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Lender Automation and Racial Disparities in Credit Access
Abstract
Process automation reduces racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach, and removing human biases from decision‐making. We document these findings amid the Paycheck Protection Program (PPP), where private lenders faced no credit risk but decided which firms to serve. Black‐owned firms obtained PPP loans primarily from automated fintech lenders, especially in areas with high racial animus. After traditional banks automated their loan processing procedures, their PPP lending to Black‐owned firms increased. Our findings cannot be fully explained by racial differences in loan application behaviors, pre‐existing banking relationships, firm performance, or fraud rates.
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... Additionally, Black-owned businesses were more likely to be denied loans from traditional lending sources (Howell et al., 2024). ...
... Methods for addressing the lack of self-reported business-owner race vary depending on the unit of analysis employed. Papers using loan-level analysis attempt to correct or fill in unreported race observations (e.g., Atkins et al., 2022;Garcia and Darity, 2022;Howell et al., 2024). Another method is to aggregate loan distribution data (e.g., Fairlie and Fossen, 2022), often at the ZIP code level, as this was the smallest standard geographical variable reported in the loan data. ...
... The cost of this primary data collection is a significantly smaller sample size, as they limit their analysis solely to Durham, NC. Rather than hand-collecting data, Howell et al. (2024) employ a two-part process involving machine learning to predict borrowers' races. First, they estimate the probability that an individual belongs to a certain racial group based on their last name and location, using data from the 2000 Census on last names and census tract racial distributions from the American Community Survey. ...
We investigate racial disparities in the distribution of Paycheck Protection Program (PPP) loans, focusing on the third tranche and rural business impacts. Using a Cragg-Hurdle regression model, we analyze how a community's racial composition influenced PPP loan allocation. Our findings reveal a large increase in funding for majority-minority communities, particularly those with higher Black populations, during the third tranche compared to earlier rounds. This tranche introduced notable shifts in loan distribution patterns, influenced by nontraditional lenders and policy changes under the Biden-Harris Administration. We observe a marked difference in loan allocation based on racial composition and urban-rural distinctions: the Black population's share consistently correlates positively with both loan numbers and amounts, a trend amplified in the third tranche. In contrast, the relationship between the White population share and loan distribution varies, becoming less positive or more negative in different tranches and regions. These findings challenge prior assessments of racial equity in PPP loan distribution, underscoring the third tranche's critical role in shaping the overall program. Our study emphasizes the need for a revised understanding of racial disparities in PPP loan allocation, particularly in light of evolving lender practices and policy changes.
... As a result, banks may exhibit preferences for certain types of loans (e.g., larger ones) or borrowers (e.g., ones with existing banking relationships (Bartik et al., 2020)). Minority entrepreneurs are less likely to secure credit through these relationships (Federal Reserve Bank of Atlanta, 2019), which may explain the difficulties Black small business owners faced in accessing PPP funds, especially early in the program (Atkins et al., 2022;Chernenko & Scharfstein, 2022;Evans, 2021;Fairlie & Fossen, 2022;Lester & Wilson, 2023) and why they obtained a disproportionately high share of PPP loans from fintechs (Erel & Liebersohn, 2022;Howell et al., 2024;Schweitzer & Guo, 2024). Forgiveness outcomes, however, are attained only by borrowers who cleared their lenders' loan approval hurdles. ...
... Their high efficiency may partially arise from analyzing customers' digital footprints, which can replace some aspects of soft information (Berg et al., 2020). Consistent with these advantages, fintechs expand access to the PPP (Erel & Liebersohn, 2022;Howell et al., 2024). ...
... When considering borrower race, we hypothesize that Black borrowers will have poorer PPP loan forgiveness outcomes compared to White borrowers, holding lender and loan characteristics constant. In other words, we expect that the racial disparities in small business lending observed in the presence of credit risk (Chen et al., 2021) and in the absence of such risk at the PPP loan approval stage (Atkins et al., 2022;Chernenko & Scharfstein, 2022;Evans, 2021;Fairlie & Fossen, 2022;Howell et al., 2024;Lester & Wilson, 2023) extend to the PPP forgiveness stage. Structural inequalities in the financial system and business support networks, including historical factors, discriminatory lending practices, other forms of institutional racism, and poorer access to resources (including reliable information) for Black entrepreneurs, can contribute to racial disparities in lending outcomes (Aaronson et al., 2021;Bates & Robb, 2013;Lynch et al., 2021;Popick, 2022;Quillian et al., 2020;Ross & Yinger, 2002). ...
Existing research establishes that minority borrowers, particularly Black small business owners, faced significant challenges in accessing funds from the Paycheck Protection Program (PPP), especially in its early stages. We find that institutional and racial disparities persist during the PPP loan forgiveness stage. Controlling for various loan- and borrower-level characteristics, we demonstrate that relationship lenders—community banks, credit unions, and farm credit institutions—are associated with higher rates of PPP loan forgiveness. In contrast, automated lenders—fintechs and fintech banks—exhibit the lowest forgiveness rates. Black borrowers experience the poorest outcomes, except for loans issued by non-depository fintech and lenders categorized as “other,” where they outperform White borrowers. Loan forgiveness rates improve, and racial disparities diminish, with increased lender concentration in specific economic sectors. Thus, specialized relationship lenders may have the highest odds of achieving the best and most equitable lending outcomes.
