Impact of Port Reform, Political and Economic Events on Maritime

Impact of Port Reform, Political and Economic Events on
Maritime Traffic in Chinese Ports
Dong Yanga1
Anthony TH Chinb
Shun Chenc
a
Centre for Maritime Studies, National University of Singapore, Singapore
Department of Economics, National University of Singapore, Singapore
c
College of Transport & Communications, Shanghai Maritime University, Shanghai, China
b
Abstract
Chinese ports have undergone a series of dynamic international and domestic events, economic
and political reform since the founding of the People‟s Republic. This paper attempts to ascertain
if these political, economic events and port reforms have had an impact on maritime traffic of
Chinese ports by using econometric methods. Findings suggest that foreign trade drives the
increase in throughput of Chinese ports, especially foreign and coastal port throughput. In
contrast, this increase in port throughput has led to an increase in domestic retail sales (or
domestic demand) as well as more port investment. In its development, port traffic was
influenced by multiple shocks. Among all the events, the Great Leap Forward has exerted the
biggest influence to the throughput of Chinese ports, especially in short run. China‟s accession to
WTO brought an exclusive and minor effect to port throughputs. Sudden rises in port throughput
were largely a consequence of implementation of port policy but not as prominent as effect from
economic and political events. Port structural reform is proved to be more efficient and long
lasting than simple investment in the port infrastructure construction.
Keywords: Economic and political events, Port reforms, Maritime traffic in ports, Structural
break
1
Corresponding Author‟s Address: Research Fellow, Dong Yang, Centre for Maritime Studies, National University of Singapore, 12
Prince George‟s Park, Singapore 118411. Email: [email protected]
1
1. Introduction.
The Chinese economy has undergone several dramatic transformations since 1949. As such some
of the economic indicators may not be trend stationary. Li (Li 2000) and Smyth (Smyth 2004)
analyzed national and provincial GDPs and concluded that these GDPs present a stationary
process with one or two breaks as opposed to a unit-root process. Wang et.al (2008) revealed that
Chinese export trend is a piecewise stationary series subject to two or more breaks. Ma (2008)
recognized a structure break for Chinese saving rate at 1978. The structural break in these
historical data may be due to a series national political or economic events, such as the Great
Leap Forward (1958–1960), Three years of natural disasters (1959–1961), Cultural Revolution
(1966–1976), Open Door Policy and accompanying Economic Reforms (1978–1979),
Macroeconomic Austerity Program (1989–1990), Deng‟s Southern Tour (1992), China's World
Trade Organization Accession (2001), SARS (2003) and so on.
The throughput of Chinese port is also found be a non-stationary series. Potential shocks may
come not only from above-mentioned national economic or political events but also a series of
policies implemented in the port sector. In the first two decades after 1949, Chinese port
throughput was influenced by domestic trade and the development of ports was slow. Foreign
shipping traffic grew rapidly with the establishment of foreign diplomatic relations. The capacity
of coastal ports was not able to catch up with the increase in shipping activities. In 1973 a threeyear project to upgrade the Chinese coastal port's facilities was initiated where CNY 2.3 billion
has been invested in the port facilities construction. This was followed by decentralization of
authority as an outcome of port structural reform after 1984 where 37 out of 38 major ports were
jointly operated jointly managed by the Ministry of Communications and the local governments
until 1987. The latest change in Chinese ports policy was initiated after China‟s accession to
WTO in 2001. The expected growth in trade and the economy led to the introduction of Port Law
and its complementary, regulations such as the Rules on Port Operation and Management, in
2004. A modern enterprise system was gradually introduced into the port sector allowing
operators greater autonomy in operations and management.
In addition to the above events, several international shocks should also be taken into account in
the study of port throughput such as the Oil crises (1973, 1979 and 1990), Asian Financial Crisis
(1997) and the Global Financial Crisis (2008). Table 1 gives a timeline of events which might
have an impact on throughput of port.
The objective of this paper is to ascertain the relative impact of the above events on the maritime
traffic of Chinese ports since 1952. The study is organized into three stages. Stage one firstly tests
for unit roots with the assumption of no structural breaks in Chinese ports throughput series data.
If a series contains a unit root, then any certain event or reform is of limited value and long run
growth in throughput is probably affected by multiple factors. However, if the series is stationary,
this implies that only huge shock will have at least a semi-permanent effect on the growth path
(Li, 2000). Unit root test allowing for structural break is then performed to identify the exact
point of the most significant „shock‟ which affected port traffic. In stage two, we control for the
effects of economics variables such as foreign trade, Chinese domestic retails sales and port
investment to verify a co-integration relationship with Chinese ports throughput. If co-integration
exists, then granger causality test will be employed based on error correction model estimation
(VECM) in an attempt to explain the interaction between ports throughputs and those economics
variables. Stage three analyzes specifically with events which may have impact on port
performance. We test for co-integration between ports throughput and corresponding economic
variables allowing structural break. A potential break in co-integration implies deviation. The
timing and significance of break point will indicate exclusive impact of events on port throughput.
