## _wp15193

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---

### I. Introduction — scope and approach
- Objective: explore the complex relationship between capital inflows and credit growth using a granular panel dataset.
- Data and sample: panel for 33 countries covering the period 1980–2011.
- Granularity:
  - Capital inflows disaggregated into FDI, portfolio, and other flows.
  - Credit broken down by sectors: households and non-financial corporations.
  - Complementary firm-level analysis (listed firms) to strengthen causal inference.
- Channels and mechanisms examined:
  - Role of domestic financial system development and structure in mediating inflow–credit linkages.
  - Demand- and supply-side channels through which capital inflows translate into credit growth.
- Identification strategy highlights:
  - Firm-level analysis treats country-level capital inflows as exogenous to individual firms and controls for firm fixed effects.
  - Interaction of industry external-finance dependence (RZ index) with capital inflow components (difference-in-difference flavor) to isolate effects.

### Key empirical findings
- General:
  - Capital inflows boost credit growth and increase the likelihood of credit booms in both household and corporate sectors.
  - Composition matters: only non-FDI inflows are significantly associated with higher credit growth rates and credit booms for both household and corporate sectors.
- Country- and firm-level consistency:
  - Firm-level evidence consistent with country-level evidence.
  - Net other inflows, but not net FDI inflows, are related to more rapid credit growth for firms that rely more heavily on external financing.
- Financial system heterogeneity:
  - Household credit growth:
    - Composition of capital inflows more important than financial system characteristics.
    - Net other inflows associated with faster growth in both shallow and deep markets.
  - Corporate credit growth:
    - Financial system characteristics matter more.
    - Capital inflows positively associated with faster growth in more shallow and more bank-based systems.
    - Regardless of financial system, net other inflows are linked to rapid credit growth.
- Channels:
  - Evidence indicates both demand-side and supply-side factors play a role.

### Contributions relative to prior literature
- Greater granularity:
  - Disaggregates capital inflows into FDI, portfolio, and other categories.
  - Distinguishes private credit to households and to non-financial corporations using a BIS dataset.
- Micro-level causal leverage:
  - Firm-level analysis reduces endogeneity concerns because country-level inflows are outside firm control and firm fixed effects attenuate omitted variable bias.
- Financial structure focus:
  - Financial development measured as sum of stock market capitalization and private credit (percent of GDP).
  - Financial structure measured as log(stock market capitalization / private credit).
- Methodological improvement in boom identification:
  - Identifies boom periods using only information available at a specific time point via a rolling cubic trend and ad hoc thresholds.

### Section III — Methodology overview (country- and firm-level)
- Two-step empirical analysis:
  - Country-level analysis: association of different types of capital inflows with credit growth at the macro level.
  - Firm-level analysis: impact of capital inflows on credit growth using firm-level data.
- Samples:
  - Country-level panel: 33 countries, 1980–2011.
  - Firm-level panel: listed firms in the same 33-country sample, 1991–2011.
- Country-level baseline specification (Equation (1)):
  - Dependent variable y_it = growth rate of the credit-to-GDP ratio.
  - NCI = net-capital-inflows-to-GDP ratio (variable of interest).
  - Controls X include lagged macro variables; country fixed effects v and year fixed effects η; all explanatory variables lagged one period.
- Capital inflow decomposition: net FDI inflows, net portfolio inflows, net other inflows (from WEO).
- Credit-boom identification (rolling cubic trend; Dell’Ariccia et al. (2012)):
  - Peak criteria: deviation from trend > 1.5 times its standard deviation AND annual growth rate of credit-to-GDP ratio exceeds 10 percent; OR annual growth rate of credit-to-GDP ratio exceeds 20 percent.
  - Exclude peaks when GDP growth is negative.
  - Start criteria: deviation > three-fourths of its historical standard deviation AND annual growth rate exceeds 5 percent; OR annual growth rate exceeds 10 percent.
  - End criteria: growth of credit-to-GDP ratio turns negative; OR credit-to-GDP ratio falls within three-fourths of one standard deviation from its trend AND annual growth rate is lower than 20 percent.
  - Credit-boom dummy used in linear probability model with fixed effects (logit yields broadly same results).
- Robustness and identification checks:
  - Alternative specifications adding level terms of RHS variables.
  - IV/2SLS using one-period lag of capital inflows (or components) as instruments for levels; results basically the same.
- Financial system heterogeneity testing:
  - Sample split at median of financial development and financial structure to estimate subsample regressions.

- Firm-level baseline specification (Equation (2)):
  - Dependent variables: change in ratio of total debt to (total debt + total equity), or growth rate of total debt to total assets, or growth rate of total debt.
  - RZ = Rajan-Zingales index measuring industry dependence on external financing; interaction RZ × NCI of interest with parameter γ.
  - Controls F (firm-level): property, plant and equipment / total assets; Tobin’s q; EBIT / total assets; log sales; lagged one period.
  - Controls M (country-level): same macro variables as country-level regressions.
  - Fixed effects: firm and year; robust standard errors clustered at industry level.
  - Firm-level data winsorized top and bottom 5 percent by default.

