The impacts of port infrastructure and logistics performance on economic growth: the mediating role of seaborne trade
© The Author(s) 2018
Received: 27 August 2017
Accepted: 9 January 2018
Published: 22 January 2018
Considering 91 countries with seaports, this study conducted an empirical inquiry into the broader economic contribution of seaborne trade, from a port infrastructure quality and logistics performance perspective. Investment in quality improvement of port infrastructure and its contribution to economy are often questioned by politicians, investors and general public. A structural equation model (SEM) is used to provide empirical evidence of significant economic impacts of port infrastructure quality and logistics performance. Furthermore, analysis of a multi-group SEM is performed by dividing countries into developed and developing economy groups. The results reveal that it is vital for developing countries to continuously improve the quality of port infrastructure as it contributes to better logistics performance, leading to higher seaborne trade, yielding higher economic growth. However, this association weakens as the developing countries become richer.
Trade between nations has always made a significant contribution in terms of increasing wealth among the world population (Smith, 1776). Today, over 80% of all trade is seaborne (Stopford, 2009; UNCTAD, 2015). World merchandise trade volumes have grown at a modest rate of 2.3% in 2014 following the global gross domestic product (GDP) growth rate of 2.5%, indicating a strong correlation between trade and GDP (UNCTAD, 2015). The history of urban development also reveals that economic advancement is especially apparent in cities with seaports (Shan et al., 2014).
Globalisation of complex industrial production processes has increased the importance of seaports in the global supply chain. Port activity is no longer limited to just cargo handling; logistics service provision in an international context has become a core part of the business (Wang and Cullinane, 2006). In this situation, the most imperative aspects of logistics performance are logistics costs and reliability of supply chains. Poor logistics facilitation takes a large toll on a country’s competitive advantage, and insights in this respect were conferred by Arvis et al. (2007). In a world of just-in-time production processes, it is not only the time and cost of delivery of shipments that matters, but also its reliability and predictability. A firm’s hedging costs due to poor reliability and predictability of logistics services can be significantly high in terms of higher inventory maintenance requirements (Arvis et al., 2010). The trade-off between direct freight costs and reliability varies depending on a country’s commodity trade and logistics performance, which can limit the potential of developing countries to diversify from time-insensitive commodities to value-added goods (Arvis et al., 2010). Despite such significance, the impacts of port infrastructure quality and logistics performance on a country’s trade and economy have been largely overlooked in the existing port economics literature.
Economic impact studies in general are essential for justifying the economic contribution of large infrastructure facility developments. “They are especially controversial when used prospectively to justify public subsidy or extraordinary planning permission” (Hall, 2004). Therefore, the aim of port impact studies is to inform the general public about the economic contribution of ports. This aim alone is not a small task, as ports facilitate socio-economic infrastructure and generate external economies that are often not visible to the general public, but consent is required whenever port facilities are established or expanded (Chang, 1978). Today, it remains undecided as to whether or not ports contribute to their surrounding national or regional economies. Some researchers (e.g. Yochum & Agarwal, 1987, Ferrari et al., 2010, Bottasso et al., 2013, 2014, Shan et al., 2014, Chang, et al., 2014) have noted that ports stimulate the economic growth of a country or region, whereas others (e.g. Kinsey, 1981, Gripaios & Gripaios, 1995, Jung, 2011, Deng et al., 2013) have argued that ports do not play any key role therein. The constant decline in the number of jobs at ports due to automation and the containerisation of goods has extricated the direct economic contribution of ports.
Meanwhile, many countries are planning to build up regional hub ports, following successful cases such as Singapore, Shenzhen, Hong Kong, Dubai, to name a few and expecting additional growth of their economies in forms of new service markets. This could be aided by developing transshipment facility and efficient transport network. However, the port–city relationship has changed and the urban structure of cities is no longer important for explaining the intensity and spatial distribution of maritime transport networks (Ducruet et al., 2016). Slack and Gouvernal (2015) argued that the potential for economic development through hub port development is more limited than suggested in most maritime literature. Due to structural changes in the global shipping industry, neither a port’s throughput projection nor its economic contribution performs with the degree of certainty expected by the planners (Hesse, 2006). Also, the demolition of the shipping conference system in 2008 and the global financial crisis in 2009 hit the shipping industry adversely (Munim and Schramm, 2017). According to Grossmann (2008), “economic growth has shifted to newer economic sectors which require investments into different locational factors, a high quality of life and an attractive, well-function city-core” (p. 2063). Hence, before investing millions of dollars in building up or expanding port infrastructure, it is important to understand the extent to which ports impact national or regional economy.
