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Journal of Economic and Social Research Vol 15(2) 2013, 41-63
Transportation- Communication Infrastructure
and Economic Growth: The Panel VAR Application
Rudra P. Pradhan*, Bele Samadhan** and Shashikant
Pandey***
Abstract. Previous studies generally find mixed empirical evidence on the
relationship between transportation- communication infrastructure investment and
economic growth. In this paper, we re-examine the causal relationship between
transportation- communication infrastructure investment and economic growth by
panel Granger causality test and by utilizing a richer panel data set that includes 34OECD countries over the period 1960-2012. Our empirical findings strongly support
the existence of cointegration between transportation- communication infrastructure
investment and economic growth in the long run and have bidirectional Granger
causality effects.
JEL Classification Codes: L96, O32, O33, O43.
Keywords: Transportation- Communication Infrastructure, Economic Growth,
Panel VAR.
*
Assistant Professor, Vinod Gupta School of Management, Indian Institute of
Technology
Kharagpur,
India.
Corresponding
author.
Email:
[email protected]
**
Research Scholar, RCG School of Infrastructure Design and Management, Indian
Institute of Technology Kharagpur, India.
***
Research Scholar, Vinod Gupta School of Management, Indian Institute of
Technology Kharagpur, India.
Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
1. Background of the Study
The role of infrastructure investment in economic development is an
important issue in growth literature, particularly for the development
research community, government and international development agencies
(see, for instance, Wilhelmsson and Wigren, 2011; Pradhan, 2010a; Glass,
2009; Delgado and Alvarez, 2007; Bose and Haque, 2005; Turnovsky, 1997;
Rebelo, 1991; Barro, 1990). Over the past several years, the relationship
between infrastructure investment and economic growth has been studied in
a vast range of papers since the seminar work of Aschaure (1989) and
become a controversial issue (see, for instance, Wahab, 2011; Chakraborty
and Nandi, 2011; Rodriguez, 2010; Gramlich, 1994; Munnell 1992).
There are two groups of thoughts in this notorious issue between
infrastructure investment and economic growth: first, high infrastructure
investment can bring high economic growth; second, high economic growth
can increase the demand for infrastructure services and so induces the
increased supply (Kruger, 2012). Hence, it is not just sufficient to establish
an empirical relationship between infrastructure investment and economic
growth; the problem of the direction of causality between the two has to be
explicitly addressed. This is because the direction of causality between
infrastructure investment and economic growth has significant policy
implications for infrastructure policy and hence economic growth (Pradhan
and Bagchi, 2012; Wolde-Rufael, 2007). For instance, a unidirectional
causality running from infrastructure investment to economic growth implies
that reducing infrastructure investment could lead to a decrease in economic
growth. Similarly, for the case of reverse causality, running from economic
growth to infrastructure investment, policies aimed at stimulating the
economy by accelerating investment in the infrastructure sector may not be
successful. Again for the existence of bidirectional causality between the
two, policies aimed at stimulating investment in infrastructure can induce
economic growth while economic growth in turn can stimulate the growth of
infrastructure activity. Moreover, if there is no causality in any direction,
increase or decrease of infrastructure may or may not have any effect on
economic growth and economic growth may or may not stimulate the
demand for infrastructure.
Over and above, it might well be the case that high economic growth
and high infrastructure investment are vastly correlated, but that there is no
causal relationship, which has important implications for public policy
(Kruger, 2012; Ozbay et al., 2007). A summary of brief controversial results
42
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
are reported in Table 1. So the diverse empirical evidence combined with
infrastructure investment being increasingly identified as one of the strong
potential forces for improving economic growth, not only necessitates
further research but also the use of alternative testing methodology.
The paper addresses the debate by re-examining the empirical
evidence using panel vector autoregressive (VAR) model, originally
developed by Chamberlain (1982) and Holtz- Eakin et al. (1988).
The remaining of the paper can be summarized as follows. Section 2
describes the methodology we used and data descriptions. Section 3
discusses the empirical results. The final section provides concluding
remarks and policy implications.
2. Methodology
The focus of the analysis is to investigate the causal relationship between
public expenditure on transport-communication infrastructure and economic
growth. The variables used in this paper are public investment in transportcommunication infrastructure and per capita GDP†. The data are obtained
from World Development Indicators, World Bank, Washington. It consists
of annual observations from 1960-2012 for 34 OECD‡ countries. The
variables incorporated in the panel VAR model are used in natural
logarithmic so that their first differences approach the growth rates. The
descriptive statistics and correlation of these data is presented in Table 2.
