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Managing the Teacher Workforce in Austere Times: The Determinants and Implications of Teacher Layoffs

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  • Dan Goldhaber

    (Center for Education Data and Research, University of Washington)

  • Roddy Theobald

    (Center for Education Data and Research, University of Washington)

Abstract

Over 2,000 teachers in the state of Washington received reduction-in-force (RIF) notices across the 2008–09 and 2009–10 school years. We link data on these RIF notices to an administrative data set that includes student, teacher, school, and district variables to determine the factors that predict the likelihood of a teacher receiving a RIF notice. Not surprisingly, we find that a teacher's seniority is the strongest predictor, but we also find (all else equal) that teachers with master's degrees and those credentialed in the high-need areas of math, science, and special education were less likely to receive a RIF notice. Value-added measures of teacher effectiveness, which can be calculated for a subset of the teachers, were not correlated with the probability of receiving a RIF notice. Finally, simulations suggest that a very different group of teachers would be targeted for layoffs under an effectiveness-based layoff scenario than under the seniority-driven system that exists today. © 2013 Association for Education Finance and Policy

Suggested Citation

  • Dan Goldhaber & Roddy Theobald, 2013. "Managing the Teacher Workforce in Austere Times: The Determinants and Implications of Teacher Layoffs," Education Finance and Policy, MIT Press, vol. 8(4), pages 494-527, October.
    • Handle: RePEc:tpr:edfpol:v:8:y:2013:i:4:p:494-527
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    References listed on IDEAS

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    Cited by:

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    2. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education,, Elsevier.
    3. Dan Goldhaber & Cyrus Grout & Nick Huntington-Klein, 2017. "Screen Twice, Cut Once: Assessing the Predictive Validity of Applicant Selection Tools," Education Finance and Policy, MIT Press, vol. 12(2), pages 197-223, Spring.
    4. Cory Koedel & Eric Parsons & Michael Podgursky & Mark Ehlert, 2015. "Teacher Preparation Programs and Teacher Quality: Are There Real Differences Across Programs?," Education Finance and Policy, MIT Press, vol. 10(4), pages 508-534, October.
    5. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    6. Fraenkel, Rebecca Cannon, 2022. "Local labor markets and job match quality: Teachers," Labour Economics, Elsevier, vol. 78(C).
    7. 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.
    8. Cory Koedel & Jiaxi Li & Matthew G. Springer & Li Tan, 2016. "The Impact of Performance Ratings on Job Satisfaction for Public School Teachers," Working Papers 1617, Department of Economics, University of Missouri.
    9. Benjamin Master & Min Sun? & Susanna Loeb, 2018. "Teacher Workforce Developments: Recent Changes in Academic Competitiveness and Job Satisfaction of New Teachers," Education Finance and Policy, MIT Press, vol. 13(3), pages 310-332, Summer.
    10. Brian Jacob & Jonah E. Rockoff & Eric S. Taylor & Benjamin Lindy & Rachel Rosen, 2016. "Teacher Applicant Hiring and Teacher Performance: Evidence from DC Public Schools," NBER Working Papers 22054, National Bureau of Economic Research, Inc.

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    More about this item

    Keywords

    teacher layoffs; teacher workforce;

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

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