Ng (2013) pointed out that, with present port research concentration on day-to-day port operations (that is, port performance and competition, port management and governance, ports and supply chains), research on port-region relationships has decreased considerably since the 1990s. In order to address the significance of port infrastructure quality, logistics performance and seaborne trade, the present study investigates the following research questions (RQs): (1) Does the port infrastructure quality, logistics performance and seaborne trade of a country have any significant impact (positive or negative) on the country’s economy? (2) Does the impact differ between developed and developing economies? To answer RQ1, a structural equation model (SEM) is developed to analyse how port infrastructure and logistics performance of a country affects seaborne trade, as well as the economy of a country. To answer RQ2, a multi-group SEM is formed considering two groups: developed and developing economies.
Section 2 presents a comprehensive literature review of port economic impact studies and the conceptual framework of this study. In Section 3, data and methodological issues are discussed, before the results of the empirical analysis are presented in Section 4. Section 5 discusses policy implication of the findings and concludes with future research directions.
Literature and research framework
Although many studies have justified investment into transport facilities as a stimulator of economic growth of a country or region, most of the economic impact studies concerning seaborne trade have focused on a particular seaport or a region and a clear picture of how seaborne trade benefits the world economies remains elusive. In the context of the Port of Liverpool, Kinsey (1981) argued that the impact of ports on the local economy was declining, with a decreased number of jobs directly dependent on the port at that time. British ports were no longer major employers and the industrial inter-related complexities no longer existed, further reducing the impact of ports on the local economy (Gripaios and Gripaios, 1995). Two relatively recent studies, one in the context of South Korea (Jung, 2011) and another in the context of China (Deng et al., 2013), have also argued that ports are having declining effects on economy. In particular, Jung (2011) identified that from 1990 to 2008, South Korea experienced 87.5% decrease in the direct port employment creation effect per billion Korean won. Despite no significant impact of seaborne trade on economic growth, Deng et al. (2013) revealed significant positive association between regional economy and value added activity at Chinese ports. The reasons for such association could be that Deng et al. (2013) included total volume of imports and exports in the value-added activity construct, which is actually part of the port demand (i.e. seaborne trade) construct.
The benefits of investing in transport infrastructure are not limited to travel-time saving (Banister and Berechman, 2001). Lakshmanan (2011) showed that improved freight services lead to growing trade, followed by improved labor supply and technical diffusion. Some port impact studies in the context of the USA (Yochum and Agarwal, 1987), European countries (Bottasso et al., 2013; Bottasso et al., 2014; Ferrari et al., 2010), China (Shan et al., 2014) and South Africa (Chang et al., 2014) have observed significant impact of port activity on regional/national economies. Yochum and Agarwal (1987) concluded that some firms located in Hampton, USA, would experience a severe economic penalty due to a shortage of ports. Bottasso et al. (2013) analysed the impact of ports on local employment, using a sample of 560 regions located in 10 West European countries. They found that every million tons of net port throughput would create about 400–600 jobs in the region. Furthermore, Bottasso et al. (2014) found that every 10% increase in port throughput can generate a 6–20% increase in the GDP of the regions and can have a spillover effect on neighbouring regions in the range of 5–18%. In the context of China, Shan et al. (2014) found that 1% increase in port cargo throughput can increase GDP per capita growth by 7.6%, and the port throughput of a country has a positive impact on neighbouring economies. Similarly, Chang et al. (2014) revealed that the South African economy could suffer a 17% loss due to a single unit shortage in port activity.
A summary of selected port impact studies since the 1980s is presented in Appendix A. Most of these studies employed either input–output analysis or regression analysis, and focused only on a particular port of a country or region. What is lacking is that, none of those studies considered port infrastructure quality and logistics performance but focused solely on port throughputs. Therefore, the present study examines the wider economic benefits of port infrastructure quality, logistics performance and seaborne trade through analysis of national-level data of 91 countries.
