## 3.1    Three stylized facts about financial openness indices

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### Stylized facts about openness measures
- Fact 1 — Large cross-country dispersion, little time-series variation
  - Cross-sectional dispersion of openness among emerging market countries is very large in any given year.
  - Country dummies explain more than 80% of the total variation in openness in the data for any index.
  - In the dataset range cited, explained-variation measures span from 82.3% (Chinn-Ito) to 86.3% (Wang-Jahan).
  - Implication: empirical estimates that include country fixed effects must exploit cross-sectional variation rather than relying on limited time-series changes in de jure openness.

- Fact 2 — Different measures often yield contradicting time trends
  - Pairwise country-level time-series correlations between different openness measures are frequently low:
    - Even for Wang-Jahan and FKRSU (which tend to agree more), for one-quarter of emerging markets the correlation is below 0.5.
    - In all other pairings, for more than a quarter of countries the correlation is negative.
  - Implication: low consistency over time across standard openness indicators produces measurement uncertainty for time-series identification of policy change effects.

- Fact 3 — Different measures tend to rank countries similarly (cross-section)
  - Cross-sectional correlations of the four openness indicators are high:
    - Correlation coefficients are in the 0.75-0.95 range for all years and all indicator pairs.
  - Implication: while time-series agreement is weak, cross-sectional rankings of de jure openness are broadly consistent across methodologies, enabling cross-country inference.

### Empirical approach and motivation
- Introduced a theory-consistent empirical framework augmenting standard push-pull regressions with interaction terms between financial openness and push/pull factors to capture how sensitivity of capital inflows varies with capital account restrictiveness.
- Rationale:
  - Leverage large cross-sectional dispersion (where most variation resides).
  - Address measurement uncertainty due to low time-series consistency by focusing on cross-sectional comparisons (where measures are consistent).

### Key empirical findings (summary of Figures and Tables)
- General
  - Within R-squared reported in panel regressions is in line with literature; higher institutional quality is associated with larger inflows across openness measures.

- Portfolio equity inflows (Figure 4; Table 1)
  - More open countries are more susceptible to changes in U.S. interest rates.
    - A one-percentage-point higher Fed Funds rate:
      - almost no change for a country at the 25th percentile of openness;
      - a decline in inflows of between 0.1 and 0.3 percent of GDP for a country at the 75th percentile of financial openness (depending on openness measure).
  - Domestic growth: a one-percentage-point higher expected domestic growth may yield about 0.1 percent-of-GDP higher equity inflows for a 75th-percentile country versus a 25th-percentile country (interaction generally not statistically significant at standard levels).
  - Expected domestic returns: mixed evidence — two measures show statistically-significant effects between 0.03 and 0.08 (equity inflows in percent of GDP) for a one-percentage-point higher expected domestic return at the 75th percentile; other measures show virtually no differential effect.
  - No robust evidence that more open countries are systematically more susceptible to equity outflows from higher volatility or stronger U.S. growth.

- Portfolio debt inflows (Figure 5; Table 2)
  - At the 25th percentile of openness, portfolio debt inflows do not respond to changes in expected domestic returns.
  - More open countries show differential effects with point estimates ranging from 0.05 to 0.25 percent of GDP when expected domestic returns are one percentage point higher (interaction significant under two measures).
  - Differential effect of U.S. interest rates is broadly consistent (more open country seeing a 0.1-percent-of-GDP larger inflow decline than more closed) but imprecisely estimated.
  - Differential effects of expected U.S. growth and volatility are mixed across measures.
  - Counter-intuitive result: more closed (open) countries see an increase (decrease) in inflows when domestic growth is higher; interaction term statistically significant under some openness measures.
  - Robustness checks:
    - Results virtually unchanged when using asset-class-specific Wang-Jahan and FKRSU indices, and after outlier treatments (Cook’s distance: dropped observations with Cook distance greater than 4/N; robust regression per Yohai (1987)).

- FDI inflows (Figure 6; Table 3)
  - Most interaction terms are not statistically significant and point estimates vary across measures.
  - Exception: expected domestic growth — depending on openness measure, a 75th-percentile country may receive almost one percentage point of GDP higher FDI inflows when expected domestic growth is one percentage point higher, compared to a 25th-percentile country.

### Robustness, data, and measurement notes
- Data sources and comparisons
  - IIF monthly portfolio debt and portfolio equity capital flows series aggregated to quarterly frequency correlate well with official BOP data in most cases; some mismatches due to revisions not reflected in monthly series.
  - EPFR monthly flows show relatively poor match with official BOP data; analysis relies solely on IIF-produced data.

