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E C O N O M I C
1 9 8 7
Can Services Be a Source o f Export-Led
Growth? Evidence From the Fourth
District. Major structural changes in the U.S.
labor market during the past few years have
resulted in a dramatic increase in the number
of service jobs— and in speculation about
whether service exports can sustain a
regional economy. Economist Erica L. Groshen
examines this question in the context of
economic activity in the four largest metro
politan statistical areas (MSAs) in the Fourth
Federal Reserve District.
2
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IS S N 0013-0281
Can Services Be a Source
of Export-Led Growth?
Evidence From the
Fourth District
by Erica L. Groshen
Erica L . Groshen is an economist at
the Federal Reserve Bank of Cleve
land. The author would like to thank
Patricia Beeson, Randall Eberts, Lorie
Jackson, and Owen Flumpage for
helpful comments, and Ralph Day for
expert programming.
Introduction
The U.S. labor market is currently undergoing dra
matic structural change as service jobs rapidly re
place manufacturing jobs. In I960, manufacturing
jobs clearly dominated the labor force, claiming
42 percent of total employment, compared with
11 percent for the service sector. Today, servicesector jobs (not including trade or transportation)
claim 23 percent of employment, roughly the
same percentage as manufacturing jobs.
The change in employment com
position within cities in the Fourth Federal
Reserve District is even more pronounced. As this
trend continues both locally and nationally, it is
important to know whether services can sustain
an economy in the same way manufacturing has
done. More specifically, can the service sector
pull new dollars into the local economy by
exporting services?
Interest in the exportability of serv
ices stems from the widely held view that the
vigor of regional and national economies is
linked to the health of their export sectors. Trade
among regions of a country plays much the same
role in regional health as does international trade
in the growth of national economies. When
viewed within this export-base model of regional
growth, the relative decline of manufacturing
employment raises several issues related to the
prospects and process of future regional growth.
Is the export base vanishing, reducing the poten
tial for further regional growth? Are there other
sectors that could be transformed into part of a
regional export base?
This paper explores the exportabil
ity of services in order to address these questions.
First, the service sector and exportation methods
are described, particularly for those service indus
tries most likely to be exported directly. Employ
ment in service industries, particularly business
services, is growing faster than employment in
most other sectors of the economy, and faster in
the Fourth District than in the U.S. Two possible
explanations for this growth suggest that trade in
services may increase: services may be exported
directly to consumers out of the region, or they
may be exported indirectly, embodied in
exported manufactured goods. Differences in
consumption of services among cities are not part
of the export base, while direct exports are.
Service-sector export activity can be
measured indirectly by estimating the variation
across the U.S. in the relative concentration of
service employment in local economies. Under
various assumptions discussed below, large varia
tions in the location quotients of a service activity
across cities can be indicative of trade across
areas. This technique allows identification of
highly traded service industries and offers evi
dence of strengths and weaknesses in individual
service industries in the four largest MSAs (met
ropolitan statistical areas) in the Fourth District:
Cincinnati, Cleveland, Columbus, and Pittsburgh.
I. What Are Services and How and Why
Are They Exported?
Kendrick (1986) states that “...the distinguishing
characteristic of service-producing versus goodsproducing industries is that service outputs are in
tangible and cannot be stored.” Although this defi
nition encompasses many more economic
activities than those usually classified as services,
it captures the essence of what services have in
common. In the discussion that follows, the term
“services” refers specifically to the aggregate of
lodging places, personal services, business serv
ices, health services and hospitals, repair services,
recreational services, legal services, educational
services and schools, engineering services,
accounting services, and social services. These
comprise standard industrial classification (SIC)
codes 70 through 89.
Some discussions of the service
sector include many or all of the other industries
that are commonly considered to comprise the
“service-producing” sector: communication; utili
ties; finance, insurance, and real estate; wholesale
and retail trade; and administration. This paper
takes a narrower definition of services for two
reasons. First, most of the growth in the serviceproducing sector of late has been in the narrower
class of services, particularly in business services,
which seem particularly amenable to export activ
ity. Second, the data that were available for this
study cover only this portion of the service sector.
This paper concentrates on profes
sional and business services (also called the
“producer” services), which together account for
more than a third of employment in services. The
professional services include legal, accounting,
engineering, and educational services. Business
services include services normally rendered to
places of business rather than to final consumers,
and comprise the following: advertising; services
to buildings; computer and data processing serv
ices; management, consulting, and public rela
tions services; equipment rental and leasing;
credit reporting and collection agencies; direct
mail advertising services; blueprinting and photo
copying services; commercial photography, art,
and graphics; stenographic and duplicating serv
ices; personnel supply agencies; and commercial
research and development.
As is evident from this list, these
activities often require a face-to-face meeting, or
at least telephone contact, between supplier and
consumer. In many cases they are done at the
behest or on the premises of the consumer, so
that the services are not storable. Although these
features imply that services cannot be exported
by the same means as manufactured goods (for
example, shipping by rail or truck), they do not
eliminate the possibility of service-sector exports.
There are two ways to export services directly:
activities may be transported and sold to persons
outside the area, or individuals may travel to the
area to purchase services. Sometimes consultants
visit their clients; other times clients travel to
consultants. Data is transmitted to programmers
or to a distant mainframe computer. Construction
equipment is transported to leasors.
Establishing the possibility that serv
ices may be transported addresses only one side
of the issue: the necessary condition. The other
side of the question is, why would they be traded?
The export-base growth model, the simplest
explanation for the existence of regional trade, is
based on production economies of scale. If largescale production reduces average production cost
for some products, the minimum efficient scale
(MES) may exceed the needs of the surrounding
community. Then, welfare of all the communities
will be maximized by specialization and trade
among communities. Each community produces a
subset of the products with scale economies, and
this becomes their export base. The communities
use proceeds from exports to import goods that
are not produced locally. Regional growth is the
result of expansion of the export base. Products
with no economies of scale (that is, in wiiich
MES is small relative to local demand) are pro
duced and consumed locally. The prevalence of
interregional and international trade in manufac
tured goods is assumed to stem from larger MES
in manufacturing than in service production.
This export-base growth model is
the source of the conventional view of the
service-producing sector as one that grows only
as a result of a healthy manufacturing sector and
that does not generate “new” income for an area.
The manufacturing sector, characterized by larger
firms, generates income for the area through the
sale of goods outside the region or country. Serv
ices, on the other hand, are provided by small
local companies, and merely recycle within the
local economy the income created by the manu
facturing sector.
This perception of the service sec
tor as dependent upon the manufacturing sector,
however, has changed recently. To the extent that
technological changes increase the MES of service
provision, we can expect increases in servicesector trade. And, although they are not storable,
services can be exported directly and conse
quently have the potential to spur local economic
expansion. The question centers on the extent to
which services are, and will be, traded relative to
the manufacturing sector.
3
II. Changing Industrial Composition
o f Employment in the U.S. and Ohio
This section begins with a description of recent
changes in the composition of employment in the
United States and in the Fourth District, focusing
particularly on employment growth in the services
and their components. The rest of the section
considers the reasons to expect growth in servicesector employment and to link growth with trade
in services.
Industrial Composition o f Employment in Ohio and the U.S.:
1970 and 1986
O h io
IVQ
United States
IVQ
IVQ
IVQ
SOURCE: U.S. Bureau o f Labor Statistics.
FI GURE
1
Figure 1 summarizes the changes
in industrial composition of the work force in
Ohio and in the nation. Overall, Ohio employ
ment since 1970 has become more similar to that
of the nation as a whole. Ohio entered the 1970s
with only 15 percent of its employment in the
service industries, compared to 17 percent for the
U.S. By 1986, Ohio had almost matched the
national figure of 23 percent of the labor force
employed in the service industries. Since 1970,
the number of jobs in U.S. service industries has
doubled. In contrast, the manufacturing indus
tries gained no jobs, so manufacturing’s share of
total U.S. employment fell from 28 to 19 percent.
In the U.S. and in Ohio, the number of service
jobs now almost equals or exceeds the number
of manufacturing jobs.
But the relative growth of services in
Ohio and in the major cities of the Fourth District
followed a different pattern than in the U.S. At
first, the region lagged behind national growth;
now it appears to be catching up. Table 1 sum
marizes the pattern of growth of employment in
the service industries in the U.S., in Ohio, and in
the four largest cities of the Fourth District.
The relative growth of services in
Ohio since 1970 came in two phases. Until 1983,
shrinkage of manufacturing employment in Ohio,
combined with modest (but below nationalaverage) service growth, led to increases in the
service industries’ share of employment. However,
since late 1983/early 1984, above-average growth
in business services in Ohio has led to growth
above the national average for the state’s service
industries as a whole. Even though the state’s
share of employment in the service industries
now nearly matches that of the U.S., Ohio’s 1986
rate of service-industry job creation of 7.1 percent
continues to exceed the U.S. rate of 5.6 percent.
Because manufacturing was heavily
concentrated in Ohio, the decline in manufactur
ing employment since 1970 was particularly dra
matic here: employment share shrank from 37 to
24 percent. This resulted from the net elimination
of almost a quarter of the state’s manufacturing
jobs and from the growth of other sectors, partic
ularly the service industries.
Where is this recent growth taking
place? Nationally, the two largest components of
services are health services and business services,
which together account for more than half of total
service-industry employment. Health services sup
plied 38 percent of the growth in services until
1982. Since then, it has supplied only 17 percent
of service-sector growth and has not increased its
share of national employment. In contrast, busi
ness services contributed 22 percent of service
growth until 1982 and 38 percent of growth since
then. Thus, although health services were an impor
tant source of service growth through the 1970s
and early 1980s, the mid-1980s have seen a rapid
expansion of employment in business services.
Ohio has consistently kept pace
with the growth of health services, maintaining an
edge over the U.S. in percent employed in that in
dustry. In contrast, throughout the 1970s and early
1980s, the state lagged the U.S. in the level and
growth of business service employment, but now
exceeds the national pace of expansion. In 1986,
the growth of business services in the state was
13-1 percent, compared to 8.5 percent for the U.S.
Patterns of growth vary somewhat
among MSAs within the Fourth District. Pittsburgh
and Cleveland have the largest proportion of
employment in the service sector. The strongest
similarity among the four MSAs is the widening
gap between their expansion in services and that
of the U.S. (which includes rural areas) since
1984. Because services tend to be concentrated in
urban areas, a city that only matches, instead of
exceeds, the national average in service employ
ment probably has a relative lack of services.
Summary o f Service Employment Growth, 1970-1986
Total
% of
19861
1986
Average Annual G row th Rate
1970-79
1980-84
1985
1986
United States
Total
Manufacturing
Services
Health
Business
100,167
19,186
23,072
6,586
4,809
100.0
19.2
23.0
6.6
4.8
2.7
1.0
4.5
5.6
6.3
1.1
-1.1
3-8
3.8
7.0
4.8
-0.2
6.1
3.5
10.6
4.2
0.2
5.6
4.4
8.5
Ohio
Total
Manufacturing
Services
Health
Business
4,475
1,109
999
344
192
100.0
24.8
22.3
7.7
-0.6
-2.8
2.2
5.2
0.0
6.4
4.3
1.6
-0.2
4.0
5.5
n.a.
3.5
5.8
3.9
11.8
5.0
-0.7
7.1
5.1
13-1
Cincinnati
Total
Manufacturing
Services
Health
Business
651
148
155
49
35
100.0
22.7
23.8
7.5
5.4
2.0
0.2
4.7
n.a.
n.a.
0.0
-2.7
2.9
n.a.
n.a.
6.5
3.5
7.2
1.7
12.5
6.2
0.1
8.5
4.6
14.0
Cleveland
Total
Manufacturing
Services
Health
Business
881
206
224
72
50
100.0
23.4
25.4
8.2
5.7
0.8
-0.8
3.2
n.a.
n.a.
-1.2
-4.3
0.9
n.a.
n.a.
4.0
-1.7
6.5
5.0
13.9
4.3
-0.7
7.0
6.1
8.9
Columbus
Total
Manufacturing
Services
Health
Business
630
106
146
38
32
100.0
16.8
23.2
6.0
5.1
2.6
0.2
5.5
n.a.
n.a.
0.7
-1.8
4.1
n.a.
n.a.
7.3
1.3
7.7
6.7
-0.1
6.3
7.9
9.3
5.1
15.3
Pittsburgh
Total
Manufacturing
Services
Health
Business
842
129
253
80
n.a.
100.0
15.3
30.0
9.5
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
-1.9
-8.1
3.6
3.5
n.a.
3.5
-4.8
4.7
4.1
n.a.
3-3
-6.7
4.5
2.7
n.a.
1. In thousands,
n.a.: not available.
SOURCE: Bureau o f Labor Statistics Employment and Earnings Reports.
In the four largest MSAs in the Dis
trict, business service growth has risen sharply
since 1984. In 1983, the proportion of people
employed in business services in Cincinnati,
Cleveland, and Columbus almost equaled the
national average. As of the end of 1986, all three
cities had 18 to 20 percent more employees than
the national average in this sector.
This recent growth in the service
industries (particularly in business services) has
been dramatic, and service employment growth
can be expected to continue to exceed manufac
turing growth for four reasons: increasing afflu
ence, increased standardization, lower delivery
costs, and technological changes that raise the
relative cost of small-scale internal provision.
In general, employment growth in
manufacturing has been limited by rapid produc
tivity increases, not by decline in demand for its
output. Measured in terms of its share of gross
national product (GNP), manufacturing has not
shrunk. This has meant greater affluence for con
sumers, who have spent an increasing portion of
6
their wealth on services due to a high income
elasticity of demand for services. Consumers who
are already affluent tend to spend disproportion
ately more of further increases in income on the
purchase of services, rather than on agricultural
products or manufactured goods. Beeson and
Bryan (1986) argue that just as increasing produc
tivity in nonmanufactured goods (for example,
agriculture) in the early twentieth century was
associated with a shift toward manufacturing in
consumption and employment, so an increase in
manufacturing productivity now leads to the serv
ice boom. The growth of services is a sign of our
increased affluence.
