July 6, 2021

Global Economy and Development

City playbook for advancing the SDGs

BY Report, Brookings Institute

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Originally published on by Brookings Institute. Link to original report

( 2 mins)
City playbook for advancing the SDGs 1

This “City Playbook for Advancing the SDGs” compiles a series of how-to briefs and case studies on advancing sustainable development and social progress locally. These short, digestible, and practical briefs are written by city government officials for other city officials, based on their direct experience.

This playbook responds to significant appetite expressed by city leaders for capturing and sharing the “how” of innovations and practices to achieve the Sustainable Development Goals (SDGs) locally. These briefs come from cities participating in the Brookings SDG Leadership Cities community of practice and others to elevate innovations or processes with concrete positive outcomes for equity and sustainability. These best practices and tools help disseminate recommendations to a wider range of communities and stakeholders eager to play a pivotal role in achieving the 2030 Agenda.

Co-edited by Anthony F. Pipa and Max Bouchet, these briefs are published in collaboration with the global learning platform for government innovators apolitical.co. We intend to add content to this collection on a rolling basis throughout 2021 and 2022.

If you’re using this playbook to apply an innovation locally or have questions or suggestions, please fill out this short survey.

This playbook is part of the Local Leadership on the Sustainable Development Goals project.

An AI fair lending policy agenda for the federal financial regulators

( 31 mins) Algorithms, including artificial intelligence and machine learning models (AI/ML), increasingly dictate many core aspects of everyday life. Whether applying for a job or a loan, renting an apartment, or seeking insurance coverage, AI-powered statistical models decide who will have access to the foundational drivers of opportunity and equality.1

Michael Akinwumi

Chief Tech Equity Officer – National Fair Housing Alliance

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John Merrill

Chief Technology Officer – FairPlay AI

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Lisa Rice

President and CEO – National Fair Housing Alliance

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Kareem Saleh

Founder & CEO – FairPlay AI

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Maureen Yap

Senior Counsel – National Fair Housing Alliance

These models present both great promise and great risk. They can minimize human subjectivity and bias, facilitate more consistent outcomes, increase efficiencies, and generate more accurate decisions. Properly conceived and managed, algorithmic, and AI-based systems can be opportunity-expanding. At the same time, a variety of factors—including data limitations, lack of diversity in the technology field, and a long history of systemic inequality in America—mean that algorithmic decisions can perpetuate discrimination against historically underserved groups, such as people of color and women.
In light of the growing adoption of AI/ML, federal regulators—including the Consumer Financial Protection Bureau (CFPB), Federal Trade Commission (FTC), the Department of Housing and Urban Development (HUD), Office of the Comptroller of the Currency (OCC), Board of Governors of the Federal Reserve (Federal Reserve), Federal Deposit Insurance Corporation (FDIC), and National Credit Union Administration (NCUA)—have been evaluating how existing laws, regulations, and guidance should be updated to account for the advent of AI in consumer finance. Earlier this year some of these regulators issued a request for information on financial institutions’ use of AI and machine learning in the areas of fair lending, cybersecurity, risk management, credit decisions, and other areas.2
The adoption of responsible AI/ML policies will continue to receive serious attention from regulators. This paper proposes policy and enforcement steps regulators can take to ensure AI/ML is harnessed to advance financial inclusion and fairness. As many other papers have already focused on methods for embracing the benefits of AI, we focus here on providing recommendations to regulators on how to identify and control for the risks in order to build an equitable market.
I. Background
A. AI/ML and consumer finance
For decades, lenders have used models and algorithms to make credit-related decisions, the most obvious examples being credit underwriting and pricing. Today, models are ubiquitous in consumer markets and are constantly being applied in new ways, such as marketing, customer relations, servicing, and default management. Lenders also commonly rely on models and modeled variables provided by third-party vendors.
Recent increases in computing power and exponential growth in available data have spurred the advancement of even more sophisticated statistical techniques. In particular, entities are increasingly using AI/ML, which involves exposing sophisticated algorithms to historical “training” data to discover complex correlations or relationships between variables in a dataset.  The set of discovered relationships—typically referred to as a “model”—is then run against real-world information to predict future outcomes.
In the consumer finance context, AI/ML is similar to traditional forms of statistical analysis in that both are used to identify patterns in historical data to draw inferences and future behavior.  What makes AI/ML unique is the ability to analyze much larger amounts of data and discover complex relationships between numerous data points that would normally go undetected by traditional statistical analysis. AI/ML tools are also capable of adapting to new information—or “learning”—without human intervention. These tools are becoming increasingly popular in both the private and public sectors. As two United States senators recently put it, “algorithms are increasingly embedded into every aspect of modern society.”3
B. The risks posed by AI/ML in consumer finance
While AI/ML models offer benefits, they also have the potential to perpetuate, amplify, and accelerate historical patterns of discrimination. For centuries, laws and policies enacted to create land, housing, and credit opportunities were race-based, denying critical opportunities to Black, Latino, Asian, and Native American individuals. Despite our founding principles of liberty and justice for all, these policies were developed and implemented in a racially discriminatory manner. Federal laws and policies created residential segregation, the dual credit market, institutionalized redlining, and other structural barriers. Families that received opportunities through prior federal investments in housing are some of America’s most economically secure citizens. For them, the nation’s housing policies served as a foundation of their financial stability and the pathway to future progress. Those who did not benefit from equitable federal investments in housing continue to be excluded.
Algorithmic systems often have disproportionately negative effects on people and communities of color, particularly with respect to credit, because they reflect the dual credit market that resulted from our country’s long history of discrimination.4 This risk is heightened by the aspects of AI/ML models that make them unique: the ability to use vast amounts of data, the ability to discover complex relationships between seemingly unrelated variables, and the fact that it can be difficult or impossible to understand how these models reach conclusions. Because models are trained on historical data that reflect and detect existing discriminatory patterns or biases, their outputs will reflect and perpetuate those same problems.5

