## wpiea2024020-print-pdf - 2015. Other studies, using mostly subnational data on fintech transactions in China, find a

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---

### Contribution and approach
- Uses a novel dataset of direct measures of fintech and implements dynamic modeling to investigate the empirical relationship between fintech and economic growth in a panel of 198 countries over the period 2012–2020.
- Addresses potential endogeneity using the system generalized method of moments (GMM) approach.
- Distinguishes fintech instruments into digital lending and digital capital raising and analyzes their differential impacts on real GDP per capita growth.

### Main empirical findings
- Impact depends on fintech instrument type:
  - Digital lending as a share of GDP has a statistically significant positive effect on economic growth.
  - Digital capital raising as a share of GDP has a large but statistically insignificant effect.
- Aggregate results:
  - Overall impact of fintech including all instruments is positive and statistically significant because of the overwhelming share of digital lending in total.
  - Positive relationship between fintech and economic growth remains after controlling for other factors including the lagged dependent variable.
- Heterogeneity:
  - When estimated separately, the positive relationship is stronger in magnitude in advanced economies.
  - The statistical significance of the effect is higher in developing countries in some specifications.
- Relative size and implications:
  - Average volume of fintech instruments: 0.1 percent of GDP.
  - Domestic credit to the private sector: 55 percent of GDP.
- Policy-relevant conclusions:
  - Maintaining financial stability is essential for sustainable growth and requires:
    - Strong regulatory institutions.
    - Better use of technology in regulation.
    - Extensive cross-border coordination.
    - Appropriately calibrated prudential regulations for a level playing field and effective monitoring and supervision of traditional and emerging financial institutions.

### Data overview (sample and variables)
- Sample: unbalanced panel of annual observations covering 198 countries over 2012–2020.
- Dependent variable: annual real GDP per capita growth rate and gross fixed capital formation as a share of GDP (from World Bank WDI).
- Key explanatory variable: volume of fintech transactions (excluding cryptocurrencies) as a share of GDP from the Cambridge Centre for Alternative Finance (CCAF) dataset.
- CCAF fintech categories used:
  - Digital lending (balance sheet lending, peer-to-peer and marketplace lending, debt-based lending, invoice trading).
  - Digital capital raising (investment-based crowdfunding, non-investment-based crowdfunding).
  - Total fintech: combination of digital lending, digital capital raising, and other fintech types (such as micro finance and pension-led funding) scaled by GDP.
- Note: The CCAF dataset excludes mobile money and internet banking, which are also operated by traditional financial institutions.

### Control variables included
- Level of real GDP per capita.
- Consumer price inflation.
- Trade openness (share of exports and imports in GDP).
- Financial development (domestic credit to the private sector as share of GDP).
- Government size (government spending as share of GDP).
- Population growth.
- Educational attainments (share of labor force with basic education).
- Institutional and political controls: government stability and bureaucratic quality (ICRG composite indices).

### Descriptive statistics (selected exact values from Table 1)
- Real GDP growth
  - Observations: 1,738
  - Mean: 2.2
  - Std. dev.: 5.9
  - Minimum: -54.2
  - Maximum: 86.8
- Gross fixed capital formation
  - Observations: 1,485
  - Mean: 23.1
  - Std. dev.: 8.0
  - Minimum: 1.4
  - Maximum: 78.0
- 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: 5.3
  - Std. dev.: 21.1
  - Minimum: -4.3
  - Maximum: 557.2
- Trade openness
  - Observations: 1,581
  - Mean: 90.9
  - Std. dev.: 58.4
  - Minimum: 10.0
  - Maximum: 442.6
- Domestic credit to the private sector
  - Observations: 1,528
  - Mean: 55.0
  - Std. dev.: 43.5
  - Minimum: 1.1
  - Maximum: 258.9
- Government spending
  - Observations: 1,517
  - Mean: 17.1
  - Std. dev.: 8.6
  - Minimum: 3.6
  - Maximum: 84.2
- Population growth
  - Observations: 1,773
  - Mean: 1.3
  - Std. dev.: 1.4
  - Minimum: -6.9
  - Maximum: 11.8
- Educational attainments
  - Observations: 944
  - Mean: 47.8
  - Std. dev.: 17.0
  - Minimum: 12.6
  - Maximum: 100.0
- 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

