## 3.1    A Novel Indicator of Capital Openness

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### Key features of the FinOpen index
- TheFinOpenIndex (Li (2026)) covers 193 countries over the period 1996-2022 and takes values in the [0,1] range, with higher values indicating a more liberalized capital account.
- Quantifies the intensity of capital liberalization, updating daily by incorporating changes in CFMs.
- Moves beyond binary measures by quantifying intensity rather than only recording presence/absence of controls, enabling systematic cross-country comparisons over time.
- Distinguishes between controls on nonresidents’ inflows and nonresidents’ outflows, and provides subindexes for FDI, portfolio equity, portfolio debt, and other investments.
- Subindexes provide three components for each flow category: controls on nonresidents’ inflows (NRin), nonresidents’ outflows (NRout), and residents’ outflows (Rout).
- Limitation: as a de jure index, theFinOpenIndex cannot capture different degrees of de facto enforcement of restrictions across countries or over time.

### Construction of composite openness variables (exact formulas)
- Two main explanatory variables for nonresidents’ inflows, each ranging in [0, 1]:
  - FinOpen_{e,NRin} = (FinOpen_{FDI, NRin} + FinOpen_{PortEquity, NRin}) / 2
  - FinOpen_{d,NRin} = (FinOpen_{PortDebt, NRin} + FinOpen_{Loans, NRin}) / 2
- Composite indices capturing overall openness to foreign flows (FinOpen_{eNR} and FinOpen_{dNR}) are computed as the average of the inflow and outflow subindexes.
- Structure permits linking differentiated policy treatment of equity and debt inflows to the composition of external liabilities.

### Advantages and empirical role
- Captures sectoral and policy-intensity distinctions that binary labels miss (examples: advanced economies restricting mainly real estate vs. China and India simplifying FDI approval requirements).
- Positive correlation between openness to nonresidents’ portfolio debt and the foreign share of government debt securities supports index relevance for external liability composition.
- FinOpen subindexes are used to construct measures of:
  - Equity inflow openness (non-residents)
  - Debt inflow openness (non-residents)
  - Analogous measures for nonresidents’ outflows and composite openness (FinOpen_{eNR}, FinOpen_{dNR})

### Data coverage, complements, and limits
- Can be combined with BoP flow definitions mapped to financial account subcomponents.
- Comparison: Bergant, Fernández, Teoh, and Uribe (2026) extract high-frequency de jure restrictions from AREAER using LLMs but do not map controls to specific financial-account subcomponents.
- Dependent-variable data on external liabilities and subcomponents drawn from the “External Wealth of Nations” (EWN) database by Lane and Milesi-Ferretti (2018), covering 212 economies for 1970-2023.
- External liabilities comprise FDI, portfolio equity, debt (portfolio debt, loans, currency, and deposits), and financial derivatives.
- “Total equity” = sum of FDI and portfolio equity relative to total international liabilities.
- Important limitation: EWN does not distinguish between public and private liabilities; sovereign and state-owned enterprise holdings are aggregated with private-sector positions.

### Sample and explanatory variables
- Baseline sample: 118 countries (25 advanced and 93 emerging or developing economies) with requisite variables available for at least one year between 1995 and 2022.
- Excluded 28 offshore financial centers to avoid distortions from tax-driven Phantom FDI.
- Explanatory controls include:
  - Size of the economy: total GDP in trillions of USD, constant 2010 prices
  - Level of development: GDP per capita in thousands of USD, constant 2010 prices
  - Trade openness: sum of imports and exports relative to GDP
  - Importance of natural resources: exports of fuels, metals, and ores as a share of GDP
  - Human capital: Barro and Lee (2013)
  - Financial development: composite index from Svirydzenka (2016)
  - Institutional quality: simple average of six governance indicators from Kaufmann, Kraay, and Mastruzzi (2010)

### Descriptive statistics (averages 1995-2022, preserved exactly)
- Whole sample (118 observations):
  - Total equity: Minimum 0.09, Maximum 0.84, Mean 0.40, Median 0.39, Standard deviation 0.14
  - Institutional quality index: Minimum -1.56, Maximum 1.81, Mean -0.05, Median -0.26, Standard deviation 0.83
  - GDP (constant US$ trillions): Minimum 0.001, Maximum 6.44, Mean 0.49, Median 0.05, Standard deviation 1.75
  - GDP p.c. (constant US$ thousands): Minimum 0.29, Maximum 72.18, Mean 11.70, Median 4.89, Standard deviation 15.70
  - Financial development index: Minimum 0.05, Maximum 0.89, Mean 0.31, Median 0.23, Standard deviation 0.22
  - Natural resources: Minimum 0.00, Maximum 0.98, Mean 0.26, Median 0.14, Standard deviation 0.27
  - Trade openness: Minimum 0.23, Maximum 2.02, Mean 0.76, Median 0.70, Standard deviation 0.34
  - Human capital: Minimum 0.23, Maximum 7.00, Mean 2.84, Median 2.69, Standard deviation 1.66
  - Equity inflow openness (non-residents): Minimum 0.34, Maximum 1.00, Mean 0.84, Median 0.88, Standard deviation 0.14
  - Debt inflow openness (non-residents): Minimum 0.28, Maximum 1.00, Mean 0.81, Median 0.85, Standard deviation 0.18
  - Equity openness wedge: Minimum -0.30, Maximum 0.37, Mean 0.03, Median 0.00, Standard deviation 0.12
- Non-high income countries (93 observations):
  - Total equity: Minimum 0.09, Maximum 0.84, Mean 0.40, Median 0.39, Standard deviation 0.15
  - Institutional quality index: Minimum -1.56, Maximum 1.02, Mean -0.38, Median -0.40, Standard deviation 0.54
  - GDP (constant US$ trillions): Minimum 0.008, Maximum 8.02, Mean 0.23, Median 0.04, Standard deviation 0.87
  - GDP p.c. (constant US$ thousands): Minimum 0.29, Maximum 61.05, Mean 5.89, Median 3.01, Standard deviation 9.33
  - Financial development index: Minimum 0.05, Maximum 0.62, Mean 0.23, Median 0.18, Standard deviation 0.14
  - Natural resources: Minimum 0.00, Maximum 0.98, Mean 0.30, Median 0.17, Standard deviation 0.29
  - Trade openness: Minimum 0.23, Maximum 2.02, Mean 0.75, Median 0.70, Standard deviation 0.34
  - Human capital: Minimum 0.23, Maximum 6.96, Mean 2.34, Median 2.16, Standard deviation 1.45
  - Equity inflow openness (non-residents): Minimum 0.34, Maximum 1.00, Mean 0.81, Median 0.83, Standard deviation 0.14
  - Debt inflow openness (non-residents): Minimum 0.28, Maximum 1.00, Mean 0.77, Median 0.79, Standard deviation 0.18
  - Equity openness wedge: Minimum -0.30, Maximum 0.37, Mean 0.05, Median 0.03, Standard deviation 0.12
- Note: Variables are time-series means for available years during 1995-2022.

