Can FinTech reduce disparities in access to finance? Evidence from the Paycheck Protection Program

Erel, Isil; Liebersohn, Jack

doi:10.1016/j.jfineco.2022.05.004

Cited by 110 publications

(40 citation statements)

References 37 publications

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“…The fintech level is assessed with the development and application ability of fintech in the financial platform (Erel and Liebersohn, 2022 ). Especially, it refers to the ability of technology to help users invest and manage their money.…”

Section: Methodsmentioning

confidence: 99%

“…Fintech could help us to develop various technologies for innovating financial products and services (Erel and Liebersohn, 2022 ). In terms of the fintech level, previous studies have focused on the level of fintech in various regions and interpreted the level of fintech as the fintech development status of a region (Yao et al, 2021 ; Wu et al, 2022 ).…”

Section: Theoretical Analysis and Research Hypothesismentioning

confidence: 99%

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How does platform's fintech level affect its word of mouth from the perspective of user psychology?

et al. 2023

Front. Psychol.

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IntroductionThe rapid development of fintech has brought opportunities for business operations and economic development. Currently, few researches have focused on how fintech level affects word-of-mouth (WOM) from the perspective of user psychology. Therefore, studying the effect of fintech level on WOM is a worthwhile scientific question.MethodsBased on motivation theory and reinforcement theory, this paper proposes a new psychology-based theoretical framework model to study the relationship between fintech level and WOM and constructs a structural equation model including fintech level, user experience, user trust, user stickiness and WOM through the analysis of 732 questionnaires.ResultsThe results indicate that the improvement of fintech level can enhance WOM. More specifically, fintech level has a significantly positive influence on user stickiness through two mediation variables (user experience and user trust), and further, user stickiness has a significantly positive influence on WOM.DiscussionThis paper analyzes the internal mechanism of fintech level's influence on WOM from the micro psychological perspective, which enriches the psychology theoretical research. And, the conclusions provide specific suggestions for marketing and promotion of financial platforms in the future.

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Section: Methodsmentioning

confidence: 99%

Section: Theoretical Analysis and Research Hypothesismentioning

confidence: 99%

How does platform's fintech level affect its word of mouth from the perspective of user psychology?

et al. 2023

Front. Psychol.

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“…Yet their reliance on completely impersonal data-based transactions, automation, and online operations simultaneously reduces the scope for personal and racial bias in lending and tempers minority borrowers’ expectations of such treatment. Fintech systems allow them to make small loans in volume without branch networks, making them key conduits for PPP loans to minority-owned businesses that were quite often too small to be profitable for banks ( Chernenko & Scharfstein, 2022 ; Erel & Liebersohn, 2022 ; Howell et al, 2021 ). LMCBs, in contrast, stand as intermediate cases.…”

Section: Theory and Hypothesesmentioning

confidence: 99%

“…Yet, the interest in both studies is not in the direct impact of racial animus or other community characteristic on loan flows, but only in how animus moderates the microimpacts of individual ethno-racial characteristics. Only Erel and Liebersohn (2022) examined first-order community effects in analyzing inclusivity and types, using data on ZIP codes to show that communities relied more heavily on fintechs when they had large minority populations, low incomes, and sparse branch networks. But they, too, focused only on fintechs versus banks.…”

mentioning

confidence: 99%

Bank Types, Inclusivity, and Paycheck Protection Program Lending During COVID-19

Cassell

Schwan

Schneiberg

2023

Economic Development Quarterly

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How do differences in bank or lending institution type shape access to credit for small businesses in poor and/or minority communities in the United States? Banking systems are populated by lenders that differ qualitatively in their organizational forms, business models and missions, and that connect—or fail to connect—to small business borrowers and local communities in divergent ways. The authors analyze data on the Paycheck Protection Program and its over 11 million loans made to businesses across the United States to trace how these differences shaped the flow of credit to poor and minority communities. The authors find substantial differences across seven lender types, both in their propensities to avoid or lend to firms in traditionally marginalized communities, and in how much they lend to poor and majority–minority communities relative to their nonpoor and majority White counterparts. From this variety within American banking, the authors identify two potential pathways for more inclusive lending.

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“…Big data are often key in their business model, and they can reduce the impact of negative prejudice in the credit market (Philippon, 2019), such as racial disparities by automating the lending processes (Howell et al, 2021). FinTech lenders also serve in areas with less bank presence, lower incomes, and more minority households (De Roure et al, 2022;Erel and Liebersohn, 2022).…”

Section: Related Literaturementioning

confidence: 99%

Open Banking and Customer Data Sharing: Implications for Fintech Borrowers

Nam

2022

SSRN Journal

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Can FinTech reduce disparities in access to finance? Evidence from the Paycheck Protection Program

Cited by 110 publications

References 37 publications

How does platform's fintech level affect its word of mouth from the perspective of user psychology?

How does platform's fintech level affect its word of mouth from the perspective of user psychology?

Bank Types, Inclusivity, and Paycheck Protection Program Lending During COVID-19

Open Banking and Customer Data Sharing: Implications for Fintech Borrowers

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