Many large urban school districts match students to schools using algorithms that incorporate an element of random assignment. We introduce two simple empirical strategies to harness this randomization for value-added models (VAMs) measuring the causal effects of individual schools. The first...

Conventional value-added models (VAMs) compare average test scores across schools after regression-adjusting for students’ demographic characteristics and previous scores. This paper tests for VAM bias using a procedure that asks whether VAM estimates accurately predict the achievement...

Lottery estimates suggest oversubscribed urban charter schools boost student achievement markedly. But these estimates needn't capture treatment effects for students who haven't applied to charter schools or for students attending charters for which demand is weak. This paper reports estimates...

Many large urban school districts match students to schools using algorithms that incorporate an element of random assignment. We introduce two simple empirical strategies to harness this randomization for value-added models (VAMs) measuring the causal effects of individual schools. The first...

This paper tests for bias in consumer lending decisions using administrative data from a high-cost lender in the United Kingdom. We motivate our analysis using a simple model of bias in lending, which predicts that profits should be identical for loan applicants from different groups at the...