## Chapter 3 of the October 2015 Global Financial Stability Report

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### I. Introduction and research questions
- Corporate debt of nonfinancial EM firms increased from about US$4 trillion in 2004 to well over US$18 trillion in 2014.
- The EM corporate debt-to-GDP ratio rose by 26 percentage points over the same period.
- Central research question: Are more accommodative global financial conditions associated with higher EM corporate leverage growth?
- Secondary empirical questions:
  - Role of country-specific characteristics such as financial openness and the exchange rate regime.
  - Through which channels do global financial conditions influence EM leverage growth (e.g., domestic interest rates, relaxation of borrowing constraints)?

### II. Conceptual channels
- Two broad transmission channels emphasized:
  - Capital flows and policy reaction channel: advanced-economy monetary policy loosening → greater EM capital inflows → EM central bank rate cuts to mitigate appreciation → lower domestic rates stimulate corporate borrowing.
  - Financial-frictions / borrowing-constraints channel: accommodative global conditions raise collateral values and relax borrowing constraints (financial accelerator), disproportionately benefiting firms more dependent on external finance (SMEs, firms with limited collateral).

### III. Data coverage and identification strategy
- Firm-level dataset and sample:
  - ORBIS database; about 400,000 nonfinancial EM firms over 2004–2013.
  - More than 1.3 million firm-year observations (unbalanced panel).
  - Focus on private EM non-financial corporations with total assets in excess of $1 million.
  - About 60 percent of the sample covers SMEs.
  - All variables winsorized at 2.5 percent.
- Key variables and definitions:
  - Baseline leverage: total (non-equity) liabilities-to-total assets ratio, TLTA.
  - Alternative leverage: total liabilities-to-total equity, total assets-to-total equity; cash-netted variants; long-term debt limited by ORBIS availability.
  - Firm controls: Size (log sales), Profitability (return on assets), Tangibility (net PPE / total assets).
  - Sector financial dependence: updated Rajan and Zingales (1998) index (Tong and Wei 2011 update).
  - Country controls: ICRG Economic and Financial Risk Ratings; domestic credit to private sector / GDP; Chinn and Ito (2006) capital account openness; Reinhart and Rogoff (2004) exchange rate regime inverted for flexibility; monetary policy synchronization (Laeven and Tong 2012).
- Global financial conditions proxies:
  - Primary: U.S. shadow rate (Krippner 2014) entered as its inverse (shadow rate × -1) so positive = looser monetary conditions.
  - Complementary: global shadow rate (first principal component of four central banks’ shadow rates), inverse Federal funds rate, Treasury rates, U.S. monetary policy shocks (Gertler and Karadi 2015, inverted so positive = looser), inverse VIX.
- Identification strategy:
  - Treat global monetary conditions as exogenous to any individual EM firm.
  - Interact inverse shadow rate with sector financial dependence and with country traits (financial openness, exchange rate rigidity) to capture heterogeneous effects and isolate mechanism from other global factors.

### IV. Key empirical findings
- Baseline association:
  - Coefficient on inverse U.S. shadow rate (Table 2, Col 1): 0.088 (positive and statistically significant).
  - Interpretation: a 1 percentage point increase in the (inverse) U.S. shadow rate (looser U.S. monetary conditions) corresponds to an increase in EM leverage growth of 9 basis points per year.
  - Sample average leverage growth: 35 basis points per year.
- Heterogeneity by financial dependence and firm traits:
  - Interaction (inverse U.S. shadow rate × sector financial dependence) (Table 2, Col 2): 0.039 (statistically significant).
  - A one standard deviation increase in inverse U.S. shadow rate associated with about 5 basis points greater leverage growth for firms at the 75th percentile of financial dependence versus firms at the 25th percentile.
  - SMEs and low-tangibility firms disproportionately increase leverage amid accommodative U.S. monetary conditions.
  - SME definition used: operating revenues, total assets, and employees below €10 ($13) million, €20 ($26) million, and 150, respectively.
- Country- and sector-level interactions:
  - Impact of U.S. monetary policy on EM leverage growth is greater for sectors more dependent on external funding in financially open EMs with relatively more rigid exchange rate regimes.
  - Interaction in financially open + rigid exchange rate EMs (Table 4, Col 6): 0.072 (statistically significant at the 1 percent level).
  - With country-time and sector-time dummies (Table 2, Col 5) interaction declines to 0.017 but remains statistically significant at 5 percent.
- Transmission channels inferred:
  - Evidence consistent with two channels: influence on domestic interest rates and relaxation of corporate borrowing constraints.

### V. Contributions relative to existing literature
- Considers total debt (bond- and bank-based) rather than primarily bond issuance.
- Includes SMEs and private (non-listed) firms, providing broader coverage beyond listed firms.
- Identifies global financial conditions as a quantitatively important determinant of capital structure for small, financially integrated emerging or advanced economies.
- Provides novel empirical evidence that financial frictions matter in the transmission of monetary policy to the non-financial corporate sector across EMs.

