Today we are pleased to introduce to you from Jamel Saadawi (University of Strasbourg) and William Ginn (LabCorp, Artificial Intelligence).
How do geopolitical risk shocks affect monetary policy? In the wake of the global financial crisis, international trade relations have become increasingly influenced by geopolitical considerations. Indeed, it is now widely recognized that geopolitical risks and bilateral political tensions can have a significant impact on economic operations (Caldara and Iacoviello, 2022). Geopolitical risk shocks affect the economy through different channels. Some of these are inflationary, such as the commodity price pipeline, especially oil prices (Mignon and Saadaoui, 2024) [Econbrowser post] and monetary pipelines (Gopinath, 2015). Furthermore, other channels are also deflationary, such as the consumer sentiment channel and the financial conditions channel (Forbes and Warnock, 2012). It is difficult to determine ex ante whether a geopolitical risk shock will be inflationary or deflationary. Recent research shows that geopolitical shocks historically tend to lead to inflation (Caldara et al., 2022).
According to a panel of 20 economies, William Ginn and I develop and estimate enhanced panel Taylor rules through linear and nonlinear local projection (LP) regression models. First, the linear model shows that interest rates remain relatively unchanged during geopolitical risk shocks. Second, the results from nonlinear models differ in that policy responses are attenuated in expansionary states, which operate in proportion to transient shocks. However, geopolitical risks may amplify policy responses in non-expansionary periods.
To consider the global impact of geopolitical risks, we analyze the impact of GPR on interest rates using rich industrial production, consumer price index (CPI), short-term interest rates, GPR and EPU data for 20 economies that account for approximately 82% of global GDP. The twenty economies include: Brazil (BRA), Switzerland (CHE), Chile (CHL), Canada (CAN), China (CHN), Colombia (COL), Czech Republic (CZE), the Eurozone (19 countries ;Euro)), United Kingdom (GBR), Hungary (HUN), Ireland (IRL), India (IND), Israel (ISR), Japan (JPN), Mexico (MEX), South Korea (KOR), Poland (POL), Russia (RUS), Sweden (SWE) and the United States (USA). We use monthly data covering January 1999 to February 2022.
The international data of explanatory variables and explained variables for 20 economies are shown in Figure 1. In the output growth data, we can clearly see three phases of global economic slowdown, namely the dot-com bubble in 2001, the global financial crisis in 2008-2009, and the pandemic in 2020. Except in the aftermath of the global financial crisis and pandemic, inflation charts show greater dispersion over time and between countries. In terms of monetary tightening and easing, we have also observed that the monetary cycles triggered by the global financial crisis (easing) and after the epidemic (tightening) are the most synchronized. In addition, economic policy will be more uncertain after the epidemic. In recent times, we can observe an increase in GPR levels due to the war in Ukraine. More generally, as discussed in Mignon and Saadaoui (2024), GPR emerged around 2001 as a result of 9/11 and the post-2009 increase in tensions between the United States and China and the election of Donald Trump surged significantly.
figure 1: international data
The Taylor rule is designed to capture central bank responses to inflation and output deviations (Taylor, 1993). By examining rules in expansionary and non-expansionary countries, this study provides insight into how central banks adjust interest rates in response to economic conditions during geopolitical shocks. The LP model developed by Jordà (2005) is used to estimate the enhanced Taylor rule based on GPR shocks. Periods of high GPR can have adverse effects on the economy. Central banks take current economic conditions, including uncertainty and geopolitical tensions, into account when implementing monetary policy. The Taylor rule provides a framework for central banks to adjust interest rates based on economic indicators, in which we test whether this adjustment will be affected by the level of GPR.
figure 2: Linear LP model
Note: The shock is a one standard deviation shock to the change in GPR. The confidence interval is 90%.
image 3: Non-Linear LP Model (Transfer Variable: Twelve Month Center Shiftaverage output growth rate) – baseline
Figure 4: Nonlinear LP model (transition variable: recession dummy variable)
Overall, the linear LP (Figure 2) model shows a negative relationship between monetary policy responses and GPR shocks, where policy responses are attenuated and statistically insignificant. Nonlinear models (Figures 3 and 4) show that in an expansionary state, GPR shocks lead to a weakened interest rate policy response. If interest rate responses operate in a manner that is proportional to the transitory nature of shocks, and allow for lags in the effects of monetary policy, then there is no policy dilemma. In the non-expansionary state, the impact of GPR shocks on monetary policy is different. The findings show that responses become more relaxed and statistically significant over multiple periods. The last result is robust to the choice of transition variables (GDP, OG with HP filter, recession dummy, EPU). Having said that, looser monetary policy following geopolitical risk shocks is observed among more independent groups of central banks and emerging countries (Figures 5 to 8).
Figure 5: Baseline Mode Nonlinear LP – Advanced Economies: CAN, CHE, DNK, EUR, GBR, JPN, KOR, NOR, SWE, USA
Figure 6: Benchmark Model Nonlinear LP – Emerging Economies: BRA, CHL, CHN, COL, HUN, IND, ISR, MEX, POL, RUS
Figure 7: Baseline Mode Nonlinear LP – More Independent Central Bank (Central Bank Independence – Dicer and Eichengreen, 2014): CAN, CHL, EUR, HUN, MEX, NOR, RUS, SWE
Figure 8: Baseline Model Nonlinear LP – Central Banks with Less Independence (Central Bank Independence – Dicer and Eichengreen, 2014): CHN, COL, DNK, GBR, IND, ISR, JPN, KOR, POL, USA
main reference
Caldara, D., Conlisk, S., Iacoviello, M. and Penn, M. (2022), “Do geopolitical risks raise or lower inflation”, Fed Board of Governors.
Caldara, D. and Iacoviello, M. (2022), “Measuring geopolitical risk”, American Economic Review 112(4), 1194-1225.
Dincer, NN and Eichengreen, B. (2014), “Central Bank Transparency and Independence: Updates and New Measures”, international journal of central banks 10(1), 189-259.
Mignon, V. and Saadaoui, J. (2024), “How do political tensions and geopolitical risks affect oil prices?”, Energy Economics 129,107219.
* The authors thank Menzie Chinn for helpful suggestions and Elena Pesavento for guidance on local forecasts for each state. Interested readers can find the latest version of the paper on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4762672
The author of this article is Jamel Saadawi and William Jean.
