The Illusion of Cheap Credit: Why Interest-Rate Caps Backfire
Executive Summary
Policymakers have proposed capping credit card interest rates to provide relief to consumers. A recent Vanderbilt Policy Accelerator study argues that banks’ margins are sufficient to absorb such caps while continuing to lend profitably, yielding net consumer savings.
That conclusion relies on a static framework that asks only whether lending remains arithmetically viable under a cap. It does not consider how lenders, borrowers, and competing providers will respond when risk-based pricing is constrained. Economic theory and evidence show that credit markets adjust along other margins: supply contracts for higher-risk borrowers, shifts toward lower-risk segments, and migrates to substitute or informal channels.
The Vanderbilt study also rests on unsupported assumptions. It assumes issuers will uniformly price at the cap, even though that could raise costs for prime borrowers and turn a ceiling into a de facto floor. It assumes banks will offset losses by cutting advertising, despite likely effects on competition and innovation. And it evaluates viability using accounting returns, rather than the risk-adjusted returns that drive capital allocation.
A proper welfare analysis must account for borrowers pushed out of the market, substitution into costlier alternatives, fee shifting, and reduced innovation. Evidence from prior reforms and international experience confirms these effects.
Credit markets are dynamic. Price ceilings do not eliminate risk or create credit; they reallocate it. The relevant question is whether restricting risk-based pricing improves welfare once these adjustments are considered. The evidence suggests it does not.
I. Introduction
Policymakers across the political spectrum have recently proposed bills to impose price controls on credit card interest rates. In February 2025, Sens. Josh Hawley (R-Mo.) and Bernie Sanders (I-Vt.) introduced S.381, the 10 Percent Credit Card Interest Rate Cap Act.[1] Sanders argued that the legislation would provide “desperately needed financial relief” to working families struggling to pay their bills.[2]
Economists have long viewed such proposals with skepticism. Price controls are blunt instruments, and their unintended but predictable effects often outweigh any intended benefits. It is therefore notable that in September 2025 the Vanderbilt Policy Accelerator published a study concluding that interest-rate caps would benefit consumers.[3]
This brief evaluates that study’s methodology, analysis, and conclusions. The study asks whether banks’ current profit margins are sufficient to absorb a mandated rate cut—and answers yes. But it does not address the more relevant question: how banks, borrowers, and substitute lenders will respond to a binding price ceiling. That question has been studied extensively, and the evidence does not favor rate-cap proponents.
II. The Limits of Static Analysis
The Vanderbilt study, authored by Brian Shearer—a lawyer and former assistant director at the Consumer Financial Protection Bureau (CFPB)—uses comparative static analysis to estimate the effects of capping credit card interest rates at 10%, 15%, or 18%. It relies on data from a Federal Reserve Bank of New York report by Itamar Drechsler (hereinafter the “Drechsler Report”), which decomposes credit card profitability into key components: interest spread, charge-off losses, interchange revenue, rewards costs, fee revenue, and operating expenses. The data cover borrower and transactor accounts across 14 FICO score bins, ranging from 600 to 850.[4]
Shearer replaces the observed interest spread with a hypothetical spread consistent with each proposed cap—for example, a 5% spread under a 10% cap, assuming a 5% federal funds rate. He then recalculates return on assets (ROA) and asks whether it remains positive.
The results appear striking. Under a 15% cap, nearly every FICO tier generates positive returns without adjustments to rewards or operating costs, suggesting that existing margins could absorb a meaningful rate reduction. Under a 10% cap, ROA turns negative in tiers below FICO 780 before adjustments. Shearer argues, however, that banks could restore profitability by cutting rewards in those tiers and trimming what he characterizes as “bloated advertising budgets,”[5] while still leaving consumers better off because interest savings would exceed rewards reductions by at least three to one.
These calculations are internally consistent but rest on a critical assumption: that a federal price control on credit card rates would produce no behavioral response. The analysis assumes no credit rationing, no product substitution, no fee shifting, and no portfolio rebalancing. That assumption is not merely contestable—it conflicts with extensive empirical evidence on how credit markets respond to binding price ceilings.
