Business Review Q1 2011 9www.philadelphiafed.orgWhy do.docx

Business Review Q1 2011 9www.philadelphiafed.org
Why do people in densely
populated areas tend to be more
productive? In countries like the U.S.,
places dense in workers, machines,
D
BY JEFFREY LIN
Urban Productivity Advantages from Job
Search and Matching*
*The views expressed here are those of the
author and do not necessarily represent
the views of the Federal Reserve Bank of
Philadelphia or the Federal Reserve System.
ensely populated areas tend to be more
productive. Of course, the cost of living and
producing in these locations is higher because
congestion raises the cost of scarce fixed
resources such as land. But despite the higher prices,
many people and businesses continue to live and work in
these areas. Why? One explanation is that these locations
have natural advantages, such as proximity to a river.
Another says that this concentration of households and
businesses by itself generates productivity advantages in
the form of agglomeration economies. In studying these
agglomeration economies, economists have pursued two
other questions. Do agglomeration economies exist and

how big are they? And what are the precise sources of
these agglomeration economies? In this article, Jeffrey
Lin describes the evidence for agglomeration economies
from job search and matching and then asks whether it
may be large enough to offer meaningful explanations for
differences in productivity and density.
Jeffrey Lin is a
senior economist
in the Research
Department of
the Philadelphia
Fed. This article
is available free
of charge at www.
philadelphiafed.
org/research-and-data/publications/.
firms, and households also tend to
be places where people are able to
produce more things. Of course, these
places are also usually more expensive
to produce in and to live in because
congestion raises the price of scarce
fixed resources such as land. Despite
these high prices, many businesses
and people continue to choose these
locations.
A typical first explanation is that
these densely populated areas enjoy
intrinsic natural advantages, such as
Philadelphia’s proximity to a navigable
waterway and a relatively deep harbor.
Advantages like these can reduce the

costs of shipping and the price of trad-
ed goods, attracting both businesses
and households. This story can often
be compelling, even though, today,
many people in the Philadelphia region
do not experience direct benefits from
the Delaware River. An intriguing
alternative explanation is that bring-
ing together workers, businesses, and
households can, by itself, generate
these productivity advantages. These
kinds of advantages are often called
agglomeration economies, and they
describe situations in which geographic
concentrations of economic activity al-
low businesses and households to save
on the costs of transporting people,
materials, and ideas.
Urban economists have pursued
two related research questions. First,
do these agglomeration economies
exist, and, if so, how big are they?
Second, what are the precise sources of
these agglomeration economies?
Many researchers have already
discovered evidence that these agglom-
eration economies do exist and that
they are big enough to offer mean-
ingful explanations of present-day
differences in productivity and density.
For example, in an attempt to answer
the first question, economists Antonio
Ciccone and Robert Hall, using data
for U.S. states, found that a doubling of

employment density increased average
labor productivity by about 6 percent.
Although other studies have provided
different estimates of the exact mag-
10 Q1 2011 Business Review www.philadelphiafed.org
nitude of this effect, many have noted
that agglomeration economies make an
important contribution to differences
in productivity across locations.1 In ad-
dition, research by Satyajit Chatterjee
(discussed in his 2003 Business Review
article) also suggests that agglomera-
tion economies play some explanatory
role in these differences, even after
accounting for natural advantages.
For both academic and policy
reasons, an important next step is
to investigate the specific sources of
agglomeration economies. In this
article, I will discuss some of my recent
research on one potential source:
opportunities to better match work-
ers’ skills to job requirements. Dense
urban areas have thick labor markets
–– that is, markets with many differ-
ent kinds of workers and jobs –– and
might therefore benefit from improved
job search and matching. This idea —
that markets with more participants
can offer better matches — is typically
attributed to Alfred Marshall, and the

idea was formalized in economist Peter
Diamond’s “coconut” model. (If con-
sumers have tastes for a particular va-
riety of “coconut,” they are more likely
to find the one they prefer in a large
market where more types of coconuts
are sold.) Intuitively, we know that
workers have varying skills and jobs
have varying skill requirements. From
the perspective of a worker, search-
ing for a suitable job may be easier
in a large city with many potential
employers. Put another way, workers
in large cities may find a job that is
better matched to their talents, for the
same search costs. This is a potential
source of agglomeration economies;
geographic concentration increases
productivity because workers need not
let their acquired skills lapse by taking
less-suitable jobs.
It is important to note that, in
theory, there are a number of different
sources of agglomeration economies.
In a 2005 Business Review article, Jerry
Carlino discusses a few of the many
possible economic mechanisms respon-
sible for agglomeration economies. His
2001 Business Review article talks about
one possible mechanism — knowledge
spillovers — related to the increased
production and flow of (new) ideas
and information in dense cities. In a

later Business Review article (2009), he
describes his paper in which he evalu-
ates another potential mechanism:
Urban population density may increase
the amount and variety of goods and
services available for households to
consume. As another example, I show
evidence for yet another mechanism
in a recent working paper: Geographic
concentrations of skilled workers and
potential users of new products or pro-
cesses can increase the rate of adapta-
tion to new technologies. In general,
as explained by Gilles Duranton and
Diego Puga, agglomeration economies
might arise from mechanisms related
to sharing, learning, or matching.
Sharing refers to advantages that arise
from distributing the costs of large in-
divisible investments across many pro-
ducers or consumers, as might be the
case with a large factory or consump-
tion amenities, as in Carlino’s article.
Learning refers to advantages in either
the creation of new technologies, as
described by Jane Jacobs; the forma-
tion of human capital, as described by
Edward Glaeser and David Maré; or
adaptation to new technologies, as in
my working paper.
In order to evaluate alternative
proposals, policymakers concerned
with city growth, the productivity of
local workers, or the welfare of local