### VI. Robustness and checks
- Alternative global financial condition measures (all yielding qualitatively similar results):
  - Inverse global shadow rate, inverse Federal funds rate, Treasury rates of various maturities, U.S. monetary policy shocks (Gertler and Karadi 2015), inverse VIX.
  - Inverse Federal funds rate coefficient reported: 0.028 (Table 6).
  - Inverse global shadow rate interactions: e.g., inverse global shadow rate × financial dependence up to 0.134 (Table 5, selected columns) and lower in other columns; inverse shadow rate coefficients in Table 9: 0.0561 to 0.0676.
- Alternative proxies for financial constraints:
  - SME dummy: inverse shadow rate × SME positive and significant (SME interaction up to 0.0665 in some columns).
  - Low tangibility (bottom tertile) interactions: inverse shadow rate × TAN up to 0.0794 in some specifications.
  - Triple interactions (SME × low tangibility × inverse shadow rate) show even larger leverage responses (e.g., 0.0683 to 0.0886 in reported columns).
- Alternative leverage measures and firm fundamentals:
  - Interaction remains significant across TLTE, TATE, NTLTA, NTLTE, NTATE variants (Table 16); example TLTE interaction = 0.00693 (SE 0.00190).
  - Replacing sales with total assets and including median industry leverage retains significance (Table 15).
- Clustering and inference:
  - Baseline clustered by sector; two-way clustering by sector and time leaves interaction statistically significant (Table 13).
  - Alternative clustering schemes (Table 14) and Driscoll-Kraay standard errors (reported as available) preserve statistical significance.
- Leave-one-out and sample robustness:
  - Excluding each country one-by-one (including China) leaves baseline interaction statistically significant at the 1 percent level.
  - Excluding sectors one-by-one robust except removal of general contractors (construction) lowers statistical significance notably.

### VII. Selected quantitative details and summary statistics
- Sample counts and winsorization:
  - About 400,000 nonfinancial EM firms; more than 1.3 million firm-year observations.
  - All variables winsorized at 2.5 percent.
- Table 1 key summary statistics (exact values as reported):
  - Leverage:
    - Observations: 3,996,138
    - Mean: 0.58
    - Median: 0.59
    - Standard deviation: 0.30
    - First quartile: 0.34
    - Third quartile: 0.81
    - Minimum: 0.01
    - Maximum: 1.41
  - Sales:
    - Observations: 3,210,832
    - Mean: 15.43
    - Median: 15.41
    - Standard deviation: 1.47
    - First quartile: 14.56
    - Third quartile: 16.35
    - Minimum: 10.53
    - Maximum: 19.38
  - Profitability:
    - Observations: 3,902,401
    - Mean: 0.08
    - Median: 0.03
    - Standard deviation: 0.16
    - First quartile: 0.00
    - Third quartile: 0.11
    - Minimum: -0.28
    - Maximum: 0.85
  - Tangibility:
    - Observations: 3,675,210
    - Mean: 0.32
    - Median: 0.27
    - Standard deviation: 0.27
    - First quartile: 0.08
    - Third quartile: 0.51
    - Minimum: 0.00
    - Maximum: 0.97
  - Financial dependence:
    - Observations: 56
    - Mean: 0.14
    - Median: 0.06
    - Standard deviation: 0.67
    - First quartile: -0.08
    - Third quartile: 0.30
    - Minimum: -2.19
    - Maximum: 3.84
  - Macroeconomic conditions (ICRG):
    - Observations: 196
    - Mean: 40.50
    - Median: 42.48
    - Standard deviation: 4.46
    - First quartile: 36.88
    - Third quartile: 44.17
    - Minimum: 27.69
    - Maximum: 45.71
- Shadow-rate sample summary (inverse U.S. shadow rate, 10 observations reported):
  - Mean: 0.48
  - Median: 0.80
  - Standard deviation: 3.35
  - First quartile: -1.23
  - Third quartile: 2.87
  - Minimum: -5.23
  - Maximum: 4.58
- Selected regression coefficients (reported magnitudes and statistical significance in main tables):
  - Baseline inverse U.S. shadow rate coefficient: 0.088 (Table 2, Col 1).
  - Interaction (inverse U.S. shadow rate × sector financial dependence): 0.039 (Table 2, Col 2); 0.038 (with year dummies); 0.017 (with sector-time and country-time dummies).
  - Interaction in financially open + rigid exchange rate EMs: 0.072 (Table 4, Col 6).
  - Inverse Federal funds rate coefficient: 0.028 (Table 6).
  - Sample average leverage growth: 35 basis points per year.
  - Interpretation: 1 percentage point increase in U.S. shadow rate (looser) → 9 basis points per year increase in leverage growth.

