory is also tested for
Pakistan.
III. MODEL SPECIFICATION, METHODOLOGY AND DATA SOURCES
This study employs annual time series of real gross domestic
product (RGDP), real exports (RX), physical capital (PC) and human
development (HD) in Pakistan for the period 1970-71 to 2008-09, drawn
from various issues of Pakistan Economic Survey and Annual Reports of
State Bank of Pakistan. A time series for HDI for the period 1970-71 to
2008-09 has been constructed by using UNDP methodology developed in
1999- 2000. The variables GDP and exports have been converted into real
terms by GDP deflator and export prices, respectively. Where as physical
capital (PC) has been measured by the real fixed capital formation.
Keeping in view the theoretical postulates of the relationship
among RGDP, PC, RX and HD the following models have been specified as:
ln RGDP =f(/n PC, In RX, In HD) ... ... ... ... ... (1)
ln RX = f (In PC, In RGDP, In HD) ... ... ... ... ... (2)
ln HD = f (In PC, In RGDP, In RX) ... ... ... ... ... (3)
Where In stands for natural logarithm, and
RGDP = Real GDP; a measure of economic growth: current GDP at
market prices is deflated by GDP deflator.
PC = Since time series data on capital stock is not directly
available for Pakistan. Physical capital is proxied by real value of
gross fixed capital formation. GFCF deflated by GDP deflator; a proxy
used to measure real physical capital. "Fixed capital formation
measures both private and public national investment"
[Balasubramanyam, Salisu and Sapsford (1996); Hansen and Rand (2006)].
This proxy for real PC has been used by Kohpaiboon (2004) and Mansouri
(2005).
RX = Real exports; ah important component of trade and is
considered as important ingredient of progress and prosperity of a
nation. Here exports ate converted into real exports by using unit value
indexes of exports.
HD = HDI; a composite measure of human development.
The Model 1 is a kind of production function augmented by RX and
HD. The relationship among variables under consideration is expected to
be positive.
In literature human capital development was measured by using
different proxy variables like labour force employment, average years of
schooling, educational attainments, the number of employed workforce
with tertiary education, public expenditures on education and health,
R&D expenditures, secondary school achievement test scores and
literary rates etc., however, these proxy variables cannot fully capture
the notion of HD and has been questioned and criticised [e.g. see Judson
(2002)]. In order to
capture the effect of HD on EGr, a direct and more reliable measure
of HD is needed. In this study, a composite measure of HD known as HDI
is constructed by using UNDP latest methodology for the period 1970-71
to 2008-09.
Several methods such as residual based Engle-Granger (1987) test,
Johansen (1988), Johansen-Juselius (1990), Gregory and Hansen (1996),
Saikkonen and Lutkepohl (2000), and ARDL approach to cointegration are
available in literature. Since this study aims at detecting SR and LR
linkages between EGr, HD and exports, it uses a relatively new
estimation technique known as Bounds Testing Approach to Cointegration
within ARDL framework. A brief description of ARDL approach is given
below:
Autoregressive Distributive Lag (Ardl) Approach to Cointegration
The Proposed ARDL approach to cointegration is developed by Pesaran
and Pesaran (1997), Pesaran and Shin (1995 and 1998) and further
advanced by Pesaran, et al. (2001). It is a unification of
autoregressive models and distributed lag models. In an ARDL model, a
time series is a function of its lagged values and current and lagged
values of one or more explanatory variables. There are several benefits
for the use of ARDL approach to cointegration. Bivariate cointegration
test and multivariate cointegration techniques given by Stock and Watson
(1988), Johansen (1988, 1991) and Johansen and Juselius (1990) perform
better for large samples. However, ARDL technique to cointegration is
more appropriate for small samples (30 to 80 values). ARDL technique to
cointegration not only can distinguish dependent and explanatory
variables (i.e. it avoids the problem of endogeneity) but also ARDL
method can simultaneously estimate the LR and SR components of the
model. This technique also removes the problems related to omitted
variables and autocorrelation. The parameter estimates obtained from the
ARDL approach are unbiased and efficient because they avoid the problems
that may arise due to serial correlation and endogeneity [Pesaran, Shin,
and Smith (2001)].
A dynamic error correction model (ECM) through linear
transformation can be derived from ARDL [Banerjee, et al. (1993)] that
permits to draw inference for LR estimates that is not available in
other alternative cointegation procedures [Sezgin and Yildirim (2002)].
ARDL approach to cointegration has some superiority over Engle and
Granger (1987) single equation cointegration technique. The ARDL method
to cointegration can be applied irrespective of whether the regressors
are of I(0), I(1) or mutually integrated but it is still prerequisite
that the dependent variable is of I(1) in levels and none of the
explanatory variables is I(2) of higher order. In ARDL procedures to
cointegration, different variables may have diverse optimal number of
lags, which in other standard cointegration techniques like Johansen
type approaches, is not possible. Apart from the superiority of ARDL
model over other cointegration techniques, this study preferred to apply
ARDL approaches to cointegartion because of the following two main
reasons:
(i) Bounds test procedure's results are robust in case of
small of finite samples (i.e. 30 to 80 observations as is the case in
this study).
(ii) Real income and real exports variables are of I(1), while HDI
is I(0) or fractionally integrated.
All these justify the application of ARDL model to determine the
relationship among EGr, HD and exports in Pakistan.
Bounds Testing Approach to Cointegration
The 2nd stage procedure of this paper involves the testing of the
existence of short-run (SR) and long-run (LR) relationship between real
gross domestic product (RGDP), real exports (RX), physical capital (PC)
and human capital (HD) within a multivariate framework. To examine the
existence of SR and LR relationship the error-correction version of ARDL
model of Equations 1, 2, and 3 by following Pesaran and Pesaran (1997:
397-9) and Pesaran and Shin (1999), can be used as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)
The coefficients (a, b, c, d, e) of part first of Equations (4, 5
and 6) measure the SR dynamics of the model whereas [delta]s represent
the LR relationships.
ARDL model uses a three-step procedure:
(a) Dynamic analysis.
(b) Long-run relationship.
(c) ECM analysis.
The 1st step in the ARDL approach to cointegration is to examine LR
relationship among the variables by carrying out familiar F-test on the
differenced variables components of Unrestricted Error Correction
Mechanism (UECM) model for the joint significance of the coefficients of
lagged level of the variables. In this first step, the regression
equation estimated for the dependent variable RGDP (Y) is defined as
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)
To create error correction mechanism in this step, the first lag of
the level of each variable is added to the Equation (7) and a variable
Addition Test is conducted by calculating F-test on the joint
significance of all the added lagged level variables.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8)
The null hypothesis of no cointegration for RGDP against
alternative hypothesis is tested by taking into account the UECM model
as:
[H.sub.0]: [[gamma].sub.1Y] = [[gamma].sub.2Y] = [[gamma].sub.3Y] =
[[gamma].sub.4Y] = 0
H1: None of the coefficients ([[gamma].sub.1Y], [[gamma].sub.2Y,
[[gamma].sub.3Y], [[gamma].sub.4Y]) = 0
This is denoted as [F.sub.Y](Y|PC, RX, and HD).
