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The Role of US Monetary Policy in Banking Crises Across the World

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Abstract

We examine the role of US monetary policy in banking crises across the world by using a cross-country database spanning 69 countries over the 1870–2010 period. US monetary policy tightening increases the probability of a banking crisis for those countries with direct linkages to the USA, either in the form of trade links or significant share of USD-denominated liabilities. Conversely, if a country is integrated globally, rather than having a direct exposure, the effect is ambiguous. These findings suggest that the effect of US monetary policy in global banking crises is not uniform and is largely dependent on the nature of linkages with the USA.

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Notes

  1. 1.

    We check the sensitivity of our findings by employing other systemic crisis databases of Bordo et al. (2001), Laeven and Valencia (2012), Gourinchas and Obstfeld (2012), Schularick and Taylor (2012). The results discussed in Sect. 3.4 are qualitatively similar to those using Reinhart and Rogoff’s (2009) definition.

  2. 2.

    These data are available online at www.measuringworth.com/datasets/interestrates/.

  3. 3.

    RR shocks are available since the late-1960s. Using the full sample of RR shocks or a shorter sample consistent with GK shocks, available from the early 1990s, does not affect our results.

  4. 4.

    Local monetary policy decisions and changes in the exchange rates are also expected to affect the economic and financial conditions. However, historical coverage for both series for many of the countries are poor. When we include changes in the short-term interest rates and exchange rate in our baseline specification, the sample size shrinks by three quarters; hence, we do not include these local variables in our baseline regressions and instead present them as part of robustness analysis in Sect. 3.4.

  5. 5.

    As we mentioned previously, Romer and Romer shocks are available since the late-1960s. We examine our results with the Romer and Romer shocks for this period as well and find that our results remain generally similar.

  6. 6.

    There might be other channels present, such as confidence or expectations channels. In the interest of space, we do not delve further into discussing how possible other channels might be at play.

  7. 7.

    See also Neumeyer and Perri (2005), Uribe and Yue (2006) on how countercyclicality of world interest rate could affect emerging market economies.

  8. 8.

    If the capital outflows are orderly, they can help rein in financial excesses and lean against the wind (Adrian and Liang 2018). However, our results indicate that the adverse effects of capital outflows dominate the leaning-against-the-wind effects.

  9. 9.

    This result is counterintuitive, and it might be the result of the definition of currency crisis in the Carmen Reinhart data library. 15% or more annual depreciation criteria might not be effective in systematically capturing true crises episodes (for most developing countries, 15% annual declines might be frequently observed without leading to abrupt effects on the economy). Hence we argue that banking crises might be more relevant episodes to focus on.

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Acknowledgements

We would like to thank Elena Afanasyeva, Luca Guerrieri, and the seminar participants at the Bank of Finland, the Federal Reserve Board, IMF 19th Jacques Polak Annual Research Conference, Central Bank of Turkey Conference on “Changing Global Economic Landscape,” Barcelona GSE summer forum, CEBRA Meetings at the Bank of Mexico, and the Computing in Economics and Finance Conference for helpful discussions. We also thank Michael Siemer for his contributions to an earlier version of this project. All remaining errors are exclusively our responsibility. The views expressed in this paper are those of the authors and should not be attributed to the Board of Governors of the Federal Reserve System or its staff.

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Correspondence to C. Bora Durdu or Alex Martin or Ilknur Zer.

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Appendices

Appendix 1: Definition of Variables

Monetary Policy Shocks

  • MP: US monetary policy change, defined as the change in US short-term interest rates from the Jorda et al. (2017) macrohistory database.

  • GK: US monetary policy shocks introduced in Gertler and Karadi (2015), and defined as the surprises in the 3 months ahead federal funds rate futures.

  • RSW: US monetary policy shocks introduced in Rogers et al. (2014), and constructed through the surprises on the 6-month Eurodollar contracts.

  • RR: US monetary policy shocks introduced in Romer and Romer (2004). The authors use the Fed Greenbook forecasts of output growth and inflation along with the fed funds rates to estimate shocks.