### Model specification and estimation strategy
- Baseline panel specification:
  - 푦푖푡 = 훽1 + 훽2푓푖푛푡푒푐ℎ푖푡 + 훽3푋푖푡 + 휂푖 + 휇푡 + 휀푖푡
  - 푦푖푡 denotes real GDP per capita growth or gross fixed capital formation as share of GDP in country i and time t.
  - 푓푖푛푡푒푐ℎ푖푡 represents (i) digital lending as a share of GDP, (ii) digital capital raising as a share of GDP, or (iii) all fintech instruments as a share of GDP.
  - 푋푖푡 includes controls: log real GDP per capita at t-1, consumer price inflation, trade openness, domestic credit to the private sector, government size, population growth, educational attainments, government stability, and bureaucratic quality.
  - 휂푖 and 휇푡 are country fixed effects and time effects; 휀푖푡 is the idiosyncratic error term.
- Sample and period:
  - Panel of 198 countries over the period 2012–2020.
- Inference:
  - Driscoll-Kraay (1998) standard errors used to account for heteroskedasticity, autocorrelation and cross-sectional dependence in an unbalanced panel with a shorter time dimension.
- Endogeneity and identification:
  - Potential reverse causality addressed by system GMM (Arellano and Bover (1995); Blundell and Bond (1998)).
  - One-step system GMM estimator used to avoid downward bias in standard errors with short T.
  - Use Roodman (2009) strategy to limit instrument proliferation.
  - Validation via AR(1) and AR(2) tests and Hansen J-test for overidentifying restrictions.

### Static estimation findings (selected reported values)
- Overall sample:
  - Growth impact of fintech is negligible for the sample as a whole at conventional levels.
- Representative coefficients and values (as reported):
  - Digital lending (All): 0.406 [0.329]
  - Digital capital raising (All): 9.805 [8.621]
  - Total fintech (All): 0.789 [0.323]
  - Real GDP per capita t-1 (All, some columns): -12.802** [4.121], -9.936*** [3.803], -9.539*** [3.783]
  - Number of observations (varies by column): 358, 519, 530, 322, 182, 231, 743, 01307 (as reported)
  - Number of countries (varies): 84, 99, 100, 32, 33, 35, 266, 67 (as reported)
- Subsample patterns:
  - Digital lending: statistically and economically significant positive effect (at the 10 percent level) on economic growth in advanced economies; much smaller and statistically insignificant effect in developing countries.
  - Digital capital raising: much greater in magnitude but statistically insignificant overall; positive in advanced economies and negative in developing countries.
  - Total fintech: statistically insignificant across all static specifications.

### Dynamic estimation findings (system GMM, selected reported values)
- Sample as a whole:
  - Digital lending as a share of GDP:
    - Coefficient: 0.849*** [0.176] — statistically and economically significant positive effect on real GDP per capita growth (at the 1 percent level).
  - Digital capital raising:
    - Coefficient: 17.610 [9.039] — substantially greater in magnitude but statistically insignificant.
  - Total fintech (all instruments):
    - Coefficient: 0.718*** [0.183] — positive and statistically significant; effect driven by the overwhelming share of digital lending.
- Heterogeneity by income group:
  - Advanced economies:
    - Digital lending coefficient: 1.909* [0.785] (positive, significant at 10 percent in some specifications).
  - Developing countries:
    - Positive relationship between fintech and growth is statistically significant and in some specifications more significant in developing countries despite smaller magnitude in advanced economies.
- Dynamic model diagnostics (examples as reported):
  - AR(1) p-values: 0.005, 0.000, 0.000
  - AR(2) p-values: 0.217, 0.425, 0.317
  - Hansen J-test p-values: 0.267, 0.369, 0.363
- Selected coefficients and statistics from Table 3 (All columns, as reported):
  - Real GDP per capita growth t-1: 0.354*** [0.039], 0.429*** [0.095], 0.439*** [0.099]
  - Total fintech (All): 0.718*** [0.183]
  - Number of observations (examples): 352, 510, 518, 182, 214, 218, 170, 296, 300
  - Number of countries (examples): 84, 99, 100, 32, 33, 35, 266, 67

