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Federal Reserve Bank
of Chicago
Second Quarter 2001

perspectives
2

Are the large central cities of the Midwest reviving?

15

Polycentric urban structure: The case of Milwaukee

28

Central banking and the economics of information

38

Competition among banks: Good or bad?

Economic

perspectives

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Contents

Second Quarter 2001, Volume XXV, Issue 2

Are the large central cities of the Midwest reviving?
William A. Testa

Most central cities of the Midwest experienced revival in the 1990s in comparison with
the previous two decades, according to such broad measures as population, employment,
unemployment, and income. This article evaluates such gains in light of the overall
turnaround of the Midwest economy and finds that underlying urban/suburban differences
in performance have not changed radically in most metropolitan areas.

Polycentric urban structure: The case of Milwaukee
Daniel P. McMillen
The author finds that Milwaukee has one employment subcenter, located at the western edge
of the city. The subcenter has significant but highly localized effects on both employment
and population densities in the Milwaukee area.

28

Central banking and the economics of information
Edward J. Green
This article concerns the potential relevance of information technology to three aspects of
central banking: setting the objectives of monetary policy, ensuring the integrity and security
of financial system infrastructure, and maintaining the transparency of decision-making.
Regarding integrity and security of infrastructure, a revised role for central banks may be
appropriate. However, recent innovations in technology and advances in learning confirm the
wisdom of central banks’ efforts to control inflation and maintain their own transparency.

38

Competition among banks: Good or bad?
Nicola Cetorelli
What are the pros and cons of bank competition? This article presents an overview of the
most recent research on the economic role of bank competition. Contrary to the received
wisdom that competition in the banking industry is necessarily welfare enhancing, theoretical
analyses and empirical evidence also identify possible negative economic effects. This
broader view offers food for thought for regulators and policymakers.

Are the large central cities of the Midwest reviving?
William A. Testa

Introduction and summary
Most central cities of the large metropolitan areas of
the Midwest showed signs of improvement during
the 1990s compared with the previous two decades,
according to such broad measures as population, employment, unemployment, and income. If such gains
can be sustained, it will be welcome news for households residing in central cities who experienced erosion of their income and tax base during the second
half of the 20th century. Such gains might also provide
important evidence of the results of the recent policies
of big city mayors, who have been very active in both
improving the quality of urban life—through transportation, crime, and school reform initiatives—and
engaging in economic development initiatives—such
as work force training and rebuilding city infrastructure. In this article, I analyze broad measures of 11
central city economies since 1970 to assess whether
there has been any underlying structural improvement
in big city performance beyond the effects of the
general U.S. and regional economic expansion.
I relate each city’s performance to that of its surrounding suburban areas. In this way, I can control
for many factors that may be peculiar to a given metropolitan area—such as a change in the performance
of an area’s key industry and overall economy or its
location on a particular interstate highway. Within this
framework, I ask whether the city’s share of metropolitan population and employment is growing over time,
or at least whether its loss of share is abating, and
whether other performance measures such as household income and unemployment rate are improving
in the city relative to its suburbs. Such a standard for
improvement may be stringent. Most of the 11 large
central cities of the Midwest have fixed boundaries;
they are unable to annex land to accommodate population growth in the metropolitan area, while the surrounding suburban areas are able to do so.

2

I find that, on average, the population of the 11
cities almost stabilized in the 1990s, a marked improvement compared with the 1970s. In part, however,
it appears that central city population recovery largely reflects buoyant regionwide recovery rather than
structural change; central cities continue to lose share
of population to their suburbs. However, my analysis
of total permits to construct residential housing units
indicates that, although cities continued to lose ground
to their suburbs in the 1990s relative to the 1980s,
single-family construction showed the opposite trend,
perhaps reflecting the much-touted recovery of central cities as a livable place for families. So too, the
Midwest’s economic recovery of the 1990s has lifted
labor force participation and income in both city and
suburb. Furthermore, tightening labor markets in the
1990s clearly narrowed the gap between suburban
unemployment rates and those of the city, although
the low ratios of household income in cities versus
their suburbs have not improved.
It appears that city residents continue to look to
the periphery of metropolitan areas to earn their income. At least through 1997, job sites continued to
decentralize from the center of the metropolitan area.
Overall, I conclude that, although there are several
individual instances to the contrary, central cities in
the Midwest continued to struggle to keep pace with
their suburbs in the 1990s in terms of job growth and
economic development. Nonetheless, there are some
positive indications for the future, and it is quite evident that the large central cities of the Midwest have

William A. Testa is vice president and director of regional
programs in the Economic Research Department of the
Federal Reserve Bank of Chicago. The author thanks
Margrethe Krontoft and Loula Merkel for research
assistance.

2Q/2001, Economic Perspectives

shared in the bounty of the general economic recovery.

FIGURE 1

Major midwestern cities and their metropolitan areas

Are cities gaining population
and housing?
In the U.S. and in most developed countries, households exercise choice in where
Minneapolisto locate their residences. Accordingly,
St. Paul
population growth is a frequently examined indicator of the health and attractiveMilwaukee
ness of a locale. In the 11 metropolitan areas
chosen for this article, the central cities
Detroit
Buffalo
continue to comprise a major, though dePittsburgh
Chicago
clining, share of the populations of their
Cleveland
Columbus
respective metropolitan areas (see figure 1
Indianapolis
and table 1). According to recent data from
the Bureau of the Census, these cities comSt. Louis
prised 28 percent of their metropolitan staCincinnati
tistical area (MSA) population in 2000.
Combined, the cities represented a 50 percent share of the population of the metropolitan area at mid-century and a 55
Source: U.S. Department of Commerce, Bureau of the Census.
percent share in 1900 (table 1).1
How did these cities fare during the 1990s in
central cities accelerated in the 1990s, supported by
comparison to the 1980s? Looking first at population
the economic turnaround in the Midwest. Migration
growth in central cities, we see that six cities experiout of the Midwest has slowed to a trickle in recent
enced an improvement in their average annual
years, and population growth in the 11 sample metrogrowth rate of population in the 1990s—Chicago,
politan areas accelerated from .2 percent per year in
Cleveland, Detroit, Indianapolis, Minneapolis-St.
the 1980s to .7 percent per year in the 1990s. But was
Paul, and Pittsburgh (table 2). Of these, only Chicago,
there a shift in residential preferences between city
Indianapolis, and Minneapolis-St. Paul actually
grew; the population of Cleveland, Detroit,
and Pittsburgh declined more slowly than
TABLE 1
in the previous decade. The population
Eleven Great Lakes cities
changes in these six cities combined were
2000 population
City as share of MSA
sufficient to offset the deterioration in the
City
MSA
1900
1950
2000
other five central cities, so that the average
(thousands)
(percent)
growth of the total city population registered an improvement from the 1980s to
Buffalo
293
1,170
69.3
53.3
25.0
the 1990s, wherein the annual growth rate
Chicago
2,896
8,273
74.9
62.4
35.0
Cincinnati
331
1,646
44.0
41.3
20.1
climbed from –1.3 percent per year to staCleveland
478
2,251
54.2
41.0
21.3
ble population on average. An unweighted
Columbus
711
1,540
39.3
53.1
46.2
average, whereby each city is given equal
Detroit
951
4,442
40.8
49.8
21.4
weight, shows that average annual populaIndianapolis
782
1,607
39.2
51.4
48.6
tion growth improved slightly from a loss
Milwaukee
597
1,501
63.3
56.7
39.8
of .5 percent per year over 1980–90 in comMinneapolisparison to a loss of .2 percent per year over
St. Paul
670
2,969
60.5
64.3
22.6
the 1990–2000 period.
Pittsburgh
335
2,359
25.7
27.1
14.2
In comparing the 1980s to the 1990s,
St. Louis
348
2,604
60.7
46.6
13.4
the improvements are more widespread.
All 11 cities
8,392
30,361
54.7
50.4
27.6
All 11 central cities experienced improveNotes: MSA is metropolitan statistical area. MSA reflects 1998
ments in population change. This is not too
definition for all years.
surprising since overall population growth
Source: U.S. Department of Commerce, Bureau of the Census, various years.
of the metropolitan areas that overlie the
● ●

●

●

●

●

●

●

●

●

●

●

Federal Reserve Bank of Chicago

3

TABLE 2

Average annual change in population and share of MSA
Population
1970–80

1980–90

Share of MSA
1990–2000

1970–80

(percent)

1980–90

1990–2000

(percentage points)

Buffalo
Chicago
Cincinnati
Cleveland
Columbus
Detroit
Indianapolis
Milwaukee
Minneapolis-St. Paul
Pittsburgh
St. Louis

–2.3
–1.1
–1.5
–2.4
0.5
–2.0
–0.6
–1.1
–1.4
–1.8
–2.7

–0.8
–0.7
–0.6
–1.2
1.2
–1.5
0.5
–0.1
0.0
–1.3
–0.6

–1.1
0.4
–0.9
–0.5
1.2
–0.7
0.7
–0.5
0.5
–1.0
–1.2

–1.6
–1.2
–1.8
–1.9
–0.3
–2.0
–1.0
–1.1
–2.1
–1.5
–2.6

–0.4
–0.9
–1.0
–1.0
0.1
–1.3
–0.1
–0.4
–1.3
–0.6
–1.6

–0.9
–0.7
–1.6
–0.7
–0.2
–1.2
–0.8
–0.9
–1.5
–0.8
–1.6

Weighted avg.

–1.4

–1.3

0.0

–1.4

–0.8

–0.7

Unweighted avg.

–1.5

–0.5

–0.2

–1.5

–0.8

–1.0

11 MSAs

0.0

0.2

0.7

n.a.

n.a.

n.a.

U.S.

1.4

1.2

1.0

n.a.

n.a.

n.a.

Notes: n.a. indicates not applicable. MSA is metropolitan statistical area.
Source: U.S. Department of Commerce, Bureau of the Census, various years.

and suburbs in the 1990s? Here again we see that most
central cities are indeed moving in a positive direction
in comparison to the 1970s (table 2). All appear to be
either experiencing a deceleration in loss of share or
an acceleration in gains. However, in the aggregate a
modest deterioration occurred from the 1980s to 1990s
as measured by the unweighted average. Buffalo,
Cincinnati, Columbus, Indianapolis, Milwaukee,
Minneapolis-St. Paul, and Pittsburgh saw an increased
rate in the erosion of population share to their suburbs.
In assessing the importance of these population
losses in central cities, it is important to note that the
municipal boundaries of the cities have remained essentially fixed while those of their metropolitan areas
have expanded to accommodate growth in households
and rising demand for housing and land. The rising
demand for space means, for example, that there will
be a growing share of population in those parts of the
metropolitan area where land area can expand. In point
of fact, the boundaries of large midwestern cities have
not grown much. Notable exceptions to stagnant city
boundaries are Columbus, Ohio, which has used its
strategic assets of water and sewerage treatment capacity to induce annexation of neighboring development;
Indianapolis, which became roughly coincident with
its surrounding county government all in one fell

4

swoop in the 1970s; and Milwaukee, which undertook an aggressive, but short-lived, annexation policy during the 1950s (table 3). The remaining eight
cities taken together expanded their land area by only
3.7 percent from 1950 to 1990.
The overall population densities of metropolitan
areas have been falling steeply since the early decades
of the twentieth century, thereby spreading out existing population. Households tend to live today in a
fashion that consumes more housing—both land and
structure—than earlier in the century. Accordingly,
even had no further population increase taken place
in metropolitan areas, households would have jumped
the fixed city boundary in achieving lower densities
of living (and working), thereby reducing population
of central cities. The trend toward declining densities
in central cities can be seen between 1920 and 1990
(table 4). For all 11 cities taken together, and not adjusting for changing city boundaries and land area,
average density declined by almost one-half over the
period. Even if we exclude Indianapolis, Columbus,
and Milwaukee—whose boundaries were highly expansionary—average city density declined by approximately one-half over this period. The second two
columns of table 4 measure the rate at which population density in the entire metropolitan area falls for
every mile of distance from the center of the city.

2Q/2001, Economic Perspectives

TABLE 3

Land area (square miles) and density (population per square mile)
1910

1920

1930

1940

1950

1960

1970

1980

1990

Buffalo
Land area
Density

38.7
10,949

38.9
13,028

38.9
14,732

39.4
14,617

39.4
14,724

39.4
13,522

41.3
11,205

41.8
8,561

40.6
8,082

Chicago
Land area
Density

185.1
11,806

192.8
14,013

201.9
16,723

206.7
16,434

207.5
17,450

224.2
15,836

222.6
15,126

228.1
13,174

227.2
12,252

Cincinnati
Land area
Density

49.8
7,301

71.1
5,643

71.4
6,319

72.4
6,293

75.1
6,711

77.3
6,501

78.1
5,794

78.1
4,935

77.2
4,716

Cleveland
Land area
Density

45.6
12,295

56.4
14,128

70.8
12,718

73.1
12,016

75.0
12,197

81.2
10,789

75.9
9,893

79.0
7,264

77.0
6,566

Columbus
Land area
Density

20.3
8,941

22.6
10,488

38.5
7,547

39.0
7,848

39.4
9,541

89.0
5,296

134.6
4,009

180.9
3,123

190.9
3,315

Detroit
Land area
Density

40.8
11,416

77.9
12,748

137.9
11,375

137.9
11,773

139.6
13,249

139.6
11,964

138.0
10,953

135.6
8,874

138.7
7,411

33.0
7,080

43.6
7,206

54.2
6,719

53.6
7,220

55.2
7,739

71.2
6,689

379.4
1,963

352.0
1,991

361.7
2,022

22.8
16,397

25.3
18,069

41.1
14,069

43.4
13,536

50.0
12,748

91.1
8,137

95.0
7,548

95.8
6,641

96.1
6,536

102.3
5,045

101.9
6,038

107.6
6,840

106.0
7,359

106.0
7,859

108.7
7,326

107.3
6,937

107.5
5,964

107.7
5,948

Pittsburgh
Land area
Density

41.4
12,896

39.9
14,745

51.3
13,057

52.1
12,892

54.2
12,487

54.1
11,171

55.2
9,422

55.4
7,652

55.6
6,653

St. Louis
Land area
Density

61.4
11,189

61.0
12,670

61.0
13,475

61.0
13,378

61.0
14,046

61.0
12,296

61.2
10,167

61.4
7,379

61.9
6,408

All 11 cities
Land area
Density

641.2
10,176

731.4
11,464

874.6
11,812

884.6
11,845

902.4
12,496

1,036.8
10,582

1,388.6
7,513

1,415.6
6,319

1,434.6
5,862

Indianapolis
Land area
Density
Milwaukee
Land area
Density
Minneapolis-St. Paul
Land area
Density

Source: U.S. Department of Commerce, Bureau of the Census, various years.

From this we see that population densities have been
declining both within and outside of central cities. What
are the underlying reasons for these falling densities?
Changing technologies and standards of living
are generally thought to have given rise to decisions
of city residents to decentralize. Significant technological forces spurring lower-density living and working are described as pervasive by urban analysts and
are reflected in the trend of suburbanization around
the world.2 Rising household incomes pushed families to desire more housing and land, trading off longer working commutes to the central city for more (and
distant) housing where land was cheaper. Falling
automobile prices and better highways in the early

Federal Reserve Bank of Chicago

twentieth century lent a further impetus to suburban
living. Meanwhile, on the production and employment
side, there was also strong impetus to decentralization. Highways freed factories from their ties to water
ports, railroads, and rail spurs. Intermediate goods
could be shipped in from afar on trucks, and final goods
sent out the same way. So too, workers at inner-city
factories increasingly gave way to machinery, and those
few workers no longer needed to walk or take a streetcar to the factory site. With assembly-line production
assisted by electric tools and conveyor belts, multistory factories converted or moved to sprawling land
intensive one-story buildings. And why not build
those low-slung modern factories where land was

5

On the other hand, some observers
suggest that tastes may change back toPopulation density
ward a preference for residential living
Density
Percent falloff in
in a more compact form. One school of
(population per
density per mile
thought called “new urbanism” is now
City
square mile)
from city center
promoting higher density residential life1920
1990
1920
1990
styles within walking distance to shopping,
Buffalo
13,028
8,082
0.15
0.13
entertainment, and public transportation.
Chicago
14,013
12,252
0.15
0.09
In fact, observers have reported on the
Cincinnati
5,643
4,716
0.23
0.13
pickup in the pace of residential building
Cleveland
14,128
6,566
0.22
0.11
in some central cities in the late 1990s.
Columbus
10,488
3,315
0.22
0.12
This phenomenon has been attributed to
Detroit
12,748
7,411
0.19
0.11
Indianapolis
7,206
2,022
0.24
0.07
a revived interest in city living by both
Milwaukee
18,069
6,536
0.31
0.16
young and old, but mostly childless, houseMinneapolisholds. An expected demographic moveSt. Paul
6,038
5,948
0.18
0.11
ment toward larger numbers of childless
Pittsburgh
14,745
6,653
0.17
0.12
households as baby-boomers pass their
St. Louis
12,670
6,408
0.22
0.11
child-rearing years may presage a continAll 11 cities
11,707
6,355
0.21
0.11
ued revival of interest in city living. MeanStandard deviation
0.05
0.02
while, in attempting to retain and attract
Source: Author’s calculations based on decennial census data.
families, central cities such as Milwaukee,
Cleveland, Detroit, and Chicago have
launched ambitious and innovative initiacheaper and transportation/warehousing more comtives to improve their public school systems.
modious, that is, far distant from the city center. In
As to hard evidence of growth in housing activity,
the latter part of the twentieth century, job location
municipal governments typically report permits that
followed population in suburbanizing, so much so that
are filed in advance of construction (and conversion)
metropolitan areas can often be characterized as conof new housing units. An unknown portion of these
taining several large employment centers dispersed
permits are not acted on, and there is no timely data
throughout the metropolitan area.3
source available on abandonment or tear-downs with
This portrayal implies that midwestern cities may
which to assess changes to the overall net stock of
now be in the process of lowering or equalizing their
housing. Nonetheless, these data do indicate the expectdensities to match their surrounding suburbs. Adjusted and planned level of new residential construction
ment to lower densities cannot take place instantaactivity. Figure 2 shows the pace of building permits
neously. Both residential and nonresidential capital
of residential units back to 1980, and there is clearly
in the form of housing, commercial buildings, and
steady growth in the 1990s, with a marked accelerapublic infrastructure are far from perfectly malleable.4
tion in the past two to three years. Single-family home
Even as demand favors less dense residential and combuilding growth is especially steady in its upward
mercial space, rents will tend to fall below the costs
climb, with both total (and multi-unit) housing being
of new construction, thereby forestalling de-concenmuch more volatile. However, in the context of busitration pending the depreciation of the stock of existness cycle movements, the recent rise in building is
ing buildings. Thus, some observers propose that city
somewhat less impressive; most midwestern cities are
decline is partly a transitory and delayed adjustment
only now reaching the levels of residential building
of density to new technology, which further implies
activity that were previously attained in the mid to late
that the cities’ population decline will bottom out at
1980s. For all 11 cities combined, the number of resisome point when an equilibrium density is achieved.
dential permits issues for the last five years of the 1990s
The fact that the technologies of overland transportareached only 90.6 percent of the levels for the late
tion and industrial production are no longer making
1980s (table 5, column 2). However, the data are more
those significant technological leaps that have lowered
sanguine for single-family housing permits. In the last
preferred density gives rise to some optimism that city
five years of the 1990s, single-family permits were
population decline may soon bottom out to an equitaken out at a much more rapid clip in central cities
librium state of land use density with the surrounding
compared with the last five years of the 1980s (table
metropolitan area.
5, column 5). In fact, the improvement in the pace of
TABLE 4

6

2Q/2001, Economic Perspectives

FIGURE 2

Authorized residential units
(11 midwestern cities)
thousands
25
20

Total

15
10

Single family
5
0
1981

’84

’87

’90

’93

’96

’99

Sources: U.S. Department of Commerce, Bureau of the Census,
and U.S. Department of Labor, Bureau of Labor Statistics, various
years, Current Population Survey.

permits for single-family housing in cities even compares favorably with the suburban areas of MSAs.
Are city residents doing better?
We have seen that population and housing
growth, or a slowing in the pace of decline, may be a
sign of city revival as households increasingly come
to view the city favorably and choose to live there.