First, we examine the effects of QPI on LP, seaborne trade (ST) and national economy (NE) based on the conceptual framework in Fig. 1. We then look at the effects of LP on ST and NE, and then the effect of ST on NE is examined. Finally, the indirect (or mediated) effects of QPI on NE via LP, QPI on NE via ST, and QPI on NE via LP and ST are investigated.
H1 (a): The quality of port infrastructure has a positive effect on logistics performance.
H1 (b): The quality of port infrastructure has a positive effect on seaborne trade.
H1 (c): The quality of port infrastructure has a positive effect on national economy.
H1 (d): The quality of port infrastructure has a positive effect on national economy mediated through logistics performance.
H1 (e): The quality of port infrastructure has a positive effect on national economy mediated through seaborne trade.
H1 (f): The quality of port infrastructure has a positive effect on national economy mediated through logistics performance and seaborne trade.
H2 (a): Logistics performance has a positive effect on seaborne trade.
H2 (b): Logistics performance has a positive effect on national economy.
H2 (c): Logistics performance has a positive effect on national economy mediated through seaborne trade.
H3: Seaborne trade has a positive effect on national economy.
Data and methodology
List of observed and latent variables
Quality of port infrastructure (QPI)
Quality of port infrastructure
Logistics performance (LP)
Ability to track and trace consignments
Competence and quality of logistics services
Ease of arranging competitively priced shipments
Efficiency of customs clearance process
Frequency with which shipments reach consignee within scheduled or expected time
Quality of trade and transport-related infrastructure
Seaborne trade (ST)
Container port traffic (‘000 TEUs)
Liner shipping connectivity index
National economy (NE)
GDP per capita, PPP (Int. $)
The quality of port infrastructure (QPI) construct is formed with one observed variable, named QPI, which covers business executives’ perception of their country’s port facilities. Data are on a Likert scale from 1 to 7, where 1 represents the port infrastructure considered extremely underdeveloped and 7 represents efficient by international standards (see http://data.worldbank.org/indicator/IQ.WEF.PORT.XQ for details).
The logistics performance construct consists of six indicators as listed in Table 1. Lun et al. (2016) used the same set of LP data as a proxy for trade facilitation. The set of LP indicators is based on empirical survey data collected by the World Bank on a regular basis. The LP Index measures logistics performance on the country level through asking operators on the ground (global freight forwarders and express carriers) to provide feedback on the logistics “friendliness” of the countries in which they operate (see http://lpi.worldbank.org/ for further details).
Seaborne trade can be expressed by container or cargo throughput, as used by Deng et al. (2013) and Shan et al. (2014). However, throughput alone cannot totally represent the value and intensity of seaborne trade of a country. Therefore, a latent construct with country-level container throughput in TEUs and liner shipping connectivity index is formed. The LSC index represents the connectivity of countries with each other in the form of a global liner shipping network. The LSC index is based on five maritime transport components (i.e. number of ships handled, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country’s ports; see http://data.worldbank.org/indicator/IS.SHP.GCNW.XQ for details). A correlation matrix of all the latent constructs is depicted in Appendix B.
Descriptive statistics of variables
Empirical analysis and findings
This section presents empirical analysis and findings with respect to the research questions stated in Section 1. First, normality of latent variables has been checked through the Shapiro-Wilk test and Q-Q plots of residuals of variables. As none of the variables are normally distributed, the Satorra-Bentler rescaling method has been employed for SEM estimation, as suggested by Rosseel (2012). Validity and reliability statistics of the conceptual model (proposed in Section 2) are then performed. After validation and reliability check, the final SEM and multi-group SEM are presented.
Validity and reliability
Summary Results of Measurement Model
Unstandardized factor loadings
Standardized factor loadings
R2 (item reliability)
In addition to the model fit indices and SMC, we also checked the reliability of the latent constructs. Reliability is referred to by the value of Cronbach’s Alpha. The Cronbach’s Alpha (Cronbach, 1951) values of the constructs LP and ST are 0.97 and 0.86, respectively; both values exceed the required level of 0.70 as suggested by Nunnally (1978). This also confirms internal consistency of the latent constructs (Garver and Mentzer, 1999).