A sound policy analysis must also look beyond borrowers who remain in the market. Consumer welfare cannot be measured solely by comparing interest payments before and after a cap. It must account for borrowers who are rationed out of credit markets, the costs of shifting to substitute or informal credit, changes in fees and other less-salient pricing terms, reduced competition and innovation, and the reallocation of capital toward uncapped products. A policy that lowers rates for some borrowers while excluding others—or pushing them into inferior alternatives—may produce visible short-term savings while reducing overall welfare once these broader adjustments are considered. Because the Vanderbilt study focuses only on projected savings within the existing portfolio, it does not capture the full economic consequences of a binding cap.
III. Credit Rationing Under Price Controls
In a seminal 1981 paper, Andrew Weiss and Nobel Prize–winning economist Joseph Stiglitz showed that when lenders cannot adequately price risk—whether due to a binding usury cap or asymmetric information—markets do not clear through prices.[6] Instead, they clear through quantity rationing. When lenders cannot raise interest rates to reflect higher risk, they restrict the supply of credit to riskier borrowers.
Credit card markets follow this logic. A binding rate cap prevents lenders from charging risk-adjusted prices to higher-risk borrowers. Lenders respond by tightening nonprice terms: raising minimum credit score thresholds, lowering credit limits, shortening maturities, requiring additional collateral, or denying credit altogether. The result is both a reduction in total credit and a reallocation of credit toward lower-risk borrowers.
Shearer acknowledges this dynamic only at the margins. He notes, for example, that lending to borrowers with FICO scores of 600 and below may decline under a 10% cap because those accounts remain unprofitable even after eliminating rewards and cutting advertising. He treats this as a narrow and acceptable edge case, suggesting that “at that risk-level, we might see increases in other fees or a reduction in lending.”[7]
That framing understates the stakes. Even if the effects were limited to borrowers with FICO scores of 600 and below, excluding them from the credit card market would raise serious concerns. This group may represent “only 12% of Americans,” but dismissing granting their ability to access credit cards as resembling “predatory lending”[8] overlooks why their borrowing costs are higher in the first place: elevated default risk. Higher APRs reflect that risk; they do not create it. Nor does the possibility of debt cycles justify denying access to credit altogether. If anything, evidence on product substitution suggests that consumers denied mainstream credit often turn to more expensive or less regulated alternatives.
At bottom, this reasoning reflects a paternalistic premise: that individuals with low FICO scores are better off excluded from credit markets than allowed to borrow at risk-adjusted prices. That premise is both contestable and inconsistent with evidence on how consumers respond to credit constraints.
Rationing, moreover, is not confined to the lowest FICO tiers. A 2025 Federal Reserve Bank of New York study by Rajashri Chakrabarti et al. examined the introduction of 36% APR caps in several states and found that credit to higher-risk borrowers contracted, while credit to lower-risk borrowers expanded—exactly the reallocation predicted by theory.[9] The effects were concentrated among borrowers whose risk profiles implied market-clearing rates above the cap. If a 36% cap produces measurable rationing, a 10% or 15% federal cap would likely have far more pronounced effects.
A static accounting framework cannot capture these dynamics because it does not model behavior. Shearer’s approach shows only that bank profitability at each FICO tier is arithmetically consistent with lower interest rates. That is true in the same narrow sense that a restaurant’s gross margins might appear sufficient to absorb a price ceiling. What the arithmetic cannot show is whether the restaurant will remain open, change its menu, shift costs, or exit the market. Credit markets are not vending machines that dispense loans at regulator-set prices.
IV. Substitution Effects and Credit Migration
The Vanderbilt study largely ignores a central question: what happens to borrowers who lose access to credit cards—or face sharply reduced credit limits—under a binding rate cap? This is not a theoretical concern. A World Bank review of interest-rate caps across more than 70 countries identifies “migration to the least-constrained channel” as a pervasive and well-documented response.[10] When regulation targets a specific product, lenders adjust, and borrowers seek substitutes.
One such substitute is buy now, pay later (BNPL) financing—e.g., pay-in-four loans financed by merchants—as well as installment loans repaid over time with interest. The CFPB’s January 2025 report finds that about 21% of Americans with a credit score have used BNPL.[11] Use is heavily concentrated among lower-credit borrowers: “subprime” consumers (FICO scores of 580–619) account for 16% of BNPL loans, while “deep subprime” consumers (FICO scores of 300–579) account for 45%.[12] These are precisely the borrowers most likely to face credit restrictions under a binding cap on credit card interest rates.
