Learning When to Quit: An Empirical Model of Experimentation in Standards Development
利用互联网工程任务组数据，估计了研究者决定继续或放弃研究项目的动态离散选择模型，并模拟了研发补贴和出版奖励两种创新政策的效果。
Using data from the Internet Engineering Task Force (IETF), a voluntary organization that develops protocols for managing internet infrastructure, we estimate a dynamic discrete choice model of the decision to continue or abandon a line of research. The model's key parameters measure the speed at which authors learn whether their project will become a technology standard. We use the model to simulate two innovation policies: an R&D subsidy and a publication prize. While subsidies have a larger impact on research output, the optimal policy depends on the level of R&D spillovers.