### 4.1    Results — Cross-Sectional Estimates (selected coefficients, whole sample)
- Main empirical finding: lower restrictions on equity-based capital flows (and higher restrictions on debt-based flows) are associated with a higher equity share in foreign liabilities.
- Key estimated coefficients (whole sample, Table 2):
  - FinOpen(e,NR) = 0.326* (0.168)
  - FinOpen(d,NR) = -0.463** (0.217)
  - Equity openness wedge = 0.214* (0.111)
  - I(Equity openness wedge > 0) = 0.075*** (0.027)
  - Inst.Quality x Equity openness wedge = 0.445** (0.195)
- Robust correlates of higher equity share:
  - Institutional Quality = 0.103*** (0.028)
  - Financial Development = 0.284*** (0.091)
  - Natural Resources = 0.203*** (0.061)
  - Trade openness = 0.131*** (0.035)
- Subsample evidence:
  - Non-high-income sample: Observations = 93, R-squared up to 0.489; patterns broadly stable though precision declines.
  - Expanded non-high-income sample (Table 4): Observations = 121; similar patterns.
- Disaggregated inflow types (Table 5, whole sample, sub-indices):
  - FinOpen(FDI,NRin) = 0.209* (0.107) — associated with higher Equity Liability Share and higher FDI Liability Share (0.226** (0.108))
  - FinOpen(loans,NRin) = -0.152** (0.069) — associated with lower Equity Liability Share
  - FinOpen(Portequity,NRin) and FinOpen(Portdebt,NRin) show smaller, less robust effects on aggregate equity share
- Interpretation: evidence consistent with policy stances that favor equity inflows (or constrain debt inflows) increasing the equity share of external liabilities; multicollinearity can limit precision when equity and debt controls included jointly.

### 4.1.2    Dynamic effects (local projections)
- Identification challenges: external liability composition is slow-moving and capital account restrictions are persistent with limited within-country time variation.
- Methodology: local projection (Jordà (2005)) on panel data with country and year fixed effects, two lags of dependent and policy variables, and controls: institutional quality, GDP (constant 2010 USD), GDP per capita, financial development, natural resources, trade openness.
- Dynamic results:
  - Cumulative impulse response to a relative liberalization of equity inflows (equity openness wedge) is modest short-run, builds gradually, and peaks after roughly six to eight years.
  - Decomposition: positive response to the overall wedge is primarily driven by tighter restrictions on debt inflows; loosening equity inflow restrictions shows weaker and less precisely estimated effects.
  - By inflow type: tightening of debt-type inflows (portfolio debt and other investment) tends to raise the equity share gradually and persistently; changes in equity-type inflow restrictions (FDI and portfolio equity) have smaller and more delayed responses.
  - Results robust to controls for macroprudential policies and foreign exchange interventions and to an alternative foreign equity share measure from BoP flows.

### 4.1.3    Firm-level evidence and channels
- Data: Orbis Historical Financial Database (August 2022 release), ~12 million firm-year observations across 30 emerging economies, mid-1980s to 2022.
- Firm-level proxy for equity issuance:
  - EquityIssuanceRatio_it = (∆Shareholders’Fundsit − ProfitAfterTaxit) / Shareholders’Fundsi,t−1
- Firm-level regression (Equation (6)) includes firm fixed effects and controls: size, leverage, profitability, age; SEs clustered at country level.
- Key firm-level estimates (Table 6; sample period 1983–2022):
  - ∆ Equity openness wedge = 0.421** (0.197) (column (1))
  - ∆ FinOpen(e, NRin) = 0.045 (0.273) (not significant)
  - ∆ FinOpen(d, NRin) = -0.511* (0.253)
  - FKRSU-based measures generally insignificant
- Firm-level control estimates:
  - Size = 0.063*** (0.007) to 0.073*** (0.008)
  - Leverage = -0.004*** (0.001) to -0.006** (0.003)
  - Profitability = -0.002** (0.001) to -0.004** (0.002)
- Sample and fit: Observations up to 12,894,738; Number of Countries = 30; R-squared ≈ 0.409–0.416
- Interpretation:
  - Easing equity inflow restrictions relative to debt inflows increases firms’ propensity to issue equity; tightening debt inflow openness associated with reduced equity issuance in some specifications, consistent with substitution and prudential channels.
  - Non-listed firms likely capture FDI-type equity financing behavior; firm-level proxy correlates with changes in equity liabilities and FDI at the country-year level.
  - Intensity-based FinOpen index provides the time variation needed to detect firm-level responses; coarser binary indices lack sufficient short-run variation.

