## Prometheus Unbound: What Makes Fintech Grow? (Section 1)

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---

### Abstract and Introduction
- Research objective:
  - Investigate emergence and spread of fintech using a comprehensive dataset covering 98 countries over the period 2012–2020 to ascertain economic, demographic, technological and institutional factors that enable fintech development.
- Key takeaways:
  - The total value of start-up investments into fintech worldwide increased from US$1 billion in 2008 to over US$200 billion before the COVID-19 pandemic.
  - The magnitude and statistical significance of enabling factors vary by type of fintech instrument and level of economic development (advanced economies vs. developing countries).
  - Policies and structural reforms can promote financial innovation—particularly by strengthening technological and institutional infrastructures and reducing cybersecurity threats.
- Definitions and scope:
  - Fintech (per Financial Stability Board): “technologically enabled financial innovation that could result in new business models, applications, processes, or products with an associated material effect on financial markets and institutions, and the provision of financial services.”
  - This study excludes cryptocurrencies and excludes mobile money and internet banking (which are operated by traditional financial institutions, per the CCAF dataset).

### Motivation and Literature Context
- Observed heterogeneity:
  - Significant variation in fintech development across countries, especially with respect to income level.
  - Prior literature is nascent on enabling factors for fintech development; mixed evidence exists on fintech’s effects on financial stability, economic growth, and financial inclusion.
- Prior study findings summarized:
  - Some studies: fintech mitigates financial risks via decentralization, diversification, deeper markets, efficiency, and transparency.
  - Other studies: fintech can increase vulnerabilities—cybersecurity risks, market volatility, amplified aggregate risk-taking, contagious behavior—potentially undermining financial stability.
  - On growth and inclusion: empirical studies document a positive association between fintech and economic growth and tend to find a positive relationship with financial inclusion, though many use indirect measures (mobile phone penetration, broadband access, prevalence of digital payments).
  - Using direct measures, fintech may have so far failed to promote financial inclusion across all countries but helped expand inclusion to some extent in developing countries (Cevik, 2024c).

### Data Overview
- Sample and primary measures:
  - Panel dataset: 98 countries, period 2012–2020.
  - Dependent variable: amount of fintech transactions (excluding cryptocurrencies) as a share of GDP.
  - Primary fintech data: Cambridge Centre for Alternative Finance (CCAF) dataset covering more than 4,400 fintech entities.
  - CCAF fintech categories: (i) digital lending and (ii) digital capital raising; CCAF excludes mobile money and internet banking.
- Fintech measure definitions:
  - Digital lending: balance sheet lending, peer-to-peer/marketplace lending, debt-based lending, invoice trading.
  - Digital capital raising: investment-based crowdfunding, non-investment-based crowdfunding.
  - Total fintech: digital lending + digital capital raising + other fintech (micro finance, pension-led funding) scaled by GDP.
- Selected explanatory variables:
  - Real GDP per capita; consumer price inflation; trade openness (exports + imports as % of GDP); domestic credit to the private sector (% of GDP); share of adult population covered by public credit registry; ATMs per 100,000 adults; commercial bank branches per 100,000 adults; educational attainments (share of labor force with basic education); population growth; urbanization; old-age dependency; mobile phone subscriptions per 100 people; fixed broadband subscriptions per 100 people; secure internet servers per 1 million people; government stability; bureaucratic quality.
- Table 1: Descriptive statistics (selected exact figures)
  - Fintech
    - Digital lending: Observations 594, Mean 0.1, Std. dev. 0.3, Minimum 0.0, Maximum 3.4
    - Digital capital raising: Observations 1,093, Mean 0.0, Std. dev. 0.0, Minimum 0.0, Maximum 0.5
    - Total: Observations 1,118, Mean 0.1, Std. dev. 0.2, Minimum 0.0, Maximum 3.4
  - Real GDP per capita: Observations 1,738, Mean 13,706, Std. dev. 18,765, Minimum 263, Maximum 167,809
  - Inflation: Observations 1,620, Mean 5.32, Std. dev. 21.1, Minimum -4.35, Maximum 57.2
  - Trade openness: Observations 1,581, Mean 90.95, Std. dev. 58.4, Minimum 10.0, Maximum 442.6
  - Domestic credit to the private sector: Observations 1,528, Mean 55.04, Std. dev. 43.5, Minimum 1.1, Maximum 258.9
  - Public credit registry: Observations 1,492, Mean 12.6, Std. dev. 23.0, Minimum 0.0, Maximum 100.0
  - ATMs: Observations 1,557, Mean 50.0, Std. dev. 47.8, Minimum 0.1, Maximum 324.2
  - Bank branches: Observations 1,576, Mean 17.8, Std. dev. 18.5, Minimum 0.4, Maximum 218.1
  - Population growth: Observations 1,773, Mean 1.3, Std. dev. 1.4, Minimum -6.9, Maximum 11.8
  - Urbanization: Observations 1,764, Mean 58.7, Std. dev. 23.3, Minimum 11.2, Maximum 100.0
  - Old-age dependency: Observations 1,773, Mean 8.5, Std. dev. 6.1, Minimum 0.3, Maximum 29.6
  - Educational attainments: Observations 944, Mean 47.8, Std. dev. 17.0, Minimum 12.6, Maximum 100.0
  - Mobile phone subscriptions: Observations 1,744, Mean 105.94, Std. dev. 41.1, Minimum 7.4, Maximum 420.9
  - Fixed broadband subscriptions: Observations 1,693, Mean 13.0, Std. dev. 13.5, Minimum 0.0, Maximum 61.3
  - Secure internet servers: Observations 1,745, Mean 5,140.6, Std. dev. 19,054.7, Minimum 0.0, Maximum 277,330.6
  - Government stability: Observations 1,242, Mean 7.1, Std. dev. 1.1, Minimum 4.0, Maximum 11.0
  - Bureaucratic quality: Observations 1,242, Mean 2.2, Std. dev. 1.1, Minimum 0.0, Maximum 4.0
- Data coverage notes:
  - CCAF covers 198 countries but dataset is unbalanced; fintech variables are not consistently available for all countries (e.g., 594 observations for digital lending, 1,093 for digital capital raising).
  - Results remain similar when dataset is winsorized at 5th and 95th percentiles.