### Country-level empirical results (selected findings and magnitudes)
- Overall:
  - Capital inflows significantly associated with credit growth for both household and corporate sectors.
  - Net other inflows are the most consistently significant component.
- Composition specifics:
  - Household credit: net portfolio inflows and net other inflows significant; net FDI inflows not significant.
  - Corporate credit growth: only net other inflows significant; net FDI and portfolio inflows significant for corporate credit-boom probability (FDI marginally significant).
  - Net other inflows always significant across cases.
- Reported effect magnitudes (from reported regressions):
  - One standard deviation increase in net-capital-inflows-to-GDP ratio → household and corporate credit growth increase by 0.07 and 0.21 standard deviations, respectively (based on columns (1) and (5)).
  - One standard deviation increase in net other inflows → household and corporate credit growth increase by 0.09 and 0.07 standard deviations, respectively (based on columns (2) and (6)).
- Financial development and structure heterogeneity (selected exact magnitudes):
  - Household credit:
    - One standard deviation increase in net-other-inflows-to-GDP ratio → household credit growth rises by 0.35 standard deviations in high banking development group and 0.08 standard deviations in low banking development group.
  - Corporate credit:
    - One standard deviation increase in net-other-inflows-to-GDP ratio → corporate credit growth rises by 0.25 standard deviations in highly-developed stock markets and 0.39 standard deviations in less-developed stock markets.
    - Corresponding figures in more market-based vs less market-based systems are 0.31 and 0.46 standard deviations, respectively.
- Selected baseline coefficients (Table 2; sample: 604 observations; period 1980–2011; country and year fixed effects included):
  - Household sector:
    - Net capital inflows to GDP ratio: 0.337** (0.140) for Growth of Credit to GDP; 0.010** (0.005) for Credit Boom.
    - Net FDI inflows to GDP ratio: 0.102 (0.180) and 0.007 (0.005).
    - Net portfolio inflows to GDP ratio: 0.329* (0.160) and 0.011** (0.004).
    - Net other inflows to GDP ratio: 0.380*** (0.130) and 0.010** (0.005).
  - Corporate sector:
    - Net capital inflows to GDP ratio: 0.252** (0.100) and 0.007** (0.003).
    - Net portfolio inflows to GDP ratio: 0.161 (0.110) and 0.008** (0.004).
    - Net other inflows to GDP ratio: 0.341*** (0.090) and 0.006** (0.003).
    - Exchange rate regime: -2.172** (0.820) and -2.054*** (0.720) in relevant columns.

### Firm-level empirical results (selected findings and magnitudes)
- Interaction evidence (RZ × inflows):
  - Coefficient on interaction between net other inflows and RZ index robustly positive across specifications and dependent variable choices.
  - RZ index×FOI (net other inflows) example coefficients: 0.036* (0.022); 0.047* (0.025); 0.045* (0.025) in selected Growth of Leverage 1 specifications.
  - RZ index×FDI not significant in baseline; RZ index×FPI significant only in some specifications (e.g., 0.050** (0.024)).
- Firm-level control results (selected exact coefficients):
  - Property, plant and equipment / assets: negative and highly significant, e.g., -0.200*** (0.024).
  - (Market capitalization + total debt) / assets: strong negative, e.g., -5.188*** (0.435).
  - Log sales: -3.238*** (0.472).
  - Broad Money to GDP ratio: -0.050*** (0.014).
  - Capital account openness: 3.347*** (0.595).
- Observations (selected):
  - Growth of Leverage 1 regressions: 212955; 193440; 189001 (various specifications).
  - Growth of Leverage 2 regressions: 230377; 209944; 205268 (various specifications).
- Robustness:
  - Results robust to alternative firm-level measures, alternative winsorization and outlier handling, and decomposition into debt vs equity growth (effects primarily on debt growth).

### Demand-side vs supply-side mechanisms — evidence
- Demand-side conceptual channel:
  - Capital inflows boost asset prices, increase collateral/wealth, reduce external finance premium, and raise demand for credit.
  - Empirical test (Table 8): interact capital inflows with change in net equity or collateral value in firm regressions.
    - Indicator×Net other inflows to GDP ratio: 0.297** (0.146); 0.368** (0.162); 0.340* (0.174) across selected columns — consistent with demand-side channel.
- Supply-side conceptual channel:
  - Capital inflows increase liquidity available to banks; foreign capital entry intensifies competition, may loosen lending standards.
  - Empirical test (Table 9): split sample by banking soundness indicators (capital adequacy ratio, liquid assets to deposits and short-term financing, Z-score, NPL ratio).
    - Example results (selected exact coefficients):
      - Capital Adequacy Ratio sample:
        - Net FDI inflows to GDP ratio: overall 0.075 (0.156); high -0.185 (0.213); low 0.301*** (0.091).
        - Net other inflows to GDP ratio: overall 0.265** (0.102); high 0.351** (0.170); low 0.258** (0.097).
      - Liquid Assets to Deposits and Short Term Financing:
        - Net other inflows to GDP ratio: overall 0.293*** (0.088); high 0.192* (0.101); low 0.480*** (0.162).
      - Z-score:
        - Net other inflows to GDP ratio: overall 0.273*** (0.096); high 0.153 (0.196); low 0.347*** (0.098).
      - NPL Ratio:
        - Net other inflows to GDP ratio: overall 0.281** (0.103); high 0.641*** (0.226); low 0.231* (0.113).
- Overall inference:
  - Evidence supports both demand-side and supply-side channels operating in linking capital inflows to credit growth.

### Policy implications and recommendations
- No one-size-fits-all response:
  - Policymakers should consider the composition of capital inflows (FDI versus non-FDI) and the characteristics of the domestic financial system when judging credit market implications.
- Targeted policy attention:
  - Surges in non-FDI inflows warrant particular attention because of their stronger association with rapid credit growth and booms across sectors.
  - For corporate credit exposures, policymakers should account for financial system depth and bank/market orientation when assessing vulnerability to inflow-driven credit growth.

### Key quantitative facts and dataset summaries (preserved exactly)
- Country sample size: 33 countries.
- Country sample period: 1980–2011.
- Firm-level period: 1991–2011.
- Credit-boom peak criteria:
  - deviation from trend > 1.5 times its standard deviation AND annual growth rate > 10 percent; OR annual growth rate > 20 percent.
- Boom start criteria:
  - deviation > three-fourths of historical standard deviation AND annual growth rate > 5 percent; OR annual growth rate > 10 percent.
- Boom end criteria:
  - growth turns negative; OR deviation within three-fourths of one standard deviation from trend AND annual growth rate < 20 percent.
- Selected summary statistics (Table 1, Panel A — Macro-Level Variables; Mean / Std. / Min / Max):
  - Growth of Credit to Household Sector to GDP (%) — 5.85 / 27.70 / -60.89 / 637.03
  - Household Credit Boom (dummy) — 0.12 / 0.32 / 0 / 1
  - Growth of Credit to Non-financial Corporations to GDP (%) — 1.66 / 6.44 / -23.49 / 28.16
  - Corporate Credit Boom (dummy) — 0.04 / 0.20 / 0 / 1
  - Net Capital Inflows to GDP (%) — 0.50 / 5.41 / -22.35 / 21.56
  - Net FDI Inflows to GDP (%) — 0.14 / 3.51 / -22.69 / 15.15
  - Net Portfolio Inflows to GDP (%) — 0.01 / 6.59 / -26.68 / 54.93
  - Net Other Inflows to GDP (%) — 0.35 / 6.32 / -60.16 / 48.15
  - Sum of Market Capitalization and Private Credit to GDP (%) — 163.36 / 102.09 / 16.40 / 706.34
  - Log of Stock Market Capitalization to Private Credit Ratio — -0.34 / 0.76 / -3.20 / 1.57
- Selected firm-level summary statistics (Table 1, Panel B; Mean / Std. / Min / Max):
  - Growth Rate of Total Debts/(Total Debts+Equity) (%) — 7.20 / 65.94 / -100 / 432.68
  - Growth Rate of Total Debts/Total Asset (%) — 8.56 / 73.07 / -100 / 468.72
  - RZ Index — -0.06 / 1.94 / -29.47 / 10.65
  - Property, Plant and Equipment to Total Asset (%) — 30.39 / 21.74 / 0 / 98.64
  - Log Sales — 12.19 / 2.11 / 1.39 / 18.00
  - Log Total Assets — 12.32 / 1.96 / 2.94 / 18.15
  - Z-score — 2.64 / 4.53 / -304.46 / 241.73
  - Cash and Investment to Total Asset (%) — 14.09 / 15.14 / 0 / 99.55