### Fintech, fixed investment, and mechanisms (selected reported values)
- Dynamic system GMM for gross fixed capital formation as share of GDP (Table 4):
  - Total fintech (All):
    - All countries: 0.322*** [0.091] — fintech positively associated with gross fixed capital formation.
    - Advanced economies: 0.566*** [0.115] — positive effect on fixed investment.
    - Emerging/developing markets (EM): -0.680*** [0.204] — negative association with fixed investment.
- Interpretation:
  - Fintech, like broader financial development, affects growth via contribution to physical capital accumulation.
  - Divergent directions by income group may reflect infancy and volatility of fintech in developing countries.
- Selected diagnostics from Table 4:
  - Gross fixed capital formation t-1: 0.854*** [0.145], 0.700*** [0.136], 0.790*** [0.134]
  - AR(1) p-values: 0.002, 0.003, 0.005
  - AR(2) p-values: 0.335, 0.225, 0.584
  - Hansen J-test p-values: 0.474, 0.931, 0.348
  - Number of observations (All/AE/EM): 533, 228, 305
  - Number of countries (All/AE/EM): 96, 32, 64

### Control variables — consistent patterns (selected qualitative findings)
- Real GDP per capita (level) is inversely correlated with growth (income convergence).
- Inflation is generally negatively associated with growth, especially in developing countries.
- Trade openness has a positive effect on growth; statistically significant only in developing countries in static estimates; significant in some dynamic specifications.
- Financial development (domestic credit to private sector) shows a negative coefficient across specifications, but generally not statistically significant.
- Government spending (government size) has a statistically significant negative effect on growth in developing countries.
- Population growth and educational attainments contribute positively to real GDP per capita growth.
- Institutional variables (government stability and bureaucratic quality) have expected signs but are often not statistically significant.

### Conclusions and policy implications
- Empirical conclusions:
  - Impact of fintech on real GDP per capita growth depends on fintech instrument type:
    - Digital lending as a share of GDP: statistically significant positive effect on growth.
    - Digital capital raising as a share of GDP: large magnitude but statistically insignificant.
  - Overall fintech (all instruments) is positively and statistically significantly associated with economic growth, driven primarily by digital lending.
  - While positive relationship is stronger in magnitude in advanced economies, statistical significance is higher in developing countries in some specifications.
  - Average volume context: fintech instruments average 0.1 percent of GDP, compared to 55 percent of GDP in domestic credit to the private sector.
- Policy recommendations and considerations:
  - Fintech can promote economic growth by increasing financial intermediation and providing resources for fixed capital formation, consistent with Schumpeterian predictions, but not all fintech types accelerate growth equally.
  - As fintech scales, it is likely to have a greater effect on economic growth.
  - Maintaining financial stability is essential for sustainable growth, requiring:
    - Strong regulatory institutions.
    - Better use of technology in regulation.
    - Extensive cross-border coordination.
    - Appropriately calibrated prudential regulations to ensure a level playing field and effective monitoring and supervision of traditional and emerging financial institutions.

*Source: Author's estimations.*

### 2015. Other studies, using mostly subnational data on fintech transactions in China, find a

### wpiea2024020-print-pdf - 2015. Other studies, using mostly subnational data on fintech transactions in China, find a

### Contribution and approach
- Uses a novel dataset of direct measures of fintech and implements dynamic modeling to investigate the empirical relationship between fintech and economic growth in a panel of 198 countries over the period 2012–2020.
- Addresses potential endogeneity using the system generalized method of moments (GMM) approach.
- Distinguishes fintech instruments into digital lending and digital capital raising and analyzes their differential impacts on real GDP per capita growth.

