Appendix to Part II: Data Requirements for the SPICS II.1 Data Sets for South Pacific Island Countries—SPICs In this section we describe the data sets collected for the case study of the South Pacific Island Countries. We also include statistical analyses to describe the status quo of these countries. To begin, the collected variables are summarised in Table II.1 and fully described in the next sections. One of the limitations of the study, however, has been the lack of robust and comprehensive data sets for the Fig. II.1 Visualisation of the geographic location of the container ports considered in the study © Springer International Publishing AG 2017 113 F.R. Medda et al., Collaborative Approach to Trade, Advances in Spatial Science, DOI 10.1007/978-3-319-47039-9 Table II.1 List of model variables and collected data sets 114 Appendix to Part II: Data Requirements for the SPICS Completeness Model Notation Variable Information Unit Year (% or no.) Source component n ¼ 1, 2, ..., Ports (35) Container ports in SPICS Latitude, – 100% www.sea-rates.com Horizontal N and relevant Pacific RIM Longitude Containerisation countries International sw (u) Throughput Volumes handled per year TEU Various 69% – Port Authorities website (or TON) – Secretariat of the Pacific Community (2011) Max_draft Max Draft Max Draft Mt 2011 100% Secretariat of the Pacific Community (2011) Vessel_size Max vessel size Size of the largest vessel mLOA 2011 80% Secretariat of the Pacific that has visited each port (or dwt) Community (2011) Rij,k Container Shipping Container services that Non- Various 54 shipping – Asian Development Services (CSS) operate in SPICs ports dimensional services Bank Report (2004) – shipping lines LPI_EC Efficiency of cus- Efficiency of customs Non- 2010 62% World Bank toms: Logistics clearance process dimensional Performance Index (LPI) Internet_users Internet Users Population with access to Percentage 2010 100% World Bank the worldwide network www.internetworldstats.com Ease_business Ease of Doing Measures if a country’s Ranking 2012 77% World Bank Business regulatory environment is conducive to business LSCI Liner Shipping Captures countries’ Non- 2010 91% UNCTAD Connectivity Index connectivity to global dimensional shipping networks LPI Logistics Perfor- Reflects perceptions of Non- 2010 62% World Bank mance Index: a country’s logistics dimensional Overall operations II.1 Data Sets for South Pacific Island Countries—SPICs 115 Ei,k; Ij,k Supply/demand Import/export at country US$ 2010–2012 100% UN Trade Dataset Vertical level by product GDP GDP GDP US$ Until 2012 100% – World Bank – IMF TA Economic Free trade agreements Non- Various 4 Agreements WTO Agreement between SPICs and between dimensional (SPARTECA, SPICs and major Pacific PICTA, Rim countries PACER, APEC) Migration M Major demographic figures Various Until 2012 100% World Bank Cult Cultural ties Cultural links Non- 2005 100% CEPII dimensional Lang Common Language Common language Non- 2005 100% CEPII dimensional TCuv , k Transport cost US$/TEU 100% Transport huv , k Handling cost Handling cost per unit US$/TEU 100% Korea Maritime Institute cost τuv Port tariff US$/TEU 76% b duv Travel time Travel time between ports Days 100% Own elaboration on GIS map (distance) and vessel speed T. Notteboom and P. Cariou (2009) cpe Fuel cost IFO 380 prices for the US$/ton 2013 100% Asian-Pacific market in Nov 2013 ε Vessel Fuel Vessel fuel consumption tons/day 2009 100% T. Notteboom and P. Cariou Efficiency according to vessel size (2009) and travel speed 116 Appendix to Part II: Data Requirements for the SPICS region under consideration. For this reason, the data have been drawn from different sources and merged together in order to produce a satisfactory and robust data set. Below in Fig. II.1 is the geo-referred visualisation of the 35 ports under examination. II.2 The Shipping Network Maritime shipping is the dominant mode of transport for international trade for the SPICs and is the main focus of the model. We reconstruct the container trade shipping network within SPICs and between SPICs and major Pacific RIM coun- tries. The remainder of this section is dedicated to the description of the shipping network we have reconstructed from the available sources. Port Selection We have collected information on 35 container ports in 25 countries in the region: 12 belong to major Pacific RIM countries and 23 to the SPICs. The collection has been performed such that we have selected any container port in the SPICs which is served by at least one international liner service. For modelling purposes, we have chosen one port for each Pacific RIM country that has trade relationships with SPICs. In Table II.2 we report the list of container