IDEAS home Printed from https://ideas.repec.org/a/kap/revind/v64y2024i1d10.1007_s11151-023-09929-7.html
   My bibliography  Save this article

Public Communication as a Mechanism for Collusion in the Broiler Industry

Author

Listed:
  • Qiwen Sheng

    (North Carolina State University)

  • Tomislav Vukina

    (North Carolina State University)

Abstract

In this paper, we investigate whether the U.S. broiler companies used public information sharing mechanisms to collude to reduce output and fix prices. Utilizing natural language processing methods, we converted quarterly earnings call transcripts of publicly traded broiler companies into structured data. We identified six keywords that could be used as signals: cut; balance; constrain; discipline; reduction; and adjustment. Our results show statistically significant and mildly elastic negative relationship between the keywords signals and three different broiler production precursors. For example, a 1% increase in the aggregated signal is associated with 1.5% reduction in the broiler-type hatchery supply flocks. The results are corroborated when using LASSO estimation to identify the most important elements in the vector of collusion signals.

Suggested Citation

  • Qiwen Sheng & Tomislav Vukina, 2024. "Public Communication as a Mechanism for Collusion in the Broiler Industry," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 64(1), pages 57-91, February.
    • Handle: RePEc:kap:revind:v:64:y:2024:i:1:d:10.1007_s11151-023-09929-7
    DOI: 10.1007/s11151-023-09929-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11151-023-09929-7
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11151-023-09929-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    2. Gaurab Aryal & Federico Ciliberto & Benjamin T Leyden, 2022. "Coordinated Capacity Reductions and Public Communication in the Airline Industry [Exchangees of Cost Information in the Airline Industry]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(6), pages 3055-3084.
    3. Alex Chinco & Adam D. Clark‐Joseph & Mao Ye, 2019. "Sparse Signals in the Cross‐Section of Returns," Journal of Finance, American Finance Association, vol. 74(1), pages 449-492, February.
    4. David Spector, 2022. "Cheap Talk, Monitoring and Collusion," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 60(2), pages 193-216, March.
    5. Green, Edward J & Porter, Robert H, 1984. "Noncooperative Collusion under Imperfect Price Information," Econometrica, Econometric Society, vol. 52(1), pages 87-100, January.
    6. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    7. David P. Byrne & Nicolas de Roos, 2019. "Learning to Coordinate: A Study in Retail Gasoline," American Economic Review, American Economic Association, vol. 109(2), pages 591-619, February.
    8. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    9. Awaya, Yu & Krishna, Vijay, 2019. "Communication and cooperation in repeated games," Theoretical Economics, Econometric Society, vol. 14(2), May.
    10. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    11. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    12. Tarek A Hassan & Stephan Hollander & Laurence van Lent & Ahmed Tahoun, 2019. "Firm-Level Political Risk: Measurement and Effects," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(4), pages 2135-2202.
    13. Robert Clark & Jean-François Houde, 2014. "The Effect of Explicit Communication on pricing: Evidence from the Collapse of a Gasoline Cartel," Journal of Industrial Economics, Wiley Blackwell, vol. 62(2), pages 191-228, June.
    14. Tomislav Vukina & Porametr Leegomonchai, 2006. "Oligopsony Power, Asset Specificity, and Hold-Up: Evidence from the Broiler Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(3), pages 589-605.
    15. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    16. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    17. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    18. Vukina, Tomislav & Zheng, Xiaoyong, 2015. "The Broiler Industry: Competition and Policy Challenges," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 30(2), pages 1-6.
    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. Massimo Ferrari Minesso & Frederik Kurcz & Maria Sole Pagliari, 2022. "Do words hurt more than actions? The impact of trade tensions on financial markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1138-1159, September.
    2. Freire, Gustavo, 2021. "Tail risk and investors’ concerns: Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    3. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    4. Istrefi, Klodiana & Odendahl, Florens & Sestieri, Giulia, 2023. "Fed communication on financial stability concerns and monetary policy decisions: Revelations from speeches," Journal of Banking & Finance, Elsevier, vol. 151(C).
    5. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
    6. Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022. "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
    7. Daniel Borup & Jorge Wolfgang Hansen & Benjamin Dybro Liengaard & Erik Christian Montes Schütte, 2023. "Quantifying investor narratives and their role during COVID‐19," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 512-532, June.
    8. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    9. 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.
    10. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    11. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    12. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
    13. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    14. Leilane de Freitas Rocha Cambara & Roberto Meurer, 2023. "News sentiment and foreign portfolio investment in Brazil," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3332-3348, July.
    15. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
    16. John Garcia, 2021. "Analyst herding and firm-level investor sentiment," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 461-494, December.
    17. Wang, Jiqian & Ma, Feng & Bouri, Elie & Zhong, Juandan, 2022. "Volatility of clean energy and natural gas, uncertainty indices, and global economic conditions," Energy Economics, Elsevier, vol. 108(C).
    18. García, Diego & Hu, Xiaowen & Rohrer, Maximilian, 2023. "The colour of finance words," Journal of Financial Economics, Elsevier, vol. 147(3), pages 525-549.
    19. Husted, Lucas & Rogers, John & Sun, Bo, 2020. "Monetary policy uncertainty," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 20-36.
    20. Chen, Chung-Chi & Huang, Yu-Lieh & Yang, Fang, 2024. "Semantics matter: An empirical study on economic policy uncertainty index," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1286-1302.

    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:kap:revind:v:64:y:2024:i:1:d:10.1007_s11151-023-09929-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.