Randal Quarles, the first Trump appointment to the Federal Reserve’s Board of Governors, finished his four-year term as the Vice Chair for Supervision on October 13, 2021. To replace him, President Biden has nominated no one. The Fed replaced him with no one. For now, the Fed’s vital supervisory and regulatory priorities will be managed by the Fed’s Board of Governors, through their committee structure.

There is much to lament with this state of affairs. Quarles was the first to hold the position: it was created in 2010 in the Dodd-Frank Act to encourage the Fed to focus more completely on the vital work of bank regulation and supervision, areas that many feared had become neglected during the Greenspan years. Even though the position was created under a signature law of the Obama administration, that administration did not prioritize the formal appointment, relying instead on Fed Governor Tarullo to manage the portfolio, just as former Fed Governors had done. Today, for reasons known only to the administration itself, if known at all, the Biden administration has been plagued by delays in filling Fed and other financial regulatory vacancies. Even though the Vice Chair’s term is fixed by statute at four years, we still have no insight into the people the administration is even considering to succeed Quarles, as the administration has not even announced an intent to nominate anyone to any position at the Fed.

Quarles, a Republican, pursued a bank regulatory and supervisory agenda with expertise and a clear vision. He is no favorite of some Democrats, who do not endorse his vision, have little use for his expertise, and have been eager to see him depart the scene. Whether the Democrats would prefer it otherwise or not, Quarles is not going anywhere for now. He remains a Fed Governor, with the same important responsibilities over regulatory, supervisory, and monetary policy as his colleagues on the Board. That term is fixed for fourteen years and will not expire until 2032.

Here is the good news. Despite the mishandling of these vacancies from the Biden administration, the Fed’s decision not to reassign these priorities to another Governor is exactly the right thing to do. Its other alternatives are not attractive. It could have given now-Governor Quarles the responsibilities despite the expired term, but his ability to operate without the benefit of his statutory status would be significantly curtailed. The other option is hardly better: the Fed could have given these responsibilities to a candidate more in line with Democratic priorities—Fed Governor Lael Brainard, an expert on virtually every regulatory and supervisory question before the Fed, would fit this bill nicely. But Governor Brainard herself is a candidate to succeed Fed Chair Jay Powell, whose term as Chairman expires in January, and any move to reassign her portfolio could look like meddling in the Fed Chair sweepstakes that is still ongoing.

And so, the Vice Chair for Supervision—that unique creature of governance created by Congress just a decade ago—remains vacant, creating the possibility that financial regulation and supervision will not take their place at the forefront of the Fed’s policymaking. What’s more, the replacement of the Vice Chair position with a committee will devolve more authority to the Fed’s staff to handle this highly political and politicized portfolio.

So why is this good news? Because public oversight of the Federal Reserve System is primarily a product of public governance. We need, as a public, to have rigorous debates about who we want our central bankers to be. One such debate is underway as the Biden administration continues to consider the president’s appointment of the Fed Chair. Those who support Jay Powell, the incumbent, praise his leadership during the 2020 pandemic crisis and his management of a major shift in monetary policy regime. His detractors argue that his regulatory priorities are insufficiently aligned with those of the president, especially around bank regulation, financial stability, and climate change. While the tone of this debate can veer toward hyperbole—an American political tradition as old as the Republic—this is what politics looks like. We should welcome it.

What we are not having, however, is that same level of debate around the priorities that the Fed should pursue as a regulator and supervisor. For this debate, we need to have time to consider viable candidates for this position. And we need the Fed not to do this work for us by pretending that the work of bank regulation and supervision has no political content in it.

The position obviously does have political content. The act of regulating and supervising the financial system is almost top to bottom a political exercise. We have elections to let that content and those exercises dictate the course that regulation and supervision should take. Just because the Biden administration has inexplicably dodged its responsibility for sponsoring that debate does not mean that the Fed should skip the debate entirely. By failing to appoint a successor to Quarles, the Fed has turned up the heat on the politicians to give us—the people and institutions affected most by the Fed’s regulatory and supervisory work—the chance to perform our role in vetting the nominees for this job.

Let’s hope the president accepts the Fed’s invitation as quickly as possible.

The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment.  A list of donors can be found in our annual reports published online here. The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.

      

On December 13, Anton Korinek, the David M. Rubenstein fellow at the Center on Regulation and Markets at Brookings, will sit down with Acemoglu to discuss the complexities of the impact of new AI technologies and how AI could be regulated to achieve its promising and wide-reaching potential.

This event will be part of the Brookings Center on Regulation and Markets’ series, “The economics and regulation of artificial intelligence and emerging technologies,” which focuses on analyzing how AI and other emerging technologies impact the economy, markets, and society, and how they can be regulated most effectively.

Viewers can submit questions for speakers by emailing events@brookings.edu or via Twitter using #FutureofAI.

      

Over the past decade there have been rapid increases in the performance of artificial intelligence (AI). For example, since 2015, AI has performed better than humans at image recognition, as measured in an annual image recognition contest called ImageNet. These achievements have led to a similarly rapid increase in corporate investment in AI and funding for venture-backed AI startups in a range of applications. According to the 2021 “AI Index Report,” of the categories in which it tracks investment, the use of AI in drugs and drug discovery received $13.8 billion in private investment in 2020, 4.5 times higher than 2019. There have been similarly dramatic advances in the capabilities of and interest in other emerging technologies, including applications of blockchain (such as cryptocurrency and non-fungible tokens) and recombinant DNA (perhaps best exemplified by the rapid development of COVID-19 vaccines).

Such rapid technological advances are likely to herald an unprecedented period of innovation and technological change which will fundamentally alter current industries and markets. What is different from previous periods of technological progress is the speed at which these developments are happening and the extent to which they will shape the operations of markets around the world. These technological breakthroughs garner excitement as well as trepidation. On the one hand, new technologies may lead to rapid economic growth and improvements in human well-being. On the other hand, they may affect workers, customers, and society in unanticipated or inequitable ways. Indeed, a common theme in popular press articles is that AI and robots will take jobs away from humans.

Policymakers around the world have started to grapple with these competing claims, seeking to implement policies that harness the benefits of AI and other emerging technologies while mitigating any downsides. The 2021 “AI Index Report” reveals that mentions of AI in the U.S. Congressional Record for the 116^th Congress (2019 – 2021) are triple that of the 115^th Congress (2017 – 2019). However, these technological breakthroughs pose a particular challenge for policymakers and regulators: they will have to be much more flexible and adaptable in their approaches to governing these new markets. In some respect, the technology is so far ahead of the regulations that new approaches to policymaking and regulatory decision making are needed.

Drawing on methods and insights from economics, political economy, public policy, business strategy, political science, and law, the Center on Regulation and Markets’ new series on “The Economics and Regulation of Artificial Intelligence and Emerging Technologies” will explore these issues. The series will feature contributions from experts of diverse viewpoints to elucidate underexplored ways in which AI and other emerging technologies affect the economy and society, and whether—and how—to regulate them most effectively. It is our hope that the new series provides a valuable resource for policymakers, regulators, business executives, academics, and the public.

The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment. A list of donors can be found in our annual reports published online here. The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.

      

In sectors as diverse as health care, criminal justice, and finance, algorithms are increasingly used to help make complex decisions that are otherwise troubled by human biases. Imagine criminal justice decisions made without race as a factor or hiring decisions made without gender preference. The upside of AI is clear: human decisionmakers are far from perfect, and algorithms hold great promise for improving the quality of decisions. But disturbing examples of algorithmic bias have come to light. Our own work has shown, for example, that a widely-used algorithm recommended less health care to Black patients despite greater health needs. In this case, a deeply biased algorithm reached massive scale without anyone catching it—not the makers of the algorithm, not the purchasers, not those affected, and not regulators.¹

Since both human and algorithmic decisionmakers introduce the possibility of bias, removing algorithms entirely isn’t always the best approach. In fact, in some cases, biased algorithms may be easier to fix than biased humans. However, it falls on policymakers to ensure that the algorithms helping make complex decisions are doing so in a just and equitable way. To do so, we believe three key steps are required. First, regulators must define bias practically, with respect to its real-world consequences. Second, once the goalposts are clear, regulators must use them to provide much-needed guidance for industry and to define targets for objective, hard-hitting investigations into biased algorithms. Third, as in other fields, regulators should insist on specific internal accountability structures, documentation protocols, and other preventative measures that can stop bias before it happens.