### 4.2    The Role of Capital Outflow Measures
- Overview:
  - Foreign outflow controls have been substantially more liberalized than foreign inflow controls across countries and over time; domestic outflow controls remain the most restrictive.
  - Since the mid-1990s, most economies removed restrictions on nonresident capital repatriation; exceptions include China and India.
  - Outflow measures are episodic and often crisis-driven (examples: Greece and Cyprus); episodic, short-lived usage implies limited persistent effects on external liability composition.
- Case evidence:
  - Tranquil periods: holding-period requirements or administrative approvals may discourage new equity investment by signaling repatriation risk.
  - Stress episodes:
    - Argentina (early-2000s crisis and post-2018 tightening): tight outflow restrictions coincided with a sharp increase in the share of equity liabilities (interpreted as existing equity harder to liquidate while debt liabilities declined).
    - Ecuador (2010s): tax on outflows coincided with a decline in the equity share (interpreted as deterring new foreign equity investment).
  - Conclusion: outflow controls can either “trap” existing equity positions or discourage new equity inflows depending on design and circumstances.
- Regression evidence (Table 7: between-estimator controlling outflow openness; whole sample):
  - General finding: outflow openness coefficients typically have expected signs but are generally statistically insignificant; inflow-related measures remain strong and robust.
  - Notable exception: FinOpen(tot,out) = 0.222** (column (2))
  - Selected coefficient patterns (point estimates only, significance noted in source):
    - FinOpen(d,NRin): -0.225* (col 1), -0.286*** (col 2), -0.233** (col 3), -0.264** (col 4)
    - FinOpen(tot,out): 0.222** (col 2)
    - Equity inflow openness wedge: 0.214* (col 1), 0.199* (col 2)
    - Num of large outflow tightenings: generally statistically insignificant across columns
  - Robust covariates across columns:
    - Institutional Quality: 0.103*** (cols 1–4), 0.095*** (col 5), 0.094*** (col 6)
    - GDP (constant 2010 USD, rescaled to trillions): 0.013** (cols 1–4), 0.014** (col 5), 0.014* (col 6)
    - GDP per capita: -0.007*** (all columns)
    - Financial Development: positive and significant (e.g., 0.290*** col 1)
    - Natural Resources and Trade openness: positive and significant across columns
  - Model fit: Observations = 118; R-squared ranges reported (e.g., 0.350 to 0.379 across columns)
- Interpretation:
  - Outflow liberalization may modestly increase equity shares by boosting investor confidence in predictable, transparent capital account management.
  - Crisis-period outflow policies have heterogeneous effects (trap vs. deter).
  - Aggregate impact ambiguous due to heterogeneity in design and timing; episodic use limits persistent compositional effects.
- Policy implication:
  - Baseline conclusions remain robust when accounting for outflow openness: inflow-related CFMs and the inflow openness wedge drive most variation in external liability composition.
  - Outflow measures function primarily as short-term crisis-management tools rather than standing instruments to shift liability composition over the medium-to-long term.

### Data sources, variable definitions, and country samples (selected)
- World Development Indicators, World Bank: percentage of ore, metals and fuels in total exports (1995–2023); openness (1995–2023); other macro controls.
- Financial Development Index Database, IMF (Svirydzenka (2016)): available years 1995–2021.
- Human capital: Barro and Lee (2013), 2021 September Update (available 1995–2015 at 5-year intervals; linear interpolation for intermediate years).
- Institutional quality: average of six Worldwide Governance Indicators (1996–2023).
- Capital flow management (CFM) indices: FinOpen database by Li (2026), distinguishing inflow/outflow, residents/nonresidents, equity/debt.
  - Main explanatory variable: ∆EquityOpennessWedge_ct = ∆CFM_equity, NR-in,ct − ∆CFM_debt, NR-in,ct
- Baseline country sample for macro regressions: 118 countries listed in the chapter (countries marked with * excluded from the non-high income sample following Lane and Milesi-Ferretti (2018)).
- Excluded offshore financial centers as listed in Lane and Milesi-Ferretti (2018).
- Firm-level baseline country sample (30 emerging economies) listed in the chapter.
- Firm-level dependent variable construction, cleaning, and final estimation sample: approximately 12 million firm-year observations; winsorized and restricted per documented rules.

### Robustness and auxiliary findings (selected)
- Firm-level and macro robustness checks confirm:
  - FKRSU and other coarse/binary indices often yield insignificant estimates due to limited time variation.
  - Results robust across alternative institutional measures (ICRG), sample splits (1995–2007; 2009–2021), and specifications excluding valuation effects.
- Representative robustness coefficients (examples from appendix tables):
  - Table A2: FinOpen(e,NR) = 0.122* (0.073); FinOpen(d,NR) = −0.202** (0.097); I(Equity openness wedge > 0) = 0.061** (0.024); Inst.Quality x wedge = 0.210** (0.085)
  - Table A7 (2009–2021): FinOpen(e,NR) = 0.433* (0.247); FinOpen(d,NR) = −0.610*** (0.232); I(Equity openness wedge > 0) = 0.088*** (0.028); Inst.Quality x wedge = 0.686*** (0.194)
- Figures referenced include distributions of wedges and changes, and cumulative IRFs with 68 percent and 90 percent confidence intervals.

_Source: Extracted from the chapter "3.1 A Novel Indicator of Capital Openness" and sections 4.1–4.2 (wpiea2026037-source-pdf)._

### 3.1    A Novel Indicator of Capital Openness

### 3.1    A Novel Indicator of Capital Openness

### Key features of the FinOpen index
- TheFinOpenIndex (Li (2026)) covers 193 countries over the period 1996-2022 and takes values in the [0,1] range, with higher values indicating a more liberalized capital account.
- It quantifies the intensity of capital liberalization, updating daily by incorporating changes in CFMs.
- Moves beyond binary measures by quantifying intensity rather than only recording presence/absence of controls, enabling systematic cross-country comparisons over time.
- Distinguishes between controls on nonresidents’ inflows and nonresidents’ outflows, and provides subindexes for FDI, portfolio equity, portfolio debt, and other investments.
- Subindexes provide three components for each flow category: controls on nonresidents’ inflows (NRin), nonresidents’ outflows (NRout), and residents’ outflows (Rout).
- Limitation noted: similar to other de jure indexes, theFinOpenIndex cannot capture different degrees of de facto enforcement of restrictions across countries or over time.

### Advantages illustrated (as described in source)
- Captures sectoral and policy-intensity distinctions that binary labels miss (example: advanced economies that restrict mainly real estate or screen for national security vs. China and India where approval requirements for new foreign direct capital have been simplified).
- Positive correlation between openness to nonresidents’ portfolio debt and the foreign share of government debt securities highlights the index’s effectiveness in capturing policy intensity relevant for external liability composition.

### Construction of composite openness variables (exact formulas)
- Two main explanatory variables for nonresidents’ inflows, each ranging in [0, 1]:
  - FinOpen_{e,NRin} = (FinOpen_{FDI, NRin} + FinOpen_{PortEquity, NRin}) / 2
  - FinOpen_{d,NRin} = (FinOpen_{PortDebt, NRin} + FinOpen_{Loans, NRin}) / 2
- Composite indices capturing overall openness to foreign flows (FinOpen_{eNR} and FinOpen_{dNR}) are computed as the average of the inflow and outflow subindexes.
- The structure allows linking differentiated policy treatment of equity and debt inflows to the composition of external liabilities.

