# Business Cycles

Paper Session

#### Friday, Jan. 5, 2018 2:30 PM - 4:30 PM

Pennsylvania Convention Center, 107-A
Hosted By: American Economic Association
• Chair: Emily Marshall, Dickinson College

### Monetary Policy and the Firm: Some Empirical Evidence

#### Abstract

We use novel micro data to examine the impact of monetary policy shocks on the employment decisions of incumbent firms. The firm-level data covers both listed and unlisted firms, manufacturing and non-manufacturing firms, and contains detailed balance-sheet information. Monetary policy shocks are identified using high frequency market reactions to policy meetings as an external instrument. The dynamic response of aggregate employment and the (weighted) firm-level responses align, with a peak employment decrease of around 0.3% in response to a 25bp contractionary monetary policy shock. This average response masks heterogeneity. We establish that monetary policy shocks have a greater impact on firms that are initially smaller, younger, more highly levered, and have a poorer credit rating, suggestive of a financial frictions narrative.

### The Behavior of Small and Large Firms Over the Cycle

#### Abstract

In this paper we examine the behavior of small and large firms over the business cycle. A widely held view is that monetary policy fluctuations play a central role in the business cycle and that these fluctuations affect small firms disproportionately. We ask whether the data support this view.
We use data from the Annual Survey of Manufacturing, County Business Patterns, the Longitudinal Research Database, and the Quarterly Financial Reports to examine the behavior of small and firms over postwar business cycles. For the Great Depression period, we have constructed a new data set using Moody's database, which together with the Census of Manufacturing, can shed light on this behavior during the Great Depression.
Our tentative findings from the data are that the two types of firms behave similarly over the business cycle, but during monetary contractions, small firms fall more.
We construct on equilibrium model with collateral constraints that quantitatively rationalize these findings.

### Uncertainty and Economic Activity: A Multi-Country Perspective

#### Abstract

Empirical measures of uncertainty behave countercyclically, but economic theory does not provide definite guidance on the direction of causation between economic activity and uncertainty. This paper proposes a new multi-country approach to the analysis of interaction between uncertainty and economic activity, without \textit{a priori} restricting the direction of causality. Two common factors, real' and a financial', are identified assuming different patterns of cross-country correlations of country-specific innovations to real GDP growth and realized stock market volatility. Specifically, it is assumed that the real factor is sufficient to model cross-country correlations of growth innovations, but both real and financial factors are needed to model the cross-country correlations of volatility innovations. We then document that the assumptions made are consistent with the data, and quantify the absolute and the relative importance of the common factor shocks as well as country-specific volatility and GDP growth shocks. The paper highlights three main empirical findings. First, it is shown that that most of the unconditional correlation between volatility and growth can be accounted for by the real common factor, which is proportional to world growth in our model and linked to the risk-free rate in a standard consumption-based CAPM framework. Second, it is found that the share of volatility forecast error variance explained by the real common factor and by country-specific growth shocks amounts to less than 5 percent. Third and finally, shocks to the common financial factor explain about 10 percent of the growth forecast error variance, and when such shocks occur, their negative impact on growth is large and persistent. In contrast, country-specific volatility shocks account for less than 1-2 percent of the growth forecast error variance.
##### JEL Classifications
• E3 - Prices, Business Fluctuations, and Cycles