However, because technologies have universally
changed living and working for the better, those who
choose to remain in the city may also be better off.
Apart from geographic growth measures, then, what
are the more direct measures of the well-being of city
residents that we can compare with suburban counterparts? Both average household income and the unemployment rate are powerful and widely accepted
measures of well-being. Household income estimates
for cities and their surrounding metropolitan areas
can be constructed from sample data collected annually by the Bureau of the Census and the Bureau of
Labor Statistics in their Current Population Survey.
A second measure reflects the degree to which city
residents have access to opportunities to participate
in the work force. Local unemployment rates are
constructed through sampling of the members of
working age households by the Bureau of Labor Statistics in cooperation with state employment agencies.
These indicators show absolute improvements
for city residents in the 1990s (figure 3). Unemployment rates averaged over the central cities peaked at
over 15 percent in the early 1980s, and have since
declined to a recent level of approximately 6 percent
for workers aged 16 years and older. Similarly for
real average household income (deflated by the Consumer Price Index calculated for all urban areas), the

TABLE 5

Residential permits (ratios x 100)
Total residential units
1990–94
1980–84
Buffalo

1995–99
1985–89

Single family units
1990s
1980s

1990–94
1980–84

1995–99
1985–89

1990s
1980s

239.4

202.1

216.2

234.0

76.8

118.3

70.0

129.2

98.4

196.9

167.3

178.7

Chicago
Cincinnati

101.6

83.7

94.9

530.5

133.2

244.9

Cleveland

90.5

143.7

120.3

738.6

803.3

785.4

Columbus

110.3

108.7

77.1

88.5

133.2

95.0

Detroit

47.3

96.0

62.1

51.7

479.8

125.3

Indianapolis

93.4

86.4

89.2

200.1

140.8

161.4

Milwaukee

59.8

58.6

59.2

41.9

75.1

52.1

Minneapolis-St. Paul

24.1

94.9

45.2

51.2

227.4

102.6

Pittsburgh

30.4

76.7

49.6

59.4

122.5

86.9

St. Louis

12.6

71.6

35.3

143.4

129.7

132.9

All 11 cities

78.3

90.6

85.0

154.0

129.3

139.2

U.S.

88.6

95.4

92.3

123.8

111.6

116.8

Note: Ratios of earlier versus later five-year period or decade.
Source: U.S. Department of Commerce, Bureau of the Census, various years.

Federal Reserve Bank of Chicago

7

trend was for sideways movement from the late 1970s
up until the early 1990s, from which point the current
expansion has lifted mean incomes by 15 percent to
20 percent. There is no question, then, that the 1990s
have on average lifted the fortunes of city residents.
How have city residents fared versus suburban
residents? Average household incomes in comparison
to suburban counterparts have not changed appreciably from the 1980s (table 6). Again, we can look at
these over comparable periods of the 1980s and 1990s.
Interestingly, it appears that city incomes are somewhat countercyclical—really less procyclical—compared with the suburbs; the income ratio of city to
suburb tends to climb during contractions and fall during expansions (figure 4). Perhaps one explanation is
that a greater proportion of city residents depend on
fixed income streams from pensions and government
income support programs than their suburban counterparts. Such income streams are less likely to evaporate
during a downturn. In any event, the relative income
of city residents versus suburbs has not improved from
the latter 1980s, which was a similar business cycle
period to the latter 1990s.5 More formal trend analysis (not reported) using ordinary least squares (OLS)
multiple regression does not suggest that the procyclicality of the suburb to city ratio is statistically significant. Moreover, a binary variable for 1990–95 and one
for 1996–99 suggest that the suburb to city ratio of
mean household income widened during the booming
1990s. Real household income has risen in both city
and suburb alike, but more so for suburban households.
What do unemployment rates say about the economic well being of city residents? Currently, there
is little doubt that the Midwest’s tight labor markets

FIGURE 3

Unemployment rates and real income
(11 midwestern cities)
thousands
30

Real mean
household income

percent
20

(left scale)

25

15

10

20

Unemployment rate
(right scale)

15

5

0

10
1978

’81

’84

’87

’90

’93

’96

’99

Sources: U.S. Department of Commerce, Bureau of the
Census, and U.S. Department of Labor, Bureau of Labor
Statistics, various years, Current Population Survey.

are lifting the employment rates of city populations.
Though these are an imperfect measure of employment
participation, unemployment rates in both city and
suburb alike are the lowest seen in 30 years. To assess
whether cities are coming back within the context of
their surrounding regions, I focus on explaining the
difference between each city’s unemployment rate
minus the adjacent suburban area’s unemployment
rate (in March of each year) for adults aged 16 years
and over. Over a combined sample of each of the years
from 1977 to 1999, I regressed this unemployment rate
gap against each city’s overarching MSA unemployment rate (see box 1). This MSA unemployment rate—
an independent variable in the regression—accounts

TABLE 6

Average city to suburb ratios of mean income
1960

1970

1980–85

1986–89

1990–95

1996–99

Buffalo
Chicago
Cincinnati
Cleveland
Columbus
Detroit
Indianapolis
Milwaukee
Minneapolis-St. Paul
Pittsburgh
St. Louis

0.81
0.80
0.83
0.74
0.77
0.81
0.79
0.86
0.87
0.88
0.76

0.69
0.71
0.76
0.64
0.78
0.69
1.01
0.71
0.70
0.82
0.67

0.61
0.67
0.70
0.72
0.75
0.62
1.00
0.64
0.73
0.82
0.58

0.63
0.66
0.74
0.56
0.74
0.58
0.88
0.69
0.66
0.81
0.57

0.60
0.65
0.84
0.51
0.75
0.55
0.81
0.67
0.78
0.79
0.50

0.67
0.63
1.09
0.50
0.70
0.50
0.63
0.61
0.69
0.92
0.64

All 11 cities

n.a.

0.74

0.71

0.68

0.67

0.66

Note: n.a. indicates not available. 1960 data represent median family income for central cities
and urban fringes of urbanized areas.
Sources: 1960 and 1970 data are from the decennial census. All other data are from the March CPS.

8

2Q/2001, Economic Perspectives

FIGURE 4

City/suburbs mean income
(11 midwestern cities)
percent
75

70

65

60

55
1978

’81

’84

’87

’90

’93

’96

’99

Sources: U.S. Department of Commerce, Bureau of the Census
and U.S. Department of Labor, Bureau of Labor Statistics,
various years, Current Population Survey.

for the specific point of the business cycle for each
particular metropolitan area, as well as accounting
for the overall MSA-specific labor market condition.
As an estimation strategy, I include so-called fixed effects—that is, a binary or “shift” variable for each
metropolitan area—in the regression equation to account for differences in each individual region’s industry and work force composition.
In reviewing the regression results, I find clear
evidence that unemployment rates in the city gained
on the suburbs during the very strong labor markets
of the 1990s (table 7). The estimated effects of the shift
variables for the 1990s and for the 1995–99 period indicate that the gap has narrowed in unemployment rate
between suburb and city. Lower metropolitan unemployment rates during the 1990s have tended to dampen
city unemployment rates even more. In the event that
the current tight labor markets persist, as the ongoing
trend toward slower growth of the U.S. work force
suggests, the city’s working age residents may continue to enjoy abundant employment opportunities.
Are cities a better workplace?
The location of employment is an important indicator of a city’s economic base. For one reason, such
employment usually reflects the richness of the taxable base from which municipal and school district governments can raise revenues to provide services to city
residents. Secondly, such jobs importantly reflect employment opportunities to residents that are accessible
and proximate—jobs from which city households can
generate their own wealth and income. How, then, are
the large midwestern cities faring as sites for employment, especially in relation to their suburbs?

Federal Reserve Bank of Chicago

Jobs have been suburbanizing at a phenomenal
pace in recent decades, so much so that the “reverse
commute” from city to suburbs now rivals that of suburb to city. As of the 1960 Census of Population, the
net flow of workers to central city job sites (on a population-adjusted basis) clearly favored the city; 36.6
percent of employed suburban residents worked in
the 11 major central cities, while only 9.4 percent of
city residents worked in their suburbs. This has changed
dramatically. By the 1990 census, 26.2 percent of
city residents commuted outward, while 28.4 percent
of suburbanites headed for city job sites.6
Data covering jobs located in central cities is
sparser than that for population, income, and employment. Indeed, the decennial census provides our only
intermittent glimpse of the evolution of jobs in central cities. On a timely and consistent historical basis,
BOX 1

Analyzing MSA growth trends by analyzing
employment rates
To formally test for a changing trend in the unemployment rate of central cities versus their own suburbs, I use an ordinary least squares regression
equation, with the difference in city minus suburban unemployment rate as the dependent variable
to be explained. I use annual observations for each
of the 11 cities for each year from 1977 to 1999 as
the dependent variable. The regression equation
becomes
URDIFit = bi Pi + b2 URit + b3 Yt + et,
where URDIFit represents the difference of the city’s
unemployment rate in metropolitan area i from the
suburban area’s unemployment rate in the same
region at time t. Coefficients bi (i = 1, 2, 3, 11)
are estimated for each metropolitan region i observed as Pi. Since these observations are loaded as
zero or one (indicating place), the coefficients bi
act as shifters to pick up region-specific differences
in suburban minus city labor markets. The effect on
URDIF of each metropolitan area’s overall labor
market condition is estimated by the coefficient,
b2, acting through URit, the overall metropolitan
area unemployment rate, which is observed to vary
across time t and place i. The coefficient b3 is the
estimated effect of the particular year acting on
the observations Yt observed as period 1990–99 or
1995–99, respectively. Since these observations as
loaded as zero or one (for the specified period), the
coefficient reflects another shifter, testing whether
URDIF has shifted during these periods relative to
previous years 1977–89.

9

TABLE 7

Effect of place and time on city versus suburban unemployment
Independent
variable

(- - - - - - - - - - - - - - - - - - - - - - - Dependent variable: ( URcity –

Buffalo
2.54 (2.1)*
Chicago
3.89 (3.6)*
Cincinnati
2.33 (2.1)*
Cleveland
6.27 (6.0)*
Columbus
2.19 (2.2)*
Detroit
8.39 (6.9)*
Indianapolis
0.94 (0.9)
Milwaukee
3.40 (3.4)*
Minneapolis-St. Paul 1.03 (1.1)
Pittsburgh
1.42 (1.3)
St. Louis
4.08 (3.9)*

1.89
3.29
1.73
5.70
1.67
7.73
0.39
2.85
0.54
0.81
3.52

Unemployment
rate in metro area 0.29 (3.1)*

0.33 (3.6)*

Year shifter
1990–99
1995–99

–1.35(–2.6)*
—

Interaction of
place and time
1990–99
1995–99
R2
Durbin–Watson

—
–1.15 (–1.9)*

—
—
0.73
1.91

(1.6)
(3.1)*
(1.6)
(5.6)*
(1.8)*
(6.6)*
(0.4)
(2.9)*
(0.6)
(0.7)
(3.5)*

5.52 (7.5)*
6.41 (8.7)*
4.89 (6.6)*
8.62 (11.6)*
4.22 (5.7)*
11.45 (15.5)*
3.15 (4.1)*
5.58 (7.5)*
2.82 (3.8)*
4.09 (5.5)*
6.38 (8.6)*

5.12 (7.0)*
6.02 (8.2)*
4.50 (6.1)*
8.23 (11.2)*
3.83 (5.2)*
11.05 (15.0)*
2.74 (3.6)*
5.19 (7.0)*
2.42 (3.3)*
3.70 (5.0)*
5.99 (8.1)*

—

—

–2.22 (–5.0)*
—

—
—
0.73
1.92

URsubs), 1977–99 - - - - - - - - - - - - - - - - - - - - - - - - - -)

—
–2.26 (–4.3)*

—
—
0.72
1.85

—
—
0.72
1.85

2.54
3.88
2.31
6.26
2.18
8.39
0.94
3.39
1.02
1.41
4.07

(2.0)*
(3.4)*
(2.0)*
(5.6)*
(2.1)*
(6.6)*
(0.9)
(3.1)*
(1.0)
(1.2)
(3.8)*

0.29 (2.8)*

1.86
3.26
1.66
5.65
1.63
7.69
0.35
2.82
0.50
0.78
3.49

(1.6)
(3.1)*
(1.5)
(5.5)*
(1.7)*
(6.4)*
(0.4)
(2.8)*
(0.5)
(0.7)
(3.5)*

0.34 (3.5)*

–1.32 (–1.0)
—

—
–0.82(–0.5)

–0.01 (0)
—

—
–0.07(–0.2)

0.73
1.91

0.73
1.92

* Denotes significance at 90 percent level.
Notes: T-stats in parentheses; data not available for 1994 (all cities) and Indianapolis for 1989.
Source: U.S. Department of Commerce, Bureau of the Census, various years, CPS supplement, March.

there has been no data series collected to reflect city
boundaries. For this reason, it is difficult to measure
the decentralization of job sites into the 1990s and to
analyze it in the context of previous decades. As a substitute, I use the comprehensive annual estimates of
employment by location at the county level of geography from the Bureau of Economic Analysis, which
are reported back to 1969.7 I can use these data to compare central county data trends with those of surrounding suburbs to assess the progress of central areas as
job sites in the 1990s. To corroborate my findings, I
piece together job data covering many (but not all) individual industries in the city versus the suburbs, as
reported in various census reports of industry sectors
from the U.S. Census Bureau. These, admittedly incomplete, data tend to corroborate the assertion that,
while conditions have definitely improved, there is
little in the way of structural or comparative improvement of cities in relation to suburban growth.
Beginning with the county data, the pattern that
emerges is much like that of population trends. As
shown in table 8, the average annual employment
growth rate in central counties improved modestly
from .7 percent per year from the 1969–79 period to

10

.9 percent during the 1979–89 period. Perhaps that
improvement is not too surprising given the propensity for there to be a mutual attraction between job
location and residential location. However, job growth
showed no improvement from the decade of the
1980s to the decade of the 1990s (up through 1998).
Taken together, employment growth remained constant at .9 percent per year; taken as a group with
each observation given equal weight, growth deteriorated from 1.2 percent per year to 1.0 annual growth
in the 1990s.
Has there been any underlying structural improvement in the trends for central counties? When I compare
the performance of central counties to their surrounding
counties, I find that little if any overall improvement
has taken place. The 1980s display an easing of the rate
of loss in comparison to the 1970s (table 8, columns 4,
5, and 6). Yet, on average, the 1990s appear to have experienced acceleration in share loss from the 1980s, and
in fact to have performed no better and perhaps worse
than the 1970s rate of decline. If anything, employment
decentralization has fared somewhat worse than population decentralization using this measure (table 2). Population loss of share has improved over time; the pace of

2Q/2001, Economic Perspectives

employment share loss has deteriorated or, at least,
remained about the same. Perhaps the inner suburbs of
midwestern metropolitan areas are also faring poorly as
job locales in relation to the periphery. At least it appears
that they are not doing well enough to pull up measured
central county employment in relation to the peripheral
counties of the metropolitan regions. Employment share
erosion of the suburban portion of the central county is
consistent with the findings of Myron Orfield, who documents that the problems once thought to characterize
large inner cities—loss of tax base, population, and
jobs—are now typical of the inner ring suburbs of
older “inelastic” cities as well.8
Can we corroborate the finding of city job site
decline any further? Comprehensive data on jobs by
location over time are extremely spotty at the city level
of geography—at least with regard to data sets that are
consistently constructed so as to be comparable from
state to state. However, I can use data from the censuses
of business to shed some light on city-specific employment trends in the 1990s versus earlier decades.
The business censuses do report accurately payroll employment by city geography. The downside is that
coverage of industries is incomplete. Several service
sectors are not covered for years before 1987, along
with finance, insurance, real estate, transportation, communication, and public utilities. These are admittedly

some sizable industries, and some of those that we
know from other data sources to be most prominent
(and central city durable) in central city locales. Nonetheless, a sizable amalgam of total employment remains
that can be used to construct a “total employment” measure, comprising manufacturing, retail trade, wholesale trade, services (part), and government (part). The
Census Bureau estimates that the business census data
cover 75 percent of total payroll employment for 1987.9
We can see that the data trends displayed for
central counties tend to be confirmed—even magnified—according to the business census data. On the
whole for the 11 cities, the decline in the average annual employment trend accelerated from 1977–87 to
1987–97 (table 9). In measuring each city as an observation with equal weight, employment growth
from 1977 to 1987 turned from slightly positive on
an average annual basis to a negative annual decline
of 1.4 percent per year during the 1987–97 period.
This pattern was repeated for the city performance
taken in aggregate—the so-called weighted average.
Here, Chicago’s large size and somewhat superior
performance pulls up the average for all 11 cities. It
is also notable that these city job losses were a stark
contrast to the pace of job growth in the overall
MSAs, which experienced gains of over 1 percent
per year over the latter period. The consequences of

TABLE 8

Average annual change in central county employment
County employment growth
1969–79

1979–89

1989–98

Share of MSA employment
1969–79

(percent)
Buffalo
Chicago
Cincinnati
Cleveland
Columbus
Detroit
Indianapolis
Milwaukee
Minneapolis-St. Paul
Pittsburgh
St. Louis
All 11 central counties
(weighted avg.)

1979–89

1989–98

(percentage points)

0.4
0.3
1.1
0.0
2.6
–0.8
1.2
1.1
2.2
0.5
3.2

0.8
0.6
1.3
0.2
3.1
–1.0
1.8
0.3
2.1
0.4
3.7

0.5
0.7
1.1
0.8
2.4
–0.3
2.1
0.2
1.6
0.8
1.3

0.0
–0.8
–0.7
–0.7
–0.1
–1.9
–0.5
–0.9
–0.8
–0.3
1.8

0.1
–0.6
–0.6
–0.3
0.3
–2.1
–0.3
–0.7
–0.6
0.3
2.1

0.1
–0.9
–1.1
–0.6
–0.2
–1.7
–0.5
–1.3
–0.7
–0.4
0.1

0.7

0.9

0.9

–0.6

–0.4

–0.7

All 11 central counties
(unweighted avg.)

1.1

1.2

1.0

–0.5

–0.2

–0.6

All 11 MSAs

1.4

1.3

1.6

n.a.

n.a.

n.a.

U.S.

2.3

2.1

1.8

n.a.

n.a.

n.a.

Notes: n.a. indicates not applicable. MSA is metropolitan statistical area.
Source: U.S. Department of Commerce, Bureau of Economic Analysis, various years,
Regional Economic Information System.

Federal Reserve Bank of Chicago

11

this city–suburban disparity are that the
central city lost share to the suburbs in the
second period, and did so at an accelerated
rate of 2 percent to 2.5 percent loss of
share per year in 1987–97 versus the pace
of 1 percent to 1.5 percent per year in the
1977–87 period. The generally buoyant
Midwest economy has not lifted the central
city as job domicile over the recent period
in relation to the suburbs, though some
central cities, such as Chicago, have bucked
the trend. There has not been any slowing
in the pace of erosion of job share for the
central city. In observing this subset of
payroll jobs, the evaporation of the city’s
importance in the wide metropolitan area
seems to be accelerating.
Conclusion

TABLE 9

Annual average change in city employment
(percent)
City share of MSA
employment

City employment
1977–87
Buffalo
Chicago
Cincinnati
Cleveland
Columbus
Detroit
Indianapolis
Milwaukee
MinneapolisSt. Paul
Pittsburgh
St. Louis
Weighted avg.
Unweighted average
Weighted average
of 11 MSAs
U.S.