BNPL, however, is an imperfect substitute for credit cards. Merchant offers of BNPL remain limited, and much more limited than the general prevalence of credit card acceptance. BNPL products also typically offer weaker fraud protections (for example, completely lacking zero-liability policies), along with more cumbersome dispute-resolution processes than credit card chargebacks. They also generally do not provide insurance benefits. In addition, pay-in-four products are usually limited to smaller purchases.
For larger purchases, borrowers may turn to installment loans. But annual percentage rates (APRs) on such loans for lower-FICO borrowers often match or exceed credit card rates. At the same time, many states have imposed caps on installment-loan APRs, which has reduced or eliminated their availability for the very consumers most likely to be rationed out of the credit card market.
Borrowers who cannot access credit cards, BNPL, or installment loans may turn to other sources, such as payday lending, which is often more expensive. If caps extend across all legal credit products—through a federal usury rule, for example—some borrowers will turn to illegal lending markets.
International experience underscores this risk. After Japan tightened rate caps under the 2006 revision of the Money Lending Business Act, consumer-finance balances fell sharply, while illegal lending expanded. Some illegal lenders reportedly charged rates as high as 10% per week—equivalent to an APR of 14,104%.[13] Korea’s experience is similar. After progressively lowering its statutory maximum rate to 20% by 2021, one study estimates that a single 2018 rate reduction pushed roughly 659,000 borrowers into informal or illegal credit channels.[14]
The Vanderbilt study’s failure to engage with these substitution effects is not a minor omission. It overlooks one of the most consistent empirical findings in the literature on price controls in credit markets.
V. Fee Shifting and Hidden Price Adjustments
When governments cap one source of revenue, lenders typically recover losses elsewhere. Fee shifting is a well-documented response to interest-rate restrictions. It can take many forms: higher annual fees, increased penalty and late fees, reduced benefits such as credit insurance, tighter grace periods, and higher minimum payment requirements that raise the effective cost of revolving balances.
Shearer argues that such fee shifting is unlikely, relying on a 2015 study by Sumit Agarwal, Souphala Chomsisengphet, Neale Mahoney, and Johannes Stroebel on the effects of the CARD Act. That study estimated that the act’s fee restrictions saved consumers $11.9 billion annually, with “no evidence of an offsetting increase in interest charges or a reduction in the volume of credit.”[15] But more recent research—using longer time horizons, richer data, and structural models unavailable to Agarwal et al.—reaches a different conclusion. The evidence now indicates that the CARD Act produced substantial fee shifting, altered competitive dynamics, restricted credit access for higher-risk borrowers, and imposed welfare costs that the study’s framework did not capture.
Agarwal et al. based their conclusions on a difference-in-differences model applied over a short observation window. Broader evidence tells a different story. The CFPB’s own 2013 report found that “[d]ue to the increase in both the incidence and average dollar amount of annual fees, consumers…paid an additional $475 million in annual fees in 2012.”[16]
Fee shifting has also intensified over time. Using CFPB data, the Consumer Bankers Association reports that total annual fees have doubled since 2015. While increased card issuance explains part of that growth, issuers also shifted the fee burden away from subprime borrowers and toward higher-credit tiers.[17] Lenders did not simply absorb lost revenue; they restructured pricing in ways that short-run models could not detect.
Agarwal et al. acknowledged these limits. With only two years of post–CARD Act data, they could not evaluate longer-run effects on market entry and exit, contract structure, or investment.[18] Subsequent evidence shows precisely those adjustments: rising annual fees, redesigned rewards programs, and changes in competitive behavior.
The most rigorous challenge to Agarwal et al. comes from Scott Nelson’s structural model of the credit card market.[19] Accounting for private information in repricing—the mechanism the CARD Act restricts—Nelson finds distributional effects absent from earlier work. Pricing became less responsive to both public and private measures of borrower risk, and price dispersion fell by roughly one-third, indicating more pooled pricing. Rates declined for higher-risk and less price-sensitive borrowers, while rising elsewhere—including for some borrowers with low FICO scores but favorable private risk profiles.
