## Marketplace lending: cross-country drivers and segmental differences (Content unit)

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---

### Introduction — context and motivation
- Limited access to credit is an important hurdle for consumers and small and medium-sized enterprises (SMEs) in many countries; International Finance Corporation estimates that 41 percent of SMEs in the formal sector in developing countries have unmet financing needs.
- According to the World Bank, about 60 percent of adults in developing countries do not use any formal financial services.
- Common barriers to credit: lack of physical access to bank branches, lack of documentation and credit history, absence of credit bureaus/registries, weak legal protection for creditors, weak collateral registry systems, and legal constraints on use of movable collateral.

### Definition and scope of fintech credit studied
- Fintech credit in this paper refers to marketplace lending (the debt component of crowdfunding), specifically:
  - P2P lending (funding entirely open to the public; platform matches borrowers with a pool of lenders).
  - Balance sheet lending when the platform also uses its own funds (included where funding is partly or completely open to retail investors).
  - Invoice trading (lending against account receivables).
- Excluded: Big tech lending, digital lending by banks, mobile-platform lending not open to the public, mortgage lending, and equity crowdfunding.

### Data and sample
- Data source: Cambridge Center for Alternative Finance (CCAF) Alternative Finance Industry Benchmarking Survey.
- Coverage: 109 countries, annual data for 2015 to 2017, country-level aggregates of gross new originations of debt-based alternative finance (consumer and business).
- Note: CCAF data captures gross new originations and does not account for repayments.

### Stylized facts and key statistics
- Global scale and growth:
  - Marketplace lending more than tripled from 2015 to 2017, reaching US$400 billion.
  - Composition in 2017: 69 percent of total marketplace lending originations were to consumers.
  - 2017 growth: fintech consumer credit originations grew 62 percent versus fintech business credit originations at 26 percent.
- Global concentration:
  - China, the United States, and the United Kingdom account for 98 percent of global marketplace lending activity.
  - China alone accounts for 86 percent of global marketplace lending.
  - The source also reports China, US and UK comprising 98.3 percent of global activity in an alternative phrasing.
- Systemic importance and relative shares:
  - Marketplace lending originations exceeded 1 percent of credit from traditional intermediaries only in Georgia (2.1 percent) and China (1.3 percent) in 2017.
  - Apart from Georgia and China, all other countries had marketplace lending originations less than 0.4 percent of credit from traditional intermediaries.
  - In terms of GDP, other than Georgia and China, all countries had marketplace lending originations less than 0.4 percent; most countries have less than 0.5 percent of GDP in marketplace lending.
- Country-level usage examples:
  - United Kingdom: less than 0.1 percent of UK SMEs have borrowed from a marketplace lender; about 4 of every 1,000 adults has borrowed from a marketplace lender.
  - China: about 5 of every 1,000 adults has borrowed from a marketplace lender versus over 427 per 1,000 adults from commercial banks.
- Industry concentration in markets:
  - United Kingdom: three top marketplace lenders accounted for 60 percent of business lending through online crowdfunding platforms in 2017Q1; business lending by platforms rose from US$660 million in 2014 to US$2.9 billion in 2017; consumer lending by top platforms rose from US$790 million in 2014 to US$2.5 billion in 2017.
  - China: top five P2P platforms had a 25 percent market share in 2019 (out of more than 500 platforms).
- China P2P time series (platform-reported WDZJ.com):
  - Q1 2014: US$6 billion in transaction volume of loans originated via P2P platforms.
  - Q3 2017: US$113 billion.
  - Q3 2019: US$30 billion.
  - Average term of a marketplace loan in China: 15.8 months as of April 2019.
  - WDZJ’s 2017 survey: more than 80 percent of borrowers from P2P platforms in China were between 20 and 40 years old; more than half of borrowers made a monthly salary less than US$600.

### Methodology (empirical strategy)
- Panel regression analysis with country fixed effects; also report between regressions to capture slow-moving factors.
- Dependent variables: logarithm of new originations of marketplace lending as a share of a country’s nominal GDP; regressions run for total, business, and consumer marketplace lending.
- Key identification assumption: marketplace lending is small relative to aggregate economy and traditional lenders, so explanatory variables are treated as exogenous.
- Explanatory domains: economic development, financial development (depth, access, efficiency), internet adoption, information, legal infrastructure, geographical barriers, banking sector features.
- Interaction terms: financial development interacted with economic development dummies (Advanced Economies (AE) and Low-Income Countries (LIC)).