Rather than prescriptive rules, which would quickly become obsolete in a rapidly evolving field, we believe these practices will both set a foundation for mitigating bias and provide clear protocols for investigating bad actors.

Defining the goalposts

We believe the key reason AI is still mostly unregulated is that regulators don’t currently have a vocabulary to articulate the benefits and harms of algorithms and hold them accountable. When we regulate a toaster oven, we know it should not catch on fire. When we regulate a pharmaceutical, we know the benefits should outweigh any side effects. But what do we measure when we regulate algorithms?

Algorithms provide decisionmakers with information—a forecast, a probability, or some other key unknown—in order to improve the quality of decisions. Building on this simple observation, we propose that regulators should ask two questions to hold AI accountable: first, what is the ideal information that an algorithm should be providing? And second, is it doing so accurately, both overall and for protected groups?

Consider the example we examined in Obermeyer et al (2019) of a biased health algorithm that caused untold harm. We used data from one health system to study the way a large family of algorithms works. Population health management algorithms like this one are used around the world by health systems and insurers alike to decide who gets access to so-called “extra help” programs. These ‘care management’ programs provide additional up-front care to patients with chronic conditions with the aim of reducing flare-ups and complications in the future. Patients avoid health problems, and the health care system saves the money it would have spent on ER visits and hospitalizations—a win-win.

What is the ideal information the algorithm should be providing in this instance? It was supposed to identify patients who were going to get sick tomorrow so hospitals could enroll them in the extra help program today. We call that goal of the algorithm its ideal target. But what was the algorithm actually doing? In fact, it was doing something subtly but importantly different: it was predicting not who was going to get sick but who was going to generate high costs for the health care system. This is the algorithm’s actual target. The wedge between these two is a key driver of bias.

The algorithm made its predictions based on cost because an assumption was made that health care costs are a fitting proxy for health care need. While that seems reasonable, not everyone who needs health care gets health care—which means some patients end up having lower costs than others, even though they need the same care. This wedge—between what the algorithm was supposed to be doing and what it was actually doing—led to Black patients being deprioritized for the program, resulting in unmeasured harm against many patients. The ideal target, the decision we care about, was need for care, but the algorithm’s actual target was cost of care. We wanted the algorithm to answer one question, but it was answering something else.

Algorithms are like genies. A man asks a genie for his wish, “I want to be rich!” The genie replies, “Ok, Rich, what’s your next wish?” Algorithms are concrete and literal to a fault. When we investigate the decisions an algorithm is informing, we can understand the gap between what we want it to do and what we actually told it to do. This gap between the ideal target and the actual target results in what we call label choice bias. In follow-up work since the example above, we’ve found bias in a wide range of other algorithms, nearly all driven by biased proxies, leading to biased outcomes. This bias, disturbing as it is, is just a symptom of a deeper problem: algorithms are often not doing what they are supposed to be doing. To catch problems like these, we need to understand algorithms in context. What do we want them to do? What are they actually doing? Is there a discrepancy, and if so, is it different for different groups?

To summarize, algorithms provide a key piece of information to a decisionmaker. So regulation must ensure that they are providing that information accurately, both overall and for specific groups. This approach, which focuses on the output of the algorithm—the accuracy of its predictions on an ideal target—has several key advantages. It does not require ‘opening the black box’: algorithms can be audited simply based on the scores they produce. This avoids compromising trade secrets. It also means regulators do not need to regulate the inputs of algorithms or understand the many reasons why they might be biased—non-representative training data, use of an explicit race correction, etc. Instead, regulators can focus on one simple question: is the algorithm predicting its ideal target accurately and equitably? This test will detect many forms of bias, like failure of an algorithm to generalize from one population to another. But unlike many other measures of bias proposed, it will also detect label choice bias, which we’ve found is a major driver of algorithmic bias in many settings.

Translating the goalposts into action

Defining the goalposts can be transformative. In our experience, most people who make algorithms do not want to scale up racial biases. They want to design products that work and to stay on the right side of regulators, but because this is a new and fast-moving field, they often don’t know how. By providing guidance to industry on what bias looks like and how to avoid it, regulators can have a transformative effect on the future of algorithms in many fields.

At the same time, regulators also need to be prepared to enforce the standards and make sure the industry is staying within those goalposts that have been set. There are a couple key tools regulators can use to investigate suspected cases of bias.

First, start with some simple statistical tests to see if the algorithm is performing well on the variable it is actually predicting. These tests are as simple as making a graph or running a regression in any standard statistical package: simply compare the variable being predicted to the algorithms score, by group. This should detect problems caused by non-diverse training data, failures to generalize, or poor performance in underserved groups (this is termed “calibration” in the literature). But keep in mind: good performance in one protected group does not mean the algorithm is free of bias.

Second, hold the algorithm accountable for predicting the ideal target for underserved populations. Once you define your ideal target variable, you can check whether the algorithm does a good job of predicting that variable across populations. Again, this is as simple as a graph or a regression comparing the ideal target to the algorithm score, by group. Notice that we don’t need to understand or explain the inner workings of the “black box” in order to find bias. We just need the outputs of that box (the algorithm’s predictions) and some data representing the outcome we care about (the ideal target) for our population.

Often, we find that algorithms are poorly calibrated when you consider the outcome that is truly important. In the health care example above, where an algorithm informs whether people are offered an extra help program, we found that if you take two patients, one Black and one white, who are equally sick (as defined by active number of chronic conditions), the algorithm is more likely to recommend extra care for the white patient than the Black one (see graph below). This is a case where something has certainly gone wrong.

Solving these problems is simply a matter of matching the actual target (what the algorithm is predicting) more closely to the ideal target (what we want the algorithm to predict). Again considering the health care example, instead of predicting cost, we should predict the variable we truly care about—health care needs. In our research, adjusting the target led to almost double the number of Black patients being selected for the program. Fixing algorithmic bias means solving the problems we are really trying to solve.

Accountability structures to prevent algorithmic bias

Policymakers also need to provide guidance on how to set up preventative structures and practices within one’s organization. In our Algorithmic Bias Playbook we recommend a few steps, and we think that most of these are broadly applicable to most organizations.

In any organization, it is crucial that a single person be responsible for algorithmic bias. This person, whom we’ve termed a steward, should have broad oversight over strategic decisions so that they are equipped to provide executive support to the team but also so they can be truly accountable for mitigating bias. While a single person should be ultimately responsible, they shouldn’t work alone. They must be advised by a diverse group of stakeholders for this effort to be truly successful. Individuals from different organizational capacities with different backgrounds, both internal and external voices, should all be included in an advisory group to the steward so that decisions about algorithmic bias are made collectively.

Aside from a clear accountability structure, organizations also need clear documentation. Algorithms live throughout an organization and provide input on decisions that affect thousands or even millions of people. We recommend that the steward and her team create a master inventory of algorithms that can provide a record of items such as the following: the source, the owner, the intended purpose/ideal target, the actual target, the population served, and any other information that the organization deems useful. Keeping records like these will help uncover where bias can arise and help prioritize efforts to ensure equitable outcomes.

Finally, be clear that ignorance is no protection—as our own ongoing work with state law enforcement agencies has taught us. It is no longer reasonable for an organization to be unaware of algorithmic bias as a potential problem. Negligence is in not doing the work to find, mitigate, and prevent it.

Conclusions

Algorithmic bias is everywhere. Our work has shown that label choice bias is a particularly dangerous form of bias: it is widespread because we often don’t have the specific variable we’re interested in. We’ve discussed an example from health care, where it’s difficult to measure the true outcome we care about—health—and instead organizations often fall back on convenient proxies like cost. Any industry where the variables we care about are not truly available will have this issue. In finance, we may want to measure “creditworthiness,” but instead we measure income, employment, demographics, etc. In crime, we may want to know “propensity to commit crime,” but instead we look at arrest record, education, employment, etc. These are all proxies for the true variable of interest, which means there is room in every one of these cases for label choice bias to exist. They must be investigated and prevented.

The good news is that we can reduce bias in algorithms by asking organizations to do a better job defining that “ideal target” —the thing we really care about. Organizations across the health care industry have come to us for support as they’ve worked to identify and mitigate bias in their own contexts. Some problems we’ve helped address include population health management, operational efficiency, strategic patient engagement, and others. In addition to workshopping specific algorithms, we help organizations apply our framework in practice and establish processes for proactively preventing bias.

