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The Transmission Mechanisms of International Business Cycles: International Trade and the Foreign Effects of US Monetary Policy

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Abstract

This paper studies the channels through which shocks from one country transmit in the global economy. Using local projections with instrumental variables (LP-IV), we document significant effects of US monetary shocks on output, interest rates, and trade flows in a large panel of countries. We find substantial heterogeneity in the foreign output responses depending on international trade. This association is due to the output share of international trade (extensive margin) and not the elasticity of trade (intensive margin), the exchange rate, or the foreign interest rate responses. To distinguish between the direct and indirect transmission of shocks, we further collect detailed data on bilateral trade linkages and combine the LP-IV approach with spatial econometric models. The indirect spillovers account for nearly half of the total response of foreign output, indicating substantial amplification engendered by the trade network. But the heterogeneity in the total effects is almost entirely due to the direct effects. Moreover, accounting for network effects within this framework leads to estimates of the mean output response up to one-third larger in magnitude than in the nonspatial specifications. Thus, studies that do not explicitly model cross-country linkages may present an incomplete view of international business cycles.

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Notes

  1. We also take advantage of a long-standing literature dedicated to the identification and analysis of the real effects of monetary shocks in the USA (e.g., Romer and Romer 2004; Nakamura and Steinsson 2018). Our paper extends these analyses to foreign economies, focusing on the transmission channels of international business cycles.

  2. Recent work by di Giovanni and Hale (2021) studies the role of the global production network in explaining international spillovers of US monetary policy shocks to stock returns.

  3. Several papers focus on the spillovers stemming from the Federal Reserve’s unconventional monetary policy to emerging markets (e.g., Bhattarai, Chatterjee, and Park 2021).

  4. These data are publicly available on the authors’ websites.

  5. The list of countries and their characteristics are provided in the online appendix (Table A.1).

  6. While several papers focus on unconventional policies (e.g., Eberly, Stock, and Wright 2020), we do not consider the Gürkaynak, Sack, and Swanson (2005) path shocks, as the analysis of forward guidance is beyond the scope of this paper. Our baseline results are robust to removing the period when the path shocks were likely important.

  7. We estimate symmetric responses, as we do not find significant quantitative differences between the effects of monetary tightening and easing in our sample.

  8. Due to space constraints, these and many other additional results are relegated to the online appendix.

  9. This response is strong regardless of the exchange rate regime (Appendix Figure A.3), and could be attributed to several possible reasons. For example, a large pass-through for the floaters could be due to “fear of floating” (Calvo and Reinhart 2002). Bhattarai, Chatterjee, and Park (2020) provide evidence that some emerging markets, to avoid sharp capital outflows, raise their policy rate in response to rising US uncertainty.

  10. Due to a small number of countries in each subsample, we cannot cluster the errors by country, because this procedure is valid only for large N. Instead, we report Driscoll–Kraay standard errors. Because this procedure relies on the Bartlett (Newey–West) truncated kernel within each panel, Driscoll–Kraay standard errors are valid for large T but possibly small N. As required by local projections, we set the bandwidth to \(h+1\) quarters.

  11. The stronger impact on high-trade countries is consistent with the dollar’s dominance as the global currency of trade invoicing (Gopinath et al. 2020; Boz et al. 2022).

  12. The role of exchange-rate responses in the transmission of US monetary shocks is emphasized in Ilzetzki and Jin (2021), among others.

  13. We show the differential responses of other key macroeconomic variables in the online appendix (Fig. A.7).

  14. For example, if \(\lambda _{i,\text {US}}=0.01\) and \(\xi _{i,\text {US}}=\varphi _\text {US}\), the resulting partial effect would be characterized by a semielasticity of \(-0.02\), more than an order of magnitude smaller, in absolute value, than implied by our baseline estimates. We thank our discussant Javier Cravino for providing this example.

  15. For example, in 2019 the GDP share of total exports was 31.9 percent in Canada and 35.0 percent in Spain.

  16. Spatial autoregressions are closely related to the global vector autoregressions used to study international spillovers (e.g., Georgiadis 2016). A key difference is that the spatial models explicitly account for endogenous feedback loops, thereby enabling us to estimate separately the direct and indirect effects ( Elhorst, Gross, and Tereanu 2021).

  17. In the online appendix, we show robustness of our results to setting \(\lambda ^h=0\) or \(\kappa ^h_k=0\), or both (Table A.9).

  18. We also consider alternative normalizations that account for overall trade openness, such as the dominant eigenvalue normalization. We reach qualitatively similar conclusions.

  19. With standard normalizations, the model converges if \(\left| \rho ^h\right| < 1\).

  20. In Online Appendix B, we illustrate this decomposition using a stylized example with \(N=3\) countries.

  21. The nodes’ locations are not informative. They are chosen subjectively to enhance visibility.

  22. The high share of the indirect effect comes almost entirely from the spatial structure of the model and not from the heterogeneity in \(\beta _i\). When we estimate specification (10) with country-invariant \(\beta\), the share of the indirect effect, at 46 percent, remains close to the baseline (see Fig. A.9 in the online appendix).

  23. At the 12 and 13 quarter horizons (the peak absolute response in the nonspatial model), the total effects in the spatial model are larger by 7.0 percent and 14.3 percent, respectively (cf. Table 2 and Fig. 3).

  24. We plot the corresponding distributions in the online appendix (Fig. A.10). Only in two countries does the indirect effect make up less than 30 percent of the total. In seven countries, this share is 70 percent or above.

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Acknowledgements

The authors thank Keith Barnatchez and Sam Tugendhaft for excellent research assistance. For comments and suggestions, they thank Paul Bergin, Ryan Chahrour, Javier Cravino, Mario Crucini, Bill Dupor, Sacha Gelfer, Gita Gopinath, Yuriy Gorodnichenko, Pierre-Olivier Gourinchas, Laura Jackson Young, Ralph Koijen, Ezgi Kurt, Andrei Levchenko, Giovanni Olivei, Nick Sander, Eric Sims, and Eva Van Leemput as well as seminar and conference participants at the Boston Area Macro-Finance Juniors meeting, Society for Economic Dynamics 2021 meeting, Bentley University, National Bank of Ukraine, and the IMF 22nd Jacques Polak Annual Research Conference. The authors thank Haver Analytics for providing some of the publicly available data used in this research. They are especially grateful to Rob Johnson and Eric Swanson for sharing their data. An earlier version of this paper circulated under the title “Output Spillovers from U.S. Monetary Policy: The Role of International Trade and Financial Linkages.” The views expressed in this paper are those of the authors and do not indicate concurrence by the Federal Reserve Bank of Boston, the principals of the Board of Governors, or the Federal Reserve System.

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    Falk Bräuning & Viacheslav Sheremirov

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Bräuning, F., Sheremirov, V. The Transmission Mechanisms of International Business Cycles: International Trade and the Foreign Effects of US Monetary Policy. IMF Econ Rev 71, 300–325 (2023). https://doi.org/10.1057/s41308-022-00179-3

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