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Examples of discriminatory models abound, particularly in the finance and housing space. In the housing context, tenant screening algorithms offered by consumer reporting agencies have had serious discriminatory effects.6 Credit scoring systems have been found to discriminate against people of color.7 Recent research has raised concerns about the connection between Fannie Mae and Freddie Mac’s use of automated underwriting systems and the Classic FICO credit score model and the disproportionate denials of home loans for Black and Latino borrowers.8
These examples are not surprising because the financial industry has for centuries excluded people and communities from mainstream, affordable credit based on race and national origin.9 There has never been a time when people of color have had full and fair access to mainstream financial services. This is in part due to the separate and unequal financial services landscape, in which mainstream creditors are concentrated in predominantly white communities and non-traditional, higher-cost lenders, such as payday lenders, check cashers, and title money lenders, are hyper-concentrated in predominantly Black and Latino communities.10
Communities of color have been presented with unnecessarily limited choices in lending products, and many of the products that have been made available to these communities have been designed to fail those borrowers, resulting in devastating defaults.11 For example, borrowers of color with high credit scores have been steered into subprime mortgages, even when they qualified for prime credit.12 Models trained on this historic data will reflect and perpetuate the discriminatory steering that led to disproportionate defaults by borrowers of color.13
Biased feedback loops can also drive unfair outcomes by amplifying discriminatory information within the AI/ML system. For example, a consumer who lives in a segregated community that is also a credit desert might access credit from a payday lender because that is the only creditor in her community. However, even when the consumer pays off the debt on time, her positive payments will not be reported to a credit repository, and she loses out on any boost she might have received from having a history of timely payments. With a lower credit score, she will become the target of finance lenders who peddle credit offers to her.14 When she accepts an offer from the finance lender, her credit score is further dinged because of the type of credit she accessed. Thus, living in a credit desert prompts accessing credit from one fringe lender that creates biased feedback that attracts more fringe lenders, resulting in a lowered credit score and further barriers to accessing credit in the financial mainstream.
In all these ways and more, models can have a serious discriminatory impact. As the use and sophistication of models increases, so does the risk of discrimination.
C. The applicable legal framework
In the consumer finance context, the potential for algorithms and AI to discriminate implicates two main statutes: the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act. ECOA prohibits creditors from discriminating in any aspect of a credit transaction on the basis of race, color, religion, national origin, sex, marital status, age, receipt of income from any public assistance program, or because a person has exercised legal rights under the ECOA.15  The Fair Housing Act prohibits discrimination in the sale or rental of housing, as well as mortgage discrimination, on the basis of race, color, religion, sex, handicap, familial status, or national origin.16
ECOA and the Fair Housing Act both ban two types of discrimination: “disparate treatment” and “disparate impact.”  Disparate treatment is the act of intentionally treating someone differently on a prohibited basis (e.g., because of their race, sex, religion, etc.). With models, disparate treatment can occur at the input or design stage, for example by incorporating a prohibited basis (such as race or sex) or a close proxy for a prohibited basis as a factor in a model. Unlike disparate treatment, disparate impact does not require intent to discriminate.  Disparate impact occurs when a facially neutral policy has a disproportionately adverse effect on a prohibited basis, and the policy either is not necessary to advance a legitimate business interest or that interest could be achieved in a less discriminatory way.17
II. Recommendations for mitigating AI/ML Risks
In some respects, the U.S. federal financial regulators are behind in advancing non-discriminatory and equitable technology for financial services.18 Moreover, the propensity of AI decision-making to automate and exacerbate historical prejudice and disadvantage, together with its imprimatur of truth and its ever-expanding use for life-altering decisions, makes discriminatory AI one of the defining civil rights issues of our time. Acting now to minimize harm from existing technologies and taking the necessary steps to ensure all AI systems generate non-discriminatory and equitable outcomes will create a stronger and more just economy.
The transition from incumbent models to AI-based systems presents an important opportunity to address what is wrong in the status quo—baked-in disparate impact and a limited view of the recourse for consumers who are harmed by current practices—and to rethink appropriate guardrails to promote a safe, fair, and inclusive financial sector. The federal financial regulators have an opportunity to rethink comprehensively how they regulate key decisions that determine who has access to financial services and on what terms. It is critically important for regulators to use all the tools at their disposal to ensure that institutions do not use AI-based systems in ways that reproduce historical discrimination and injustice.
A. Set clear expectations for best practices in fair lending testing, including a rigorous search for less discriminatory alternatives
Existing civil rights laws and policies provide a framework for financial institutions to analyze fair lending risk in AI/ML and for regulators to engage in supervisory or enforcement actions, where appropriate. However, because of the ever-expanding role of AI/ML in consumer finance and because using AI/ML and other advanced algorithms to make credit decisions is high-risk, additional guidance is needed. Regulatory guidance that is tailored to model development and testing would be an important step towards mitigating the fair lending risks posed by AI/ML.
Below we propose several measures that would mitigate those risks.
1. Set clear and robust regulatory expectations regarding fair lending testing to ensure AI models are non-discriminatory and equitable 
Federal financial regulators can be more effective in ensuring compliance with fair lending laws by setting clear and robust regulatory expectations regarding fair lending testing to ensure AI models are non-discriminatory and equitable. At this time, for many lenders, the model development process simply attempts to ensure fairness by (1) removing protected class characteristics and (2) removing variables that could serve as proxies for protected class membership. This type of review is only a minimum baseline for ensuring fair lending compliance, but even this review is not uniform across market players. Consumer finance now encompasses a variety of non-bank market players—such as data providers, third-party modelers, and financial technology firms (fintechs)—that lack the history of supervision and compliance management. They may be less familiar with the full scope of their fair lending obligations and may lack the controls to manage the risk. At a minimum, the federal financial regulators should ensure that all entities are excluding protected class characteristics and proxies as model inputs.19
Removing these variables, however, is not sufficient to eliminate discrimination and comply with fair lending laws. As explained, algorithmic decisioning systems can also drive disparate impact, which can (and does) occur even absent using protected class or proxy variables. Guidance should set the expectation that high-risk models—i.e., models that can have a significant impact on the consumer, such as models associated with credit decisions—will be evaluated and tested for disparate impact on a prohibited basis at each stage of the model development cycle.
Despite the need for greater certainty, regulators have not clarified and updated fair lending examination procedures and testing methodologies for several years. As a result, many financial institutions using AI/ML models are uncertain about what methodologies they should use to assess their models and what metrics their models are expected to follow. Regulators can ensure more consistent compliance by explaining the metrics and methodologies they will use for evaluating an AI/ML model’s compliance with fair lending laws.
2. Clarify that the federal financial regulators will conduct a rigorous search for less discriminatory alternatives as part of fair lending examinations, and set expectations that lenders should do the same 
The touchstone of disparate impact law has always been that an entity must adopt an available, less discriminatory alternative (LDA) to a practice that has discriminatory effect, so long as the alternative can satisfy the entity’s legitimate needs. Consistent with this central requirement, responsible financial institutions routinely search for and adopt LDAs when fair lending testing reveals a disparate impact on a prohibited basis. But not all do. In the absence of a robust fair lending compliance framework, the institutions that fail to search for and adopt LDAs will unnecessarily perpetuate discrimination and structural inequality. Private enforcement against these institutions is difficult because outside parties lack the resources and/or transparency to police all models across all lenders.
Given private enforcement challenges, consistent and widespread adoption of LDAs can only happen if the federal financial regulators conduct a rigorous search for LDAs and expect the lenders to do the same as part of a robust compliance management system. Accordingly, regulators should take the following steps to ensure that all financial institutions are complying with this central tenet of disparate impact law:
a. Inform financial institutions that regulators will conduct a rigorous search for LDAs during fair lending examinations so that lenders also feel compelled to search for LDAs to mitigate their legal risk. Also inform financial institutions how regulators will search for LDAs, so that lenders can mirror this process in their own self-assessments.
b. Inform financial institutions that they are expected to conduct a rigorous LDA search as part of a robust compliance management system, and to advance the policy goals of furthering financial inclusion and racial equity.
c. Remind lenders that self-identification and prompt corrective action will receive favorable consideration under the Uniform Interagency Consumer Compliance Rating System20 and the CFPB’s Bulletin on Responsible Business Conduct.21 This would send a signal that self-identifying and correcting likely fair lending violations will be viewed favorably during supervisory and enforcement matters.
The utility of disparate impact and the LDA requirement as a tool for ensuring equal access to credit lies not only in enforcement against existing or past violations but in shaping the ongoing processes by which lenders create and maintain the policies and models they use for credit underwriting and pricing. Taking the foregoing steps would help ensure that innovation increases access to credit without unlawful discrimination.
3. Broaden Model Risk Management Guidance to incorporate fair lending risk
For years, financial regulators like the OCC and Federal Reserve have articulated Model Risk Management (“MRM”) Guidance, which is principally concerned with mitigating financial safety and soundness risks that arise from issues of model design, construction, and quality.22 The MRM Guidance does not account for or articulate principles for guarding against the risks that models cause or the perpetuation of discrimination. Broadening the MRM Guidance scope would ensure institutions are guarding against discrimination risks throughout the model development and use process. In particular, regulators should clearly define “model risk” to include the risk of discriminatory or inequitable outcomes for consumers rather than just the risk of financial loss to a financial institution.
Effective model risk management practices would aid compliance with fair lending laws in several ways. First, model risk management practices can facilitate variable reviews by ensuring institutions understand the quality of data used and can identify potential issues, such as datasets that are over- or under-representative for certain populations. Second, model risk management practices are essential to ensuring that models, and variables used within models, meet a legitimate business purpose by establishing that models meet performance standards to achieve the goals for which they were developed. Third, model risk management practices establish a routine cadence for reviewing model performance. Fair lending reviews should, at a minimum, occur at the same periodic intervals to ensure that models remain effective and are not causing new disparities because of, for example, demographic changes in applicant and borrower populations.
To provide one example of how revising the MRM Guidance would further fair lending objectives, the MRM Guidance instructs that data and information used in a model should be representative of a bank’s portfolio and market conditions.23 As conceived of in the MRM Guidance, the risk associated with unrepresentative data is narrowly limited to issues of financial loss. It does not include the very real risk that unrepresentative data could produce discriminatory outcomes. Regulators should clarify that data should be evaluated to ensure that it is representative of protected classes. Enhancing data representativeness would mitigate the risk of demographic skews in training data being reproduced in model outcomes and causing financial exclusion of certain groups.
One way to enhance data representativeness for protected classes would be to encourage lenders to build models using data from Minority Depository Institutions (MDIs) and Community Development Financial Institutions (CDFIs), which have a history of successfully serving minority and other underserved communities; adding their data to a training dataset would make the dataset more representative. Unfortunately, many MDIs and CDFIs have struggled to report data to consumer reporting agencies in part due to minimum reporting requirements that are difficult for them to satisfy. Regulators should work with both consumer reporting agencies and institutions like MDIs and CDFIs to identify and overcome obstacles to the incorporation of this type of data in mainstream models.
4. Provide guidance on evaluating third-party scores and models
Financial institutions routinely rely on third-party credit scores and models to make major financial decisions. These scores and models often incorporate AI/ML methods. Third-party credit scores and other third-party models can drive discrimination, and there is no basis for immunizing them from fair lending laws. Accordingly, regulators should make clear that fair lending expectations and mitigation measures apply as much to third-party credit scores and models as they do to institutions’ own models.