residents need to understand the
specific economic forces that generate
productivity advantages and attract
businesses and households to certain
places. Should local leaders sponsor
arts and cultural programs or invest in
transportation infrastructure? What
kinds of businesses should cities be
interested in attracting? The answer to
these questions depends on the relative
strength of different kinds of agglom-
eration economies. In other words, for
both intellectual and practical reasons,
it is useful to know what is happening
inside the “black box” of agglomera-
tion economies.
However, finding evidence that
distinguishes one kind of agglomera-
tion economy from another can be
challenging. Different mechanisms
often have similar predictions for ag-
gregate city-level data. For example,
most (if not all) kinds of agglomera-
tion economies predict higher wages
and higher land prices in denser cities.
1 See the paper by Gerald Carlino and Richard
Voith; the recent working paper by Morris
Davis, Jonas Fisher, and Toni Whited; and the
2004 article by Stuart Rosenthal and William
Strange.
In order to evaluate alternative proposals,
policymakers concerned with city growth, the

productivity of local workers, or the welfare of
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economic forces that generate productivity
advantages and attract businesses and
households to certain places.
(These facts are in line with conven-
tional wisdom and easily confirmed
using aggregate census data.) There-
fore, looking inside the “black box”
of agglomeration economies often
requires creative research strategies.
Recent work in this area, including my
own, has been made possible by the
increasing availability of large data sets
that contain detailed information at
the plant, household, or worker level.
Using micro-data, it is sometimes pos-
sible to test predictions that are unique
to one kind of agglomeration economy
and not associated with another kind.
In this way, it becomes possible to
highlight variables that should be of
interest to policymakers.
I will describe the evidence for

agglomeration economies from job
search and matching using just such
a strategy. An important caveat is
that the research strategy described
here does not rule out other sources of
agglomeration economies. Instead, I
evaluate whether there is evidence for
this source of agglomeration economies
and then ask whether it may be large
enough to offer meaningful explana-
tions for differences in productivity
and density.
JOB SEARCH AND MATCHING
IN CITIES
In my recent working paper with
Hoyt Bleakley, we test for agglomera-
tion economies from job search and
matching. The intuition for our test
is as follows. Consider a worker in a
small city who loses her job. She has
some specialized skills (either innate
or gained through experience) suited
to the activities she performed or the
output she produced in her previous
job. If the separation from her previous
job is permanent, the worker now faces
a choice: She could wait a long time
before finding employment performing
similar tasks but at a different firm. Or,
because waiting is costly, it may make
more sense to accept a job elsewhere
in the local economy that is less suited
to her unique skill set. (Alternatively,

she might choose to move to a location
where there is greater demand for her
skills, but of course, moving is also
costly.) Since her skills are less suited
to this job, some of her skills go un-
used, and she may be less productive.
This worker, in a small city,
faces a “small numbers” problem: She
happens to be without a job, but does
there happen to be another firm that
needs a worker with her skill set? On
the other hand, workers in dense cities
benefit from market thickness: They
are less likely to be in a narrow labor
market at a moment in which their
skills are in excess supply. This poten-
tial source of agglomeration economies
yields an interesting, and potentially
unique, prediction: Workers should
choose to eschew their specialized
skills less frequently in large, dense cit-
ies, where they are more likely to find
job openings suited to their talents.
We evaluate this prediction by
examining the likelihood that workers
change occupations or industries. These
job classifications, characterizing either
the tasks or activities performed or
the kinds of output produced, have
been used in a number of labor-market
studies on specific human capital.2 We
expect that in the presence of agglom-
eration economies from job search and
matching, workers should choose to

change occupations and industries less
frequently in denser labor markets.
Further, this agglomeration econ-
omy should also affect workers’ early
decisions about skill specialization.
In separate studies, economists Kevin
Murphy and Sunwoong Kim have
proposed how density might change
the market for specialized skills.
In Kim’s model, sparsely populated
areas have fewer firms in each sector,
and therefore, a worker might have
invested less in narrow skills because
she anticipated that there would be
fewer potential employers in the event
of a separation.3 Therefore, in large
cities, workers choose to invest more in
specialized skills, making it even less
likely that they would want to change
occupations or industries in dense
cities and compounding density’s effect
on productivity.4
Using data from the decennial
U.S. census and the monthly Current
Population Survey (CPS), Bleakley
and I confirm this prediction. We find
that workers are less likely to change
occupation or industry in metropolitan
areas with high population density
(Figure 1). The data are at the worker
level, and the key outcome of interest
is a change in each worker’s reported
occupation or industry.5 Respondents

to the 1970 census reported these
changes for 1965 and 1970. The
CPS samples in the 1990s and 2000s
reported these changes for individual
workers, both for the year of the survey
and up to three years earlier. The key
explanatory variable is local population
density, measured for each worker’s
metropolitan area of residence. Figure
1 summarizes our main result. Here,
each point represents a metropolitan
2 For example, see the study by Derek Neal and
the one by Daniel Parent on industry-specific
skills; see Gueorgui Kambourov and Iourii
Manovskii’s recent paper on occupation-specific
skills.
3 Alternatively, workers in small cities with
specialized skills might choose to move to
denser cities.
4 For example, James Baumgardner found
that doctors are more specialized in big cities;
similarly, Luis Garicano and Thomas Hubbard
found more specialization among lawyers in
larger markets.
5 We obtain similar results whether our outcome
of interest measures a change in each worker’s
reported occupation, a change in reported
industry, or a change in either reported occupa-
tion or reported industry.