### Conceptual Framework and Econometric Strategy
- Conceptual framing:
  - Fintech diffusion parallels new technology adoption: driven by demand-side factors (income growth, demographic changes) and supply-side factors (technological advancements, regulatory underpinnings).
  - Financial innovation addresses adverse selection, agency problems, information asymmetries; reduces verification and transaction costs; enhances credit issuance and availability; transfers and shares risks; and responds to economic and regulatory changes.
- Baseline empirical specification:
  - fintechi,t = α + βX i,t + ηi + μt + εi,t
    - fintechi,t: digital lending as a share of GDP, digital capital raising as a share of GDP, or all fintech instruments as a share of GDP.
    - X i,t: vector of explanatory variables listed above.
    - ηi: time-invariant country-specific effects.
    - μt: time effects controlling for common shocks.
- Estimation considerations:
  - Driscoll-Kraay (1998) standard errors to account for heteroskedasticity, autocorrelation and cross-sectional dependence.
  - Alternative techniques: two-stage least squares (2SLS) with instrumental variables (IV) to address omitted variable bias and potential endogeneity.

### Empirical Evidence (Main Patterns and Results)
- General patterns:
  - Macroeconomic factors are significant in creating a conducive environment for fintech.
  - Financial factors—from overall financial development to prevalence of ATMs and commercial bank branches and availability of credit registry—are associated with fintech levels.
  - Demographics—population growth, urbanization, and younger population composition—contribute to fintech development.
  - Technological infrastructure—mobile phone and broadband subscriptions and secure internet servers—is positively and decisively associated with fintech; fintech depends on information and telecommunication technologies and is vulnerable to cybersecurity risks.
  - Institutional and political factors foster fintech expansion, with effects varying by instrument type and country characteristics.
- Baseline regression highlights (full sample, country and time fixed effects):
  - Real GDP per capita: positively and significantly correlated with digital lending (column [1]); income effect statistically insignificant for digital capital raising (column [2]) and total fintech (column [3]).
  - Inflation: positive association with fintech development; magnitude small.
  - Trade openness: no notable effect.
  - Overall financial development: significant positive relationship with all fintech activity.
- Key quantitative regression evidence (selected reported coefficients and significance indicators as presented)
  - Table 2 excerpts:
    - Real GDP per capita: 0.267***; 0.962*** in another column.
    - Inflation: 0.003***; other columns show 0.000***.
    - Financial development: 0.001***; 0.029*** in one column for total fintech.
    - ATMs: -0.042; -0.003**; -0.029*** across columns.
    - Commercial bank branches: -0.125***; -0.005***; -0.054*** across columns.
    - Population growth: 0.040***; 0.029***; 0.022** in various columns.
    - Old-age dependency: -0.150; -0.027***; -0.141*** across columns.
    - Mobile phone subscriptions: 0.093**; 0.143*** in some columns.
    - Broadband internet subscriptions: 0.011; 0.352**; 0.035*** across specifications.
    - Secure internet servers: 0.010***; 0.010***; 0.003*** across columns.
  - Appendix Table A1 (2SLS-IV) selected coefficients:
    - Real GDP per capita t-1: 0.250, 0.158, 0.385
    - Financial development t-1: 0.001**, 0.001***, 0.001***
    - ATMs: -0.052, -0.002**, -0.026***
    - Commercial bank branches: -0.138***, -0.005***, -0.055***
    - Secure internet servers: 0.013***, 0.018***, 0.005***
  - Sample sizes and fit (examples):
    - Table 2 number of observations: 291, 451, 457 (varies by column).
    - Table 2 number of countries: 81, 97, 98.
    - Appendix A1 observations: 282, 440, 442; countries: 81, 89, 99.
    - R^2 examples: 0.44, 0.21, 0.38 in Table 2; Appendix A1 R^2: 0.43, 0.21, 0.37.
- Heterogeneity and robustness:
  - Advanced economies: per capita income and financial development are important drivers.
  - Developing countries: inflation matters more; demographics (population growth, urbanization, younger populations) are more critical; technological factors have broadly similar effects across income groups.
  - Endogeneity checks: 2SLS-IV via GMM using lagged values of real GDP per capita growth and domestic credit to the private sector; Appendix A1 confirms baseline findings.
- Notable empirical regularities from Section 2:
  - Higher financial development associated with higher fintech.
  - Greater prevalence of ATMs and commercial bank branches correlates with reduced fintech activity (coefficients consistently negative and often highly significant).
  - Credit registry coverage: positive but generally not statistically significant.
  - Population growth: positive and often statistically significant (example coefficient 0.040***).
  - Old-age dependency: negative association (example -0.141***).
  - Secure internet servers: consistently positive and often highly significant (examples 0.013***, 0.018***, 0.005***).

