Port infrastructure investment and regional economic growth in China: Panel evidence in port regions and provinces

Transport Policy 36 (2014) 173–183

Contents lists available at ScienceDirect

Transport Policy journal homepage: www.elsevier.com/locate/tranpol

Port infrastructure investment and regional economic growth in China: Panel evidence in port regions and provinces Lili Song a,b,n, Marina van Geenhuizen c a

School of Management, Harbin Normal University, 1 Shida Road, Harbin, China School of Economics and Management, Harbin Institute of Technology, 92 Xidazhi Street, Harbin, China c Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands b

art ic l e i nf o

Keywords: Port investment Regional economic growth Economic structure Panel data China

a b s t r a c t China's seaports belong to the largest in the world. The question is to what extent port infrastructure investment in China also contributes to growth of the regional economies involved, through mainly direct and indirect relations. We estimate the output elasticity of port infrastructure through production function, applying panel data analysis for the period of 1999–2010, and calculate the model at the level of four port regions as well as the port province level. The results indicate clear positive effects of port infrastructure investment in all regions, however, the strength varies considerably among the four regions, with the Yangtze River Delta region (Shanghai) at the strongest level, followed by the Bohai Rim region (Tianjin), the Southeast region (Guangzhou) and the Central region, where the influence is the weakest. The analysis indicates that differences are related to the character of the port (land or sea), stage of economic development of the region, international network connectivity, and the spillover effects from adjacent regions. Overall, the weakest relation tends to be with landside transport infrastructure density. The paper closes with some policy implications. & 2014 Elsevier Ltd. All rights reserved.

1. Introduction 1.1. China's seaport in the world Ports are traditionally seen as economic catalysts for the regions they serve, where the agglomeration of services and manufacturing activities generate economic benefits and socioeconomic wealth (Warf and Cox, 1989; Pettit and Beresford, 2009; Zhang et al., 2009; Danielis and Gregori, 2013). Chinese ports play a key role in the world port system of 2011, as indicated in Table 1. The 10 Chinese ports rank high with a share in total cargo volume and container traffic of the top-20 world ports of 52.9% and 53.0%, respectively. In both rankings, China is present with three ports among the five largest ones in the world, with Shanghai in first place. The rankings also show differentiation between cargo and container traffic. For example, Tianjin port enjoys a higher rank (rank 3) in cargo volume and a relatively lower rank in container traffic (rank 11), reflecting the port's specialization in raw materials like coal and mineral. However, the rankings are only a description of relative size of transport flows, while this is just

n Corresponding author at: School of Management, Harbin Normal University, 1 Shida Road, Harbin, China. Tel.: þ86 13936307866. E-mail address: [email protected] (L. Song).

http://dx.doi.org/10.1016/j.tranpol.2014.08.003 0967-070X/& 2014 Elsevier Ltd. All rights reserved.

one part of port activity in a situation of manifold and systematic relationships between ports and ports' regional economies. Accordingly, the relation with local industries, economic characteristics of the port regions, and transport network connectivity of the region, etc. could also have an impact on port activity, as well as on regional economic growth (Berechman et al., 2006; Banister, 2012; Ducruet et al., 2013). The important position of Chinese ports indicates that China has made substantial capital investment in its port facilities in recent years. What is actually less known is to what extent the port investments contribute to growth of the regional economy through various multiplier effects, including the direct, indirect and induced effects, and whether there are large regional disparities in these effects. 1.2. Port infrastructure and the regional economy: a literature review Over the last decades, a large number of studies has focused on the impact of transport infrastructure and accessibility in general on regional economic growth, most of which were concerned with transport investments, aiming to assess whether positive economic impacts are a sufficient rationale for traffic infrastructure investments (Ozbay et al., 2003; Canning and Bennathan, 2007). However, in the recent literature, impacts on the regional economy are increasingly seen as influenced by the level of traffic infrastructure

174

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

Table 1 Top 20 world ports in 2011. Source: Institute of Shipping Economics & Logistics, Containerization International Yearbook 2012. Rank

Port, Country

Cargo volume (thousands of tons)

Rank

Port, Country

Container traffic (TEUS)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Shanghai, China Singapore, Singapore Tianjin, China Rotterdam, Netherlands Guangzhou, China Qingdao, China Ningbo, China Qinhuangdao, China Bushan, South Korea Hong Kong, China Port Hedland, Australia South Louisiana (LA), U.S.A Houston (TX), U.S.A Dalian, China Shenzhen, China Port Kelang, Malaysia Antwerp, Belgium Nagoya, Japan Dampier, Australia Ulsan, South Korea

590,439 531,176 459,941 434,551 431,000 372,000 348,911 284,600 281,513 277,444 246,672 223,633 215,731 211,065 205,475 193,726 187,151 186,305 171,844 163,181

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Shanghai, China Singapore, Singapore Hong Kong, China Shenzhen, China Bushan, South Korea Ningbo, China Guangzhou, China Qingdao, China Dubai Ports, United Arab Emirates Rotterdam, Netherlands Tianjin, China Kaohsiung, Taiwan Port Kelang, Malasyia Hamburg, Germany Antwerp, Belgium Los Angeles, U.S.A Tanjung Pelepas, Malaysia Xiamen, China Dalian, China Long Beach, U.S.A

31,739,000 29,937,700 24,384,000 22,570,800 16,163,842 14,719,200 14,260,400 13,020,100 12,617,595 11,876,920 11,587,600 9,636,289 9,435,408 9,014,165 8,664,243 7,940,511 7,302,461 6,454,200 6,400,300 6,061,091

accumulation in the region at the start of the study period, with an emphasis on a non-linear relationship between transport infrastructure provision and economic growth (Banister, 2012). The idea has been forwarded that below a certain level of infrastructure endowment and above a certain level, the growth effect of expanding transport infrastructure tends to be relatively small (Deng et al., 2013a, 2013b). Threshold values have also been addressed by Hong et al. (2011), but only as a lower threshold. In the remaining section, we discuss the statistical models used in investigations of the relationship between port investment and the regional economy, and studies using a broader network and value chain view on port development, including spill-over effects. Mainly three empirical methods are used to investigate the relationship between transport investment and regional economy which are Cobb–Douglas production function framework, time series models and structural equation modeling. Most previous research used a Cobb–Douglas production function framework in estimating the impacts of transport investment (Blum, 1982; Biehl, 1986; Nijkamp, 1986; Del Bo and Florio, 2012). The result of these studies is a positive relationship between transport investment and economic growth which is now commonly accepted (Berechman et al., 2006). Yoo (2006) and Jiang (2010) investigated the influence of seaport infrastructure investment on economic growth in Korea and China respectively by applying time series data. A positive impact of port investment on economic growth could be found both in Korea and China. In addition, Jiang's empirical findings also show regional disparities: the port investments in Pearl River Delta have the highest short-term output elasticity, whereas the short-term output elasticity in Yangtze River Delta is the lowest, indicating a larger amount of new construction and related activity in the first region compared to the last one. Another study on China, by Deng et al. (2013a, 2013b), used structural equation modeling to unravel the different influences on regional economic growth related to port investments, by distinguishing between port supply, port demand, and value added-activity in ports. They observed no direct relation between port supply and growth in the regional economy, but port supply was connected to this growth through the relations with port demand and port value added activity. Many recent studies analyze port activities and relations with the regional or local (port city) economy from wider network perspectives, including territorial embedding of port areas in commodity