... In light of the program, questions were raised about the equitable fairness of loan distributions. Countless studies found disparities within the program (e.g., Lederer and Oros 2020;Wang and Zhang 2021;Kelly and van Holm 2021;Santellano 2021;Kickul et al. 2021;Howell et al. 2022;Atkins et al. 2022;Chernenko et al. 2023;Lester and Wilson 2023;Howell et al. 2024;Chernenko and Scharfstein 2024;Kotomin et al. 2024;Lelo de Larrea et al., 2024). The PPP only reached up to 20% of all eligible firms in rural-dominated states with the highest concentration of Black-owned small businesses. ...
... This, in turn, reduces bias on race/ethnicity within banking institutions in their PPP lending activity. In a subsequent study, Howell et al. (2024) found Black-owned businesses were about 12% points more likely than other firms to receive their PPP loans from a fintech lender, mainly due to preference-based discrimination. After conventional lenders automated their lending processes, their rates of lending to Black-owned businesses increased substantially. ...
Introduced under the Trump-Pence Administration, the Paycheck Protection Program (PPP) provided short-term relief loans to small American businesses during the peak of the Coronavirus pandemic. The initial design of the PPP faced significant criticism from researchers due to racial disparities, among other issues, in its lending process. Minority groups received smaller PPP loan amounts during the original two tranches released in 2020. To increase equitable access for all, in February 2021 the Biden-Harris Administration enforced swift changes to the initial PPP aimed at favouring access to PPP loans for minority-owned small businesses that had been disadvantaged by the program’s original design under the Trump-Pence Administration. By exploiting a granular dataset of 1,759,270 PPP loans granted between Q2 2020 and Q2 2021 and by implementing a difference-in-differences approach (DID), this paper provides novel evidence on the effectiveness of the Biden reforms in reducing racial disparities within the Paycheck Protection Program. Indeed, we observe a significant increase in the volume of PPP loans granted to minority-owned businesses in the period following the Biden-Harris Administration’s reforms. Furthermore, among different minority groups, the reforms appear most effective for Native American minority groups (including American Indians, Alaska Natives, Native Hawaiians and/or Other Pacific Islanders), followed by Black Americans and Asian business owners. Our findings offer novel contributions to the existing literature on institutional discrimination, particularly regarding the initial PPP design. Our findings are especially valuable for policy makers as they underscore the importance of radical changes in addressing racial disparities. Our paper also offers evidence of how a public credit guarantee program should be designed to empower and promote economic inclusion for all, regardless of ethnicity, aligning with the UN’s Sustainable Development Goals.
... For example, loan process automation has reduced racial disparities in credit access by providing small loans, expanding banking reach, and removing human bias from decision making. Such automation can help mitigate bias in credit decisions and promote more equitable access to financial services [6]. ...
The rapid development of fintech over the past decade has dramatically changed global financial markets and profoundly influenced investor behavior. This paper examines the impact of fintech innovation, particularly robo-advisors, blockchain technology, and social trading platforms, on investor behavior through the lens of behavioral finance. By reviewing the existing literature, this paper explores how these techniques affect decision-making processes, market efficiency, and investor biases such as overconfidence, loss aversion, and herding behavior. The study found that through algorithms and automated investment management, robo-advisors can mitigate certain behavioral biases, but can also introduce new challenges, such as over-dependence. The inherently volatile and decentralized nature of blockchain technology and cryptocurrencies magnifies speculation and introduces new biases. Social trading platforms, while democratizing access to financial markets, have exacerbated herding behavior and short-term speculation. The study identifies gaps in current research, including the need for long-term impact studies and ethical considerations, and suggests directions for future research, such as exploring new behavioral biases and improving regulatory frameworks. Overall, fintech innovation offers great potential for improving market efficiency and financial inclusion, but it also presents new challenges that require ongoing investigation and adaptation strategies.
... Some individuals face institutionalised bias and discrimination when accessing or using financial services in the financial sector (Brock and De Haas, 2023;Howell et al, 2024). These individuals are also at risk of facing institutionalised bias and discrimination when accessing or using central bank digital currency in the financial system. ...
The purpose of this article is to explore the role of artificial intelligence, or AI, in a central bank digital currency project and its challenges. Artificial intelligence is transforming the digital finance landscape. Central bank digital currency is also transforming the nature of central bank money. This study also suggests some considerations which central banks should be aware of when deploying artificial intelligence in their central bank digital currency project. The study concludes by acknowledging that artificial intelligence will continue to evolve, and its role in developing a sustainable CBDC will expand. While AI will be useful in many CBDC projects, ethical concerns will emerge about the use AI in a CBDC project. When such concerns arise, central banks should be prepared to have open discussions about how they are using, or intend to use, AI in their CBDC projects.