More specifically, regulators should clarify that, in connection with supervisory examinations, they may conduct rigorous searches for disparate impact and less discriminatory alternatives related to third-party scores and models and expect the lenders to do the same as part of a robust compliance management system. The Federal Reserve Board, FDIC, and OCC recently released the “Proposed Interagency Guidance on Third-Party Relationships: Risk Management,” which states: “When circumstances warrant, the agencies may use their authorities to examine the functions or operations performed by a third party on the banking organization’s behalf. Such examinations may evaluate…the third party’s ability to…comply with applicable laws and regulations, including those related to consumer protection (including with respect to fair lending and unfair or deceptive acts or practices) ….”24  While this guidance is helpful, the regulators can be more effective in ensuring compliance by setting clear, specific, and robust regulatory expectations regarding fair lending testing for third-party scores and models. For example, regulators should clarify that protected class and proxy information should be removed, that credit scores and third-party models should be tested for disparate impact, and that entities are expected to conduct rigorous searches for less discriminatory alternative models as part of a robust compliance management program.25
5. Provide guidance clarifying the appropriate use of AI/ML during purported pre-application screens
Concerns have been raised about the failure to conduct fair lending testing on AI/ML models that are used in purported pre-application screens such as models designed to predict whether a potential customer is attempting to commit fraud. As with underwriting and pricing models, these models raise the risk of discrimination and unnecessary exclusion of applicants on a prohibited basis. Unfortunately, some lenders are using these pre-application screens to artificially limit the applicant pool that is subject to fair lending scrutiny. They do so by excluding from the testing pool those prospective borrowers who were purportedly rejected for so-called “fraud”-based or other reasons rather than credit-related reasons. In some cases, “fraud”26 is even defined as a likelihood that the applicant will not repay the loan—for example, that an applicant may max out a credit line and be unwilling to pay back the debt. This practice can artificially distort the lender’s applicant pool that is subject to fair lending testing and understate denial rates for protected class applicants.
Regulators should clarify that lenders cannot evade civil rights and consumer protection laws by classifying AI/ML models as fraud detection rather than credit models and that any model used to screen out applicants must be subject to the same fair lending monitoring as other models used in the credit process.
B. Provide clear guidance on the use of protected class data to improve credit outcomes
Any disparate impact analysis of credit outcomes requires awareness or estimation of protected class status. It is lawful—and often necessary—for institutions to make protected-class neutral changes to practices (including models) to decrease any outcome disparities observed during fair lending testing. For example, institutions may change decision thresholds or remove or substitute model variables to reduce observed outcome disparities.
Institutions should also actively mitigate bias and discrimination risks during model development. AI/ML researchers are exploring fairness enhancement techniques to be used during model pre-processing and in-processing, and evidence exists that these techniques could significantly improve model fairness. Some of these techniques use protected class data during model training but do not use that information while scoring real-world applications once the model is in production. This raises the question of the ways in which the awareness or use of protected class data during training is permissible under the fair lending laws. If protected class data is being used for a salutary purpose during model training—such as to improve credit outcomes for historically disadvantaged groups—there would seem to be a strong policy rationale for permitting it, but there is no regulatory guidance on this subject. Regulators should provide clear guidance to clarify the permissible use of protected class data at each stage of the model development process in order to encourage developers to seek optimal outcomes whenever possible.
C. Consider improving race and gender imputation methodologies
Fair lending analyses of AI/ML models—as with any fair lending analysis—require some awareness of applicants’ protected class status. In the mortgage context, lenders are permitted to solicit this information, but ECOA and Regulation B prohibit creditors from collecting it from non-mortgage credit applicants. As a result, regulators and industry participants rely on methodologies to estimate the protected class status of non-mortgage credit applicants to test whether their policies and procedures have a disparate impact or result in disparate treatment. The CFPB, for example, uses Bayesian Improved Surname Geocoding (BISG), which is also used by some lenders and other entities.27  BISG can be useful as part of a robust fair lending compliance management system. Using publicly available data on names and geographies, BISG can allow agencies and lenders to improve models and other policies that cause disparities in non-mortgage credit on a prohibited basis.28
Regulators should continue to research ways to further improve protected class status imputation methodologies using additional data sources and more advanced mathematical techniques. Estimating protected class status of non-mortgage credit applicants is only necessary because Regulation B prohibits creditors from collecting such information directly from those applicants.29 The CFPB should consider amending Regulation B to require lenders to collect protected class data as a part of all credit applications, just as they do for mortgage applications.
D. Ensure lenders provide useful adverse action notices
AI/ML explainability for individual decisions is important for generating adverse action reasons in accordance with ECOA and Regulation B.30 Regulation B requires that creditors provide adverse action notices to credit applicants that disclose the principal reasons for denial or adverse action.31 The disclosed reasons must relate to and accurately describe the factors the creditor considered. This requirement is motivated by consumer protection concerns regarding transparency in credit decision making and preventing unlawful discrimination. AI/ML models sometimes have a “black box” quality that makes it difficult to know why a model reached a particular conclusion. Adverse action notices that result from inexplicable AI/ML models are generally not helpful or actionable for the consumer.
Unfortunately, a CFPB blog post regarding the use of AI/ML models when providing adverse action notices seemed to emphasize the “flexibility” of the regulation rather than ensuring that AI providers and users adhere to the letter and spirit of ECOA, which was meant to ensure that consumers could understand the credit denials that impact their lives.32 The complications raised by AI/ML models do not relieve creditors of their obligations to provide reasons that “relate to and accurately describe the factors actually considered or scored by a creditor.”[33] Accordingly, the CFPB should make clear that creditors using AI/ML models must be able to generate adverse action notices that reliably produce consistent, specific reasons that consumers can understand and respond to, as appropriate. As the OCC has emphasized, addressing fair lending risks requires an effective explanation or explainability method; regardless of the model type used: “bank management should be able to explain and defend underwriting and modeling decisions.”34
There is little current emphasis in Regulation B on ensuring these notices are consumer-friendly or useful. Creditors treat them as formalities and rarely design them to actually assist consumers.  As a result, adverse action notices often fail to achieve their purpose of informing consumers why they were denied credit and how they can improve the likelihood of being approved for a similar loan in the future. This concern is exacerbated as models and data become more complicated and interactions between variables less intuitive.
The model adverse action notice contained in Regulation B illustrates how adverse action notices often fail to meaningfully assist consumers. For instance, the model notice includes vague reasons, such as “Limited Credit Experience.” Although this could be an accurate statement of a denial reason, it does not guide consumer behavior. An adverse action notice that instead states, for example, you have limited credit experience; consider using a credit-building product, such as a secured loan, or getting a co-signer, would provide better guidance to the consumer about how to overcome the denial reason. Similarly, the model notice in Regulation B includes “number of recent inquiries on credit bureau report” as a sample denial reason. This denial reason may not be useful because it does not provide information about directionality. To ensure that adverse action notices are fulfilling their statutory purpose, the CFPB should require lenders to provide directionality associated with principal reasons and explore requiring lenders to provide notices containing counterfactuals—the changes the consumer could make that would most significantly improve their chances of receiving credit in the future.
E. Engage in robust supervision and enforcement activities
Regulators should ensure that financial institutions have appropriate compliance management systems that effectively identify and control risks related to AI/ML systems, including the risk of discriminatory or inequitable outcomes for consumers. This approach is consistent with the Uniform Interagency Consumer Compliance Rating System35 and the Model Risk Management Guidance. The compliance management system should comprehensively cover the roles of board and senior management, policies and procedures, training, monitoring, and consumer complaint resolution. The extent and sophistication of the financial institution’s compliance management system should align with the extent, sophistication, and risk associated with the financial institution’s usage of the AI system, including the risk that the AI system could amplify historical patterns of discrimination in financial services.
Where a financial institution’s use of AI indicates weaknesses in their compliance management system or violations of law, the regulators should use all the tools at their disposal to quickly address and prevent consumer harm, including issuing Matters Requiring Attention; entering into a non-public enforcement action, such as a Memorandum of Understanding; referring a pattern or practice of discrimination to the U.S. Department of Justice; or entering into a public enforcement action. The Agencies have already provided clear guidance (e.g., the Uniform Consumer Compliance Rating System) that financial institutions must appropriately identify, monitor, and address compliance risks, and the regulators should not hesitate to act within the scope of their authority. When possible, the regulators should explain to the public the risks that they have observed and the actions taken in order to bolster the public’s trust in robust oversight and provide clear examples to guide the industry.
F. Release additional data and encourage public research
Researchers and advocacy groups have made immense strides in recent years studying discrimination and models, but these efforts are stymied by a lack of publicly available data. At present, the CFPB and the Federal Housing Finance Agency (FHFA) release some loan-level data through the National Survey of Mortgage Originations (NSMO) and Home Mortgage Disclosure Act (HMDA) databases. However, the data released into these databases is either too limited or too narrow for AI/ML techniques truly to discern how current underwriting and pricing practices could be fairer and more inclusive. For example, there are only about 30,000 records in NSMO, and HMDA does not include performance data or credit scores.
Adding more records to the NSMO database and releasing additional fields in the HMDA database (including credit score) would help researchers and advocacy groups better understand the effectiveness of various AI fairness techniques for underwriting and pricing. Regulators also should consider how to expand these databases to include more detailed data about inquiries, applications, and loan performance after origination. To address any privacy concerns, regulators could implement various measures such as only making detailed inquiry and loan-level information (including non-public HMDA data) available to trusted researchers and advocacy groups under special restrictions designed to protect consumers’ privacy rights.
In addition, NSMO and HMDA both are limited to data on mortgage lending. There are no publicly available application-level datasets for other common credit products such as credit cards or auto loans. The absence of datasets for these products precludes researchers and advocacy groups from developing techniques to increase their inclusiveness, including through the use of AI. Lawmakers and regulators should therefore explore the creation of databases that contain key information on non-mortgage credit products. As with mortgages, regulators should evaluate whether inquiry, application, and loan performance data could be made publicly available for these credit products.
Finally, the regulators should encourage and support public research. This support could include funding or issuing research papers, convening conferences involving researchers, advocates, and industry stakeholders, and undertaking other efforts that would advance the state of knowledge on the intersection of AI/ML and discrimination. The regulators should prioritize research that analyzes the efficacy of specific uses of AI in financial services and the impact of AI in financial services for consumers of color and other protected groups.
G. Hire staff with AI and fair lending expertise, ensure diverse teams, and require fair lending training
AI systems are extremely complex, ever-evolving, and increasingly at the center of high-stakes decisions that can impact people and communities of color and other protected groups. The regulators should hire staff with specialized skills and backgrounds in algorithmic systems and fair lending to support rulemaking, supervision, and enforcement efforts that involve lenders who use AI/ML. The use of AI/ML will only continue to increase. Hiring staff with the right skills and experience is necessary now and for the future.
In addition, the regulators should also ensure that regulatory as well as industry staff working on AI issues reflect the diversity of the nation, including diversity based on race, national origin, and gender. Increasing the diversity of the regulatory and industry staff engaged in AI issues will lead to better outcomes for consumers. Research has shown that diverse teams are more innovative and productive36 and that companies with more diversity are more profitable.37 Moreover, people with diverse backgrounds and experiences bring unique and important perspectives to understanding how data impacts different segments of the market.38 In several instances, it has been people of color who were able to identify potentially discriminatory AI systems.39
Finally, the regulators should ensure that all stakeholders involved in AI/ML—including regulators, financial institutions, and tech companies—receive regular training on fair lending and racial equity principles. Trained professionals are better able to identify and recognize issues that may raise red flags. They are also better able to design AI systems that generate non-discriminatory and equitable outcomes. The more stakeholders in the field who are educated about fair lending and equity issues, the more likely that AI tools will expand opportunities for all consumers. Given the ever-evolving nature of AI, the training should be updated and provided on a periodic basis.
III. Conclusion
Although the use of AI in consumer financial services holds great promise, there are also significant risks, including the risk that AI has the potential to perpetuate, amplify, and accelerate historical patterns of discrimination. However, this risk is surmountable. We hope that the policy recommendations described above can provide a roadmap that the federal financial regulators can use to ensure that innovations in AI/ML serve to promote equitable outcomes and uplift the whole of the national financial services market.

