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Economic Review

■ Quarter I 1987
Concentration and Profitability in
Non-MSA Banking Markets

■ Quarter III 1987
Can Services Be a Source of Export-led
Growth? Evidence from the Fourth District

by Gary Whalen

by Erica L Groshen

The Effect of Regulation on
Ohio Electric Utilities

Identifying Amenity and Productivity Cities
Using Wage and Rent Differentials

by Philip Israilevich
and K.J. Kowalewski

by Patricia E. Beeson
and Randall W. Eberts

Views from the Ohio Manufacturing Index

FSLIC Forbearances to Stockholders and
the Value of Savings and Loan Shares

by Michael F. Bryan
and Ralph L Day

■ Quarter II 1987
A New Effective Exchange Rate Index
for the Dollar and Its Implications
for U.S. Merchandise Trade
by Gerald H. Anderson,
Nicholas V. Karamouzis
and Peter D. Skaperdas

How Will Tax Reform Affect Commercial
Banks?
by Thomas M. Buynak

by James B. Thomson

■ Quarter IV 1987
Learning, Rationality, the Stability of
Equilibrium and Macroeconomics
by John B. Carlson

Airline Hubs: A Study of Determining Factors
and Effects
by Paul W. Bauer

A Comparison of Risk-Based Capital and
Risk-Based Deposit Insurance
by Robert B. Avery
and Terrence M. Belton

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E C O N O M I C
1 9 8 7

2

Concentration and Profitability in
Non-MSA Banking Markets. Industrial

organization economists have traditionally
viewed market structure as the primary
determinant o f firm conduct and perfor­
mance. Recently, as barriers to competition
in financial services have eroded, this view
has been increasingly criticized. Using
recent data from a sample o f 191 banks,
economist Gary Whalen examines the
nature o f the relationship between market
structure and bank performance and finds
that the traditional view is not supported by
the evidence.

R E V I E W

Q U A R T E R

Economic Review

1

is published quar­

terly by the Research Department of
the Federal Reserve Bank of Cleve­
land. Copies of the issues listed
here are available through our Public
Information Department,
216/579-2047.

Editor: William G. Murmann
Assistant Editor: Robin Ratliff
Design: Michael Galka
Typesetting: Liz Hanna

Opinions stated in

Economic Review

are those of the authors and not
necessarily those of the Federal
Reserve Bank of Cleveland or of the
Board of Governors of the Federal

"I
The Effect of Regulation on Ohio
JL \J Electric Utilities. Previous

researchers have neglected to look for a
regulatory bias on the rate o f technical
change implemented by electric utilities.
Economists Philip Israilevich and K.J. Kowa­
lewski find that regulation has retarded the
rate o f technical change experienced by a
sample o f Ohio electric utilities over the
1965 to 1982 period.

O

Views from the Ohio Manu-

Ld \ J facturing Index. Interest in U.S.

manufacturing trends has heightened the
need for more timely data on regional
manufacturing production. Recently, a set o f
experimental indexes o f manufacturing in
Ohio has been developed by the Federal
Reserve Bank o f Cleveland. This article
introduces the Ohio Manufacturing Index
and briefly examines the patterns o f manu­
facturing growth occurring in the state over
this expansion.

Reserve System .

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IS S N 0013-0281

Concentration and
Profitability in Non-MSA
Banking Markets
by Gary Whalen
Gary Whalen is an economist at the
Federal Reserve Bank of Cleveland.

Introduction
Until quite recently, industrial-organization econ­
omists, bank regulators, and the Justice Depart­
ment shared the view that market structure, that is,
the number and size distribution of competitors in
a market, is the primary determinant of the con­
duct and performance of banks operating in that
market. More particularly, the traditional structur­
alist view is that the greater the share of the mar­
ket controlled by the largest competitors or,
alternatively, the higher the market concentration,
the greater the likelihood that the firms will be
able to agree collusively to raise prices above
costs and so earn supranormal or monopoly
profits.
Concentration and bank profitability
have been found to be positively related in a
number of empirical studies, and these findings
have been interpreted by structuralists as evi­
dence that their position is correct.1
The presumption that the structur­
alist view is valid is reflected in the Justice
Department’s merger guidelines, which are used
by regulators to identify bank acquisitions and
mergers likely to have anti competitive effects. In
essence, the guidelines generally proscribe bank
regulators from approving acquisitions and
mergers that would cause market concentration
to rise above an assumed critical collusionfacilitating level.