... Despite FinTech can potentially reduce the physical and psychological barriers, some evidence shows how changes in technology could increase the disparity in the US credit market outcomes across different groups of borrowers in the economy based, for example, on race (Fuster et al., 2022). However, process automation can reduce racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach and removing human biases from decision-making, as demonstrated by Howell et al. (2024) analyzing the context of the Paycheck Protection Program (PPP). ...
Purpose
This review aims to present the state of the art regarding the impact of financial technology (FinTech) on financial inclusion and its implications for consumers and institutions in terms of accessibility, usage and quality. An integrated framework is developed to illustrate the primary thematic areas for future research.
Design/methodology/approach
We performed a systematic literature review (SLR) to summarize and synthesize existing research published in peer-reviewed academic journals. Forty-two eligible studies were identified from the Web of Science database and a cross-reference search.
Findings
The results suggest that FinTech promotes financial inclusion for consumers and businesses by increasing the accessibility, usage and quality of financial products. We present a multidisciplinary integrative framework that links the three dimensions of financial inclusion (i.e. access, usage and quality) to financial technology. Finally, we propose several avenues for future research.
Originality/value
To the best of the author’s knowledge, this is the first SLR on how FinTech is associated with the accessibility, usage and quality of financial products. We provide an integrative framework for understanding the topic with implications in different fields.
... Some individuals face institutionalised bias and discrimination when accessing or using financial services in the financial sector (Brock and De Haas, 2023;Howell et al, 2024). These individuals are also at risk of facing institutionalised bias and discrimination when accessing or using central bank digital currency in the financial system. ...
The purpose of this article is to explore the role of artificial intelligence, or AI, in a central bank digital currency project and its challenges. Artificial intelligence is transforming the digital finance landscape. Central bank digital currency is also transforming the nature of central bank money. This study also suggests some considerations which central banks should be aware of when deploying artificial intelligence in their central bank digital currency project. The study concludes by acknowledging that artificial intelligence will continue to evolve, and its role in developing a sustainable CBDC will expand. While AI will be useful in many CBDC projects, ethical concerns will emerge about the use AI in a CBDC project. When such concerns arise, central banks should be prepared to have open discussions about how they are using, or intend to use, AI in their CBDC projects.
... In a DB space, banks also require digital authetification (Wu et al., 2023). The customer-disclosed information can bring about discrepancies in credit access to different population groups (Howell et al., 2023). Given the sensitivity of digital technology-based banking services, it is logical to state that issues of inclusiveness should be part of what banks offer and communicate to customers. ...
AI technology-based banking services development has disrupted the way people participate in banking transactions. It has created easier and faster banking transaction possibilities with the use of electronic gadgets. However, ethical concerns about these applications have also been amplified together with the need for management communication of safety features and protocols for customer information protection, and redress when infringements occur. The study was an attempt to highlight how AI-enabled banking services safety communication affects customers’ ethical concerns and how the concerns shape their banking services value perception, attitude, and loyalty intentions. A conceptual framework based on the generic AI technology, ethical concerns, and loyalty intentions was used as a basis for this study. It attempted to test the link between management communication, ethical concerns, satisfaction/dissatisfaction, and customer loyalty to AI-based banking services in a developing economy context. The study used three theoretical grounding bases to empirically test the proposed hypotheses. The results analysis followed Structural equation modeling (SEM). The results confirmed the impact of management communication on customers’ ethical concerns of security, privacy, diversity, and discrimination, and the positive influence of privacy and security on satisfaction/dissatisfaction. However, the relationship between diversity and discrimination concerns with customer satisfaction was not confirmed. Lastly, customer satisfaction was proven to impact their loyalty intentions.
Data-driven lending relies on the calibration of models using training periods. We find that this type of lending is not resilient in the presence of economic conditions that are materially different from those experienced during the training period. Using data from a small business fintech lending platform, we document that the small business credit supply collapsed during the COVID-19 crisis of March 2020 even though the demand for loans doubled relative to pre-pandemic levels. As the month progressed, most lenders significantly reduced or halted their lending activities, likely due to the heightened risk of model miscalibration under the new economic conditions.
As governments around the world embrace the digital revolution, AI-driven innovation emerges as a powerful tool for transforming public sector operations and e-government services. AI-driven innovation offers significant opportunities for governments, such as increasing operational efficiency, fostering citizen engagement, and enabling more effective decision-making. However, the integration of AI in the public sector also presents key challenges, such as data privacy concerns and algorithmic bias. This chapter provides a comprehensive analysis of AI integration in the public sector across various key domains. It explores both the significant opportunities and the critical considerations that governments must address to ensure the effective and ethical utilization of AI technologies.
Severe climate events pose an increasing threat to households’ financial well-being. Households affected by these events can face a significant financing challenge as disaster expenses and damages may exceed tens or even hundreds of thousands of dollars. We review the literature to describe how households fund recovery, with a focus on the role of insurance. While insurance is a key financial tool for managing such large losses, many households are uninsured and rely on a patchwork of savings, credit, federal assistance programs, and help from family and friends. Low-income households and racial or ethnic minority households have more limited access to recovery funding. Households who are unable to fund repairs appear at greater risk of financial distress in the years after the disaster. Our findings highlight the importance of disaster insurance and motivate a comprehensive view of financial recovery where income and race play critical roles.