*Source: _Section III provides a detailed discussion of the methodology, _wp15193 (PDF).*

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

### _wp15193 - References .............................................................................................................

### I. Introduction — scope and approach
- Objective: explore the complex relationship between capital inflows and credit growth using a granular panel dataset.
- Data and sample: panel for 33 countries covering the period 1980–2011.
- Granularity:
  - Capital inflows disaggregated into FDI, portfolio, and other flows.
  - Credit broken down by sectors: households and non-financial corporations.
  - Complementary firm-level analysis to strengthen causal inference.
- Channels and mechanisms examined:
  - Role of domestic financial system development and structure in mediating inflow–credit linkages.
  - Demand- and supply-side channels through which capital inflows translate into credit growth.
- Identification strategy highlights:
  - Firm-level analysis treats country-level capital inflows as exogenous to individual firms and controls for firm fixed effects.
  - Interaction of industry external-finance dependence with capital inflow components (difference-in-difference flavor) to isolate effects.

### Key empirical findings
- General:
  - Capital inflows boost credit growth and increase the likelihood of credit booms in both household and corporate sectors.
  - Composition matters: only non-FDI inflows are significantly associated with higher credit growth rates and credit booms for both household and corporate sectors.
- Country- and firm-level consistency:
  - Firm-level evidence is consistent with country-level evidence, reinforcing robustness.
  - Net other inflows, but not net FDI inflows, are related to more rapid credit growth for firms that rely more heavily on external financing.
- Financial system heterogeneity:
  - For household credit growth: composition of capital inflows is more important than financial system characteristics.
    - Net other inflows associated with faster growth in both shallow and deep markets.
  - For corporate credit growth: financial system characteristics matter more.
    - Capital inflows positively associated with faster growth in more shallow and more bank-based systems.
    - Regardless of financial system, net other inflows are linked to rapid credit growth.
- Channels:
  - Evidence indicates both demand-side and supply-side factors play a role in translating capital inflows into credit growth.

### Contributions relative to prior literature
- Greater granularity:
  - Disaggregates capital inflows into FDI, portfolio, and other categories.
  - Distinguishes private credit to households and to non-financial corporations using a BIS dataset.
- Micro-level causal leverage:
  - Firm-level analysis reduces endogeneity concerns common in country-level studies because country-level inflows are outside firm control and firm fixed effects attenuate omitted variable bias.
- Financial structure focus:
  - Examines how financial depth (sum of stock market capitalization and private credit, in percent of GDP) and financial structure (log ratio of stock market capitalization to private credit) condition inflow–credit linkages.
- Methodological improvement in boom identification:
  - Identifies boom periods using only information available at a specific time point.
  - Combines country-specific, trend-based approach with ad hoc thresholds, avoiding full-sample HP filter reliance or invariant cross-country thresholds.

### Context within related literature (select comparisons and contrasts)
- Prior studies documenting links between capital inflows and credit/booms:
  - Hernandez and Landerretche (1999): support that surges in capital inflows tend to finance a credit boom.
  - Sa (2006): examines 27 emerging countries during 2002–06 and does not find a clear-cut causal relationship.
  - Mendoza and Terrones (2012): find credit booms often follow surges in capital inflows.
  - Calderon and Kubota (2012): surges in gross debt inflows predict subsequent credit booms in a sample of 71 countries from 1975q1 to 2010q4.
  - Lane and Mcquade (2013): domestic credit growth in European countries strongly related to net debt inflows but not to net equity inflows.
- Micro-level literature connections:
  - Abiad et al. (2012): sectors more dependent on external finance suffer more during creditless recoveries (post-boom).
  - Tong and Wei (2012): pre-crisis exposure to non-FDI inflows worsens credit crunch for firms dependent on external finance for working capital.
- Asset-price and demand-side linkages:
  - Aizenman and Jinjarak (2009): positive associations between current account deficits and real estate price appreciation.
  - Sa and Wieladek (2010): capital inflows played a bigger role than monetary loosening in the U.S. housing boom.
  - Vasquez-Ruiz (2012): capital flows affect house prices positively in a dataset covering 45 countries from 1990 to 2012.
  - Olaberria (2011): linkages between capital inflows and asset price booms vary across countries using panel data on 40 countries from 1990 to 2010.
  - Laibson and Mollerstrom (2010): movements in home prices explain half of variation in trade deficits in a sample of 18 OECD countries plus China.
  - Jansen (2003); Kim and Yang (2011): capital inflows contribute to asset price appreciation in Thailand and other emerging Asian economies.

### Policy implications and recommendations (suggestive)
- No one-size-fits-all response:
  - Policymakers should consider the composition of capital inflows (FDI versus non-FDI) and the characteristics of the domestic financial system when judging credit market implications.
- Targeted policy attention:
  - Surges in non-FDI inflows warrant particular attention because of their stronger association with rapid credit growth and booms across sectors.
  - For corporate credit exposures, policymakers should account for financial system depth and bank/market orientation when assessing vulnerability to inflow-driven credit growth.