The correlation matrix provides signal that the relationship between
transport-communication infrastructure investment and economic growth are
positive and significant. However, the present study looks for this evidence
by the cointegration and causality analysis. It simply tries to assess the
importance of infrastructure development to economic growth, by
investigating whether the development of infrastructure sector has
contributed to economic growth, or whether the expansion of infrastructure
sector is simply a consequence of rapid economic growth.
†
GDP stand for Gross domestic product.
OECD stands for Organization of Economic Cooperation and Development. The
countries include in this analysis are namely Australia, Austria, Belgium, Canada,
Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece,
Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea Republic, Luxembourg,
Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic,
Slovenia, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United
States.
43
‡
Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
The causality between transport-communication infrastructure and economic
growth is examined within the framework of Granger causality. The
definition of Granger causality between two series, given by Granger (1969),
is exclusively based on the predictability. Essentially, Xt is said to cause Yt,
if Xt contains information in the past terms that helps in the prediction of Yt.
For the reverse causation, the feedback from Yt to Xt can exist if a prediction
of Xt can be significantly improved by taking into account the past values of
Yt (Granger, 1988). Hence, Granger causal relation between Yt and Xt can be
bidirectional if the causation is found to run in both-sided directions
simultaneously (Yu et al., 2012).
For this study, the Granger causality between transportcommunication infrastructure and economic growth is addressed by using
the panel data of 34 OECD countries, covering the period 1960-2012. In
other words, we used panel VAR§ model to examine the nexus between
transport-communication infrastructure and economic growth. The panel
VAR model is the application of cointegration and causality test on a panel
of cross sectional units. It is a powerful technique to examine the long run
nexus between time variables (Canova and Ciccarelli, 2004). The panel
VAR model has an advantage to improve the power of test, which is
available from the cross sectional units like states, countries, regions, etc.
The VAR technique is not something new; we have extensive literature on
this technique, particularly with respect to long run relationship between
various time series variables. The estimation process of panel VAR model
involves three steps. First, the deployment of panel unit root test to know the
stationarity (i.e., order of integration) of time series variables. Second, the
deployment of panel cointegration test to know the existence of long run
relationship between the time series variables and estimate long run equation
by using fully modified OLS (FMOLS**). Third, the deployment of VAR
model to investigate the size and direction of causality between the time
series variables, particularly in the panel setting. The detail descriptions of
these three techniques are given below.
§
VAR stands for Vector-autoregressive.
FMOLS is a non-parametric approach, takes into account the possible correlation
between the error term and the first differences of the regressor as well as the
presence of a constant term, to dealing with corrections for serial correlation (see,
for instance, Maeso-Fernandez et al., 2006; Pedroni, 2000, 2001).
**
44
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
2. 1. Panel Unit Root Test
The definitions of Granger causality have assumed that only stationary
variables are involved. We deployed panel unit root tests, LLC (Levin-LinChu; Levin et al., 2002) and IPS (IM- Pesaran- Shin; Im et al., 2003), for the
same. They have been deployed on the principles of conventional ADF††
test. The LLC allows for heterogeneity of the intercepts across members of
the panel, while IPS allows for heterogeneity in intercepts as well as in the
slope coefficients. Both the tests are applied by averaging individual ADF tstatistics across cross-section units. The test follows the estimation of
following equation:
pi
∆Yt = µ i + γ i Yit −1 + ∑ β ij ∆Yit − j + λi t + ε it
(1)
j =1
Where i = 1, 2….N; t = 1, 2…. T; Yit is the series for country i in the
panel over period t; pi is the number of lags selected for the ADF regression;
∆ is the first difference filter (I –L); and εit are independently and normally
distributed random variables for all i and t with zero means and finite
heterogeneous variances (σi2).
The IPS tests the null hypothesis of unit root for each individual in
the panel, that is, H0: γi = 0 for ∀i against an alternative HA: γi < 0, i = 1, 2…
N1; γi = 0, i = N1 + 1, …., N, which allows for some of the individual series
to be integrated. The IPS develops the t-bar statistic calculated as a simple
average across groups of the individual ADF t statistics. This is as follows:
t =
1
N
N
γˆi
∑ σˆ
i =1
(2)
γˆi
The standardized t-bar statistic, Ztbar, converges to standard normal
distribution sequentially, as N tends to very high.
The LLC unit root test is also based on model (1) but it differs from
IPS in some ways. On the one hand, IPS allows the coefficients of the
autoregressive term, γi, to differ across section units, while LLC considers
the coefficients of the autoregressive term as homogenous across all
individuals, i.e., γi = γ for ∀i . The LLC unit root test tests the null
hypothesis that each individual in the panel has integrated time series, i.e.,
††
ADF stands for Augmented Dickey Fuller test.