flows and value chains. Ducruet et al. (2013), in a comparative study of almost 200 port regions in advanced economic areas, argue that port-region linkages develop in subtle interdependencies, while pointing to noticeable differences between traffic volumes, types and local economic structures, as apparent from commodity traffic data and regional economy data. Accordingly, economically and demographically larger and richer regions that are specialized in (private sector) producer services, concentrate larger and more diversified traffic volumes as well as higher valued goods. By contrast, agricultural and industrial regions are more specialized in bulk traffic (Ducruet et al., 2010, 2013). The study of Jacobs et al. (2011) on maritime advanced producer services, fits into the wider network perspective on influences on port activity and traffic flow. Studies paying attention to spillover effects to nearby regions also fit into the broader perspective. We mention Bottasso et al. (2014) who observed in 13 West European countries that a 10% increase in port throughput gave a growth in regional GDP of the port regions by 0.01–0.03%, while the effect in nearby regions turned out to be larger, namely 0.05–0.18%. Merk and Hesse (2012) found for the port of Hamburg (Germany) not only considerable regional spillover effects, but also large distances involved. Only 13% of the multiplier effects have an impact on Hamburg and its neighboring regions, while almost a third spills over to two large southern regions at a distance from the port and more than half to the rest of Germany. The previous studies illustrate a myriad of interrelationships between port infrastructure investment, connectivity of the port with land infrastructure, size and type of transport flow, value chains and production networks embedded in the port and stretching (spilling over) in adjacent and more distant regions, and local geographical and historical specificities, like local economic specialization. This situation would mean that each estimation of impacts of port infrastructure investment on the regional economy shows a relatively small impact and shows some differentiation between regions. 1.3. Research aim and questions Most previous port investment studies have a limited scope that is often neglecting (part of) the above indicated influences, like connected land traffic infrastructures, profile of the regional

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

economy and types of connected value chains. In addition, small attention has been paid to the regional spillover effects in modeling the impacts of port investments. Against this background, this paper takes a broader networkrelated look at the effects of port infrastructure investment on the regional economy in China. Accordingly, the following questions are addressed. What is the output elasticity of port infrastructure investment in the four regions and in port provinces, and which differences do exist between these regions and between these provinces? What are the reasons behind these differences? For example, what is the role, aside from port capital investment, of multi-modal connectivity of the ports, presence of neighboring ports, and the economic structure in terms of manufacturing size in the region? To what extent are inland port areas different from seaport areas? The structure of the paper is as follows. The next section gives a brief descriptive analysis of the regional distribution of port facilities, as well as an overview of port investment in China (Section 2). This is followed by analysis of the factors included in our model in Section 3. Section 4 introduces the methodology and database to quantify economic effects of port infrastructure on the four regions and 13 port provinces. The results of the model estimations are presented in Section 5, on the regional and provincial level, including a discussion of the results. The paper ends with conclusions and policy implications.

175

2. Port infrastructure in China: an overview 2.1. Four port regions China developed route schedule oriented shipping with Japan and Korea and dug the world's first canal while owning a highly advanced shipbuilding technology already during the Han Dynasty (256–220 BC) (Wang and Ducruet, 2013). More recently, China's port construction is based on a development policy to gradually create port clusters with a hub port as the core (Li and Yuan, 2010). The sample ports in the current study are the scale ports in China, including seaports and inland ports. According to the Chinese Ministry of Transport, ‘scale ports’ are defined as ports facing throughputs of over 15 and 10 million tons in 2002 for a sea port and an inland port respectively. Our definition of the port region (see Fig. 1) complies with the Coastal Port layout in China (MOCOPRC, 2006). The only difference is that in the current study, inland port provinces are being included as an independent port region (named Central region or Center) to get a better understanding of the disparity between seaports and inland ports. We now briefly characterize the industrial specialization of the four port regions. Bohai Rim is an important base of energy and raw materials production in China (for example, heavy chemical and steel industry) and an area of abundant mineral resources. These two assets enabled the ports in Bohai Rim (Tianjin and Dalian, etc.)

Fig. 1. The four port regions and 13 port provinces in this study.

176

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

to become the most important coastal transport nodes to the southern part of China. Next, the Yangtze River Delta region includes the port of Shanghai as one of the world's largest seaports and among China's biggest manufacturing and commercial services centers. Yangshan port, as an extension project of Shanghai port, is a value-added and integrated industrial, logistics, and shipping complex rather than a sole transshipment node, with the Yangshan Bonded Port Area as a multimodal logistics center for transshipment, distribution, insurance, finance, and entrepot trade. This facility was also designed to provide a local manufacturing base aimed at limiting truck and shipping flows to and from mainland China in addition to its first transshipment function (Wang and Ducruet, 2012). In this region we find more than 100 industrial parks (distributed over the urban areas of Shanghai, Hangzhou, Nanjiang, etc.) and many large enterprises (e.g. Wanxiang Group, Jinshan petro-chemical, Yangzi ethylene, Volkswagen, Shanghai Bell, EastCom) (Deng et al., 2013a, 2013b). By contrast, the Southeast region is an important production area for imported tropical cash crops such as rubber, cane sugar, tobacco, etc., and it is also China's earliest “opened-up” region, including open coastal economic zones (cities of Shenzhen, Zhuhai and Xiamen, etc.). In particular, Guangdong province benefited from the “Opening Up” policy since 1978, through which many multinationals were attracted, including Canon, Epson, Samsung, Coca-Cola, Philip, etc. And finally, there is the central region (or Center), which can be regarded as the latest developed region, as more and more of China's manufacturing operations are shifting inland. The results of the “Go West” policy “Central Rise” strategy – adopted since the early 2000s – can now be clearly seen in inland locations, although spread effects may go hand-in-hand with backwash effects (Ke and Feser, 2010). Major cities like Chongqing and Wuhan are booming as manufacturing hubs, home of some of the world's largest manufacturers across a diverse range of products and industries, including Ford, Intel, Hewlett-Packard, General Electric, Procter & Gamble, Siemens, and Samsung, etc. 2.2. Scale port investment After the Chinese fiscal decentralization, in the early 1990s, many local administrative units (provincial and municipal) have

received substantial financial power (Zhang et al., 2007). This enabled them to have their own independency with respect to the distribution of transport investments and investment decisions in view of their individual economic growth. However, the main lines of port investment policy are determined at the national level in a situation in which most of the scale ports are state-owned. The size of port investment (port infrastructure investment, including container terminals, cargo terminals, road and rail in port area, cranes, etc.) per region suggests substantial regional differences between seaports, but also between inland ports, and of course part of the differences can be understood based on the different numbers of ports per region (Table 2). The investments in the Yangtze River region – 11 ports – are the largest at the start of the period (23,800 million RMB) and also in 2010 (150,700 million RMB). In addition, the initial port investment in Center area, with merely inland ports and only four of them, is the lowest in 1999 (2600 million RMB), but – due to a quick growth – equals investments in the Southeast region in 2010 (30400 million RMB). The annual growth rate of port investment in Center area is higher than the Yangtze River and Southeast areas, as witnessed by 22.8% versus 16.6% and 15.7%, respectively. The same holds for Bohai Rim, with merely sea ports, nine of them (23.7%). By contrast, Southeast, including seven seaports compares with Bohai Rim with regard to size of initial investments in 1999 but tends to stay behind in 2010. Whether the high increase of port investment in the Center and Bohai Rim, with annual growth exceeding 20%, have resulted and result in a stronger growth of the regional economy is still not known.