1987–97

1977–87

1987–97

–0.4
–1.3
1.3
–1.9
3.5
–2.1
2.5
–0.6

–2.9
0.5
–2.0
–1.9
1.7
–3.6
1.3
–0.8

–0.7
–2.3
–1.0
–2.5
0.0
–3.0
0.2
–1.5

–3.4
–0.4
–3.3
–2.6
–0.4
–4.1
–0.8
–2.2

2.3
0.4
–0.1
–0.2
0.3

–2.8
–1.9
–3.2
–1.0
–1.4

–1.4
0.6
–1.7
–1.5
–1.2

–4.2
–2.8
–3.8
–2.0
–2.5

The central cities of the Midwest’s
1.5
1.3
n.a.
n.a.
large metropolitan areas are riding the fa3.0
3.8
n.a.
n.a.
vorable growth trends of the overlying
Notes: n.a. indicates not applicable. MSA is metropolitan statistical
Midwest economy. The 1970s were a terriarea. Total employment as calculated from business census data for
ble decade for central cities that followed
manufacturing, wholesale, retail, services, and government for 1977,
1987, and 1997. Government employment reflects only local government
upon the tumultuous times of the 1960s.
employment for the MSAs and the U.S. and only municipal employment
Despite a profound Midwest recession that
for the city.
unfolded during the first three years of the
Source: Business census data for manufacturing, wholesale, retail,
services, and government for 1977, 1987, and 1997.
1980s, subsequent economic recovery was
strong enough to make the 1980s look like
an improvement over the 1970s. The late
1980s and 1990s solidified and magnified overall
the 1990s in relation to their suburbs, at least in
gains in the Midwest economy. As a consequence,
terms of the pace of loss of share.
central cities are now enjoying very strong rates of
Of course, there may be evidence of revival that
work force participation, a slowing of population
underlies these broad and aggregate statistics. So too,
loss, and rising real household incomes. Nonetheless,
there are exceptional cities that are flashing recovery
when we look beneath these statistics for signs of a
statistics, such as Chicago, that may be studied for
structural change that would indicate that cities may
clues to success and redevelopment. And the bright
regain their former prominence, there is less to cheer
side should not be discounted. The improved absolute
about. Relative to their suburbs, and accounting for
conditions brought about by U.S. economic expansion
the national business cycle, cities are faring little betand Midwest revival in the 1990s may provide the
ter than the 1980s (though better than the 1970s
foundation and resources on which to fashion an uralong some dimensions). Average household income
ban revival. However, this look at the current trends
in central cities relative to their suburbs continues to
for improvement in the structural growth of central
erode. Central city residents are finding employment,
cities does not justify any complacency on the part of
but increasingly in the suburbs. As the domicile of
urban leaders and policymakers.
job location, central cities appear less attractive in

12

2Q/2001, Economic Perspectives

NOTES
The circumstances of annexation differ greatly from city to city.
The state legislature mandated a merger between the old city of
Indianapolis and most of its surrounding county area. Indianapolis
then merged many of its services with the remainder of Marion
County as of 1970 into what is called Unigov. However, schools
remain part of independent local governments, and townships remain, which include fire and relief responsibilities. So too, police
services remain part of the former city of Indianapolis, while four
former suburbs were allowed to retain their independence. In Columbus, Ohio, a forward-looking mayor named Jack Sensenbrenner
adopted an aggressive policy of trading municipal services for
annexation in the 1950s, allowing that city to gather up prime land
around the emerging interstate highways and beltways in the 1960s
and beyond. Milwaukee used its monopoly over Lake Michigan
water to encourage annexation in the post WWII era. Milwaukee
mayors were mostly annexation-minded throughout the first half
of the century, though the city met resistance from industrial intensive fringe areas that feared higher property tax rates. A state
legislative statute largely greatly impeded city annexation in 1956
by greatly easing the ability of mostly rural areas surrounding Milwaukee to incorporate.
The reasons some cities vigorously annexed and others
chose not to remain cloudy. Surely, some city leaders pursued a
self-interested fiscal calculus in pursuing annexation. For example,
see Saffran (1952). Dye’s (1964) study of U.S. urbanized areas
for 1960 concluded that age of central city, social inequity between city and suburb, and form of government were partially explanatory factors. For a review of related studies, see Klaff and
Fuguitt (1978).

1

For a discussion see Mieszkowski and Mills (1993) and
Brueckner (2000).
2

3

See White (1999).

Models have been explored in which capital stock, once built, is
either abandoned or remains forever, or is durable but replaceable.
So too, initial investment may take place myopically, or with degrees of foresight. See Wheaton (1983).
4

Regression analysis confirms this finding; Indianapolis may be an
exception in that average city household incomes appear to have
strengthened in the 1990s.
5

See U.S. Department of Commerce, Bureau of the Census, (various years), Journey to Work statistics.
6

These data gather county level statistics from a number of
sources so as to achieve complete industry coverage; estimates
of self-employed workers along with payroll workers are included in the data.
7

8

Orfield and Rusk (1998).

Economic census data covered 75 percent of “economic activity”
in 1987. In 1992, it covered 98 percent. See Micarelli (1998),
p. 372.
9

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Aaronson Daniel, and Daniel G. Sullivan, 1998,
“Recent trends in job displacement,” Chicago Fed
Letter, December, No. 136.
Brennan, John, and Edward W. Hill, 1999, Where
are the Jobs? Cities, Suburbs, and the Competition
for Employment, Washington DC: The Brookings Institution, Center on Urban and Metropolitan Policy,
November.
Brueckner, Jan K., 2000, “Urban sprawl: Diagnosis
and remedies,” International Regional Science Review,
Vol. 23, No. 2, April, pp. 160–171.
, 1987, “The structure of urban equilibria:
A unified treatment of the Muth–Mills model,”
in Handbook of Urban Economics, Vol. II, Edwin S.
Mills (ed.), Amsterdam: Elsevier Science B.V.
Dye, T. R., 1964, “Urban political annexation: Conditions associated with annexation in American
cities,” Midwest Journal of Political Science, Vol. 8,
No. 4, pp. 430–446.
Gurda, John, 1999, The Making of Milwaukee,
Milwaukee, WI: Milwaukee County Historical
Society.

Federal Reserve Bank of Chicago

Illinois Department of Employment Security, 2000,
Where Workers Work, 1999, Chicago, IL.
Klaff, Vivian Z., and Glenn V. Fuguitt, 1978,
“Annexation as a factor in the growth of cities,
1950–60 and 1960–70,” Demography, Vol. 15, No. 1,
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Knepper, George W., 1997, Ohio and Its People,
Kent, OH: Kent State University Press.
McMillen, Daniel P., and John F. McDonald, 1998,
“Suburban subcenters and employment density,”
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Micarelli, William F., 1998, “Evolution of the United
States economic censuses: The nineteenth and twentieth centuries,” Government Information Quarterly,
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Mieszkowski, Peter, and Edwin S. Mills, 1993, “The
causes of suburbanization,” Journal of Economic Perspectives, Vol. 7, No. 3, Summer, pp. 134–147.
Mills, Edwin S., 1972, Studies in the Structure of the
Urban Economy, Resources for the Future, Baltimore,
MD: Johns Hopkins Press.

13

Mills, Edwin S., and Luan Sende Lubuele, 1997,
“Inner cities,” Journal of Economic Literature, Vol.
35, June, pp. 727–756.
Orfield, Myron W., and David Rusk, 1998, Metropolitics: A Regional Agenda for Community and Stability,
Washington, DC: The Brookings Institution, June.
Pagano, Michael A., 1999, “Metropolitan limits:
Intrametropolitan disparities and governance in U.S.
laboratories of democracy,” in Governance and
Opportunity in Metropolitan America, Alan Altshuler,
William Morrill, Harold Wolman, and Faith Mitchell
(eds.), Washington DC: National Academy Press, pp.
253–295.
Saffran, George C., 1952, Annexation Practices in
Milwaukee, Study 52034, Milwaukee, WI: City of
Milwaukee, Budget Supervisor, Administration Survey
Committee, June.
U.S. Department of Commerce, Bureau of Economic Analysis, 1998, Regional Economic Information System, Washington, DC.
U.S. Department of Commerce, Bureau of the
Census, 1997, Census of Manufactures, Washington,
DC.
ington, DC.
ington, DC.
ington, DC.
ington, DC.

, 1992, Census of Manufactures, Wash, 1987, Census of Manufactures, Wash, 1982, Census of Manufactures, Wash, 1977, Census of Manufactures, Wash-

, various issues, Census of Population,
Washington, DC.
ington, DC.
ington, DC.
ington, DC.
ington, DC.
ington, DC.

14

, 1997, Census of Retail Trade, Wash, 1992, Census of Retail Trade, Wash, 1987, Census of Retail Trade, Wash, 1982, Census of Retail Trade, Wash-

, 1997, Census of Selected Services,
Washington, DC.
, 1992, Census of Selected Services,
Washington, DC.
, 1987, Census of Selected Services,
Washington, DC.
, 1982, Census of Selected Services,
Washington, DC.
, 1977, Census of Selected Services,
Washington, DC.
, 1997, Census of Wholesale Trade,
Washington, DC.
, 1992, Census of Wholesale Trade,
Washington, DC.
, 1987, Census of Wholesale Trade,
Washington, DC.
, 1982, Census of Wholesale Trade,
Washington, DC.
, 1977, Census of Wholesale Trade,
Washington, DC.
, various issues, County Business Patterns,
available on the Internet at www.census.gov/epcd/
cbp/view/cbpview.html.
U.S. Department of Commerce, Bureau of the
Census, and U.S. Department of Labor, Bureau of
Labor Statistics, various years, Current Population
Survey, available on the Internet at
www.bls.census.gov/cps/cpsmain.htm.
U.S. Department of Housing and Urban Development, 2000, The State of the Cities 2000, Washington, DC.
Wheaton, William C., 1983, “Theories of urban
growth and metropolitan spatial development,” in
Research in Urban Economics: A Research Annual,
Vol. 3, J. Vernon Henderson (ed.), Greenwich, CT:
JAI Press Inc., pp. 3–36.
White, Michelle J., 1999, “Urban areas with decentralized employment: Theory and empirical work,”
in Handbook of Regional and Urban Economics, E.
S. Mills and P. Cheshire (eds.), Amsterdam: Elsevier
Science B.V.

, 1977, Census of Retail Trade, Wash-

2Q/2001, Economic Perspectives

Polycentric urban structure: The case of Milwaukee
Daniel P. McMillen

Introduction and summary
Theoretical models of urban structure are based on
the assumption that all jobs are located in the central
business district (CBD). Although this assumption
was never literally true, it is a useful approximation
for a traditional city in which the CBD holds the only
large concentration of jobs. As metropolitan areas
have become increasingly decentralized, traditional
CBDs have come to account for a much smaller proportion of jobs than in the past. Large employment
districts have arisen outside of central cities that rival
the traditional city center as places of work. When
these districts are large enough to have significant
effects on urban spatial structure, they are referred
to in the urban economics literature as “employment
subcenters.”
The distinction between a metropolitan area with
multiple subcenters (or a polycentric urban structure)
and one with much more dispersed suburban employment has important policy implications. Public transportation can be designed to serve subcenters. Buses
can help alleviate severe congestion, and commuter
rail lines may be able to serve large subcenters. Large
subcenters may have enough jobs to warrant designing
public transportation that brings central city workers
to suburban job locations, which can help alleviate
problems of a “spatial mismatch” between jobs and
central city workers (Kain, 1968, and Ihlanfeldt and
Sjoquist, 1990). The term “urban sprawl” appears to
be used to describe an urban area whose residents
have moved farther and farther from the central city,
while driving past pockets of farmland and open space
to get to their suburban jobs. Sprawl is likely to be
less of a problem in an urban area whose suburban
jobs are concentrated in subcenters. If jobs are confined to a relatively small number of suburban sites,
workers will attempt to reduce their commuting costs
by living nearby. This tendency toward suburban

Federal Reserve Bank of Chicago

centralization is reinforced when transportation facilities are designed to serve the subcenters.
Spatial modeling of traditional monocentric cities
is relatively easy because the site of the CBD is known
in advance. Housing prices, land values, population
density, and other variables of interest can be modeled as functions of distance to the CBD, with the addition of other variables of local importance, such as
distance to Lake Michigan in Chicago or proximity
to freeway interchanges and commuter train stations.
In contrast, subcenter locations are not always obvious beforehand. The U.S. Census lists central places,
which are generally older suburbs that once were satellite cities. However, subcenters are often relatively
new developments (dubbed “edge cities” by Garreau,
1991) that may not have been incorporated as recently
as 1960. Subcenter locations are an empirical issue:
Does an area have enough employment that it has a
significant local effect on variables such as employment density?
In this article, I critique various procedures for
identifying employment subcenters and then use a
procedure developed in McMillen (2002) to analyze
subcenters in Milwaukee, Wisconsin. Milwaukee is
interesting because it has not been the subject of a
great deal of study, yet it is representative of older
industrial cities that have maintained strong CBDs.
I identify subcenters as local peaks in an estimated
employment density function. I find that Milwaukee
has one subcenter, which is located at the western
edge of the city. It is notable for being the site of a
Harley-Davidson manufacturing plant, although other

Daniel P. McMillen is a professor of economics at the
University of Illinois at Chicago and a consultant to the
Federal Reserve Bank of Chicago.

15

firms also are located in the area. The subcenter has
significant but highly localized effects on both employment and population densities in the Milwaukee
area. Milwaukee remains a largely monocentric city.
Although Milwaukee has a monocentric spatial
structure, it has ample suburban employment that is
highly dispersed. Its single subcenter is readily accessible by central city residents, but the subcenter
has fewer than 25,000 jobs in a metropolitan area of
821,158 workers. The dispersed nature of Milwaukee’s
suburban jobs makes it difficult to design a public
transportation system that would help carry central
city residents to suburban jobs. Milwaukee’s dispersed employment increases the probability of central city unemployment and increases urban sprawl
as suburban residents move still farther from the
central city.
The rise of the polycentric city
The monocentric city model of Alonso (1964),
Muth (1969), and Mills (1972) remains the most
popular and influential model of urban spatial structure. The model depicts a stylized nineteenth century
city, in which all jobs are located in the CBD. To
reduce the cost of their daily commute, workers bid
more for housing close to the city center. As a result,
housing and land prices are predicted to fall with distance from the CBD. Spatial patterns for other variables
of interest—population density, lot sizes, building
heights, and the like—are all predicted to be simple
functions of distance from the CBD.

Although these predictions have ample empirical support,1 the central idea of the monocentric city
model—that urban employment is concentrated in
the traditional CBD—is no longer a suitable representation of urban spatial structure. Indeed, McDonald
and McMillen’s (1990) evidence of multiple peaks
in land value functions in early twentieth-century
Chicago suggests that the assumption of monocentricity was always more of a mathematical convenience than an accurate depiction of reality. Recent
theoretical and empirical research in urban economics
treats metropolitan areas as polycentric, that is, having
multiple employment centers with varying degrees of
influence on urban spatial patterns. Anas, Arnott, and
Small (1998) present an excellent survey of theoretical
and empirical models of polycentric cities.
The polycentric structure of urban areas has become more evident over time. Table 1 presents evidence of declining employment concentration in 11
midwestern urban areas. Across all 11 cities, 36.6
percent of suburban residents worked in the central
city in 1960, whereas only 9.4 percent of city residents
worked in the suburbs. The percentage of suburban
residents working in the city ranged from 16.8 percent in Pittsburgh to 62.3 percent in Indianapolis. By
1990, the percentage of suburban residents working
in the city had declined in every metropolitan area
except Pittsburgh. Overall, only 28.4 percent of suburban residents worked in the central city in 1990,
while 26.2 percent of city residents worked in the
suburbs. Pittsburgh is an outlier because the large

TABLE 1

Journey to work patterns
City residents
working in the suburbs

Buffalo
Chicago
Cincinnati
Cleveland
Columbus
Detroit
Indianapolis
Milwaukee
Minneapolis-St. Paul
Pittsburgh
St. Louis
All

Suburban residents
working in the city

1960

1970

1980

1990

1960

1970

1980

1990

17.1
6.6
11.2
7.7
7.8
17.3
6.1
8.9
6.6
11.2
8.3
9.4

26.8
16.1
24.8
24.4
19.1
32.1
18.5
23.7
19.7
19.1
21.1
21.2

25.3
18.4
24.4
28.6
17.7
34.3
9.8
26.3
24.5
20.1
24.0
21.8

27.9
22.5
29.5
30.3
24.2
36.4
12.1
30.3
29.8
21.4
35.9
26.2

36.5
34.6
45.0
52.4
50.6
33.5
62.3
48.0
52.1
16.8
36.7
36.6

30.4
27.1
39.5
43.5
54.5
24.6
44.8
36.1
43.5
24.6
30.0
31.8

28.0
22.5
36.3
34.8
48.1
16.9
48.7
33.7
31.2
26.4
25.4
27.0

29.6
25.6
31.6
32.5
49.7
19.4
48.9
37.2
30.5
24.3
27.9
28.4

Note: Data for 1990 reflect all central cities in the consolidated metropolitan statistical areas.
Source: U.S. Department of Commerce, Bureau of the Census, various years.

16

2Q/2001, Economic Perspectives

suburban steel plants closed during this period, leading to renewed employment centralization. Table 1
clearly shows that the CBD is not the dominant employment site in any of these cities, and that city residents are now nearly as likely to work in the suburbs
as suburban residents are to work in the city.
The diminishing role of the CBD has come about
despite the advantages it offers for firms wishing to
locate in metropolitan areas. In-place public transportation, such as light rail, and radial boulevards and
highways are designed to carry workers from outlying areas into the city. Reverse commuting and intrasuburban commuting is very difficult other than by
automobile. Whereas highways lead from many directions in to the city, a suburban firm may find that
its potential labor pool is limited to a relatively small
geographic area around the workplace. In addition,
theories of agglomeration such as Anas and Kim
(1996), Berliant and Konishi (2000), and Fujita and
Ogawa (1982) suggest that firms may enjoy significant cost advantages by locating near other firms. The
close proximity of firms in the CBD facilitates faceto-face communication. Lawyers, bankers, and myriad
consultants are all nearby in the CBD. Both suppliers
and customers are likely to require only a short trip
to visit a CBD firm.
But suburban locations offer different advantages.
Land is significantly cheaper than in the CBD, and
access to interstate highways is better and subject to
less congestion. Large manufacturing firms are more
likely to prefer suburban locations, as are distributors
and wholesalers that have customers outside the metropolitan area. Suburban locations may reduce the
wage bills of firms whose workers live in the suburbs
because less compensation is needed for an expensive
and time-consuming commute.
Employment subcenters combine many of the
advantages of CBD and suburban locations. Highways
and public transportation can serve subcenters much
as they serve the CBD, bringing in an ample supply
of workers from distant locations. Costs may be lower than in the CBD because land is cheaper and many
workers like to live and work in the suburbs. Personal
communication may be as easy as in the CBD when
firms locate near one another in subcenters. Restaurants and other services find enough business to form
concentrations in the vicinity. The diversity of business
types may be lower than in the city, but large subcenters sometimes appear to mimic the diversity of
CBDs while offering lower land and commuting costs.
Large subcenters offer employment and shopping opportunities for which nearby residents are willing to
pay a premium. As predicted by the monocentric city
model for locations near the CBD, the rise in land

Federal Reserve Bank of Chicago

values near subcenters leads to configurations with
smaller lot sizes and higher population density that
look like small cities.
Subcenter identification procedures
Empirical researchers have long recognized that
cities are not truly monocentric. Variables representing distance from various employment sites other
than the CBD are frequently included as explanatory
variables in empirical studies of housing prices, employment density, and population density.2 Sites that
are significant enough to affect the overall urban spatial structure must be specified beforehand using this
ad hoc approach. Forming the list of potential subcenters often draws on ample local knowledge, but
may well be inconsistent with the data. Although statistically insignificant subcenter distance variables
help indicate that the subcenter list is incorrect, they
do not reveal subcenter sites that are omitted from
the regressions.
The first formal procedure for identifying employment subcenters was proposed by McDonald (1987).
He begins by estimating a simple employment density function for a standard monocentric city: log yi =
a + bxi + ei, where yi represents the number of employees per acre and xi is distance from the CBD.
Subcenters produce clusters of positive residuals in
the estimated function. McDonald inspects the list
of statistically significant positive residuals, and finds
that O’Hare Airport is the dominant subcenter in the
Chicago metropolitan area.
McDonald’s novel approach poses several problems in practice. The notion of a “cluster” is subject
to interpretation. Are two significant positive residuals among ten observations in a two-mile radius a
cluster? A reasonable change in either the radius or
the requisite number of positive residuals can potentially change the results dramatically. The procedure
also suffers from statistical problems. The results are
sensitive to the unit of analysis. Using extremely large
tracts, McDonald (1987) finds a single subcenter in
the Chicago area near O’Hare Airport. In a follow-up
paper using square mile tracts, McDonald and Prather
(1994) find additional subcenters in Schaumburg and
central DuPage County. The local rise in employment
density produced by a subcenter tends to flatten the
estimated employment density function, which reduces
the probability of identifying subcenters. Although
the monocentric employment density function implies
that gradients do not vary across the urban area, multiple subcenters or distinctive topographical features
may lead to variations in gradients. Such functional
form misspecification can hide potential subcenters.

