PhD graduates hold the highest education degree, are trained to conduct research and can be considered a key element in the creation, commercialization and diffusion of innovations. The impact of PhDs on innovation and economic development takes place through several channels such as the accumulation of scientific capital stock, the enhancement of technology transfers and the promotion of cooperation relationships in innovation processes. Although the placement of PhDs in industry provides a very important mechanism for transmitting knowledge from universities to firms, information about the characteristics of the firms that employ PhDs is very scarce. The goal of this paper is to improve understanding of the determinants of the demand for PhDs in the private sector. Three main potential determinants of the demand for PhDs are considered: cooperation between firms and universities, R&D activities of firms and several characteristics of firms, size, sector, productivity and age. The results from the econometric analysis show that cooperation between firms and universities encourages firms to recruit PhDs and point to the existence of accumulative effects in the hiring of PhD graduates.
García-Quevedo, J. (IEB), Mas-Verdú, F. (IEB), Polo-Otero, J. (IEB)
We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.
When actuaries face with the problem of pricing an insurance contract that contains different types of coverage, such as a motor insurance or homeowner’s insurance policy, they usually assume that types of claim are independent. However, this assumption may not be realistic: several studies have shown that there is a positive correlation between types of claim. Here we introduce di®erent multivariate Poisson regression models in order to relax the independence assumption, including zero-in°ated models to account for excess of zeros and overdispersion. These models have been largely ignored to date, mainly because of their computational di±culties. Bayesian inference based on MCMC helps to solve this problem (and also lets us derive, for several quantities of interest, posterior summaries to account for uncertainty). Finally, these models are applied to an automobile insurance claims database with three different types of claims. We analyse the consequences for pure and loaded premiums when the independence assumption is relaxed by using different multivariate Poisson regression models and their zero-inflated versions.