Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/16240.html
   My bibliography  Save this paper

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

Author

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
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w16240.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. repec:bla:jfinan:v:43:y:1988:i:3:p:593-616 is not listed on IDEAS
    2. 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.
    3. 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, President and Fellows of Harvard College, vol. 126(4), pages 1593-1660.
    4. 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.
    5. 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.
    6. 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(1), pages 95-135.
    7. Nicholas Bloom & Benn Eifert & Aprajit Mahajan & David McKenzie & John Roberts, 2013. "Does Management Matter? Evidence from India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(1), pages 1-51.
    8. 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.
    9. Ginger Zhe Jin & Phillip Leslie, 2003. "The Effect of Information on Product Quality: Evidence from Restaurant Hygiene Grade Cards," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(2), pages 409-451.
    10. 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, vol. 6(1), pages 18-42, January.
    11. Jovanovic, Boyan, 1979. "Job Matching and the Theory of Turnover," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 972-990, October.
    12. Brian A. Jacob, 2010. "Do Principals Fire the Worst Teachers?," NBER Working Papers 15715, National Bureau of Economic Research, Inc.
    13. Brian A. Jacob & Lars Lefgren, 2008. "Can Principals Identify Effective Teachers? Evidence on Subjective Performance Evaluation in Education," Journal of Labor Economics, University of Chicago Press, vol. 26(1), pages 101-136.
    14. Baker, G.P. & Jensen, M.C. & Murphy, K.J., 1988. "Compensation And Incentives: Practice Vs. Theory," Papers 88-05, Rochester, Business - Managerial Economics Research Center.
    15. Kevin J. Murphy, 1986. "Incentives, Learning, and Compensation: A Theoretical and Empirical Investigation of Managerial Labor Contracts," RAND Journal of Economics, The RAND Corporation, vol. 17(1), pages 59-76, Spring.
    16. Rockoff, Jonah E. & Speroni, Cecilia, 2011. "Subjective and objective evaluations of teacher effectiveness: Evidence from New York City," Labour Economics, Elsevier, vol. 18(5), pages 687-696, October.
    17. Holmstrom, Bengt & Milgrom, Paul, 1991. "Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 7(0), pages 24-52, Special I.
    18. Justine S. Hastings & Jeffrey M. Weinstein, 2008. "Information, School Choice, and Academic Achievement: Evidence from Two Experiments," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 123(4), pages 1373-1414.
    19. Jonah E. Rockoff & Douglas O. Staiger & Thomas J. Kane & Eric S. Taylor, 2012. "Information and Employee Evaluation: Evidence from a Randomized Intervention in Public Schools," American Economic Review, American Economic Association, vol. 102(7), pages 3184-3213, December.
    20. 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.
    21. Cecilia Elena Rouse & Jane Hannaway & Dan Goldhaber & David Figlio, 2013. "Feeling the Florida Heat? How Low-Performing Schools Respond to Voucher and Accountability Pressure," American Economic Journal: Economic Policy, American Economic Association, vol. 5(2), pages 251-281, May.
    22. Jonah E. Rockoff, 2004. "The Impact of Individual Teachers on Student Achievement: Evidence from Panel Data," American Economic Review, American Economic Association, vol. 94(2), pages 247-252, May.
    23. Douglas O. Staiger & Jonah E. Rockoff, 2010. "Searching for Effective Teachers with Imperfect Information," Journal of Economic Perspectives, American Economic Association, vol. 24(3), pages 97-118, Summer.
    24. Eric A. Hanushek & Steven G. Rivkin, 2010. "Generalizations about Using Value-Added Measures of Teacher Quality," American Economic Review, American Economic Association, vol. 100(2), pages 267-271, May.