To test for convergent validity, we examined the statistical significance of the factor loadings through their z-values (also stated as t-values, Dunn et al., 1994). As a rule of thumb, acceptable estimates should have z-values higher than 2 or less than − 2 (Hair et al., 2006; Koufteros, 1999). As depicted in Table 3, all z-values of indicators are higher than 2, which means that all indicators measure their respective latent construct, and confirm the uni-dimensionality and convergent validity of each construct (Anderson and Gerbing, 1988). As all the R2 values are above 0.50, item reliability is also confirmed. To assess discriminant validity, a series of pairwise confirmatory factor analyses (CFA) were conducted. In this process, a non-constrained CFA of one pair of constructs was compared with a constrained CFA at a time to avoid the influence of construct pairs with significant values over non-significant ones (Anderson and Gerbing, 1988). All the chi-square difference test results were statistically significant (p < 0.001), providing evidence of the discriminant validity of the constructs.
Structural equation model
As the measurement model and reliability tests have confirmed validity and reliability, the structural equation model is proceeded. The parameter estimations, model fit indices, and the results of hypotheses proposed in Section 2.2 are presented and discussed.
Results of Structural Equation Modelling
QPI → LP
QPI → ST
QPI → NE
QPI → LP → NE
QPI → ST → NE
QPI → LP → ST → NE
LP → ST
LP → NE
LP → ST → NE
ST → NE
Based on the statistical significance of the regression coefficients depicted in Table 4, this study finds support for H1 (a), H1 (c), H1 (d), H2 (a) and H2 (b). The other hypotheses are not supported. Thus, quality of port infrastructure has a positive effect on logistics performance and national economy. Logistics performance has a positive effect on seaborne trade and national economy. The effects of quality of port infrastructure and logistics performance on national economy were found to be significant. While the mediation effect of port infrastructure quality on national economy via logistics performance is found significant, the mediation effect via seaborne trade was found to be insignificant. Also, the mediation effect of logistics performance on national economy through seaborne trade was found to be insignificant. To further investigate whether the findings are similar for both developed and developing economies, a multi-group SEM is formed and estimated in the next section.
Multi group analysis
A useful extension of the SEM is multi-group analysis, which allows for the investigation of model fit of a specific model for different groups. Some studies have compared different groups in terms of inequality. Due to immense inequality of regional economic development, Chu (2012) and Li et al. (2017) compared the impact of logistics development on regional economic growth of China for coastal provinces and inland provinces. Similarly, port infrastructure quality, logistics performance and seaborne trade may have varying impact on different economies of the world. Therefore, the sample of 91 countries has been divided into two groups: developed and developing economies. The World Bank classification of country economics was used to group the countries. Based on World Bank classification, countries with gross national income (GNI) per capita higher than 12,475 USD were considered as developed economies (N = 103), while others with GNI per capita below 12,475 USD were considered as developing economies (N = 125).
Comparison of model fit statistics for invariance test
MG: Configural (χ2 = 182.38)
MG Vs. MG2: Equal loadings
MG Vs. MG3: Equal intercepts
Comparison of regression coefficients
QPI → LP
QPI → ST
QPI → NE
QPI → LP → NE
QPI → ST → NE
QPI → LP → ST → NE
LP → ST
LP → NE
LP → ST → NE
ST → NE
The multi-group SEM demonstrates good model fit as the ratio of χ 2 and degrees of freedom is below three (that is, 132.33/68 = 1.95). All other fit indices, such as CFI, TLI, AGFI, RMSEA and SRMR, are also within the recommended level. The findings of the multi-group analysis are remarkable. Quality of port infrastructure has a positive effect on logistics performance in both developed and developed economies, but the quality of port infrastructure affects national economy only in developed economies. However, the mediated effect of port infrastructure quality on national economy through logistics performance and seaborne trade is significant in developing economies. Furthermore, logistics performance has a positive effect on national economy for both developed and developing economies.