### Main empirical findings
- The impact magnitude and statistical significance of fintech on real GDP per capita growth depend on the type of instrument:
  - Digital lending as a share of GDP has a statistically significant positive effect on economic growth.
  - Digital capital raising as a share of GDP has a large but statistically insignificant effect.
- The overall impact of fintech including all instruments is positive and statistically significant because of the overwhelming share of digital lending in total.
- The positive relationship between fintech and economic growth remains after controlling for other factors including the lagged dependent variable.
- When estimated separately:
  - The positive relationship is stronger in magnitude in advanced economies.
  - The statistical significance of the effect is higher in developing countries.
- Fintech is still small relative to traditional finance, but could have significant effects on economic growth even at current sizes:
  - Average volume of fintech instruments: 0.1 percent of GDP.
  - Domestic credit to the private sector: 55 percent of GDP.
- Maintaining financial stability is essential for sustainable growth and requires:
  - Strong regulatory institutions.
  - Better use of technology in regulation.
  - Extensive cross-border coordination.
  - Appropriately calibrated prudential regulations for a level playing field and effective monitoring and supervision of traditional and emerging financial institutions.

### Data overview (sample and variables)
- Sample: unbalanced panel of annual observations covering 198 countries over 2012–2020.
- Dependent variable: annual real GDP per capita growth rate and gross fixed capital formation as a share of GDP (from World Bank WDI).
- Key explanatory variable: volume of fintech transactions (excluding cryptocurrencies) as a share of GDP from the Cambridge Centre for Alternative Finance (CCAF) dataset.
- CCAF fintech categories used:
  - Digital lending (balance sheet lending, peer-to-peer and marketplace lending, debt-based lending, invoice trading).
  - Digital capital raising (investment-based crowdfunding, non-investment-based crowdfunding).
  - Total fintech: combination of digital lending, digital capital raising, and other fintech types (such as micro finance and pension-led funding) scaled by GDP.
- Note: The CCAF dataset excludes mobile money and internet banking, which are also operated by traditional financial institutions.

### Control variables included
- Level of real GDP per capita.
- Consumer price inflation.
- Trade openness (share of exports and imports in GDP).
- Financial development (domestic credit to the private sector as share of GDP).
- Government size (government spending as share of GDP).
- Population growth.
- Educational attainments (share of labor force with basic education).
- Institutional and political controls: government stability and bureaucratic quality (ICRG composite indices).

### Descriptive statistics (selected exact values from Table 1)
- Real GDP growth
  - Observations: 1,738
  - Mean: 2.2
  - Std. dev.: 5.9
  - Minimum: -54.2
  - Maximum: 86.8
- Gross fixed capital formation
  - Observations: 1,485
  - Mean: 23.1
  - Std. dev.: 8.0
  - Minimum: 1.4
  - Maximum: 78.0
- 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: 5.3
  - Std. dev.: 21.1
  - Minimum: -4.3
  - Maximum: 557.2
- Trade openness
  - Observations: 1,581
  - Mean: 90.9
  - Std. dev.: 58.4
  - Minimum: 10.0
  - Maximum: 442.6
- Domestic credit to the private sector
  - Observations: 1,528
  - Mean: 55.0
  - Std. dev.: 43.5
  - Minimum: 1.1
  - Maximum: 258.9
- Government spending
  - Observations: 1,517
  - Mean: 17.1
  - Std. dev.: 8.6
  - Minimum: 3.6
  - Maximum: 84.2
- Population growth
  - Observations: 1,773
  - Mean: 1.3
  - Std. dev.: 1.4
  - Minimum: -6.9
  - Maximum: 11.8
- Educational attainments
  - Observations: 944
  - Mean: 47.8
  - Std. dev.: 17.0
  - Minimum: 12.6
  - Maximum: 100.0
- 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

### Interpretation and implications
- Results are consistent with the Schumpeterian prediction that financial innovation can promote economic growth by increasing financial intermediation and providing financial resources for fixed capital formation.
- Not all fintech instruments act as accelerators; digital lending is the primary driver of the observed positive aggregate effect.
- Given the small current size of fintech relative to traditional finance (fintech mean 0.1 percent of GDP vs. domestic credit mean 55 percent of GDP), continued rapid growth and adoption—including by large established institutions and big-tech companies—could amplify fintech’s impact on growth.
- Maintaining financial stability is critical to realizing sustainable growth from fintech expansion; this requires robust regulation, technology-enabled supervision, cross-border coordination, and prudential measures ensuring a level playing field.