In the 1980s, however, a number
of legal, regulatory, and technological develop­
ments and additional theoretical and empirical
work have raised questions about the appropri­
ateness of using the structuralist paradigm as a
basis for antitrust policy. In particular, the grow­
ing importance of potential competitors in an
increasingly deregulated environment has been
emphasized by critics of the traditional view.2
Other critics have suggested that
the positive relationship between concentration
and profitability found in previous empirical stud­
ies may not be attributable to collusion and does
not necessarily indicate unidirectional causation
running from structure to performance.3They
suggest that performance determines market
structure rather than the reverse. One author has
dubbed this the “efficient structure” hypothesis.4
Superior efficiency, management, or luck cause
firms to be profitable and to increase their market
share, resulting in market concentration. Market
share, a proxy for relative firm efficiency, is thus
positively related to profitability. The positive
relationship between concentration and profita­
bility is spurious and simply reflects the correla­
tion between market share and concentration.

2

For a discussion of these developments and their implications,
see McCall and M cFadyen (1986). See also the work on contest-

able market theory in Baumol, et al. (1982) and the discussion of the
structuralist view in Brozen (1982).

1

See, for example, Rhoades (1982).

3

See Dem setz (19 74) and Smirlock (1985).

4

Smirlock, op. cit.

This study represents an attempt to
provide additional insight on the nature of the re­
lationship between market structure and bank per­
formance. Specifically, the relationship between
bank profitability and concentration w ill be exam­
ined using recent data for a sample of 191 institu­
tions drawn from non-metropolitan statistical area
(MSA) counties in O hio and Pennsylvania.
In the following section, some criti­
cisms of the traditional view will be discussed
and previous empirical studies will be briefly
reviewed. Next, the data and sample design will
be discussed. In the fourth section, the data will
be analyzed in several ways. Finally, a summary of
the results and conclusions will be presented.

I. Problems with the Traditional View
The traditional structuralist view reflects several
implicit assumptions that appear to be question­
able. The first is that creating and enforcing tacit
collusive agreements is relatively easy. For a col­
lusive agreement to be stable, participating firms
must institute some mechanism to set and adjust
price(s) and allocate market shares. This is not a
trivial exercise, particularly for banks, which are
multiproduct firms selling complex, heterogene­
ous products and services in a number of differ­
ent geographic markets.
The second is that technological
conditions, regulation, other barriers to entry, or
the threat of predation allow colluding firms in
concentrated markets to disregard potential com­
petitors. Concentration-related monopoly power
and profits can exist and persist only when entry
by potential competitors can be effectively pre­
vented by incumbent firms. In recent work, theo­
rists have demonstrated that when barriers to
market entry and exit are low, or a market is con­
testable, it is possible to have outcomes approx­
imating those of perfect competition even if the
number of actual competitors is quite small or
concentration is high.5
Geographic and product market
barriers to competition faced by banks and other
financial intermediaries admittedly were formid­
able prior to the 1980s. Price competition was
constrained by interest rate ceilings on deposits
and on some types of loans as well. However,
this situation has changed dramatically in the past
few years. Intrastate and interstate barriers to geo­
graphic expansion by commercial banks and by
savings and loan institutions (S&Ls) have been
removed in a large number of states. Remaining
barriers have been circumvented in various ways

See Baumol, et a!., op. cit.

(with loan production offices and nonbanking
holding company subsidiaries, for example). The
Monetary7Control Act of 1980 and the Gam-St
Germain Act of 1982 essentially allow S&Ls to
offer all the financial products and services of
commercial banks. Largely unregulated nonbank
financial companies also now compete aggres­
sively for both loan and deposit customers of
banks. In addition, the increasing sophistication
and declining cost of computer and telecommu­
nications technology have made it possible for
financial institutions to compete effectively in a
geographic area without an extensive investment
in brick and mortar offices. Financial intermediar­
ies also now are basically free to compete on a
price as well as a nonprice basis.
These developments have made it
much easier for banks and other types of
financial -services providers to compete for cus­
tomers in any given local loan or deposit market.
The implication is that market structure may not
be the primary determinant of bank performance
in the current environment.