Regulators are central to solving the problem of algorithmic bias. While some examples like the Federal Trade Commission guidelines and Food and drug administration regulatory framework are good starts, much more needs to be done. Regulators must identify targets for prompt investigations grounded in real world use cases and provide clear guidance and accountability structures for organizations to follow. Working together, we can move the needle towards more equitable outcomes across sectors.

Ziad Obermeyer has received speaking or consulting fees from AcademyHealth, Anthem, Independence Blue Cross, Premier Inc, and The Academy. The authors did not receive financial support from any firm or person for this article or, other than the aforementioned, from any firm or person with a financial or political interest in this article. They are currently not an officer, director, or board member of any organization with an interest in this article.

      

After years of inactivity, momentum is gathering for policy action on issues related to consumer financial data in the United States. In July, the president issued an executive order encouraging the Consumer Financial Protection Bureau (CFPB) to enable data portability in financial services. The CFPB issued an advance notice of proposed rulemaking last year and expects to commence a rulemaking process in spring 2022. Congress has shown interest in the subject as well, most recently by holding a Task Force on Financial Technology hearing on consumers’ right to access financial data.

Such momentum is long overdue. Data portability in financial services has the potential to help consumers in their choice of financial service provider and enable innovation by new entrants seeking to offer a better deal or a novel product or service. While data portability is necessary to realize a more competitive and innovative financial services sector, other consumer data rights and protections are also needed. Our research indicates that consumers are demanding greater control than the current legal and regulatory framework governing financial data provides. To be responsive to these important interests, both regulatory and legislative action is needed to ensure that consumers have appropriate data rights and protections.

Background

In the wake of the global financial crisis and the ensuing public outrage over the behavior of “too big to fail” banks, policymakers in the early 2010s found themselves looking for ways to promote competition in financial services. While many debated the merits of breaking up large banks or a new Glass-Steagall Act to separate retail and investment banking, others looked for ways to promote competition from the ground up. Around the world, policymakers began to contemplate data portability measures as a way to loosen banks’ hold on dissatisfied customers.¹

In the United States, this responsibility fell to the CFPB. Under Section 1033 of the Dodd Frank Wall Street Reform and Consumer Protection Act of 2010, the CFPB was empowered to prescribe rules to enable data portability in financial services.² However, with numerous other priorities on the CFPB’s to-do list, rulemaking on Section 1033 never took place. Instead, the CFPB issued non-binding principles for data sharing and closely monitored developments in the market.

Meanwhile, consumer demand for data portability accelerated, driven by the burgeoning fintech revolution. To meet this demand, “data aggregation” companies such as Plaid began to connect consumers’ favorite fintech apps to their bank accounts. Data aggregators often used online banking login credentials shared by consumers to gain entry to consumer accounts and “screen-scrape” data available to consumers via online banking portals. Though this practice is still in use, aggregators have more recently begun to enter into contracts with banks, credit unions, core technology providers, and others to lessen dependence on credential-sharing and screen-scraping in favor of the use of tokenized account access and application program interfaces (APIs).

The financial data sharing ecosystem largely built on this technological framework has given rise to a vibrant fintech market, including many innovative companies who use consumer financial data to design products and services that help consumers improve their financial health. Today, fintechs offer products that use consumers’ financial data to help them avoid costly overdraft fees when their balances dwindle, build emergency savings when their balances grow, and optimize their bill payments to ensure that bills are paid on time without creating a liquidity shortfall. Other fintechs use cashflow data for underwriting purposes, a practice that shows evidence of increasing access to credit among those without a credit history or a credit score and those whose credit scores understate their creditworthiness.³ Still other fintechs use financial data to enable their customers to send money to friends and family within and between countries. These services are widely used, and their popularity has only increased as more and more banking activity moved online during the COVID-19 crisis.

In early 2021, the Financial Health Network conducted a nationally representative survey to explore consumers’ interactions with, and attitudes towards, the financial data ecosystem.  According to our research, more than two thirds of banked consumers are fintech users, having linked financial apps to their checking account. In contrast with banks and credit unions,⁴ young people and people of color are particularly likely to use fintech apps, with apps used to send money to friends and family being the most common type of fintech app and the type of fintech app used most frequently.

The need for data portability

The lack of a comprehensive legal framework designed to govern the rights and duties of the various players in this ecosystem creates risks for individual consumers, financial institutions, and the functioning of the financial data ecosystem as a whole. Last year, the Financial Health Network partnered with FinRegLab, Flourish Ventures, and the Mitchell Sandler law firm to produce a comprehensive analysis of the legal and regulatory landscape governing consumer financial data. This analysis uncovered numerous open interpretive and policy questions related to Section 1033 as well as older statutes covering a set of interlocking issues including privacy and security under the Gramm-Leach-Bliley Act, accuracy and privacy under the Fair Credit Reporting Act, fairness under the Equal Credit Opportunity Act, and liability under the Electronic Funds Transfer Act.

Unless regulators take action, these open questions will continue to fester and have the potential to impede data portability. Already there are reports of some financial institutions restricting access to consumer data.⁵ Such restrictions can serve to entrench incumbent institutions and limit competition to the detriment of consumers. These restrictions also are out of step with consumer preferences. According to our research, 62 percent of consumers are in favor of data portability, believing that their bank or credit union should be required to share their personal data with another company (such as a fintech provider) if the consumer directs it to do so.

Importantly, this majority holds across demographic groups, including age, gender, education, race/ethnicity, and household income. Support for data portability in financial services is also bipartisan, with majorities of self-identified Democrats, Republicans, and Independents in favor of it.

Support for data portability holds regardless of the type of institution that serves as a consumer’s primary bank or credit union. This underscores the importance of ensuring that customers of small financial institutions with more limited technological resources have access to secure, affordable solutions to enable data portability.

These results confirm a broad consensus in favor of data portability that has been increasingly apparent for some time. Indeed, at the CFPB’s Symposium on Consumer Access to Financial Records in early 2020, few participants disputed that data portability is a right that should be available to consumers and that rulemaking on Section 1033 should guarantee.⁶ What they did not agree on was what other rights and protections should be guaranteed and how best to do so.

The data minimization principle

Among the issues dividing large banks, small banks, fintechs, data aggregators, and other market participants at the CFPB’s 2020 Symposium was the question of the scope. What kind of data fields should be able to be shared under Section 1033, and who should decide what kind of data are appropriate for what use case?

In the absence of regulatory guidance, the scope of data available to be shared at a consumer’s direction today varies greatly depending on where a consumer banks. Practically, this means that while some consumers currently enjoy a high degree of data portability, others have a much more limited ability to consistently share their data. As a result, consumers are unlikely to understand the scope of the data they share unless they carefully read complex legal disclosures.

The Financial Health Network asked fintech app users who had connected their fintech app to their checking account how much of their checking account data their fintech app is capable of accessing. 41 percent reported believing it could only access the data it needed, 25 percent reported believing it could access all of their checking account data, and the remaining third of respondents reported that they did not know.

When asked about how much of their checking account data fintech apps should be able to access, 87 percent reported believing that their fintech app should only be able to access the data it needs. Only 11 percent reported believing it should be able to access all the data in their checking account. In other words, consumers know what rules they want, but they are not sure if the current system is aligned with their preferences.

As with data portability, this preference for data minimization holds across demographic groups, including age, gender, education, race/ethnicity, household income, and political party affiliation. Unlike data portability, the preference for data minimization is overwhelming, with support usually in the high 80s to low 90s, with at least 75 percent of each demographic group in favor.

This indicates that while consumers desire the right to data portability, they have a strong preference for discretion as they share their data and do not wish to share any data beyond what is required for a given use case. Some data holding financial institutions (such as banks) have also emphasized this data minimization principle. However, those entities have their own competitive incentives to limit data flows and would not be impartial arbiters of what data are needed for a given use case.

With this market dynamic in mind, the CFPB should use its authority under Section 1033 to determine what data must be accessible, how often they must be made available, how long those data can be accessed for, and to whom they may be made available. If the CFPB does not feel it has the authority to impose data minimization limitations on data aggregators and recipients without impeding data portability, further legislative action may be needed to empower the Bureau to ensure that those entities are only accessing the data they need for a given use case, and are only storing that data for the minimum amount of time necessary. Congress will find strong support for this principle across the political and socio-economic spectrums.

