Since the onset of the “Great recession”, economists have struggled to explain why the recovery has been so slow, despite the many policy measures that have been passed to re-invigorate economic activity. One candidate explanation that several have pointed to, for instance Baker, Bloom, Davis and Van Reenen (2012), is economic uncertainty. The argument is that uncertainty is likely to induce cautious behaviour, as decisions made today are more likely to prove “wrong” in the future, in which case they will have to be reversed at some cost.

Uncertainty-induced cautiousness influences the economy in two ways.

  • First, there is the direct effect that economic activity contracts because fewer firms hire workers and make investments.

Much recent research, such as Bloom (2007), Bachman, Elstner and Sims (2012), Baker, Bloom and Davis (2013) and Jurado, Ludvigsson and Ng (2013), find support of this effect in aggregate data.

  • Second, there is a somewhat subtler effect that economic policy might become less effective as firms are less willing to make decisions of any kind, and therefore less sensitive to marginal changes in investment and hiring incentives.

While well-understood in theory, this mechanism has not received much empirical attention. We have therefore empirically explored how the macroeconomic impact of monetary policy is affected by the prevailing degree of economic uncertainty.

New research

Our recent research estimates how uncertainty interacts with the effectiveness of monetary policy “shocks” (Aastveit, Natvik and Sola, 2013). We consider four countries: The US, Canada, the United Kingdom and Norway. For each country, we identify the innovations to monetary policy using conventional techniques suggested elsewhere in the rich literature on monetary policy. Compared to this literature, the novelty of our study is to let the impact of policy interact with a variety of uncertainty measures.1 Following the recent studies of uncertainty we use seven different US-based measures of uncertainty:

  • The US stock-market volatility index2
  • The US corporate-bond spread
  • Forecasters’ disagreement taken from the Federal Reserve Bank of Philadelphia’s Business Outlook Survey
  • A Google-based measure of uncertainty3
  • Two measures of macroeconomic uncertainty created by Jurado et al. (2013)
  • The “economic policy uncertainty index” created by Baker et al. (2013).
Muted influence of monetary policy when uncertainty is high

Figure 1 shows the US-responses of GDP, investment and consumption to a monetary policy shock, for two different levels of uncertainty, as measured by stock market volatility. The red curve is computed holding the uncertainty level at the 90th percentile of its historical distribution. The blue curve is computed holding uncertainty at its tenth percentile.

We see that when uncertainty is low, real activity falls as conventional monetary theory would predict. In contrast, when uncertainty is high the picture looks very different: The policy impulse has only negligible effects. Investment falls by more than 1 % when volatility is low, while it falls by less than 0.5 % when uncertainty is high. Consumption falls by a maximum of almost three percentage points when uncertainty is low, but less than one percentage point when uncertainty is high. The GDP response is consistent with the investment and consumption movements, as its maximum response is around 1 % under low uncertainty, but less than 0.5 % under high uncertainty.

Figure 1.

Interestingly, the same qualitative relationship between uncertainty and policy effectiveness is repeated when we consider other uncertainty indicators. Figure 2 shows the investment responses to a monetary impulse under high and low uncertainty, using each of the six remaining uncertainty measures. For five of these indicators, we see the same pattern, namely that policy effects are weaker when uncertainty is high. The one measure for which results deviate is the Economic Policy Uncertainty index of Baker (2013).

Figure 2.

Canada, the UK, Norway

When we extend our investigation to Canada, the United Kingdom and Norway, we should expect weaker effects, as the uncertainty indicators we use are measured in the US. This is also what we find.

  • For Canada, the qualitative pattern is the same as in the US for all uncertainty measures, but the influence of uncertainty on policy effectiveness is quantitatively weaker.
  • In the UK and Norway, the dampened policy influence emerges primarily when the financial market based uncertainty indicators are used, and less so or not at all for the other measures.

A likely reason why the financial measures of US economic interact more strongly, is that financial markets are internationally integrated, and therefore tend to reflect aspects of the economic environment that are relevant across countries, whereas the non-financial measures are more specific to the US, and less relevant elsewhere.


Over the last years economic uncertainty has been given much attention, both by policymakers and in the academic literature, as a potential influence in business cycle fluctuations. Much of the debate has been motivated by concerns that elevated uncertainty might motivate firms and households to delay decisions that are costly to reverse and make them less responsive to policy shocks.

Our findings indicate that indeed monetary policy is less effective when uncertainty is high. This implies that when uncertainty is high, monetary policymakers may face a trade-off between acting decisively and acting correctly, as policy must be more aggressive than otherwise in order to stabilise economic activity.

Our results are consistent with the “caution effect” suggested by economic theory, which maintains that in presence of fixed adjustment costs uncertainty increases agents’ opportunity cost of waiting and therefore makes policy less effective.

Our findings, however, show that the pattern of reduced policy effect is particularly stark when uncertainty measures from financial markets are utilised. This could indicate that financial channels are playing a role. Further research on the exact mechanism behind the policy ineffectiveness effects we find seems warranted.

Editors' Note: The views expressed in this article are those of the author(s) and do not necessarily represent those of the Norges Bank, IMF or their policy.


Aastveit K A, Natvik G J, and Sola S (2013), "Economic Uncertainty and the Effectiveness of Monetary Policy", Norges Bank Working Paper N. 17/2013.

Bachmann R, S Elstner, and E Sims (2013), "Uncertainty and economic activity: Evidence from business survey data", American Economic Journal: Macroeconomics, forthcoming.

Baker, S R, N Bloom, and S J Davis (2012), "Measuring economic policy uncertainty" Working paper.

Baker, Scott and Nicholas Bloom (2011), “Does Uncertainty Drive Business Cycles? Using Disasters as a Natural Experiment”, Working Paper, Stanford University.

Bachmann, R and C Bayer (2011), "Uncertainty business cycles - really?", NBER Working Papers 16862, National Bureau of Economic Research, Inc.

Bernanke, B S (1983), "Irreversibility, uncertainty, and cyclical investment", The Quarterly Journal of Economics 98 (1), 85–106.

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Bloom, N. (2009), "The impact of uncertainty shocks", Econometrica 77 (3), 623–685.

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Dixit, A and R Pindyck (1994). Investment under Uncertainty. Princeton University Press.

Sa F, P Towbin, and T Wieladek (2013), "Capital inflows, financial structure, and housing booms", Journal of the European Economic Association, forthcoming.

IMF (2012), “Coping with high debt and sluggish growth”, World Economic Outlook, Washington DC.

Jurado K, S C Ludvigson, and S Ng (2013), "Measuring uncertainty", technical report, Columbia University

Kose A and Terrones M E (2012), “Uncertainty Weighting on the Global Recovery”,, October 18.

Sa F, P Towbin, and T Wieladek (2013), "Capital inflows, financial structure, and housing booms", Journal of the European Economic Association, forthcoming.

Towbin, P and S Weber (2013), "Limits of floating exchange rates: The role of foreign currency debt and import structure", Journal of Development Economics 101 (1), 179–101.

Vavra, J (2013), "Inflation dynamics and time-varying uncertainty: New evidence and an Ss interpretation", Working paper, University of Chicago.

1 We utilise structural vector autoregressions with interaction terms between economic uncertainty and monetary policy. The interaction SVAR methodology is the same as in Towbin and Weber 2013 and Sa et al. 2013.

2 For the period before 1986 we use Bloom’s measure of stock-market volatility and for the period after 1986 we use the Chicago Board Options Exchange index of implied volatility.

3 Measures (ii) to (iv) are taken from Bachmann et al. (2013), see their paper for more details.

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