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Digital Inclusion and Financial Inclusion: Evidence from Peer-to-Peer Lending

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Abstract

We explore whether digital inclusion, a public policy designed to provide high-speed internet infrastructure for historically digitally excluded populations, is associated with the social and ethical challenge of financial inclusion. Using evidence from a sizable P2P lender in the U.S., we document that digital inclusion is positively associated with P2P lending penetration and that this relation is more pronounced in counties with limited commercial bank loan penetration and higher minority populations. Our new evidence from cross-sectional tests suggests that digital inclusion plays a key role in financial inclusion, particularly in regions with more vulnerable and/or underserved populations. In consequence tests, we document that high-risk borrowing is less likely to be denied in counties with higher digital inclusion and that digital inclusion is positively associated with P2P lending efficiency in the form of more repeated borrowing, decreased funding time, and improved funding fulfillment. In addition, we show that the availability of alternative information, a plausible channel through which digital inclusion is related to financial inclusion, is positively associated with efficiency in P2P lending. Our findings indicate that digital inclusion can empower financial service providers and other stakeholders to collaboratively fulfill their ethical and social responsibilities to meet the financial needs of historically marginalized groups.

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Notes

  1. For example, according to the 2021 World Bank Global Financial Inclusion database, the global average percentage of adults with an account increased from 51% in 2011 to 76% in 2021 (Demirgüç-Kunt et al., 2022).

  2. Babalola et al. (2022) provide commentaries from the editors of the Journal of Business Ethics on the future of business ethics, containing topics such as leadership, psychology, finance, and accounting. Our paper borrows from ideas and insights that span the commentary section provided by Guedhami, Liang, and Shailer.

  3. Such practices include, for example, the redlining with the National Housing Act in 1934 that excluded Black populations from mortgage lending and the Affordable Care Act failing to expand to states with more Black Americans (Florant et al. 2020).

  4. According to a 2022 report from the Joint Economic Committee (JEC), 50 million American consumers do not have a valid credit bureau score, or are ‘credit invisible,’ of which most are minorities or low-income individuals.

  5. For example, small business entrepreneurs often need such loans as an important source of leverage to start or expand their small businesses.

  6. A 2022 JEC report suggests that there are two groups of U.S. consumers with no valid credit scores. The first group are those without a credit record in any of the nationwide credit reporting agencies; the second group are those who are considered “unscorable” by the nationwide credit reporting agencies. In particular, these people are disproportionately minorities or low-income individuals.

  7. A 2020 market research report shows that LendingClub and Prosper are the two largest P2P lenders in the U.S. (https://mangosoft.tech/blog/top-5-peer-to-peer-lending-companies-2020-full-market-research).

  8. The high-speed and basic internet connections data are from FCC Form 477, and the households’ high-speed internet subscriptions data are from the U.S. Census Bureau.

  9. We choose 2013 as the cutoff year for the following reasons: Although the NBP funding deployment was in 2010, the exact years it takes for a county to materialize the digital inclusion effect can vary. Our trend analysis illustrated in Fig. 1 suggests that most counties started to exhibit tangible divergence in P2P penetration after 3 years of NBP deployment. Indeed, FCC (2010) argues that internet infrastructure investments typically require a multi-year horizon to manifest tangible effects, stemming from the complexities associated with implementation, integration, and user adaptation.

  10. For example, in November 2021, U.S. President Joe Biden signed the Infrastructure Investment and Jobs Act (IIJA) into law and provided $65 billion for broadband: https://broadbandusa.ntia.doc.gov/resources/federal/federal-funding.

  11. This study also corroborates the regulators’ initiatives to utilize big data in the traditional financial institutions. For example, Jelena McWilliams, the former Chairman of Federal Deposit Insurance Corporation (FDIC), stressed that FDIC is “actively engaged with” financial institutions to provide “affordable access to financial services and products” for the “most vulnerable populations.” Source: https://www.fdic.gov/news/events/banking-on-data/index.html.

