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The effects of technological development on fisheries production

  • Jae Bong Chang
  • Yoonsuk Lee
Original Article Social Science
  • 1 Downloads

Abstract

Demand for seafood is continuously growing, while fish resources have been progressively exploited. Many countries treat technological development as a catalyst to improve the productivity of aquaculture and sustainable development of capture fisheries and they support fisheries technological development through government R&D expenditures. This study estimates the effects of fisheries technological development stimulated by government R&D expenditures on fisheries production aggregated by capture and aquaculture. Understanding the structure and dynamics of complex systems in the fisheries industry is necessary to analyze the effects of technological development. Thus, this study constructs a fisheries technology input–output-outcome system to demonstrate the systematic flow of technological effects. Based on the flow of technological effects, the relationships between inputs, fisheries technological development, and fisheries production are estimated by the mediated path analysis. From the path analysis conducted, it was found that fisheries technological development positively influence fisheries production. Fisheries technology development in this study is based on R&D activities supported by government funds. Such results imply that the input of government R&D expenditure stimulates R&D activities in the fisheries industry and that technology development from R&D activities leads to increases in fisheries production.

Keywords

Fisheries Government R&D expenditure Input–output-outcome system Missing values Path analysis Technological development 

Notes

Acknowledgements

This paper was supported by Konkuk University in 2017.

References

  1. Allison DP (2000) Multiple imputation for missing data: a cautionary tale. Sociol Methods Res 28:301–309CrossRefGoogle Scholar
  2. Burnell G, Allan G (2009) New technologies in aquaculture: improving production efficiency, quality and environmental management. Woodhead Publishing, CambridgeCrossRefGoogle Scholar
  3. Capron H (1993)​ Economic quantitative methods for the evaluation of the impact of R and D programmes. A state-of-the-art. EUR 14864 EN. Research evaluation. Science and Technology Policy SeriesGoogle Scholar
  4. Chang JB (2018) The effects of forage policy on feed costs in Korea. Agriculture 8:72CrossRefGoogle Scholar
  5. Christopher Y (2015) Imputing missing data using SAS. Paper 3295–2015. SAS Institute Inc, USAGoogle Scholar
  6. FAO (1996) Precautionary approach to capture fisheries and species introductions. FAO, RomeGoogle Scholar
  7. FAO (2007) Information and communications technologies benefit fishing communities, new directions in fisheries: a series of policy briefs on development issues. FAO, RomeGoogle Scholar
  8. Gallini TN (2002) The economics of patents: lessons from recent US patent reform. J Econ Perspect 16:131–154CrossRefGoogle Scholar
  9. Godin B (2002) Measurement and statistics on science and technology. Routledge, New YorkGoogle Scholar
  10. Griliches Z (1990) Patent statistics as economic indicator: a survey. J Econ Lit 28:1661–1707Google Scholar
  11. Grossman MG, Helpman E (1994) Endogenous innovation in the theory of growth. J Econ Perspect 8:23–44CrossRefGoogle Scholar
  12. HLPE (2014) Sustainable fisheries and aquaculture for food security and nutrition. A report by the high level panel of experts on food security and nutrition of the committee on world food security. HLPE, RomeGoogle Scholar
  13. Honaker J, King G (2010) What to do about missing values in time-series cross-section data. Am J Polit Sci 54(2):561–581CrossRefGoogle Scholar
  14. Jeon J (2015) The strengths and limitations of the statistical modeling of complex social phenomenon: focusing on SEM, path analysis, or multiple regression models. Int Sch Sci Res Innov 9:1634–1642Google Scholar
  15. Jones IC, Williams CJ (2000) Too much of a good thing? The economics of investment in R&D. J Econ Growth 5:65–85CrossRefGoogle Scholar
  16. Joseph LR, Alan N (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42:59–66Google Scholar
  17. Mark GB, Raynold AS (2016) Measuring R and D productivity. Res Tech Manage 31(4):11–15Google Scholar
  18. Matthew SF, David PM (2008) A graphical representation of the mediated effect. Behav Res Methods 40:55–60CrossRefGoogle Scholar
  19. Mcarthur MJ, Sachs DJ (2002) The growth competitiveness index: measuring technological advancement and the stages of development. In: Porter ME, Sachs JD et al (eds) The global competitiveness report 2001–2002. Oxford University Press, New York, pp 28–51Google Scholar
  20. Mittag N (2013) Imputation: benefits, risks and a method for missing data. Unpublished manuscript, cerge-ei.czGoogle Scholar
  21. Nicholas T (2012) Technology, innovation and economic growth in Britain since 1970. Working paper, prepared for the Cambridge Economic History of Modern BritainGoogle Scholar
  22. Pakes A, Schankerman M (1984) ​The rate of obsolescence of patents, research gestation lags, and the private rate of return to research resources, NBER Chapters. In: R and D, Patents, and Productivity. National Bureau of Economic Research Inc, pp 73–88Google Scholar
  23. Park G, Park Y (2006) On the measurement of patent stock as knowledge indicators. Technol Forecast Soc Change 73:793–812CrossRefGoogle Scholar
  24. Pedhazur JE (1997) ​​Multiple regression in behavioral research: explanation and prediction. Harcourt Brace College Publisher, San DiegoGoogle Scholar
  25. Rubin BD (1976) Inference and missing data. Biometrika 63:581–592CrossRefGoogle Scholar
  26. Semmes JG (1972) Protection of inventions and know-how in the common market. Law Contemp Probl 37:351–358CrossRefGoogle Scholar
  27. Sewall W (1934) The method of path coefficients. Ann Math Stat 5:161–215CrossRefGoogle Scholar
  28. Smith AF, Brown HJ, Valone JT (1997) Path analysis: a critical evaluation using long-term experimental data. Am Nat 149:29–42CrossRefGoogle Scholar
  29. Wakelin K (2001) Productivity growth and R&D expenditure in UK manufacturing firms. Res Policy 7:1079–1090CrossRefGoogle Scholar
  30. Wälde K, Woitek U (2004) R&D expenditure in G7 countries and implications for endogenous fluctuations and growth. Econ Lett 82:91–97CrossRefGoogle Scholar
  31. Walsh JS, Engas A, Ferro R, Fonteyne R, Marlen B (2002) To catch or conserve more fish: the evolution of fishing technology in fisheries science. ICES Mar Sci Symp 215:493–504Google Scholar
  32. Watanabe C, Zhu B, Griffy-Brown C, Asgari B (2001) Global technology spillover and its impact on industry’s R&D strategies. Technovation 5:281–291CrossRefGoogle Scholar
  33. Wooldrige JM (2002)​ ​Econometric analysis of cross section and panel data. MIT Press, Cambridge, MAGoogle Scholar
  34. World Bank (2008) Global economic perspective: technology diffusion in the developing world. The World Bank, Washington, DCGoogle Scholar
  35. Yagi N, Senda Y, Ariji M (2008) Panel data analyses to examine effects of subsidies to fishery production in OECD countries. Fish Sci 74:1229–1234CrossRefGoogle Scholar

Copyright information

© Japanese Society of Fisheries Science 2018

Authors and Affiliations

  1. 1.Department of Food Marketing and SafetyKonkuk UniversitySeoulKorea
  2. 2.Department of Agricultural and Resources EconomicsKangwon National UniversityChuncheonKorea

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