FINANCIAL RISK AND UNEMPLOYMENT

• Author: Zvi Eckstein,David Weiss,Ofer Setty
• DOI: http://doi.org/10.1111/iere.12360
• Published date: 01 May 2019
• Date: 01 May 2019

INTERNATIONAL

ECONOMIC

REVIEW

May 2019

Vol. 60, No. 2

DOI: 10.1111/iere.12360

FINANCIAL RISK AND UNEMPLOYMENT∗

BYZVI ECKSTEIN,OFER SETTY,AND DAVID WEISS1

Interdisciplinary Center, Herzliya, Israel; Tel Aviv University, Israel; Tel Aviv University, Israel

There is a strong correlation between corporate interest rates, their spreads relative to Treasuries, and the

unemployment rate. We model how corporate interest rates affect equilibrium unemployment and vacancies,

in a Diamond–Mortesen–Pissarides search and matching model. Our simple model permits the exploration of

U.S. business cycle statistics through the lens of financial shocks. We calibrate the model using U.S. data without

targeting business cycle statistics. Volatility in the corporate interest rate can explain a quantitatively meaningful

portion of the labor market. Data on corporate firms support the hypothesis that firms facing more volatile

financial conditions have more volatile employment.

1. INTRODUCTION

We document a strong correlation between corporate financial conditions, as measured by the

Baa interest rate (r) and their spread relative to Treasuries, and unemployment (u) or vacancies

(v).2Baa interest rates rise during recessions even as Treasury rates and the Federal Funds

rate decline, reflecting a countercyclical interest rate spread. Table 1 shows that the corporate

interest rate and spread are very volatile, significantly more so than productivity, and have high

cross-correlations with unemployment.3Our main question is: How important are corporate

financial conditions for understanding the volatility of unemployment and vacancies?

We address our question using the classic Search and Matching model, as in the Diamond

(1982), Mortensen (1982), and Pissarides (1985) (DMP) model with capital. Each firm is either

∗Manuscript received November 2016; revised April 2018.

1We thank our editor Guido Menzio and three anonymous referees for greatly improving this article. We thank

Gadi Barlevy, Lawrence Christiano, Martin Eichenbaum, Jesus Fernandez-Villaverde, Jordi Gali, Joao Gomes, Jeremy

Greenwood, Bob Hall, Moshe Hazan, Elhanan Helpman, Urban Jermann, Fatih Karahan, Nobu Kiyotaki, Iourii

Manovskii, Kurt Mitman, Stan Rabinovich, Itay Saporta-Eksten, Ali Shourideh, Mathieu Taschereau-Dumouchel,

Venky Venkateswaran, Gianluca Violante, Yaniv Yedid-Levi, and seminar participants at numerous conferences and

workshops for their very helpful comments. Setty’s research is supported by the Marie Curie International Reinte-

gration Grant, European Commission, EC Ref. No. 276770. Please address correspondence to: David Weiss, The

Eitan Berglas School of Economics, Tel Aviv University, P.O.B. 39040, Tel Aviv, Gush Dan, Israel (IL). E-mail:

davidweiss@post.tau.ac.il.

2Baa is a credit rating of corporate default risk. For the U.S. Treasury, we use the five-year Constant Maturity Rate.

We frequently refer to the corporate interest rate as the interest rate and to the corporate interest rate spread relative

to the five-year Treasury interest rate as the spread. We always use real interest rates under a variety of definitions of

inflation. The interest rate spread is unaffected by inflation.

3We choose the time period 1982–2012, as Gali and van Rens (2014) claim that during this time, labor productivity

became less procyclical, opening the door for other mechanisms to be explored. Jermann and Quadrini (2012) use

a similar time period in their analysis of financial shocks and the macroeconomy. We include both lagged and con-

temporaneous values here for completeness. During the rest of the article, unless otherwise specified, we use only

contemporaneous values.

