## 1. Sample of Countries

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### I. Introduction and context
- The collapse in international capital inflows during the global financial crisis has proven to be very persistent.
- Capital inflows fell sharply during the crisis, and have only partially recovered in recent years to about one third of their pre-crisis (2003-07) global GDP average.
- Most of the decline in capital inflows has been experienced in Advanced Economies (AEs) rather than in Emerging Markets (EMs).
- In terms of type of capital flows, most of the decline has taken place in portfolio inflows (equity and debt securities) and ‘other investments’, while FDI inflows have proved to be more resilient.
- At the aggregate worldwide level:
  - Portfolio inflows went from representing about half of total non-FDI inflows during the pre-crisis period (2003-07) to representing above 80 percent during 2008-16.
  - The ‘other investment’ category's share dropped to below 20 percent after the crisis.
- Breakdown by borrower sectors exhibits high heterogeneity:
  - Share of non-FDI inflows to corporates slightly declined to below half during the crisis period (2008-12) and then jumped to about three quarters during the post crisis (2013-16).
  - Non-FDI gross inflows to banks represent about 15 percent during the post-crisis period (about half of their pre-crisis share).
  - Share to sovereign borrowers increased from negative inflows during the pre-crisis, to about half of inflows during the crisis, and 10 percent during the post crisis period.
- Negative gross inflows indicate that the flow has reversed, with non-residents liquidating their claims.

### II. Data and methodology
- Dataset
  - Novel dataset constructed from IMF BOP data with borrower breakdowns by instrument for 43 countries (21 AEs and 22 EMs).
  - Time coverage: 2003Q1 to 2016Q1.
  - Instruments distinguished: Portfolio Debt, Portfolio Equity, Other Investment: Loans, Other Investment: Miscellaneous (per BPM6 breakdown).
  - Borrower types: Banks, Corporates, Sovereigns.
  - BPM6 used where available; selected BPM5 series used to fill gaps for specific country-periods as documented in the source.
  - Certain borrower-country observations excluded from regressions due to unreliable decompositions (banks: Canada, Germany, Israel, Spain, UK, US, Colombia, India; sovereigns: Hong Kong SAR, Australia, India, US).
- Empirical frameworks
  - Country-level cross-country panel regressions (equations (1) and (2)) with dependent variable = loans inflows by borrower type; controls include contemporaneous instrument inflows (normalized by GDP), lagged GDP growth, country and time fixed effects.
  - Crisis dummy: 1 if observation is between 2008Q1 and 2012Q4; Post-crisis (post): 1 if observation is from 2013Q1 onwards.
  - Directional analysis augments regressions with outflow dummy indicating negative inflows (equation (3)).
  - Dynamic latent factor model (equation (4)) using Bayesian estimation (Kose et al. 2003 approach) to extract:
    - Global factor for each instrument, I_{t,k,global}.
    - Borrower-specific subfactor for each instrument and borrower type, I_{t,k,s}.
    - Idiosyncratic (residual) components per country-sector.
  - Bayesian estimation details:
    - Posterior drawing via MCMC: 200,0000 MCMC replications after 20,000 burn-in replications (as reported).
    - Priors and normalization as specified in equations (8)–(12).
  - Decomposition used: predicted inflows = factor loadings × factors; residuals = actual inflows − predicted inflows. Regression (13) uses predicted and residual components to assess role of common vs idiosyncratic dynamics.

### III. Stylized facts and descriptive patterns
- Aggregate and composition patterns (figures referenced)
  - Collapse in Other Investment: Miscellaneous during the crisis in both AEs and EMs; even negative inflows for AEs.
  - Increase in share of Portfolio Debt inflows over time for both AEs and EMs.
  - Portfolio Equity inflows increased for AEs but declined for EMs over the sample.
  - Other Investment: Loans generally declined in share in post-crisis relative to pre-crisis period (heterogeneous across groups).
  - By borrower type: Non-FDI non-resident inflows to sovereigns increased since the crisis; banks’ shares declined (especially in AEs); corporates’ shares increased in AEs during the post-crisis period and remained relatively stable during the crisis.
- Sector-instrument patterns (quarterly time-series evidence)
  - AE corporates: portfolio debt and other investment loans were positively correlated pre-crisis; switched to apparent substitution after the crisis.
  - EM corporates: complementarity between portfolio debt and loans present over the entire period (especially post-crisis).
  - EM sovereigns: substitution between portfolio debt and other investment loans, especially pre-crisis.