### Descriptive statistics (selected exact entries)
- log (total fintech credit/GDP): mean -9.62, Std. Dev. 2.08, Min -15.87, Max -3.55.
- log (business fintech credit/GDP): mean -10.26, Std. Dev. 2.14, Min -18.53, Max -4.70.
- log (consumer fintech credit/GDP): mean -10.09, Std. Dev. 2.32, Min -15.45, Max -3.92.
- log (GDP ppp per capita): mean 9.39, Std. Dev. 1.19, Min 6.60, Max 11.48.
- Internet Users (% of population): mean 52.56, Std. Dev. 28.36, Min 1.76, Max 97.30.
- Financial Development Index (Sahay and others (2015)): mean 0.48, Std. Dev. 0.22, Min 0.12, Max 1.00.
- Average Bank Concentration (2010-2014): mean 76.01, Std. Dev. 15.68, Min 27.51, Max 100.00.
- Average Return on Bank Assets (2010-2014): mean 1.29, Std. Dev. 1.13, Min -2.78, Max 5.53.

### Regression evidence — selected fixed-effect results (exact reported coefficients and significance)
- Drivers of total marketplace lending (dependent variable: log(total marketplace lending/GDP); sample 2015–2017):
  - GDP per capita: positive and highly significant (example: coefficient 10.66***, (0.002) in column (1); 14.11***, (0.000) in column (5)).
  - Internet users: positive and highly significant (example: 0.26***, (0.000) in column (1); 0.25***, (0.000) in column (5)).
  - Financial Depth: negative and significant (example: -24.60***, (0.000) in column (3); -54.20**, (0.026) in column (5)).
  - Bank Concentration * Financial Efficiency: positive and significant (0.34***, (0.000) in column (5)).
  - R-squared range: 0.52 to 0.60; Number of Countries 102–103; Observations ~207–208.
- Drivers of business marketplace lending (dependent variable: log(business marketplace lending/GDP)):
  - GDP per capita: positive and highly significant (example: 14.18***, (0.001) in column (1)).
  - Financial Development: negative and significant in some specifications (example: -26.17***, (0.006) in column (1); -35.48**, (0.019) in column (2)).
  - Financial Efficiency: negative and significant in several specifications (example: -6.55**, (0.029) in column (1); -14.39*, (0.072) in column (5)).
  - Interaction effects: LIC * Financial Access negative and significant in some specs (example: -186.41***, (0.002) in column (5)); LIC * Financial Efficiency positive and significant in some specs (16.39**, (0.026) in column (5)).
  - R-squared: ~0.26–0.29; Number of Countries 68; Observations 154.
- Drivers of consumer marketplace lending (dependent variable: log(consumer marketplace lending/GDP)):
  - GDP per capita: positive and significant in some specifications (example: 12.73***, (0.005) in column (3); 13.29***, (0.007) in column (5)).
  - Internet users: positive and highly significant across specifications (example: 0.30***, (0.000) in column (1); 0.27***, (0.000) in column (5)).
  - Financial Depth: negative and significant in some specifications (example: -28.32***, (0.001) in column (1)).
  - Financial Efficiency: positive and significant in some specifications (example: 6.75**, (0.036) in column (1)).
  - Interaction AE * Financial Development: negative and significant in example (-49.68***, (0.001) in column (3)); LIC * Financial Development: large positive in example (289.72*, (0.054) in column (3)).
  - R-squared range: 0.57 to 0.70; Number of Countries 93–95; Observations 161–162.

### Key empirical interpretations
- Economic development:
  - Higher income per capita is highly significant and positively associated with marketplace lending across total, consumer, and business segments in panel regressions.
- Technological infrastructure:
  - Internet access is an important and robust driver of marketplace lending, especially for total and consumer segments; alternative proxies (e.g., internet servers) show significance for business fintech.
- Financial development (depth, access, efficiency):
  - Business marketplace lending: the broad index of financial development is strongly significant with a negative sign—marketplace lending to businesses tends to expand when traditional financial sector development erodes.
  - Interaction patterns: the negative relationship between traditional financial development and fintech is substantially weaker in LICs compared with advanced and developing economies for the business segment (narrative reports implied coefficients such as -3.9 for LICs versus -35.5 for advanced/developing economies).
  - For consumer fintech, controlling for economic development reveals a highly significant negative coefficient for advanced economies (narrative reports a coefficient estimate -31.9), while in some specifications the relationship flips sign for other country groups.
- Geography, information and banking features:
  - Availability of credit information, legal system strength, geographical barriers, and banking sector profitability and concentration are examined as channels explaining cross-country differences; detailed coefficients reported in tables.