Purpose
This paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.
Design/methodology/approach
This study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.
Findings
Using a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.
Originality/value
This paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.
This study explores integrating artificial intelligence (AI) into the operational frameworks of FinTech enterprises, prompting novel leadership and marketing strategies. AI presents opportunities for heightened efficiency, enhanced customer connectivity, and accelerated business expansion. As a prime example, we analyze Kabbage a thriving FinTech business and examine how they've successfully harnessed AI across innovation management and digital marketing.
Existing research establishes that minority borrowers, particularly Black small business owners, faced significant challenges in accessing funds from the Paycheck Protection Program (PPP), especially in its early stages. We find that institutional and racial disparities persist during the PPP loan forgiveness stage. Controlling for various loan-and borrower-level characteristics, we demonstrate that relationship lenderscommunity banks, credit unions, and farm credit institutionsare associated with higher rates of PPP loan forgiveness. In contrast, automated lendersfintechs and fintech banksexhibit the lowest forgiveness rates. Black borrowers experience the poorest outcomes, except for loans issued by non-depository fintechs and lenders categorized as "other", where they outperform White borrowers. Loan forgiveness rates improve, and racial disparities diminish, with increased lender concentration in specific economic sectors. Thus, specialized relationship lenders may have the highest odds of achieving the best and most equitable lending outcomes.
Banks’ information technology (IT) capabilities affect their ability to serve customers during the COVID-19 pandemic, which generates an unexpected and unprecedented shock that shifts banking services from in-person to digital. Amid mobility restrictions, banks with better IT experience larger reductions in physical branch visits and larger increases in website traffic, implying a larger shift to digital banking. Stronger IT banks are able to originate more Paycheck Protection Program loans to small business borrowers, especially in areas with more severe COVID-19 outbreaks, higher internet use, and higher bank competition. Those banks also attract more deposit flows and receive better mobile customer reviews during the pandemic.
Innovations in statistical technology, in functions including credit-screening, have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.
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We assess the role of race in loans made through the Paycheck Protection Program (PPP). The PPP program, created by the U.S. government as a response to the Covid-19 pandemic, provides loans to small businesses so they can keep employees on their payroll. We argue that the historical record and PPP program design choices made it likely that many Black-owned businesses received smaller PPP loans than White-owned businesses. Using newly released data on the PPP program, we find that Black-owned businesses received loans that were approximately 50% lower than observationally similar White-owned businesses. The effect is marginally smaller in areas with more bank competition and disappeared over time as changes to the PPP program were implemented allowing for entry by fintechs and other non-traditional lenders.
Plain English Summary
We find that Black-owned businesses received loans through the Paycheck Protection Program that were approximately 50 percent lower than White-owned businesses with similar characteristics. However, this difference in loan size shrank over time as more non-bank lenders such as fintechs were allowed to participate in the program and began approving PPP loans. Loan size differences were also slightly smaller in zip codes containing a larger number of bank branches. These results are consistent with prior research which shows lending discrimination by commercial banks against Black borrowers. It is also consistent with studies showing that greater access to and competition among banks and other lenders can reduce discrimination. In light of these results we recommend that policy makers account for existing racial inequalities within banking or other systems in their program design to produce more equitable outcomes.
The Paycheck Protection Program (PPP) extended 669 billion dollars of forgivable loans in an unprecedented effort to support small businesses affected by the COVID-19 crisis. This paper provides evidence that information frictions and the “first-come, first-served” design of the PPP program skewed its resources towards larger firms and may have permanently reduced its effectiveness. Using new daily survey data on small businesses in the U.S., we show that the smallest businesses were less aware of the PPP and less likely to apply. If they did apply, the smallest businesses applied later, faced longer processing times, and were less likely to have their application approved. These frictions may have mattered, as businesses that received aid report fewer layoffs, higher employment, and improved expectations about the future.
Significance
Drawing on a survey of more than 5,800 small businesses, this paper provides insight into the economic impact of coronavirus 2019 (COVID-19) on small businesses. The results shed light on both the financial fragility of many small businesses, and the significant impact COVID-19 had on these businesses in the weeks after the COVID-19–related disruptions began. The results also provide evidence on businesses’ expectations about the longer-term impact of COVID-19, as well as their perceptions of relief programs offered by the government.
We introduce a list that offers information on the relation between first names and race or ethnicity. Drawing information from mortgage applications, the list includes 4,250 first names and information on their respective count and proportions across six mutually exclusive racial and Hispanic origin groups. These six categories are consistent with the categories used in the Census Bureau's list on surnames' demographic information. Also, just like the Census Bureau's list of surnames, the list of first names is highly aggregated, so as to not identify any specific individuals.
