Contributing to Fisheries Sustainability: Inequality Analysis in the High Seas Catches of Countries

Abstract

The uneven exploitation of scarce natural resources threatens their sustainability by altering the commitment of agents. In fisheries, a great portion of catches is known to be concentrated in a few countries. Aiming to provide a more complete view on the distributional issues associated to the exploitation of common marine resources, this article focuses on the analysis of catches from high seas, which can be understood as the common marine resources under the current legislation. The analysis focuses on the evolution of several inequality indexes (the Gini index as well as others from the Atkinson and General Entropy families) from 1960 to 2014. Additionally, the Theil index is decomposed to observe whether this inequality is given by biological (between inequality) or technological (within inequality) reasons. All inequality indexes confirm that the exploitation of fishing resources in high seas is very unequal across countries. However, this inequality has decreased between 29% and 65% from 1960 to 2014. When considering the origin of catches, between 46% and 82% of the inequality observed is due to technological and fishermen capacity differences across the countries operating within fishing areas, while between 18% and 54% of the inequality can be attributed to biological differences between the fishing areas. Over time, the within component has decreased more than 35%, reflecting the greater reliance of more countries on high seas fisheries and their catching up on fishing technology. Being aware on the existence and the nature of catches inequality observed is necessary to develop successful policies for maintaining the sustainability of the fishery resources.

Equitable Allocations in Northern Fisheries: Bridging the Divide for Labrador Inuit

“…The Theil index is estimated as the weighted average of inequality among subgroups and measures the entropic distance between the observed and perfect equality distribution (Bellanger et al, 2016). It has been used at a regional scale for decomposing within and between components of different features of a fishery such as fishing gear, vessel length, distance from fishing grounds and production (Bellanger et al, 2016) as well as at a global scale to distinguish the between and within inequality attributed to biological and technological conditions, respectively (Gutiérrez and Inguanzo, 2019). In other words, the Theil index can answer the question of how much of the observed inequality can be explained by differences between fishing areas (e.g., species diversity, climate, nutrients, and other productivity factors) and how much by differences between different fishing actors in these areas (e.g., technological features of the fleet such as gear length, power, and distance) (Gutiérrez and Inguanzo, 2019).…”

“…Similar to the GI, the AI ranges from 0 for an egalitarian distribution to 1 for maximum inequality, with higher values of the aversion parameter making the index more sensitive to inequalities at the bottom of the distribution (De Maio, 2007). It has been used in a fishery context, through scenarios for different levels of inequality aversion, primarily to reflect equity in distribution of fisheries resources but also to spatially demonstrate the impact of competing ocean uses (Hoagland et al, 2015;Gutiérrez and Inguanzo, 2019). One can interpret the AI as the percentage of total income that would have to be sacrificed to achieve equally distributed incomes (see more details in section "Inequality Aversion").…”

“…Other indices with useful properties include the Theil index which can decompose "within" and "between-group" inequality (Gutiérrez and Inguanzo, 2019) to provide an idea of how much of the inequality can be explained by differences among stakeholders in the fishery. For differently sized groups though, such as those in the turbot fishery, the Theil index may not be as helpful for direct comparisons, especially across time and space.…”

Distributional impact of COVID-19: regional inequalities in cases and deaths in Spain during the first wave

“…This is a standard methodology that has been applied to several topics, especially in the social sciences. A non-exhaustive list includes studies on the distribution of variables as diverse as income (Ram 2015;Chongvilaivan and Kim 2016;Bui, Nguyen, and Pham 2017), health resources (De Maio 2007;Alcalde-Unzu, Ezcurra, and Pascual 2009;Morita et al 2018;Saito et al 2020), education facilities (Quadrado, Loman, and Folmer 2001), sports results (Borooah and Mangan 2012), demographic behaviour (Bleha and Ďurček 2019;Pagliacci 2019), natural resources use (Duro, Schaffartzik, and Krausmann 2018;Gutiérrez and Inguanzo 2019;Cetrulo et al 2020;Tang et al 2020) and pollutant emissions (Xia, Wang, and Ji 2019;Bolea, Duarte, and Sánchez-Chóliz 2020;Pakrooh et al 2020).…”