### VIII. Main conclusions and policy implications
- Empirical conclusions:
  - Accommodative U.S. monetary conditions are economically meaningful and statistically robust determinants of faster EM corporate leverage growth.
  - Effects are larger for sectors more dependent on external financing, for SMEs, and for EMs that are more financially open with less flexible exchange rate regimes.
  - Evidence consistent with two transmission channels: influence on domestic interest rates and relaxation of corporate borrowing constraints.
- Policy implication:
  - Given the role of global factors during exceptionally favorable periods, EMs must prepare for the implications of potential tightening of global financial conditions.
- Suggested future research:
  - Explore role of institutional environments, particularly corporate governance, in explaining firm capital structure and leverage dynamics across countries and firms.

*Chapter 3 of the October 2015 Global Financial Stability Report. All remaining errors are our own.*

### Chapter 3 of the October 2015 Global Financial Stability Report. All remaining errors are our own.

### Chapter 3 of the October 2015 Global Financial Stability Report

### I. Introduction and research questions
- Corporate debt of nonfinancial EM firms increased from about US$4 trillion in 2004 to well over US$18 trillion in 2014.
- The EM corporate debt-to-GDP ratio rose by 26 percentage points over the same period.
- Central research question: Are more accommodative global financial conditions associated with higher EM corporate leverage growth?
- Secondary empirical questions:
  - What is the role of country-specific characteristics such as financial openness and the exchange rate regime?
  - Through which channels do global financial conditions influence EM leverage growth (e.g., domestic interest rates, relaxation of borrowing constraints)?

### II. Conceptual channels
- Accommodative global monetary conditions can encourage EM leverage growth via:
  - Greater EM capital inflows when advanced-economy monetary policy loosens.
  - Central bank responses (e.g., cutting domestic rates to mitigate appreciation), which lower domestic interest rates and stimulate corporate borrowing.
  - Relaxation of financial (borrowing) constraints, especially benefiting firms dependent on external finance (e.g., SMEs, firms with limited collateral).

### III. Data coverage and identification strategy
- Firm-level dataset covers more than 400,000 firms in 24 EMs, including SMEs and private firms.
- Global financial conditions proxied primarily by a measure of the U.S. monetary policy stance; alternative indicators include “shadow rates” and estimated monetary policy shocks.
- Identification advantage: global monetary conditions treated as exogenous to any individual EM firm.
- Empirical strategy differentiates firms by degree of financial constraints (including dependence on external finance) to isolate the effect of global financial conditions from other global factors like global growth or commodity prices.

### IV. Key empirical findings
- Baseline association:
  - A 1 percentage point decline in the U.S. policy rate corresponds to an increase in EM leverage growth of 9 basis points, on average.
  - Sample average leverage growth is 35 basis points per year.
- Heterogeneity by financial dependence and firm traits:
  - A decrease in the U.S. policy rate of one standard deviation is associated with leverage growth about 5 basis points greater for firms at the 75th percentile of financial dependence versus firms at the 25th percentile.
  - SMEs and firms with less collateral disproportionately increase leverage amid accommodative U.S. monetary conditions.
- Country- and sector-level interactions:
  - The impact of U.S. monetary policy on EM leverage growth is greater for sectors that are more heavily dependent on external funding in financially open EMs with relatively more rigid exchange rate regimes.
- Transmission channels inferred:
  - Results are consistent with two channels: influence on domestic interest rates and relaxation of corporate borrowing constraints.

### V. Contributions relative to existing literature
- Considers total debt (including bond- and bank-based debt) rather than focusing primarily on bond issuance.
- Includes SMEs and private (non-listed) firms in addition to listed firms, providing a more comprehensive picture of leverage dynamics.
- Identifies global financial conditions as a new, quantitatively important determinant of capital structure relevant for small, financially integrated emerging or advanced economies.
- Provides novel empirical evidence that financial frictions are important in the transmission of monetary policy to the non-financial corporate sector across EMs.

### VI. Robustness and checks (overview)
- Alternative global financial condition measures examined: shadow rates and estimated monetary policy shocks.
- A battery of checks underscores the robustness of the main findings.
- Heterogeneity and interaction analyses (financial openness, exchange rate regime, sectoral dependence on external funding) support the main conclusions.

*Chapter 3 of the October 2015 Global Financial Stability Report. All remaining errors are our own.*

### Section III gives an overview of the data and variable definitions while relegating additional

### _wp16243 - Section III gives an overview of the data and variable definitions while relegating additional

### Methodology: conceptual framework and empirical approach
- Two broad transmission channels from global monetary conditions to EM leverage growth:
  - Capital flows and policy reaction channel: monetary policy loosening in advanced economies → greater EM capital inflows → possible EM central bank rate cuts to relieve appreciation pressures → lower domestic rates stimulate corporate borrowing.
  - Financial-frictions / borrowing-constraints channel: accommodative global conditions raise collateral values and relax borrowing constraints (financial accelerator), disproportionately benefiting firms more dependent on external finance (SMEs, firms with limited collateral).
- Three proxies for borrowing constraints:
  - Sectoral dependence on external finance (main proxy, Rajan and Zingales 1998 approach).
  - SMEs indicator (Gertler and Gilchrist 1993 spirit).
  - Asset tangibility (Braun and Larrain 2005).
- Baseline empirical specification (annual panel, firm i, sector s, country c, time t):
  - Firm-level leverage growth regressed on inverse shadow rate, firm controls (lagged first differences of profitability, size, tangibility), macro conditions (ICRG in some specs), firm fixed effects, and combinations of time, country-time, and sector-time fixed effects.
  - Standard errors clustered by sector in baseline; alternative clustering considered (e.g., two-way).
- Identification strategy:
  - Interaction between inverse shadow rate and sector financial dependence captures heterogeneous effects across financially constrained firms.
  - Further interactions between inverse shadow rate and country traits (financial openness, exchange rate rigidity) test cross-country heterogeneity.