Discussion and conclusion
This study examined associations among quality of port infrastructure, logistics performance and seaborne trade, and their effects on national economy. Overall, the results show that improvement in quality of port infrastructure and logistics performance would bring the greatest benefits to the economy of a country. The study revealed that the quality of port infrastructure has a significant positive effect on national economy, which is similar to Ferrari et al. (2010), Bottasso et al. (2014), Park and Seo (2016) and others, who observed positive effects of seaports on the economy. Our findings are also similar to those of Deng et al. (2013) as we found no association between seaborne trade (i.e. port demand) and national economy.
However, quality of port infrastructure significantly affects the logistics performance of a country. Similar to Hausman et al. (2013), we also found that logistics performance affects the seaborne trade of a country. Most of the studies that foresaw diminishing impact of seaports on economy emphasised employment generation within ports (Gripaios and Gripaios, 1995; Kinsey, 1981; Jung, 2011). However, Helling and Poister (2000) mentioned that ports that retain direct port-related employment lose their ability to compete for cargo, which leads to a lower number of jobs in the long-run. Economic development is associated much more with the long-term capability of a port to attract more customers while creating and retaining employment and income (Helling, 1997). A dollar’s worth of maritime transportation requires inputs from at least 10 interrelated transport and logistics industries (Helling and Poister, 2000). Therefore, if the quality of port infrastructure is not improved continuously, it may have a substantial adverse impact on the economy of a country.
Further, the extension to multi-group analysis reveals important findings, especially for developing economies. Seaborne trade partially mediates the impact of port infrastructure quality and logistics performance on economic growth in developing countries. Portugal-Perez and Wilson (2012) found that the impact of transport efficiency (a component of logistics performance) on export performance decreases as the economy becomes richer. Similarly, the present study found that logistics performance has a higher impact on seaborne trade in developing economies than developed ones. For developing economies, we also found that quality of port infrastructure positively affects logistics performance; better logistics performance yields higher seaborne trade, and higher seaborne trade yields economic growth. Therefore, policy makers in developing countries should consider investing in quality improvement of port infrastructure and logistics performance, compared to larger investments in the building of new physical infrastructures (Portugal-Perez and Wilson, 2012). Policy makers in developed countries should also consider maintaining high-quality port infrastructure, as this has a positive effect on logistics performance and national economy. Korinek and Sourdin (2011) stated that “as developed nations shift from traditional manufacturing and agriculture and are increasingly engaging in international vertical specialisation, the need for efficient logistics services becomes ever more important” (p. 2). As of 1990, although smaller countries of the OECD database had a higher share of vertical specialisation than the overall OECD share; the overall share increased by about 30% between 1970 and 1990 (Hummels et al., 2001).
However, the reasons for the lack of any significant association between seaborne trade and national economy for developed economies could be: (1) the growth rates of GDP per capita compared to seaborne trade of the developed countries is lower than that of developing countries in general, and (2) developed countries are service-based economies and the role of seaborne trade is often one-way (imports), while developing countries tend to be more industry-based and trade plays a two-way role (both imports and exports). Meanwhile, attempts to stimulate economic growth by major developed economies – such as Brexit by the United Kingdom, and the United States President Donald Trump’s approach towards bringing back major industrial production facilities to the USA – could, if followed by other developed nations, change the current association between seaborne trade and economy in the future.
Overall, the findings of the study are consistent with the existing transport economics literature, which underlines the fundamental contributions of port infrastructure quality and logistics performance to the economic growth of a country. However, associations among the quality of port infrastructure, logistics performance and seaborne trade, and their effects on yearly growth of country economy, should be further examined using latent growth models. It would be interesting for future studies to investigate the interaction effect between port size and economy classification. Investigation of the comparative economic impact of hub and gateway ports could also be considered. Studies should also examine the impact that quality of port infrastructure and logistics performance has on the growth of neighbouring landlocked countries’ economy. Finally, economic contribution of value added activities at ports (e.g. through development of logistics parks) may also be investigated in future research (Munim and Saeed, 2016).
The authors would like to thank Prof. Adolf K. Y. Ng for useful suggestions.
There are no sources of funding to be decleared.
