## sdnea2020001

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### Executive summary — key findings
- Global income inequality has fallen in the past two decades, driven largely by high economic growth and poverty reduction in emerging market and developing economies.
- Financial sector policies and advances in financial technology are enabling financial inclusion, particularly in large economies such as China and India.
- Within-country inequality is rising in many economies and remains high in several others; economic mobility has stalled in recent decades.
- Finance can both reduce and amplify inequality depending on how financial depth, inclusion, and stability evolve and are managed.

### Financial depth — empirical relationship with income inequality
- Question: Does greater financial depth reduce or increase within-country income inequality?
- Core empirical result: Financial deepening reduces within-country income inequality up to a point, after which inequality starts rising (inverse U-shaped relationship).
- Quantitative example: As the financial depth index increases from the emerging market and developing economy average to the advanced economy average (a difference of about 0.4), Gini increases by about 0.1 (keeping all other variables at their sample averages).
- Context and ranges:
  - Gini observations in the sample range from about 0.2 to about 0.7.
- Mechanisms and explanations:
  - Rising fees associated with asset management and higher compensation in the financial industry.
  - Proliferation of financial products with high risk and high return accessible mostly to the well-off.
  - Size and lobbying power of big business to access finance more easily.
  - “Easy credit” policies.
- Conclusion: At high levels of development, deeper financial systems are associated with a surge in top incomes and financial sector rents.

### Financial inclusion — empirical relationship with income inequality
- Question: Does greater financial inclusion reduce within-country income inequality?
- Core empirical result: Greater financial inclusion tends to be associated with reductions in inequality; the effect is stronger for payments services and when women have greater access.
- Key data points:
  - About 1.7 billion adults (31 percent) remained unbanked in 2017.
  - Globally in 2017 there were about 1.4 accounts per adult with financial institutions.
  - Small- and medium-size companies account for about 40 percent of the world’s GDP and 50 percent of employment but receive only about 10 percent of corporate loans.
  - Cross-country correlation between financial inequality (inequality in account penetration across income groups) and income inequality: correlation coefficient significant at 0.33.
- Representative magnitudes and nonlinearities:
  - Payments (proxied by ATM density): independent of depth, greater financial inclusion is associated with lower inequality; inequality falls more at lower depth.
    - Example: For a country with low financial depth (25th percentile), an increase in availability of ATMs from the 25th to the 75th percentile is associated with a Gini reduction of 9 percentage points. For high financial depth, the same increase corresponds to a Gini decline of 7 percentage points.
  - Credit (number of borrowers per 1,000 adults): at low and medium depth, greater access reduces inequality; at high depth, inequality increases with credit expansion (relationship becomes inverted at high depth).
- Robustness: Results hold across advanced economies as well as emerging market and developing economies and across alternative inequality measures and estimation methods.

### Financial instability — links with inequality and crises
- Question: Is stability (absence of financial distress) related to within-country income inequality?
- Empirical results:
  - Higher inequality is associated with greater financial risks; increases in inequality tend to be accompanied by higher growth in credit.
  - Finding: inequality increases before a financial crisis. Logit models show change in the Gini index is statistically significant in predicting crises.
  - Dynamics around crises: inequality falls temporarily during crisis onset and early part, then rises in postcrisis years; postcrisis increases driven by disproportionate income losses for lower-income households.
- Examples and historical episodes:
  - United States: too much credit, including to lower-income households, contributed to the 2007–08 crisis.
  - Other episodes: rapid consumer credit expansion in several emerging markets in 2004–06; microcredit crisis in India’s Andhra Pradesh in 2010.
- Stability metrics used:
  - z-score (distance-to-distress): capital to assets + return on assets / standard deviation of return on assets.
  - Presence/absence of banking crises (Laeven and Valencia database).

### Measurement: financial depth, inclusion, instability (Box 1)
- Financial depth:
  - Defined as size of the financial sector relative to the economy.
  - Depth indices used: Financial Development Index (FDI) depth indices for institutions and markets; 0 and 1 correspond to least and most deep systems.
  - Financial institutions depth index components: private sector credit to GDP; pension fund assets to GDP; mutual fund assets to GDP; insurance premiums to GDP.
  - Financial markets depth index components: stock market capitalization to GDP; stocks traded to GDP; international debt securities of government to GDP; total debt securities of financial corporations to GDP; total debt securities of nonfinancial corporations to GDP.
  - Series converted to a 0–1 scale using min-max procedure and winsorized at the 5th and 95th percentiles.
- Financial inclusion:
  - Defined as “access to and use of formal financial services,” covering transactions, savings, credit, insurance, and so on, for individuals and firms.
  - Primary sources: IMF Financial Access Survey (FAS) and Global Findex (demand-side survey).
  - FAS coverage: 121 time series and 64 indicators for 189 jurisdictions spanning 2004–18.
  - Global Findex: published every three years since 2011; survey of about 150,000 adults in 144 economies (latest 2017).
- Financial instability:
  - Described as downside risk of expanding financial services manifesting in asset price declines, rising nonperforming loans, depleted capital buffers, and real-economy feedbacks.
  - Empirical proxies: z-score and banking crisis indicators.

### Methods to address endogeneity and identification
- Instrumentation and identification strategies:
  - Instruments include existence of private credit bureaus and public credit registries, initial endowments (wealth), and use of internal lags (Arellano and Bond GMM).
  - Local projection method à la Jordà (2005) used to examine impulse-response behavior of inequality and inclusion.
  - Robustness checks include adoption of national financial inclusion strategy as an identifier and synthetic matching.
- Estimation approaches:
  - OLS panel fixed effects, GLS with panel-wide AR(1) correction, dynamic panel GMM (Arellano and Bond 1991), and 2SLS with external instruments.
- Sample and timing:
  - Panel of more than 180 advanced and emerging market and developing economies in many analyses; specific baseline depth–inequality regressions use 128 economies during 1980–2015 with five-year averages.

### Gender and distributional effects
- Finding: Financial inclusion of women has a stronger link to reducing inequality.
- Representative coefficients (gender-specific):
  - account_f (female account ownership): -0.108** (standard error 0.049); IV: -0.624*** (standard error 0.186).
  - account_m (male account ownership): -0.079 (standard error 0.051); IV: -1.324 (standard error 0.864).
  - borrow_f: -0.289** (standard error 0.120).
  - Interpretation: Coefficients of account_f and borrow_f are negative and statistically significant while account_m and borrow_m are not, suggesting improving access for women is more strongly associated with lower income inequality.

### Microlevel evidence on firms, inclusion, and stability
- Data: World Bank’s Enterprise Survey covering more than 135,000 firms in 140 countries.
- Key micro result: expansions in credit-related aspects of inclusion that favor small firms lower the overall risk of credit allocation.
  - A pro–small-firm bias in expanding inclusion is associated with smaller increases (or improvements) in riskiness of credit allocation.
- Regression sample: 166 country-level observations for 92 countries (Annex analysis).
- Two-thirds of the time, increases in financial inclusion of firms are accompanied by improvements in income equality.

