## _wp15243 - References

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### Introduction and research question
- Examines whether the increase in global financial integration is behind some of the increase in income inequality, using an unbalanced panel of 149 countries from 1970 to 2010.
- Motivated by observed trends:
  - Average Gini rose in high-income and middle-income economies since the late-1980s.
  - Capital account openness increased across income groups with a significant rise at the beginning of the 1990s.
- Potential channels through which capital account liberalization can affect inequality:
  - Risk-sharing and domestic consumption smoothing, conditional on strength of financial institutions.
  - Increased likelihood of financial crises, which may have opposing distributional impacts.
  - Increased foreign direct investment (FDI) changing demand for skilled vs. unskilled labor.
  - Effects on the labor share of income through bargaining and threat of production reallocation.

### Key findings (empirical results)
- Capital account liberalization reforms are associated with a statistically significant and persistent increase in inequality:
  - Typically increased the Gini coefficient by about 0.8 percent in the very short term (1 year after a liberalization reform).
  - Typically increased the Gini coefficient by about 1.4 percent in the medium term (5 years after).
- Robustness:
  - Results robust to different sets of controls, different estimation techniques (including WLS), alternate measures of capital account openness (including Quinn and Toyoda), and endogeneity checks.
  - WLS medium-term effect reported as about 1½ percent (difference from OLS not statistically significant).
  - Using Quinn and Toyoda measure produced a medium-term effect of about 2½ percent.
- Depth and direction:
  - Magnitude of the inequality effect tends to increase with the depth of capital account liberalization (tested using thresholds of one and three standard deviations).
  - Capital account restriction episodes tend to reduce inequality, but the estimated effect is not statistically significantly different from zero.
- Heterogeneity across income groups:
  - Effect largest in middle-income countries, smallest in low-income countries.
  - Effects across income groups are not statistically different from whole-sample estimates, though estimates for high-income countries are more precisely estimated.
- Role of financial institutions and crises:
  - Increase in inequality after liberalization is larger in countries with weak financial institutions and when liberalizations are followed by financial crises.
  - Crisis-linked liberalization episodes associated with a medium-term increase in inequality of more than 3.5 percent; non-crisis episodes associated with about 1 percent medium-term increase. The difference becomes statistically significant after the third year.
  - Financial crises per se estimated to increase the Gini by about 0.1 percent in the very short term (1 year) and by about 2.5 in the medium term (5 years).
- Labor share channel:
  - Capital account liberalization episodes have statistically significant and long-lasting negative effects on the labor share of income:
    - Decrease in labor share of about 0.7 percentage point in the very short term (1 year after reform).
    - Decrease in labor share of about 0.7 percentage point in the medium term (5 years after reform).
  - Largest medium-term labor share effects in high- and middle-income countries; effect in low-income countries not statistically significant.

### Data and measurement
- Income inequality:
  - Gini coefficients from the Standardized World Income Inequality Database (SWIID), covering 173 countries from 1960 to 2010; in sample Gini ranges from 18 to 78.
- Capital account openness:
  - Chinn and Ito (Kaopen) index, unbalanced panel of 182 countries from 1970 to 2010, range from -1.856 (more restricted) to 2.456 (less restricted).
- Identification of liberalization episodes:
  - An annual change in Kaopen exceeding two standard deviations of the average annual change (exceeds 0.76) identifies liberalization; this yields 224 liberalization episodes.
- Capital account restriction episodes:
  - Annual change in Kaopen two standard deviations below average annual change; 157 restriction episodes identified.
- Labor share:
  - Ratio of compensation of employees to GDP from UN national accounts, table 203 using SNA 1993 methodology.
- Other indicators used:
  - Quinn and Toyoda (alternative capital openness measure).
  - EFW composite indicator of credit market freedom (0 to 10 scale).
  - Private credit to GDP (Global Financial Development Database).
  - Financial inclusion: ratio of adults borrowing from formal financial institutions (Demirguc-Kunt et al. 2015).
  - Crisis dates: Laeven and Valencia (2010).