### Cross-country (between) regression findings — selected points (exact figures preserved where reported)
- Aggregate interpretation:
  - Marketplace lending remains a small fraction of the financial system and too small to pose financial stability concerns.
- Economic development in between regressions:
  - GDP per capita generally shows a negative coefficient in between regressions (marketplace lending depth negatively correlated with the economy’s size).
- Technological infrastructure in between regressions:
  - Internet use (% of population) is highly insignificant across specifications for total, business, and consumer marketplace lending in between regressions.
- Information infrastructure:
  - Depth of credit information index (range 0 to 8) shows some evidence of explaining total marketplace lending differences and is more relevant for consumer marketplace lending; results are specification-sensitive.
- Regulation and legal environment:
  - Banking regulation stringency has a strongly negative and statistically significant relationship with marketplace lending across specifications and segments.
  - Strength of legal rights index (range 0 to 12) shows no statistically significant coefficient for total and business marketplace lending but a significant and positive coefficient for consumer marketplace lending in some regressions; caveat: the index focuses on credit secured with movable collateral while marketplace lending is generally unsecured.
- Geographical barriers:
  - Ratio of urban area to land area: interaction results show marketplace lending is higher in advanced economies with geographical barriers; effect holds for consumer segment but not business segment.
- Banking profitability:
  - Banking ROA (past 5 years) is highly insignificant in all regressions.

### Segment-specific patterns (summary)
- Consumer marketplace lending:
  - Negative relationship with financial depth, stronger for low-income countries.
  - Positive association with traditional financial inclusion index in cross-country regressions; fintech-payment financial inclusion index is highly insignificant (limited coverage).
  - Depth of credit information shows a more consistent positive relation for the consumer segment.
- Business marketplace lending:
  - Expands where financial efficiency of traditional institutions in granting credit declines.
  - Expands when access to financial institutions decreases.
  - Relationship with financial development indices is less consistent compared with the consumer segment.

### Specific reported estimates for developing and low-income economies
- Marketplace lending to consumers in low-income and developing economies is driven by the same factors that drive the traditional financial sector; reported estimated coefficients include "17.8 for developing economies and 307.5 for low-income countries (both significant at 10% level)."

### Policy implications and recommendations (as presented)
- Marketplace lending tends to fill gaps where traditional financial development shows imperfections (negative relationship between traditional financial development and marketplace lending).
- Regulatory stance matters:
  - Stringent bank regulation is associated with lower marketplace lending; while prudential regulation is essential, unnecessarily burdensome regulation can hinder marketplace lending expansion.
- Market structure and entry barriers:
  - High bank concentration and large banking sectors can suppress marketplace lending due to entry barriers; policies that reduce anti-competitive barriers could facilitate fintech entry where appropriate.
- Information infrastructure and inclusion:
  - Improving availability of credit information and traditional financial inclusion can support marketplace lending adoption, particularly for consumer credit in lower-income contexts.

*Source: wpiea2020150-print-pdf — INTRODUCTION and empirical results (CCAF data and authors’ calculations, 2015–2017).*

### Introduction ...........................................................................................................

### wpiea2020150-print-pdf - Introduction ...........................................................................................................

### Table of contents (section headings with page references)
- Introduction ......................................................................................................................... 5
- Literature Review .............................................................................................................. 7
- Stylized Facts .................................................................................................................... 9
- Enabling Conditions for Fintech Credit ....................................................................... 13
  - A. Methodology .................................................................................................................. 13
  - B. Results: Fintech Credit Drivers ...................................................................................... 14
  - C. Results: Cross-country Differences in Fintech Credit.................................................... 21
- Conclusions ....................................................................................................................... 28
- References .............................................................................................................................. 29

### Figures
- 1. Sample Coverage from the Universe of Fintech Financing .................................................. 6
- 2. Marketplace Lending Across the Globe ............................................................................. 10
- 3. Composition of Marketplace Lending ................................................................................ 12

### Tables
- 1. Descriptive Statistics of Variables ...................................................................................... 15
- 2. Drivers of Total Marketplace Lending: Fixed-Effect Regressions ..................................... 16
- 3. Drivers of Business Marketplace Lending: Fixed-Effect Regressions ............................... 17
- 4. Drivers of Consumer Marketplace Lending: Fixed-Effect Regressions ............................. 18
- 5. Cross-Country Determinants of Total Marketplace Lending: Between Regressions ......... 22
- 6. Cross-Country Determinants of Business Marketplace Lending: Between Regressions ... 23
- 7. Cross-Country Determinants of Consumer Marketplace Lending: Between Regressions . 24

### Glossary (abbreviations as listed)
- APD Asian and Pacific Countries
- AFR African Countries
- CCAF The Cambridge Center for Alternative Finance
- EME Emerging Market Economies
- EUR European Countries
- LIC Low-Income Countries
- MCD Middle East and Central Asia
- P2P lending Peer-to-Peer Lending
- SMEs Small and Medium-Sized Enterprises
- WEF The World Economic Forum
- WHD Western Hemisphere Countries

