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Interlinkages between external debt financing, credit cycles and output fluctuations in emerging market economies

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Abstract

This paper examines the role of external debt financing (EDF) in shaping the credit cycles and the joint implications of EDF flows and credit growth for the output volatility in ten major emerging economies. We find that extreme phases—known as surge and stop episodes—in EDF flows are significantly associated with credit surges and stop episodes as shown by a panel multinominal logit estimation. However, the association is asymmetric—EDF stop episodes are more likely to bring about a credit stop episode compared to the occurrence of credit surges due to EDF surges. The results suggest vulnerabilities of credit cycles of EMEs to the sharp movement in EDF flows which in turn is typically synchronized with global liquidity conditions. In the second part of our analysis, using the bias-corrected LSDV estimation for the dynamic panels, we find that EDF flows and credit jointly have a strong amplifying impact on the output volatility. Moreover, EMEs also face large output volatility when EDF stop and credit stop episodes occur together. The results implicate a broader welfare loss in the form of output fluctuations due to a strong synchronization between external debt financing and domestic credit conditions. Significant output fluctuations are also a cause of concern for policymakers in EMEs who seek to insulate their economies from external and domestic financial shocks.

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Notes

  1. Credit cycle consists of two phases; credit boom and bust. The credit boom is typically defined by a phase in which the cyclical component of private sector credit to GDP is more than 1.65 times its standard deviation in at least one year or that in which an annual rate of credit to GDP ratio is above 20 percent (Ohnsorge and Yu, 2016; Bakker et al., 2012). An average credit boom lasts for 1.7 years and a maximum of up to 5 years.

  2. The paper also documents several external and domestic factors responsible for this dynamic such as low world commodity prices, weak global trade, a slowdown in domestic productivity growth and policy uncertainty in EMEs.

  3. Average growth in EMEs declined post-GFC period as shown in summary statistic Table 1

  4. Surge and stop episodes are defined as the economic phases that exceed the levels of key macroeconomic variables justified by economic fundamentals or the potential level (Forbes and Warnock, 2012; Sula, 2010). In the context of this paper, it indicates a large increase or sudden reduction in cross-border flows and credit growth.

  5. Extreme capital inflows episodes are known as surge, stop, flight, and retrenchment episodes. For more details, please see Forbes and Warnock (2012).

  6. At the compositional level of debt flows, foreign currency credit to non-financial corporates has increased in EMEs which exposes them to different types of risks—exchange rate risk, rollover risks, and other market risks (Chui et al., 2016).

  7. Sudden stop episode refers to a negative phase in which growth in capital inflows drops at least a standard deviation below average in the last five years and at least two standard deviations below the prior average in one quarter.

  8. Countries with financial frictions are more likely to be susceptible to a sudden stop in the form of capital flow reversal and a larger decline occurs in those industries that are more sensitive to financial frictions and those that benefit less from price changes to tradable sectors. Several features of financial frictions are commonly found in EMEs such as capital controls, micro, and macro-prudential regulations, and incomplete financial markets.

  9. The definition of credit to non-financial sector is provided on the BIS data portal of credit statistics—https://www.bis.org/statistics/totcredit/changes.htm. Credit to the non-financial sector only excludes credit given to financial sector to avoid double counting since the credit to financial sector would be eventually lent to the non-financial sector. The credit to non-financial sector covers core debt which includes loans, debt securities, currency, and deposits.

  10. Credit to non-financial sector as a measure of domestic credit is extensively used in the related literature (Mendoza and Terrones, 2008; Avdjiev et al., 2017; Meng and Gonzalez, 2017).

  11. Moving correlation presents correlation coefficient calculated between the two data sets for all points within a particular time window which is 12 quarters in our case. For the correlation plot, the time window shifts along with the data, and the correlations are calculated at every fixed time interval. Moving correlation mainly describes how the relationship between two variables changes over time

  12. For further details on the periods of the surge and stop episodes for EDF and credit, please see the Appendix Tables 10, 11, 12 and 13.

  13. These papers mainly focus on estimating early warning systems for predicting banking crises for EMEs and OECD countries. Their findings suggest that the multinomial logit model improves the predictive power of early warning systems for the systemic banking crisis.

  14. EMEs faced substantial financial instability risk and output loss post-GFC (2008) period. Financial market volatility further rose during taper tantrum episode (2013) (Ghosh et al., 2018; Tillmann, 2016).

  15. Monte Carlo simulation also show that difference GMM estimators have a severe finite sample bias and weak instrument bias in a highly persistent panel with small cross-section (Bruno et al., 2017).

  16. We use STATA procedure ‘xtlsdvc’ to estimate the model as given by (Bruno, 2005b). We choose the Anderson-Hsia estimator as the initial estimator to avoid using invalid and too many instruments (Roodman, 2009, 2015). Further, we use bootstrapped standard errors with 100 replications following Bruno (2005a).

  17. For our model setup, the inclusion of the interaction term of moderately correlated variables remains valid for two reasons. First, the correlation between the key explanatory variables—EDF and credit growth—is quite low (− 0.25) for our sample EMEs as shown in Appendix Table 7. Second, Balli and Sørensen (2013) theoretically demonstrate that collinearity is not a particular problem for regression with interaction effects; they further confirmed its validity through replicating several key macroeconomic studies and related robustness checks. In addition, several related studies support the use of interaction among similar types of macroeconomic variables within dynamic panel setup (Ductor and Grechyna, 2015; Aizenman et al., 2013).

  18. For a preliminary analysis to understand the role of EDF surge/stop in affecting output volatility, we tested for the difference in output volatility between EDF surge and EDF stop episodes and find significant results; results are shown by the Appendix Table 8.

  19. RRR values > 1 mean that the risk of outcome falling in the reference category increases as the variable increases whereas RRR < 1 means the opposite.

  20. This paper finds strong empirical evidence that countries with excess capital inflows are more sensitive to global liquidity conditions regardless of domestic monetary policy and exchange rate regime. Our results are on similar lines with more focus on the impact of EDF flows on credit cycles in the context of EMEs.

  21. Roodman (2015) suggest that standard panel fixed effects model could also be used for the dynamic panel with small cross-section and large periods. When T is relatively large, dynamic panel bias is not a concern.

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Acknowledgements

We would like to thank Prof. Ashima Goyal and Prof. Subrata Sarkar for their helpful comments and feedback. All remaining errors are our own.

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    Akhilesh K. Verma & Rajeswari Sengupta

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Appendix

Appendix

1.1 A.1: Robustness checks: regression results

See Table 6.

Table 6 Output volatility, credit and EDF (panel fixed effects)
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1.2 A.2: Cross correlation

See Tables 7, 8 and 9.

Table 7 Cross-correlations
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Table 8 Average output volatility during EDF surge and EDF stop episodes
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Table 9 Panel Unit root tests (Maddala and Wu, 1999)
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1.3 A.3: Surge and stop episodes of credit and EDF flows for sample EMEs

See Tables 10, 11, 12 and 13.

Table 10 Credit surges
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Table 11 Credit stops
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Table 12 EDF surges
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Table 13 EDF stops
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Verma, A.K., Sengupta, R. Interlinkages between external debt financing, credit cycles and output fluctuations in emerging market economies. Rev World Econ 157, 965–1001 (2021). https://doi.org/10.1007/s10290-021-00424-3

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