### Data and variable definitions (Section III and Appendix overview)
- Firm-level dataset: ORBIS (Bureau van Dijk), annual global panel of over 130 million public and private companies; sample used:
  - About 400,000 nonfinancial EM firms over 2004-2013.
  - More than 1.3 million firm-year observations (unbalanced panel).
  - Focus on private EM non-financial corporations with total assets in excess of $1 million.
  - About 60 percent of the sample covers SMEs.
  - All variables winsorized at 2.5 percent to address outliers.
- Measures of leverage:
  - Baseline: total (non-equity) liabilities-to-total assets ratio, TLTA.
  - Alternative leverage definitions considered: total liabilities-to-total equity, total assets-to-total equity; cash-netted variations; long-term debt considered but data limited in ORBIS.
- Global financial conditions:
  - Primary proxy: U.S. shadow rate (Krippner 2014) and its inverse (shadow rate × -1) to facilitate interpretation (positive = looser monetary conditions).
  - Complementary proxies: global shadow rate, Federal funds rate (inverse), Treasury rates, U.S. monetary policy shocks (Gertler and Karadi 2015), inverse VIX.
- Firm-level controls:
  - Size (log sales), profitability (return on assets), asset tangibility (net PPE / total assets).
- Sector financial dependence:
  - Rajan and Zingales (1998) index (updated version based on Tong and Wei 2011 over 1990-2006) used to identify sectors more intrinsically dependent on external finance.
- Country controls:
  - ICRG Economic and Financial Risk Ratings (average) used in some specifications.
  - Measures used in cross-country heterogeneity tests: domestic credit to private sector (scaled by GDP) as proxy for financial development; Chinn and Ito (2006) capital account openness; exchange rate regime (Reinhart and Rogoff 2004 classification inverted for flexibility); monetary policy synchronization (Laeven and Tong 2012: correlation of monthly money market rates between U.S. and each EM).

### Baseline empirical results (Section IV.A)
- Main baseline result (Table 2, Column 1):
  - Coefficient on inverse U.S. shadow rate: 0.088 (positive and statistically significant).
  - Interpretation: an increase in the U.S. shadow rate (looser monetary conditions) of 1 percentage point corresponds to an increase in leverage growth of 9 basis points per year.
  - Sample average leverage growth: 35 basis points (per year).
- Heterogeneity by sectoral financial dependence (Table 2, Column 2):
  - Interaction coefficient estimate: 0.039 (statistically significant).
  - A one standard deviation increase in inverse U.S. shadow rate associated with about 5 basis points greater leverage growth for firms at the 75th percentile of financial dependence (Chemicals and Pharmaceuticals) versus 25th percentile (Construction).
- Robustness to fixed effects:
  - With year dummies (Column 3): interaction coefficient = 0.038 (statistically significant at 1 percent).
  - With country-time dummies (Column 4): interaction remains highly significant.
  - With sector-time and country-time dummies (Column 5): interaction coefficient declines to 0.017 but remains statistically significant at 5 percent.
- Summary of baseline conclusions:
  - (1) Accommodative U.S. monetary conditions are associated with faster EM corporate leverage growth.
  - (2) The impact is more pronounced for sectors relatively more dependent on external financing.

### Country-level heterogeneity (Section IV.B)
- Financial development:
  - Interaction of domestic financial development (domestic credit to private sector / GDP) and inverse U.S. shadow rate: negative and statistically significant → more financially developed countries are less sensitive to global financing conditions.
  - Finding corroborated using the Sahay and others (2015) financial development index.
- Capital account openness and exchange rate rigidity:
  - More open capital accounts → firms’ leverage growth more responsive to U.S. monetary conditions (Columns III and IV).
  - Interaction tests (Table 4):
    - In financially open EMs with more rigid exchange rate regimes, the interaction between inverse U.S. shadow rate and sector financial dependence is larger.
    - Column 6: interaction coefficient = 0.072 (statistically significant at the 1 percent level).
    - Column 5: coefficient statistically not different from zero.
  - Interpretation: U.S. monetary conditions influence EM corporate leverage in part by affecting domestic interest rates, especially where capital mobility is high and exchange rate flexibility is low.