3. Factors of influence 3.1. Regional economic structure As previously indicated, economically and demographically larger and richer regions specialized in the commercial service sector tend to be involved with larger and more diversified transport volumes as well as with more value-added goods. By contrast, agricultural and heavy manufacturing regions tend to be more specialized in bulk transport. Moreover, the level of value

Table 2 Scale port investment per port region and province in China. Source: China Port Statistical Yearbook (2000–2011). Region Province

Bohai Rim Tianjin Liaoning Hebei Shandong Yangtze River Shanghai Jiangsu Zhejiang Southeast Fujian Guangdong Center Chongqing Anhui Hubei Hunan

Accumulated port investment per region (100 million RMB)

Annual growth (%)

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

57 11 6 14 25 238 97 109 32 52 17 34 26 14 2 10 0.3

62 12 8 13 28 255 97 121 37 58 20 37 29 16 2 11 0.4

72 14 9 13 35 286 104 135 47 61 21 39 34 19 2 12 0.4

89 16 11 14 47 349 114 177 59 67 24 43 38 22 3 13 0.4

133 21 16 19 78 483 128 276 79 84 31 53 51 31 4 15 0.6

186 25 24 26 110 598 161 337 101 101 39 62 64 39 5 19 0.6

250 30 34 36 149 716 185 412 119 119 47 72 80 49 6 25 0.8

285 37 45 45 158 819 205 483 132 141 60 81 100 60 8 31 1.0

343 48 64 59 172 946 232 569 144 176 86 90 131 77 13 40 1.3

444 68 83 76 217 1079 252 663 164 199 98 102 170 98 18 52 1.8

583 100 113 115 255 1283 275 817 192 240 115 125 228 129 26 70 2.6

732 130 146 143 313 1507 277 1018 212 300 151 149 304 169 36 87 12.4

23.7 22.6 29.7 21.5 23.3 16.6 9.2 20.4 17.1 15.7 19.7 13.0 22.8 23.3 27.4 19.8 36.4

Notes: Port regions are bold. SP denotes seaports and IP inland ports. Bohai Rim: Dalian (SP), Yingkou (SP), Qinhuangdao (SP), Huanghua (SP), Tangshan (SP), Qingdao (SP), Rizhao (SP), Yantai (SP) and Tianjin (SP). Yangtze River: Lianyungang (SP), Nanjing (IP), Zhenjiang (IP), Suzhou (IP), Nantong (IP), Taizhou (IP), Wuxi (IP), Shanghai (SP), Ningbo (SP), Hangzhou (IP) and Huzhou (IP). Southeast: Fuzhou (SP), Quanzhou (SP); Xiamen (SP), Shenzhen (SP), Guangzhou (SP), Zhanjiang (SP) and Foshan (IP). Center: Chongqing (IP), Wuhu (IP), Wuhan (IP) and Yueyang (IP).

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

adding relatedness between flows of good and local economies is a strong component of the wealth of port regions, with higher levels of local value added activity bringing higher incomes (Ducruet et al., 2013). Unfortunately, the available statistics on regional economic structure in China can only picture a broad pattern. The economic structure in port provinces in 1999 and 2010 (Table 3) indicates that the share of the manufacturing sector in GDP remained stable in the port provinces in Center area from 1999 to 2010 except for Chongqing, with an increase at the level of 5%; in addition, this share significantly decreased in the port provinces in Yangtze River Delta and Southeast regions, with Shanghai facing the largest decrease, namely, at the level of 8%. For Bohai Rim, this

177

share obviously increased in all port provinces within the region. Moreover, the share of the tertiary industry in GDP, including transport and other services, increased or remained stable in most of the port provinces. The only provinces facing a substantial increase, are in Yangtze River Delta, namely, Shanghai, Jiangsu and Zhejiang, the last one at a level of 10%. These provinces may also attract more diversified and value-added port-related activity, and accordingly a larger output elasticity of port investment. Though the Chinese economy will remain being driven by the expansion of manufacturing sector for a long period, since China is still in the early stage of industrialization, the economic structure transition is emerging in some regions in China, mainly Yangtze

Table 3 Changes in economic structure per port province in 1999 and 2010. Source: China Regional Economy Statistical Yearbook (2000 and 2011). Province

Primary industry in GDP (%)

Secondary industry in GDP (%)

Manufacturing

Tertiary industry in GDP (%) (including transport)

Non-manufacturing

1999

2010

1999

2010

1999

2010

1999

2010

Bohai Rim Tianjin Liaoning Hebei Shandong

5.0 12.0 18.0 16.0

2.0 9.0 13.0 9.0

39.0 37.0 36.0 37.0

41.0 41.0 40.0 41.0

12.0 11.0 12.0 12.0

11.0 13.0 13.0 13.0

45.0 40.0 34.0 35.0

46.0 37.0 35.0 37.0

Yangtze River Shanghai Jiangsu Zhejiang

2.0 13.0 11.0

1.0 6.0 5.0

41.0 43.0 46.0

33.0 40.0 39.0

10.0 13.0 13.0

9.0 13.0 13.0

51.0 36.0 34.0

57.0 41.0 44.0

Southeast Fujian Guangdong

18.0 11.0

9.0 5.0

36.0 43.0

37.0 40.0

11.0 11.0

14.0 10.0

40.0 42.0

40.0 45.0

Center Chongqing Anhui Hubei Hunan

17.0 28.0 20.0 24.0

9.0 14.0 13.0 14.0

35.0 37.0 42.0 33.0

40.0 38.0 41.0 34.0

12.0 10.0 11.0 10.0

15.0 14.0 13.0 12.0

41.0 30.0 34.0 37.0

36,0 34.0 38.0 40.0

Notes: Regions are in bold. According to National Industry Classification, Chinese industry is divided into three industries: Primary industry (including Agriculture, Forestry, Animal husbandry and Fishing); Secondary industry (including Mining, Manufacturing, Production and distribution of electricity, gas and water, and Construction); Tertiary industry (including all sectors except for Primary industry and Secondary industry).

