G2TT
来源类型Discussion paper
规范类型论文
来源IDDP12733
DP12733 Learning When to Quit: An Empirical Model of Experimentation
Emanuele Tarantino; Timothy S. Simcoe; Bernhard Ganglmair
发表日期2018-02-19
出版年2018
语种英语
摘要We study a dynamic model of the decision to continue or abandon a research project. Researchers improve their ideas over time and also learn whether those ideas will be adopted by the scientific community. Projects are abandoned as researchers grow more pessimistic about their chance of success. We estimate the structural parameters of this dynamic decision problem using a novel data set that contains information on both successful and abandoned projects submitted to the Internet Engineering Task Force (IETF), an organization that creates and maintains internet standards. Using the model and parameter estimates, we simulate two counterfactual policies: a cost-subsidy and a prize-based incentive scheme. For a fixed budget, subsidies have a larger impact on research output, but prizes perform better when accounting for researchers' opportunity costs.
主题Industrial Organization
关键词Learning Experimentation Standardization Dynamic discrete choice
URLhttps://cepr.org/publications/dp12733
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/541543
推荐引用方式
GB/T 7714
Emanuele Tarantino,Timothy S. Simcoe,Bernhard Ganglmair. DP12733 Learning When to Quit: An Empirical Model of Experimentation. 2018.
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