### Lithuania: an example of a fast-growing fintech hub (Box 1)
- Country scale and timeline:
  - Lithuania population: 2.8 million people.
  - Number of fintech enterprises: increased from 45 in 2013 to 276 by the end of 2023.
- Fintech transaction growth:
  - Total fintech transactions (excluding mobile money and internet banking) increased by 5,567 percent from less than 0.01 percent of GDP in 2015 to over 0.43 percent of GDP in 2020 (CCAF dataset).
- Regulatory and ecosystem features:
  - More than half of fintech firms licensed as electronic money institutions, payment institutions, or specialized banks—the highest in the EU.
  - Bank of Lithuania operates a “sandbox” facility and a one-stop-shop program for new market entrants (guidance and feasibility analysis during preapplication).
  - Lithuania ranked 9th lowest-risk country in the Basel AML Index in 2023 (Basel AML Index ranking: 9th; France 12th; the UK 13th; Germany 32nd; US 33rd).
- Labor market and spillovers:
  - Skilled workers in software development, data analytics, and cybersecurity are key contributors.
  - Fintech growth prompts traditional banks to innovate and offer comparable products.

### Policy Implications and Concluding Points
- Policy recommendations (from empirical findings):
  - Strengthen technological infrastructures (information and telecommunication networks) to realize the potential of digital technologies and financial innovation.
  - Strengthen institutional infrastructures and bureaucratic quality to foster fintech expansion.
  - Reduce cybersecurity threats and increase capacity for digital security; secure internet servers highlighted as crucial.
  - Develop adequate regulatory frameworks that:
    - Foster innovation for growth.
    - Ensure sustainable and inclusive financial development.
    - Balance promotion of fintech with mitigation of associated risks (financial stability, cybersecurity).
  - Align policies with the Bali Fintech Agenda (IMF, 2018) in harnessing benefits and opportunities of fintech.
- Overall conclusion:
  - Fintech can revolutionize financial services and create new finance sources for households and firms, but fintech transactions remain small relative to credit by traditional financial institutions.
  - Policies and structural reforms can promote financial innovation—particularly via technological and institutional improvements—and must balance fostering innovation with managing associated risks.

*Source: IMF Working Paper WP/25/44, Serhan Cevik, February 2025.*

### Section 1

### Prometheus Unbound: What Makes Fintech Grow? (Section 1)

### Abstract and Introduction
- Research objective: Investigate emergence and spread of fintech using a comprehensive dataset covering 98 countries over the period 2012–2020 to ascertain economic, demographic, technological and institutional factors that enable fintech development.
- Key takeaways:
  - The total value of start-up investments into fintech worldwide increased from US$1 billion in 2008 to over US$200 billion before the COVID-19 pandemic.
  - The magnitude and statistical significance of enabling factors vary by type of fintech instrument and level of economic development (advanced economies vs. developing countries).
  - Policies and structural reforms can promote financial innovation—particularly by strengthening technological and institutional infrastructures and reducing cybersecurity threats.
- Definitions and scope:
  - Fintech (per Financial Stability Board): “technologically enabled financial innovation that could result in new business models, applications, processes, or products with an associated material effect on financial markets and institutions, and the provision of financial services.”
  - This study excludes cryptocurrencies and excludes mobile money and internet banking (which are operated by traditional financial institutions, per the CCAF dataset).

### Motivation and Literature Context
- Observed heterogeneity:
  - Significant variation in fintech development across countries, especially with respect to income level.
  - Prior literature is nascent on enabling factors for fintech development; mixed evidence exists on fintech’s effects on financial stability, economic growth, and financial inclusion.
- Prior study findings summarized:
  - Some studies: fintech mitigates financial risks via decentralization, diversification, deeper markets, efficiency, and transparency.
  - Other studies: fintech can increase vulnerabilities—cybersecurity risks, market volatility, amplified aggregate risk-taking, contagious behavior—potentially undermining financial stability.
  - On growth and inclusion: empirical studies document a positive association between fintech and economic growth and tend to find a positive relationship with financial inclusion, though many use indirect measures (mobile phone penetration, broadband access, prevalence of digital payments).
  - Using direct measures, fintech may have so far failed to promote financial inclusion across all countries but helped expand inclusion to some extent in developing countries (Cevik, 2024c).