- Panel regression summaries (selected metrics from Tables)
  - Portfolio equity regressions:
    - Observations: 2041 to 2065 across specifications.
    - Countries: 16.
    - R-squared values: 0.047, 0.058, 0.041, 0.039 across Chinn-Ito, Quinn, FKRSU, Wang-Jahan respectively.
  - Portfolio debt regressions:
    - Observations: 2228 to 2251 across specifications.
    - Countries: 17.
    - R-squared values: 0.025, 0.021, 0.018, 0.020 across Chinn-Ito, Quinn, FKRSU, Wang-Jahan respectively.
  - FDI regressions:
    - Observations: 2163 to 2341 across specifications (country counts vary: 38-44).
    - R-squared values: 0.007, 0.013, 0.011, 0.011 across Chinn-Ito, Quinn, Wang-Jahan, FKRSU respectively.
  - Statistical significance notation in tables: * 20 percent significance, ** 10 percent, *** 5 percent.

### Conclusions and policy-relevant implications
- Main conclusion: cross-country data rarely yields statistically robust confirmation of theoretical predictions about the effects of capital controls; only a few relationships are stable to measurement uncertainty.
- Stable patterns suggest:
  - More financially open countries: greater exposure of portfolio equity inflows to external interest rate environment.
  - Portfolio debt inflows: greater responsiveness to domestic business cycle in more open countries.
  - Overall, evidence is consistent with countries’ ability to dampen capital flow volatility using capital flow management measures, but openness increases susceptibility to some (not all) push and pull factors.
- Methodological implication for policy evaluation:
  - Researchers and policymakers should exploit cross-sectional variation in de jure openness when assessing capital account policies because:
    - Cross-sectional dispersion is the dominant source of variation.
    - De jure openness measures are more consistent across countries than over time.

*Source: 3.1 Three stylized facts about financial openness indices (content unit: wpiea2019194-print-pdf).*

### 3.1    Three stylized facts about financial openness indices

### 3.1    Three stylized facts about financial openness indices

### Fact 1 — Large cross-country dispersion, little time-series variation
- Cross-sectional dispersion of openness among emerging market countries is very large in any given year.
- Country dummies explain more than 80% of the total variation in openness in the data for any index.
- In the dataset range cited, explained-variation measures span from 82.3% (Chinn-Ito) to 86.3% (Wang-Jahan).
- Implication: empirical estimates that include country fixed effects must exploit cross-sectional variation rather than relying on limited time-series changes in de jure openness.

### Fact 2 — Different measures often yield contradicting time trends
- Pairwise country-level time-series correlations between different openness measures are frequently low:
  - Even for Wang-Jahan and FKRSU (which tend to agree more), for one-quarter of emerging markets the correlation is below 0.5.
  - In all other pairings, for more than a quarter of countries the correlation is negative.
- Implication: low consistency over time across standard openness indicators produces measurement uncertainty for time-series identification of policy change effects.

### Fact 3 — Different measures tend to rank countries similarly (cross-section)
- Cross-sectional correlations of the four openness indicators are high:
  - Correlation coefficients are in the 0.75-0.95 range for all years and all indicator pairs.
- Implication: while time-series agreement is weak, cross-sectional rankings of de jure openness are broadly consistent across methodologies, enabling cross-country inference.

### Empirical approach and motivation
- The paper introduces a theory-consistent empirical framework that augments standard push-pull regressions with interaction terms between financial openness and push/pull factors to capture how sensitivity of capital inflows varies with capital account restrictiveness.
- Rationale:
  - Leverage large cross-sectional dispersion (where most variation resides).
  - Address measurement uncertainty due to low time-series consistency by focusing on cross-sectional comparisons (where measures are consistent).