2
Table 1 Timeline of Political, Economic and Port Reforms
Year
Events
Scope
1958-1960
Great Leap Forward
National
1959–1961
Three years of natural disasters
National
1966–1976
Cultural Revolution
National
1973–1975
Three-year project to upgrade the Chinese coastal ports
Coastal Ports
1978–1979
Open Door Policy and accompanying Economic Reforms
National
1984-1987
Reform: semi-decentralization or dual-administration system
Major Ports
1989–1990
Macroeconomic Austerity Program
National
1992
Deng‟s Southern Tour
National
1997
Asian Financial Crisis
Asia
2001
China‟s World Trade Organization Accession
National
2003
SARS
National
1973, 1979,1990
Oil Crisis
Global
2004
Reform: Rules on Port Operation and Management
All Ports
2. Literature Review
There is a lot literature on economic analysis of port development and reform. Pallis et al. (2010)
reviewed 395 relevant journal papers in port economics, policy and management during the
period 1997-2008. Among which, “Port Governance” as one of total seven classifications,
achieves a lot of research attention (61 relevant papers out of all 395 papers). Ircha,(1997, 2001)
reviewed and evaluated the Canadian port reform and also outlined the evolution of strategic
planning and its applicability to Canadian ports. He kept a positive attitude to the port reform in
Canada and concluded that the reform will lead to further rationalization and enhanced
competitiveness of Canadian ports. Everett (Everett and Robinson 1998; Everett 2007) argued in
a corporatization port regime, where government has specifically chosen to retain ownership but
policy prescribes that government businesses will be profitable, bureaucrat and minister impose
significant constraints on achieving competitive efficiency. She also examined the impacts and
constraints the state government regulator imposed on terminal expansion and operations and
suggested the transfer of state government ports to a single national regulator. Hadi Baaj and Issa
(2001) presented the institutional reform of the Lebanese maritime transport sector. He listed
different port autonome models and asserted that the State Corporation model is the most high
scoring and powerful model. Mexico‟s port system was centrally managed by public firms until
1993. Then reforms liberalized and decentralized it to regional port authorities to improve its
efficiency. Estache (2004) measured the changes and sources of efficiency since the reforms. He
suggested that port reforms were quite successful in contributing to improvements in Mexico‟s
economic competitiveness. Serebrisky and Trujillo (2005) showed that structural reform caused
significant efficiency gains to Argentine ports and also identifies outstanding issues, such as
vertical mergers in the port of Buenos, could impact the long-run sustainability of the gains
achieved. Castillo-Manzano et al (2008) applied an estimated econometric model to investigate
whether there is an important impact of legislative changes on Spanish port traffic with
econometric technology by using data over the period of 1966–2003. He provided evidence
supporting that greater port autonomy had beneficial effects for the Spanish port system as a
whole. Cullinane and Wang (2006) described China‟s policies of economic reform since the
3
inauguration of its open door policy in 1978. They divide the development of Chinese ports into
three distinct phases: 1978 to 1984, 1984 to 2004 and 2004 to now according to different reform
contents. Still with regard to Chinese ports reform, Qiu (Qiu 2008) focused on the economic
background, motivations and progress, and discusses issues associated with relevant planning
events. He also recognized three phases in the development of Chinese ports. However he
regarded the 2001 as the beginning of the third phase instead of 2004 which Cullinane and Wang
believed. Qiu (2008) made his conclusion that the reforms are necessary for the ports industry to
raise funds for infrastructure expansion and to enhance the industry efficiency but Cullinane and
Wang asserted that it is still too early to tell whether the latest phase of reforms will prove to be
successful in solving China‟s port problems.
This paper builds upon the above literatures in two ways. First, the analysis in this paper is drawn
from a long time series data starting from 1952 considering the impacts from not only reforms but
also a series of economic and political events. Previous studies began from early 1980‟s and only
focus on reform. Second, most previous studies on port reform and performance tend to be
qualitative. We employ an empirical approach controlling for the endogenous structural breaks.
Instead of ascertaining a simple structural break test, this paper proposes both unit-root and cointegration tests allowing structural break so as to understand impacts from events in different
perspectives.
3. Data Description
The annual data utilized covers the period 1952 to 2009 drawn from various sources such as
Comprehensive Statistical Data and Materials on 50 Years of New China (1999), Transportation
Statistical Data and Materials on 50 Years of New China:1949~ 1999 (1999), China Shipping
Development Annual Report 2009 (2010), China Statistical Yearbook 2010 (2010), China Trade
and External Economic Statistical Yearbook 2010 (2010), China Marine Statistical Yearbook
2009 (2010) and Chinese Statistical Bulletin for Shipping and Road transportation (2001 ~ 2010).
The data is divided into two groups, among which, different Chinese ports throughputs are
primary research objects and economic variables as proxy.
Chinese ports throughput is recorded in two classifications: main coastal ports and main inland
ports. “Main ports” are also described as “Ports above a designated size” and this refers to: 1)
Coastal ports with over one million handling capacity in one year; 2) Inland port with over 2
million handling capacity in one year; 3) Ports with license of foreign transportation business.
These ports actually account for dominant traffic amount over all Chinese ports. For each
classification of port, it contains two time series which are “Domestic Trade throughput” and
“Foreign Trade throughput”. Therefore, totally, there are four records for ports throughput, which
are 1) Domestic throughput of main inland ports; 2) Foreign throughput of main inland ports; 3)
Domestic throughput of main coastal ports and 4) Foreign throughput of main coastal ports. We
recompose these throughputs and look at five definitions of throughput time series data in this
article, i.e. i) Domestic and Foreign throughput of Chinese main INland ports (DFIN); ii)
Domestic and Foreign throughput of Chinese main COastal ports (DFCO); iii) Domestic
throughput of Chinese main INland and COstal ports (DINCO); iv) Foreign throughput of
Chinese main INland and COastal ports (FINCO); and, v) Domestic and Foreign throughput of
Chinese main INland and Coastal ports (DFINCO).