Table 4 Transport infrastructure density in 1999 and annual growth rate from 1999 to 2010 for port provinces and four regions. Source: China Statistics Yearbook (2000 and 2011). Region Port Province (a)

Railway in 1999 (m/km2)

Railway growth rate (%)

Road in 1999 (m/km2)

Road growth rate (%)

Inland waterways in 1999 (m/km2)

Inland waterways growth rate (%) (b)

Bohai Rim Tianjin Liaoning Hebei Shandong

22.04 51.02 25.68 21.31 17.18

1.83 2.43 1.04 1.71 2.89

357,05 748,30 304,05 310,08 431,40

8.90 4.43 7.01 8.46 10.71

7.55 34.01 5.41 0.53 15.91

 6.49  10.91  5.61 0.00  5.93

Yangtze River Shanghai Jiangsu Zhejiang

10.91

4.94

342,60

11.70

172,72

 0.07

47.31 8.77 10.81

2.43 6.42 4.19

662,36 269,98 395,87

9.14 15.13 8.74

331,18 232,94 102,16

0.39 0.10  0.58

Southeast Fujian Guangdong

10.31 10,48 10,01

3.71 4.08 3.44

485,01 404,84 531,66

5.62 5.08 5.90

48.23 29.84 60.06

0.28  1.20 0.74

Center Chongqing Anhui Hubei Hunan

12.59 7.28 15.78 11.83 13.22

3.21 1.71 2.33 3.69 2.35

298,21 341,01 293,34 297,87 285,14

11.74 8.46 11.40 11.57 11.71

40.83 27.91 40.16 39.25 47.68

1.35 0.00 0.00 1.08 1.09

Notes: (a) Port regions are in bold. (b) Negative developments may be due to the filling of superfluous, narrow, canals.

178

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

River, in which the share of manufacturing in GDP goes down in favor of services. 3.2. Landside traffic infrastructure and air traffic A well-developed landside traffic infrastructure enables the emergence of regional economic growth derived from goods from overseas. It allows the development of multimodal chains based on an increased accessibility. According to the China Port Statistical Yearbook (2011), the percentage of the goods transported between ports and hinterland by road, waterways and railway are 84%, 14% and 2% respectively, meaning that almost all goods in ports are delivered through road and waterways. By taking rail, road and waterways into account – in terms of density – the following broad pattern becomes clear concerning the year 1999 (Table 4): Bohai Rim has the best developed rail system, especially the province of Tianjin, while the road system is at a medium level and the waterway system at a very low level. However, railways to date turn out to be an irrelevant mode in China for freight. Yangtze River has by far the best developed system of waterways, with a medium level as far as roads are concerned, and a relatively weak level of railway development, this with the exception of Shanghai which is endowed with relatively high densities. Southeast has the best developed road system, especially the province of Guangdong, with inland waterways at a medium level, and railways at a low level. Given the most often use of roads, Southeast is the best endowed area. Central has relatively low scores, with waterways and railways at a medium level, and a road system that is clearly behind. We may understand this situation regarding port-related economic growth as follows. The Center, as an inland port region, can be assumed not having reached the threshold value in the existing traffic infrastructure in 1999 that allows a quick contribution of port investment to economic growth (Banister, 2012). The relatively strong investment in the road system here in the years that followed (Table 4), may not yet be effective in the early 1990s. By contrast, both Yangtze River and Southeast, the first endowed with a well-developed waterway system and medium-level road system, and the second with a well-developed road system, can be assumed to have crossed this threshold already before 1999, potentially causing a relatively strong impact of port investments on economic growth. Bohai Rim may be positioned somewhere inbetween the last two regions and the Center. Connectivity is not only involved with landside freight traffic within the port region and regions nearby and on larger distances, it also deals with other parts of the world, in particular with large cities where major economic decisions are taken. International airports connect the port regions with first-tier world cities, mainly concerning passengers as decision-makers in various domains, such as multinational companies' strategies, location and composition of global freight flows, and global financial services (e.g. Matsumoto, 2007). This is the reason why international air traffic flows are included in the analysis. Data constraints, however, make us to focus on aircraft movements from/to international airports in the four regions and respective provinces, although we realize that this is a very broad indicator of world city linkages, because origin and destination are not known and the purpose of flights (passengers) are also not given (Derudder and Witlox, 2005). The region facing the highest number of air craft movement in international airports is the Yangtze River, and the region facing the lowest level is the Center. Within the regions, the following provinces stand out with high levels: Liaoning (Bohai Rim), Shanghai (Yangtze River), Chongqing (Center) and Guangdong (Southeast). 3.3. Regional spillover effects In principle, economic growth from seaports might generate spillover effects in adjacent port provinces and sometimes also

across not adjacent areas (Notteboom and Rodrigue, 2005). Such patterns depend on the spatial organization of the value chains involved and are connected with the presence of strong economic activity in adjacent areas and at larger distances in the ‘hinterland’. With regard to China, we may assume still weak but increasing spillover effects over large distances, because there are not (yet) many strongly developed inland provinces. However, we do assume that multiplier effects spill over to adjacent (port) provinces. To include this in our analysis would be a study in itself, therefore we count for each province the number of scale ports in adjacent provinces as a proxy, and may expect positive impacts if the ports are specialized to a certain extent. The number of scale ports in adjacent provinces is largest for Zhejiang province and smallest for Chongqing.

4. Methodology 4.1. Model specification The starting point of the analysis is a production function and panel data (1999–2010). The baseline empirical model is constructed derived from a production function as: Y ¼ f ðK; MAN; TID; S; ICÞ

ð1Þ

where Y denotes output, K represents port infrastructure capital stock, MAN is the size of manufacturing sector, TID represents aggregate land traffic density, S stands for spillover effect from adjacent provinces, and IC represents the international connectivity. We estimate model (1) at the whole port region level, four regions level, as well as the port provincial level. In addition, for the whole port region, dummy variables are applied to explore the existence of regional disparities, as shown in Eq. (2). For the four regions and 13 port provinces, Eq. (3) will be estimated. As usual, in the log-linearized reduced version of production function (Mera, 1973), the estimated parameters can be thought of as Y elasticities to each regressor: ln Y it ¼ β0 þ β 1 ln K it þ β 2 ln MAN it þ β3 ln TIDit þ β4 ln Sit þ β 5 ln IC it þ β 6 D1it þ β7 D2it þ β 8 D3it þ εit

ð2Þ

ln Y it ¼ β0 þ β 1 ln K it þ β 2 ln MAN it þ β3 ln TIDit þ β4 ln Sit þ β 5 ln IC it þ εit ð3Þ where Y denotes real gross domestic product; i and t are the indices port province and year respectively; K is actual port infrastructure investment stock; MAN is the share of manufacturing output in the gross domestic product; TID is the aggregated density of road, railway and inland waterways per port province, S stands for spillover effects from the ports in adjacent port provinces measured as a proxy using number of scale port in adjacent provinces. IC is the number of total aircraft movements of international airports in the port provinces, which is used as a proxy for international connectivity of the port provinces. Taking the central region as a reference, D1, D2, D3 in Eq. (2) are dummy variables to indicate the other three individual regions: Bohai Rim, Yangtze River and Southeast. To estimate the model, unit root tests and co-integration tests need to be performed to ensure the reliability of the regression results. LLC (Levin et al., 2002) test is employed in this study for unit root test. If the unit root test indicates the series are nonstationary, which will result in a spurious regression, then we have to test whether the series are integrated of the same order d or for short I (d) process, if so, Kao (1999) co-integration test needs to be employed to examine the long-term equilibrium relationships of the non-stationary panels, namely to investigate whether the early changes in port infrastructure, economic structure, land traffic

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

density, spillovers from ports in adjacent provinces and international connectivity can effectively explain the changes in economic growth in the entire port region and the port regions and provinces individually. Ultimately, we estimate the coefficients of the models by the ordinary least squares (OLS) regression procedure using the panel data model as implemented in Eviews7.

