| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP12733 |
| 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 |
| URL | https://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. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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