area or a group of co-terminous coun-
ties in 1970, and population density is
measured on the horizontal axis. The
vertical axis measures the probability
that a worker in each location changed
either occupation or industry between
1965 and 1970. The fitted line shows
that workers in locations with higher
population densities are less likely
to switch occupations or industries.
Further, the magnitude of this thick-
market effect is large enough to be
relevant in understanding differences
across locations. For example, a change
in density from, say, Tucson, Arizona,
to Philadelphia, is associated, on aver-
age, with a decrease of 1 percent in
occupation or industry switching over
a five-year period.
This negative correlation between
switching and local population density
supports the existence of agglom-
eration economies in job search and
matching. But we also rule out other
important alternative explanations.
For example, we compare similar
workers by controlling for charac-
teristics such as gender, age, race,
ethnicity, and educational attainment,
and whether or not they have moved
recently. We also control for fixed
characteristics of a worker’s previous

occupation and industry, so that our
comparison is among workers shar-
ing the same initial occupation and
industry. Jobs in different occupations
and industries may require different
levels of specialized skills. If we control
for previous occupation and industry,
the results do not simply reflect differ-
ences in the composition of occupation
or industry across cities. The graph
in Figure 1 already controls for all of
these effects.
Metropolitan areas are also dif-
ferent along a lot of other dimensions.
We control for other characteristics
of cities, such as industry composition
(e.g., the relative size of the manufac-
turing sector), average educational
attainment, and climate, with little
impact on our main result. There is an
additional issue of potential measure-
ment error associated with using met-
ropolitan-area-level population density.
Since metropolitan areas are based on
county boundaries, we are more likely
to mis-measure local density in western
states that feature relatively large
counties. For example, the Los Angeles
metropolitan area includes coun-
ties that stretch to the Arizona and
Nevada borders, including desert lands
that are sparsely populated. Our results
are similar when we adjust our density
measure using census tract data.

Another story to consider is that
changing jobs or employers by workers
(as opposed to changing occupation or
industry) may also depend on the size
of the local labor market. Other stud-
ies have found mixed evidence of den-
sity’s effect on job switching.6 One way
we can check to see how this might
affect our results is to use information
available in the U.S. CPS supplements.
This is the survey conducted every
month to estimate important statistics
such as the unemployment rate. In
addition, the CPS also periodically
includes supplemental questions of
interest to researchers or policymakers.
In January and February, these supple-
ments usually include questions related
to job changing. In these supplements,
the CPS reports workers’ reasons for
changing jobs; many lost their jobs be-
cause their plant or firm closed. Thus,
increased opportunities due to popula-
tion density probably did not cause
them to change jobs, since they lost
their jobs involuntarily. These workers
also change occupation or industry
less frequently in larger cities, so job
6 See the papers by Bruce Fallick, Charles
Fleischman, and James Rebitzer; Jeffrey Groen;
Guido de Blasio and Sabrina Di Addario; and
Jeremy Fox for conflicting evidence on this
question.

Occupation and Industry Switching and Local
Population Density
FIGURE 1
Adjusted occupation and industry switching probability
Hundreds of people per square mile, 1970 (log scale)
Tucson
Philadelphia
.1
.05
0
-.05
10 20 4030 50
Source: Author’s calculations and the 1970 U.S. census
changing is probably not an important
explanation of our main result.
Some workers may have innate
specialized skills and may also “sort”
themselves into large metropolitan
areas. The fact that they have innate

specialized skills implies that they
may choose to switch occupations or
industries less frequently. However,
in this story, these workers choose to
live in large labor markets for reasons
other than improved opportunities for
job search and matching. For example,
they may be interested in the con-
sumption amenities available in such
cities. If this is an important explana-
tion for our main result, workers whose
location choice is not influenced by
such considerations should not experi-
ence a similar pattern relating density
to occupation or industry switching.
In fact, using information on workers’
places of birth, we find that our results
are similar for those workers whose
choice of location was influenced by
the state in which they were born.
Taking all of these pieces of evidence
together, we argue that agglomeration
economies from job search and match-
ing are the likeliest explanation for our
results.
YOUNGER WORKERS
An additional piece of evidence
weighs in favor of agglomeration econ-
omies from job search and matching.
If job searching is less costly in large
cities, we can make another interesting
prediction: People may find it easier
to shop around for a good occupation
or industry match in a dense city. Of

course, it makes sense to do this for
younger workers who are just starting
their careers: They have fewer spe-
cialized skills accumulated, and they
have the rest of their careers to gain
from great matches. In contrast, older
workers have spent many more years
accumulating specialized skills: Instead
of sampling different occupations,
these workers choose jobs more closely
matched to their existing skills.
Following this logic, the correla-
tion between changing occupation and
industry and population density may
depend on workers’ potential experi-
ence. (Potential experience measures
how long workers have potentially
been in the labor market: their age,
minus the number of years they spent
in school, minus six, the number
of years between birth and school.)
We find that this is indeed the case.
Figure 2 shows the effect of density
on occupation and industry switching
for different levels of potential labor
market experience.
For young workers with less than
10 years of potential experience, being
in a large city actually increases the
likelihood that they will change occu-
pations or industries. (In Figure 2, this
can be seen in the positive estimated
effect of density on occupation and

industry switching.) In contrast, for
older workers, density lowers the likeli-
hood of such changes. (On average,
the effect due to older workers domi-
nates the overall effect seen in Figure
1, since older workers constitute much
of the total workforce.) This positive
effect of density on switching early
in workers’ careers provides further
support for the thick-market matching
hypothesis, but it is harder to reconcile
with other stories of how density might
affect occupation and industry switch-
ing. If there are benefits from match-
ing in dense cities, workers could take
advantage of low search costs to search
more intensively for the right occupa-
tion or industry match. This occupa-
tion and industry shopping could po-
tentially be greater than the negative
effect of density on switching shown
in the previous section (and thus
be, on net, positive). However, since
search intensity is like an investment
whose gains are realized throughout
the working lifetime, this new, positive
effect should be strongest at younger
ages. Compare this with a story in
Effect of Density on Occupation and Industry
Switching Depends on Potential Experience
Source: Author’s calculations and the 1970 U.S. census
FIGURE 2

