A compensating differential is the difference in the wage between a job with desirable non-monetary characteristics and a job with undesirable non-monetary characteristics, holding all other factors (like human capital or skill requirements, experience of the worker, etc.) constant. When a job has attractive non-monetary characteristics (e.g. it is clean, safe, or fun), then more people will be willing to do that job. This leads to a higher supply of labour for that job, which leads to lower equilibrium wages. In contrast, when a job has negative non-monetary characteristics (e.g. it is dirty, dangerous, or boring), then fewer people will be willing to do that job. This leads to a lower supply of labour for that job, which leads to higher equilibrium wages. It is the difference in wages between jobs with attractive non-monetary characteristics and jobs with negative non-monetary characteristics that we refer to as a compensating differential (essentially, workers are being compensated for taking on jobs with negative non-monetary characteristics, through higher wages).
The current pandemic presents a situation where many jobs have suddenly had a new and negative non-monetary characteristic added to them - the risk of becoming infected with the coronavirus. The idea of compensating differentials suggests that workers who suddenly face a job that is riskier than before should receive an increase in wages (and indeed, we have seen that, such as the pay bonus that some supermarket workers have received).
There hasn't been much in the way of systematic research on the compensating differentials arising from the pandemic. No doubt we can expect some in the future. An early example is this new paper by Duha Altindag, Samuel Cole, and Alan Seals (all Auburn University). It turns out that Auburn University didn't strictly follow the CDC requirements for safe social distancing in class, leading to some classes having too many students, and therefore being higher risk. As Altindag et al. note:
Possibly due to the cost concerns, Auburn University did not implement any policy about maintaining six feet of distance between students within the classrooms... Instead, the university set an enrollment limit of half of the normal seating capacity in classrooms, despite the Center for Disease Control (CDC) guidelines and the public health orders of the state... This practice of the university led to about 50% of all face-to-face (F2F) courses in Spring 2021 being delivered in “risky” classrooms, in that the number of enrolled students in classes exceeded their classrooms’ CDC-prescribed safe capacity (the maximum number of students that can be seated in the room while allowing a six-foot distance between all students).
Altindag et al. looked at differences in which staff taught the risky (or 'very risky' - classes where the number of enrolled students was more than double the safe room capacity) classes, and then looked at differences in pay between those teaching risky classes and those teaching less risky classes. For the differences in pay, they are able to adopt an instrumental variables approach, using the presence of fixed furniture in the teaching room as an instrument. As they explain:
Our instrument, Dispersible Class, is an indicator for whether students in a classroom can spread away from each other while attending the lectures. This can only happen in in-person classes that take place in rooms with movable furniture or in online courses in which students have already spread away from one another.
I worry a little about the sensitivity of the results to the inclusion of fully online classes. By construction, the instrument (Dispersible Class) always takes a value of one for online classes, and the online classes are by definition non-risky, so the variation they are picking up is entirely driven by the riskiness of the face-to-face classes. That is what you want in this analysis, but why include the online classes in the analysis at all since they aren't contributing any variation?
Anyway, nit-picking aside, when Altindag et al. look at who teaches the risky classes, they find that:
...GTAs [Graduate Teaching Assistants] and adjunct instructors, who are ranked low within the University hierarchy, are about eight to ten percentage points more likely to teach a risky class compared to the tenured faculty (full and associate professors) and administrators (such as the department chairs, deans, and others) who teach courses in the same department...
...female instructors are more likely to be teaching risky classes. Additionally... younger faculty face higher risk in their classrooms.
The results are similar for 'very risky' classes. Young faculty and low-ranked faculty (and, possibly, female faculty) have less bargaining power with departmental chairs, so are more likely to acquiesce to a request to teach particular classes, and that is what Altindag et al. find. Those academics have consequently taken on more COVID-19 risk. But, are they compensated for this risk? In their instrumental variables analysis, Altindag et al. find that:
...instructors who teach at least one risky class earn 22.5 percent more than their counterparts who deliver only safe course sections... Relative to the average monthly wage of an instructor in our sample, this effect corresponds to approximately $2,100. In a four-month semester, this impact corresponds to $8,400.
Again, the results are similar for 'very risky' classes. So, even though junior faculty and female faculty take on more risky classes, they are compensated for that additional risk. Note that the estimates of the compensating differential control for the instructor's demographic characteristics, academic level, and experience at Auburn. Altindag et al. find that the compensating differential is roughly the same at all academic levels.
One other criticism is that perhaps the types of classes that junior faculty typically teach happen to be those that are riskier. Altindag et al. address this by running their analysis using data on classes from the previous year, when COVID-19 was not a thing. They find no statistically significant differences in who teaches 'hypothetically-riskier' classes, and no statistically significant wage premium for those teaching 'hypothetically-riskier' classes. That provides some confidence that the effects they pick up in their main analysis relate to risk in pandemic times.
This paper raises an interesting question. Faculty are compensated for coronavirus risk, through higher wages. However, it isn't only faculty who face higher risk. Students attending those classes are at higher risk as well. Is there a compensating differential for students, and if so, how would we measure it? That is a question for future research.
[HT: Marginal Revolution]
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