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Count-data regression models of the time to adopt new technologies

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  • Bruce Mcwilliams
  • Yacov Tsur
  • Eithan Hochman
  • David Zilberman

Abstract

This paper presents a framework for interpreting and using the count-data model for estimating the time of technology adoption. The Bernoulli trials of the negative binomial model are interpreted as the stages involved in a potential adopter learning and updating information relevant to a new technology. Empirically, the paper estimates the Poisson, the generalized negative binomial, and the geometric models in order to identify the determinants of computer adoption on farms in California.

Suggested Citation

  • Bruce Mcwilliams & Yacov Tsur & Eithan Hochman & David Zilberman, 1998. "Count-data regression models of the time to adopt new technologies," Applied Economics Letters, Taylor & Francis Journals, vol. 5(6), pages 369-373.
    • Handle: RePEc:taf:apeclt:v:5:y:1998:i:6:p:369-373
    DOI: 10.1080/135048598354744
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    References listed on IDEAS

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    Cited by:

    1. Mishra, Ashok K. & Park, Timothy A., 2005. "An Empirical Analysis of Internet Use by U.S. Farmers," Agricultural and Resource Economics Review, Cambridge University Press, vol. 34(2), pages 253-264, October.
    2. Marra, Michele & Pannell, David J. & Abadi Ghadim, Amir, 2003. "The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve?," Agricultural Systems, Elsevier, vol. 75(2-3), pages 215-234.

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