Effect of Density on Occupation and Industry Switching
1
.5
0
-.5
-1
100 20 30 40 50
which workers in dense cities are more
specialized for some other reason (not
better job search and matching), such
as faster learning or greater returns
to specialization because of improved
opportunities for the division of labor.
If there are no differences in search
costs across cities, it is unlikely that
we would observe more occupation
and industry switching in dense cities
among the youngest workers.
POTENTIAL IMPLICATIONS
FOR PRODUCTIVITY AND
WAGES
Finally, our estimated differences

in occupation and industry switch-
ing could be large enough to offer
meaningful explanations of differences
in productivity. We can get a feel for
what our estimates might mean for
the relationship between density and
wages by doing some quick calcula-
tions. First, in small cities, specialized
skills fall into disuse faster, as workers
churn through more occupations and
industries. There are earlier estimates
by Derek Neal (1995) and Daniel Par-
ent (2000) on how much of a worker’s
wage is due to industry-specific skills.
Neal estimates that 10 percent of in-
come is derived from industry-specific
skills for men with 10 years of experi-
ence; Parent estimates that 10 to 20
percent of workers’ income is derived
from industry-specific skills. To span
the range of likely possibilities, say that
the fraction is somewhere between 5
and 25 percent. We multiply this by
our own estimates of density-driven
differences in industry switching —
approximately 0.6 percent measured
over a five-year horizon or about 4.8
percent over a 40-year career. These
calculations suggest that, over 40 years,
a doubling of labor market density im-
plies somewhere between 0.2 percent
and 1.2 percent higher wage growth
through this mechanism. In com-
parison, the extra growth in wages in
dense areas, in the same units, is about

2 percent over 40 years.
Second, in small cities, work-
ers might be less inclined to invest
in specialized skills. Note that the
previous calculation does not account
for differences in behavior that might
result from expectations about the
usefulness of specialized skills in big
cities. Calculating the potential effect
on wages is difficult, since it depends
on how costly it is to acquire special-
ized skills and how quickly those skills
fall into disuse, even without changing
occupation or industry. In our related
working paper, we find that, for rea-
sonable values of these variables, this
mechanism can explain nearly all of
the observed differences in productiv-
ity levels across locations. To sum up,
our back-of-the-envelope calculations
suggest that the relationship between
density and occupation and industry
switching can account for most of the
differences across cities in workers’
income growth and nearly all of the
differences in income levels.
PHILADELPHIA AND THE
THIRD FEDERAL RESERVE
DISTRICT
These differences in occupation
changing can be seen even among the
handful of metropolitan areas within

the Third District. The Table displays
population density, taken from recent
U.S. Census Bureau estimates, and
occupation switching in Third District
and selected nearby metropolitan
areas, calculated using recent samples
from the CPS. Overall, workers in
metropolitan areas with lower popula-
tion density tend to be more likely to
change occupations. (Of course, these
are raw numbers, without some of the
controls for other factors that vary
across cities used in creating Figure
1.) For example, in our District, the
Altoona, Vineland–Millville–Bridge-
ton, and Johnstown metropolitan areas
have the highest average occupation-
changing rates and also relatively low
population densities. In contrast, the
Trenton–Ewing metropolitan area has
both the lowest rate of occupation
changing and the highest population
density of any metropolitan area in the
Third District. Even within our region,
some of the differences in density and
productivity seem to be related to
differences in the accumulation and
preservation of specialized skills.
CONCLUSION
In this article, I have discussed
new evidence for one potential source
of agglomeration economies: better

job search and matching. The broader
agenda for this kind of work is to
provide support for appropriate local
policy choices. If urban productiv-
ity advantages are due mostly to job
matching advantages, that may suggest
that local development strategies that
don’t take advantage of these thick-
market effects may not be effective.
An important caution is that policy
effects are likely to be small relative to
the magnitudes needed for noticeable
changes in local productivity. This
can be seen in the persistence of city
characteristics: Places that are densely
populated or that have highly educated
workforces also had similar character-
istics in decades or even centuries past.
Finally, an important further step
is to understand the relative impor-
tance of different sources of agglom-
Overall, workers in metropolitan areas with
lower population density tend to be more likely
to change occupations.
TABLE
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Persons per square mile, 2007
Percent of workers switching occupations
last year, 2005-2009 average
Third District Metropolitan Areas
Trenton-Ewing, NJ 1,617.5 6.4
Philadelphia-Camden-Wilmington,
PA-NJ-DE-MD
1,258.8 10.9
Allentown-Bethlehem-Easton, PA-NJ 550.8 10.4
Atlantic City, NJ 482.4 8.8
Reading, PA 468.0 9.6
Harrisburg-Carlisle, PA 324.7 13.9
Vineland-Millville-Bridgeton, NJ 317.9 14.6
Scranton-Wilkes-Barre, PA 314.6 11.4
Lancaster, PA 267.4 8.7
Dover, DE 258.2 10.0
Altoona, PA 238.7 15.4
Johnstown, PA 210.7 14.3

Metropolitan Areas Outside the Third District
New York-Northern New Jersey
Long Island, NY-NJ-PA
2,797.6 10.1
Boston-Cambridge-Quincy, MA-NH 1,278.3 10.3
Cleveland-Elyria-Mentor, OH 1,045.9 9.1
Baltimore-Towson, MD 1,022.6 9.7
Washington-Arlington-Alexandria,
DC-VA-MD-WV
943.0 10.5
Cincinnati-Middletown, OH-KY-IN 485.1 11.1
Pittsburgh, PA 446.2 12.7
Source: Author’s calculations, U.S. Census Bureau, and the
2005-09 Current Population
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eration economies. Stuart Rosenthal
and William Strange, in their 2001
study, and Glenn Ellison, Edward
Glaeser, and William Kerr have some

intriguing early results in this area.
Using industry locations as observa-
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a measure of spatial concentration
with industry-location characteris-
tics that proxy for the presence of
knowledge spillovers, input sharing,
natural advantages, and other types of
agglomeration economies. Their results
indicate that industry concentrations
are correlated with a number of these
measures, in particular, measures
related to labor market concentration.
Ellison, Glaeser, and Kerr adopt a
similar methodology but use industry
pairs as the unit of observation. Their
results suggest that linkages between
industries are an important reason for
co-location patterns. Despite these
early efforts, much remains unknown
about this important question. One of
the priorities for future work should
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Murphy, Kevin M. “Specialization and
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Rosenthal, Stuart S., and William
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Geography, History, Economies of Density,
and the Location of Cities*