The economic explanatory variables used in the analysis are, Chinese foreign trade (TRADE)
which contains both import and export trade, Chinese Domestic Retail Sales (RS) which also
indicates the level of Domestic Demand, Chinese main INland and Coastal ports inVEstment
(INCOVE) including INland ports inVEstment (INVE), COastal ports inVEstment (COVE) and
4
Chinese main Inland and Coastal ports Berth Number (INCOBN) including main INland ports
Berth Number (INBN) and main COastal ports Berth Number (COBN).
All variables are in natural logarithmic form for the purpose of scaling. Values of throughputs are
valued in thousand ton in the data source. Values of variables such as TRADE, RS, INCOVE,
INVE and COVE (deflated by Chinese Consumer Price Index, CPI) are in Chinese Yuan (CNY).
4. Results
4.1 Unit root tests without structural breaks
Figure 1 summarizes the throughput for Chinese ports. All throughput data except for foreign
throughput of Chinese main ports (FINCO) experienced a fall in the beginning of 1960s. The
foreign throughput of main ports (FINCO) grew faster than throughputs of other ports. Figure 2
shows the trends of relevant economic variables. Retail sales (RS) and ports investment
(INCOVE) also exhibit a dip around 1961. However, the growth of foreign trade is higher than
that of all the other variables while port investment (INCOVE) fluctuates with time.
DINCO
FINCO
DFCO
DFIN
DFINCO
INCODF
14
TRADE
RS
INCOBN
INCOVE
20
12
15
10
Figure 1 Trends of Different Chinese Ports Throughput
2008
2004
2000
1996
1992
1988
1984
1980
1976
1972
1968
1964
1960
1952
2008
2004
2000
1996
1992
1988
1984
1980
1976
1972
1968
1964
0
1960
4
1956
5
1952
6
1956
10
8
Figure 2 Trends of Relevant Proxy Varibles
We perform the Augmented Dickey-Fuller (Dickey 1979) unit root test and ensure the robustness
of its results by Phillips-Perron test for all the time series data. The results of unit-root test are
summarized in Tables 2 and 3.
Table 2: Unit Root Test for All Derived Time Series of Chinese Ports Throughput
DFIN
DDFlN
DFCO
DDFCO
DINCO
DDINCO
FINCO
DFINCO
DFINCO
DDFINCO
t-stat
-1.48
-5.67
-1.47
-3.85
-1.36
-4.70
-3.43
-5.72
-1.31
-4.56
Prob
0.83
0
0.83
0
0.86
0
0.06
0
0.88
0
t-stat
-1.48
-5.67
-1.47
-3.85
-1.83
-4.34
-3.68
-9.35
-1.77
-4.18
Prob
0.83
0
0.83
0
0.68
0
0.03
0
0.70
0
ADF
PP
Notes: ADF is the Augmented Dickey and Fuller (1981) test. The lag length of the ADF test is determined by minimizing the SBIC. PP is the
Philip and Perron(1988) test. The most optimal lagged term for the model is selected according to Schwarz Information Criterion (SIC).
Table 3: Unit Root Test for Potential Proxy variables
TRADE
DTRADE
RS
DRS
INVE
DINVE
COVE
DCOVE
INCOVE
DINCOVE
COBN
INCOBN
t-stat
-2.58
-5.13
-1.42
-5.62
-2.87
-6.23
-2.64
-6.69
-2.65
-5.94
-0.98
-6.54
Prob
0.29
0
0.843
0
0.18
0
0.26
0
0.26
0
0.94
0
t-stat
-1.82
-4.20
-0.69
-5.46
-3.16
-7.74
-2.62
-7.76
-2.66
-8.32
-0.98
6.53
Prob
0.68
0
0.97
0
0.10
0
0.27
0
0.26
0
0.94
0
ADF
PP
5
Results show that except for foreign throughput of Chinese main ports (FINCO), the null
hypothesis of a unit root cannot be rejected on the log levels of all the other series at the 1%
significance level, while it is rejected on the log-first differences of all the series. These variables
are then considered as I(1), i.e. integrated of order one. The FINCO rejects the null hypothesis of
a unit-root at the 10% significance level by ADF test and the Phillips-Perron test at the 5%
significance level. The results imply that Chinese port domestic traffic as well as Chinese port
traffic as a whole are affected by multiple shocks. However, it also suggests the FINCO is some
degree of trend stationary and relatively less influenced by less shocks than the domestic
throughputs of Chinese main ports.
4.2 Unit root tests with structural breaks
The traditional unit root test fails to reject the unit root hypothesis for the series that are actually
trend stationary with structural breaks. Over fifty years after the foundation of P.R.China, as the
macro-economic environment, society and ports policy experienced several significant, it is
necessary to ask whether or not the traditional unit root test results are in a biased interpretation
when one or two stationary alternatives are true in port throughput series and a structural break is
ignored. It is interesting to ascertain what event that could has contributed a potential break.
Perron (1989) firstly proposed a unit root test allowing for a structure break with three alternative
models: crash model (shift in the intercept), changing growth model (change in slope) and the
model containing change both in intercept and slope. However the models have been criticized
for treating the time of breaks as exogenous (i.e., the time of break is known a priori). Building
upon Perron‟s models, Zivot and Andrews (1992) developed three forms of the sequential trend
break to endogenous break test (ZA model). We employ methodology A and C of ZA model.