### Data Overview
- Sample:
  - Panel dataset of annual observations covering 98 countries over the period 2012–2020.
  - Dependent variable: amount of fintech transactions (excluding cryptocurrencies) as a share of GDP.
  - Primary fintech data: Cambridge Centre for Alternative Finance (CCAF) dataset covering more than 4,400 fintech entities.
  - CCAF divides fintech into two main categories: (i) digital lending and (ii) digital capital raising.
  - CCAF excludes mobile money and internet banking.
- Fintech measures:
  - Digital lending: loans issued through digital platforms (balance sheet lending, peer-to-peer/marketplace lending, debt-based lending, invoice trading).
  - Digital capital raising: capital raising through digital platforms (investment-based crowdfunding, non-investment-based crowdfunding).
  - Total fintech: combination of digital lending, digital capital raising, and other fintech (micro finance, pension-led funding) scaled by GDP.
- Explanatory variables (selected):
  - Real GDP per capita; consumer price inflation; trade openness (exports + imports as % of GDP); domestic credit to the private sector (% of GDP); share of adult population covered by public credit registry; ATMs per 100,000 adults; commercial bank branches per 100,000 adults; educational attainments (share of labor force with basic education); population growth; urbanization; old-age dependency; mobile phone subscriptions per 100 people; fixed broadband subscriptions per 100 people; secure internet servers per 1 million people; government stability; bureaucratic quality.
- Table 1: Descriptive statistics (selected exact figures)
  - Fintech
    - Digital lending: Observations 594, Mean 0.1, Std. dev. 0.3, Minimum 0.0, Maximum 3.4
    - Digital capital raising: Observations 1,093, Mean 0.0, Std. dev. 0.0, Minimum 0.0, Maximum 0.5
    - Total: Observations 1,118, Mean 0.1, Std. dev. 0.2, Minimum 0.0, Maximum 3.4
  - Real GDP per capita: Observations 1,738, Mean 13,706, Std. dev. 18,765, Minimum 263, Maximum 167,809
  - Inflation: Observations 1,620, Mean 5.32, Std. dev. 21.1, Minimum -4.35, Maximum 57.2
  - Trade openness: Observations 1,581, Mean 90.95, Std. dev. 58.4, Minimum 10.0, Maximum 442.6
  - Domestic credit to the private sector: Observations 1,528, Mean 55.04, Std. dev. 43.5, Minimum 1.1, Maximum 258.9
  - Public credit registry: Observations 1,492, Mean 12.6, Std. dev. 23.0, Minimum 0.0, Maximum 100.0
  - ATMs: Observations 1,557, Mean 50.0, Std. dev. 47.8, Minimum 0.1, Maximum 324.2
  - Bank branches: Observations 1,576, Mean 17.8, Std. dev. 18.5, Minimum 0.4, Maximum 218.1
  - Population growth: Observations 1,773, Mean 1.3, Std. dev. 1.4, Minimum -6.9, Maximum 11.8
  - Urbanization: Observations 1,764, Mean 58.7, Std. dev. 23.3, Minimum 11.2, Maximum 100.0
  - Old-age dependency: Observations 1,773, Mean 8.5, Std. dev. 6.1, Minimum 0.3, Maximum 29.6
  - Educational attainments: Observations 944, Mean 47.8, Std. dev. 17.0, Minimum 12.6, Maximum 100.0
  - Mobile phone subscriptions: Observations 1,744, Mean 105.94, Std. dev. 41.1, Minimum 7.4, Maximum 420.9
  - Fixed broadband subscriptions: Observations 1,693, Mean 13.0, Std. dev. 13.5, Minimum 0.0, Maximum 61.3
  - Secure internet servers: Observations 1,745, Mean 5,140.6, Std. dev. 19,054.7, Minimum 0.0, Maximum 277,330.6
  - Government stability: Observations 1,242, Mean 7.1, Std. dev. 1.1, Minimum 4.0, Maximum 11.0
  - Bureaucratic quality: Observations 1,242, Mean 2.2, Std. dev. 1.1, Minimum 0.0, Maximum 4.0
- Data coverage notes:
  - CCAF covers 198 countries but dataset is unbalanced; fintech variables are not consistently available for all countries (e.g., 594 observations for digital lending, 1,093 for digital capital raising).
  - Results remain similar when dataset is winsorized at 5th and 95th percentiles.