ZHM conceptualised the framework, conducted literature review, data curation, analysis and wrote the first draft. HJS supervised the complete process, enriched the literature review, and reviewed and edited the final draft of the paper. Both authors have read and approved the final version of the manuscript.
Ziaul Haque Munim is a PhD Research Fellow at the University of Agder, Norway. His main research interests include maritime economics and logistics, supply chain management and international trade. He holds a MSc degree in Supply Chain Management with specializations in transport and logistics, and transport geography modelling from WU Vienna University of Economics and Business. He received the Best Paper Award at the IAME Annual Conference 2016 in Hamburg, Germany.
Hans-Joachim Schramm is a Senior Lecturer at WU – Vienna University of Economics and Business and an external lecturer at Copenhagen Business School (CBS). He holds a diploma degree in economics from Humboldt-University at Berlin and a doctoral degree from Dresden University of Technology. Being a forwarding agent by profession, his main focus of his research is about economics and policy issues in sea, air, rail and road transport markets. He authored several papers and monographs and contributed to peer-review processes at JBL, IJPDLM, IJLM, MPM, LR and JTG. Furthermore, he was visiting lecturer in Belgium, Finland, France, Hungary, Sweden, China and Cuba.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Anderson JC, Gerbing DW (1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull 103(3):411–423View ArticleGoogle Scholar
- Arvis J-F, Mustra MA, Ojala L, Shepherd B, Saslavsky D (2010) Connecting to compete: trade logistics in the global economy, The Logistics Performance Index and Its Indicators. The World Bank, Washington DCGoogle Scholar
- Arvis J-F, Mustra MA, Panzer J, Ojala L, Naula T (2007) Connecting to compete: trade logistics in the global economy. The World Bank, Washington DCGoogle Scholar
- Banister D, Berechman Y (2001) Transport investment and the promotion of economic growth. J Transp Geogr 9(3):209–218View ArticleGoogle Scholar
- Bollen KA (1989) Structural equations with latent variables. Wiley, New YorkGoogle Scholar
- Bollen KA, Long JS (1993) "introduction" in testing structural equation models. Sage, Newbury ParkGoogle Scholar
- Bottasso A, Conti M, Ferrari C, Merk O, Tei A (2013) The impact of port throughput on local employment: evidence from a panel of European regions. Transp Policy 27:32–38View ArticleGoogle Scholar
- 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 65:44–55View ArticleGoogle Scholar
- Chang S (1978) In defense of port economic impact studies. Transp J 17:79–85Google Scholar
- Chang Y-T, Shin S-H, Lee PT-W (2014) Economic impact of port sectors on south African economy: an input–output analysis. Transp Policy 35:333–340View ArticleGoogle Scholar
- Chu Z (2012) Logistics and economic growth: a panel data approach. Ann Reg Sci 49(1):87–102View ArticleGoogle Scholar
- Clark X, Dollar D, Micco A (2004) Port efficiency, maritime transport costs, and bilateral trade. J Dev Econ 75(2):417–450View ArticleGoogle Scholar
- Coto-Millán P, Agüeros M, Casares-Hontañón P, Pesquera MÁ (2013) Impact of logistics performance on world economic growth (2007–2012). World Review Intermodal Transp Res 4(4):300–310View ArticleGoogle Scholar
- Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16(3):297–334View ArticleGoogle Scholar
- Deng P, Lu S, Xiao H (2013) Evaluation of the relevance measure between ports and regional economy using structural equation modeling. Transp Policy 27:123–133View ArticleGoogle Scholar
- Ducruet C, Cuyala S, Hosni AE (2016) The changing influence of city-systems on global shipping networks: an empirical analysis. J Shipping Trade 1(4)Google Scholar
- Dunn SC, Seaker RF, Waller MA (1994) Latent variables in business logistics research: scale development and validation. J Bus Logist 15(2):145–172Google Scholar