45
Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
H0: γi = γ = 0 for ∀i against an alternative HA: γi = γ < 0 for ∀i . Hence,
under an alternative hypothesis, all single series are stationary. LLC
considers pooling the cross-section time series data and it follows the t- star
statistics, which is as follows:
tγ =
*
)
γ
)
s.e(γ )
(3)
The t- statistic also asymptotically follows standard normal
distribution. The computation of this statistics along with the determination
of order of integration for each variable completes the first phase of testing
for cointegration.
2. 2. Panel Cointegration Test
If the series are individually integrated of same order, then they may be
cointegrated (Granger, 1988). That means there is possibility of some linear
combination between them. Traditional cointegration tests, such as Engle
and Granger (1987) and Johansen (1988), have low power when the length
of the series is low. Pedroni (2000) proposes a methodology to test for panel
data cointegration, which is considered as an extension of traditional Engle
and Granger (1987) two step residual-biased methods. The Pedroni’s method
is used in this paper for investigating co-integration in a heterogeneous panel
data (see Larsson et al., 2001). The technique starts with the following
regression equation.
TCI it = β 0i + β 1i t + β 2i GDPit + ε it
(4)
and ε it = γ i ε it −1 + ξ it
(5)
Where TCI is public investment in transport- communication
infrastructure; GDP is per capita economic growth; i = 1, 2, ….., N; t = 1,
2…. T; β0i is the fixed effect or individual specific effect that is allowed to
vary across individual cross-sectional units. The β1it is a deterministic time
trend specific to individual countries in the panel. The slope coefficients β2i
can vary from one individual to another allowing the cointegrating vectors to
be heterogeneous across countries.
Pedroni proposed seven different statistics for the cointegration test
in the panel data setting (see Pedroni, 1999). Of the seven proposed
statistics, first four are known as panel cointegration statistics and that is
46
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
within-dimension statistic, while the last three are known as group mean
panel cointegrating statistics and that is between-dimension statistic. Their
levels are based on the way the autoregressive coefficients are manipulated
to arrive at the final statistic. There are basically five steps to obtain these
cointegration statistics.
Step 1: compute the residuals ( εˆit ) from the panel regression (equation 4).
The estimation involves the inclusion of all appropriate fixed effects, time
trends or common time dummies.
Step 2: Compute the residuals ( ζˆit ) from the following regression:
∆Yit = β 1i ∆X it + β 2i ∆X it + ... + β mi ∆X mit + ξ it
(6)
2
ˆ
Step 3: Compute ( Lˆ11
i ), the long run variance of ζ it :
1
2 Ki 
S  T
2
2
ˆ
 ∑ cˆit cˆit − s
L11i = ∑ cˆit + ∑ 1 −
T
T S =1  K i + 1 t − s +1
(7)
Step 4: Compute the residuals of the ADF test for εˆit ( uˆ it ) and compute the
following variances of these residuals
1 T
Sˆ i2 = ∑ uˆ it2
T t =1
and
~2
1 T
S NT
= ∑ Sˆ i2
T t =1
(8)
Step 5: Computation of panel-t and group-t statistics (see, for details,
Pedroni, 2000). These statistics are asymptotically normally distributed.
The null of no cointegration is then tested, based on the above
description of standard normal distribution. The null hypothesis of no
cointegration is H0: γi = 1 for ∀i against an alternative hypothesis HA: γi < 1
for ∀i , in the residuals from the panel cointegration. In contrast, the group
means panel cointegration statistics test the null hypothesis of no
cointegration against an alternative HA: γi < 1 for ∀i , which allows the
possibility of an additional heterogeneity source across the countries. These
statistics diverge to negative infinity under the alternative hypothesis. So, the
left tail of the normal distribution is usually employed here to reject the null
hypothesis (see, for more detail, Pedroni, 1999).
47
Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
2. 3. Fully Modified OLS Panel Estimates
Pedroni proves that the panel OLS estimator is biased when the variables are
cointegrated and suggests estimating and testing hypothesis for cointegrating
vectors in dynamic panels by fully modified OLS (FMOLS). The model of
FMOLS is described as follows (Pedroni, 2004):
Yit = δ i + β i X it + ξ it
(9)
X it = X it −1 + ζ it
(10)
Where Y is the log of TCI or log of GDP and X represents the
corresponding
vector
of
independent
variables.