### Key empirical findings (summary of Figures and Tables)
- General:
  - Within R-squared reported in panel regressions is in line with literature; higher institutional quality is associated with larger inflows across openness measures.
- Portfolio equity inflows (Figure 4; Table 1):
  - More open countries are more susceptible to changes in U.S. interest rates.
    - A one-percentage-point higher Fed Funds rate:
      - almost no change for a country at the 25th percentile of openness;
      - a decline in inflows of between 0.1 and 0.3 percent of GDP for a country at the 75th percentile of financial openness (depending on openness measure).
  - Domestic growth: a one-percentage-point higher expected domestic growth may yield about 0.1 percent-of-GDP higher equity inflows for a 75th-percentile country versus a 25th-percentile country (interaction generally not statistically significant at standard levels).
  - Expected domestic returns: mixed evidence — two measures show statistically-significant effects between 0.03 and 0.08 (equity inflows in percent of GDP) for a one-percentage-point higher expected domestic return at the 75th percentile; other measures show virtually no differential effect.
  - No robust evidence that more open countries are systematically more susceptible to equity outflows from higher volatility or stronger U.S. growth.
- Portfolio debt inflows (Figure 5; Table 2):
  - At the 25th percentile of openness, portfolio debt inflows do not respond to changes in expected domestic returns.
  - More open countries show differential effects with point estimates ranging from 0.05 to 0.25 percent of GDP when expected domestic returns are one percentage point higher (interaction significant under two measures).
  - Differential effect of U.S. interest rates is broadly consistent (more open country seeing a 0.1-percent-of-GDP larger inflow decline than more closed) but imprecisely estimated.
  - Differential effects of expected U.S. growth and volatility are mixed across measures.
  - Counter-intuitive result: more closed (open) countries see an increase (decrease) in inflows when domestic growth is higher; interaction term statistically significant under some openness measures. Robustness checks:
    - Results virtually unchanged when using asset-class-specific Wang-Jahan and FKRSU indices, and after outlier treatments (Cook’s distance: dropped observations with Cook distance greater than 4/N; robust regression per Yohai (1987)).
- FDI inflows (Figure 6; Table 3):
  - Most interaction terms are not statistically significant and point estimates vary across measures.
  - Exception: expected domestic growth — depending on openness measure, a 75th-percentile country may receive almost one percentage point of GDP higher FDI inflows when expected domestic growth is one percentage point higher, compared to a 25th-percentile country.

### Robustness, data, and measurement notes
- Data sources and comparisons:
  - IIF monthly portfolio debt and portfolio equity capital flows series aggregated to quarterly frequency correlate well with official BOP data in most cases; some mismatches due to revisions not reflected in monthly series.
  - EPFR monthly flows show relatively poor match with official BOP data; analysis relies solely on IIF-produced data.
- Panel regression summaries (selected metrics from Tables):
  - Portfolio equity regressions:
    - Observations: 2041 to 2065 across specifications.
    - Countries: 16.
    - R-squared values: 0.047, 0.058, 0.041, 0.039 across Chinn-Ito, Quinn, FKRSU, Wang-Jahan respectively.
  - Portfolio debt regressions:
    - Observations: 2228 to 2251 across specifications.
    - Countries: 17.
    - R-squared values: 0.025, 0.021, 0.018, 0.020 across Chinn-Ito, Quinn, FKRSU, Wang-Jahan respectively.
  - FDI regressions:
    - Observations: 2163 to 2341 across specifications (country counts vary: 38-44).
    - R-squared values: 0.007, 0.013, 0.011, 0.011 across Chinn-Ito, Quinn, Wang-Jahan, FKRSU respectively.
- Statistical significance notation in tables: * 20 percent significance, ** 10 percent, *** 5 percent.

### Conclusions and policy-relevant implications
- Main conclusion: cross-country data rarely yields statistically robust confirmation of theoretical predictions about the effects of capital controls; only a few relationships are stable to measurement uncertainty.
- Stable patterns suggest:
  - More financially open countries: greater exposure of portfolio equity inflows to external interest rate environment.
  - Portfolio debt inflows: greater responsiveness to domestic business cycle in more open countries.
  - Overall, evidence is consistent with countries’ ability to dampen capital flow volatility using capital flow management measures, but openness increases susceptibility to some (not all) push and pull factors.
- Methodological implication for policy evaluation:
  - Researchers and policymakers should exploit cross-sectional variation in de jure openness when assessing capital account policies because:
    - Cross-sectional dispersion is the dominant source of variation.
    - De jure openness measures are more consistent across countries than over time.

*Source: 3.1 Three stylized facts about financial openness indices (content unit: wpiea2019194-print-pdf).*