Certainly, the growth of health and
personal services fits the pattern of increased
affluence, but how does this explain the expan
sion of business services? Affluence may have
shifted consumption toward final products whose
component industries tend to use business serv
ices most. For instance, an increasingly litigious
society needs more legal photocopy shops for its
attorneys. Or, increased demand for differentiated
or luxury goods will raise demand for advertising
services, because luxuries and differentiated products are advertised more heavily than are essen
tials or standardized goods. While this explana
tion predicts growth in the service sector, it does
not predict increases in service-sector trade.
The other three explanations for
growth (standardization, falling delivery costs,
and technological change) have implications for
trade because they suggest that the production of
services is now increasingly subject to economies
of scale. That is, larger size may now enhance
efficiency in service provision.
First, management and other tech
nologies have become specialized and routinized
to the extent that there are new economies of
scale in consolidating them across company lines.
Stanback, et al. (1981) suggest that increased spe
cialization found in large firms leads to routinization of functions. Once routine, these functions
can be separated from other functions of manage
ment. Firms with consistent demand for the serv
ice may still provide it internally. Firms with
intermittent demand will purchase the service as
needed from vendors who specialize in its rou
tine provision.
To accelerate the process, informa
tion, transportation, and communication have
become less costly, reducing the necessity for
essential components of management to be
located near the scene of production, either geo
graphically or within the same firm.
Furthermore, technological changes
may have raised the relative cost of providing
intermittent or small amounts of services inter
nally. Business services provide a way to purchase
some portion of the services of an indivisible
technology7, or to meet peak loads (for example,
due to seasonal, cyclical, or unanticipated de
mand growth). The complexity or the larger effi
cient scale of new products used by businesses
could make it more economical to contract out
for services rather than provide them internally,
especially for small establishments. Examples are
the use of external computer time-sharing, data
processing, and photocopying services, as well as
the use of temporary personnel.
Taken together, these three points
suggest that net costs to separation of compo
nents of management and production processes
have fallen; that is, scale economies have risen in
business service provision. This may explain why
between 1975 and 1984 (according to County
Business Pattern Data), large (more than 100
employees) establishments’ share of employment
in business services rose from 44 to 49 percent.
In addition, the Census of Services notes that the
percentage of business service establishments
that were part of firms with three or more estab
lishments rose from 4.7 in 1972 to 11.4 in 1982.
An increase in the MES of service provision
makes services more similar to manufacturing. In
particular, it makes trade more likely.
Business service growth comprises
two elements: growth of employment because of
increased production of services, and increased
outsourcing of services formerly provided inter
nally (that is, transfer of employment to service
firms). During 1975 to 1984, while the size of
business service establishments grew, large
manufacturing establishments decreased their
employment share from 74 to 63 percent, and the
average size of manufacturing establishments
shrank from 60 to 55 employees. The decline in
the average size of manufacturing establishments
should increase demand for business services; of
course, outsourcing of business services may also
be a source of the decline.
Growth resulting from increased
demand for business services could be due to the
increased affluence previously mentioned, or to
increased productivity of services (assuming a
highly price-sensitive demand). Unfortunately,
the intangible nature of services makes it difficult
to measure productivity of these industries. Serv
ice output (work performed) is difficult to distin
guish from input (person-hours). Thus, usual
attempts to measure productivity changes have
detected only small or negative improvements in
the service sector. However, the purchasers of
services provide a clue to the direction of
changes in productivity. If management acts to
maximize profits, their purchase of a service (as
opposed to internal provision) indicates that they
consider the purchase to be the least-cost alterna
tive. It follows, then, that increasing demand may
be the strongest available evidence of increasing
productivity in business services.
The strength of service-sector em
ployment growth in the Fourth District may be
due to catch-up growth of locally consumed serv
ices or to establishment of service exports to non
local consumers. Although growth is encouraging
for the region in either case, only growth due to
exports adds to the economic base of the region.
III. The Regional Economic Base
and Measurement of Service Export Activity
Three explanations for service-sector growth sug
gest that many services may be increasingly ex
portable from one location to another. Thus, a
region (or city or country) could become a service
exporter as part of its economic base. Export activ
ity may be direct (sales of services across boun
daries) or indirect (sales of goods containing
embodied services across boundaries). Because of
the regional specialization of economic activity,
and because business services are purchased pri
marily as intermediate goods, differences in local
production of business services are related to dif
ferences in the regional concentration of other
industries, as well as to direct export activity.
If a region’s export industries are
intensive users of services, that region indirectly
exports services. But these are exports consistent
with the old view of services as a secondary, sup
porting sector, rather than as an independent part
of the economic base. Services that are primarily
exported indirectly do not attract “new” dollars
into the region directly, unless they attract other
producers by making the region more competitive.
Tliis is one reason for the concern that services
are not a viable part of an economic export base
for a region (see, for example, Cohen and Zysman
[1987] and Perna [1987]). For policy purposes,
identification of potential indirect exports may be
less relevant than identification of direct exports.
Alternatively, expansion in the mar
ket for business services outside the firm implies
the potential both for nonlocal provision of serv
ices, and for incentives for the formation of direct
service-exporting companies. In this case, services
are clearly part of the economic base of the area.
Services may be traded across city,
regional, or national boundaries. In 1981, the
United States was a net exporter of business serv
ices. The growing importance of international
trade in services has been recognized by the Con
ference Board, which recently issued a report
emphasizing the need for lower import restric
tions for services among our trade partners
(Basche [1986]).
Keil and Mack (1986) suggest that
a useful measure of the export potential of an
industry is the extent to which employment share
(relative to the national average) varies among
cities. Local employment share divided by
national share is called the location quotient. In
their framework, if location quotients for an
industry vary strongly among cities, the cities with
larger shares are probably exporting that industry
to cities (or, perhaps, countries) with smaller
quotients. Cities use the proceeds to purchase the
products of industries in which they have low
location quotients.
If export activity is heavy in an
industry, one would expect to see many cities
with very little employment in the industry, and
others with heavy concentrations of jobs in the
industry. If little export activity occurs, all cities
will have about the same percentage of employ
ment in the industry. Thus, a service industry with
a high variance of location quotients across areas
is likely to be an industry with trade activity and,
therefore, export potential. O f course, the prod
ucts of such an industry are also likely to be
imported (and, thus, to be a source of dollar out
flow) for many cities.
Exports per capita (based on size
of the city’s labor force) of industry in city
are defined as quantity produced
minus local consumption (
all in per capita
terms:
i
(X'j)
(1 )
Csp ,
v„
y„-c;„.
j
(Q,j),
Qi}
Keil and Mack measure
by em
ployment in industry as a proportion of total
employment in city divided by the industry aver
age employment share across the nation’s cities.
This is defined as the location quotient for indus
try in city (/.. ). Two assumptions are made.
First, labor productivity is constant across cities
and industries (that is,
for all
Second, all consumption patterns are con
stant across cities (that is,
=
for all
so:
(2)
=
Under these assumptions, we can
take the sample variance of each side for each
industry as follows:
(3)
=
If industry is characterized by a
high trade volume, some cities thus will have
high imports ( ^ < < 0 ) and others will have high
exports (X ;/» 0 ) . Therefore, the variance of the
for industry7 across cities will be high. On
the other hand, if little of an industry’s product is
traded, all
will be of similar size, so their var
iance across cities will be small for the industry.
If the two assumptions of identical
productivity and consumption patterns across
regions hold, variation in the relative size of the
labor force in industries across cities is directly
j
i
i,j).
i
j
Qjj/L,j = Q/L,
C{j Ct,
Xtj L'j . (Q/L) . Cr
i
s)(X tp
Xtj s
(Q/L)2
i
Xtj s
i
j),
7
Business Service Industry Consumption in 1981
All
Industries
Business
Services
Intermediate demand
Final demand
Personal consumption
Inventory and investment
Net exports
Government purchases
Total
52.4
32.8
8.9
0.6
10.1
100.0%
82.0%
18.0
8.6
0.0
1.5
7.8
100.0%
Industry
Share of
Final
Demand
Share of
of all
Industries
Total Output1
Share of Business
Services
Intermediate
Output2
Relative
Use of
Business
Services3
0.6
11.8
7.3
7.4
15.3
15.6
3-0
1.6
2.8
15.1
3.9
11.3
1.9
1.9
3.8
5.1
9.8
0.4
19.3
16.7
3.6
1.6
4.2
11.0
3.8
8.8
1.8
15.3
14.0
0.3
2.1*
0.7
0.6
0.8
0.9
0.2
2.0*
1.8*
0.5
1.2
1.5
0.9
1.2
1.2
1.2
100.0
100.0
Disposition of Total Receipts____________________
Oil, mining, agriculture,
ordnance and forestry
Construction
Nondurable manufacturing
Durable manufacturing
Transportation
Communication
Utilities
Wholesale and retail trade
Finance and insurance
Real estate
Hotels, personal and repair services
Business services
Eating and drinking places
Automobile and recreation services
Health and professional services
Government enterprises
Total
47.6%
1.3
5.3
2.5
2.0
5.1
0.5
10.3
2.9
1.5
0.7
21.9
7.0
3.8
1.5
8.1
2.2
2.3
5.9
0.6
100.0
1. Total measured output by enterprises in each industry7, including d ou ble counting due to use o f output as intermediate g o o d s by other
enterprises.
2. Total consum ption o f business services as an intermediate g o o d by each industry.
3. This is the num ber in the third colum n divided by the number in the second colum n. Numbers over 1.0 indicate greater-than-average
use o f business services as an intermediate g o o d ; numbers b e lo w 1.0 indicate less-than-average use o f business services.
‘ Industry with relative use greater than 1.7.
SOURCE: Planting, Mark A , “Input-Output Accounts o f the U.S. Econom y, 1981,” Survey’ o f Current Business, vol. 67, no. 1, January 1987.
TABLE
2
related to variation in trade activity among cities
for that industry. If the two assumptions do not
hold, the variations in the location quotients may
not be detecting trade activity. The remainder of
this section and the next section explore the
plausibility of these assumptions.
Differences in the relative cost of
local factors of production can lead to differences
in location quotients among areas. Because of
cost-minimizing substitution among inputs by
service providers, such differences cause variation
in the labor input even if quantity produced does
not vary. Conversely, if labor is more productive
in one industry than in another, variation in
employment will understate the relative value of
exports in the more productive industry. (How
ever, to the extent that the focus of the exercise is
to identify employment creation by export activ
ity, this bias is appropriate.) A related problem
arises if there are systematic biases in the way
labor input is measured. For example, industries
with more variation in their use of part-time labor
may appear to have more export activity by this
measure than one where the full-time/part-time
ratio is consistent among most employers.
It is difficult to account for regional
differences in service-sector productivity, because
service-sector output is not available. National
level estimates use income accounts, which are not
available for regions. As a first attempt to check
the plausibility of the equal productivity assump
tion, the results that follow (based on employ
ment) were compared to calculations based on
variation in receipts. Differences were negligible.
IV. Differences in Consumption of Services
Among Areas
Differences in regional concentration of services
may be related to consumption patterns of cities’
residents and businesses, rather than to direct
export activity. Most variations in personal serv
ices location quotients are probably due to varia
tion in city residents’ consumption. For example,
regional differences in the taste for hairdressing
or in climate could generate nonexport-based
variations in location quotients. For this reason,
the analysis below excludes personal services.
Controlling for variation due to
indirect exports (differences in consumption by
cities’ businesses) is more problematic. The
technique applied below does not distinguish
between direct and indirect export activity. How
ever, if the users of services are regionally
dispersed, indirect exports are likely to be a
smaller portion of total exports than if the users
tend to be concentrated geographically.
Table 2 presents national consump
tion patterns for business services in 1981, the
latest year available. The upper panel compares
the disposition of total output, as measured by
receipts, of all domestic industries to that of bus
iness services. On average, about half (47.6 per
cent) of the output of U.S. firms is purchased by
other firms as an input to their own production.
The other half is produced for final demand, pri
marily personal consumption (about 33 percent
of the total) and government purchases (10.1
percent of the total). Investment and inventory
changes consume another 8.9 percent, while net
exports were less than 1 percent of total output.
The pattern for business services is
markedly different. Intermediate demand con
sumes 82.0 percent of total output, with the
remainder fairly evenly split between personal
consumption and the government. In short, the
demand for business services is indeed primarily
a derived demand from that for other industries.
Thus, variation in the level of personal consump
tion is not likely to be a significant source of vari
ation in the provision of business services. It is
also interesting to note that the U.S. balance of
trade in business services, although small (only
1.5 percent of business service output), is better
than the average for U.S. industries.
The second panel of table 2 indi
cates which industries are the largest consumers
of business services. Two factors are important in
the level of consumption: the relative size of the
consuming industry and its relative use of busi
ness services. The first column of the table com
pares industries by their share of output consumed
as final demand. The second column shows each
industry’s share of total output, which includes
products sold to other firms as intermediate
goods. The third column lists the share of the
total output of business services consumed by
each industry.
Four industries emerge as heavy
(double-digit) users of business services: whole
sale and retail trade; construction; durable manu
facturing; and nondurable manufacturing. The
trade industries alone use almost 22 percent of
the output of business services, not only because
the sector is large, but also because the industries
have a high relative use of these services. Con
struction also combines the influences of large
industry and heavy use. In contrast, manufactur
ing firms are below-average users of business serv
ices. However, because of the size of the sector,
they consume 24 percent of the output of busi
ness service firms.