### Data highlights, extreme inequality, and additional metrics
- Aggregate global wealth mid-2019: US$360 trillion, or about 420 percent of GDP.
- An equivalent of 190 percent of GDP was in the hands of the wealthiest 1 percent.
- Oxfam (2019) estimate cited: in 2018, the wealth of 26 people was the same as that of the 3.8 billion who make up the poorest half of humanity, down from 43 people in 2017.
- Extreme-inequality proxies (2004–15) constructed from Forbes’ billionaires list and Global Wealth Databook; findings indicate increases in extreme inequality are more marked than standard measures may suggest and that improving access to financial services can contribute to reducing extreme inequality.

### Robustness, diagnostics, and representative regression diagnostics
- Representative baseline diagnostics for depth–inequality GMM estimations (1980–2010, five-year averages):
  - Observations: 573, 567, 567, 567 (by column).
  - Number of countries: 128, 126, 126, 126 (by column).
  - Financial institutions squared coefficient example: 3.728** (standard error 1.245).
  - Financial markets squared coefficient example: 2.587** (standard error 1.183).
  - Serial correlation p-values reported and Hansen J test p-values indicate instrument validity in representative columns.
- Financial inclusion baseline example (atm per 100,000 adults, representative columns):
  - OLS: -0.027 (standard error 0.016)
  - OLS (alternative): -0.023*** (standard error 0.005)
  - GLS: -0.022** (standard error 0.009)
  - GMM: -0.045** (standard error 0.019)
  - IV/GMM: -0.060*** (standard error 0.012)
- Interaction results indicate financial inclusion’s effect on inequality is stronger when GDP growth is higher and when financial depth or stability conditions are favorable.

### Takeaways and policy implications (five main points)
- Policymakers should assess financial sector policies’ impact on inequality; finance can amplify inequalities when access barriers persist, but a well-functioning financial sector can help compensate for differences in starting positions.
- First: Financial inclusion policies are important for reducing inequality. Recommended measures include:
  - Greater financial literacy.
  - Stronger consumer protection.
  - Reducing biases in data collection.
  - Protection of private data.
  - Policies to enhance access of lower-income households and small businesses to financial services while minimizing financial risks.
  - Increase coverage and discussion of financial inclusion in financial stability reports and mandates of financial sector regulators.
- Second: Promote women’s financial inclusion; inequality falls even more when policies are inclusive of women.
- Third: Regulatory and supervisory policies should help rein in excessive growth of the financial sector; at high levels of financial depth, further deepening can create challenges for growth, stability, and inequality.
- Fourth: Provided quality of regulation and supervision is high, financial inclusion and financial stability can be pursued simultaneously; a pro–small-firm bias in expanding inclusion is beneficial for financial stability.
- Fifth: Fiscal and competition policy tools will still be needed to fully address inequality; financial sector policies are a complement, not a substitute, for other policy tools.

### Data gaps and research priorities
- Lack of granular data on access to and use of financial services limits assessment of transmission channels.
- Priority research areas:
  - Effects of fintech on inequality.
  - How financial sector policies—including nonbank regulation, supervision, macroprudential policies, market infrastructures, and financial safety nets—affect economic opportunities and distribution of income and wealth.
- Need for emerging datasets to clarify transmission during initial stages of financial development and during late stages when relationships may invert.

*Source: EXECUTIVE SUMMARY and Box 1, sdnea2020001*

### EXECUTIVE SUMMARY __________________________________________________________________________ 4

### EXECUTIVE SUMMARY

### Key findings
- Global income inequality has fallen in the past two decades, driven largely by high economic growth and poverty reduction in emerging market and developing economies.
- Financial sector policies and advances in financial technology are enabling financial inclusion, particularly in large economies such as China and India, allowing more low-income households and small businesses to participate productively in the formal economy.
- Within-country inequality is rising in many economies and remains high in several others; economic mobility has stalled in recent decades.
- Finance can both reduce and amplify inequality depending on how financial depth, inclusion, and stability evolve and are managed.

### Financial depth and income inequality
- Question: Does greater financial depth (financial sector size relative to the economy) reduce or increase within-country income inequality?
- Empirical result: Initially, financial depth is associated with lower inequality, but only up to a point, after which inequality rises.
- Mechanisms and explanations noted in the literature:
  - Rising fees associated with asset management and higher compensation in the financial industry.
  - Proliferation of financial products with high risk and high return accessible mostly to the well-off.
  - The size and lobbying power of big business to access finance more easily.
  - “Easy credit” policies.
- Conclusion: At high levels of development, deeper financial systems are associated with a surge in top incomes and financial sector rents.

### Financial inclusion and income inequality
- Question: Does greater financial inclusion (access to and use of financial services by individuals and firms) reduce within-country income inequality?
- Empirical result: Greater financial inclusion tends to be associated with reductions in inequality.
  - For payment services, evidence shows benefits are greater for those at the low end of the income distribution, reducing inequality.
  - Both men and women benefit from financial inclusion, but inequality falls more when women have greater access.
  - Results for access to and use of credit are mixed.
- Conceptual point on collateral (Figure 4b): Collateral plays a key role in managing credit risk; collateral-based lending can reinforce existing inequalities. Emphasizing lending decisions based on future cash flows (less on collateral) can help reduce inequality.

### Financial instability and income inequality
- Question: Is there a relationship between stability (absence of financial distress such as financial crises) and within-country income inequality?
- Empirical result: Higher inequality is associated with greater financial risks. Increases in inequality tend to be accompanied by higher growth in credit.
- Example noted: In the United States, too much credit, including to lower-income households, contributed to the 2007–0  8 crisis. Crises lead to higher default rates and make lower-income households worse off, increasing inequality after a crisis.

### Empirical scope, data, and limitations
- The study examines macroeconomic relationships between income inequality and three features of finance: depth, inclusion, and stability. Box 1 defines the concepts and measurements.
- Data highlights and numbers:
  - Aggregate global wealth in mid-2019 was estimated at US$360 trillion, or about 420 percent of GDP.
  - An equivalent of 190 percent of GDP was in the hands of the wealthiest 1 percent.
  - Oxfam (2019) estimate cited: in 2018, the wealth of 26 people was the same as that of the 3.8 billion who make up the poorest half of humanity, down from 43 people in 2017.
- Limitations acknowledged:
  - Focus is on within-country inequality, not on global or between-country inequality.
  - Causality between macroeconomic and macrofinancial variables is difficult to determine; reverse causation and omitted factors may exist despite econometric controls described in Annex II.
  - Precision of some commonly used inequality indicators is debated.
  - Financial sector indicators approximate true depth and scope of finance and focus on payments and credit; may not fully capture fintech developments.
  - Fintech could reduce inequality by expanding access but could also disproportionately benefit the rich; it is too early to draw net-benefit conclusions.

### Policy implications (five takeaways)
- First, financial inclusion policies help reduce inequality.
- Second, there is a case for promoting women’s financial inclusion, as inequality falls even more when policies are inclusive of women.
- Third, regulatory policies have a role to play in reining in excessive growth of the financial sector.
- Fourth, provided quality of regulation and supervision is high, financial inclusion and stability can be pursued simultaneously.
- Fifth, financial sector policies are a complement, not a substitute, for other policy tools—fiscal and macro-structural policies are still needed to help address inequality.