*Source: wpiea2020150-print-pdf - Introduction ...........................................................................................................*

### INTRODUCTION

### INTRODUCTION

### Context and motivation
- Limited access to credit is an important hurdle for consumers and small and medium-sized enterprises (SMEs) in many countries, with potentially significant macroeconomic consequences.  
- International Finance Corporation estimates that 41 percent of SMEs in the formal sector in developing countries have unmet financing needs.  
- According to the World Bank, about 60 percent of adults in developing countries do not use any formal financial services.  
- Barriers to credit include lack of physical access to bank branches, lack of documentation and credit history, absence of credit bureaus/registries, weak legal protection for creditors, weak collateral registry systems, and legal constraints on use of movable collateral.

### Definition and scope of fintech credit studied
- Fintech credit in this paper refers to marketplace lending (the debt component of crowdfunding), specifically:
  - P2P lending (funding entirely open to the public, platform matches borrowers with a pool of lenders).
  - Balance sheet lending when the platform also uses its own funds (included in sample where funding is partly or completely open to retail investors).
  - Invoice trading (lending against account receivables).
- Excluded from the study: Big tech lending (e.g., e-commerce platforms), digital lending by banks, mobile-platform lending not open to the public, mortgage lending, and equity crowdfunding.

### Data and sample
- Data source: Cambridge Center for Alternative Finance (CCAF) Alternative Finance Industry Benchmarking Survey.  
- Coverage: 109 countries, annual data for 2015 to 2017, country-level aggregates of gross new originations of debt-based alternative finance (consumer and business).  
- Note: CCAF data captures gross new originations of alternative finance and does not account for repayments.

### Stylized facts and key statistics
- Global scale and growth:
  - Marketplace lending more than tripled from 2015 to 2017, reaching US$400 billion.
  - Composition in 2017: 69 percent of total marketplace lending originations were to consumers.
  - Growth in 2017: fintech consumer credit originations grew 62 percent versus fintech business credit originations at 26 percent.
- Global concentration:
  - China, the United States, and the United Kingdom account for 98 percent of global marketplace lending activity.
  - China alone accounts for 86 percent of global marketplace lending.
  - China, US and UK dominate marketplace lending comprising 98.3 percent of global activity (alternative phrasing in source: 98 percent and 98.3 percent both reported).
- Systemic importance and relative shares:
  - Marketplace lending originations exceeded 1 percent of credit from traditional intermediaries only in Georgia (2.1 percent) and China (1.3 percent) in 2017.
  - Apart from Georgia and China, all other countries had marketplace lending originations less than 0.4 percent of credit from traditional intermediaries.
  - In terms of GDP, other than Georgia and China, all countries had marketplace lending originations less than 0.4 percent; most countries have less than 0.5 percent of GDP in marketplace lending.
- Country-level usage examples:
  - United Kingdom: less than 0.1 percent of UK SMEs have borrowed from a marketplace lender; about 4 of every 1,000 adults has borrowed from a marketplace lender.
  - China: about 5 of every 1,000 adults has borrowed from a marketplace lender versus over 427 per 1,000 adults from commercial banks.
- Industry concentration in markets:
  - UK: three top marketplace lenders accounted for 60 percent of business lending through online crowdfunding platforms in 2017Q1; business lending by platforms rose from US$660 million in 2014 to US$2.9 billion in 2017; consumer lending by top platforms rose from US$790 million in 2014 to US$2.5 billion in 2017.
  - China: top five P2P platforms had a 25 percent market share in 2019 (out of more than 500 platforms).
- China P2P time series (platform-reported WDZJ.com):
  - Q1 2014: US$6 billion in transaction volume of loans originated via P2P platforms.
  - Q3 2017: US$113 billion.
  - Q3 2019: US$30 billion (decline after 2017).
  - Average term of a marketplace loan in China: 15.8 months as of April 2019.
  - WDZJ’s 2017 survey: more than 80 percent of borrowers from P2P platforms in China were between 20 and 40 years old; more than half of borrowers made a monthly salary less than US$600.

### Methodology (empirical strategy)
- Panel regression analysis with country fixed effects; also report between regressions to capture slow-moving factors.
- Dependent variables: logarithm of total new originations of marketplace lending as a share of a country’s nominal GDP; regressions run for total, business, and consumer marketplace lending.
- Key identification assumption: marketplace lending is small relative to aggregate economy and traditional lenders, so explanatory variables are treated as exogenous relative to marketplace lending development.
- Explanatory domains: economic development, financial development (depth, access, efficiency), internet adoption, information, legal infrastructure, geographical barriers, banking sector features.
- Interaction terms: financial development interacted with economic development dummies (Advanced Economies (AE) and Low-Income Countries (LIC)).