In Equation (2) where exports is the regress and, the null
hypothesis of no cointegration for exports against alternative
hypothesis of cointegration is as under:
[H.sub.0]: [[gamma].sub.1RX] = [[gamma].sub.2RX] =
[[gamma].sub.3RX] = [[gamma].sub.4RX] = 0
[H.sub.1]: None of the coefficients ([[gamma].sub.1RX],
[[gamma].sub.2RX], [[gamma].sub.3RX], [[gamma].sub.4RX]) = 0 and is
denoted as FRX (RXI Y, PC, HD)
In Equation (3), where HC is the dependent variable, the null
hypothesis of no cointegration against alternative hypothesis of
cointegration is as under:
[H.sub.0]: [[gamma].sub.1HD] = [[gamma].sub.2HD] =
[[gamma].sub.3HD] = [[gamma].sub.4HD]= 0
[H.sub.1]: None of the coefficients ([[gamma].sub.1HD],
[[gamma].sub.2HD], [[gamma].sub.3HD], = [[gamma].sub.4HD]) = 0
and is denoted as [F.sub.HD](HD| Y, PC, RX)
The above hypotheses can be tested by applying standard
F-statistic. However, the asymptotic distribution of this F-statistic is
non-standard irrespective of whether the variables included in the model
are I(0) or I(1). The value of F depends upon (i) number of explanatory
variables, (ii) intercept and/or a trend of ARDL, and (iii) sample size.
Pesaran, et al. (2001) have "tabulated two sets of appropriate
critical values. One set assumes all variables are I(1) and another
assumes that they are all I(0). This provides a band covering all
possible classifications of the variables into I(1) of I(0) or even
fractionally integrated." Critical values of Pesaran, et al. (2001)
are valid for large sample while Narayan (2005) and Tuener (2006) have
provided two sets of critical values for small sample size (30 to 80
observations).
The value of F-statistic found from the data is then compared to
the non- standard two sets of critical bound values developed by
Pesaran, et al. (2001). This comparison is made as follows. If the
calculated value of F-statistic lies outside the critical bounds then a
conclusive decision about cointegration can be made having no knowledge
of order of integration of the regressors. If the calculated value of
F-statistic is bigger than the asymptotically upper bound value,
cointegration will establish. On the other hand, cointegration is not
established if the calculated value of F-statistic is smaller than the
critical lower bound value. The F-test becomes inconclusive about
cointegration if the value of F-statistic lies between the critical
lower and upper bounds values. In such cases, the order of integration
of the variables under consideration is checked by following the
procedure developed by Johansen and Juselius (1990) for detection of
cointegration. When the order of integration of the variables under
consideration is known already and the variables are of I(l) upper
bounds are used to make the decision. The decision of optimum lag length
can be made by using either Akaike Information Criteria or
Schwartz-Bayesian criteria or R-bar criteria or Human-Quinn criteria. In
case of inclusive situation, use of ECM version of ARDL model is
regarded as the efficient way of establishing cointegration by Kremers,
et al. (1992) and Bannerjee, et al. (1998). Cointegration is established
if the ECM coefficient is negative and highly significant.
Stability of the model is checked as the second step in ARDL bounds
testing procedure. After establishing cointegration, the model based on
Equation (4) to Equation (6) is estimated by using an appropriate lag
criterion such as Akaike Information Criterion or Schwarz Bayesian
criteria. Completion of second stage gives estimates of LR elasticities
as well as enables the use of CUSUM and CUSUM Sum of Squares tests to
the residuals of Equation (4) to Equation (6) for testing the stability
of LR elasticities along with SR dynamics.
To establish the stability of SR estimated coefficients of the
first differenced variables as well as LR parameters, CUSUM and CUSUM
Sum of Squares tests proposed by Brown, et al. (1975) were employed by
Pesaran and Pesaran (1977). The statistics of CUSUM and CUSUM Sum of
Squares are updated recursively and are plotted against the break points
after breaking the sample period. The coefficient estimates are called
stable if the plot of CUSUM and CUSUM Sum of Squares stay within 5
percent significance level (portrayed by two straight lines based on
equations developed by Brown, et al. (1975).
Though the existence of LR relationship among the variables is of
very interest, it may be even more relevant to examine the SR dynamics.
So the next step of the analysis is the formulation of ECM, as has been
done previously in the 2nd stage procedure of the paper. ECM best
describes the SR dynamics consistently with the LRrelationship. The
coefficient of ECM(-I) term known as adjustment parameter indicates
speed of adjustment and the negative sign and its highly statistical
significant confirms cointegration and determines the LRcausal effect.
The negative sign of the adjustment parameter also ensures stability of
the model. The negative and statistical significant sign of the
coefficient of [ECM.sub.t-1] also implies that the series me
non-explosive and LR equilibrium is attained.
IV. EMPIRICAL RESULTS AND ANALYSIS
Unit Root Tests
Using the ARDL technique avoids the classification of variables
into I(1) or 1(0) of mutually integrated and there is no need for unit
root pre-testing unlike other standard cointegration tests [Pesaran, et
al. (2001)]. Sezgin and Yildirim (2002) and Ouattara (2004) reported
that the calculated F-statistic provided by Pesaran, et al. (2001)
becomes invalid in the existence of I(2) variables. Therefore, testing
the unit root in the ARDL procedures is necessary to avoid the
possibility of spurious regression and to ensure that not a single
variable is of I(2) of above. For this purpose, the order of integration
of the variables under study was tested using Augmented Dickey-Fuller
(ADF), Pbillip-Perron (PP), Dickey-Fuller Generalised Least Square
(DF-GLS) and Ng-Perron tests. Phillip-Perron test has the extra merit
over the ADF test, i.e. it has been adjusted to capture serial
correlation. This study also employed the Ng-Perron (2001) modified unit
root test because it is considered more suitable for small samples than
the traditional tests. Applying the Ng- Perron unit root test, the null
hypothesis of unit root is not over rejected [Ng-Perron (2001); Sinha
Dipendra (2007); Omisakin (2008)]. Different unit root tests results for
the variables under consideration are reported in Tables 1, 2 and 3. The
results reveal that the log of real GDP, PC and RX series are not
stationary in their level but ate stationary at their first difference
at 1 percent level of significance in PP, DF-GLS and ADF unit roots
tests, while log of real GDP is stationary at its first difference at 10
percent level of significance according to Ng-Perron test. Table 1 also
indicates that log of HDI series is stationary at its level form at 2
percent and 1 percent level of significance according to ADF and PP unit
root tests but Tables 2 and 3 reveal that HDI is stationary at its first
difference with constant and intercept according to both DF-GLS and
Ng-Perron unit root criteria. Therefore, it is concluded that log of
real GDP, PC, RX are integrated of order 1, i.e. I(1), while log of HDI
series is integrated of order 0 with constant, i.e. 1(0) in both ADF and
PP unit roots tests. HDI series is of I(1) at both Ng-Perron and DF-GLS
criteria. AII the unit root tests ensure that none of the variables is
I(2) or higher order.