*Source: EXECUTIVE SUMMARY, sdnea2020001*

### Box 1. Financial Depth, Inclusion, and Instability: Measurement

### Box 1. Financial Depth, Inclusion, and Instability: Measurement

### Measurement: financial depth and inclusion
- Financial depth: size of the financial sector relative to the economy.  
- Depth indices used: financial depth index developed in Sahay and others (2015a), with 0 and 1 corresponding to the least and most deep financial system in terms of financial institutions and markets.
  - Financial institutions depth index components:
    - private sector credit to GDP
    - pension fund assets to GDP
    - mutual fund assets to GDP
    - insurance premiums to GDP
  - Financial markets depth index components:
    - stock market capitalization to GDP
    - stocks traded to GDP
    - international debt securities of government to GDP
    - total debt securities of financial corporations to GDP
    - total debt securities of nonfinancial corporations to GDP
  - Construction details:
    - Each series is converted to a 0–1 scale using a min-max procedure.
    - Indicators are winsorized with 5th and 95th percentiles set at the cutoff levels (global distribution—across countries and time—used to determine cutoff levels).
- Financial inclusion definition: “access to and use of formal financial services,” covering transactions, savings, credit, insurance, and so on, for individuals and firms (Sahay and others 2015b).
  - Data sources used:
    - IMF’s Financial Access Survey: 121 time series and 64 indicators for 189 jurisdictions spanning 2004–18; provider-side administrative sources (national central bank and other statistical authorities).
    - Global Findex: published every three years since 2011 and covering about 150,000 adults in 144 economies.
  - Annex I discusses features and limitations of available data. Different measures of financial inclusion are defined in relevant sections of the study.

### Financial instability: definition and measurement
- Financial instability described as the potential downside risk of expanding financial services, typically manifesting as asset price declines, increasing nonperforming loans, declining or depleted capital buffers, and feedbacks into the real economy.
- Two empirical proxies for financial instability used:
  - z-score (distance-to-distress): defined as capital to assets + return on assets / standard deviation of return on assets (the buffers that banks have against shocks to earnings).
  - data on presence and absence of banking crises.
- Risks highlighted:
  - Financial booms followed by distress can force vulnerable financial firms to reduce debt and lending, amplifying declines in asset prices, employment, and growth.
  - Disproportionate reductions in loans to small firms and poorer individuals can put equal opportunity at risk and potentially increase inequality.
  - Misaligned incentives and weak regulation/supervision increase the risk of leverage, distress, and crises.

### Key empirical findings: financial depth and income inequality
- Literature context:
  - Theoretical models (Banerjee and Newman 1993; Galor and Zeira 1993; Greenwood and Jovanovic 1990) suggest better credit availability can reduce income inequality, especially at lower development levels.
  - At high development levels, deepening financial systems can disproportionately boost top incomes and financial sector rents (Philippon and Reshef 2012; Greenwood and Scharfstein 2013; Axelson and Bond 2015; others).
- New empirical results (panel of more than 180 advanced and emerging market and developing economies):
  - Financial deepening reduces within-country income inequality up to a point, after which inequality starts rising (inverse U-shaped relationship).
  - The effect is observed for both financial institutions and financial markets; the effect of financial markets is relatively stronger.
  - The nonlinear relationship holds across economies at all developmental levels, with more visible effects in advanced economies.
  - Quantitative example: as the financial depth index increases from the emerging market and developing economy average to the advanced economy average (a difference of about 0.4), Gini increases by about 0.1 (keeping all other variables at their sample averages).
  - Context: Gini observations in the sample range from about 0.2 to about 0.7.
- Estimation approach and controls:
  - Uses financial depth subindices of the Financial Development Index (FDI) and inequality approximated by the Gini coefficient (baseline), with robustness checks using first quintile income share, the Palma ratio, and the quintile ratio.
  - Controls include initial values of economic and financial development, legal origin, ethnic and religious fractionalization, geographical latitude, logarithm of industrial value added to GDP, average years of primary and secondary schooling, and inflation.
  - Regression method: instrumental variable generalized method of moments with country fixed effects; quadratic and higher polynomial terms included to check nonlinearity. Annex II summarizes methodology and regression results.

### Key empirical findings: financial inclusion and income inequality
- Literature context:
  - Theoretical models show lack of access to finance can lead to poverty traps and persistent inequality (Aghion and Bolton 1997; Banerjee and Newman 1993; Galor and Zeira 1993).
  - Field experiments and natural experiments provide evidence that access to savings and payment services can increase savings, women’s empowerment, productive investment, consumption, preventive health investment, productivity, and income—effects typically larger for lower-income households (Ashraf, Karlan, and Yin 2010; Dupas and Robinson 2013; Beck, Levine, and Levkov 2010).
  - Cross-country analyses historically limited by data availability; newer studies extend the evidence (Mookerjee and Kalipioni 2010; Kim 2015; Sahay and others 2015a; Aslan and others 2019).
- New data patterns:
  - Financial inclusion and income inequality are strongly correlated in new global data (Global Findex 2017).
  - Real-world patterns: about 1.7 billion adults (31 percent) remained unbanked in 2017.
  - Globally in 2017 there were about 1.4 accounts per adult with financial institutions (IMF’s Financial Access Survey), implying those banked had two accounts on average while the rest had zero.
  - Example distributional facts: small- and medium-size companies—which account for about 40 percent of the world’s GDP and 50 percent of employment—receive only about 10 percent of corporate loans.
  - Cross-country correlation between financial inequality (inequality in account penetration across income groups) and income inequality: correlation coefficient significant at 0.33.
- Regression results and magnitudes:
  - Financial inclusion is strongly associated with lower inequality across a broad range of economies.
  - The effect of financial inclusion on inequality is higher when economic growth is higher and when the financial system is more stable.
  - The relationship between financial inclusion and inequality depends on financial depth:
    - Payments (basic access to payment services, proxied by ATM density): independent of depth, greater financial inclusion is associated with lower inequality; inequality falls more at lower depth.
      - Quantitative example: For a country with low financial depth (25th percentile), an increase in availability of ATMs from the 25th to the 75th percentile is associated with a Gini reduction of 9 percentage points. For high financial depth, the same increase in FI means a smaller decline in inequality (7 percentage points). (Asterisk denotes significance at the 5 percent level in the study.)
    - Credit (number of borrowers per 1,000 adults as FI proxy): at low and medium depth, greater access reduces inequality; at high depth, inequality increases with credit expansion (relationship becomes inverted at high depth).
  - Longer time-series evidence (U.S. data, 1940–2014): analyses confirm that inequality falls as financial inclusion rises (vector autoregression using branches per 100,000 people; cumulative response of income share of top 1 percent to a 1 percentage point increase in financial inclusion reported).
- Robustness:
  - Findings hold across advanced economies as well as emerging market and developing economies and withstand a wide range of robustness checks (alternative samples, measures of inequality, and estimation methods). Annex II and Annex Table 3 provide detailed results.

*International Monetary Fund — Box 1, “Financial Depth, Inclusion, and Instability: Measurement”*

### 20.      Our study takes steps to address concerns about endogeneity and reverse causality.

### 20.      Our study takes steps to address concerns about endogeneity and reverse causality.

### Methods to address endogeneity and identification
- Regressions use instruments for institutional characteristics in instrumental variable regressions.
- Instruments include the existence of private credit bureaus and public credit registries as well as initial endowments (wealth), following Mookerjee and Kalipioni (2010) and Beck and others (2007).
- A local projection method à la Jordà (2005) is used to examine the impulse-response behavior of inequality and inclusion.
- Robustness checks include:
  - Using the adoption of a national financial inclusion strategy to identify changes in inclusion.
  - Employing synthetic matching to examine the impact of changes in inclusion.