475

C

(2018) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social

and Economic Research Association

476 ECKSTEIN,SETTY,AND WEISS

TABLE 1

SUMMARY STATISTICS OF THE U.S.QUARTERLY TIME SERIES DATA, 1982–2012

uvrSpread Productivity

Standard Deviation 0.11 0.12 0.14 0.35 0.01

Correlation with u{ two-quarter lags 0.26 0.71 −0.32

contemporaneous 0.32 0.62 0.05

NOTES: The table reports statistics from quarterly U.S. time series data, in HP-log deviations with a smoothing parameter

of 1,600. The values for the standard deviation of the HP deviations, instead of HP-log deviations, of the interest rate

and spread are 0.8% and 1.0%, respectively. The cross correlations (lagged) with unemployment rates is 0.21 (0.17)

for the interest rate and 0.60 (0.73) for the spread. Nominal interest rates converted to real interest rates use realized

changes in the core PPI (producer price index). See Appendix A.1 for data definitions.

vacant or matched with one worker. Firms borrow from banks in order to finance their capital,

which is used either by workers or in a vacancy.4The firms pay the corporate interest rate

and cover the depreciation costs of capital. Banks borrow from workers (depositors) and

experience a financial intermediation cost while lending to firms. We study exogenous shocks

to those costs, which represent monitoring costs, changing default risk and recovery rates,

intermediation costs, uncertainty shocks, or other shocks that would affect the interest rate

perceived by firms. Free entry of banks pins down the corporate interest rate to be the rate paid

to workers plus financial intermediation costs. Workers are risk neutral, are either employed

or unemployed, and make a consumption/savings choice. Matches between unemployed

workers and vacant firms occur in a frictional matching market. Wages are determined by

Nash bargaining based on the dynamic value functions of firms and workers. We solve for

the closed-form solution of the equilibrium market tightness (θ=v

u) of the model given the

stochastic process of interest rates, which allows us to solve for the general equilibrium of the

model, including unemployment, vacancies, and wages.

Interest rates affect firm profits directly by influencing capital costs and thus the incentive to

hire. We call this the ow prot channel. The interest rate shock also affects vacancy posting

costs, as we assume that vacancies require the capital a worker would use to be available. We

call this the vacancy cost channel. We follow much of the literature in assuming exogenous and

constant separations between workers and firms.5

We calibrate the model to U.S. data to match vacancy costs, average job-finding rates, and

average labor market tightness. We use the observed Baa interest rate to discipline financial

shocks. In our calibration, we do not target business cycle statistics related to unemployment,

vacancies, or market tightness in setting parameter values. The main result of our article is that a

simple model with empirically disciplined financial shocks can go a long way toward explaining

observed unemployment, vacancies, market tightness, and other business cycle volatility.

Quantitatively, our benchmark calibration generates about 60%–70% of observed labor

market volatility in the sample period of 1982–2012. About 60% of model volatility is explained

by the flow profit channel, whereas the other 40% comes from the vacancy cost channel.

Delving deeper into the model (Section 4) shows that our results depend on the assumption

that all capital is debt financed, whereas in the data debt is roughly 40%–60% of capital. If we

take the extreme assumption that the nondebt portion of capital, such as equity, is not subject to

cost fluctuations, then the fraction of observed labor market volatility generated by the model

is roughly linear in the fraction of capital assumed to be subject to financial shocks. However,

4The assumption that firms use debt to finance investment and rental of capital is common in many studies discussed

below, including Jermann and Quadrini (2012), which is closely related to our article. More recently, Bigio (2015) builds

a model in which capital is financed under both limited enforcement contracts and asymmetric information and applies

it to the Great Recession. In a different context, Bocola and Lorenzoni (2017) model the decisions of a central bank

with self-fulfilling financial crises. Banks intermediate capital, with the central bank acting as a lender of last resort in

multiple currencies.