17

Giuliano and Small (1991) propose another influential subcenter identification procedure. It has been
employed in subsequent work by Bogart and Hwang
(1999), Cervero and Wu (1997, 1998), and Small and
Song (1994). Defining a subcenter as a set of contiguous tracts that have a minimum employment density
of 10 employees per acre each and, together, have at
least 10,000 employees, Giuliano and Small identify
32 subcenters in the Los Angeles area. This reasonable subcenter definition is sensitive to the cutoff
points used for minimum employment density and
total subcenter employment. The same cutoff points
imply an unreasonably large subcenter in the northern
Chicago suburbs with over 400,000 employees, leading McMillen and McDonald (1998) to raise the
cutoffs to 20 employees per acre and 20,000 total
employees. Local knowledge must guide the choice
of cutoff points, limiting the analysis to familiar metropolitan areas.
Giuliano and Small’s procedure is also sensitive
to the unit of analysis. Their data set includes 1,146
tracts covering an area of 3,536 miles. In contrast,
McMillen and McDonald’s Chicago data set has
14,290 tracts in an area of 3,572 square miles. Data
sets with small tracts are more likely to have pockets
with low employment density, which reduces the
number of subcenters identified using the Giuliano
and Small procedure. This observation led McMillen
and McDonald (1998) to work with proximity instead
of contiguity: Two tracts are proximate to one another
if they are within 1.5 miles. The number of subcenters
is again sensitive to the definition of proximity.
Giuliano and Small define a subcenter as an area
with large employment, with the definition of “large”—
the cutoff points—being up to the analyst. Subsequent
statistical analysis determines whether the subcenters
have significant effects on such variables as employment density, population density, and housing prices.
The cutoffs do not vary over the data set, which means
that the minimum subcenter size is the same near the
CBD as in distant suburbs. This characteristic of
their procedure is not desirable if a subcenter is defined as an area with larger employment density than
surrounding areas. Since densities tend to decrease
with distance to the CBD, the minimum cutoffs should
tend to decrease also. Then the question becomes
how to vary the cutoffs.
Craig and Ng (2001) propose a procedure that
eliminates many of the problems with the earlier
methods. They use a nonparametric estimation procedure to obtain smoothed employment density estimates
for Houston. Using a quantile regression approach,
they focus on the 95th percentile of the employment

18

density distribution. The quantile regression approach
is attractive in this context because a subcenter is
defined using the extremes of the distribution. Craig
and Ng’s estimated density function is symmetric
about the CBD because they only use distance from
the CBD as an explanatory variable for the estimates.
They first look for local rises in the density–CBD
relationship, and then inspect the rings to find sites
with unusually high density and employment. They
use their knowledge of Houston to accept or reject
high-density sites as subcenters.
Craig and Ng’s procedure is not as sensitive to
the unit of analysis as the McDonald and Giuliano–
Small procedures. Though larger tracts lead to smoother
employment density functions, a large subcenter will
produce a rise in the function whether the data set includes acres, quarter sections, or square miles. The
procedure is readily reproducible by other researchers
and requires scant knowledge of the local area. Much
of the arbitrariness of the Giuliano–Small procedure
is eliminated because the local rise that defines a
subcenter is subject to tests of statistical significance.
However, the Craig–Ng procedure requires some local knowledge to choose which sites are subcenters
within rings around the CBD, and the imposition of
symmetry around the CBD is unsuited to cities that
are distinctly asymmetric due to varied terrain or
multiple subcenters.
A nonparametric subcenter identification
procedure
Nonparametric approaches offer significant
advantages over simple linear regression procedures.
Nonparametric estimators are flexible, allowing the
slope of density functions to vary across the metropolitan area. As an example, suppose that employment density declines more rapidly on the north side
of the city than on the south. The standard linear regression estimator used by McDonald (1987) imposes
the same gradient on both sides of the city, which
tends to produce positive residuals on the north side
and negative residuals to the south. This functional
form misspecification increases the probability of
finding a subcenter on the north side of the city even
if none exists. Craig and Ng’s (2001) estimator is more
flexible than standard linear regression, but does not
avoid this type of misspecification because it imposes symmetry about the CBD. In contrast, nonparametric estimation procedures are sufficiently flexible
to detect the difference in gradients across the two
sides of the city.
McMillen (2002) proposes a nonparametric procedure for identifying subcenters in a variety of cities,

2Q/2001, Economic Perspectives

including those with which the analyst is largely unfamiliar. It is a two-stage procedure that combines
features of both the McDonald (1987) and Craig and
Ng (2001) approaches. As in McDonald (1987), the
first stage of the procedure identifies subcenter candidates through an analysis of the residuals of a smoothed
employment density function. The procedure differs
in that McMillen uses a nonparametric estimator,
locally weighted regression, to estimate the employment density function.3 The estimation procedure
involves multiple applications of locally weighted regression. McMillen estimates a separate regression
for locations for which a log-employment density
estimate is desired. Observations closer to the target
location receive more weight in the regressions.
McMillen (2002) identifies subcenter candidates as
significant residuals (at the 5 percent level) from the
first-stage locally weighted log-density estimates.
When significant residuals cluster together, he narrows
the list of subcenter candidate sites to those with the
highest predicted log-employment density among all
observations with significant positive residuals in a
three-mile radius.
The second stage of the procedure uses a semiparametric procedure (Robinson, 1988) to assess the
significance of the potential subcenter sites in explaining employment density. The nonparametric
part of the regression controls in a general way for
the nuisance variable, DCBD, which is an acronym
for distance from the central business district. Following Gallant (1981, 1983, and 1987), McMillen
(2002) uses a flexible Fourier form to approximate
the nonparametric part of the regression (see box 1).
Distances to potential subcenter sites are included as
explanatory variables in the parametric part of the

regression. If the regression indicates that densities
fall significantly with distance from a potential subcenter site, then the site is included in the final list
of subcenters.
This procedure reflects the definition of subcenters listed earlier: Subcenters are sites that cause
a significant local rise in log-employment densities,
after controlling for distance from the CBD. Unlike
Giuliano and Small (1991), McMillen (2002) uses
statistical tests to determine the significance of subcenter sites. This feature makes it possible to apply
the procedure for a variety of cities, including unfamiliar ones. Basing the procedure on a semiparametric regression analysis allows the analyst to conduct
statistical tests of significance, while reducing the sensitivity of the analysis to restrictive functional form
specifications, the size of the unit of observation, and
the specification of arbitrary cutoff points.
Data

The data come from the Urban Element of the
Census Transportation Planning Package, which is
produced by the Department of Transportation’s Bureau
of Transportation Statistics (BTS). The BTS produced
special tabulations of 1990 U.S. census data to match
standard census data with their unit of analysis, which
they term the transportation analysis zone, or “taz.”
The zones vary in size across metropolitan areas, but
are usually smaller than census tracts or zip codes.
All data for this study cover the Milwaukee metropolitan area, which comprises Milwaukee, Kenosha,
Ozaukee, Racine, Washington, and Waukesha counties.4
The taz sizes average 2.1 square miles in this
sample of 1,206 observations. Total population is
1,805,245, and total employment is 821,158, or 45.5
percent of the population. Average densities imply that population is more disBOX 1
persed than employment. Employment
density averages 2,598 workers per square
Fourier terms
mile, or 4.1 employees per acre. In conThe Fourier expansion uses sine and cosine terms to approxitrast, population density averages 3,244
mate the general function g(DCBD). To implement the propeople per square mile, or 5.1 people
cedure, the variable DCBD is first transformed to lie between
per acre.
0 and 2p, with the transformed variable denoted by z.
2
The Fourier expansion is g(DCBDi) » l0 + l1zi + l2 zi +
The Milwaukee subcenter

Sq(gqcos(qzi) + dqsin(qzi)), where q = 1,

, Q. The Schwarz
(1978) information criterion is used to choose the expansion
length, Q. The optimal Q is the value that minimizes S(m) =
log(s2) + mlog(n)/n, where m is the number of estimated coefficients (m = 3 + 2Q), s2 is the estimated variance of the errors
from the semiparametric regression, and n is the number of
observations. Larger values of Q reduce the estimated variance but increase the second term. The subcenter distance
variables are omitted when choosing Q.

Federal Reserve Bank of Chicago

Figure 1 presents a map showing
employment densities in the Milwaukee
area. Aside from pockets of high densities
in Racine and Kenosha, the map suggests
that Milwaukee is not far from a stylized
monocentric city. This finding is reflected in the McMillen (2002) procedure,
which identifies a single employment

19

its midpoint. Both areas include 11 observations. Only 6.7 percent of Milwaukee’s
Employment density in Milwaukee and subcenter location
employment is in the CBD (as defined
here), but the CBD is nonetheless more
than twice as large as the subcenter, which
_
_
has 3.0 percent of total employment in
the metropolitan area. As predicted by
urban theory, median earnings are highest in the CBD, but it is interesting to note
that earnings on average are higher in the
subcenter than in the rest of the city. The
earnings differences are not large, but they
suggest that either marginal productivity
Brookfield
is higher in sites with high employment
density or that firms must compensate
Milwaukee
workers for longer commutes. In keeping
Waukesha
West Allis
_
with the spatial mismatch hypothesis,
New Berlin
African-Americans comprise a larger perGreenfield
centage of total employment in the CBD.
_
In contrast to the spatial mismatch hypothesis, however, this tendency toward CBD
employment may increase the average
earnings of African-Americans because
Racine
average earnings are lower elsewhere.
In part because the subcenter is only 8.1
miles from the CBD, the percentage of
African-Americans in the subcenter is
_
Kenosha
_
closer to that in the CBD than in the rest
_
of the city. This result is significant because it indicates that the commute to a
Employment density (employees per square mile)
10–180
180–800
800–2,700
2,700–1,000,000
nearby subcenter may be only slightly
Subcenter location
more burdensome than a commute to the
CBD for central city residents.
0
6
12
18
Table 2 shows the employment mix
Miles
in the CBD, subcenter, and the rest of the
Source: Author’s calculations based on data from the U.S. Department of
Commerce, Bureau of the Census, transportation planning package.
city for five traditional industry categories. The CBD specializes in the finansubcenter. Its location is shown in figure 1. The subcial, insurance, and real estate sector (26.61 percent
center is at the edge of the City of Milwaukee, at the
of CBD employment) and service industries (34.27
intersection of State Highway 45 and Route 190,
percent of CBD employment). In contrast, a larger
near Wauwatosa. The site includes the main Harleypercentage of the subcenter’s employment (30.48
Davidson manufacturing plant. It meets the Giuliano
percent) is engaged in manufacturing, with a signifiand Small (1991) criterion for a subcenter by includcant concentration in retail also. Service industries
ing two tracts with more than 10 employees per acre.
are underrepresented in the subcenter compared with
The larger tract, which includes the Harley-Davidson
the CBD or the rest of the city. On the whole, the
plant, has 17.0 employees per acre and 10,344 total
employment mix in the subcenter is closer to the mix
workers. The other tract has 10.5 employees per acre
in the rest of the city than to the CBD.
and 3,759 workers.
Comparison of employment density
Table 2 provides more information on employment
estimates
patterns in the Milwaukee area. The CBD is defined
as an area one mile in diameter around the tract at the
Figure 2 presents graphs of the estimated logcity center with the largest employment density. The
employment densities along a ray from the CBD to
subcenter is an area three miles in diameter around
the subcenter. The grey line shows that the initial
FIGURE 1

H

H

20

2Q/2001, Economic Perspectives

detect a sharp rise in employment density
around the subcenter, although they too
Employment mix
tend to overestimate densities in distant
CBD
Subcenter
Rest of city
locations. Figure 2 shows that McDonald’s
estimator would have trouble finding subTotal employment
54,669
24,967
741,522
centers in distant areas because the overNumber of residents
4,508
19,260
1,781,477
estimate of densities will tend to produce
Median earnings ($)
21,397
20,715
19,064
negative rather than positive residuals.
(- - - - - - - % of total employment- - - - - - -)
Just as simple exponential function
White
87.06
89.29
89.60
overestimates densities along the ray between the CBD and the subcenter, figure
Black
9.75
9.05
7.82
3 shows that it tends to underestimate
Manufacturing
10.92
30.48
26.13
Transportation,
densities along a ray due south from the
communications,
CBD. Densities do not decline as rapidly
utilities, and
on the south side of Milwaukee as to the
wholesale
11.08
10.85
10.57
north. Together, figures 2 and 3 show the
Retail
8.79
23.48
17.03
advantages of locally weighted regresFinancial, insurance,
sion’s flexibility over the symmetric
and real estate
26.61
9.99
5.56
McDonald (1987) and Craig–Ng (2001)
Services
34.27
21.95
31.91
estimators.5 Figure 4 shows an advantage
Note: CBD is central business district.
of the nonparametric approach over the
Source: Author’s calculations based on data from the U.S. Department of
Commerce, Bureau of the Census, transportation planning package.
Giuliano–Small (1991) procedure. The
entire log-employment density function
lies below the cutoff point of 10 employees
per
acre,
which is why only two tracts—those
locally weighted regression estimates decline rapidly
with
large
positive
residuals—meet the cutoff. If the
with distance from the CBD up to about 18 miles,
cutoff
were
raised
to
20 employees per acre, the
after which the decline is nearly linear. The black
Giuliano–Small
procedure
would miss the subcenter
line shows that the simple exponential function used
entirely.
If
the
cutoff
point
were
lowered too far, the
by McDonald (1987) is badly misspecified here, insubcenter
would
simply
be
part
of
the CBD, or it
dicating a much less rapid rate of decline in densities
would
be
so
large
as
to
be
meaningless
(as found in
after about seven miles than found using the more flexiMcMillen
and
McDonald,
1998,
for
Chicago).
ble nonparametric estimator. The Fourier estimates
TABLE 2

FIGURE 2

FIGURE 3

Estimated employment density functions—
ray between CBD and subcenter

Estimated employment density functions—
ray from CBD to south

log-employment density
10

log-employment density
10.0

9

Fourier
estimate

Fourier
estimate

8

7.5

7

Linear
density

Linear
density

6

Initial
smooth

5
4

5.0

3

Initial
smooth

2

2.5

1
0

10

20

distance from CBD (miles)

Federal Reserve Bank of Chicago

30

40

0

10

20

30

40

distance from CBD (miles)

21

FIGURE 4

Cutoff points for Giuliano–Small method
log-employment density
10

10 per acre

9
8

2.3 per acre
7

Fourier
estimate

6
5
4
3
0

10

20
30
distance from CBD (miles)

40

Subcenters and urban sprawl
I define subcenters here as sites that cause significant local rises in employment densities. A question arises as to the extent of the subcenter’s influence
on the overall urban spatial structure. Traditionally in
urban economics, urban decentralization is measured
by the CBD gradient, which is the slope coefficient
from a regression of the natural logarithm of population density on distance from the CBD (Clark, 1951;
Macauley, 1985; McDonald, 1989; McDonald and
Bowman, 1976; Mills, 1972; and Mills and Tan, 1980).
The gradient measures the percentage decline in densities associated with a movement of one mile from
the CBD. The relatively slow decline of densities in
decentralized metropolitan areas is reflected in small
gradients. Density gradients are thus a useful measure
of urban sprawl.
The first column of results in table 3 presents the
average gradients from various specifications of employment and population density functions. In a simple regression of log density on DCBD, employment
density is estimated to decline by 11.7 percent and
population density is estimated to decline by 7.6 percent with each mile from the CBD. These figures are
consistent with those found previously for relatively
centralized cities (for example, Macauley, 1985; or
Mills and Tan, 1980). However, the apparent centralization of Milwaukee becomes more pronounced when
more flexible functional forms are used in estimation.
Flexible Fourier functions of DCBD imply much larger gradients: 28.2 percent per mile for employment
density and 17.7 percent per mile for population density. Such steep declines in densities with distance to
the CBD indicate a centralized urban area.