Kareem Saleh and John Merrill are CEO and CTO, respectively, of FairPlay, a company that provides tools to assess fair lending compliance and paid advisory services to the National Fair Housing Alliance. Other than the aforementioned, the authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. Other than the aforementioned, they are currently not an officer, director, or board member of any organization with an interest in this article.

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China’s payments u-turn: Government over technology

( 11 mins) China has been at the forefront of a technological revolution in payments in both its private and public sectors. China’s tech firms succeeded in replacing the bank-based magnetic striped card world with a tech-based QR code system. Then the People’s Bank of China (PBOC) launched its central bank digital currency, followed by a series of government actions that appear designed to steer the Chinese system away from these tech firms. What is going on in Chinese payments is a fascinating battle of private sector innovation versus government control and big-tech versus big-banks, putting the usually staid and boring world of payment systems into the spotlight allowing for examination of broader narratives about the future of China and how it is playing the global economic game. It also offers insight into how the Federal Reserve plans to approach digital payments in America.

Aaron Klein

Senior Fellow – Economic Studies

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The Chinese payment wars stand in sharp contrast to the standard analysis of the global economic game. In the standard model, the United States is the advanced incumbent economy while China is playing economic catch-up. China is simultaneously modernizing its own domestic system to resemble western economies while at various levels integrating into the broader global financial system. The story in payments begins along this common narrative. The U.S. created and essentially dominates global retail payments through a magnetic stripe card-based interface running through the global banking system. This system has its roots in a series of inventions from roughly 50 years ago in New York, which began as a set of solutions for restaurants and frequent customers who were unable to access cash over the weekend and sought an alternative to the paper-based check payment system. These ‘Diners Cards’ eventually transformed into a series of plastic cards, building a set of payment rails that process more than 130 billion transactions a year in the United States, which is more than 350 million transactions per day. To put that in perspective, the peak number of daily transactions in Bitcoin is estimated around 400,000.
Magnetic striped cards came to dominate the world of retail payments in developed economies. At an earlier point in its economic development, China attempted to emulate and graft onto this system, with multiple banks introducing their own sets of magnetic stripes and cards including Union Pay as the most prominent example. Founded in 2002, Union Pay’s prevalence rose sharply to achieve over 3.5 billion cards in circulation in just a decade and volume that was roughly half of what Visa was processing in the mid 2010s.
The story diverges with Chinese technology companies, WeChat and Alibaba, who appreciated the inherent inefficiencies in the card-based system: the interchange fees, design apparatus of cards and card readers, and the costs borne by merchants. Chinese merchants, particularly small ones, lacked interest in such a costly system. Exploiting these opportunities, the two tech firms created a QR code digital wallet scan-based system, which essentially leapfrogged the debit magnetic cards. The new system was faster and more efficient than debit magnetic cards, producing a host of direct and indirect benefits for those two companies as well as for broader society. This innovation allowed China to leapfrog the magnetic striped card system that dominates much of the western world’s retail payment system.
China’s new payment system exploded from inception to dominance in under a decade. With over a billion users on each platform, the power of network incentives has been unleashed. The new payment system has replaced cards and cash at registers, changed how families give gifts, and even evolved the way how beggars ask for money, with QR codes replacing tin cups.
This is a powerful example of Chinese innovation, competition, and adoption. It appears, at least to outside observers, to be highly organic and internally driven, not a product of central planning or committees. For example, the two companies diverged in the origin of their payment systems. WeChat Pay is based on a social media platform (for Americans think Facebook) and is heavily engaged in person-to-person payments. WeChat Pay first rolled out as a service to facilitate personal funds in the form of ‘Red Envelopes’ (traditional gifts of cash) around the Lunar New Year in 2014. WeChat Pay proposed digitizing this exchange, which given their person-to-person social media network, was clearly synergistic. The popularity of Red Envelope exchanges seeded many customers’ WeChat Pay accounts with initial funds. WeChat launched the Red Packet digital payment idea in 2014, and 16 million packets were sent. The next year, 1 billion packets were sent. By 2016, it was over 8 billion and in 2017, 46 billion.

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Alipay’s origin differs. Alipay is a payment platform developed by Chinese tech conglomerate Alibaba with roots in digital commerce (think Amazon) and hence more likely to be used for business purposes. Internet commerce requires electronic payment systems, which were integrated with credit and debit cards. The lack of such a system in China incentivized Alibaba to develop Alipay to support its Taobao online shopping platform. With Alipay’s main competitor, UnionPay, having only recently launched and not having gained many customers, the payment market was wide open. Alibaba offers incentives for merchants to use Alipay for purchases throughout their platform. They offer feeless purchases for both parties, preferential placement on digital platforms for merchants, and the ease of payment integration into business processing. Those differences provide economic benefits of lower costs and potentially greater transaction volumes that are not widely available in the bifurcated credit/debit card system.
There are potential drawbacks to this integrated model, including the lack of fees to provide services customers want with payments – such as interest-free grace periods of credit – and anti-competitive concerns of integrating business platforms and social networks with payment platforms.
With this technological advance, China had many of the ingredients necessary to challenge the existing retail payments system and seemed poised to leap into the global payments contest, which is in desperate need of an advance from the 50-year-old plastic cards that seem woefully out of place in the digital environment.
However, it appears that China has not chosen to do this, instead making a u-turn and now heading in the other direction. Rather than aggressively expanding the system and opening it to a broader network in the way that the American card-based system did, China has taken a series of measures to slow the tech companies, enhance the government’s role, and possibly bring payments back into a bank-centric system.
China’s government intervened with the creation of a central bank digital currency. This digital yuan uses much of the same infrastructure as the Ali and WeChat pay systems: digital wallets, QR codes, scanners, etc. Just this month PBOC Governor Yi Gang stated a goal of “interoperability with existing payment tools” for the digital yuan.
The digital yuan is currently running in more than 10 regions of China with more than 150 million users. It was first launched in Shenzhen, the home city of Tencent (the company that runs WeChat Pay). It does not take a skilled U.S.-China international diplomat with a keen understanding of history to understand that deciding to roll out the digital yuan in the hometown of the payment giant sends a clear message. If the U.S. government started its own online bookstore/retailer and happened to choose the city of Seattle, the message would be globally clear.
Couple this with Alibaba’s aborted initial public offering of its financial arm Ant and the sweeping set of problems cited by government officials and regulators and there is a message that China is pausing any potential for global expansion of the Alipay and WeChat payment systems. To the contrary, what seems to be happening is that rather than exporting Chinese-based digital wallets in hopes of becoming as ubiquitous as the Visa, MasterCard and American Express networks are currently, there is instead a desire to reorient the internal Chinese system to be focused on a central bank digital currency run through digital wallets more directly tied to the Chinese banking system.
Now, it is plausible that this change ultimately sets up a digital yuan using very similar technological rails of QR codes, first piloted by Ali and WeChat that would in fact be analogous to history repeating. The original American charge card, Diners Club, coordinated between restaurants (merchants) and consumers, not banks. This model ultimately lost the race. MasterCard is itself a consortium of financial institutions with a very different history than Visa, which was born from Bank of America, and American Express which began as a closed loop payment system and today is part of a bank holding company.
Previously, it seemed plausible that a digital wallet from Alipay or linked to the WeChat network could be a global phenomenon spreading far beyond China in the phones and pockets of billions of people worldwide. That now feels very unlikely. Instead, digital Chinese wallets through Chinese banks appear where China is headed. That model seems an unlikely mode to facilitate international commerce throughout Europe, or even Africa, let alone to challenge the United States for domestic market share. Though Alipay and WeChat are accepted in the United States in retail stores, they are almost exclusively used by Chinese individuals, not by Americans.
This begs the question: when China does make technological advances in globally competitive industries such as payments, is China’s ultimate goal to export this technology and create a network for global commerce? Or is it ultimately an internal process where the benefits and costs will be felt by Chinese nationals and control will be maintained by the Chinese government? Gunpowder was invited in China centuries before the formula came to Europe who used it very differently.
From an American perspective, there’s a bit of a sigh of a relief because China had built a better mousetrap in many respects. It is also a shot in the arm for the Federal Reserve, which has devoted significant resources to considering launching its own central bank digital currency. China was not the only entity pushing the Federal Reserve. Facebook’s original announcement of launching a digital currency (then called Libra, now known as Diem) was another key moment energizing the Fed to consider alternatives. The Fed’s consideration of a central bank digital currency has been heavily impacted by the payments actions proposed by both China and Facebook. This helps explain how the same Federal Reserve that failed to adopt a real-time payment in the U.S. despite the European Union, United Kingdom, Japan, Mexico, and many more countries adopting such a system years and decades earlier is now devoting significant attention to creating a new central bank digital currency. Whether the Fed launches a new digital currency or not, is years away. In the meantime, low income consumers still pay billions as a result of the Fed’s failure to modernize its payment system. By my estimate more than $100 billion has already been taken as a result of the Fed’s failure to act when the United Kingdom transitioned more than a decade ago. It marks one the largest failures of policy that contributes to income inequality and needless inequity in America in my lifetime.
In conclusion, whereas it is currently unclear whether the Federal Reserve will launch a central bank digital currency, it appears that China is committed to a path of a digital yuan. It seems likely that such a move will also favor moving payments more broadly back into its banking system, away from its two technological companies. However, the technological system of QR codes and digital wallets appears likely to remain in China regardless of who operates the system.

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.

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Credit scoring: Improve or eliminate?

( 2 mins) Your credit score plays a major role in your life, impacting your ability to rent an apartment, buy a house, get a credit card, and even how much you pay for auto insurance. These three-digit numbers, graded on a scale that resembles the SAT, have become more accessible to consumers due to recent changes in law, technology, and business. Credit scores are clearly impactful in the lives of Americans, but are they being created accurately, fairly, and with proper regulatory oversight? Is there a better way?
Credit scores are built on credit reports, files kept on most Americans by several large credit reporting bureaus. What are these reports and scores made of? How accurate are they? Who ensures they are fair and accurate?
On December 7, the Center on Regulation and Markets will convene a group of experts to discuss these questions and get to the core of the issue: Are credit scores and credit reports the right method for society to allocate credit? If so, how can they be improved? If not, what should replace them?
Viewers can submit questions for speakers by emailing [email protected] or via Twitter using #CreditScore.

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How Fintech Companies Can Mitigate the Racial Wealth Gap

( 2 mins) On November 2, 2021, Brookings Metro Fellow Kristen Broady testified to the U.S. House Financial Services Committee’s Task Force on Financial Technology, during a hearing titled Buy Now, Pay More Later? Investigating Risks and Benefits of BNPL and Other Emerging Fintech Cash Flow Products.

Broady’s research, as detailed in her written testimony, shows how fintech companies can mitigate racial financial health and wealth gaps that hamper Black and Hispanic families’ financial security through product offerings and policies they put in place. Through technology and automation, they can reduce costs and prices, speed up delivery and increase convenience for underserved populations (Saunders, 2019). Over the past 20 years, fintech companies have provided new ways to capture data, reach broader audiences, and expand access to credit (Strochak, 2017). These companies also have the potential to think differently about policies and programming that can amplify opportunities for Black and minority communities. These private sector innovations can be paired with public policy interventions as well as to address some of the systemic issues that have contributed to the financial health and wealth gaps.
Broady provides several steps that public policymakers can take to increase financial health, including:

Increase investments in the CDFI Fund and make any relevant programs that sunset (like NMTC) permanent.
Create a mandatory financial health curriculum for middle and high schoolers.
Enhance broadband deployment.
Raise minimum wage for companies with over 500 employees.
Foster utilization of the CFPB Special Purpose Credit Program (SPCP).
Revise and revive the SBIC program under the SBA to incentivize private sector investments in BIPOC founders.
Revise SBA 7(a) program to enable fintechs to more easily engage with the program.