*Source: CCAF; World Bank; author's calculations.*

### 2020. Taking advantage of the panel structure in the data, I estimate the following baseline

### 2020. Taking advantage of the panel structure in the data, I estimate the following baseline specification:

### Model specification and data
- Baseline panel specification:
  - 푦푖푡 = 훽1 + 훽2푓푖푛푡푒푐ℎ푖푡 + 훽3푋푖푡 + 휂푖 + 휇푡 + 휀푖푡
  - 푦푖푡 denotes real GDP per capita growth or gross fixed capital formation as share of GDP in country i and time t.
  - 푓푖푛푡푒푐ℎ푖푡 represents (i) digital lending as a share of GDP, (ii) digital capital raising as a share of GDP, or (iii) all fintech instruments as a share of GDP.
  - 푋푖푡 includes controls: log real GDP per capita at t-1, consumer price inflation, trade openness, domestic credit to the private sector, government size, population growth, educational attainments, government stability, and bureaucratic quality.
  - 휂푖 and 휇푡 are country fixed effects and time effects; 휀푖푡 is the idiosyncratic error term.
- Sample and period:
  - Panel of 198 countries over the period 2012–2020.
- Standard errors and inference:
  - Driscoll-Kraay (1998) standard errors used to account for heteroskedasticity, autocorrelation and cross-sectional dependence in an unbalanced panel with a shorter time dimension.

### Endogeneity and estimation strategy
- Endogeneity concern:
  - Potential reverse causality: faster-growing economies might demand more fintech, biasing estimates.
- Identification strategy:
  - Instrumental variable (IV) estimation not feasible due to lack of suitable time-varying IVs.
  - Implement system GMM (Arellano and Bover (1995); Blundell and Bond (1998)) to:
    - Include lagged dependent variable as regressor.
    - Control for potential endogeneity of all explanatory variables, including fintech measures.
  - Use one-step system GMM estimator to avoid downward bias in standard errors present in two-step estimators with short T.
- Instrument proliferation and mitigation:
  - Use Roodman (2009) strategy to limit weak and excessively numerous instruments.
  - Validate identification via:
    - AR(1) and AR(2) tests (p-values reported; high first-order autocorrelation, no evidence of significant second-order autocorrelation).
    - Hansen J-test for overidentifying restrictions (Hansen J-test results indicate validity of internal instruments).

### Static estimation findings (Table 2) — key results summarized
- Overall sample:
  - The growth impact of fintech is negligible for the sample as a whole at conventional levels.
- Digital lending:
  - Statistically and economically significant positive effect (at the 10 percent level) on economic growth in advanced economies.
  - Much smaller and statistically insignificant effect in developing countries.
- Digital capital raising:
  - Much greater in magnitude but statistically insignificant overall.
  - Positive impact in advanced economies; negative impact in developing countries.
- Total fintech (all instruments):
  - Statistically insignificant across all specifications for the static estimations.
- Representative coefficient and test details from Table 2 (selected values as reported):
  - Digital lending (All): 0.406 [0.329]
  - Digital capital raising (All): 9.805 [8.621]
  - Total fintech (All): 0.789 [0.323]
  - Real GDP per capita t-1 (All, some columns): -12.802** [4.121], -9.936*** [3.803], -9.539*** [3.783]
  - Number of observations (varies by column): 358, 519, 530, 322, 182, 231, 743, 01307 (as reported)
  - Number of countries (varies): 84, 99, 100, 32, 33, 35, 266, 67 (as reported)

