Teacher quality is widely believed to be important for education, despite little ev-idence that teachers ’ credentials matter for student achievement. To accurately measure variation in achievement due to teachers ’ characteristics–both observ-able and unobservable–it is essential to identify teacher fixed effects. Unlike previous studies, I use panel data to estimate teacher fixed effects while control-ling for fixed student characteristics and classroom specific variables. I find large and statistically significant differences among teachers: a one standard deviation increase in teacher quality raises reading and math test scores by approximately.20 and.24 standard deviations, respectively, on a nationally standardized scale. In addition, teaching experience has statistically significant positive effects on reading test scores, controlling for fixed teacher quality.

Research on the impact of teachers on student achievement (e.g., Jonah E. Rockoff 2004; Steven G. Rivkin, Hanushek, and John Kain 2005) has established two stylized facts: (1) teacher effectiveness varies widely, and (2) outside of experience, qualifications that determine a teacher’s certification and salary bear little relation to outcomes. This provides motivation to understand how to identify effective and ineffective teachers, particularly early in their careers. Studies that examine how student achievement data can predict teachers’ impacts on student outcomes in the future (e.g., Robert Gordon, Thomas J. Kane, and Douglas O. Staiger 2006; Dan Goldhaber and Michael Hansen 2010) conclude that using such data to selectively retain teachers could yield large benefits. However, “value-added” measures of effectiveness are noisy and can be biased if some teachers are persistently given students that are difficult to teach in ways that are hard to observe. Thus, using other information may achieve more stability and accuracy in teacher evaluations. There is also a literature on subjective teaching evaluations (i.e., evaluations by the school principal or evaluations based on classroom observation protocols or “rubrics”), which also finds significant relationships between evaluations and achievement gains. However, these studies typically investigate how evaluations predict the exam performance of current, not

We estimate the willingness to pay for reductions in crime risk using the location and move-in dates of sex offenders. We find significant effects of sex offenders' locations that are geographically localized. House prices within 0.1 miles of a sex offender fall by 4 percent on average. We then use this finding to estimate the costs to victims of sexual offenses, and find costs of over $1 million per victim—far greater than previous estimates. However, we cannot reject the alternative hypotheses that individuals overestimate risks posed by offenders or that living near an offender poses significant costs exclusive of crime risk.

Rothstein (2017) successfully replicates Chetty, Friedman, and Rockoff's (2014a, b)—henceforth, CFR's—results using data from North Carolina, but raises concerns about CFR's methods. We show that Rothstein's methodological critiques are invalid by presenting simulations and supplementary empirical evidence which show that (i) his preferred imputation of missing data generates bias; (ii) his “placebo test” rejects valid research designs; and (iii) his method of controlling for covariates yields inconsistent estimates of teachers' long-term effects. Consistent with the conclusions of Bacher-Hicks, Kane, and Staiger (2016) using data from Los Angeles, we conclude that Rothstein's replication study ultimately reinforces CFR's methods and results.

We examine how employers learn about worker productivity in a randomized pilot experiment which provided objective estimates of teacher performance to school principals. We test several hypotheses that support a simple Bayesian learning model with imperfect information. First, the correlation between performance estimates and prior beliefs rises with more precise objective estimates and more precise subjective priors. Second, new information exerts greater influence on posterior beliefs when it is more precise and when priors are less precise. Employer learning affects job separation and productivity in schools, increasing turnover for teachers with low performance estimates and producing small test score improvements.