Model A aims to test short term effect and Model C treats the long run effects. Our hypothesis is
that if events had short term effect it would be reflected by the intercept coefficient γ in model A
and long term effect would be represented by the slope coefficient θ in model C. The following
models is designed to explain the breaks in maritime traffic evolution of Chinese ports
Model A: ∆yt = c + α*y-1 + β*t + γ*DUt +
Model C: ∆yt = c + α*y-1 + β*t + θ*DTt + γ*DUt +
DUt =
DTt =
Here, DUt and DTt are dummy variables used to capture the effect of shocks at break time Tb. DUt
indicates level (intercept) shift and DTt specifies the slope change.
To apply these models, the break point is searched for over range of the sample (0.10-0.90T),
which means the possible breaks before 1958 and after 2003 cannot be recognized due to data
limitation. The “t-sig” approach suggested by Hall (1994) is applied to decide the optimal lag
length k. The choice of sample size and “t-sig” approach will also be applied for the Gregory and
Hansen‟s co-integration test in the following section. Although Zivot and Andrews (1992)
provide an asymptotic critical value for this test, it may deviate substantially in terms of different
sample sizes. Therefore, we calculate the “exact” critical values for our cases following the
methodology recommended in Zivot and Andrews (1992, p.262).
The results of ZA test for different Chinese ports throughputs and economic variables are shown
in Table 4 and Table 5:
6
Table 4 Zivot and Andrew Test for Unit Roots with One Structural Break: Model A
DFIN
Break
1961
DFCO
DINCO
FINCO
DFINCO
TRADE
RS
COVE
INCOVE
COBN
INCOBN
2002
1961
2002
1961
1960
1961
1961
1961
1986
1986
*
*
-2.32
-5.26
tα
-4.34
-2.94
-3.39
-5.47
k
1
2
2
γ
-0.39***
0.15***
-0.32***
-3.2
-4.62
-4.81
-4.13
-5.58
0
2
2
1
1
1
0
0
0.25***
-0.28***
-0.40***
-0.20***
-0.61***
-0.75***
0.19***
0.55***
Note: Exact Critical Value (tα): -5.97(1%), -5.68(5%), -5.32(10%); ***,**,* indicate significance at 1%, 5% and 10% levels, respectively.
Table 5 Zivot and Andrew Test for Unit Roots with One Structural Break: Model C
DFIN
DFCO
DINCO
FINCO
DFINCO
TRADE
RS
COVE
INCOVE
COBN
INCOBN
Break
1961
1960
1961
1998
1961
1967
1961
1972
1961
1986
1986
tα
-4.36
-3.39
-3.5
-5.68
-3.02
-4.83
-4.90
-4.79
-5.63
-3.68
-5.2
k
1
2
2
0
2
2
1
1
1
0
0
γ
θ
-0.45
***
-0.02
-0.40
***
-0.07***
-0.40
***
-0.03
-0.05
0.04***
-0.30
***
-0.01
-0.14
0.08***
-0.24
***
-0.01
0.95
***
0.06**
-0.92
***
-0.05
0.24
***
0.02***
0.55***
0.01
Note: Exact Critical Value (tα): -6.49(1%), -6.37(5%), -6.25(10%); ***,**,* indicate significance at 1%, 5% and 10% levels, respectively.
The results of unit root test allowing one structural break are basically consistent with those of the
standard ADF and Phillips-Perron tests. There are no evidences against accepting the unit root
null hypothesis at the 5% significance level for all the time series data in both model A and model
C. It confirms that most throughputs of Chinese ports on the long-run growth path have been
influenced by multiple events.
In model A, the most significant break point occurs following the Great Leap Forward for some
time series including domestic and foreign throughput of Chinese ports (DFINCO), domestic and
foreign throughput of Chinese main inland ports (DFIN), domestic throughput of Chinese main
inland and costal ports (DINCO), economics variables (TRADE, Retail Sales) and ports
investments (INCOVE and COVE). The foreign throughput of Chinese main ports (FINCO) is
trend stationary at the 10% significance level in model A with an upward break in 2002 when
China entered the WTO. The most significant break of throughput of Chinese main coastal ports
(DFCO) is also in 2002, but it cannot reject the null hypothesis of a unit root at the 10%
significance level. All the coefficients of dummy variables are significant at 1% significance level.
The results from Model C suggest that the null hypothesis of a unit root cannot be rejected for
time series at level at 10% significance level. The most significant breaks for most of the series
data are around 1961. This include domestic and foreign throughput of Chinese main inland ports
(DFIN); domestic and foreign throughput of Chinese main coastal ports (DFCO), domestic
throughput of Chinese main inland and costal ports (DINCO), domestic and foreign throughput of
Chinese main inland and coastal ports (DFINCO), Retail Sales (RS) and Chinese main ports
investment (INCOVE). The coefficients of dummy variables (γ) are all significant at 1%
significance level while those of dummy variables (θ) are only significant in some cases. Some of
them (DFIN, DINCO and DFINCO) are not significant at the 10% significance level. A positive
change in slope at 1% significance level is observed for foreign throughput of Chinese ports
(FINCO) in 1998, a year after the Asian Financial Crisis, the same as the foreign trade. But the
intercept coefficients of them are negative. This suggests that the crisis affected not only the trade
but also the foreign throughput of Chinese ports, followed by a quick recovery. Coastal port
investment is not trend stationary with one break, the intercept coefficient is significantly positive
at a 1% significance level and the slope coefficient is significantly positive at a 5% significant
7
level given a structural break in 1972. Therefore, the implementation of the three-year project to
upgrade the Chinese coastal port is proved to generate a rise of coastal ports investment. Similarly,
the intercept coefficients and slope coefficient of Chinese coastal ports berth number (COBN)
and the intercept coefficients of Chinese main ports berth number (INCOBN) are significantly
positive at 1% significance level with a break in 1986. It confirms an increase of port construction
from 1986, two years after the beginning of the first port structure reform.
