Comment
Karen Helene Ulltveit-Moe, University of Oslo and CEPR
I. Contribution of the Paper
Recessions hit differently. They hit differently across sectors and regions. Focusing on the latter, Fogli, Hill, and Perri emphasize that recessions are not purely aggregate phenomena, but vary significantly across
space. They document geographical features of the US business cycle
over the last 30 years, and argue that—although usually not included
in macro analysis—geography is important in explaining the transmission, amplification, and propagating of business cycles.
Their paper starts by presenting the spatial patterns of unemployment in the United States during the Great Recession (2007 to 2009).
They move on to analyzing spatial correlation and dispersion of unemployment during this last recession as well as the three previous
recessions, and present three main findings: (a) at any point in time,
unemployment across US counties is significantly spatially dispersed;
(b) during most recessions, spatial correlation falls before and at the
start of the recession, then increases sharply at the beginning of the
recession and stabilizes toward the end; (c) spatial dispersion typically
increases during the recession, then stabilizes and falls thereafter.
Motivated by geographical differences in unemployment, the authors
build a simple theoretical model of unemployment with many counties and spatial heterogeneity, as well as spatial transmission of countyspecific shocks. They calibrate the model, and find that the model can
generate patterns of spatial correlation and spatial dispersion that are
broadly consistent with the patterns observed under most recessions.
Finally, the model is used to evaluate the importance of local geographical connections for the transmission of shocks and unemploy-
© 2013 by the National Bureau of Economic Research. All rights reserved.
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ment dynamics. It is found that local connections amplify large aggregate shocks, while the same local connections serve to mute the effect
of small aggregate shocks.
II. Location Matters
Unemployment maps illustrating the geographical differences across
US counties during the Great Recession work well to motivate the paper. Looking at deviation of unemployment within each county from
its long term, we see that unemployment is not randomly distributed
across space. Instead we observe that groups of counties that are located close to each other either experience higher or lower unemployment than the average county.
In line with what we would expect from just eyeing the map, countyspecific unemployment is both significantly spatially correlated and
spatially dispersed. This result forms an important basis for the analysis. However, the argument may be made that the spatial characteristics of unemployment that we observe have nothing to do with location
as such, but rather to do with geographical specialization stretching
across the borders of single counties. It is well recognized that the
United States is characterized by significant industrial specialization
and localization (see, e.g., Krugman 1991; Kim 1995).
The presence of strong industrial specialization and localization implies that spatial correlation may not be the result of local transmission
channels of shocks from one county to another, but instead can be explained by similar industrial structure in neighboring counties.
To account for similarity in industrial structure, industry controls are
introduced. It almost goes without saying that it is important that these
controls are not based on too much of an aggregate industry classification, as this would hide patterns of specialization and make the controls
ineffective. But nor does one want a too disaggregated classification,
since this does not allow us to take into account the effect of industrial
cluster and supplier networks stretching across county borders. Finally,
it is important to recognize that industrial structures change over time,
and thus use time-specific controls.
In the present version of the paper, industry controls are time-specific,
and based on NAICS three-digit code (which includes 86 sectors), while
in an earlier version of the paper a much cruder industry classification
was employed. Regardless of aggregation level, we see that accounting
for industrial structure reduces spatial correlation as well as spatial dis-
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Ulltveit-Moe
persion, but both statistics still take on values that are still significantly
different from zero.
Hence, there is reason to conclude that location does indeed matter,
and that local transmission channels may be important for explaining
the dynamics of county-specific unemployment.
III. Are Neighbors Good or Bad?
Fogli, Hill, and Perri build a simple model with two channels of local
geographic connection between counties: (a) contemporaneous correlation of local conditions (e.g., due to intercounty trade); and (b) unemployment in a county depends on the lagged unemployment of neighboring counties (e.g., due to migration). Calibrating this model allows
them to assess the importance of local geographic connections for aggregate unemployment dynamics and in particular to examine whether
these connections serve to amplify or mute the effect of shocks.
The authors find that the role of neighboring counties differs, and depends on the magnitude of the aggregate shock that hits the economy.
If the economy is hit by a small aggregate shock, then local transmission
serves as a means to mute the effect of the shock and entail less aggregate unemployment. On the other hand, if the economy is hit by a large
aggregate shock, then neighbors have the opposite effect and amplify
the effects of an aggregate shock.
However, as the paper does not provide a structural estimation of the
labor market, we are left with something like a black box when it comes
to understanding the type, and working of, the local interactions that
are responsible for (sometimes) amplifying and (sometimes) muting the
effect of shocks.
IV. Concluding Remarks
The authors are convincing in their argument that geographical factors
are important for understanding aggregate business cycle dynamics.
But they also leave a set of questions open.
They show that the statistics of spatial correlation and dispersion follow certain patterns during most recessions. Moreover, they provide
evidence suggesting that having neighboring counties may help or hurt
depending on the magnitude of the recession. However, the reason
why spatial statistics follow specific patterns, and why neighbors mat-
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ter for business cycle dynamics, should indeed be the subject of future
research.
In their seminal paper, Blanchard and Katz (1992) investigated how
US states adjusted after having been affected by an adverse shock to
employment. They found that labor mobility across states served as
the most important mechanism of adjustment and helped to smooth
out shocks. In light of their results, the more ambiguous results on the
impact of neighboring counties presented here definitely ask for further investigation. Is there, for instance, a differential impact of neighbors depending on geographical aggregation? In other words, do local
transmission channels work differently between counties than between
states?
It is well known that US labor mobility has gradually declined over
the last two decades, and has more recently almost come to a halt. This
suggests that the impact and importance of various local geographical
connections may have changed over time. This time variation is obviously also an issue that research into the transmission channels and
their workings should account for.
Fogli, Hill, and Perri have reminded us that geography matters, also
for aggregate macro phenomena, and that stabilization policy can benefit from the insights of spatial dynamics.
Endnote
For acknowledgments, sources of research support, and disclosure of the author’s
material financial relationships, if any, please see http://www.nber.org/chapters/c12790
.ack.
References
Blanchard, O. J., and L. F. Katz. 1992. “Regional Evolutions.” Brookings Papers on
Economic Activity 1992:1–75.
Kim, S. 1995. “Expansion of Markets and the Geographic Distribution of Economic Activities: The Trends in US Regional Manufacturing Structure, 1860–
1987.” Quarterly Journal of Economics 110:881–908.
Krugman, P. R. 1991. Geography and Trade. Cambridge, MA: MIT Press.
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