Let
Z it = (Yit , X it )′ ~ I (1) andϖ it = (ξ it , ζ it )′ ~ I (0) with long run covariance
matrix Ω i = Li Li′ . Li is the lower triangular decomposition of Ω i , which can
be decomposed as Ω i = Ω 0i + Γi + Γi′ . Where, Ω i0 is the contemporaneous
covariance and Γi is a weighted sum of co-variances. We can also augment
the above cointegrating regression with lead and lagged differences of the
regressors to control for endogenous feedback. This can be presented as
follows:
ki
Yit = δ i + β i X it + ∑ λik ∆X it − k + ξ it
(11)
k = ki
The panel FMOLS estimator of the β is:
−1
T
 T

2 
β = N ∑  ∑ ( X it − X i )   ∑ ( X it − X i )Yit* − Tτˆi 
i =1  i =1
  i =1

ˆ
L
Where, Yit* = (Yit − Yi ) − 21i ∆X it
Lˆ
*
NT
−1
N
(12)
22 i
and
ˆ0 −
τˆi = Γˆ 21i + Ω
21i
Lˆ 21i ˆ
ˆ0 )
(Γ22i + Ω
22 i
Lˆ
22 i
48
(13)
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
2. 4. Panel Causality Test
The panel causality test, proposed by Holtz-Eakin et al. (1988), is deployed
to know the direction of causality. The proposed panel VAR model is as
follows:
p
q
k =1
k =1
∆TCI it = η1 j + ∑ α 11ik ∆TCI it − k + ∑ α 12ik ∆GDPit − k + λ1i EC1it −1 + ε 1it
(14)
p
q
k =1
k =1
∆GDPit = η 2 j + ∑ α 21ik ∆GDPit − k + ∑ α 22ik ∆TCI it − k + λ 2i EC1it −1 + ε 2it
(15)
Where TCI is public investment in transportation- communication
infrastructure and GDP is per capita economic growth. The ECT is the
lagged error correction term derived from the long run cointegrating
relationship. The ε1it & ε2it are the disturbance terms. The null hypotheses are
to test α12i ≠ 0 & λ1i ≠ 0 in equation (14) and α22i ≠ 0 & λ2i ≠ 0 in equation
(15). The significance of α12 and α22 represent the possibility of short run
causality, while the significance of λ1i & λ2i represent the possibility of long
run causality. Moreover, the coefficients of λ1i & λ2i should be negative
(Engle and Granger, 1987; Hamilton, 1994).
The variables incorporated in the panel VAR model are used in
natural logarithms so that their first differences approach the growth rates.
The descriptive statistics and correlation of these data is presented in Table
2.
3. Empirical Results
This section scans the empirical results. It deals in three parts: panel unit
roots test, cointegration test; and Granger causality test. The Tables 3 and 4
summarizes the estimated results of panel unit root tests and panel
cointegration tests respectively, while Table 5 summarizes the panel
causality test. As the empirical findings show, when we run the panel unit
root test on the original values of transport-communication infrastructure
investment (TCI) and economic growth (GDP), the results show that the null
hypotheses of a unit root test cannot be rejected at the 5% level. However,
when we conduct the joint unit root test for the first difference of each of the
two variables, we are able to reject the null hypotheses (see, Table 3). Hence,
we can conclude that both variables (TCI and GDP) are integrated of order
49
Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
one, i.e. I (1). It now opens the path to know, whether there is a long run
equilibrium relationship between TCI and GDP.
Given that each variable is integrated of order one, we test for panel
cointegration using Engle and Granger’s (1987) two-step test procedure:
first, estimating the long run model specified in equation (4) for obtaining
the estimated residuals; and second, to check whether the residuals are
stationary. If ζit are stationary, we can conclude that the two series are cointegrated. As the empirical findings show, all the statistics (Pedroni, 2000)
significantly reject the null hypotheses of no cointegration and obtain a
strong evidence of integration among the series. Hence, it can be concluded
that TCI and GDP move together in the long run, which indicates that
transport-communication infrastructure can facilitate the economic growth of
34 OECD countries and vice versa. That means it concludes that there is a
long run equilibrium relationship between transport-communication
infrastructure and per capita economic growth.
Following Pedroni (2000), the long run equation (2) is estimated by
fully modified OLS (FMOLS) in order to avoid the bias of the OLS
estimator. The FMOLS results indicate that the coefficient of TCI is
statistically significant and positive at 1% significance level. It justifies that
a 1% increase in TCI lead to an increase of economic growth by 0.60% in
the sample of OECD- 34. The estimates for individual countries show the
significance of the coefficient of TCI for all countries. The results are not
reported here due to space constraints and can be available with request.
After knowing the status of cointegration, the next step is to check
the direction of causality between transport-communication infrastructure
and economic growth. According to Granger (1969), the existence of
cointegration relationship implies that there will be at least a unidirectional
Granger causality, which is also applicable to the panel data analysis. The
panel cointegration results already cleared that GDP and TCI are cointegrated, which means that the Granger causality between GDP and TCI
exists in the long run. However, we are not sure whether it is a bidirectional
or unidirectional causality. Given that the variables are co- integrated, the
vector error correction model (VECM) is deployed to perform the Granger
causality tests in order to identify the direction of long run causality and to
examine its causal relationship in the short term (Pedroni, 2004; Everaert
and Heylen, 2001; Kao, 1999).