The ongoing COVID-19 pandemic has disproportionately impacted the Black community in terms of health and economic effects and shined a light on historical racial wealth and financial health gaps in America. Closing these gaps will require that structural, systemic, and historical economic disparities are addressed through significant public policy changes.
To read Broady’s full testimony, click here. To watch the testimony video, click here.

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Expensive and growing: Loudoun County, Va.

( 7 mins) Navigation

November 4, 2021

Loudoun County is rapidly growing, high-priced county located in a growing, high-priced metropolitan area (Washington, D.C.). All but one of the metro area’s 25 jurisdictions saw positive population growth from 2009 to 2019, and eight jurisdictions fall into the highest cost category (housing value-to-income ratios over 4). Loudoun had the second highest population growth rate in the metro area (0.25).

To develop a more complete picture of housing market conditions in Loudoun County, we draw on a broader set of measures that capture demand, affordability of both owner-occupied and rental housing, and housing quality (Table 1). 

Key findings from this comparison are:

Loudoun County’s population growth rate, 0.35, is more than double that of the average county in the D.C. metro area, and well above the national average. Fast population growth drives the demand for additional housing.The typical household in the Washington D.C. metro area would have to pay 4.9 times their annual income to purchase the median home in Loudoun County. Home value-to-income ratios between 2.5-3.5 are considered healthy.Households earning less than $74,800 (or 78% of the metro area median income) would have difficulty paying rent for the median rental home in Loudoun County, while spending no more than 30% of their income on rent. While most middle-income households in the metro area can afford median rent in Loudoun, most low- and moderate-income households in the region will fall below this threshold.20.2% of renters in Loudoun County are severely cost burdened, meaning they spend more than half their income on rent. That is slightly below the severely cost-burdened share for the entire D.C. metro area and below the national average.The vacancy rate, 3.3%, is very low. Vacancy rates of 6-10% are considered healthy. Low vacancy rates are an indication that supply is not keeping up with demand.The housing stock is quite new relative to the region and country: only 2.8% of homes were built prior to 1940, while 72.6% were built after 1990. New housing is generally higher quality than older homes and more expensive to buy or rent, while having lower maintenance costs.

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Increase housing supply. Housing is expensive because supply has not kept up with demand. High prices and rents, combined with low vacancy rates, indicate that there is unmet demand for housing. It is important to realize that no single county can produce enough housing to meet demand for the entire metro area. Reducing housing costs in expensive, supply-constrained metro areas will require sustained periods of increased housing production across multiple jurisdictions. All high-cost counties within a metro area adopting the strategies described below will have better results than actions by a single county, and county officials can play a leading role in coordinating across jurisdictions and sectors to achieve those goals.

About the Authors

Jenny Schuetz

Senior Fellow – Metropolitan Policy Program, Future of the Middle Class Initiative

Tim Shaw

Associate Director of Policy – Aspen Institute

Make it easier to build small, moderately-priced homes. In expensive metro areas, the size of homes and the amount of land used per home are major factors in the price of individual homes. Single-family detached homes on large lots are the most expensive structure type. Rowhouses, townhomes, two-to-four family homes, and low-rise apartment buildings have lower per-unit development costs than detached homes. These structures are also well suited for rental housing, which is more affordable to moderate-income households, and as “starter homes” for prospective first-time buyers who are currently priced out of the market. Zoning changes that enable smaller, less land-intensive structures to be built as-of-right in more parts of the county will increase the diversity of housing choices and widen the price range of available homes. Companion zoning reforms include relaxing dimensional requirements, such as minimum lot sizes, setbacks, lot coverage, or floor-to-area ratios. Reducing minimum parking requirements and allowing flexibility in design standards can also result in cost savings for newly built homes.

Developing a specific menu of zoning reforms will require an assessment of the county’s current housing types, density, and land availability. Exactly what types of zoning reform will yield the largest supply increases and cost reductions will vary across high-cost counties. A locality that has predominantly detached homes on one-acre lots—typical of many outer suburbs—could realize substantial cost savings by allowing rowhouses on 4,000 square foot lots. For urban counties and inner-ring suburbs that currently have many small-lot homes and little undeveloped land, increasing housing supply may require zoning reforms that allow redeveloping single-family homes, parking lots, or commercial buildings as low- or mid-rise apartments. High-rise construction has the highest per-square-foot costs, and will typically only occur in places with very expensive land and high rents.

Make the development process simpler and shorter. The length of time required to complete development projects, combined with the complexity of the process, are significant factors in the price of newly built housing. Local development processes that make decisions on a case-by-case basis, rather than following consistent, transparent rules, increase the uncertainty and risk of development, which translates into higher costs. Discretionary processes include requiring special permits (also called conditional use permits), site plan reviews, environmental impact reviews, and negotiations over impact fees all add to development costs. Policy changes that reduce development time and complexity include allowing more development as-of-right; integrating approvals for multiple parts of the development process through a one-stop-shopping approach; setting a clear and transparent impact fee schedule; and setting deadlines that require decisions to be made within a set period of time.

Expand vouchers or income supports for low-income renters. Even in communities where enough housing is built to accommodate increased demand, market-rate housing remains unaffordable to many low-income households. The poorest 20% of households everywhere in the U.S. spend more than half their income on housing, well above the threshold HUD defines as affordable. Only one in four eligible households receives federal rental assistance, including vouchers and public housing. Local governments that have sufficient resources can supplement these programs through locally funded rental vouchers or direct income supports. These programs require an ongoing funding source; high-income counties may be able to finance local vouchers from general tax revenues such as property or sales taxes, while lower-income counties will require support from state or federal governments. 

An alternative to household-based subsidies for low-income households is to provide land or financial support for acquisition or construction of affordable housing. Local jurisdictions often own or have significant control over physical assets—such as publicly owned land or airspace—that can be leveraged to increase the availability of affordable housing in the community. Affordable housing trust funds are a flexible financing vehicle to support these activities.

Housing market conditions can vary across submarkets within counties. These policy recommendations are based on an assessment of overall county-level housing metrics. Larger counties often have multiple distinct submarkets with varying affordability, physical quality, infrastructure availability, and development regulations. Cities, towns, and neighborhoods that offer the best economic opportunity—proximity to well-paid jobs, transportation, good schools, and other amenities—often have housing that is too expensive for moderate-income households in the county. Lower-cost communities tend to have older, poorer quality housing. Addressing within-county disparities in housing costs, availability, and quality may require coordinating between independent political entities (e.g., separate cities and towns) in counties with more fragmented local government.

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Focus on bank supervision, not just bank regulation

( 12 mins) Introduction
Last month, the Biden administration made headlines nominating Cornell Law School Professor Saule Omarova to serve as Comptroller of the Currency, a position from which she would oversee the National Banking System. Omarova’s nomination has drawn sharp criticism from the financial services industry, placing her alongside other Biden appointments within financial regulation such as chair of the Securities and Exchange Commission Gary Gensler and director of the Consumer Financial Protection Bureau (CFPB) Rohit Chopra. In each case, the appointments represent a sea change, embracing an approach to regulation that starkly differs from the priorities of the Trump administration. These existing appointments and nominations set the stage for the financial regulatory appointments that the administration has not yet made, including three vacancies on the Federal Reserve — the Fed Chair, Vice Chair for Supervision, and a member of Fed’s Board of Governors —and vacancies at the Federal Deposit Insurance Corp (FDIC), among others.

Debate over these key personnel focus on different visions of regulation, the rules political appointees write that apply to the entire financial system. These include what financial regulation should do about climate change, how it should support under-represented minorities, how it can ensure financial stability, and much more. But the biggest piece of the puzzle is still missing: these agencies and appointments also control the supervision of the financial system, not just its regulation. The difference between these two concepts is very important. If regulation sets the rules of the road, supervision is the process that ensures obedience to these rules (and sometimes to norms that exist outside these rules entirely). Regulation is the highly choreographed process of generating public engagement in the creation of rules. Supervision is the mostly secret process of managing the public and private responsibilities over the risks that the financial system generates.
Political groups organize in support or opposition of various regulatory nominees usually on the basis of the candidates’ perceived regulatory priorities. This is important, but this exclusive focus is a mistake. We, all of us, should pay far more attention to the candidates’ vision and philosophy of supervision. This part of the public vetting is all the more important given the culture of secrecy that surrounds bank supervision. If the public is going to have a say in the kind of supervisory system we should have, then the appointment process is likely the first and last chance to do it.
The question for senators who provide advice and consent necessary to obtain these jobs and for the general public in vetting these candidates for appointment should focus on how these nominees view the tradeoffs inherent in the supervisory process wholly independent of financial regulation. It should focus on what they will do—now—to maintain the culture of supervision or to change it.
What Supervision Means
The idea that supervision and regulation should receive separate priorities is not new. More recently, in contemporary debates, some view supervision as part of a long-standing settlement of monetary questions of special relevance during the Civil War, others as the implementation of regulatory priorities, others as a kind of regulatory monitoring. We view it differently. While many of these conceptions of supervision capture elements of what makes this mode of public governance so unusual, the full picture is more historically contingent and flexibly comprehensive. In our view, supervision is the management of residual risk at the boundary of public and private, the space where private banks and public officials sometimes spar, sometimes collaborate, over responsibility over the financial system. Because risks in the financial system are constantly evolving,  supervision has done the same.
In our work on the history of bank supervision, we offer a typology that captures this range of functions. The typology has two parts. First, supervision functions as a distinct mechanism of legal obedience—a means by which government or private actors seek to alter bank behavior. These mechanisms can be displayed on two axes, between public and private mechanisms, which require the exercise of coercive and non-coercive power.

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In this sense, supervision represents a choice for policymakers, distinct from other alternatives. It is a choice, as the graphic indicates, which authorizes government officials to exercise substantial discretion about how to alter behavior.