We examine whether male investors are biased against female entrepreneurs. To do so, we use a proprietary dataset from AngelList covering fundraising startups. We find that female founders are less successful with male investors compared to observably similar male founders. In contrast, the same female founders are more successful than male founders with female investors. The results do not appear to be driven by differences across founder gender in startup quality, sector focus, or risk. Given that investors are predominately male, our results suggest that an increase in female investors is likely necessary to support an increase in female entrepreneurship.
We develop a novel empirical test of racial bias based on comparisons between forward-looking, expectations-based credit scores and backward-looking, repayment-history-based credit scores. We then test for racial bias using confidential-access data from the Kauffman Firm Survey. Businesses founded by disadvantaged minorities have much lower average business credit scores, but these scores show no evidence of racial bias. If anything, forward-looking credit-score models under-predict the rate of payment delinquency among minority-owned businesses.
This data archive includes Race Implicit Association Test (IAT) scores of 2,355,303 Internet volunteers who completed educational/demonstration versions of the Race IAT at https://implicit.harvard.edu from 2002 to 2012. Data in this archive can be downloaded for all years, either separately by year or in a single file. Codebooks, indicating the variable labels and value labels, and changes of variables over years, are available for both individual-year data sets and the entire data set. Participation in the (still on-going) Race IAT “study” at the Project Implicit (PI) demonstration site includes completion of the Race IAT along with demographic questions, self-report measures of racial attitude, and various additional measures received by a portion of the participants. These data allow analyses involving changes in responding over time and interrelations among IAT and self-report measures of race attitudes, as well as the association of each of these with demographics. This archive is available at http://osf.io/project/52qxL/. Dataset The Data described in this paper is available from the Open Science Framework: https://osf.io/52qxl/ [1]
ACKNOWLEDGEMENTS We would like to thank Peter Morrison of the RAND Corporation for initial encouragement to work on this project and for his comments; Signe Wetrogan, John Long, and Nancy Gordon for enabling this work; Maureen Lynch, Bert Kestenbaum (Social Security Administration), James Farber, and Matthew Falkenstein for providing data; Emmett Spiers for help on modifying the Lynch-Winkler string comparator program to enable Edit #2; Susan Love for providing the definition of data-defined person; Rodger Johnson, Campbell Gibson and Frank Hobbs for demographic review; Robert Fay for comments, information, and revisions; Gregg Robinson for comments; and Marjorie Hanson for editorial review of this report.
This paper employs data from the 1998 and 2003 Survey of Small Business Finances to analyze whether, after controlling for observable factors that influence loan decisions, there is a significant difference in the loan approval rate and the interest rate charged on approved loans for businesses owned by minority or white females and firms owned by white males.
How did the diffusion of the internet affect regional wage inequality? We examine the relationship between business investment in advanced internet technology and local variation in US wage growth between 1995 and 2000. We identify a puzzle. The internet is widespread, but the economic payoffs are not. Advanced internet technology is only associated with substantial wage growth in the 6 percent of counties that were already highly wealthy, educated, and populated and had IT-intensive industry. Advanced internet and wage growth appear unrelated elsewhere. Overall, advanced internet explains over half the difference in wage growth between already well-off counties and all others. (JEL J31, L86, O33, R11, R23)
Through a field experiment set in academia (with a sample of 6,548 professors), we found that decisions about distant-future events were more likely to generate discrimination against women and minorities (relative to Caucasian males) than were decisions about near-future events. In our study, faculty members received e-mails from fictional prospective doctoral students seeking to schedule a meeting either that day or in 1 week; students' names signaled their race (Caucasian, African American, Hispanic, Indian, or Chinese) and gender. When the requests were to meet in 1 week, Caucasian males were granted access to faculty members 26% more often than were women and minorities; also, compared with women and minorities, Caucasian males received more and faster responses. However, these patterns were essentially eliminated when prospective students requested a meeting that same day. Our identification of a temporal discrimination effect is consistent with the predictions of construal-level theory and implies that subtle contextual shifts can alter patterns of race- and gender-based discrimination.
Empirical studies on information communication technologies (ICT) typically aggregate the
“information” and “communication” components together. We show theoretically and empirically that
these have very different effects on the empowerment of employees, and by extension on wage
inequality. If managerial hierarchies are devices to acquire and transmit knowledge and information,
technologies that reduce information costs enable agents to acquire more knowledge and ‘empower’
lower level agents. Conversely, technologies reducing communication costs substitute agent’s
knowledge for directions from their managers, and lead to centralization. Using an original dataset of
firms in the US and seven European countries we study the impact of ICT on worker autonomy, plant
manager autonomy and spans of control. Consistently with the theory we find that better information
technologies (Enterprise Resource Planning for plant managers and CAD/CAM for production
workers) are associated with more autonomy and a wider span of control. By contrast, communication
technologies (like data networks) decrease autonomy for both workers and plant managers. Our
findings are robust to using exogenous variation in cross-country telecommunication costs arising
from differential regulatory regimes.
Using confidential microdata from the Characteristics of Business Owners survey, we examine why African American–owned businesses lag substantially behind white-owned businesses in sales, profits, employment, and survival. Black business owners are much less likely than white owners to have had a self-employed family member owner prior to starting their business and less likely to have worked in that family member’s business. Using a nonlinear decomposition technique, we find that the lack of prior work experience in a family business among black business owners, perhaps by limiting their acquisition of general and specific business human capital, negatively affects black business outcomes.