### Methodology overview
- Baseline specification:
  - Univariate autoregressive inequality equation (ARDL-style) with country fixed effects and time fixed effects.
  - Dependent variable g = annual change in the (log of the) Gini coefficient.
  - D: dummy equal to 1 at the start of a capital account liberalization episode.
  - Controls X: include current account reforms and regulation reforms; extensions include GDP growth, level and square of log GDP per capita, change in trade openness, change in government expenditure share, change in sectoral shares, dependency ratios, and product/labor/credit market regulation changes.
- Estimation:
  - OLS on unbalanced panel 1970–2010 for 149 countries, typically using 2 lags (robustness to different lag lengths tested).
  - Impulse response functions derived by simulating a shock in the liberalization dummy.
  - Weighted Least Squares (WLS) used as robustness check, with analytical weights equal to inverse of SWIID standard errors.
- Nonlinear and heterogeneity analyses:
  - Smooth transition autoregressive (STAR) specification to allow effect to vary with measures of financial development (G(z_it) transition function with γ = 1.5).
  - Interaction specifications and regime splits by income group and crisis occurrence.
  - Instrumental variables approach using lag of capital account openness indicator also reported as robustness (results similar to OLS).

### Mechanisms tested empirically
- Financial development and inclusion:
  - Effect of liberalization on inequality decreases with higher levels of credit market institutions (EFW), private credit to GDP, and financial inclusion.
  - In countries with very high credit-to-GDP ratio, medium-term effect may be negative (not statistically different from zero).
  - In countries with low financial inclusion, medium-term increase in inequality of more than 3 percent; with high inclusion, medium-term increase less than 0.1 percent.
- Financial crises:
  - Liberalization episodes followed by crises produce larger increases in inequality than non-crisis episodes (see magnitudes above).
- Labor share:
  - Capital account liberalization lowers labor share by about 0.7 percentage point in both very short and medium term; impacts concentrated in high- and middle-income countries.

### Policy implications and conclusions
- Main conclusion:
  - Capital account liberalization episodes are associated with a statistically significant and persistent increase in income inequality on average.
  - Typical magnitudes reported: about 0.8 percent increase in Gini at 1 year, and about 0.7–2½ percent increase in Gini at 5 years.
- Policy messages:
  - Findings do not imply countries should avoid liberalization, but provide additional reason for caution—especially where reducing inequality is a policy objective.
  - Consistent with the IMF’s institutional view: liberalization is generally more beneficial and less risky if countries have reached certain levels or thresholds of financial and institutional development.
  - Supporting policies to strengthen financial institutions, increase financial inclusion, and manage crisis risks can mitigate the adverse distributional consequences of liberalization.
  - Financial integration should be pursued as part of a broader package of reforms and supportive macroeconomic policies.

### Key statistics and empirical results (selected)
- Table 1. Descriptive statistics by income groups (Panel A. All countries):
  - Gini: N 4334; Average 44.531; SD 9.274; Min 17.590; Max 77.965
  - D.Gini: N 4020; Average -0.014; SD 1.836; Min -13.567; Max 19.571
  - Kaopen: N 6023; Average -0.002; SD 1.529; Min -1.856; Max 2.456
  - D.Kaopen: N 5829; Average 0.024; SD 0.370; Min -3.253; Max 3.253
- Table 2. Number of capital account liberalization reforms (1970-2010 totals):
  - All: 1970-2010 224 (70s 38; 80s 25; 90s 100; 2000s 61)
  - High income: 1970-2010 58 (70s 15; 80s 7; 90s 23; 2000s 14)
  - Upper middle income: 1970-2010 79 (70s 11; 80s 9; 90s 28; 2000s 31)
  - Lower middle income: 1970-2010 54 (70s 5; 80s 6; 90s 31; 2000s 12)
  - Lower income: 1970-2010 33 (70s 7; 80s 3; 90s 18; 2000s 5)
- Table 3. The effect of capital account liberalization on inequality (1970-2010), OLS (selected coefficients and statistics):
  - Gini growth(t-1) coefficient 0.272*** (t-statistic 4.52)
  - Gini growth(t-1) coefficient (alternate) 0.127*** (t-statistic 2.77)
  - Capital account reform (t) coefficient 0.766*** (t-statistic 3.12)
  - Capital account reform (t-1) coefficient 0.143 (t-statistic 0.45)
  - Capital account reform (t-2) coefficient -0.048 (t-statistic -0.16)
  - Current account reform (t-2) coefficient 0.579* (t-statistic 1.65)
  - Regulation reform (t) coefficient -0.333 (t-statistic -1.05)
  - N 2071
  - R^2 0.21
  - Note: T-statistics based on robust clustered standard errors in parenthesis. ***,**, * denote significance at 1 percent, 5 percent and 10 percent, respectively.