### Data coverage, complements, and comparisons
- FinOpenindex can be combined with data on external liabilities following BoP flow definitions and mapped to specific financial account subcomponents.
- Comparison noted: Bergant, Fernández, Teoh, and Uribe (2026) extract high-frequency de jure restrictions from IMF’s AREAER using large language models; their index quantifies intensity but does not map controls to specific financial-account subcomponents, making it less suited for linking to external liability composition.
- Methodological details and comparisons with existing indices are available in Li (2026).

### Use in empirical analysis (role and variables)
- TheFinOpen subindexes are used to construct measures of:
  - Equity inflow openness (non-residents)
  - Debt inflow openness (non-residents)
  - Analogous measures for nonresidents’ outflows and composite openness (FinOpen_{eNR}, FinOpen_{dNR})
- These measures are employed to assess how differentiated policies toward equity versus debt inflows translate into differences in countries’ external liability structures.

### Data sources and limits relevant to empirical application
- Dependent variable data on external liabilities and subcomponents drawn from the “External Wealth of Nations” (EWN) database by Lane and Milesi-Ferretti (2018), covering 212 economies for 1970-2023.
- External liabilities comprise FDI, portfolio equity, debt (portfolio debt, loans, currency, and deposits), and financial derivatives.
- “Total equity” is defined as the sum of FDI and portfolio equity relative to total international liabilities.
- Important limitation: EWN does not distinguish between public and private liabilities; sovereign and state-owned enterprise holdings are aggregated with private-sector positions. Results therefore capture composition of aggregate external liabilities rather than isolating public vs. private balance sheets.

### Sample and variable set used with the index
- Baseline sample: 118 countries (25 advanced and 93 emerging or developing economies) for which all key explanatory variables are available for at least one year between 1995 and 2022.
- Excluded 28 offshore financial centers to avoid distortions from tax-driven Phantom FDI.
- Explanatory variables augmenting the specification include:
  - Size of the economy: total GDP in trillions of USD, constant 2010 prices
  - Level of development: GDP per capita in thousands of USD, constant 2010 prices
  - Trade openness: sum of imports and exports relative to GDP
  - Importance of natural resources: exports of fuels, metals, and ores as a share of GDP
  - Human capital: Barro and Lee (2013)
  - Financial development: composite index from Svirydzenka (2016)
  - Institutional quality: simple average of six governance indicators from Kaufmann, Kraay, and Mastruzzi (2010)

### Descriptive statistics (averages 1995-2022, preserved exactly)
- Whole sample (118 observations):
  - Total equity: Minimum 0.09, Maximum 0.84, Mean 0.40, Median 0.39, Standard deviation 0.14
  - Institutional quality index: Minimum -1.56, Maximum 1.81, Mean -0.05, Median -0.26, Standard deviation 0.83
  - GDP (constant US$ trillions): Minimum 0.001, Maximum 6.44, Mean 0.49, Median 0.05, Standard deviation 1.75
  - GDP p.c. (constant US$ thousands): Minimum 0.29, Maximum 72.18, Mean 11.70, Median 4.89, Standard deviation 15.70
  - Financial development index: Minimum 0.05, Maximum 0.89, Mean 0.31, Median 0.23, Standard deviation 0.22
  - Natural resources: Minimum 0.00, Maximum 0.98, Mean 0.26, Median 0.14, Standard deviation 0.27
  - Trade openness: Minimum 0.23, Maximum 2.02, Mean 0.76, Median 0.70, Standard deviation 0.34
  - Human capital: Minimum 0.23, Maximum 7.00, Mean 2.84, Median 2.69, Standard deviation 1.66
  - Equity inflow openness (non-residents): Minimum 0.34, Maximum 1.00, Mean 0.84, Median 0.88, Standard deviation 0.14
  - Debt inflow openness (non-residents): Minimum 0.28, Maximum 1.00, Mean 0.81, Median 0.85, Standard deviation 0.18
  - Equity openness wedge: Minimum -0.30, Maximum 0.37, Mean 0.03, Median 0.00, Standard deviation 0.12
- Non-high income countries (93 observations):
  - Total equity: Minimum 0.09, Maximum 0.84, Mean 0.40, Median 0.39, Standard deviation 0.15
  - Institutional quality index: Minimum -1.56, Maximum 1.02, Mean -0.38, Median -0.40, Standard deviation 0.54
  - GDP (constant US$ trillions): Minimum 0.008, Maximum 8.02, Mean 0.23, Median 0.04, Standard deviation 0.87
  - GDP p.c. (constant US$ thousands): Minimum 0.29, Maximum 61.05, Mean 5.89, Median 3.01, Standard deviation 9.33
  - Financial development index: Minimum 0.05, Maximum 0.62, Mean 0.23, Median 0.18, Standard deviation 0.14
  - Natural resources: Minimum 0.00, Maximum 0.98, Mean 0.30, Median 0.17, Standard deviation 0.29
  - Trade openness: Minimum 0.23, Maximum 2.02, Mean 0.75, Median 0.70, Standard deviation 0.34
  - Human capital: Minimum 0.23, Maximum 6.96, Mean 2.34, Median 2.16, Standard deviation 1.45
  - Equity inflow openness (non-residents): Minimum 0.34, Maximum 1.00, Mean 0.81, Median 0.83, Standard deviation 0.14
  - Debt inflow openness (non-residents): Minimum 0.28, Maximum 1.00, Mean 0.77, Median 0.79, Standard deviation 0.18
  - Equity openness wedge: Minimum -0.30, Maximum 0.37, Mean 0.05, Median 0.03, Standard deviation 0.12
- Notes: Variables are time-series means for available years during 1995-2022. The whole sample consists of 118 observations and the non-high income countries sample consists of 93 observations.