### Conceptual Framework and Econometric Strategy
- Conceptual framing:
  - Fintech is similar to development and diffusion of new technologies; driven by demand-side factors (income growth, demographic changes) and supply-side factors (technological advancements, regulatory underpinnings).
  - Financial innovation aims to address adverse selection, agency problems, information asymmetries; reduce verification and transaction costs; enhance credit issuance and availability; transfer and share risks; and respond to economic and regulatory changes.
- Baseline empirical specification:
  - fintechi,t = α + βX i,t + ηi + μt + εi,t
    - fintechi,t: digital lending as a share of GDP, digital capital raising as a share of GDP, or all fintech instruments as a share of GDP.
    - X i,t: vector of explanatory variables listed in the Data Overview.
    - ηi: time-invariant country-specific effects.
    - μt: time effects controlling for common shocks.
  - Estimation considerations:
    - Driscoll-Kraay (1998) standard errors used to account for heteroskedasticity, autocorrelation and cross-sectional dependence, suitable for unbalanced panels with shorter time dimension.
    - Alternative estimation techniques include two-stage least squares (2SLS) with instrumental variables (IV) to address omitted variable bias and potential endogeneity.

### Empirical Evidence (Main Patterns and Results)
- General patterns:
  - Macroeconomic factors play a significant role in creating a conducive environment for fintech endeavors.
  - Financial factors—from overall financial development to prevalence of ATMs and commercial bank branches and availability of credit registry—are associated with higher levels of fintech.
  - Demographic forces—population growth, urbanization, and a young population composition—contribute significantly to fintech development.
  - Technological infrastructure—mobile phone and broadband subscriptions and the number of secure internet servers—is positively and decisively associated with faster-growing fintech; fintech depends on information and telecommunication technologies and is vulnerable to cybersecurity risks.
  - Institutional and political factors foster fintech expansion, but effects depend on instrument type and country characteristics.
- Baseline regression highlights (as reported for the full sample with country and time fixed effects):
  - Real GDP per capita: positively and significantly correlated with digital lending (column [1]). Income effect is statistically insignificant for digital capital raising (column [2]) and total fintech (column [3]).
  - Inflation: shows a positive association with fintech development; magnitude small but suggests higher inflation may encourage financial innovation and adoption.
  - Trade openness: no notable effect on fintech.
  - Overall financial development: significant positive relationship with all types of fintech activity.

### Policy Implications and Concluding Points (from Section 1)
- Policymaker actions suggested by empirical findings:
  - Strengthen technological infrastructures (information and telecommunication networks) to realize the potential of digital technologies and financial innovation.
  - Strengthen institutional infrastructures and reduce cybersecurity threats to cultivate fintech ventures.
  - Develop adequate regulatory frameworks that foster innovation for growth and ensure sustainable and inclusive financial development, while acknowledging and mitigating potential risks associated with fintech (financial stability, cybersecurity).
- Overall conclusion:
  - Fintech has potential to revolutionize financial services and create new finance sources for households and firms, but fintech transactions remain small relative to credit by traditional financial institutions.
  - Policies and structural reforms can promote financial innovation—particularly via technological and institutional improvements—and must balance fostering innovation with managing associated risks.

*Source: IMF Working Paper WP/25/44, Serhan Cevik, February 2025.*

### Section 2

### Section 2

### Key empirical findings on the development of fintech
- Higher financial development (measured by domestic credit to the private sector as a share of GDP) is associated with higher levels of fintech. (Table 2: Financial development coefficients reported as positive and frequently statistically significant.)
- Greater prevalence of ATMs and commercial bank branches correlates with reduced fintech activity. Estimated coefficients for ATMs and commercial bank branches are consistently negative and statistically highly significant in many specifications.
- Credit registry coverage has a positive but not statistically significant effect on fintech spread across the full sample (Table 2: Credit registry coverage coefficients near zero, generally not significant).
- Demographics:
  - Population growth: positive and often statistically significant association with fintech development (Table 2: population growth coefficients positive, e.g., 0.040*** in one specification).
  - Urbanization: positive association with fintech, significance varies by instrument and specification.
  - Old-age dependency ratio: tends to have a significant negative association with fintech (Table 2: negative coefficients for old-age dependency, e.g., -0.141*** in some columns).
  - Educational attainments: positive but negligible magnitude and often statistically insignificant (author notes basic education measure may fail to capture advanced skills required for fintech ventures).
- Technological infrastructure:
  - Mobile phone subscriptions: positive association with fintech; magnitude and significance vary by fintech instrument (Table 2: mobile phone subscriptions coefficients positive, e.g., 0.093** in one column; Appendix Table A1 shows 0.113** for digital lending).
  - Broadband internet subscriptions: positively associated with fintech; effects vary by instrument and specification (Table 2 and Appendix Table A1 show positive coefficients, some significant, e.g., Appendix: 0.018*).
  - Secure internet servers: appear most crucial for fintech development; consistently positive and often highly significant (Table 2 and Appendix Table A1 show positive and significant coefficients, e.g., Appendix: 0.013***, 0.018***, 0.005***).
- Institutional and political factors:
  - Bureaucratic quality and government stability foster fintech growth, with effects varying by instrument and country group. Bureaucratic quality shows positive coefficients (e.g., Table 2 and Appendix Table A1 report positive coefficients, some significant).
- Heterogeneity by country group:
  - Advanced economies: per capita income and financial development are important drivers of fintech endeavors.
  - Developing countries: inflation matters more for fintech adoption (higher inflation is a greater obstacle to inclusion via conventional banks); demographics (faster population growth, urbanization, younger populations) are more critical; technological factors have broadly similar effects across income groups; institutional and political variables matter, notably in emerging market economies.