50
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
The estimated results reflect that the coefficients of error correction term are
negative and significant from both directions, which means the existence of
bidirectional causality between transportation- communication infrastructure
investment and economic growth in the panel of 34 OECD countries. This
represents that economic growth is the Granger cause of transportcommunication infrastructure development (GDP => TCI) and transportcommunication infrastructure development is also Granger cause of
economic growth (TCI => GDP).
For GDP to TCI, it reflects that economic growth is indeed a major
cause of rapid development of transport-communication infrastructure. This
is justified on the ground that economic growth usually provides necessary
financial and technical support for transport-communication infrastructure
investment and improvement. Typically, most infrastructures have been
financed, built, owned and operated by the governments at the various levels
(see, for instance, Newell et al., 2009). To fulfill the growing demand for
transport-communication infrastructure induced by increasing economic
growth, the governments are supposed to make an effort on transportcommunication infrastructure creation. In this context, government should
take the initiative to develop the transport-communication infrastructure,
through both public-finance projects and public-private partnership modes
(Mu et al., 2011).
In summary, economic growth can increase the investment in
transport-communication infrastructure and thus promoted infrastructure
development. For TCI to GDP, it reflects that transport-communication
infrastructure investment is also a major cause of economic growth. That
means transport-communication investment is a productive stimulus
contributing to its economic growth. The estimated results are also supported
by the generalized impulse response functions (GIRFs), which are very
responsive to panel VAR results. The results are not reported here due to
space constraints and can be available with request.
4. Concluding Remarks and Policy Implications
Understanding the policy implications of the nexus between public
investment in transportation- communication infrastructure and economic
growth is of great importance in the development economics. Much still
needs to be understood about the various integrations between the two in
order for the policy makers to make the right decisions about investments in
transportations and communications in terms of not only the most efficient
51
Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
use of funds, but also land use and air quality, to name a few (Kustepeli et
al., 2012; Eruygur et al., 2012; Ozkan et al., 2012; Pradhan, 2010b; Ozbay
et al., 2007; Pereira and Andraz, 2005; Haque and Kim, 2003; Ozbay et al.,
2003; Banister and Berechman, 2003; Eberts, 2000; World Bank, 1996;
Cullison, 1993; Munnell, 1992; Aschauer, 1989).
We presume that public investment in transportation- communication
infrastructure is a key to long run economic growth (Bose and Haque, 2005).
This is since transportation- communication infrastructure is considered as a
vehicle through which capital, ideas, technology, and skills are transferred
across borders and hence, provide substantial spillover effect. The debate is,
however, always whether transportation- communication infrastructure
determines economic growth or economic growth determines transportationcommunication infrastructure development. The existing literature on the
nexus between the two is far from settled (see Table 1). This paper
contributes to the existing literature by exploring short- and long-run
relationships between transport-communication infrastructure investment
and economic growth.
The study examined the causal relationship between transportcommunication infrastructure investment and economic growth in a panel
Cointegration and Granger causality framework. The results show that there
is a stable long run equilibrium relationship transport-communication
infrastructure investment and economic growth in the panel of 34 OECD
countries, namely Australia, Austria, Belgium, Canada, Chile, Czech
Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary,
Iceland, Ireland, Israel, Italy, Japan, Korea Republic, Luxembourg, Mexico,
Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic,
Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom and United
States.
The results of Granger causality show that the bidirectional causality
between the two variables exists. That means there is feedback hypothesis,
which emphasizes the interdependence relationship between transportcommunication infrastructure investment and economic growth. The
complementary relationship opens the possibility that transportcommunication infrastructure is a productive stimulus contributing to its
economic growth and at the same time, economic growth can provide
necessary support (both financial and technical) to transport-communication
infrastructure investment and its improvement. Furthermore, a booming
economy always creates demand for more transport-communication
52
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
infrastructure development. This is in line with the expectations of decision
makers.
Figure 1 can reflect the feedback loop of transport-communication
infrastructure and economic growth. That means governments have to make
corresponding transport-communication infrastructure policies to achieve
economic goals on the analysis of evaluating transportation and
communications network. Over and above, the findings suggest that
increased economic growth can induce additional investment in transportcommunications infrastructure because of the high income elasticity of
transport-communication usage. It also suggests that investment in transportcommunication infrastructure may prove to be a critical tool for enhancing
economic growth and closing the developmental gap in OECD countries. So
the paper complements the previous studies by providing more robust results
on the causal relationships between transportation- communication
infrastructure investment and economic growth.