We test the predictions from Becker's (1957) seminal work on employer prejudice and find that relative black wages (a) vary negatively with the prejudice of the "marginal" white in a state, (b) vary negatively with the prejudice in the lower tail of the prejudice distribution but are unaffected by the prejudice of the most prejudiced persons in a state, and (c) vary negatively with the fraction of a state that is black. Our estimates suggest that one-quarter of the racial wage gap is due to prejudice, with nontrivial consequences for black lifetime earnings. (c) 2008 by The University of Chicago. All rights reserved..
We propose a simple model of trooper behavior to design empirical tests for whether troopers of different races are monolithic in their search behavior, and whether they exhibit relative racial prejudice in motor vehicle searches. Our test of relative racial prejudice provides a partial solution to the well-known infra-marginality and omitted-variables problems associated with outcome tests. When applied to a unique dataset from Florida, our tests soundly reject the hypothesis that troopers of different races are monolithic in their search behavior, but the tests fail to reject the hypothesis that troopers of different races do not exhibit relative racial prejudice.
U.S. fair-lending law prohibits lenders from making credit determinations that disparately affect minority borrowers if those determinations are based on characteristics unrelated to creditworthiness. Using an identification under this rule, we show risk-equivalent Latinx/Black borrowers pay significantly higher interest rates on GSE-securitized and FHA-insured loans, particularly in high-minority-share neighborhoods. We estimate these rate differences cost minority borrowers over $450 million yearly. FinTech lenders’ rate disparities were similar to those of non-Fintech lenders for GSE mortgages, but lower for FHA mortgages issued in 2009–2015 and for FHA refi mortgages issued in 2018–2019.
The incidence of misselling, fraud, and poor customer service by retail banks is significantly higher in areas with higher proportions of poor and minority borrowers and in areas where government regulation promotes an increased quantity of lending. Specifically, low-to-moderate-income (LMI) areas targeted by the Community Reinvestment Act have significantly worse outcomes, and this effect is larger for LMI areas with a high-minority population share. The results highlight an unintended adverse consequence of such quantity-focused regulations on the quality of credit to lower-income and minority customers.
We analyze the information content of a digital footprint—that is, information that users leave online simply by accessing or registering on a Web site—for predicting consumer default. We show that even simple, easily accessible variables from a digital footprint match the information content of credit bureau scores. A digital footprint complements rather than substitutes for credit bureau information and affects access to credit and reduces default rates. We discuss the implications for financial intermediaries’ business models, access to credit for the unbanked, and the behavior of consumers, firms, and regulators in the digital sphere. (JEL G20, G21, G29)
We test for pricing disparities in mortgage contracts using a novel data set that allows us to observe the race and ethnicity of both parties to the loan. We find that minorities pay between 3% and 5% more in fees than similarly qualified whites when obtaining a loan through the same white broker. Critically, we find that the premium paid by minorities depends on the race of the broker. We also examine recent policy changes around broker compensation rules that may not only reduce these price disparities but may also limit access to credit for minorities.
We test for racial discrimination in the prices charged by mortgage lenders. We construct a unique data set from which we observe the three dimensions of a mortgage’s price: the interest rate, discount points, and fees. Although we find statistically significant gaps by race and ethnicity in interest rates, these gaps are offset by differences in discount points. We trace out point-rate schedules and show that minorities and whites face identical schedules, but sort to different locations on the schedule. Such sorting may reflect systematic differences in liquidity or preferences. Finally, we find no differences in total fees by race or ethnicity.
Social‐distancing restrictions and health‐ and economic‐driven demand shifts from COVID‐19 are expected to shutter many small businesses and entrepreneurial ventures, but there is very little early evidence on impacts. This paper provides the first analysis of impacts of the pandemic on the number of active small businesses in the United States using nationally representative data from the April 2020 Current Population Survey—the first month fully capturing early effects. The number of active business owners in the United States plummeted by 3.3 million or 22% over the crucial 2‐month window from February to April 2020. The drop in active business owners was the largest on record, and losses to business activity were felt across nearly all industries. African‐American businesses were hit especially hard experiencing a 41% drop in business activity. Latinx business owner activity fell by 32%, and Asian business owner activity dropped by 26%. Simulations indicate that industry compositions partly placed these groups at a higher risk of business activity losses. Immigrant business owners experienced substantial losses in business activity of 36%. Female business owners were also disproportionately affected (25% drop in business activity). Continuing the analysis in May and June, the number of active business owners remained low—down by 15% and 8%, respectively. The continued losses in May and June, and partial rebounds from April were felt across all demographic groups and most industries. These findings of early‐stage losses to small business activity have important implications for policy, income losses, and future economic inequality.