22

Milwaukee’s subcenter has only a marginal impact on the estimated gradients. The gradients for
distance from the CBD are virtually unchanged when
the inverse of distance from the subcenter is added
as an explanatory variable in the density regressions.
For example, the employment density gradient only
falls from –11.7 percent to –11.2 percent when the
variable is added to a regression of log-employment
density on DCBD. The second column of results in
table 3 presents the corresponding gradients for distance from the subcenter, estimated using the same
regressions as for the CBD gradients. The gradients,
which are averages over the entire metropolitan area,
are not statistically significant. Together, these results
suggest that the subcenter has only a local effect on
Milwaukee’s spatial structure. It raises densities
enough to have a statistically significant effect in the
estimated functions, but not enough to be significant
across the full metropolitan area or to cause severe
bias in the estimated CBD gradients when omitted
from the density functions.
The last column of table 3 presents the results
of Lagrange multiplier (LM) tests for spatial autocorrelation (Anselin, 1988; Anselin et al., 1996; and
Burridge, 1980). Spatial autocorrelation will be present
if the residuals of the estimated density functions are
correlated over space. If firms tend to cluster together,
then the residuals of the employment density functions will be positively correlated spatially. The LM
tests are thus a useful measure of spatial clustering.
They are complementary to but different from our
definition of a subcenter. Whereas a subcenter is an
area with extremely high density, spatial autocorrelation may be found in areas without sharp peaks in
density, yet with more clustering of employment than
would be implied by random variation. Just as a metropolitan area with subcenters is less decentralized
than an otherwise identical city with randomly distributed suburban employment, an area with a high
degree of spatial autocorrelation in employment density is more centralized than an area with random
variation in densities.
The LM tests presented in table 3 are highly significant in every case.6 For the simple models in which
only DCBD is included as an explanatory variable,
the LM test statistics are 1,486.27 for employment
density and 1,616.90 for population density. These
values are far greater than the critical value of 3.84,
and indicate an extremely high degree of spatial
clustering of the residuals. The test statistics fall to
859.17 and 536.31 when the inverse of distance to
the subcenter is added to the regressions. The decrease
in the test statistics suggests that the residuals are

2Q/2001, Economic Perspectives

TABLE 3

Employment and population density

Explanatory variables

CBD
gradient

Subcenter
gradient

Spatial autocorrelation
LM test

Log-employment density
Distance from CBD

–0.117
(0.006)

1,486.27

Fourier terms

–0.282
(0.073)

602.39

jobs. Even simple exponential functions
imply large gradients for both employment and population density. More flexible functional forms imply still steeper
gradients. Both employment and population are spread across Milwaukee in clusters, with densities that decline rapidly
with distance from the city center.
Conclusion

Milwaukee’s CBD still dominates
metropolitan-wide employment and population density patterns. Nevertheless,
Fourier terms and
jobs are spread throughout the metropoliinverse of distance
–0.295
–0.033
592.13
to subcenter
(0.074)
(0.019)
tan area. Table 1 shows that a majority
of Milwaukee’s suburban residents worked
Log-population density
in the suburbs in 1990, and over 30 percent
Distance from CBD
–0.076
1,616.90
of its central city residents also worked in
(0.004)
the suburbs. One area at the edge of the
Fourier terms
–0.177
327.39
(0.037)
city is large enough to qualify for subDistance from CBD
center status. It is the location for a
and inverse of distance
–0.074
–0.009
536.31
Harley-Davidson manufacturing plant
to subcenter
(0.004)
(0.009)
and is the site for more than 20,000 jobs.
Fourier terms and
The subcenter has significant effects on
inverse of distance
–0.182
–0.013
321.20
to subcenter
(0.037)
(0.008)
employment density and population den2
sity patterns in the vicinity. However,
Notes: The Fourier terms include z, z , cos(z), and sin(z), where z denotes
the distance from the CBD multiplied by 2p/50. See box 1, p. 19, for
the effects are highly localized. Milwaucomplete details on Fourier terms. Heteroscedasticity consistent standard
kee is still primarily a monocentric city.
errors (White, 1980) are in parentheses.
Source: Author’s calculations based on data from the U.S. Department of
Although it has ample suburban employCommerce, Bureau of the Census, transportation planning package.
ment, the CBD dominates overall spatial
density patterns in a manner largely consistent with Brueckner’s (1979) version
of
the
monocentric
city model.
much less clustered after allowing densities to rise
With
only
one
subcenter
set in the midst of ample
near the subcenter. The higher degree of clustering in
suburban
employment,
little
can
be done in Milwaukee
the model without the subcenter distance variable is
to
relieve
problems
associated
with
congestion and a
a direct result of a large number of positive residuals
spatial
mismatch
between
jobs
and
workers.
If firms
near the subcenter site. Adding the Fourier expansion
2
in
the
Milwaukee
area
had
moved
to
a
few
large
subterms—z, z , cos(z), and sin(z)—leads to further reducurban
subcenters,
public
transportation
could
be
detions in the LM test statistics. In the most general
signed to carry commuters efficiently to suburban
models, which include both the Fourier expansion
jobs. Central-city residents would not be at a serious
terms and the inverse of distance to the subcenter, the
disadvantage in taking suburban jobs if they could
LM test statistics are 592.13 for employment density
easily take buses to the large subcenters. Milwaukee’s
and 321.20 for population density. Thus, the LM
single subcenter can indeed be reached easily by centests suggest that spatial autocorrelation remains sigtral-city residents. However, the majority of Milwaunificant even after controlling for the effects of the subkee’s jobs are now scattered across the metropolitan
center and when using a very general functional form
area. This spatial pattern of employment opportunities
for DCBD. Whereas estimated density functions imply
makes it difficult for central-city residents to find
that densities decline smoothly with distances from the
jobs, and increases the probability that suburbanites
CBD and subcenter, the spatial autocorrelation tests
will move still farther from the city center.
suggest that densities are in fact much more highly clusResearchers have identified subcenters for only
tered than implied by smooth functions of distance.
a
small
number of cities—Chicago, Cleveland,
Overall, these results indicate that Milwaukee reDallas,
Houston,
Los Angeles, New Orleans, the San
mains a centralized city, although it has many suburban
Distance from CBD
and inverse of distance
to subcenter

–0.112
(0.006)

Federal Reserve Bank of Chicago

–0.021
(0.020)

859.17

23

Francisco Bay Area, and now Milwaukee. It remains
an open question whether there are systematic patterns across metropolitan areas concerning subcenters.
Is there a critical population level at which subcenters
become more likely? Are subcenters more likely in
old or new cities or in cities with good public transportation service or those that rely predominantly on
the automobile? Do subcenters increase the probability of reverse commuting and the probability of central

city unemployment? Do subcenters increase the degree of sprawl by allowing suburbanites to live still
farther from the center of the city? Do subcenters
tend to specialize in particular types of employment,
such as manufacturing or financial services? Recently
developed procedures for identifying subcenters make
it possible for researchers to answer these questions
after determining the number, size, and employment
mix of subcenters across metropolitan areas.

NOTES
Examples include Clark (1951), Fales and Moses (1972), Macauley
(1985), McDonald (1989), McDonald and Bowman (1976; 1979),
McMillen (1996), and Mills (1969; 1970).
1

2
Examples include Bender and Hwang (1985), Dowall and
Treffeisen (1991), Gordon et al. (1986), Greene (1980), Griffith
(1981), Heikkila et al. (1989), Richardson et al. (1990), and
Shukla and Waddell (1991).

Stone (1977) and Cleveland (1979) first proposed the locally
weighted regression procedure, which has since been extended by
Cleveland and Devlin (1988), Fan (1992, 1993), Fan and Gijbels
(1992), and Ruppert and Wand (1994). It is a simple extension of
the kernel regression estimator. Locally weighted regression has
been used extensively in spatial modeling. Examples include
Brunsdon et al. (1996), McMillen and McDonald (1997), McMillen
(2002), Meese and Wallace (1991), Pavlov (2000), and Yuming
and Somerville (2001).
3

5
As employed here, the Fourier estimator also imposes symmetry
about the CBD. This misspecification is less critical in the second
stage of the analysis, where the objective is only to assess the statistical significance of the subcenters. The misspecification could
be eliminated by estimating g(x1,x2) nonparametrically rather than
g(DCBD), where x1 and x2 represent distances north and east of
the CBD.
6
The test statistic is (e¢We/s2)2/tr(W¢W + WW), where e is the vector of residuals and s2 is the estimated variance of the regression.
W is a “spatial contiguity matrix,” representing the spatial relationship between observations. For the models in table 3, Wij = 1
when observation j is among the nearest 1 percent of the observations to observation i, and Wij = 0 otherwise. The rows of the n ´
n matrix W are then normalized such that each sums to one. The
test statistic is distributed c2 with one degree of freedom, which
implies a critical value of 3.84 for a test with a 5 percent significance level.

4
I used a mapping program to measure the area of each taz (in
square miles) and to provide coordinates for the taz center points.
These coordinates are used to measure distance to the CBD.

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27

Central banking and the economics of information
Edward J. Green

Introduction and summary
Advances in the economic management of information have worked pervasive change throughout the
world economy and its financial system. Change due
to the adoption of electronic computing and communications has been highly visible. Another kind of advance, in the design of organizations (including
financial institutions) to allow them to function well
in private-information environments, has been equally significant. In this article, I examine how both
types of advance affect a key sector of the financial
system: central banking. This examination focuses
on the following three areas.
First, I study the implications of innovation
induced by information technology in payment arrangements for monetary policy objectives. Some observers have suggested that such innovations, including
the introduction of electronic money products or emoney, may nullify the relationship between issuance
of money by the central bank and the price level.1 I
explain why I think that this will not happen. More
precisely, I explain why innovative payment arrangements will not nullify the long-run identity between
the rate of money growth and the rate of increase of
the price level.
Second, I consider operational and design requirements for electronic information systems to ensure the integrity and security of the financial system
(including central bank settlement of large-value payments). I conclude that an enlarged role for central
banks may be warranted. Specifically, to a considerably greater extent than in the past, central bankers
need to understand themselves as customers and advocates in a market for information technology, where
the quality of goods and services provided by the
market, rather than only the individual choices of the
central bank itself, determines how high a level of integrity and security is feasible.
Third, I examine the meaning of, and requirements
for, transparency of central bank decision-making.

28

A central bank is usually said to be transparent if it
makes its decisions visible to the public. For example, the Federal Reserve increased its transparency in
the 1990s when it began to announce the new target
for the federal funds rate immediately after the Federal Open Market Committee (FOMC) meeting in
which the target was set. I suggest extending the concept of transparency beyond decisions per se to the
full range of information about the central bank that
is relevant to the formulation and implementation of
present and future monetary policy. I argue that the decentralized structure that establishes 12 Reserve Banks
as independent corporations enhances the transparency
of the Federal Reserve System.
Information technology, the payments
system, and monetary policy
Payments innovation as a central banking issue
Information technology is engendering rapid innovation in payment systems. E-money designed for
small-value payments is the family of innovations
within the past decade that is most visible to the broad
public.2 In addition, there are new technology applications that promise dramatic expansion of the applicability of netting and related procedures for large-value
payments. The most conspicuous (albeit not yet operational) worldwide example is the continuous linked
settlement (CLS) system, which was designed in response to market and supervisory pressure to reduce
the current lag of several days between the initiation
Edward J. Green is a senior policy advisor at the Federal
Reserve Bank of Chicago. The author is indebted to
Charles Goodhart and Staffan Viotti for their helpful
comments on a draft of this article. This article was
originally published as Edward Green, “Central Banking
and the Economics of Information,” in Challenges for
Central Banking, Santomero, Viotti, Vredin (eds.), Kluwer
Academic Publishers, April 2001, pp. 155–171. It has been
reprinted here with the publisher’s permission. The article
has been edited to suit the format of this publication.

2Q/2001, Economic Perspectives

of a foreign exchange transaction and its completion.3 In the U.S., the Clearing House Interbank Payments System (CHIPS) has incorporated a sophisticated
trade-matching algorithm in its payments system for
making large-value dollar funds transfers. The new
protocol makes CHIPS settlement a closer substitute
for Fedwire, the Federal Reserve’s real-time gross
settlement system, as a way to transfer funds for
most purposes.4
Retail e-money is more similar to these largevalue payment innovations than may be apparent. As
typically conceived, an electronic-money product resembles a check in being transferable prior to settlement, although in detail, the e-money is a virtual bearer
security while the check is a negotiable instrument.5
An electronic-money product resembles a credit card
in giving the payee a claim on the issuer rather than
on the payor or drawer. What is new about e-money,
at least relative to recent payment arrangements, is
the combination of these two features. While the negotiability of a check is seldom used in practice because
each drawer’s ability to settle is not widely established,
e-money is envisioned to be routinely transferred because the issuer’s ability to settle will be public knowledge. The familiar payment instrument (although no
longer used in most countries) that e-money will most
closely resemble, then, is a banknote.6 Banks used to
accept outside money (typically gold or silver coin, in
the old days) as payment for a paper certificate, the
banknote, that the bank would exchange for outside
money on demand. Someone who accepted a banknote
could pay it to someone else who would redeem it,
and the payor and payee would thus avoid the risk
and inconvenience of using outside money directly
for their transaction.
One way of looking at the common use of an instrument’s transferability is as a netting arrangement.
The issuer or intermediary accepts a payment from
the first payor and makes a payment to the last payee
to use the instrument, with an arbitrary number of intervening transactions being settled without the issuer’s
direct involvement, but simply by transfer of the instrument. Moreover, the profitability of e-money, which
derives from the issuer being able to convert money
received in payment to an illiquid security with positive yield until having to make payment, relies on this
netting to forestall premature liquidation of the security. From this perspective, e-money shares the same
main feature of economic interest of the large-value
payment innovations currently being developed.
With that preamble, I propose that increased
scope for net settlement is the predominant aspect
of payments system innovation that might raise a

Federal Reserve Bank of Chicago

fundamental issue of monetary policy.7 (This is not to
say that it would be the only aspect relevant for other
purposes, such as addressing prudential, competitive,
and consumer-protection issues.) By a fundamental
issue, I mean one that current understanding would
not provide a good way to address. Extensive conversion of reservable deposits, or of deposits included in
a monetary aggregate, to e-money might be a serious
problem for monetary policy in some sense, for example, but it would not be a fundamental problem, because
reformulation of the basis for computing the reserve
requirement or monetary aggregate to include e-money
would be an obvious solution. By this premise, a potentially open-ended examination of the implications
of innovation for monetary policy can be narrowed
to an examination of the monetary implications of increasing the scope of net settlement.
Several years ago, central bankers concluded
that the implications of payments system innovation
for the formulation and implementation of monetary
policy in the context of e-money were not fundamental.8 This conclusion was based partly on the argument that the use of money for small-value transactions
is insignificant for monetary policy. That argument,
of course, provides no assurance regarding innovation
in large-value payments. However, there were other,
independent arguments that reached the same conclusion. The central bankers suggested that, in general,
as long is there is a way to induce demand for reserves,
and as long as the central bank can finance openmarket operations to affect the supply of reserves, conduct of monetary policy broadly along present lines
should remain feasible. Inducing demand might require some regulatory distortion if interest were not
to be paid on the reserves, and providing resources
for open-market operations might require funding of
the central bank with tax revenue (as is already done
in the UK) if interest were to be paid, but even such
measures were not generally viewed as serious obstacles to the conduct of monetary policy.
The question that past discussion has not resolved
or even framed very explicitly is whether innovation
in payment arrangements might conceivably cause
the objective of monetary policy to change. Central
bankers view themselves as making a tradeoff between the avoidance (or control) of inflation and other policy objectives such as growth and full employment.
If innovation in payment arrangements were to reduce
the sensitivity of the economy to inflation, then central bankers presumably should respond by giving
greater weight to those other policy objectives relative to the inflation objective.

29

Two models of payments innovation and inflation
I discuss two economic models that purport to
address the effect of payment innovation on the welfare cost of inflation. The first model suggests that innovation should reduce the welfare cost of a given level
of inflation. However, the model incorporates an assumption inappropriate to studying this question. I formulate an alternative model that suggests that the
welfare cost of inflation is unaffected by innovation.
I start off with a well-known model, according
to which payment innovation potentially can reduce
the sensitivity of the economy to inflation.9 This model
posits that transactions can be made by using either
money or an alternative, nonmonetary, technology.
The researchers who developed the model call the alternative technology credit, financial intermediation,
or e-money. A buyer’s decision which technology to
use is based on cost minimization. The cost of using
money is the interest income that is forgone by holding money rather than an interest-bearing, but illiquid, security.10 The buyer’s cost of using the alternative
technology—call it credit—for a particular transaction depends on the seller. For any buyer, there are a
few sellers—think of them as being his immediate
neighbors—to whom it is very inexpensive to establish creditworthiness, a few others to whom it is very
costly, and other sellers at every level in between. Each
buyer desires the differentiated goods of all sellers,
so he will use credit to buy from his neighbors but
money to buy from distant strangers. There will be
some critical distance at which buyers switch from
using money to credit.
Now suppose that, for every distance, the cost of
establishing creditworthiness to a seller at that distance
falls by half. Then, if the seignorage tax does not
change, the critical distance will double. Some payments that would have been made with money before
will now be made with credit—in particular, those
payments from buyers to sellers who are located farther away than the old critical distance but closer than
the new one. Such a fall in the cost of establishing
creditworthiness is how a payment innovation is represented in this model.
The utility loss due to a marginal increase in the
price of a good is proportional to the quantity of the
good consumed.11 More generally, the utility loss due
to a uniform marginal increase in the prices of a set
of goods is proportional to the sum of the quantities
of those goods consumed. An increase in the rate of
inflation (that is, in the seignorage tax rate) translates
into an after-tax price increase on all goods bought
with money. The upshot is that the fewer are the
goods that a buyer buys with money, the smaller is

30

the buyer’s utility loss from an increase in the rate of
inflation. Since innovation in payment arrangements
reduces the set of goods that each buyer buys with
money, innovation reduces the aggregate welfare cost
of inflation according to this analysis. An implication
would seem to be that, as innovation decreases the
cost of alternative payments technology and correspondingly increases its use, central bankers should
care less about inflation and should turn their attention more to other policy goals.
The most satisfactory way of modeling an innovation in payment arrangements is to represent it in a
general equilibrium model having the feature that
people’s willingness to accept money in exchange for
valuable commodities arises naturally as an equilibrium phenomenon, rather than being imposed by an ad
hoc constraint against goods-for-goods trades not involving money.12 The foregoing analysis is best regarded as an essay to think in rough and ready terms
about how such a fully articulated model would work.
The analysis leans heavily on the premise that money
is not used at all in making credit payments. In contrast, actual credit is almost always nominal, so money
is essential to extinguish or settle it. Since the payment
innovations that are being made on the basis of information technology are specifically means of economizing on the use of money for settlement, the purpose
itself must be important for understanding the innovations. The observation that there is no debt-settlement role for money in the model just presented
should be a warning bell about its appropriateness
for this use.
A more appropriate proxy for a fully articulated
analysis, I think, is to imagine a payment arrangement as being a protocol according to which a buyer
can costlessly issue real debt (that is, make enforceable promises to provide specified quantities of goods
at future dates) to finance part of a purchase, but according to which at least a specified fraction of the
value of the purchase must be paid in money. This
fraction corresponds to the “netting ratio,” that is, to
the ratio of the aggregate value of gross payments
settled via the arrangement to the aggregate value of
net payments made in money to effect settlement.
Thus an increase in the netting ratio of the economy
is a good representation of a payments innovation.
Now, as a rough and ready analogy, think of the netting ratio as being just the traditional money multiplier with net payment playing the role of inside money.
The clear intuition from this analogy ought to be that
a payments innovation will raise the price level (by
increasing the amount of inside money) but have no
other effect. This intuition follows from the idea that

2Q/2001, Economic Perspectives

money is neutral in the long run, that is, that an increase in the stock of money may have transitory
effects on real economic activity but will have no effect
asymptotically. In particular, the long-run welfare
effects of monetary policy should be identical in the
post-innovation economy to what they were in the
pre-innovation economy. This is the conclusion that
I would expect a sound, fully articulated analysis to
yield. On the basis of this expectation, I do not believe
that innovation in payment arrangements constitutes
a fundamental change.
Requirements for integrity and security of
the financial system
Central banks have undertaken to promote the
integrity and security of the financial system infrastructure, and a central bank is directly accountable
for the integrity and security of its own operations.13
Integrity means immunity from failure when operated
and used even under extreme conditions (such as during
a period of financial market volatility) but in good faith.
Security means immunity from failure due to attempted
impairment or bad-faith use by an authorized or unauthorized user.
Major components of financial system infrastructure have relied on electronic computing and
communication technology for several decades. In
most countries these components include, for example, the real-time gross settlement system for largevalue transfers and the system of ownership registration
for government securities. Other components, such
as securities-market trading systems and systems for
assessing and documenting the credit quality of assets
intended for securitization are progressively becoming
dominated by electronic technology as well.
Old-fashioned requirements of security and integrity continue to be relevant in the context of electronic information technology. For example, physical
facilities have to be guarded adequately; the authorization, execution, and recording of transactions ought
not to be done by the same person; and there must be
sufficient investment in maintenance and redundancy
of equipment to control the risk of mechanical and
electrical failures.
In addition, three features of electronic technology,
and of software in particular, create problems that are
new or much more intense than before. First, a software defect is present in exactly the same form on all
machines that run the software, so that redundancy of
equipment provides no protection from such a defect.
Second, there is the problem that software tends to
be subject to dramatic failure on account of a defect
in any one of a profusion of details. For example, in

Federal Reserve Bank of Chicago

1985, when the number of distinct issues of U.S. government securities grew too large to be represented
by the address field in a program instruction (analogous to the recent century-date-change problem, in
which the commonly used two-digit representation
of a year ceased to be adequate), the unintended behavior of the program had business consequences that
required the Bank of New York to borrow more than
$20 billion at the discount window of the Federal
Reserve Bank of New York.14 More important, this
episode highlighted the potential for serious disruptions
to the payments system and the financial markets, although they were avoided in this instance.15 Third,
besides the problem of integrity in each of many individual components, these components—often programmed independently of one another—must interact
in precisely specified ways in order to be compatible.
An example of what can happen otherwise was provided last year by the Chicago Board of Trade, which
temporarily had to suspend activity on its electronic
trading system for financial derivatives (called Project
A) because of such a system-programming problem.16
Project A is a demonstration project being conducted
by an exchange that is still mainly organized as an
open-outcry trading pit. If this suspension had taken
place on an exchange that relies primarily on electronic trading, as some of the world’s principal exchanges already do and others envision doing soon,
then there would have been an exchange-wide suspension of trading with potential implications beyond the exchange itself.
In the past, central banks and other financial intermediaries often have programmed idiosyncratic,
proprietary systems suited to their individual needs.
The critical need for this software to perform accurately
and reliably, in view of the features that I have described above, makes such an approach increasingly
risky and inordinately expensive as software becomes
highly complex. The preferable approach is to synthesize a system by relying as far as possible on generic modules that are widely enough used to justify
(and to share) the heavy cost of exhaustive testing,
and that preferably have been used together in various combinations sufficiently often that there is a
high degree of confidence in their compatibility. Besides mitigating in the first instance the problems of
integrity that I have described, maintaining a system
of components in widespread, current use helps to
ensure that the most skilled technicians will be available (as both employees and contractors) to maintain
the system and to make prompt, effective repairs
when necessary.

