Local Risk, Local Factors, and Asset Prices

• Published date: 01 February 2017
• DOI: http://doi.org/10.1111/jofi.12465
• Date: 01 February 2017

THE JOURNAL OF FINANCE •VOL. LXXII, NO. 1 •FEBRUARY 2017

Local Risk, Local Factors, and Asset Prices

SELALE TUZEL and MIAO BEN ZHANG∗

ABSTRACT

Firm location affects firm risk through local factor prices. We find more procycli-

cal factor prices such as wages and real estate prices in areas with more cyclical

economies, namely,high “local beta” areas. While procyclical wages provide a natural

hedge against aggregate shocks and reduce firm risk, procyclical prices of real estate,

which are part of firm assets, increase firm risk. We confirm that firms located in

higher local beta areas have lower industry-adjusted returns and conditional betas,

and show that the effect is stronger among firms with low real estate holdings. A

production-based equilibrium model explains these empirical findings.

MOST WORK IN THE FINANCE literature treats labor and capital markets as per-

fectly competitive and homogeneous at the aggregate level, where wages and

rental rates equalize across locations. In reality, workers face frictions when

moving from place to place (e.g., transaction costs in housing, job search fric-

tions, family coordination issues, etc.). Moreover, a significant part of physical

capital, such as land and structures, is immobile. Production factors that are

subject to such geographical immobility, namely, local factors, account for a

large part of economic output.1Fluctuations in factor prices due to local eco-

nomic conditions can thus have important effects on the firms using them.

In this paper, we show that local factor prices respond to aggregate shocks

differently across localities based on the types of industries that dominate

∗Both authors are at the Marshall School of Business, University of Southern California.

We have benefited from conversations with Ken Ahern, Frederico Belo (discussant), Jonathan

Berk, Sandra Black, Daniel Carvalho, Riccardo Colacito, Ed Coulson, Ian Dew-Becker (discus-

sant), Wayne Ferson, Mike Gallmeyer, Mark Grinblatt, Daniel Hirschleifer, Ayse Imrohoroglu,

Chris Jones, Chang-Mo Kang, Leonid Kogan, Erica Li, Debbie Lucas, Hanno Lustig (discussant),

Oguzhan Ozbas, Vincenzo Quadrini, Clemens Sialm, Sheridan Titman, Hakon Tretvoll (discus-

sant), John Wei (discussant), Jiro Yoshido; seminar participants at McGill, UC Irvine, Oregon,

Penn State, CKGSB, UVA McIntire, USC, UT Austin, Jackson Hole Finance Meetings, West-

ern Finance Association Meetings, University of Washington Summer Finance Conference, CAPR

Workshop at BI, USC-UCLA-UCI Finance Day, SFM Conference, and Econometric Society Meet-

ings. Special thanks to Andres Donangelo, Ken Singleton (Editor), and two anonymous referees.

We thank Joseph Engelberg for sharing his data on firm headquarters location changes and Diego

Garcia and Oyvind Norli for providing data on geographic locations of firm operations. The authors

do not have any potential conflicts of interest, as defined in the JF Disclosure Policy.

1The estimates for the output share of labor range from 60% (Cooley and Prescott (1995)) to

75% (Imrohoroglu and Tuzel (2014)). Campbell (1996) uses two-thirds. The output share of land

and structures is roughly 15% (Tuzel (2010)). The two local factors jointly claim more than 75% of

total economic output.

DOI: 10.1111/jofi.12465

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326 The Journal of Finance R

those areas. Conceptually,if local factors are immobile across areas, the market

for these factors clears within each area. As a result, local factor prices—for

example, wages and real estate rents—aggregate the shocks to the firms in the

area since all firms tap into the same local labor pool and real estate market. If

the major industries that drive the economy of an area covary highly with the

aggregate economy, local factor prices in that area are likely to be procyclical.

Take, for example, the accommodation industry, which is fairly cyclical. It is the

main industry in three metropolitan areas: Las Vegas, Norwich-New London,

and Atlantic City. Given its significant weight in these economies, these areas

have some of the most cyclical economies in the United States. Thus, during

the 2009 economic downturn, real wages in these three metropolitan areas

declined more than twice the national average. More importantly, the excess

wage decline in these areas relative to the national average occurred not only

in the accommodation sector but also in unrelated sectors such as plastics,

nonmetallic minerals, and transportation equipment manufacturing. These

areas also experienced much larger declines in rents and real estate prices

compared to other metropolitan areas in 2009. Larger fluctuations in input

prices during economic cycles have implications for the risk and returns of

firms located in these areas. We explore these implications in two steps.

