Using longitudinal data from a cohort of middle school students from a large school district, we estimate separate “value‐added” teacher effects for two subscales of a mathematics assessment under a variety of statistical models varying in form and degree of control for student background characteristics. We find that the variation in estimated effects resulting from the different mathematics achievement measures is large relative to variation resulting from choices about model specification, and that the variation within teachers across achievement measures is larger than the variation across teachers. These results suggest that conclusions about individual teachers' performance based on value‐added models can be sensitive to the ways in which student achievement is measured.

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This study drew on teacher survey responses from randomized experiments exploring three different pay-for-performance programs to examine the extent to which these programs motivated teachers to improve student achievement and the impact of such programs on teachers' instruction, number of hours worked, job stress, and collegiality. Results showed that most teachers did not report their program as motivating. Moreover, the survey responses suggest that none of the three programs changed teachers' instruction, increased their number of hours worked or job stress, or damaged their collegiality. Future research needs to further examine the logic model of pay-for-performance programs and test alternative incentive models such as rewarding teachers based on their practices and job responsibilities rather than on student outcomes.