Since the results presented in Tables 1, 2 and 3 indicate that all
the variables expect HD are of I(1). According to ADF and PP tests, HD
is integrated of order 0, while according to DF-GLS and Ng-Perron Unit
Roots HD is integrated of order 1. None of the variables is of I(2). So
this study applied the ARDL procedure with such mixed integrated result
(i.e. of I(1) of I(0) or fractionally integrated).
Bahmani-Oskooee and Nasir (2004) have argued that the first step in
any cointegration technique is "to determine the degree of
integration of each variable in the model", but different unit root
tests yield different results. For example, while applying the
traditional ADF and PP tests one may mistakenly conclude that a unit
root is present in a series that is actually stationary around a 1-time
structural break [for further details, please consult Perron (1989);
Perron (1997)]. This study uses ARDL procedures to avoid this unit root
related problem.
Cointegration
Following the first step in the ARDL approach to cointegration,
this study examines LR relationship between the variables by conducting
partial F-test. This test is sensitive to the number of lags used on
each first differenced variable [Bahmani-Oskooee and Brooks (1999)]. In
this study lags up to four periods have been imposed on each first
differenced variable. The estimated F-statistic for RGDP, RX and HD of
Models 1, 2 and 3 are reported in Table 4.
It is at least one F-value that is higher than the upper critical
value, supporting cointegration among RGDP, RX, PC and HD when HD is the
dependent variable. The null hypothesis of no cointegration is not
rejected when real GDP and RX are serving as dependent variables in
Models 1 and 2 respectively because at least one F-value is not higher
than the upper critical bounds value. However, the results at this stage
are considered preliminary and this study seeks for more evidence of
cointegration in the second stage of the analysis when an appropriate
lag selection criterion is employed. Once cointegration among the
variables of interest is established, then models 1, 2, and 3 were
estimated by using ARDL approach.
By using ARDL approach, Equation (4) was estimated with and without
HD variable in addition to PC and RX. The estimated results are
presented in Table 5. The results of estimated dynamic ARDL models
presented in Table 5 clearly support the fact that the RX is not
significant in explaining the real GDP in Pakistan. This also seems to
refute the 'export-led growth hypothesis' in Pakistan.
The stability of the LR coefficients together with SR dynamics was
tested by plot of cumulative sum of recursive residuals (CUSUM) and by
plot of cumulative sum of squares of recursive residuals (CUSUM SQUARE)
tests. The results of CUSUM and CUSUM SQUARE tests proposed by Brown,
Durbin and Evans (1975) reside within a 5 percent level of significance
(portrayed by two straight lines). This reveals the significant and
stable relation among the variables. This also indicates that there is
no evidence of any significant structural instability (Figures 1 and 2).
[FIGURE 1a OMITTED]
[FIGURE 1b OMITTED]
[FIGURE 2a OMITTED]
[FIGURE 2b OMITTED]
Once the stability and LR relationship have been established, the
results of LR coefficients using ARDL approach are presented in Table 6.
The LR elasticity coefficients of PC and HD in ARDL (1, 2, 0, l)
model are positive and statistically significant, indicating that both
PC and HD are enhancing economic growth in the LR. HD has the highest
positive and significance effect among other explanatory variables on
EGr in the LR. This is consistent with the findings of Emadzadeh, et al.
(2000), Nili and Nafisi (2003), Mohamadi (2006), Dargahi and Gadiri
(2003), and Komijani and Memernejad (2004). RX have positive but
insignificant effect on EGr in the LR.
The next stage of analysis is based on error correction model
(ECM). After examining LR relationships among variables, the SR dynamics
of these variables can be determined by error correction representation
of ARDL model based on Equation (4). ECM specification for ARDL (1, 2,
0, 1) model is reported in Table 7.
The coefficient of lagged error correction term reveals how much
rapidly variable returns to equilibrium and it must be statistically
significant with negative sign. The absolute value of ECM(-I) indicates
speed of adjustment to return to equilibrium and the negative sign shows
convergence in the SR dynamic model. The negative and highly significant
sign of lagged error correction term (ECM(-I)) is also a more efficient
way of establishing cointegration and LR causality. The coefficient of
ECM(-I) for ARDL (1,2,0,1) in Table 7 is -0.227 and this means that in
each period, 22.7 percent of shocks can be justified as a LR trend. 22.7
percent of the deviation from equilibrium is eliminated within one year.
The small absolute value of coefficient of ECM(-1), i.e. 22.7 percent,
implies that RGDP is not rapidly adjusted to changes in the LR
equilibrium component. This coefficient of [EC.sub.t-1] in this model is
negative and significant at 99 percent level of confidence. It implies
that, in Pakistan, EGr, PC, exports and HD are cointegrated, which is
not in line of results presented in Table 4 for cointegration. Moreover,
the results presented in Table 7 refute any SR significant of the RX and
HD in explaining real GDP. The significant effect of PC on EGr is found
in the both SR and LR. The effect of HC is more pivotal in explaining
real GDP than both RX and PC in LR as had been expected. So it is
recommended that the Government of Pakistan should continue its quest
for HC promotion policies. HC-based endogenous growth theory seems valid
only in LR. The effect of RX on RGDP is positive both in the LR and SR
but not statistically significant. This finding does not seem to support
the validity of 'export-led growth hypothesis' in case of
Pakistan. As a conclusion LR elasticity of PC, RX and HD on RGDP are
found to be bigger and more significant than SR counterparts. Tables 5,
6 and 7 also reveal that with the inclusion of HDI as a measure of HD
reduces the PC share in real GDP (i.e. from 109 percent to 42 percent in
LR and from 6.2 percent to 1.5 percent in SR) and boosts up over all
share of capital (both PC and HC) in determining real GDP in the LR
(i.e. from 109 percent to 126.4 percent) whereas it improves the
robustness of the regression model. The negative LR effect of RX on RGDP
also became positive with the inclusion of HD variable. This finding
justifies the inclusion of HD as a third variable in the model.
To assess Equation (5), concerning the effects of RGDP, HD, PC on
RX, it was estimated by using ARDL approach. The results of dynamic ARDL
(1, 1, 0, 0) of Model 2 are reported in Table 8.
The results of the dynamic ARDL (1, l, 0, 0) model for RX presented
in Table 8 support the hypothesis that both PC and RGDP are significant
in the explanation of RX in Pakistan. The results in Table 8 also seem
to support the 'growth-driven export hypothesis' in case of
Pakistan. The results of CUSUM and CUSUM SQUARE tests exist within a 5
percent level showing the significant and stable SR and LR relation
among the variables (Figure 3).
[FIGURE 3(a) OMITTED]
[FIGURE 3(b) OMITTED]
After examining the stability and establishing LR relationship, the
results of LR coefficients using ARDL Model 2 for the variable RX are
presented in Table 9.
The LR elasticity coefficient of RGDP in Table 9 is positive and
statistically significant at 95 percent level of confidence, indicating
that the RGDP will enhance RX in the LR. The LR elasticity coefficient
of HD on RX is negative and highly insignificant. The SR dynamic
coefficients of RGDP, PC and HD for RX can be determined by error
correction representation of ARDL model based on Equation (5) by
repeating the third stage of the model. Its results are reported in
Table 10.
