IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/19803.html
   My bibliography  Save this paper

Using School Choice Lotteries to Test Measures of School Effectiveness

Author

Listed:
  • David J. Deming

Abstract

Value-added models (VAMs) are increasingly used to measure school effectiveness. Yet random variation in school attendance is necessary to test the validity of VAMs, and to guide the selection of models for measuring causal effects of schools. In this paper, I use random assignment from a public school choice lottery to test the predictive power of VAM specifications. In VAMs with minimal controls and two or more years of prior data, I fail to reject the hypothesis that school effects are unbiased. Overall, many commonly used VAMs are accurate predictors of student achievement gains.

Suggested Citation

  • David J. Deming, 2014. "Using School Choice Lotteries to Test Measures of School Effectiveness," NBER Working Papers 19803, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:19803
    Note: CH ED LS
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w19803.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. Jesse Rothstein, 2010. "Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement," The Quarterly Journal of Economics, Oxford University Press, vol. 125(1), pages 175-214.
    3. C. Kirabo Jackson, 2012. "Non-Cognitive Ability, Test Scores, and Teacher Quality: Evidence from 9th Grade Teachers in North Carolina," NBER Working Papers 18624, National Bureau of Economic Research, Inc.
    4. 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.
    5. David J. Deming, 2011. "Better Schools, Less Crime?," The Quarterly Journal of Economics, Oxford University Press, vol. 126(4), pages 2063-2115.
    6. 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.
    7. 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.
    8. David J. Deming & Sarah Cohodes & Jennifer Jennings & Christopher Jencks, 2016. "School Accountability, Postsecondary Attainment, and Earnings," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 848-862, December.
    9. Donald B. Rubin & Elizabeth A. Stuart & Elaine L. Zanutto, 2004. "A Potential Outcomes View of Value-Added Assessment in Education," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 103-116, March.
    10. Daniel F. McCaffrey & J. R. Lockwood & Daniel Koretz & Thomas A. Louis & Laura Hamilton, 2004. "Models for Value-Added Modeling of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 29(1), pages 67-101, March.
    11. Atila Abdulkadiroğlu & Joshua D. Angrist & Susan M. Dynarski & Thomas J. Kane & Parag A. Pathak, 2011. "Accountability and Flexibility in Public Schools: Evidence from Boston's Charters And Pilots," The Quarterly Journal of Economics, Oxford University Press, vol. 126(2), pages 699-748.
    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. Stacy, Brian & Guarino, Cassandra & Wooldridge, Jeffrey, 2018. "Does the precision and stability of value-added estimates of teacher performance depend on the types of students they serve?," Economics of Education Review, Elsevier, vol. 64(C), pages 50-74.
    2. Hinnerich, Björn Tyrefors & Vlachos, Jonas, 2017. "The impact of upper-secondary voucher school attendance on student achievement. Swedish evidence using external and internal evaluations," Labour Economics, Elsevier, vol. 47(C), pages 1-14.
    3. David Blazar, 2018. "Validating Teacher Effects on Students’ Attitudes and Behaviors: Evidence from Random Assignment of Teachers to Students," Education Finance and Policy, MIT Press, vol. 13(3), pages 281-309, Summer.
    4. 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.
    5. Vosters, Kelly N. & Guarino, Cassandra M. & Wooldridge, Jeffrey M., 2018. "Understanding and evaluating the SAS® EVAAS® Univariate Response Model (URM) for measuring teacher effectiveness," Economics of Education Review, Elsevier, vol. 66(C), pages 191-205.
    6. Papay, John P. & Kraft, Matthew A., 2015. "Productivity returns to experience in the teacher labor market: Methodological challenges and new evidence on long-term career improvement," Journal of Public Economics, Elsevier, vol. 130(C), pages 105-119.
    7. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    8. Azam, Mehtabul & Kingdon, Geeta Gandhi, 2015. "Assessing teacher quality in India," Journal of Development Economics, Elsevier, vol. 117(C), pages 74-83.
    9. Gershenson, Seth & Holt, Stephen B. & Papageorge, Nicholas W., 2015. "Who Believes in Me? The Effect of Student-Teacher Demographic Match on Teacher Expectations," IZA Discussion Papers 9202, Institute of Labor Economics (IZA).
    10. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education,, Elsevier.
    11. Naven, Matthew, 2019. "Human-Capital Formation During Childhood and Adolescence: Evidence from School Quality and Postsecondary Success in California," MPRA Paper 97716, University Library of Munich, Germany.
    12. Lindsay Fox, 2016. "Playing to Teachers’ Strengths: Using Multiple Measures of Teacher Effectiveness to Improve Teacher Assignments," Education Finance and Policy, MIT Press, vol. 11(1), pages 70-96, Winter.
    13. 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.
    14. Cassandra M. Guarino & Mark D. Reckase & Jeffrey M. Woolrdige, 2014. "Can Value-Added Measures of Teacher Performance Be Trusted?," Education Finance and Policy, MIT Press, vol. 10(1), pages 117-156, November.
    15. Rothstein, Jesse & von Wachter, Till, 2016. "Social Experiments in the Labor Market," Institute for Research on Labor and Employment, Working Paper Series qt6605k20b, Institute of Industrial Relations, UC Berkeley.
    16. 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.
    17. Dan Goldhaber & Duncan Chaplin, "undated". "Assessing the Rothstein Test: Does It Really Show Teacher Value-Added Models Are Biased?," Mathematica Policy Research Reports 77f489fc94a34a0e96a42c419, Mathematica Policy Research.
    18. Lee Crawfurd, 2017. "School Management and Public–Private Partnerships in Uganda," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 26(5), pages 539-560.
    19. David J. Deming & Justine S. Hastings & Thomas J. Kane & Douglas O. Staiger, 2014. "School Choice, School Quality, and Postsecondary Attainment," American Economic Review, American Economic Association, vol. 104(3), pages 991-1013, March.
    20. 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.

    More about this item

    JEL classification:

    • I2 - Health, Education, and Welfare - - Education
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:19803. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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: (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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.