1 September 2025
Economic uncertainty has been elevated recently due to geopolitical conflicts and trade tensions. This blog post investigates whether, and how much, high economic uncertainty affects monetary policy transmission in the euro area.
Changes in interest rates make it cheaper or more expensive for businesses to get a loan or and for households to get a mortgage. But when the economic future is uncertain, businesses and households are less likely to invest in big, long-term plans in the first place. This means they may not react as much to interest rates changes as they would under more predictable circumstances. In this blog post, we investigate this effect and show that changes in monetary policy have a weaker impact on the euro area economy when uncertainty is high.
Measuring economic uncertainty
Almost everyone knows what uncertainty feels like. But it's not easy to define it as a concept. In economics, “risk” describes an outcome that may or may not occur, but there is some objective sense of how likely it is. Uncertainty is different. It means that we are faced with several possible outcomes, but we don’t know the chances of each one happening. Economists often measure economic uncertainty by looking at things such as how much stock prices or GDP go and up and down, how much forecasts of economic growth differ from each other, or even how often the word “uncertainty” shows up in the news (Bloom 2014).
In this blog post, we focus on uncertainty about economic growth. As a proxy for uncertainty we use the interquartile range of the distribution of euro area industrial production – that is, how much industrial production varies over time.[1] We estimate it using the methodology by Falconio and Manganelli (2025). Industrial production is an important variable for our purpose because monetary policy transmits mainly through capital-intensive activities, such as manufacturing.[2]
Chart 1 shows our economic uncertainty indicator. In early 2025 it reached its highest level since the COVID-19 pandemic. Before that, such levels occurred only during the euro area sovereign debt crisis. The recent increase is likely to reflect the effects of global trade frictions and geopolitical tensions.
Chart 1
Interquartile range of euro area industrial production
Percentage points
Source: ECB staff calculations.
Notes: The 25-75% interquartile range, our proxy for economic uncertainty, is endogenously obtained by jointly estimating different quantiles of euro area industrial production (excluding Ireland) as a function of industrial production lagged quantiles and a set of control variables, following Falconio and Manganelli (2025). The latest observation is for April 2025.
How does economic uncertainty influence transmission?
We analyse how monetary policy affects the economy when uncertainty is high and when it is low. We use financial market movements in a tight time window around ECB monetary policy decisions to spot any unexpected changes in monetary policy (Altavilla et al., 2019). We then include an interaction term between these monetary policy shocks and economic uncertainty in a local projections framework (Jordà, 2005). This allows us to see how economic outcomes, such as inflation and unemployment, respond to monetary policy in times of high and low uncertainty.
Chart 2 shows the effects of an expansive monetary policy shock on inflation and unemployment depending on the level of economic uncertainty. We look at the results of a surprise 100-basis point drop in the three-month overnight index swap rate under high and low uncertainty. The uncertainty regimes are defined as the top and bottom 10% of the distribution of the uncertainty indicator. When economic uncertainty is low, an unexpected interest rate cut leads to higher inflation and lower unemployment, just as economic theory suggests. But when uncertainty is high, the same shock has a much more muted impact on the economy. Under such conditions, economic agents apparently respond less to changes in borrowing costs. For inflation, the peak impact of a monetary policy shock, reached after two years, is about 9 basis points lower and not statistically different from zero when uncertainty is high. For unemployment, the peak impact after one year is about 17 basis points smaller under high uncertainty.
Our findings suggest that the strength of monetary policy transmission depends on the degree of uncertainty in the economy. This means that, to achieve a given intended effect, central banks may need to act more forcefully during times of high uncertainty than under low uncertainty. This is in line with similar analyses for other jurisdictions (e.g. Aastveit, et al., 2017), and with theoretical models focusing on the degree of price flexibility during periods of high and low volatility (Vavra, 2014).
This is not to say that central banks should always ease or tighten monetary policy more aggressively during periods of high uncertainty. Volatile economic conditions are likely to make firms and households more hesitant to spend on consumption and investments independent of what the central bank does. While such a slowdown in activity leans in the same direction that central banks want to steer the economy to during a tightening cycle, it works against the effects that an easing of monetary policy usually wants to achieve. Our results do not speak to the total effect that monetary policy needs to have in the presence of uncertainty. Instead, they show that if central banks need to adjust the needle by a given amount, then they may find that they have to step more forcefully on the pedals when uncertainty is high.
Chart 2
Peak impact of expansive monetary policy shocks under different macroeconomic uncertainty regimes
Percent
Source: ECB staff calculations.
Notes: Peak impulse responses to a 100-basis point monetary policy shock estimated using local projections, with the interquartile range indicator in its upper (“high uncertainty”) and lower (“low uncertainty”) decile. Monetary policy shocks are identified using the “poor man’s sign restrictions” as in Jarociński and Karadi (2020). The response peaks at the 25-month horizon for inflation and the 12-month horizon for unemployment. The local projections control, among other things, for the interquartile range of euro area industrial production (excluding Ireland), our proxy for economic uncertainty, and its interaction with monetary policy shocks. The uncertainty indicator is endogenously obtained by jointly estimating different quantiles of industrial production as a function of industrial production lagged quantiles and a set of control variables, following Falconio and Manganelli (2025). The outcome variables are the euro area Harmonised Index of Consumer Prices (left panel) and unemployment (right panel). All variables are in log-levels. All data are at monthly frequency for the 2002m1-2025m4 period. Standard errors are derived from the Newey and West (1987) estimator to account for autocorrelation and heteroskedasticity.
References
Aastveit, K., Natvik, G., and Sola, S. (2017), “Economic uncertainty and the influence of monetary policy“, Journal of International Money and Finance, Vol. 76, pp. 50-67.
Altavilla, C., Brugnolini, L., Gürkaynak, R.S., Motto, R., and Ragusa, G. (2019), “Measuring euro area monetary policy”, Journal of Monetary Economics, Vol. 108, pp. 162-179.
Bloom, N. (2014), “Fluctuations in Uncertainty”, Journal of Economic Perspectives, Vol. 28(2), pp. 153-176.
Falconio, A. and Manganelli, S. (2025), “Financial conditions, business cycle fluctuations and growth-at-risk”, Journal of Economic Dynamics and Control, Vol. 176.
Hauptmeier, S. and Holm-Hadulla, F. (2023), “Industry structure and the real effects of monetary policy”, Economic Bulletin, Issue 7, ECB.
Jarociński, M. and Karadi, P. (2020), “Deconstructing Monetary Policy Surprises—The Role of Information Shocks”, American Economic Journal: Macroeconomics, Vol. 12(2), pp. 1-43.
Jordà, Ò. (2005), “Estimation and Inference of Impulse Responses by Local Projections”, American Economic Review, Vol. 95(1), pp. 161–182.
Vavra, J. (2014), “Inflation Dynamics and Time-Varying Volatility: New Evidence and an Ss Interpretation”, Quarterly Journal of Economics, Vol. 129(1), pp. 215-258.
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We exclude industrial production in Ireland due to its highly volatile components.