### Dynamic estimation findings (system GMM, Table 3) — key results summarized
- For the sample as a whole:
  - Digital lending as a share of GDP:
    - Coefficient: 0.849*** [0.176] — statistically and economically significant positive effect on real GDP per capita growth (at the 1 percent level).
  - Digital capital raising:
    - Coefficient: 17.610 [9.039] — substantially greater in magnitude but statistically insignificant.
  - Total fintech (all instruments):
    - Coefficient: 0.718*** [0.183] — positive and statistically significant; effect driven by the overwhelming share of digital lending.
- Heterogeneity by income group:
  - Advanced economies:
    - Digital lending coefficient: 1.909* [0.785] (positive, significant at 10 percent in some specifications).
  - Developing countries:
    - Positive relationship between fintech and growth is statistically significant and in some specifications more significant in developing countries despite smaller magnitude in advanced economies.
- Dynamic model diagnostics (as reported in Table 3):
  - AR(1) p-values (examples): 0.005, 0.000, 0.000 (first-order autocorrelation present as expected).
  - AR(2) p-values (examples): 0.217, 0.425, 0.317 (no significant second-order autocorrelation).
  - Hansen J-test p-values (examples): 0.267, 0.369, 0.363 (internal instruments valid).

- Selected coefficients and statistics from Table 3 (All columns, as reported):
  - Real GDP per capita growth t-1: 0.354*** [0.039], 0.429*** [0.095], 0.439*** [0.099]
  - Total fintech (All): 0.718*** [0.183]
  - Number of observations (examples): 352, 510, 518, 182, 214, 218, 170, 296, 300
  - Number of countries (examples): 84, 99, 100, 32, 33, 35, 266, 67

### Fintech, fixed investment, and mechanisms (Table 4)
- Investigation: dynamic system GMM for gross fixed capital formation as share of GDP.
- Main findings:
  - Total fintech (All):
    - All countries: 0.322*** [0.091] — fintech positively associated with gross fixed capital formation.
    - Advanced economies: 0.566*** [0.115] — positive effect on fixed investment.
    - Emerging/developing markets (EM): -0.680*** [0.204] — negative association with fixed investment.
  - Interpretation:
    - Fintech, like broader financial development, affects growth via contribution to physical capital accumulation.
    - Divergent directions by income group may reflect infancy and volatility of fintech in developing countries.
- Selected diagnostics from Table 4:
  - Gross fixed capital formation t-1: 0.854*** [0.145], 0.700*** [0.136], 0.790*** [0.134]
  - AR(1) p-values: 0.002, 0.003, 0.005
  - AR(2) p-values: 0.335, 0.225, 0.584
  - Hansen J-test p-values: 0.474, 0.931, 0.348
  - Number of observations (All/AE/EM): 533, 228, 305
  - Number of countries (All/AE/EM): 96, 32, 64

### Control variables — consistent patterns
- Real GDP per capita (level) is inversely correlated with growth (income convergence).
- Inflation:
  - Generally negative association with growth, especially in developing countries.
- Trade openness:
  - Positive effect on growth; statistically significant only in developing countries in static estimates; significant in some dynamic specifications.
- Financial development (domestic credit to private sector):
  - Negative coefficient across specifications, but generally not statistically significant.
- Government spending (government size):
  - Statistically significant negative effect on growth in developing countries.
- Demographics and human capital:
  - Population growth and educational attainments contribute positively to real GDP per capita growth.
- Institutional variables:
  - Government stability and bureaucratic quality have expected signs but are often not statistically significant.