### Regression evidence — selected estimation results and interpretations
- Data table summary statistics (select entries preserved exactly):
  - log (total fintech credit/GDP): mean -9.62, Std. Dev. 2.08, Min -15.87, Max -3.55.
  - log (business fintech credit/GDP): mean -10.26, Std. Dev. 2.14, Min -18.53, Max -4.70.
  - log (consumer fintech credit/GDP): mean -10.09, Std. Dev. 2.32, Min -15.45, Max -3.92.
  - log (GDP ppp per capita): mean 9.39, Std. Dev. 1.19, Min 6.60, Max 11.48.
  - Internet Users (% of population): mean 52.56, Std. Dev. 28.36, Min 1.76, Max 97.30.
  - Financial Development Index (Sahay and others (2015)): mean 0.48, Std. Dev. 0.22, Min 0.12, Max 1.00.
  - Average Bank Concentration (2010-2014): mean 76.01, Std. Dev. 15.68, Min 27.51, Max 100.00.
  - Average Return on Bank Assets (2010-2014): mean 1.29, Std. Dev. 1.13, Min -2.78, Max 5.53.

- Drivers of total marketplace lending (Fixed-Effect regressions; dependent variable log(total marketplace lending/GDP); sample 2015–2017; robust p-values in parentheses):
  - GDP per capita: positive and highly significant across specifications (e.g., coefficient 10.66***, (0.002) in column (1); 14.11***, (0.000) in column (5)).
  - internet users: positive and highly significant (e.g., 0.26***, (0.000) in column (1); 0.25***, (0.000) in column (5)).
  - Financial Depth: negative and significant (e.g., -24.60***, (0.000) in column (3); -54.20**, (0.026) in column (5)).
  - Bank Concentration * Financial Efficiency: positive and significant (0.34***, (0.000) in column (5)).
  - R-squared ranges: 0.52 to 0.60 across columns; Number of Countries 102–103; Observations ~207–208.

- Drivers of business marketplace lending (Fixed-Effect regressions; dependent variable log(business marketplace lending/GDP)):
  - GDP per capita: positive and highly significant (e.g., 14.18***, (0.001) in column (1)).
  - Financial Development: negative and significant in some specifications (e.g., -26.17***, (0.006) in column (1); -35.48**, (0.019) in column (2)).
  - Financial Efficiency: negative and significant in several specifications (e.g., -6.55**, (0.029) in column (1); -14.39*, (0.072) in column (5)).
  - Interaction effects: LIC * Financial Access negative and significant in some specs (e.g., -186.41***, (0.002) in column (5)); LIC * Financial Efficiency positive and significant in some specs (16.39**, (0.026) in column (5)).
  - R-squared ~0.26–0.29; Number of Countries 68; Observations 154.

- Drivers of consumer marketplace lending (Fixed-Effect regressions; dependent variable log(consumer marketplace lending/GDP)):
  - GDP per capita: positive and significant in some specifications (e.g., 12.73***, (0.005) in column (3); 13.29***, (0.007) in column (5)); weaker in others.
  - internet users: positive and highly significant across specifications (e.g., 0.30***, (0.000) in column (1); 0.27***, (0.000) in column (5)).
  - Financial Depth: negative and significant in some specifications (e.g., -28.32***, (0.001) in column (1)).
  - Financial Efficiency: positive and significant in some specifications (6.75**, (0.036) in column (1)); but interaction AE * Financial Development shows negative and significant coefficient (-49.68***, (0.001) in column (3)) and LIC * Financial Development shows large positive (289.72*, (0.054) in column (3)).
  - R-squared range: 0.57 to 0.70; Number of Countries 93–95; Observations 161–162.

### Main empirical interpretations highlighted in the text
- Economic development:
  - Higher income per capita is highly significant and positively associated with marketplace lending (total, consumer, and business segments).
- Technological infrastructure:
  - Internet access is an important and robust driver of marketplace lending, especially for total and consumer segments. The fraction of population using internet is a significant predictor; alternative proxies (e.g., supply of internet servers) show significance for business fintech.
- Financial development (depth, access, efficiency):
  - For business marketplace lending, the broad index of financial development is strongly significant with a negative sign, suggesting marketplace lending to businesses tends to expand when traditional financial sector development erodes (fintech filling gaps).
  - The relationship between traditional financial development and fintech differs across country groups:
    - Interaction with AE and LIC dummies shows the negative relationship is substantially weaker in LICs compared to advanced and developing economies for the business segment (implied coefficient for LICs reported as -3.9 versus -35.5 for advanced and developing economies in text interpretation).
    - For consumer fintech, controlling for economic development reveals a highly significant negative coefficient for advanced economies (coefficient estimate -31.9 reported in narrative), but the relationship flips sign for other groups in some specifications.
- Geography, information and banking features:
  - The paper also examines availability of information, strength of legal systems, geographical barriers, and banking sector profitability and concentration as channels explaining cross-country differences (detailed coefficients and interaction effects reported in regression tables summarized above).