Protecting consumers’ privacy

Consumers’ preference for discretion is not limited to the data they choose to share with fintech apps. Indeed, our research indicates that consumers are equally sensitive to financial or personal data about them being shared without their affirmative consent, no matter what institution is doing the sharing. Just as consumers do not want big tech companies sharing data about their browsing patterns without consent, consumers likewise do not want their bank or fintech app sharing financial data about them without their consent. Our survey shows consumers seem to view these forms of data sharing in much the same way, despite other research indicating that consumers have differing levels of trust for these institutions more broadly.⁷

Almost 90 percent of consumers (consistent among all demographic groups) expressed a preference for data sharing by their primary bank or credit union to be bound by an opt-in standard rather than an opt-out standard.

This strong preference for an opt-in standard stands in sharp contrast with current legal requirements which cannot be changed without legislative action. At present, consumers who do not want their data to be shared must opt-out, and even their ability to do that is limited. Banks are still permitted under the Gramm-Leach-Bliley Act to share consumer data with non-affiliated third parties if the information sharing is subject to one of the numerous exceptions under the law, regardless of whether a consumer might prefer them not to share.⁸ In other words, the current law places the burden of protecting privacy on consumers, who are expected to carefully parse complex legal disclosures provided by their financial institution and affirmatively opt-out of any optional data sharing.  According to our research, only about 1 in 4 consumers reports having done this. As low as that is, it may under-state how rare it is for consumers to opt-out of data sharing.  The plurality of banks interviewed in a 2020 study by the Government Accountability Office reported opt-out rates less than 5 percent.

In order to ensure that privacy protections are reflective of consumers’ preferences, we believe that legislative change is needed. The United States is past due for comprehensive data privacy legislation that not only addresses open issues in financial services but also ensures that consumers are afforded strong and consistent data rights and protections when they interact with tech platforms, healthcare providers, educational institutions, and others. However, if such a comprehensive effort remains beyond the reach of Congress, lawmakers should nevertheless build on the bipartisan consensus among consumers and past interest from both Republicans and Democrats in updating consumers’ data rights and protections in financial services. At the very least, data sharing by financial institutions should be bound by an opt-in standard.

Conclusion

As the financial data ecosystem evolves, regulatory and legislative action to ensure that consumers have strong data rights and protections is increasingly urgent. With momentum for action building and consumers having an unusual level of agreement on the need for data portability, data minimization, and data privacy, policymakers should proceed with the clear goal of ensuring that consumers are the primary beneficiary of the use of their financial data.

      

In this fireside chat with Circle co-founder, chairman, and CEO Jeremy Allaire, we will discuss the rise of stablecoins, the state of regulation of stablecoins, and the potential for greater inclusion through new financial technology (fintech). The dialogue will cut through much of the hype of cryptocurrency – stablecoins in particular – and dive into the two important and distinct issues surrounding stablecoins: financial stability and inclusion.

This event will be part of the Brookings Center on Regulation and Markets’ Series on Financial Markets and Regulation, which looks at financial institutions and markets broadly and explores how regulatory policy affects consumers, businesses, investors, fintech, financial stability, and economic growth.

Viewers can submit questions for speakers by emailing events@brookings.edu or via Twitter using #Stablecoin.

      

In 2017, the notice-and-comment rulemaking process entered the popular consciousness in a way it never previously had. The reason: The Federal Communications Commission decided to rescind a rule relating to net neutrality that it had only issued a few years prior. The original rule garnered nearly 4 million comments, then the largest response ever. The rescission shattered that record, eliciting almost 22 million comments.

All of a sudden, a fairly obscure mechanism for agency policymaking was the talk of the town. Late night comic John Oliver urged his viewers to weigh in against the rescission, and thousands obliged. And, as an investigation by the New York Attorney General released earlier this year has now revealed, far more underhanded efforts to influence the process also took place. For instance, one college student alone generated 7.7 million comments (i.e., around a third of the total) by using a computer algorithm. And several broadband companies hired so-called lead generators who collectively submitted in excess of 8 million comments (i.e., another third of the total), often by misappropriating living or deceased individuals’ identities and submitting computer-generated comments on their behalf.

The entire ugly episode has undermined confidence in the notice-and-comment process and sparked an investigation by Congress. At the same time, it has laid bare some of the fundamental tensions that have always surrounded public participation in agency rulemaking. When enacting the Administrative Procedure Act, Congress was not entirely clear on the extent to which it intended the agency to take into account public opinion as reflected in comments or merely to sift the comments for relevant information. This tension has simmered for years, but it never posed a major problem since the vast majority of rules garnered virtually no public interest.

Even now, most rules still generate a very anemic response. Internet submission has vastly simplified the process of filing a comment, however, and a handful of rules generate “mass comment” responses of hundreds of thousands or even millions of submissions. In these cases, as the net neutrality incident showed, individual commenters and even private firms have begun to manipulate the process by using computer algorithms to generate comments and, in some instances, affix false identities. As a result, agencies can no longer ignore the problem.

Nevertheless, technological progress is not necessarily a net negative for agencies. It also presents extraordinary opportunities to refine the notice-and-comment process and generate more valuable feedback. Moreover, if properly channeled, technological improvements can actually provide the remedies to many of the new problems that agencies have encountered. And other, non-technological reforms can address most, if not all of, the other newly emerging challenges. Indeed, if agencies are open-minded and astute, they can both “democratize” the public participation process, creating new and better tools for ascertaining public opinion (to the extent it is relevant in any given rule), and “technocratize” it at the same time, expanding and perfecting avenues for obtaining expert feedback.

Challenges of new technology

As with many aspects of modern life, technological change that once was greeted with naive enthusiasm has now created enormous challenges. As a recent study for the Administrative Conference of the United States (for which I served as a co-consultant) has found, agencies can deploy technological tools to address at least some of these problems. For instance, so-called “deduplication software” can identify and group comments that come from different sources but that contain large blocks of identical text and therefore were likely copied from a common source. Bundling these comments can greatly reduce processing time. Agencies can also undertake various steps to combat unwanted computer-generated or falsely attributed comments, including quarantining such comments and issuing commenting policies discouraging their submission. A recently adopted set of ACUS recommendations partly based on the report offer helpful guidance to agencies on this front.

Unfortunately, as technology evolves, new challenges will emerge. As noted in the ACUS report, agencies are relatively unconcerned with duplicate comments since they possess the technological tools to process them. Yet artificial intelligence has evolved to the point that computer algorithms can produce comments that are both indistinguishable from human comments and at least facially appear to contain unique and relevant information. In one recent study, an algorithm generated and submitted 1001 “deepfake” comments in connection with an agency rulemaking, and the officials screening the comments were unable to flag them as computer-generated.

Imagine if an algorithm were to comb the literature on the subject of a particular rule and submit millions of distinct comments containing seemingly relevant arguments and citations. At least at present agency staffing levels, this would crash the entire notice-and-comment process.  Agencies are not, as a general matter, equipped to sift through such a large volume of distinct comments. Yet existing law, which requires the agency to consider the “relevant matter presented” in comments, arguably mandates that agencies actually consider and respond to such submissions, at pain of being sued if they fail to do so. The fact that a human being did not generate the comments is arguably irrelevant (especially since a human being produced the algorithm). Congress could address this issue by amending the statute to clarify that agencies need only consider comments composed by actual human beings, though artificial intelligence may eventually evolve to the point that an algorithm could generate a sophisticated and relevant comment that an agency should take into account.

Democratizing the rulemaking process

If technology is the problem, however, it also may provide at least part of the solution. On the one hand, the internet has made it far easier to comment on individual rules, and members of the public clearly expect that the agency will take their opinions into account. Though agencies have always been clear that notice and comment is not an “up-or-down vote,” they often treat it as having a democratic element. Indeed, agencies have occasionally cited the percentage of comments favoring a particular approach as evidence in favor of pursuing it, and the popular response clearly affects how members of Congress and other political actors perceive particular rules. Moreover, regardless of the official line on the purpose of notice and comment, it is abundantly clear that most members of the public view it as something like a plebiscite. Why else would John Oliver mount a mass comment campaign or public influence firms mine the internet for names to falsely affix to auto-generated comments?

If technology is the problem, however, it also may provide at least part of the solution.