The Great Leap Forward has greatest influence on Chinese ports as throughputs fell together with
other economic indices, foreign trade (TRADE), Domestic Demand (RS) and ports investment
(INCOVE). This however didn‟t lead to a drastic decline to the foreign throughput of Chinese
ports (FINCO) and is in short run and. Although the implement of ports project in 1972 generated
substantial investment in port construction and ports structural reform during 1984 to1987
resulted in a sharply increase in berth number, a lack of evidences to support the hypothesis that
these measures promote the throughput of Chinese ports in a long run significantly. The sign of
port boom appeared during the turn of the new century when China recovered from the Asian
Financial Crisis and accessed to WTO.
4.3 Co-integration tests without structural breaks
It is noted that the ports maritime traffic series exhibit similar trend in relation to economic
indications (see Figures 1 and 2). Figures 3 to Figure 8 show the cyclical trends of their changing
percentage. Most series show large down turn around 1961 and almost keep positive thereafter.
Three cycles are apparent around 1978 to1989, 1991 to1996 and after 1999 with some differences.
Ports investments suffer from cyclical changes with greater downturns and upswings. Trade and
ports throughputs are similarly affected. The retail sale is relatively stable.
TRADE
RS
INCOVE
0.5
0.5
0
0
-0.5
-1
-1
-1.5
-1.5
Figure 3 Percent Change of DFINCO, TRADE, RS and INCOVE
FINCO
1
TRADE
INCOVE
RS
INCOVE
Figure 4 Percent Change of DINCO, RS and INCOVE
DFIN
1.5
RS
INVE
1
0.5
0.5
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
0
-0.5
DINCO
0
-0.5
-1
-1
-1.5
-1.5
-2
-2
Figure 5
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
-0.5
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
1
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
DFINCO
1
Percent Change of FINCO, TRADE and INCOVE
Figure 6
Percent Change of DFIN, RS and INVE
8
TRADE
COVE
2009
2005
2001
1997
1993
1989
1985
1981
1977
1973
0
1969
0
1965
0.5
1961
0.5
1957
1
1953
1
-0.5
INCODF
1.5
INCOVE
INCOBN
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
DFCO
1.5
-0.5
-1
-1
-1.5
-1.5
Figure 7
Percent Change of DFCO, TRADE and COVE
Figure 8 Percent Change of DFINCO, INCOVE and INCOBN
Given the apparent changes, we now proceed to test the relationships between port throughputs
and other economic indices.
Although the Johansen (Johansen 1988) co-integration test has seen a commonly used, we
employ the Engle and Granger (1987)‟s approach2 to test the co-integration without structural
break in this article because the methodology complements the Gregory-Hansen approach which
will be used later in the article.
Table 6 Engle and Granger co-integration test
Endogenous
c
te
µTRADE
µRS
µINCOVE
β
Critical value (te)
DFINCO
TRADE+RS+INCOVE
TRADE+RS
-3.47*
-4.46***
**
***
-3.49
-4.54
-0.8
1.31***
0.008
---
-4.55(1%)
-3.89(5%)
-3.56(10%)
-0.08
1.32***
---
---
-4.09(1%)
-3.44(5%)
-3.12(10%)
RS
-3.67***
-3.86***
---
1.19***
---
---
-3.55(1%)
-2.91(5%)
-2.59(10%)
TRADE
-2.85
5.54***
0.33***
---
---
0.045***
-4.12(1%)
-3.49(5%)
-3.17(10%)
*
***
---
-3.55(1%)
-2.91(5%)
-2.59(10%)
INCOVE
-2.59
6.37
---
---
0.68
***
DINCO
RS+INCOVE
-3.28*
-2.90***
---
1.08***
0.02
---
-4.09(1%)
-3.44(5%)
-3.12(10%)
RS
-3.31**
-3.17***
---
1.11***
---
---
-3.55(1%)
-2.91(5%)
-2.59(10%)
***
---
---
-3.55(1%)
-2.91(5%)
-2.59(10%)
INCOVE
-2.56
6.37
---
0.64
***
FINCO
TRADE+INCOVE
TRADE
INCOVE
-3.35*
-4.08
**
-2.88
0.33
0.65***
---
0.22**
---
-4.09(1%)
-3.44(5%)
-3.12(10%)
**
---
---
0.09***
-4.12(1%)
-3.49(5%)
-3.17(10%)
---
0.84
---
-3.55(1%)
-2.91(5%)
-2.59(10%)
0.19***
---
-4.09(1%)
-3.44(5%)
-3.12(10%)
---
-3.55(1%)
-2.91(5%)
-2.59(10%)
---
-3.55(1%)
-2.91(5%)
-2.59(10%)
***
0.12
3.83***
---
5.03
DFIN (INVE)
RS+INVE
-3.50**
-1.64**
---
0.83***
RS
-3.53**
-4.29***
---
1.12***
**
***
INVE
-3.43
6.04
---
---
0.71
***
DFCO (COVE)
TRADE+INCOVE
-3.14*
2.84***
0.59***
---
0.12*
---
-4.09(1%)
-3.44(5%)
-3.12(10%)
**
***
0.27
**
---
---
---
-3.55(1%)
-2.91(5%)
-2.59(10%)
6.35***
---
---
0.66***
---
-3.55(1%)
-2.91(5%)
-2.59(10%)
TRADE
-3.52
COVE
-2.60*
5.57
Note: ***,**,* indicate significance at 1%, 5% and 10% levels, respectively. The bold indicate a co-integration with above 5% significance
level.