### Role of gender in financial inclusion and inequality
- Findings indicate that financial inclusion of women has a stronger link to reducing inequality (Annex Table 5).
- Analysis builds on Sahay and others (2015a, 2015b, 2018) using a range of instrumental variable estimates.
- All indicators of financial inclusion are negatively and significantly correlated with the Gini index.
- Both men and women benefit from financial inclusion, but the effect for women’s financial inclusion is quantitatively larger.
- Conclusion: women’s financial inclusion has a stronger association with lower income inequality.

### Financial instability and income inequality — literature summary
- Theory: higher inequality poses greater macro-financial risks which can engender greater inequality (Annex Figure 1).
- Leverage is central to transmission: inequality may compromise financial stability via implications for leverage; inequality is a factor behind credit bubbles.
- Historical examples where policy-driven inclusion contributed to instability: US subprime crisis of 2007–08; rapid consumer credit expansion in several emerging markets in 2004–06; microcredit crisis in India’s Andhra Pradesh in 2010.
- Empirical evidence:
  - Schularick and Taylor (2012): role of credit growth as predictor of financial crises (14 countries, 1870–2008).
  - Borio and Drehmann (2009): unusually strong increases in credit and asset prices tended to precede banking crises (18 advanced economies, 1980–2013).
  - Han and Melecky (2015): broader access to deposits associated with less large deposit outflows in stress.
  - Čihák, Mare, and Melecky (2016): relationship between inclusion and stability systematically influenced by country characteristics (financial openness, tax rates, education, credit information systems).
  - Sahay and others (2015b): nonmonotonic relationship between financial inclusion (number of commercial bank borrowers) and financial stability (banking system’s distance-to-distress); key conditioning factor is quality of banking supervision.
- Updated analysis with data to 2019 (Figure 8) underscores that quality of regulation and supervision matters for the inclusion-stability trade-off.

### New data and empirical findings
- Analysis combines updated crisis data (Laeven and Valencia 2018) with cross-country inequality datasets (advanced and emerging market economies).
- Finding: inequality increases before a financial crisis.
  - Logit models regressing the binary financial crisis indicator on inequality measure (change in Gini), credit growth, and country fixed effects show the inequality measure was statistically significant.
- Dynamics around crises:
  - Inequality falls temporarily during the crisis onset and early part, then rises in postcrisis years.
  - Postcrisis increases in inequality driven by disproportionate income losses for lower-income households, constraints on consumption smoothing, dent in net wealth.
- Consistent with prior work indicating benefits of finance for growth decline or disappear when aggregate leverage is high (Sahay and others 2015a; IMF 2017).
- Trade-off: short-term benefits of rising debt for activity versus medium-term costs to macroeconomic and financial stability and inequality; adverse effects stronger when debt is higher and more pronounced for advanced than for emerging market economies.
- Institutional characteristics—such as higher quality of regulation and supervision—can mitigate risks associated with rising debt (Sahay and others 2015a).

### Microlevel evidence on firms, inclusion, and stability
- Accompanying microlevel analysis uses World Bank’s Enterprise Survey covering more than 135,000 firms in 140 countries (Annex I).
- Key result: expansions in credit-related aspects of inclusion that favor small firms lower the overall risk of credit allocation.
  - A pro–small-firm bias in expanding inclusion is beneficial for financial stability: the greater the increase in inclusion of small relative to larger firms, the smaller the change in riskiness (Figure 10).
- Regression analysis based on 166 country-level observations for 92 countries (Annex Table I).
- Two-thirds of the time, increases in financial inclusion of firms are accompanied by improvements in income equality.
- Expansions in bank credit use by firms were associated with increases in the riskiness of credit allocation, but a small-firm bias reduces that risk.
- Countries with higher-quality supervision and regulation (World Bank’s Bank Regulation and Supervision Survey) are less likely to pay the price of expanding inclusion in terms of greater credit risk.

### Takeaways
- Financial services can help reduce inequality but can also be associated with greater inequality if the financial system is not well managed.
- Financial deepening has a nonlinear relationship with inequality:
  - Initially associated with lower inequality, but beyond a point inequality rises.
  - At high levels of development, deeper financial systems are associated with a surge in top incomes and financial sector rents.
- Greater financial inclusion tends to be associated with reductions in inequality:
  - Access to and use of payments services yields strong evidence of benefits greater for poorer households.
  - Both men and women benefit, but association is larger for women.
  - Access to credit has mixed effects depending on quality of regulation and supervision.
- There is a virtuous relationship between income equality and financial stability:
  - Higher inequality is associated with greater financial risks; increased inequality tends to be associated with rising credit and higher likelihood of crisis; crisis exacerbates inequality.
  - Measures supporting inclusive growth enhance macro-financial stability, provided regulation and supervision quality is high.
- Better-functioning, more inclusive financial sector can reduce financial frictions and improve economic efficiency, potentially reducing the fiscal burden and complementing redistributive policies.

### Additional empirical observations and institutional coverage
- Meta-analysis findings:
  - About 1 in 10 legal mandates of central banks and other financial supervisors contain an explicit financial inclusion mandate.
  - Income inequality is mentioned infrequently in country authorities’ financial stability reports (fewer than 5 percent).
  - Frequency of references to financial inclusion in those reports has been increasing (Figure 11).
- The United Nations sustainable development agenda (SDG goal 10) recognizes the need to improve regulation and monitoring of global financial markets and institutions (SDG target 10.5).

### Policy implications (five main points)
- Policymakers should look to financial sector policies’ impact on inequality when formulating policies. Financial sectors can amplify inequalities when inherited wealth, race, gender, and other barriers drive access to and use of financial services; conversely, a well-functioning financial sector can compensate for differences in “starting positions.”
- First: Financial inclusion policies are important for reducing inequality. Recommended measures include:
  - Greater financial literacy.
  - Stronger consumer protection.
  - Reducing biases in data collection.
  - Protection of private data.
  - Policies to enhance access of lower-income households and small businesses to financial services while minimizing financial risks.
  - An initial step: increase coverage and discussion of financial inclusion in financial stability reports and mandates of financial sector regulators.
- Second: Promote women’s financial inclusion. Inequality falls even more when policies are inclusive of women.
- Third: Regulatory and supervisory policies should help rein in excessive growth of the financial sector. Policymakers should be aware that at high levels of financial depth, further deepening can create challenges for growth, stability, and inequality.
- Fourth: Provided quality of regulation and supervision is high, financial inclusion and financial stability can be pursued simultaneously. There is no substitute for high-quality regulation and supervision; a pro–small-firm bias in expanding inclusion is beneficial for financial stability.
- Fifth: Fiscal and competition policy tools will still be needed to fully address inequality. Financial sector policies are a complement, not a substitute, for other policy tools such as fiscal and competition policies.

*Source: IMF Staff Discussion Note (sdnea2020001) — chapter section provided in the source PDF.*

### 38.      We need more data and research to examine the channels through which finance

### sdnea2020001 - 38.      We need more data and research to examine the channels through which finance affects inequality

### Data gaps and research priorities
- Lack of granular data on access to and use of financial services limits assessment of channels through which finance affects inequality.
- Emerging new data sets could clarify transmission during:
  - initial stages of financial development (e.g., whether credit strengthening the macroeconomy or inclusion boosting the previously excluded), and
  - late stages when the relationship may invert and different channels may operate across countries.
- Specific research needs:
  - Effects of fintech on inequality.
  - How financial sector policies—including nonbank regulation, supervision, and oversight; macroprudential policies; financial market infrastructures; and financial safety nets—affect economic opportunities and the distribution of income and wealth.