  12. A more illustrative version of the Prosper credit rating system is shown in Table 1, Panel A and Panel B.

  13. Source: https://www.fcc.gov/general/broadband-deployment-data-fcc-form-477.

  14. Source: https://www.fcc.gov/form-477-census-tract-data-internet-access-services.

  15. Released by the FCC on March 17, 2020, the NBP aims to provide universal high-speed internet access, bridging the digital divide. Source: https://www.fcc.gov/general/national-broadband-plan. In addition, the data related to the county-level NBP deployment can be downloaded at: https://transition.fcc.gov/national-broadband-plan/broadband-availability-gap-data.zip.

  16. Prosper claims that the “custom risk model” uses historical Prosper data and is built on the Prosper borrower population, i.e., the model inputs all historical Prosper loan records and makes predictive analysis.

  17. Considering that starting from 2020, Prosper added a new product—home equity line of credit—we also use a cutoff sample period up to the end of 2019. We find consistent results on all tests.

  18. Call Report is short for Consolidated Reports of Condition and Income. All national banks, state member banks, insured state nonmember banks, and savings associations are required to submit Call Report data to bank regulators.

  19. While not tabulated, we document similar empirical results with a winsorization at the top and bottom 2% levels, considering the prevalence of outliers of the P2P penetration measures.

  20. Using Column (1) as an example, the impact of a one standard deviation increase in DI on P2PPEN is computed as 0.9953 (the coefficient of DI) × 1.1357 (the sample standard deviation of DI) ÷ 4.6092 (the sample mean of P2PPEN) × 100% = 24.52%.

  21. For example, FCC (2010) argues that internet infrastructure investments typically require a multi-year horizon to manifest tangible effects, stemming from the complexities associated with implementation, integration, and user adaptation.

  22. A February 2013 report by the FCC also showed that the nationwide average subscribed speed by consumers by the end of third quarter of 2012 was 15.6 Mbps, representing an average annualized speed increase of about 20%. Source: https://www.fcc.gov/reports-research/reports/measuring-broadband-america/measuring-broadband-america-february-2013.

  23. The survey data are at the state level, which is available from NTIA: https://www.ntia.doc.gov/data/digital-nation-data-explorer#sel=socialNetworkUser&disp=map.

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Acknowledgements

We gratefully acknowledge helpful comments from Hao Liang (the Section Editor) and two anonymous reviewers. Jia and Kanagaretnam thank the CPA Ontario and Schulich School of Business Joint Centre in Digital Accounting Information for its financial support. Kanagaretnam thanks the Social Sciences and Humanities Research Council of Canada (SSHRC) for its financial support.

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Authors and Affiliations

  1. Lazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, ON, Canada

    Xiaoran Jia

  2. Schulich School of Business, York University, Toronto, ON, Canada

    Kiridaran Kanagaretnam

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  1. Xiaoran Jia
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Correspondence to Xiaoran Jia.

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Appendix A: Variable Definitions

Appendix A: Variable Definitions

P2PPEN

 = 

The main measure of P2P penetration, which is calculated as the county-year-level successfully funded Prosper loans amount per person

P2PPEN_CT

 = 

The second measure of P2P penetration, which is calculated as the county-year-level number of successfully funded Prosper loans per person, scaled by 10,000

P2PPEN_RK

 = 

The third measure of P2P penetration, which is calculated as the rank of a county-year’s P2PPEN across all county-years. This measure minimizes the bias of the prevalence of extreme values of P2PPEN

P2PPEN_CS

 = 

The fourth measure of P2P penetration, which is calculated as the county-year-level successfully funded Prosper loans amount divided by the county-year-level commercial bank consumer loans amount

P2PPEN_TL

 = 

The fifth measure of P2P penetration, which is calculated as the county-year-level successfully funded Prosper loans amount divided by the county-year-level commercial bank total loans amount

