The new craft skills of engineering: The impact of innovation technology on engineering practices

This chapter explores the impact of innovation technologies such as simulation, modelling, and rapid prototyping on engineering practice. Innovation technologies help redefine the role of engineers in the innovation process, creating a new division of innovative labour both with and across organizations. This chapter also explores the boundaries of experimentation and inertia within particular domains of problem-solving to create new opportunities and value.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.

Globalisation and the technology gap: Regional and time evidence

This chapter examines the relationship between globalisation and technological progress. It computes an annual and country specific measure of technological gap, the technology ratio (TGR), using a recently proposed method known as metafrontiers. The TGR is measured as the distance from a group frontier to the global (or meta) frontier. The TGRs provide a measure to compare technological capability across countries. The ranking obtained from the metafrontiers method is first compared to other methods based on the direct measure of patents, science articles, schooling etc. The TGRs are then related to levels of trade openness and inbound and outbound foreign direct investment within regions and overtime in an effort to identify the relationship between technological gap and outward orientation.