Behavioral economics has become an additional tool at the disposal of antitrust agencies and defense counsel. While the findings of behavioral economists are often considered justification for additional government regulation of the free market, a growing behavioral literature suggests caution against excessive intervention. It is sometimes overlooked that behavioral biases that affect consumers and firms, can and often do affect policymakers. Furthermore, because of the nature of the political process, policies may rather institutionalize rather than overcome behavioral biases. As such, regulatory solutions to overcome behavioral biases may be inferior to market dynamics which may succeed in eliminating behavioral biases over time. As the debate over the alleged failure of antitrust policy in the past forty years and the need for more aggressive antitrust enforcement intensifies, it becomes vital to understand if and how best to reform antitrust in light not only of the behavioral biases of consumers and firms, but of policymakers as well.
By Andrea Asoni[1]
I. COGNITIVE BIASES EVERYWHERE
Economists have long recognized that among the justifications for government intervention are so-called “market failures,” conditions that prevent the market economy to lead to efficient outcomes.[2] Broadly speaking market failures include market power, which limits the incentives of firms to compete for consumers and perhaps innovate, asymmetries of information, which may prevent consumers to drive competition between firms, and externalities, which lead markets to produce too much or too little of certain goods.
Since Tversky’s and Kahneman’s seminal work on risk, economists have identified several “behavioral biases,” i.e. deviations from standard assumptions on firms’ and individuals’ rationality embedded in neoclassical economics, which either reinforce some of the market failures identified above[3] or provide justification – according to some – for additional government intervention. Behavioral economics is not only a successful field within the economic profession[4] but has influenced economic policy and firms’ behavior: in 2010 the “Behavioral Insights Unit” was instituted within the UK cabinet to design more effective economic policies using behavioral economics insights.[5] A similar unit was established within the US government in 2015.[6] Many more governments, large organizations, and universities have created similar units since. The OECD counted 202 organizations with behavioral units in 2020.[7]
Antitrust enforcement is driven by a careful analysis of the facts on the ground by the agencies, as well as by private practitioners and the courts. As such, behavioral economics can be another tool at the disposal of enforcers and courts, when the facts are better explained by behavioral biases rather than the standard economic framework. However, it is sometimes overlooked that behavioral biases affect not only consumers and firms, but also enforcers, regulators, and legislators. While it has been well understood for many decades that policymakers do not necessarily pursue the “public good” but respond to private and public incentives, only recently social scientists have focused on the implications of behavioral economics on the actions and choices of regulatory bodies.
A growing body of research has shown that government agencies do indeed share many of the same behavioral biases as consumers and firms. Furthermore, this literature suggests that the political process may lead regulators to institutionalize behavioral biases rather than overcome them. As the debate over the need for more aggressive antitrust enforcement and/or regulation intensifies, it becomes crucial to understand if and how best to reform antitrust in light not only of the behavioral biases of consumers and firms, but of policymakers as well. After briefly summarizing the implications of consumer and firm behavioral biases on antitrust policy, this article will present a few insights from this growing literature on behavioral policy making and discuss its implications for the antitrust debate.
II. WHAT DOES THE IRRATIONALITY OF FIRMS AND INDIVIDUALS PRESCRIBE FOR ANTITRUST?
Behavioral economics initially focused on individuals’ tolerance for risk, uncertainty, and how people assessed gains and losses. While neoclassical economics treats individuals as rational actors, maximizing their expected utility, fully assessing the information available to them, behavioral economists suggest that individuals have limited, bounded rationality and exhibit several cognitive biases. For example, according to the standard expected utility theory people should weigh gains and losses the same way and treat equivalently lotteries with the same expected value. It turns out that this is not the case: individuals dislike losses more than they like gains, are prone to overestimate small risks, and dislike uncertainty over potential risks (the latter referring to imprecision in estimating the likelihood of an event).
In the field of Industrial Organization, the field of economics closest to antitrust and competition, economists have analyzed several forms of irrationality, both related to consumers’ behavior as well as firms conduct.[8] Most of the recent work focuses on behavioral issues on the consumer side of the market and how firms exploit consumers’ weaknesses. For example, consumers may look at relative rather than absolute search costs when it comes to purchasing goods: people are typically willing to travel one hour across town to save $10 on a $20 t-shirt but are not willing to make the same trip to save $10 on a $1,000 laptop computer.[9] Standard economics would predict that consumers would make the same decisions in both cases, but behavioral economists have shown this not to be the case. This means that search costs may be more important for large-ticket items than small-ticket items.