*Source: _wp15243 - References .............................................................................................................*

### References .............................................................................................................

### _wp15243 - References .............................................................................................................

### Introduction and research question
- Examines whether the increase in global financial integration is behind some of the increase in income inequality, using an unbalanced panel of 149 countries from 1970 to 2010.
- Motivated by observed trends: average Gini rose in high-income and middle-income economies since the late-1980s; capital account openness increased across income groups with a significant rise at the beginning of the 1990s.
- Identifies potential channels through which capital account liberalization can affect inequality:
  - Risk-sharing and domestic consumption smoothing, conditional on strength of financial institutions.
  - Increased likelihood of financial crises, which may have opposing distributional impacts.
  - Increased foreign direct investment (FDI) changing demand for skilled vs. unskilled labor.
  - Effects on the labor share of income through bargaining and threat of production reallocation.

### Key findings (empirical results)
- Capital account liberalization reforms are associated with a statistically significant and persistent increase in inequality:
  - Typically increased the Gini coefficient by about 0.8 percent in the very short term (1 year after a liberalization reform).
  - Typically increased the Gini coefficient by about 1.4 percent in the medium term (5 years after).
- Robustness:
  - Results robust to different sets of controls, different estimation techniques (including WLS), alternate measures of capital account openness (including Quinn and Toyoda), and endogeneity checks.
  - WLS medium-term effect reported as about 1½ percent (difference from OLS not statistically significant).
  - Using Quinn and Toyoda measure produced a medium-term effect of about 2½ percent.
- Depth and direction:
  - Magnitude of the inequality effect tends to increase with the depth of capital account liberalization (tested using thresholds of one and three standard deviations).
  - Capital account restriction episodes tend to reduce inequality, but the estimated effect is not statistically significantly different from zero.
- Heterogeneity across income groups:
  - Effect largest in middle-income countries, smallest in low-income countries.
  - Effects across income groups are not statistically different from whole-sample estimates, though estimates for high-income countries are more precisely estimated.
- Role of financial institutions and crises:
  - The increase in inequality after liberalization is larger in countries with weak financial institutions and when liberalizations are followed by financial crises.
  - Crisis-linked liberalization episodes associated with a medium-term increase in inequality of more than 3.5 percent; non-crisis episodes associated with about 1 percent medium-term increase. The difference becomes statistically significant after the third year.
  - Financial crises per se estimated to increase the Gini by about 0.1 percent in the very short term (1 year) and by about 2.5 in the medium term (5 years).
- Labor share channel:
  - Capital account liberalization episodes have statistically significant and long-lasting negative effects on the labor share of income:
    - Decrease in labor share of about 0.7 percentage point in the very short term (1 year after reform).
    - Decrease in labor share of about 0.7 percentage point in the medium term (5 years after reform).
  - Largest medium-term labor share effects in high- and middle-income countries; effect in low-income countries not statistically significant.

### Data and measurement
- Income inequality: Gini coefficients from the Standardized World Income Inequality Database (SWIID), covering 173 countries from 1960 to 2010; in sample Gini ranges from 18 to 78.
- Capital account openness: Chinn and Ito (Kaopen) index, unbalanced panel of 182 countries from 1970 to 2010, range from -1.856 (more restricted) to 2.456 (less restricted).
- Identification of liberalization episodes: an annual change in Kaopen exceeding two standard deviations of the average annual change (exceeds 0.76) identifies liberalization; this yields 224 liberalization episodes.
- Capital account restriction episodes: annual change in Kaopen two standard deviations below average annual change; 157 restriction episodes identified.
- Labor share: ratio of compensation of employees to GDP from UN national accounts, table 203 using SNA 1993 methodology.
- Other indicators used:
  - Quinn and Toyoda (alternative capital openness measure).
  - EFW composite indicator of credit market freedom (0 to 10 scale).
  - Private credit to GDP (Global Financial Development Database).
  - Financial inclusion: ratio of adults borrowing from formal financial institutions (Demirguc-Kunt et al. 2015).
  - Crisis dates: Laeven and Valencia (2010).