### Robustness and endogeneity checks
- Endogeneity concerns noted: potential omitted variables and reverse causality (fintech may contribute to economic growth and financial development).
- 2SLS-IV via GMM approach used to alleviate endogeneity: lagged values of real GDP per capita growth and domestic credit to the private sector used as instruments (Appendix Table A1).
- Appendix Table A1 results confirm baseline findings (examples from Appendix A1: Real GDP per capita t-1 coefficients reported as 0.250, 0.158, 0.385 across columns; financial development t-1 coefficients 0.001**, 0.001***, 0.001***; secure internet servers 0.013***, 0.018***, 0.005***).

### Representative quantitative details from regression tables (selected reported values)
- Table 2 excerpts (selected coefficients and significance indicators as presented):
  - Real GDP per capita: 0.267***; 0.962*** in another column.
  - Inflation: 0.003***; other columns show 0.000***.
  - Financial development: 0.001***; 0.029*** in one column for total fintech.
  - ATMs: -0.042; -0.003**; -0.029*** across columns.
  - Commercial bank branches: -0.125***; -0.005***; -0.054*** across columns.
  - Population growth: 0.040***; 0.029***; 0.022** in various columns.
  - Old-age dependency: -0.150; -0.027***; -0.141*** across columns.
  - Mobile phone subscriptions: 0.093**; 0.143*** in some columns.
  - Broadband internet subscriptions: 0.011; 0.352**; 0.035*** across specifications.
  - Secure internet servers: 0.010***; 0.010***; 0.003*** across columns.
- Appendix Table A1 (2SLS-IV) selected coefficients:
  - Real GDP per capita t-1: 0.250, 0.158, 0.385 (with reported brackets for standard errors).
  - Financial development t-1: 0.001**, 0.001***, 0.001***.
  - ATMs: -0.052, -0.002**, -0.026***.
  - Commercial bank branches: -0.138***, -0.005***, -0.055***.
  - Secure internet servers: 0.013***, 0.018***, 0.005***.
- Sample sizes and fit (as reported in Table 2 and Appendix A1):
  - Number of observations in Table 2: example entries 291, 451, 457 (varies by column and sample).
  - Number of countries in Table 2: example entries 81, 97, 98.
  - Appendix A1 number of observations: 282, 440, 442; number of countries: 81, 89, 99.
  - R^2 examples: 0.44, 0.21, 0.38 in Table 2; Appendix A1 R^2 reported as 0.43, 0.21, 0.37.

### Lithuania: an example of a fast-growing fintech hub (Box 1)
- Country context and scale:
  - Lithuania population cited as a country of 2.8 million people.
  - Number of fintech enterprises increased from 45 in 2013 to 276 by the end of 2023.
- Fintech transaction growth:
  - Total amount of fintech transactions excluding mobile money and internet banking increased by 5,567 percent from less than 0.01 percent of GDP in 2015 to over 0.43 percent of GDP in 2020 (CCAF dataset).
- Regulatory and ecosystem features:
  - More than half of fintech firms licensed as electronic money institutions, payment institutions, or specialized banks—the highest in the EU.
  - Bank of Lithuania operates a “sandbox” facility for testing under regulatory supervision and a one-stop-shop program for new market entrants (guidance on legal/regulatory matters and feasibility analysis during preapplication).
  - Lithuania ranks as the 9th lowest-risk country in the Basel AML Index in 2023 (Basel AML Index ranking mentioned as 9th, ahead of France 12th, the UK 13th, Germany 32nd, US 33rd).
- Labor market and spillovers:
  - Skilled workers in software development, data analytics, and cybersecurity noted as key contributors.
  - Fintech growth prompts traditional banks to innovate and offer comparable products and services.

### Policy implications and recommendations (from conclusion)
- Strengthen technological infrastructure: advancement of information and telecommunication networks is key to realizing the potential of digital technologies and financial innovation.
- Strengthen institutional infrastructure and bureaucratic quality to foster fintech expansion.
- Reduce cybersecurity threats and increase capacity for digital security (secure internet servers highlighted as crucial).
- Develop an adequate regulatory framework that:
  - Fosters innovation for growth.
  - Ensures sustainable and inclusive financial development.
  - Balances the promotion of fintech with acknowledgment of associated risks and threats.
- Reference to policy guidance: align with the Bali Fintech Agenda (IMF, 2018) in harnessing benefits and opportunities of fintech.