2
Engle and Granger expression: et = yt - c - µ1x1t - µ2x1t -… µnxnt – βt, the exact critical values of ei are calculated according to
James (2010) in terms of different number of repressors and the existence or non-existence of constant and trend items.
9
The choice of repressors for port throughput is based on the following assumptions: 1) Domestic
ports throughput and inland ports throughput interact with Chinese Retail Sale; 2) Foreign ports
throughput and coastal port throughput interact with Chinese foreign trade; 3) Ports throughput
interacts with corresponding Chinese ports investment, for example, throughput of all ports
(DFINCO) corresponds to all ports investment (INCOVE), throughput of coastal ports (DFCO)
corresponds to coastal ports investment (COVE) and 4) Because the different in the capacities of
berths, berth number is not taken into consideration in co-integration test. Table 6 shows the
results of Engle and Granger co-integration test for different pairs of variables.
According to the test results, the throughputs of DFINCO, DINCO and DFIN which are relevant
to domestic ports throughput are all co-integrated with Retail Sales (RS) at 5% significance level.
In addition, a co-integration relationship at the 5% significance level is also observed between the
total throughput of inland ports (DFIN) and inland ports investment (INVE). Similarly, the
coastal ports relevant throughput (FINCO and DFCO) all show a co-integration relationship with
foreign trade (TRADE) at 5% significance level. When TRADE and RS are tested for cointegration with total throughput of all ports (DFINCO) simultaneously, the RS coefficient µRS is
significant at 1% significance level while TRADE coefficient µTRADE is not significant at 10%
significance level. It shows the Chinese total ports throughput (DFINCO) has a more similar
pattern with the Chinese Retail Sales (RS) in trend compared to foreign trade (TRADE) which
maybe due to the “lock States” of China before 1980s.
4.4 Granger Causality test on the lead-lag relationship
We next employ VECM (Vector Error Correction Model) to identify the lead-lag causal
relationship among variables where a co-integration relationship exists ( Granger 1988).
Table 7 summarizes the results of Granger causality test performed, it shows that Chinese main
ports all throughput (DFINCO), domestic throughput of Chinese main ports (DINCO),
throughput of Chinese inland ports (DFIN) granger cause Retail Sales (RS) at the 10%
significance level without feedback effect. Meanwhile, throughput of Chinese inland ports
(DFIN), throughput of Chinese coastal ports (DFCO) granger cause inland ports investment
(INVE) and coastal ports investment (COVE) at the 10% significance level without feedback
effect respectively. The causality from the throughput of Chinese coastal ports (DFCO) to coastal
ports investment (COVE) is found to be significant at the 1% significance level. A unidirectional
causality effect can be observed from Chinese foreign trade (TRADE) on foreign throughput of
main ports (FINCO) at the 1% significance level. A bilateral causality can be recognized between
Chinese main coastal ports throughput (DFCO) and foreign trade (TRADE).
Table 7: Granger-Causality Test
Causality Test
1
Port throughput to proxy variables1
Statistics (Prob.)
Proxy variables to port throughput2
Statistics (Prob.)
DF
DFINCO ↔ RS
4.25(0.039)
0.025(0.87)
1
DINCO ↔ RS
4.24(0.039)
0.004(0.95)
1
FINCO ↔ TRADE
3.65(0.16)
3.65(0.009)
2
DFIN ↔ RS
5.78(0.055)
0.87(0.65)
2
DFIN ↔ INVE
5.21(0.0738)
0.756(0.685)
2
DFCO ↔ TRADE
5.41(0.0668)
5.109(0.0777)
2
DFCO ↔ COVE
9.19(0.0024)
0.572(0.45)
1
Notes: means the null hypothesis is port throughput does not granger cause the proxy variables;
variables do not granger cause port throughout. Figures in (.) indicate the exact significance levels.
2
means the null hypothesis is that proxy
10
This result reveals that the foreign trade is the main driving force to Chinese ports throughput,
especially the foreign throughput and coastal port throughput. The foreign throughput is
counteractive to the foreign trade. The rise of ports throughput boosts domestic demand and lead
to substantial port investment.
4.5 Co-integration tests with structural breaks
We now consider the long run relationship between Chinese ports throughput and other
economics variables by allowing a structure break indicating an exclusive impact on port.