E
*The views expressed here are those of the au-
thor and do not necessarily represent the views
of the Federal Reserve Bank of Philadelphia or
the Federal Reserve System.
By JEffrEy Lin
What determines the location of
cities? Sometimes, we can clearly iden-
tify instances when city locations were
conomists believe that people choose to
live and work at sites that have productive
or amenity value such as a river, harbor, or
some other natural resource. Another factor
that may determine the location of a city is the benefits
derived from density itself: agglomeration economies.
Although these complementary explanations both have
something useful to say about the locations and sizes
of cities, they also have important limitations. While
natural features seem important, it is difficult to point to
one or even several that are valuable enough to explain
a very large metropolitan area. And if there are large
economies of density, then any location could be the
potential site for a city, since density itself provides a
reason for further concentration. If you were to replay
the settlement of some large expanse of land, perhaps
cities in this alternative history would be of different
sizes and locations. This “path dependence” or “history
dependence” is a potentially important theoretical
implication of models featuring economies of density.
In this article, Jeff Lin helps shed light on why cities are

located where they are.
Jeff Lin is a
senior economist
in the Research
Department of
the Philadelphia
Fed. This article
is available
free of charge
at www.
philadelphiafed.org/research-and-data/
publications/.
chosen to achieve specific development
or political goals, in remote or sparsely
populated areas. For example, the site
of Canberra, Australia’s capital city,
was selected in the early 20th century
as a compromise between rival cities
Sydney and Melbourne. For many old-
er cities, we can make only educated
guesses about their origins. In general,
economists believe that people choose
to concentrate at sites that have some
productive or amenity value. A river, a
harbor, or some other natural resource
nearby might encourage settlement.
There is also the role of local institu-
tions — for example, well-defined
property rights — that might make
some places more attractive. If these
kinds of local features aren’t available
everywhere, economic activity will be

attracted to locations that are superior
in resources and institutions.
Another factor that may de-
termine the location of cities is the
benefits derived from density itself —
so-called agglomeration economies.
Living or working in close proximity to
businesses or other people can make
workers more productive. For example,
similar businesses might cluster togeth-
er in order to have access to cheaper
specialized inputs. Jerry Carlino’s 2001
and 2009 Business Review articles and
my own from 2011 discuss several po-
tential sources of these agglomeration
economies. (Of course, the effect of ag-
glomeration economies on the location
of cities does not preclude the influ-
ence of natural amenities.)
These complementary explana-
tions both have something useful to
say about the locations and relative
sizes of cities. Of course, great agglom-
erations today are located near rivers,
oceans, or other prominent features
of the natural landscape. And many
people who live in densely populated
areas experience clear benefits from
proximity to customers, employers, and
producers.
What is perhaps less clear is how
to judge the contributions of loca-
tional “fundamentals” and agglomera-

tion economies — or more generally,
economies of density — independently.
Note that both natural fundamentals
and economies of density have impor-
tant limitations as stories for under-
standing the geographic distribution
of economic activity. While natural
features seem important, it is difficult
to point to one or even several natural
features that are valuable enough to
explain a very large metropolitan area.
For example, in Philadelphia, is prox-
imity to the Delaware and Schuylkill
rivers alone really so valuable as to
encourage millions of people to crowd
together on their banks? Similarly, on
their own, stories featuring economies
of density are also limited. If there are
large economies of density, people will
want to locate near existing concentra-
tions of population, but these stories
are silent on how a city comes to be in
a particular location in the first place.
Why is the greatest agglomeration in
the Third Federal Reserve District1
near the confluence of the Delaware
and Schuylkill rivers and not, say,
further upstream on the Schuylkill or
closer to the Atlantic Ocean?
Furthermore, if there really are

large economies of density — that is,
density itself provides incentive for
people to concentrate, in a virtuous
circle — it’s possible that any location
could be the potential site for a city.
All that is required for a large agglom-
eration is a smaller agglomeration or,
in a sense, a city “seed.” Intuitively, if
you were to rewind history and replay
the settlement of some large expanse
of land, perhaps cities in this alterna-
tive history would be of different sizes
and locations. Economists sometimes
call this “path dependence” or “history
dependence” — that is, present-day or
long-run outcomes can depend on a
series of historical events or shocks —
and it is a potentially important, and
unique, theoretical implication of mod-
els featuring economies of density.
EVIDENCE ON GEOGRAPHY
FROM WAR AND DISEASE
In two papers, economists Donald
Davis and David Weinstein reported
a historical example paralleling this
thought experiment. They analyzed
settlement patterns in Japan before
and after widespread Allied bombings
during World War II. They interpreted
these devastating bombings, and the
resulting destruction of homes, capital,
and lives, as akin to “starting history
over” — many new location decisions

were to be made in the vastly changed
human geography of postwar Japan.
However, contrary to their expecta-
tions, they found that the locations
and relative sizes of Japanese cities re-
mained unchanged from the prewar pe-
riod — even Hiroshima and Nagasaki
returned to their prewar growth trends
within 20 years (Figure 1). Similarly,
a 2006 working paper by economists
Patricia Beeson and Werner Troesken
found that epidemics of yellow fever in
Philadelphia in the 17th and 18th cen-
turies had no long-run effects. Despite
severe epidemics in 1699, 1792–1793,
and 1797–1799, each of which killed
about 8 to 10 percent of the city’s popu-
lation, Philadelphia, after each episode,
returned quickly to its preexisting
population growth trend.2
The tendency for Japanese cities
to quickly revert to preexisting trends
suggests that there was very little his-
tory dependence following the shocks
of World War II. Otherwise, Davis and
1 The Third District covers eastern Pennsylva-
nia, southern New Jersey, and Delaware.
FIGURE 1
Populations of Hiroshima and Nagasaki
Returned to Trend Growth Quickly