### Annex I. Data — Inequality measures and sources
- Primary income-inequality measure: Gini index (scale from 0 to 1).
- Robustness measures used: first quintile income share, Palma ratio, quintile ratio, share of top 1 percent in income and wealth, and an extreme-inequality measure based on wealth.
- Palma ratio definition preserved: ratio of the richest 10 percent of the population’s share of total gross national income divided by the poorest 40 percent’s income share.
- Quintile ratio definition preserved: ratio of the richest 20 percent of the population’s share of income divided by the income share of the poorest 20 percent.
- Main cross-country source: Standardized World Income Inequality Database (SWIID), covering 196 jurisdictions for as many years as possible from 1960 to the present; harmonizes thousands of reported Gini indices from multiple published sources.
- Additional robustness sources:
  - Fiscal Monitor October 2017 Gini coefficients (on a five-year basis starting in 1980).
  - Atkinson and Morelli (2012): 25 countries, 1911 to 2010.
  - Atkinson, Piketty, and Saez (2011) and updates (WID) for 30 countries.
- Equality of opportunities source: Global Database on Intergenerational Mobility (GDIM), covering intergenerational economic mobility in 96 percent of the world’s population over 50 years.
- Notes on "top 1 percent" calculations: sensitivity discussed (Auten and Splinter 2019); analysis in this note is not dependent on top 1 percent calculations and is robust to country-sample changes.
- Alternative wealth-equality perspective: Bhalla (2017) measure capturing human capital accumulation.

### Annex I. Data — Extreme inequality proxies (2004–15)
- Data sources: Forbes’ list of billionaires (by country) and Global Wealth Databook (Credit Suisse).
- Two constructed indicators:
  - Per capita wealth index: ratio of the richest billionaire’s stock of wealth to the country’s wealth per adult.
  - Average per capita wealth index: average wealth per billionaire (total billionaires’ wealth divided by the number of billionaires) divided by the aggregate wealth per adult in the country.
- Empirical observations:
  - Increase in extreme inequality is more marked than standard income-inequality measures (such as the Gini index) may suggest.
  - The gap between top richest individuals and country-level average is larger in the developing world than in high-income countries.
  - Initial full-sample data suggest a negative correlation between access to financial services and the proxy for extreme inequality, indicating potential for financial inclusion to reduce wealth gaps across countries.

### Annex I. Data — Financial depth
- Source: Financial Development Index (FDI) database (introduced in Sahay and others 2015a; updated at http://data.imf.org/fdindex).
- Coverage: more than 180 advanced and emerging market and developing economies with annual frequency from 1980 onward.
- Two indicators of financial depth:
  - Financial institutions depth index: combines four series—(1) private sector credit to GDP, (2) pension fund assets to GDP, (3) mutual fund assets to GDP, and (4) insurance (life and nonlife) premiums to GDP.
  - Financial markets depth index: combines five variables—(1) stock market capitalization to GDP, (2) stocks traded to GDP, (3) international debt securities of government to GDP, (4) total debt securities of financial corporations to GDP, and (5) total debt securities of nonfinancial corporations to GDP.
- Normalization procedure:
  - Each series converted to a 0–1 scale using a min-max procedure and winsorized at the 5th and 95th percentiles.
  - Min-max normalization maps global minimum to 0 and global maximum to 1; range is [0,1].

### Annex I. Data — Financial inclusion
- Primary source: IMF Financial Access Survey (FAS).
  - FAS contains 121 time series and 64 indicators for 189 jurisdictions spanning 2004–18.
  - Measures outreach (branch network, nonbranch agents, ATMs, mobile-money agent outlets) and use (deposits, loans, insurance).
  - Provides disaggregation for households (including by gender) and for small and medium enterprises; based on administrative/provider-side sources.
- Complementary source: Global Findex database.
  - Compiles demand-side data on how adults save, borrow, make payments, and manage risk.
  - Published every three years since 2011; latest survey carried out in 2017 as part of the Gallup World Poll.
  - Survey of about 150,000 adults in 144 economies; target population ages 15 and older.
  - Offers decompositions by gender, age, income, employment, living area, and education level.
- Firms: World Bank Enterprise Survey.
  - Conducted since 2002; covers over 135,000 firms in 140 countries.
  - Regression sample availability: 110–166 country-level observations for 66–92 countries (Annex Table 1).

### Annex I. Data — Financial stability
- Main sources:
  - Financial crises: Laeven and Valencia 2018 database.
  - Household and corporate leverage: database for Chapter 2 of the October 2017 Global Financial Stability Report and ORBIS-Worldscope.
  - Financial sector and macroeconomic variables: Bloomberg, L.P.; Haver Analytics; IMF databases.
- Instability measures used:
  - Presence/absence of banking crises.
  - z-score (distance-to-distress): defined as capital to assets + return on assets / standard deviation of return on assets.
- Riskiness of credit allocation indicator:
  - Based on Greenwood and Hanson (2013) approach; constructed from four firm-level vulnerability indicators: leverage, debt overhang, interest coverage ratio, and expected default frequency.
  - The indicator captures the vertical distance between average vulnerability of top and bottom issuers; distributions of indicators have a bell curve shape and a standard deviation of about 1.
  - Recent work (Brandão-Marques and others 2019) shows this measure provides early warning of financial crises and sharp real downturns; it behaves cyclically and is related to macroprudential policies.

### Annex I. Selected empirical tabulations (from Annex Table 1)
- Variable names excerpted: fbankinv1 fbankw1 fbankinv2 fbankwk2 fcredobst fbankln fsm bankln
- Number of observations (selected): 166, 121, 164, 121, 166, 110, 110
- Number of countries (selected): 91, 92, 90, 92, 89, 66, 66
- Correlation with change in Gini (selected): 0.04, 0.15, 0.10, 0.18, -0.11, 0.10, 0.16
- Number of increases in financial inclusion/Total observations (%): 64.5, 61.2, 57.3, 59.5, 61.4, 44.5, 42.7
- Number of common observations of change in financial inclusion and Gini (selected): 89, 538, 95, 38, 95, 75, 57
- Number of increases in financial inclusion with declines in Gini (selected %): 41.6, 39.6, 36.0, 39.6, 39.3, 29.8, 28.1
- Number of decreases in financial inclusion with increases in Gini (selected %): 14.6, 9.4, 19.1, 11.3, 12.4, 15.8, 19.3
- Increases in financial inclusion with declines in Gini (selected % of increases): 66.1, 65.6, 68.1, 65.6, 63.6, 65.4, 69.6
- Decreases in financial inclusion with increases in Gini (selected % of decreases): 39.4, 23.8, 40.5, 28.6, 32.4, 29.0, 32.4