Relatedly, what supervision looks like on the ground depends almost entirely on what supervisors think they are trying to accomplish, self-conceptions that divide not only according to an external logic of coercion vs. non-coercion and public vs. private but also an internal one. These self-conceptions operate within a framework with axes spanning punitive to collaborative and from retrospective to prospective, as summarized in Figure 2.

Together, the two typologies indicate a range of possible supervisory actions (external) and the motivations behind such actions (internal). They imply constant trade-offs that supervisors must make as they share risk management responsibilities with participants in the private sector. Despite the extraordinary flexibility that this model of supervision permits, supervisors cannot hold all ground at once and be all things to all people. They must choose and in choosing navigate the often conflicting and sometimes contradictory policy goals placed by Congress on bank supervisors: between safety and soundness and firm competitiveness, between consumer protection and facilitation of financial innovation, between punitive and collaborative approaches, and many others.
When a new public official is appointed to lead one of the major elements of bank supervision, she inherits a toolbox with many different kinds of tools. Members of Congress should ask nominees which tools they prefer for which kinds of jobs, how they view these trade-offs and what they would prioritize, and how they think about alternatives. A short-hand method is to listen for the metaphors nominees use to describe supervision. Do nominees conceive of supervisors as cops on the beat? As fire wardens? As referees and umpires? As compliance officers? As management consultants? Are banks their customers? What tools will they use in accomplishing this vision? What flexibility will they use and under what circumstances?
Who Supervisors Are
Because supervision is fundamentally flexible and evolving, personnel decisions are vital. Supervisory officials—independent of legislative and regulatory processes—constantly reshape the methods, tools, and rationales of supervision in relation to their understandings of financial risk and their evaluation of relevant policy tradeoffs.
History provides rich examples of this process. Looking back to the nineteenth century, comptrollers, and later officials at the Federal Reserve and FDIC, created supervisory bureaucracies with little congressional guidance on how those bureaucracies should be structured. In doing so, they crafted supervisory tools, like standardized examination forms or “schools” of supervision complete with simulated banks getting practice exams, which guided frontline agency staff and bankers through the thicket of managing residual financial risk. Sometimes appointees proved too lenient or too eager to encourage bank chartering and growth at the expense of systemic safety. At other times they proved too harsh, making a theatrical display of cracking down on shoddy bank oversight and in doing so potentially undermining agency credibility with bankers who doubt supervisors’ intentions.
Two recent examples highlight the ways conceptions of supervisory purpose translate into agency action. First, the CFBP emerged in the wake of the 2008 financial crisis in part because existing supervisory agencies tended to sacrifice consumer protection. In its early years, the architects of the CFPB adopted Senator Elizabeth Warren’s “cop on the beat” approach, bringing enforcement lawyers to routine exams even when there was no enforcement action pending. Thus, while other agencies tended to have a collaborative and prospective view of consumer protection—identifying potential problems and helping bankers navigate past them—the CFPB was looking to punish past mistakes and ensure compliance in the future. Bankers struggled to reconcile the agency’s seemingly contradictory positions. The enforcement attorneys left the examination teams, but the tone set from the top continued to be decisively important. Under Democrat Richard Cordray, the CFPB leveled more than $5.5 million in fines a day compared to slightly less than $2 million per day under Trump appointee Kathy Kraninger. The CFPB used civil enforcement aggressively and then didn’t.
Second, the design and implementation of stress tests for the nation’s largest, most systemically important banks has also undergone significant change at the hands of Federal Reserve appointees Dan Tarullo and Randy Quarles. There is much that is purely regulatory about these changes—the pace of stress tests, the reliance on qualitative versus quantitative metrics. But stress tests are ultimately a supervisory activity leaving a huge amount of space for supervisors to shape individual responses to idiosyncratic factors. The question for a new head of an agency is not simply what regulatory rules will govern stress tests but how that official thinks supervisory interactions with individual banks through the stress test process should occur.
Finally, changes in the approach to and methods of supervision seldom spring fully-formed from the head of a Senate-confirmed nominee. Rather, supervisory officials must also develop plans for recruiting, training, and retaining a superb corps of supervisors who can be independent of banks but also expert at managing those relationships to alter bank behavior. In doing so, they face two challenges. First, political appointees necessarily take the helm of an unwieldy ship. Frontline supervisors are in place and working hard in offices across the country. They have methods, routines, and ingrained expectations developed over years of experience and training by distant political appointees whose visions and ideologies of supervision may have been repudiated by a recent election. Supervisory leaders must have a strategy for learning their agencies’ bearing and changing course as necessary. They must do so, secondly, while holding onto experienced staff who may be attracted to the lure of more lucrative private-sector jobs. Retaining and retraining staff is a signature challenge of bank supervision. Senators should inquire about nominees’ plans for doing so.
Ten questions policymakers should ask
If Biden administration officials and senators heed our calls to take supervision seriously, then there are a number of questions they should direct to candidates and nominees alike.

What do you see as the purpose of supervision? What is the supervisory agency’s primary role? What, if any, secondary goals would you emphasize?
How would you plan to balance the inherent trade-offs between these goals? How would this balance differ—if at all—from your predecessor?
Which supervisory tools do you view as most important for accomplishing these goals? How would you use these tools differently than your predecessors?
How do you plan to learn about the existing supervisory culture at your agency? How do you plan to realign that culture around your goals?
How will you recruit, train, and retain supervisory staff?
How will you organize your office such that regulatory and supervisory functions inform each other but do not absorb each other?
How do you plan to work with other bank supervisory agencies—at the federal level, internationally, and in the states? Do you see these other agencies as competitors or collaborators?
What is the relationship between supervision and enforcement in your agency? How will you manage the process through which supervisors learn sensitive information that may be relevant to an enforcement action but that may also be an opportunity to change bank behavior without enforcement?
How will you ensure that bank supervisors do not unduly adopt the point of view of the banks supervised?
How will you ensure that bank supervisors understand the point of view of the banks supervised?

Although regulatory issues such as climate change, federal bank chartering, diversity, and fintech dominate conversations about regulatory appointments, a lack of focus on supervisory issues comes at a great cost to public governance and financial stability. Fights over regulation that ignore supervision may obscure these critical issues more than they illuminate. Bank supervision is an unusual set of institutions, homegrown in the United States and refined by federalism, financial crisis, and historical accident. Supervision remains the most important tool in the federal administrative toolkit for changing the way we understand the business of banking. The process of public governance should give it its due.

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.
Sean Vanatta is a Lecturer in U.S. Economic and Social History at the University of Glasgow, and an un-paid member of the Federal Reserve Archival System for Economic Research (FRASER) Advisory Board. The authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. Other than the aforementioned, they are currently not an officer, director, or board member of any organization with an interest in this article.

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Greening Asia for the long haul: What can central banks do?

( 5 mins) Here in Hong Kong, Category 8 typhoons used to be infrequent. Just as floods in Germany and massive wildfires in California were disasters we might see every decade or so. Sadly, this is no longer the case. In one week alone this October, Hong Kong saw two Category 8 typhoons. Earlier this year, floods devastated parts of central Germany, and California saw five of the largest wildfires in its history in 2020. Hong Kong, Germany, and California are not outliers. Extreme weather conditions have been documented in much of the world.

It is widely recognized that climate change implies more frequent and severe weather events, greatly increasing the physical risks to financial and economic stability. In the absence of urgent action, the impact will be widespread and affect most countries, people’s lives and livelihoods, and many industries. The financial sector will be no exception.
Central banks have an important role to play in responding to this challenge. They are interested in these issues for at least three reasons: (i) preserving financial stability as societies move toward reducing their carbon footprints; (ii) diversifying the investments of central bank reserves to minimize unnecessary risks; and (iii) in keeping with their mandates and expertise, providing support to global efforts to achieve the objectives of the Paris climate accord.
The Asia-Pacific region has the largest need for infrastructure investments, around $1.7 trillion per year for developing Asia alone, according to the Asian Development Bank (ADB). Much of this needs to be green investment. It is also among the most vulnerable regions if actions to combat climate change are not taken urgently. At the same time, Asia has, relatively speaking, an extremely large pool of foreign exchange reserves, at around $5.7 trillion by the end of 2019. So, there is a clear case that we need to find a way to bring these two together and do so creatively and safely. The BIS Asian Green Bond Fund is an attempt in this direction.
The fund is designed to provide central banks with opportunities to invest in high quality bonds issued by sovereigns, supranationals, and corporations that comply with strict international green standards. It has two distinct features. First—compared to the BIS’ previous green bond funds—it has a broader group of eligible issuers. It will invest in corporations, including financial firms, because much of the financing in Asia is through commercial banks rather than directly from capital markets. Second, the BIS has engaged with multiple international financial institutions and development finance institutions—the ADB, the Asian Infrastructure Investment Bank, the World Bank, and the International Finance Corporation—to explore opportunities for collaboration to develop a pipeline of green products in the region to invest in. These institutions also have expertise on the ground to ensure that the highest standards are being followed in forming these green investment opportunities to allay concerns about greenwashing.
On the technical side, in line with reserve managers’ appetite for safety, liquidity, and return, all central features of the BIS’s financial products, the Asian Green Bond Fund would be established as a BIS Investment Pool (BISIP), a collective investment scheme structured under Swiss law that is commonly used by the BIS for its fixed income investment products.

While the fund would provide the opportunity for central banks to invest their reserves into greening the economies in the region, the fund is not restricted to investors from Asia. Broad interest to invest in the fund has been expressed by central banks well beyond Asia, reflecting the need of the global central banking community for reserve diversification. Regardless of the domicile of the central bank, central bank investments are generally made with a long-term investment horizon in mind. The fund will thus help channel global central bank reserves to green projects for long-term sustainable growth in the region, which has contributed more than two-thirds of global growth in the past decade.
In light of the fast-changing developments of the green bond market in the Asia-Pacific region, the Asian Green Bond Fund will be an evolving fund, allowing it to be agile and make changes as needed. For example, while the fund will be denominated in U.S. dollars initially, green bonds denominated in Asian local currency will be considered at its first and/or second anniversary. Similarly, while ICMA (International Capital Market Association) and CBI (Climate Bond Initiative) standards will be used at the start, the BIS is open to alternative standards, for example, for bonds funding projects that may not be green at present but are aligned with a transition toward low-carbon activities.
Technologists often say: “Think big, start small and scale fast.” That is how the BIS too plans to approach this endeavor. The Asian Green Bond Fund will represent an important addition to the existing suite of green bond funds at the BIS. As the fund evolves, it will also allow central banks to consider ways of expanding green financial markets, either through diversifying into regional currencies, or by considering the next generation of sustainable bonds adhering to even stricter standards and aligned to the objectives of the Paris accord.

