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Uncertain Times, Bad Times


Dr. Giovanni Caggiano

By

May 20th, 2017


This article first appeared in Short Supply 2017 – check out the full magazine via the Short Supply tab at the top of this page!


In January 2009, in the midst of the Global Financial Crisis, Olivier Blanchard, then Chief Economist of the International Monetary Fund, wrote “Crises feed uncertainty. And uncertainty affects behavior, which feeds the crisis. […] [Uncertainty] affects consumption and investment decisions, and is largely behind the dramatic collapse in demand we have observed over the last three months. […] Given the uncertainty, why build a new plant or introduce a new product now? Better to pause until the smoke clears”. [1]

As Blanchard clearly put it, heightened uncertainty is bad for economic performance as it fosters a “wait-and-see” attitude in households and firms, depressing consumption, investment and employment. Uncertainty is typically a by-product of bad economic times: when the economy is in a recession, then uncertainty increases. But uncertainty can also be a cause of bad economic times. In an analysis of the drivers of the Great Recession in the U.S., James Stock (Harvard University) and Mark Watson (Princeton University) wrote: “the shocks that produced the recession primarily were associated with financial disruptions and heightened uncertainty”. [2] In light of these concerns, uncertainty has been one of the most discussed topics by both macro-economists and policy-makers in recent years. This article provides a brief overview of two major issues related to the analysis of uncertainty: i) how to measure uncertainty, and ii) under which conditions uncertainty will have negative effects on the economy.

Uncertainty is a vague concept. Its modern definition can be traced back to Frank Knight, a famous Chicago economist, who in 1921 defined uncertainty as people’s inability to forecast the likelihood of events happening. [3] Uncertainty differs from the concept of risk, which Knight defines as the known probability distribution over a set of events. Tossing a coin is risky, since we do not know the outcome but we know the odds of the outcome. Uncertainty refers, instead, to cases in which we do not even know the odds.

A major challenge that macro-economists have recently faced has been how to measure the level of uncertainty in the economy. One approach looks at the volatility in financial markets. Why? The value of stocks should reflect the underlying value of the firm that issues them. Looking in aggregate, the value of a stock index should therefore reflect the fundamentals of the economy. Volatility in stock prices should then reflect uncertainty about the fundamentals of the economy. In particular, measures of implied volatility (i.e. measures of expected short-term volatility) have been used as proxies of uncertainty. The most popular implied volatility measure is the Chicago Board Option Exchange volatility index, or VIX, also known as the ‘investor fear gauge’. A second popular way of measuring uncertainty has been to measure the extent of disagreement among business and professional forecasters. [4] A third approach to measuring uncertainty looks at the common component in the volatility of forecast errors of several macro and financial variables in a data-rich environment. [5]

What is the information content of these measures? Do they capture the same phenomena? Figure 1 plots four common proxies of uncertainty for the United States from February 1990 to April 2017, all standardised to have mean zero and unit variance for the sake of comparability. A few observations are in order. All measures of uncertainty tend to spike when some recognisable exogenous shocks have hit the economy, such as the 9/11 attacks, the Second Gulf War, and the collapse of Lehman-Brothers. A second observation is that, despite their tendency to move together, different measures of uncertainty convey different information. For example, the level of uncertainty related to economy policy decisions, summarised by the EPU index, has been relatively higher after the GFC, when both monetary and fiscal policy authorities have started to move into uncharted territories to respond to the deep recession of 2007-09.

But why is uncertainty bad for the economy? After all, an increase in uncertainty would simply reflect the fact that, according to households and firms, both very good and very bad outcomes have become equally more likely. Economists have highlighted a few channels through which uncertainty can damage the real economy. Two explanations, however, have become predominant and found empirical support in academic literature. The first relates to the concept of real options and partial irreversibilities, while the second looks at risk aversion and the existence of frictions in financial markets.