O f the four largest consumers of
business services, the top two (construction and
trade) are very regionally dispersed, one is some
what dispersed (nondurable manufacturing), and
one is fairly concentrated (durable manufactur
ing). Construction and wholesale and retail trade
have two features in common: seasonal demand
and small establishment sizes. Their prominence
is consistent with the hypothesis that business
services provide smoothing and scale economies
to their customers. Thus, by this cursory analysis,
the evidence is somewhat mixed. Some of the
regional variation in service employment no
doubt derives from regional variation in manufac
turing consumption. Nevertheless, much of the
regional concentration of services is probably due
to direct exports, and therefore is a viable part of
an economic export base.
V.
Signs of Service Export Activity by Industry
Which service industries are characterized by the
most trade activity? Unfortunately, statistics on the
service sector are not plentiful. However, in 1982
the U.S. Department of Commerce conducted an
economic census of the service sector. From that
snapshot of services in the U.S. and in Fourth Dis
trict cities, we can get some indication of our serv
ice industry strengths and weaknesses. Because
much of the most interesting growth in services
took place after 1982, the conclusions we can
reach about current strengths and export activity
from these data are at best limited. The data allow
identification of the baseline distribution of
industries. However, it is not clear whether sub
sequent growth took place in those industries the
region lacked or in those industries showing rela
tive strength.
Table 3 ranks three- and four-digit
hotel and producer service industries by the
standard deviation of employment location quo
tients across MSAs in 1982. The data used are
employment totals in taxable (that is, for-profit)
9
Service Industry Export Activity Among MSAs in 1982
Service Industry
10
Percent
of Total
Employment1
Export
Activity
Group2
0.06
0.02
1.26
0.06
0.03
0.02
0.61
0.04
0.08
0.05
0.03
0.53
0.03
0.09
0.23
0.55
0.19
0.14
0.55
0.86
0.08
0.47
0.25
0.83
0.81
High
High
M-High
M-High
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
M-Low
M-Low
M-Low
Low
Low
Low
Low
Low
Low
Research and development laboratories
Schools and educational services, not elsewhere classified
Hotels, motels, and lodging places
Direct mail advertising services
Surveying services
Interior designing
Engineering services
Testing laboratories and facilities
Commercial photography, art and graphics
Correspondence and vocational schools
Stenographic and reproduction services
Commercial sports and recreation
Blueprinting and photocopying
Photographic finishing labs
Advertising services
Computer and data processing services
Equipment rental and leasing services
Architectural services
Management and public relations consulting
Personnel supply services
Credit reporting and collection agencies
Detective agencies and protection services
Other repair shops and related services
Legal services
Services to dwellings and other buildings
Electrical and electronic repair shops
Accounting, audit, and bookkeeping services
0.13
0.47
Standard Deviation
of Location
Quotients
0.192
0.161
0.106
0.100
0.076
0.075
0.074
0.073
0.071
0.066
0.065
0.063
0.061
0.059
0.056
0.056
0.054
0.052
0.045
0.042
0.038
0.031
0.028
0.028
0.026
0.023
0.018
1. Industry em ploym ent as a percent o f total em ploym ent in all industries in all U.S. MSAs.
2. Export activity group is a som ewhat arbitrary grouping o f the industries on the basis o f standard deviation o f the location quotients in
that industry' across all U.S. MSAs (sh ow n in the last tw o colum ns). The follow ing service industries w ere excluded from this analysis: trailering parks and camps; all health services; bow ling alleys and billiards and p o o l establishments; telephone answering services; other serv
ices; photographic portrait studios; funeral services and crematories; automotive sendees; autom obile parking; all personal services; reup
holstery and furniture repair; other health services; other business services, not elsewhere classified; auto rental and leasing without
drivers.
SOURCES: U.S. Department o f Com m erce, Census o f Service Industries 1982 (service industry em ploym ent by city); Department o f C om
m erce County Business Patterns 1982 (total em ploym ent by city).
TABLE
3
establishments by industry and MSA from the 1982
U.S. Department of Commerce Census of Services.
Personal services are excluded from the analysis
because variations are probably due primarily to
variations in demographic characteristics. Health
services are excluded because these data omit the
tax-exempt sector and hospitals, which are both
important employers in health services.
The industries are grouped from
high evidence of export activity to low export
activity, based on the standard deviation of loca
tion quotients for the industry. At the top of the
list are research and development laboratories
and private technical schools, reflecting the
inherent exportability of knowledge (although
the variation in research and development
employment may be due to indirect exports).
The large variance in employment by hotels,
motels, and other lodging places among MSAs
reflects the variation in the extent of tourism and
convention activity among cities. On the other
hand, at the bottom of the list are legal services
and accounting, audit, and bookkeeping services.
Most of the output of these two industries is
probably consumed locally.
For comparison, table 4 presents
standard deviations for two-digit manufacturing
industries. The higher level of aggregation in
these data should tend to reduce variation. Never
theless, the standard deviations of location quo
tients among MSAs of these industries are, in
general, higher than those of the service indus
tries. Application of the same export activity
groupings used in table 3 puts more than half (10
of 19) of the manufacturing industries in the high
or moderately high categories, compared to four
of the 27 service industries included. There is,
however, considerable overlap in the ranges
covered by the standard deviations in services
and manufacturing.
For example, three service indus
tries (R&D labs, schools not elsewhere classified,
and hotels) show more evidence of export activ
ity to other regions than does primary metals.
Thus, while the more traditional view of manufac
turing as inherently export industries and services
as inherently local industries has some validity, a
subset of service industries is at least as geo
graphically concentrated as the bulk of manufac
turing industries.
The pattern of export activity
across MSAs in the District shows no strong con
sistency, except perhaps for the lack of concentra
tion in the most heavily traded industries. Each
city has a unique pattern of strengths, which is to
be expected if the proximity of cities increases
the probability that they trade heavily with one
another. Cleveland’s concentration in private, forprofit schools and educational services, not else
where classified, is the only entry from the high
or moderately high exportability groups. A few
industries appear more than once. For instance,
personnel supply services is prominent in three
of the four cities. Management and public rela
tions consulting; detective and protection agen-
Manufacturing Industry Export Activity Among MSAs in 1982
Manufacturing Industry (SIC)
Percent
of Total
Employment1
Export
Activity
Group2
0.12
High
High
High
High
High
High
M-High
M-High
M-High
M-High
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
M-Low
Low
Petroleum and coal (29)
Textile mill products (22)
Leather and leather products (31)
Furniture and fixtures (25)
Lumber and wood products (24)
Instruments and related products (38)
Primary metals (33)
Miscellaneous manufacturing (39)
Paper and allied products (26)
Transportation equipment (37)
Apparel and other textile products (23)
Stone, clay, and glass products (32)
Chemical and allied products (28)
Rubber and miscellaneous plastics (30)
Electrical and electronic equipment (36)
Food and kindred products (20)
Nonelectrical machinery (35)
Fabricated metal products (34)
Printing and publishing (27)
0.53
0.15
0.36
0.29
0.75
0.85
0.42
0.56
1.43
1.17
0.49
0.99
0.69
2.31
1.63
2.63
1.87
1.68
Standard Deviation
of Location
Quotients
0.250
0.237
0.213
0.151
0.145
0.131
0.105
0.101
0.094
0.088
0.082
0.078
0.078
0.061
0.055
0.050
0.049
0.042
0.033
1. Industry em ploym ent as a percent o f total em ploym ent in all U.S. MSAs.
2. Export activity group is relative to the service industry standard deviations, as defined in the text and in table 3SOURCES: U.S. Department o f Com m erce, Census o f Manufactures 1982 (manufacturing industry em ploym ent by city); Department o f
C om m erce County Business Patterns 1982 (total em ploym ent by city).
TABLE
4
VI. Exportability and the Strengths
and Weaknesses of the Service Sector
in the District
Does the Fourth District export services? This sec
tion examines evidence on the extent of concen
tration of Fourth District MSAs in export-intensive
service industries.
Table 5 shows the industries in
which the four major MSAs in the District were
particularly strong. Also listed are the exportabil
ity group of each industry and the estimated
number of jobs over the national average.
cies; and accounting, audit, and bookkeeping
services each appear twice. These are the only
cases of repetition.
Personnel supply services consists
of two main components: employment agencies
and temporary help suppliers. From 1982 to 1984,
temporary employment was the fastest-growing
industry, with more than 50,000 employees in the
U.S. (see Carey and Hazelbaker [1986]). Although
many of the jobs are low-skill (laborer and cleri
cal positions), there are two high-skill sectors of
the market: engineering and technical.
Fourth District Service Industry Employment Surpluses by MSA in 1982
Export Activity
_______ Group1_______
Employment
_______ Surplus2
Cincinnati
Commercial photography, art and graphics
Commercial sports and recreation
Photographic finishing labs
Management and public relations consulting
Personnel supply services
Services to dwellings and other buildings
Moderate
Moderate
Moderate
M-Low
M-Low
Low
220
230
270
300
1,550
570
High
Moderate
M-Low
Low
Low
Low
230
220
1,150
860
550
610
Moderate
M-Low
M-Low
Low
Low
400
1,960
1,330
540
250
Moderate
Moderate
M-Low
Low
5,810
370
1,010
840
Cleveland
Schools and educational services, not elsewhere classified
Stenographic and reproduction services
Personnel supply services
Detective agencies and protection services
Other repair shops and related services
Accounting, audit, and bookkeeping services
Columbus
Architectural services
Management and public relations consulting
Credit reporting and collection agencies
Other repair shops and related services
Services to dwellings and other buildings
Pittsburgh
Engineering services
Testing laboratories and facilities
Personnel supply services
Detective agencies and protection services
1. Export activity group is a grouping o f industries by the standard deviation o f the location quotients in that industry across all U.S. MSAs.
See table 1 and text for explanation.
2. Surpluses are rounded to the nearest 10. Only industries with em ploym ent surpluses o f m ore than 200 are included.
SOURCES: U.S. Department o f Com m erce, Census o f Service Industries 1982 (service industry em ploym ent by city); Department o f C om
m erce County Business Patterns 1982 (total em ploym ent by city).
TABLE
5
Temporary work affords workers
an opportunity for flexible schedules and exper
imentation with positions. It allows employers to
adjust to temporary employment needs due to
seasonal or cyclical fluctuations, to employee
absences, or to demand shifts of dubious per
manence. The size of the industry in these Fourth
District cities may indicate that local employers
were more hesitant about adding permanent per
sonnel than were others nationally. On the other
hand, it may have signaled the beginning of
growth: that is, as an indication of positions soon
to be added to permanent staff.
If the prominence of personnel
supply services results from the high-skill sectors,
it may signal that the engineering and technical
schools in Cleveland, Columbus, and Pittsburgh
produce a concentration of technically skilled
people who export some of their services to
areas without such schools.
The most striking entry among the
surpluses is engineering services in Pittsburgh;
these exports generate about 5,800 jobs for the
city’s economy, over and above the jobs
demanded for the local economy. Also of interest
is the concentration by some of the cities in
industries that are not, in general, characterized
by export activity. In particular, Cleveland and
Columbus show evidence of concentration in
accounting, audit, and bookkeeping services,
although this industry ranks the lowest in signs of
export activity of all 27 industries analyzed. Per
haps this signals the beginning of a trend toward
trade in these industries.
Patterns of consistency across cities
are much stronger in the region’s service
employment deficits. Table 6 shows the indus
tries in which the four MSAs were apparently net
importers. In general, these four large cities
import legal, research, hotel, computing, and
engineering services. R&D labs and legal services
both employ significantly fewer people than the
national average in all four major Fourth District
MSAs. The following industries appear three
times on the lists: engineering services; computer
Fourth District Service Industry Employment Deficits by MSA in 1982
Export Activity
_______ Group1_______
Employment
_______ Deficit2
Cincinnati
Research and development laboratories
Hotels, motels, and lodging places
Engineering services
Computer and data processing services
Architectural services
Legal services
Accounting, audit, and bookkeeping services
High
M-High
Moderate
Moderate
Moderate
Low
Low
240
680
740
610
280
1,700
410
High
M-High
Moderate
Moderate
Moderate
M-Low
Low
Low
220
3,080
490
1,280
300
990
550
990
High
Moderate
Moderate
M-Low
Low
Low
240
490
480
490
1,840
360
High
M-High
Moderate
Moderate
Moderate
Moderate
M-Low
Low
Low
Low
Low
480
3,070
1,210
1,440
440
420
350
200
1,800
1,000
400
Cleveland
Research and development laboratories
Hotels, motels, and lodging places
Engineering services
Computer and data processing services
Equipment rental and leasing services
Management and public relations consulting
Legal services
Services to dwellings and other buildings
Columbus
Research and development laboratories
Engineering services
Commercial sports and recreation
Personnel supply services
Detective agencies and protection services
Legal services
Pittsburgh
Research and development laboratories
Hotels, motels, and lodging places
Commercial sports and recreation
Computer and data processing services
Equipment rental and leasing services
Architectural services
Management and public relations consulting
Other repair shops and related services
Legal services
Services to dwellings and other buildings
Accounting, audit, and bookkeeping services
1. Export activity group is a grouping o f industries by the standard deviation o f the location quotients in that industry across all U.S. MSAs.
See table 1 and text for explanation.
2. Deficits are rounded to the nearest 10. Only industries with em ploym ent deficits o f m ore than 200 are included.
SOURCES: U.S. Department o f Com m erce, Census o f Service Industries 1982 (service industry em ploym ent by city); Department o f C om
merce County Business Patterns 1982 (total em ploym ent by city).
TABLE
6
and data processing services; and hotels, motels,
and other lodging places.
Hotels, in particular, stand out as a
major deficit in Cleveland and Pittsburgh. This
suggests that these cities “import” conventions
and tourism; that is, people leave these cities to
vacation or to attend conventions. The lack of
local engineering services employment in Cin
cinnati, Cleveland, and Columbus may be due to
imports of those services from Pittsburgh. The
regional deficits in computer and data processing
services employment suggest heavy importation
of these services or slowness to begin using them
(that is, deficient local demand), as of 1982. This
deficit is particularly troubling because between
1974 and 1984, employment nationwide in this
industry grew by 250 percent.