In general, many of these biases will arise because of what Kahneman called “fast thinking,” i.e. the tendency of our brains to adopt heuristics, cognitive shortcuts, and simple decision rules that lead to fast, and typically good-enough, decisions without expending considerable cognitive resources. Fast thinking is opposed by “slow thinking” which instead is more methodical, rational, effortful.[10] Another behavioral bias is that consumers tend not to look at pricing terms that are not provided upfront: this is the kind of behavioral bias that firms will try to exploit, for example, by “hiding” prices behind add-ons, employing differently structured tariffs and strategies as “drip pricing.”
The tech sector has come under scrutiny as a particularly fertile ground for use (and abuse) of consumers’ behavioral biases. Tech firms typically can collect detailed data on consumer behavior and use sophisticated algorithms to manipulate it. The Federal Trade Commission has recently released a report on “dark patterns” in web commerce which refer to “deceptive design elements” and “practices that raise consumer protection concerns.”[11] The FTC discusses several dark patterns designed to either hide prices (for example, burying additional fees, mandatory charges, etc.), induce consumers to pay for products they do not want to purchase, or steer consumers towards sharing their personal information. Additional research has shown that dark patterns mostly impact the poor and uneducated, and concluded that dark patterns are particularly harmful when combined with market power.[12]
Firms are often considered less prone to behavioral biases for three reasons: (1) they rely on expert consultants for their strategic, financial, marketing, and pricing decisions; (2) firms focus on a limited number of markets, accumulating knowledge and experience, while consumers often deal with many markets; (3) competition will more promptly force “irrational” firms out of the market, than irrational consumers. Nevertheless, economists have identified several instances of behavioral biases for firms. For example, boundedly rational owners/managers or overconfident managers may affect firm behavior.[13]
Given the broad array of potentially irrational behaviors from either consumers or firms, or both, it is challenging to identify how behavioral economics affects antitrust policy and enforcement as a whole. However, it seems fair to say that behavioral economics has become complementary, rather than alternative, to traditional economics when it comes to antitrust enforcement. It should come as no surprise since antitrust is highly fact-specific; as such, economists and attorneys strive to find the best economic and legal models that fit the facts of the case. As discussed more in detail in Bailey (2015), there are several antitrust cases that relied on behavioral models, as opposed to standard economics.[14]
III. PUBLIC CHOICE THEORY
Let us now turn from consumers and firms to policymakers. Even before the rise of behavioral economics, social scientists understood that, rather than disinterested servants of the public interest, policymakers are rational actors that respond to incentives. In its seminal work, “The Theory of Economic Regulation,” George Stigler framed the issue in terms of supply and demand of regulation and posited that, in democracies, organized minorities can often benefit at the expense of the general public. Regulations are not the outcome of public minded, or even neutral, individuals but rather the result of political preferences, private and public interests, and their ability to organize and exert pressure on elected officials and unelected bureaucrats. Recent work from the University of Chicago Stigler Center, following in the footsteps of its namesake economist, suggests that potentially lax enforcement of antitrust laws in the past forty years is not the result of a change in voters’ preferences but rather the result of the influence of special interests, such as “big business,” that benefited from less aggressive antitrust enforcement and managed to push this agenda among unelected bureaucrats such as judges and regulators.[15]
IV. BEHAVIORAL PUBLIC CHOICE THEORY
The main contribution of public choice theory is to show that regulations are the product of a political process and may not reflect (only) the interests of the public. Public choice theory, however, never posited irrational, or otherwise cognitively biased, policymakers but rather assumed rational actors operating in their own self-interest, as is typically done in neo-classical economics. Building on the insights offered by both public choice theory and behavioral economics, a recent and growing strand of literature has been studying if and how policymakers are affected by the same behavioral biases as the firms and consumers that they purport to regulate. There are several key insights from this literature: first, policymakers are subject to the same cognitive biases as consumers and firms. Second, because of the nature of the political process and accountability of policymakers to the public, government policies may institutionalize such behavioral biases rather than overcome them. For two surveys of this relatively new field see Schnellenbach & Schubert (2014)[16] and Lucas & Tasic (2015).[17] Below I will discuss a few insights from this body of work.
