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Credit Booms in Developing Countries: Are They Different from Those in Advanced and Emerging Market Countries?

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Abstract

While earlier studies focus on credit booms in advanced and emerging market countries, this paper examines the characteristics and determinants of credit booms in developing countries. The results find that credit booms in developing countries are less likely to be associated with systemic banking crises. Rather, they are more likely to be the result of financial deepening than of dangerous buildups of financial risks; the prevention of credit booms in developing countries may thus be associated with higher opportunity costs in terms of foregone growth opportunities. Random effect probit and tobit regressions find some evidence that credit booms are likely to start when the economy is expanding and if the financial sector is larger. Although monetary and fiscal policies do not help in preventing credit booms in developing countries, we find that prudential regulations and supervision can play a much more effective role in preventing “bad” booms, while incurring substantially lower costs. Although “bad” booms are hard to identify ahead of time, the duration and size of booms, as well as the level of credit aggregates, appear to be useful indicators in determining them.

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Notes

  1. The macroeconomic variables previously studied include domestic investment and consumption booms, housing price booms, real exchange rate appreciation, current account deterioration, and reserve position.

  2. The changing macroeconomic variables include output gap, investment, consumption, domestic real interest rate, inflation, real exchange rate, current account balance, private capital inflows, short-term external debt, and terms of trade.

  3. Some studies use only bank credit, while others stress the importance of using credit by both banks and non-bank financial institutions. Also, some use credit-to-GDP ratio, while others use real credit per capita to proxy credit development.

  4. The method used by Mendoza and Terrones (2012) are different in a number of ways from the previous studies, especially Gourinchas (2001), which was widely followed by Cottarelli et al. (2005), International Monetary Fund (2004), Hilbers et al. (2005). Firstly, Mendoza and Terrones (2012) measure credit by using real credit per capita instead of credit-to-GDP ratio. Secondly, they use the Hodrick-Prescott (HP) filter in its standard form, instead of using an “expanding HP trend” to construct the trend of credit. Thirdly, instead of a common threshold for all countries, they use different thresholds for different countries depending on each country’s cyclical variation of credit. With these measures, they better capture the incidence of credit booms.

  5. The value 1.65 is chosen because it falls in the 5% upper tail of the standardized normal distribution.

  6. Studies by Gourinchas (2001), Barajas et al. (2007), Dell’Ariccia et al. (2016) use the credit-to-GDP ratio.

  7. The classifications are done based on the assessment of each country’s level of economic development as well as financial market structure, accessibility, and development, for example, market size, liquidity, openness, competition, operational efficiency, regulatory framework, and supporting infrastructure, etc.

  8. Our results are mainly based on the parameter value of 0.25 for boom start/end. Using a higher parameter value will shorten the boom duration; however, the comparative conclusion remains the same.

  9. We use banking crisis data from Laeven and Valencia (2012), where a country is considered to have experienced a systemic banking crisis if its banking system experienced significant signs of financial stress (indicated by significant bank runs, losses, and bank liquidations); and also if significant policy interventions can be observed in response to losses in the banking system. Policy interventions are considered to be significant if the following forms of interventions have been used: significant guarantees are put in place, extensive liquidity support (5% of deposits and liabilities to nonresidents), bank restructuring costs (at least 3% of GDP), or significant bank nationalizations took place.

  10. A few interesting variables including TFP shock and macroprudential policy index are not included in our study due to data limitations for the developing countries.

  11. We do not include credit to the public sector and financial sector for a few reasons. Credit to the public sector tends to be policy-oriented and counter-cyclical. It tends to increase rapidly after the stress is realized in accordance with government’s intervention policies, while it slows down during the booms, which thus might produce false signals for our analysis. On the other hand, including credit to the financial sector (such as banks) might create a double counting issue, as credit to financial sectors will mainly be lent to the private non-financial sector.

  12. Several recent studies highlight considerable differences in the behavior of gross versus net inflows (Forbes and Warnock 2012; Calderón and Kubota 2012; Crystallin et al. 2015). Following those studies, we use gross flows instead of net flows, as gross flows enable us to better examine the impact on credit booms from the foreign source of funds, which is more relevant in the study of credit booms, and especially the ones leading to financial crises (‘bad’ credit booms). Using net flows may not allow us to properly distinguish the behavior of the foreign investors from that of domestic ones. It may also provide misleading implication on the amount of capital coming from abroad.

  13. The responsiveness of monetary and fiscal policies is shown by negative values of residuals at mean. For example, for monetary policy, the actual policy rates on average are lower than the mean values predicted by inflation and GDP growth. Similarly, the actual fiscal balance is lower than the predicted mean values obtained from regressing general government balance on GDP growth.

  14. We also test unit roots of the series using Levin et al. (2002); Im et al. (2003); and augmented Dickey-Fuller tests, and the results show that the series are stationary, as shown in the Appendix 4.

  15. Capital inflows appear to be not statistically significant in our sample possibly due to the definition of variable used to capture capital inflows. Some studies indicate that components of capital inflows matter with the credit boom incidence, especially for other investment flows and portfolio flows, but not for FDI (See, for example, Furceri et al. 2012; Calderón and Kubota 2012, and Blanchard et al. 2015).

  16. There are at least two main challenges, including (1) political economy biases that contribute to overspending when revenues are abundant in good times, and (2) the inability of developing countries to access external finance to pay for fiscal expansion during downturns.

  17. Given the cross-sectional nature of the variable Duration, we collapse our panel data into cross-sectional form and estimate probit models, where the dependent variable takes value of 1 if a credit boom episode is followed within two years by a banking crisis episode and 0 if the credit boom ends softly without a crisis. Each observation reflects the mean value of all variables except Duration over each boom period.

  18. Llewellyn (2000) also suggests that effective regulation (internal and external) and supervision of banks and financial institutions have the potential to significantly contribute to financial stability and robustness.

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Acknowledgements

The authors highly appreciate helpful comments by two anonymous referees, and the author, Dr. Channarith Meng, is grateful for the research grants provided by the National Bank of Cambodia and research facilities offered by the National Graduate Institute for Policy Studies (GRIPS) during his stay as a visiting scholar.

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Authors and Affiliations

  1. National Bank of Cambodia, Phnom Penh, Cambodia

    Channarith Meng

  2. National Graduate Institute for Policy Studies (GRIPS, Japan), Tokyo, Japan

    Roberto Leon Gonzalez

  3. Rimini Center for Economic Analysis (RCEA, Italy), Rimini, Italy

    Roberto Leon Gonzalez

Authors
  1. Channarith Meng
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  2. Roberto Leon Gonzalez
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Correspondence to Channarith Meng.

Appendices

Appendix 1

Table 11 Sample of economies
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Appendix 2

Table 12 List of credit boom episodes and peak years
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Appendix 3: Credit Boom Episodes based on Boom Parameter of 2.00

Table 13 Number of credit boom episodes based on real credit per capita (boom parameter = 2.00)
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Table 14 Probability of a boom occuring in a country in a year, average
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Table 15 Duration of credit boom episodes, average
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Table 16 Magnitude of credit boom episodes (boom parameter = 2.00)
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Table 17 Credit booms and banking crises (boom parameter = 2.00)
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Appendix 4

Table 18 Panel unit root test
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Meng, C., Gonzalez, R.L. Credit Booms in Developing Countries: Are They Different from Those in Advanced and Emerging Market Countries?. Open Econ Rev 28, 547–579 (2017). https://doi.org/10.1007/s11079-016-9425-9

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