### Methodological notes and robustness
- Firm-level approach advantages:
  - Treats country-level capital inflows as exogenous to firm-level shocks.
  - Uses firm fixed effects and industry external-finance dependence interactions to sharpen causal interpretation.
- Boom identification:
  - Uses a country-specific, trend-based approach with ad hoc thresholds feasible for policy use in real time, contrasting with full-sample HP filters or invariant thresholds.

*Source: _wp15193 - References .............................................................................................................*

### Section III provides a detailed discussion of the methodology.

### _wp15193 - Section III provides a detailed discussion of the methodology.

### Overview
- Empirical analysis carried out in two steps:
  - Country-level analysis: association of different types of capital inflows with credit growth at the macro level.
  - Firm-level analysis: impact of capital inflows on credit growth using firm-level data.
- Sample scope:
  - Country-level panel of 33 countries covering the period 1980–2011.
  - Firm-level panel of listed firms in the same 33-country sample spanning 1991–2011 (data limitations).

### Country-Level: data, variable construction, and baseline specification
- Sample and data sources:
  - 33 countries: Australia, Austria, Belgium, Canada, China, Czech Republic, Denmark, Finland, France, Germany, Greece, Hong Kong SAR, Hungary, India, Indonesia, Ireland, Italy, Japan, Korea, Mexico, Netherlands, Norway, Poland, Portugal, Singapore, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, United Kingdom, and United States.
  - Data from BIS credit to private sector database, IMF WEO, World Bank Financial Development and Financial Structure database, Reinhart and Rogoff (2004), Chinn and Ito (2008).
- Baseline panel specification (Equation (1)):
  - y_it = growth rate of the credit-to-GDP ratio.
  - NCI = net-capital-inflows-to-GDP ratio (variable of interest).
  - X = control variables set.
  - Country fixed effects v and year fixed effects η; all explanatory variables lagged one period.
- Credit series construction:
  - BIS private sector credit, adjusted for breaks; broken down into credit to non-financial corporations and credit to households.
  - Credit-to-GDP ratio constructed using geometric average of GDP in years t and t+1 (following Dell’Ariccia et al. (2012)).
- Capital inflow decomposition (from WEO):
  - net FDI inflows, net portfolio inflows, net other inflows.
- Control variables included in X (from WEO unless noted):
  - log real GDP per capita and its square, real GDP growth rate, broad-money-to-GDP ratio, inflation, deposit interest rate, change of nominal exchange rate, exchange rate regime (Reinhart and Rogoff (2004) coarse classification with Ilzetski, Reinhart, and Rogoff (2010) update; AREAER updates for 2008–11), import-and-export-to-GDP ratio (openness), capital control index (updated Chinn and Ito (2008) index).
- Credit-boom identification (rolling cubic trend method following Dell’Ariccia et al. (2012)):
  - Rolling cubic trend estimated between t and t–10 for each country-year.
  - A data point is a peak of a boom if either:
    - (i) deviation from trend > 1.5 times its standard deviation AND annual growth rate of credit-to-GDP ratio exceeds 10 percent; OR
    - (ii) annual growth rate of credit-to-GDP ratio exceeds 20 percent.
  - Exclude peaks when GDP growth is negative.
  - Boom start defined as earliest year when either:
    - (i) credit-to-GDP ratio exceeds trend by > three-fourths of its historical standard deviation AND annual growth rate exceeds 5 percent; OR
    - (ii) annual growth rate exceeds 10 percent.
  - Boom ends when either:
    - (i) growth of credit-to-GDP ratio turns negative; OR
    - (ii) credit-to-GDP ratio falls within three-fourths of one standard deviation from its trend AND annual growth rate is lower than 20 percent.
  - Replace credit growth dependent variable by credit-boom dummy and estimate a linear probability model with fixed effects (logit yields broadly same results).
- Robustness and identification checks:
  - Alternative specifications adding level terms of RHS variables.
  - IV/2SLS using one-period lag of capital inflows (or components) as instruments for levels; results basically the same.
- Financial system heterogeneity:
  - Financial development = private-credit-to-GDP ratio + stock-market-capitalization-to-GDP ratio.
  - Financial structure = log(stock market capitalization / bank loans to private sector) (higher values → more market-based).
  - Sample split at median of financial development and financial structure to estimate subsample regressions.

### Firm-Level: data, specification, and identification
- Data:
  - Thomson Reuters Worldscope financial statements for listed firms located in the 33-country sample.
  - Time span 1991–2011.
- Baseline firm-level specification (Equation (2)):
  - Dependent variables: change in ratio of total debt to (total debt + total equity), or growth rate of total debt to total assets, or growth rate of total debt (depending on specification).
  - RZ = Rajan-Zingales index measuring industry dependence on external financing (constructed annually at SIC 2-digit using U.S. firm medians; dependence = (capital expenditures – cash flow) / capital expenditures; cash flow = cash flow from operations + decrease in inventories + decrease in receivables + increase in payables).
  - Interaction term of interest: RZ × NCI (capital inflows); parameter γ interpretation: positive γ → firms more dependent on external finance experience more credit growth in response to capital inflows (difference-in-difference in industry × country).
  - Controls F (firm-level): ratio of property, plant and equipment to total assets, Tobin’s q (sum of market capitalization and total debt to total assets), EBIT to total assets, log of sales; lagged one period.
  - Controls M (country-level): same macro variables as in country-level regressions.
  - Fixed effects: firm and year; robust standard errors clustered at industry level.
- Data processing:
  - Winsorize top and bottom 5 percent observations of firm-level variables for each country in baseline; alternative winsorization/testing described below.
- Robustness checks at firm level:
  - Alternative tangibility measures: receivables and inventory to total assets.
  - Alternative Tobin’s q: (market capitalization + asset – equity) / total assets.
  - Alternative firm size: log total assets.
  - Additional controls added stepwise: interest expense to net income, Z-score, cash & short-term investment to total assets, (net income + depreciation) / total assets.
  - Outlier handling: further dropping top and bottom 5 percent of dependent variables and top/bottom 1 percent of Z-score and (net income + depreciation)/total assets.
  - Separate tests splitting debt and equity growth to confirm effects reflect debt growth.