- A. Risk Perceptions and Public Policy
Viscusi & Gayer (2015)[18] focus on risk perceptions and document several areas in which government agencies exhibit the same behavioral biases as individuals. One well documented finding in behavioral economics is that individuals overestimate small risks and the benefits derived from eliminating such risks, while underestimating the benefits from eliminating large risks. Furthermore, people tend to underestimate the benefits from reducing any risk, unless the risk has been completely eliminated. Viscusi & Gayer suggest that government agencies suffer from the same bias: for example, the Environmental Protection Agency systematically overestimates small risks associated with exposure to certain dangerous chemicals because it compounds conservative estimates which often lead to estimated risks that are significantly higher than the actual risks. Viscusi & Gayer also suggest that the significant changes to airport security introduced after the terrorist attacks of 9/11 may have been spurred by the same bias. While these measures have had some benefits, they also generated costs to civil liberties and privacy. Both costs and benefits, however, are hard to assess in part because these are low probability events, and it is nearly impossible to estimate what reduction in probability of another 9/11 attack actually occurred.
Another well documented behavioral bias is “ambiguity aversions,” which is people’s aversion to hard-to-estimate probabilities. For example, consider a car that will fail 2 out 100 times and a car that will fail with 50 percent chance 1 out of 100 times and with 50 percent chance will fail 3 out of 100 times. Even though the expected probability that the cars will fail is the same, people will tend to choose the first car which offers a “certain” probability of failure. According to Viscusi & Gayer, government policies often reflect the same ambiguity aversion towards novel risks. For example, research discussed by Viscusi & Gayer has found that court rulings often are biased against innovation. Furthermore, judges studied in a lab setting tend to favor the existing drugs with a known, higher risk when offered a choice between a new drug with uncertain risk and an existing drug . Another instance of ambiguity aversion is the regulatory approach to new drugs or new products with uncertain risks such as GMOs which places more weight on worst-case outcomes and assigns the burden of proof on the manufacturers.
Finally, people have been found to prefer avoiding losses to incurring gains. Viscusi & Gayer document this bias in the FDA regulatory approval process for new drugs: the FDA would rather approve a new drug which leads to modest health benefits but no harm than another drug which may lead to some harm but also to significantly more benefits (on net). This fallacy is compounded by how errors of commission (approving a drug which leads to harm) are weighted significantly more than errors of omissions (not approving a drug that could have led to significant benefits) as the losses are typically more visible in the case of errors of commission: patients who die after taking a dangerous drug are identifiable. In contrast, patients whose lives are lost because they failed to get the benefits of a promising new drug often cannot be identified.
B. Political Oversight and Regulation
In the spirit of public choice, Cooper & Kovacic (2012)[19] posit a simple model of a regulator that serves as agent to a political overseer. The regulator balances two potentially competing goals: what she perceives as the optimal long run policy and the rewards she gets from her political overseer. This framework is then used to evaluate the effects of bounded rationality on policymaking and specifically on competition policy, given the experience of both authors at the antitrust agencies.
The article considers several behavioral biases. Overall, they find that flawed heuristics such as optimism (the tendency to underestimate one’s own probability of experiencing a bad outcome), availability (the tendency to highlight recent, particularly salient events), representativeness (the tendency to ignore the base line rate of an event), and hindsight (the tendency to overestimate the ex-ante probability of an event occurring, after it has occurred) are more likely to make the regulators adopt policies that are closer to the preferences of the political overseers, rather than optimal long-term policies. Even an unbiased regulator has an incentive to choose populist policies due to the political rewards that come from immediate action, especially with limited time horizons.
The effect of confirmation bias, i.e. the tendency to interpret ambiguous or even contradictory information as supporting one’s initial position, is more uncertain and depends on the existing status quo as well as on the order in which new information is received. The authors speculate that it may create a weak tendency to adopt politically expedient policies since the first evidence a regulator may view on a matter is a call to action by its political overseer. In the field of antitrust, for example, confirmation bias could lead enforcers challenging a merger to interpret documents from the merging parties that cast the merger in a competitive light as either neutral or supporting their view of the case.
As discussed supra, consumers and firms may correct in the long run their cognitive biases due to the pressures of the competitive markets. However, since such pressure does not exist for policymakers, Cooper & Kovacic argue that even a regulator with a preference for maximizing long-term social welfare will over time tend to focus excessively on short-term rewards, especially if suffering from certain behavioral biases. The authors then indicate several correcting mechanisms including a greater use of internal and external adversarial reviews and greater accountability through ex-post evaluations of previous interventions (or lack thereof).
V. WHAT ARE THE LESSONS OF BEHAVIORAL PUBLIC CHOICE FOR ANTITRUST?
What are the relevant lessons for reforming antitrust enforcement and potentially regulating the tech industry? The literature discussed above emphasizes the existence of certain cognitive bias among all people, including policymakers. Some of these biases seems particularly relevant for antitrust matters and, perhaps, especially for antitrust matters related to the tech industry.