### Annex II. Methodologies and results — Financial depth and inequality
- Research question: Does greater financial depth mean lower or higher inequality within countries?
- Theoretical background:
  - One strand suggests deeper financial systems reduce income inequality (e.g., Banerjee and Newman 1993; Galor and Zeira 1993; Greenwood and Jovanovic 1990)—credit availability improves allocation and reduces inequality.
  - Another strand highlights mechanisms where finance increases inequality via rent extraction or harmful financial innovations (e.g., Korinek and Kreamer 2014; Gennaioli, Shleifer, and Vishny 2012; Thakor 2012; Bolton, Santos, and Scheinkman 2016).
  - Possible complementarity: early-stage benefits for developing economies; later-stage inequality effects in advanced economies—implying a U-shaped relationship between inequality and financial depth.
- Empirical context:
  - Recent empirical findings: studies focused on developing economies often find financial depth improves incomes of the poor (e.g., Burgess and Pande 2005); studies including advanced economies often find more finance increases inequality (Jaumotte, Lall, and Papageorgiou 2013; Jauch and Watzka 2016; de Haan and Sturm 2017).
  - Brei, Ferri, and Gambacorta (2018) attribute nonlinearities to a shift from banks to financial markets.
- Estimated baseline equation:
  - Gini_it = α + β0 Gini_it−1 + β1 FD_it + β2 FD_it^2 + β3 (FD_it × Interact_i) + β4 X_it + ε_it
  - FD is the financial depth index (or subcomponents for institutions and markets); quadratic functional form chosen based on prior literature.
- Controls included:
  - Initial values of economic and financial development, legal origin, ethnic and religious fractionalization, geographical latitude, logarithm of industrial value added to GDP, average years of primary and secondary schooling, trade-to-GDP, consumer price index inflation, government-consumption-to-GDP ratio, foreign-direct-investment-to-GDP ratio, banking crisis dummy (Laeven and Valencia 2018), and country fixed effects.
- Country sample and periods:
  - Panel data for 128 economies during 1980–2015 (exact years vary across regressions).
  - Baseline estimation uses nonoverlapping five-year periods.
- Regression approaches:
  - Ordinary least squares (OLS) panel fixed effects.
  - Generalized least squares (GLS) with panel-wide AR(1) correction.
  - Dynamic panel generalized method of moments (GMM) (Arellano and Bond 1991) to address potential endogeneity.

*Source: sdnea2020001 (IMF).*

### introduction of the lagged dependent variable among the regressors, reverse causality

### introduction of the lagged dependent variable among the regressors, reverse causality

### Endogeneity and estimation strategy
- Sources of endogeneity: introduction of the lagged dependent variable among the regressors, reverse causality, and measurement errors.
- Instrumentation approach: Given limited availability of external instruments, the Arellano and Bond estimator uses an internal instrumentation procedure in which the lags of the regressors are used as instruments.
- Estimation techniques used: OLS panel fixed effects, GLS with panel-wide AR(1) correction, and dynamic panel GMM (Arellano and Bond 1991). A complementary 2SLS approach is also applied using existence of private credit bureaus and public credit registries, and initial endowment (absolute latitude of the capital city) as instruments.

### Annex Table 2 — Inequality and Financial Depth: baseline results (representative coefficients and diagnostics)
- Dependent variable: log of Gini index (market).
- Financial depth index (linear terms):
  - Financial Institutions: -0.368 (standard error 0.917)
  - Financial Institutions (alternative): -2.103** (standard error 0.907)
  - Financial Markets: -0.368 (standard error 0.917)
  - Financial Markets (alternative): -1.782** (standard error 0.819)
- Financial depth index squared:
  - Financial Institutions (squared): 3.728** (standard error 1.245)
  - Financial Markets (squared): 2.587** (standard error 1.183)
- Sample and diagnostic statistics:
  - Observations: 573, 567, 567, 567 (by column)
  - Number of countries: 128, 126, 126, 126 (by column)
  - Serial correlation (p-value): 0.001, 0.008, 0.023, 0.038 (by column)
  - Hansen J test (p-value): 0.158, 0.263, 0.147, 0.179 (by column)
- Notes: GMM estimations, 1980–2010 (five-year averages). Control variables include initial values of economic and financial development, legal origin, ethnic and religious fractionalization, geographical latitude, logarithm of industrial value added to GDP, average years of primary and secondary schooling, trade-to-GDP, consumer price index inflation, government-consumption-to-GDP ratio, and foreign-direct-investment-to-GDP ratio, and a “banking crisis” dummy variable. Robust standard errors in parentheses. Country fixed effects included.

### Robustness checks (summary)
- Robustness procedures performed:
  - Changing the sample of countries
  - Changing the sample years
  - Removal of outliers—the 5 percent highest and lowest observations of the financial development variable
  - Replacing the market Gini index by disposable income Gini
  - Replacing the Gini index by other proxies for inequality: first quintile income share, the Palma ratio, and the quintile ratio
- Result: In all cases, results were similar to the baseline estimate.

### Financial Inclusion and Inequality — model, controls, and nonlinear specification
- Baseline equation:
  - INE_it = α + β FI_it + Θ X_it + η_i + ε_it
  - INE = income inequality; FI = financial inclusion; X = vector of control variables; η = country fixed effects.
- Control variables included (exactly as listed):
  - Inflation – (percent change in consumption price index)
  - Trade – (imports plus exports, in percent of GDP)
  - Telephone – (fixed telephone subscriptions per 100 people)
  - Population – (growth rate of total population)
  - Remittances – (remittance inflows, in percent of GDP)
  - Education – (enrollment rate in primary school)
  - Rule of law – (an indicator of the perception of confidence in the rules of society)
  - Dummy variables for country grouping – high income (dhi), upper middle income (dumi), lower middle income (dlmi), low income (dli)
- Nonlinear interaction specification:
  - INE_it = α + β FI_it + δ β FI_it * Z̃_it + λ Z_it + Θ X_it + η_i + ε_it
  - Z̃ is the difference between Z_it and its sample mean.
  - Conditional variables investigated: GDP growth, index of financial market development, deviations of the short-term interest rate from its fundamental value (money market conditions), and aggregate banking system Z-score (financial stability).
  - Marginal effect of FI on INE: ∂INE_it/∂FI_it = β + δ Z̃_it

### Annex Table 3 — Financial inclusion baseline results (representative findings)
- Key finding: The financial inclusion proxy has the expected negative sign; Annex Table 3 shows negative and statistically significant coefficients, suggesting a negative correlation between financial inclusion and income inequality.
- Notable numeric examples (emerging market/developing economies and full sample, representative):
  - atm (number of ATMs per 100,000 adults):
    - OLS: -0.027 (standard error 0.016)
    - OLS (alternative): -0.023*** (standard error 0.005)
    - GLS: -0.022** (standard error 0.009)
    - GMM: -0.045** (standard error 0.019)
    - GMM (alternative): -0.040*** (standard error 0.006)
    - IV/GMM: -0.060*** (standard error 0.012)
  - branch (number of commercial bank branches per 100,000 adults):
    - OLS: -0.022** (standard error 0.010)
    - OLS (alternative): -0.014*** (standard error 0.004)
    - GLS: -0.016** (standard error 0.006)
    - GMM: -0.025** (standard error 0.012)
    - GMM (alternative): -0.017*** (standard error 0.005)
    - IV/GMM (selected column): -0.004 (standard error 0.006)
- Selected control variable coefficients and diagnostics (representative figures):
  - population: 0.034, 0.279, 0.443**, 0.438***, 0.913***, 0.697** (standard errors vary by column)
  - education: -0.056, -0.141, -0.080***, -0.119***, -0.126***, -0.210*** (standard errors vary)
  - L.wdigini (lagged dependent Gini): 0.703***, 0.751***, 0.638***, 0.757*** (standard errors reported)
  - Observations across columns: 497, 516, 470, 489, 295, 298, 311, 324, 284, 297, 158, 161 (by column)
  - Number of countries across columns: 104, 107, 77, 80, 56, 56, 68, 08, 35, 63, 33, 33 (as reported by column)
  - Adjusted R-squared examples: 0.112, 0.130, 0.155, 0.160 (by column)
  - Hansen J test p-value examples: 0.866, 0.939, 0.983, 0.963 (by column)
- Interpretation: Impact of financial inclusion on inequality appears stronger in the developing world; the coefficient associated with atm is twice as large for the sample of lower- and middle-income countries compared with the full sample (statement in main text).