*Source: INTRODUCTION (wpiea2020150-print-pdf) — CCAF data and authors’ calculations as presented in the source text.*

### 17.8 for developing economies and 307.5 for low-income countries (both significant at 10%

### Marketplace lending: cross-country drivers and segmental differences (Content unit)

### Key empirical findings
- Marketplace lending to consumers in low-income and developing economies is driven by the same factors that drive the traditional financial sector; estimated coefficients include "17.8 for developing economies and 307.5 for low-income countries (both significant at 10% level)."
- Sample and scope:
  - Data for 109 countries, sample period 2015 to 2017 (CCAF survey).
  - Total marketplace lending in 2017: US$400 billion.
  - Market concentration: top three countries (China, the United States and the United Kingdom) account for 98 percent of the market in 2017.
- Aggregate interpretation:
  - Marketplace lending remains a small fraction of the financial system and too small to pose financial stability concerns.

### Financial-development components and fintech credit
- Relationships by component:
  - Financial depth: highly significant and negative effect on total and consumer fintech credit.
  - Financial efficiency: business fintech credit grows where financial efficiency is lower.
  - Financial access: interacting sub-indices with economic development shows greater financial access is a key driver of marketplace lending in lower-income countries.
- Interpretation:
  - Findings are consistent with a financial literacy story in underdeveloped financial sectors: increased access signals basic improvements in financial literacy among previously unbanked populations, enabling consumer marketplace lenders to attract users by providing small, flexible loans.

### Role of bank concentration and interaction effects
- Bank concentration as barrier:
  - Regression results indicate the negative relation between financial efficiency and marketplace lending is weaker in countries with highly concentrated banking sectors (positive interaction coefficient).
  - Higher bank concentration is an impediment to marketplace lending growth entering credit markets that suffer from high inefficiency in credit pricing.
- Interaction of bank concentration and financial development:
  - Marketplace lending is smaller in countries with a highly concentrated yet large banking sector; the negative coefficient on the interaction term supports the role of entry barriers.
  - Marketplace lending is larger when the banking sector is more competitive but small.

### Cross-country determinants (between regressions — total, business, consumer segments)
- Economic development:
  - GDP per capita generally shows a negative coefficient in between regressions: marketplace lending depth is negatively correlated with the economy’s size, implying marketplace lending is more prevalent in less developed economies, even though income per capita positively drives marketplace lending controlling for country features in panel regressions.
  - Advanced-economy interactions sometimes attenuate the inverse relationship (e.g., AE * GDP per capita coefficients reported as positive in some specifications).
- Technological infrastructure:
  - Internet use (% of population) is highly insignificant across specifications for total, business, and consumer marketplace lending.
  - Robustness checks using (a) indicator of internet use above sample mean (52 percent) and (b) log of number of internet servers did not yield significant coefficients.
- Information infrastructure:
  - The depth of credit information index (ranges from 0 to 8) shows some evidence of explaining total marketplace lending differences across countries and is more relevant for consumer marketplace lending than for business lending; results are sensitive to specification.
- Regulation and legal environment:
  - Banking regulation stringency has a strongly negative and statistically significant relationship with marketplace lending across all specifications and segments (robust p-values shown in tables).
  - Strength of legal rights index (ranges from 0 to 12) shows no statistically significant coefficient for total and business marketplace lending but a significant and positive coefficient for consumer marketplace lending in unreported regressions; caveat: the index focuses on credit secured with movable collateral, while marketplace lending is generally unsecured.
- Geographical barriers:
  - Ratio of urban area to land area used as proxy for geographical barriers: interaction results show marketplace lending is higher in advanced economies with geographical barriers (negative interaction coefficient); effect holds for consumer segment but not for business segment.
  - Alternative measure (bank branches per 1,000 square km) produced highly insignificant coefficients in unreported regressions.
- Banking profitability:
  - Banking ROA (past 5 years) is highly insignificant in all regressions; no support that higher bank profitability predicts marketplace lending emergence.