For example, Cooper & Kovacic suggest that confirmation bias may lead the regulator to dismiss available evidence that counters a pre-existing view and suggest that internal adversarial reviews may be able to counter this problem. The authors suggest that the regulators could set up an internal “B” team whose role is to act as defense counsel; they also flag the FTC traditional approach of having the Bureau of Economics provide a separate recommendation from the legal counsel as one mechanism to implement this adversarial review. The authors do not discuss this, but perhaps such mechanism is more important when the economists, or staff as a whole, have a view that diverges significantly from the pre-existing view of the Commissioners. A policy implication of this view is that such mechanisms at the agencies should be created/reinforced and that “dissenting staff” should be given a fair hearing by management. Another implication not discussed by the authors is that perhaps the long run harm caused by confirmation bias is more severe when the pre-existing view of the regulator (or its overseer) is formed while evidence is scant and still developing.[20]
A large share of antitrust enforcement is concerned with predicting the effects of current decisions on the future state of competition. As such, it is an exercise in risk assessment, weighing future losses and gains, often considering events with small probabilities. For example, acquisitions of potential competitors may involve all these factors. The literature discussed above suggests that enforcers may weigh potential losses, for example the loss of the “next-big-thing,” more than potential gains, for example due to the combination of the innovation efforts of the target and the buyers. Similarly, enforcers may overestimate the likelihood of small probability events, such as the probability that a nascent competitor may become a powerful rival to the buyer in the future. Finally, ambiguity aversion may lead the enforcers to prefer a “certain” outcome today, such as maintaining the current status quo, to the uncertainty created by the acquisition.
Finally, the literature discussed above suggests that regulators may have a bias against innovation. Since innovation characterizes the tech industry more than other segments of the economy, one can read the behavioral public choice literature as cautioning against a new regulatory body which may institutionalizes a bias against innovation.[21]
VI. CONCLUSION
Behavioral economics has found its place among the tools used by antitrust agencies and practitioners to evaluate the state of competition in the United States. While behavioral biases are often considered a reason for government intervention, antitrust scholars have suggested that the picture is more nuanced. Behavioral biases may often be resolved by market dynamics and the pressure imposed on consumers and firms to behave more rationally, i.e. not leave money on the table. However, this is not always the case; for example, when firms have an incentive to exploit consumers’ behavioral biases. A recent strand of research, behavioral public choice theory, adds further nuance to the debate suggesting that policymakers may exhibit the same behavioral biases as consumers and firms. And perhaps more importantly, that the dynamics of the political process may institutionalize such behavioral biases rather than overcome them.
Where does this leave us when it comes to the current debate over reforming competition policy and antitrust enforcement in the United States? It is probably a safe bet that both sides of the debate, those who want stricter enforcement and more regulations and those who see the other side as overreaching, see severe behavioral biases that may justify their position. And they may both be right! If so, the debate would benefit from a clear effort from each side to identify its behavioral biases and a careful read of the accumulating evidence to identify areas were progress can be found by both sides. If nothing else, behavioral economics suggests putting fast thinking aside and embrace slow thinking, careful research, and dispassionate reading of the available evidence. The debate will certainly benefit from additional insights and evidence from the behavioral public choice literature, especially on what mechanisms ought to be deployed to ensure that no biases affect the creation and enforcement of antitrust policy.
[1] Dr. Asoni is a principal in the Antitrust & Competition Economics Practice at Charles River Associates (“CRA”) in Washington, DC. The views expressed herein are the views and opinions of the author and do not reflect or represent the views of Charles River Associates or any of the organizations with which the author is affiliated. I would like to thank Elizabeth Bailey for helpful comments. All remaining errors are mine.
[2] The existence of market failures is not the only justification for government intervention. Another example is the pursuit of certain redistributive goals. A discussion over the role of the government in the economy is outside the scope of this article.
[3] For example, consumer “stickiness” may create or reinforce market power.
[4] Daniel Kahneman was awarded the Nobel prize in economics “for having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty” in 2002, and Richard Thaler was awarded the Nobel prize in economics “for his contributions to behavioral economics” in 2017.
[5] The “Nudge Unit,” as it is also known, has since been incorporated into a limited company fully owned by British charity Nesta.
[6] The Social and Behavioral Science Team was established in September 2015 (see, https://obamawhitehouse.archives.gov/blog/2015/09/15/designing-federal-programs-american-people-mind). This team stopped being operative in 2017 and its work is currently done under the General Services Administration’s Office of Evaluation Sciences (see, https://www.psychologicalscience.org/policy/the-us-office-of-evaluation-sciences-releases-2016-2017-results.html).
