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Information and Employee Evaluation: Evidence from a Randomized Intervention in Public Schools

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Listed:
  • Jonah E. Rockoff
  • Douglas O. Staiger
  • Thomas J. Kane
  • Eric S. Taylor

Abstract

The evidence that productivity varies greatly across teachers has given rise to the idea that student achievement data should be included in performance evaluation, despite limited empirical evidence on subjective evaluation or the use of objective performance measures in U.S. public schools. In this paper, we examine the results of a randomized pilot program in which school principals were provided with estimates of the performance of individual teachers in raising their students' test scores in math and English. Our analysis establishes several facts consistent with a simple Bayesian learning model of employee evaluation in the presence of imperfect information. First, objective teacher performance estimates based on student data and principals' prior beliefs are positively correlated, and the strength of this relationship rises with the precision of the objective estimates and the precision of subjective priors. Second, principals who are provided with objective performance data incorporate this information into their posterior beliefs, and do so to a greater extent when the data are more precise and when their priors are less precise. Moreover, after the provision of performance data, the probability of job separation rises for teachers with low performance estimates, and, in line with this change in attrition patterns, student achievement exhibits small improvements the following year. These results suggest that objective performance data provides useful information to principals in constructing employee evaluations and using these evaluations to improve productivity.

Suggested Citation

  • Jonah E. Rockoff & Douglas O. Staiger & Thomas J. Kane & Eric S. Taylor, 2010. "Information and Employee Evaluation: Evidence from a Randomized Intervention in Public Schools," NBER Working Papers 16240, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:16240 Note: ED LS
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    References listed on IDEAS

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    1. Baker, George P & Jensen, Michael C & Murphy, Kevin J, 1988. " Compensation and Incentives: Practice vs. Theory," Journal of Finance, American Finance Association, vol. 43(3), pages 593-616, July.
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    3. Thomas J. Kane & Douglas O. Staiger, 2008. "Estimating Teacher Impacts on Student Achievement: An Experimental Evaluation," NBER Working Papers 14607, National Bureau of Economic Research, Inc.
    4. Raj Chetty & John N. Friedman & Nathaniel Hilger & Emmanuel Saez & Diane Whitmore Schanzenbach & Danny Yagan, 2011. "How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project Star," The Quarterly Journal of Economics, Oxford University Press, vol. 126(4), pages 1593-1660.
    5. Jesse Rothstein, 2009. "Student Sorting and Bias in Value-Added Estimation: Selection on Observables and Unobservables," Education Finance and Policy, MIT Press, vol. 4(4), pages 537-571, October.
    6. Sass, Tim R. & Semykina, Anastasia & Harris, Douglas N., 2014. "Value-added models and the measurement of teacher productivity," Economics of Education Review, Elsevier, vol. 38(C), pages 9-23.
    7. Daniel Aaronson & Lisa Barrow & William Sander, 2007. "Teachers and Student Achievement in the Chicago Public High Schools," Journal of Labor Economics, University of Chicago Press, vol. 25, pages 95-135.
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    9. Petra E. Todd & Kenneth I. Wolpin, 2003. "On The Specification and Estimation of The Production Function for Cognitive Achievement," Economic Journal, Royal Economic Society, vol. 113(485), pages 3-33, February.
    10. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2011. "The Long-Term Impacts of Teachers: Teacher Value-Added and Student Outcomes in Adulthood," NBER Working Papers 17699, National Bureau of Economic Research, Inc.
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    12. Cory Koedel & Julian R. Betts, 2011. "Does Student Sorting Invalidate Value-Added Models of Teacher Effectiveness? An Extended Analysis of the Rothstein Critique," Education Finance and Policy, MIT Press, pages 18-42.
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    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J45 - Labor and Demographic Economics - - Particular Labor Markets - - - Public Sector Labor Markets

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