### Empirical results — country-level (summary of key findings)
- Overall:
  - Capital inflows significantly associated with credit growth for both household and corporate sectors.
  - Greater capital inflows boost credit growth rate and increase probability of a credit boom.
  - Composition matters: net other inflows are the most consistently significant; net portfolio inflows and net FDI inflows show more limited or conditional significance.
- Composition specifics:
  - Household credit: net portfolio inflows and net other inflows significant; net FDI inflows not significant.
  - Corporate credit growth: only net other inflows significant; net FDI and portfolio inflows significant for corporate credit-boom probability (FDI marginally significant).
  - Net other inflows always significant across cases.
- Economic magnitudes (based on reported regressions):
  - From columns (1) and (5): an increase of net-capital-inflows-to-GDP ratio by one standard deviation is associated with an increase in household and corporate credit growth rate by 0.07 and 0.21 standard deviations, respectively.
  - From columns (2) and (6): the corresponding figures for net other inflows are 0.09 and 0.07 standard deviations.
- Other notable country-level coefficients:
  - Higher real GDP growth associated with more rapid household credit growth and higher probability of household credit boom.
  - More flexible exchange rate regime reduces corporate credit growth rate.
- Heterogeneity by financial development and structure:
  - Household sector:
    - Net other inflows positively linked with household credit growth in both low- and high-financial-development subsamples.
    - Net portfolio inflows become significant in the high financial development group (driven by banking sector development rather than stock market development).
    - Numerical magnitudes: a one standard deviation increase in net-other-inflows-to-GDP ratio → household credit growth rises by 0.35 standard deviations in high banking development group and 0.08 standard deviations in low banking development group.
    - Financial structure (market vs bank relative size) does not materially alter linkages for household credit growth.
  - Corporate sector:
    - Net other inflows always significant regardless of financial system characteristics.
    - Portfolio inflows significant only in less developed financial systems.
    - FDI and portfolio inflows significant in more bank-based systems but not in more market-based systems.
    - Stock market development matters: one standard deviation increase in net-other-inflows-to-GDP ratio → corporate credit growth rises by 0.25 standard deviations in highly-developed stock markets and 0.39 standard deviations in less-developed stock markets; corresponding figures in more market-based vs less market-based systems are 0.31 and 0.46 standard deviations.

### Empirical results — firm-level (summary of key findings)
- Interaction evidence:
  - Coefficient on interaction between net other inflows and RZ index is robustly positive across specifications and dependent variable choices.
  - Interpretation: firms more dependent on external finance experience faster debt growth in response to net other inflows — supporting a causal link (difference-in-difference exploiting industry and country variation and firm fixed effects).
  - Interaction of net FDI inflows × RZ not significant; net portfolio inflows × RZ significant only in some specifications — consistent with country-level patterns.
- Firm-level control findings:
  - Tangibility (property, plant and equipment / total assets) coefficient negative → firms with more tangible assets tend to issue less debt growth relative to equity (more equity issuance).
  - Tobin’s q significantly negative → consistent with debt overhang argument (Myers, 1977).
  - Log sales coefficient negative → larger firms have smaller debt growth rates (easier access to equity/stock market).
  - Lower interest payment pressure, lower bankruptcy probability (higher Z-score), and higher liquidity associated with faster debt growth.
- Country-level firm regressors:
  - Higher real GDP growth, lower inflation, lower money supply, higher interest rates, and greater capital account openness relate to faster firm debt-ratio growth.
- Robustness:
  - Results robust to alternative firm-level measures (tangibility, Tobin’s q, firm size), outlier treatments, and decomposition into debt vs equity growth (effects primarily on debt growth).
- Conclusion from firm-level evidence:
  - Firm-level results corroborate country-level findings: net other inflows linked to more rapid credit/debt growth for externally reliant firms; net FDI inflows not associated.

### Demand-side vs supply-side mechanisms
- Conceptual channels:
  - Demand-side: capital inflows boost asset prices (stock and house prices), increase household and firm wealth/collateral, reduce external finance premium, and increase demand for credit.
  - Supply-side: capital inflows increase liquidity and resources available to banks; entry of foreign capital intensifies competition and may induce domestic banks to loosen lending standards and expand credit.
- Empirical tests:
  - Demand-side test: interact capital inflows with change in net equity or collateral value in firm regressions (drop RZ interaction to avoid multicollinearity).
    - Results (Table 8): net other inflows boost credit growth for firms experiencing greater increases in equity value or tangible asset value → consistent with demand-side channel.
  - Supply-side test: split sample by banking soundness indicators (median-based subsamples on capital adequacy ratio, asset liquidity, Z-score, non-performing-loan ratio) and run country-level regressions (Equation (1)).
    - Results (Table 9): link between capital inflows and credit growth stronger in subsamples with lower capital adequacy ratio, lower asset liquidity, lower Z-score, and higher non-performing-loan ratio.
    - In subsample with higher capital adequacy ratio: only net other inflows significant.
    - In subsamples with weaker bank indicators: coefficients on all kinds of capital inflows significant (portfolio inflows significant in lower asset liquidity/lower Z-score/higher NPL subsamples).
- Overall inference:
  - Evidence supports both demand-side and supply-side channels operating in linking capital inflows to credit growth. Further investigation left for future research.

### Key quantitative facts and thresholds preserved exactly as reported
- Country sample size: 33 countries.
- Country sample period: 1980–2011.
- Firm-level period: 1991–2011.
- Credit-boom peak criteria:
  - deviation from trend > 1.5 times its standard deviation AND annual growth rate > 10 percent; OR annual growth rate > 20 percent.
- Boom start criteria:
  - deviation > three-fourths of historical standard deviation AND annual growth rate > 5 percent; OR annual growth rate > 10 percent.
- Boom end criteria:
  - growth turns negative; OR deviation within three-fourths of one standard deviation from trend AND annual growth rate < 20 percent.
- Reported effect magnitudes (selected):
  - One standard deviation increase in net-capital-inflows-to-GDP → household and corporate credit growth increase by 0.07 and 0.21 standard deviations, respectively (based on columns (1) and (5)).
  - One standard deviation increase in net other inflows → household and corporate credit growth increase by 0.09 and 0.07 standard deviations, respectively (based on columns (2) and (6)).
  - Household credit: one standard deviation increase in net-other-inflows-to-GDP → growth rate rises by 0.35 standard deviations (high banking development) and 0.08 standard deviations (low banking development).
  - Corporate credit: one standard deviation increase in net-other-inflows-to-GDP → growth rate rises by 0.25 standard deviations (highly-developed stock markets) and 0.39 standard deviations (less-developed stock markets); in market-based vs less market-based systems: 0.31 and 0.46 standard deviations, respectively.

