News and Uncertainty shocks

The hypothesis that future technology expectations play an important role in driving business cycles was formalized in Beaudry and Portier (2004). By using stock prices as the basis for forming expectations about future economic conditions and by using two sequential identification schemes; first which makes innovations

7 . 4 N e w s a n d U n c e rta i n t y s h o c k s 91 to stock prices orthogonal to TFP shocks, and second which drives the long term movement in TFP, Beaudry and Portier (2006) demonstrate that their news-driven shocks anticipate TFP growth by a couple of years. Beaudry and Portier (2005), Beaudry and Lucke (2010), Beaudry, Dupaigne, and Portier (2011) reach similar conclusions. Jaimovich and Rebelo (2009) in a DSGE model further posit that news shocks about economic fundamentals generate comovement in aggregate productivity and account for comovement in sectoral productivity as well. However, in a different VAR scheme by identifying news shocks as orthogonal to technology innovation and one which maximizes future variation in technology, Barsky and E. R. Sims (2009) show that the positive wealth effect generated from positive news about future productivity cannot lead to an expansion in RBC models. The increase in consumption and leisure from the wealth effect leads to a fall in output and hours. Suppose instead, because of the high elasticity of intertemporal substitution, the real rate of return effect dominates. In that case, investment and hours increase, but the increase in output does not compensate for the increase in investment, and so consumption falls.

The strength of the news-driven business cycle is also challenged when con-sidering the significant relation between periods of economic downturn and high uncertainty. The volatility of the stock market or GDP is an often-used mea-sure of uncertainty. This volatility surges during recessions. However, this surge cannot be explained by a measure of bad news or an increase in risk aversion (during recessions) alone. Only 1 of the 17 instances of volatility jumps from 1962 to 2008 that lowered the expected GDP growth and led to an increase in eco-nomic uncertainty was due to ‘bad news’ (Bloom, 2009). It is not surprising then that the macroeconomic research since the Global financial crisis has emphasized considering uncertainty, volatility, information, and sentiment-based shocks to understand the business cycle fluctuations.

On the one hand, uncertainty negatively impacts growth and spending. Romer (1990) says uncertainty near the Global Depression is responsible for a fall in durable consumer spending. In an influential paper Bloom (2009) depicts the cyclical variation in the standard deviation of firm-level stock returns, which he calls as uncertainty, to be an important determinant of business cycles. Uncer-tainty results in cautious decision-making on behalf of firms, as they deliberate on hiring and investment decisions since adjustment costs make those decisions expensive to reverse. It also results in cautious decision-making on behalf of con-sumers, as during high uncertainty, they delay consumption, especially of durable goods. Both these responses also reduce the efficacy of monetary and fiscal policy.

On the other hand, negative growth creates uncertainty. Period of negative growth, or recessions, also raise uncertainty by slowing down trading activity, difficult forecasting ability, policy miscommunication, and hyper-activism. Baker, Bloom, and Davis (2016) show that due to slackness in business activity, there is an increase in micro-level uncertainty since businesses try out new ideas for the reason that they are now cheaper to try. Nakamura, Sergeyev, and Steinsson (2017) using consumption and growth data from 16 OECD countries find that periods of lower growth have high fluctuations in long-run volatility. Income and wages, especially for low-wage earners, show volatility surge during recessions.

Contrary to traditional business cycle models (Kydland and Prescott,1982), the uncertainty based results from Bloom (2014) and Baker, Bloom, and Davis (2016) further provide evidence that a fall in productivity is an effect of an increase in uncertainty, rather than a response to technological regress. They find that increase in uncertainty has a chilling effect on the productivity-enhancing reallocation of high productivity and low productivity firms. As uncertainty increases, high productivity firms do not want to be aggressive in their productivity allocation, and low productivity firms do not want to cut back on their aggressive propositions. We know that the reallocation of resources tends to drive most of the observed productivity growth. Therefore this hiatus in productivity reallocation during high uncertainty stalls productivity growth and such a stalling effect of uncertainty underlies the theory of uncertainty driven business cycle.

Uncertainty driven business cycle hypothesis finds support from micro-level evidence of Panousi and Papanikolaou (2011), which discusses the impact of CEO level decision making from an increase in uncertainty. CEOs do not make risky investments if their net worth is tied to or highly exposed to the firm’s equity valuation and its risk valuation. The structural model in Bloom (2014) estimates that an average uncertainty shock has reduced the GDP by 1.3%. The uncertainty after the great recession was thrice as compared to previous uncertainty shocks.

So around 3-4% of the fall in GDP during GFC could be attributed to uncertainty.

A sudden increase in uncertainty due to natural disasters, terrorist events explain about 50% of the variation in output (Baker, Bloom, and Davis, 2016) following the event.

Arguably, the business cycle impact of uncertainty is limited only for the short term (Bloom, 2014). In the short run, investment and output reduce, but as uncertainty is reduced and once pent-up demand increases, an increase in hiring and investment leads to a rebound. Similarly, L. Stein and Stone (2010) show