A Bayesian Measure of Research Productivity

Lin Qin and Steven T. Buccola ()
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Lin Qin: comScore, Inc.
Steven T. Buccola: Oregon State University

A chapter in From Agriscience to Agribusiness, 2018, pp 465-481 from Springer

Abstract: Abstract We use Bayesian probability theory to develop a new way of measuring research productivity. The metric accommodates a wide variety of project types and productivity sources and accounts for the contributions of “failed” as well as “successful” investigations. Employing a mean-absolute-deviation loss functional form with this new metric allows decomposition of knowledge gain into an outcome probability shift (mean surprise) and outcome variance reduction (statistical precision), a useful distinction, because projects scoring well on one often score poorly on the other. In an international aquacultural research program, we find laboratory size to moderately boost mean surprise but have no effect on precision, while scientist education improves precision but has no effect on mean surprise. Returns to research scale are decreasing in the size dimension but increasing when size and education are taken together, suggesting the importance of measuring human capital at both the quantitative and qualitative margin.

Keywords: Mean Surprise; Lab Size; Resulting Variance Reduction; Topic Area Category; US Agency For International Development (USAID) (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:innchp:978-3-319-67958-7_23

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DOI: 10.1007/978-3-319-67958-7_23

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