*Italicized source attribution:* _Section III provides a detailed discussion of the methodology, _wp15193 (PDF)._

### References

### _wp15193 - References

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- Beck, T., A. Demirguc-Kunt, and R. Levine, 2000, “A New Database on Financial Development and Structure,” World Bank Economic Review, Vol. 14, pp. 597–605.
- Beck, T., and R. Levine, 2002, “Industry Growth and Capital Allocation: Does Having a Market or Bank-Based System Matter?” Journal of Financial Economics, Vol. 64, pp. 147–180.
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- Bruno, V., and H. Shin, 2013, “Capital Flows, Cross-Border Banking and Global Liquidity,” mimeo, Princeton University.
- Calderon, C., and M. Kubota, 2012, “Gross Capital Inflows, Credit Booms and Crises,” World Bank Policy Research Working Paper No. WPS6270.
- Chinn, M., and H. Ito, 2008, “A New Measure of Financial Openness,” Journal of Comparative Policy Analysis, Vol. 10, pp. 307–320.
- Dell’Ariccia, G., and R. Marquez, 2006, “Lending Booms and Lending Standards,” Journal of Finance, Vol. 61, No. 5, pp. 2511–2546.
- Dell’Ariccia, G., D. Igan, L. Laeven, and H. Tong, 2012, “Policies for Macrofinancial Stability: How to Deal with Credit Booms,” IMF Staff Discussion Note 12/06, June.
- Dell’Ariccia G., D. Igan, and L. Laeven, 2012, “Credit Booms and Lending Standards: Evidence from the Subprime Mortgage Market,” Journal of Money, Credit and Banking, Vol. 44, Issue 2-3, pp. 367–384.
- Dembiermont, C., M. Drehmann, and S. Muksakunratana, 2013, “How Much does the Private Sector Really Borrow? A New Database for Total Credit to the Private Non-financial Sector,” BIS Quarterly Review, March 2013, pp. 65–81.
- Djankov, S., C. McLiesh, and A. Shleifer, 2007, “Private Credit in 129 Countries,” Journal of Financial Economics, Vol. 84, No. 2, pp. 299–329.
- Elekdag, S., and Y. Wu, 2011, “Rapid Credit Growth: Boon or Boom-Bust?” IMF Working Paper No. 11/241.
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- Ilzetzki, E., C. Reinhart, and K. Rogoff, 2010, “Exchange Rate Arrangements into the 21st Century: Will the Anchor Currency Hold?” unpublished manuscript.
- Jansen, W. J., 2003, “What do Capital Inflows Do? Dissecting the Transmission Mechanism for Thailand: 1980-1996,” Journal of Macroeconomics, Vol. 25, No. 4, pp. 457–480.
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- Myers, S., 1977, “Determinants of Corporate Borrowing,” Journal of Financial Economics, Vol. 5, pp. 147–175.
- Olaberria, E., 2011, “Capital Inflows and Asset Price Appreciation: Evidence from a Panel of Countries,” Central Bank of Chile Working Paper No. 675.
- Rajan, R., and L. Zingales, 1995, “What do We Know about Capital Structure? Some Evidence from International Data,” Journal of Finance, Vol. 50, No. 5, pp. 1421–1460.
- Rajan, R., and L. Zingales, 1998, “Financial Dependence and Growth,” American Economic Review, Vol. 88, No. 3, pp. 559–586.
- Reinhart, C., and K. Rogoff, 2004, “The Modern History of Exchange Rate Arrangements: A Reinterpretation,” Quarterly Journal of Economics, Vol. 119, No. 1, pp. 1–48.
- Sa, F., and T. Wieladek, 2010, “Monetary Policy, Capital Inflows and the Housing Boom,” Bank of England Working Paper No. 405.
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### Table 1 — Summary Statistics for Key Variables (selected exact values)
Panel A: Macro-Level Variables (Mean / Std. / Min / Max)
- Growth of Credit to Household Sector to GDP (%) — 5.85 / 27.70 / -60.89 / 637.03
- Household Credit Boom (dummy) — 0.12 / 0.32 / 0 / 1
- Growth of Credit to Non-financial Corporations to GDP (%) — 1.66 / 6.44 / -23.49 / 28.16
- Corporate Credit Boom (dummy) — 0.04 / 0.20 / 0 / 1
- Net Capital Inflows to GDP (%) — 0.50 / 5.41 / -22.35 / 21.56
- Net FDI Inflows to GDP (%) — 0.14 / 3.51 / -22.69 / 15.15
- Net Portfolio Inflows to GDP (%) — 0.01 / 6.59 / -26.68 / 54.93
- Net Other Inflows to GDP (%) — 0.35 / 6.32 / -60.16 / 48.15
- Log Real GDP Per Capita in National Currency — 11.26 / 2.09 / 6.81 / 16.89
- Real GDP Growth Rate (%) — 2.92 / 3.10 / -10.51 / 14.78
- Broad Money to GDP (%) — 87.84 / 55.86 / 17.67 / 416.18
- Inflation (%) — 5.26 / 11.43 / -3.95 / 104.54
- Deposit Interest Rate (%) — 7.04 / 10.20 / 0.00 / 105.52
- Growth of Nominal Exchange Rate (National Currency /$, %) — 1.90 / 14.77 / -29.35 / 170.93
- Exchange Rate Regime Coarse Classification — 2.37 / 1.19 / 1 / 5
- Import and Export to GDP (%) — 84.88 / 74.27 / 16.21 / 446.29
- Capital Account Openness (Chinn-Ito Index) — 1.60 / 1.22 / -1.86 / 2.44
- Sum of Market Capitalization and Private Credit to GDP (%) — 163.36 / 102.09 / 16.40 / 706.34
- Log of Stock Market Capitalization to Private Credit Ratio — -0.34 / 0.76 / -3.20 / 1.57