In other words, the CARD Act induced substantial fee shifting and cross-subsidization. It also contributed to partial market unraveling among relatively safe subprime borrowers, who were priced out of credit they would otherwise have received. Nelson further shows that part of the earlier study’s measured increase in “consumer surplus” reflects reduced lender profits—a transfer to remaining borrowers, not a net welfare gain. That transfer came at the expense of borrowers who exited the market entirely, whose losses are not captured in account-level data.[20]
Other studies corroborate these findings. Yiwei Dou, Julapa Jagtiani, Joshua Ronen, and Kenechukwu Maingi show that the ratio of credit limits to total available credit declined for subprime borrowers relative to higher-score groups after the CARD Act.[21] Crucially, this shift began with the legislation—not during the Great Recession—undermining claims that macroeconomic conditions drove the change. Song Han, Benjamin Keys, and Geng Li find similar effects in credit card solicitation data, documenting a contraction in supply to subprime borrowers.[22] Gregory Elliehausen and Simona Hannon find that restrictions on risk-management practices reduced credit card holding among higher-risk consumers and increased reliance on consumer finance loans in states where those loans remained viable.[23] In short, borrowers who lost access to credit cards shifted to costlier alternatives—a welfare loss not reflected in the original estimates.
Finally, Shearer contends that consumers choose credit cards based on salient features such as annual fees and rewards, limiting issuers’ ability to shift costs. But if consumers focus on visible terms, issuers have stronger incentives to adjust less salient ones: tighter grace periods, higher minimum payments, and more aggressive penalty structures. The study does not model these margins of adjustment.
VI. The Uniform Pricing Assumption
A further key assumption in the Vanderbilt study is that all issuers will charge the capped interest rate to all borrowers. Shearer describes this as a “conservative” assumption, reasoning that if banks charge less than the cap, consumers save even more. But the assumption is neither conservative nor empirically grounded.
It may hold for lower-FICO tiers, where current rates exceed any proposed cap. For higher-FICO borrowers—particularly those currently paying interest spreads in the 8–12% range—the assumption implies that rates would rise under a cap, not fall. Shearer acknowledges as much, noting that under an 18% cap, borrowers with FICO scores above 760 would pay roughly $7 billion more each year.
The assumption draws on the payday-lending literature, where lenders often charge the maximum permissible rate.[24] But that pattern reflects the structure of payday lending, which targets high-risk borrowers with few alternatives. In that context, the cap may approximate the minimum viable rate. Credit card markets are different. Issuers serve borrowers across a wide range of risk profiles and compete on multiple price and nonprice dimensions. By assuming uniform pricing at the cap, the study imports a model that does not fit the market it seeks to analyze.
If issuers continue to price below the cap for prime borrowers, the study overstates consumer savings. If they instead converge on the cap, then the policy functions as a price floor for low-risk borrowers—raising their borrowing costs while purporting to help consumers. Either way, the study’s savings estimates are unreliable.
VII. The Advertising Assumption
In FICO tiers where static ROA turns negative under a rate cap, the Vanderbilt study assumes that banks will restore profitability by cutting advertising expenditures. That assumption is flawed in several respects.
First, the study assumes that marketing accounts for 20% of expenses across all issuers and FICO tiers.[25] This figure derives from a single observation about Capital One’s expense structure in the Drechsler Report—and refers to “marketing,” not advertising.[26] That is a thin empirical basis for a system-wide assumption with significant analytical consequences.
Second, marketing is not a discretionary expense that banks can reduce without tradeoffs. It is an endogenous investment in customer acquisition and retention. Marketing allows issuers to inform consumers about existing products and introduce new ones. A uniform reduction in marketing might leave relative market shares unchanged, but only by dampening competition and slowing product differentiation. That outcome appears implicit in the study’s assumption.
Reduced marketing would also affect innovation. If expected returns fall, banks will scale back investment in new products and features. Lower marketing spend reduces product adoption and revenue, weakening incentives to invest in improvements such as fraud protection, insurance benefits, and digital account management tools. These are not marginal features; they are central to the value proposition of modern credit cards.
More fundamentally, the claim that advertising budgets are “bloated” lacks a clear benchmark. The assumption that banks will absorb the effects of rate caps primarily through advertising cuts reflects the same static logic that underpins the broader analysis. In reality, banks would adjust along multiple margins—credit rationing, product substitution, and fee restructuring among them. Assigning the entire adjustment burden to marketing is inconsistent with both theory and evidence.