*Source: Extracted from the chapter "3.1 A Novel Indicator of Capital Openness" (wpiea2026037-source-pdf).*

### 4.1    Results

### 4.1    Results

### 4.1.1    Cross-Sectional Estimates
- Main empirical finding: lower restrictions on equity-based capital flows (and higher restrictions on debt-based flows) are associated with a higher equity share in foreign liabilities.
- Key estimated coefficients (whole sample, Table 2):
  - FinOpen(e,NR) = 0.326* (0.168)
  - FinOpen(d,NR) = -0.463** (0.217)
  - Equity openness wedge = 0.214* (0.111)
  - I(Equity openness wedge > 0) = 0.075*** (0.027)
  - Inst.Quality x Equity openness wedge = 0.445** (0.195)
- Robust correlates of higher equity share (Table 2): 
  - Institutional Quality = 0.103*** (0.028) (column (1) baseline)
  - Financial Development = 0.284*** (0.091)
  - Natural Resources = 0.203*** (0.061)
  - Trade openness = 0.131*** (0.035)
- Subsample evidence:
  - Results remain stable when restricting to non-high-income countries (Table 3), though statistical precision declines (Observations = 93, R-squared up to 0.489).
  - Expanding the non-high-income sample (Table 4) yields similar patterns (Observations = 121).
- Disaggregated inflow types (Table 5, whole sample, sub-indices):
  - FinOpen(FDI,NRin) = 0.209* (0.107) — associated with a higher Equity Liability Share and higher FDI Liability Share (0.226** (0.108))
  - FinOpen(loans,NRin) = -0.152** (0.069) — associated with a lower Equity Liability Share
  - FinOpen(Portequity,NRin) and FinOpen(Portdebt,NRin) show smaller, less robust effects on aggregate equity share
- Interpretation and robustness:
  - Evidence consistent with a policy stance that favors equity inflows (or constrains debt inflows) increasing the equity share of external liabilities.
  - Multicollinearity between equity and debt control variables can limit precision when both are included.
  - Use of long-run stock measures (external liability stocks) reduces concerns about relabeling of flows (e.g., debt relabeled as FDI).

### 4.1.2    Estimating Dynamic Effects
- Identification challenges:
  - External liability composition is a slow-moving stock; capital account restrictions are persistent with limited within-country time variation.
  - Median country shows no asymmetric stance, but roughly two-thirds maintain a more restrictive policy toward one instrument, with a slight tilt toward favoring equity; policy stances remain stable over time.
- Methodology:
  - Local projection (LP) approach following Jordà (2005) on panel data to estimate impulse responses of equity share to changes in the equity openness wedge.
  - Regression framework (Equation (3a) and (3b)) includes country fixed effects, year fixed effects, two lags of dependent and policy variables, and controls: institutional quality, GDP (constant 2010 USD), GDP per capita, financial development, natural resources, trade openness.
  - Decomposition (Equation (4)) estimates separate dynamic responses to ∆FinOpen(e,NRin) and ∆FinOpen(d,NRin).
- Dynamic results:
  - Cumulative impulse response to a relative liberalization of equity inflows (the equity openness wedge) is modest short-run but builds gradually and peaks after roughly six to eight years.
  - Decomposition shows the positive response to the overall wedge is primarily driven by tighter restrictions on debt inflows; loosening equity inflow restrictions shows weaker and less precisely estimated effects.
  - Panel-level decomposition by inflow type (FDI, portfolio equity, portfolio debt, loans) shows:
    - Tightening of debt-type inflows (portfolio debt and other investment) tends to raise the equity share gradually and persistently.
    - Changes in equity-type inflow restrictions (FDI and portfolio equity) have smaller and more delayed responses, consistent with FDI’s long-term strategic nature.
  - Results robust to controlling for macroprudential policies and foreign exchange interventions and to an alternative foreign equity share measure constructed from BoP flows.

### 4.1.3    Firm-Level Evidence and Possible Channels
- Data and proxy:
  - Firm-level panel from Orbis Historical Financial Database (August 2022 release), covering ~12 million firm-year observations across 30 emerging economies, mid-1980s to 2022.
  - Proxy for equity issuance (Equation (5)): EquityIssuanceRatioit = (∆Shareholders’Fundsit − ProfitAfterTaxit) / Shareholders’Fundsi,t−1
  - Aggregation uses median for country–year binned plots; main regressions use firm-level data with firm fixed effects.
- Firm-level regression (Equation (6)) specification:
  - Dependent variable: EquityIssuance (firm-level)
  - Regressor of interest: ∆CFM (change in capital flow management measures; wedge or components)
  - Controls: firm fixed effects, size, leverage, profitability, age; standard errors clustered at country level.
- Key firm-level estimates (Table 6; sample period 1983–2022):
  - ∆ Equity openness wedge = 0.421** (0.197) (column (1))
  - ∆ FinOpen(e, NRin) = 0.045 (0.273) (not significant)
  - ∆ FinOpen(d, NRin) = -0.511* (0.253) — tightening debt inflow openness reduces equity issuance at the firm level (column (3))
  - FKRSU-based measures (columns (5)–(6)) generally insignificant, illustrating limited time variation in coarse/binary openness indices
  - Firm-level control estimates:
    - Size = 0.063*** (0.007) to 0.073*** (0.008)
    - Leverage = -0.004*** (0.001) to -0.006** (0.003)
    - Profitability = -0.002** (0.001) to -0.004** (0.002)
    - Age effects small or mixed
  - Sample size and fit: Observations up to 12,894,738; Number of Countries = 30; R-squared ≈ 0.409–0.416
- Interpretation of micro evidence:
  - Easing equity inflow restrictions relative to debt inflows increases firms’ propensity to issue equity; tightening debt inflow openness is associated with reduced equity issuance in some specifications, consistent with substitution and prudential channels.
  - Non-listed firms (baseline focus) likely capture FDI-type equity financing behavior; firm-level proxy correlates with changes in equity liabilities and FDI at the country-year level (Figure 6).
  - Rich, intensity-based FinOpen index provides the time variation needed to detect firm-level responses; coarser binary indices (Fernández et al. (2016) / FKRSU) lack sufficient short-run variation for identification in the micro panel.
- Complementary evidence and mechanisms:
  - Firm-level results align with aggregate patterns: debt-targeted controls can trigger substitution toward equity financing.
  - Recent granular studies (e.g., Fabiani, Fernández, and Gupta (2025)) document that tightening nonresident purchases of domestic corporate bonds reduces bond issuance, especially among riskier firms, with substitution by more productive firms toward alternative funding — consistent with a prudential channel lowering leverage.
  - Overall, capital flow management measures influence both the volume and composition of external financing: debt restrictions reduce leverage and debt-based external financing; equity liberalizations operate over a longer horizon and influence steady-state composition.

*Italic: Source: wpiea2026037-source-pdf - 4.1 Results*

### 4.2    The Role of Capital Outflow Measures

### 4.2    The Role of Capital Outflow Measures

### Overview
- Foreign outflow controls have been substantially more liberalized than foreign inflow controls across countries and over time, while domestic outflow controls remain the most restrictive category.
- Since the mid-1990s, most economies have removed restrictions on nonresident capital repatriation, leaving foreign outflows largely unrestricted; a few countries (e.g., China and India) maintain relatively permanent foreign outflow controls.
- Outflow measures are typically episodic and introduced in response to crises (examples: Greece and Cyprus), suggesting they are used primarily as short-term crisis-management instruments rather than systematic policy tools.
- Because outflow measures are infrequent and short-lived, they are unlikely to generate persistent effects on the overall composition of external liabilities.

