How Restrictive Zoning Hurts Personal Trainers

A lot of the focus on the economics of cities centers on the idea of agglomeration externalities boosting the production of tradeable goods. So you have your entertainment cluster in Los Angeles, your IT cluster in Silicon Valley, a finance cluster in New York, and so forth. This is all very interesting and important, but an aspect of it that I’m interested in is the impact of urbanization on the kind of localized service provision that accounts for a larger and larger share of the labor force in a globalized world.

Take, for example, Catherine Rampell’s article about personal trainers:

From 2001 to 2011, the number of personal trainers grew by 44 percent, to 231,500, while the overall number of workers fell by 1 percent, according to the Labor Department. […]

For personal trainers, the median hourly wage is less than $15. Because they have to find clients and set up their businesses, trainers must be flexible, adapting to client schedules and physical abilities, as well as the availability of exercise machines and accommodating weather.

They must also be able to engage with all sorts of personalities — precisely the skills that help keep these jobs around while others are replaced by algorithms.

A few relevant points about urban policy:

One is simply that a trainer in a high-income metro area (Seattle or DC) is going to be able to earn higher wages than a trainer in a lower-income one (Phoenix or Philadelphia) simply because the clients can afford to pay more. So when restrictive zoning makes housing expensive, not only does that reduce real incomes of the people who live in the expensive city (because their rent is so expensive), it generally reduces the earnings potential of the people who are pushed away.

The second is that a personal trainer’s productivity—the value he generates per hour worked—is going to be higher in a bigger, denser metropolitan area. That’s because being a “good personal trainer” isn’t a one-dimensional thing. Different people have different preferences, and different trainers are better matches for different clients. The denser the metropolitan area, the better the matching between clients and trainers, and thus the more value created.