Background

Reports of modern slavery in marine fisheries indicate a need to properly assess the scale of the issue globally. Given most countries around the world are involved in marine fishing, a clearer understanding of the risk factors associated with vulnerability to modern slavery in fisheries is required to effectively allocate resources to research and remedy in those countries most at risk.

Joint research undertaken by researchers from the Sea Around Us at the University of Western Australia and the University of British Columbia, and the Walk Free Foundation has sought to identify those characteristics which most strongly suggest modern slavery aboard fishing vessels. The goal was to develop a risk model that indicates where undetected modern slavery issues in the global fishing industry may exist. The research methodology and results are the subject of a forthcoming peer reviewed paper. 1

In summary, the research sought to use statistical testing to understand the relationship between data on prevalence of modern slavery, and data on fisheries governance and performance sourced from the Sea Around Us.2 The analysis was limited to the 20 largest fishing countries, which collectively land 80percent of the world’s fisheries catch. The analysis was based on the prevalence data from the 2016 Global Slavery Index3, and media and NGO reports of slavery incidents in fisheries, while the fisheries sector data were derived from the Sea Around Us project and other key sources4. The analysis identified six key characteristics of the fisheries sector that predict vulnerability to forced labour at a national level:

  1. The percentage of national catch caught outside a country’s Exclusive Economic Zone (EEZ)5 with higher values indicating greater vulnerability.
  2. The mean distance (km) from a fishing country to the location of catch, calculated at a resolution of 0.25 degree and weighted by tonnes caught in each cell,6 with greater distances indicating greater vulnerability.
  3. The percentage of harmful subsidies as a percentage of the total (2009) landed value of the fishery.7 Harmful subsidies distort the market by, for instance, reducing fuel costs or increasing fishing capacity and thus support fishing even when it is uneconomical, with higher values indicating greater vulnerability.
  4. Per capita GDP based on purchasing power parity in 2016 US$8 as an indicator of relative national wealth with higher values indicating lower vulnerability.
  5. The value of the fishery per fisher (US$) as an indicator of the average return to fishers within the sector. We averaged the value of reported industrial fisheries catch between 2005-20149 and divided this number by the estimated number of individuals employed in industrial fisheries in 2003 as more recent data were unavailable, with higher values indicating lower vulnerability.
  6. The percentage of unreported fish catch divided by the total of all catch, reported and unreported, for industrial fishing as an indicator of governance and effective fisheries management, with higher values indicating greater vulnerability.

These six characteristics reflect two major sets of drivers:

  • National Fisheries Policy that determines the degree to which fisheries focus on distant waters vs national EEZs and the degree to which countries subsidize their fisheries, a typical requirement of distant water fleets. This driver reflects the first three characteristics that drive vulnerability to forced labour.
  • Wealth and Institutional Capacity that determines the degree to which a country has the resources to maintain appropriate working conditions and report on fishing activity. This is reflected in national GDP, value of the fisheries, and the degree to which countries accurately report on their fish catch. This driver reflects the latter three characteristics that drive vulnerability to forced labour.

Assessing vulnerability to modern slavery at sea

It is reasonable to assume that these six risk factors are relevant, not just for the top 20 fishing nations but for all fishing nations. In other words, an examination of these risk factors may point us to areas of risk that may otherwise be completely out of sight. To enable a broader examination of this issue, researchers at the University of Western Australia used the results from the analysis described above to model Risk of Modern Slavery at Sea for all fishing countries assessed in the Global Slavery Index 2016. The six risk factors identified can be explained in terms of two dimensions which drive vulnerability to modern slavery in a country’s fishing industry: first, National Fisheries Policy and second, Wealth and Institutional Capacity.

For each of the six characteristics identified in the initial analysis described above, a category was assigned to each country based on the country’s value for that characteristic. For instance, where the percentage of fishing outside a country’s EEZ was less than 5percent, a value of “1” was assigned. For all six characteristics, vulnerability with respect to forced labour increases from “1” to “4”.

Table 1Parameters used to determine a country’s rating for each of the six characteristics
 Fishery characteristicCategory
 1234
1Outside EEZ (%)< 5%5-29%30-69%> 70%
2Distance to fishing grounds (km)< 150150-500500-1300> 1300
3Harmful subsidies (%)< 1%1-5%6-20%> 20%
4Per capita GDP (US$)> $50,000$17,000-$49,000$7,000-$16,999< $7,000
5Value per fisher (US$)> $25,000$4,000-$25,000$1,000-$3,999< $1,000
6Unreported catch (%)0%1-15%16-40%> 40%

These generated six categorical values for each country. We then took the average values of the three characteristics associated with National Fisheries Policy and Wealth and Institutional Capacity. As the six characteristics have similar influence in the original analysis, their categorical values did not have to be weighted when calculating the average for each driver.

The average values for National Fisheries Policy, and Wealth and Institutional Capacity were then ranked from lowest to highest, representing low to high vulnerability respectively. Countries were assigned traffic light colours of green (< 2.00), orange (2.00 - 2.99) and red (3.00 - 4.00). These traffic lights represent low, moderate, and high vulnerability to forced labour in the global fishing sector. The results are in Table 1 of Modern slavery in the fishing industry.

Footnotes

1Tickler, D, Bryant, K, David, F, Forrest, J A, Gordon, E, Larsen, J J, Meeuwig, J, Oh, B, Pauly, D, Sumaila, U R and Zeller, D, Common causes, shared solutions: The relationship between modern slavery and the race to fish, [undergoing review for publication].
2Pauly, D & Zeller, D (Editors) 2015, Sea Around Us Concepts, Design and Data, Sea Around Us. Available from: www.seaaroundus.org [23 July 2017].
3Walk Free Foundation 2016, Global Slavery Index 2016. Available from: https://www.globalslaveryindex.org/download/ [23 July 2017].
4Pauly, D & Zeller, D (Editors) 2015, Sea Around Us Concepts, Design and Data, Sea Around Us. Available from: www.seaaroundus.org [23 July 2017]; International Monetary Fund 2016, IMF World Economic Outlook 2016, available from: https://www.imf.org/external/pubs/ft/weo/2016/02/weodata/index.aspx [23 July 2017].
5Pauly, D & Zeller, D (Editors) 2015, Sea Around Us Concepts, Design and Data, Sea Around Us. Available from: www.seaaroundus.org [23 July 2017].
6As above.
7Sumaila, UR, Lam, V, Le, F, Swartz, W & Pauly, D 2016, ‘Global fisheries subsidies: An updated estimate’, Marine Policy, 69. Available from: https://www.sciencedirect.com/science/article/pii/S0308597X16000026 [4 August 2018].
8International Monetary Fund 2016, IMF World Economic Outlook 2016, available from: https://www.imf.org/external/pubs/ft/weo/2016/02/weodata/index.aspx [23 July 2017].
9Pauly, D & Zeller, D (Editors) 2015, Sea Around Us Concepts, Design and Data, Sea Around Us. Available from: www.seaaroundus.org. [23 July 2017].