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What topic modelling can show about the development of agricultural economics: evidence from the Journal Citation Report category top journals

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  • Leonardo Cei
  • Edi Defrancesco
  • Gianluca Stefani

Abstract

Throughout its history, several attempts have been made to map the structure and subfields of agricultural economics; however, these attempts either rely on the experience of distinguished scholars or require processing a massive amount of textual data. This paper investigates the structural dynamics of agricultural economics, focusing on the changing frequency of different subfields and the diversification of the discipline over time and on the differences between European and non-European scholars. A quantitative text analysis is carried out on abstracts from the major agricultural economics journals in the Journal Citation Reports category ‘Agricultural Economics and Policy’. The topics identified are consistent with findings from traditional studies, but their importance differs between the two areas. However, a convergence process has been observed in the last years.

Suggested Citation

  • Leonardo Cei & Edi Defrancesco & Gianluca Stefani, 2022. "What topic modelling can show about the development of agricultural economics: evidence from the Journal Citation Report category top journals," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(2), pages 289-330.
  • Handle: RePEc:oup:erevae:v:49:y:2022:i:2:p:289-330.
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    File URL: http://hdl.handle.net/10.1093/erae/jbab055
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    References listed on IDEAS

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    1. Alex McCalla & Emery Castle & Vernon Eidman, 2010. "The AAEA: Ever Growing and Changing Research Challenges," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(2), pages 334-355.
    2. David Blandford & Martin Banse, 2021. "What's Past is Prologue: Twenty Years of EuroChoices Analysed," EuroChoices, The Agricultural Economics Society, vol. 20(1), pages 4-10, April.
    3. Beatrice Cherrier, 2017. "Classifying Economics: A History of the JEL Codes," Journal of Economic Literature, American Economic Association, vol. 55(2), pages 545-579, June.
    4. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    5. B. L. Gardner & G. C. Rausser (ed.), 2001. "Handbook of Agricultural Economics," Handbook of Agricultural Economics, Elsevier, edition 1, volume 1, number 2.
    6. Reisch, E., 2000. "Entwicklungslinien in der agrarökonomischen Forschung in Westdeutschland von 1959 bis 1999," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 36.
    7. Hanf, Claus-Henning, 1997. "Agricultural Economics in Europe: A Thriving Science for a Shrinking Sector?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 24(3-4), pages 565-578.
    8. David Zilberman, 2019. "Agricultural Economics as a Poster Child of Applied Economics: Big Data & Big Issues," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(2), pages 353-364.
    9. B. L. Gardner & G. C. Rausser (ed.), 2001. "Handbook of Agricultural Economics," Handbook of Agricultural Economics, Elsevier, edition 1, volume 1, number 1.
    10. Robert Evenson & Prabhu Pingali (ed.), 2010. "Handbook of Agricultural Economics," Handbook of Agricultural Economics, Elsevier, edition 1, volume 4, number 1.
    11. Angela Ambrosino & Mario Cedrini & John B. Davis & Stefano Fiori & Marco Guerzoni & Massimiliano Nuccio, 2018. "What topic modeling could reveal about the evolution of economics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 25(4), pages 329-348, October.
    12. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    13. Sckokai, Paolo, 2012. "Agricultural and Applied Economics: What is This?," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(1), pages 1-16, April.
    14. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    15. Robert Evenson & Prabhu Pingali (ed.), 2007. "Handbook of Agricultural Economics," Handbook of Agricultural Economics, Elsevier, edition 1, volume 3, number 1.
    16. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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