### Conclusions and policy implications
- Main empirical conclusions:
  - The impact of fintech on real GDP per capita growth depends on fintech instrument type:
    - Digital lending as a share of GDP: statistically significant positive effect on growth.
    - Digital capital raising as a share of GDP: large magnitude but statistically insignificant.
  - Overall fintech (all instruments) is positively and statistically significantly associated with economic growth, driven primarily by digital lending.
  - While the positive relationship is stronger in magnitude in advanced economies, statistical significance is higher in developing countries in some specifications.
  - Average volume context: fintech instruments average 0.1 percent of GDP, compared to 55 percent of GDP in domestic credit to the private sector.
- Policy recommendations and considerations:
  - Fintech can promote economic growth by increasing financial intermediation and providing resources for fixed capital formation, consistent with Schumpeterian predictions, but not all fintech types accelerate growth equally.
  - As fintech scales, it is likely to have a greater effect on economic growth.
  - Maintaining financial stability is essential for sustainable growth, requiring:
    - Strong regulatory institutions.
    - Better use of technology in regulation.
    - Extensive cross-border coordination.
    - Appropriately calibrated prudential regulations to ensure a level playing field and effective monitoring and supervision of traditional and emerging financial institutions.

*Source: Author's estimations.*

### REFERENCES

### REFERENCES

### Fintech, Financial Stability, and Regulation
- Adrian, T., M. Moretti, A. Carvalho, H. Chon, K. Seal, F. Melo, and J. Surti (2023). “Good Supervision: Lessons from the Field,” IMF Working Paper No. 23/181 (Washington, DC: International Monetary Fund).
- Arner, D., D. Zetzsche, R. Buckley, and J. Barberis (2017). “FinTech and RegTech: Enabling Innovation While Preserving Financial Stability,” Georgetown Journal of International Affairs, Vol. 18, pp. 47–58.
- Bains, P., and C. Wu (2023). “Institutional Arrangements for Fintech Regulation: Supervisory Monitoring,” IMF Fintech Notes No. 2023/004 (Washington, DC: International Monetary Fund).
- Boot, A., P. Hoffmann, L. Laeven, and L. Ratnovski (2021). “Fintech: What’s Old, What’s New?” Journal of Financial Stability, Vol. 53, 100836.
- Cevik, S. (2023). “The Dark Side of the Moon? Fintech and Financial Stability,” IMF Working Paper No. 23/253 (Washington, DC: International Monetary Fund).
- Daud, S., A. Ahmad, A. Khalid, and W. Azman-Saini (2022). “FinTech and Financial Stability: Threat or Opportunity?” Finance Research Letters, Vol. 47, 102667.
- 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 Papers No. 117 (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.
- 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).
- Kanga, D., C. Oughton, L. Harris, and V. Murinde (2021). “The Diffusion of Fintech, Financial Inclusion and Income Per Capita,” European Journal of Finance, Vol. 28, pp. 108–136.
- Magnuson, W. (2018). “Regulating Fintech,” Vanderbilt Law Review, Vol. 71, pp. 1167–1226.
- 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.
- Pierri, N., and Y. Timmer (2020). “Tech in Fin before FinTech: Blessing or Curse for Financial Stability?” IMF Working Paper No. 20/14 (Washington, DC: International Monetary Fund).
- 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.
- 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.