1925 1930 1935 1940 1947 1950 1955 1960 1965 1970 1975
Year*
Hiroshima
Nagasaki
1925 - 1940 Hiroshima Trend
1925 - 1940 Nagasaki Trend
12.2
12.4
12.6
12.8
13.0
13.2
13.4
13.6
13.8
Log of Populaton
* Data for 1945 were unavailable, so the authors used data for
1947.
Source: Davis and Weinstein (2002), used with permission

2 Papers by Steven Brakman, Harry Garret-
sen, and Marc Schramm; Paul F. Paskoff; and
Edward Miguel and Gérard Roland show similar
results for cities following war-related destruc-
tion in Germany after World War II, the U.S.
South after the Civil War, and Vietnam after
the Vietnam War.
Weinstein might have found different
patterns of concentration in postwar
Japan; perhaps cities that had experi-
enced relatively less destruction would
have grown faster. Instead, the authors’
preferred interpretation was that natu-
ral features are probably very impor-
tant for understanding the locations
and sizes of cities, with economies
of density perhaps playing a second-
ary role. Their research left open an
important question: If economies of
density really do play an important role
in determining location patterns, why
didn’t they observe any changes in the
geographic distribution of activities
following the massive destruction of
World War II?
INTEGRATING EXPLANATIONS
BASED ON NATURAL
FEATURES AND ECONOMIES
OF DENSITY

A satisfying understanding of the
locations and sizes of cities probably
includes both economies of density
and natural features. However, finding
evidence on the relative contributions
of locational fundamentals and econo-
mies of density can be challenging.
First, there are many natural features
(e.g., rivers, forests, minerals, climate,
etc.), and we may not have been able
to include the value of all of these fea-
tures. This leads to an “unobservable
variables” problem: Although there
may be a preferred explanation for a
particular agglomeration, there lurks
the possibility that some unobserved
factor is the true reason for concentra-
tion at that site.
Furthermore, the natural features
that first attracted people and busi-
nesses to a location very often contin-
ue to have value, even today. Consider
long-lasting features like access to an
ocean port or nice weather. These
things continue to attract economic
activity to particular locations to
the present day and provide value to
households who live there. Their con-
tinued value can confound attempts to
attribute today’s spatial distribution of
population to economies of density.
In a previous Business Review
article, Satyajit Chatterjee discussed

one way to better understand the
relative roles of natural features and
agglomeration economies. His strategy
was to construct an economic model
that included both natural features and
agglomeration economies. Then, he
used this model to match the observed
distribution of employment across U.S.
counties and metropolitan areas. This
exercise implied certain values for
key parameters of the model. Having
matched the actual geographic distri-
bution of employment with this model,
he then simulated a counterfactual
geographic distribution of employment
without agglomeration economies; that
is, he assumed that the benefits to den-
sity were zero, but the other parameters
were the same as before. Chatterjee
found that, in the simulated economy,
the distribution of economic activity
without agglomeration economies was
very similar to the observed distribu-
tion. His work supports the idea that
some factor besides agglomeration
economies is important for under-
standing the distribution of economic
activity, although his method is silent
on what the factor or factors might be.
EVIDENCE ON HISTORY
DEPENDENCE AND INDUSTRY
LOCATION FROM GERMANY
Economists Stephen Redding,

Daniel Sturm, and Nikolaus Wolf
have also explored these issues in two
papers. They examined the effects of
Germany’s division and reunification
on its economic geography. In their
2011 paper, Redding, Sturm, and Wolf
found that the division of Germany
led to a shift in the location of air hub
traffic from Berlin, where it had been
concentrated, to Frankfurt. Following
reunification, they found no evidence
of a shift back to Berlin. They inter-
preted this evidence in the following
way: The division of Germany after
World War II made continued hub
operations in Berlin less profitable
because that city became more isolated
relative to other cities in the new West
Germany. Frankfurt became relatively
more attractive and subsequently be-
came the preeminent air hub. Finally,
reunification made Berlin less isolated
and therefore a more attractive loca-
tion for hub activities relative to its
Cold War value. However, the authors
found no evidence of a return of air
traffic to Berlin; in fact, hub traffic
continued to rise in Frankfurt and
decline in Berlin following reunifica-
tion. Thus, a historical shock had a
permanent effect on the distribution of
economic activity.
The authors interpreted this

as evidence of history dependence.
While these facts suggest the impor-
tance of economies of density (versus
natural fundamentals), there remains
the possibility that the division of
Germany also created some unobserv-
able, persistent change in the attrac-
tiveness of Berlin (or Frankfurt) as a
A satisfying understanding of the locations
and sizes of cities probably includes both
economies of density and natural features.
However, finding evidence on the relative
contributions of locational fundamentals and
economies of density can be challenging.
hub, so that following reunification,
Berlin’s value was not high enough to
serve as a viable hub, no matter what
the alternative historical sequence of
events. (Alternatively, perhaps some
event after German division greatly
increased Frankfurt’s value as an air
traffic hub.) Much of Redding, Sturm,
and Wolf’s paper focuses on ruling out
changes in locational fundamentals.
In fact, probably the strongest case for
history dependence (and against this
criticism) is that hub traffic has not
returned to Berlin, despite its being by
far the largest city in Germany. Still,
there is some ambiguity to interpreting

