IDEAS home Printed from https://ideas.repec.org/p/ags/aaea25/361106.html

Exaggeration Bias and Article Citations in Agricultural Economics

Author

Listed:
  • Han, Donggeun
  • Adom, Enoch
  • Lambert, Dayton M.

Abstract

Research credibility in agricultural economics is compromised by two interrelated factors: selective reporting and low statistical power. These factors contribute to exaggerated findings that appear more persuasive and garner more citations. This study analyzes 849 articles published in leading U.S. agricultural economics journals between 2018 and 2023, with 48,962 observations. Two empirical analyses are conducted. The first regresses citation counts on p-values reported in article tables, a proxy for statistical power, article topics, and journal and year fixed effects. The second predicts the time it takes a journal to be cited ‘10’ times, given p-values and statistical power. We hypothesized that citation counts would be negatively associated with p-values (i.e., lower p-value attract more citations), while no specific hypothesis was formed for statistical power, as it is unobservable to readers. The results show that citation counts are strongly influenced by topic novelty and journal prestige, with studies reporting lower p-values receiving more citations, whereas adequately powered studies receive fewer. The misalignment between research rigor and citation counts raises concerns that farmers may adopt recommendations based on less reliable findings, as agricultural extension services may rely on citation metrics when evaluating scientific research. Thus, aligning citation-based evaluations with empirical credibility is important not only for maintaining trust in science but also for informing decisions made by farmers and extension agents.

Suggested Citation

  • Han, Donggeun & Adom, Enoch & Lambert, Dayton M., 2025. "Exaggeration Bias and Article Citations in Agricultural Economics," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO 361106, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea25:361106
    DOI: 10.22004/ag.econ.361106
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/361106/files/75307_110296_105300_Exaggeration_Bias_and_Article_Citations_in_Agricultural_Economics_Donggeun_Han.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.361106?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Thelwall, Mike & Wilson, Paul, 2014. "Regression for citation data: An evaluation of different methods," Journal of Informetrics, Elsevier, vol. 8(4), pages 963-971.
    2. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    3. Larsen, Vegard H. & Thorsrud, Leif A., 2019. "The value of news for economic developments," Journal of Econometrics, Elsevier, vol. 210(1), pages 203-218.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    2. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2023. "Measuring partisan media bias in US newscasts from 2001 to 2012," European Journal of Political Economy, Elsevier, vol. 78(C).
    3. Ntentas, Raphael, 2021. "Quantifying political populism and examining the link with economic insecurity: evidence from Greece," LSE Research Online Documents on Economics 112579, London School of Economics and Political Science, LSE Library.
    4. Helena Seibicke & Asimina Michailidou, 2022. "The Challenges of Reconstructing Citizen-Driven EU Contestation in the Digital Media Sphere," Politics and Governance, Cogitatio Press, vol. 10(1), pages 97-107.
    5. Lin, Annie E. & Young, Jimmy A. & Guarino, Jeannine E., 2022. "Mother-Daughter sexual abuse: An exploratory study of the experiences of survivors of MDSA using Reddit," Children and Youth Services Review, Elsevier, vol. 138(C).
    6. Yasuhiro Hara, 2024. "Dynamic Relationship between Information Dissemination by Local Governors and Mobility during the COVID-19 Pandemic," Discussion papers ron373, Policy Research Institute, Ministry of Finance Japan.
    7. repec:rim:rimwps:22-04 is not listed on IDEAS
    8. Bastiaan Bruinsma & Moa Johansson, 2024. "Finding the structure of parliamentary motions in the Swedish Riksdag 1971–2015," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3275-3301, August.
    9. Anselm Küsters, 2022. "Applying Lessons from the Past? Exploring Historical Analogies in ECB Speeches through Text Mining, 1997–2019," International Journal of Central Banking, International Journal of Central Banking, vol. 18(1), pages 277-329, March.
    10. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    11. Keith Carlson & Michael A. Livermore & Daniel N. Rockmore, 2020. "The Problem of Data Bias in the Pool of Published U.S. Appellate Court Opinions," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(2), pages 224-261, June.
    12. Rauh, Christian, 2015. "Communicating supranational governance? The salience of EU affairs in the German Bundestag, 1991–2013," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 16(1), pages 116-138.
    13. Pratima (Tima) Bansal & Jury Gualandris & Nahyun Kim, 2020. "Theorizing Supply Chains with Qualitative Big Data and Topic Modeling," Journal of Supply Chain Management, Institute for Supply Management, vol. 56(2), pages 7-18, April.
    14. Ajiferuke, Isola & Famoye, Felix, 2015. "Modelling count response variables in informetric studies: Comparison among count, linear, and lognormal regression models," Journal of Informetrics, Elsevier, vol. 9(3), pages 499-513.
    15. Heinemann, Friedrich & Kemper, Jan, 2022. "Inflation of objectives instead of focus on inflation? Evidence on the ECB objective function from a textual analysis," ZEW Expert Briefs 22-07, ZEW - Leibniz Centre for European Economic Research.
    16. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
    17. Grajzl, Peter & Murrell, Peter, 2025. "From status to contract? A macrohistory from early-modern English caselaw and print culture," Explorations in Economic History, Elsevier, vol. 97(C).
    18. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    19. Copiello, Sergio, 2019. "Peer and neighborhood effects: Citation analysis using a spatial autoregressive model and pseudo-spatial data," Journal of Informetrics, Elsevier, vol. 13(1), pages 238-254.
    20. Julia Seiermann, 2018. "Only Words? How Power in Trade Agreement Texts Affects International Trade Flows," UNCTAD Blue Series Papers 80, United Nations Conference on Trade and Development.
    21. Jiahang Lyu & Saralees Nadarajah, 2022. "Discrete lognormal distributions with application to insurance data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1268-1282, June.

    More about this item

    Keywords

    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea25:361106. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.