Research Brief
Not All Changes Are Equal
May 2, 2016
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).
A Structural Modeling Approach
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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.
Chapter One
Introduction
Chapter Two
Overview of the Chicago Teachers' Employment Context
Chapter Three
Insights from the Teacher Retention Literature
Chapter Four
A Dynamic Retention Model of Chicago Public School Teacher Retention
Chapter Five
Chicago Teacher Retention Data and Teacher and Nonteacher Wage Profiles
Chapter Six
DRM Parameter Estimates and Model Fit
Chapter Seven
Policy Simulations
Chapter Eight
Conclusion
Appendix A
Selected CTPF Provisions
Appendix B
Teacher Years of Service, Teacher and Nonteacher Earnings Profiles, and Social Security
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