| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w24358 |
| 来源ID | Working Paper 24358 |
| Learning When to Quit: An Empirical Model of Experimentation | |
| Bernhard Ganglmair; Timothy Simcoe; Emanuele Tarantino | |
| 发表日期 | 2018-02-26 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | Research productivity depends on the ability to discern whether an idea is promising, and a willingness to abandon the ones that are not. Economists know little about this process, however, because empirical studies of innovation typically begin with a sample of issued patents or published papers that were already selected from a pool of promising ideas. This paper unpacks the idea selection process using a unique dataset from the Internet Engineering Task Force (IETF), a voluntary organization that develops protocols for managing Internet infrastructure. For a large sample of IETF proposals, we observe a sequence of decisions to either revise, publish, or abandon the underlying idea, along with changes to the proposal and the demographics of the author team. Using these data, we provide a descriptive analysis of how R&D is conducted within the IETF, and estimate a dynamic discrete choice model whose key parameters measure the speed at which author teams learn whether they have a good (i.e., publishable) idea. The estimates imply that sixty percent of IETF proposals are publishable, but only one-third of the good ideas survive the review process. Author experience and increased attention from the IETF community are associated with faster learning. Finally, we simulate two counterfactual innovation policies: an R&D subsidy and a publication-prize. Subsidies have a larger impact on research output, though prizes perform better when accounting for researchers' opportunity costs. |
| 主题 | Microeconomics ; Economics of Information ; Development and Growth ; Innovation and R& ; D |
| URL | https://www.nber.org/papers/w24358 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/582030 |
| 推荐引用方式 GB/T 7714 | Bernhard Ganglmair,Timothy Simcoe,Emanuele Tarantino. Learning When to Quit: An Empirical Model of Experimentation. 2018. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w24358.pdf（1381KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