From Table 10, it is obvious that the ECM(-1) has a correct sign,
i.e. negative and its coefficient is statistically significant. It
implies that, in Pakistan, RX, PC, HD and EGr are cointegrated when real
exports served as dependent variable. This result is not in line with
the results in Table 4 for cointegration. The absolute value of
coefficient of ECM(-1) in Table 10 is 0.445 and this implies that in
each period, about 44.5 percent of shocks can be justified as LR trends.
It also implies that 44.5 percent of the previous year's
disequilibrium in RX from its equilibrium path will be corrected in the
recent year. The positive and significant effect of real GDP on exports
is supported in both the LR and the SR at 95 percent level of
confidence. This supports the validity of 'growth-driven exports
hypothesis' in Pakistan. Furthermore, long run elasticity of real
GDP on RX is found bigger than its SR counterpart. This implies that the
impact of increasing real GDP on RX is higher in the LR than in the SR.
The highly insignificant and negative effect of HD on exports is found
both in LR and SR dynamic models. This might be the outcome that exiting
stock of knowledge and skill do not match with desired technology for
enhancing RX. This also might be the result that large portion of
Pakistan's exports still constituted primary and semi-manufactured
commodities.
To examine Equation (6), concerning the effect of RGDP, RX and PC
on HD. it was estimated by using ARDL approach. The results of dynamic
ARDL (3, 2, 0, 3) of model 3 are reported in Table 11.
The results of the estimated dynamic ARDL (3, 2, 0, 3) model for HD
presented in Table 11 indicate that lagged values of HD itself, lagged
values of RGDP and lagged values of RX are helpful and significant in
the explanation of HD. Since the results of CUSUM and CUSUM SQUARE tests
proposed by Brown, et al. (1975) exist within a 5 percent level show the
significant and stable relation among the variables under consideration
(Figure 4). There is ample evidence of structural stability in the
model.
[FIGURE 4(a) OMITTED]
[FIGURE 4(b) OMITTED]
After having tested the stability and LR relationship among
variables, the results of LR coefficients using ARDL approach are
presented in Table 12 below.
The LR elasticity coefficient of RGDP is positive and highly
statistically significant, indicating that RGDP will enhance HD in the
LR. The elasticity coefficient of RX is positive and highly
insignificant.
After examining LR relationship among HD, RX and RGDP, the SR
dynamics of these variables can be determined by error correction
representation of ARDL model based on Equation (6). The results are
reported below in Table 13.
The absolute value of coefficient of ECM(-1) in Table 13 is 0.559,
indicating a moderate speed of adjustment to equilibrium following SR
shocks. This also means that 55.9 percent of the disequilibrium caused
by previous period shocks, converses back to equilibrium and this also
means that in each period, 55.9 percent of shocks can be justified as a
LR trend. The coefficient of [EC.sub.t-1] in the model is negative and
significant at 97 percent level of confidence. It implies that, in
Pakistan, exports, PC, EGr, and HC are cointegrated. This finding about
cointegration among the variables, when HD is the dependent variable is
in accordance with the results presented in Table 4 for cointegration.
The positive and significant effect of real GDP on HD is supported both
in LR (confidence at 99 percent) and SR (confidence at 96 percent). The
effect of two period lagged RX on HD is negative and highly significant
indicating the reverse trend and behaviour of both the labour force and
exporters about improving their HC stock in the SR.
In short, the ARDL results indicate that (i) the inclusion of HD as
ah explanatory variable in addition to PC, RX in augmented growth
function raises the robustness of the model; (ii) cointegration among
real GDP, PC, RX and HD raises their significance when HD serves as a
dependent variable; (iii) RX do not support and promote both the real
GDP and HD both in SR and LR; (iv) HD promotes real GDP only in the LR
and at the same time it does not explain RX in Pakistan; and (v) real
GDP proves itself to be a significant source of explaining and promoting
both the RX and HD both in SR and LR.
Diagnostic Tests
Some other diagnostic tests were used for serial correlation, model
specification, heteroskedasticity and conflict to normality that is
based on a test of skewness and kurtosis of residuals. All the models
satisfied and qualify all the above diagnostic tests.
Toda-Yamamoto Augmented Granger Causality Test
The causal linkages among real GDP, PC, RX and HD are being
investigated by following the Granger causality procedures adopted by
Toda and Yamamoto (1995) and interpreted and further expanded by
Rambaldi and Doran (1996) and Zapata and Rambaldi (1997). Zapata and
Rambaldi (1997) argue that this test needs no prior knowledge of the
cointegration among the variables and the usual lag selection scheme to
the systems can still be applied in a case where there exists no
cointegration or the rank conditions and stability are not satisfied
"so long as the order of integration of the process does not exceed
the true lag length of the model" [Toda and Yamamoto (1995), p.
225]. The attractiveness of applying Toda and Yamamoto (1995) technique
to test Granger causality lies in its simplicity to apply and ability to
overcome many a shortcomings of other alternative cumbersome econometric
procedures such as developed by Toda and Phillips (1993) and Mosconi and
Giannini (1992). Toda-Yamamoto Augmented Granger Causality Test applied
Modified WALD test (MWALD) for restrictions on the parameters of a
VAR(k), where k is the lag length in the system of equations. This test
statistic follows a [chi square]-distribution when VAR (k + [d.sub.max])
is estimated. Here, d, .... shows the maximum order of integration
likely to happen in the system of equations. Here, we utilise Seemingly
Unrelated Regression (SUR) because it has been proved by Rambaldi and
Doran (1996) that MWALD test for testing Granger causality can be easily
applied by using SUR. One of the advantages of utilising SUR is that it
also takes care of the possible simultaneity bias in the system of
equations. One of the characteristics of VAR model is that it permits
the researcher to test the direction of causality. Use of VAR can also
overcome the problem of simultaneity bias. In VAR, all the variables are
taken as endogenous variables.
