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Drivers Of Climate Change Adaptation In Artisanal Fisheries. A Case Of Malawi

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  • KAMBA, Yanjanani
  • MAGANGA, Assa
  • KATENGEZA, Sam

Abstract

This study was conducted to understand fishers’ climate change adaptation decisions in order to move climate informed policy for artisanal fisheries in developing economies forward. Data were collected from a random sample of 220 fishers in Mangochi District in Malawi. A binary probit model and a multivariate probit model were used to assess factors that affect fishers’ decision to adapt to climate change and their choice of adaptation strategies respectively. The study found that factors such as sex, education level, fishing experience, household size, fishing income, perception of catch rate trend, social capital and access to extension service corresponded in an increase in the probability of fishers adapting to impacts of climate change by increasing fishing effort, engaging in migratory fishing, investing in improved gear and livelihood diversification. The study recommends strengthening the education system in riparian communities to equip fishers with skills employable outside fishing and at the same time relive pressure off aquatic ecosystems.

Suggested Citation

  • KAMBA, Yanjanani & MAGANGA, Assa & KATENGEZA, Sam, 2020. "Drivers Of Climate Change Adaptation In Artisanal Fisheries. A Case Of Malawi," Review of Agricultural and Applied Economics (RAAE), Faculty of Economics and Management, Slovak Agricultural University in Nitra, vol. 23(1), March.
    • Handle: RePEc:ags:roaaec:308390
    DOI: 10.22004/ag.econ.308390
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    References listed on IDEAS

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    1. Channing Arndt & Adam Schlosser & Kenneth Strzepek & James Thurlow, 2014. "Climate Change and Economic Growth Prospects for Malawi: An Uncertainty Approach," Journal of African Economies, Centre for the Study of African Economies, vol. 23(suppl_2), pages 83-107.
    2. Geweke, John, 1996. "Monte carlo simulation and numerical integration," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 15, pages 731-800, Elsevier.
    3. Khanal, Uttam & Wilson, Clevo & Hoang, Viet-Ngu & Lee, Boon, 2018. "Farmers' Adaptation to Climate Change, Its Determinants and Impacts on Rice Yield in Nepal," Ecological Economics, Elsevier, vol. 144(C), pages 139-147.
    4. Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, vol. 3(3), pages 278-294, September.
    5. Channing Arndt & Adam Schlosser & Kenneth Strzepek & James Thurlow, 2014. "Climate Change and Economic Growth Prospects for Malawi: An Uncertainty Approach," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 23(suppl_2), pages 83-107.
    6. Tim M Daw & Joshua E Cinner & Timothy R McClanahan & Katrina Brown & Selina M Stead & Nicholas A J Graham & Joseph Maina, 2012. "To Fish or Not to Fish: Factors at Multiple Scales Affecting Artisanal Fishers' Readiness to Exit a Declining Fishery," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-10, February.
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