The problem with treating the rulemaking process as a vote is that soliciting public comment is a uniquely terrible way of ascertaining public opinion. Anyone with even rudimentary training in survey design knows that allowing participants to opt in ensures that the result is wildly unrepresentative. People with extreme views are the ones who deem it worthwhile to participate. Prior to the rise of the internet, however, soliciting paper comments was the only cost-effective way of hearing from the public.

If agencies are willing to reconceive the commenting process, new technology offers at least a partial solution to this problem. Though the APA mandates that agencies undertake notice and comment for any legislative rule, it does not foreclose the possibility of supplementing it. In those instances in which an agency deems public opinion to be relevant to its decision, it can employ a variety of technology-enabled tools to ensure that it is getting a truer sense of popular sentiment than a mere comment solicitation could provide.

One possible approach offered by Connor Raso and Bruce Kraus would involve allowing “upvoting” comments. Rather than taking the time to file a comment herself, an interested party can do the equivalent of “liking” someone else’s comment. This reduces the barrier to entry and increases the likelihood that a more demographically and ideologically representative sample of individuals participate. At the same time, it is also susceptible to manipulation, as a clever programmer could easily design an algorithm to repeatedly “like” (or “dislike”) her own or someone else’s comment, and agencies would likely need to consider implementing a reCAPTCHA system or some other mechanism to ensure that the submissions are genuine and unique.

Another possible approach that I have explored in a different context would involve convening a citizen advisory panel. The agency could assemble a demographically representative group of citizens, provide them with briefing materials, and then ask them to offer their input on the issue the agency is considering. To ensure that the agency achieves demographic representativeness and avoids conflicts of interest, the agency could either task an independent team of officials with assembling the panel or contract out the function to a private firm. This approach ensures that the public perspective is not only representative but also well-informed. Of course, agencies have always been able to convene citizen advisory committees, but the cost of doing so was once prohibitive (around $200,000 per year, by my estimate). One of the few silver linings of the COVID-19 pandemic, however, is the fact that videoconferencing has become far more sophisticated and far cheaper. Agencies are already using videoconferencing to expand participation in other contexts, and convening an advisory committee by Zoom or another online platform would eliminate virtually all associated expenses other than staff time.

A third approach is a public opinion poll. In many ways, this is the most attractive and cost-effective approach, and it does not require any advances in technology. However, it also poses the most legal challenges. Under the Paperwork Reduction Act (PRA), agencies must undergo an elaborate, months-long approval process prior to circulating any survey instrument to ten or more individuals. To the extent agencies express interest in conducting such polls, Congress might consider amending the PRA to facilitate this process.

By taking any of these approaches, agencies could alleviate some of the pressure on the notice-and-comment process. No one could credibly attack an agency for ignoring public opinion expressed in comments in favor of public opinion ascertained by a much more reliable mechanism. Interest groups and even individual commenters would hopefully take notice and modify their behavior accordingly, abandoning efforts to flood the notice-and-comment process, which would become increasingly fruitless, and instead focus on engaging with the more constructive methods of public participation.

While “Technocratizing” it at the same time

Of course, notice-and-comment is not exclusively or even primarily a democratic process. Most regulatory scholars and practitioners view it as a technocratic process, in which the agency is gathering decentralized knowledge from experts dispersed throughout society. Indeed, most rules deal with esoteric subjects that are of little to no interest to the general public, which often lacks any particularly relevant information to contribute. Here, too, technology can actually improve the notice and comment process and render it more valuable to the agency and stakeholder communities alike.

First, much as virtual meeting platforms facilitate the convening of citizen advisory panels, the same technology could be used to assemble panels of technical experts. Rather than attempting to hold an in-person discussion among the key players, which can become prohibitively expensive when the leading experts must be flown in from throughout the country, agencies could simply arrange virtual meetings. In so doing, agencies must be careful of triggering the so-called Federal Advisory Committee Act, which can be avoided by having the experts offer individual input rather than collaborating on a group recommendation.

Second, AI tools can help agencies process the comments they do receive. Consulting firms such as Deloitte have developed programs that allow agencies to sort comments based on subject matter, sentiment, and other dimensions. These tools go well beyond deduplication software and actually allow agencies to process conceptually distinct comments efficiently.

As technology continues to evolve and computer-generated comments become more prevalent, agencies will likely need to abandon the practice of having human beings review every comment and rely primarily or even exclusively upon these sorts of tools to perform the initial screening function. A human will, of course, need to review the output and decide how to proceed, but some use of AI is likely to become a practical necessity. Law firms already make extensive use of AI in the eDiscovery context, and there is no reason why agencies cannot or should not follow suit.

Third, and relatedly, as agencies begin to deploy AI screening tools, computer-generated comments could become an asset rather than a liability. Bridget Dooling and Michael Livermore (two of the other consultants for the previously mentioned Administrative Conference project) have argued that computer algorithms could alert potential commenters to topics of interest, screen rule text to identify technical errors, or even comb the technical literature and prepare a credible comment to submit to an agency. To the extent that a computer generates a comment that consists of nothing more than credible-sounding gibberish (e.g., furnishing studies that do not stand for the propositions for which they are cited), the agency’s algorithm should be able to identify the comment’s logical flaws and discount it. If, on the other hand, an external algorithm formulates a legitimate argument, the agency’s algorithm should flag it and call it to the human decisionmaker’s attention. In this sense, the agency can actually harness the intelligence of algorithms, which surpasses human intelligence in certain key respects, and leverage the expertise of private sector players who write the algorithms.

Conclusion

If technology runs the risk of breaking the notice-and-comment process as we know it, it appears to be a case of creative destruction. On the one hand, agencies will almost certainly be overwhelmed with technological innovations that challenge their ability to continue with business as usual in the notice and comment space. On the other hand, technology offers a solution to at least some of those very problems and otherwise enhances the process in a way that makes it more useful and satisfactory to agencies, key institutional players, and the general public alike.

At present, traditional notice-and-comment is serving neither the democratic nor technocratic function especially well, at least in high profile rules, as the circus surrounding mass commenting campaigns serves both to frustrate everyday citizens who feel that their views are being ignored and to drown out information-rich comments with masses of comments that contain nothing more than simple expressions of opinion. By thoughtfully deploying new technologies and carefully deciding which rules would benefit from a clearer sense of public opinion and which from a richer array of expert inputs (including those from non-human experts), agencies can re-tool an outmoded process for the 21st century while allowing it to more effectively accomplish the twin goals it was originally designed to serve.

ACUS disclaims responsibility for any private publication or statement of any ACUS employee. The article expresses the author’s views and does not necessarily reflect those of ACUS, the federal government, or the Brookings Institution. The author did not receive any financial support from any organization or person for this article or from any organization or person with a financial or political interest in this article. He is currently not an officer, director, or board member of any organization with a financial or political interest in this article.

      

On October 27, ahead of the 2021 United Nations Climate Change Conference in Glasgow (October 31 to November 12), the Urban-Brookings Tax Policy Center and the Brookings Center on Regulation and Markets brought together climate and tax policy experts to examine recent proposals for U.S. energy tax policy. Catherine Wolfram, deputy assistant secretary of climate and energy economics at the U.S. Department of the Treasury, shared her perspective on the Biden administration’s climate strategy. Following her keynote, an expert panel consisting of Gilbert Metcalf (Tufts University), Carole Nakhle (Crystol Energy), and Kurt Van Dender (OECD), moderated by Thornton Matheson (Urban-Brookings Tax Policy Center), further discussed the U.S. approach to energy tax policy.

Viewers submitted questions for speakers by emailing events@brookings.edu or via Twitter using #taxandclimate.

      

Introduction

From the start of his term, President Biden has indicated that he wishes to pursue an ambitious climate agenda. On his first day in office, he recommitted the U.S. to the Paris Climate Agreement and ordered agencies to review a slew of climate-related (de-)regulations enacted by the Trump administration. One week later, he signed the Executive Order on Tackling the Climate Crisis, which outlined a “whole-of-government” approach to mitigating and responding to climate change. And in April, he announced a new target for U.S. emissions reductions: to halve emissions from 2005 levels by 2030.

Now, President Biden and Democrats in Congress have to find a way to meet these goals. Democrats are in the midst of negotiations over what could be a very impactful climate bill. As part of the budget reconciliation process, Democrats are proposing a $3.5 trillion spending bill, a sizable portion of which would be allocated to climate-related provisions. Representatives have floated such ideas as investing in electric vehicle infrastructure, launching a “Civilian Climate Corps,” and even imposing a carbon border tax.

