IDEAS home Printed from https://ideas.repec.org/p/ags/umaesp/329835.html
   My bibliography  Save this paper

Slow Magic: Agricultural vs Industrial R&D Lag Models

Author

Listed:
  • Alston, Julian M.
  • Pardey, Philip G.
  • Serfas, Devin
  • Wang, Shanchao

Abstract

R&D is slow magic. It takes many years before research investments begin to affect productivity, but then they can affect productivity for a long time. Many economists get this wrong. Here we revisit the conceptual foundations for R&D lag models used to represent the temporal links between research investments and impact, review prevalent practice, and document and discuss a range of evidence on R&D lags in agriculture and other industries. Our theory and evidence consistently support the use of longer lags with a different overall lag profile than is typically imposed in studies of industrial R&D and government compilations of R&D knowledge stocks. Many studies systematically fail to recognize the many years of investment and effort typically required to create a new technology and bring it to market, and the subsequent years as the technology is diffused and adopted. Consequential distortions in the measures and economic understanding are implied.

Suggested Citation

  • Alston, Julian M. & Pardey, Philip G. & Serfas, Devin & Wang, Shanchao, 2022. "Slow Magic: Agricultural vs Industrial R&D Lag Models," Staff Papers 329835, University of Minnesota, Department of Applied Economics.
    • Handle: RePEc:ags:umaesp:329835
    DOI: 10.22004/ag.econ.329835
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/329835/files/Alston%20_etal2022Slow%20Magic2022-12-27.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.329835?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
    ---><---

    More about this item

    Keywords

    Agricultural Finance; Industrial Organization; Production Economics; Research and Development/Tech Change/Emerging Technologies;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:umaesp:329835. 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.

    We have no bibliographic references for this item. You can help adding them by using 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/daumnus.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.