The Palawan Scientist Fast Track Articles Livelihood assets and poverty among fishing households in Bicol Region, Philippines during the COVID-19 pandemic

Livelihood assets and poverty among fishing households in Bicol Region, Philippines during the COVID-19 pandemic

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Authors:
Cherrylyn P. Labayo and Emmanuel M. Preña*
Center for Policy Studies and Development, Bicol University, Legazpi City, Philippines 4500
*Correspondence: emmanuel.prena@bicol-u.edu.ph

How to cite:
Labayo CP and Preña EM. 2024. Livelihood assets and poverty among fishing households in Bicol Region, Philippines during the COVID-19 pandemic. The Palawan Scientist, 16 (1): 69-80.

ABSTRACT
Research efforts concerning COVID-19 primarily focused on the macro-level impacts of the pandemic on multiple fronts. Less attention was paid to individuals and less still to the socio-economic condition of the poorest sectors. This research addresses this gap by utilizing the theory of change (ToC) of asset ownership to examine the effects of livelihood asset ownership on the poverty status of 200 fisherfolk households in the Bicol Region, Philippines, during the pandemic. The study employed descriptive measures and logistic regression with principal component analysis (PCA) to examine the survey data. Results revealed that ownership of productive assets increased the likelihood of households maintaining the status of nonpoor compared to households who owned less to nothing. Whereas, households with more physical assets were more likely to fall below the poverty line during the pandemic. Defining poverty in terms of livelihood asset ownership has important implications for policymakers. Addressing these evidence gaps enables a nuanced analysis of the socio-economic condition of fishing households during the pandemic. The study suggests that aid organizations and funding agencies should complement grants with efforts that promote asset ownership through capacity-building services like training, mentoring, and providing market links for fishing communities.

Keywords: binary logistic regression, fisheries sector, household poverty, principal component analysis

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
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