VIII. Capital Allocation and Opportunity Cost
A final—and more fundamental—weakness in the Vanderbilt study lies in its treatment of capital allocation and opportunity cost. The study assumes that a positive ROA is sufficient to sustain lending within a given FICO tier. If projected ROA remains above zero—or above the federal funds rate—lending is deemed “viable.” That framing misstates how banks allocate capital and leads to an overly optimistic view of credit card lending under rate caps.
Banks do not evaluate business lines against a zero-profit threshold. They allocate scarce regulatory capital based on risk-adjusted return on capital (RAROC).[27] The relevant question is not whether a product generates a positive accounting return, but whether it generates a return commensurate with the capital it consumes, the volatility of its cash flows, its correlation with macroeconomic risk, and the returns available from alternative uses of capital.
Credit cards score poorly on these dimensions. They are unsecured, operationally intensive, and highly procyclical. Charge-off rates rise sharply in downturns, reflecting both income volatility and the absence of collateral.[28] Under Basel standards and U.S. implementing regulations, unsecured revolving credit carries materially higher capital charges than many secured products.[29] Banks must hold capital against unexpected losses, not just expected losses, precisely because these portfolios exhibit elevated loss volatility.[30]
Rate caps compress the risk premium available to compensate for that volatility. If a 15% cap reduces projected ROA in binding tiers to roughly 2%, as the Vanderbilt study suggests, that figure must be evaluated in context. A 2% ROA on a high-risk, high-volatility unsecured product is not equivalent to a 2% ROA on secured lending or fee-based activities. Nor is it equivalent to alternative uses of capital once returns are evaluated on a risk-adjusted basis.
The federal funds rate is also the wrong benchmark. Banks cannot freely redeploy capital into reserves and earn the policy rate without regard to capital requirements, liquidity rules, leverage constraints, and shareholder expectations.[31] The relevant comparison is the marginal risk-adjusted return available elsewhere. If capped returns fall below a bank’s internal hurdle rate—typically set above its cost of equity and calibrated to portfolio risk—capital will have to be reallocated.[32]
That reallocation need not be dramatic to matter. Even modest shifts toward prime borrowers, secured lending, commercial credit, or fee-based services would reduce supply in the segments where caps bind most tightly. The Vanderbilt framework assumes capital remains in place so long as returns are positive. In practice, capital flows to its highest risk-adjusted use.[33]
Static ROA analysis also misses the dynamic nature of credit card lending. These portfolios depend on sustained investment in underwriting, fraud detection, rewards systems, customer acquisition, and digital servicing. Lower expected lifetime returns reduce incentives to invest. Banks will respond by tightening underwriting, lowering credit limits, scaling back acquisitions, and reducing exposure to cyclical segments. Over time, these adjustments contract effective supply—even if accounting ROA never turns negative.
Price controls also distort risk across the business cycle. In expansions, upside is capped; in downturns, losses are not. This asymmetric payoff further discourages capital allocation to capped products. Banks earn less in good times while bearing full downside risk in bad times. The rational response is to shift capital toward assets where pricing can adjust with risk—a pattern well documented in the banking literature.[34]
In short, a positive projected ROA under a rate cap does not establish economic viability. The relevant test is whether returns exceed the full opportunity cost of capital on a risk-adjusted basis. The Vanderbilt study does not perform that test. By treating positive accounting returns as sufficient, it understates the likelihood of capital reallocation and credit contraction in the very segments the policy aims to help.
IX. Conclusion
The Vanderbilt Policy Accelerator study offers an ambitious attempt to quantify the effects of federal credit card interest-rate caps. Its central claim is that banks’ existing profit margins can absorb a mandated reduction in interest spreads while remaining profitable, thereby delivering net savings to consumers. That conclusion rests on a narrow comparative-static framework that asks only whether credit card lending remains arithmetically viable under a capped rate. It does not ask how market participants will respond once risk-based pricing is constrained.
Credit markets are dynamic. They rely on risk differentiation, portfolio optimization, and capital allocation under regulatory constraints. Theory and empirical evidence show that when a binding price ceiling prevents lenders from pricing for risk, adjustment occurs along other margins. Credit supply contracts for higher-risk borrowers, shifts toward lower-risk borrowers, and migrates into substitute or informal channels. Lenders restructure revenue through fees and less-salient pricing terms. Capital flows toward products with stronger risk-adjusted returns.
