Where the Sharing Economy Runs Aground.


Living in what is usually called ‘Flyover country’ we don’t get to see a lot of the more interesting ideas found under the definition of the ‘Sharing Economy.’ While that could be explained away in a number of ways like things just take a while to get here ( a good example is fashion, which, for some of the more unsavory trends, also seem to take too long to leave), that startups aren’t ready to expand into our market at this time, or that there’s just not the steaming cauldron of tech savvy people in the area, it’s lack of arrival also brings up musings about the limiting bounds of such services, namely density and anonymity.

Seeing as these services like Uber, TaskRabbit and any number of other “I have free time, how about I use an app to make a few bucks” services usually originate in the larger metropolises like New York, Seattle or the startup mecca of San Francisco, birth locations seem obvious. There is a certain density of pre-existing potential customers in these cities and probably a greater than average amount of willing early adopters as well. I won’t speak to the rest of the world because I won’t pretend to assume their functioning, but what happens when these sorts of services begin to be translated to less dense, ‘more conservative’ areas of the US? I think this is when the seams of theses services start to show.

The first thing that happens when you leave the high density city world is the pool of potential customers shrinks quickly. The customer base density plays a large part in the economics of the services. At a certain point in this migration, the reduction in population will move the service providers in Lyft or other services from the potential of full time employment to part time or even less. This may be a much larger issue than the companies let on.

If these things can’t be done as a primary job, most service providers will need a full time gig. I’d think this will cause a dearth of operators particularly during the 9-5s of the week (when nearly all of us work the regular job or go to school) and in drive time when the services may be needed most. Of course this reduction is self fulfilling as once there is less service providers there will be less utility for the customers and as an extension, less opportunity for the service to be useful to providers as there just isn’t enough supply to feel the gig lucrative.

When talking about areas outside of the largest US cities, population per area usually decreases as well. Of course, when the service extends to locations where the pure density of the city is sufficiently spread across a larger area, the density of service provider workforce also reduces. This will tend to reduce the convenience of the service. At a certain point it will reach the hurdle of just as convenient as an alternative, like just doing it yourself.

Yep, this is to scale – just think 800,000 in the inset versus 600,000 in the big image.

A good example of how geographies across the country differ might be comparing Oklahoma City to San Francisco. The OKC has the population of around 630,000 which could be considered almost similar to San Francisco with 860,000-ish – but the former stretches those people over 620 square miles while the latter consolidates its population in less than 50 square miles. With that amount of sprawl, the costs of the workforce will increase as transportation costs will begin to become a larger and larger factor in the choosing of assignments. Not to mention you’d just need more drivers or task people just to provide the same speed of service in OKC as in San Francisco. Driving across Oklahoma City is an investment in time. I can’t imagine doing it pedaling – even with the benefits of my carbon road bike. The costs of travel become a bigger issue.

With Uber and now Bodega, the sprawl offers another inherent issue. People are already used to driving their own conveyances and the cities are designed for driving. With sprawl comes more abilities to park making the drive more second nature than messing with new, possibly awkward outcomes. Who knows when you’ll get that Lyft back from the store. If you just drive your own car to the store you can almost guarantee you’ll get everything you need and more – no machine learning cycles are needed to get the peanut butter you like stocked in the vending machine.

Customers only change habits when the benefit is significantly larger than the pain of learning new things. If you’re already driving everywhere and it’s not too bad, the cost may be higher to figure out an app and wait than to keep driving to the Wal*mart.

The second, and perhaps most interesting situation that develops is as population shrinks, the possibility for anonymity does as well – and perhaps one of the central pillars of these services is the sharing app is necessary for connecting people who don’t know each other.  Conversely, if the area isn’t large enough to sufficiently provide anonymity of the service provider, the chances of customers sidestepping the app to directly contact providers becomes an increasing concern.

This image is from a site called Taxi Fare Finder, which looks rather interesting all by itself.

Flyover country is typically portrayed as more personable – maybe the riders would get to know the service providers. Think that’s crazy? I know people in Chicago that know and only use certain cabbies. They would call them personally for rides rather than calling dispatch. If it happens there, it will certainly happen with the likes of Uber or TaskRabbit in a smaller city where there isn’t hundreds of Lyft drivers. It’ll be a nice 100% profit ride for the service provider, too, because they wouldn’t have to share with Lyft.

While I’m certainly not against the sharing economy – I lean on Uber quite a bit to be sure and would certainly love TaskRabbit to show up here in force – not all business models can be strapped onto every market.

Thinking more lucratively, perhaps there needs to be developed another set of sharing business models for the great midsection of the USA (or midsection of Germany, Russia or China for that matter) that takes into account the difference in resident behaviors, geography and density. Or maybe this is just where we enter the Craigslist zone?

When these models do develop, I’d doubt they would come from the coastal startup hot spots of today. What I wouldn’t doubt would be the value of these models may actually outpace their city-based cousins. It might be easier to scale these up rather than to scale the current ones down.

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