In considering how the U.S. can meet the targets set out by Biden, one fact becomes clear: the U.S. needs a carbon price.

In recent weeks, carbon pricing has entered the reconciliation debate as high-profile officials including Senate Finance Chair Ron Wyden (D-OR) and Senator Sheldon Whitehouse (D-RI) have publicly supported the policy. Some suggest that introducing a carbon price will be a key component of raising enough revenue to fund other provisions of the bill. Others argue that even if a price on carbon is offset with other policy changes so as to be budget neutral, it is the most effective way to ensure sustainable, long-term reductions in emissions. Yet while a growing number of members of Congress appear interested in carbon pricing, it is unclear if the proposal will garner enough support to make it into any final version of the reconciliation bill.

However, carbon pricing is the most basic and effective tool to reduce carbon emissions, as much of the world has already discovered. If the U.S. continues to stand by while others move forward with carbon pricing, it risks hampering progress towards climate mitigation goals, reducing the global competitiveness of American companies, and diminishing the credibility of its commitment to climate issues on the global stage.

What is carbon pricing?

Carbon pricing is exactly what the name implies: imposing a price on carbon emissions to mitigate the negative externalities created by greenhouse gas emissions. There are two common structures for carbon pricing schemes.

The first—and administratively simpler—approach is imposing a carbon tax. Under this approach, governments levy a fixed fee that firms must pay on every ton of carbon they emit. The level of emissions may fluctuate, but officials set the level of the tax according to the projected amount of carbon emissions at that price.

The second approach is implementing an emissions trading scheme (ETS, also known as a “cap-and-trade” system) for carbon emissions. This system caps carbon emissions at a specified level for a group of companies or industrial plants and then issues emissions allowances according to this level. Firms must obtain an allowance—either directly from the government or through trading with one another—for every ton of carbon they wish to emit. Under an ETS, the price on carbon fluctuates according to market demand for emissions, but the total amount of emissions is known.

While there are substantial differences between the two systems, the core benefit of carbon pricing remains the same: carbon pricing forces firms to internalize the cost of carbon emitted during production, such that they have to incorporate the cost of environmental damage in their production decisions.

The effectiveness of carbon pricing in reducing emissions depends in large part on their design. There are many considerations that policymakers have to take into account when designing a carbon pricing system. How much should emitting a ton of carbon cost, and how should this amount change over time? Who should be responsible for paying the carbon price—fossil fuel producers, consumers, or someone in between? Will the carbon pricing scheme be a source of revenue, and how should this revenue be used? The idiosyncrasies of the design influence popular support for the pricing system, the net cost of emitting carbon, and the environmental justice implications of the system, all of which can shape the system’s effectiveness in reducing carbon emissions.

Where does carbon pricing work?

Currently, 64 carbon pricing initiatives have been implemented across one supranational jurisdiction, 45 national jurisdictions, and 35 subnational jurisdictions, covering over one fifth of global greenhouse gas emissions. The largest and most famous of these is the European Union Emissions Trading Scheme (EU ETS), a “cap-and-trade” system that covers emissions from factories, power plants, and other installations in 30 countries (all EU countries plus Iceland, Liechtenstein, and Norway), resulting in coverage of around 40% of the EU’s greenhouse gas emissions. Other national initiatives include ETSs in Kazakhstan, New Zealand, Mexico, and (recently) China, as well as carbon taxes in countries such as South Africa, Chile, Argentina, and Canada.

Even some U.S. states have enacted carbon pricing systems: California launched its cap-and-trade system in 2013, while the state of Washington voted to enact its own carbon pricing system in April 2021. Eleven states¹ in the northeastern U.S. participate in the Regional Greenhouse Gas Initiative, a localized cap-and-trade system that covers 18 percent of emissions in participating states. The Hawaii state senate has stated its intent to consider a carbon tax in 2022, while legislators in Oregon unsuccessfully tried to create a cap-and-trade system in 2019.

A major critique of existing carbon pricing systems has been that their price is too low to effectively reduce emissions. According to the World Bank Carbon Pricing Dashboard, prices vary wildly among different systems, from around $0.30 per ton in Ukraine to nearly $75 per ton in the EU. Many EU member states impose their own carbon tax in addition to participating in the EU ETS. For instance, in Sweden, companies pay a combined price of approximately $200 per ton of carbon emissions. While carbon prices tend to be high in Europe, outside the continent, most carbon pricing systems charge less than $20 per ton of carbon, and many charge less than $5.

Determining the “right” price on carbon has proven to be a challenge. Many argue that the carbon price should be tied to the social cost of carbon (SCC)—an estimate of the total economic damages associated with each ton of carbon emissions. Climate economist William Nordhaus estimates that the SCC was $31 per ton in 2015, but will grow to $44 per ton by 2025 and $52 per ton by 2030. The Obama administration EPA calculated similar estimates: $36 per ton in 2015, growing to $46 per ton by 2025 and $50 per ton by 2030. Taking a different approach, the High Level Commission on Carbon Prices—drafted by the UN Framework Convention on Climate Change—estimated that achieving the Paris Agreement’s goal of limiting warming to two degrees would require a universal carbon price of $40-80 per ton by 2020 and $50-100 by 2030 to achieve. Only 3.76% of global emissions are currently covered by a $40-80 price. Economists at the International Monetary Fund went even further, suggesting that major emitters would need a carbon price of $75 per ton to achieve sufficient emissions reductions.

How can the U.S. benefit from a carbon price?

Implementing carbon pricing as part of the United States’ development of a 21^st century climate change mitigation strategy could accomplish four key goals:

1. Mitigate climate change

First and foremost, carbon pricing is the most direct and most efficient way to achieve the emissions reductions that are necessary to mitigate climate change. The U.S. will have to take drastic action if it is to meet its climate goals. The current “command-and-control” methods that U.S. regulatory agencies use to govern greenhouse gas (GHG) emissions are likely not sufficient to meet President Biden’s goal of halving emissions by 2030. While U.S. carbon emissions have fallen over the past two decades from their peak in 2005, they have not fallen rapidly enough. The Rhodium Group projects that, under current policies, the U.S. will only reduce emissions by 20-22 percent from 2005 levels by 2025, and by 20-26 percent by 2030. This is only half of the goal set by President Biden. When considering the turbulence created by the COVID-19 pandemic, this figure could fall to only a 17 percent reduction by 2030.

Carbon pricing can reverse this trend. Implementing a sufficiently high carbon price has been projected to have significant impacts on carbon emissions. A 2019 Brookings Institution report projects that a $25 per ton carbon tax that rises by one percent per year would reduce emissions by 17 to 38 percent relative to 2005 benchmark levels by 2030. Under their calculations, a $50 per ton carbon tax rising by five percent per year would reduce emissions by 26 to 47 percent relative to 2005 levels—up to 90 percent of the reductions needed to achieve President Biden’s Paris Agreement goal.

Some estimates find that even a smaller fee that grows over time may still have a significant impact. One of the most serious carbon pricing proposals under consideration by members of Congress involves a $15 per ton tax levied on oil and gas producers. Resources for the Future estimates that if this tax were to begin in 2023 and rise by five percent annually, it would reduce emissions to close to 40 percent below 2005 levels by 2030. If the tax were to rise by $10 per year, it would reduce emissions by approximately 45 percent below 2005 levels. While the U.S. cannot rely on carbon pricing alone to achieve its climate goals, the right carbon pricing system has the potential to be the country’s most effective tool for climate mitigation.

In addition, any revenue earned from carbon pricing can be used to reduce the effects of climate change on the most vulnerable communities. Climate change has already disproportionately impacted vulnerable communities, and its impact is projected to worsen in the coming decades. Furthermore, the carbon pricing mechanisms and other environmental regulations needed to mitigate climate change can increase costs for consumers if costs are passed through by companies. Carbon pricing revenue should therefore be used to offset any potential increased energy costs for low-income households, as well as to build climate resilience in vulnerable communities. In addition, such revenue could be used to provide job retraining for fossil fuel workers.

2. Justify a carbon border tax

In July, several Democrats in Congress proposed to include a carbon border adjustment tax in the $3.5 trillion budget reconciliation bill that is currently in negotiation. Broadly speaking, carbon border adjustments are meant to protect domestic firms from having to unfairly compete with firms producing in countries with weaker greenhouse gas regulations. This has several advantages. First, it ensures that both domestic and foreign firms face similar production costs. Second, it discourages domestic firms from relocating production in response to strengthened climate regulations. This not only protects domestic jobs but also reduces the potential for carbon leakage, where environmental regulations do not reduce emissions but simply facilitate relocating them. Third, the possibility of tariffs can encourage countries and individual firms to improve their environmental practices.