31

Following this modular-design approach means
depending more than previously on the general market for software and software-operated information
services to meet information-technology needs. A caveat regarding this dependence has to do with the unusually high premium that financial system customers
place on integrity and security. Constituting part of a
niche market in this respect, the financial services industry may sometimes not be a priority customer of
the software industry. The market for advanced encryption technology provides a case in point. The Digital
Encryption System (DES) has been widely incorporated as a security measure in financial system software since its introduction in the 1970s. For most of
that time, DES has been regarded as a commercially
reasonable security measure for large-value transactions. Progressively through the 1990s, however, advances in code-breaking techniques have raised some
doubt regarding the adequacy of DES encryption.
A more secure encryption algorithm based on DES,
the Triple Data Encryption Algorithm (TDEA, informally known as “triple DES”), has been regarded by
experts for some time to offer a preferable level of
protection.17 However, despite this situation having
developed somewhat predictably as code-breaking
research continued and triple DES having been identified early as a reasonable response to it, the current
state of the market is such that conversion of a computer system to triple-DES encryption remains a
costly and managerially challenging project.18
A parallel situation exists with respect to software integrity. For example, current industry efforts
to ensure the interoperability essential to the modular
design approach envisioned above may not be stringent enough to meet fully the needs of the financial
system.19 The financial system is likely to look to
central banks for leadership in working with the information technology industry and its regulators (including, perhaps, defense-related agencies charged with
safeguarding communications and other economic
infrastructure) to ensure that needs are met. Because
the character of that industry is heavily affected by
the special attributes of information technology as an
economic good, bringing the needs of the financial
system effectively to its attention is likely to require
considerable exercise of judgment and creativity, as
well as tenacity.20
Central bank transparency
As I mentioned at the outset, the economic management of information includes design decisions regarding the structure of institutions, as well as decisions
about the employment of electronic technology for

32

computing and communication. Central bank transparency is an issue to which both kinds of decision
are relevant.
For purposes of this discussion, I call a central
bank transparent to the extent that it makes public the
information about itself that is relevant to the formulation
and implementation of present and future monetary
policy. Such information might include its objectives,
its understanding (in terms of both broad concepts
and specific formal models) of the structure economy,
its knowledge about the current state of the economy,
and its decision-making protocol.
This definition is intended to separate as clearly
as possible the issue of transparency from the issue
of intellectual decisiveness within the central bank itself. For example, if decision-makers within the central
bank are confused or divided at a point in time regarding the significance of unexpected developments,
then the public’s inability to attribute a coherent view
of the economy to the central bank merely reflects
the true situation of the central bank, not any lack of
transparency.21
During the past decade or two, central banks have
espoused transparency to a substantial extent. One
reason may be that the electorate has grown to regard
this as something for which the central bank is accountable, and that central bank independence is therefore
politically dependent on transparency. Another reason
is that central bankers have recognized that greater
transparency may favorably affect the scope of action
within which they can maintain credibility while responding to macroeconomic developments. This broader scope of action may make monetary policy more
effective. I am not concerned here with the justification of transparency, however, but rather with the
question of how to achieve it.
Some early research on this topic (for example,
Canzoneri, 1985) modeled transparency in terms of
disclosure by the central bank of its information regarding the economy. The advances in information
technology that have been the focus of the article thus
far make such a modeling perspective less convincing than it may have previously been. A central bank
does possess some private information (for example,
more timely access than the public to some economic
statistics compiled by the government), but large corporations—particularly multinationals—presumably
possess some information that the central bank lacks.
Thus, on the whole, central banks do not seem unique
in point of privileged access to information or to the
judgment of sophisticated market participants. Where
central banks may have been unique a generation ago
was in possession of techniques and equipment for

2Q/2001, Economic Perspectives

sophisticated formal modeling and forecasting, which
was the province of a small community of central
bank experts and university researchers. Today such
econometric expertise is widely available to the public. Moreover, in some economies, the dramatic growth
of financial derivatives markets and the concurrent
issuance of indexed and unindexed bonds (that are
approximately comparable in other respects) have generated price information that is available to the general
public and have facilitated the public’s direct acquisition of accurate information about expectations, especially regarding inflation.22
A more recent approach to analyzing transparency, taken by Faust and Svensson (1998), focuses on
communication by the central bank of its objective.
Faust and Svensson base their work on a modified
version of a model of Cukierman and Meltzer (1986),
according to which the central bank has a preferred
solution to an inflation/employment tradeoff, and this
preference is private information. They informally
recognize that this assumption could be given more
satisfactory foundations by assuming, as in Wallace
(1984), that the public is heterogeneous and that monetary policy can work to the advantage of some sectors but to the disadvantage of others. The tradeoff
that the central bank intends to make regarding the
welfare of these sectors is its private information,
which the public must infer from the subset of economic outcomes that it can observe.
My impression is that this focus on a private incentive is much closer to the gist of the actual problem
of transparency than a focus on private knowledge of
specific facts. Recall, however, that when I proposed
a definition of transparency above, I mentioned two
other domains of private information besides these
two. One is the central bankers’ understanding of the
structure of the economy and the other is their protocol for reaching group decisions. Despite the wisdom
of Faust and Svensson’s decision to simplify their
formal model by focusing on objectives rather than
general understandings or decision protocols, transparency in these other domains is equally important
and presents problems for central bankers that are at
least as thorny. For example, if a central bank has a
staff econometric model that is routinely discussed
when monetary policy is set, should the model be described to the public and should its software even be
disclosed in full? Whether or not such an initiative
would do harm in any respect, I do not believe that it
would give an accurate or helpful picture of the overall thinking of the monetary policy committee. The
collective state of mind of such a committee would
better be described by Paul Feyerabend’s description

Federal Reserve Bank of Chicago

of the collective state of mind of a scientific community as “a whole set of partially overlapping, factually
accurate, but mutually inconsistent theories.”23 How
does one accurately and informatively disclose such
a state of mind to the public? I can only hope to scratch
the surface of this question in this article.
There is one asymmetry in Faust and Svensson’s
modeling approach that would disappear if a more
thoroughly game-theoretic approach were to be taken.
That is, they present credibility as an issue of the central bank’s ability to make incentive-compatible disclosure of private information, while they present
transparency as an issue of the extent to which the
central bank’s information regarding its own objectives (or, more generally, its type in the sense that I
have discussed above) is private or public. In my view,
this latter information is private and the public’s ability to know it is highly dependent, exactly as in the
case of information about central bank actions, on
there being an institutional framework that gives the
central bank an incentive for accurate and informative reporting.
Discussion of institutional design to enhance transparency tends to focus on specific proposals such as
the prompt publication of minutes of meetings where
policy is set. My sense is that such proposals rely for
their effectiveness on more fundamental structural
features of the central bank. To illustrate this idea, let
me cite a structural feature of the Federal Reserve that
I believe plays a most significant role in achieving
transparency: its decentralized structure. There are 19
persons, the seven governors of the Federal Reserve
Board and the 12 presidents of the Federal Reserve
Banks, who participate directly in the deliberations
of the Federal Open Market Committee (FOMC),
which sets monetary policy.24 A substantial part of
the ongoing analytical support of FOMC decisionmaking is provided by the staffs of the Board of
Governors and the Federal Reserve Bank of New
York. However, the fully independent participation
of each Reserve Bank president is buttressed by the
president’s status as the head of a separately chartered
corporation that comprises, among other things, a research department under the unilateral control of that
president. The autonomy that is built into this structure has produced, over time, open discussion of a
number of policy foundations and alternatives that I
believe might have received less or later exposure in
a more centralized institutional framework.
Several important examples from recent decades
support this case. Beginning in the 1960s, the Federal
Reserve Bank of St. Louis conducted a sustained program of research and advocacy regarding the control

33

of monetary aggregates as a basis for conducting monetary policy.25 In the 1970s and 1980s, the Federal
Reserve Bank of Minneapolis played a significant role
in developing general equilibrium monetary models
for policy analysis as an alternative to the macroeconomic modeling approach that was then dominant in
the Federal Reserve.26 In the early 1990s, the Federal
Reserve Bank of Cleveland persistently made a case
that the benefits of bringing inflation under control
would not be fully garnered until exact price stability
had been achieved.27 These essays in analysis and
persuasion have been both more vigorous and more
open to public scrutiny than I believe they would have
been if they had been led by policymakers of equivalent seniority, but operating within a more hierarchically organized central bank. In all three cases, the
advocates of heterodox positions within the central
bank have had to depend heavily on informed public
opinion, and particularly on the endorsement of economists in the academic community, to affirm the correctness of their views. Thus, the decentralized design
of the U.S. central banking system systematically
forces policy debate out into the open marketplace of
ideas, to the benefit of both the transparency of the
Federal Reserve System and the intellectual caliber
of the discussion. The history of the three initiatives
that I have mentioned, and of others as well, suggests
that this process succeeds in identifying and evaluating
significant new ideas and, where merited, progressively
infusing them into the policymaking of the central
bank as a whole, albeit usually not in the uncompromising form that they initially tend to be proposed.
In my view, this sort of institutional design for the
central bank is an important complement to the various,
specific regulations (regarding, for example, the exact timing and format of public release of minutes of
policy-setting meetings) that are usually recommended as means to achieve transparency and to ensure
that monetary policy is publicly accountable.
Other design approaches, adopted by various
central banks in recent years, have analogous roles in

providing transparency. The common feature of these
approaches is that, rather than attempting to achieve
transparency by mandate, they set in place systems
of incentives that result in an institutional culture of
transparency.28 Both a conducive culture and a clear
public mandate have a place in achieving transparency. Indeed, for the central bank to have an appropriate institutional culture is probably a necessary
condition for a mandate to be effective.
Conclusion
Recent, dramatic innovations in the economic
management of information, and particularly their
application in the payments system, might seem potentially to change the nature of central banking. On
close examination, however, these developments do
not significantly change the role or responsibilities of
a central bank. They do not render obsolete the established body of knowledge regarding what constitutes
a well designed central bank and sound central banking practice.
Similarly, intellectual advances in understanding
how organizations should optimally be designed reinforce established thinking about how a central bank
should be designed to achieve transparency. Indeed,
these advances provide a clearer understanding of
how the decentralized structure of the Federal Reserve
System contributes to the effectiveness of the U.S.
central bank and to the public welfare.
The one area where innovations in information
technology do seem to call for new understanding is
in the involvement of central banks with the technology itself. Such involvement is required to discharge
both oversight and operational responsibilities. On
behalf of the financial system, as well as on its own
behalf, a central bank must manage problems that are
rooted in the structure of the information-technology
industry. Adept management is required to maintain
the integrity and security of a financial system that,
because of its scope and complexity, is critically dependent on information technology for its functioning.

NOTES
1
Money issued by the central bank is known as outside money.
Commercial banks and other such depository institutions also issue
money, in effect, when they make loans. This is known as inside
money. A requirement that depository institutions must hold reserves of outside money constrains their ability to create inside
money. Reserve requirements in the U.S. and some other countries are deposit reserves based on the value of deposits that a
depository institution holds, and in other countries are clearing
balances based on the value of payments that a depository institution makes on behalf of its depositors. In this article, “money”
means outside money unless otherwise indicated, and “reserves”

34

is used as a generic term for either deposit reserves or clearing
balances.
E-money refers to a family of payment methods that include
stored-value cards and “Internet cash” designed for widespread
use. Payment methods designed for convenient purchasing from a
single seller, such as the fare cards issued by some public transit
authorities, are not within the meaning that is usually intended.

2

The CLS system is described in Bank for International Settlements (1998).

3

2Q/2001, Economic Perspectives

4

De Santis (1998) describes the proposed system and its riskmanagement implications. Marjanovic (1998) also provides a
brief description.

intermediaries themselves. Therefore, oversight of such arrangements involves coordination between the central bank and the supervision authorities for various types of intermediaries.

A negotiable instrument is one that has a particular, named individual as its beneficiary, but that allows that beneficiary to designate another person as beneficiary instead (typically, as payment
for a good or service received from the new beneficiary). A bearer
security is a financial instrument, such as currency, whose beneficiary is whoever happens to possess it. Some types of e-money differ
from a literal bearer security in point of requiring proof of ownership beyond physical possession. That difference is not material
to the analogy drawn here.

14

5

6
That is, a bearer security issued by a bank and redeemable for coin
or other legal money. Wallace (1986) and Summers and Gilbert
(1996) have previously emphasized this analogy.

Friedman (1999) and King (1999) suggest that extensive use of
information technology might make it possible in principle for
the private sector to operate a comprehensive settlement network
that would be wholly outside the influence of the central bank. In
that case, my premise would be violated. The gist of my argument
in this section is that, although it might seem that being able to
settle large gross payments with much smaller net payments is
tantamount to the situation that Friedman and King have in mind,
the implications for monetary policy may be materially different.

7

Bank for International Settlements (1996) reflects some of that
discussion.

8

9
The model is an elaboration of a cash-in-advance model of Lucas
and Stokey (1987) in which, for each trader, some goods are
credit goods that are exempt from the cash-in-advance constraint
that holds for the remaining cash goods. The elaboration is to
endogenize the cash/credit distinction as explained here. Such
models were introduced by Schreft (1992) and Aiyagari, Braun,
and Eckstein (1998).

This foregone interest is seignorage that is captured by the government, which would have to issue interest-bearing debt to finance
expenditure if people would not accept money. Seignorage is thus
an implicit tax on holding money.

10

This is Roy’s identity; see Deaton and Muellbauer (1980), p. 40.
This principle is highly intuitive. For example, if someone purchases
five daily newspapers and one weekly newsmagazine a week, then
a penny increase in the price of a newspaper hurts five times as
much as a penny increase in the price of a magazine. This is evidently true if consumption does not change. If the reader cuts down
to four newspapers a week, then he was getting just one penny’s
worth of utility from the fifth paper beyond what alternative expenditure of its price would have yielded (since he elected to give
it up when an extra penny was charged), so (on the simplifying
assumption that utility is measured in whole penny’s-worth units)
he still loses a penny’s worth of utility despite changing his budget allocation.

11

Freeman (1996a, b, and 1999) and Green (1997) analyze central
bank operations, and also clearinghouse operations closely akin
to netting, in this way.

12

Such a responsibility is widely conferred to, and considered an
appropriate role for, central banks. Consensus to this effect is reflected, for example, in a series of documents issued under the
auspices of the Bank for International Settlements during the past
decade. Payment arrangements operated by private financial intermediaries (either directly or via jointly owned subsidiaries)
evidently cannot be supervised in isolation from the sponsoring

13

Federal Reserve Bank of Chicago

Statements of Paul A. Volcker, Chairman, Board of Governors
of the Federal Reserve System, and J. Carter Bacot, Chairman and
Chief Executive Officer of the Bank of New York and the Bank
of New York Company, Inc., before the Subcommittee on Domestic Monetary Policy of the Committee on Banking, Finance, and
Urban Affairs, House of Representatives, December 12, 1985.
Reprinted in U.S. Congress (1986).
Statement of Paul A. Volcker, Chairman, Board of Governors of
the Federal Reserve System, before the Subcommittee on Domestic Monetary Policy of the Committee on Banking, Finance, and
Urban Affairs, House of Representatives, December 12, 1985.
Reprinted in U.S. Congress (1986).

15

16

Chicago Board of Trade (1999).

In 1998, the Accredited Standards Committee X9 on Financial
Services, a U.S. financial services industry committee working
under the aegis of the American National Standards Institute (ANSI),
adopted standard ANSI X9.52, which specifies triple DES as an
interim encryption method for large-value financial transactions
until a more durable method can be developed. In 1999, the X9
Committee issued technical guideline TG-25-1999, which expresses
a consensus that (single) DES encryption no longer provides adequate security for large-value transactions. An analogous guideline for U.S. government applications and public recommendation
for nongovernmental applications, “Data encryption standard
(DES),” Federal Information Processing Standards (FIPS) Publication 46-3, was issued by the U.S. Department of Commerce/
National Institute of Standards and Technology (NIST) in 1999.
This guideline designates triple DES as an encryption algorithm
of choice and permits DES for legacy systems only. The NIST is
also in the process of adopting an Advanced Encryption Standard
that will co-exist with, and eventually supplant, triple DES.

17

To provide adequate electronic security, conversion to triple DES
must be done in conjunction with other measures such as setting
up a cryptographic key-management system. For non-U.S. entities,
a further complication recognized by NIST FIPS-46-3 is that export of encryption systems deemed to provide commercially reasonable security for large-value financial applications is subject
to U.S. export-control regulation.

18

19

Summers (1999) raises this issue.

Arrow (1974) provides a classic introduction to the issues of
market structure engendered by characteristics of informationrelated industries.

20

Transparency is relevant in other contexts besides monetary
policy, but I have qualified the definition in this respect to screen
off issues that involve special considerations (such as whether “constructive ambiguity” about the extent of the central bank’s willingness to provide emergency credit is a justifiable strategy to
deter implicitly subsidized risk-taking).

21

Roughly speaking, the interest rate premium of an unindexed bond
above an indexed bond of the same maturity provides a measure
of expected inflation from the present to the maturity date. Techniques to extract information regarding expectations from assetprice data are surveyed by Soderlind and Svensson (1997).

22

23

Feyerabend (1978), p. 39.

35

24
All but one of the Reserve Bank presidents are voting members
only in rotation, but their participation in deliberations is continuous.
25

See Andersen and Carlson (1974).

26
See Miller (1994), which is a collection of papers reprinted from
the Federal Reserve Bank of Minneapolis, Quarterly Review.
27

Having such incentive systems in place is a very helpful means
to attract capable, principled, intellectually independent persons
to serve on the governing board of the central bank. It is also helpful to guarantee the ability of the governing board to hire and retain staff with those characteristics. Perception of the high character
and ability of the central bank leadership and staff seems to contribute materially to public support for central bank independence.

28

See Gavin (1991).

REFERENCES

Aiyagari, Rao, R. Anton Braun, and Zvi Eckstein,
1998, “Transaction services, inflation, and welfare,”
Journal of Political Economy, Vol. 106, pp. 1274–1301.

De Santis, Vincent, 1998, “CHIPS update,” presentation to the Money Transfer 98 conference sponsored
by Bank Administration Institute.

American National Standards Institute, Accredited
Standards Committee X9 on Financial Services,
1999, “A guide to understanding data remanence in
automated information systems,” technical guideline,
No. TG-25-1999, Washington, DC.