The real options argument goes as follows. [6] Investment and hiring choices, from the firm’s perspective, can be considered as a series of options. A firm has an option of building a new plant or hiring a new worker today, or postponing the action into the future. Acting today can be profitable if certain conditions are realised. If the firm becomes uncertain about the realisation of these conditions, then it may prefer to wait, given that actions like investment and hiring have adjustment costs that make them expensive to reverse. More formally, if uncertainty increases, the option value of delay for the firm increases. An increase in uncertainty, therefore, makes firms more cautious: they will stop hiring and stop investing. Because of labour attrition and capital depreciation, this will lead to a decrease in the aggregate level of investment and employment in the economy. There is also another negative side effect of heightened uncertainty: firms become less sensitive to the conditions prevailing in the economy, such as interest rate levels. This complicates the task of economic stabilisation through tools such as monetary policy, when compared to periods of low uncertainty.

The second channel works via investors’ risk aversion and financial constraints. [7] Heightened uncertainty increases risk premia, which in turn raises the cost of finance for firms. Moreover, increased uncertainty triggers a higher probability of borrowers defaulting. Default premiums will increase and borrowing costs will go up. In the presence of financial frictions, heightened uncertainty leads firms to build up cash to hedge against future potential shocks and to reduce debt, particularly short-term debt, to increase their flexibility to respond to adverse shocks. The overall effect is a reduction in investment and employment.

Is there empirical evidence that high uncertainty stifles the economy? Yes indeed. Studies have shown that an increase in perceived uncertainty leads to an overall decline in real activity, particularly investment but also employment and consumption. This is especially true when the economy is already experiencing extreme conditions, such as a recession, periods of financial stress or abnormally low interest rates. As such, the present global economic conditions, characterised by low growth, high levels of financial strain, and low interest rates, are a dangerous ground for uncertainty. Events such as Brexit, for example, have been considered particularly worrying not only for the expected effects on trade, but also for the negative effects due to the uncertainty that it generates. Policy-makers and regulators need to ensure that they counteract the uncertainty generated by such events, in order to reduce the chances of another economic slowdown.

 

Dr Giovanni Caggiano is an Associate Professor in the Department of Economics. His main research interests are in the area of empirical macroeconomics and monetary policy.

 

[1] “(Nearly) Nothing to Fear but Fear Itself”, guest article published in The Economist on 29 January 2009.

[2] James Stock and Mark Watson (2012): “Disentangling the Channels of the 2007-09 Recession”, Brookings Papers on Economic Activity, 44: 81-135.

[3] Frank H. Knight (1921): Risk, Uncertainty, and Profit. Boston, MA: Hart, Schaffner & Marx; Houghton Mifflin Company.

[4] The two references are: Rudiger Bachmann, Steffen Elstner, and Erik R. Sims (2013): “Uncertainty and Economic Activity: Evidence from Business Survey Data”, American Economic Journal: Macroeconomics, 5: 217-49. Barbara Rossi and Tatevik Sekhposyan (2015): “Macroeconomic Uncertainty Indices Based on Nowcast and Forecast Error Distributions”, American Economic Review: Papers & Proceedings, 105: 650-55.

[5] Kyle Jurado, Sydney Ludvigson, and Serena Ng (2015): “Measuring Uncertainty”, American Economic Review, 105: 1177-1216. Sydney Ludvigson, Sai Ma, and Serena Ng (2016): “Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?”, New York University and Columbia University.

[6] The main contribution in this respect is: Nicholas Bloom (2009): “The Impact of Uncertainty Shocks”, Econometrica, 77: 623-85.

[7] Important contributions in this area are: Larry Christiano, Roberto Motto, and Massimo Rostagno (2014): “Risk Shocks”, American Economic Review, 104: 27-65; Simon Gilchrist, Jae Sim, and Egon Zakrajsek (2014): “Uncertainty, Financial Frictions, and Investment Dynamics”, Boston University; Ivan Alfaro, Nicholas Bloom, and Xiaoji Lin (2016): “The Finance-Uncertainty Multiplier”, Stanford University and Ohio State University.

The views expressed within this article are those of the author and do not represent the views of the ESSA Committee or the Society's sponsors. Use of any content from this article should clearly attribute the work to the author and not to ESSA or its sponsors.

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