*Source: Author's estimations.*

### Section 3

### Section 3

### References cited

- Zhu (2018). “Fintech Credit Markets Around the World: Size, Drivers, and Policy Issues,” BIS Quarterly Review, September, pp. 29–49.  
- Comin, D., and B. Hobijn (2004). “Cross-Country Technology Adoption: Making the Theories Face the Facts,” Journal of Monetary Economics, Vol. 51, pp. 39–83.  
- Daud, S., A. Ahmad, A. Khalid, and W. Azman-Saini (2022). “FinTech and Financial Stability: Threat or Opportunity?” Finance Research Letters, Vol. 47, 102667.  
- Didier Brandao,T., E. Feyen, R. Llovet Montanes, and O. Ardic Alper (2022). “Global Patterns of Fintech Activity and Enabling Factors : Fintech and the Future of Finance Flagship Technical Note,” (Washington, DC: World Bank).  
- Driscoll, J., and A. Kraay (1998). “Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data,” Review of Economics and Statistics, Vol. 80, pp. 549–560.  
- Erumban, A., and S. de Jong (2006). “Cross-Country Differences in ICT Adoption: A Consequences of Culture,” Journal of World Business, Vol. 41, pp. 302–314.  
- Feyen, E., J. Frost, L. Gambacorta, H. Natarajan, and M. Saal (2021). “Fintech and the Digital Transformation of Financial Services: Implications for Market Structure and Public Policy,” BIS Working Papers No. 117 (Basel: Bank for International Settlements).  
- Frame, S., and L. White (2009). “Technological Change, Financial Innovation, and Diffusion in Banking,” Federal Reserve Bank of Atlanta Working Paper No. 2009-10 (Atlanta: Federal Reserve Bank of Atlanta).  
- Frame, S., L. Wall, and L. White (2019). “Technological Change and Financial Innovation in Banking: Some Implications for Fintech,” in A. Berger, P. Mullineaux, and J. Wilson (eds.) Oxford Handbook of Banking (Oxford: Oxford University Press).  
- Frost, J. (2020). “The Economic Forces Driving Fintech Adoption Across Countries,” BIS Working Papers No. 838 (Basel: Bank for International Settlements).  
- Fung, D., W. Lee, J. Yeh, and F. Yuen (2020). “Friend or Foe: The Divergent Effects of FinTech on Financial Stability,” Emerging Markets Review, Vol. 45, 100727.  
- Gomber, P., R. Kauffman, C. Parker, and B. Weber (2018). “On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services,” Journal of Management Information Systems, Vol. 35. pp. 220–265.  
- Guerrieri, P., M. Luciani, and V. Meliciani (2011). “The Determinants of Investment in Information and Communication Technologies” Economics of Innovation and New Technology, Vol. 20, pp. 387–403.  
- Haddad, C., and L. Hornuf (2019). “The Emergence of the Global Fintech Market: Economic and Technological Determinants,” Small Business Economics, Vol. 53, pp. 81–105.  
- Haddad, C., and L. Hornuf (2023). “How Do Fintech Start-Ups Affect Financial Institutions’ Performance and Default Risk?” European Journal of Finance, Vol. 29, pp. 1761–1792.  
- Hargittai, E. (1999). “Weaving the Western Web: Explaining Differences in Internet Connectivity AMONG OECD Countries” Telecommunications Policy, Vol. 23, pp. 701–718.  
- Hausman, A., and W. Johnston (2014). “The Role of Innovation in Driving the Economy: Lessons from the Global Financial Crisis,” Journal of Business Research, Vol. 67, pp. 2720–2726.  
- He, D., R. Leckow, V. Haksar, T. Mancini-Griffoli, N. Jenkinson, M. Kashima, T. Khiaonarong, C. Rochon, and H. Tourpe (2017). “Fintech and Financial Services: Initial Considerations,” IMF Staff Discussion Note No. 17/05 (Washington, DC: International Monetary Fund).  
- Hooks, D., Z. Davis, V. Agrawal, and Z. Li (2022). “Exploring Factors Influencing Technology Adoption Rate at the Macro Level: A Predictive Model,” Technology in Society, Vol. 68, 101826.  
- IMF (2018). “The Bali Fintech Agenda,” IMF Policy Paper (Washington, DC: International Monetary Fund).  
- Kane, E. (1986). “Technology and the Regulation of Financial Markets,” in A. Saunders and L. White (eds.), Technology and the Regulation of Financial Markets: Securities, Futures and Banking (Lexington, MA: Lexington Books).  
- Kanga, D., C. Oughton, L. Harris, and V. Murinde (2022). “The Diffusion of Fintech, Financial Inclusion and Income Per Capita,” European Journal of Finance, Vol. 28, pp. 108–136.  
- Kiiski, S., and M. Pojola (2002). “Cross-Country Diffusion of the Internet,” Information Economics and Policy, Vol. 14, pp. 297–310.  
- Kowalewski, O., and P. Pisany (2023). “The Rise of Fintech: A Cross-Country Perspective,” Technovation, Vol. 122, 102642.  