Faust, Jon, and Lars E. O. Svensson, 1998, “Transparency and credibility: Monetary policy with unobservable goals,” Board of Governors of the Federal
Reserve System, international finance discussion paper,
No. 605.

, 1998, “Triple data encryption algorithm
modes of operation,” ANSI standard, No. X9.52,
Washington, DC.

Feyerabend, Paul, 1978, Against Method, London:
Verso Editions.

Andersen, Leonall, and Keith Carlson, 1974, “The
St. Louis model revisited,” International Economic
Review, Vol. 15, pp. 305–327.

Freeman, Scott, 1999, “Rediscounting under aggregate risk,” Journal of Monetary Economics, Vol. 43,
pp. 197–216.

Arrow, Kenneth J., 1974, The Limits of Organization,
New York: W. W. Norton.

, 1996a, “Clearinghouse banks and banknote over-issue,” Journal of Monetary Economics,
Vol. 38, pp. 101–115.

Bank for International Settlements, 1998, “Reducing
foreign exchange settlement risk: A progress report,”
Basel, Switzerland.

, 1996b, “The payments system, liquidity, and rediscounting,” American Economic Review,
Vol. 86, pp. 1126—1138.

, 1996, “Implications for central banks
of the development of electronic money,” Basel,
Switzerland.

Friedman, Benjamin, 1999, “The future of monetary policy: The central bank as an army with only a
signal corps?,” National Bureau of Economic Research, working paper, No. 7420.

Canzoneri, Matthew B., 1985, “Monetary policy
games and the role of private information,” American
Economic Review, Vol. 75, pp. 1056–1070.
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August 17.
Cukierman, Alex, and Allan H. Meltzer, 1986,
“A theory of ambiguity, credibility, and inflation under discretion and asymmetric information,” Econometrica, Vol. 54, pp. 1099–1128.
Deaton, Angus, and John Muellbauer, 1980, Economics and Consumer Behaviour, Cambridge, UK:
Cambridge University Press.

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Gavin, William T., (special issue ed.), 1991, “Price
stability: A conference sponsored by the Federal Reserve Bank of Cleveland, November 8–10, 1990,”
Journal of Money, Credit, and Banking, Vol. 23,
pp. 433–631.
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structure of payments,” Bank of Japan Monetary and
Economic Studies, Vol. 15, pp. 63–87, reprinted in
1999, Quarterly Review, Federal Reserve Bank of
Minneapolis, Vol. 23, pp. 13–29.

2Q/2001, Economic Perspectives

King, Mervyn, 1999, “Challenges for monetary
policy, new and old,” presentation to the New Challenges for Monetary Policy symposium of the Federal
Reserve Bank of Kansas City, Jackson Hole, WY,
August 26–28.

Summers, Bruce, 1999, “Integrity and trust in electronic banking,” presentation to the 1999 Software
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“Money and interest in a cash-in-advance economy,”
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“Clearing and settlement of U.S. dollar payments:
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final leg of overhaul,” American Banker, Vol. 163,
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Miller, Preston J., (ed.), 1994, The Rational Expectations Revolution: Readings from the Front Line,
Cambridge, MA: Massachusetts Institute of Technology
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techniques to extract market expectations from financial
instruments,” Journal of Monetary Economics, Vol. 40,
pp. 383–429.

Federal Reserve Bank of Chicago

37

Competition among banks: Good or bad?
Nicola Cetorelli

Introduction and summary
In recent years we have witnessed a substantial convergence of research interest and the opening of a debate on the economic role of market competition in
the banking industry. The need for such a debate may
seem unjustified at first. The common wisdom would
hold that restraining competitive forces should unequivocally produce welfare losses. Banks with monopoly
power would exercise their ability to extract rents by
charging higher loan interest rates to businesses and
by paying a lower rate of return to depositors. Higher
lending rates would distort entrepreneurial incentives
toward the undertaking of excessively risky projects,
thus weakening the stability of credit markets and
increasing the likelihood of systemic failure. Higher
lending rates would also limit firms’ investment in
research and development, thus slowing down the
pace of technological innovation and productivity
growth. Lower supply of loanable funds, associated
with higher lending rates, should also be reflected in
a slower process of capital accumulation and, therefore, in a lack of convergence to the highest levels
of income per capita.
These are some of the conventional effects that
market power in the banking industry is commonly
thought to generate. However, in more recent years,
researchers have begun analyzing additional issues
in the matter of bank competition, highlighting potentially negative aspects and so raising doubts regarding
the overall beneficial welfare impact of bank competition on the economy. The research effort devoted
to this issue has picked up noticeably, a sign that the
time is ripe for an open debate regarding the costs
and benefits of bank competition.1
The policy implications associated with this issue, related to the regulation of the market structure
of the banking industry, are especially relevant. In
fact, banking market structure is a traditional policy

38

variable for the regulator. Implicitly or explicitly motivated by the desire to restrain banks’ ability to extract rents, policymakers would typically recommend
measures aimed at fueling competition, promoting
the liberalization of financial markets and removing
barriers to entry (see, for example, Vittas, 1992). In
light of the most recent regulatory changes affecting
the U.S. financial industry, the policy relevance for
U.S. regulators is more current than ever. In 1992 intrastate branching restrictions were relaxed, followed
in 1994 by the passage of the Riegle–Neal Interstate
Banking and Branching Efficiency Act, which allows
bank holding companies to acquire banks in any state
and, as of June 1, 1997, to branch across state lines.
Finally, 1999 saw the passage of the Financial Services Modernization (Gramm–Leach–Bliley) Act, allowing the operation of commercial banking,
investment banking, and insurance underwriting
within the same holding company. Such regulatory
changes continue to have a significant impact on the
market structure of the banking industry and on
banks’ competitive conduct. A deeper analysis of the
economic role of bank competition should thus contribute to our understanding of the role of the regulator and the consequences of regulatory action and,
therefore, support more effective policymaking.
The goal of this article is to summarize some of
the arguments that have recently emerged and to suggest some new lines of investigation. In the next section, I describe theoretical contributions that have
identified both positive and negative effects of bank
competition. Subsequently, I illustrate the results of

Nicola Cetorelli is an economist at the Federal Reserve
Bank of Chicago. The author thanks David Marshall
for his useful comments.

2Q/2001, Economic Perspectives

existing empirical studies, which present mixed evidence regarding the economic role of bank competition. The main conclusion that seems to emerge from
the review of the current literature is that the market
structure of the banking industry and the related conduct of banking firms affect the economy in a much
more complicated way than through the simple association: more market power equals higher lending rates
and lower credit quantities. By combining the various
research studies, I identify multiple effects of bank
competition, acting along different economic dimensions, suggesting the existence of tradeoffs and leading
us toward more sophisticated normative considerations
associated with bank competition. For example, as I
describe in detail later, there is evidence from recent
work to support the conventional wisdom that a more
concentrated banking industry imposes a deadweight
loss in the credit market as a whole, resulting in a reduction in the total quantity of loanable funds and
slower economic growth. However, the effect appears
to be heterogeneous across industry sectors, and younger
firms in industries that are heavily dependent on banks
for investment funds actually seem to grow faster if
they deal with a concentrated banking sector.
The final section of the article explores some
additional lines of research on the economic role of
bank competition. For instance, does it matter whether banks are government owned? To what extent do
government-owned banks behave differently from
privately owned banks? Could common ownership
across different government-owned banks imply a
cartel-like behavior?
A separate question is whether the role of bank
competition varies depending on how restrictive is the
regulatory environment of the banking sector. Banks
may be or may not be allowed, for example, to own
and control nonfinancial firms, or to engage in securities or insurance underwriting and selling, or real
estate investments. The possibility for banks to be active in multiple markets and face competition from
nonbank firms in such markets may have an impact
on the role of bank competition in the economy. For
example, the possibility to offer a wider array of
products and services may allow banks to “capture”
and retain clients even while facing intense competition in traditional banking markets.
Finally, another dimension of analysis is the exploration of the possible relationship between market
power in the banking industry and that in other sectors of the economy. Does a concentration of market
power in banking lead banks to extend credit to few
firms, which grow in size and make their sectors concentrated, or rather does bank concentration promote

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the continuous entry of firms, thus contributing to
lower concentration in other industries? Theoretical
conjectures could be presented suggesting either effect.
I present an illustration of these separate lines of
inquiry and some empirical evidence. This evidence
confirms that the market structure of the banking industry and the related conduct of banking firms have
an important role in maintaining a well-functioning
economy and that normative action regarding bank
regulation requires careful consideration.
Theoretical arguments
I begin by illustrating the most common theoretical arguments used to identify positive and negative
economic effects of bank competition. In a stylized
model of economic growth, Pagano (1993) showed
that market power, by allowing banks to charge higher
loan rates and compensate savers with lower deposit
rates, does indeed reduce the equilibrium quantities
of funds available for credit, hence generating a direct
negative effect on the rate at which the economy can
grow. Guzman (2000) confirms this negative effect
of market power in a general equilibrium model of
capital accumulation. He compares two identical economies, one with a monopolistic bank and the other with
a competitive banking sector, and shows that the monopolistic bank produces an unequivocally depressing
effect on capital accumulation for two possible reasons.
If the conditions exist for credit rationing, quantities
are rationed more with a monopolistic bank than within a competitive setting. Without credit rationing,
monopoly power in banking is still inefficient because
it leads to excessive monitoring. As Guzman argues,
this is due to the fact that with monopoly power loan
rates are higher, and with higher rates the likelihood
of default also increases (moral hazard). Consequently, the monopolistic bank has to sustain a higher cost
to monitor entrepreneurs, thus diverting resources
that could otherwise be available for lending.
In perhaps the most widely cited article about
this issue, Petersen and Rajan (1995) focus on the role
banks play in financing new businesses. In a stylized
theoretical model, the authors show that young firms
with no record of past performance may actually receive more credit, and at better rates, if they are in a
market where banks have monopoly power. The intuition is the following. Lenders facing a pool of risky
(because yet unknown) borrowers should incorporate
an appropriate premium in their lending rates to cover
a likelihood of default potentially higher than that
among already established entrepreneurs. Consequently,
lending rates for this category of borrowers should be
high and credit partially rationed. However, in such a

39

scenario, a bank with market power has an alternative
lending strategy. It can charge “introductory” lower
rates, attract more—and possibly on average better—
young entrepreneurs, and establish a lending relationship with them, with the prospect of extracting rent
(charging higher rates) in the future from those who
are eventually successful. This strategy of initial “subsidization” and subsequent “participation” in successful firms’ profits is feasible if the bank has market
power. The bank relies on the fact that the successful
firms will not be bid away by competitor banks in
the future. On the other hand, in a competitive market,
a bank sustaining the initial cost of offering credit at a
lower rate could not count on its ability to retain the
successful customers.
In a more recent paper, Shaffer (1998) points out
another possible shortcoming associated with bank
competition. One of banks’ main functions is that of
performing screening, separating prospective entrepreneurs by quality categories. Shaffer shows that the
average quality of a bank’s pool of borrowers declines
as the number of competitors in the market increases.
The intuition is based on the possibility that banks’
screening technologies may not accurately report the
borrowers’ true characteristics. Suppose the screening model used by banks is indeed imperfect, in the
sense that with a certain probability entrepreneurs of
high quality can be identified as being of low quality,
and vice versa. Also assume that a bank cannot distinguish between a new loan applicant and someone
who has already been denied credit by another institution. As a result, rejected applicants (either of high
or low quality) can continue to apply to other banks;
the more banks there are in the market, the higher the
likelihood that a low-quality applicant receives credit. This occurrence is known as “winner’s curse”: A
bank that agrees to extend a loan may be “winning”
the right to fund a lemon.
Also focusing on bank screening, Cao and Shi
(2000) argue that, because an increase in the number
of banks operating in the market exacerbates the winner’s curse, the number of banks active in performing
screening and competing in supplying credit would
actually fall; as a result, loan rates would be higher
and credit quantities smaller than in a market with
fewer banks.
Dell’Ariccia (2000) explores another model of
bank screening, showing that as the number of banks
increases, the likelihood that banks will actually
screen entrepreneurs, as opposed to lending indiscriminately, decreases. His argument is based on the
observation that entrepreneurs may be averse to being screened. For instance, the screening process

40

may be time-consuming and in the process the firm
may miss profit opportunities. Alternatively, an entrepreneur may not want to reveal the true creditworthiness of the project. In slow-growth periods or during
recessions, screening may be the optimal strategy, since
there is a high probability that entrepreneurs demanding credit may be of low quality and have already been
rejected by other banks. However, in periods of economic expansion, when there is a higher proportion
of new, untested entrepreneurs, banks competing for
market share may choose to offer lending contracts
involving no screening. The interesting result is that
by bearing a higher risk in the upswing of the economic
cycle, banks are more likely to plant the seeds for a
subsequent recession.
Manove, Padilla, and Pagano (2000) observe that
screening and collateralizing are substitutes from the
point of view of a bank’s lending strategy. A bank
screens to select high-quality entrepreneurs and reduce
the risk of default among low-quality ones. However,
if an entrepreneur posts full collateral, then the bank
may not have an incentive to screen (where screening
is a costly activity) since the bank would be protected
in the event of default. Consider a world with high- and
low-quality entrepreneurs, where high-quality ones
have a higher probability of picking a good project.
Entrepreneurs know whether they are of high or low
quality. In a competitive banking market, banks would
offer loans only to those entrepreneurs whose projects
were screened and thus recognized as successful.
However, banks have to offer them credit at a rate
high enough to recoup the total cost of screening (including the screening cost component of the entrepreneurs whose applications were rejected). High-quality
entrepreneurs can separate themselves from the pool
by offering to post collateral on their loan (low-quality
entrepreneurs would not offer to post collateral, since
they face a high probability of loss if their project
turns out to be unsuccessful). Hence, banks would
only screen low-quality borrowers and extend credit
to those who were able to pick a successful project.
All high-quality entrepreneurs (some of which will
still be unsuccessful) would receive credit since they
posted collateral and, therefore, do not constitute a
risk for the bank.
What happens in a market with a monopolistic
bank? According to the authors, such a bank may
not have the incentive to accept collateral from highquality entrepreneurs. This is because the monopolistic bank is able to appropriate the surplus generated
by successful projects. Hence, for this bank, screening implies a higher rate of return and, therefore, may
be preferred to accepting collateral. In this case, the

2Q/2001, Economic Perspectives

monopolistic bank screens all projects, thus eliminating the allocation of resources to entrepreneurs destined to fail.
Multiple effects of banking
market structure
If banks’ role were simply that of intermediating
between supply and demand of credit, then market
power in the hands of banks could only generate the
conventional negative effect associated with rent extraction. However, banks fulfill other important functions—in particular their role in screening prospective
entrepreneurs and in allocating capital resources to
the best social uses. The studies described above share
the insight that market competition may distort banks’
incentive to perform these additional roles. A legitimate
observation, therefore, is that banking market structure
produces multiple effects, (and of opposite directions)
on the economy. On the one hand, market power
may enable banks to extract rents and distort the
equilibrium of the credit market away from one
where the quantity of funds supplied is the highest.
On the other hand, market power may be necessary
to allow banks to achieve an efficient allocation of
funds, thus enhancing the quality of the pool of
selected entrepreneurs.
The identification of a tradeoff between quantity
of credit made available in the market and banks’ role
in allocating funds efficiently is an important insight
that emerges from the most recent analysis of bank
competition. Cetorelli (1997) and Cetorelli and Peretto
(2000) identify both roles and model the tradeoff. Both
papers analyze the role of banking market structure for
an economy’s path of capital accumulation and growth
using a dynamic, general equilibrium framework. The
first paper compares only two benchmark economies,
one with perfect competition and the other with a
monopolistic bank, while the second analyzes banks
in a fully specified Cournot oligopoly model. The
Cournot model has the nice feature that competition
and monopoly are the two extremes of a continuum
of market structures, wherein market power is fully
captured by the number of firms if the model is symmetric, or corresponding measures of market concentration if the model is asymmetric. Cetorelli and
Peretto (2000) analyze N banks competing with each
other in gathering individual savings and in loaning
funds to entrepreneurs. Banks have access to a screening technology that, at a cost, allows them to discriminate between high- and low-quality entrepreneurs.
While the outcome of the screening test may not be
observable by third parties, competitor banks can extract information about the screened entrepreneurs by

Federal Reserve Bank of Chicago

simply observing whether the bank extends or denies
the loan.2 In other words, there is an informational
externality that generates a free-riding problem, which
weakens banks’ incentives to incur the cost of screening and to carry out an information-based (efficient)
lending strategy. Cetorelli and Peretto’s model shows
that the bank’s optimal strategy entails screening entrepreneurs only with some probability, and thereby
extending both “safe” (screened) and “risky” (unscreened) loans. The credit market is thus endogenously segmented: A fraction of entrepreneurs are
always screened, with credit extended only to those
of high quality, while the remaining proportion of entrepreneurs receive credit indiscriminately, regardless
of characteristics of quality. The relative size of these
two components of the credit market evolves along,
and has feedback into, the path of economic development. Within this theoretical framework, two major
effects of banking market structure on economic growth
are identified. On the one hand, the fewer the number
of banks, the smaller the total quantity of credit available to entrepreneurs, exactly as conventional wisdom
suggests. On the other hand, the fewer the number of
banks, the greater the incentive for banks to screen
and, consequently, the larger the proportion of funds
that is allocated efficiently to high-quality entrepreneurs.3 Therefore, the number of banks governs the
tradeoff between the overall size of the credit market
and its efficiency. The size and efficiency of the credit
market, in turn, determine the return to capital accumulation and, therefore, to saving. The main result of
this model is that, because of this tradeoff, the relationship between banking market structure and steadystate income per capita may not be monotonic. In
other words, the market structure that maximizes
economic development is neither a monopoly nor
perfect competition, but an oligopoly.
Empirical evidence
Simultaneously with the development of the theoretical debate, researchers have also begun to investigate empirically the economic role of banking
market structure. As with the theoretical contributions,
the empirical findings suggest that banking market
structure has both positive and negative economic effects, and it is hard to establish which one ultimately
dominates. For example, a few studies provide evidence
of a clearly negative role of banking market power.
Shaffer (1998) uses data on household income growth
between 1979 and 1989 in U.S. metropolitan statistical areas (MSAs). He finds that, after controlling
for other determinants of income growth, household
income grows faster in MSAs with a higher number

41

of banks. Black and Strahan (2000) focus instead on
the impact of banking market structure in fostering
entrepreneurial activity. Looking at cross-industry,
cross-state U.S. data, they find that the number of
new firms and the number of new business incorporations are smaller in states where bank concentration is higher. Jayaratne and Strahan (1996) estimate
the effect of the removal of U.S. bank branching restrictions on state income growth. The removal of such barriers should presumably enhance competition. They
find that both personal income and output growth
accelerated after states implemented the regulatory
change. Hence, their findings suggest, indirectly, a positive effect of bank competition on economic growth.
At the same time, however, some empirical contributions have suggested a positive effect of bank
concentration. For example, Petersen and Rajan (1995)
analyze credit availability for a cross-section of U.S.
small businesses located in markets characterized by
different degrees of banking concentration. They find
that firms are less credit constrained if they are in more
concentrated markets. In addition, they find that younger firms pay lower loan rates in markets with higher
bank concentration. Shaffer (1998), cited earlier, also
finds evidence of higher loan chargeoff rates in MSAs
with a higher number of banks. Collender and Shaffer
(2000) report evidence that while the effect of bank
concentration on household income in U.S. metropolitan areas was negative between 1973 and 1984, it
was positive during the 1984–96 period. Bonaccorsi
and Dell’Ariccia (2000) analyze cross-industry, crossprovince Italian data and find that the rate of creation
of new firms is higher in provinces with a more concentrated banking sector (an Italian province is roughly
equivalent to a U.S. metropolitan statistical area). In
fact, the effect is especially strong on new firms belonging to industry sectors that can be considered more informationally opaque, that is, where the technologies
adopted are such that banks need to put more effort into
screening and selecting entrepreneurs.
Evidence on multiple effects of banking
market structure
Empirical evidence of both a positive and a negative channel through which banking market structure
may affect an economy, implied by the various theoretical contributions and hinted at by the empirical
evidence surveyed in the previous section, is confirmed in Cetorelli and Gambera (2001). They test
the role of banking market structure using data on the
growth of 36 industry sectors in 41 different countries,
both developing and developed economies, expanding
on the existing and well-established methodologies
employed in the literature on finance and growth. The