DI

 = 

The main proxy for digital inclusion, which measures the county-year-level residential fixed connections of at least 10 Mbps (download) / 1 Mbps (upload) (i.e., high-speed internet) per 1000 households, which is coded from 0 to 5, with 5 indicating the highest level of digital inclusion. Specifically, let X be the number of high-speed internet connections per 1,000 housing units. We denote DI = 0 if X = 0; DI = 1 if 0 < X ≤ 200; DI = 2 if 200 < X ≤ 400; DI = 3 if 400 < X ≤ 600; DI = 4 if 600 < X ≤ 800; and DI = 5 if 800 < X. The tables are based on Federal Communications Commission (FCC) Form 477 data

Data Link: https://www.fcc.gov/form-477-census-tract-data-internet-access-services

INTERNET_

BASIC

 = 

A measure of the level of basic internet connections (i.e., a measure of residential fixed internet connections of more than 200 kbps per 1000 households). Data are from FCC Form 477. Because the basic connections are prevalent across the U.S., we do not use this directly as the measure for digital inclusion. Instead, this measure is used in a principal component analysis (PCA)

INTERNET_

SUBSCRIPTION

 = 

The number of county-year households with high-speed internet subscriptions scaled by county-year population. The data from 2017 to 2020 are queried from the U.S. Census 5-Year American Community Survey (ACS). The data from 2013 to 2016 are queried from the U.S. Census 1-Year ACS. However, the 1-Year ACS only covers census blocks with populations of 65,000 or more, which is why we only use this measure in an auxiliary PCA

DI_ALT

 = 

The alternative measure of digital inclusion, which is the first principal component of a factor analysis on three variables, namely DI, T1_INT, and HI_SUB. These three variables used in factor analysis are described in detail in this appendix

POST

 = 

The ‘after’ versus ‘before’ indicator variable for the event study utilizing the National Broadband Plan deployed in 2010. Hence, POST = 1 if the year of the observation is the ‘after’ year. In addition, we use two event windows for the analyses. In the first window, the ‘before’ year is 2010 and the ‘after’ year is 2015. In the second window, the ‘before’ year is 2009 and the ‘after’ year is 2015. We use a window of 5 or 6 years to allow for the prolonged effects of internet infrastructure investment

TREATED

 = 

The treatment indicator variable for the event study utilizing the National Broadband Plan deployed in 2010. We define a county as ‘treated’ if the county receives the largest quartile of investment as a result of the national broadband plan. A county is defined as ‘not treated’ if the county receives the lowest quartile of investment, including counties that do not receive any funding at all

EXCLUDED

or

EXCLUDED_STRICT

 = 

For the event study, we hypothesize that the treatment effect of national broadband plan investment should be significantly positive for the P2P lending penetration for ex-ante digitally excluded counties – i.e., those counties with poorer broadband infrastructure (i.e., DI ≤ 2), lower GDP per capita (i.e., counties with below-75th-percentile GDP per capita), and lower local banking penetration (i.e., counties with below-75th-percentile bank consumer loans per capita). Hence, EXCLUDED = 1 suggests subsamples of ex-ante excluded counties. In addition, we define EXCLUDED_STRICT as a robustness test, in which we change the criterion to counties with the poorest existing internet infrastructure (i.e., DI ≤ 1)

DI_HIGH

 = 

A dummy variable measure of digital inclusion, which equals 1 if DI is above median across all county-years and equals 0 if DI is below median across all county-years

AA_HIGH

 = 

A dummy variable measure of county-level Black/African Americans (AA) population, which equals 1 if the county-level proportion of Black/AA population is in the highest quantile, and 0 if it is in the lowest quartile

HL_HIGH

 = 

A dummy variable measure of county-level Hispanic/Latino population, which equals 1 if the county-level proportion of Hispanic/Latino population is in the highest quartile, and 0 if it is in the lowest quartile

TLPC_HIGH

 = 

A dummy variable measure of county-level commercial bank loans penetration, which equals 1 if the county-level commercial bank total loans per capita is in the highest quartile, and 0 if it is in the lowest quartile

CSPC_HIGH

 = 

A dummy variable measure of county-level commercial bank loans penetration, which equals 1 if the county-level commercial bank consumer loans per capita is in the highest quartile, and 0 if it is in the lowest quartile