This paper studies whether, in the consumer credit market, peer-to-peer (P2P) lending platforms serve as substitutes for banks or instead as complements. I develop a conceptual framework and derive testable predictions to distinguish between these two possibilities. Using a regulatory change as an exogenous shock to bank credit supply, I find that P2P lending is a substitute for bank lending in terms of serving infra-marginal bank borrowers yet complements bank lending with respect to small loans. These results indicate that the credit expansion resulting from P2P lending likely occurs only among borrowers who already have access to bank credit.
Received June 1, 2017; editorial decision September 15, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Technology-based (“FinTech”) lenders increased their market share of U.S. mortgage lending from 2% to 8% from 2010 to 2016. Using loan-level data on mortgage applications and originations, we show that FinTech lenders process mortgage applications 20% faster than other lenders, controlling for observable characteristics. Faster processing does not come at the cost of higher defaults. FinTech lenders adjust supply more elastically than do other lenders in response to exogenous mortgage demand shocks. In areas with more FinTech lending, borrowers refinance more, especially when it is in their interest. We find no evidence that FinTech lenders target borrowers with low access to finance.
Received June 1, 2017; editorial decision November 5, 2018 by Editor Wei Jiang. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Small businesses are the backbone of the U.S. economy. They are the biggest job creators and offer a path to the American Dream. But for many, it is difficult to get the capital they need to operate and succeed. In the Great Recession, access to capital for small businesses froze, and in the aftermath, many community banks shuttered their doors and other lenders that had weathered the storm turned to more profitable avenues. For years after the financial crisis, the outlook for many small businesses was bleak. But then a new dawn of financial technology, or “fintech,” emerged.
Beginning in 2010, new fintech entrepreneurs recognized the gaps in the small business lending market and revolutionized the customer experience for small business owners. Instead of Xeroxing a pile of paperwork and waiting weeks for an answer, small businesses filled out applications online and heard back within hours, sometimes even minutes. Banks scrambled to catch up. Technology companies like Amazon, PayPal, and Square entered the market, and new possibilities for even more transformative products and services began to appear.
In Fintech, Small Business & the American Dream, former U.S. Small Business Administrator and Senior Fellow at Harvard Business School, Karen G. Mills, focuses on the needs of small businesses for capital and how technology will transform the small business lending market. This is a market that has been plagued by frictions: it is hard for a lender to figure out which small businesses are creditworthy, and borrowers often don’t know how much money or what kind of loan they need. New streams of data have the power to illuminate the opaque nature of a small business’s finances, making it easier for them to weather bumpy cash flows and providing more transparency to potential lenders.
Mills charts how fintech has changed and will continue to change small business lending, and how financial innovation and wise regulation can restore a path to the American Dream. An ambitious book grappling with the broad significance of small business to the economy, the historical role of credit markets, the dynamics of innovation cycles, and the policy implications for regulation, Fintech, Small Business & the American Dream is relevant to bankers, fintech investors, and regulators; in fact, to anyone who is interested in the future of small business in America.
This article develops a new test for identifying racial bias in the context of bail decisions-a high-stakes setting with large disparities between white and black defendants. We motivate our analysis using Becker's model of racial bias, which predicts that rates of pretrial misconduct will be identical for marginal white and marginal black defendants if bail judges are racially unbiased. In contrast, marginal white defendants will have higher rates of misconduct than marginal black defendants if bail judges are racially biased, whether that bias is driven by racial animus, inaccurate racial stereotypes, or any other form of bias. To test the model, we use the release tendencies of quasi-randomly assigned bail judges to identify the relevant race-specific misconduct rates. Estimates from Miami and Philadelphia show that bail judges are racially biased against black defendants, with substantially more racial bias among both inexperienced and part-time judges. We find suggestive evidence that this racial bias is driven by bail judges relying on inaccurate stereotypes that exaggerate the relative danger of releasing black defendants. © The Author(s) 2018. Published by Oxford University Press on behalf of the President and Fellows of Harvard College. All rights reserved.
Shadow bank market share in residential mortgage origination nearly doubled from 2007 to 2015, with particularly dramatic growth among online “fintech” lenders. We study how two forces, regulatory differences and technological advantages, contributed to this growth. Difference in difference tests exploiting geographical heterogeneity induced by four specific increases in regulatory burden–capital requirements, mortgage servicing rights, mortgage-related lawsuits, and the movement of supervision to Office of Comptroller and Currency following closure of the Office of Thrift Supervision–all reveal that traditional banks contracted in markets where they faced more regulatory constraints; shadow banks partially filled these gaps. Relative to other shadow banks, fintech lenders serve more creditworthy borrowers and are more active in the refinancing market. Fintech lenders charge a premium of 14–16 basis points and appear provide convenience rather than cost savings to borrowers. They seem to use different information to set interest rates relative to other lenders. A quantitative model of mortgage lending suggests that regulation accounts for roughly 60% of shadow bank growth, while technology accounts for roughly 30%.
This paper examines racial and ethnic differences in high-cost mortgage lending in seven diverse metropolitan areas from 2004 to 2007. Controlling for credit score and other risk factors, African American and Hispanic borrowers are 103% and 78% more likely to receive high-cost mortgages for home purchases. A large part of the increase is attributable to sorting across lenders (55%-65%), and this, in turn, can be largely accounted for by the lender’s ex post foreclosure risk. The remaining within-lender differences are also concentrated in high-risk lenders, revealing the central role of these institutions in explaining market-wide racial and ethnic differences. (JEL G21, I28, J15, J71, R21).