VII.
Conclusion
The major points of this paper may be summarized
as follows:
1.
The composition of employ
ment in the United States and in the Fourth Dis-
14
trict is shifting toward services. The Fourth Dis
trict currently exceeds the nation in the growth of
services as a whole and in the fast-growing busi
ness services.
2. Increased minimum efficient
scale (MES) for the provision of producer serv
ices may be a basic reason for their growth. This
implies that trade in services may increase,
although as of 1982, there was apparently less
trade in producer services than in manufacturing.
Services, to the extent that they are exported
directly to consumers outside a region, are viable
members of the regional economic base.
3. In the producer services (in
1982), the four largest cities in the Fourth District
each specialized in a different combination of
services; only personnel supply services was an
industry of concentration for more than two cit
ies. The largest concentration was engineering
services in Pittsburgh, which generated about
5,800 extra jobs.
4. Fourth District import patterns
were more consistent across cities; employment
deficits were pronounced in legal, research,
hotel, computing, and engineering services for at
least three cities out of four.
This information is particularly
relevant to the Fourth District because of the
recent national and regional decline in manufac
turing employment. Can we expect the service
industries to replace lost manufacturing dollars? If
economies of scale rise in the services, interre
gional and international trade in services should
continue to grow. There is no reason to expect
dollars drawn into a region by services sales to
have a smaller impact on wealth than dollars
earned through manufacturing activity (assuming
that income earned from service firms is spent
similarly to that from manufacturing firms). The
recent growth in services in the Fourth District
suggests that they may be able to replace some of
the lost manufacturing dollars, but it is unclear
just how much replacement any region, and the
Fourth District in particular, can expect.
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16
Introduction
Many studies have explored the existence of
nominal wage differentials between regions. The
irrefutable conclusion is that wage differentials
exist and that they persist over time.1 Such differ
entials are difficult to explain within a neoclassi
cal framework in which regions and factors are
identical and all factors are free to move in
response to interregional factor price differentials.
In this case, one must resort to explanations
based on institutional barriers and other impedi
ments to free mobility.
The key to understanding how
wage differentials (and other factor price differen
tials) can persist in the presence of free mobility is
to recognize that some factors are inherently immo
bile. For instance, each region has geographic
and climatic characteristics that are unique to the
area. Even for those areas that share common fea
tures, the quality and quantity of the site-specific
characteristics may differ. Therefore, firms or
households will be willing to pay or accept dif
ferent levels of wages depending upon the value
they place on these attributes.
For instance, firms may find that
proximity to improved harbors reduces shipping
costs and thus reduces production costs. In this
case, firms can offer higher wages and still remain
competitive with firms in lower-wage regions be
cause of the cost advantage of the harbor. Since
I
Bellante (19 79 ), Johnson (1983), and Eberts and Stone (1986) are
examples of numerous studies that have examined interregional
wage differentials.
land next to the harbor is limited, the influx of
firms attracted by the harbor will increase the de
mand for both labor and land. Wages and rents
will be bid up until the cost advantage of the
harbor is completely offset by the increase in fac
tor prices. Thus, wages and land rents vary across
regions according to the value firms place on the
site-specific attributes in each region and their
ability to substitute between factors of production.
A similar story can be told about
households. Households may value the same
harbor that firms find attractive, except for differ
ent reasons. The harbor that reduced shipping
costs for firms may be attractive to households as
a place to enjoy water sports. Consequently, as
more households move into the area to take
advantage of the harbor, the supply of labor
increases and the demand for land increases.
Thus, wages fall and land rents rise until individ
uals are no longer willing to accept proximity to
the harbor as compensation for lower wages and
higher land rents.
The resultant wage differential
between an area with a harbor and one without
depends upon the relative magnitudes of the
demand and supply responses to site characteris
tics. If wages are observed to be higher in the
harbor area than in the area without a harbor,
then the demand response (the firm’s response)
dominates the wage determination process. If
wages are relatively lower in the harbor area, then
the supply response (the household’s response)
dominates the process. In both cases, land rents
will be higher because both households and
firms value the harbor. Land rents would be
lower in the harbor area than in an otherwise
comparable area if the harbor was detrimental to
both parties. Consequently, by observing relative
wages and rents, it is possible to identify whether
a region’s bundle of site characteristics has a
greater net effect on firm location decisions or
household location decisions.
The purpose of this paper is to
identify metropolitan areas according to the
extent to which they are dominated by supply
and demand responses to their net bundle of
site-specific characteristics. To do this, we esti
mate hedonic wage and rent equations for a
sample of metropolitan areas. From these esti
mates, we derive quality-adjusted wage and rent
differentials for each area. The metropolitan areas
are then classified into four groups based on the
relative values of an area’s wage and rent differen
tial vis-a-vis the national average. The metropoli
tan areas are identified as high amenity (low
wage, high rent), low amenity (high wage, low
rent), high productivity (high wage, high rent),
and low productivity (low wage, low rent). Classi
fication of this sort provides information about
the relative attractiveness to firms and households
of the total bundle of attributes indigenous to
each metropolitan area.
I. A Model of Household and Firm Equilibrium
In this section, we first present a model, based on
the work of Roback (1982), of the effects of inter
area differences in amenities and productivity on
wages and rents. We then show how this model
can be used to determine the relative importance
of amenity and productivity differences as sources
of factor price differentials across cities.
Several simplifying assumptions
are made in modeling the relationship between
interarea differences in amenities and productiv
ity and interarea differences in wages and rents.
Workers are assumed to be identical in tastes and
skills and completely mobile across cities. Sim
ilarly, capital is assumed to be completely mobile
and production technologies are assumed to be
identical across firms.2
2
If people have different preferences, the value of certain areas will
be understated in our approach, which uses a comparison of cost-
of-living differences as an indication of the value individuals place on cit
In this model, cities are character
ized as bundles of attributes, which can affect the
utility of households and the costs of production
for firms. Individuals in these cities consume and
produce a composite consumption good. The
price of the good is determined by international
markets and for convenience is normalized to
one. Each worker supplies a single unit of labor
independently of the wage rate. We assume that
individuals work in the city in which they live,
and we treat differences in leisure resulting from
differences in intracity commuting as a site char
acteristic.3 Equilibrium in this model is character
ized by equal utility for identical workers and
equal unit costs for firms across all regions.
Workers choose the location that
maximizes their utility, subject to an income con
straint. Utility depends upon consumption of the
composite commodity
residential land (Zc),
and amenities ( 5). Equivalently, the problem can
be stated in terms of an indirect utility function,
which is a function of wages (
rents ( r),
and amenities
Equilibrium for workers
requires that utility is the same at all locations, or
(1)
The equilibrium relationship between wages,
rents, and amenities for households can be
determined by totally differentiating the indirect
utility7function. In log form, this relationship can
be stated as:
(2)
+
0.
(X),
V,
(s).
w),
V(w,r;s)=V°.
dlnV d ln w + dlnV dlnr dlnV =
dlnw ds dlnr ds
ds
Using Roy’s identity, the marginal
valuation of amenities in a city evaluated relative
to the marginal utility of income is
(3)
-
Ps = k xdlnr/ds dlnw/ds,
W~
where Ps is the monetized value of the amenities,
and kx is the portion of consumer income spent
on land. Equation 3 states that individuals pay for
amenities through reductions in real income in
the form of higher land rents (which reduce
income by
times the increase in rents) and
lower wage income.
Firms are assumed to employ local
residents and to use land to produce the compos
ite commodity,
according to a constantreturns-to-scale production technology. Under
these assumptions, equilibrium for firms requires
that unit costs are equal in all locations and equal
to the price of
assumed to be 1,
(4)
1.
kx
X,
X,
C(w,r,s) =
ies (see Roback [1982]). The second set of assumptions refers to the
mobility of households and firms. W e assume that migration is costless
and that, given the relative wages, rents, and site characteristics across
cities, both firms and households have chosen locations such that they
could not be made better off by relocating. If moving is not costless, we
m ay have biased estimates of the attractiveness of areas. Individuals or
3
Roback's model ignores intracity commuting. Hoehn, et al. (1986)
have pointed out that this leads to incorrect estimates of the
value of other site characteristics. Since w e are not interested in deriv
firms m ay perceive that they would be better off by moving, but if it is
ing values for specific characteristics, but simply the net impact of these
costly to do so they will move only if the extra benefits of moving out
characteristics, our model is not subject to this criticism. W e therefore
weigh the costs of moving. W e may then be over- or underestimating
simply assume that intracity commuting is another site characteristic
the attractiveness of an area since w e ignore the costs of moving.
that reduces leisure time and therefore is a disamenity for workers.
17
The relationship between wages,
rents, and site characteristics (s), which are con
sistent with equilibrium for firms, can be
expressed in log form as:
(5)
+
+
= 0.
3
The marginal value to firms of different locations
is
(6)
)
where - is the price that firms are willing to pay
to locate in one city rather than another, and
and
are the cost shares of land and labor,
respectively.
If the site characteristics of a city
provide a net productivity advantage to firms,
then firms will pay for this advantage in terms of
higher wages and rents and
will be positive.
Wages and rents in each city are determined by
the interaction of the location decisions of the
households and firms.
dlnC dlnw dlnC dlnr
Inw ds dlnr ds
dlnC
ds
Cs - - 6r(dlnr/ds -6u,(dlnw/ds),
Cs
Qr
dw
-Cs
Inw
Inr
Combinations of
and
for
which the unit costs of firms are equal are depict
ed in figure lb. The value of site characteristics to
firms is fixed along each quasi-isocost curve, and
the curves shift up (down) as the site characteris
tics of a city increase (decrease) the productivity
of firms. The slope of the quasi-isocost curve is
equal to the elasticity of substitution between
land and labor, which from equation 6 is
According to figure lb, site characteristics in city
enhance productivity more than site character
istics in city S ,.
Each city is characterized by a
bundle of amenities and site characteristics that
are associated with a specific pair of isocost and
iso-utility curves in figures la and lb. The inter
section of any two curves for each city then
determines relative wages and rents. In figure 2,
equilibrium wages and rents in city Sj will be
and ]. Using city Sj as a reference point
- 0w/6 r.
S2
wl
r
Equilibrium Conditions for Households and Firms
1A
1B
SOURCE: Authors.
FIGURE
1
Classification of Cities as Amenity
or Productivity Cities
The model described above is illustrated in figure
1. The upward sloping curves in figure la [labeled
V(.)], show combinations of
and
for
which utility is equal. The slope of these curves is
the trade-off that households are willing to make
between wages and rents for any given level of
amenities. From equation 3, this trade-off is equal
to the inverse of the budget share of land,
[
Along each curve, the value of amenities is fixed
and the curves shift up (down) as the amenities
of one city are valued more (less) than the amen
ities of other cities. The value of amenities in the
city labeled
is greater than the value of ameni
ties in the city labeled
since individuals are
willing to pay higher rents at every wage rate.
Inw
Inr
k \
S2
(which could be thought of as the average city),
we can see how intercity differences in amenities
and productivity will be reflected in differences
in wages and rents.
Consider a city
that differs from
5, only in that the site characteristics of city
provide a greater productivity advantage to firms
than the site characteristics of city S,. In figure 2,
this is illustrated by
2) lying above C(5'1).
Assuming there is no difference in amenities
between the two cities, we can see that equilibrium
requires that wages and rents in city
be high
relative to city . These higher wages and rents re
flect the amount firms are willing to pay to locate
in city
rather than Sj and, therefore, the pro
ductivity value of
relative to the average city.
S2
S2
C(S
S2
S2
S2
Productivity Differences and Equilibrium Wages and Rents
SOURCE: Authors.
FI GURE
in amenities or productivity by examining the
pattern of wages and rents across cities. If wage
and rent differences primarily reflect amenity dif
ferences across cities, we would see a negative
relationship between wages and rents. If they
reflect productivity differences, the relationship
would be positive.
Within the same framework, we
can also classify individual cities on the basis of
whether their wages and rents differ from the
average because of above-average amenities,
below-average amenities, above-average produc
tivity, or below-average productivity. These classi
fications are summarized in table 1 and figure 4.
O f course, cities may differ in
characteristics that affect both household utility
and production costs. The problem of classifying
cities by the relative magnitudes of these two
effects becomes one of identifying the portion of
the wage and rent differentials due to a shift in
each curve. This can be done by identifying the
combinations of
and
that would result
from equal shifts of both curves and determining
how wages and rents in each city fall relative to
these shifts. The combinations of
and
that would result from equal shifts of both
curves will form two lines with slopes that
depend upon
1 and
If j-1 (the
slope of the
curve) is equal to
(the
negative of the slope of the
curve), the com
binations of
and
resulting from equal
shifts of both curves would coincide with the x
and y axis.
Assuming for illustration that this is
the case, for any city with above-average wages and
rents, the shift of the
(productivity) curve
must be greater than the shift of the
(amenity)
curve. Therefore, any city with wage and rent com
binations in quadrant A in figure 4 is classified as
a “high productivity” city, because the
reason that this city’s wages and rents differ from
those of the average city is the above-average
productivity it affords firms. This above-average
productivity is reflected in the ability of firms in
these cities to pay above-average wages and rents.
Similarly, cities with below-average
wages and rents (quadrant C in figure 4) are clas
sified as “low productivity” cities, since firms in
these cities are compensated for the belowaverage productivity related to site characteristics
with below-average factor costs.
Above-average amenities in a city
are associated with increases in rents and
decreases in wages reflecting households’ will
ingness to pay for the amenities. Quadrant D
then identifies cities where the dominant factor
determining relative wages and rents is high
amenities. For cities in quadrant B, the dominant
factor is their below-average amenity value.