these facts.
EVIDENCE ON HISTORY
DEPENDENCE FROM PORTAGE
SITES IN THE U.S.
Having better knowledge about
some fundamental natural feature that
affected economic geography and the
change in its value over time might
provide better evidence of history
dependence. In addition, perhaps it
would be interesting to examine popu-
lation in general, rather than a specific
(but interesting) industry like airline
services. In a recent working paper,
Hoyt Bleakley and I attempt to provide
this kind of evidence. We examine
historic portage sites in the U.S. South,
Mid-Atlantic, and Midwest.
Portage is the carrying of a boat or
its cargo over land between navigable
waterways or to avoid a navigational
obstacle such as rapids or falls. Por-
tages are the places where this activity
occurs. During the settlement of North
America, when long-distance shipping
was mostly waterborne, portages were a
focal point for commerce. Traders were
obliged to stop because of the natural
obstacle to navigation; in turn, these
sites offered easy opportunities for
exchange and commerce. While these
opportunities were valued histori-
cally, they became obsolete long ago.

Thanks to changes in transportation
technology (e.g., railroads, trucks),
traders no longer walk canoes around
rapids. Similarly, some falls were
sources of waterpower during early
industrialization, and these advantages
also declined with the advent of other,
cheaper power sources. (Electrifica-
tion, by allowing for transmission of
power over long distances, uncoupled
the location of manufacturing from the
location of power generation.) Nota-
bly, despite the obsolescence of canoe
transport and water wheels, concentra-
tions of economic activity continue to
exist at many of these sites.
Historical Portages and the
Economic Geography of the Third
District. Historical portage sites af-
fected the economic geography of the
Third District in early America and
continue to do so even today (selected
historical portages are shown in Figure
2 as green points). Several places in the
Third District are portage-descended
cities, including Trenton, Philadelphia,
and Wilmington.
For example, the Schuylkill River
was a major water transportation
route in early America, and the falls
of the Schuylkill (near the present-day
section of East Falls in Philadelphia)

first attracted Delaware and Iroquois
Indian activity prior to European
settlement.3 (Later, William Penn
directed his surveyors to find a site on
the Delaware River where it was “most
navigable, high, dry, and healthy; that
is, where most ships may best ride, of
deepest draught of water, if possible to
load or unload at the bank or key side,
without boating or lightering of it. It
would do well if the river coming into that
creek be navigable, at least for boats, up
into the country.”4 Thus, a key feature
FIGURE 2
Selected Historical Fall-Line Portages
in the Third District
D
elaw
are R
iver
B
randyw
ine Creek
Schuylkill River
TRENTONTRENTON
PHILADELPHIAPHILADELPHIA

WILMINGTONWILMINGTON
Christina R
iver
3 See p. 11 of the book by Thomas Scharf and
Thompson Westcott.
4 See the article by John Reps, p. 29, emphasis
mine.
Background is nighttime lights layer from National Geophysical
Data Center (2003); Version 2
DMSP-OLS Nighttime Lights Series, Boulder, CO;
http://www.ngdc/noaa.gov/. DMSP data col-
lected by U.S. Air Force Weather Agency.
that Penn sought for his city, Phila-
delphia, was access to and trade with
the interior of Pennsylvania. Penn’s
commission set out for Pennsylvania in
the early summer of 1682 with these in-
structions for finding a suitable site for
Philadelphia. There is some evidence
that the commission initially selected a
more southerly site in present-day Ches-
ter County.5 It’s plausible that recogniz-
ing the value of better navigation and
waterpower along the Schuylkill, Penn’s
surveyors rejected the Chester County
site in favor of the present-day site near
the falls of the Schuylkill River.

Swedish, Dutch, and later English
settlers took advantage of both the
trading opportunities and waterpower
at the falls of the Schuylkill. Farmers
used the Schuylkill to transport goods
and exchange grew near the falls. In
1706, farmers in Lower Merion asked
for a road to the landing place just be-
low the falls to better facilitate trade.6
As early as 1686, water mills were
erected to take advantage of the falls.7
And Donald Davis, who owned a mill
near the falls, said in 1749 that the
site of the falls was “very convenient
for water carriage, both for bringing
loads to the mill, and rafting timber
to Philadelphia, it being by the river
Schuylkill.”8 Thus, early Philadelphia
benefitted from its location near the
falls of the Schuylkill and was able to
attract both commerce and industry.
The site of present-day Trenton
is at the falls of the Delaware River
and its head of navigation, that is, the
point at which navigation is no longer
possible. It was inhabited by the Sanhi-
can tribe of the Lenape Indian nation
as early as 1400. The first Europeans
settled there in 1679. William Trent, a
Philadelphia merchant, recognized the
value of the falls and bought 800 acres
near them; he then began develop-
ing the area, including a stone mill.