BRANCH_HIGH

 = 

A dummy variable measure of the county-year level commercial bank branch coverage per 1,000 population, which equals 1 if the county-level bank branch per 1000 people is in the highest quartile, and 0 if it is in the lowest quartile

DEPOSITS_HIGH

 = 

A dummy variable measure of the county-year level commercial bank total branch deposits per capita, which equals 1 if the county-level deposits per capita is in the highest quartile, and 0 if it is in the lowest quartile

RATE

 = 

The county-year mean of Prosper loan borrowers’ interest rates. This is used as a proxy for county-year level of P2P borrowers’ credit risk

SCORE

 = 

The county-year mean of Prosper loan borrowers’ credit bureau score

TERM

 = 

The county-year mean of Prosper loan terms

AMOUNT

 = 

The county-year mean of Prosper loan listing amount in thousands

INCOME_RANGE

 = 

The county-year mean of Prosper loan borrowers’ self-disclosed income range

EMPLOY_LENGTH

 = 

The county-year mean of Prosper loan borrowers’ self-disclosed months of employment

REPEATED

 = 

The county-year percent of borrowers who previously borrowed from Prosper

REPEATED = 1

 = 

A dummy variable used in loan-level regressions, which equals 1 if the loan listing is from a repeated borrower, and zero it the loan listing is from a new borrower

FUNDED_TIME

 = 

The time elapsed from the listing creation date to the successful loan origination date

PARTIAL = 1

 = 

A dummy variable that equals 1 if the funded amount is less than the listing amount

FUND_PERCENT

 = 

The percentage of funded amount to listing amount

INCOME_COUNTY

 = 

The county-year-level personal income per capita in thousands. Source: U.S. Bureau of Economic Analysis (BEA)

EDU

 = 

The county-year-level proportion of adult population receiving post-secondary education. Source: U.S. Census 5-Year ACS

RACE

 = 

The county-year-level proportion of population that is not White. Source: U.S. Census 5-Year ACS

GENDER

 = 

The county-year-level proportion of population that is male. Source: U.S. Census 5-Year ACS

MARRY

 = 

The county-year-level proportion of adult population that is currently married. Source: U.S. Census 5-Year ACS

TLPC

 = 

The county-year-level total commercial bank loans per capita (Call Report)

TAPC

 = 

The county-year-level total commercial bank assets per capita (Call Report)

CSLPC

 = 

The county-year-level total commercial bank consumer loans per capita (Call Report)

LLP

 = 

The county-year-level commercial bank loan loss provisions scaled by one-year lagged total loans (Call Report)

LLA

 = 

The county-year-level commercial bank loan loss allowances scaled by one-year lagged total loans (Call Report)

NCO

 = 

The county-year-level commercial bank net loan charge-offs scaled by one-year lagged total loans. Net charge-offs equal to charge-offs minus recoveries (Call Report)

NPL

 = 

The county-year-level commercial bank non-performing loans scaled by one-year lagged total loans (Call Report)

T1R

 = 

The county-year-level commercial bank Tier 1 capital ratio (Call Report)

UNEMPRATE

 = 

The county-year-level unemployment rate. Source: U.S. Bureau of Labor Statistics

HPI

 = 

The county-year-level housing price index. Source: Federal Housing Finance Agency

LNGDP

 = 

The natural logarithm of county-year-level GDP per capita. Source: U.S. Bureau of Economic Analysis

ALT_INFO

 = 

A state-year-level indicator variable for the availability of alternative information. This proxy is constructed by taking the sum of two percentages provided by National Telecommunications and Information Administration (NTIA), including the percentage of 15 + persons using online financial services and the percentage of 15 + persons using online shopping. The data are available only at the state-year level for years 2013, 2015, 2017, and 2019

Source: NTIA, United States Department of Commerce. https://www.ntia.doc.gov/data/digital-nation-data-explorer#sel=socialNetworkUser&disp=map

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Jia, X., Kanagaretnam, K. Digital Inclusion and Financial Inclusion: Evidence from Peer-to-Peer Lending. J Bus Ethics (2024). https://doi.org/10.1007/s10551-024-05689-w

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