IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v27y2020i8p673-678.html
   My bibliography  Save this article

What types of public R&D support increase employment performance? Evidence from Korean firm-level data

Author

Listed:
  • Kang Ho Bong
  • Sunyoung Park
  • Jaemin Park

Abstract

If employment is the result of a series of decisions based on a firm’s performance at its growth phase, then an increase in firm employment should be the result of labor demand or optimal choices made in this process. The study aims to investigate the effects of the various forms of public support on employment decisions within a firm’s innovation process. For empirical analysis, an ordered logit model and a hurdle model were employed on a sample of 3,300 Korean firms. While non-financial support had a statistically significant positive effect on employment performance in both models, the results were mixed on other forms of policy support. Overall, the results of the empirical analysis clearly indicate that firms’ behavior can be better understood by approaching employment in a two-step decision-making process such as the hurdle model.

Suggested Citation

  • Kang Ho Bong & Sunyoung Park & Jaemin Park, 2020. "What types of public R&D support increase employment performance? Evidence from Korean firm-level data," Applied Economics Letters, Taylor & Francis Journals, vol. 27(8), pages 673-678, May.
    • Handle: RePEc:taf:apeclt:v:27:y:2020:i:8:p:673-678
    DOI: 10.1080/13504851.2019.1644425
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2019.1644425
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2019.1644425?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.

    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:taf:apeclt:v:27:y:2020:i:8:p:673-678. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.