IDEAS home Printed from https://ideas.repec.org/a/bla/ajecsc/v82y2023i1p5-14.html
   My bibliography  Save this article

Measuring a contract's breadth: A text analysis

Author

Listed:
  • Bryan McCannon
  • Joshua Hall
  • Yang Zhou

Abstract

We use a computational linguistic algorithm to measure the topics covered in teacher contracts. Topic modeling metrics are used to assess a contract's expansiveness. Our topic, diversity measurement, is then related to the prevalence of support staff. If more specialized services are provided, then contracts should be broader as they cover more employment relationships. We confirm a strong, statistically significant relationship and, thus, have a valid measurement of contract breadth.

Suggested Citation

  • Bryan McCannon & Joshua Hall & Yang Zhou, 2023. "Measuring a contract's breadth: A text analysis," American Journal of Economics and Sociology, Wiley Blackwell, vol. 82(1), pages 5-14, January.
    • Handle: RePEc:bla:ajecsc:v:82:y:2023:i:1:p:5-14
    DOI: 10.1111/ajes.12486
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/ajes.12486
    Download Restriction: no
    File URL: https://libkey.io/10.1111/ajes.12486?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
    ---><---

    References listed on IDEAS

    as
    1. Joshua C. Hall & Donald J. Lacombe & Joylynn Pruitt, 2017. "Collective bargaining and school district test scores: evidence from Ohio bargaining agreements," Applied Economics Letters, Taylor & Francis Journals, vol. 24(1), pages 35-38, January.
    2. Grimmer, Justin, 2010. "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases," Political Analysis, Cambridge University Press, vol. 18(1), pages 1-35, January.
    3. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    4. McCannon, Bryan C., 2020. "Wine Descriptions Provide Information: A Text Analysis," Journal of Wine Economics, Cambridge University Press, vol. 15(1), pages 71-94, February.
    5. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
    6. Carlo Schwarz, 2018. "ldagibbs: A command for topic modeling in Stata using latent Dirichlet allocation," Stata Journal, StataCorp LP, vol. 18(1), pages 101-117, March.
    7. Dyer, Travis & Lang, Mark & Stice-Lawrence, Lorien, 2017. "The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation," Journal of Accounting and Economics, Elsevier, vol. 64(2), pages 221-245.
    8. Kevin M. Quinn & Burt L. Monroe & Michael Colaresi & Michael H. Crespin & Dragomir R. Radev, 2010. "How to Analyze Political Attention with Minimal Assumptions and Costs," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 209-228, January.
    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. McCannon, Bryan & Zhou, Yang & Hall, Joshua, 2021. "Measuring a Contract’s Breadth: A Text Analysis," Working Papers 11013, George Mason University, Mercatus Center.
    2. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    3. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(2), pages 358-375, May.
    4. 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.
    5. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    6. Arina Wischnewsky & David‐Jan Jansen & Matthias Neuenkirch, 2021. "Financial stability and the Fed: Evidence from congressional hearings," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 1192-1214, July.
    7. Lino Wehrheim, 2019. "Economic history goes digital: topic modeling the Journal of Economic History," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 13(1), pages 83-125, January.
    8. Leonardo Bursztyn & Aakaash Rao & Christopher Roth & David Yanagizawa-Drott, 2023. "Opinions as Facts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1832-1864.
    9. Lino Wehrheim, 2017. "Economic History Goes Digital: Topic Modeling the Journal of Economic History," Working Papers 177, Bavarian Graduate Program in Economics (BGPE).
    10. Md Nazrul Islam & Md Mofazzal Hossain & Md Shafayet Shahed Ornob, 2024. "Business research on Industry 4.0: a systematic review using topic modelling approach," Future Business Journal, Springer, vol. 10(1), pages 1-15, December.
    11. Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021. "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 507-520.
    12. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    13. Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    14. Brian Fabo & Martina Jancokova & Elisabeth Kempf & Lubos Pastor, 2020. "Fifty Shades of QE: Conflicts of Interest in Economic Research," Working and Discussion Papers WP 5/2020, Research Department, National Bank of Slovakia.
    15. James P. Ryans, 2021. "Textual classification of SEC comment letters," Review of Accounting Studies, Springer, vol. 26(1), pages 37-80, March.
    16. Hector Carcel-Villanova, 2022. "Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    17. Yang Bao & Anindya Datta, 2014. "Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures," Management Science, INFORMS, vol. 60(6), pages 1371-1391, June.
    18. Damane Moeti, 2022. "Topic Classification of Central Bank Monetary Policy Statements: Evidence from Latent Dirichlet Allocation in Lesotho," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 10(1), pages 199-227, September.
    19. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
    20. Ping-Yu Hsu & Hong-Tsuen Lei & Shih-Hsiang Huang & Teng Hao Liao & Yao-Chung Lo & Chin-Chun Lo, 2019. "Effects of sentiment on recommendations in social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 253-262, June.

    More about this item

    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:bla:ajecsc:v:82:y:2023:i:1:p:5-14. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0002-9246 .

    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.