- Ellis PD (2011) Social ties and international entrepreneurship: opportunities and constraints affecting firm internationalization. J Int Bus Stud 42(1):99–127View ArticleGoogle Scholar
- Ferrari C, Percoco M, Tedeschi A (2010) Ports and local development: evidence from Italy. Int J Transport Econ 37(1):9–30Google Scholar
- Garver MS, Mentzer JT (1999) Logistics research methods: employing structural equation modeling to test for construct validity. J Bus Logist 20(1):33–57Google Scholar
- Gordon JR, Lee P-M, Lucas HC (2005) A resource-based view of competitive advantage at the port of Singapore. J Strateg Inf Syst 14(1):69–86View ArticleGoogle Scholar
- Gripaios P, Gripaios R (1995) The impact of a port on its local economy: the case of Plymouth. Marit Policy Manag 22(1):13–23View ArticleGoogle Scholar
- Grossmann I (2008) Perspectives for Hamburg as a port city in the context of a changing global environment. Geoforum 39(6):2062–2072View ArticleGoogle Scholar
- Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL (2006) Multivariate data analysis. Pearson Prentice Hall Upper Saddle RiverGoogle Scholar
- Hall PV (2004) “We’d have to sink the ships”: impact studies and the 2002 west coast port lockout. Econ Dev Q 18(4):354–367View ArticleGoogle Scholar
- Hausman WH, Lee HL, Subramanian U (2013) The impact of logistics performance on trade. Prod Oper Manag 22(2):236–252View ArticleGoogle Scholar
- Helling A (1997) Transportation and economic development a review. Public Works Manag Policy 2(1):79–93View ArticleGoogle Scholar
- Helling A, Poister TH (2000) US maritime ports: trends, policy implications, and research needs. Econ Dev Q 14(3):300–317View ArticleGoogle Scholar
- Hesse M (2006) Global chain, local pain: regional implications of global distribution networks in the German north range. Growth Change 37(4):570–596View ArticleGoogle Scholar
- Hummels D, Ishii J, Yi K-M (2001) The nature and growth of vertical specialization in world trade. J Int Econ 54(1):75–96View ArticleGoogle Scholar
- Jung B-M (2011) Economic contribution of ports to the local economies in Korea. Asian J Shipping Logist 27(1):1–30View ArticleGoogle Scholar
- Kinsey J (1981) The economic impact of the port of Liverpool on the economy of Merseyside—using a multiplier approach. Geoforum 12(4):331–347View ArticleGoogle Scholar
- Kline RB (2005) Principles and practice of structural equation modeling. The Guilford PressGoogle Scholar
- Korinek J, Sourdin P (2011) To what extent are high-quality logistics services trade facilitating?, OECD Trade Policy Papers, No. 108. OECD publishing, ParisView ArticleGoogle Scholar
- Koufteros XA (1999) Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling. J Oper Manag 17(4):467–488View ArticleGoogle Scholar
- Lakshmanan T (2011) The broader economic consequences of transport infrastructure investments. J Transp Geogr 19(1):1–12View ArticleGoogle Scholar
- Lean HH, Huang W, Hong J (2014) Logistics and economic development: experience from China. Transp Policy 32:96–104View ArticleGoogle Scholar
- Li KX, Jin M, Qi G, Shi W, Ng AK (2017) Logistics as a driving force for development under the belt and road initiative–the Chinese model for developing countries. Transp Rev:1–22. https://doi.org/10.1080/01441647.2017.1365276
- Limao N, Venables AJ (2001) Infrastructure, geographical disadvantage, transport costs, and trade. World Bank Econ Review 15(3):451–479View ArticleGoogle Scholar
- Lu C-S, Lai K-H, Cheng TE (2007) Application of structural equation modeling to evaluate the intention of shippers to use internet services in liner shipping. Eur J Oper Res 180(2):845–867View ArticleGoogle Scholar
- Lun YV, Carlton J, Bichou K (2016) Examining the economic impact of transport complex economies. J Shipping Trade 1(1):1–17View ArticleGoogle Scholar
- Memedovic O, Ojala L, Rodrigue J-P, Naula T (2008) Fuelling the global value chains: what role for logistics capabilities? International Journal of Technological Learning. Innov Dev 1(3):353–374Google Scholar