4.2. Data collection and preparation The sample of scale ports and four port regions is derived from the National Coastal Port Layout (MOCOPRC, 2006). Although Hong Kong Port is an important port in Southeast region, data on this port are not given in the China Port Statistical Yearbook, therefore, in this study, Hong Kong port is excluded from Southeast in the analysis of ports. The data in this research are collected from various official Chinese sources, including the China Statistical Yearbook (2000–2011), China Regional Economy Statistical Yearbook (2000–2011), China Port Statistical Yearbook (2000–2011) and China Airport Production Statistics Bulletin (1999–2010). Data are used from panels of 10 provinces and 3 municipalities where the 31 scale ports are located, and from panels of four port regions at the aggregate level, for the period 1999–2010. We calculate the port infrastructure capital stock based on investment data according to the perpetual inventory method (Goldsmith, 1951). The values of GDP, port investment and gross industrial output are the absolutes in 1999–2010 and will be recalculated based on the price in 1999 in such a way that the factors influencing the price in this period are removed. Table 5 gives the descriptive statistics of the variables applied in the model estimations. Note that the level of detail of some data is limited, meaning that the broad scope adopted in the paper, is sometimes narrowed down due to lack of data. Before calculating Eqs. (2) and (3), unit root test and co-integration test are applied to test the data used in the regression to ensure the accuracy of regression results. The results of unit root test (Table 6) show that the variables in the model are non-stationary for levels at except for traffic infrastructure density, which is stationary at 5% level of significance. However, non-stationary can be rejected for first-

179

differences of all variables at 10% level of significance meaning that the series are integrated of the same order 1. The results of co-integration residual test (Table 6) clearly imply that during the research period, the five independent variables can effectively explain the GDP growth. Therefore, we can conduct the regression analysis to estimate the contributions of port infrastructure, the size of manufacturing sector, traffic infrastructure density, spillover effect and international connectivity to the economic growth in the whole port area, four regions and 13 port provinces. As a final step in the preparation, we check for multicollinearity among the five independent variables (port investment, economic structure, transport infrastructure density, port spillover effect and international connectivity). All the correlation coefficients are below 0.50, meaning that there is no serious concern about multi-collinearity.

5. Impacts from port investments 5.1. Estimation results: regional level Estimation results at the level of the entire port region, without dummy variables for the region (Model I), are listed in Table 7, indicating that port infrastructure investment, economic structure, traffic infrastructure density, port spillover effects and international connectivity positively influence the growth of regional economy. Next, by including the regional dummy variables (Model II), the model results (R2) improve from 0.428 to 0.827, and the coefficients of D1, D2, and D3 are all significant at the 5% level. We may thus conclude that the model including the regions can better explain the relationship of port infrastructure investment, economic structure, traffic infrastructure density, port spillover effects and international connectivity with regional economic growth compared to the model without the regions, pointing to some relevant differences between the four regions. Overall, the coefficient of port investment (Model II) (0.191) indicates a positive impact on economic growth, which is in line with results on port supply by Deng et al. (2013a, 2013b).

Table 5 Variables per port province and descriptive statistics. Variables

Y K MAN TID S IC

Indicator

Descriptive statistics

GDP in 100 million RMB Port capital investment stock in 100 million RMB Share of manufacturing in GDP Average traffic infrastructure density (road, railway and inland waterways) in m/ km2 Number of scale ports in adjacent provinces Number of aircraft movements in international airports

Average

S.D.

Max

Min

10,811.4 93.89 0.43 848.3 6.308 116,843.1

8858.0 140.46 0.063 440.8 3.157 122,272.9

46,013.0 1017.71 0.54 2302.5 12 58,3762

1450.1 0.30 0.28 331.9 2 6296

Note: The units for Y, K and MAN are 100 million RMB; the unit for TID is m/km2.

Table 6 Results for panel unit root test and co-integration test for all data. Unit root test

Residual co-integration test (Kao)

Levels

LLC

First-differences

LLC

ln Y ln K ln MAN ln TID ln S ln IC

9.218 3.007  1.271  2.766** 3.851 1.170

Δln Y Δln K Δln MAN Δln TID Δln S Δln IC

 4.797***  4.115***  8.572***  11.252*** 9.146***  6.398***

Note: **Statistical significance at the 5% level, ***at 1% level.

ADF Residual variance HAC variance

t-Statistics

Prob.

 2.703 0.007 0.009

0.0034

180

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

Table 7 Regression results (coefficients) of regional economic growth.

Model I Model II

ln K

ln MAN

ln TID

ln S

ln IC

D1

D2

D3

Adj. R2

0.132 (2.71) *** 0.191 (6.02) ***

0.757(2.02)** 0.611 (2.75) ***

0.478 (4.75) *** 0.227 (3.12) ***

0.383 (4.42) *** 0.534 (9.84) ***

0.362 (6.51) *** 0.489 (10.78) ***

0.212 (2.50) **

0.369 (2.92) ***

0.295 (2.14) **

0.428 0.827

Note: t-statistics in parenthesis. nn

Statistical significance at 5% level. Statistical significance at 1% level.

nnn

Table 8 Regression results (coefficients) of regional economic growth for four port regions. Port region

ln K

Bohai Rim (SP) Yangtze River Delta (SP and IP) Southeast (SP) Center (IP)

0.541 1.428 0.506 0.093

ln MAN (6.588)*** (11.994)*** (6.585)*** (2.426)**

3.811 1.163 1.039 0.318**

ln TID (6.843)*** (1.370) (1.987) (1.331)

0.107 0.386 0.024 0.041

ln S (1.333) (1.676) (0.098) (0.220)

0.600 3.026 0.596  0.814

Adj. R2

ln IC (4.673) *** (12.939)*** (3.103)*** (  6.097)***

0.227 0.256 0.598 0.719

(3.680)*** (2.081)** (5.816)*** (7.094)***

0.850 0.789 0.782 0.759

Note: t-statistics in parenthesis. nn

Statistical significance at 5% level. Statistical significance at 1% level.

