We use the Survey of Doctorate Recipients to examine the question of who in US universities is patenting. Because standard methods of estimation are not directly applicable, we use a zero-inflated negative binomial model to estimate the patent equation, using instruments for the number of articles to avoid problems of endogeneity. We also estimate the patent model using the generalized method of moments estimation of count data models with endogenous regressors. We find work context and field to be important predictors of the number of patent applications. We also find patents to be positively and significantly related to the number of publications. This finding is robust to the choice of instruments and method of estimation. The cross-sectional nature of the data preclude an examination of whether a trade-off exists between publishing and patenting, holding individual characteristics constant over time. But the strong cross-sectional correlation that we find does not suggest that commercialization has come at the expense of placing knowledge in the public domain.Academic research productivity, Patenting, Publishing, Technology transfer, Count data models, Bayh-Dole Act,

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SUMMARYThis paper develops a semi-parametric estimation method for hurdle (two-part) count regression models. The approach in each stage is based on Laguerre series expansion for the unknown density of the unobserved heterogeneity. The semi-parametric hurdle model nests Poisson and negative binomial hurdle models, which have been used in recent applied literature. The empirical part of the paper evaluates the impact of managed care programmes for Medicaid eligibles on utilization of health-care services using a key utilization variable, the number of doctor and health centre visits. Health status measures and age seem to be more important in determining health-care utilization than other socio-economic and enrollment variables. The semi-parametric approach is particularly useful for the analysis of overdispersed individual level data characterized by a large proportion of non-users, and highly skewed distribution of counts for users.

"We estimate a knowledge production function for university patenting using an individual effects negative binomial model. We control for Research and Development expenditures, research field, and the presence of a Technology Transfer Office. We distinguish between three kinds of researchers: faculty, postdoctoral scholars (postdocs), and PhD students. For the latter two, we also distinguish by visa status. We find patent counts to relate positively and significantly to the number of PhD students and number of postdocs. Our results also suggest that not all graduate students and postdocs contribute equally to patenting but that contribution is mediated by citizenship and visa status." (JEL C25, O31, O32, O34, O38) Copyright (c) 2008 Western Economic Association International.