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Greening Asia for the long haul: What can central banks do?

( 5 mins) Here in Hong Kong, Category 8 typhoons used to be infrequent. Just as floods in Germany and massive wildfires in California were disasters we might see every decade or so. Sadly, this is no longer the case. In one week alone this October, Hong Kong saw two Category 8 typhoons. Earlier this year, floods devastated parts of central Germany, and California saw five of the largest wildfires in its history in 2020. Hong Kong, Germany, and California are not outliers. Extreme weather conditions have been documented in much of the world.

It is widely recognized that climate change implies more frequent and severe weather events, greatly increasing the physical risks to financial and economic stability. In the absence of urgent action, the impact will be widespread and affect most countries, people’s lives and livelihoods, and many industries. The financial sector will be no exception.
Central banks have an important role to play in responding to this challenge. They are interested in these issues for at least three reasons: (i) preserving financial stability as societies move toward reducing their carbon footprints; (ii) diversifying the investments of central bank reserves to minimize unnecessary risks; and (iii) in keeping with their mandates and expertise, providing support to global efforts to achieve the objectives of the Paris climate accord.
The Asia-Pacific region has the largest need for infrastructure investments, around $1.7 trillion per year for developing Asia alone, according to the Asian Development Bank (ADB). Much of this needs to be green investment. It is also among the most vulnerable regions if actions to combat climate change are not taken urgently. At the same time, Asia has, relatively speaking, an extremely large pool of foreign exchange reserves, at around $5.7 trillion by the end of 2019. So, there is a clear case that we need to find a way to bring these two together and do so creatively and safely. The BIS Asian Green Bond Fund is an attempt in this direction.
The fund is designed to provide central banks with opportunities to invest in high quality bonds issued by sovereigns, supranationals, and corporations that comply with strict international green standards. It has two distinct features. First—compared to the BIS’ previous green bond funds—it has a broader group of eligible issuers. It will invest in corporations, including financial firms, because much of the financing in Asia is through commercial banks rather than directly from capital markets. Second, the BIS has engaged with multiple international financial institutions and development finance institutions—the ADB, the Asian Infrastructure Investment Bank, the World Bank, and the International Finance Corporation—to explore opportunities for collaboration to develop a pipeline of green products in the region to invest in. These institutions also have expertise on the ground to ensure that the highest standards are being followed in forming these green investment opportunities to allay concerns about greenwashing.
On the technical side, in line with reserve managers’ appetite for safety, liquidity, and return, all central features of the BIS’s financial products, the Asian Green Bond Fund would be established as a BIS Investment Pool (BISIP), a collective investment scheme structured under Swiss law that is commonly used by the BIS for its fixed income investment products.

While the fund would provide the opportunity for central banks to invest their reserves into greening the economies in the region, the fund is not restricted to investors from Asia. Broad interest to invest in the fund has been expressed by central banks well beyond Asia, reflecting the need of the global central banking community for reserve diversification. Regardless of the domicile of the central bank, central bank investments are generally made with a long-term investment horizon in mind. The fund will thus help channel global central bank reserves to green projects for long-term sustainable growth in the region, which has contributed more than two-thirds of global growth in the past decade.
In light of the fast-changing developments of the green bond market in the Asia-Pacific region, the Asian Green Bond Fund will be an evolving fund, allowing it to be agile and make changes as needed. For example, while the fund will be denominated in U.S. dollars initially, green bonds denominated in Asian local currency will be considered at its first and/or second anniversary. Similarly, while ICMA (International Capital Market Association) and CBI (Climate Bond Initiative) standards will be used at the start, the BIS is open to alternative standards, for example, for bonds funding projects that may not be green at present but are aligned with a transition toward low-carbon activities.
Technologists often say: “Think big, start small and scale fast.” That is how the BIS too plans to approach this endeavor. The Asian Green Bond Fund will represent an important addition to the existing suite of green bond funds at the BIS. As the fund evolves, it will also allow central banks to consider ways of expanding green financial markets, either through diversifying into regional currencies, or by considering the next generation of sustainable bonds adhering to even stricter standards and aligned to the objectives of the Paris accord.

Read More »

Status check: Managing debt sustainability and development priorities through a ‘Big Push’

( 9 mins) Executive Summary
Emerging market and developing economies (EMDEs) have seen development prospects fade in the two years since the onset of COVID-19. Growth turned negative in 2020, is forecast to snap back in 2021, but then revert to a declining trend.1 Investment levels in Latin America and Africa are forecast to remain in the range of 20-25 percent of GDP in the medium term. Outside of Asia, prospects for growth and for convergence with advanced economies are dim. Unlike in advanced economies, the GDP trajectory in EMDEs post-COVID-19 is significantly lower than pre-COVID-19 estimates; 31 developing countries may have lower levels of GDP per capita in 2025 than in 2019.

Meanwhile, general government debt levels in EMDEs have risen by 9 percentage points of GDP. At current low levels of world interest rates, the debt service implications are manageable for most countries, but risks remain if inflation causes major central banks to raise interest rates. As a result, EMDEs are under pressure to cut public spending, even in face of higher needs to respond to the pandemic.
The present trajectory, therefore, is one of slow growth, low investment and public spending, and rising debt service burdens in many, if not most, EMDEs. There is significant risk that this trajectory will prove unsustainable for economic, social, or political reasons.
The current trajectory is also highly inefficient, with high-return projects in EMDEs left unfunded due to debt overhang considerations, and highly inequitable, with poor and vulnerable countries and populations left to manage the pandemic with limited support.

Related Content

Global aspirations for a universal transformation to a low-carbon economy and a “just transition” are not likely to be met in the current baseline scenario for the global economy because EMDEs are central to both objectives and without additional public spending neither transition will happen.
There is another way forward, one that offers better prospects for global growth and equity, with lower risks of systemic debt defaults. Rather than relying on austerity, it is a path that seeks to accelerate green, inclusive and resilient growth. This path takes advantage of historically low prices of energy, made possible by technological advances in renewables, and of historically low interest rates on international capital markets to undertake a “big push” to transform economic structures and accelerate growth.

There are four key ingredients of the “big push” approach.
First, a set of investments needs to be identified to achieve the desired transformations. The country-by-country analytical work on which this paper draws suggests that EMDEs (ex-China) should be increasing their investment rates by about 3-4 percent of GDP above pre-pandemic levels in order to provide adequate growth of zero-carbon energy and infrastructure, sustainable agriculture, forestry, and land use (AFOLU), adaptation and resilience, and human capital. This translates into incremental annual investments of about $1.3 trillion by 2025, and more thereafter.
Second, a financing plan is needed that is aligned with the types of expenditures being considered. The proposal advanced here is for an even split between domestic and external financing. The latter, in turn, can be mobilized from ODA, multilateral and other official financial institutions, and private capital. These are not fully fungible—each has a role to play.
Domestic resource mobilization is a core component of any investment strategy. It is essential for general purpose financing like human capital and recurrent spending on nature and adaptation. Thus, a key part of the big push strategy is improving developing country tax administrative capacity, while reducing fossil fuel subsidies. The needed increase of 2.7 percentage points of GDP is well within the range of possibilities identified by the IMF. Additional revenues may accrue from new regulations governing base erosion and profit shifting (BEPS), but the current G-20 agreement may not yield much for many developing countries in the medium term. Stronger international efforts are also needed to stem illicit financial flows and encourage greater information sharing between tax authorities in advanced and developing countries.
Concessional finance is needed to help poor countries, to promote equity, and to incentivize countries to invest adequately in global public goods that have international spillovers—for example, mitigation, nature, and pandemic preparedness. Bilateral donors have already pledged to double climate finance from $30 billion to $60 billion, and agreement seems likely on a $100 billion IDA20 replenishment by year’s end. However, more is needed. ODA in 2020 from DAC countries amounted to only 0.32 percent of their GDP. A new collective agreement is needed to back the transformational change that is proposed here. Our approach calls for a 50 percent increase in concessional finance relative to 2019 levels, an incremental $96 billion by 2025. This is equivalent to 0.15 percent of donor GDP.
Our proposal is not just a call for more ODA, defined as money designed to promote the welfare of developing countries. As the past year has shown, weak health systems and pandemic surveillance in one country have global repercussions. The point is that provision of concessional finance for implementation of global public goods in developing countries benefits advanced countries as well as developing countries. Our proposal calls for a mixture of ODA and a fair funding of global public goods on concessional terms.
Multilateral finance and other official finance. Multilateral development banks (MDBs) are able to offer lower cost loans at longer maturities than other lenders, making debt more sustainable. They could stretch their current balance sheets by making better use of callable capital and reforming statutory lending limits—perhaps freeing up headroom for an additional $750 billion to $1.3 trillion of loans. Other reform efforts, including balance sheet optimization, greater risk pooling, greater use of blended finance and guarantee facilities, and asset sales could also help expand MDB lending. Our proposal calls for MDBs to triple their lending levels, from $63 billion in 2019 to $189 billion by 2025.
Private capital can be attracted into sustainable infrastructure projects, which generate revenue streams to cover equity returns and the debt service associated with the project. There are currently both supply and demand side obstacles that have prevented the scale-up of greater private investment in developing countries: a lack of bankable projects, and a limited appetite for long term investments with perceived high risk. New institutional innovations, such as the development of country platforms, standardized processes, and experience with risk-mitigating official finance suggest that a rapid ramp-up in private finance is now feasible. Our proposal calls for an approximate doubling of the 2019 level of private finance for infrastructure in developing countries by 2025. MDBs and other development partners will need to be more proactive to mobilize and catalyze private finance on this scale.
The third element of the “big push” is policy and institutional reform in countries to ensure that investments generate maximum returns. These reforms are partly sectoral (carbon equivalent taxes and removal of fossil fuel subsidies are clear examples) and partly cross-cutting. Reducing corruption and bureaucratic red tape, increasing voice and citizen participation, and consideration of gender issues are examples. Many countries have the capacity to absorb far larger amounts of investment than they actually receive. To illustrate, the IMF’s public investment management assessment scores African countries at 4.4 compared to 4.5 for Asia. Yet Asian countries profitably invest 15 percentage points of GDP more than African countries. The dispersion in scores is large, suggesting some countries have considerable room to improve, but equally suggesting that other countries have reasonable policy frameworks in place already.
The fourth and final element of the “big push” is international coordination. A “big push” will not work without a concerted global plan and agreement. In some cases, this reflects the need for global policy coordination—the BEPS agreement, proposed measures for carbon taxes and standardized approaches in blended finance are examples. More fundamentally, however, international coordination is needed to change mindsets about appropriate policy. No finance minister in an EMDE will support a big push strategy if this is not approved by the international financial institutions that s/he will rely on to provide needed resources. No private investor will willingly provide funds into a high-debt situation without clarity on how debt work-out mechanisms will operate. No civil society advocate will support a big push without greater transparency on how funds will be spent by their government.
All this suggests the need for a process of consensus building around how to scale up cross-border financing. There are many ongoing discussions and ideas for strengthening parts of the system, but less has been done on forging a common approach so that all parts of the system act in a linked-up fashion. There must be international oversight of core recovery programs, the building of consensus around which individual actors can orient their actions, and the inclusion of regional partners to augment the voice of developing countries. Transparency will be a crucial pillar of any financial architecture reform effort.
The response to COVID-19 thus far has bought us some time, but it is far from enough. The world faces a host of looming medium-term challenges in addition to the short-term imperatives of managing COVID-19 and ensuring recovery. Foremost among these are a low-carbon transition, increasing biodiversity and nature needs, growing adaptation and resilience challenges, and a large deficit in human capital spending. Big problems require big solutions. A stepped-up “big push” investment of around $1.3 trillion in EMDEs by 2025 would enable greater spending on all of these global challenges. A financing strategy must match the right source of financing to each of these needs and deliver this in a timely fashion to enable transformational change over the next decade. The moment is ripe for greater international collaboration and action. In 2020, advanced economies spent over $12 trillion on domestic response efforts. In the next decade, we must mobilize a similarly ambitious effort to tackle a global response.
Download the full working paper

