Fisheries Crisis and its People: An econometric Sustainable Livelihoods application for low-income artisanal fishers in Sierra Leone, The
thesisposted on 18.05.2018, 00:00 by Nathan Brock
The global fisheries crisis is a topic of significant research across fields, including economics. It is evident that the current global fish stocks are declining, which has motivated environmental protection measures to be implemented. Low-income countries and their inhabitants are unevenly harmed by these environmental impacts, as artisanal fishing villages are dependent on fisheries for subsistence and livelihood. This case is particularly true in Sierra Leone, where poverty is evident, artisanal fishing communities exist, and dependence on fisheries is high (Kassam, et al. 2017; Teh, et al. 2016). Policy makers in these countries are faced with the challenge of mitigating environmental disasters while still ensuring that livelihoods of local people are protected. The present paper argues that an econometric model would provide useful, empirical evidence to these policy makers as to the specific factors of livelihood, responding to the question: which factors in the livelihoods of low-income artisanal fishers are most significant, and therefore should be considered in policy decisions? The econometric model in this paper follows the Sustainable Livelihoods Approach to environmental protection and poverty reduction (Krantz 2001). Livelihood, measured by the United Nations Development Programme Human Development Index, is explained by economic, social, and environmental variables in a sustainable livelihoods framework. Results and viability of this model are discussed, followed by a discussion of further research to sharpen these results. The model is then tested to show its practical application for policy-makers in maximizing poverty alleviation outcomes. Research concluded that insufficient data has caused present results to be relatively inconclusive for Sierra Leone today, but the model design and rationale can be useful given a substantially larger dataset.