### Methodology overview
- Baseline specification: univariate autoregressive inequality equation (ARDL-style) with country fixed effects and time fixed effects:
  - Dependent variable g = annual change in the (log of the) Gini coefficient.
  - D: dummy equal to 1 at the start of a capital account liberalization episode.
  - Controls X: include current account reforms and regulation reforms, and later extensions include GDP growth, level and square of log GDP per capita, change in trade openness, change in government expenditure share, change in sectoral shares, dependency ratios, and product/labor/credit market regulation changes.
- Estimation:
  - OLS on unbalanced panel 1970–2010 for 149 countries, typically using 2 lags (robustness to different lag lengths tested).
  - Impulse response functions derived by simulating a shock in the liberalization dummy.
  - Weighted Least Squares (WLS) used as robustness check, with analytical weights equal to inverse of SWIID standard errors.
- Nonlinear and heterogeneity analyses:
  - Smooth transition autoregressive (STAR) specification to allow effect to vary with measures of financial development (G(z_it) transition function with γ = 1.5).
  - Interaction specifications and regime splits by income group and crisis occurrence.
  - Instrumental variables approach using lag of capital account openness indicator also reported as robustness (results similar to OLS).

### Mechanisms tested empirically
- Financial development and inclusion:
  - Effect of liberalization on inequality decreases with higher levels of credit market institutions (EFW), private credit to GDP, and financial inclusion.
  - In countries with very high credit-to-GDP ratio, medium-term effect may be negative (not statistically different from zero).
  - In countries with low financial inclusion, medium-term increase in inequality of more than 3 percent; with high inclusion, medium-term increase less than 0.1 percent.
- Financial crises:
  - Liberalization episodes followed by crises produce larger increases in inequality than non-crisis episodes (see magnitudes above).
- Labor share:
  - Capital account liberalization lowers labor share by about 0.7 percentage point in both very short and medium term; impacts concentrated in high- and middle-income countries.

### Policy implications and conclusions
- Main conclusion: capital account liberalization episodes are associated with a statistically significant and persistent increase in income inequality on average.
  - Typical magnitudes reported: about 0.8 percent increase in Gini at 1 year, and about 0.7–2½ percent increase in Gini at 5 years.
- Policy messages:
  - Findings do not imply countries should avoid liberalization, but provide additional reason for caution—especially where reducing inequality is a policy objective.
  - Consistent with the IMF’s institutional view: liberalization is generally more beneficial and less risky if countries have reached certain levels or thresholds of financial and institutional development.
  - Supporting policies to strengthen financial institutions, increase financial inclusion, and manage crisis risks can mitigate the adverse distributional consequences of liberalization.
  - Financial integration should be pursued as part of a broader package of reforms and supportive macroeconomic policies.

*Source: _wp15243 - References .............................................................................................................*

