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The Rise of Tech-Based Matching Markets: Uber, Lyft, and Airbnb. What’s next?

The rise of the market matching economy: Uber, Lyft, and Airbnb. What’s next?

In traditional economics, a market is described as a means of equalizing demand and supply through an adjustment in price. While this model works well for a typical commodity market, it doesn’t fully describe what goes on in more complicated markets. An example of this is a matching market. According to Alvin Roth, a noble prize-winning economist, a matching market is “a market in which prices don’t do all the work.” In their book, Networks, Crowds and Markets, Professor Easley and Professor Kleinberg of Cornell University give reasons for the popularity of the study of matching markets.

Market matching embodies the way in which people may have different preferences for different kinds of goods, the way in which prices can decentralize the allocation of goods to people, and the way in which such prices can in fact lead to allocations that are socially optimal. (Easley and Kleinberg, 277)

Driven by the rapid growths in processing power, mobile technology, and the internet of the last few decades, unicorns with market matching roles have started entering the global economy. The ride-sharing companies Uber and Lyft that match travelers with drivers and, Airbnb, which matches hosts with travelers are a few good examples. Primarily, these services significantly lower an individual’s barrier to entry in their respective markets. Today, anyone with a well-functioning car and a driving license can transport goods and passengers by becoming a Lyft driver. Furthermore, using complex algorithms, location information through google maps, and data in terms of user information, they increase the rate at which a market reaches perfect matching. One good question to ask is how much value the matching efficiency of these unicorns have added to the economy. Although their long-term economic impact is yet to be seen, they have certainly made the market more efficient and socially optimal. On the other hand, like most new technology-based companies, they have disrupted traditional markets.  While Uber and Lyft are disrupting the taxi system, most famously in the medallion-based cab system of New York City, Airbnb is disrupting hotel services. Nevertheless, their positive impact on the global economy through an increase in market participation and efficient market matching is undeniable.

Another interesting question we can ask is which markets can highly benefit from a technology-driven market matching model. A few of markets that already use such a model are kidney donation, college admissions, and hiring. Let’s take an example of jumpstart, a startup founded in 2017 connecting students with employers. According to crunchbase, jumpstart is “a machine learning platform that enables students to learn, discover and connect with the most innovative companies in the world.” Students create a profile and enter information about their interests, values, and experiences which the platform uses to make intelligent matches to employers using a machine learning model.

The move towards making markets efficient through market making algorithms and emerging technologies such as machine learning will not only decrease current market inefficiency, it will also increase the number of participants in a market leading to more alternatives and a lower price. Moreover, the lowering of the barrier to entry in combination with efficient and socially optimal matching systems will create a fair, socially sensible and merit-based society.

Easley, David, and Jon Kleinberg. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, 2010.




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October 2018