II. Review of Previous Empirical Studies
Comprehensive reviews of structure-performance
studies in banking published prior to 1984 have
been done by Rhoades (1982) and Gilbert (1984).
Although the two authors reviewed many of the
same studies, their evaluation of the empirical
evidence differs considerably. The former con­
cluded that the results suggest that bank market
structure influences both profit and price perfor­
mance in the manner predicted by the structural­
ist paradigm. The latter concluded that the results
do not consistently support or reject the hypothe­
sis that market concentration influences bank per­
formance. Both concur that where a significant
positive concentration impact on prices or profit­
ability was found, the magnitude of the impact
was typically slight. Gilbert emphasizes that the
positive impact does not necessarily imply that
collusion is the cause.
More recent studies of the structureperformance relationship have been done by
Burke and Rhoades (1985), Smirlock (1985),
Smirlock and Brown (1986), and Whalen (1986).
Burke and Rhoades explore the relationship
between bank profitability averaged over the
1980-84 interval and the number of bank compet­
itors faced using a national sample of more than
7600 institutions. First, they calculate and com­
pare mean profit rates for sample banks operating
in 1-bank, 2-bank, 3-bank and 4-bank non-MSA
markets and MSA markets and find results con­
sistent with the traditional structuralist view. The
mean profitability of banks in 1-bank markets is
significantly greater than the means of the other
classifications. Consistent results were found for

3

the other non-MSA markets (that is, mean returns
in 2-bank markets are above those in markets
with a larger number of competitors, and so on).
Burke and Rhoades also explore the relationship
between their profitability variable and a binary7
market structure variable (equal to one for MSA
banks, equal to zero otherwise) using regression
analysis. Additional nonstructural control varia­
bles are also employed in the regression. Again,
the results are in line with the traditional view.
The estimated coefficient on the market structure
variable is negative and significant, indicating
banks operating in urban markets with large num ­
bers of competitors are less profitable than ruralmarket banks facing four or fewer competitors.
The authors conclude that the re­
sults suggest “...banks in monopolistically or oligopolistically structured markets likely pay lower
rates on deposits, charge higher rates on loans
and services, or both... [suggesting] that out-of­
market and limited-purpose competitors do not
provide effective competition to banks in highly
concentrated markets. Such markets are appar­
ently not contestable probably because barriers to
entry exist in real-world markets.”6
Although the results were inter­
preted by the authors as support for the traditional
structure-performance view, alternative explana­
tions for the findings exist. In particular, the sig­
nificant differences in mean returns may be largely
due to temporary regional differences in economic
activity rather than differences in the number of
competing banks faced in local markets. Mean
returns were calculated for each sample bank
over the 1980-84 interval. Over the first three
years of this period, the energy and agricultural
sectors were booming. As a result, banks located
in agricultural and energy-producing states were
highly profitable. Coincidentally, many of these
states have restrictive geographic branching laws
and so have a relatively large number of local
markets with few competing banks. Thus, it is
possible that local economic conditions rather
than the number of competitors are responsible
for the observed differences in mean bank profit­
ability in the sample.
In the regression analysis, the
authors attempt to control for other factors
thought to impact bank profitability. However,
several potentially important variables were not
included and may have affected the reported
results. In particular, no thrift-presence variable
was employed even though S&Ls possessed
much the same powers as banks after 1982. Also,
a bank-market-share variable was not employed.

Burke and Rhoades (1985), p. 11 .

As noted above, it has been argued by some that
the positive relationship between profitability and
market concentration found in empirical studies
is spurious and will not be evident if differences
in market share are taken into account.7
Finally, it is not clear that the report­
ed results suggest that potential competition is
unimportant. The mean returns used in the t-tests
are computed for each market type using all such
banks in the sample. That is, banks in each
class are pooled regardless of differences in state
branching laws. Since differences in bank branch­
ing restrictions should have an important impact
on the intensity of potential competition, the
mean-profitability test results do not provide any
insight on the potency of this force. In fact, the
regression results do provide support for the
hypothesis that potential competition is impor­
tant. Specifically, the two state branching dum ­
mies included in the estimated equation (for unit
banking and limited branching states) have posi­
tive significant coefficients, indicating that bank
profitability is higher in states with branching
restrictions.
Smirlock (1985) uses regression
analysis to investigate the profitabilityconcentration relationship using a sample of
more than 2,700 banks drawn from unit-banking
states in the Tenth Federal Reserve District. The
relationship was examined for a single year, 1978.
In essence, the study represents an attempt to
determine if a positive concentration-profitability
relationship remains evident when a bank-marketshare variable is also included in the estimated
equation. If it does, it suggests that the traditional
view is the correct one. If not, and if the marketshare variable is significant, it suggests that the
“efficient structure” hypothesis is correct. The
market structure variable used was the three-bankconcentration ratio. The market-share variable is
each bank’s share of commercial bank market
deposits. Several other additional common con­
trol variables are also employed.
Smirlock concludes that the regres­
sion results support the efficient structure rather
than the traditional concentration-collusion view.
Market share is positively and significantly related
to profitability even when concentration is includ­
ed in the estimated equation. However, he finds
a significant positive concentration-profitability
relationship only when the market-share variable
is omitted from the estimated equation. When
both are included, the coefficient on the concen­
tration variable becomes insignificant.