### REFERENCES

### _wp15243 - REFERENCES

### References cited
- Abiad, Abdul G. and Furceri, Davide and Topalova, Petia B., The Macroeconomic Effects of Public Investment: Evidence from Advanced Economies (May 2015). IMF Working Paper No. 15/95.
- Agnello, Luca and Ricardo M. Sousa (2012), "How do banking crises impact on income inequality?" Applied Economics Letters, Taylor and Francis Journals, 19(15), 1425-1429, October.
- Atkinson, A. B. and S. Morelli (2011), Inequality and banking crises: A first look. Paper prepared for the Global Labour Forum in Turin organized by the International Labour Organization.
- Auerbach, Alan, and Yuriy Gorodnichenko. 2013. “Fiscal Multipliers in Recession and Expansion.” In Fiscal Policy After the Financial Crisis, eds. Alberto Alesina and Francesco Giavazzi, NBER Books, National Bureau of Economic Research, Inc., Cambridge, Massachusetts.
- Bernal-Verdugo, L., D. Furceri, and D. Guillaume (2013), “Banking crises, labor reforms and unemployment,” Journal of Comparative Economics, vol. 4, 1202-1219.
- Bouis, Romain, Orsetta Causa, Lilas Demmou, Romain Duval, and Aleksandra Zdzienicka (2012), "The Short-Term Effects of Structural Reforms: An Empirical Analysis," OECD Economics Department Working Papers 949, OECD Publishing.
- Cerra. Valerie. and Sweta .C. Saxena, (2008). “Growth Dynamics: The Myth of Economic Recovery”, American Economic Review, 98, 439-457.
- Chen, S.L., J.L. Wu (2005), Long-run Money Demand Revisited: Evidence from a Non-linear Approach. Journal of International Money and Finance, Vol. 24, pp. 19-37.
- Chinn, M. and H. Ito (2008), "A New Measure of Financial Openness", Journal of Comparative Policy Analysis , 10(3), 309 – 322, September.
- Claessens, S. and E.C. Perotti (2007), “Finance and Inequality,” Journal of Comparative Economics 35, 748-773.
- Collins, S. M. (2007), “Comments on “Financial globalization, growth, and volatility in developing countries” by Eswar Prasad, Kenneth Rogoff, Shang-Jin Wei, and M. Ayhan Kose”. In: Harrison, A. (Ed.), Globalization and Poverty, National Bureau of Economic Research Conference Report. University of Chicago Press, Chicago.
- Cragg, M.I. and M. Epelbaum (1996), “Why Has Wage Dispersion Grown in Mexico? Is it the Incidence of Reforms of the Growing Demand for Skills?” Journal of Development Economics 51, 99-116.
- Cukierman, Alex and Mariano Tommasi (1998), “When Does It Take a Nixon to Go to China?” American Economic Review, 88(1), 180–97.
- Das, Mitali and Mohapatra Sanket (2003), “Income inequality: the aftermath of stock market liberalization in emerging markets” Journal of Empirical Finance, 10, 217-248.
- Demirguc-Kunt, Asli, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden. 2015. “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, DC.
- Fok D., D. van Dijk, P. H. Franses (2005), A multi-level panel STAR model for US manufacturing sectors. Journal of Applied Econometrics, Vol. 20(6), pp. 811-827.
- Granger, Clive W. J., and Timo Teräsvirta. 1993. Modelling Nonlinear Economic Relationships. New York: Oxford University Press.
- Harrison, A. E. (2002), ‘Has Globalization Eroded Labour’s Share’ mimeo, University of California Berkeley.
- Helpman, Elhanan, Oleg Itskhoki, Marc-Andreas Muendler, and Stephen Redding. 2015. “Trade and Inequality: From Theory to Estimation”. Harvard University, manuscript.
- Henry, P.B. 2007. Capital Account Liberalization: Theory, Evidence, and Speculation, Journal of Economic Literature, vol. XLV (December), 887-935.
- Kose, M. Ayhan, Eswar S. Prasad, and Marco E. Terrones (2009), "Does financial globalization promote risk sharing?," Journal of Development Economics, Elsevier, 89(2), 258-270, July.
- Kose, Ayhan M, Eswar S. Prasad, and Ashley D. Taylor (2011), "Thresholds in the process of international financial integration," Journal of International Money and Finance, Elsevier, 30(1), 147-179, February.
- IMF (2012), “The Liberalization and Management of Capital Flows: An Institutional View”, IMF Policy Paper.
- Jayadev, Arjun (2007), "Capital account openness and the labour share of income," Cambridge Journal of Economics, Oxford University Press, 31(3), 423-443, May.
- Jaumotte, Florence, Subir Lall, and Chris Papageorgiou (2013), "Rising Income Inequality: Technology, or Trade and Financial Globalization? " IMF Economic Review, 61(2).
- Quinn, Dennis P. and A. Maria Toyoda. (2008), Does Capital Account Liberalization Lead to Economic Growth? Review of Financial Studies, 21(3), 1403-1449.
- Laeven, Luc and Fabian Valencia (2010), “Resolution of Banking Crises: The Good, the Bad, and the Ugly.” IMF Working Paper WP/10/146 (Washington: International Monetary Fund).
- Larrain, Mauricio (2015), “Capital Account Opening and Wage Inequality”, Review of Financial Studies, Society for Financial Studies, vol. 28(6), pp. 1555-1587.
- Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica: Journal of the Econometric Society, 1417-1426.
- Romer, Christina D. and David H. Romer (2010), “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks,” American Economic Review, 100(3), 763–801.
- Sarno, L., M. P. Taylor (2002), Purchasing power parity and the real exchange rate. IMF Staff Papers 49, pp. 65-105.
- Solt, Frederick. (2009), “Standardizing the World Income Inequality Database.” Social Science Quarterly, 90(2), 231-242. SWIID Version 3.1, December 2011.
- Teulings, C. N. and Zubanov, N. (2014), Is economic recovery a myth? robust estimation of impulse responses. Journal of Applied Econometrics, 29: 497–514.