Inw
2
Consider another city, S3, that
differs from
only in that households find it to
be more amenable. This relationship is illustrated
in figure 3, where city
is represented by
which is to the left of
Sj). If no productivity
differences exist, [that is, 6’(5 1)= C(S3)], the dif
ference in the households’ valuation of amenities
across cities leads to lower wages and higher
rents in the more amenable city, S
Sx
Inr
Inr
V(S5), Inw
S}
V(
3.
Amenity Differences and Equilibrium Wages and Rents
k x
Vs
Inw
- 0w/0r. k
6u,/6r
Cs
Inr
Cs
Vs
primary>
SOURCE: Authors.
Within this simple framework in
which cities differ in either amenities or produc
tivity, but not both, we can determine whether
factor price differences reflect intercity differences
1 9
Classification of Cities
rent
D: High Amenity
A: High Productivity
These labels may be misleading in
that what we are referring to as “high productiv
ity” cities are not necessarily more or less attrac
tive to households than the “high amenity” cities.
A city like the one represented by point A in fig
ure 5 is relatively attractive to both households
and firms. This relationship can be seen by the
positions of
and
relative to the
average city. The effect that dominates, however,
is the productivity effect, since the shift of the
curve is greater than the shift of the
curve.
Another city like the one repre
sented by point B may be less attractive to both
firms and households than city A (again reflected
in the relative positions of the amenity and pro
ductivity curves). However, the dominant trait of
city B is its amenities, which are above average.
C(SA)
Cs
C. Low Productivity
I B: Low Amenity
w
II. Estimation
4
Classification of Cities
D irection o f Price Differential
Rent
Shift
City Classification
Wage
High productivity
High
High
Low productivity
Low
Low
High amenity
Low
High
Low amenity
High
Low
C ( 5;) curve up
C(S,) curve down
ViS,) curve up
ViS,) curve down
SOURCE: Authors.
TABLE
Vs
wage
SOURCE: Authors.
FI GURE
V(SA)
1
Classification of Cities and the Relative Productivity
and Amenity Effects
The analysis is based on wage and rent data for a
sample of recent movers drawn from the Public
Use Microdata Sample of the 1980 Census of Popu
lations. This subsample includes individuals who
lived and worked in the same Standard Metropoli
tan Statistical Area (SMSA) in 1980 and who
changed addresses between 1975 and 1980. This
subsample of movers was chosen because housing
prices of recently acquired or rented dwellings
more accurately reflect current land market
conditions.
The rent equation includes both
owner-occupied and rental units for which posi
tive values of unit or gross rent are reported. The
dependent variable in the rent equation is gross
monthly housing expenditures. For homeowners,
the monthly housing expenditure is based on the
value of the dwelling using 7.85 percent as the
discount rate.4 The monthly housing expenditure
is the sum of this imputed rent and monthly util
ity charges. For renters, the monthly expenditure
is gross rent (contract rent plus utilities).
Individuals included in the wage
sample had to meet the following criteria. Indi
viduals had to be between the ages of 25 and 55;
work more than 25 hours per week; not be selfemployed; and have positive wage and salary
income. The dependent variable in the wage
equation is average weekly earnings, which is
calculated by dividing annual wage and salary
income by the number of weeks worked.
SOURCE: Authors.
4
The discount rate is from a study of the user cost of capital by
Peiser and Smith (1985).
Quality-Adjusted Wages
A hedonic approach is used to estimate wage dif
ferentials across SMSAs. This approach uses
regression analysis to determine the value the
market places on different worker characteristics.
An individual’s wage is then predicted based on
the value of his or her characteristics. The first
step in constructing the wage indexes is to spec
ify estimable equations that reflect appropriate
individual characteristics of workers that could
Estimates of Wage Equation
Variables
Mean
Coefficient
Intercept
—
4.33
(50.19)
-.083
(-5.00)
-.161
(-11.57)
.043
(5.16)
.0007
(2.81)
.043
(25.12)
-.0008
Sex (Female =1 )
.42
Race (Black =1)
.16
Education
Education squared
Experience
Experience squared
Part time
Usual hours worked
per week
15.55
250.37
10.29
192.33
.04
42.05
Head of household
.64
Veteran
.20
Sex x Race
.08
Sex x (Marital status)
.22
Sex x Experience
Sex x (Experience squared)
4.10
76.82
Marital status
.62
Union member
.25
(-15.63)
-.308
(-14.44)
.006
(10.84)
.111
(10.20)
-.017
(1.53)
.111
(5.47)
-.058
(3.14)
-.019
(-7.81)
.0003
(3-54)
.108
(9.62)
.434
(14.12)
affect wages. Our approach follows the human
capital specification of individual wages set forth
by Hanoch (1967) and Mincer (1974). Thus, we
specify individual wages (expressed in loga
rithms) as a function of education level (entered
as a quadratic), potential experience (age minus
years of education minus six, also entered as a
quadratic), a binary variable indicating part-time
employment status (less than 35 hours per
week), and 42 binary occupation variables (with
one omitted as a constant). Binary variables are
also entered to account for gender, race, marital
status, union affiliation, and whether or not an
individual is a veteran.5 In addition, the gender
variable is interacted with other characteristics in
order to control for male/female differences in
the rate of return to these attributes.
The estimated coefficients of the
wage equation are presented in table 2, except for
the occupation variables, which are omitted for
brevity. The estimated coefficients are as expected.
Education and experience are valued positively in
the labor market, while part-time, female, and
nonwhite workers receive lower wages than their
otherwise identical counterparts. We also find
that individuals who are married, heads of
households, and in highly unionized industries
earn more than their counterparts. Females
receive less return on experience than males.
The predicted wage level for each
worker in the sample is obtained by multiplying
the estimated coefficients by each worker’s char
acteristics. The predicted wage can be interpreted
as the compensation a worker could expect to
receive, given his or her characteristics, regardless
of geographic location. Subtracting the predicted
wage from the actual wage nets out the portion
of the actual wage that is related to the individual
worker’s characteristics. The skill-adjusted metro
politan wage differentials are then obtained by
averaging the wage residuals (actual minus pre
dicted wage) for all workers in a particular metro
politan area. Average wage differentials are calcu
lated for each of 38 cities. The 38 metropolitan
areas are chosen by including only those SMSAs
for which 100 or more individuals in the sample
were recorded as movers between 1975 and 1980.
The quality-adjusted wage differentials are dis
played in table 4.
(42 Occupation Dummies)
R-square
No. observations
Dependent variable:
log (weekly earnings)
.34
22,313
5.50
Note: Estimates derived from Public Use Microdata Sample. T-statistics in
Rent Equation
The method used to calculate quality-adjusted
rent differentials is similar to the one used to cal
culate quality-adjusted wage differentials. The log
of monthly housing expenditures is regressed
parentheses.
SOURCE. Authors.
The measure of unionization in the wage equation is the industry
unionization rate taken from Kokkelenberg and Sockell (1985).
21
Estimates of Rent Equation
Variables
Intercept
Dwelling rented (=1)
Central city (=1)
x rental
Mean
Coefficient
.53
.14
9.93 (248.36)
.084 (1.35)
-.05 (-3.29)
.021 (1.70)
Number of floors
x rental
1.10
.122 (5.43)
-.056 (-2.62)
Attached dwelling (=1)
x rental
.06
.06 (2.41)
.027 (1.17)
Year dwelling built
x rental
3-65
-.06 (-17.98)
-.018 (-4.94)
Number of rooms
x rental
7.07
.11 (22.80)
-.032 (-5.64)
Number of bedrooms
x rental
4.25
.10 (9.96)
.011 (1.03)
.14
.06 (3.70)
-.027 (-.83)
.52
.12 (9.13)
.038 (2.82)
.91
.12 (6.35)
-.058 (-4.14)
.96
-.046 (1.62)
2.92
-.003 (-.65)
.007 (1.41)
2.72
.179 (32.03)
-.056 (-6.73)
Well water (=1)
x rental
Central air
conditioning (=1)
x rental
Central heating (=1)
x rental
Dwelling other than
condominium (=1)
Number of units
at address
x rental
Number of bathrooms
x rental
against housing attributes. These characteristics
include the number of rooms, number of bed
rooms, number of bathrooms, and separate binary
variables indicating location of the dwelling in
the central city, and whether or not the dwelling
is a single structure, has central air conditioning
and/or heating, is connected to a city sewer sys
tem, and has well water. The year the dwelling
was built is entered to proxy the vintage. Dwell
ing characteristics are interacted with rental status
in order to account for differences in the valua
tion of these attributes between rented and
owner-occupied dwellings.
Coefficient estimates are reported
in table 3- The results are as expected. Larger,
newer dwellings with central air and heating and
that are located outside the central city have
higher market value than otherwise identical
homes. In general, attributes of rentals are valued
less than otherwise identical owner-occupied
dwellings. The predicted rent is calculated by
multiplying the estimated coefficients by the
housing characteristics of each household. The
quality-adjusted rent differentials presented in
table 4 are the differences between the actual and
predicted house values.
By including a number of housing
characteristics in the rent equation, the difference
between actual and predicted house values can
be interpreted to reflect primarily land values in
specific geographical locations. Thus, qualityadjusted rent differentials relative to the national
average reflect differences in city land values,
which are due primarily to the capitalized effects
of differences in site characteristics.
Land Shares
City sewer
connection (=1)
x rental
.87
Lot size less than
one acre (=1)
x rental
.92
-.130 (8.72)
.185 (8.07)
Elevator (=1)
.04
.065 (2.45)
R-square
.053 (4.27)
.004 (.18)
.63
No. observations
16,017
Dependent variable:
log (house value)
11.07
Note: Estimates derived from Public Use Microdata Sample. T-statistics in
parentheses. The entry “ x rental” indicates that the rental dum m y variable
has been interacted with the variable listed immediately above it.
SOURCE: Authors.
In addition to the quality-adjusted wage and rent
differentials, our classification of cities requires
estimates of the share of household income
spent on land
}) and the ratio of the income
shares of land and labor in production (0r/0 M,).
These values are not readily available for each
specific metropolitan area. Thus, we use national
estimates and assume that the portion of house
hold income spent on land and the ratio of labor
income to land income in production are con
stant across metropolitan areas and equal to the
national average.
The budget share of land is calcu
lated by multiplying the fraction of income spent
on housing (27.0 percent in our sample) by the
ratio of land value to the total value of the house
(estimated to be 19 6 percent).6 From these esti
mates, land’s share of household income ( x) is
(k
k
6
The ratio of land value to total house value w as estimated by
Roback using F H A housing data. Unfortunately, the census data
used in this study cannot be used to make a new estimate.
(dr/d w)
kx
5.3 percent. The ratio
is calculated by sub
tracting our estimate of
from the ratio of the to
tal income to land (6.4 percent of national
income) relative to total labor income (73 percent
of national income).7 The ratio of these income
shares is 8.8 and the estimate of
is 3-5.
SMSAs lie in the “productivity” quadrants than in
the “amenity” quadrants. This is confirmed by a
positive correlation coefficient of 0.46. The rela
tively small value of the coefficient suggests that
the relationship is not the same across all SMSAs.
We now proceed to determine
whether deviations from the average wages and
rents for individual SMSAs primarily reflect a)
above-average amenities, b) below-average amen
ities, c) above-average productivity, or d) belowaverage productivity.
In order to determine the combi
nations of wages and rents that fall into each of
these categories, we must first determine the
wage and rent combinations that form the boun
daries for these categories. These boundaries are
determined by the combinations of wages and
rents that would result from equal shifts of the
and
curves relative to the average SMSA
Using the estimates of land shares discussed
above, we find that for all practical purposes
these boundaries coincide with the x and y axis
in figure 6.8
A listing of cities in each category
is presented in table 5. Most of the SMSAs fall
0r/6 u,
III. Classification of Cities
As discussed in section I, we can determine
whether wage and rent differentials reflect varia
tions in productivity or amenities across SMSAs by
examining the pattern of wage and rent differen
tials across SMSAs. If intercity wage and rent dif
ferentials primarily reflect amenity7differences, we
should observe a negative relationship between
wages and rents. If they primarily reflect produc
tivity differences, the relationship should be
positive.
The quality-adjusted wages and
rents for the SMSAs in our sample are presented
in figure 6. It appears from figure 6 that there is a
slight positive relationship between wages and
rents in our sample. Using the same amenity and
productivity quadrants found in figure 4, more
Vs
Cs
2 3
Standard Metropolitan Statistical Areas Included in Sample
0.3075
•
rent
differential
.
Los Angeles, CA •
•I Boston, MA
*
• Newark, NJ
San Diego, CA
#
San Francisco, CA
• Anaheim, CA
^an Jo s e ’ CA *
Nassau, NY
New York, NY
Washington, D.C.
•
•
Miami, FL
Milwaukee, WI
•
• Portland, OR
• Fort Lauderdale, FL
• Denver, CO
•
•
•
• Sacramento, CA
• Phoenix, AZ
Minneapolis, MN
Seattle, WA
Houston, TX •
Riverside, CA
------- T "
0.0129
,•
Chicago, IL
Philadelphia, PA
----------------------------- •-Detroit, M I Cleveland, OH
•
•
Tampa, FL
*
•
Dallas, TX
Columbus, OH
•
-0.2029
-0.1194
•
Salt Lake City, UT
• New Orleans, LA
Baltimore, MD
*
• Pittsburgh, PA
St. Louis, MO
* Cincinnati, OH
Kansas City, MO
•
Atlanta, GA
Indianapolis, IN
San Antonio, TX
0.0000
wage
differential
SOURCE: Authors.
FI GURE
0.1493
6
7
The estimate of labor compensation is taken from the national
income account data reported in Table B-23 of the Economic
Report of the President (1987). Unfortunately, the national income
accounts do not include land income as a separate category of income.