nnn

The results of the model for the four port regions (Table 8) show that the coefficients of port investment, port spillover effects and international connectivity are significant at the 5 or 1% level for all four port regions. However, the coefficients of the share of manufacturing in GDP are only statistically significant for Bohai Rim and Center at the 1 and 5% level of significance respectively, while these coefficients are not significant in other regions. Remarkably, the coefficients of landside transport infrastructure density are all not significant in the four port regions, this may be due to the previously indicated non-linear relationships with portrelated economic growth, which cannot be grasped in our linear models. With regard to the effects of port infrastructure investment, we observe the following implications. For Bohai Rim area, the GDP will increase by 0.54% if the port infrastructure increases by 1%, and the GDP will increase by 3.81% if the size of manufacturing sector increases by 1%. This is also the region with the comparatively highest manufacturing share in GDP in 2010. For Yangtze River, endowed with a modest road density in 1999 and facing a relatively strong traffic infrastructure density increase in the research period, the relatively high coefficient of port investment (0.1428) is in line with the idea that a better developed land traffic infrastructure system, ‘produces’ higher impacts of port investment on economic growth due to benefits from network effects, primarily accessibility (Banister, 2012). The development towards a relatively small manufacturing sector in Yangtze River in favor of the services sector, however, is not sufficiently strong to give a negative coefficient that is significant for manufacturing share, most probably because the indicator used does only partially ‘grasp’ size of advanced producer services. For the Southeast region, as the best endowed area with road infrastructure in 1999, the contribution of land traffic infrastructure is the lowest (a coefficient of 0.024), most probably because the Southeast has surpassed the threshold of accumulated transport infrastructure in 1999, and the extra investments in transport infrastructures cannot generate higher economic growth in the region. For the Center part, the contribution of port infrastructure investment is on the lowest level (a coefficient of 0.09), reflecting a different character of the region, endowed only with land-ports. However this may change in the next coming years due to a relatively strong improvement of the road system (Table 4). The contribution of the size of manufacturing in the Center is significant (a coefficient of

0.318), indicating that the quickly growing manufacturing (shift to the West) has a positive impact on regional economic growth here. Table 8 also indicates that the spillover effects from scale ports in neighboring provinces are rather different. The Yangtze River region enjoys the highest positive spillovers (a coefficient of 3.026) from scale ports in neighboring port provinces. While the Bohai Rim region and Southeast are facing spillover effects of a similar modest size (coefficients of 0.600 and 0.596 respectively), this coefficient in the Center is negative (  0.814), which implies that the port development in adjacent provinces tends to hinder economic growth in here. A similar result is also found by Yu et al. (2013), in that the transport infrastructure spillover effects are negative in central China, including Hubei, Hunan, Anhui, Jiangxi, Henan and Shanxi provinces. With regard to the international connectivity, in the Center, the contribution to the regional economy is the highest (a coefficient of 0.719), while the impact of international connectivity in the Southeast is at a medium level (a coefficient of 0.596) compared to the two other port regions, Bohai Rim and Yangtze River (coefficients of 0.227 and 0.256, respectively). The trend of strong influence of international connectivity in the Center may be understood in the context of the shift to the West, which is mainly undertaken by large multinationals and has increased flights from and to international airports here. 5.2. Estimation results: provincial level The coefficients concerning port investment of all provinces are significant at the 5% level (Table 9). Within the regions of Bohai Rim, Yangtze River and Southeast region, there are no considerable disparities between port investment outputs per province; in contrast, within the Center, a huge gap exists between the output in Chongqing at the 0.903 level and that in Hunan which is at the 0.092 level. Considering the size of manufacturing, only in Tianjin, Liaoning, Hebei, Shanghai and Hubei the coefficients are positive and significant. These provinces, except for Shanghai and Hubei, enjoy a comparatively larger increase of the size of manufacturing in the period 1999–2010. With respect to transport infrastructure density, only Tianjin and Shandong, holding a comparatively high road density in 1999 and also a high growth rate of road in Shandong, show coefficients that are significant. This means that only in these two provinces an

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

181

Table 9 Regression results (coefficients) of regional economic growth for 13 port provinces. Port province

ln K

ln MAN

ln TID

ln S

ln IC

Bohai Rim Tianjin Liaoning Hebei Shandong

0.399(2.74)*** 0.282(3.72)*** 0.521(6.09)*** 0.333(2.25)**

1.252(3.66)*** 0.998(5.57)*** 2.204(2.02)** 0.464(0.90)

1.136 (1.73)** 0.081(1.24) 0.052(0.26) 0.212(2.67)***

1.86(0.49) 12.41(2.18)** 5.48(5.16)*** 0.415(0.39)

0.173(2.61)** 0.493(2.33)** 0.110(1.39) 0.482(1.60)

Yangtze River Shanghai Jiangsu Zhejiang

0.685(2.90)*** 0.661(6.00)*** 0.452(1.70)**

0.716(2.46)** 1.150(1.91) 0.183(0.20)

0.041(0.14) 0.240(1.27) 0.295(1.59)

0.015(0.02) 1.589(2.86)*** 0.722(0.71)

0.368(1.65) 0.026(0.67) 0.304(0.75)

Southeast Fujian Guangdong

0.315(2.94)*** 0.494(2.84)***

0.209(0.90) 0.510(1.55)

0.116(1.17) 0.237(1.52)

1.09(0.86) 3.454(1.99)**

0.889(3.73)*** 0.831(3.19)***

Center Chongqing Anhui Hubei Hunan

0.903(3.81)*** 0.406(9.35)*** 0.752(12.4)*** 0.092(2.57)**

0.519(1.04) 0.052(0.14) 0.830(4.34)*** 0.504(1.17)

0.128(1.25) 0.004(0.07) 0.064(0.81) 0.009(0.08)

12.411(2.18)**  2.656(-4.67)*** 5.176(8.76)***  0.412(  0.37)

0.473(1.07) 0.149(1.36) 0.049(0.47) 0.922(5.11)***

Note: t-statistics in parenthesis. Adj. R2 for the panel model of 13 port provinces is 0.698. nn

Statistical significance at 5% level. Statistical significance at 1% level.

nnn

increase of transport infrastructure density tends to result in local economic growth. In addition, various disparities in the impact from spillovers among provinces could be found, Liaoning, Hebei, Jiangsu, Guangdong, Chongqing and Hubei enjoy positive and significant coefficients, meaning that in these provinces, the development of the ports in neighboring port provinces tends to promote the economic growth in these provinces. In contrast, Anhui province enjoys negative and significant spillovers. The adjacent province of Anhui is Jiangsu which is a welldeveloped province in the Yangtze River region, indicating that the port development in Jiangsu tends to absorb production factors from neighboring area (Anhui province), and this is confirmed by a positive and significant coefficient for regional spillovers for Jiangsu. Furthermore, regarding the international connectivity, some obvious disparities could be detected, namely, only five provinces (Tianjin, Liaoning, Fujian, Guangdong and Hunan) enjoy a positive and significant coefficient, indicating that in these provinces, the strengthening of international connectivity tends to stimulate economic growth. 5.3. Discussion Port infrastructure investment has a clear influence on the economy of the four port regions in China. Using a broader network perspective in regression analysis, in this study, the coefficients of port infrastructure investment turn out to be significant both on the regional and on the provincial level. However, the influence is substantially different in strength between the four regions and that needs to be seen in relation to various other significant influences on the regional economy. The Yangtze River enjoys the highest port investment output, with highest spillover effects. The Bohai Rim and Southeast regions are somewhat behind with lower output elasticity. For Bohai Rim, this is most probably related to absence of a dense inland waterway system and less dense highway infrastructure, but also not yet a sufficient level of diversification with high valued manufacturing and producer services in the port-city and hinterland. For Southeast, though endowed with well-developed traffic system, the relatively low output of port investment probably because of the low spillovers from neighboring ports, while the port of Hong-Kong falls outside the current analysis. For