### Segment-specific patterns
- Consumer marketplace lending:
  - Negative relationship with financial depth, stronger for low-income countries.
  - Positive association with traditional financial inclusion index (Sahay and others, 2020) in cross-country (between) regressions; fintech-payment financial inclusion index is highly insignificant (limited coverage: about one third of sample).
  - Depth of credit information shows more consistent positive relation for consumer segment.
- Business marketplace lending:
  - Increases where financial efficiency of traditional institutions in granting credit declines.
  - Increases when access to financial institutions decreases.
  - Relationship with financial development indices is less consistent compared with consumer segment.

### Policy implications and interpretation
- Marketplace lending tends to fill gaps where traditional financial development shows imperfections (negative relationship between traditional financial development and marketplace lending).
- Regulatory stance matters:
  - Stringent bank regulation is associated with lower marketplace lending; while prudential regulation is essential, unnecessarily burdensome regulation can hinder marketplace lending expansion.
- Market structure and entry barriers:
  - High bank concentration plus large banking sectors can suppress marketplace lending due to entry barriers; policy that reduces anti-competitive barriers could facilitate fintech entry where appropriate.
- Information infrastructure and inclusion:
  - Improving availability of credit information and traditional financial inclusion can support marketplace lending adoption, particularly for consumer credit in lower-income contexts.

*Source: IMF working paper (between- and fixed-effects regressions, CCAF survey data, 2015–2017).*

### REFERENCES

### REFERENCES

### FinTech, financial inclusion, and policy papers
- Bazarbash, M. (2019). FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk. International Monetary Fund. Working Paper 19/109  
- Carstens, A. (2019) Central banking and innovation: partners in the quest for financial inclusion. BIS Speech. Available at https://www.bis.org/speeches/sp190425.htm  
- Claessens, S., J. Frost, G. Turner, and F. Zhu (2018). “Fintech credit markets around the world: size, drivers and policy issues,” BIS Quarterly Review, September.  
- International Monetary Fund (2019a). “Fintech—The Experience So Far”, Policy Paper No. 19/024.  
- International Monetary Fund, (2019b). “Financial Inclusion of Small and Medium-Sized Enterprises in the Middle East and Central Asia,” Departmental Paper No. 19/02.  
- International Monetary Fund (2018). “The Bali Fintech Agenda.” October, available at https://www.imf.org/en/Publications/Policy-Papers/Issues/2018/10/11/pp101118-bali-fintechagenda (accessed December 17, 2018).  
- Sahay, R., Eriksson von Allmen, U., Lahreche, A., Khera, P., Ogawa, S., Bazarbash, M., & Beaton, K. (2020). The Promise of Fintech; Financial Inclusion in the Post COVID-19 Era. IMF Departmental Policy Papers 20/09, International Monetary Fund.  
- US Department of the Treasury (2016): Opportunities and challenges in online marketplace lending, May.  
- US Department of the Treasury (2018): A financial system that creates economic opportunities: nonbank financials, fintech, and innovation, July.  
- World Economic Forum (2015). “The Future of FinTech: A Paradigm Shift in Small Business Finance,” White Paper. October, available at http://www3.weforum.org/docs/IP/2015/FS/GAC15_The_Future_of_FinTech_Paradigm_Shift_Small_Business_Finance_report_2015.pdf (accessed December 17, 2018).

### Peer-to-peer lending, marketplace lending, and alternative finance
- Berg, T., Burg, V., Gombović, A., & Puri, M. (2018). On the rise of FinTechs–Credit scoring using digital footprints (No. w24551). National Bureau of Economic Research.  
- De Roure, C., Pelizzon, L., & Tasca, P. (2016). How does P2P lending fit into the consumer credit market? Bundesbank Discussion Paper No. 30/2016.  
- Freedman, S., & Jin, G. Z. (2017). The information value of online social networks: lessons from peer-to-peer lending. International Journal of Industrial Organization, 51, 185-222.  
- Havrylchyk, O., Mariotto, C., Rahim, T., & Verdier, M. (2018). What has driven the expansion of the peer-to-peer lending. Universite Paris, Boston University, and the European Commission Working Paper.  
- Huang, R. H. (2018). Online P2P lending and regulatory responses in China: opportunities and challenges. European Business Organization Law Review, 19(1), 63-92.  
- Jagtiani, Julapa and Catharine Lemieux, 2017. “ Fintech Lending: Financial Inclusion, Risk Pricing, and Alternative Information,” Federal Reserve Bank of Philadelphia Working Paper No. 17-17.  
- Nesta (2014). Understanding alternative finance. The UK alternative finance industry report.  
- Rau, P. Raghavendra, Law, Trust, and the Development of Crowdfunding (May 31, 2019). Available at SSRN: https://ssrn.com/abstract=2989056  
- Tang, Huan. "Peer-to-peer lenders versus banks: substitutes or complements? "The Review of Financial Studies 32, no. 5 (2019): 1900-1938.  
- Zhang, Y., Jia, H., Diao, Y., Hai, M., & Li, H. (2016). Research on credit scoring by fusing social media information in online peer-to-peer lending. Procedia Computer Science, 91, 168-174.