8
The exact boundaries are tw o lines that pass through the origin,
one with a slope of -.003, the other with slope 333. W e classified
Our estimate of land's share of income is taken from Mills and Hamilton
cities based on these boundaries, but the classifications do not change if
(1984).
one uses the x and y axis as reference points.
Quality-Adjusted Rent and Wage Differentials3
M etropolitan Area________________
Anaheim, CA
Atlanta, GA
Baltimore, MD
Boston, MA
Chicago, IL
Cincinnati, OH
Cleveland, OH
Columbus, OH
Dallas, TX
Denver, CO
Detroit, MI
Ft. Lauderdale, FL
Houston, TX
Indianapolis, IN
Kansas City, MO
Los Angeles, CA
Miami, FL
Milwaukee, WI
Minneapolis, MN
Nassau-Suffolk, NY
New Orleans, LA
New York, NY
Newark, NJ
Philadelphia, PA
Phoenix, AZ
Pittsburgh, PA
Portland, OR
Riverside-San Bernardino, CA
Sacramento, CA
St. Louis, MO
Salt Lake City, UT
San Antonio, TX
San Diego, CA
San Francisco, CA
San Jose, CA
Seattle, WA
Tampa, FL
Washington, D.C.
Rent
Quality-Adjusted
________ W age
.281
-.145
-.075
.220
.104
-.082
-.053
-.126
-.103
.036
.013
.039
.023
-.172
-.155
.261
.076
.100
.073
.240
-.110
.145
.195
-.013
-.029
-.079
.059
.016
-.014
.085
-.099
-.203
.148
.308
.269
.048
-.142
.116
.078
.014
.031
-.001
.081
.064
.108
-.074
.001
-.013
.149
-.029
.142
.041
-.037
.049
-.112
-.002
.065
.077
-.079
.036
.045
.017
-.047
.047
-.027
-.008
-.047
.019
-.081
-.105
-.014
.073
.125
.047
-.119
.103
a. Quality-adjusted differentials are obtained by subtracting the predicted
cities receive compensation for this low amenity
value in the form of above-average wages and
below-average rents.
SMSAs that can be characterized as
“high productivity” include Chicago, Houston,
Los Angeles, and San Jose, among others. For
these cities, both wages and rents are above aver
age, suggesting that the firms in these cities are
compensated for high factor costs by other loca
tional characteristics of these cities. SMSAs like
Tampa, New Orleans, and San Antonio can be
characterized as “low productivity.” Firms in
these areas are compensated for the belowaverage productivity value of site characteristics in
the form of lower wages and rents.
Classifying SMSAs according to the
dominant effect of their site characteristics does
not mean that a high-productivity city has no amen
ity value. It simply means the city is dominated by
its productivity characteristics. Using equations 3
and 6, we can develop relative rankings of cities
within the productivity groups by amenities and
within the amenity groups by productivity. The
ordering of cities in table 5 reflects this sort of
cross classification. For example, of the highproductivity cities, New York, Los Angeles, and
Seattle are considered more amenable than Chi
cago, Houston, and Detroit. O f the high-amenity
cities, Boston is more attractive to firms than
Miami.
The classifications of some cities
are questionable, especially for cities near the
boundaries. For some cities like Boston and Mil
waukee, rents are considerably higher than aver
age, but wages are so close as to be indistinguish
able from the average. As a result, we cannot be
confident in our classification of these cities as
high productivity or high amenity, although we
can be fairly confident that they are not lowamenity or low-productivity cities. Philadelphia
and Riverside are examples of cities that are so
close to the average in both wages and rents that
their classifications may also be meaningless.
estimate from the actual value. The reference point for these estimates is
the sample average.
IV. Conclusion
SOURCE: Authors.
TABLE
4
within expected classifications. For instance,
Miami, Denver, Portland, Ft. Lauderdale, and San
Diego are classified as high-amenity cities, since
these cities are characterized by below-average
wages but above-average rents, both of which
reduce the income of households.
In cities like Baltimore, Cleveland,
Pittsburgh, and Atlanta, wages and rents primarily
reflect the below-average amenity value to
households of these cities. Households in these
In this paper, we have utilized the relationship
between regional wage and rent differentials to
identify cities by the net effect of their bundle of
site characteristics on firms and households. We
have found that, on average, firms respond more
to site characteristics than households, as is
revealed in the relatively large contribution of
demand effects to determining regional wage dif
ferentials. Nevertheless, the amenity (or house
hold) component of the total regional differential
is also significant. Thus, regional wage differen
tials result from the interplay of the forces of
supply and demand and exist even though indi
viduals move freely in response to factor price
References
Classification of Cities
High Productivity
L ow Productivity
New York, NY
Newark, NJ
Los Angeles, CA
Seattle, WA
San Francisco, CA
Minneapolis, MN
Anaheim, CA
Nassau-Suffolk, NY
Chicago, IL
Washington, D.C.
San Jose, CA
Houston, TX
Detroit, MI
Tampa, FL
San Antonio, TX
Salt Lake City, UT
New Orleans, LA
Columbus, OH
Sacramento, CA
Phoenix, AZ
Kansas City, MO
High Amenity
Low Amenity
Boston, MA
San Diego, CA
Milwaukee, WI
Denver, CO
Riverside, CA
Portland, OR
Ft. Lauderdale, FL
Miami, FL
Cleveland, OH
Cincinnati, OH
Pittsburgh, PA
Philadelphia, PA
Baltimore, MD
St. Louis, MO
Indianapolis, IN
Dallas, TX
Atlanta, GA
Amer
ican Economic Rei’iew,
Eberts, Randall W., and Joe A. Stone. “Metropoli
tan Wage Differentials: Can Cleveland Still
Compete?”
Federal Reserve
Bank of Cleveland, Quarter 2 (1986), pp. 2-8.
Economic Review,
Hanoch, Giora. “An Economic Analysis of Earn
ings and Schooling,”
vol. 2, no. 3 (Summer 1967), pp.
310-29.
Journal of Human
Resources,
NOTE: Productivity cities are listed from the most amenable to the least.
Amenity cities are listed from the most productive to the least.
SOURCE: Authors.
TABLE
Bellante, Don. “The North-South Differential and
the Migration of Heterogeneous Labor,”
vol. 69, no. 1 (March
1979), pp. 166-75.
Hoehn, John P., Mark C. Berger, and Glenn C.
Bloomquist. “A Hedonic Model of Interre
gional Wages, Rents and Amenity Values,”
University of Kentucky Working Paper No.
E-91-86 (1986).
Johnson, George E. “Intermetropolitan Wage Dif
ferentials in the United States,” in Jack E. Tri
plett, ed.,
Chicago: University of Chicago Press, 1983,
pp. 309-30.
The Measurement of Labor Cost.
Kokkelenberg, Edward C., and Donna R. Sockell.
“Union Membership in the United States:
1973-1981,”
vol. 38, no. 4 (July 1985), pp. 497-543.
Industrial and Labor Relations
Revieu\
5
differentials. Thus, so long as regions differ in the
amount and quality of their site-specific character
istics, wage differentials will continue to exist.
Urban
Mills, Edwin S., and Bruce W. Hamilton.
Glenview, 111.: Scott Foresman and
Company, 3rd ed. (1984).
Economics.
Schooling Experience, and Earn
Mincer, Jacob.
New York: National Bureau of Economic
Research, Distributed by Columbia University
Press, 1974.
ings.
Peiser, Richard B., and Lawrence B. Smith.
“Homeownership Returns, Tenure Choice and
Inflation,”
vol. 13, no. 4
(Winter 1985), pp. 343-360.
American Real Estate and Urban
Economics Association Journal,
Roback, Jennifer. “Wages, Rents and the Quality
of Life,”
vol. 90,
no. 6 (December 1982), pp. 1257-78.
Journal of Political Economy,
25
FSLIC Forbearances to
Stockholders and the Value
of Savings and Loan Shares
by James B. Thomson
Jam es B. Thomson is an economist
at the Federal Reserve Bank of
Cleveland. The author thanks Lynn
Downey for excellent research assis
tance, and Randall Eberts, Christopher
Jam es, William Osterberg, and
Walker Todd for helpful comments
and suggestions.
26
Introduction
Policies of forbearance to stockholders of insol
vent firms by federal deposit guarantors represent
a wealth transfer from federal deposit-insurance
agencies, and ultimately from federal taxpayers,
to the stockholders of the insured institutions.
Kane (1985, 1986), Pyle (1986), and Thomson
(1987) discuss theoretical determinants of the
value of forbearances to stockholders of financial
institutions by the Federal Deposit Insurance
Corporation (FDIC) and the Federal Savings and
Loan Insurance Corporation (FSLIC). Brickley and
James (1986) show empirically that the stockmarket returns of thrifts increase with the exten
sion of FSLIC capital forbearances.
This paper investigates the rela
tionship between the market and book values of
the firm’s equity. It demonstrates that the market
value of a thrift is positively related to its book
value and to the value of its unbooked assets. We
argue that one of the major unbooked assets of a
thrift is its FSLIC insurance guarantee. Measures of
FSLIC forbearance policy are shown to be related
to the market value of the thrifts whose market
values exceed their book values.
Section I of this paper discusses
the relationship between the market value and
book value of a firm. It outlines the reasons that
these values may diverge and argues that FSLIC
guarantees are one of the unbooked assets valued
by the market. Section II gives a brief overview of
the empirical evidence and theoretical arguments
regarding the value of federal deposit guarantees
and forbearances. Section III describes the data,
the sample selection criteria, the regression
experiment used to test the forbearance hypothe
sis, and the empirical results. The conclusions
and policy implications of the paper are pre
sented in section IV.
I. The Relationship Between Market
and Book Values
The book value of a firm’s equity is measured as
the difference between the book value of the
firm’s assets-in-place and the par value of its lia
bilities. The book value of assets may not equal
their market value for three reasons. First, the
accounting conventions used by most firms carry
assets at their par, or acquisition, value and do
not reflect subsequent changes in the market value
of the assets. The market value of the assets would
include these unbooked gains and losses. Sec
ond, because book values tend to include only
assets-in-place, they do not measure the value of
options for future business that are unique to the
firm.1 Finally, to avoid taxes, burdensome regula
tions, or restrictive debt covenants, some firms
may engage in activities that are not carried on
their books. The assets (liabilities) associated
with these activities would not show up in book
measures of assets (liabilities), but would none
theless be reflected in their market values.
1
Myers (19 77) and Warner (19 77) argue that the market value of
the firm's assets includes both the market value of the assets-in-
place and the market value of the firm’s options for profitable future
business opportunities. Therefore, if the firm carried its assets-in-place at
market value, the book value of the firm would understate its market
value.
On the other side of the ledger, the
firm carries its liabilities at par. Like the assets, the
liabilities’ market value includes unbooked
changes. The market value of the firm’s liabilities
also includes off-balance-sheet financing and other
types of contingent liabilities not reflected in
book values (see Bennett [1986] and Forde
[1987]). Therefore, the book value of the firm’s
equity will differ from its market value if the errors
in the book measures of the firm’s assets and lia
bilities do not completely offset one another.
Unbooked Losses and Gains in Thrift Portfolios
The market value of a thrift institution’s assets can
be separated into the market value of its assets-inplace and the market value of its charter. The mar
ket value of the assets-in-place may not equal their
book value because the accounting procedures
that thrifts and their regulators use to calculate
book values do not take into account unrealized
gains and losses on the thrift’s asset portfolio.
For example, thrifts hold a large vol
ume of fixed-rate mortgages, whose market values
fluctuate inversely with interest rates. When inter
est rates rise, the market values of the mortgages
decrease while the face value of the mortgage
portfolio remains constant. Because thrifts are not
forced to recognize capital losses on the mort
gages until they are sold (or until the customer
defaults), an increase in interest rates causes the
book value of the mortgage portfolio to exceed
its market value and the market value of the
assets-in-place to be less than their book value.
Another source of unbooked capi
tal gains and losses in the thrift’s portfolio are real
estate holdings. Thrifts tend to carry real estate on
their books at acquisition price, which may not
equal the current value of the real estate. The real
estate portfolios of many thrifts are likely to be
carried on their books at a discount from market
value, which may cause the book value of the
thrifts to be less than their market value.
The Value of Thrift Charters
The charter value of a thrift reflects the value of
its unbooked assets.2 We can divide the value of
the thrift’s charter into five categories. The first is
the value of business relationships built over
time. Kane and Malkiel (1965) argue that long
standing customer banking relationships have
2
Buser, Chen, and Kane (1981) maintain that the FD IC attempts to
preserve the value of the banking charter when disposing of a
failed bank, by using the charter's value to reduce the disposal costs. If the
bank is disposed of via a purchase-and-assumption transaction, the pur
chase premium paid by the bank acquiring the failed bank reflects the
value of the charter to the acquiring institution.
value because they lower the information and
contracting costs associated with doing business.
The reduction in the cost of servicing long
standing customers is available only to the servic
ing thrift and is a source of profitable future busi
ness opportunities.
Firm-specific options for profitable
future business opportunities are the second
source of the charter’s value. These options may
be available to the thrift because it has developed
expertise in servicing a particular segment of the
market. The third source is monopoly rents that
may accrue to the thrift from restrictive branching
laws and other regulations that restrict competition.
The fourth source of the charter’s
value is access to Federal Home Loan Bank Board
(FHLBB) advances. The FHLBB makes secured
loans to member thrifts at subsidized rates. These
advances represent both a direct subsidy and an in
expensive source of backup liquidity. The fifth com
ponent of the charter’s value is federal deposit
guarantees. Kane (1985, 1986) maintains that the
mispricing of deposit insurance and the use of
forbearance policy by federal deposit guarantors
has made the value of deposit guarantees an
important source of thrift charter values.