### IV. Main empirical findings
- Substitution evidence
  - AE sovereigns: portfolio debt and loan inflows show negative correlations throughout the sample.
  - AE corporates: substitution between loans and debt securities emerged since the crisis.
- Complementarity evidence
  - EM corporates: complementary relationship between portfolio debt inflows and other investment loans during crisis and post-crisis periods.
- Significance patterns
  - No statistically significant relationships frequently found for EM corporates and banks in some specifications unless sub-periods considered.
- GDP growth controls
  - Lag GDP growth: Positive and statistically significant for private sector (corporates, banks) loan inflows.
  - Lag GDP growth: Negative and statistically significant for sovereign loan inflows (higher GDP growth reduces sovereign borrowing needs).
- Magnitude example
  - Pre-crisis across countries: an increase in portfolio debt inflows of 1 percent of GDP was correlated with an increase of about 0.3 percent of GDP in loan inflows for AE corporates (pre-crisis). This relationship became one of substitution during the crisis with interacted coefficients (debt*crisis) producing strong negative effects (reported debt*crisis ≈ -0.870*** for corporates in one specification).
- Directionality (inflows vs outflows)
  - Statistically significant complementarity or substitution relationships were mostly present when there was an increase in non-residents’ debt securities inflows.
  - AE corporates:
    - Pre-crisis complementarity between debt securities and loans was driven by increases in both instruments.
    - Post-crisis substitution driven by increases in debt securities that partially replaced international bank loans.
  - EM corporates:
    - Complementarity since the crisis reflects increases in both debt securities and loans.
  - EM banks (crisis peak):
    - Complementarity seemed driven by outflows of both debt securities and loans (less precisely estimated).

### V. Role of global/common factors versus idiosyncratic factors
- Dynamic factor analysis results
  - Global common factors and borrower-specific subfactors were estimated for each instrument and borrower type; results show:
    - AE global factors: commonality across countries in portfolio debt and equity inflows during pre-crisis and peak crisis; variability in loans and miscellaneous with notable movements during crisis.
    - EM global factors: stronger common variability in portfolio debt and miscellaneous during crisis; loan and equity global factors flatter, indicating heterogeneity.
- Decomposition regression (predicted vs residual)
  - Many substitution/complementarity relationships are explained more by idiosyncratic country-specific residuals than by predicted (global + subfactor) components.
  - AE sovereign substitution between loans and debt is primarily captured by residuals (country-specific factors).
  - AE corporates’ pre-crisis complementarity switching to substitution is driven by both common factors (predicted component) and residuals.
  - EM corporates’ complementarity since the crisis is driven by the predicted component (consistent with global financial cycle influence) but residuals also play a role.
  - Overall: the global financial cycle alone does not fully explain the complementarity/substitution patterns; country-specific factors matter substantially and their importance varies by borrower type and instrument.

### VI. Policy implications and recommendations
- For empirical analysis and policy discussion:
  - Distinguish as much as possible between sovereign and non-sovereign capital inflows when analyzing patterns and drivers of gross flows.
  - Exclude inflows to the public sector where possible when analyzing gross inflows along the global financial cycle due to different behavior and substantial heterogeneity across sectors.
- Interpretation of flow dynamics:
  - Substitution of bank loans by debt securities in AEs (since the crisis) is consistent with supply-side bank retrenchment and increased debt issuance; policymakers should account for differential effects of global supply-side changes on borrower sectors.
  - Complementarity in EM private inflows suggests that favorable global financing conditions enable simultaneous increases in multiple instruments for corporates; policy should consider aggregate external borrowing constraints and exposure accumulation risks in EM private sectors.
- Surveillance and risk assessment:
  - Assess country-specific drivers (demand and supply) in addition to global push factors when evaluating capital flow episodes; reliance on global proxies alone (e.g., single VIX-type measures) may miss important borrower- and country-level dynamics.