### Robustness exercises (Section IV.C)
- Alternative monetary condition measures:
  - Global shadow rate (inverse) in place of U.S. shadow rate (Table 5): similar positive, statistically significant results.
  - Inverse Federal funds rate (Table 6): expected sign, significant at 1 percent, coefficient 0.028 (lower than shadow-rate-based estimate 0.038).
  - Treasury rates of various maturities: reinforce baseline results.
  - U.S. monetary policy shocks (Gertler and Karadi 2015, inverted so positive = looser) (Table 7): positive and statistically significant relationship; coefficients somewhat lower than Table 2 estimates.
  - Inverse VIX (Table 8): results align with main findings.
  - Including global growth and oil prices (Table 9): coefficients on shadow rates essentially unaltered; global oil prices and EM leverage move in tandem in intuitive ways.
- Alternative proxies for financial constraints:
  - SME dummy (Table 10): (1) positive relationship between SME leverage growth and U.S. shadow rate; (2) SMEs increase leverage disproportionately amid looser U.S. monetary conditions.
    - SME definition: firms with operating revenues, total assets, and employees below €10 ($13) million, €20 ($26) million, and 150, respectively.
  - Asset tangibility (Table 11): firms in bottom tertile of tangible assets ratio disproportionately increase leverage amid loose U.S. monetary conditions.
  - Triple interactions (Table 12): SMEs with less tangible assets (most binding constraints) show even greater leverage increases when global conditions favorable.
- Standard errors and clustering:
  - Baseline clustered by sector; alternative clustering by sector and time (Table 13) leaves interaction term statistically significant.
  - Other clustering methods (Table 14) also preserve statistical significance.
  - Driscoll-Kraay standard errors (not reported) also yield statistically significant results (available upon request).
- Firm fundamentals and alternative controls:
  - Replacing sales with assets and/or including median industry leverage (Table 15): interaction remains significant.
- Alternative leverage measures:
  - Interaction remains significant when using alternative leverage ratios (Table 16).
- Leave-one-out exercises:
  - Excluding each country one-by-one (including China): baseline interaction remains statistically significant at the 1 percent level.
  - Excluding sectors one-by-one: results robust except exclusion of general contractors (construction) lowers statistical significance notably.

### Additional empirical notes and selected quantitative details
- Sample: about 400,000 nonfinancial EM firms over 2004-2013; more than 1.3 million firm-year observations.
- Winsorization: all variables winsorized at 2.5 percent.
- Krippner (2014) U.S. shadow rate: entered negative territory in November 2008; bottomed out in May 2013; global shadow rate virtually flat in recent years as of the sample discussion.
- Key coefficients and magnitudes:
  - Baseline inverse U.S. shadow rate coefficient: 0.088 (Table 2, Col 1).
  - Interaction (inverse U.S. shadow rate × sector financial dependence): 0.039 (Table 2, Col 2); 0.038 (with time dummies); 0.017 (with country-time and sector-time dummies).
  - Interaction in financially open + rigid exchange rate EMs: 0.072 (Table 4, Col 6).
  - Inverse Federal funds rate coefficient: 0.028 (Table 6).
  - Sample average leverage growth: 35 basis points per year.
  - Impact interpretation: 1 percentage point increase in U.S. shadow rate → 9 basis points per year increase in leverage growth.

### Main conclusions and implications (Section V)
- Empirical findings:
  - Accommodative U.S. monetary conditions are economically meaningful and statistically robust determinants of faster EM corporate leverage growth.
  - The effect is larger for sectors more dependent on external financing, for SMEs, and for EMs that are more financially open with less flexible exchange rate regimes.
  - Evidence consistent with two channels: U.S. monetary conditions affect domestic interest rates and relax corporate borrowing constraints.
- Policy implication:
  - Given the role of global factors during exceptionally favorable periods, EMs must prepare for the implications of potential tightening of global financial conditions.
- Suggested area for future research:
  - Explore role of institutional environments, particularly corporate governance, in explaining firm capital structure and leverage dynamics across countries and firms.

*Source: _wp16243 - Section III gives an overview of the data and variable definitions while relegating additional*

### 1. Aggregate Corporate Debt

### 1. Aggregate Corporate Debt

### Aggregate corporate debt levels and ratios
- Time series coverage shown for years: 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014.
- Aggregate corporate debt plotted in "Billio n s o f U.S. do llars" with y-axis grid values indicated at: 40, 45, 50, 55, 60, 65, 70, 75, 80.
- Aggregate corporate debt ratio shown as "Percent of  GDP" with a y-axis value indicated at 25 in the figure.

### Shadow rates and policy context
- The U.S. federal funds rate and an estimated U.S. shadow rate are presented across years: 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015 in the firm-level liabilities-to-equity figure.
- The global shadow rate is defined as "the first principal component of the shadow rates of the four central banks (Bank of England, Bank of Japan, European Central Bank, and U.S. Federal Reserve)."
- Inverse U.S. shadow rate sample size: 10 observations with summary statistics:
  - Mean: 0.48
  - Median: 0.80
  - Standard deviation: 3.35
  - First quartile: -1.23
  - Third quartile: 2.87
  - Minimum: -5.23
  - Maximum: 4.58

### Firm-level leverage and liabilities-to-equity
- Firm-level liabilities-to-equity ratio ("Firm-Level Data:  Liabilities-to-Equity  Ratio (Percent)") plotted with Average and Median series across years: 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015; y-axis range shown from -6 to 8.
- The figure juxtaposes the federal funds rate and the shadow rate over the same sample years.

