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Firm Risk, Misallocation, Disasters and Aggregate Uncertainty

Paper Session

Sunday, Jan. 3, 2021 10:00 AM - 12:00 PM (EST)

Hosted By: American Economic Association
  • Chair: Sebnem Kalemli-Ozcan, University of Maryland

Financial Structure and Volatility of Firms

Emin Dinlersoz
,
U.S. Census Bureau
Sebnem Kalemli-Ozcan
,
University of Maryland
Vincenzo Quadrini
,
University of Southern California
Veronika Penciakova
,
Federal Reserve Bank of Atlanta

Abstract

We use data on privately owned firms to investigate the relationship between the financial structure of firms---specifically leverage---and their idiosyncratic volatility. From a theoretical point of view, the nexus between volatility and financial structure is a two-way stream. On the one hand, more volatile firms are discouraged from borrowing because, in the presence of operational uncertainty, leverage is risky. On the other, leverage could create the conditions for higher operational uncertainty. Both streams could be relevant in practice: firms facing higher operational volatility choose to borrow less (from volatility to leverage) and firms that are more leveraged experience higher idiosyncratic volatility because of the higher leverage (from leverage to volatility). Although it is difficult to separate these two streams empirically, the sign of the relationship could inform us, indirectly, about their prevalence: a negative correlation could be indicative of the prevalence of the first channel (from volatility to leverage) while a positive correlation could be indicative of the prevalence of the second channel (from leverage to volatility). Using firm-level data for a cross-section of US privately-owned firms we find that more leveraged firms display higher operational volatility (sales, earnings and profits). This suggests that the causal link going from leverage to volatility may be more important. We also replicate these findings using data from European firms.

This finding is important for understanding the economic recovery in the aftermath of the COVID-19 crisis. The profitability collapse that many businesses are currently experiencing will result in increased debt burdens. This is likely to increase their idiosyncratic uncertainty which will discourage investments once the pandemic is under control. The consequence could be a much slower and longer recovery with sluggish investment.

The Investment Network, Sectoral Comovement, and the Changing U.S. Business Cycle

Christian vom Lehn
,
Brigham Young University
Thomas Winberry
,
University of Chicago

Abstract

We argue that the network of investment production and purchases across sectors is an important propagation mechanism for understanding business cycles. Empirically, we show that the majority of investment goods are produced by a few ``investment hubs" which are more cyclical than other sectors. We embed this network into a multisector business cycle model and show that sector-specific shocks to the investment hubs and their key suppliers have large effects on aggregate employment and drive down labor productivity. Quantitatively, we find that sector-specific shocks to hubs and their suppliers account for an increasing share of aggregate fluctuations over time, generating the declining cyclicality of labor productivity and other changes in business cycle patterns since the 1980s.

Using Disasters to Estimate the Impact of Uncertainty

Scott R. Baker
,
Northwestern University
Nicholas Bloom
,
Stanford University
Stephen J. Terry
,
Boston University

Abstract

Uncertainty rises in recessions and falls in booms. But what is the causal relationship? We construct cross-country panel data on stock market levels and volatility and use natural disasters, terrorist attacks, and political shocks as instruments in regressions and VAR estimations. We find that increased volatility robustly lowers growth. We also structurally estimate a heterogeneous firms business cycle model with uncertainty and disasters and use this to analyze our empirical results. Finally, we used our VAR results in early 2020 to produce and circulate a real-time forecast, based on the initial stock market returns and volatility response to COVID-19, which accurately predicted the magnitude of the initial drop in US GDP.

The Aggregate Consequences of Default Risk: Evidence from Firm-Level Data

Timothy Besley
,
London School of Economics
Isabelle Roland
,
University of Cambridge
John Van Reenen
,
Massachusetts Institute of Technology

Abstract

This paper studies the implications of perceived default risk for aggregate output and productivity. Using a model of credit contracts with moral hazard, we show that a firm’s probability of default is a sufficient statistic for capital allocation. The theoretical framework suggests an aggregate measure of the impact of credit market frictions based on firm-level probabilities of default which can be applied using data on firm-level employment and default risk. We obtain direct estimates of firm-level default probabilities using Standard and Poor’s PD Model to capture the expectations that lenders were forming based on their historical information sets. We implement the method on the UK, an economy that was strongly exposed to the global financial crisis and where we can match default probabilities to administrative data on the population of 1.5 million firms per year. As expected, we find a strong correlation between default risk and a firm’s future performance. We estimate that credit frictions (i) cause an output loss of around 28% per year on average; (ii) are much larger for firms with under 250 employees and (iii) that losses are overwhelmingly due to a lower overall capital stock rather than a misallocation of credit across firms with heterogeneous productivity. Further, we find that these losses accounted for over half of the productivity fall between 2008 and 2009, and persisted for smaller (although not larger) firms.
Discussant(s)
Nicolas Crouzet
,
Northwestern University
David Rezza Baqaee
,
University of California-Los Angeles
Tarek Alexander Hassan
,
Boston University
Thomas Drechsel
,
University of Maryland
JEL Classifications
  • E3 - Prices, Business Fluctuations, and Cycles
  • O4 - Economic Growth and Aggregate Productivity