The Vanderbilt framework cannot capture these dynamics. By treating positive projected ROA as evidence of continued supply, it overlooks the relevant benchmark: risk-adjusted return on capital. Credit card lending is unsecured, operationally intensive, and procyclical. When caps compress margins, they reduce the risk premium available to compensate for volatility and downturn exposure. Even if accounting returns remain positive, capital may rationally move elsewhere.
The study’s uniform-pricing assumption further undermines its conclusions. If issuers continue to price below the cap for prime borrowers, the study overstates consumer savings. If they instead converge on the cap, the policy risks functioning as a price floor for low-risk borrowers. Either outcome calls the projected savings into question.
Most importantly, the study evaluates welfare only for borrowers who remain in the capped market. It does not account for those excluded from credit, those pushed into higher-cost or less-regulated alternatives, reduced innovation, or long-run contractions in supply. Transfers within a shrinking market do not constitute net welfare gains.
Price ceilings in credit markets do not eliminate risk or create credit. They reallocate it. They change who receives credit, on what terms, and from which providers. Whether that reallocation improves welfare depends on general-equilibrium effects—not on whether a mandated rate aligns with current accounting margins.
The relevant policy question is not whether banks can absorb lower interest spreads in a static model. It is whether restricting risk-based pricing improves consumer welfare once rationing, substitution, fee restructuring, and capital reallocation are taken into account. On that question, the weight of theory and evidence is far less reassuring than the Vanderbilt study suggests.
[1] 10 Percent Credit Card Interest Rate Cap Act, S. 381, 119th Cong. (2025), https://www.congress.gov/bill/119th-congress/senate-bill/381.
[2] Bernie Sanders, News: Sanders, Hawley Introduce Bill Capping Credit Card Interest Rates at 10%, U.S. Senate (Feb. 4, 2025), Press Release, https://www.sanders.senate.gov/press-releases/news-sanders-hawley-introduce-bill-capping-credit-card-interest-rates-at-10.
[3] Brian Shearer, Capping Credit Card Rates, Vanderbilt Policy Accelerator (Sept. 2025), https://cdn.vanderbilt.edu/vu-URL/wp-content/uploads/sites/412/2025/10/01144344/Capping-Credit-Card-Rates.pdf [hereinafter Vanderbilt Study].
[4] Itamar Drechsler et al., Credit Card Banking, Fed. Rsrv. Bank of N.Y. Staff Rep. No. 1143 (2025), https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr1143.pdf [hereinafter Drechsler Report].
[5] Vanderbilt Study, supra note 3, at 7, 17.
[6] Joseph E. Stiglitz & Andrew Weiss, Credit Rationing in Markets with Imperfect Information, 71 Am. Econ. Rev. 393 (1981).
[7] Vanderbilt Study, supra note 3, at 24.
[8] Id. at 25.
[9] Rajashri Chakrabarti et al., Less for You, More for Me: Credit Reallocation and Rationing Under Usury Limits, Fed. Rsrv. Bank of N.Y. Staff Rep. No. 1173 (2025).
[10] Samuel Munzele Maimbo & Claudia Alejandra Henriquez Gallegos, Interest Rate Caps Around the World: Still Popular, But a Blunt Instrument, World Bank Pol’y Rsch. Working Paper No. 7070 (2014).
[11] Consumer Fin. Prot. Bureau, Consumer Use of Buy Now, Pay Later and Other Unsecured Debt 1 (Jan. 2025).
[12] Id. at 14.
[13] Shigeru Sato & Shingo Kawamoto, Loan-Shark Lending Surge Feared in Japan, Bloomberg (Aug. 8, 2012), https://www.bloomberg.com/news/articles/2012-08-07/loan-shark-lending-surge-feared-in-japan.
[14] Aurora Ferrari et al., Interest Rate Caps: The Theory and the Practice, World Bank Pol’y Rsch. Working Paper No. 8398, at 18–20 (2018).
[15] Sumit Agarwal et al., Regulating Consumer Financial Products: Evidence from Credit Cards, 130 Q.J. Econ. 111, 112 (2015).
