We introduce a model in which agents observe signals about the state of the world, some of which are open to interpretation. Our decision makers first interpret each signal and then form a posterior on the sequence of interpreted signals. This ‘double updating’ leads to confirmation bias and can lead agents who observe the same information to polarize. We explore the model’s predictions in an on-line experiment in which individuals interpret research summaries about climate change and the death penalty. Consistent with the model, there is a significant relationship between an individual’s prior and their interpretation of the summaries; and - even more striking - over half of the subjects exhibit polarizing behavior.
Starting in the 2013-2014 school year, I conducted a randomized field experiment in fortysix traditional public elementary schools in Houston, Texas designed to test the potential productivity benefits of teacher specialization in schools. Treatment schools altered their schedules to have teachers specialize in a subset of subjects in which they have demonstrated relative strength (based on value-add measures and principal observations). The average impact of encouraging schools to specialize their teachers on student achievement is -0.11 standard deviations per year on a combined index of math and reading test scores. Students enrolled in special education and those with less experienced teachers demonstrated marked negative results. I argue that the results are consistent with a model in which the benefits of specialization driven by sorting teachers into a subset of subjects based on comparative advantage is outweighed by inefficient pedagogy due to having fewer interactions with each student, though other mechanisms are possible.
This paper explores racial diﬀerences in police use of force. On non-lethal uses of force, blacks and Hispanics are more than ﬁfty percent more likely to experience some form of force in interactions with police. Adding controls that account for important context and civilian behavior reduces, but cannot fully explain, these disparities. On the most extreme use of force –oﬃcer-involved shootings – we ﬁnd no racial diﬀerences in either the raw data or when contextual factors are taken into account. We argue that the patterns in the data are consistent with a model in which police oﬃcers are utility maximizers, a fraction of which have a preference for discrimination, who incur relatively high expected costs of oﬃcer-involved shootings.
We propose a theory of social interactions based on self-selection and comparative advantage. In our model, students choose peer groups based on their comparative advantage within a social environment. The eﬀect of moving a student into a diﬀerent environment with higher-achieving peers depends on where in the ability distribution she falls and the shadow prices that clear the social market. We show that the model’s key prediction—an individual’s ordinal rank predicts her behavior and test scores—is borne out in one randomized controlled trial in Kenya as well as administrative data from the U.S. To test whether our selection mechanism can explain the eﬀect of rank on outcomes, we conduct an experiment with nearly 600 public school students in Houston. The experimental results suggest that social interactions are mediated by self-selection based on comparative advantage.
We present a two-armed bandit model of decision making under uncertainty where the expected return to investing in the "risky arm" increases when choosing that arm and decreases when choosing the "safe" arm. These dynamics are natural in applications such as human capital development, job search, and occupational choice. Using new insights from stochastic control, along with a monotonicity condition on the payo dynamics, we show that optimal strategies in our model are stopping rules that can be characterized by an index which formally coincides with Gittins' index. Our result implies the indexability of a new class of restless bandit models