5See, for instance, Shimer (2005) and Hagedorn and Manovskii (2008). See Shimer (2012) for an empirical study that

shows that the job-finding probability accounts for 77% of fluctuations in the unemployment rate since 1948, rising to

90% after 1987.

FINANCIAL RISK AND UNEMPLOYMENT 477

if the cost of capital that is not financed by debt moves closely with the Baa interest rate, the

results would be roughly equivalent to the results of our benchmark model.6

The model generates a correlation between unemployment and financial conditions that is

large relative to the data, which we discuss in the context of the DMP literature. Simulating the

time series of the model shows that financial shocks are important for the first two recessions of

the 2000s but that the model is not consistent with previous recessions. We additionally compare

the model implications for investment volatility with empirical counterparts.

Beyond assumptions about debt financing, model results are robust to a wide variety of

different parameter values, measures of the real interest rate, and other modeling assumptions

such as the assumptions on the production function. Replacing financial shocks with productivity

shocks in our benchmark calibration results in negligible unemployment and vacancies volatility.

This finding is similar to that found in Shimer (2005), which launched a large literature on

whether the DMP model with productivity shocks can create realistic business cycle volatility.7

We confirm that labor market volatility in the model is due to our use of a large financial

shock instead of a small surplus or another mechanism through which a small shock, such as

productivity, can create large effects.

It is well known that the volatility of the labor share of income in the data is small, as wages

do not fluctuate much. The benchmark model, with its simplifying assumptions, overstates labor

share volatility somewhat, as wages adjust immediately to large financial shocks. We study two

extensions of the benchmark model that significantly reduce labor share volatility. In the first,

we introduce long-term contracts between firms and banks such that the interest rate is steady

during a match, although we allow firms to refinance should interest rates drop. This reduces

the volatility of flow surplus and thus of the labor share of income. This deviation does not have

a significant impact on unemployment volatility. In the second, we introduce alternating-offer

wage bargaining, as in Hall and Milgrom (2008), to the long-term contract model. This form of

bargaining reduces the dependence of the wage on the outside option of the worker and thus

further reduces the volatility of wages and the labor share of income. It also increases business

cycle volatility, indicating that the benchmark model’s overstatement of labor share volatility

is not a large concern. We further discuss the current model’s shortcoming in explaining the

cross-correlation of the labor share with the unemployment rate.

Our model makes a stark prediction about the relationship between interest rate volatility

and employment volatility. To evaluate this prediction, we use Compustat data on firm-level

employment volatility and credit ratings to show that lower credit ratings are associated with

both more volatile employment and higher interest rate volatility. This evidence supports the

prediction of a positive correlation between interest rate volatility and employment volatility.

Since the financial crisis, interest in examining these effects empirically has grown. For example,

Chodorow-Reich (2014), who studies the variation in borrowing and employment for firms

depending on precrisis financial relationships, finds that firms with a precrisis relationship with

a bank that became less healthy after the Lehman crisis had more difficulty obtaining loans,

paid higher interest rates on those loans, and reduced employment to a greater extent than

firms linked with banks that performed relatively well.

There is a growing literature that uses financial conditions in macroeconomics. Jermann and

Quadrini (2012) study shocks to firms’ ability to finance through debt, which is cheaper than

equity, and explain hours fluctuation. They infer a time series shock from the Flow of Funds

account. Decreases in a firm’s ability to borrow implicitly raises the cost of capital to that

firm, as it must switch to costlier equity financing. Although we abstract from the corporate

finance aspects of debt versus equity financing, implicitly focusing entirely on debt financing,

we are similar to Jermann and Quadrini (2012) in our study of stochastic capital costs, which

6This argument is consistent with papers that allow for debt to be only a fraction of capital financing, such as Jermann

and Quadrini (2012), as we explain below. In Section 4, we extend the discussion on these issues, as well as on whether

financial conditions can be taken as exogenous to the firm.

7See Ljungqvist and Sargent (2017) for a survey explaining the economics behind generating volatility in the DMP

model.