    25. Prendergast, Canice & Topel, Robert, 1993. "Discretion and bias in performance evaluation," European Economic Review, Elsevier, vol. 37(2-3), pages 355-365, April.
    26. repec:pri:cepsud:156rouse is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    2. Koedel Cory & Leatherman Rebecca & Parsons Eric, 2012. "Test Measurement Error and Inference from Value-Added Models," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 12(1), pages 1-37, November.
    3. Eric S. Taylor & John H. Tyler, 2011. "The Effect of Evaluation on Performance: Evidence from Longitudinal Student Achievement Data of Mid-career Teachers," NBER Working Papers 16877, National Bureau of Economic Research, Inc.
    4. Dan Goldhaber & Michael Hansen, 2013. "Is it Just a Bad Class? Assessing the Long-term Stability of Estimated Teacher Performance," Economica, London School of Economics and Political Science, vol. 80(319), pages 589-612, July.
    5. Harris, Douglas N. & Sass, Tim R., 2014. "Skills, productivity and the evaluation of teacher performance," Economics of Education Review, Elsevier, vol. 40(C), pages 183-204.
    6. Hanushek, Eric A., 2011. "The economic value of higher teacher quality," Economics of Education Review, Elsevier, vol. 30(3), pages 466-479, June.
    7. Sean Corcoran & Dan Goldhaber, 2013. "Value Added and Its Uses: Where You Stand Depends on Where You Sit," Education Finance and Policy, MIT Press, vol. 8(3), pages 418-434, July.
    8. Marine de Talancé, 2015. "Better Teachers, Better Results? Evidence from Rural Pakistan," Working Papers DT/2015/21, DIAL (Développement, Institutions et Mondialisation).
    9. Matthew A. Kraft, 2015. "Teacher Layoffs, Teacher Quality, and Student Achievement: Evidence from a Discretionary Layoff Policy," Education Finance and Policy, MIT Press, vol. 10(4), pages 467-507, October.
    10. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education,, Elsevier.
    11. Rockoff, Jonah E. & Speroni, Cecilia, 2011. "Subjective and objective evaluations of teacher effectiveness: Evidence from New York City," Labour Economics, Elsevier, vol. 18(5), pages 687-696, October.
    12. Douglas O. Staiger & Jonah E. Rockoff, 2010. "Searching for Effective Teachers with Imperfect Information," Journal of Economic Perspectives, American Economic Association, vol. 24(3), pages 97-118, Summer.
    13. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates," American Economic Review, American Economic Association, vol. 104(9), pages 2593-2632, September.
    14. Goldhaber, Dan & Cowan, James & Walch, Joe, 2013. "Is a good elementary teacher always good? Assessing teacher performance estimates across subjects," Economics of Education Review, Elsevier, vol. 36(C), pages 216-228.
    15. Azam, Mehtabul & Kingdon, Geeta Gandhi, 2015. "Assessing teacher quality in India," Journal of Development Economics, Elsevier, vol. 117(C), pages 74-83.
    16. Cory Koedel & Mark Ehlert & Eric Parsons & Michael Podgursky, 2012. "Selecting Growth Measures for School and Teacher Evaluations," Working Papers 1210, Department of Economics, University of Missouri.
    17. Gershenson, Seth, 2021. "Identifying and Producing Effective Teachers," IZA Discussion Papers 14096, Institute of Labor Economics (IZA).
    18. Allison Atteberry & Susanna Loeb & James Wyckoff, 2013. "Do First Impressions Matter? Improvement in Early Career Teacher Effectiveness," NBER Working Papers 19096, National Bureau of Economic Research, Inc.
    19. Hanushek, Eric A. & Rivkin, Steven G. & Schiman, Jeffrey C., 2016. "Dynamic effects of teacher turnover on the quality of instruction," Economics of Education Review, Elsevier, vol. 55(C), pages 132-148.
    20. Peter Z. Schochet & Hanley S. Chiang, 2013. "What Are Error Rates for Classifying Teacher and School Performance Using Value-Added Models?," Journal of Educational and Behavioral Statistics, , vol. 38(2), pages 142-171, April.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:16240. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.