To examine the causality between real GDP, PC, RX and HD, this
study utilised the Toda-Yamamoto Augmented Granger Causality Test. The
following system of equations is being estimated to investigate the
Augmented Granger causality test:
[Y.sub.t] = [[alpha].sub.1] + [3.summation of over (i=1)]
[[beta].sub.1i] [Y.sub.t-i] + [3.summation of over (i=1)]
[[gamma].sub.1i] [PC.sub.t-i] + [u.sub.1t] ... ... ... ... ... (9)
[PC.sub.t] = [[alpha].sub.2] + [3.summation of over (i=1)]
[[beta].sub.2i] [Y.sub.t-i] + [3.summation of over (i=1)]
[[gamma].sub.2i] [PC.sub.t-i] + [u.sub.2t] ... ... ... ... ... (10)
[Y.sub.t] = [[alpha].sub.3] + [3.summation of over (i=1)]
[[beta].sub.3i] [Y.sub.t-i] + [3.summation of over (i=1)]
[[gamma].sub.3i] [RX.sub.t-i] + [u.sub.3t] ... ... ... ... ... (11)
[RX.sub.t] = [[alpha].sub.4] + [3.summation of over (i=1)]
[[beta].sub.4i] [Y.sub.t-i] + [3.summation of over (i=1)]
[[gamma].sub.4i] [HD.sub.t-i] + [u.sub.4t] ... ... ... ... ... (12)
[Y.sub.t] = [[alpha].sub.5] + [3.summation of over (i=1)]
[[beta].sub.5i] [Y.sub.t-i] + [3.summation of over (i=1)]
[[gamma].sub.5i] [HD.sub.t-i] + [u.sub.5t] ... ... ... ... ... (13)
[HD.sub.t] = [[alpha].sub.6] + [3.summation of over (i=1)]
[[beta].sub.6i] [Y.sub.t-i] + [3.summation of over (i=1)]
[[gamma].sub.6i] [HD.sub.t-i] + [u.sub.6t] ... ... ... ... ... (14)
The above three systems of two equations each is estimated by SUR
method. To explore that PC does not Granger cause GDP, the null
hypothesis will be [H.sub.0[: Y]i = 0 where [[gamma].sub.1i] are the
coefficients of [PC.sub.t-i], i = 1, 2, 3 ([H.sub.0]: [[gamma].sub.11] =
[[gamma].sub.12] = [[gamma].sub.13] = 0) in the first equation of the
system. Likewise the other null hypothesis for second equation is
[H.sub.0]: [[beta].sub.2i] = 0 where [[beta].sub.2i] are the
coefficients of [GDP.sub.t-i], i = 1, 2, 3 ([H.sub.0]: [[beta].sub.21],
= [[beta].sub.22]: = [[beta].sub.23] = 0) that is the GDP does not
Granger cause PC. This was carried out by means of a Wald test with the
null hypothesis that the values of the estimated coefficients (132i and
Y2i) are zero. The other hypothesis for remaining system of two
equations was also formulated in the same manner. The results of the
Toda-Yamamoto test of augmented Granger causality are given in Table 14.
The statistical results of bivariate causality indicate that the
null hypothesis of no Granger causality between PC and real GDP is
rejected at 1 percent level of significance. Similar hypothesis
regarding no Granger causality between real GDP and PC is rejected at 1
percent level of significance. These results support the presence of
bidirectional causality between real GDP and PC. To test causality
between real exports (RX) and real GDP (RGDP), the system of Equations
(11) and (12) has been estimated by SUR. The null hypothesis that RX do
not Granger cause RGDP cannot be rejected at 95 percent level of
confidence. On the other hand, the hypothesis that RGDP does not Granger
cause exports can be rejected at 95 percent level of confidence. It was
found that there is one way causality running from RGDP to RX in a
bivariate case. The causal flow from real output to real export is
termed as 'growth-driven exports'. Exports are thus not seen
as the significant source of EGr in Pakistan. Similarly another
bivariate analysis between RGDP and HD (Equations 13 and 14) also
indicates a unidirectional causality running only from RGDP to HD. In
conclusion, in case of bivariate analysis, 'export-led growth
hypothesis' is not seen to be valid while bivariate results support
the validity of 'growth-driven hypothesis'. Human
capital-based endogenous growth theory does not seem to be valid in case
of bivariate causality analysis.
Now moving to trivariate system of equations (Equations (15), (16)
and (17)) to analyse the Augmented Granger Causality for the variables
RX, real GDP and HD. As found above in bivariate analysis, RX do not
Granger cause RGDP at 95 percent level of confidence, whereas RGDP does
Granger cause RX at 99 percent level of confidence in case of trivariate
analysis. Similarly no Granger causality between HD and RGDP and between
HD and RX was established in trivariate analysis. In conclusion, in case
of trivariate analysis, 'export-led growth hypothesis' is not
valid whereas 'growth-driven exports hypothesis' found valid.
The 'human based-endogenous growth theory' still found not
valid in case of trivariate analysis.
To test the tetravariate causality between RGDP, RX, HD and PC,
again use of SUR was made. It was found that RGDP does Granger cause RX
while exports do not Granger cause real GDP. It was also found that PC
does Granger cause RGDP.
Regarding causality running from HD to real GDP in all cases
(bivariate, trivariate and tetravariate analyses) the null hypothesis
that HD does not Granger cause real GDP cannot be rejected. Thus, it can
be concluded that 'human capital based- endogenous growth
theory' is not valid in case of Pakistan. In sum, only
'growth-driven exports hypothesis' was found valid in case of
Pakistan.
The statistical results also reveal that causality running from
real GDP to HD does Granger cause only in bivariate analysis. This can
be explained as: when people get richer because of EGr they prefer to
send their children for higher education, knowledge and skills instead
of sending them in the labour market. Similarly, because of increase in
EGr, R&D expenditure will also grow. Finally, the statistical
results do not support the presence of Granger causality between HD and
RX. Thus, it could be the result of mismatch between existing HC stock
and the required HC to produce exportables.
[Y.sub.t] = [[alpha].sub.7] + [3.summation of over (i=1)]
[[beta].sub.7i] [Y.sub.t-i] + [3.summation of over (i=1)]
[[lambda].sub.7i] [HD.sub.t-i] + [3.summation of over (i=1)]
[[gamma].sub.7i] [RX.sub.t-i] [u.sub.7t] ... ... ... ... ... (15)
[RX.sub.t] = [[alpha].sub.8] + [3.summation of over (i=1)]
[[beta].sub.8i] [Y.sub.t-i] + [3.summation of over (i=1)]
[[lambda].sub.8i] [HD.sub.t-i] + [3.summation of over (i=1)]
[[gamma].sub.8i] [RX.sub.t-i] [u.sub.8t] ... ... ... ... ... (16)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (17)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (18)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (19)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (20)
From the results of the ARDL models and Augmented Granger Causality
tests, the qualitative results of the study are summarised in Table 15.
'Export-led growth hypothesis' cannot be supported at 95
percent level of confidence in case of Pakistan. However, the sign of
the regression coefficients of real GDP in all causality tests remained
positive. This materialised situation could be the result of trade
composition and trade policy of Pakistan. Although, Pakistan's
trade has significantly shifted from primary and semi-manufactured goods
and services to manufactured goods, but exports share of the country in
GDP remained almost the same. The results of this study are consistent
with those of Akbar and Naqvi (2000), Ahmed, et al. (2000) and Afzal,
Rehman, and Rehman (2008).
On the other hand, the null hypothesis that real GDP does not
Granger cause RX is rejected in case of Pakistan. This finding is
consistent with Doganlar and Fisunoglu (1999) and Afzal, Rehman and
Rehman(2008). In addition, the ARDL results presented in Tables 8, 9 and
10 do support the 'growth-driven export hypothesis'. Thus, it
can be concluded that 'growth-driven hypothesis' is valid in
case of Pakistan. This finding about growth-driven hypothesis is
consistent with Afzal, Rehman, and Rehman (2008). According to ARDL
model 1, HD promotes real GDP only in the LR but Granger causality
analysis do not support the human capital-based endogenous growth theory
in case of Pakistan. Thus, it can be concluded that 'human
capital-based endogenous growth theory' is not valid in case of
Pakistan.