### Annex Table 4 — Interaction with growth and financial depth (representative findings)
- Interaction with GDP growth:
  - atm coefficients and interactions:
    - atm: -0.025, -0.021***, -0.029***, -0.046**, -0.037***, -0.049*** (by column)
    - atm*GDP growth: -0.002**, -0.001***, -0.003***, -0.002, -0.002***, -0.001 (by column)
  - branch and branch*GDP growth:
    - branch: -0.021**, -0.013***, -0.008**, -0.025*, -0.016***, -0.010 (by column)
    - branch*GDP growth: -0.003***, -0.003***, -0.001, -0.002, -0.003***, 0.001 (by column)
  - GDP growth main effects (examples): 0.042, 0.047*, 0.035***, 0.032***, 0.136***, 0.043*** (by column)
- Interaction with financial depth (FM):
  - atm: -0.029*, -0.024***, -0.026***, -0.042**, -0.040***, -0.076*** (by column)
  - atm*FM: -0.014, -0.010, -0.080***, 0.039, 0.020, -0.107 (by column)
  - branch: -0.038***, -0.026***, -0.052***, -0.076, -0.045**, -0.111** (by column)
  - branch*FM: -0.091*, -0.067**, -0.226***, -0.275, -0.148, -0.535** (by column)
  - FM main effects (examples): 6.243*, 5.413*, 0.969, 1.228, 2.111, 1.719 (by column)
- Interpretation: Financial inclusion (atm, branch) retains negative and statistically significant impacts on the Gini index; interaction coefficients mostly negative, suggesting financial inclusion has a stronger impact on inequality in countries that grow faster or with higher financial development.

### Gender gaps in financial inclusion (Annex Table 5) — setup and key results
- Model for gender-specific analysis:
  - INE_i = α + β FI_i + Θ X_i + η_i + ε_i
  - FI indicators used: account (percentage with an account), borrow (percentage who borrowed from a financial institution in past 12 months), credit card, debit card. Specifications use aggregate FI and FI_m (male) and FI_f (female).
- Representative results (selected coefficients):
  - account:
    - OLS: account -0.099* (standard error 0.051)
    - IV: account -0.497*** (standard error 0.136)
  - account by gender:
    - account_f: -0.108** (standard error 0.049); IV: -0.624*** (standard error 0.186)
    - account_m: -0.079 (standard error 0.051); IV: -1.324 (standard error 0.864)
  - borrow:
    - borrow: -0.247** (standard error 0.118); IV: -3.135 (standard error 2.022)
    - borrow_f: -0.289** (standard error 0.120); IV: -2.804* (standard error 1.541)
    - borrow_m: -0.186 (standard error 0.112); IV: -3.611 (standard error 2.947)
- Interpretation:
  - All four indicators of financial inclusion are negatively and significantly correlated with the Gini index in the full table summary.
  - Coefficients of account_f and borrow_f are negative and statistically significant, while account_m and borrow_m are not, suggesting the impact of financial inclusion on inequality is stronger when access improves for women.
  - For credit card indicators, gender gaps do not seem to make a difference.
  - Debit card results suggest inclusion lowers inequality for both men and women, with larger magnitude for women.
  - Overall conclusion: Financial inclusion of women matters more in reducing income inequality.

### Robustness, identification, and longitudinal analysis
- Identification concerns:
  - Dynamic GMM (Arellano and Bond) is used to address endogeneity and persistence of inequality, but lagged instruments have limitations if financial inclusion variables are persistent.
  - Complementary 2SLS results using credit bureaus and registries as instruments confirm baseline results; instruments are valid across specifications.
- Robustness to alternative inequality measures:
  - Re-estimated baseline regressions using alternative measures of income inequality: percentage of total income accruing to the bottom 20 percent, the Palma ratio, and the quintile ratio. Exercise unchanged in substance.
- Longitudinal case study:
  - US data, 1935 to 2014: large increase in financial services infrastructure (commercial bank branches per 100,000 people) between early 1930s and late 1970s associated with a significant decline in income and wealth inequality. Inequality has since reverted while availability of financial services continued to increase at a much lower pace.

*Source: sdnea2020001 - introduction of the lagged dependent variable among the regressors, reverse causality (authors’ calculations and tables as provided in the source content).*

### Annex Figure 2. Financial Inclusion and Income Inequality, United States, 1930–2015

### Annex Figure 2. Financial Inclusion and Income Inequality, United States, 1930–2015

### Data and measurement
- Sources: World Wealth and Income Database; Federal Deposit Insurance Corporation; U.S. Census Bureau; and authors’ calculations.
- Note: series in natural logs.
- Financial inclusion measure: number of commercial bank branches per 100,000 people.
- Inequality measures: based on income and wealth distribution.
- All variables are used in first difference of natural log to ensure stationarity.

### Econometric approaches and specifications
- OLS specification (as reported):
  - INE_t = α + β FI_t + Θ X_t + ε_t
  - Control variables include the income tax rate, private credit to GDP, and school enrollment.
- VAR specification (baseline):
  - Y_t = A + B(L) Y_t + e_t
  - Y is the vector containing the variables included in the model; A is the vector of intercept terms; B(L) is the matrix of coefficients associated with the right-side variables, with L the lag operator; e is the vector of error terms.
  - Optimal number of lags, as determined by the Bayesian Information Criterion, is 1.
  - Identification: Cholesky decomposition to ensure financial inclusion shocks are orthogonal.
  - Baseline models include income inequality and financial inclusion (bank branches); expanded models control for income tax rate, private credit to GDP, or school enrollment.
- Dynamic household credit specification (following Perugini, Hölscher, and Collie (2016)):
  - HouseholdCredit_{i,t} = ρ_1 HouseholdCredit_{i,t−1} + β_1 Inequality_{i,t−1} + β_2 Others_{i,t−1} + α_i + γ_t + ε_{it}
  - Estimation: generalized method of moments (GMM) to address persistence and endogeneity, with instruments for income inequality.
- Credit growth volatility cross-section:
  - σ_i = β_1 Inequality_i + Others_i + ε_i
  - σ_i is the country-specific volatility or credit cycle quality (amplitude, degree of asymmetry between up- and downswings), computed over a period (for example, past 30 years or more).
- Effects of systemic banking crises on inequality:
  - ΔΔI... (equation shown in source) where Recession and Crisis are dummy variables for recessions and systemic banking crises (Laeven and Valencia 2018). Controls include country characteristics and other factors such as taxation and capital flows.

