Teacher Layoff Threat
During times of layoff-inducing budget uncertainties, teachers fall into three categories:
1) those who have received a Reduction-In-Force (RIF) notice and consequently been laid off by their school districts;
2) those who are under severe threat of layoff because they have received a RIF notice which was then rescinded or they were re-hired; and
3) teachers who face little threat of layoff because they have not yet received a RIF notice. Teachers in each of these categories face a number of different career choices based on the level of layoff threat that they face, and the policies that dictate the layoff process.
To better understand the implications of these levels of layoff threat on the quality composition of the teacher workforce, the proposed research project will explore the following question: To what extent does the variation in layoff threat associated with policies governing layoffs influence the career paths of teachers of varying effectiveness?
To answer this question, we base our work in two different contexts that capitalize on variations in layoff policy: the Los Angeles Unified School District (LAUSD), which is the setting for the Reed vs. State of California, et al. court case and decision that each year protects teachers within 45 schools from traditional LIFO policies; and Washington State, in which individual districts' teachers' union contracts allow for varying layoff policies based more or less on teacher seniority. Both LAUSD and Washington have careful longitudinal data that enable us to follow individual teachers over time, generate value-added estimates of teacher effectiveness for many of their teachers, and identify which teachers are under each level of layoff threat.
We will use competing risk survival analyses that estimates the risk that teachers choose each of multiple ("competing") career options. These models estimate teachers' propensities to make each career choice in each year of their employment, taking into account important teacher-, school- and district-level characteristics that may influence these choices. Importantly, these models will allow us to estimate the impact of the layoff-induced job threat on teachers in each of the different risk categories in each year that teachers are laid off, and to do so for teachers with different levels of effectiveness (as measured by teachers' "value-added" to student achievement).