V. CONCLUSION AND RECOMMENDATIONS
The ARDL results indicate that the inclusion of human development
as an explanatory variable in addition to physical capital, real exports
in augmented growth function raises the robustness of the model. The
ARDL Approach to Cointegration results show cointegration between
economic growth, physical capital, real exports and human development
when human development is taken as dependent variables. The statistical
results and their analysis support the 'growth-driven exports
hypothesis'. However, the hypotheses of export-led growth and human
capital based endogenous growth are not found valid for Pakistan. The
invalidity of export-led growth is also supported by the existing data
on exports to GDP ratio [Pakistan Economic Survey (2008-09), p. 61]. It
might be because of the two main reasons: firstly, the result of brain
drains of highly skilled labour force and, secondly, the outcome of
mismatch between existing human capital stock and required human capital
stock to produce and enhance real GDP. Real GDP is found to be a
significant source of explaining and promoting both the real exports and
human development both in short-run and long-run, while human capital
accumulations and real exports do not seem to accelerate real GDP in the
short-run. It is, therefore, recommended that Government of Pakistan
should allocate more resources for the promotion of human capital. There
is a need for serious effort on the part of the Government to revise its
export promotion policies. The goal of export promotion can be achieved
by restructuring export composition and by exploring new markets. The
'export- led growth hypothesis', 'growth-driven exports
hypothesis' and 'human capital-based endogenous growth
theory' may further be tested and generalised in case of Pakistan
by including other economic and non-economic variables like foreign
direct investment, terms of trade, imports, financial development,
energy, debt and debt servicing and political turmoil etc.
Comments
The paper titled 'A Dynamic Analysis of the Relationship among
Human Development, Exports and Economic Growth in Pakistan'
examines linkages among these three variables using latest econometric
techniques. Specifically it tests three hypotheses; export-led growth
hypothesis, human capital-based endogenous growth hypothesis and
growth-driven export hypothesis. The theoretical construct of this paper
is derived from endogenous growth theory which clearly supports second
hypothesis and to some extent the first one. However, results of this
paper reject the first two hypotheses and accept only the third one. So,
there seems to be some contradiction of theory and results that needs to
be explained in more detail.
Also recommendations of authors that government should allocate
more resources for the development of human capital and government
should revive export promotion schemes do not fit with the results of
this paper. Similarly, their conclusion that invalidity of export-led
growth hypothesis is due to brain drain and mismatch of existing and
desired human capital is not based on data and analysis used in this
paper.
M. Mazhar Iqbal
Quaid-i-Azam University, Islamabad.
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Muhammad Afzal <muhamlnad afzalch@yahoo.com> is Lecturer,
Department of Economics, University of the Punjab, Lahore. A. Rauf Butt
<drarbutt@yahoo.com> is Professor, School of Business and
Economics, University of Management and Technology, Lahore. Hafeez ur
Rehman <dehrehmankhan@gmail.com> is Associate Professor/Chairman,
Department of Economics, University of the Punjab, Lahore. Ishrat Begum
<ask4ishratbegum@gmail.com> is Lecturer in Political Science,
Queen Marry College, Lahore.
Table 1
Unit Root Analysis: ADF and PP
Augmented Dickey-Fuller Test (ADF)
Variables Intercept Intercept and Trend
In Y -2.4430 (0.1373) -1.3875 (0.8486)
[DELTA] In Y -4.8816 (0.0003)
In PC -0.8267 (0.7999) -2.2951 (0.4263)
[DELTA] In PC -4.8394 (0.0004)
In RX -0.8830 (0.7828) -2.7279 (0.2318)
[DELTA] In RX -6.4063 (0.0000)
In HD -3.3506 (0.0198)
Phillips-Perron Test (PP)
Variables Intercept Intercept and Trend
In Y -2.2227 (0.2019) -1.4682 (0.8230)
[DELTA] In Y -4.8786 (0.0003)
In PC -1.1908 (0.6685) -2.0294 (0.5670)
[DELTA] In PC -5.4943 (0.0001)
In RX -0.8756 (0.7851) -2.7882 (0.2101)
[DELTA] In RX -6.4077 (0.0000)
In HD -4.3180 (0.0015)
Values in parentheses are p-values.
Table 2
Unit Root Analysis: DF-GLS
Variables Dickey-Fuller Generalised
Least Square Test Statistic (DF-GLS)
Intercept Intercept and Trend
In Y 0.8684 -0.9883
[DELTA] In Y -2.8392 *
In PC 1.0012 -2.2889
[DELTA] In PC -4.4047 *
In RX 0.3168 -2.88330
[DELTA] In RX -4.4432 *
In HD 0.1771 -2.3291
[DELTA] In HD -0.4329 -8.4393 *
*, **, *** Indicate 1 percent, 5 percent and 10 percent
level of significance.
Table 3
Unit Root Analysis: Ng-Perron
Variables Mza Mzt
ln Y with constant 0.8892 0.6426
ln Y with constant and trend -2.8280 -1.0754
[DELTA] ln Y with constant -7.6100 -1.8656
In PC with constant 1.5305 1.7467
In PC with constant and trend -8.0820 -2.0026
[DELTA] ln PC with constant -17.99927 -2.9481
ln RX with constant 0.8025 0.7912
ln RX with constant and trend -11.9087 -2.3020
[DELTA] ln RX with constant -15.3935 -2.7060
ln HD with constant 0.9562 0.9936
ln HD with constant and trend -6.4746 -1.6499
[DELTA] ln HD with constant -1.0385 -0.6010
[DELTA] ln HD with constant and trend -15.0962 -2.7472
1%, Level of Significance (with constant) -13.8000 -2.5800
5%, Level of Significance (with constant) -8.1000 -1.9800
10% Level of Significance (with constant) -5.7000 -1.6200
1%, Level of Significance (with constant -23.8 -3.42
and trend)
5%, level of Significance (with constant -17.3 -2.9
and trend)
10% Level of Significance (with constant -14.20 -2.62
and trend)
Variables MSB MPT
ln Y with constant 0.7227 38.7505
ln Y with constant and trend 0.3802 28.9286
[DELTA] ln Y with constant 0.2451 3.5259
In PC with constant 1.1412 97.8290
In PC with constant and trend 0.2478 11.2968
[DELTA] ln PC with constant 0.1638 1.5470
ln RX with constant 0.9860 65.0975
ln RX with constant and trend 0.1933 8.3644
[DELTA] ln RX with constant 0.1713 1.9340
ln HD with constant 1.0391 73.8157
ln HD with constant and trend 0.2549 14.0830
[DELTA] ln HD with constant 0.5789 18.4891
[DELTA] ln HD with constant and trend 0.1820 6.0374
1%, Level of Significance (with constant) 0.1740 1.7800
5%, Level of Significance (with constant) 0.2.130 3.1700
10% Level of Significance (with constant) 0.2750 4.4500
1%, Level of Significance (with constant 0.14.4 4.03
and trend)
5%, level of Significance (with constant 0.168 5.48
and trend)
10% Level of Significance (with constant 0.185 6.67
and trend)
Table 4
Bounds Testing Approach to Cointegration: Results of F-Test for
Cointegration
Lag Length
1 2 3 4
[DELTA] {[F.sub.Y] (Y|PC, RX, HD)} 0.53 0.45 0.70 0.60
[DELTA] RX {[F.sub.RX] (RX| PC,Y, HD)} 2.27 3.17 1.56 2.94
[DELTA] HD {[F.sub.HD](HD|PC, Y, RX)} 4.48 10.17 6.37 12.35
Outcome
[DELTA] {[F.sub.Y] (Y|PC, RX, HD)} No Cointegration
[DELTA] RX {[F.sub.RX] (RX| PC,Y, HD)} No Cointegration
[DELTA] HD {[F.sub.HD](HD|PC, Y, RX)} Cointegration
Lower and upper Critical values for bounds testing ARDL for 1
percent, 5 percent and 10 percent significance levels are 3.65-
4.66, 2.79-3.67 and 2.37-3.20 respectively.