Exposure Variables

  • UStradeIntensity Trade intensity to USA, calculated as total trade with the USA divided by total trades of the country. Data are from COW trade project.

  • Gravity–UStradeIntensity The instrument of trade intensity, introduced in (2).

  • Debt_in_USD Debt liabilities minus debt assets in USD (% of GDP) in log terms, constructed by using data from the IMF’s Coordinated Portfolio Investment Survey (CPIS) and the BIS locational banking statistics as detailed by Lane and Shambaugh (2010), Benetrix et al. (2015).

  • EconInteg Economic integration, calculated as a country’s total exports and imports as a % of GDP (trade openness). Trade data are from COW trade project, and GDP data are from Maddison project.

  • Gravity–EconInteg The instrument of trade openness, introduced in (3).

  • FOPEN_dejure De-jure financial openness index. The index is from KOF globalization index (KOFFiGIdj), introduced by Dreher (2006) and developed by Gygli et al. (2019). The index aggregates regulations to international capital flows, Chinn-Ito index of capital account openness and bilateral investment agreements.

  • FOPEN_defacto De-facto financial openness index. The index is from KOF globalization index (KOFFiGIdf), introduced by Dreher (2006) and developed by Gygli et al. (2019). It aggregates foreign direct investments, portfolio investments. international income payments, debt and reserves.

  • TOPEN_dejure De-jure trade openness index. The index is from KOF globalization index (KOFFiGIdf), introduced by Dreher (2006) and developed by Gygli et al. (2019). It considers trade regulations, tariffs, and trade agreements.

Capital Flows

  • \(\Delta {\text {CF}}\): The change in total portfolio flows as a percentage of the local country’s GDP, taken from the IMF Balance of Payments statistics (BPM5).

Control Variables

  • GDPgrowth Real GDP per capita growth rate. Data from the Maddison project.

  • INF Inflation rate calculated as the annual percentage change of the CPI index. Data from the Global Financial Data.

  • POLCOMP Political competition as a proxy for institutional quality. Data are from the Polity IV Project database. POLCOMP is the combination of the degree of institutionalization or regulation of political competition and the extent of government restriction on political competition. The higher the value of the POLCOMP, the better the institution quality of a given country.

  • \(\Delta\) INT_RATES Change in local 3-month Treasury yields. Used as a proxy for the local monetary policy surprises. Data from the Global Financial Data.

  • \(\Delta XR\) The change in the exchange rate of the local currency to the dollar, from Global Financial Data.

  • ANCHOR A dummy variable equal to 1 if a country’s currency is pegged to the US dollar in year t and 0 otherwise. Data from Ilzetzki et al. (2017).

  • Gravity variables

    • areap is the log of the product of the areas in km2 of two countries. Data are from the GeoDist database–CEPII (Mayer and Zignago 2011)

    • \(T_{i,j}\) is the bilateral trade value between countries i and j. Data are from the COW project

    • pop is the population of a country. Data from the Maddison project

    • distw is the bilateral distances between the biggest cities of two countries, those inter-city distances being weighted by the share of the city in the overall country’s population (see Mayer and Zignago 2011, for details).

    • areap is the log of the product of the areas (in squared kilometers) of countries i and USA

    • comlang is equal to 1 if the countries share the same official language and 0 otherwise

    • border is equal to 1 if the countries share a border and 0 otherwise

    • landlocked is equal to 1 if the local country is landlocked (i.e., entirely enclosed by land) and 0 otherwise

    • colony is equal to 1 if the countries have ever had a colonial link with the USA

Appendix 2: Sample Details

See Table 8.

Table 8 This table lists the countries in our sample and sample coverage, divided into panels by IMF classification

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Durdu, C.B., Martin, A. & Zer, I. The Role of US Monetary Policy in Banking Crises Across the World. IMF Econ Rev 68, 66–107 (2020). https://doi.org/10.1057/s41308-020-00109-1

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Keywords

  • Banking crises
  • Financial stability
  • Monetary policy shocks
  • Sudden stops
  • Global financial cycles

JEL Classification

  • E44
  • E52
  • F42
  • G15