### Summary statistics: Key variables (from Table 1)
- Sources: Orbis database; Reserve Bank of New Zealand; PRS Group; Authors’ calculations.
- Notes: Leverage is the ratio of Total non-equity liabilities to Total assets. Sales is the logarithmic transformation of total sales. Profitability is Return-on-assets. Tangibility is defined as Net property, plant, and equipment to Total assets. Financial dependence is the updated version of the original Rajan-Zingales (1998) index based on Tong and Wei (2011). Macroeconomic conditions are proxied by the ICRG economic and financial index. The (inverse) shadow rate is estimated from a term-structure model based on Krippner (2014).

Key variable summary rows (Observations, Mean, Median, Standard deviation, First quartile, Third quartile, Minimum, Maximum):
- Leverage
  - Observations: 3,996,138
  - Mean: 0.58
  - Median: 0.59
  - Standard deviation: 0.30
  - First quartile: 0.34
  - Third quartile: 0.81
  - Minimum: 0.01
  - Maximum: 1.41
- Sales
  - Observations: 3,210,832
  - Mean: 15.43
  - Median: 15.41
  - Standard deviation: 1.47
  - First quartile: 14.56
  - Third quartile: 16.35
  - Minimum: 10.53
  - Maximum: 19.38
- Profitability
  - Observations: 3,902,401
  - Mean: 0.08
  - Median: 0.03
  - Standard deviation: 0.16
  - First quartile: 0.00
  - Third quartile: 0.11
  - Minimum: -0.28
  - Maximum: 0.85
- Tangibility
  - Observations: 3,675,210
  - Mean: 0.32
  - Median: 0.27
  - Standard deviation: 0.27
  - First quartile: 0.08
  - Third quartile: 0.51
  - Minimum: 0.00
  - Maximum: 0.97
- Financial dependence
  - Observations: 56
  - Mean: 0.14
  - Median: 0.06
  - Standard deviation: 0.67
  - First quartile: -0.08
  - Third quartile: 0.30
  - Minimum: -2.19
  - Maximum: 3.84
- Macroeconomic conditions
  - Observations: 196
  - Mean: 40.50
  - Median: 42.48
  - Standard deviation: 4.46
  - First quartile: 36.88
  - Third quartile: 44.17
  - Minimum: 27.69
  - Maximum: 45.71

### Figure and data sources
- Sources cited for figures: Bank for International Settlements; Dealogic; IMF; Orbis; and authors’ calculations.
- Additional source noted: Reserve Bank of New Zealand home page; and authors' calculations.
- Note: The selected emerging markets are presented in Appendix Table 1.

*Content based on IMF working paper section "1. Aggregate Corporate Debt" (figures and Table 1 summary statistics as presented in the source).*

### 2. The Shadow Rates in Other Countries

### 2. The Shadow Rates in Other Countries

### Baseline: EM Corporate Leverage and Global Financial Conditions
- Dependent variable: total liabilities-to-total assets ratio (first differenced).
- Key firm controls:
  - Sales: -1.651*** (SE 0.123) to -1.813*** (SE 0.122) across specifications (columns (1)–(5)).
  - Profitability: 0.107*** (SE 0.00549) to 0.108*** (SE 0.00513).
  - Tangibility: 0.0764*** (SE 0.00297) to 0.0782*** (SE 0.00315).
- Macroeconomic conditions: 0.197*** (SE 0.0147) to 0.132*** (SE 0.0148) where reported.
- Inverse shadow rate effects:
  - Inverse shadow rate: 0.0879*** (SE 0.0122) and 0.0794*** (SE 0.00941) in reported columns.
  - Interaction: Inverse shadow rate x Financial dependence = 0.0386*** (SE 0.0108) to 0.0174** (SE 0.00727).
- Observations: 1,424,409 down to 1,363,844 depending on specification.
- R-squared (within): 0.010 to 0.015.
- Fixed effects: Firm (all), Time/ Country-time/ Sector-time as indicated per regression.

### Leverage, Global Financial Conditions, and Country Traits
- Interaction of Inverse shadow rate (ISR) with financial dependence:
  - ISR x Financial dependence = 0.0230*** (SE 0.00725) to 0.0311*** (SE 0.00832).
- Credit-to-GDP and interactions:
  - Credit-to-GDP: 0.0204*** (SE 0.00476).
  - Credit-to-GDP x ISR: -0.000763*** (SE 0.000229).
- Financial development index (FDI) and interactions:
  - FDI: 0.0661*** (SE 0.00981).
  - FDI x ISR: -0.00157*** (SE 0.000542).
- Per capita income (PCI) and interaction:
  - PCI: 2.825*** (SE 0.405).
  - PCI x ISR: -0.0197 (SE 0.0185) (not statistically significant at conventional levels reported).
- Capital account openness (KAO) and interaction:
  - KAO: -0.285 (SE 0.376) (not significant).
  - KAO x ISR: 0.0969*** (SE 0.0186).
- Observations: 1,328,563 to 1,361,768.
- R-squared (within): 0.008.