### VII. Conclusions
- There is sectoral heterogeneity in substitution and complementarity patterns across borrower types and instruments during 2003Q1–2016Q1:
  - AE corporates and AE sovereigns exhibit substitution between loans and portfolio debt inflows (not evident in aggregate-only studies).
  - EM corporates exhibit complementarity between loans and portfolio debt, especially since the crisis.
  - EM sovereigns show substitution between loans and debt securities similarly to AEs’ sovereigns.
- The global financial cycle contributes to some co-movements (especially for EM corporates), but a large part of the substitution/complementarity relationships is driven by country-specific idiosyncratic factors.
- Recommendation: analyses of gross cross-border capital flows and policy responses should incorporate borrower heterogeneity (banks vs corporates vs sovereigns) and instrument breakdowns (debt securities vs equity vs loans vs miscellaneous) rather than relying on aggregate flow measures.

*Source: IMF Working Paper wp1829, Section 1 and Section 2 (sample description, data, methodology, results and policy implications).*

### 1. Sample of Countries .................................................................................................

### 1. Sample of Countries

### I. Introduction and context
- The collapse in international capital inflows during the global financial crisis has proven to be very persistent.
- Capital inflows fell sharply during the crisis, and have only partially recovered in recent years to about one third of their pre-crisis (2003-07) global GDP average.
- Most of the decline in capital inflows has been experienced in Advanced Economies (AEs) rather than in Emerging Markets (EMs).
- In terms of type of capital flows, most of the decline has taken place in portfolio inflows (equity and debt securities) and ‘other investments’, while FDI inflows have proved to be more resilient.
- At the aggregate worldwide level:
  - Portfolio inflows went from representing about half of total non-FDI inflows during the pre-crisis period (2003-07) to representing above 80 percent during 2008-16.
  - The ‘other investment’ category's share dropped to below 20 percent after the crisis.
- Breakdown by borrower sectors exhibits high heterogeneity:
  - Share of non-FDI inflows to corporates slightly declined to below half during the crisis period (2008-12) and then jumped to about three quarters during the post crisis (2013-16).
  - Non-FDI gross inflows to banks represent about 15 percent during the post-crisis period (about half of their pre-crisis share).
  - Share to sovereign borrowers increased from negative inflows during the pre-crisis, to about half of inflows during the crisis, and 10 percent during the post crisis period.
- Negative gross inflows indicate that the flow has reversed, with non-residents liquidating their claims.

### II. Literature and data emphasis
- Recent literature highlights the importance of disaggregate gross capital inflows data; aggregate inflows may not reveal underlying disaggregate relationships (e.g., Bluedorn et al 2013; Bussière et al 2016; Advjiev et al 2017).
- Galstyan et al (2016) using IMF CPIS data find that understanding cross-border portfolio positions requires granular-level analysis, distinguishing portfolio debt and equity by holder sector.
- Advjiev et al (2017), using BOP data complemented by BIS and World Bank sources, analyze borrower-type decomposition of portfolio and other investment inflows for 85 countries during 1996-2014 and find that empirical regularities for aggregate inflows do not hold for all borrowing sectors.
- This paper adds:
  - Inclusion of equity flows to distinguish between debt securities and equity in portfolio inflows.
  - Breakdown of ‘other investment’ into loans (made by international banks) and the remainder (mostly currency and deposits, trade credit and advances, and other accounts receivable/payable), labeled “other investment miscellaneous”.

### III. Central research questions
- Was there substitution of international bank-related inflows with either portfolio debt or equity inflows during 2003-16?
- If so, are these relationships driven by negative or positive gross inflows?
- Was this a common phenomenon across countries and type of borrowers (banks, corporates, and sovereigns)?

### IV. Figures and decomposition (as described)
- Figure 1: Global Evolution of Capital Inflows by Instrument Type (Source: IMF BOP statistics).
- Figure 2: Breakdown of Non-resident Inflows by Type of Flows.
- Figures 3–7 and 8.1–9.2 provide further decompositions and analyses:
  - Decomposition of Capital Inflows by Instruments and by Borrower Type.
  - Evolution of Gross Capital Inflows to Corporates (AEs and EMs) and to Sovereigns (EMs).
  - Common factors and subfactors by instruments and borrowers for Advanced and Emerging Economies.