On the basis of Engle and Granger co-integration test and unit-root test allowing structure break
suggested by Perron (1987), Gregory and Hansen (1996) propose three models for co-integration
test allowing one endogenous structural (GH model). The break in GH model implies a maximal
deviation between ports throughput and proxy variables in this study. The GH model has been
broadly applied in many research fields. For example, Budget balance (Keho 2010), Public
investment in agriculture (Lee and Hsu 2009), Fiscal Synchronization (Murat Aslan 2009),
Energy consumption (Altinay and Karagol 2004), Fiscal policy (Hjelm and Johansson 2002),
Japanese demand system (Ogura 2011), Saving and investment nexus for China (Narayan 2005)
and so on. We control for our economic indices in the co-integration test models with break as:
Model A(C): allow a change in the intercept which is represented as a level shift
yt = c +
+ γ*DUt + εt
Model C(C/S): it is called regime shift where changes in both intercept and slope coefficients are
included
yt = c +
+ γ*DUt +
* DUt +εt
Here, DUt is different from DUt in ZA models, DUt=1 when t=Tb instead of t=Tb-1.
Castillomanzano (2008) used world maritime traffic and Spanish GDP as proxy economic
variables to study the effect of legal structural reform on Spanish ports. We hence noticed and
tested the international level economic variables such as world or regional maritime traffic, trade
and Chinese GDP. Analysis shows that there are very large deviations between international or
regional maritime traffic, economics indices such as regional trade and GDP, and all sorts of
Chinese port throughputs. The possible reason is that China was isolated from the world economy
prior to 1978. As an alternative, this paper controls for the domestic economic variables as proxy
which have been used in Engle-Granger co-integration test. By doing this, the exclusive effect
brought especially by events relevant to ports can be identified through comparing the results of
Engle-Granger test and Gregory and Hansen test. The results of Gregory-Hansen test can be seen
in Table 8.
In contrast to the results of Engle and Granger test, in most cases the null hypothesis of no cointegration between variables cannot be rejected by GH model. The implication is that there is no
overwhelming shock to throughputs brought by implementation of port policy or reform.
In all pairs of variables, a co-integration relationship at 1% significance level is only found
between foreign throughput of Chinese ports (FINCO) and foreign trade (TRADE) in both model
A and model C. The break occurred in 1961 following the Great Leap Forward. A positive
intercept coefficient (θTRADE) at 1% significance level in model A and a negative slope coefficient
(γTRADE) in mode C imply that the foreign throughput of Chinese ports (FINCO) suffers much less
than foreign trade during the Great Leap Forward. But in a long run from 1961 to now, it grows
slower than the foreign trade. Throughput of Chinese inland ports (DFIN) has a co-integration
11
relationship with Retail Sales at 5% significance level if a break at 1963 is considered both in
model A and model C. The reason is unclear. Measuring by Retail Sales and foreign trade
simultaneously, the null hypothesis of no co-integration is rejected by throughput of Chinese
main ports (DFINCO) at a 5% significance level in model C. The break year is 1972. The
intercept coefficient (θTRADE+RS) is significantly positive at 1% significance level while the slope
coefficient for both foreign trade and retails sales (γTRADE and γRS) are negative and insignificant.
This break coincides with “Three-year Project” in 1972. It suggests that the project has had a
positive impact in raising ports throughput in short run but not in the long term.
Table 8 Gregory and Hansen test for co-integration with structural break
Model
Break
ADF
αTRADE
αRS
αINCOVE
γ
θ x1
θ x2
k
Critical Value (1%, 5%, 10%)
DFINCO ↔ TRADE +RS
m=2
A(C)
2002
-4.05
-0.11*
1.32***
---
1.32***
---
---
0
-5.44
-4.92
-4.69
C(C/S)
1972
-5.65**
0.22
1.47***
---
5.38***
-0.15
-0.37
5
-5.97
-5.50
-5.23
---
0
-5.13
-4.61
-4.34
0
-5.47
-4.95
-4.68
DFINCO↔ RS
m=1
A(C)
2002
-4.38*
---
1.15***
---
0.22***
---
C(C/S)
2002
-4.38
---
1.15***
---
-0.26
0.03
-4.10
0.67***
---
---
0.21
---
---
0
-5.13
-4.61
-4.34
-4.22
***
---
---
-2.86
0.21
---
0
-5.47
-4.95
-4.68
DFINCO↔ TRADE
A(C)
C(C/S)
2002
1998
m=1
0.68
DFINCO↔ INCOVE
A(C)
C(C/S)
1972
1986
m=1
-3.40
-3.99
-----
---
0.79***
-0.55***
---
***
***
0.47
-1.84
--0.34
---
0
***
-5.13
-4.61
-4.34
-5.47
-4.95
-4.68
DINCO ↔ RS
A(C)
C(C/S)
m=1
2001
2001
-3.96
-4.50
---
1.07***
---
0.23***
---
---
0
-5.13
-4.61
-4.34
---
***
---
-3.95
0.30
---
0
-5.47
-4.95
-4.68
1.07
DINCO ↔ INCOVE
A(C)
C(C/S)
1972
1986
m=1
-3.53
-4.18
-----
---
0.76***
-0.59***
---
---
***
-1.78***
0.33***
0.43
---
0
-5.13
-4.61
-4.34
0
-5.47
-4.95
-4.68
FINCO ↔ TRADE
A(C)
C(C/S)
m=1
1961
-5.20***
0.77***
---
---
1.11***
---
---
0
-5.13
-4.61
-4.34
1961
***
***
---
---
5.55*
-0.48
---
0
-5.47
-4.95
-4.68
-5.57
1.25
FINCO↔ INCOVE
A(C)
C(C/S)
1957
m=1
-4.03
---
---
0.78***
0.95***
---
1.68
-0.20
1
-5.13
-4.61
-4.34
1
-5.47
-4.95
-4.68
---
1
-5.13
-4.61
-4.34
-0.9***
---
1
-5.47
-4.95
-4.68
0.38***
---
---
0
-5.13
-4.61
-4.34
0.40***
-0.91***
0.33***
---
0
-5.47
-4.95
-4.68
---
0.21
---
---
0
-5.13
-4.61
-4.34
0
-5.47
-4.95
-4.68
---
0
-5.13
-4.61
-4.34
---
0
-5.47
-4.95
-4.68
*
1957
-3.92
---
---
0.98
A(C)
1963
-5.05**
---
1.19***
---
-0.30***
---
C(C/S)
1963
-5.07**
---
2.07***
---
9.30***
A(C)
2002
-3.71
---
---
0.66***
C(C/S)
1973
-4.24
---
---
-3.90
0.69***
---
-4.04
0.69
***
-3.47
---
---
DFIN ↔ RS
m=1
DFIN ↔ INVE
m=1
DFCO ↔ TRADE
A(C)
C(C/S)
2002
1998
m=1
---
---
-2.52
0.19
---
0.54***
-0.85***
---
---
***
***
DFCO ↔ COVE
A(C)
C(C/S)
1995
1986
m=1
-3.82
---
0.44
-1.84
0.37
***
Note: SIC is used as lag selection standard. Maxlag is selected according to “t-sig” method. Critical value are extracted from Table in Gregory
and Hansen (1996, p. 109).