[16] Consumer Fin. Prot. Bureau, CARD Act Report 25 (2013), https://files.consumerfinance.gov/f/201309_cfpb_card-act-report.pdf.
[17] Consumer Bankers Ass’n, Facts Matter: CARD Act Report Reveals Credit Card Fee Landscape in Stark Contrast to CFPB’s Misleading Headlines (2024), https://consumerbankers.com/press-release/facts-matter-card-act-report-reveals-credit-card-fee-landscape-in-stark-contrast-to-cfpbs-misleading-headlines.
[18] Agarwal et al., supra note 15, at 157–58.
[19] Scott T. Nelson, Private Information and Price Regulation in the US Credit Card Market, 93 Econometrica 1371, 1371 (2025).
[20] Id. at 1372.
[21] Yiwei Dou et al., The Credit Card Act and Consumer Debt Structure, 7 J.L. Fin. & Acct. 91, 91-92 (2022).
[22] Song Han, Benjamin J. Keys & Geng Li, Information, Contract Design, and Unsecured Credit Supply: Evidence from Credit Card Mailings, Bd. of Governors of the Fed. Rsrv. Sys., Fin. & Econ. Discussion Series No. 2015-103 (2015).
[23] Gregory Elliehausen & Simona M. Hannon, The Credit Card Act and Consumer Finance Company Lending, 34 J. Fin. Intermediation 109, 109-10 (2018).
[24] Vanderbilt Study, supra note 3, at 14 (“[P]ayday lenders uniformly charge the maximum interest rate allowed by state law.”).
[25] Id. at 17-18.
[26] Drechsler Report, supra note 4, at 29 (noting that Capital One’s marketing accounts for about 20% of total expenses); Vanderbilt Study, supra note 3, at 18 (assuming 20% of operating expenses in each tier go to advertising based on that estimate).
[27] Joel M. Bessis, Risk Management in Banking 197–205 (4th ed. 2015) (explaining RAROC frameworks, internal capital allocation, and risk-adjusted performance measurement in banking institutions).
[28] Ronald J. Mann, Charging Ahead: The Growth and Regulation of Payment Card Markets 96–101 (2006) (describing cyclicality and loss volatility in credit card portfolios); Fed. Deposit Ins. Corp., Quarterly Banking Profile tbl. RC-N (various years) (showing elevated, recession-sensitive net charge-off rates for credit card loans relative to other loan categories).
[29] Basel Comm. on Banking Supervision, Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems ¶¶ 49–54, 153–159 (rev. June 2011) (describing capital buffers and the treatment of credit risk exposures); 12 C.F.R. pt. 3, subpt. D (2024) (OCC risk-based capital rules); 12 C.F.R. pt. 217, subpt. D (2024) (Federal Reserve capital rules implementing Basel standards).
[30] Basel Comm. on Banking Supervision, International Convergence of Capital Measurement and Capital Standards ¶¶ 330–335 (June 2006) (assigning risk weights and capital treatment for qualifying revolving retail exposures, including credit card receivables).
[31] Basel Comm. on Banking Supervision, Basel III: The Liquidity Coverage Ratio and Liquidity Risk Monitoring Tools ¶¶ 14–25 (Jan. 2013) (establishing liquidity coverage requirements); 12 C.F.R. pt. 249 (2024) (U.S. liquidity coverage ratio rule).
[32] Robert C. Merton & André F. Perold, Theory of Risk Capital in Financial Firms, 1 J. Applied Corp. Fin. 16, 19–24 (1993) (developing internal risk-capital allocation principles and hurdle-rate logic for financial institutions).
[33] Franklin Allen & Douglas Gale, Competition and Financial Stability, 55 J. Money, Credit & Banking 453, 458–63 (2004) (analyzing capital-allocation decisions and risk-taking incentives under competitive constraints).
[34] Anil K. Kashyap & Jeremy C. Stein, Cyclical Implications of the Basel II Capital Standards, 49 Econ. Persp. 18, 20–24 (2004) (discussing the procyclicality of bank capital requirements and lending behavior); Viral v. Acharya et al., Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks, 102 Am. Econ. Rev. 59, 61–64 (2012) (analyzing capital adequacy and downside risk exposure in financial institutions).