*Source: wp1829 - 1. Sample of Countries (IMF working paper chapter).*

### 2.  Furthermore, for  each  of  these  categories,  we  identify  how  much  of  the  flows  by  each

### 2.

### Data and methodology
- Dataset
  - Novel dataset constructed from IMF BOP data with borrower breakdowns by instrument for 43 countries (21 AEs and 22 EMs).
  - Time coverage: 2003Q1 to 2016Q1.
  - Instruments distinguished: Portfolio Debt, Portfolio Equity, Other Investment: Loans, Other Investment: Miscellaneous (per BPM6 breakdown).
  - Borrower types: Banks, Corporates, Sovereigns.
  - BPM6 used where available; selected BPM5 series used to fill gaps for specific country-periods as documented in the source.
  - Certain borrower-country observations excluded from regressions due to unreliable decompositions (banks: Canada, Germany, Israel, Spain, UK, US, Colombia, India; sovereigns: Hong Kong SAR, Australia, India, US).

- Empirical frameworks
  - Country-level cross-country panel regressions (equations (1) and (2)) with dependent variable = loans inflows by borrower type; controls include contemporaneous instrument inflows (normalized by GDP), lagged GDP growth, country and time fixed effects.
  - Crisis dummy: 1 if observation is between 2008Q1 and 2012Q4; Post-crisis (post): 1 if observation is from 2013Q1 onwards.
  - Directional analysis augments regressions with outflow dummy indicating negative inflows (equation (3)).
  - Dynamic latent factor model (equation (4)) using Bayesian estimation (Kose et al. 2003 approach) to extract:
    - Global factor for each instrument, I_{t,k,global}.
    - Borrower-specific subfactor for each instrument and borrower type, I_{t,k,s}.
    - Idiosyncratic (residual) components per country-sector.
  - Bayesian estimation details:
    - Posterior drawing via MCMC: 200,0000 MCMC replications after 20,000 burn-in replications (as reported).
    - Priors and normalization as specified in equations (8)–(12).
  - Decomposition used: predicted inflows = factor loadings × factors; residuals = actual inflows − predicted inflows. Regression (13) uses predicted and residual components to assess role of common vs idiosyncratic dynamics.

### Stylized facts and descriptive patterns
- Aggregate and composition patterns (Figures referenced in source)
  - Collapse in Other Investment: Miscellaneous during the crisis in both AEs and EMs; even negative inflows for AEs.
  - Increase in share of Portfolio Debt inflows over time for both AEs and EMs.
  - Portfolio Equity inflows increased for AEs but declined for EMs over the sample.
  - Other Investment: Loans generally declined in share in post-crisis relative to pre-crisis period (heterogeneous across groups).
  - By borrower type: Non-FDI non-resident inflows to sovereigns increased since the crisis; banks’ shares declined (especially in AEs); corporates’ shares increased in AEs during the post-crisis period and remained relatively stable during the crisis.

- Sector-instrument patterns (quarterly time-series evidence)
  - AE corporates: portfolio debt and other investment loans were positively correlated pre-crisis; switched to apparent substitution after the crisis.
  - EM corporates: complementarity between portfolio debt and loans present over the entire period (especially post-crisis).
  - EM sovereigns: substitution between portfolio debt and other investment loans, especially pre-crisis.

### Main empirical findings
- Country-level panel regressions (selected highlights)
  - Evidence of substitution between portfolio debt and loans:
    - AE sovereigns: portfolio debt and loan inflows show negative correlations throughout the sample.
    - AE corporates: substitution between loans and debt securities emerged since the crisis.
  - Complementarity:
    - EM corporates: complementary relationship between portfolio debt inflows and other investment loans during crisis and post-crisis periods.
  - No statistically significant relationships frequently found for EM corporates and banks in some specifications unless sub-periods considered.
  - Lag GDP growth:
    - Positive and statistically significant for private sector (corporates, banks) loan inflows.
    - Negative and statistically significant for sovereign loan inflows (higher GDP growth reduces sovereign borrowing needs).