a better understanding, it needs to be mentioned that the previous three regions, apart from the Center, are the most important economic zones in China benefiting from the reform and opening up policy that started in 1978, aiming to introduce capitalist market principles. Especially Guangdong province in Southeast and Yangzte Delta have the priority in carrying out the economic reform, with the result three decades later of market factors playing an essential role in the regional economy and value chains stretching around the globe. In contrast, Bohai Rim is a new growth pole developed only in the late 20th century, later than Southeast and Yangzte Delta regions, and is still in a transition phase from a planning economy to a market economy, of which the maturity and competition of the market are behind Southeast and Yangzte Delta. The reason why the port investment output elasticity of the Center is relatively low, is mainly the concentration of the inland scale ports here, inhibiting a much smaller scale and efficiency than many sea ports and specifically Yangtze river inland ports (Wang and Meng, 2013), where market competition has developed for decades (Yuen et al., 2013). Meanwhile, the negative spillovers might be another reason for the low level of port infrastructure output. A situation of rapid economic growth does not apply for the Center at the beginning of the research period in this study, though today this region is catching up. In addition, China's regions are facing different stages of the industrialization process (Chen et al., 2006), according to Chen et al. (2012), eastern China has accomplished the industrialization process, and the rest of China is in the late industrialization phase. Liu and Li (2002) pointed out that for developing countries which have not accomplished their industrialization stage, economic growth is essentially driven by manufacturing. Our empirical findings show that the size of the manufacturing sector in Bohai Rim (a coefficient of 3.81), which is in the late industrialization phase, has a larger impact on economic growth compared to Yangzte Delta and Southeast (coefficients of 1.163 and 1.039, respectively) which have already finished the industrialization stage, except for Fujian province. Regarding Central area, which is an underdeveloped region that benefits from the Central China Plan in 2009, many manufacturing companies have shifted from coastal areas to this region, most probably including relatively low value-added manufacturing activity. Hence, our findings further underpin the role of manufacturing in the regional economy (a coefficient of 0.318).

182

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

6. Conclusion, policy suggestions and issues for future research In this paper, we evaluated the economic impact of port infrastructure in China, which has attracted attention among researchers only very recently. Based on this study, we may conclude that port infrastructure investment has a positive impact on regional economic growth in China, but with obvious differences at the regional and provincial level. These differences could be connected with differences between seaports and inland ports, different densities in land transport infrastructure, phases of economic development, spillovers from ports in neighboring regions, as well as reform policies carried out by the central government. In terms of policy making, we may forward some interesting suggestions and ideas. Based on the empirical results, we believe that at the regional level, central government and local government should balance the investments in port infrastructure through specific policies, particularly in Center, where output elasticity of port investment is relatively low. Improving port efficiency is much more important than the increase of physical infrastructure only, meaning better port management and inland transport connectivity, but also a much quicker integration of the ports with supply chains connected to the regional economy. This could be enhanced by the creation of specialized, high valueadded, clusters of port-related economic activity, including advanced maritime producer services, but this is a long-term effort. On the short term, feasible strategies of fast establishing the linkages between ports and existing clusters or growth poles, through which value added can be increased (Pettit and Beresford, 2009) should be carried out, this is in the frame of an overall urban policy for knowledge-based development (Geenhuizen and Nijkamp, 1998). In such scenario, also so-called breeding places for development and experimentation in new port and maritime technology and logistics need to be established in the port area in connection with local research institutes (universities), this to enhance a stronger emergence and growth of small technologybased firms. In addition, the empirical results on the landside traffic infrastructure density indicate that the government should pay more attention on investment in connecting traffic infrastructures because at the entire port region level, the landside traffic infrastructure density has a positive and significant impact on economic growth, but at the provincial level, only two provinces (Tianjin and Shandong) tend to benefit from landside traffic infrastructure. Therefore, for the port provinces, intermodality among various traffic modes should be considered to improve the connectivity of transport network within the province rather than merely increasing investment in the ports. Furthermore, for the Yangtze and Southeast regions, our results indicate that the size of the manufacturing sector is somewhat decreasing in favor of services, pointing to the need of a reconsideration of port development in the next coming years. There is the question of reaching the optimal level in the near future, above which additional port investments only produce small growth. Also, and related, there is the question of increased competition between ports in the same region, like between Hong Kong, Shenzhen and Guangzhou in container transport, not yet depicted in the current study. The magnitude of these developments and their implications need to be investigated before new decisions on large infrastructure investment are taken. This is connected with a general increasing uncertainty that is replacing the stability of the global business environment and growth of the Chinese economy, whereas ports have a long lifetime and port investments are often irreversible. This situation requires to pay a stronger attention to how the implications of uncertainty can be incorporated in the

way of planning and design of ports, like flexibility in design (adaptive port planning) and real options analysis (Walker et al., 2001; De Neufville and Scholtes, 2011). Despite the interesting results, the study also suffers from some weaknesses which could be addressed in future research. Firstly, we adopted the idea of non-linear economic growth inhibiting threshold values, only by assuming that the relatively smaller port areas have not exceeded a lower threshold, among others due to less developed land transport infrastructure. In future research, this idea, including a second threshold as an optimal level above which growth diminishes, could be incorporated in a dynamic model inhibiting non-linearity, like in the study of Deng et al. (2013a, 2013b) on highways in China. Secondly, due to data limitations, we used various relatively broad indicators, of which we mention the number of scale ports in neighboring port provinces to indicate regional spillovers effects and the regional economic structure to indicate structural shifts but in which advanced producer services remained unidentified. In future research, spillovers should be disaggregated to specific port activities to explore the spillovers in greater detail and the services sector should be measured at a more disaggregated level as well. Thirdly, our study has remained broad as we made no distinction between different value chains and different port activity in the modeling. In future research, differences could be made between investments in ports predominantly active in bulk, container transport, other cargo, or a mix, and ports connected with (petro) chemical industry, steel industry, food industry, assemblage industry, etc. As a fourth point we forward that the observations in this study are the scale ports in China, which do not offer a complete picture of port development in China, many inland ports are not taken into account, which also have an impact on the regional economy. Hong Kong port as part of China could also be included in future research.