For a country with strong climate regulations (and a large domestic market), a carbon border tax can be an effective tool to maintain the competitiveness of domestic firms. However, it makes little sense to institute a border adjustment in the U.S. without first imposing a domestic carbon price.

Carbon border adjustments are difficult to design even with a carbon price. In theory, policymakers determine the carbon border adjustment rate by setting it equal to the domestic carbon price, thereby ensuring that all firms—foreign and domestic—pay the same price for emissions generated during production. However, they are then faced with the decision of which goods should be subject to the tariff, which trading partners should be exempt, and how and whether the adjustment should take other greenhouse gas regulations into account. Without a domestic carbon price, setting a fair price for the border adjustment involves calculating an effective carbon price based on existing environmental and emissions regulations. Resources for the Future outlines one approach where importing firms are exempt from the border adjustment up to the average level of carbon emissions on a sector-by-sector basis, then face a per ton charge that is based on the estimated marginal cost of emissions abatement. Such an approach, and any similar approaches, would be enormously complex for both administering agencies and for importing firms. Furthermore, it would be prone to inaccuracy, as firms in the same sector may still face a broad range of effective carbon prices due to differences in regulatory environments among states.

Imposing a carbon border tax without a domestic carbon price would also make the U.S. vulnerable to challenges and retaliation in global trade. The U.S. has no standing to implement a border tax if it does not have a carbon price of its own. Even the EU’s new carbon border adjustment risks setting off a trade war. The U.S. border tax proposal currently under consideration would almost certainly face challenges from countries with weaker environmental regulations at the World Trade Organization (WTO). If these challenges succeeded, the U.S. would contend with retaliatory tariffs from these countries, which include major economies such as China and India. In short, a border tax without a domestic carbon price is likely to achieve more protectionist goals than climate goals.

In summary, without a domestic carbon price, the U.S. cannot credibly implement a carbon border adjustment tax.

3. Boost the global, long-term competitiveness of American companies

The lack of a carbon price has created uncertainty for U.S. companies. Without knowing if or when the U.S. will institute carbon pricing, companies cannot accurately plan future investment decisions. Some companies have tried to rectify this issue by instituting internal carbon pricing, where business units incorporate a predetermined price on carbon emissions into their present and future budgets. Yet without guidance from the federal government on what this price should be, companies might set their price too low. A report from McKinsey & Company finds that most firms set their internal carbon price below the minimum price of $40 per ton recommended by economists and climate experts. These prices vary by industry: while the median internal carbon price set by energy companies is $25, the median price set by financial services companies is $6 per ton. Instituting a federal carbon price would allow firms to  plan their long-term investment decisions better.

Imposing carbon pricing in the U.S. might also ensure continued access for American companies to markets abroad. Recently, the EU announced that it was planning to impose a carbon border tax on carbon-intensive imports. Importers can avoid this tax if they can prove that “a carbon price has already been paid during the production of the imported goods.” If the U.S. continues to delay carbon pricing, then imports from the U.S. will be subject to this tax from the EU, one of the United States’ most important trading partners. As more countries choose to enact carbon pricing, the risk of this happening elsewhere grows stronger. A robust carbon price in the U.S. would ensure that American firms can continue to access international markets without frictions.

Finally, instituting a carbon price in the U.S. can prepare American firms for global technological transitions, strengthening their competitiveness in the future. A carbon price will force companies to reevaluate their long-term investment decisions, shifting away from emissions-intensive production toward low-carbon technologies. Globally, a shift toward low-carbon production is inevitable, and the sooner U.S. companies can begin this transition, the more competitive they will be. A U.S. carbon price will help to ensure that U.S. companies can lead the new industries centered on low-carbon technologies that will become the lynchpin of the global economy in the coming decades.

4. Restore the U.S.’ global reputation as a leader on climate issues

Over the past few decades, the United States’ global reputation on climate issues has steadily declined. While other countries and nations, such as the EU, have set ambitious climate goals, the U.S. continues to grapple with climate deniers blocking any substantive action on climate change. It has rejected membership in both of the key global climate accords of the past two decades—the Kyoto Protocol and the Paris Agreement—even though it recently rejoined the latter. Rather than government leaders and policymakers committing to significant action on climate change, the majority of reductions in emissions over the past decade have been achieved through shifts in fossil fuel usage and increased efficiency in industry.

A carbon price could help the U.S. reverse its lagging global standing on climate issues and demonstrate its commitment to reducing GHG emissions using the most modern and effective techniques available.

Conclusion

In order to achieve its climate targets, the U.S. will need a carbon price. This will not only enable the U.S. to meet its emissions reduction goals, but also support American firms in a future low-carbon economy and signal the U.S.’ commitment to effective climate mitigation policies to the larger global community.

The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment. A list of donors can be found in our annual reports published online here. The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.

      

It’s good news that financial regulators are focused on figuring out what to do about stablecoins because their growth is creating significant risks. But there’s a bigger picture here than how to bring these new instruments within the regulatory perimeter, or how to regulate crypto generally, even though these are important. The bigger issue is how do we modernize our payments system?  This should be a Biden administration priority because it would help low-income people in particular.

The connection between stablecoins and helping low-income people might seem unlikely because stablecoins are primarily used today to speculate on cryptocurrencies. While their growth has led financial regulators to worry about potential systemic risks, that growth is partly because of deficiencies in our payments system, and those deficiencies are a major reason why the U.S. lags behind other developed nations in financial inclusion. Stablecoins have potentially much broader application. Properly regulated, they are one means—although clearly not the only means—of curing some of those payment system deficiencies. Thus, financial regulators should not only address the risks that stablecoins pose but keep their aim on that broader goal of modernizing our payments system and improving access to the financial system.

This paper discusses (1) why stablecoins are a problem; (2) how we should regulate stablecoins; and (3) the bigger picture about modernizing payments and improving financial access.

The risks of stablecoins

Stablecoins are digital tokens whose value is pegged to the dollar (or another currency or asset). They serve to grease the wheels of the crypto industry, enabling investors to easily transfer value between different crypto exchanges and cryptocurrencies without converting back and forth into dollars. Settlement is instant, thus avoiding delays of other means of payment. This function coupled with explosive growth in the crypto currency market explains why the market capitalization of stablecoins has increased from $20 billion twelve months ago to over $120 billion today¹.

Figure 1: Market capitalization of stablecoins, January 2017 to August 2021

Stablecoins are currently not regulated in any meaningful way. While some issuers have state licenses, these impose minimal requirements. There are no standards requiring issuers to protect reserves or maintain liquidity. I have written about how a sudden spike in demand for repayment could cause a stablecoin to “break the buck” the same way the Reserve Primary Fund did in September of 2008, which triggered  a run on money market mutual funds that was only stopped when the Treasury issued a guarantee of money market mutual fund liabilities.

Although stablecoins are currently not used widely outside of the crypto industry, they have the potential for much broader application. Stablecoins first garnered wide attention in June 2019 when Facebook proposed creating “a simple global currency”² or stablecoin called Libra that would be pegged to a basket of fiat currencies including the dollar and the euro. That proposal provoked harsh criticism, both because of its sponsor³ as well as its design. Central bankers feared it would undermine sovereign currencies and monetary policies⁴. The proposal has since been renamed Diem and redesigned as a set of stablecoins, each tied to an individual fiat currency.  It is not operational, in part, because Facebook promised in Congressional hearings that it would not launch the idea unless regulators approved, and they have not done so. Other stablecoin issuers did not ask for permission, and their tokens have grown enormously, which has finally prompted regulators to consider acting.

How to regulate stablecoins

In July, Treasury Secretary Yellen convened the President’s Working Group on Financial Markets (PWG) to discuss stablecoins. The PWG does not have any power to actually do anything about stablecoins, however. Instead, Treasury staff will soon issue a report that will recommend a path forward, which could include a mix of recommendations for actions by different regulatory agencies and potentially Congress.

