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Econometric Assessment of Research Programs: A Bayesian Approach

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  • Qin, Lin
  • Buccola, Steven T.

Abstract

Effective research-project assessment typically is impeded by project variety. In particular, bibliometric approaches to science assessment tend to offer little information about the content of the projects examined. We introduce here a new approach – based on Bayesian theory – of econometrically evaluating the factors affecting scientific discovery, and use the method to assess a biological research program comprised of numerous heterogeneous projects. Our knowledge metric not only flexibly accommodates project variety but accounts for information in “failed” as well as “successful” studies. Using a mean-absolute-deviation utility functional form to measure new scientific knowledge, we decompose knowledge gain into a mean-surprise and statistical-accuracy effect. The two effects are econometrically examined independently, and then combined into the net knowledge production function. Research FTE and distance to study site have statistically significant but moderate effects on the amount by which research shifts the prediction of scientific outcome. However, scientist education powerfully improves the research study’s predictive accuracy or precision, a one-percent boost in the average investigator’s formal schooling improving precision by 4.3 percent. Largely on the basis of that precision effect, increasing returns to research project scale are evident.

Suggested Citation

  • Qin, Lin & Buccola, Steven T., 2012. "Econometric Assessment of Research Programs: A Bayesian Approach," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124948, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea12:124948
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    References listed on IDEAS

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    1. Bronwyn H. Hall & Adam B. Jaffe & Manuel Trajtenberg, 2000. "Market Value and Patent Citations: A First Look," NBER Working Papers 7741, National Bureau of Economic Research, Inc.
    2. Pardey, Philip G, 1989. "The Agricultural Knowledge Production Function: An Empirical Look," The Review of Economics and Statistics, MIT Press, pages 453-461.
    3. Jaffe, Adam B, 1989. "Real Effects of Academic Research," American Economic Review, American Economic Association, vol. 79(5), pages 957-970, December.
    4. Schimmelpfennig, David E. & Norton, George W., 2000. "What Value Is Agricultural Economics Research?," 2000 Annual meeting, July 30-August 2, Tampa, FL 21773, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. David E. Schimmelpfennig & George W. Norton, 2003. "What is the Value of Agricultural Economics Research?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 81-94.
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