- Magnitude example reported
  - Pre-crisis across countries: an increase in portfolio debt inflows of 1 percent of GDP was correlated with an increase of about 0.3 percent of GDP in loan inflows for AE corporates (pre-crisis). This relationship became one of substitution during the crisis with interacted coefficients (debt*crisis) producing strong negative effects (reported debt*crisis ≈ -0.870*** for corporates in one specification).

- Directionality (inflows vs outflows)
  - Statistically significant complementarity or substitution relationships were mostly present when there was an increase in non-residents’ debt securities inflows.
  - AE corporates:
    - Pre-crisis complementarity between debt securities and loans was driven by increases in both instruments.
    - Post-crisis substitution driven by increases in debt securities that partially replaced international bank loans.
  - EM corporates:
    - Complementarity since the crisis reflects increases in both debt securities and loans.
  - EM banks (crisis peak): complementarity seemed driven by outflows of both debt securities and loans (less precisely estimated).

- Role of global/common vs idiosyncratic factors (dynamic factor analysis and decomposition)
  - Global common factors and borrower-specific subfactors were estimated for each instrument and borrower type; results show:
    - AE global factors: commonality across countries in portfolio debt and equity inflows during pre-crisis and peak crisis; variability in loans and miscellaneous with notable movements during crisis.
    - EM global factors: stronger common variability in portfolio debt and miscellaneous during crisis; loan and equity global factors flatter, indicating heterogeneity.
  - Decomposition regression (predicted vs residual):
    - Many substitution/complementarity relationships are explained more by idiosyncratic country-specific residuals than by predicted (global + subfactor) components.
    - AE sovereign substitution between loans and debt is primarily captured by residuals (country-specific factors).
    - AE corporates’ pre-crisis complementarity switching to substitution is driven by both common factors (predicted component) and residuals.
    - EM corporates’ complementarity since the crisis is driven by the predicted component (consistent with global financial cycle influence) but residuals also play a role.
  - Overall: the global financial cycle alone does not fully explain the complementarity/substitution patterns; country-specific factors matter substantially and their importance varies by borrower type and instrument.

### Policy implications and recommendations
- For empirical analysis and policy discussion:
  - Distinguish as much as possible between sovereign and non-sovereign capital inflows when analyzing patterns and drivers of gross flows.
  - Exclude inflows to the public sector where possible when analyzing gross inflows along the global financial cycle due to different behavior and substantial heterogeneity across sectors.
- Interpretation of flow dynamics:
  - Substitution of bank loans by debt securities in AEs (since the crisis) is consistent with supply-side bank retrenchment and increased debt issuance; policymakers should account for differential effects of global supply-side changes on borrower sectors.
  - Complementarity in EM private inflows suggests that favorable global financing conditions enable simultaneous increases in multiple instruments for corporates; policy should consider aggregate external borrowing constraints and exposure accumulation risks in EM private sectors.
- Surveillance and risk assessment:
  - Assess country-specific drivers (demand and supply) in addition to global push factors when evaluating capital flow episodes; reliance on global proxies alone (e.g., single VIX-type measures) may miss important borrower- and country-level dynamics.

### Conclusions
- There is sectoral heterogeneity in substitution and complementarity patterns across borrower types and instruments during 2003Q1–2016Q1.
  - AE corporates and AE sovereigns exhibit substitution between loans and portfolio debt inflows (not evident in aggregate-only studies).
  - EM corporates exhibit complementarity between loans and portfolio debt, especially since the crisis.
  - EM sovereigns show substitution between loans and debt securities similarly to AEs’ sovereigns.
- The global financial cycle contributes to some co-movements (especially for EM corporates), but a large part of the substitution/complementarity relationships is driven by country-specific idiosyncratic factors.
- Recommendation: analyses of gross cross-border capital flows and policy responses should incorporate borrower heterogeneity (banks vs corporates vs sovereigns) and instrument breakdowns (debt securities vs equity vs loans vs miscellaneous) rather than relying on aggregate flow measures.

*Source: IMF Working Paper wp1829, Section 2 (pages and figures as provided in the supplied content).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2018/wp1829.pdf_