ports included in our case study. As reported in Table II.3 below for each port (or at the national level for economic variables), we introduce the following information: Throughput, Max Draft, Logistics Performance Index (Efficiency of Customs) (LPI), Internet Users, Ease of Doing Business, Liner Shipping Connectivity Index (LSCI), Logistics Performance Index (LPI) (Overall), and Port Fees. Most of the selected variables in Table II.3 have a high variance since our data set comprises both international hubs (e.g., Hong Kong, Singapore, Busan, etc.) and under-developed ports in the SPICs. As expected, ports in the SPICs show low traffic volumes and poor port characteristics (Table II.3). In Fig. II.2 we visualise throughput and Fig. II.3 shows the maximum vessel draft allowed for ports in our case study. We use vessel maximum draft as a proxy for ports’ technical characteristics. It is noteworthy that, although ports in the SPICs are on average poorly equipped, some ports (e.g., in PNG, Fiji and Majuro Atoll) can accommodate fairly large vessels. In Figs. II.4, II.5 and II.6 we visualise GDP, Ease of Doing Business, and percentage of Internet Users, respectively, in our case study countries. As expected, SPIC countries all have very low GDP, and new business activity has relatively high difficulty in getting started. Although the SPICs are better positioned than some of the strong emergent economies (e.g., India, China and Indonesia), a less rosy picture appears when we consider the percentage of people with Internet access. Apart II.2 The Shipping Network 117 Table II.2 Container ports and countries examined in our case study Country List of ports (3-digit code) American Samoa (S) Apia (APW), Pago Pago (PPG) Australia (R) Brisbane (BNE) China (R) Hong Kong (HKG) Fiji (S) Lautoka (LTA), Suva (SUV) Guam (S) Port of Guam (GUM) Indonesia (R) Jakarta (JKT) India (R) Calcutta (CCU) Japan (R) Nagoya (NGO) Kiribati (S) Tarawa Betio (TRW) South Korea (R) Busan (BNU) Majuro Atoll (S) Majuro Atoll (MAJ) New Caledonia (S) Noumea (NOU) Malaysia (R) Port Kelang (PKL) New Zealand (R) Tauranga (TRG) Philippines (R) Manila (MNL) Palau (S) Koror (KOR) Papua New Guinea (S) Kimbe (KIM), Lae (LAE), Madang (MAG), Oro Bay (ORO), Port Moresby (PMB), Rabaul (RAB) French Polynesia (S) Papeete (PPT) Singapore (R) Singapore (SIN) Solomon Islands (S) Honiara (HIR), Noro (NOR) Thailand (R) Bangkok (BKK) Tonga (S) Nukualofa (TBU) Tuvalu (S) Funafuti (FUN) USA (R) Los Angeles (LAX) Vanuatu (S) Port Vila (VLI), Santo (GBS) (S)—SPICs and (R)—Pacific RIM countries Table II.3 Relevant statistics of port variables N Min Max Mean Std. dev. Variance Skew. Kurtos. Throughput 29 2000 2.8E+7 3,446,542 6,997,488 4.9E+13 2.59 6.41 Max Draft 35 6.4 22 11.68 3.56 12.64 1.25 2.24 Efficiency 22 1.95 4.02 2.7 0.7185 0.5162 0.46 À1.42 customs (LPI) Internet users 35 2 83.7 29.01 28.57 816.19 0.86 À0.82 Ease of Doing 27 1 133 72.11 45.08 2032.54 À0.47 À1.39 Business LSCI 31 2.9 143.6 27.0 35.6527 1271.2 1.82 2.61 Total LPI 22 1.91 4.22 2.8 0.85 0.73 0.39 À1.49 Port fees 35 489.9 10,421 4056 2412 5,819,234 0.74 0.16 118 Appendix to Part II: Data Requirements for the SPICS Fig. II.2 Geo-referred visualisation of Port Throughput Fig. II.3 Geo-referred visualisation of Maximum allowed vessel Draft II.2 The Shipping Network 119 Fig. II.4 Geo-referred visualisation of national GDP in 2010 Fig. II.5 Geo-referred visualisation of national Ease of Doing Business. Small values are associated with countries where it is easier to start up a business 120 Appendix to Part II: Data Requirements for the SPICS Fig. II.6 Geo-referred visualisation of national percentages of Internet Users from the more dynamic examples of PNG and the Solomon Islands, most of the SPICs have low to medium values of Internet usage. Container Shipping Services We construct the shipping network in the study region by collecting information from 14 shipping companies. We have surveyed a total of 54 container services in the region and included every port in the SPICs that is served by at least one shipping service; in other words, we have collected and analysed the data for 23 ports in the SPICs (shown below in Table II.4). Moreover, we have considered one port for each country outside the SPICs that has trade relationships with the SPICs (12 ports). The reasons for selecting only one port for each neighbour country are: (i) we suppose that the transport cost from a port in the SPICs to any port in a selected neighbour country does not fluctuate substantially (for example, we assume transport cost between Suva and Brisbane or Suva and Melbourne to be similar); and (ii) we do not have detailed information on supply and demand locations within each country.