We integrate tools to monitor information acquisition in field experiments on discrimination and examine whether gaps arise already when decision makers choose the effort level for reading an application. In both countries we study, negatively stereotyped minority names reduce employers' effort to inspect resumes. In contrast, minority names increase information acquisition in the rental housing market. Both results are consistent with a model of endogenous allocation of costly attention, which magnifies the role of prior beliefs and preferences beyond the one considered in standard models of discrimination. The findings have implications for magnitude of discrimination, returns to human capital and policy.
In both political behavior research and voting rights litigation, the turnout and vote choice for different racial groups
are often inferred using aggregate election results and racial composition. Over the Past several decades, many statistical
methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation
bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature,
we use Bayes’s rule to combine the Census Bureau’s Surname List with various information from geocoded voter registration
records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records
in Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among
Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our
predictions to estimate the turnout by race and find that our estimates yields substantially less amounts of bias and root
mean squared error than standard ecological inference estimates. The open-source software is available for implementing the
proposed methodology.
How can we know how much racial animus costs a black presidential candidate, if many people lie to surveys? I suggest a new proxy for an area’s racial animus from a non-survey source: the percent of Google search queries that include racially charged language. I compare the proxy to Barack Obama’s vote shares, controlling for the vote share of the previous Democratic presidential candidate, John Kerry. An area’s racially charged search rate is a robust negative predictor of Obama’s vote share. Continuing racial animus in the United States appears to have cost Obama roughly four percentage points of the national popular vote in both 2008 and 2012. The estimates using Google search data are 1.5 to 3 times larger than survey-based estimates.
In this paper we explore the effects of bank–borrower physical proximity on price and non-price aspects of small business lending in local credit markets. Along the price dimension, our analysis reveals that interest rates increase with bank–borrower distance and decrease with the distance between borrower and other competing banks. Along the quantity dimension, we observe that more distant borrowers are more likely to experience binding credit limits. We also show that the quantity effects of bank–borrower distance are concentrated among less transparent firms. Our findings are consistent with pricing based on marginal costs that reflect information-based factors, and are in contrast to the established paradigm, where banks adopt spatial discriminatory pricing rules when lending to small-sized enterprises.
We use data from the 1993 and 1998 National Surveys of Small Business Finances to examine the existence of racial discrimination in the small-business credit market. We conduct an econometric analysis of loan outcomes by race and find that black-owned small businesses are about twice as likely to be denied credit even after controlling for differences in creditworthiness and other factors. A series of specification checks indicates that this gap is unlikely to be explained by omitted variable bias. These results indicate that the racial disparity in credit availability is likely caused by discrimination. Copyright (c) 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.
Historically, lenders have been accused of 'redlining' minority neighborhoods as well as refusing to lend to minority applicants. Considerable bank regulation is designed to prevent both actions. However, the strong correlation between race and neighborhood makes it difficult to distinguish the impact of geographic discrimination from the effects of racial discrimination. Previous studies have failed to untangle these two influences, in part, because of severe omitted variable bias. The data set in this paper allows the distinct effects of race and geography to be identified and it shows that the evidence for redlining is weak. Copyright 1996, the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
Racial differences in the receipt of financial inheritances help to explain why the average difference in wealth between black and white households is larger than the average difference in income. Using data from a panel of prime-aged males and from a representative survey of the U.S. population, the authors document the greater likelihood of white households receiving an inheritance than black households. Controlling for other factors which contribute to racial differences in wealth, the authors estimate that financial inheritances may account for between 10 percent and 20 percent of the average difference in black-white household wealth. Copyright 1997 by Oxford University Press.
African- American motorist in the United States are much more likely than white motorists to have their car searched by police checking for illegal drugs and other contraband. The courts are faced with the task of deciding on the basis of traffic-search data whether police behavior reflects a rackial bias. We discuss why a simple test for racial bias commonly applied by the courts is inadequate and develop a model of law enforcement that suggests an alternative test. The model assumes a population with two racial types who also differ along other dimensions relevant to criminal behavior. Using the model, we construct a test for whether racial disparities in motor vehicle searches reflect racial prejudice, or instead are consistent with the behavior of non-prejudiced police maximizing drug interdiction. The test is valid even when the set of characteristics observed by the police is only partially observable by the econometrician. We apply the test to traffic-search data from Maryland and find the observed black-white disparities in search rates to be consistent with the hypothesis of no racial prejudice. Finally, we present a simple analysis of the tradeoff between efficiency of drug interdiction and racial fairness in policing. We show that in some circumstances there is no trade-off; constraining the police to be color-blind may achieve greater efficiency in drug interdiction.
Blattner Laura andScottNelson andJannSpiess 2021 Unpackingthe Black Box: Regulating algorithmic decisions
Minority entrepreneurs struggled to get small-business reliefloans The New York Times
- Cowley Stacy