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How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value-added

Author

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  • Dieterle, Steven G.

    (University of Edinburgh)

  • Guarino, Cassandra M.

    (University of California, Riverside)

  • Reckase, Mark D.

    (Michigan State University)

  • Wooldridge, Jeffrey M.

    (Michigan State University)

Abstract

The federal government's Race to the Top competition has promoted the adoption of test-based performance measures as a component of teacher evaluations throughout many states, but the validity of these measures has been controversial among researchers and widely contested by teachers' unions. A key concern is the extent to which nonrandom sorting of students to teachers may bias the results and lead to a misclassification of teachers as high or low performing. In light of this, it is important to assess the extent to which evidence of sorting can be found in the large administrative data sets used for VAM estimation. Using a large longitudinal data set from an anonymous state, we find evidence that a nontrivial amount of sorting exists – particularly sorting based on prior test scores – and that the extent of sorting varies considerably across schools, a fact obscured by the types of aggregate sorting indices developed in prior research. We also find that VAM estimation is sensitive to the presence of nonrandom sorting. There is less agreement across estimation approaches regarding a particular teacher's rank in the distribution of estimated effectiveness when schools engage in sorting.

Suggested Citation

  • Dieterle, Steven G. & Guarino, Cassandra M. & Reckase, Mark D. & Wooldridge, Jeffrey M., 2012. "How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value-added," IZA Discussion Papers 7112, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp7112
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Gershenson, Seth, 2016. "Should Value-Added Models Control for Student Absences?," IZA Discussion Papers 9978, Institute of Labor Economics (IZA).
    3. Ajzenman, Nicolás & Bertoni, Eleonora & Elacqua, Gregory & Marotta, Luana & Méndez, Carolina, 2020. "Altruism or Money?: Reducing Teacher Sorting Using Behavioral Strategies in Peru," IDB Publications (Working Papers) 10576, Inter-American Development Bank.
    4. J. R. Lockwood & D. McCaffrey, 2020. "Using hidden information and performance level boundaries to study student–teacher assignments: implications for estimating teacher causal effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1333-1362, October.
    5. Christopher Belfield & Imran Rasul, 2020. "Cognitive and Non‐Cognitive Impacts of High‐Ability Peers in Early Years," Fiscal Studies, John Wiley & Sons, vol. 41(1), pages 65-100, March.
    6. Ali Protik & Steven Glazerman & Julie Bruch & Bing-ru Teh, 2015. "Staffing a Low-Performing School: Behavioral Responses to Selective Teacher Transfer Incentives," Education Finance and Policy, MIT Press, vol. 10(4), pages 573-610, October.
    7. Seth Gershenson & Cassandra M. D. Hart & Joshua Hyman & Constance A. Lindsay & Nicholas W. Papageorge, 2022. "The Long-Run Impacts of Same-Race Teachers," American Economic Journal: Economic Policy, American Economic Association, vol. 14(4), pages 300-342, November.
    8. Dieterle, Steven G., 2015. "Class-size reduction policies and the quality of entering teachers," Labour Economics, Elsevier, vol. 36(C), pages 35-47.
    9. Ajzenman, Nicolás & Elacqua, Gregory & Marotta, Luana & Westh Olsen, Anne Sofie, 2021. "Order Effects and Employment Decisions: Experimental Evidence from a Nationwide Program," IDB Publications (Working Papers) 11541, Inter-American Development Bank.
    10. 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.
    11. Huang, Wei & Li, Teng & Pan, Yinghao & Ren, Jinyang, 2023. "Teacher characteristics and student performance: Evidence from random teacher-student assignments in China," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 747-781.
    12. Canales, Andrea & Maldonado, Luis, 2018. "Teacher quality and student achievement in Chile: Linking teachers' contribution and observable characteristics," International Journal of Educational Development, Elsevier, vol. 60(C), pages 33-50.
    13. 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.
    14. David Card & Laura Giuliano, 2016. "Can Tracking Raise the Test Scores of High-Ability Minority Students?," American Economic Review, American Economic Association, vol. 106(10), pages 2783-2816, October.
    15. Gershenson, Seth, 2021. "Identifying and Producing Effective Teachers," IZA Discussion Papers 14096, Institute of Labor Economics (IZA).
    16. Guarino, Cassandra M. & Reckase, Mark D. & Stacy, Brian & Wooldridge, Jeffrey M., 2014. "A Comparison of Growth Percentile and Value-Added Models of Teacher Performance," IZA Discussion Papers 7973, Institute of Labor Economics (IZA).
    17. Steven Dieterle, 2013. "Development Class-size Reduction Policies and the Quality of Entering Teachers," Edinburgh School of Economics Discussion Paper Series 224, Edinburgh School of Economics, University of Edinburgh.
    18. David Card & A. Abigail Payne, 2021. "High School Choices And The Gender Gap In Stem," Economic Inquiry, Western Economic Association International, vol. 59(1), pages 9-28, January.
    19. Roberto V. Penaloza & Mark Berends, 2022. "The Mechanics of Treatment-effect Estimate Bias for Nonexperimental Data," Sociological Methods & Research, , vol. 51(1), pages 165-202, February.
    20. Hermann, Zoltán & Horváth, Hedvig, 2022. "Tanári eredményesség és tanár-diák összepárosítás az általános iskolákban. Empirikus mintázatok három magyarországi tankerület adatai alapján [Teacher effectiveness and teacher-student matching in ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(11), pages 1377-1406.

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    More about this item

    Keywords

    teacher labor markets; teacher quality; value added; education;
    All these keywords.

    JEL classification:

    • I0 - Health, Education, and Welfare - - General
    • I20 - Health, Education, and Welfare - - Education - - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J44 - Labor and Demographic Economics - - Particular Labor Markets - - - Professional Labor Markets and Occupations
    • J45 - Labor and Demographic Economics - - Particular Labor Markets - - - Public Sector Labor Markets

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