Panel B: Firm-Level Variables (Mean / Std. / Min / Max)
- Growth Rate of Total Debts/(Total Debts+Equity) (%) — 7.20 / 65.94 / -100 / 432.68
- Growth Rate of Total Debts/Total Asset (%) — 8.56 / 73.07 / -100 / 468.72
- RZ Index — -0.06 / 1.94 / -29.47 / 10.65
- Property, Plant and Equipment to Total Asset (%) — 30.39 / 21.74 / 0 / 98.64
- Receivables and Inventory to Total Asset (%) — 33.86 / 20.24 / 0 / 91.58
- Sum of Market Capitalization and Total Debts to Total Asset — 1.30 / 1.54 / 0.09 / 50.00
- (Market capitalization+Asset-Equity) to Total Asset — 1.59 / 1.38 / 0.26 / 23.94
- Earnings before Interest and Tax to Total Asset — 0.02 / 0.27 / -26.22 / 0.68
- Log Sales — 12.19 / 2.11 / 1.39 / 18.00
- Log Total Assets — 12.32 / 1.96 / 2.94 / 18.15
- Interest Expense to Net Income — 0.19 / 1.22 / -81.27 / 26.49
- Z-score — 2.64 / 4.53 / -304.46 / 241.73
- Cash and Investment to Total Asset (%) — 14.09 / 15.14 / 0 / 99.55
- Sum of Net Income and Depreciation to Total Asset (%) — 3.08 / 27.14 / -2769.3 / 49.48

Notes (exact formulations from source)
- Top and bottom 1 percent observations of firm-level variables are winsorized.
- Z-score = 0.717*Z1 + 0.847*Z2 + 3.107*Z3 + 0.420*Z4 + 0.998*Z5. Z1 is working capital to total asset. Z2 is retained earnings to total asset. Z3 is EBIT to total asset. Z4 is market value of equity to book value of total liabilities. Z5 is sales to total asset.
- RZ index is based on the dependence on external finance and is calculated year by year following the methodology of Rajan and Zingales (1998). Dependence on external finance = (Capital expenditures - cash flow) / capital expenditures. Cash flow = cash flow from operations + decrease in inventories + decrease in receivables + increase in payables.

### Table 2 — Capital Inflows and Credit Growth: Baseline Results (selected coefficients and exact significance)
Sample: 604 observations; sample period 1980–2011. Country and year fixed effects included.
Household Sector (columns (1)–(4))
- Net capital inflows to GDP ratio: 0.337** (0.140) for Growth of Credit to GDP; 0.010** (0.005) for Credit Boom.
- Net FDI inflows to GDP ratio: 0.102 (0.180) for Growth of Credit to GDP; 0.007 (0.005) for Credit Boom.
- Net portfolio inflows to GDP ratio: 0.329* (0.160) for Growth of Credit to GDP; 0.011** (0.004) for Credit Boom.
- Net other inflows to GDP ratio: 0.380*** (0.130) for Growth of Credit to GDP; 0.010** (0.005) for Credit Boom.
- Log real GDP per capita: -47.070 (32.600) and -49.660 (32.100) in columns (1) and (2).
- Real GDP growth rate: 1.426* (0.820) and 1.409* (0.830).
- Broad money to GDP ratio: -0.077* (0.043) and -0.086** (0.041).
- Deposit interest rate: 1.248* (0.660) and 1.248* (0.650).

Corporate Sector (columns (5)–(8))
- Net capital inflows to GDP ratio: 0.252** (0.100) for Growth Rate of Credit to GDP; 0.007** (0.003) for Credit Boom.
- Net portfolio inflows to GDP ratio: 0.161 (0.110) and 0.008** (0.004).
- Net other inflows to GDP ratio: 0.341*** (0.090) and 0.006** (0.003).
- Exchange rate regime: -2.172** (0.820) and -2.054*** (0.720) in columns (5) and (6).
- Observations: 604 in all columns.

Notes (exact)
- Control variables lagged one period. Credit boom is a dummy variable indicating whether a country is experiencing a credit boom in a given year. Exchange rate regime is from Reinhart and Rogoff (2004)’s coarse classification. Capital account openness is measured by the Chinn-Ito index. Robust standard errors in parentheses. ***, **, and * indicate significance level at 1%, 5% and 10%, respectively.

### Table 3 — Household Credit Growth: Role of the Financial System (selected exact coefficients)
Sample period: 1989–2011. Observations: 508 overall; 254 split groups.
- Net FDI inflows to GDP ratio: Overall -0.044 (0.171); High financial development 0.060 (0.084); Low financial development 0.128 (0.712).
- Net portfolio inflows to GDP ratio: Overall 0.217 (0.159); High 0.180** (0.073); Low 0.687 (0.812).
- Net other inflows to GDP ratio: Overall 0.280* (0.146); High 0.190** (0.081); Low 0.857* (0.490).
- Log real GDP per capita: Overall -79.408* (44.457).
- Capital account openness: Overall 5.743* (3.151); Low financial structure 2.138** (0.855).

Notes (exact definitions)
- Financial development = sum of stock market capitalization and private credit to GDP ratio. Financial structure = log ratio of stock market capitalization to private credit. Columns (2) and (3) use median split on financial development; columns (4) and (5) use median split on financial structure. Robust standard errors in parentheses.

### Table 4 — Corporate Credit Growth: Role of the Financial System (selected exact coefficients)
Sample period: 1989–2011. Observations: 508 overall; 254 split groups.
- Net other inflows to GDP ratio: Overall 0.315*** (0.091); High development 0.235** (0.104); Low development 0.438** (0.168).
- Net portfolio inflows to GDP ratio: Overall 0.157 (0.110); Low financial development 0.388* (0.225); Low financial structure 0.315** (0.130).
- Exchange rate regime: Overall -2.221*** (0.806); High development -2.237** (0.903); Low development -1.717** (0.713).

Notes (exact)
- Financial development and structure defined as in Table 3. Robust standard errors in parentheses.

### Table 5 — Firm-Level Credit Growth: Baseline Results (selected exact coefficients and counts)
Observations:
- Growth of Leverage 1 regressions: 212955, 193440, 189001 (various specifications).
- Growth of Leverage 2 regressions: 230377, 209944, 205268 (various specifications).