“Trent’s energy and financial backing
launched the settlement, which he
called Trent’s Town, into a period of
steady growth. Its position at the head
of sloop navigation made the town
a shipping point for grain and other
products of the area, and a depot for
merchandise between New york and
Philadelphia.”9
The first permanent European
settlement in Delaware — by Swedes
in 1638 — was near the confluence of
the Delaware and Christina rivers and
the falls of the Brandywine Creek, the
present-day site of Wilmington. The
falls of the Brandywine and several
smaller nearby rivers provided water-
power for early mills and attracted
industrial activity. The first mill on the
Brandywine opened in 1687. By the
1790s, the flour mills near Wilmington
and the falls of the Brandywine were
the largest in the U.S.10
THE PERSISTENCE OF
PORTAGE CITIES AFTER THE
OBSOLESCENCE OF PORTAGE
Of course, in our District many
portage cities are close enough to the
ocean to continue to serve as port
cities. In that sense, some natural ad-
vantage survives to this day. However,
the Schuylkill, Christina, and Bran-
dywine rivers serve little commercial

traffic today. Similarly, the waterpower
produced at these falls today is negli-
gible, compared with power from other
sources.
In my study with Hoyt Bleakley,
we consider many other portage sites
where the disappearance of the origi-
nal advantages is even clearer. In spite
of the obsolescence of these original
natural advantages, these portage sites
are often still the location of major
agglomerations today. In our study, we
pay particular attention to rivers that
intersect the fall line, a geomorpho-
logical feature dividing the Piedmont
and the coastal plain. The fall line
describes the last set of falls or rapids
found along a river before it empties
into the Atlantic Ocean or the Gulf of
Mexico. Many historical portages, at
intersections between the fall line and
major rivers, are sites of major cities
today (Figure 3).
An advantage of examining
fall-line portages is that nearby loca-
tions are often very similar, in terms
of other natural advantages. On land,
the transition from the coastal plain
to the Piedmont is quite gradual. This
smoothness allows us to use com-
parison areas — places along the same
river — that, except for an initial por-
tage advantage, share features similar

to these historical portage sites. For
example, we can compare Philadel-
phia with other locations along the
Schuylkill. This similarity also helps to
rule out the existence of features co-lo-
cated with portage that might continue
to have value today. We also control for
other observable differences, such as to-
pography and climate. Thus, the main
comparison is between sites that seem
nearly identical except for the initial dif-
ference in value due to portage.
We found that not only are
present-day populations concentrated
at portage sites (relative to similar lo-
cations), these differences have shown
no tendency to diminish over a long
period of time — over a century after
portage-related advantages became
obsolete. Figure 4 shows the difference
between population densities at por-
tage sites and comparison sites for each
decade relative to 1850. We also con-
trol for other observable differences.
What the graph shows is that the dif-
5 See p. 594 in the book by Samuel Hazard.
6 See the article by Charles Barker, p. 345.
7 See the article by Edwin Iwanicki, p. 326.
8 See Barker, p. 345.
9 See the Federal Writers’ Project, p. 400.

10 See the book by John Munroe, p. 58.
ferences in density have actually gotten
larger over time. (In a separate analy-
sis, we also compared portage cities to
other cities of comparable density in
1850. There is no tendency for portage
cities to decline relative to these cities
as portage’s value declined.)
Thus, even though initial differ-
ences in value due to portage have
declined to zero, there is no tendency
for populations to equalize across these
comparison locations. If fundamen-
tals were the only force that mattered,
we would expect, over the long run,
that these differences would attenuate
toward zero. However, the evidence
suggests otherwise. Thus, a historical
difference, now obsolete, strongly and
permanently affected the pattern of
development across a wide swath of the
U.S. We view this as strong evidence
for path dependence in the location of
economic activity.11
So why didn’t Davis and Wein-
stein find permanent responses to the

bombings of World War II in their
study of Japan? A comparison with
the studies of Germany and the fall
line in the U.S. suggests a few hy-
potheses. Perhaps the magnitude of
the shock associated with the Allied
bombings of Japan was transitory, that
is, not “large” enough to have perma-
nent effects. Roads, lot divisions, and
many other forms of capital survived
the bombings and may have provided
anchors for redevelopment. Also, the
division of Germany lasted a half-
century and, at the time, was likely to
have been perceived as permanent or
near permanent. Similarly, many por-
tage sites in the U.S. were in active use
and provided value for many decades
or even a century or more. A plau-
sible explanation is that these latter
two episodes were larger shocks to the
economic geography of the respective
regions, which accounts for the differ-
ence in results.
Another possibility relates to the
large amount of geographic variation
in Japan. Japan’s islands contain rug-
ged mountainous areas and a few flat
coastal plains. These large differences
can actually suppress the effects of
history. Intuitively, if only a few loca-
tions in a larger region are suitable for
economic activity, it seems likely that,
no matter the sequence of histori-
cal events, people would continue to

Population Density Differences Over Time,
Portage vs. Nonportage Sites
Source: Adapted from Bleakley and Lin, Figure 5
FIGURE 4
Fall Line, Rivers, and Population Density Today
Source: Adapted from Bleakley and Lin, Figure A.1
FIGURE 3
Effect (relative to 1850) of portage proximity
1800 1850 1900 1950 2000
Year
.5
.4
.3
.2
.1
0
-.1
11 If we were to replay the history of the U.S., it
seems likely that a similar sequence of location

decisions might have taken place near fall-line
portages, given the existence of these physical
obstacles to water navigation. However, a broad-
er definition of path dependence, in which the
location of economic activity depends on the
past sequence of events and not necessarily lo-
cational fundamentals, seems applicable to the
history of portage cities. In this view, portages
are like accidents of geography that affected the
historical location of population, which, in turn,
affected the location of cities today.
REFERENCES
settle in the same places. By analogy,
as a thought experiment, if we were to
replay the history of settlement in Cali-
fornia (a very heterogeneous region),
it seems likely that in our alternative
history, the views of the Pacific coast,
the harbors in San Francisco and San
Diego, the soil quality in the Central
Valley, and the sunshine in the Los
Angeles basin would result in similar
kinds of economic activity locating in
similar places.
In contrast, in our study of por-
tages, we are examining an area of the
world that is relatively homogeneous:
The U.S. South, Midwest, and Mid-
Atlantic are all relatively featureless
plains, or, at least, the terrain and oth-

er natural features change slowly over
space. Compared with Japan, a sample
area that minimizes changes in natural
features seems like a more ideal labora-
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dependence in the location of cities.
Recent research in economic
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major contributors to the distribution
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major explanations for the distribution
of people and businesses. If economies
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fundamentals matter little and histori-
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