### References

### wpiea2019194-print-pdf - References

### Capital flows, capital controls, and policy analysis
- Calvo, Guillermo A., Leonardo Leiderman, and Carmen M. Reinhart.1993. “Capital Inflows and Real Exchange Rate Appreciation in Latin America: The Role of External Factors.”Staff Papers (International Monetary Fund), 40(1): 108–151.
- Eichengreen, Barry, and Andrew Rose.2014. “Capital Controls in the 21st Century.” Journal of International Money and Finance, 48: 1 – 16.
- Fern ́andez, Andr ́es, Michael W Klein, Alessandro Rebucci, Martin Schindler, and Mart ́ın Uribe.2016. “Capital Control Measures:  A New Dataset.”IMF Economic Review, 64(3): 548–574.
- Fernandez-Arias, Eduardo.1996. “The new wave of private capital inflows: Push or pull?”Journal of Development Economics), 48(2): 389–418.
- Forbes, Kristin.2005. “The Microeconomic Evidence on Capital Controls: No Free Lunch.”NBER Working Paper 11372.
- Ghosh, Atish R., Mahvash S Qureshi, and Naotaka Sugawara.2014. “Regulating Capital Flows at Both Ends : Does it Work?”IMF Working Paper 14/188.
- Gosh, Atish R., Jonathan D. Ostry, and Mahvash S. Qureshi.2018.Taming the Tide of Capital Flows : A Policy Guide.MIT Press.
- Giordani, Paolo E., Michele Ruta, Hans Weisfeld, and Ling Zhu.2017. “Capital flow deflection.”Journal of International Economics, 105: 102 – 118.
- Horn, Sebastian, and Futoshi Narita.forthcoming. “Opening Up: Capital Flows and Financial Sector Dynamics in Low-Income Developing Countries.”IMF Working Paper.
- IMF.2016. “Capital Flows – Review of Experience with the Institutional View.”International Monetary Fund.
- Jeanne, Olivier, and Anton Korinek.2010. “Excessive Volatility in Capital Flows: A Pigouvian Taxation Approach.”American Economic Review, 100(2): 403–07.
- Koepke, Robin.2015. “What Drives Capital Flows to Emerging Markets? A Survey of the Empirical Literature.” University Library of Munich, Germany MPRA Paper 62770.
- Korinek, Anton.2018. “Regulating capital flows to emerging markets: An externality view.”Journal of International Economics, 111: 61 – 80.
- Magud, Nicolas E., Carmen M. Reinhart, and Kenneth S. Rogoff.2018. “Capital Controls: Myth and Reality–A Portfolio Balance Approach.”Annals of Economics and Finance, 19(1): 1–47.
- Nier, Erlend, Tahsin Saadi Sedik, and Tomas Mondino.2014. “Gross Private Capital Flows to Emerging Markets: Can the Global Financial Cycle be Tamed?”IMF Working Paper), , (14/196).
- Giordani, Paolo E., Michele Ruta, Hans Weisfeld, and Ling Zhu.2017. “Capital flow deflection.”Journal of International Economics, 105: 102 – 118.

### Financial openness, integration, and measurement
- Chinn, Menzie D., and Hiro Ito.2008. “A New Measure of Financial Openness.”Journal of Comparative Policy Analysis: Research and Practice, 10(3): 309–322.
- Jahan, Sarwat, and Daili Wang.2016. “Capital Account Openness in Low-income Developing Countries: Evidence from a New Database.”IMF Working Paper 16/252, 1–26.
- Quinn, Dennis.1992. “Measuring International Financial Openness.”paper presented at the 1992 Annual Meetings of the American Political Science Association.
- Quinn, Dennis, and A. Maria Toyoda.2008. “Does Capital Account Liberalization Lead to Growth.”Review of Financial Studies, 21: 1403–49.
- Quinn, Dennis, Martin Schindler, and A. Maria Toyoda.2011. “Assessing Measures of Financial Openness and Integration.”IMF Economic Review, 59(3): 488–522.
- Schindler, Martin.2009. “Measuring Financial Integration: A New Data Set.”IMF Staff Papers, 56(1): 222–238.

### Sovereign debt, reserves, rollover risk, and external positions
- Arellano, Cristina, and Ananth Ramanarayanan.2012. “Default and the Maturity Structure in Sovereign Bonds.”Journal of Political Economy, 120(2): 187–232.
- Bianchi, Javier, Juan Carlos Hatchondo, and Leonardo Martinez.2018. “International Reserves and Rollover Risk.”American Economic Review, 108(9): 2629–70.
- Lane, Philip R., and Gian Maria Milesi-Ferretti.1999. “The External Wealth of Nations: Measures of Foreign Assets and Liabilities for Industrial and Developing Countries.” IMF Working Paper 99/115, 1–57.
- Lane, Philip R., and Gian Maria Milesi-Ferretti.2007. “The external wealth of nations mark II: Revised and extended estimates of foreign assets and liabilities, 19702004.” Journal of International Economics, 73(2): 223 – 250.

### Methodology, robustness, and macro-financial measurement
- Cook, R. Dennis.1977. “Detection of Influential Observation in Linear Regression.”Technometrics, 19(1): 15–18.
- Rey, H ́el`ene.2015. “Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence.”NBER Working Paper 21162.
- Wu, Jing Cynthia, and Fan Dora Xia.2016. “Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound.”Journal of Money, Credit and Banking, 48(2-3): 253–291.
- Yohai, Victor J.1987. “High Breakdown-Point and High Efficiency Robust Estimates for Regression.”The Annals of Statistics, 15: 642–656.
- Cook, R. Dennis.1977. “Detection of Influential Observation in Linear Regression.”Technometrics, 19(1): 15–18.

*References list from wpiea2019194-print-pdf - References*

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_Source: https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019194-print-pdf.pdf_