II. FSLIC Subsidies, Forbearances, and the Market
Value of Thrift Institutions
A new and growing body of literature addresses
the value of federal deposit insurance subsidies
and forbearances to insured depository institu
tions. Kane (1985, 1986) argues that the aggre
gate net worth of the thrift industry, net of the
value of deposit guarantees and forbearances, is
negative. Pyle (1986) shows that the the use of
capital forbearances increases the value of deposit
guarantees. Brickley and James (1986) empirically
demonstrate a positive relationship between the
adoption of a capital forbearance policy by the
FHLBB and the market value of thrift institutions.
Ronn and Verma (1986) show that estimates of the
fair value of deposit guarantees are extremely
sensitive to assumptions regarding the forbear
ance policy the FDIC employs when disposing of
failed banks. Thomson (1987) breaks down the
value of the deposit guarantee into three compo
nents: the value of the guarantee on insured
deposits, the value of a conditional guarantee on
the uninsured deposits, and the value of a condi
tional guarantee of the stockholders’ claim on the
residual future earnings of the insured institution.
This paper is concerned with the value of forbear
ances to the stockholders of insured institutions.
The federal deposit insurance agen
cies extend forbearances to stockholders of insol
vent institutions in two ways. The first, and politi
cally preferred, method is to allow the institutions
to operate after they are discovered to be insol-
27
28
vent.3 The de jure failure of a federally insured
bank or thrift is an event timed by the regulators.
The extension of explicit or implicit guarantees
to the claims of uninsured depositors and general
creditors of the insolvent bank or thrift removes
the incentives of these individuals to force the
closing and reorganization of the institution.4
A forbearance policy that does not
at least close out the position of stockholders in
insured depository institutions that are found to
be insolvent has value to the stockholders (see
Thomson [1987]). It represents an option on the
future residual earnings of the institution. The
behavior of the stock of Beverly Hills Savings and
Loan (BHSL) of California is evidence that this
type of forbearance has value. At the end of
March 1985, roughly one month before it was
closed by the FHLBB, the stock of BHSL had a
market value of $19.21 million, while the book
value of its equity was -$58,091 million.5
The second way stockholders re
ceive forbearances from the federal deposit
insurer is when the federal deposit guarantor
uses open-bank assistance to handle the failure
(or to head off the imminent failure) of an
insured institution.6 In this case, the federal de
posit guarantor may preserve some or all of the
value of the stockholders’ claim on the residual
future income of the institution.
For example, when the FDIC
bailed out the Continental Illinois Bank and Trust
Company of Chicago (Continental) in 1984, it
gave the original stockholders warrants allowing
them to purchase shares in the reorganized insti
tution. The estimated value of these warrants was
approximately $155 million (close to 20 percent
of the estimated equity value of the reorganized
Continental) on the day after the bailout package
was announced.
3
Net worth certificates and capital forbearances are two of the
tools that politicians and industry regulators use to forestall the
closing of insolvent institutions (see Nash [19 8 7] and McTague [19 87]).
4
a guarantee of the market value of their claim at the time the
5
ment program on April 2 5 ,19 8 5 . A t that time, the book value of
The deposit guarantor must provide the uninsured depositors with
institution is discovered to be insolvent on a market-value basis.
The B H S L was admitted to the F H L B B ’s management consign
its assets w as $2,939 billion, and its
June 6 ,1 9 8 6 , the reported
TNW
TNW
Empirical Issues
The Data
The sample consists of 43 thrifts that meet the fol
lowing criteria. First, to measure the market value
of equity, we had to be able to obtain stock price
and share data on the thrifts from Data Resources
Incorporated’s (DRI) Security Price File from
March 1984 to the end of June 1986. Second, the
thrifts had to be insured by the FSLIC. Third,
balance-sheet and income-statement data had to
be available from the FHLBB’s Quarterly Reports
of Condition and Income. Finally, to remove the
effects of nonthrift subsidiaries from the results,
we excluded all thrift holding companies.
The requirement that the thrifts’
stock must trade on the market restricts the sam
ple to the largest firms in the industry. For exam
ple, at the end of June 1986, the average size
(measured in total assets) of the thrifts in our
sample was $1,895 billion.7 This is considerably
larger than the size of the average thrift in the
population. Therefore, one should be careful in
generalizing the results of the tests on this sam
ple to the population. We do not expect the
other sample selection restrictions to materially
affect the results.8
To construct proxy variables for
our tests, we draw on theoretical arguments (see
Beaver, et al. [1970], Bowman [1979], Myers
[1977], and Unal and Kane [1987]); empirical
findings (see Barth, et al. [1985], Benston [1986],
w as -$58,091 million. On
ment program occurred when interest rates were falling. The one-year
secondary market Treasury bill rate w as 8.22 percent on April 2 6 ,19 8 5 ,
6,
III.
of B H S L w as -$540 million. In fact, the
decline in B H S L 's net worth under the F H L B B ’s management consign
and 6 .14 percent on June
The probability that federal deposit
guarantors will extend forbearances to stock
holders of insolvent insitutions is a function of
constraints on the guarantors’ ability to reorganize
insolvent institutions. Kane (1986) places these
constraints into four categories: political and legal
constraints, information constraints, staff con
straints, and funding constraints reflected in the
implicit and explicit reserves of the insurance
fund. Sprague’s (1986) account of the FDIC’s
decision to bail out Continental makes it clear
that the first three constraints played a major role
in that bailout. Barth, et al. (1985) show that the
ability of the FSLIC to close insolvent thrift institu
tions is directly related to the solvency of the
FSLIC insurance fund.
7
The largest (smallest) thrift in the sample at the end of June
1986,
measured in terms of total assets, w as $10,551 billion
($164,226 million).
1986. Thus, it is fairly clear that the posi
tive market value of B H S L before its closing was not due to unrealized
capital gains on B H S L ’s portfolio.
8
To test the sensitivity of the results to survival bias, w e replicate
the cross-section regression experiments using a sample that
includes all firms in the sample with complete information for that quar
6
On December 4 ,1 9 8 6 , the FD IC announced that it had set up
ter. Because the number of firms varies across quarters, w e do not
formal guidelines for the use of open-bank assistance in handling
attempt to pool this sample. Overall, the results over the larger sample
troubled and failed banks (see McTague [1986]).
support the paper’s main results.
Results from the SMVAM Regressions3
(U sing GAAP Net W orthb )
Quarter
1984
1985
1986
N um ber
MKTVAL0
TN W d
1
43
39506.73
60361.41
2
43
37452.41
3
43
4
k
Ue
R2
14006.781
(4.288)e
0.4224 l n
(16.243)
0.7749
62386.19
12627.56+
(3-317)
0.39792++
(14.936)
0.7039
39170.32
63704.42
10493-50+
(2.695)
0.45015++
(13.553)
0.7502
43
40985.42
65153.79
6560.23
(1.482)
0.52837+
+
(10.479)
0.7707
1
43
47330.67
67189.88
5920.71
(1.332)
0.61631 ^
(8.714)
0.8269
2
43
55950.50
71629.05
5789.46
(1.092)
0.70029n
(6.107)
0.8324
3
43
51973.94
74923.77
10687.99*
(2.044)
0.55104n
(9.660)
0.7742
4
43
62388.50
77475.47
8304.28
(1.232)
0.69808++
0.7772
(5.173)
1
43
79638.41
84108.60
10490.68
(1.354)
0.82212++
(2.937)
0.8180
2
43
83701.49
85911.07
21001.71**
(1.968)
0.72982++
0.6939
(3.569)
2 9
a. M odel: MKTVAL = Ug + kTN W + e.
f Significantly different from zero at 1%.
b. Net worth com puted using generally accepted accounting procedures.
f t Significantly different from on e at 196.
c. Average market value o f thrift stock (0 0 0 ’s).
* Significantly different from zero at 5%.
d. Average b o o k value o f thrift equity (0 00’s).
** Significantly different from zero at 10%.
e. T-statistics in parentheses.
SOURCE: Author.
TABLE
1
Brickley and James [1986], and Lee and Brewer
[1985]); and the deposit-forbearance literature
(see Kane [1986], Pyle [1986], Ronn and Verma
[1986], and Thomson [1987]). The following
proxy variables are constructed from stock-market
data and balance-sheet and income data.
MKTVAL =
market value of the thrift’s stock.
is the product of the price
of the thrift’s stock and the number
of shares outstanding, or the market
value of equity.
net worth according to generally
accepted accounting principles.
is the book value of equity.
= proxy variable for liquidity.
is
nondeposit liabilities divided by total
book liabilities.
= proxy variable for diversification of
assets.
is the sum of nonmort
gage loans and contracts and direct
investments, divided by mortgage
loans and contracts.
proxy variable for solvency and a
measure of capital adequacy.
is
divided by total book assets.
MKTVAL
TNW =
LLQ
DIV
TNW
LIQ
DLV
TNWA =
TNW
TNWA
Empirical Tests of the Forbearance Hypothesis
To test the forbearance hypothesis, we use the
Statistical Market-Value A ccounting M odel
(SMVAM) of Unal and Kane (1987):
(1)
+
+
Equation 1 is the basic SMVAM regression where
is the value of the thrift’s stock and
is the book value of the thrift’s equity. Unal
and Kane interpret the slope coefficient, as the
market’s value of $1 of book equity, and
as
the market’s value of unbooked equity. In other
words, times
is the portion of market
value accounted for by assets-in-place, and
is
the portion of market value accounted for by the
charter.
If booked assets and liabilities are
marked-to-market, then the theoretical value of
is one; and if all assets and liabilities are carried
on the books, the theoretical value of
is zero.
If the charter value net of FSLIC forbearances and
guarantees is positive (negative), FSLIC forbear
ances and guarantees will increase (decrease in
absolute value terms) the size of
Equation 1 is estimated over the
cross-section of firms in the sample for each quar
ter. As seen in table 1,
is positive in every
MKTVAL = Ue kTNW
e.
MKTVAL
TNW
k
k,
Ue
TNW
Ue
k
Ue
Ue.
Ue
Proportion of Stock-Market Value Explained by Charter Value3
Quarter
1984
1
2
3
4
1985
1986
1
2
3
4
1
2
Num ber
Ue /MKTVALh
T-Billc
GNMAd
43
43
43
43
0.35454
0.33716
0.26789
0.16006
0.0952
0.0987
0.1037
0.0806
0.1270
0.1414
0.1308
0.1254
43
43
43
43
43
43
0.12509
0.10347
0.20564
0.0852
0.0695
0.0710
0.0710
0.0656
0.0621
0.1268
0.1154
0.13311
0.13173
0.25091
0.1129
0.1070
0.0944
0.0957
a. Charter value is measured by the intercept term, LJe , in the SMVAM regressions.
b. MKTVAL is the average stock-market value o f the firms in the sample.
c. Annual equivalent yield o n 3-month Treasury bills traded on the secondary' market (from Interest Rates tables in selected Federal Reserve
Bulletins, 1984-1986).
d. Average net yields on Governm ent National Mortgage Association, mortgage-backed, fully m odified pass-through securities, assuming
12-year prepayment on 30 p o o ls o f FHA/VA mortgages (from Interest Rates tables in selected Federal Reserve Bulletins, 1984-1986).
SOURCE: Author.
TABLE
2
quarter. However, it is not significantly different
from zero in five of the 10 quarters. Table 2
shows the percent of stock-market value
accounted for by the estimated charter value,
The value of the charter, which includes the
FSLIC forbearances, ranges from a high of 35.4
percent in the first quarter of 1984 to a low of
10.3 percent in the second quarter of 1985. In
other words, the charter is a nontrivial compo
nent of stockholder equity.
The per-dollar value the market
places on book equity,
appears in the sixth
column in table 1. This value ranges from a low
of 40 cents on the dollar in the second quarter of
Ue.
3 0
k,
Pooling and Cross-Equation Equality Restrictions
for the SMVAM Regressions3
Uei
U
Ue io
Test:
= 0, e2 = 0, ...............
F( 10,410) = 5.3392896+
Test:
i =
2
............ =
F(9,410) = 0.62610870
Test:
= 1, & 2 = 1, ............ , £io = 1
F( 10,410) = 102.89425+
Test:
2 = ............ =
F(9,410) = 8.4505921+
Ue
Ue
= 0
Ue io
k\
k\ - k
k\o
a. SMVAM Regression M odel: MKTVAL = Ue + kTN W + e.
f Significant at the 1% level.
SOURCE: Author.
1984 to a high of 82 cents on the dollar in the
first quarter of 1986. In all quarters,
is positive
and significantly different from one at the 1 per
cent level. As expected, there appears to be an
inverse relationship between and the level of
interest rates. The general upward trend in
from the first quarter of 1984 to the second
quarter of 1986 coincides with the downward
trend in interest rates over this period.
Table 3 presents the results of joint
tests of the SMVAM coefficients and tests of pool
ing restrictions. A seemingly unrelated system of
equations, with each quarter estimated as a
separate regression, is used to perform the tests.
We reject the joint restriction that
is zero in
every equation at the 1 percent level, but we
cannot reject the restriction that
is equal
across equations. For the slope coefficient,
we
reject both the cross-equation equality restriction
and the joint restriction that equals one in
every quarter at the 1 percent level. Overall, the
results of the joint tests and the pooling restric
tions support the forbearance hypothesis.
Although the results of the SMVAM
regressions are consistent with the forbearance
hypothesis, the SMVAM specification does not
provide a direct test of the forbearance hypothe
sis. Recall that a thrift charter may have value
exclusive of deposit insurance subsidies and for
bearances because the charter also contains the
net value of all unbooked assets and liabilities.