Read More »

Status check: Managing debt sustainability and development priorities through a ‘Big Push’

( 9 mins) Executive Summary
Emerging market and developing economies (EMDEs) have seen development prospects fade in the two years since the onset of COVID-19. Growth turned negative in 2020, is forecast to snap back in 2021, but then revert to a declining trend.1 Investment levels in Latin America and Africa are forecast to remain in the range of 20-25 percent of GDP in the medium term. Outside of Asia, prospects for growth and for convergence with advanced economies are dim. Unlike in advanced economies, the GDP trajectory in EMDEs post-COVID-19 is significantly lower than pre-COVID-19 estimates; 31 developing countries may have lower levels of GDP per capita in 2025 than in 2019.

Meanwhile, general government debt levels in EMDEs have risen by 9 percentage points of GDP. At current low levels of world interest rates, the debt service implications are manageable for most countries, but risks remain if inflation causes major central banks to raise interest rates. As a result, EMDEs are under pressure to cut public spending, even in face of higher needs to respond to the pandemic.
The present trajectory, therefore, is one of slow growth, low investment and public spending, and rising debt service burdens in many, if not most, EMDEs. There is significant risk that this trajectory will prove unsustainable for economic, social, or political reasons.
The current trajectory is also highly inefficient, with high-return projects in EMDEs left unfunded due to debt overhang considerations, and highly inequitable, with poor and vulnerable countries and populations left to manage the pandemic with limited support.

Related Content

Global aspirations for a universal transformation to a low-carbon economy and a “just transition” are not likely to be met in the current baseline scenario for the global economy because EMDEs are central to both objectives and without additional public spending neither transition will happen.
There is another way forward, one that offers better prospects for global growth and equity, with lower risks of systemic debt defaults. Rather than relying on austerity, it is a path that seeks to accelerate green, inclusive and resilient growth. This path takes advantage of historically low prices of energy, made possible by technological advances in renewables, and of historically low interest rates on international capital markets to undertake a “big push” to transform economic structures and accelerate growth.

There are four key ingredients of the “big push” approach.
First, a set of investments needs to be identified to achieve the desired transformations. The country-by-country analytical work on which this paper draws suggests that EMDEs (ex-China) should be increasing their investment rates by about 3-4 percent of GDP above pre-pandemic levels in order to provide adequate growth of zero-carbon energy and infrastructure, sustainable agriculture, forestry, and land use (AFOLU), adaptation and resilience, and human capital. This translates into incremental annual investments of about $1.3 trillion by 2025, and more thereafter.
Second, a financing plan is needed that is aligned with the types of expenditures being considered. The proposal advanced here is for an even split between domestic and external financing. The latter, in turn, can be mobilized from ODA, multilateral and other official financial institutions, and private capital. These are not fully fungible—each has a role to play.
Domestic resource mobilization is a core component of any investment strategy. It is essential for general purpose financing like human capital and recurrent spending on nature and adaptation. Thus, a key part of the big push strategy is improving developing country tax administrative capacity, while reducing fossil fuel subsidies. The needed increase of 2.7 percentage points of GDP is well within the range of possibilities identified by the IMF. Additional revenues may accrue from new regulations governing base erosion and profit shifting (BEPS), but the current G-20 agreement may not yield much for many developing countries in the medium term. Stronger international efforts are also needed to stem illicit financial flows and encourage greater information sharing between tax authorities in advanced and developing countries.
Concessional finance is needed to help poor countries, to promote equity, and to incentivize countries to invest adequately in global public goods that have international spillovers—for example, mitigation, nature, and pandemic preparedness. Bilateral donors have already pledged to double climate finance from $30 billion to $60 billion, and agreement seems likely on a $100 billion IDA20 replenishment by year’s end. However, more is needed. ODA in 2020 from DAC countries amounted to only 0.32 percent of their GDP. A new collective agreement is needed to back the transformational change that is proposed here. Our approach calls for a 50 percent increase in concessional finance relative to 2019 levels, an incremental $96 billion by 2025. This is equivalent to 0.15 percent of donor GDP.
Our proposal is not just a call for more ODA, defined as money designed to promote the welfare of developing countries. As the past year has shown, weak health systems and pandemic surveillance in one country have global repercussions. The point is that provision of concessional finance for implementation of global public goods in developing countries benefits advanced countries as well as developing countries. Our proposal calls for a mixture of ODA and a fair funding of global public goods on concessional terms.
Multilateral finance and other official finance. Multilateral development banks (MDBs) are able to offer lower cost loans at longer maturities than other lenders, making debt more sustainable. They could stretch their current balance sheets by making better use of callable capital and reforming statutory lending limits—perhaps freeing up headroom for an additional $750 billion to $1.3 trillion of loans. Other reform efforts, including balance sheet optimization, greater risk pooling, greater use of blended finance and guarantee facilities, and asset sales could also help expand MDB lending. Our proposal calls for MDBs to triple their lending levels, from $63 billion in 2019 to $189 billion by 2025.
Private capital can be attracted into sustainable infrastructure projects, which generate revenue streams to cover equity returns and the debt service associated with the project. There are currently both supply and demand side obstacles that have prevented the scale-up of greater private investment in developing countries: a lack of bankable projects, and a limited appetite for long term investments with perceived high risk. New institutional innovations, such as the development of country platforms, standardized processes, and experience with risk-mitigating official finance suggest that a rapid ramp-up in private finance is now feasible. Our proposal calls for an approximate doubling of the 2019 level of private finance for infrastructure in developing countries by 2025. MDBs and other development partners will need to be more proactive to mobilize and catalyze private finance on this scale.
The third element of the “big push” is policy and institutional reform in countries to ensure that investments generate maximum returns. These reforms are partly sectoral (carbon equivalent taxes and removal of fossil fuel subsidies are clear examples) and partly cross-cutting. Reducing corruption and bureaucratic red tape, increasing voice and citizen participation, and consideration of gender issues are examples. Many countries have the capacity to absorb far larger amounts of investment than they actually receive. To illustrate, the IMF’s public investment management assessment scores African countries at 4.4 compared to 4.5 for Asia. Yet Asian countries profitably invest 15 percentage points of GDP more than African countries. The dispersion in scores is large, suggesting some countries have considerable room to improve, but equally suggesting that other countries have reasonable policy frameworks in place already.
The fourth and final element of the “big push” is international coordination. A “big push” will not work without a concerted global plan and agreement. In some cases, this reflects the need for global policy coordination—the BEPS agreement, proposed measures for carbon taxes and standardized approaches in blended finance are examples. More fundamentally, however, international coordination is needed to change mindsets about appropriate policy. No finance minister in an EMDE will support a big push strategy if this is not approved by the international financial institutions that s/he will rely on to provide needed resources. No private investor will willingly provide funds into a high-debt situation without clarity on how debt work-out mechanisms will operate. No civil society advocate will support a big push without greater transparency on how funds will be spent by their government.
All this suggests the need for a process of consensus building around how to scale up cross-border financing. There are many ongoing discussions and ideas for strengthening parts of the system, but less has been done on forging a common approach so that all parts of the system act in a linked-up fashion. There must be international oversight of core recovery programs, the building of consensus around which individual actors can orient their actions, and the inclusion of regional partners to augment the voice of developing countries. Transparency will be a crucial pillar of any financial architecture reform effort.
The response to COVID-19 thus far has bought us some time, but it is far from enough. The world faces a host of looming medium-term challenges in addition to the short-term imperatives of managing COVID-19 and ensuring recovery. Foremost among these are a low-carbon transition, increasing biodiversity and nature needs, growing adaptation and resilience challenges, and a large deficit in human capital spending. Big problems require big solutions. A stepped-up “big push” investment of around $1.3 trillion in EMDEs by 2025 would enable greater spending on all of these global challenges. A financing strategy must match the right source of financing to each of these needs and deliver this in a timely fashion to enable transformational change over the next decade. The moment is ripe for greater international collaboration and action. In 2020, advanced economies spent over $12 trillion on domestic response efforts. In the next decade, we must mobilize a similarly ambitious effort to tackle a global response.
Download the full working paper

Read More »