The best option is to have the Financial Stability Oversight Council (FSOC) commence a review.  Under Title VIII of the Dodd Frank Wall Street Reform and Consumer Protection Act, the FSOC can require regulation of a “payment activity” that it determines “is, or is likely to become, systemically important.”⁵  The FSOC has broad powers to get information from institutions engaged in an activity that it has reasonable cause to believe meets the standard for such a designation. That type of inquiry is much needed given the lack of transparency about stablecoins.

The law sets forth criteria for making the systemically important designation, which include size as well as the effect that the failure or disruption of the activity would have on critical markets, financial institutions, or the broader financial system. Under Title VIII, the FSOC has designated two operators of business payment systems as systemically important financial market utilities, but it has never designated an activity generally nor an entity engaged primarily in retail payments. The “likely to become” phrase is not in Title I, under which FSOC designated four entities following the global financial crisis. It is clearly relevant to the concern that, unless regulated, stablecoins could continue to grow dramatically.

Having been a member of the FSOC for three years, I believe an FSOC review will be useful even if it decides against a systemically important designation.  The FSOC can still be the forum for making a sound choice among the alternative paths, which involve different regulators who comprise the FSOC. There is a good argument that stablecoins could be regulated as bank deposits  under existing law. Section 21 of Glass Steagall (which survived despite repeal of much of that law) prohibits anyone from receiving a bank deposit unless subject to regulatory oversight under specific exceptions.

There is also the option of regulating stablecoins as securities or as money market funds.  Securities and Exchange Commission chair Gary Gensler has suggested he might move to do so and has referred to the PWG report as something that his staff is working on with Secretary Yellen. Although I have compared stablecoin risks to those of money market funds, I do not think that is the best way to regulate them. They are fundamentally payment devices and not investments. Classifying them as securities would also appear to pre-empt a systemic importance determination as part of the payment authority given by Dodd Frank since the definition of “payment, settlement and clearing activity” for purposes of FSOC’s jurisdiction excludes “any offer or sale of a security under the Securities Act.” The PWG report will presumably indicate which path of this fork the Biden administration will pursue.

Another option is to recommend to Congress that it enact new authority. Various bills have been introduced to address stablecoins, including one that would limit issuance to entities that are banks.  But at a time when the Biden administration and Congress already have many weighty legislative priorities, rapid enactment of legislation seems doubtful.

If the FSOC does reach a determination of systemic importance, the Federal Reserve would be charged with developing “risk management standards” that “promote robust risk management; promote safety and soundness; reduce systemic risks; and support the stability of the broader financial system.”⁶

The path chosen may affect the comprehensiveness of the regulatory framework that can be created, though we should put in place what we can now and add to it later if necessary.   Ideally, we need a framework that includes not just traditional prudential regulation standards, but also operational risk measures, consumer protection standards, and standards to achieve interoperability.  Regulators should require that reserves are invested in bank deposits, Treasuries, or other safe, liquid assets, and that there are liquidity requirements. If the Federal Reserve were to broaden who is eligible for a master account, then stablecoin providers which are not banks could park reserves at the Fed, an option some would argue is even safer because it would avoid the operational risk of a particular bank.   Regulators should require a capital buffer even if reserves are invested in cash or other safe assets. That is because capital can protect against other types of losses, such as operational ones. Regulators may also want to ban the payment of interest to discourage users from maintaining large deposits. That plus requiring reserves to be invested in cash or other safe assets would likely mean that stablecoins would be attractive only as payment instruments, not as investments. A prohibition on interest would put stablecoins at a disadvantage relative to bank deposits if interest rates rise, but regulators might desire that in these early days of the industry. Compliance with “know your customer,” anti-money laundering, and other laws combatting the financing of terrorism is crucial.

There should also be operational resilience standards. This is a huge area of risk often overlooked in commentary that focuses on stablecoin financial risks. Stablecoins run on decentralized blockchains and smart contracts. The software for the various layers of operation could have flaws or could be vulnerable to attack. The largest stablecoins run on multiple blockchains but are separate and distinct tokens on each such blockchain, as a recent post by Neha Narula of MIT explains. That means risks associated with the integrity and reliability of the blockchains and software are multiplied. In addition, a stablecoin could become too large in relation to the capacity of the blockchain itself. Federal Reserve staff are presumably becoming knowledgeable about these issues through their collaboration with MIT to design a hypothetical central bank digital currency platform.

The regulatory framework should also include requirements for adequate disclosure of information to customers, rights of recourse, and standards on protection of customer information, including on how a customer’s data can be used. The Consumer Financial Protection Bureau may have a role to play in this regard. Finally, we may want to create standards that ensure interoperability between different stablecoins to avoid a fragmented system.

One other advantage of an FSOC process is that the presence of representatives of state banking and securities regulators on the council may help figure out how state regulation of stablecoins—which exists, but is quite limited—should mesh with federal standards.

Both the PWG and the FSOC are well suited to examining risks generated by financial markets.   The PWG was created in response to the 1987 stock market crash; the FSOC was created in response to the 2008 global financial crisis.  But whatever path is chosen for going forward, the goal should be not just to regulate risks of this particular innovation but to address deficiencies in the payment system that are a principal reason for the growth of stablecoins.

How regulating stablecoins can advance financial inclusion

While stablecoin usage has largely been in the crypto industry, their impact has already been broader.  As Federal Reserve chairman Powell said in discussing Facebook’s Libra proposal, the concept “lit a fire” under central bankers to consider central bank digital currencies (CBDCs).   Both stablecoins and CBDCs are ways to remedy deficiencies of our payments system and potentially enhance financial inclusion. As financial regulators address the risks of stablecoins, they should articulate that larger goal of modernizing our payments system and increasing access to the financial system.

Banks handle the vast majority of U.S. dollar payments in a safe and well-tested manner. But the system is characterized by relatively high cost, weak competition, and insufficient innovation. Americans pay significantly more than Europeans for payment services, particularly because of high fees paid for credit cards. The system is also slow relative to real-time payments increasingly common in other countries.

Most Americans might say the system is fine. We don’t notice interchange fees paid by merchants because they are rolled into prices, and our credit cards give us free revolving credit, cash back, frequent flyer miles, or other rewards. Nor is anyone who has some savings likely to be inconvenienced if a check takes a couple days to clear.

But the flaws of the system weigh much more heavily on those in lower-income brackets. Those who live paycheck to paycheck are at risk of incurring significant overdraft fees when checks don’t clear quickly. The fact that approximately 70%⁷ of those who use check cashing services have bank accounts is clear evidence that something is wrong with the payments system. In addition, because people with lower incomes have fewer credit cards—they use more prepaid cards and debit cards, which don’t offer the same benefits—the credit card costs embedded in prices fall disproportionately on them.

It is shocking that with a financial system as sophisticated as ours, 25% of American households are unbanked or “underbanked,” according to the FDIC.  The latter term means they have a bank account but use nonbank options like check cashing services or payday lenders, often to avoid even more expensive bank overdraft charges. Moreover, as Aaron Klein has written in an excellent new paper, the key issue is access to digital money, and low-income people are at a distinct disadvantage in that regard.

Stablecoins are one way to speed up payments, as are CBDCs. The original Libra proposal focused on its potential financial inclusion benefits. While some might regard that as window dressing by Facebook, the fact is that slow and expensive payments burden low income people in many ways.  Remittances—a $700 billion market of sending money from one country to another—is a prime example, as the average cost exceeds 6%.

A retail CBDC could be a means of providing bank accounts as well.⁸ Of course, the banking industry is quick to cry out that this would disintermediate the banking system—because deposits would leave commercial banks and move to the Fed—and result in less lending and credit creation. But there are many design choices for CBDCs. One option is to create no frills, minimal retail accounts with deposit limits, which might help the unbanked and underbanked without draining away significant deposits. We could also mandate banks to provide such low or no-cost accounts.

These are not the only solutions, and some will argue that existing private and public sector initiatives are sufficient to modernize the system. The Federal Reserve’s new real-time payments system, FedNow, is due to be operational in 2023 and that will surely help. But it needs to be coupled with regulatory changes that will create more competition, or banks may only offer those services within their “walled gardens”.

The real issue is increasing competition—either from new private sector entrants or effectively from the government through a CBDC. Regulators can’t fix the system’s deficiencies as they move to regulate stablecoins, but they can frame the issue in that context. The big picture is that stablecoins have grown enormously because they offer distinct advantages in speed.  Banks have not sufficiently modernized the system nor addressed financial inclusion.  Changes in regulation may be needed to permit greater competition and facilitate innovation. The Biden administration should make this a national priority.