- Landes, D. (1969). The Unbound Prometheus: Technological Change and Industrial Development in Western Europe from 1750 to the Present (Cambridge: Cambridge University Press).  
- Lee, S., Y. Nam, S. Lee, and H. Son (2016). “Determinants of ICT Innovations: A Cross-Country Empirical Study,” Technological Forecasting & Social Change, Vol. 110, pp. 71–77.  
- Levine, R. (1997). “Financial Development and Economic Growth: Views and Agenda,” Journal of Economic Literature, Vol. 35, pp. 688–726.  
- Li, J., Y. Wu, and J. Xiao (2019). “The Impact of Digital Finance on Household Consumption: Evidence from China,” Economic Modeling, Vol. 86, pp. 317–326.  
- Merton, R. (1992). “Financial Innovation and Economic Performance,” Journal of Applied Corporate Finance, Vol. 4, pp. 12–22.  
- Miller, M. (1986). “Financial Innovation: The Last Twenty Years and the Next,” Journal of Financial and Quantitative Analysis, Vol. 21, pp. 459–471.  
- Minto, A., M. Voelkerling, and M. Wulff (2017). “Separating Apples from Oranges: Identifying Threats to Financial Stability Originating from Fintech,” Capital Markets Law Journal, Vol. 12, pp. 428–465.  
- Nguyen, Q., and V. Dang (2022). “The Effect of FinTech Development on Financial Stability in an Emerging Market: The Role of Market Discipline” Research in Globalization, Vol. 5, 100105.  
- Pantielieieva, N., S. Krynytsia, M. Khutorna, and L. Potapenko (2018). “Fintech, Transformation of Financial Intermediation and Financial Stability,” Presented at the 2018 International Scientific-Practical Conference Problems of Infocommunications.  
- Philippon, T. (2020). “On Fintech and Financial Inclusion,” BIS Working Papers No. 841 (Basel: Bank for International Settlements).  
- Ran, Z., P. Rau, and T. Ziegler (2022). “Sometimes, Always, Never: Regulatory Clarity and the Development of Digital Financing,” Available at SSRN: https://ssrn.com/abstract=3797886.  
- Rath, B. (2016). “Does Digital Divide Across Countries Lead to Convergence? New International Evidence,” Economic Modelling, Vol. 58, pp. 75–82.  
- Rath, B., B. Panda, and V. Akram. (2023). “Convergence and Determinants of ICT Development in case of Emerging Market Economies” Telecommunications Policy, Vol. 47, 102464.  
- Rogers, E. (1995). Diffusion of Innovations (New York: The Free Press).  
- Rosenberg, N. (1972). “Factors Affecting Diffusion of Technology,” Explorations in Economic History, Vol. 10, pp. 3–33.  
- Sahay, R., U. von Allmen, A. Lahreche, P. Khera, S. Ogawa, M. Bazarbash, and K. Beaton (2020). “The Promise of Fintech: Financial Inclusion in the Post COVID-19 Era,” IMF Departmental Paper No. 20/9 (Washington, DC: International Monetary Fund).  
- Schiller, R. (2012). The Subprime Solution: How Today’s Global Financial Criss Happened, and What to Do About It (Princeton, NJ: Princeton University Press).  
- Schindler, J. (2017). “Fintech and Financial Innovation: Drivers and Depth,” Finance and Economic Discussion Series No. 2017-081 (Washington, DC: Federal Reserve Board).  
- Smith, C., C. Smithson, and D. Wilford (1990). Managing Financial Risks (New York: Harper Business).  
- Song, N., and I. Appiah-Otoo (2022). “The Impact of Fintech on Economic Growth: Evidence from China,” Sustainability, Vol. 14, 6211.  
- Vucinic, M. (2020). “Potential Influence of Fintech on Financial Stability: Risks and Benefits,” Journal of Central Banking Theory and Practice, Vol. 9, pp. 43–66.  
- Wang, R., J. Liu, and H. Luo (2021). “Fintech Development and Bank Risk Taking in China,” European Journal of Finance, Vol. 27, pp. 397–418.  
- White, E. (2000). “Technological Change, Financial Innovation, and Financial Regulation in the US,” in P. Harker and S. Zenios (eds.), Performance of Financial Institutions (Cambridge: Cambridge University Press).  
- Wunnava, P., and D. Leiter (2009). “Determinants of Intercountry Internet Diffusion Rates,” American Journal of Economic and Sociology, Vol. 68, pp. 413–426.  
- Yang, T., and X. Zhang (2022). “FinTech Adoption and Financial Inclusion: Evidence from Household Consumption in China,” Journal of Banking and Finance, Vol. 145, 106668.  
- Zhang, X., J. Zhang, G. Wan, and Z. Lou (2020). “Fintech, Growth and Inequality: Evidence from China’s Household Survey Data” Singapore Economic Review, Vol. 65, pp. 75–93.

*Source: wpiea2025044-print-pdf - Section 3*

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_Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025044-print-pdf.pdf_