A simple bivariate count data regression model

This paper develops a simple bivariate count data regression model in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components. Unlike existing commonly used bivariate models, we obtain a computationally simple closed form of the model with an unrestricted correlation pattern.

Tobacco Chewing, Smoking and Health Knowledge: Evidence from Bangladesh

Unlike the substance abuse studies in developed countries, tobacco consumption and its adverse effects in developing countries are poorly studied. The objective of this paper is to identify which factors influence individuals' decision to smoke cigarettes, chew tobacco and their knowledge about the health hazards of tobacco use. To allow for the potential correlation among smoking tobacco, chewing tobacco, and health knowledge, we estimate a trivariate probit regression model using household survey data from Bangladesh. For both chewing tobacco and smoking, the results show how the probabilities of uninformed tobacco user and uninformed nonuser vary across different demographic groups.Chewing tobacco

PATENTS, R&D AND LAG EFFECTS: EVIDENCE FROM FLEXIBLE METHODS FOR COUNT PANEL DATA ON MANUFACTURING FIRMS

Hausman, Hall and Griliches (1984) and Hall, Griliches and Hausman (1986) investigated whether there was a lag in the patent-R&D relationship for the U.S. manufacturing sector using 1970¿s data. They found that there was little evidence of anything but contemporaneous movement of patents and R&D. We reexamine this important issue employing new longitudinal patent data at the firm level for the U.S. manufacturing sector from 1982 to 1992. To address unique features of the data, we estimate various distributed lag and dynamic multiplicative panel count data models. The paper also develops a new class of count panel data models based on series expansion of the distribution of individual effects. The empirical analyses show that, although results are somewhat sensitive to different estimation methods, the contemporaneous relationship between patenting and R&D expenditures continues to be rather strong, accounting for over 60% of the total R&D elasticity. Regarding the lag structure of the patents-R&D relationship, we do find a significant lag in all empirical specifications. Moreover, the estimated lag effects are higher than have previously been found, suggesting that the contribution of R&D history to current patenting has increased from the 1970¿s to the 1980¿s.Innovative activity, Patents and R&D, Individual effects, count panel data methods.

The Knowledge Production Function for University Patenting

We estimate a knowledge production function for university patenting using an individual effects negative binomial model. We control for R&D expenditures, research field and the presence of a TTO office. We distinguish between three kinds of researchers who staff labs: faculty, postdoctoral students and PhD students. We also examine whether PhDs and postdoctoral scholars contribute equally to patent activity or whether there is a differential effect depending upon visa status. We find patent counts relate positively and significantly to the number of faculty, 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 visa status. Working Paper 07-0