I

7
/

See the discussion in Smirlock, op. cit., pp. 70 -71

In the later Smirlock and Brown
(1986) paper, additional empirical evidence in sup­
port of the efficient structure hypothesis is pre­
sented. The same sample of banks is used to esti­
mate several variants of a profit function. If the
traditional concentration-collusion hypothesis is
valid, the expectation is that secondary or fringe
firms will act as price-setters. Conversely, if the ef­
ficient structure hypothesis is valid, the fringe
firms should act as price-takers. Leading firms may
act as price-setters under either hypothesis. The
profit function can be, and is, used to test whether
a firm is a price-setter or price-taker. The estima­
tion results indicate that leading firms exhibit
price-setting behavior, while secondary “fringe”
firms act as price-takers, regardless of market
concentration. Further, there is no evidence that
collusion increases with market concentration.
The study by Whalen (1986) repre­
sents a simple attempt to examine the relation­
ship between the number of banks competing in
a market and bank profitability for a sample of
banks drawn from O hio and Pennsylvania over
the 1976-85 interval. The study was designed to
provide insight on whether potential competition
had become an effective disciplinary force over
the past decade. Both states liberalized their
bank-branching laws over the period of observa­
tion. Further, thrifts are an important force in
both states, and possessed essentially all the
powers of banks after 1982. Thus, barriers to
competition were presumably lower at the end of
the period than they were at the outset.
Following the approach of Burke
and Rhoades, sample banks were classified
according to the number of competing banks
faced in the market. Three classes were created
for non-MSA banks: 1-3 competing banks, 4-6
competing banks, and 7 or more competing
banks. A separate class was created for MSA
banks. Mean returns were calculated for the
banks in each class for three subperiods: 1976-78,
1979-81, and 1982-85. If the traditional
concentration-collusion hypothesis is valid, the
mean profitability of banks operating in highly
concentrated markets should be significantly
higher than for banks operating in markets with
larger numbers of actual competitors in each of
the three subintervals.
Empirical support for the traditional
view was found only in the first time period, be­
fore relaxation of either state’s bank branching
laws and the expansion of S&L asset and liability
powers. The findings suggest that the lowering of
barriers to actual and potential competition during
the last two subintervals largely eliminated any
concentration-related impact on bank profitability.
Thus, researchers have found sup­
port for the concentration-collusion hypothesis in
only one of the four most recent empirical studies

of the structure-performance relationship in bank­
ing.8 Further, it is not clear that the results of this
one supportive study demonstrate that the higher
profitability observed in concentrated markets is
due to collusion. A deficiency of all of the studies
is that the market presence of thrift institutions is
not taken into account.

III. Sample and Methodology
The structure-performance relationship is reex­
amined in this study, using a sample of 191 nonMSA banks located in O hio and Pennsylvania.
Non-MSA banks are studied because potential
competition should be relatively weak in such
areas, and so the sample is likely to provide evi­
dence in favor of the concentration-collusion
hypothesis— if it is in fact valid.
The relationship is investigated
over the 1982-84 interval. This period was chosen
for several reasons. Bank branching restrictions in
both states were liberalized by early 1982.
Further, the 1982 Gam-St Germain Act had given
S&Ls essentially the same asset and liability pow­
ers as commercial banks. Both of these develop­
ments should have intensified potential as well as
actual competition in local banking markets in
both states. Thus, the sample may indicate if
these developments, in conjunction with techno­
logical changes in the funds-information transfer
area, have rendered rural banking markets
contestable.
The particular banks analyzed were
selected in the following way. In each state, all
single-market banks in continuous operation over
the 1976-85 interval headquartered in non-MSA
counties were included. Single-market banks are
those with all their offices located within their
home office county. The presumption is that nonMSA counties approximate rural banking markets.
The sample must be restricted to single-market
banks so that market structure can be related to
profits earned in that market.
The profitability measure employed
is annual return on assets (net income after taxes,
before securities transactions, divided by average
total assets) averaged over the 1982-84 interval.