[7] https://www.oecd.org/gov/regulatory-policy/behavioural-insights.htm.
[8] A complete review of this literature is well beyond the scope of this article but interested readers can refer to the Handbook of Behavioral Industrial Organization for a complete overview. See the Handbook of Behavioral Industrial Organization, edited by Victor J. Tremblay, Elizabeth Schroeder, Carol Horton Tremblay, published by Edward Elgar Publications (2018).
[9] Bennett, Matthew, John Fingleton, Amelia Fletcher, Liz Hurley & David Ruck. “What Does Behavioral Economics Mean for Competition Policy?” Competition Policy International 6, 1: 110-137, discuss this example as well as several others.
[10] Kahneman, Daniel. “Thinking, Fast and Slow.” Published by Farrar, Straus and Giroux (2013)
[11] Lesley Fair, September 19, 2022. “FTC issues illuminating report on digital dark patterns.” Available at: https://www.ftc.gov/business-guidance/blog/2022/09/ftc-issues-illuminating-report-digital-dark-patterns.
[12] Stigler Committee on Digital Platform – Final Report, section on Privacy and Data Protection (p. 206). Available at: https://www.chicagobooth.edu/research/stigler/news-and-media/committee-on-digital-platforms-final-report. Also see Luguri, Jamie & Jacob Strahilevitz. “Shining a Light on Dark Patterns.” Journal of Legal Analysis 13, 1 (2021): 43-109. Available at: https://academic.oup.com/jla/article/13/1/43/6180579.
[13] For example, see chapters 6, 7, 9, and 12 of the Handbook of Behavioral Industrial Organization, supra.
[14] Bailey, Elizabeth. “Behavioral Economics and U.S. Antitrust Policy.” Review of Industrial Organization 47 (2015): 355–366.
[15] Lancieri, Filippo, Eric A. Posner, and Luigi Zingales. “The Political Economy of the Decline in Antitrust Enforcement in the United States.” Antitrust Law Journal (forthcoming).
[16] Schnellenbach, Jan and Christian Schubert. “Behavioral political economy: A survey.” European Journal of Political Economy 40, B (2015): 395-417.
[17] Lucas, Gary & Slavisa Tasic. “Behaviora Public Choice and the Law.” West Virginia Law Review 118, 1 (2015): 199-266.
[18] Viscusi, Kip & Ted Gayer. “Behavioral Public Choice: The Behavioral Paradox of Government Policy.” Harvard Journal of Law & Public Policy 38 (2015): 973-1007.
[19] Cooper, James & William Kovacic. “Behavioral Economics and Its Meaning for Antitrust Agency Decision Making.” Journal of Law, Economics & Policy 8, 4 (2012): 779-800.
[20] For example, those advocating for more aggressive antitrust enforcement point to evidence of increasing concentration in the economy, increasing markups, a tendency by tech companies to acquire smaller startups to monopolize various markets, etc. A fair reading of the ongoing research, however, suggests that the evidence may not point unequivocally towards an increase in market power. While this is a vast debate that goes beyond this footnote, interested readers can review a recent article by Dennis Carlton which discusses some of the limitations of the research suggesting an increase in market power (Dennis W. Carlton. “How to make sensible merger policies?” Network Law Review (2022) available at: https://www.networklawreview.org/carlton-mergers/). Another recent paper suggests that the measured increase in markups may be due to changing technology rather than market power (Foster, Lucia, John Haltiwanger & Cody Tuttle. “Rising Markups or Changing Technology?” CES Working Paper 22-38.) Finally, we have recently documented that some of the assumptions informing a call to more aggressive enforcement may not be supported in the data (Asoni, Andrea & Grace Luo. “Mergers and Acquisitions in the Tech Industry: Are They Different?” George Mason Law Review (forthcoming))
[21] Another unrelated strand of literature that identifies a potential bias against innovation is the “error cost” literature, which suggests that the cost of “false positives,” i.e. identifying a competition problem where there is none, are significantly higher in dynamic environments. (See, for example, Manne, Geoffrey & Joshua Wright. “Innovation and the Limits of Antitrust.” Journal of Competition Law and Economics 6, 1 (2009): 153-202.) Others have criticized the error cost approach in antitrust. (See, for example, Hovenkamp, Herbert. “Antitrust Error Costs.” University of Pennsylvania Journal of Business Law 24, 2 (2022): 293-349.)
