Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-22T07:44:33.006Z Has data issue: false hasContentIssue false
  • English
  • Français

Illegal fishing and catch potentials among small-scale fishers: application of an endogenous Switching regression model

Published online by Cambridge University Press:  22 October 2013

Wisdom Akpalu  and
Ametefee K. Normanyo
Wisdom Akpalu
Affiliation:
Farmingdale State College, State University of New York, 2350 Broadhollow Road, Farmingdale, NY 11735, USA. E-mail: [email protected]; [email protected]
Ametefee K. Normanyo
Affiliation:
Ho Polytechnic, Ghana. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Capture fish stocks are facing an increasing threat of extinction, partly due to the use of illegal fishing methods. In developing coastal countries – where fishing activities are the mainstay of the population along the coast – livelihoods are being directly threatened. Although a number of studies exist on fishing regulations and those who violate them, little has been done on the relationship between intrinsic catch potentials/fishing skills and illegal fishing behavior. Using data on violations of light attraction regulation among small-scale fishers in Ghana, our results show that the risk of punishment, the amount of fishing experience, the skipper's age, and religious norms all influence the decision to violate fishing regulations. Most importantly, we found that violators and non-violators have different fishing skills. Consequently, policies targeting illegal fishing must focus on equalizing efficiency and/or fishing skills among the fishermen as well as on traditional variables that influence violation decisions.

Type
Research Article
Information
Environment and Development Economics , Volume 19 , Issue 2 , April 2014 , pp. 156 - 172
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Akpalu, W. (2008), ‘Fishing regulation, individual discount rate and fisherman behavior in a developing country fishery’, Environment and Development Economics 13: 591–606.Google Scholar
Akpalu, W. (2009), ‘Economics of biodiversity and sustainable fisheries management’, Ecological Economics 68: 2729–2733.Google Scholar
Akpalu, W. (2010), ‘A dynamic model of mesh size regulatory compliance’, Journal of Agricultural and Resource Economics 35(1): 34–50.Google Scholar
Akpalu, W. (2011a), ‘Fisher skills and compliance with effort-limiting fishing regulations in a developing country’, International Journal of Social Economics 38(16): 666–675.Google Scholar
Akpalu, W. (2011b), ‘Determinants of noncompliance with light attraction regulation among inshore fishers in Ghana’, Journal of Socio-Economics 40: 172–177.Google Scholar
Becker, G. (1968), ‘Crime and punishment: an economic approach’, Journal of Political Economy 76(2): 169–217.Google Scholar
Chavez, C. and Salgado, H. (2005), ‘Individual transferable quota markets under illegal fishing’, Environmental and Resource Economics 31: 303–324.Google Scholar
Di Falco, S., Veronesi, M., and Yesuf, M. (2011), ‘Does adaption to climate change provide food security? A micro-perspective from Ethiopia’, American Journal of Agricultural Economics 93(3): 829–846.Google Scholar
Furlong, W.J. (1991), ‘The deterrent effect of regulatory enforcement in the fishery’, Land Economics 67: 116–129.CrossRefGoogle Scholar
Greene, W.H. (2000), Econometric Analysis, New York: Macmillan Publishing.Google Scholar
Hatcher, A. (2005), ‘Non-compliance and the quota price in an ITQ fishery’, Journal of Environmental Economics and Management 49(3): 427–436.Google Scholar
Hatcher, A., Jaffry, S., Thébaud, O., and Bennett, E. (2000), ‘Normative and social influences affecting compliance with fishery regulations’, Land Economics 76: 448–461.Google Scholar
Heckman, J. (2001), ‘Microdata, heterogeneity and the evaluation of public policy’, Journal of Political Economy 109: 673–748.Google Scholar
Heckman, J., Tobias, J.L., and Vytlacil, E. (2001), ‘Four parameters of interest in the evaluation of social programmes’, Southern Economic Journal 68(2): 210–233.Google Scholar
Kuperan, K. and Sutinen, J.G. (1994), ‘Compliance with zoning regulations in Malaysian fisheries’, Paper presented at the 7th Conference of the International Institute of Fisheries Economics and Trade, Taipei, Taiwan, 18–21 July 1994.Google Scholar
Lee, L.F. (1978), ‘Unionism and wage rates: a simulation equation model with qualitative and limited dependent variable’, International Economic Review 19: 415–455.Google Scholar
Lokshin, M. and Sajaia, Z. (2004), ‘Maximum likelihood estimation of endogenous switching regression models’ Stata Journal 4(3): 282–289.Google Scholar
Maddala, G.S. (1983), Limited Dependent and Qualitative Variables in Econometrics, New York: Cambridge University Press.Google Scholar
OECD (2006), Why Fish Piracy Persists: The Economics of Illegal, Unreported and Unregulated Fishing, Paris: Organization for Economic Co-operation and Development.Google Scholar
Pauly, D. and Zeller, D. (2003), ‘The global fisheries crises as a rationale for improving the FAO's database of fisheries statistics’, Fisheries Centre Report 11: 1–9.Google Scholar
Quartey, R. and Bannerman, P. (2005), Observations on the Commercial Light Fishing Operation in Ghana, [Available at] http://hdl.handle.net/1834/1267.Google Scholar
Sarpong, D.B., Quaatey, S.N.K., and Harvey, S.K. (2005), ‘The economic and social contribution of fisheries to gross domestic product and rural development in Ghana – final report’, Report No. GCP/INT/735/UK, FAO, Rome.Google Scholar