A recently proposed technique for estimating nonstationary transition probabilities for a Markov process is developed for empirical implementation. Its use is then demonstrated by analyzing the process of industrial relocation for the U.S. apparel industries. Transition probabilities for twenty-one apparel industries and four time periods are estimated with aggregate frequency data and an embedded wage-adjustment model. The stationarity assumption of no wage-rate effect on the transition probabilities cannot be rejected.
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