- Munim, ZH, Saeed N (2016) Seaport competitiveness research: A bibliometric citation meta-analysis. Proceedings of the Annual conference of the International Association of Maritime Economists (IAME) 2016. August 23–26, 2016. Hamburg, GermanyGoogle Scholar
- Munim ZH, Schramm HJ (2017) Forecasting container shipping freight rates for the Far East – northern Europe trade lane. Marit Econ Logist 19(1):106–125. https://doi.org/10.1057/s41278-016-0051-7View ArticleGoogle Scholar
- Ng AK (2013) The evolution and research trends of port geography. Prof Geogr 65(1):65–86View ArticleGoogle Scholar
- Notteboom TE, Coeck C, Verbeke A, Winkemans W (1997) Containerization and the competitive potential of upstream urban ports in Europe. Marit Policy Manag 24(3):285–289View ArticleGoogle Scholar
- Nunnally JC (1978) Psychometric theory, 2nd edn. McGraw-Hill, New YorkGoogle Scholar
- Panayides PM, Parola F, Lam JSL (2015) The effect of institutional factors on public–private partnership success in ports. Transp Res A Policy Pract 71:110–127View ArticleGoogle Scholar
- Park JS, Seo Y-J (2016) The impact of seaports on the regional economies in South Korea: panel evidence from the augmented Solow model. Transport Res E-Log 85:107–119View ArticleGoogle Scholar
- Portugal-Perez A, Wilson JS (2012) Export performance and trade facilitation reform: hard and soft infrastructure. World Dev 40(7):1295–1307View ArticleGoogle Scholar
- Rosseel Y (2012) Lavaan: an R package for structural equation modeling. J Stat Softw 48(2):1–36View ArticleGoogle Scholar
- Sánchez RJ, Hoffmann J, Micco A, Pizzolitto GV, Sgut M, Wilmsmeier G (2003) Port efficiency and international trade: port efficiency as a determinant of maritime transport costs. Marit Econ Logist 5(2):199–218View ArticleGoogle Scholar
- Shan J, Yu M, Lee C-Y (2014) An empirical investigation of the seaport’s economic impact: evidence from major ports in China. Transport Res E-Log 69:41–53View ArticleGoogle Scholar
- Slack B, Gouvernal E (2015) Container transshipment and logistics in the context of urban economic development. Growth Change 47(3):406–415View ArticleGoogle Scholar
- Sleeper DM (2012) Port significance: contributions to competitiveness in Latin America and Asia. J Global Business Community 3(1):22–28Google Scholar
- Smith A (1776) An inquiry into the nature and causes of the wealth of nations. Edwin Cannan's annotated editionView ArticleGoogle Scholar
- Song D-W, Panayides PM (2008) Global supply chain and port/terminal: integration and competitiveness. Marit Policy Manag 35(1):73–87View ArticleGoogle Scholar
- Stopford M (2009) Maritime economics 3e. Routledge, AbingdonGoogle Scholar
- Subramanian U, Anderson WP, Lee K (2005) Measuring the impact of the investment climate on total factor productivity: the cases of China and Brazil. Working paper No. 3792, The Wold Bank, Washington, DCGoogle Scholar
- Subramanian U, Arnold J (2001) Forging subregional links in transport and trade facilitation. The World Bank, Washington, DCGoogle Scholar
- UNCTAD (2015) Review of maritime transport. United Nations conference on trade and development. United Nations Publication, GenevaGoogle Scholar
- van den Heuvel FP, Rivera L, van Donselaar KH, de Jong A, Sheffi Y, de Langen PW, Fransoo JC (2014) Relationship between freight accessibility and logistics employment in US counties. Transp Res A Policy Pract 59:91–105View ArticleGoogle Scholar
- Wang T-F, Cullinane K (2006) The efficiency of European container terminals and implications for supply chain management. Marit Econ Logist 8(1):82–99View ArticleGoogle Scholar
- Wilmsmeier G, Hoffmann J (2008) Liner shipping connectivity and port infrastructure as determinants of freight rates in the Caribbean. Marit Econ Logist 10(1–2):130–151View ArticleGoogle Scholar
- Yeo GT, Roe M, Dinwoodie J (2008) Evaluating the competitiveness of container ports in Korea and China. Transp Res A Policy Pract. 42(6):910–921Google Scholar
- Yochum GR, Agarwal VB (1987) Economic impact of a port on a regional economy: note. Growth Change 18(3):74–87View ArticleGoogle Scholar