Port Arrivals and Trade Volume

Port Arrivals and Trade Volume#

This page showcases results from our paper Estimating Trade Volume in the Pacific Islands using Automatic Identification System (AIS), where we validate the use of AIS data to produce port-level statistics for the Pacific Islands. The paper is available [link].

This study covers Fiji, Kiribati, Marshall Islands, Micronesia, Nauru, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu. The primary data sources are AIS data and ship registry from the UN Global Platform (UNGP), and global port boundaries from the study. The period covered by this study is from January 2019 to April 2023.

We follow two existing methodologies on trade volume estimation (; ) to estimate the cargo carried by each vessel upon arrival and departure. These papers utilize dynamic information on ship movements, static characteristics of each ship, and reported draft (depth of submergence), to estimate the amount of goods offloaded or loaded at a certain port. We find that our derived port calls data accurately capture international trade-related ships, and whilst cargo volume levels are off from official data, they can still capture variation across ports and relative trends within each port.

Map of Ports#

The map below shows the location of each port and the buffer area (22 km) used to extract AIS data.

Port Arrivals#

Trade Volume#

Data Availability#

The output data from this analysis is publicly available through the Development Data Catalog.

References#

BCJP16

Juan Gabriel Brida, Isabel Cortes-Jimenez, and Manuela Pulina. Has the tourism-led growth hypothesis been validated? a literature review. Current Issues in Tourism, 19(5):394–430, 2016.

DS19

Francis X Diebold and Minchul Shin. Machine learning for regularized survey forecast combination: partially-egalitarian lasso and its derivatives. International Journal of Forecasting, 35:1679–1691, 2019.

GR84

Clive WJ Granger and Ramu Ramanathan. Improved methods of combining forecasts. Journal of forecasting, 3(2):197–204, 1984.

LXT+18

Jingjing Li, Lizhi Xu, Ling Tang, Shouyang Wang, and Ling Li. Big data in tourism research: a literature review. Tourism management, 68:301–323, 2018.

LLXW21

Xin Li, Rob Law, Gang Xie, and Shouyang Wang. Review of tourism forecasting research with internet data. Tourism Management, 83:104245, 2021.

Nar04

Paresh Kumar Narayan. Fiji's tourism demand: the ardl approach to cointegration. Tourism Economics, 10(2):193–206, 2004.

NSB13

Paresh Kumar Narayan, Susan Sunila Sharma, and Deepa Bannigidadmath. Does tourism predict macroeconomic performance in pacific island countries? Economic Modelling, 33:780–786, 2013.

See11

Boopen Seetanah. Assessing the dynamic economic impact of tourism for island economies. Annals of tourism research, 38(1):291–308, 2011.

SQP19

Haiyan Song, Richard TR Qiu, and Jinah Park. A review of research on tourism demand forecasting: launching the annals of tourism research curated collection on tourism demand forecasting. Annals of Tourism Research, 75:338–362, 2019.

SW98

James H Stock and Mark W Watson. A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. 1998.

Tim06

Allan Timmermann. Forecast combinations. Handbook of economic forecasting, 1:135–196, 2006.

WHLK22

Xiaoqian Wang, Rob J Hyndman, Feng Li, and Yanfei Kang. Forecast combinations: an over 50-year review. International Journal of Forecasting, 2022.