42

main stylized fact recognized in this literature is that
a well-developed banking sector has an important,
causal role in economic growth. The basic question
asked in Cetorelli and Gambera is then, for a given
level of development of the banking sector, what is
the role of its market structure? They begin by evaluating whether countries with higher bank concentration are characterized by higher or lower growth across
industry sectors. Given the opposing theoretical views
described earlier, the answer to this question is not
obvious. On the one hand, if bank concentration simply results in lower credit availability, then growth
across industries should be slower in countries with
a more concentrated banking market. On the other
hand, if the market power associated with bank concentration generates positive effects, according, for
example, to the relationship-lending argument of
Petersen and Rajan (1995), then growth should be
faster in countries with a concentrated banking sector.
Cetorelli and Gambera find that bank concentration
has a negative effect, on average, on industry growth.
However, Cetorelli and Gambera’s empirical
study goes beyond the analysis of this average effect
of banking market structure. Using industry-specific
information about the intensity with which industry
sectors are dependent on external sources of finance,
they perform more refined empirical tests. Rajan and
Zingales (1998) constructed such an industry-specific measure of dependence, arguing that, due to idiosyncratic factors, different industry sectors are more
or less in need of external sources of funding to finance
capital investment. Sectors adopt different technologies, which imply different initial project scales, different gestation periods and cash harvest periods, and
different reinvestment requirements. Intuitively, sectors like tobacco or leather generate large amount of
funds internally that can be used for investment purposes. At the opposite extreme, sectors like computers or pharmaceuticals, characterized by uncertainty
in the timing and in the rate of return of their investments, will be much more dependent on external
sources of funds. Moreover, within a sector, the intensity of external financial dependence will also differ
across firms of different age, with younger firms more
in need than mature ones. Cetorelli and Gambera use
this information to test whether banking market structure has a heterogeneous effect across industry sectors.
Given the opposing theoretical views, one might argue, on the one hand, that firms in sectors especially
dependent on external finance would suffer more, and
therefore grow less than average, in a country with
a concentrated banking sector. On the other hand, if
bank concentration has positive effects, then firms in
industries especially dependent on external finance

2Q/2001, Economic Perspectives

should benefit disproportionately more when faced
with a concentrated banking sector. Cetorelli and
Gambera’s results show that industries more dependent
on external finance in fact grow relatively faster in
those countries where the banking sector is more concentrated. The effect is more pronounced for younger
firms than for mature firms.
Cetorelli and Gambera’s two main findings taken
together thus confirm the existence of multiple effects
of banking market structure. A more concentrated
banking industry does impose a deadweight loss in
the credit market as a whole, resulting in a reduction
in the total quantity of loanable funds, exactly as
conventional wisdom would suggest. However, the
effect appears to be heterogeneous across industry
sectors, and younger firms in industries that are
heavily dependent on banks for investment funds
seem to benefit from a concentrated banking sector.
New dimensions of analysis
These findings about the economic role of bank
competition draw a picture regarding the normative
implications for regulatory action that is much less
clear than what has been suggested by conventional
wisdom. In particular, it is not clear whether competition is necessarily welfare improving. Perhaps the
major insight we have gained is that policy action
related to bank competition needs to be coordinated
across multiple dimensions. There may be more funds
available in a competitive credit market, but there
may also be higher rates of default and, consequently, greater waste of resources.4 Some of these dimensions of the analysis are dependent on each other. For
example, from the last section we learned that depending on the level of concentration of the banking industry, ceteris paribus, individual sectors will grow at
different speeds. Therefore, banking market structure
plays an important role in shaping the cross-industry
size distribution within a country. Consequently, we
have identified an interesting connection between
regulation of the financial industry and industry planning. In addition, we find that bank concentration
plays a more substantial role in growth by facilitating
credit access of younger firms. To the extent that investment of younger firms is more likely to introduce
innovative technologies, regulators face an unexpected
tradeoff between the generally desirable effects of
bank competition and the promotion of technological progress.
Government ownership
Next, I explore some additional lines of research
on the economic role of bank competition. For instance, does it matter whether banks are government

Federal Reserve Bank of Chicago

owned? How does government ownership affect the
relationship between bank competition and industry
growth discussed above? La Porta et al. (2000) have
recently shown that government ownership in banking is a pervasive phenomenon observed across
countries, more so in developing economies. The
presumption is that public banks are less efficient
and perform a poorer job in allocating capital to the
best uses. The authors confirm this presumption by
showing that countries where government-owned
banks are more predominant are also characterized
by lower rates of growth in per capita income and in
productivity. In addition, these countries face slower
development in financial markets. Cetorelli and
Gambera (2001) test whether the degree of government ownership in banking affects the role of bank
concentration in industry growth. They show that in
countries with both high bank concentration and a
high degree of government ownership of banks (as a
proportion of total bank assets), the positive role of
bank concentration on the growth of sectors highly
dependent on external finance vanishes. What is left
is evidence of the standard inefficiencies associated
with market concentration. The positive role of bank
concentration described earlier supports the argument
that market power is needed for banks to be willing
to efficiently screen entrepreneurs and establish lending relationships with them. The fact that no positive
role for bank concentration is found in countries with
high bank government ownership is consistent with
the argument (see La Porta et al., 2000) that government banks are more likely to be managed to maximize
political rather than social objectives.
Regulatory restrictions
An additional route of exploration should focus
on the impact of regulatory restrictions on banks’ activities. For example, in some countries banks have
historically been authorized to own and control nonfinancial firms; and nonfinancial companies have
been able to hold equity positions in commercial
banks. In addition, banks have been able to operate
in other markets through insurance underwriting and
selling or through the underwriting and brokering of
securities. The economic role of banking market power may be affected by the regulatory environment in
which banks operate. For example, the mechanism
proposed by Petersen and Rajan (1995) through which
market power is needed for banks to establish lending relationships assumes that banks fund firms with
traditional debt rather than equity finance. If a bank
were authorized to finance via equity, the bank
would participate in future profit sharing regardless
of whether the firm maintains a lending relationship.

43

Therefore, it is possible that competitive banks allowed
to provide equity finance would have the incentive to
establish lending relationships. In such a world, the
positive effect of bank concentration for firm growth
identified in the empirical analysis described earlier
may be less important.5
Moreover, in an environment where banks are
authorized to operate in multiple markets (such as
securities, real estate, and insurance), one could argue
that, facing cross-market competitive pressures, banks
in concentrated markets may be less able to extract
rents. Therefore, in economies where banks are less
restricted in their activities, the negative effects of
bank market power may be of lower magnitude. This
line of study is all the more relevant for the U.S. in
light of the recent passage of the Financial Services
Modernization Act.
Cetorelli and Gambera (2001) look at this issue,
using a control variable that ranks countries according to how restrictive is the regulatory environment
for banks.6 However, they do not find significant evidence that the regulatory environment affects the role
of banking market structure. More research along
this line of inquiry is in order.
Bank concentration and concentration in
other sectors
Does bank concentration “transmit” to other industries? In other words, does a concentrated banking
sector lead to the formation of concentrated industry
sectors, with fewer and larger firms? The effect on
the market structure of industry sectors represents a
novel dimension of analysis of the economic role of
banking market structure. If the evidence in Cetorelli
and Gambera suggests that bank concentration may
help spur growth by favoring entry of young firms,
could it still be the case that over time the concentrated banking industry leads to the emergence of concentration of ownership and control in those sectors that
the banks helped to grow?
What determines industries’ market structure?
There is a literature in corporate finance focusing on
the determinants of firms’ size, in most cases the best
available measure of an industry’s market structure.
If, all else equal, a sector is formed by a few, large
firms, then that sector is relatively more concentrated,
while if the same sector is formed by a relatively large
number of smaller firms, then the sector is relatively
unconcentrated. In a quite exhaustive work, Kumar,
Rajan, and Zingales (1999) mention a large number
of determinants of firm size and test their empirical
significance. Evaluating industry-specific factors,
the authors suggest that capital-intensive industries,

44

industries with higher wages, and those with higher
R&D (research and development) intensity exhibit
larger firms. Looking at country (market) factors that
are common across industries, countries with a better
judicial system and those with higher human capital
have industries with larger firms.
How does banking market structure fit within the
theories of industry market structure determinants?
Theories of industrial organization suggest that barriers to entry shape market structure. To the extent that
banking market structure affects the availability of
external finance, it acts as a barrier to entry. However,
whether increasing bank concentration leads to more
or less concentration in industry sectors, that is, whether
it imposes a higher or lower barrier to entry, is a priori
ambiguous. On the one hand, one could argue, according to the empirical evidence shown above, that a
more “monopolistic” bank may enhance the growth
of firms in earlier stages. Later on, as the sector matures, it may favor lending to the now incumbent
firms over potential new entrants, a rationale that
would be consistent with Petersen and Rajan (1995).
In fact, what drove their monopolistic bank to finance
the young firms in the first place was the opportunity
to “participate,” via rent extraction, in the future stream
of profits when firms became established. The entry
of new firms at more mature stages, by increasing
market competition, would undermine the profitability of the incumbents. Hence, the bank might have an
incentive to constrain the access to credit of new firms
in more mature sectors. A second, separate argument
would maintain that managers of banks in concentrated markets might have very close relationships
with incumbent clients (for example, through membership of client companies’ boards of directors and
resulting participation in their management) and
might be led by strategic decisions, not necessarily
related to the bank’s own profit maximization, to
support these incumbents at the expense of prospective entrants. Either argument, therefore, suggests
that bank concentration should lead to increasing
concentration in industry sectors.
On the other hand, one could also argue that banks’
ultimate goal of profit maximization could lead banks
to continuously favor new entrants that, endowed with
higher return projects and more innovative technologies, would guarantee higher bank profits. In this case,
bank concentration should preserve unconcentrated industries, not contribute to the formation of large firms
with significant market power.
Cetorelli (2001) analyzes this issue, using a data
set comprising 35 manufacturing industries from 17
OECD countries. He finds that the average size of

2Q/2001, Economic Perspectives

firms in sectors highly dependent on external finance
is indeed larger in countries with a more concentrated
banking industry. Following Rajan and Zingales
(1998) and Cetorelli and Gambera (2001), Cetorelli
(2001) exploits industry variation along the dimension
of external financial dependence to establish the empirical result: Whether bank concentration has a positive or negative effect on industry concentration, the
effect should be especially strong on sectors that are
relatively more dependent on bank finance. Therefore,
Cetorelli (2001) examines whether industry concentration in sectors highly dependent on external finance is disproportionately higher or lower in
countries whose banking market is more concentrated.
The study makes a more sophisticated use of sectorspecific information. Since the theoretical underpinnings suggest that bank concentration may play a role
in industry market structure by favoring or not favoring clients with whom the banks already have longterm relationships (industry incumbents), one would
expect to see an effect in those industry sectors whose
mature firms are more dependent on external finance. If the effect is negative, it would suggest that
even in sectors where mature firms are especially dependent on external finance, banks still allow entry of
new firms, thus reducing the concentration of market
shares among old incumbents. If the effect is positive,
this would suggest that bank concentration enhances
concentration in industry sectors.
A qualitative analysis of the effect of bank concentration on firm size is presented in tables 1 and 2.
Table 1 reports mean values of average firm size, calculated for the data set of 35 manufacturing sectors
in the 17 countries used by Cetorelli (2001). The measure of average firm size is the ratio of total value
added of sector j in country k with the total number
of establishments in the same sector and the same
country. “Low” and “high” dependence refers to sectors, respectively, below and above the median in the
distribution of external financial dependence. Similarly,
low and high bank concentration refers to countries
with a level of bank concentration, respectively, below
and above the median of the cross-country distribution.
Therefore, the table indicates, for example, that the
mean of firm size of low dependent sectors in countries with relatively low bank concentration is $24.59
million, while the mean of firm size of the same sectors in countries characterized by high bank concentration is $12.3 million.
The numbers in the table allow me to make three
main observations. First, low bank concentration
countries have firms of larger size across all sectors
(24.59 > 12.30 and 6.75 > 5.16). This indiscriminate

Federal Reserve Bank of Chicago

TABLE 1

Firm size of high- and low-dependence
sectors in high and low bank
concentration countries
Low bank
concentration

High bank
concentration

(- - - - - - dollars in millions - - - - - -)
Low external
dependence

+24.59

+12.30

High external
dependence

+6.75

+5.16

Notes: Low external financial dependence sectors are
below the median of the external financial dependence
distribution. High external financial dependence sectors
are above the median of the external financial dependence
distribution. Similarly, low bank concentration countries have
a bank concentration measure below the median, while high
concentration countries have a concentration measure
above the median. The numbers in the table are mean
values across sectors of average firm size for each of the
four clusters.

effect of bank concentration, rather than being due
to the specific functioning of the banking market, is
likely to be the result of a country fixed effect, that is,
a characteristic common across all industries in the
same country that affects both bank concentration
and firm size. In particular, bank concentration is
typically inversely related to the size of a country,
as proxied by total employment, total population, or
total income. Larger countries, in other words, have
smaller bank concentration. The numbers in the table
indicate that firms are larger in larger countries.7
Second, low-dependence sectors have larger firms
across all countries, regardless of bank concentration
(24.59 > 6.75 and 12.30 > 5.16). This effect, which
is confirmed by Kumar, Rajan, and Zingales (1999),
is likely to be due to industry fixed effects, that is,
industry-specific technological factors that carry across
countries. There is not an obvious prior to explain why
sectors that are more dependent on external finance
should have smaller firms. One possibility is that this
result may indicate the indirect effect of financial constraints on firm size, assuming that there is not a
strong correlation between bank concentration and the
extent of financial constraints: Harder access to sources of finance should restrict the growth of existing
firms, and this should particularly affect the sectors
that rely more heavily on external sources of funding.8
My third observation is about the specific effect
of bank concentration. As I mentioned earlier, whatever the effect of bank concentration on firm size, I
expect it should be especially strong in sectors that
are highly dependent on external finance. Although
low-dependence sectors have larger firms, notice that

45

in countries with lower bank concentration, firms in
low-dependence sectors are about four times as large
as those in high-dependence sectors æç

24.59

è 6.75

ö
= 3.64÷ .
ø

TABLE 2

Residual firm size net of industry and
country fixed effects

However, in countries characterized by high bank
concentration, firms in low-dependence sectors are
only 2.5 times as large

æ 12.30
ç
è 5.16

ö
= 2.4÷ . These numbers
ø

are consistent with the argument that bank concentration contributes to increased firm size in industry
sectors that are more bank dependent, relative to less
dependent sectors.
I refine the analysis based on table 1 by attempting to purge the measure of firm size by industry- and
country-specific factors. First, I regress average firm
size on industry and country dummy variables. The
series of residuals of the regression is a “cleaner”
measure of sectoral firm size. The resulting numbers
indicating firm size will be either positive or negative.
A positive number shows that a certain sector in a
certain country has firm size in excess of what its industry and country factors would indicate. Vice versa,
negative numbers indicate sectors with firm size smaller
than is accounted for by industry and country factors.
Table 2 reports the mean value of the residuals for
sectors below and above the median in external financial dependence in countries below and above the
median in bank concentration. The first two observations I made regarding table 1 do not apply here, since
any industry- or country-specific effect has been
flushed out. In particular, it is no longer true that
firms in countries with low bank concentration are
larger regardless of the level of external dependence
and that firms in low-dependence sectors are larger
regardless of the level of bank concentration in a
country. What about the specific effect of bank concentration? Note that in a country with low bank concentration, firms in low-dependence sectors are larger.
Such firms have positive residuals on average, while
high-dependence sector firms in the same countries
have negative residuals. However, the pattern is exactly reversed when we move to countries with high
bank concentration. In other words, we might formulate
the following artificial experiment: What happens to
firm size across sectors if a country increases bank
concentration? The firm size of the most dependent
sectors (again, those most affected by banks) goes from
being below average to well above average. This confirms a positive effect of bank concentration on the
firm size of highly dependent sectors.
Of course, these results are only suggestive. More
convincing evidence would require a full-fledged
econometric analysis, taking into account possible

46

Low bank
concentration

High bank
concentration

Low external
dependence

+2.69

–2.99

High external
dependence

–2.47

+2.78

Notes: Low and high external financial dependence sectors
and low and high bank concentration countries are as
defined in table 1. The numbers in the table are mean
values, calculated for each of the four clusters, of the
residuals of a regression of average firm size on industry
and country dummies.

alternative explanations of this finding and testing for
robustness. A complete investigation is found in
Cetorelli (2001).
Conclusion
This article presents an overview of the latest
research on the economic role of bank competition.
Contrary to the received wisdom that competition in
the banking industry is necessarily welfare-enhancing,
recent research has identified possible channels through
which bank competition may generate negative economic effects. The main conclusion from the reading
of the current body of research is that neither extreme—
monopoly or perfect competition—may be the most
desirable market structure for the banking sector. In
advocating policies affecting the degree of bank competition, the regulator faces a tradeoff. While more
competition is likely to lead to a larger quantity of
credit, more market power should increase banks’ incentives to produce information on prospective borrowers, thus leading to a higher quality of the applicant
pool. Another related lesson to learn from this intellectual debate is that, in analyzing the role of bank
competition, we should not restrict the investigation
to its impact on the credit market, but rather support
a broader approach which takes into account the fact
that specific characteristics of the banking industry,
such as its market structure, also affect various dimensions of other sectors of the economy. Examples of
such interactions are the heterogenous effect on the
growth potential and market structure of other industry
sectors. Hence, the regulation of the banking industry
has potentially important effects on the conduct of
firms in other industries. For example, banking market structure may affect pricing strategies and incentives to innovation in other industry sectors.

2Q/2001, Economic Perspectives

NOTES
The title of this article is taken from that of a conference cosponsored by The Wharton School of the University of Pennsylvania
and by the Centre for Financial Studies of the Goethe University
in Frankfurt, held in Frankfurt, Germany on April 7–8, 2000. The
main goal of the conference, in the words of the organizers, was
to develop a debate on whether bank competition should be seen
as socially desirable. The conference program and papers are
available on the Internet at http://fic.wharton.upenn.edu/fic/wfic/
frankfurt2000.html.

1

As recognized by Bhattacharya and Thakor (1993), “bank loans
are special in that they signal quality in a way that other forms of
credit do not” (p. 3).

2

Fischer (2000) provides evidence from German data that, in
more concentrated markets, banks produce more information in
their lending activity.

3

Note that in this analysis I have not even mentioned the potential
effect of banking market structure on systemic risk and overall
financial fragility. Hellman, Murdock, and Stiglitz (2000), for

4

example, show theoretically that increases in competition, as determined by financial market liberalization, lower profits. Lower
profits reduce banks’ franchise value, and lower franchise value
encourages banks to take more risk.
In this respect, one can interpret market power as an implicit
equity stake that the bank has in the firm it is financing.
5

This cross-country indicator was put together by Barth, Caprio,
and Levine (2000).
6

The simple correlation between firm size and total income in the
data set used in Cetorelli (2001) is +0.13 and highly significant.
The correlation between bank concentration and total income is
–0.73, also highly significant.
7

8
But it could also work in the other direction: Financial constraints
may impede entry of prospective new firms. The argument above
would suggest that the first effect dominates the second one (this
argument is also in Kumar, Rajan, and Zingales, 1999).

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2Q/2001, Economic Perspectives