Table 5
Dynastic ARDL Model Based on Schwarz Bayesian Criterion (SBC)
(Dependent Variable = is Real CDP (Y))
ARDL (1, 0, 1) without HD
Repressor Coefficient t-ratio (P-value)
ln Y(-1) 0.94303 24.55531 (0.000)
ln PC 0.061905 2.5331 (0.017)
ln PC(-1)
ln PC(-2)
ln RX 0.041970 1.5403 (0.134)
ln RX(-1) -0.06058 -2.3030 (0.028)
ln HD
ln HD(-1)
Constant 0.32259 1.5260 (0.137)
[[bar.R].sup.2] = 0.99900, F-scat = 8734.1 (0.000), SBC
[[bar.R].sup.2] = 90.36, Serial Correlation (LM) = 0.0015
(0.969), Ramsey's Reset Test 0.8314
(0 .362), Heteroscedasticity (LM) = 0.3986
(0.528), Normality (LM) = 0.4875 (0.784)
ARDL (1, 2, 0, 1) with HD
Repressor Coefficient t-ratio (P-value)
ln Y(-1) 0.77323 10.6354 (0.000)
ln PC 0.10164 2.7938 (0.009)
ln PC(-1) -0.093395 -2.1006 (0.045)
ln PC(-2) 0.086539 2.5535 (0.016)
ln RX 0.021026 0.82891 (0.414)
ln RX(-1)
ln HD 0.082867 0.22360 (0.825)
ln HD(-1) 0.17102 2.1854 (0.037)
Constant 2.0780 2.6518 (0.013)
[[bar.R].sup.2] = F-stat = 5853.6 (0.000),
SBC = 89.69, Serial Correlation (LM) =
0.0435 (0.801), Ramsey's Reset Test =
1.6939 (0.1963), Heteroscedasticity (LM) =
0.02886 (0.591), Normality (LM) = 0.9489
(0.622)
The figures in parentheses are P-values.
Table 6
Estimated Long-run Coefficients Using the ARDL Approach and SBC
(Dependent Variable = ln Real GDP (Y))
ARDL (1, 0, 1) without HD
Regressor Coefficient t-ratio (P-value)
In PC 1.0867 1.7069 (0.098)
In RX -0.3267 -0.5416 (0.592)
In HD
Constant 5.66624 4.5700 (0.000)
ARDL (1, 2, 0, 1) with HD
Regressor Coefficient t-ratio (P-value)
In PC 0.4179 3.3753 (0.002)
In RX 0.0927 0.9940 (0.329)
In HD 0.8462 2.8523 (0.008)
Constant 9.1634 6.6331 (0.000)
Table 7
ECM Representation for Selected ARDL Model Based on SBC
(Dependent Variable = A ln Real GDP (Y))
ARDL (1, 0, 1) without HD
Lag Order
Variable 0 1 2
[DELTA] ln PC 0.0619
(0.016) **
[DELTA] ln PC(-1)
[DELTA] ln RX 0.0419
(0.133)
[DELTA] ln HD
ECM(-1): -0.05697
t-ratio =-1.4833 (0.148)
ECM = In Y-1.0867 In (PC)-0.3267
In RX-5.6624
[[bar.R].sup.2] = 0.25, F = 5.21 (0.005)
DW-statistic = 1.99
ARDL (1, 2, 0, 1) with HD
Lag Order
Variable 0 1 2
[DELTA] ln PC 0.1016
(0.009)
[DELTA] ln PC(-1) -0.0865
(0.016) **
[DELTA] ln RX 0.0210
(0.414)
[DELTA] ln HD 0.0209
(0.825)
ECM(-1): -0.2268
t-ratio =-3.1190 (0.004)
ECM = In Y--0.4179 In (PC)--0.0927
In RX--0.8462 In HD -9.1634
[[bar.R].sup.2] = 0.36, F = 5.34 (0.001)
DW-statistic = 1.90
Values in parentheses are P-values, and *, **, *** indicate 1 percent,
5 percent and 10 percent level of significance respectively.
Table 8
Dynamic ARDL Model: ARDL (1, 1, 0, 0) Based on SBC
(Dependent Variable = ln Real Exports (RX))
Regressor Coefficient t-ratio (P-value)
ln RX(-1) 0.55473 3.8810 (0.001)
ln PC 0.50768 2.4015 (0.032)
ln PC (-1) -0.59512 -3.1839 (0.003)
ln Y 0.84767 2.0615 (0.048)
ln HD -0.28173 -0.48380 (0.632)
Constant -6.0669 -1.3403 (0.191)
[R.sup.2] = 0.986, F-stat = 398.07 (0.000), SBC = 25.56, Serial
Correlation (LM) = 0.4192 (0.517), Ramsey's Reset Test = 0.4214
(0.516), Heteroscedasticity (LM) = 0.093 (0.923), Normality (LM) =
1.5869 (0.452).
The figures in parentheses are P-values.
Table 9
Estimated Long-run Coefficients Using the ARDL Approach and SBC
(Dependent Variable = ln Real Exports (RX))
Regressor Coefficient t-ratio (P-value)
Ln PC -0.1964 -0.4550 (0.652)
ln Y 1.9037 2.4368 (0.021)
ln HD -0.6327 -0.5058 (0.617)
Constant -13.6254 -1.5543 (0.131)
Table 10
ECM Representation for Selected ARDL Model Based on SBC
(Dependent Variable = [DELTA] ln Real Exports (RX))
Lag Order
Variable 0 1 2
[DELTA] ln PC 0.5077 (0.023)
[DELTA] ln Y 0.8477 (0.048)
[DELTA] ln HD -02817 (0.630)
ECM(-1): -0.4453, t-ratio = -3.1152 (0.004).
ECM = ln RX + 0.1964 ln (PC)--1.9037 ln (Y) + 0.6327 ln HD + 13.6254.
[R.sup.2] = 0.45, F=5.87 (0.001), DW-statistic = 1.67.
Values in parentheses are P-values.
