Nine years after starting work on this paper it finally published in the Journal of the Labor Economics. My gratitude goes to a fantastic editor, Kevin Lang at Boston University, and three anonymous referees whose suggestions made the paper a stronger and more robust contribution to labor market theory.
The main idea conveyed in this paper is as follows: When firms compete for heterogeneous workers in a well-defined labor market, larger firms have an incentive to lower wages more than their small competitors. If firms pay all their workers equally, the result is intuitive: it is more expensive for a large firm to raise wages than for its smaller competitors.
Strikingly, however, the result remains true even if firms post different wages for their positions. The decisive force behind this result is that when a firm hires a worker of high ability, the set of workers its other open positions compete for becomes less attractive. This externality is stronger for firms with more open positions, and therefore more market power. As such, firms that enjoy higher levels of market power may end up recruiting lower ability workers.
In this paper, I show that the resulting inefficiency, i.e., mismatch when assortative matching is desirable, may be quite substantial, and that the result is robust to sequential wage posting.
Search platforms (that rely on different business models) such as Amazon, Google or Yelp possess an abundance of consumer-specific data from past searches, purchases, and the behavior of ‘similar’ consumers. When a consumer searches for a product (or service) using a platform’s interface, the platform can invoke this knowledge when listing search results. In creating this list, it has become customary for platforms to sell prominently featured slots, so-called sponsored slots or positions, to firms hoping to steer consumers towards buying their products. Google founders Sergey Brin and Larry Page were on record, as early as 1998, warning of the implications of search advertising: “We expect that advertising funded search engines will be inherently biased towards the advertisers and away from the need of the consumers.” In fact, some of today’s consumers are reluctant to click on sponsored search results to circumvent steering by firms, as they believe non-sponsored (organic) search results promise to be a better match for their preferences.
In “Search Platforms: Big Data and Sponsored Positions,” co-authored by Maarten Janssen, Thomas Jungbauer, Marcel Preuss and Cole Williams, we argue that the issue at hand is more complicated. In fact, we introduce a game-theoretic model analyzing the interaction between a search platform, consumers and firms to show that, when the number of firms is large, it is in the best interest of the consumer to click on sponsored positions, while the introduction of sponsored positions in the first place may either benefit or harm consumers, depending on the platform’s competing objectives. For example, if the platform (also) maximizes sales commission revenue—as is common practice—sponsored slots are beneficial for the consumer. This is because platforms face an incentive to allocate sponsored positions to firms that constitute good matches for consumers in order to increase the revenue from auctions for sponsored positions. On the other hand, obfuscation of organic slots plays a big role in optimal search platform behavior. First, it reduces the consumer’s option value from searching beyond sponsored positions, and, second, it increases the number of positions a consumer inspects if they continue to search among organic slots, thereby increasing the probability of a sale being made.
Our results persist independently of the allocation of consumer-specific knowledge between the search platforms and firms and the variation in prices among firms. Among the major complications to overcome when characterizing optimal platform behavior is that consumers, when inspecting a firm in a given slot, learn about firms in slots they have not yet inspected when they understand the search platform’s algorithm. As a result, we can show that in environments with few firms only, the optimal algorithm of the search platform always depends on the intricacies of the specific problem, and no general rule for the ranking of search results can exist. However, when the number of firms is large—as is customary when dealing with search platforms—the platform behavior as described above is optimal almost surely. In order to prove our results with many firms, we adapt a result from the literature on social learning, the so-called mixing property of stochastic processes, for consumer search problems.
Actions that affect the value of a service are often self-reported rather than publicly observable. The diligence of a contractor, the education level of job applicant, or the true mileage of a used car are typically reported by the seller. This opens the door for lying and misrepresentation.
In “Self-Reported Actions, Signaling, and Auditing,” my co-author Mike Waldman and I present a model in which multiple receivers bid for the service of a sender, the value of which depends on a action taken by the sender. Instead of the action itself, receivers only observe a message reported by the sender indicating which action was taken. Receivers may opt for costly auditing to verify that the message matches the action.
We find that lying may increase social welfare when the action serves as a signal of a desirable trait of the sender. A positive likelihood of misrepresentation lowers the value of the action as a signal, and therefore counteracts the well-known over-investment result in the signaling literature. Therefore, factors that promote misrepresentation, such as a lower disutility of lying or a higher auditing fee, may increase social welfare.
This result stands in stark contrast to cases in which the action does not signal the sender’s type. We also find that the level of auditing is inverse U-shaped in the probability of the sender being dishonest, and that receivers may audit more often if the action does not serve as a signal, despite gaining less information when auditing. We apply our insights to education signaling, college applications, and odometer fraud in the used car market.
Find the full text paper HERE . I will present it at this year’s virtual editions of the EEA and the ESWC.
Kevin Quealy (@KevinQ) wrote a nice piece in last week’s NY Times analyzing the race for the Republican nomination from a game theory perspective. While I was slightly irritated by Rubio’s classification as a mainstream candidate, it’s an interesting application of basic game theory principles worth reading.
A STABLE MATCH 