The Circular Logic of Quality-Based Layoff Arguments

Many pundits are responding enthusiastically to the new LA Times article on quality-based layoffs – or how dismissing teachers based on Value-added scores rather than on seniority would have saved LAUSD many of its better teachers, rather than simply saving its older ones.

Some are pointing out that this new LA Times report is the “right” way to use value-added as compared with the “wrong” way that LA Times had used the information previously this year.

Recently, I explained the problematic circular logic being used to support these “quality-based layoff” arguments. Obviously, if we dismiss teachers based on “true” quality measures, rather than experience which is, of course, not correlated with “true” quality measures, then we save the jobs of good teachers and get rid of bad ones. Simple enough? Not so. Here’s my explanation, once again.

This argument draws on an interesting thought piece and simulation posted at  ( Teacher Layoffs: An Empirical Illustration of Seniority vs. Measures of Effectiveness), which was later summarized in a (less thoughtful) recent Brookings report (

That paper demonstrated that if one dismisses teachers based on VAM, future predicted student gains are higher than if one dismisses teachers based on experience (or seniority). The authors point out that less experienced teachers are scattered across the full range of effectiveness – based on VAM – and therefore, dismissing teachers on the basis of experience leads to dismissal of both good and bad teachers – as measured by VAM. By contrast, teachers with low value-added are invariably – low value-added – BY DEFINITION. Therefore, dismissing on the basis of low value-added leaves more high value-added teachers in the system – including more teachers who show high value-added in later years (current value added is more correlated with future value added than is experience).

It is assumed in this simulation that VAM (based on a specific set of assessments and model specification) produces the true measure of teacher quality both as basis for current teacher dismissals and as basis for evaluating the effectiveness of choosing to dismiss based on VAM versus dismissing based on experience.

The authors similarly dismiss principal evaluations of teachers as ineffective because they too are less correlated with value-added measures than value-added measures with themselves.

Might I argue the opposite? – Value-added measures are flawed because they only weakly predict which teachers we know – by observation – are good and which ones we know are bad? A specious argument – but no more specious than its inverse.

The circular logic here is, well, problematic. Of course if we measure the effectiveness of the policy decision in terms of VAM, making the policy decision based on VAM (using the same model and assessments) will produce the more highly correlated outcome – correlated with VAM, that is.

However, it is quite likely that if we simply use different assessment data or different VAM model specification to evaluate the results of the alternative dismissal policies that we might find neither VAM-based dismissal nor experienced based dismissal better or worse than the other.

For example, Corcoran and Jennings conducted an analysis of the same teachers on two different tests in Houston, Texas, finding:

…among those who ranked in the top category (5) on the TAKS reading test, more than 17 percent ranked among the lowest two categories on the Stanford test. Similarly, more than 15 percent of the lowest value-added teachers on the TAKS were in the highest two categories on the Stanford.

  • Corcoran, Sean P., Jennifer L. Jennings, and Andrew A. Beveridge. 2010. “Teacher Effectiveness on High- and Low-Stakes Tests.” Paper presented at the Institute for Research on Poverty summer workshop, Madison, WI.

So, what would happen if we did a simulation of “quality based” layoffs versus experience-based layoffs using the Houston data, where the quality-based layoffs were based on a VAM model using the Texas Assessments (TAKS), but then we evaluate the effectiveness of the layoff alternatives using a value-added model of Stanford achievement test data? Arguably the odds would still be stacked in favor of VAM predicting VAM – even if different VAM measures (and perhaps different model specifications). But, I suspect the results would be much less compelling than the original simulation.

The results under this alternative approach may, however, be reduced entirely to noise – meaning that the VAM based layoffs would be the equivalent of random firings – drawn from a hat and poorly if at all correlated with the outcome measure estimated by a different VAM – as opposed to experienced based firings. Neither would be a much better predictor of future value-added.  But for all their flaws, I’d take the experienced based dismissal policy over the roll of the dice, randomized firing policy any day.

In the case of the LA Times analysis, the situation is particularly disturbing if we look back on some of the findings in their own technical report.

I explained in a previous post that the LA Times value-added model had potentially significant bias in its estimates of teacher quality. For example, in my earlier post, I explain that:

Buddin finds that black teachers have lower value-added scores for both ELA and MATH. Further, these are some of the largest negative effects in the second level analysis – especially for MATH. The interpretation here (for parent readers of the LA Times web site) is that having a black teacher for math is worse than having a novice teacher. In fact, it’s the worst possible thing! Having a black teacher for ELA is comparable to having a novice teacher.

Buddin also finds that having more black students in your class is negatively associated with teacher’s value-added scores, but writes off the effect as small. Teachers of black students in LA are simply worse? There is NO discussion of the potentially significant overlap between black teachers, novice teachers and serving black students, concentrated in black schools (as addressed by Hanushek and Rivken in link above).

By contrast, Buddin finds that having an Asian teacher is much, much better for MATH. In fact, Asian teachers are as much better (than white teachers) for math as black teachers are worse! Parents – go find yourself an Asian math teacher in LA? Also, having more Asian students in your class is associated with higher teacher ratings for Math. That is, you’re a better math teacher if you’ve got more Asian students, and you’re a really good math teacher if you’re Asian and have more Asian students?????

One of the more intriguing arguments in the new LA Times article is that under the seniority based layoff policy:

Schools in some of the city’s poorest areas were disproportionately hurt by the layoffs. Nearly one in 10 teachers in South Los Angeles schools was laid off, nearly twice the rate in other areas. Sixteen schools lost at least a fourth of their teachers, all but one of them in South or Central Los Angeles.

That is, new teachers who were laid off based on seniority preferences were concentrated in high need schools. But so too were teachers with low value-added ratings?

While arguing that “far fewer” teachers would be laid off in high need schools under a quality-based layoff policy, the LA Times does not however offer up how many teachers would have been dismissed from these schools had their biased value-added measures been used instead? Recall that from the original LA Times analysis:

97% of children in the lowest performing schools are poor, and 55% in higher performing schools are poor.

Combine this finding with the findings above regarding the relationship between race and value-added ratings and it is difficult to conceive how VAM based layoffs of teachers in LA would not also fall disparately on high poverty and high minority schools. The disparate effect may be partially offset by statistical noise, but that simply means that some teachers in lower poverty schools will be dismissed on the basis of random statistical error, instead of race-correlated statistical bias (which leads to a higher rate of dismissals in higher poverty, higher minority schools).

Further, the seniority based layoff policy leads to more teachers being dismissed in high poverty schools because the district placed more novice teachers in high poverty schools, whereas the value-added based layoff policy would likely lead to more teachers being dismissed from high poverty, high minority schools, experienced or not, because they were placed in high poverty, high minority schools.

So, even though we might make a rational case that seniority based layoffs are not the best possible option, because they may not be highly correlated with true (not “true”) teaching quality, I fail to see how the current proposed alternatives are much if any better.  They only appear to be better when we measure them against themselves as the “true” measure of success.