### Leverage, Financial Openness, and Exchange Rate Regimes
- Relative capital account openness and relative exchange rate flexibility subdivisions:
  - Sales range across panels: -0.687*** (SE 0.0974) to -2.026*** (SE 0.129).
  - Profitability range: 1.041* (SE 0.608) to 4.723*** (SE 0.881).
  - Tangibility consistently positive and significant (e.g., 0.0733*** (SE 0.00383) to 0.0800*** (SE 0.00402)).
- Inverse shadow rate x Financial dependence:
  - Values: 0.0159* (SE 0.00881) to 0.0716*** (SE 0.0129) depending on openness and flexibility subgroup.
- Observations: 104,029 to 833,340 across panels.
- R-squared (within): 0.004 to 0.016.

### Robustness: Global Shadow Rate (principal component)
- Inverse global shadow rate (principal component of euro area, Japan, United States shadow rates) results:
  - Inverse shadow rate reported up to 0.322*** (SE 0.0395) and 0.300*** (SE 0.0300).
  - Inverse global shadow rate x Financial dependence = 0.134*** (SE 0.0337) down to 0.0579*** (SE 0.0211) across columns.
- Firm controls and macro conditions similar to baseline.
- Observations: up to 1,424,409 and specific subsamples 1,363,844.
- R-squared (within): 0.010 to 0.015.

### Robustness: U.S. Policy Rates and Term Structure
- Alternative U.S. rate interactions with Financial dependence:
  - Inverse Federal funds rate x Financial dependence = 0.0310** (SE 0.0140).
  - Inverse 2-year rate x Financial dependence = 0.0668** (SE 0.0302).
  - Inverse 5-year rate x Financial dependence = 0.111*** (SE 0.0353).
  - Inverse 10-year rate x Financial dependence = 0.123*** (SE 0.0391).
  - Inverse shadow rate x Financial dependence = 0.0376*** (SE 0.00972).
- Observations: 1,363,751 in each column shown.
- R-squared (within): 0.011.

### Robustness: U.S. Monetary Policy Shocks
- Inverse monetary policy shocks (surprises in year-ahead futures on the 3-month Eurodollar) x Financial dependence:
  - Interaction coefficients: 0.0316*** (SE 0.00761) to 0.0194*** (SE 0.00566) across columns.
- Inverse shadow rate: 0.0315*** (SE 0.00544) and 0.0290*** (SE 0.00426) in reported columns.
- Observations: up to 1,424,409; subsamples 1,363,844.
- R-squared (within): 0.009 to 0.015.

### Robustness: VIX and Other Global Controls
- Inverse VIX x Financial dependence:
  - 0.0112*** (SE 0.00365) to 0.0113*** (SE 0.00327) across panels.
- Oil prices and global growth controls (Table 9):
  - Oil prices: 0.0112*** (SE 0.00183) and 0.0114*** (SE 0.00186).
  - Global growth: 0.120*** (SE 0.0213) and 0.130*** (SE 0.0199).
- Inverse shadow rate coefficients in Table 9: 0.0561*** (SE 0.00837) to 0.0676*** (SE 0.00854).
- Inverse shadow rate x Financial dependence: 0.0284*** (SE 0.00734) to 0.0292*** (SE 0.00798).
- Observations: 1,361,768 to 1,422,401.
- R-squared (within): 0.006 to 0.007.

### Robustness: SMEs, Tangibility, and Their Interaction
- SME (dummy) interactions:
  - Inverse shadow rate: 0.0879*** (SE 0.0122) to 0.0479*** (SE 0.0127) in different columns.
  - Inverse shadow rate x SME: 0.0665*** (SE 0.0137) down to 0.0575*** (SE 0.0109) across columns.
- TAN (low-tangibility dummy) interactions:
  - Inverse shadow rate: 0.0879*** (SE 0.0122) to 0.0730*** (SE 0.0117).
  - Inverse shadow rate x TAN: 0.0526*** (SE 0.0122) up to 0.0794*** (SE 0.0125).
- Joint SME and TAN:
  - Inverse shadow rate x TAN x SME: 0.0683*** (SE 0.0157) to 0.0886*** (SE 0.0137).
- Observations: up to 1,424,535.
- R-squared (within): 0.010 to 0.015.