### Finance, Financial Development, and Economic Growth
- Arcand, J., E. Berkes, and U. Panizza (2015). “Too Much Finance?” Journal of Economic Growth, Vol. 20, pp. 105–148.
- Bencivenga, V., and B. Smith (1991). “Financial Intermediation and Endogenous Growth,” Review of Economic Studies, Vol. 58, pp. 195–209.
- Cecchetti, G., and E. Kharroubi (2012). “Reassessing the Impact of Finance on Growth,” BIS Working Papers No. 381 (Basel: Bank for International Settlements).
- Fisman, R., and I. Love (2007). “Financial Dependence and Growth Revisited,” Journal of the European Economic Association, Vol. 59, pp. 470–479.
- Gennaioli, N., A. Shleifer, and R. Vishny (2012). “Neglected Risks, Financial Innovation, and Financial Fragility,” Journal of Financial Economics, Vol. 104, pp. 452–468.
- Greenwood, J., and B. Jovanovic (1990). “Financial Development, Growth, and the Distribution of Income,” Journal of Political Economy, Vol. 98, pp. 1076–1107.
- King, R., and R. Levine (1993a). “Finance, Entrepreneurship, and Growth,” Journal of Monetary Economics, Vol. 32, pp. 513–542.
- King, R., and R. Levine (1993b). “Finance and Growth: Schumpeter Might Be Right,” Quarterly Journal of Economics, Vol. 108, pp. 717–737.
- Laeven, L., R. Levine, and S. Michalopoulos (2015). “Financial Innovation and Endogenous Growth,” Journal of Financial Intermediation, Vol. 4, pp. 12–22.
- 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.
- 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.
- 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.
- Liu references (related empirical work):
  - Bu, Y., X. Yu, and H. Li (2023). “The Nonlinear Impact of Fintech on the Real Economic Growth: Evidence from China,” Economics of Innovation and New Technology, Vol. 32, pp. 1138–1155.
  - Chen, X., L. Teng, and W. Chen (2022). “How Does Fintech Affect the Development of the Digital Economy? Evidence from China” North American Journal of Economics and Finance, Vol. 61, 101697.
  - Kanga, D., C. Oughton, L. Harris, and V. Murinde (2021). “The Diffusion of Fintech, Financial Inclusion and Income Per Capita,” European Journal of Finance, Vol. 28, pp. 108–136.
  - Song, N., and I. Appiah-Otoo (2022). “The Impact of Fintech on Economic Growth: Evidence from China,” Sustainability, Vol. 14, 6211.
  - 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.
  - Zhu, X., S. Asimakopoulos, and J. Kim (2020). “Financial Development and Innovation-Led Growth: Is Too Much Finance Better?” Journal of International Money and Finance, Vol. 100, 102083.

### Financial Innovation, Risks, and Crises
- Allen, F., and E. Carletti (2006). “Credit Risk Transfer and Contagion,” Journal of Monetary Economics, Vol. 53, pp. 89–111.
- Beck, T., T. Chen, C. Lin, and F. Song (2016). “Financial Innovation: The Bright and the Dark Sides,” Journal of Banking and Finance, Vol. 72, pp. 28–51.
- Gennaioli, N., A. Shleifer, and R. Vishny (2012). “Neglected Risks, Financial Innovation, and Financial Fragility,” Journal of Financial Economics, Vol. 104, pp. 452–468.
- Rajan, R. (2006). “Has Finance Made the World Riskier?” European Financial Management, Vol. 12, pp. 499–533.
- Rajan, R., and L. Zingales (1998). “Financial Dependence and Growth,” American Economic Review, Vol. 88, pp. 559–586.
- Rousseau, P., and P. Wachtel (2011). “What Is Happening to the Impact of Financial Deepening on Economic Growth?” Economic Inquiry, Vol. 49, pp. 276–288.
- Schumpeter, J. (1912). The Theory of Economic Development (Cambridge, MA: Harvard University Press).
- Shleifer, A., and R. Vishny (2010). “Unstable Banking,” Journal of Financial Economics, Vol. 97, pp. 306–318.
- Thakor, A. (2012). “Incentives to Innovate and Finance Crises,” Journal of Financial Economics, Vol. 103, pp. 130–148.

### Methodology, Econometrics, and Measurement
- Arellano, M., and O. Bover, 1995, “Another Look at the Instrumental Variable Estimation of Error-Components Models,” Journal of Econometrics, Vol. 68, pp. 29–51.
- Blundell, R., and S. Bond, 1998, “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, Vol. 87, pp. 115–143.
- 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.
- Roodman, D., 2009, “How to Do xtabond2: An Introduction to Difference and System GMM in Stata,” Stata Journal, Vol. 9, pp. 86–136.

### Miscellaneous and Sectoral Analyses
- Cambridge Center for Alternative Finance (2021). The Global Alternative Finance Market Benchmarking Report (Cambridge, UK: University of Cambridge).
- 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.
- Pierri, N., and Y. Timmer (2020). “Tech in Fin before FinTech: Blessing or Curse for Financial Stability?” IMF Working Paper No. 20/14 (Washington, DC: International Monetary Fund).
- 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.

*wpiea2024020-print-pdf - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024020-print-pdf.pdf_