8

Tw o other interesting studies provide evidence that market con­
centration need not result in anticompetitive bank performance.

Hannan (1979) finds a significant relationship between a potential

entrant variable and the rate paid on savings deposits in local markets in
Pennsylvania. Shaffer (1982) obtains estimates of the elasticity of bank
gross revenue with respect to input prices and concludes that the results
indicate that the banking markets he studied are neither perfectly com­
petitive nor monopolistic. He finds that the coefficient on a concentration
variable in his estimated equation is insignificant and concludes that the
competitive forces preventing monopolistic conduct were primarily poten­
tial rather than actual or that the concentration measure did not ade­
quately proxy actual competition.

5

Mean ROA by Market Concentration Level
(Banks only)
Market concentration

Mean ROA

S.D. ROA

T-Stat

HHI < 1800 (N=62)
HHI > 1800 (N=129)

1.179
1.015

0.529
0.621

1.89

HHI < 2 000 (N=71)
HHI > 2000 (N=120)

1.171
1.001

0.512
0.635

1.95

HHI < 2 500 (N=104)
HHI > 2500 (N=87)

1.116
1.011

0.599
0.591

1.22

HHI < 3000 (N= 133)
HHI > 3000 (N=58)

1.101
0.992

0.591
0.606

1.15

HHI < 3500 (N=155)
HHI > 3500 (N=36)

1.078
1.023

0.602

0.51

0.575

SOURCE: Author’s calculations, based o n Reports o f Incom e and Condition,
Board o f Governors o f the Federal Reserve System; and on Summary o f De
posit Data, FDIC.

TABLE

1

The deposit data for the sample banks and the
non-MSA markets comes from the FDIC Summary
of Deposits tape.
The deposit data were used to gen­
erate Herfindahl-Hirschman indexes (H H I) of
market concentration for the sample banks, both
excluding and including S&Ls.9 Others have used

6

Mean ROA by Market Concentration Level
(Banks and S&Ls)
Market concentration

Mean ROA

S.D. ROA

T-Stat

1.094

HHI < 1800 (N= 109)
HHI > 1800 (N=82)

0.594
0.600

0.70

1.033

HHI < 2000 (N=129)
HHI > 2000 (N=62)

1.100
1.001

0.598
0.590

1.09

HHI < 2500 (N=153)
HHI > 2500 (N=38)

1.087
0.991

0.599
0.585

0.90

HHI < 3000 (N= 170)
HHI > 3000 (N=21)

1.055
1.173

0.618
0.368

-1.27

HHI < 3500 (N= 180)
HHI > 3500 (N = ll)

1.061
1.190

0.607
0.368

-1.08

SOURCE: Author’s calculations, based o n Reports o f Incom e and Condition,
Board o f Governors o f the Federal Reserve System; and o n Summary o f D e­
posit Data, FDIC.

9

The H HI index is the sum of the squared market shares of firms
competing in a market. The H HI takes on its maximum value of

10,000 in

monopoly markets.

-I

The three-firm-concentration ratio is typically employed.

J . W

Stated reasons for its use are ease of computation and

tendency to exhibit the significant positive relationship between concen­
tration and profitability predicted by structuralists.

alternative concentration measures for various
reasons.10 The HHI was employed because this is
the measure used by the Justice Department and
the bank regulatory agencies in implementing
antitrust policy in banking.
The relationship between concen­
tration and bank profitability is investigated in
two ways. First, mean returns are calculated for
the sample banks after the sample has been split
into two concentration categories— “high” and
“low”— that are defined in a variety of ways. If the
concentration-collusion hypothesis is correct, the
mean return of the high-concentration class
should be significantly greater than that of the
low-concentration class.
Since this approach does not con­
trol for other factors that may impact bank profit­
ability, regression equations similar to those
employed by others are also estimated. The defi­
nitions of the variables employed in the regres­
sions appear in the appendix. Specifically, the
bank profitability variable was regressed on a
measure of bank size, a multibank holding com­
pany (MBHC) affiliation dummy, a market-size
variable, market deposit growth, and the S&L
share of total market deposits, in addition to bank
market share and market concentration.
The traditional view implies that the
estimated coefficient on the market-concentration
variable should be positive and significant when
other independent variables are included in the
equation, including a firm market-share variable.
The bank-size variable is included
to determine if larger banks realize scale econo­
mies or have diversification opportunities not
available to smaller competitors. If size does
confer advantages, the sign of the estimated coef­
ficient should be positive.
If MBHC affiliation allows subsidiary
banks to realize performance advantages relative
to independent competitors, the estimated coeffi­
cient of the MBHC dummy should be positive.
The market-size variable is included
because rural markets in the sample vary greatly in
size. It has been suggested that this variable prox­
ies ease of market entry. If this is the case, the ex­
pected sign of the coefficient should be negative.
The market-growth variable is em­
ployed to proxy the strength of demand for bank­
ing services in each market relative to supply.
Rapid market growth suggests robust demand,
and so the estimated coefficient on this variable
is expected to be positive.
The S&L variable is used to proxy
the intensity of nonbank competition in each mar­
ket. Presumably, the higher the S&L share of mar­
ket deposits, the greater their competitive impact
and the lower the level of bank profitability.