### Robustness: Clustering and Alternative Clustering Schemes
- Two-way clustering by sector and time (Table 13):
  - Sales: -1.651*** (SE 0.173) to -1.813*** (SE 0.162).
  - Inverse shadow rate: 0.0879** (SE 0.0352) and 0.0794** (SE 0.0360).
  - Interaction Inverse shadow rate x Financial dependence: 0.0386*** (SE 0.00963) to 0.0174** (SE 0.00825).
  - Observations: 1,287,828 down to 1,230,622.
- Other clustering (Table 14) and alternative cluster definitions reported; inverse shadow rate x Financial dependence = 0.0261*** (SE 0.00546) to 0.0261*** (SE 0.00858) across clustered specifications.
- Observations: 1,361,861 down to 1,228,994.
- R-squared (within): 0.006 to 0.011 depending on clustering.

### Robustness: Firm Fundamentals and Alternative Leverage Ratios
- Alternative firm-size and sector leverage controls (Table 15):
  - Total assets (alternative size): -3.749*** (SE 0.444) and -3.747*** (SE 0.444) where included.
  - Median sector leverage: -0.247*** (SE 0.0721) and -0.142* (SE 0.0754).
  - Inverse shadow rate x Financial dependence: 0.0134* (SE 0.00738) to 0.0277*** (SE 0.00736).
  - Observations: 1,669,413 and 1,361,861 depending on specification.
  - R-squared (within): 0.011 to 0.017.
- Alternative leverage measures (Table 16):
  - TLTE (total liabilities-to-total equity, first differenced):
    - Sales: -0.157*** (SE 0.0183); Profitability: 0.367*** (SE 0.0552); Tangibility: 0.00799*** (SE 0.000485).
    - Inverse Shadow Rate x Financial dependence = 0.00693*** (SE 0.00190).
    - Observations: 1,361,796; R-squared (within) 0.003.
  - TATE, NTLTA, NTLTE, NTATE variants show qualitatively similar positive interaction of inverse shadow rate with financial dependence (coefficients and SEs reported per column).
  - Example: TATE Profitability 0.382*** (SE 0.0596); Inverse Shadow Rate x Financial dependence = 0.00797*** (SE 0.00215).
  - Observations for some measures: 895,177 to 895,125; R-squared (within) up to 0.009 for TATE.

*Source: Authors’ calculations from tables in section "2. The Shadow Rates in Other Countries".*

### Appendix Table 1. Country and Firm Coverage

### Appendix Table 1. Country and Firm Coverage

### Panel A. Country coverage (Cross-sectional statistics presented for 2007)
- Argentina — N: 729; in %: 0.2
- Brazil — N: 2,833; in %: 0.7
- Bulgaria — N: 8,393; in %: 2.0
- Chile — N: 158; in %: 0.0
- China — N: 209,381; in %: 49.1
- Colombia — N: 11,472; in %: 2.7
- Croatia — N: 7,055; in %: 1.7
- Hungary — N: 11,474; in %: 2.7
- India — N: 2,754; in %: 0.6
- Indonesia — N: 335; in %: 0.1
- Kazakhstan — N: 119; in %: 0.0
- Lithuania — N: 2,313; in %: 0.5
- Mexico — N: 902; in %: 0.2
- Pakistan — N: 149; in %: 0.0
- Peru — N: 437; in %: 0.1
- Philippines — N: 1,044; in %: 0.2
- Poland — N: 24,342; in %: 5.7
- Republic of Korea — N: 48,985; in %: 11.5
- Romania — N: 15,729; in %: 3.7
- Russian Federation — N: 45,933; in %: 10.8
- Serbia — N: 6,571; in %: 1.5
- Turkey — N: 4,150; in %: 1.0
- Ukraine — N: 21,156; in %: 5.0
- Venezuela — N: 17; in %: 0.0
- Total — N: 426,431; in %: 100.00

### Panel B. Firm size categories (BvD definitions)
- Very large company — N: 20,059; in %: 4.7
- Large company — N: 144,193; in %: 33.8
- Medium sized company — N: 225,119; in %: 52.8
- Small company — N: 37,060; in %: 8.7
- Total — N: 426,431; in %: 100.00

### Panel C. BvD Major Sector coverage
- Chemicals, rubber — N: 61,197; in %: 14.4
- Construction — N: 26,515; in %: 6.2
- Education, Health — N: 3,074; in %: 0.7
- Food, beverages — N: 27,146; in %: 6.4
- Gas, Water, Electricity — N: 9,975; in %: 2.3
- Hotels & restaurants — N: 3,801; in %: 0.9
- Machinery, equipment — N: 86,453; in %: 20.3
- Metals & metal prod. — N: 36,048; in %: 8.5
- Other services — N: 23,544; in %: 5.5
- Post & telecom. — N: 1,222; in %: 0.3
- Primary sector — N: 23,218; in %: 5.4
- Publishing, printing — N: 7,326; in %: 1.7
- Textiles, wearing ap. — N: 36,243; in %: 8.5
- Transport — N: 10,145; in %: 2.4
- Wholesale & retail — N: 58,083; in %: 13.6
- Wood, cork, paper — N: 12,441; in %: 2.9
- Total — N: 426,431; in %: 100.0

*Sources: Orbis Database; Authors’ calculations.*

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