53
Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
References
Aschauer, A. D. (1989) “Is Public Expenditure Productive?” Journal of
Monetary Economics, 23: 177-200.
Banister, D. and Berechman, J. (2003) “ Transport Investment and Economic
Development”. Routledge, London.
Barro, R. (1990) “ Government Spending in a Simple Model of Endogenous
Growth”. Journal of Political Economy, 98: 103-S125.
Blomstrom, M., Lipsey, R. E. and Zejan, M. (1996) “ Is Fixed Investment
the Key to Economic Growth”. Quarterly Journal of Economics,
111: 269-76.
Bose, N. and Haque, M. E. (2005) “ Causality between Public Investment in
Transport and Communication and Economic Growth”. Journal of
Economic Development, 30: 95-106.
Canova, F. and Ciccarelli, M. (2004) “Forecasting and Turing Point
Predictions in a Bayesian Panel VAR Model”. Journal of
Econometrics, 120: 327-359.
Chakraborty, C. and Nandi, B. (2011) “Mainline Telecommunications
Infrastructure, Levels of Development and Economic Growth:
Evidence
from
a
Panel
of
Developing
Countries”.
Telecommunications Policy. 35: 441-449.
Chamberlain, G. (1982) “Multivariate Regression Model for Panel Data”.
Journal of Econometrics, 18: 5-46.
Changa, T. and Nieh, C. (2004) “A Note on Testing the Causal Link
between Construction Activity and GDP in Taiwan”. Journal of
Asian Economics, 15: 591-598.
Cullison, W. E. (1993) “Public Investment and Economic Growth”.
Economic Quarterly, Federal Reserve Bank of Richmond, 79: 19-34.
Delgado, M. J. and Alvarez, I. (2007) “Network Infrastructure Spillover in
Private Productive Sectors: Evidence from Spanish High Capacity
Roads”. Applied Economics, 39: 1583- 1597.
54
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
Eberts, R. W. (2000) “Understanding the Impact of Transportation on
Economic Development”. Transportation in the New Millennium,
TRB, Washington DC.
Engle, R. F. and Granger, C. W. J. (1987) “Cointegration and Error
Correction: Representation, Estimation and Testing”. Econometrica,
55: 251-276.
Eruygur, A., Kaynak, M. and Mert, M. (2012) “Transportation–
Communication Capital and Economic Growth: A VECM Analysis
for Turkey”. European Planning Studies, 20: 341-363.
Esfahani, H. S. and Ramirez, M. T. (2003) “Institutions, Infrastructure and
Economic Growth”. Journal of Development Economics, 70: 443477.
Everaert, G. and Heylen, F. (2001) “Public Capital and Productivity Growth:
Evidence from Belgium, 1953-1996”. Economic Modelling, 18: 97116.
Glass, A. (2009) “Government Expenditure on Public Order and Safety,
Economic Growth and Private Investment: Empirical Evidence from
the United States”. International Review of Law and Economics, 29:
29-37.
Gramlich, E. M. (1994) “Infrastructure Investment: A Review Essay”.
Journal of Economic Literature, 32: 1176-1196.
Granger C. W. J. (1969) “Investigating Casual Relations by Econometric
Models and Cross-Spectral Methods”. Econometrica, 37: 424-438.
Granger C. W. J. (1988) “Some Recent Developments in a Concept of
Causality”. Journal of Econometrics 39: 199-211.
Hamilton, D. J. (1994) “Time Series Analysis”. Princeton University Press,
Princeton.
Haque, M. E. and Kim, D. H. (2003) “Public Investment in Transportation
and Communication and Growth: A Dynamic Panel Approach”.
55
Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
Discussion Paper Series, No. 031. The University of Manchester,
Manchester, UK.
Holtz- Eakin, D., Newey, W. and Rosen, H. S. (1988) “Estimating Vector
Auto Regressions with Panel Data”. Econometrica, 56: 1371-1395.
Im, K. S., Pesaran, M. H. and Shin, Y. (1997) “Testing for unit roots in
heterogeneous panels”. Discussion paper, No. 9526. University of
Cambridge, Cambridge.
Im, K. S., Pesaran, M. H. and Shin, Y. (2003) “Testing for Unit Roots in
Heterogeneous Panels”. Journal of Econometrics, 115: 53-74.
Johansen, S. (1988) “Statistical Analysis of Cointegrating Vectors”. Journal
of Economics Dynamic and Control, 12: 231-254.
Kao, C. (1999) “Spurious Regression and Residual-based Tests for
Cointegration in Panel Data”. Journal of Econometrics, 90: 1-44.
