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Trying to measure manager vs. non-manager pay

Working with disparate data definitions

How much more do managers earn than the workers they manage? Sometimes the data can answer a question like this directly. But in this case, we must do a little work.

First, our investigation today is motivated by the fact that earnings for non-managers are persistently lower than earnings for the total pool of employees, which includes managers. But we don’t have data for the earnings of managers alone. A few developments during the pandemic make this question even more interesting: Non-manager weekly earnings rose when the pandemic hit, but non-manager hourly earnings (rate of pay) did not rise when the pandemic hit. So, did non-managers work more hours for the same rate of pay? Let’s see.

We compare weekly earnings for (i) the entire pool of workers and (ii) just non-managers so we can try to suss out what managers make. Non-managers earn about $170 per week less than the pool of all workers (which includes managers), and the graph above shows no visible evolution in that difference.

Our second FRED graph compares the same datasets but in terms of percent change from a year ago. We see no systematic difference between the two until the pandemic hits. At that point, non-managers started making significantly greater gains than the average of all workers; and they continue to do so.

Now, “weekly earnings” are a product of hourly pay and weekly hours. So, we focus on hourly pay in the graph above—again, in terms of percent change from a year ago. We see that non-manager pay didn’t immediately increase more once the pandemic hit. The increase only in the latter part of the pandemic seems to imply weekly hours must have increased more in the early stages of the pandemic. Did they?

We cannot see any difference at all in the two lines showing weekly hours. How is that possible? We would have expected the blue line to be significantly higher than the red line early in the pandemic. The problem is that the hours measured in the first graph aren’t the same as those in the last graph. The first uses the concept of average hours, which essentially represent regular work hours for an employee. The last graph considers aggregate hours, which is a total of all hours worked in the economy, and thus multiplies the average hours by employment. And the latter changed quite a bit during the pandemic. So, as it turns out, the decomposition we wanted to perform here doesn’t work. Which is why it’s so important to understand the precise definitions of the data you’re working with.

How these graphs were created: Search FRED for “average weekly earnings” and sele ct the production and non-supervisory workers series. From the “Edit Graph” panel, search for the same seriesbut pick “all employees.” You have the first graph. For the second, set units to “percent change from previous year” and apply to all. For the third and fourth graphs, repeat the operations with “average hourly earning” and “aggregate weekly hours.”

Suggested by Christian Zimmermann.

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