Selected coefficients (exact values and standard errors):
- RZ index×FDI: 0.046 (0.064) in first Growth of Leverage 1 column; other columns show 0.062 (0.076), 0.059 (0.075).
- RZ index×FPI: 0.022 (0.020) ; significance in some columns (e.g., 0.050** (0.024)).
- RZ index×FOI: 0.036* (0.022) ; other columns 0.047* (0.025) and 0.045* (0.025).
- Property, plant and equipment to assets ratio: negative and highly significant, e.g., -0.200*** (0.024), -0.146*** (0.023).
- (Market capitalization+ total debt) to assets ratio: strong negative in some columns, e.g., -5.188*** (0.435) and -5.200*** (0.416).
- EBIT to assets ratio: 15.703*** (3.006) in one specification; mixed signs across columns.
- Log sales: -3.238*** (0.472) and similar across columns.
- Broad Money to GDP ratio: negative and significant, e.g., -0.050*** (0.014).
- Capital account openness: positive and significant, e.g., 3.347*** (0.595).

Notes (exact)
- Leverage 1 = total debts/(total debts + equity). Leverage 2 = total debts/total assets. Firm and year fixed effects included. RZ index calculated year by year following Rajan and Zingales (1998). Robust standard errors clustered at industry level in parentheses. ***, **, and * denote 1%, 5% and 10% significance.

### Table 6 — Firm-Level Credit Growth: Alternative Controls (selected exact coefficients and counts)
Observations: Growth regressions range from 214122 to 194716.
- RZ index×FDI: coefficients around 0.035–0.050 with SEs e.g., (0.062), (0.074).
- RZ index×FPI: significance in some columns, e.g., 0.062** (0.031).
- RZ index×FOI: 0.039* (0.022) and similar in other columns.
- Receivables and inventory to assets ratio: negative and significant in several columns, e.g., -0.237*** (0.034).
- Cash and short term investment to assets ratio: positive and significant in some columns, e.g., 0.168*** (reported).
- Capital account openness: positive and significant across specifications, e.g., 3.086*** (0.582).

Notes (exact)
- Same definitions and estimation details as Table 5. Robust standard errors clustered at industry level in parentheses.

### Table 7 — Firm-Level Credit Growth: Additional Robustness Checks (selected exact coefficients and counts)
Observations vary by robustness sample: 170525; 184743; 163895; 159644; 205667; 214609.
Selected coefficients:
- RZ index×FDI: e.g., 0.051 (0.032) in "Drop Top and Bottom 5% of Dependent Variable" sample.
- RZ index×FPI: 0.027*** (0.008) and 0.038*** (0.012) across robustness specifications.
- RZ index×FOI: 0.026** (0.012) and 0.035*** (0.013) across specifications.
- (Market capitalization+ total debt) to assets ratio: e.g., -2.479*** (0.222) in one robustness column.
- Cash and short term investment to assets ratio: negative in some robustness checks, e.g., -0.073*** (0.023).
- Observations listed for each robustness variant as above.

Notes (exact)
- ZS and CF indicate Z-score and cash flow (sum of net income and depreciation to total asset). Dropping top and bottom percentiles yields similar results. Firm and year fixed effects included. Robust standard errors clustered at industry level in parentheses.

### Table 8 — Corporate Credit Growth: The Demand Side (selected exact coefficients and counts)
Dependent variable: Growth rate of total debt. Observations by column:
- (1) 212953; (2) 193438; (3) 188998; (4) 230631; (5) 210154; (6) 205477.

Selected indicator interactions (Indicator×Net inflows to GDP ratio):
- Indicator×Net FDI inflows to GDP ratio: (1) -0.004 (0.215); (2) 0.191 (0.215); (3) 0.160 (0.217); (4) 0.825 (1.196); (5) 1.882 (1.230); (6) 1.646 (1.252).
- Indicator×Net portfolio inflows to GDP ratio: (1) -0.128 (0.127); (2) -0.046 (0.127); (3) -0.054 (0.124); (4) 0.337 (0.784); (5) 0.552 (0.874); (6) 0.489 (0.872).
- Indicator×Net other inflows to GDP ratio: (1) 0.297** (0.146); (2) 0.368** (0.162); (3) 0.340* (0.174); (4) 1.726* (0.962); (5) 2.030* (1.092); (6) 1.929* (1.090).

Notes (exact)
- Firm and year fixed effects included; controls same as Table 5. Robust standard errors clustered at industry level in parentheses. ***, **, and * denote significance level at 1%, 5% and 10%, respectively.

### Table 9 — Corporate Credit Growth: The Supply Side (selected exact coefficients and counts)
Samples restricted to observations with bank-level indicators. Observations vary across panels.

Capital Adequacy Ratio (observations: 397 overall; 200 high; 197 low)
- Net FDI inflows to GDP ratio: 0.075 (0.156) overall; -0.185 (0.213) high; 0.301*** (0.091) low.
- Net portfolio inflows to GDP ratio: 0.148 (0.120) overall; 0.170 (0.174) high; 0.222** (0.096) low.
- Net other inflows to GDP ratio: 0.265** (0.102) overall; 0.351** (0.170) high; 0.258** (0.097) low.

Liquid Assets to Deposits and Short Term Financing (observations: 428 overall; 214 high; 214 low)
- Net other inflows to GDP ratio: 0.293*** (0.088) overall; 0.192* (0.101) high; 0.480*** (0.162) low.

Z-score (observations: 406 overall; 203 high; 203 low)
- Net other inflows to GDP ratio: 0.273*** (0.096) overall; 0.153 (0.196) high; 0.347*** (0.098) low.

NPL Ratio (observations: 395 overall; 201 high; 194 low)
- Net other inflows to GDP ratio: 0.281** (0.103) overall; 0.641*** (0.226) high; 0.231* (0.113) low.

Notes (exact definitions and estimation details)
- Capital and reserves include funds contributed by owners, retained earnings, general and special reserves, provisions, and valuation adjustments. Capital includes tier 1 capital and total regulatory capital. Total assets include all nonfinancial and financial assets.
- Z-score estimated as (ROA+equity/assets)/sd(ROA) and sd(ROA) is the standard deviation of ROA.
- “High” and “Low” indicate higher than or lower than the median value of the corresponding indicator.
- Country and year fixed effects included. Control variables same as baseline regressions and lagged by one period. Robust standard errors in parentheses. ***, **, and * indicate significance level at 1%, 5% and 10%, respectively.

*Italic: Source — _wp15193 - References (PDF).*

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