Despite all the heated talk about how to identify and dismiss low-performing teachers, there’s relatively little research on how administrators choose whom to dismiss, whether various dismissal options might actually serve to improve performance, and other aspects in this area. A paper by economist Brian Jacob, released as working paper in 2010 and published late last year in the journal Education Evaluation and Policy Analysis, helps address at least one of these voids, by providing one of the few recent glimpses into administrators’ actual dismissal decisions.
Jacob exploits a change in Chicago Public Schools (CPS) personnel policy that took effect for the 2004-05 school year, one which strengthened principals’ ability to dismiss probationary teachers, allowing non-renewal for any reason, with minimal documentation. He was able to link these personnel records to student test scores, teacher and school characteristics and other variables, in order to examine the characteristics that principals might be considering, directly or indirectly, in deciding who would and would not be dismissed.
Jacob’s findings are intriguing, suggesting a more complicated situation than is sometimes acknowledged in the ongoing debate over teacher dismissal policy.
Most importantly (but not surprisingly), he finds that there is an association between several proxies for teacher productivity and whether or not teachers were let go. For example, teachers who received lower evaluation ratings in the prior year were far more likely to be non-renewed, as were teachers with a lot more absences. Interestingly, teachers with master’s degrees and more experience were also significantly less likely to be dismissed, all else being equal.
In addition, while he was only able to calculate value-added scores for a subset of the full sample (i.e., teachers in tested grades and subjects), Jacob does find a relationship between these estimates and teacher dismissals. Specifically, a one standard deviation increase in teacher value-added (for example, comparing a teacher at the 50th percentile with one at the 84th) was associated with a decrease in the likelihood of dismissal of around 7 percentage points (a large increase relative to the probability for all these teachers). It bears noting, however, that Jacob finds no effect at all among high school teachers (ninth grade teachers only).
What these main findings suggest is less than shocking, but important nevertheless – principals appear to consider, directly or indirectly, teacher productivity (and other characteristics) when making decisions to let teachers go.* This also squares with previous research showing that there is a discernible relationship between teachers’ value-added scores and the ratings they receive from principal observations.
There were a few surprises, however, among some of the purely descriptive results (i.e., raw tabulations). Two in particular stand out.
First, Jacob found that, despite the new policy allowing principals to dismiss probationary teachers at will, a rather high proportion of them didn’t do so. During each year between 2004-05 and 2006-07, principals in around 30-40 percent of Chicago schools chose not to dismiss a single probationary teacher. Further, this phenomenon was not at all limited to “high-performing” and/or low-poverty schools, where one might expect to find a stable, well-trained teaching force. For instance, in 2005, 35 percent of the “lowest performing” schools (the bottom 25 percent) chose not to dismiss any probationary teachers, as compared with 54 percent of the school with the highest absolute achievement levels (the proportions were similar when school performance was measured in terms of value-added).
In other words, when principals were given free rein to fire for any reason, with virtually no documentation or effort, a significant proportion chose not to use this power even once.
Obviously, we wouldn’t expect every single school to dismiss teachers, and there are several reasons why so many principals might have decided not to. For example, the new policy may have influenced some teachers to resign rather than be fired, and there was indeed an increase in voluntary separations after the new policy went into effect (which would mean the dismissal rates are understated). Principals may also have been less likely to dismiss probationary teachers that they themselves hired.
Nevertheless, on average, roughly one in three CPS teachers is on probation, and the fact that so many principals didn’t dismiss even one of them might suggest, as Jacob points out, that the rules governing the dismissal of probationary teachers were already more flexible than many seem to believe. In other words, principals don’t let go of a lot of teachers because they don’t want to, not because they can’t.
A second, perhaps more surprising, finding was that, among the teachers who were dismissed in any given year, around half of them were rehired by a different school in the district. Again, there are a bunch of possible explanations here.
For example, Jacob noted that some of the dismissals were due to position cuts, which might account for some of the rehiring. Also, there are certainly cases in which good teachers just don’t “fit in” at a school for whatever reason. In these cases, it’s quite possible that the shuffling of teachers could yield benefits for all (though the rehired teachers were also substantially more likely than other first-year teachers to be let go again).
In considering these results, Jacob reported that there were, on average, ten applicants for every open position in CPS (and, almost certainly, the recession has caused this number to rise since then). Clearly, there were other people applying for the jobs, but principals hired previously-dismissed teachers at what seems like a strikingly high clip.
One other particularly plausible explanation is the labor supply – i.e., that there sometimes isn’t a pool of suitable replacements waiting to fill vacancies. That is, principals rehired previously-dismissed teachers because they were the best candidates.
As always, we must be careful about drawing strong conclusions from one analysis. This paper is a rare look at teacher dismissals, but it is necessarily limited. Most notably, it only pertains to probationary teachers. In addition, the data are from only one large district during one three-year time period.
That said, Jacob’s results portray a complicated situation. On the one hand, principals do appear to make dismissal decisions that take into account teacher productivity. This is the paper’s main finding. While it’s important to bear in mind that this analysis is not designed to isolate the effect of the new policy on outcomes, such as student achievement, it does suggest that administrators’ role in dismissal decisions may serve to improve teacher quality over the long run.
On the other hand, there is little support for the idea that principals are just dying to fire at will – or that, once dismissed, teachers can easily be replaced by “better” alternatives – despite sometimes being taken for granted in our education debates. Although they are far from conclusive, and pertain only to probationary teachers, the descriptive results discussed above tentatively suggest that the supply of appropriate replacements may not always be quite as robust as is often assumed – and/or that there may be some other reasons for low dismissal rates that are not entirely a function of the difficulty of doing so.
In short, we should be careful not to reduce the complexity of employment policies and labor markets to a simple narrative in which personnel policies are the only impediment to improvements in teaching quality.
- Matt Di Carlo
* The results also indicate that, even controlling for all the other variables, principals were significantly more likely to dismiss teachers with certain characteristics, such as men and older teachers. But it’s important to note that this does not necessarily represent evidence of discrimination. There are any number of unobserved characteristics, such as motivation or the ability to relate to children, that may be correlated with demographic characteristics such as gender and age. If that’s the case, then the models would “mistake” these factors for demographics. In other words, these methods are not designed to “detect” discrimination. Nevertheless, the differences by age and gender in the likelihood of dismissal are certainly cause for concern and further research, and Jacob dutifully acknowledges as much.