IDEAS home Printed from https://ideas.repec.org/p/ags/aaea11/104025.html
   My bibliography  Save this paper

Test of Convergence in Agricultural Factor Productivity: A Semiparametric Approach

Author

Listed:
  • Paudel, Krishna P.
  • Pandit, Mahesh
  • Poudel, Biswo N.

Abstract

We tested for club convergence in U.S. agricultural total factory productivity using a sigma convergence test. We used the same club of states as used by McCunn and Huffman as well as different states within 10 clubs identified by the cluster analysis. Results showed convergence was evident only in a few club groups. Clusters group identified using a statistical method identified only converging clubs. Variables affecting total factor productivity among states were identified using parametric, semiparametric and nonparametric methods. Semiparametric and nonparametric methods gave a better fit than a parametric method as indicated by the specification test. Our results indicated that health care expenditure, public research and extension investment, and private expenditure are important variables impacting total factor productivity differences across states.

Suggested Citation

  • Paudel, Krishna P. & Pandit, Mahesh & Poudel, Biswo N., 2011. "Test of Convergence in Agricultural Factor Productivity: A Semiparametric Approach," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 104025, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea11:104025
    as

    Download full text from publisher

    File URL: http://purl.umn.edu/104025
    Download Restriction: no

    References listed on IDEAS

    as
    1. Nguyen Van, Phu & Azomahou, Theophile, 2007. "Nonlinearities and heterogeneity in environmental quality: An empirical analysis of deforestation," Journal of Development Economics, Elsevier, vol. 84(1), pages 291-309, September.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Clubs; sigma convergence; cluster analysis; semiparametric and nonparametric methods; Productivity Analysis; Research Methods/ Statistical Methods;

    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:aaea11:104025. See general information about how to correct material in RePEc.

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

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.