Industrial targeting, experimentation and long-run specialization

• 成果类型: Article
• 署名作者: Klimenko, MM
• 署名单位: University of California System; University of California San Diego
• 刊物名称: JOURNAL OF DEVELOPMENT ECONOMICS
• ISSN/ISSBN: 0304-3878
• DOI: 10.1016/j.jdeveco.2002.09.001
• 发表日期: 2004
• 页码: 75-105
• 关键词: industrial targeting; Bayesian learning; multi-armed bandits

摘要

This paper emphasizes the experimental nature of industrial targeting policies under uncertainty in a small open economy. A government promotes entry of new firms in selected industries and updates its beliefs about the country's comparative advantage in a Bayesian way. This selective targeting policy is analyzed in the framework of a special type of statistical decision problem known as the multi-armed bandit. The paper analyzes how the costs and benefits of learning about a country's comparative advantage depend on the characteristics of the targeted industries. The framework suggests that even an optimally designed industrial targeting policy may eventually steer the country away from specializing according to its true comparative advantage. (C) 2003 Elsevier B.V All rights reserved.