Kruger, N. A. (2012) “Does Infrastructure Really cause Growth?” CTS
Working Paper, Centre for Transport Studies, Stockholm.
Kustepeli, Y., Gulcan, Y. and Akgungor, S. (2012) “Transportation
Infrastructure Investment, Growth and International Trade in
Turkey”. Applied Economics, 44: 2619-2629.
Larsson, R., Lyhagen, J. and Mothgren, M. (2001) “Likelihood Based
Cointegration Tests in Heterogeneous Panels”. Econometrics
Journal, 4:109-142.
Levin, A. and Lin, C.F. (1992) “Unit root tests in panel data: asymptotic and
finite sample properties”. Discussion paper, no. 92-93. University of
California, San Diego.
Levine, A., Lin, C. F. and Chu, C. S. (2002) “Unit Root Tests in Panel Data:
Asymptotic and Finite Sample Properties”. Journal of Econometrics,
108: 1-24.
Madsen, J. B. (2002) “The Causality between Investment and Economic
Growth”. Economic Letters, 74: 157-163.
56
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
Munnell, A. H. (1992) “Policy Watch: Infrastructure Investment and
Economic Growth”. The Journal of Economic Perspectives, 6: 189198.
Nijkamp, P. and Poot, J. (2004) “Meta-Analysis of the Effect of Fiscal
Policies on Long run Growth”. European Journal of Political
Economy, 20: 91-124.
Ozbay, K., Ozmen-Ertekin, D. and Berechman, J. (2003) “Empirical
Analysis of the Relationship between Accessibility and Economic
Development”. Journal of Urban Planning and Development, 129:
97-119.
Ozbay, K., Ozmen-Ertekin, D. and Berechman, J. (2007) “Contribution of
Transportation Investments to County Output”. Transport Policy,
14: 317-329.
Ozkan, F., Ozkan, O. and Gunduz, M. (2012) “Causal Relationship between
Construction Investment Policy and Economic Growth in Turkey”.
Technological Forecasting and Social Change, 79: 362-370.
Pedroni, P. (1999) “Critical values for Cointegration Tests in Heterogeneous
Panels with Multiple Regressors”. Oxford Bulletin of Economics and
Statistics, 61: 653-670.
Pedroni, P. (2000) “Fully Modified OLS for Heterogeneous Cointegrated
Panels. Advanced in Econometrics, 15: 93-130.
Pedroni, P. (2001) “Asymptotic and Finite Sample Properties of Pooled
Time Series Tests with an Application to the PPP Hypothesis”.
Indiana University Working Paper, Indiana University,
Bloomington.
Pedroni, P. (2004) “Panel Cointegration: Asymptotic and Finite Sample
Properties of Pooled Time Series Tests with an Application to the
PPP Hypothesis: New Results”. Econometric Theory, 20: 597-627.
Pereira, A. M. and Andraz, J. M. (2005) “Public Investment in
Transportation Infrastructure and Economic performance in
Portugal”. Review of Development Economics, 9: 177-196.
57
Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
Pradhan, R. P. (2010a) “Modelling the Nexus between Transport
Infrastructure and Economic Growth in India”. International Journal
of Management and Decision Making, 11: 182-196.
Pradhan, R. P. (2010b) ”Transport Infrastructure, Energy Consumption and
Economic Growth Triangle in India”. Journal of Sustainable
Development, 3: 167-173.
Pradhan, R. P. (2012) “Transport Infrastructure and Economic Growth in
India: The VECM Approach”. Research in Transportation
Economics, 1-10 [forthcoming].
Rebelo, S. (1991) ”Long Run Policy Analysis and Long Run Growth”.
Journal of Political Economy, 94: 56-62.
Rodriguez, F. (2010) “The Role of Infrastructure Investment in Economic
Growth”. A Century Foundation Report. The Century Foundation,
New York
Shiu,
A. and Lam, P. (2008) “Causal Relationship between
Telecommunications and Economic Growth in China and Its
Regions”. Regional Studies, 45: 705-718.
Tse, R. Y. C. and Ganesan, S. (1997) “Causal Relationship between
Construction Flows and GDP: Evidence from Hong Kong”.
Construction Management and Economics, 15: 371-376.
Turnovsky, S. (1997) “Fiscal Policy in a Growing Economy with Public
Capital”. Macroeconomic Dynamics, 1: 615-639.
Wahab, M. (2011) “Asymmetric Output growth Effects of Government
Spending: Cross Sectional and Panel Data Evidence”. International
Review of Economics and Finance, 20: 574-590.
Wang, E. C. (2002) “Public Infrastructure and Economic Growth: A New
Approach Applied to East Asian Economies”. Journal of Policy
Modelling, 24: 411-435.