12
When Retail Sale (RS) is regarded as a benchmark for different corresponding ports throughputs
in Gregory and Hansen test, the most significant break can be identified around 2002 (or 2001)
for throughput of Chinese main ports (DFINCO) and domestic throughput of Chinese main ports
(DINCO). The intercept coefficients (γRS) are significantly positive at 1% significance level in
model A and the slope coefficients (θRS) are insignificantly positive in model C.
The same break point was can be The most significant break is also found in 2002 in model C for
the co-integration relationship between the trade and port throughputs such as throughput of
Chinese main ports (DFINCO), throughput of Chinese coastal ports (DFCO). But the breaks of
model C are in 1998. The intercept coefficients in model A and slope coefficients (θTRADE) are
insignificantly positive in model C.
These results illustrate that China accession to WTO (2001) resulted in exclusive positive impact
on ports throughput compared to its impact on domestic demand and foreign trade. The Asian
Financial Crisis (1997) had a greater negative impact on trade than its on port throughput.
Thereafter the port throughput grew faster than the foreign trade. Qiu (2008) considered the 2001
as the beginning of third boom of Chinese port industry. This statement is proved to be valid
according to our analysis results based on the historic data.
Breaks between throughput of Chinese main ports (DFINCO), domestic throughput of Chinese
main inland and costal ports (DINCO), throughput of Chinese main coastal ports (DFCO) and
their corresponding ports investment (INCOVE, COVE) are observed as 1972 in model A and
1986 in model C. The coefficients of intercept (θincove) in model A are significantly negative at 1%
significance level and the slope coefficients (γincove) in model C are significantly positive at 1%
significance level. It shows the investment in ports brought by port structural reform is proved to
be more efficient to ports development.
5. Conclusion
Chinese ports throughput is largely influenced by foreign trade and retail sales (domestic demand)
in a long run. There is also correlation between throughput and port investment. This seems to
suggest that foreign trade impacts port throughput positively through foreign and coastal
throughput which in turn increases domestic retail sales (domestic demand). Port investment lags
behind port throughput. The multiple economic and reform events have an influence on maritime
traffic. For example, the implementation of “3-year project to upgrade the Chinese coastal ports”
in 1972 can be regarded as a watershed in the history of port development. Prior to 1972,
domestic inland ports accounted for almost all of throughput volume. The Great Leap Forward
had a profound negative impact on the nation‟s economics indices in 1961 in particular domestic
port throughput in a short term. Post 1972, the foreign throughput takes over the dominant
amount over domestic port throughput. The largest impact to coastal port throughput was after
accession to WTO in 2001 as foreign trade grew by leaps and bounds and this impact is exclusive
to port throughput and trade.
As such, the impact of changes in reform in of port policy was probably minimal due to the
phenomenal growth in foreign trade. Perhaps “The 3-year project to upgrade the Chinese coastal
ports” facilitated the increase in ports investment and the growth of investment was observed
much faster than the port throughput. In 1986, two years after the first port reform was introduced,
port throughput out grew port investment suggesting that the investment in ports and structural
change in port governance led to higher efficiency.
13
In conclusion, the political events such as the Great Leap Forward and economic events like
China‟s accession to WTO had greater impact on port throughput. Port investment and
infrastructure and port structural reform merely facilitated port capacity if not acted as catalyst to
increase port throughput performance. On the whole port reform did not play a major role.
Improvements to the study can be summarized as follows:

Methods for detecting unit root and co-integration test are available for two breaks
((Lumsdaine and Papell 1997), (Hatemi-J 2008)). Lumsdaine and Papell noted that the
number of structural breaks is not definitive. Perhaps, we may need to explore further
methodology that is able to identify numbers of breaks at various levels of significance.

As ports are heterogeneous in terms of level of technology employed and management
structures perhaps different functional forms should be employed to analyze port performance.
Cargo types (bulk, container, oil) by different time periods can also provide finer analysis.
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