Moreover, estimates of
could be positive and
significant when the value of FSLIC forbearances
and guarantees is zero. Estimated
could be
insignificant (or negative and significant) when
the value of FSLIC forbearances and guarantees is
positive and significant.
k
k
k
Ue
Ue
k
Ue
Ue
k,
Results from the MSMVAM Regressions3
(U sing GAAP Net W orthb )
Quarter
1984
1985
1986
U
1
Us
k
1395.67
(0.193)c
0.40942t
(17.592)
-19394.22
(-0.721)
109864.881+
(4.318)
124226.48
2
12627.61
(1.302)
0.42172+
(13-652)
-66681.97*
(-2.175)
49812.35*
(1.934)
96534.29
(0.798)
0.7649
3
4231.84
(0.388)
0.46008+
(11.881)
-40394.91
(-1.122)
27471.48
(1.094)
190151.80
(1.345)
0.7806
4
-5007.80
(0.454)
0.53010+
(9.679)
-25806.27
(-0.775)
7272.61
(0.262)
320283.78*
(2.021)
0.8080
1
-4171.15
(-0.383)
0.62986+
(7.879)
-2115.69
(-0.065)
-43843.94
(-1.508)
303247.14**
(1.941)
0.8610
2
-8187.52
(-0.597)
0.70668+
(5.408)
15703.69
(0.398)
-44814.60
(-1.443)
337791.44**
(1.751)
0.8575
3
-8847.13
(-0.645)
0.55141+
(9.093)
8946.48
(0.238)
-30860.57
(-0.874)
443532.27*
(2.413)
0.8178
4
-23639.28
(-1.265)
0.67333+
(5.129)
8533.00
(0.177)
23836.61
(0.514)
599654.62*
(2.374)
0.8097
1
-30194.55
(-1.464)
0.77275+
(3-332)
35213.95
(0.618)
35936.96
(0.618)
681527.26*
(2.417)
0.8425
2
26518.43
(1.073)
0.75192+
(2.758)
73120.49
(0.849)
-189767.15*
(-2.131)
76236.99
(0.253)
0.7293
a. M odel: MKTVAL = Ue + kTN W + f t LIQ + f t / W
+ foTN W A + e.
0.8493
(1.483)
3 1
f Significantly different from on e at 1%.
b. Net worth com puted using generally accepted accounting procedures.
f t Significantly different from zero at 1%.
c. T-statistics in parentheses.
* Significantly different from zero at 5%.
** Significantly different from zero at 10%.
SOURCE: Author.
TABLE
4
A careful reexamination of the
results in tables 1 and 2 indicates that the positive
sign on
in every quarter is due, at least in part,
to the positive value of FSLIC guarantees and for
bearances. There is an inverse relationship
between and
/
the the market
value of book equity increases, charter value as a
percent of
decreases. The value of for
bearances and guarantees should be inversely
related to
On the other hand, the value of
the charter exclusive of FSLIC forbearances and
guarantees is expected to be positively correlated
with
This suggests that FSLIC forbearances and
guarantees are a large enough portion of
that
changes in their value dominate the pattern of
across quarters.
To test the forbearance hypothesis
more directly, we modify equation 1 to include
the variables
and
to proxy for
FSLIC forbearance policy:
Ue
k
Ue MKTVAL. As
MKTVAL
k.
k.
Ue
Ue
LIQ, DIV,
(2)
TNWA
MKTVAL = Ue + kTNW + f t LIQ
DIV + f t TNWA + e.
+
LIQ,
The first forbearance proxy,
measures liquidity. Because the closing of an
insolvent institution is an event timed by the reg
ulators, insolvency is a necessary, but not suffi
cient, condition for the forced closing of a thrift
by its regulator. Given the growing insolvency of
the FSLIC insurance fund and the large number
of market-value and book-value insolvent thrifts
(see Barth, et al. [1985] and U.S. General
Accounting Office [1987]), the liquidity of the
thrift affects the probability that FSLIC forbear
ances will be extended to stockholders.
Insolvent thrifts (those that are not
running up large losses) tend to be closed when
illiquid, especially when they are insolvent
according to market-value accounting, but not
book-value accounting. Ceteris paribus, the more
liquid the thrift, the less likely a liquidity crisis
will cause the FHLBB to close the thrift. There
fore, the value of FSLIC forbearances should be
positively related to liquidity. By construction, as
increases, the thrift’s liquidity decreases.
Consequently, f t should have a negative sign.
The second forbearance proxy,
is a measure of diversification in the asset portfo
lio.
includes both direct investments and
nonmortgage loans and contracts. In March 1985,
the FHLBB issued a formal regulation that restrict
ed direct investments to less than the minimum
of 10 percent of total assets and twice the amount
of capital. This regulation, which was in effect
LIQ
DIV,
DIV
Pooling and Cross-Equation Equality Restrictions
for the MSMVAM Regressions3
Ue i
U
Test:
= 0, e 2 = 0, ............ ,
F( 10,380) = 0.5559142
Test:
i - e 2 ............ =
F(9,380) = 0.61687062
Test:
= 1, 2
1, ............
F( 10,380) = 90.82540+
Ue io
= 0
Ue
U
Ue io
k\
k =
, kio = 1
k\
DIV
TNWA,
TNWA
TNWA
k\o
Test:
= & 2 = ............ =
F(9,380) = 6.8009228+
Test:
/3i,i - 0, /?i,2 = 0, ............ , 0i,io = 0
F( 10,380) = 0.98109793
Test:
0i,i - /?i,2 = ............ = 0i,io
F(9,380) = 0.82046518
32
its policy statements emphasizing mortgage lend
ing during this period, 0 2 should be negative in
the sample period from March 1985 on. Converse
ly,
could also be a proxy for management
quality. 9 That is, the market may view a decrease
in the thrift’s reliance on mortgages as an indica
tion of the quality of management. This diversifi
cation (management quality) explanation would
make 0 2 positive before March 1985. After that
time, the sign of 0 2 should be negative if the
forbearance hypothesis holds.
The third forbearance variable,
proxies for solvency. Note that
is
solvency measured by book, not market, values.
This means that a thrift with positive
could
be insolvent on a market-value basis.10 The value
of deposit-guarantor forbearances depends on
market solvency, not on
O n the other
hand, the probability of forbearance is a function
of
FHLBB-mandated capital requirements
of 3% or more) are based on book
values. FSLIC forbearances are extended to any
institution that meets the minimum capital guide
lines, and they may be extended to institutions
with deficient capital ratios. Therefore, we use
as our proxy for solvency because the
probability of forbearance is a positive function of
The sign on 03 should be positive.
The results from the regressions
on equation 2 are reported in table 4. Joint tests
of the regression coefficients and pooling tests
for the small sample appear in table 5. For all
quarters, the estimates of
are not significantly
different from zero in the modified SMVAM
(MSMVAM) regressions. In fact, we cannot reject
the joint restriction that
is zero in every quar
ter or the cross-equation equality restriction on
In the SMVAM regressions, estimated
is
significantly different from zero in five of the 10
quarters, and we reject the joint restriction that
is zero.
However,
estimates are not
affected by the inclusion of the forbearance prox
ies. Estimated is positive and significantly less
than one in every quarter, and we cannot reject
the restriction that s m v a m = &m s m v a m in any
quarter. Furthermore, both the joint test that
equals one in every quarter and the cross
equation equality restriction on
are rejected at
the 1 percent level for both the SMVAM and the
TNWA
TNWA
( TNWA
TNWA
TNWA
Test:
0 2 ,1
- 0, 0 2 , 2 = 0, ............ ,
F( 10,380) = 2.8303445*
Test:
0 2 ,1
Test:
0 3 ,1
Test:
0 3 ,1
= 0 2 , 2 = ............ =
F(9,380) = 2.8692565*
0 2 ,1 0
= 0
Ue
0 2 ,1 0
Ue
- 0, 0 3 ,2 = 0............... ..
F( 10,380) = 2.9404988*
- 0 3 ,2 - ............ =
F(9,380) = 0.98635699
0 3 ,1 0
= 0
0 3 ,1 0
a. MSMVAM Regression M odel:
MKTVAL = Ue + kTN W + PiLlQ + /32Z W + faTN W A + e
t Significant at the 1% level.
* Significant at the 5% level.
SOURCE: Author.
Ue.
Ue
Ue
k
k
k
k
throughout the remainder of the sample period,
applies only to nationally chartered thrifts, and
not to the FSLIC-insured, state-chartered thrifts.
The FHLBB is strongly opposed to
direct investments by thrifts because it believes
such investments increase the losses to the FSLIC
fund when an insolvent thrift is closed (see Benston [1986]). Therefore, we expect there to be an
inverse relationship between FSLIC forbearances
and the level of direct investment. Given the
FHLBB’s policy regarding direct investment and
k
9
In economics, we assume that management is a scarce resource.
Therefore, firms with high-quality management will have a higher
market value than firms with lower-quality management. This, of course,
assumes that the market for managerial talent is not perfectly
competitive.
”1
The difference between market-based and accounting-based
JL \ J
measures of solvency can be quite large. A
TNWA
of 3 per
cent is often used as a proxy for the solvency threshold on a marketvalue basis.
MSMVAM regressions. The difference (similarity)
in the behavior of
(
between the SMVAM
and the MSMVAM regressions is consistent with
the forbearance hypothesis.
The coefficients on the forbearance
proxies themselves present a mixed set of conclu
sions. The coefficient on
/3i, is negative and
significant in the second quarter of 1984, support
ing the forbearance hypothesis. However, /3i is
not significantly different from zero in any other
quarter, and we cannot reject the joint restriction
that
equals zero in every quarter. Therefore,
the overall performance of
does not provide
strong support for the forbearance hypothesis. 11
The poor performance by
may be due in
large part to sample selection bias. The thrifts in
this sample are the largest in the industry and are
likely to have greater access to national capital
markets, and therefore greater sources of liquid
ity, than the average thrift in the population.
The results for the diversification
(management quality) variable,
are also
mixed,
is positive and significant in the first
two quarters of 1984 and negative and significant
in the second quarter of 1986. Moreover, /fe is
positive in six of the 10 quarters in table 4. The
cross-equation equality restriction on >82 and the
joint restriction that
is zero in every equation
are both rejected at the 5 percent level.
On the surface, the seemingly con
flicting evidence provided by DIV seems to refute
the forbearance hypothesis. But a closer inspec
tion of the results indicates that this is not the
case. Recall that the FHLBB policy restricting
direct investment did not go into effect until the
first quarter of 1985. Therefore, the positive and
significant (insignificant) 's in the first
(second) two quarters of 1984 are consistent with
both the management-quality hypothesis and the
forbearance hypothesis.
Moreover, in table 4, 2 is positive
but not significant twice, and negative and signif
icant once, after the FHLBB took a stand against
direct investment and against diversification of
the asset portfolio away from mortgage-based
assets. In fact, if we split the sample according to
this policy change, we cannot reject the cross
equation equality restriction on /fe in the preand post-policy change periods. However, in the
first period we reject the joint restriction that
/fe equals zero at the 1 percent level, but we
cannot reject it in the second period.
Ue k)
LIQ,
fii
(3i
LIQ
DIV,
fc
fc
(I
n
"I
jl
The poor performance of the liquidity proxy w as not due to
proxy variable construction. Similar results were obtained
with other specifications of
Although
Ld
TNWA
is
TNW
LIQ.
scaled by total book assets, there
is almost no correlation between
of the quarters in either sample.
TNWA
and
TNW
for any
O f all of the forbearance proxies,
the solvency proxy, provides the strongest
evidence supporting the forbearance hypothesis. 12
/fe is positive in every quarter and is significant in
six quarters. The significance of
in every quar
ter from the last quarter of 1984 through the first
quarter of 1986 coincides with the time period
when the FSLIC fund was shrinking as a result of
massive losses in the thrift industry (see U.S.
General Accounting Office [1987] and Barth, et
al. [1985]). The joint restriction that $ 3 equals
zero in every equation is rejected at the 5 percent
level. However, we cannot reject the cross
equation equality restriction on $ 3 .
Even though the results were
somewhat disappointing when we look at the
forbearance proxies individually, the overall
results are encouraging. Looking at table 4, we
see that in every quarter except the third quarter
of 1984,
is not significantly different from
zero, and at least one of the forbearance proxies
is significantly different from zero and correctly
signed. Moreover, we obtain these results using a
sample that is likely to be biased against support
ing our maintained hypothesis. That is, our sam
ple is drawn from the largest firms in the industry, and it is likely that we undersample the part
of the industry for whom the FSLIC forbearance
policy has the most value.
TNWA,
Ue
IV. Conclusions and Policy Implications
Deposit-insurance guarantees and forbearances
have value. The value of FSLIC deposit guarantees
and forbearances is reflected in the market value
of thrift institution stocks. Proxies for FSLIC for
bearances and forbearance policy are shown to
be related to thrift charter values. The empirical
results of this paper support Kane’s (1986) argu
ment that FSUC forbearances and guarantees are
an increasingly important source of thrift charter
value. Our results also support Thomson’s (1987)
theoretical result that the extension of forbear
ances to stockholders of insolvent institutions
increases the value of stockholders’ equity.
Because deposit-insurance forbear
ances to stockholders increase the value of the
stockholders’ position in the firm at the expense
of the federal deposit guarantor, and ultimately the
federal taxpayer, the federal deposit-insurance
agencies should always close out the position of
the stockholders when reorganizing insolvent insti
tutions. Capital forbearance programs, such as
those utilized by the FHLBB in dealing with thrift
insolvencies and those being used by bank regula
tors for agricultural and energy lenders, result in a
bailout of deposit institutions’ stockholders by the
federal taxpayer. Our results support the concept
of the management consignment program current
ly used by the FHLBB to reduce the unintended
3
3
value of deposit-insurance subsidies. However,
our results also indicate that the FDIC should re
think its capital forbearance and open-bank assis
tance policies, unless the bailouts of existing man
agements and shareholders of failed and failing
banks are the intended results of those policies.
Benston, George J. “An Analysis of the Causes of
Savings and Loan Association Failures,” New
York University Graduate School of Business
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5
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