### Evidence from tranquil vs. stress episodes (country cases)
- Tranquil periods:
  - Restrictions such as holding-period requirements or administrative approvals may discourage new equity investment because foreign investors perceive higher risk of delayed or restricted repatriation.
- Stress/crisis episodes:
  - Argentina (early-2000s crisis and post-2018 tightening): introduction of tight outflow restrictions coincided with a sharp increase in the share of equity liabilities—interpreted as existing equity being harder to liquidate or repatriate, while debt liabilities declined.
  - Ecuador (2010s): introduction of a tax on outflows coincided with a decline in the equity share—interpreted as deterring new foreign equity investment.
- Conclusion from cases: outflow controls can either “trap” existing equity positions or discourage new equity inflows depending on design and circumstances.

### Regression evidence (Table 7: between-estimator controlling outflow openness; whole sample)
- General finding: when measures of outflow openness are included alongside baseline specifications, estimated outflow coefficients typically have expected signs but are generally statistically insignificant; inflow-related measures remain strong and robust.
- Notable exception:
  - FinOpen(tot,out) = 0.222** (column (2)) — positive and statistically significant (two stars). Authors note this may reflect data limitations in IMF AREAER reporting where outflow details for nonresidents are less precisely captured, and residents’ outflow controls may proxy for nonresidents’ outflows in aggregate measures.
- Selected coefficient estimates (point estimate with significance as in the table and standard errors omitted here for brevity of presentation):
  - FinOpen(e,NRin): 0.153 (col 1), 0.071 (col 2), 0.164 (col 3), 0.056 (col 4) — generally not significant.
  - FinOpen(d,NRin): -0.225* (col 1), -0.286*** (col 2), -0.233** (col 3), -0.264** (col 4) — negative, several specifications statistically significant.
  - FinOpen(tot,NRout): 0.039 (col 2) — not significant.
  - FinOpen(tot,out): 0.222** (col 2) — positive and statistically significant.
  - FinOpen(e,NRout): 0.210 (col 2) — not significant.
  - FinOpen(d,NRout): -0.323 (col 2) — not significant.
  - FinOpen(e,out): 0.137 (col 5) — not significant.
  - FinOpen(d,out): -0.054 (col 5) — not significant.
  - Equity inflow openness wedge: 0.214* (col 1), 0.199* (col 2) — positive, weakly significant.
  - Equity NR outflow openness wedge: 0.069 (col 3) — not significant.
  - Equity outflow openness wedge: 0.150 (col 5) — not significant.
  - Num of large NR outflow tightenings: 0.004 (col 1), -0.019 (col 2), 0.000 (col 3) — all statistically insignificant.
  - Num of large outflow tightenings: -0.004 (col 4), -0.012 (col 5), -0.020 (col 6) — all statistically insignificant.
- Robust covariates with consistent significance across columns:
  - Institutional Quality: 0.103*** (cols 1–4), 0.095*** (col 5), 0.094*** (col 6).
  - GDP (constant 2010 USD, rescaled to trillions): 0.013** (cols 1–4), 0.014** (col 5), 0.014* (col 6).
  - GDP per capita: -0.007*** (all columns).
  - Financial Development: positive and significant across columns (e.g., 0.290*** col 1; 0.283*** col 6).
  - Natural Resources: positive and significant across columns (e.g., 0.201*** col 1; 0.198*** col 6).
  - Trade openness: positive and significant across columns (e.g., 0.129*** col 1; 0.122*** col 6).
- Model size and fit:
  - Observations: 118 (all columns).
  - R-squared: 0.350 (col 1), 0.378 (col 2), 0.355 (col 3), 0.379 (col 4), 0.349 (col 5), 0.359 (col 6).
- Authors’ interpretation of regression evidence:
  - Outflow openness shows at best a modest positive association with the equity share in aggregate measures; however, the majority of outflow-related coefficients and the history of large outflow tightening episodes are statistically insignificant.
  - The insignificance of tightening-episode counts suggests investors’ portfolio decisions respond mainly to current market and policy conditions rather than to limited, past reputational effects.

### Interpretation and mechanisms
- Outflow liberalization may modestly increase equity shares by raising investor confidence in predictable and transparent capital account management.
- During crises, outflow policies have heterogeneous effects:
  - They can lock in existing equity positions (raising measured equity shares if debt liabilities fall or if equity cannot be repatriated).
  - They can deter new equity investment (lowering equity shares).
- Aggregate impact of outflow measures is ambiguous due to heterogeneity in design and timing; episodic use limits persistent compositional effects.

### Relation to baseline conclusions and policy implications
- Baseline conclusions remain robust once outflow openness is accounted for: inflow-related measures continue to drive most of the variation in countries’ external liability composition.
- Policy implication (as discussed in the broader chapter conclusions): while inflow-side CFMs (capital flow management measures) and the inflow openness wedge exert substantial influence, outflow measures generally play a secondary, crisis-management role and do not systematically alter the composition of external liabilities over the medium-to-long term.
- Practical takeaway: outflow controls are unlikely to be effective as a standing instrument to shift liability composition; they are better understood as short-term tools whose compositional effects depend sensitively on design and context.