### Key empirical findings — Financial inclusion and inequality (U.S. 1930–2015)
- OLS results: an increase in the number of bank branches is associated with a decline in income and wealth inequality (using first differences of natural logs).
- VAR results:
  - Financial inclusion measure Granger causes income inequality at the 5 percent significance level.
  - Impulse response functions confirm a negative relationship between the number of bank branches and income inequality: a positive financial inclusion shock is associated with lower income inequality, and the effect is statistically significant and persistent over time.
  - Results hold when controlling for income tax rate, private credit to GDP, or school enrollment, and when using proxies for wealth inequality instead of income inequality.

### Extreme inequality analysis
- Definition: extreme inequality approximated as the gap between the average wealth in a country and the level of wealth at the top of the distribution (quantitative proxy for “extremes” in the distribution of wealth at the country level).
- Context: Oxfam (2019) finding quoted: just 26 people own the same wealth as the poorest half of the world.
- Empirical approach:
  - Dependent variable: proxies for extreme inequality (either the per capita wealth index or the average per capita wealth index).
  - Estimation techniques applied: OLS, GLS, and GMM.
  - Data period: 2004–15.
- Result: improving access to financial services can contribute to reducing extreme inequality; conclusion holds for subsamples of advanced economies and developing economies.

### Financial stability and inequality (cross-country analysis)
- Question/hypothesis: Is there a relationship between stability and inequality within countries?
- Coverage: 151 systemic banking crisis episodes around the globe during 1970–2017 (Laeven and Valencia (2018) data set).
- Estimated equation: logit function modeling probability of a banking crisis as a function of change in Gini index, change in financial depth, and other control variables; country fixed effects included.
- Controls: initial values of economic and financial development, legal origin, ethnic and religious fractionalization, geographical latitude, logarithm of industrial value added to GDP, average years of primary and secondary schooling, trade-to-GDP, consumer price index inflation, government-consumption-to-GDP ratio, foreign-direct-investment-to-GDP ratio.
- Baseline results (Annex Table 6):
  - Dependent variable: Log of crisis probability
  - Change in financial depth: 0.819** (0.298) in column (1); 0.822** (0.367) in column (2)
  - Change in Gini index: 0.695** (0.238) in column (2); 0.725** (0.238) in column (3)
  - Number of crises: 151
  - Number of observations: 573
  - Pseudo R squared: 0.144 (col 1), 0.210 (col 2), 0.247 (col 3)
  - Note: Robust standard errors in parentheses. ***, **, and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively. Country fixed effects are included.
- Interpretation: the change in the Gini index has predictive power for crisis likelihood in addition to the change in financial depth.

### Robustness checks and additional analyses
- Robustness checks include:
  - Varying sample of countries and years.
  - Using different definitions of Gini (disposable income).
  - Replacing the Gini index with other proxies for inequality: first quintile income share, the Palma ratio, and the quintile ratio.
- Additional analyses performed include tests on household credit growth (dynamic GMM), credit growth volatility (cross-section σ_i regressions), and the effects of systemic banking crises on inequality with interaction terms for recession and crisis.

*Source: sdnea2020001 - Annex Figure 2. Financial Inclusion and Income Inequality, United States, 1930–2015*

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- Brandão-Marques, Luis, Qianying Chen, Claudio Raddatz, Jérôme Vandenbussche, and Peichu Xie. 2019. "The Riskiness of Credit Allocation and Financial Stability." IMF Working Paper 19/207, International Monetary Fund, Washington, DC.

### Monetary policy, macroprudential policy, and distributional effects
- Coibion, Olivier, Yuriy Gorodnichenko, Lorenz Kueng, and John Silvia. 2012. "Innocent Bystanders? Monetary policy and Inequality in the US." NBER Working Paper 18170, National Bureau of Economic Research, Cambridge, MA.
- Furceri, Davide, Prakash Loungani, and Aleksandra Zdzienicka. 2016. "The Effects of Monetary Policy Shocks on Inequality." IMF Working Paper 16/245, International Monetary Fund, Washington, DC.
- Monnin, Pierre. 2017. "Monetary Policy, Macroprudential Regulation and Inequality." CEP Discussion Note 2017/2, Council on Economic Policies, Zurich.
- Korinek, Anton, and Jonathan Kreamer. 2014. "The Redistributive Effects of Financial Deregulation." Journal of Monetary Economics 68:S55–S67.
- Furceri, Davide, and Prakash Loungani. 2018. "The Distributional Effects of Capital Account Liberalization." Journal of Development Economics 130 (C): 127–44.

### Income distribution, inequality measurement, and long-run perspectives
- Atkinson, Anthony, and Salvatore Morelli. 2012. Chartbook of Economic Inequality: 25 Countries 1911-2010. New York: Institute for New Economic Thinking.
- Atkinson, Anthony, Thomas Piketty, and Emmanuel Saez. 2011. "Top Incomes in the Long Run of History." Journal of Economic Literature 49 (1): 3–71.
- Piketty, Thomas. 2013. Capital in the Twenty-First Century. Cambridge, MA: Harvard University Press.
- Kuznets, Simon. 1955. "Economic Growth and Income Inequality." American Economic Review 45 (1): 1–28.
- Lakner, Christoph, and Branko Milanovic. 2016. "Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession." World Bank Economic Review 30 (2).
- Solt, Frederick. 2016. "The Standardized World Income Inequality Database." Social Science Quarterly 97 (5): 1267–81.
- Solt, Frederick. 2019. "Measuring Income Inequality across Countries and over Time: The Standardized World Income Inequality Database." SWIID Version 8.2.
- World Inequality Database. 2018. World Inequality Report.

### Labor market, human capital, and firm structure
- Becker, Gary, and Nigel Tomes. 1986. "Human Capital and the Rise and Fall of Families." Journal of Labor Economics 4 (3): S1–39.
- Banerjee, Abhijit, and Andrew Newman. 1993. "Occupational Choice and the Process of Development." Journal of Political Economy 101 (2): 274–98.
- Philippon, Thomas, and Ariell Reshef. 2012. "Wages and Human Capital in the U.S. Finance Industry: 1909–2006." Quarterly Journal of Economics 127 (4): 1551–609.
- Blundell, Richard, Monica Costa Dias, Costas Meghir, and Jonathan Shaw. 2016. "Female Labor Supply, Human Capital, and Welfare Reform." Econometrica 84 (5): 1705–53.
- Jayachandran, Seema. 2006. "Selling Labor Low: Wage Responses to Productivity Shocks in Developing Countries." Journal of Political Economy 114 (3): 538–75.

### Data sources, databases, and methodological contributions
- Credit Suisse. 2019. Global Wealth Report 2019. Zurich.
- Davies, James, Susanna Sandstrom, Anthony Shorrocks, and Edward Wolff. 2011. "The Level and Distribution of Global Household Wealth." Economic Journal 121:223–54.
- Solt, Frederick. 2016; 2019. "The Standardized World Income Inequality Database." SWIID Version 8.2.
- World Inequality Database. 2018. World Inequality Report.
- Arellano, Manuel, and Stephen Bond. 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations." Review of Economic Studies 58:277–97.
- Jordà, Òscar. 2005. "Estimation and Inference of Impulse Responses by Local Projections." American Economic Review 95 (1): 161–82.

*International Monetary Fund. REFEREN CES list from sdnea2020001 - REFERENCES.*

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_Source: https://www.imf.org/-/media/files/publications/sdn/2020/english/sdnea2020001.pdf_