To capture the cyclicality of the local economy, we begin by constructing a

measure of local risk, which we refer to as “local beta.” Specifically,we compute

the local beta of a metropolitan statistical area (MSA) as the average of industry

betas weighted by the industry shares in the local market, where an industry’s

beta is the beta of the industry’s output on aggregate GDP. We confirm that the

sensitivity of wage growth (within industry or occupation) to aggregate shocks

is higher in MSAs with high local beta. Similarly, we find more procyclical

house prices, commercial real estate prices, and rents in MSAs with high local

beta, which suggests that demand for real estate is more cyclical in high local

beta areas. These findings support the view that industry composition is an

important driver of the heterogeneous fluctuations in local factor prices.

We next explore the implications of heterogeneous fluctuations in local factor

prices for firm risk by comparing firms in the same industry that are located in

different areas. Intuitively, if all firms in an industry are subject to the same

aggregate productivity shocks, their location will affect their risk and equity

returns only through local production factors. Two competing channels are at

work here. On the one hand, more cyclical wages absorb part of the aggregate

shocks. This provides a natural hedge for firms in high beta areas and lowers

their risk relative to industry peers located in low beta areas. On the other

hand, real estate values also respond more strongly to aggregate shocks in

high beta areas than in low beta areas. Since firm value is derived in part from

the value of its capital, which includes real estate, this mechanism implies

higher equity risk for firms in high beta areas than for firms in low beta areas.

Therefore, for firms that hold real estate, the two channels are expected to have

opposite effects on firm risk.

We find that local beta negatively predicts conditional equity betas and future

equity returns. We also show that this predictability is particularly strong for

Local Risk, Local Factors, and Asset Prices 327

the subsample of firms with few real estate holdings and gets weaker and

insignificant for the subsample of firms with high real estate holdings. These

results are obtained in both panel regressions with time-industry fixed effects

and also in portfolio sorting. Taken together, these findings suggest that both

the labor and the real estate channels are at work, but the labor hedging

channel dominates for the average firm.

To formalize these ideas, we develop a production-based equilibrium model

with local markets. In this model, firms belong to either a low beta industry or

a high beta industry, where the industry beta is determined by the sensitivity

of the industry’s output to aggregate productivity shocks. Local markets have

different compositions of low beta and high beta industries. We also introduce

heterogeneity to the real estate intensity of industries to capture their varying

real estate needs. All firms produce a homogeneous good, receive aggregate

(economy-wide) and firm-level productivity shocks, and use three factors of

production: labor, capital equipment, and land (i.e., immobile capital and real

estate). Labor and land are local factors of production with limited supply,

whereas equipment is not. Firms are ex-ante identical except for their location,

which determines the mix of firms active in their local factor markets, and their

industry affiliation, which determines their exposure to aggregate productivity

shocks and real estate intensity. Wages and land prices clear the local markets

and are determined endogenously, conditional on the industry composition of

the local market. Firms’ investment and hiring decisions are also endogenously

determined in equilibrium.

The calibration of the model generates the main empirical patterns observed

in the data: high beta areas have more procyclical wages and real estate prices

(i.e., higher covariance between aggregate productivity and local factor prices)

than low beta areas. In addition, more procyclical wages act as a natural hedge

against shocks, making the returns of firms in high beta areas less sensitive to

aggregate shocks. Therefore, their expected returns are lower relative to peer

firms in the same industry but located in low beta areas. This is especially true

for firms that belong to industries with low real estate intensity.

To simplify our empirical and theoretical analysis, we make several assump-

tions that merit some discussion. First, we assume that there is no local factor

mobility (labor and land) between local markets. Though land is truly immo-

bile, it is possible for labor to move across markets in response to shocks. Nev-

ertheless, at an annual frequency, job-related mobility is low. Between 2012

and 2013, for instance, only 3.8% of households moved across county lines,

with job-related moves making up roughly one-third of these moves.2Several

2Estimates are from the Census Bureau report, Reason for Moving: 2012 to 2013. The other

two major reasons for moves—family-related (e.g., change in marital status) and housing-related

(e.g., wanted better neighborhood) reasons—each account for one-third of intercounty moves. Chen

and Rosenthal (2008) investigate individual migration decisions using IPUMS (Integrated Public

Use Microdata Series) data. Consistent with the Census statistics, they find that, among movers,

an important reason for moving is the availability of local amenities (e.g., climate, temperature,

nonmetropolitan areas, etc.), which is not directly related to wages. In addition, Kennan and

Walk er (2011) develop an econometric model of optimal migration and estimate moving costs to