### Credit scoring, alternative data, and digital footprints
- Berg, T., Burg, V., Gombović, A., & Puri, M. (2018). On the rise of FinTechs–Credit scoring using digital footprints (No. w24551). National Bureau of Economic Research.  
- Goldstein, I., Jiang, W., & Karolyi, G. A. (2019). To FinTech and beyond. The Review of Financial Studies, 32(5), 1647-1661.  
- Liberti, J. M., & Petersen, M. A. (2018). “Information: Hard and soft.” Review of Corporate Finance Studies, 8(1), 1-41. Bureau of Economic Research.  
- Zhang, Y., Jia, H., Diao, Y., Hai, M., & Li, H. (2016). Research on credit scoring by fusing social media information in online peer-to-peer lending. Procedia Computer Science, 91, 168-174.  
- Freedman, S., & Jin, G. Z. (2017). The information value of online social networks: lessons from peer-to-peer lending. International Journal of Industrial Organization, 51, 185-222.

### MSME finance, SME access to credit, and financial development
- Ayyagari, M., A. Demirgüç-Kunt; and V. Maksimovic, 2017, SME Finance, The World Bank Group Development Research Group Policy Research Working paper 8241  
- Alvarez de la Campa, A., 2011. Increasing Access to Credit through Reforming Secured Transactions in the MENA Region. Policy Research Working Paper 5613.  
- International Finance Corporation (2017) “MSME Finance Gap: Assessment of the Shortfalls and Opportunities in Financing Micro, Small and Medium Enterprises in Emerging Markets” Available at https://openknowledge.worldbank.org/handle/10986/28881  
- International Finance Corporation (2019) https://www.smefinanceforum.org/data-sites/msme-finance-gap  
- International Monetary Fund, (2019b). “Financial Inclusion of Small and Medium-Sized Enterprises in the Middle East and Central Asia,” Departmental Paper No. 19/02.

### Macroeconomic links: finance, growth, inequality, and measurement
- Beck, T., Demirgüç-Kunt, A., & Levine, R. (2007). Finance, inequality and the poor. Journal of economic growth, 12(1), 27-49.  
- Levine, R. (2005). Finance and growth: theory and evidence. Handbook of economic growth, 1, 865-934.  
- Philippon, T. (2016). The fintech opportunity (No. w22476). National Bureau of Economic Research.  
- Philippon, T. (2019). On Fintech and Financial Inclusion (No. w26330). National Bureau of Economic Research.  
- Sahay, R., Cihak, M., & N’Diaye, P., Adolfo Barajas, Srobona Mitra, Annette Kyobe, Yen Nian Mooi, and Seyed Reza Yousefi, 2015, “Financial Inclusion: Can it Meet Multiple Macroeconomic Goals?” International Monetary Fund Staff Discussion note SDN/15/17.  
- Svirydzenka, K. (2016). Introducing a new broad-based index of financial development. International Monetary Fund.  
- Patwardhan, A. (2018). Financial inclusion in the digital age. In Handbook of Blockchain, Digital Finance, and Inclusion, Volume 1 (pp. 57-89). Academic Press.

### Empirical methods and general references
- Baltagi, B. (2008). Econometric analysis of panel data. John Wiley & Sons.  
- Cambrdige Centre for Alternative Finance https://www.jbs.cam.ac.uk/faculty-research/centres/alternative-finance/  
- Frost, Jon. (2020). The economic forces driving fintech adoption across countries. BIS Working Paper No. 838  
- Hau, H., Huang, Y., Shan, H., & Sheng, Z. (2018). Fintech credit, financial inclusion and entrepreneurial growth. Unpublished working paper. Available at https://efmaefm.org/0EFMAMEETINGS/EFMA%20ANNUAL%20MEETINGS/2018-Milan/phd/001.pdf  
- Navaretti, G, G Calzolari and A Pozzolo (2017): “FinTech and banks: friends or foes?”, European Economy: Banks, Regulation, and the Real Sector, December.  
- Khera, Purva, Stephanie Ng, Sumiko Ogawa, and Ratna Sahay. 2020. “Can FinTech Unlock Financial Inclusion in Emerging and Developing Economies?” IMF Working Paper, International Monetary Fund, Washington, DC. (forthcoming)  
- World bank   https://www.worldbank.org/en/topic/financialinclusion/brief/achieving-universal-financial-access-by-2020

*Source: REFERENCES (from wpiea2020150-print-pdf)*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020150-print-pdf.pdf_