Papers

In two 1992 papers, Card and Krueger used labor market outcomes to study the productivity of school spending. Following their lead, we examine the effects of post-1990 school finance reforms on students’ educational attainment and labor market outcomes. Using a state-by-cohort panel design, we find that reforms increased high school completion and college-going, concentrated among Black students and women, and raised annual earnings. The reforms also increased the return to education, particularly for Black students and men, driven by the return to high school.

We study the impact of post-1990 school finance reforms, during the so-called “adequacy” era, on absolute and relative spending and achievement in low-income school districts. Using an event study research design that exploits the apparent randomness of reform timing, we show that reforms lead to sharp, immediate, and sustained increases in spending in low-income school districts. Using representative samples from the National Assessment of Educational Progress, we find that reforms cause increases in the achievement of students in these districts, phasing in gradually over the years following the reform. The implied effect of school resources on educational achievement is large.

Chetty, Friedman, and Rockoff (2014a, b) study value-added (VA) measures of teacher effectiveness. CFR (2014a) exploits teacher switching as a quasi-experiment, concluding that student sorting creates negligible bias in VA scores. CFR (2014b) finds VA scores are useful proxies for teachers’ effects on students’ long-run outcomes. I successfully reproduce each in North Carolina data. But I find that the quasi-experiment is invalid, as teacher switching is correlated with changes in student preparedness. Adjusting for this, I find moderate bias in VA scores, perhaps 10-35 percent as large, in variance terms, as teachers’ causal effects. Long-run results are sensitive to controls and cannot support strong conclusions.

Teacher contracts that condition pay and retention on demonstrated performance can improve selection into and out of teaching. I study alternative contracts in a simulated teacher labor market that incorporates dynamic self-selection and Bayesian learning. Bonus policies create only modest incentives and thus have small effects on selection. Reductions in tenure rates can have larger effects, but must be accompanied by substantial salary increases; elimination of tenure confers little additional benefit unless firing rates are extremely high. Benefits of both bonus and tenure policies exceed costs, though optimal policies are sensitive to labor market parameters about which little is known.

Growing concerns over the inadequate achievement of U.S. students have led to proposals to reward good teachers and penalize (or fire) bad ones. The leading method for assessing teacher quality is “value added” modeling (YAM), which decomposes students’ test scores into components attributed to student heterogeneity and to teacher quality. Implicit in the VAM approach are strong assumptions about the nature of the educational production function and the assignment of students to classrooms. In this paper, I develop falsification tests for three widely used VAM specifications, based on the idea that future teachers cannot influence students’ past achievement. In data from North Carolina, each of the VAMs’ exclusion restrictions is dramatically violated. In particular, these models indicate large “effects” of fifth grade teachers on fourth grade test score gains. I also find that conventional measures of individual teachers’ value added fade out very quickly and are at best weakly related to long-run effects. I discuss implications for the use of VAMs as personnel tools.

Abstract Nonrandom assignment of students to teachers can bias value-added estimates of teachers’ causal effects. Rothstein (2008, 2010) shows that typical value-added models indicate large counterfactual effects of fifthgrade teachers on students’ fourth-grade learning, indicating that classroom assignments are far from random. This article quantifies the resulting biases in estimates of fifth-grade teachers’ causal effects from several valueadded models, under varying assumptions about the assignment process. If assignments are assumed to depend only on observables, the most commonly used specifications are subject to important bias, but other feasible specifications are nearly free of bias. I also consider the case in which assignments depend on unobserved variables. I use the across-classroom variance of observables to calibrate several models of the sorting process. Results indicate that even the best feasible value-added models may be substantially biased, with the magnitude of the bias depending on the amount of information available for use in classroom assignments.