Can Racially Unbiased Police Perpetuate Long-Run Discrimination?

We develop a stylized dynamic model of highway policing in which a non-racist police officer is given incentives to arrest criminals, but faces a per stop cost of stop which increases when the racial mix of the persons he stops di.ers from the racial mix of the population.We define the fair jail rate to be when the racial composition of the jail population is identical to the racial composition of the criminal population.We study the long-term racial composition of the jail population when the policeman decides whom to stop based only on his last period successes in arresting criminals.The study of this "imperfect recall" case shows, consistent with empirical findings, that the long term racial jail rate is always greater than the fair one and the gap increases when incentives are made more powerful.We then study this rate when policemen are provided with data concerning conviction rates for each race, similar to the data which is now being collected in many states.In this case, we find that although the long term rate is still greater than the fair rate, it is smaller than that obtained in the imperfect recall case.We discuss the desirability of such data collection and dissemination of information among police officers.discrimination;dynamic models;incentives;population;crime

Active learning and optimal climate policy

This paper develops a climate-economy model with uncertainty, irreversibility, and active learning. Whereas previous papers assume learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from investment in monitoring, specifically in improved observations of the global mean temperature. We find that the decision maker invests a significant amount of money in climate research, far more than the current level, in order to increase the rate of learning about climate change. This helps the decision maker make improved decisions. The level of uncertainty decreases more rapidly in the active learning model than in the passive learning model with only temperature observations. As the uncertainty about climate change is smaller, active learning reduces the optimal carbon tax. The greater the risk, the larger is the effect of learning. The method proposed here is applicable to any dynamic control problem where the quality of monitoring is a choice variable, for instance, the precision at which we observe GDP, unemployment, or the quality of education