Acknowledgments We thank the anonymous reviewers and the editor for their constructive comments. We express our appreciation for the Visiting Study Funding of China Scholarship Council (Grant no. 201207167011). We thank Mr. Liang Yan for his contribution to the making the map. References Banister, D., 2012. Transport and economic development: reviewing the evidence. Transp. Rev. 32 (1), 1–2. Berechman, J., Ozmen, D., Ozbay, K., 2006. Empirical analysis of transportation investment and economic development at state, country and municipality levels. Transportation 33 (6), 537–551. Biehl, D. 1986 The Contribution of Infrastructure to Regional Development: Final Report. Office for Publications of the European Communities (Luxembourg/ Washington DC). Blum, U., 1982. Effects of transportation investments on regional growth: a theoretical and empirical investigation. Pap. Reg. Sci. 40 (1), 169–184. Bottasso, A., Conti, M., Ferrari, C., Tei, A., 2014. Ports and regional development: a spatial analysis on a panel of European regions. Transp. Res. A: Policy Pract. July 2014, 44–45. Canning, D., Bennathan, E, 2007. The Rate of Return to Transportation Infrastructure. World Bank Working Paper no. 3293. Chen, J., Huang, Q., Zhong, H., 2006. The synthetic evaluation and analysis on regional industrialization. Econ. Res. 6, 4–15. Chen, J., Huang, Q., Lv, T., Li, X., 2012. The Report on China's Industrialization. Social Sciences Documentation Publishing House, Beijing. Danielis, R., Gregori, T., 2013. An input–output-based methodology to estimate the economic role of a port: the case of the port system of the Friuli Venezia Giulia Region, Italy. Marit. Econ. Logist. 15, 222–255. Del Bo, C., Florio, M., 2012. Infrastructure and growth in a spatial framework: evidence from the EU regions. Eur. Plan. Stud. 20 (8), 1393–1414. Deng, P., Lu, S., Xiao, H., 2013a. Evaluation of the relevance measure between ports and regional economy using structural equation modeling. Transp. Policy 27, 123–133.

L. Song, M. van Geenhuizen / Transport Policy 36 (2014) 173–183

Deng, T., Shao, S., Yang, L., Zhang, X., 2013b. Has the transport-led economic growth effect reached a peak in China? A panel threshold regression approach. Transportation, (published online 15 October). De Neufville, R., Scholtes, S., 2011. Flexibility in Engineering Design. The MIT Press, Massachusetts, The United States. Derudder, B., Witlox, F., 2005. An appraisal of the use of airline data in assessing the world city network: a research note on data. Urban Stud. 42, 2371–2388. Ducruet, C., Koster, H., Beek, D. van der, 2010. Commodity variety and seaport performance. Reg. Stud. 44, 1221–1240. Ducruet, C., Itoh, H., Joly, O., 2013. Ports and the local embedding of commodity flows. Papers in Regional Science, (published online: 22 Dec. 2013). Geenhuizen, M., Nijkamp, P., 1998. Improving the knowledge capability of cities: the case of Mainport Rotterdam. Int. J. Technol. Manag. 15 (6/7), 691–709. Goldsmith, S.B., 1951. A Perpetual Inventory of National Wealth, Studies in Income and Wealth 14. NBER, New York. Hong, J., Chu, Z., Wang, Q., 2011. Transport infrastructure and regional economic growth: evidence from China. Transportation 38, 737–752. Jacobs, W., Koster, H., Hall, P., 2011. The location and global network structure of maritime advanced producer services. Urban Stud. 48 (13), 2751–2771. Jiang, N., 2010. Seaport investment and economic development in China. Dalian Marine University Press, Dalian. Kao, C., 1999. Spurious regression and residual-based tests for co-integration in panel data. J. Econom. 90 (1), 1–44. Ke, S., Feser, E., 2010. Count on growth pole strategy for regional economic growth? Spread- backwash effects in Greater Central China. Reg. Stud. 44 (9), 1131–1147. Levin, A., Lin, C.F., Chu, C., 2002. Unit root tests in panel data: asymptotic and finitesample properties. J. Econom. 108, 1–24. Li, D.S., Yuan, L.F., 2010. Efficiency measurement of Port Group Logistics System— based on multiple subsystem fuzzy DEA model. J. Beijing Jiaotong Univ. (Soc. Sci. Ed.) 9 (2), 42–47. Liu, W., Li, S.R., 2002. Industry structure and economic growth. China Ind. Econ. 5, 14–21. Matsumoto, H., 2007. International air network structures and air traffic density of world cities. Transp. Res. Part E: Logist. Transp. Rev. 43, 269–282. Mera, K., 1973. Regional production functions and social overhead capital. Reg. Urban Econ. 3, 157–186. Merk, O., Hesse, M., 2012. The Competitiveness of Global Port-cities: The Case of Hamburg. OECD, Paris.

183

MOCOPRC (Ministry of Communications of the People's Republic of China), 2006. The Coastal Port Layout Planning. Chinese Government Nets. 〈http://news. xinhuanet.com/politics/2006-11/20/content_5352716.htm〉 (accessed 20.11.06). Nijkamp, P., 1986. Infrastructure and regional development: a multidimensional policy analysis. Empir. Econ. 11, 1–21. Notteboom, T., Rodrigue, J.P., 2005. Port regionalization: towards a new phase in port development. Marit. Policy Manag. 32, 297–313. Ozbay, K., Ozmen-Ertekin, D., Berechman, J., 2003. Empirical analysis of the relationship between accessibility and economic development. J. Urban Plan. Dev. 129 (2), 97–119. Pettit, S.J., Beresford, A.K.C., 2009. Port development: from gateways to logistics hubs. Marit. Policy Manag. 36 (3), 253–267. Walker, W.E., Rahman, S.J., Cave, J., 2001. Adaptive policies, policy analysis and policy making. Eur. J. Oper. Res. 128 (2), 282–289. Wang, C., Ducruet, C., 2012. New port development and global city making: emergence of the Shanghai-Yangshan multilayered gateway hub. J. Transp. Geogr. 25, 58–69. Wang, C., Ducruet, C., 2013. Regional resilience and spatial cycles: long-term evolution of the Chinese port system (221BC–2010AD). J. Soc. Econ. Geogr. 104 (5), 521–538. Wang, L., Meng, H., 2013. Efficiency comparison between inland port and seaport in China. Soft Sci. 27 (3), 90–95. Warf, B., Cox, J., 1989. The changing economic impacts of the port of New York. Marit. Policy Manag. 16 (1), 3–11. Yoo, S.-H., 2006. Seaport infrastructure investment and economic growth in Korea. Int. J. Crit. Infrastruct. 2 (1), 1–9. Yu, N., de Jong, M., Storm, S., Mi, J., 2013. Spatial spillover effects of transport infrastructure: evidence from Chinese regions. J. Transp. Geogr. 28, 56–66. Yuen, A., Zhang, A., Cheung, W., 2013. Foreign participation and competition: a way to improve the container port efficiency in China? Transp. Res. Part A 49, 220–231. Zhang, J., Gao, Y., Fu, Y., Zhang, H., 2007. Why does China enjoy so much better physical infrastructure? Econ. Res. 4 (3), 4–19. Zhang, Z., Wen, H., Shen, Y., 2009. Granger causality analysis on the economy and transportation infrastructure construction. J. Wuhan Univ. Technol. (Transp. Sci. Eng.) 33 (4), 699–702.