*Source: wpiea2026037-source-pdf - 4.2    The Role of Capital Outflow Measures*

### 2023.   Rescaled  to  thousands  in  the  regressions  to  make  results  more  legible.   Source:

### wpiea2026037-source-pdf - 2023.   Rescaled  to  thousands  in  the  regressions  to  make  results  more  legible.   Source:

### Data sources and variable definitions
- World Development Indicators, World Bank, https://data.worldbank.org/ used for: percentage of ore, metals and fuels in total exports (1995–2023); sum of imports and exports divided by GDP (openness) (1995–2023); other country macro controls.
- Financial Development Index Database, IMF, https://legacydata.imf.org/?sk=f8032e80-b36c-43b1-ac26-493c5b1cd33b used for Overall index of financial development (Svirydzenka (2016)) for available years 1995–2021.
- Human capital proxied by average years of secondary schooling for population aged 15-64 from Barro and Lee (2013) (2021 September Update), available for all years between 1995 and 2015 at 5-year intervals; intermediate years linearly interpolated.
- Institutional quality index constructed as average of six indicators (voice and accountability; political stability and absence of violence; government effectiveness; regulatory quality; rule of law; control of corruption) from Kaufmann et al. (2010) for available years between 1996 and 2023. Source: Worldwide Governance Indicators, www.govindicators.org.
- Capital flow management (CFM) indices from the FinOpen database by Li (2026), distinguishing inflow/outflow, residents/nonresidents, equity/debt. Main explanatory variable:
  - ∆EquityOpennessWedge_ct = ∆CFM_equity, NR-in,ct − ∆CFM_debt, NR-in,ct
  - Positive values indicate relatively greater liberalization of equity inflows.
- Alternative subcomponent indicators for robustness from Fern ández et al. (2016) (available 1995–2019) using eqi and dii for equity and boi, mmi, cii, cci, and fci for debt inflow openness.
- Macroprudential policies proxied by sum of policy action indicators of all 17 instruments from Alam et al. (2025). FXI proxied as "Total FXI proxied in percentage points of 3-year MA GDP" from Adler et al. (2025).

### Country samples and exclusions
- Baseline country sample for macro regressions: 118 countries listed explicitly (Albania, Algeria, Argentina, Armenia, Australia*, Austria*, Bangladesh, Belize, Benin, Bolivia, Botswana, Brazil, Brunei Darussalam, Bulgaria, Burundi, Cambodia, Cameroon, Canada*, Central African Rep., Chile, China, Colombia, Congo, Costa Rica, Cˆote d’Ivoire, Croatia, Czech Republic*, Denmark*, Dominican Republic, Ecuador, Egypt, El Salvador, Estonia*, Eswatini, Fiji, Finland*, France*, Gabon, Gambia, Germany*, Ghana, Greece*, Guatemala, Guyana, Haiti, Honduras, Hungary, Iceland*, India, Indonesia, Iran, Israel*, Italy*, Jamaica, Japan*, Jordan, Kazakhstan, Kenya, Korea*, Kuwait, Kyrgyz Republic, Lao People’s Dem.Rep, Latvia*, Lesotho, Libya, Lithuania*, Malaysia, Maldives, Mali, Mauritania, Mexico, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, New Zealand*, Nicaragua, Niger, Norway*, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal*, Qatar, Romania, Russia, Rwanda, Saudi Arabia, Senegal, Sierra Leone, Slovak Republic*, Slovenia*, South Africa, Spain*, Sri Lanka, Sudan, Sweden*, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Togo, Tonga, Tunisia, Turkey, Uganda, Ukraine, United Arab Emirates, United States*, Uruguay, Vietnam, Yemen, Zambia).
- Countries marked with * are excluded from the non-high income sample (classification follows Appendix 1 in Lane and Milesi-Ferretti (2018)).
- Excluded offshore financial centers (as listed in Lane and Milesi-Ferretti (2018)): Andorra, Bahamas, Bahrain, Barbados, Belgium, Bermuda, British Virgin Islands, Cayman Islands, Macao (S.A.R. of China), Curacao, Cyprus, Gibraltar, Guernsey, Hong Kong (S.A.R. of China), Ireland, Isle of Man, Jersey, Luxembourg, Malta, Mauritius, Netherlands, Netherlands Antilles, Panama, San Marino, Singapore, Switzerland, Turks and Caicos, United Kingdom.

### Firm-level data, dependent variable, controls, and sample
- Source: Orbis database (Bureau van Dijk), Orbis Historical Financial Database (August 2022 release); firm-level datasets compiled and cleaned by Díez et al. (2021).
- Coverage: mid-1980s to 2022; final sample comprises approximately 12 million firm-year observations across 30 emerging economies.
- Baseline firm-level country sample (30 emerging economies): Algeria, Argentina, Azerbaijan, Brazil, Bulgaria, Cambodia, Chile, China, Colombia, Egypt, Georgia, Ghana, India, Indonesia, Iran, Kazakhstan, Kenya, Malaysia, Mexico, Namibia, Nigeria, Philippines, Poland, Romania, Saudi Arabia, Thailand, Turkey, United Arab Emirates, Vietnam, and South Africa.
- Dependent variable: Equity issuance ratio constructed following McLean and Zhao (2014):
  - EquityIssuanceRatio_it = (∆Shareholders’Funds_it − ProfitAfterTax_it) / Shareholders’Funds_{i,t−1}
  - Observations winsorized at 1st and 99th percentiles; extreme values exceeding ±2 are excluded; dependent variable restricted to range [−2,2].
- Firm-level controls: firm fixed effects; firm size (log of total assets); leverage (ratio of total liabilities to total assets); profitability (net income over total assets); firm age (years since incorporation).
- Outlier treatment and cleaning: firm-level ratios winsorized at 1st and 99th percentiles within each country-year; observations with leverage or profitability outside [0,1] are dropped; firms with missing lagged equity omitted.
- Final estimation sample after filters: approximately 12 million firm-year observations.

### Key regression findings (firm-level Table A1 highlights)
- Sample period: 1983–2022; includes listed firms.
- Column summary (selected coefficients with standard errors in parentheses):
  - ∆ Equity openness wedge: 0.396** (0.190) in (1); 0.400** (0.194) in (2).
  - ∆ CFMe_NRin: 0.035 (0.265) in (3); 0.043 (0.279) in (4).
  - ∆ CFMd_NRin: −0.481* (0.244) in (3); −0.489* (0.254) in (4).
  - ∆ FKRSU openness wedge: 0.022 (0.032) in (5).
  - Size: coefficients range 0.062*** to 0.072*** with SEs (0.005) to (0.008).
  - Leverage: −0.004*** (0.001) to −0.006** (0.003).
  - Profitability: −0.002** (0.001) to −0.004** (0.002).
  - Age: coefficients include −0.007*** (0.002) and non-significant values.
- Observations: 13,116,290 in some specifications; 12,930,792; 10,062,681 in others.
- Number of countries: 30.
- R-squared: ranges 0.404 to 0.414 across reported specifications.
- Notes: Robust standard errors clustered at country level. Significance: *** p < 0.01, ** p < 0.05, * p < 0.1.