### Figures (captions and notes)
- Figure 1. Income inequality within income groups (1985-2005)
- Figure 2. Capital account openness within income groups (1970-2010)
- Figure 3. The evolution of inequality before and after capital account liberalizations
- Figure 4. The effect of capital account liberalization on inequality (1970-2010), OLS
  - Note: IRFs are estimated using the specification in equation (1). The solid line corresponds to the IRF; dotted lines correspond to 90 percent confidence bands.
- Figure 5. The effect of capital account liberalization on inequality (1970-2010), Depth of liberalization
  - Panel A. 1 Standard Deviations
  - Panel B. 3 Standard Deviations
  - Note: IRFs are estimated using the specification in equation (1). The solid line corresponds to the IRF; dotted lines correspond to 90 percent confidence bands.
- Figure 6. The effect of capital account restriction on inequality (1970-2010)
  - Note: IRFs are estimated using the specification in equation (1). The solid line corresponds to the IRF; dotted lines correspond to 90 percent confidence bands.
- Figure 7. The effect of capital account liberalization on inequality (1970-2010), WLS
  - Note: IRFs are estimated using the specification in equation (1). The solid line corresponds to the IRF; dotted lines correspond to 90 percent confidence bands.
- Figure 8. The effect of capital account liberalization on inequality (1970-2010), different lags
  - Note: IRFs are estimated using the specification in equation (1).
  - ARDL11, Baseline, ARDL55 (displayed variations)
- Figure 9. The effect of capital account liberalization on inequality, Quinn and Toyoda measure of capital account openness
  - Note: IRFs are estimated using the specification in equation (1). The solid line corresponds to the IRF; dotted lines correspond to 90 percent confidence bands.
- Figure 10. The effect of capital account liberalization on inequality, additional controls
  - Note: IRFs are estimated using the specification in equation (1). The solid line corresponds to the IRF; dotted lines correspond to 90 percent confidence bands.
- Figure 11. The effect of capital account liberalization on inequality across income groups
  - Note: IRFs are estimated using the specification in equation (2).
  - Series shown for Low income, Middle income, High income.
- Figure 12. The effect of capital account liberalization on inequality, the role of financial institutions
  - Panel A. EFW-Financial freedom indicator
  - Panel B. Credit-to-GDP
  - Panel C. Financial inclusion
  - Note: IRFs are estimated using the specification in equation (3).
  - Series shown: Baseline, Low Financial Deepening, High Financial Deepening; Baseline, Low Financial Inclusion, High Financial Inclusion.
- Figure 13. The effect of capital account liberalization on inequality, the role of financial crises
  - Note: IRFs are estimated using the specification in equation (4).
  - Series shown: Baseline, No crises, Crises.
- Figure 14. The effect of capital account liberalization on the labor share (1970-2010)
  - Note: IRFs are estimated using the specification in equation (5). The solid line corresponds to the IRF; dotted lines correspond to 90 percent confidence bands.

