The authors develop and estimate a stochastic dynamic programming model to analyze the relationship between compensation, including retirement benefits, and retention over the career of Chicago public school teachers. The structural modeling approach used was first developed at RAND for studying the relationship between military compensation and the retention of military personnel and is called the dynamic retention model (DRM).

Retirement Benefits and Teacher Retention

A Structural Modeling Approach

David Knapp, Kristine M. Brown, James Hosek, Michael G. Mattock, Beth J. Asch

ResearchPublished May 2, 2016

Recently, many state governments have legislated reductions in teachers' retirement benefits for new and future employees as a means of addressing the large unfunded liabilities of their pension plans. However, there is little existing capacity to predict how these unprecedented pension reforms — and, more broadly, changes to teacher compensation — will affect teacher turnover and teacher experience mix, which, in turn, could affect the cost and efficacy of the public education system. This research develops a modeling capability to begin filling that gap. The authors develop and estimate a stochastic dynamic programming model to analyze the relationship between compensation, including retirement benefits, and retention over the career of Chicago public school teachers. The structural modeling approach used was first developed at RAND for the purpose of studying the relationship between military compensation and the retention of military personnel and is called the dynamic retention model, or DRM. Although the peer-reviewed literature on teachers includes research on retirement benefits and the timing of retirement, the research does not model compensation and retention over the length of the career from entry to exit (into retirement or an alternative career), and it has limited capability to predict the effect of compensation and retirement benefit changes on retention. By comparison, the DRM is well suited to these tasks, and the DRM specification developed here for Chicago teachers fits their career retention profile well.

Key Findings

A version of the baseline DRM model incorporating an early-career preference for teaching in Chicago, in addition to the permanent taste for teaching in Chicago already included in the model, provided the best fit of teacher retention.

  • We cannot pin down specific drivers of the observed decrease in early-career preference for teaching though this deserves further research.

The largest changes to the retention profiles occur when current salaries are reduced and when the full retirement age is increased.

  • Simulations suggest a permanent 3-percent reduction in salary results in significantly lower retention for early-career teachers in years one to five.
  • An increase in the full retirement age leads to lower retention of mid-career teachers, but the retention of teachers who continue teaching beyond the full retirement age is higher given that teachers with lower taste tend to have left by the new full retirement age.

Recommendation

  • This model can be extended to include nonpecuniary factors that may affect teacher retention, such as a mentoring program for new teachers, and to explore selective retention by teacher effectiveness.

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Document Details

  • Copyright: RAND Corporation
  • Availability: Available
  • Year: 2016
  • Print Format: Paperback
  • Paperback Pages: 134
  • Paperback Price: $24.00
  • Paperback ISBN/EAN: 978-0-8330-9451-3
  • DOI: https://doi.org/10.7249/RR1448
  • Document Number: RR-1448-RC

Citation

RAND Style Manual
Knapp, David, Kristine M. Brown, James Hosek, Michael G. Mattock, and Beth J. Asch, Retirement Benefits and Teacher Retention: A Structural Modeling Approach, RAND Corporation, RR-1448-RC, 2016. As of October 8, 2024: https://www.rand.org/pubs/research_reports/RR1448.html
Chicago Manual of Style
Knapp, David, Kristine M. Brown, James Hosek, Michael G. Mattock, and Beth J. Asch, Retirement Benefits and Teacher Retention: A Structural Modeling Approach. Santa Monica, CA: RAND Corporation, 2016. https://www.rand.org/pubs/research_reports/RR1448.html. Also available in print form.
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