Wilhelmsson, M. and Wigren, R. (2011) “The Robustness of the Causal and
Economic Relationship between Construction Flows and Economic
58
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
Growth: Evidence from Western Europe”. Applied Economics 43,
891-900.
Wolde-Rufael, Y. (2007) “Another Look at the Relationship between
Telecommunications Investment and Economic Activity in the
United Sates”. International Economic Journal, 21: 199-2005.
Wong, J. M. W., Chiang, Y. H. and Ng, T. S. (2008) “Construction and
Economic Development: The Case of Hong Kong”. Construction
Management and Economics, 26: 815-826.
World Bank (1996) “ Sustainable Transport”. Priorities for the Policy
Reform. World Bank, Washington DC.
World Bank (2009) “ Africa’s Infrastructure: A Time for Transformation”.
In Foster, V. and Brice-no-Garmendia, C. (eds.), Africa
Development Forum series, International Bank for Reconstruction
and Development. The World Bank, Washington DC.
Yu, N., Jong, M. D., Storm, S. and Mi, J. (2012) ”Transport Infrastructure,
Spatial Clusters and Regional Economic Growth in China”.
Transport Reviews, 32: 3-28.
Maeso-Fernandez, F., Osbat, C. and Schnatz, B. (2006) “Towards the
Estimation of Equilibrium Exchange Rates for CEE Acceding
Countries: Methodological Issues and a panel Cointegration
Perspective”. Journal of Computational Economics, Vol. 34, No. 3,
pp. 499-517.
Pedroni, P. (2000) “Fully Modified OLS for Heterogeneous Cointegrated
Panels”. Advances in Econometrics, Vol. 15, No. 1, pp. 93-130.
Pedroni, P. (2001) “Purchasing Power Parity Tests in Cointegrated Panels”.
Review of Economics and Statistics, Vol. 83, No. 4, pp. 727-731.
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Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
Table 1: Relationship Summary of Studies
Countries
Yu et al., 2012
Series
Nature of Causality
Government
transport GDP => GTI
investment
Ozkan et al. 2012
Government construction GCI < => GDP
investment
World Bank, 2009
Government construction GCI < ≠> GDP
investment
Shiu and Lam, 2008
Government
GDP => GTI
telecommunication
investment
Wolde-Rufael, 2007
Government
GTI < => GDP
telecommunication
investment
Changa and Nieh, 2004
Government construction GDP = > GCI
investment
Nijkamp and Poot, 2004
Government investment
GI < => GDP
Esfahani and Ramirez, Government construction GCI < => GDP
2003
investment
Wang, 2002
Government investment
GI < => GDP
Madsen, 2002
Government construction GDP = > GCI
investment
Tse and Ganesan, 1997
Government construction GDP = > GCI
investment
Blomstrom et al., 1996
Government construction GDP = > GCI
investment
In this study
Government investment in GTCI <=> GDP
transport
and
communication
Note: GCI: Government Construction Investment; GTI: Government
Telecommunication/ transport Investment; GTCI: Government Transportcommunication Investment; and GDP: Per capita GDP.
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Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
Table 2: Descriptive Statistics for TCI and GDP
Note: TCI: Public investment in transportation- communication infrastructure; GDP:
Per capita GDP; Med: Median; max: Maximum; Min: Minimum; Std: Standard
Deviation; Skew: Skewness; Kur: Kurtosis.
Source: Author’s calculation.
Table 3: Results of Panel Unit Roots Test
Note 1: TCI: Public investment in transportation- communication infrastructure;
GDP: Per capita GDP; LLC: Levin-Lin-Chu panel unit root test; IPS: Im-PesaranShin panel unit root test
Note 2: *: Indicates statistical level of significance at 1%.
Note 3: The critical values for LLC test are derived from Levin and Lin (1992) [see
Table 3]; and the critical values of IPS test are derived from Im et al. (1997) [see
Table 4].
Source: Author’s calculation.
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Rudra P. Pradhan, Bele Samadhan and Shashikant Pandey
Table 4: Results of Panel Cointegration Tests for Heterogeneous Panel
Note 1: The parentheses indicate the probability level of significance.
Note 2: The critical values for the panel cointegration tests are derived from Pedroni
(2001).
Source: Author’s calculation.
Table 5: Results of Panel Causality Test
Note 1: The parentheses indicate standard errors; *: Indicate statistical level of
significance at 5%.
Note 2: The critical values are as usual structure for t-test and F-test.
Source: Author’s calculation.
62
Transportation- Communication Infrastructure and Economic Growth: The
Panel VAR Application
Figure 1: Feedback Loop of Economic Growth to TransportCommunication Infrastructure
63