### Key statistics and empirical results (tables)
- Table 1. Descriptive statistics by income groups
  - Panel A. All countries
    - Gini: N 4334; Average 44.531; SD 9.274; Min 17.590; Max 77.965
    - D.Gini: N 4020; Average -0.014; SD 1.836; Min -13.567; Max 19.571
    - Kaopen: N 6023; Average -0.002; SD 1.529; Min -1.856; Max 2.456
    - D.Kaopen: N 5829; Average 0.024; SD 0.370; Min -3.253; Max 3.253
  - Panel B. High income
    - Gini: N 1542; Average 42.653; SD 6.601; Min 25.022; Max 64.877
    - D.Gini: N 1464; Average 0.058; SD 1.716; Min -13.567; Max 10.676
    - Kaopen: N 1667; Average 1.036; SD 1.516; Min -1.856; Max 2.456
    - D.Kaopen: N 1618; Average 0.044; SD 0.299; Min -2.292; Max 2.292
  - Panel C. Upper middle income
    - Gini: N 1298; Average 45.699; SD 10.692; Min 17.590; Max 77.965
    - D.Gini: N 1187; Average -0.011; SD 1.867; Min -11.059; Max 10.844
    - Kaopen: N 1538; Average -0.138; SD 1.470; Min -1.856; Max 2.456
    - D.Kaopen: N 1488; Average 0.023; SD 0.449; Min -3.253; Max 2.556
  - Panel D. Lower middle income
    - Gini: N 937; Average 44.991; SD 9.533; Min 23.568; Max 77.480
    - D.Gini: N 863; Average -0.054; SD 1.939; Min -8.646; Max 19.571
    - Kaopen: N 1606; Average -0.352; SD 1.342; Min -1.856; Max 2.456
    - D.Kaopen: N 1551; Average 0.014; SD 0.384; Min -3.253; Max 3.253
  - Panel D. Low income
    - Gini: N 557; Average 46.235; SD 10.656; Min 25.146; Max 75.853
    - D.Gini: N 506; Average -0.161; SD 1.912; Min -8.706; Max 6.917
    - Kaopen: N 1212; Average -0.793; SD 1.017; Min -1.856; Max 2.456
    - D.Kaopen: N 1172; Average 0.011; SD 0.323; Min -1.935; Max 2.988
- Table 2. Number of capital account liberalization reforms
  - All: 70s 38; 80s 25; 90s 100; 2000s 61; 1970-2010 224
  - High income: 70s 15; 80s 7; 90s 23; 2000s 14; 1970-2010 58
  - Upper middle income: 70s 11; 80s 9; 90s 28; 2000s 31; 1970-2010 79
  - Lower middle income: 70s 5; 80s 6; 90s 31; 2000s 12; 1970-2010 54
  - Lower income: 70s 7; 80s 3; 90s 18; 2000s 5; 1970-2010 33
- Table 3. The effect of capital account liberalization on inequality (1970-2010), OLS
  - Gini growth(t-1) coefficient 0.272*** (t-statistic 4.52)
  - Gini growth(t-1) coefficient (alternate) 0.127*** (t-statistic 2.77)
  - Capital account reform (t) coefficient 0.766*** (t-statistic 3.12)
  - Capital account reform (t-1) coefficient 0.143 (t-statistic 0.45)
  - Capital account reform (t-2) coefficient -0.048 (t-statistic -0.16)
  - Current account reform (t) coefficient 0.285 (t-statistic 1.00)
  - Current account reform (t-1) coefficient 0.135 (t-statistic 0.43)
  - Current account reform (t-2) coefficient 0.579* (t-statistic 1.65)
  - Regulation reform (t) coefficient -0.333 (t-statistic -1.05)
  - Regulation reform (t-1) coefficient -0.414 (t-statistic -0.96)
  - Regulation reform (t-2) coefficient -0.059 (t-statistic -0.18)
  - N 2071
  - R^2 0.21
  - Note: T-statistics based on robust clustered standard errors in parenthesis. ***,**, * denote significance at 1 percent, 5 percent and 10 percent, respectively.
  - Note on identification: Capital account reforms are identified as episodes when, for a given country at a given time, the annual change in the Kaopen indicator exceeds by two standard deviations the average annual change over all observations. Current account reforms are identified as episodes when, for a given country at a given time, the annual change in the Quinn and Toyoda current account indicator exceeds by two standard deviations the average annual change over all observations. Regulatory reforms are identified as episodes when, for a given country at a given time, the annual change in the EFW regulatory indicator exceeds by two standard deviations the average annual change over all observations.

*Content unit: _wp15243 - REFERENCES*

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