This is a total Econ nerd post, but still, I think, informative for a casual reader.
There’s a famous sketch drawn by David Sacks – a silicon valley VC. He drew the picture below describing how Uber’s business model was based on a positive feedback loop. I think it’s pretty intuitive… have a look:
It’s an example of positive cross-network effects. When there are more suppliers, it improves the product and induces more consumers to market. The increase in consumers then has a positive cross-network effect on suppliers (drivers). Drivers have less downtime and make more — thus can charge lower prices assuming the drivers want to have a fixed hourly rate. (If you wanna get really nerdy, then we can incorporate labor supply elasticity, but that’s another post).
There’s nothing trivial about this sketch. It helps explain why in 2015 VC’s were willing to value Uber — a transportation company with no cars and no drivers — at $68 Billion. $68 BILLION.
I’d like to unpack this from an Econ 101 POV.
1. More drivers –> Wider Coverage (Bottom right)
This is the most straightforward piece. Basically, the market expands. In the image below, I assume that the supply and demand curves shift out in a way that is price neutral.
2A. Wider coverage –> faster pick ups
This is one of the key parts of network effects. The whole is greater than the sum of its parts. The fact that Uber can quickly and at low cost connect drivers and riders means the transaction costs go down dramatically. And secondly, more connections between drivers and riders can be made. This reduces the wait time for a rider and improves the quality of the product. Now riders are willing to pay more since the the product is better and Demand increases.
2B. Wider coverage –> Reduced Driver Downtime –> Reduced fare price
Drivers get more fares more frequently because of the network effects discussed above. This reduced downtime implies that drivers get more fares per hour. Wages equal (fares/hour) x (price/fare). But wages are competitive. So, as the fares/hour increases, the price/fare decreases to keep wages at the competitive labor rate*. (This is an important point. The model assumes that drivers are competitive with respect to their time, not the amount of “work” they do. For them, an hour of work is an hour of work, regardless of the number of fares they get).
To imagine how this affects the supply curve, imagine that a driver once would get 2 fares per hour and Uber charged $10 per fare. Per hour, the driver makes $20 less whatever Uber’s share is.
With network effects, the driver can now get 4 fares per hour. But the driver doesn’t get $40 (less Uber’s share) because wage prices for drivers are competitive. Uber knows this, so they reduce the fare. This an interesting dynamic and it results in a “stretching out” of the supply curve. From the market’s perspective, at a given price, there are now twice as many fares.
Very cool stuff.