Table 11
Dynamic ARDL Model: ARDL (3, 2, 0,3) Based on SBC
(Dependent Variable = ln HD)
Regressor Coefficient t-ratio (P-value)
ln HD (-1) 0.2366 1.2969 (0.208)
ln HD (-2) -0.2671 -1.7291 (0.097)
ln HD (-3) 0.4711 3.4134 (0.002)
ln PC -0.0864 -2.0035 (0.057)
ln Y -0.2571 -1.0225 (0.317)
ln Y(-1) 1.1483 3.1670 (0.004)
ln Y(-2) -0.6199 -2.1844 (0.039)
ln RX 0.0368 1.0020 (0.327)
ln RX (-1) -0.0608 -1.2286 (0.232)
ln RX (-2) -0.0970 -1.9959 (0.052)
ln RX (-3) 0.1359 3.8987 (0.001)
Constant -0.9487 -1.1182 (0.275)
[R.sup.2] = 0.991, F-stat = 293.45 (0.000), SBC = 74.09, Serial
Correlation (LM) = 1.1458 (0.284), Ramsey's Reset Test= 0.0129
(0.910), Heteroscedasticity (LM) = 2.0053 (0.157), Normality
(LM) = 0.5458 (0.761).
The figures in parentheses are P-values.
Table 12
Estimated Long-run Coefficients Using the ARDL Approach
Based oil SBC (Dependent Variable = ln HD)
Regressor Coefficient t-ratio (P-value)
In PC -0.1545 -1.66636 (0.110)
In Y 0.4849 4.2710 (0.000)
In RX 0.0267 0.2573 (0.799)
Constant -1.6957 -1.8653 (0.075)
Table 13
ECM Representation for Selected ARDL Model Based on SBC
(Dependent Variable = [DELTA] Ln HD)
Lag Order
Variable 0 1 2
[DELTA] ln HD -0.2039 (0.222)
[DELTA] ln HD -0.4711 (0.002)
[DELTA] ln PC -0.0864 (0.056)
[DELTA] ln Y -0.2571 (0.316)
[DELTA] ln Y 0.6199 (0.039)
[DELTA] ln RX 0.0368 (0.326)
[DELTA] ln RX -0.0389 (0.426)
[DELTA] ln RX -0.1359 (0.001)
ECM(-1): -0.5594 (t-ratio = -2.4255, P-value = 0.023).
ECM = In HD + 0.1545 In PC-0.4849 In Y-0.0267 In RX + 1.6957.
[R.sup.2] = 0.763, F = 8.21 (0.000), DW-statistic = 2.20.
Values in parentheses are P-values.
Table 14
Toda-Yamamoto Granger Causality Test
Equations Null Hypothesis Value
Bivariate-RGDP and Physical Capital
Equation 1 PC does not Granger cause RGDP 8.7588
Equation 2 RGDP does not Granger cause PC 12.7208
Bivariate-RGDP and Exports
Equation 3 Exports does not Granger cause RGDP .72878
Equation 4 RGDP does not Granger cause Exports 26.5535
Bivariate-RGDP and Human Development (HD)
Equation 5 HD does not Granger cause RGDP 1.4697
Equation 6 RGDP does not Granger cause HD 16.1701
Trivariate-RGDP, Exports and HD
Equation 7 Export does not Granger cause RGDP .034072
Equation 7a HD does not Granger cause RGDP 73780
Equation 8 RGDP does not Granger cause Exports 13.0664
Equation 9 RGDP does not Granger cause HD 1.4891
Equation 8a HD does not Granger cause Exports 1.9722
Equation 9a Exports does not Granger cause HD .53383
Tetravariate-RGDP, Exports, HD, and PC
Equation 10 Export does not Granger cause RGDP .47336
Equation 1a HD does not Granger cause RGDP .098718
Equation 10b PC does not Granger cause RGDP 8.0928
Equation 10c RGDP does not Granger cause Exports 13.5116
Equation 11 HD does not Granger cause Exports 1.4224
Equation 11a PC does not Granger cause Exports .62814
Equation 11b RGDP does not Granger cause HD 1.0588
Equation 11e Exports does not Granger cause HD .31531
Equation 12 PC does not Granger cause HD .53811
Equation 12a RGDP does not Granger cause PC 11842
Equation 12b Exports does not Granger cause PC 1.7246
Equation 12c HD does not Granger cause PC 1.9909
Test Statistic
Wald test ([chi square]
Equations df Prob.
Bivariate-RGDP and Physical Capital
Equation 1 1 [.003] Reject [H.sub.0]
Equation 2 1 [0.000] Reject [H.sub.0]
Bivariate-RGDP and Exports
Equation 3 1 [.393] Cannot Reject [H.sub.0]
Equation 4 1 [0.000) Reject [H.sub.0]
Bivariate-RGDP and Human Development (HD)
Equation 5 1 [.225] Cannot Reject [H.sub.0]
Equation 6 1 [0.000] Reject [H.sub.0]
Trivariate-RGDP, Exports and HD
Equation 7 1 [.854] Cannot Reject [H.sub.0]
Equation 7a 1 [.390] Cannot Reject [H.sub.0]
Equation 8 1 [.000] Reject [H.sub.0]
Equation 9 1 [.222] Cannot Reject [H.sub.0]
Equation 8a 1 [.160] Cannot Reject [H.sub.0]
Equation 9a 1 [.465] Cannot Reject [H.sub.0]
Tetravariate-RGDP, Exports, HD, and PC
Equation 10 1 [.491 ] Cannot Reject [H.sub.0]
Equation 10a 1 [.753] Cannot Reject [H.sub.0]
Equation 10b 1 [.004] Reject [H.sub.0]
Equation 10c 1 [0.000] Reject [H.sub.0]
Equation 11 1 [.233] Cannot Reject [H.sub.0]
Equation 11a 1 [.428] Cannot Reject [H.sub.0]
Equation 11b 1 [.303] Cannot Reject [H.sub.0]
Equation 11e 1 [.574] Cannot Reject [H.sub.0]
Equation 12 1 [.463] Cannot Reject [H.sub.0]
Equation 12a 1 [.731] Cannot Reject [H.sub.0]
Equation 12b 1 [.189] Cannot Reject [H.sub.0]
Equation 12c 1 [0.158] Cannot Reject [H.sub.0]
Table 15
Comparison of Results of ARDL Approach and Augmented Granger
Causality Tests Regarding Validity of Hypotheses of the Study
Validity of Hypotheses at 95 percent
Level of Confidence ARDL Approach Results
Export-led Growth Hypothesis No
Growth-driven Exports Hypothesis Yes
Human Based Endo-genous Yes in LR,
Growth Theory No in SR
Toda-Yamamoto Granger Causality
Test Results
Validity of Hypotheses at 95 percent
Level of Confidence Bivariate Trivariate
Export-led Growth Hypothesis No No
Growth-driven Exports Hypothesis Yes Yes
Human Based Endo-genous
Growth Theory No No
Toda-Yamamoto Granger Causality
Test Results
Validity of Hypotheses at 95 percent
Level of Confidence Tetravariate Overall
Export-led Growth Hypothesis No No
Growth-driven Exports Hypothesis Yes Yes
Human Based Endo-genous
Growth Theory No No