ADVANCES IN LOGISTICS AND THE GROWTH OF INTRA‐FIRM TRADE: THE CASE OF CANADIAN AFFILIATES OF U.S. MULTINATIONALS, 1984–1995*

Intra‐firm trade in intermediates between U.S. multinational parents (MNCs) and their Canadian manufacturing affiliates increased dramatically in the 1984–1995 period (i.e., it roughly doubled). Tariff and transport cost declines were far too small to explain this phenomenon. But we show that the advent of improved logistics management practices, including the ‘just‐in‐time’ (JIT) production system, can explain much of the growth of intra‐firm trade. JIT lowers the inventory carrying cost component of intra‐firm trade, and, by 1984, this was more important than tariff and transport costs in many industries. We combine regression analysis with numerous case studies to draw our conclusions.     

Non-Bayesian Testing of a Stochastic Prediction

We propose a method to test a prediction of the distribution of a stochastic process. In a non-Bayesian, non-parametric setting, a predicted distribution is tested using a realization of the stochastic process. A test associates a set of realizations for each predicted distribution, on which the prediction passes, so that if there are no type I errors, a prediction assigns probability 1 to its test set. Nevertheless, these test sets can be "small", in the sense that "most" distributions assign it probability 0, and hence there are "few" type II errors. It is also shown that there exists such a test that cannot be manipulated, in the sense that an uninformed predictor, who is pretending to know the true distribution, is guaranteed to fail on an uncountable number of realizations, no matter what randomized prediction he employs. The notion of a small set we use is category I, described in more detail in the paper.     

ARTIFACTUAL BIAS IN CONSUMER RESEARCH

Cognitive complexity has been found to affect the scope and quality of consumer information search. This research demonstrates that the characteristics that define cognitive complexity may result in an unintended subject bias in consumer information studies using college students as subject populations.