Knowledge Measurement and Productivity in a Research Program

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Abstract

We introduce a metric based on Bayesian probability theory to evaluate the determinants of scientific discovery, and use it to assess an international aquacultural research program consisting of a large number of highly varied projects. The metric accommodates not only project variety but a detailed breakdown of the sources of research productivity, accounting, for example, for the contributions of “failed” as well as “successful” investigations. A mean-absolute-deviation loss functional form permits decomposition of knowledge gain into an outcome probability shift (mean surprise) and outcome variance reduction (statistical precision), allowing productivity to be estimated for each of them separately, then combined into a single knowledge production relationship. Laboratory size is found to moderately boost mean surprise but has no effect on statistical precision, while investigator education greatly improves precision but has no effect on mean surprise. Returns to research scale are decreasing in the size dimension alone but increasing when size and education are taken together, suggesting the importance of measuring human capital at both the quantitative and qualitative margins.

JEL codes: D83, O32, O39.

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