New week new approach! As I’m easing back into publishing Aubs-ervations, I decided to take a break from the headlines and focus on research and analysis. I have included articles covering some of the many events that occurred in my absence from your inboxes.
Continuing my theme of departure from the norm, I would also like to highlight Jeff Kosseff’s new book “Liar in a Crowded Theater.” Cato will be hosting a book forum on Nov. 7 which should give all of us plenty of time to grab a copy and get reading!
Public Comments
NTIA Request for Comment
Initiative To Protect Youth Mental Health, Safety & Privacy Online
Comments due Nov. 16
Articles and Briefs
The FTC's New Normal | Truth on the Market
Gerrymandering Redux: The Relevant Market under the Draft Merger Guidelines | Mercatus Center, Gregory J. Werden
The FTC Claims Amazon is a Monopolist But Misunderstands Online Retail | Reason Foundation, Max Gulker
The Department of Justice’s Antitrust Case Against Google Search | American Action Forum, Jeffrey Westling
Antitrust Litigation Against Amazon’s Online Marketplace | American Action Forum Jeffrey Westling
New Net Neutrality Rules Could Threaten Popular Services | Reason, Will Rinehart
Net Neutrality Is a Solution in Search of a Hypothetical Problem | International Center for Law & Economics, Eric Fruits and Ben Sperry
Jawboned | Knight First Amendment Institute, Katie Harbarth and Matt Perault
Shining a Light on Censorship: How Transparency Can Curtail Government Social Media Censorship and More | Cato Institute, Andrew Grossman and Kristin Shapiro
What Does NetChoice v. Bonta Mean for KOSA and Other Attempts to Protect Children Online? | International Center for Law & Economics, Ben Sperry
Remaining Considerations: Guardian Relationships, Benefits of Algorithms and Age-Appropriate Design | R Street Institute, Shoshana Weissmann
A Link Tax Won’t Save the Newspaper Industry | Cato Institute, Paul Matzko
Key Principles For State Data Privacy Laws | Buckeye Institute, Logan Kolas
Research
The Turing Transformation: Artificial Intelligence, Intelligence Augmentation, and Skill Premiums
We ask whether a technical objective of using human performance of tasks as a benchmark for AI performance will result in the negative outcomes highlighted in prior work in terms of jobs and inequality. Instead, we argue that task automation, especially when driven by AI advances, can enhance job prospects and potentially widen the scope for employment of many workers. The neglected mechanism we highlight is the potential for changes in the skill premium where AI automation of tasks exogenously improves the value of the skills of many workers, expands the pool of available workers to perform other tasks, and, in the process, increases labor income and potentially reduces inequality. We label this possibility the “Turing Transformation.” As such, we argue that AI researchers and policymakers should not focus on the technical aspects of AI applications and whether they are directed at automating human-performed tasks or not and, instead, focus on the outcomes of AI research. In so doing, our goal is not to diminish human-centric AI research as a laudable goal. Instead, we want to note that AI research that uses a human-task template with a goal to automate that task can often augment human performance of other tasks and whole jobs. The distributional effects of technology depend more on which workers have tasks that get automated than on the fact of automation per se.
When Product Markets Become Collective Traps: The Case of Social Media
Individuals might experience negative utility from not consuming a popular product. For example, being inactive on social media can lead to social exclusion or not owning luxury brands can be associated with having a low social status. We show that, in the presence of such spillovers to non-users, standard measures that take aggregate consumption as given fail to appropriately capture welfare. We propose a new methodology to measure welfare that accounts for these consumption spillovers, which we apply to estimate the consumer surplus of two popular social media platforms, TikTok and Instagram. In large-scale, incentivized experiments with college students, we show that, while the standard welfare measure suggests a large and positive surplus, our measure accounting for consumption spillovers indicates a negative surplus, with a large share of active users deriving negative utility. We also shed light on the drivers of consumption spillovers to non-users in the case of social media and show that, in this setting, the “fear of missing out” plays an important role. Our framework and estimates highlight the possibility of product market traps, where large shares of consumers are trapped in an inefficient equilibrium and would prefer the product not to exist.
A Framework for Detection, Measurement, and Welfare Analysis of Platform Bias
Regulators are responding to growing platform power with curbs on platforms' potentially biased exercise of power, creating urgent needs for both a workable definition of platform bias and ways to detect and measure it. We develop a simple equilibrium framework in which consumers choose among ranked alternatives, while the platform chooses product display ranks based on product characteristics and prices. We define the platform's ranks to be biased if they deliver outcomes that lie below the frontier that maximizes a weighted sum of seller and consumer surplus. This framework leads to two bias testing approaches, which we compare using Monte Carlo simulations, as well as data from Amazon, Expedia, and Spotify. We then illustrate the use of our structural framework directly, producing estimates of both platform bias and its welfare cost. The EU's Digital Services Act's provision for researcher data access would allow easy implementation of our approach in contexts important to policy makers.
Always appreciate the shoutout! Also glad to see Cato has a longer piece on the link tax craziness.