### Key cross-country/regression findings (selected from robustness tables)
- Table A2 (excluding valuation effects, between-estimator, 118 observations):
  - FinOpen(e,NR): 0.122* (0.073).
  - FinOpen(d,NR): −0.202** (0.097).
  - Equity openness wedge: 0.085* (0.047) in column (1); −0.011 (0.056) in column (2).
  - Indicator I(Equity openness wedge > 0): 0.061** (0.024).
  - Inst.Quality x Equity openness wedge: 0.210** (0.085).
  - Institutional Quality: coefficients ~0.076*** to 0.101*** with SEs ~0.024–0.025.
  - Financial Development: ~0.284*** to 0.314*** (SEs ~0.083–0.089).
  - Natural Resources: ~0.185*** to 0.194*** (SEs ~0.054–0.057).
  - Trade openness: ~0.092*** to 0.103*** (SEs ~0.031–0.032).
  - GDP (constant 2010 USD): ~0.011** to 0.012** (SEs ~0.005–0.006).
  - GDP per capita: −0.006*** (0.001) across columns.
  - R-squared: ranges 0.354 to 0.383.
- Table A3 (FKRSU index, 78 observations):
  - FKRSU(e,NR): 0.037 (0.058).
  - FKRSU(d,NR): −0.066 (0.075).
  - FKRSU equity openness wedge: 0.049 (0.056) and 0.039 (0.077).
  - Institutional Quality: 0.112*** to 0.121*** (SEs ~0.035–0.036).
  - Financial Development: 0.240** to 0.281*** (SEs ~0.103–0.109).
  - Natural Resources: 0.268*** to 0.276*** (SEs ~0.077–0.079).
  - R-squared: 0.323 to 0.331.
- Table A4 (institutional quality decomposition, 118 observations):
  - Equity openness wedge coefficients not uniformly significant across decompositions (examples: −0.011 (0.128), 0.042 (0.162), 0.103 (0.132), 0.085 (0.146)).
  - Interaction terms: Control corruption x wedge: 0.395* (0.216); Political stability x wedge: 0.391* (0.217); Voice Accountability x wedge: 0.356* (0.185); other interactions not significant.
  - Individual institutional indicators significant: Control corruption 0.067** (0.026); Government effectiveness 0.118*** (0.034); Political stability 0.053** (0.026); Rule of law 0.057** (0.026); Regulatory quality 0.112*** (0.028); Voice Accountability 0.006 (0.020).
  - Financial Development coefficients range (e.g., 0.347*** (0.089) to 0.411*** (0.083)).
  - Trade openness: coefficients ~0.119*** to 0.150*** (SEs ~0.035–0.040).
  - R-squared: ranges 0.311 to 0.400 across specifications.
- Table A5 (ICRG political risk, 102 observations):
  - FinOpen(e,NR): 0.203 (0.176).
  - FinOpen(d,NR): −0.338 (0.216).
  - FinOpen(d,NRin): −0.206* (0.113).
  - Equity openness wedge: 0.200* (0.109) in one column; −0.117 (0.117) in another.
  - I(Equity openness wedge > 0): 0.059** (0.028).
  - Inst.Quality x Equity openness wedge: 0.648*** (0.177).
  - ICRG political risk: 0.005** to 0.007*** (SEs ~0.002).
  - Financial Development: 0.288*** to 0.340*** (SEs ~0.076–0.087).
  - Natural Resources: 0.144*** to 0.170*** (SEs ~0.043–0.052).
  - R-squared up to 0.463 in column (5).
- Table A6 (sample period 1995–2007, 137 observations):
  - FinOpen(e,NR): 0.454** (0.195).
  - FinOpen(d,NR): −0.519** (0.225).
  - Equity openness wedge: 0.231** (0.116) in one column; −0.142 (0.152) in another.
  - Inst.Quality x Equity openness wedge: 0.744*** (0.199).
  - Financial Development: 0.372*** to 0.428*** (SEs ~0.099–0.106).
  - Trade openness: ~0.120*** to 0.125*** (SEs ~0.039–0.041).
  - R-squared up to 0.361.
- Table A7 (sample period 2009–2021, 140 observations):
  - FinOpen(e,NR): 0.433* (0.247).
  - FinOpen(d,NR): −0.610*** (0.232).
  - FinOpen(e,NRin): 0.284* (0.168).
  - FinOpen(d,NRin): −0.342*** (0.111).
  - Equity openness wedge: 0.330*** (0.113) in one column; 0.046 (0.125) in another.
  - I(Equity openness wedge > 0): 0.088*** (0.028).
  - Inst.Quality x Equity openness wedge: 0.686*** (0.194).
  - Financial Development: 0.226** to 0.245** (SEs ~0.096).
  - Natural Resources: 0.177*** to 0.193*** (SEs ~0.052–0.057).
  - Trade openness: 0.118*** to 0.136*** (SEs ~0.039–0.047).
  - R-squared up to 0.291.

### Robustness, figures, and additional notes
- Firm-level specifications include winsorization at 1st and 99th percentiles, dropping implausible leverage/profitability outside [0,1], restricting dependent variable to [−2,2], and omitting firms with missing lagged equity.
- Figures referenced:
  - Figure A1: Distribution of the equity inflow openness wedge.
  - Figure A2: Distribution of the change in the equity inflow openness wedge.
  - Figure A3: Cumulative IRF of Equity Share to Capital Openness (note: shaded areas denote 68 percent (dark) and 90 percent (light) confidence intervals; country-clustered standard errors; macroprudential policies and foreign exchange interventions included as controls with two lags).
  - Figure A4: Distribution of reforms - equity vs. debt inflow restrictions.
  - Figure A5: Distribution of reforms - FinOpen vs. FKRSU.
  - Figure A6: Equity liability share - with and without valuation effects.
  - Figure A7: Equity liability share across countries - with and without valuation effects.
- Additional tables provide robustness checks across indices (FinOpen, FKRSU), alternative political/institutional measures (ICRG), sample period splits (1995–2007; 2009–2021), and decomposition of institutional quality components.
- Standard error and significance conventions reported: Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

*Source: wpiea2026037-source-pdf - 2023.   Rescaled  to  thousands  in  the  regressions  to  make  results  more  legible.   Source:*

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*Tilting the Balance Towards Equity: Capital Controls and the Structure of External Liabilities — Working Paper No. WP/2026/037*

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_Source: https://www.imf.org/-/media/files/publications/wp/2026/english/wpiea2026037-source-pdf.pdf_