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This paper develops the first quantitative framework for analyzing distributional effects of incentive schemes in public education. The analysis is built around a hump-shaped effort function, estimated semi-parametrically using exogenous incentive variation and rich administrative data. We identify key primitives that rationalize this effort function by estimating a flexible teacher effort-choice model. Both the model and parameter estimates are necessary components in our counterfactual framework for tracing the effects of alternative accountability systems on the entire test score distribution, with effort adjusting endogenously. We find widespread schemes that set a fixed target for all students give rise to a steep performance-inequality tradeoff. Further, counterfactual incentive policies can outperform existing schemes for the same cost -- reducing the black-white test score gap by 7% (via student-specific bonuses), and lowering test-score inequality across students by 90% (via student-specific targets). Our quantitative approach opens up new possibilities for incentive design in practice.

This paper provides the first causal evidence about how elected local school boards affect student segregation across schools. The key identification challenge is that the composition of a school board is potentially correlated with unobserved determinants of school segregation. We overcome this issue using a regression discontinuity design at the electoral contest level, exploiting quasi-random variation from narrowly-decided elections. Such an approach is made possible by a unique dataset, which combines matched information about North Carolina school board candidates with time-varying district-level racial and economic segregation outcomes. Focusing on the political identity of school board members, regression discontinuity estimates reveal that (relative to their non-Democratic counterparts) Democratic board members decrease racial segregation across schools. Our findings suggest that school boards realize such reductions in segregation by shifting attendance zones (which we infer without the need for exact geocoded boundaries) and that white families differentially exit the traditional public school system for local charter schools in response.

We develop a novel strategy to identify the relative importance of school and neighborhood factors in determining school segregation. Using detailed student enrollment and residential location data, our research design compares differences in student composition between adjacent Census blocks served by different schools to analogous differences between those schools. Our findings indicate that neighborhood factors explain around 62% of racial segregation and 44% of economic segregation across all schools, playing an even more pronounced role in urban areas, where school segregation has been especially acute. These findings suggest that the involvement of urban planners is essential when attempting to confront inequality of opportunity through education.