Regression Results*
Independent variables
Equation

HB

BSize

Mkt

MG

SLS

MBHC

Constant

-.00073
(-0.53)
R2
.025

.00027
(1.65)
F
1.80

-.00006
(-0.51)

-.00815
(-2.71)

.1207
(0.82)

1.179
(7.20)

.00798
(1.96)

-.00341
(-1.81)
R2
.045

.00051
(2.75)
F
2.47

-.00005
(-0.44)

-.00791
(-2.65)

.1438
(0.98)

1.012
(7.68)

.01682
(2.97)

-.00548

.00054

-.00004

(-2.63)
R2
.064

(2.93)
F
2.87

(-0.31)

-.00715
(-2.41)

.1732
(1.19)

1.221
(7.58)

MSB

-.000007
(-0.17)

(1)

(2 )

-.000122

(3)

(- 2 .21 )

*The dependent variable in each equation is bank return on assets averaged over the 1982-84 interval.
SOURCE: Author’s calculations, based on Reports o f Incom e and Condition, Board o f Governors o f the Federal Reserve System; and on
Summary o f Deposit Data, FDIC.

TABLE

3

IV. Results
Mean returns for the sample banks, broken down
by concentration class, appear in table 1. The
concentration measures in table 1 are calculated using
only the commercial banks operating in the mar­
ket. The first dichotomy, using HHI equal to 1800
as the breakpoint, reflects the Justice Department’s
definition of a highly concentrated banking mar­
ket, presumably prone to collusion. The other break­
downs represent an attempt to determine if there
is some higher level of market concentration at
which supranormal bank profits become evident.
The results do not support the
concentration-collusion hypothesis. In particular,
for all breakdowns examined, mean profitability
is higher for banks in the low-concentration class.
T-tests indicate that the observed differences in
mean returns are statistically significant for the
HHI=1800 and HHI=2000 breakdowns.
The results differ somewhat if S&Ls
are considered. These results appear in table 2.
Once again, for HHI breakdowns up to 2500, mean
returns are higher for the low-concentration class
than they are for the more concentrated one.
When the HHI breakpoint is 3000, mean returns
are higher for banks in the more-concentrated
class. However, none of the differences in mean
returns are statistically significant. Thus, the
results do not support the traditional view.
The regression results are presented
in tables 3 and 4.11 Once again, the concentrationcollusion hypothesis is not supported. Instead,
the results mirror those of Smirlock and suggest
that the efficient structure view is the correct one.
Specifically, whether S&Ls are included in the
concentration and market-share calculation or
not, the concentration variable has a negative,

insignificant coefficient when the market-share
variable is excluded from the estimated equation.
When a market-share variable is also employed,
the concentration-variable coefficient remains
negative and becomes significant. The estimated
coefficient on the market-share variable is con­
sistently positive and significant in equations with
and without a concentration variable.
These results are not sensitive to
the concentration measure employed. When the
three-firm concentration ratio is used, similar
results are obtained, both when thrifts are
included and excluded.
The reasons for the negative, sig­
nificant coefficient on the concentration variable
in several of the estimated equations are unclear,
although a similar result was reported in Smirlock
(1985). One possible explanation is that non­
price competition may be more intense in more
concentrated markets and so bank profitability is
lower. Another is that managers in more concen­
trated markets can more easily engage in
expense-preference behavior and so bank costs
in such markets are higher and profitability is
lower.12 Some researchers have suggested that
managers in concentrated markets will lim it the
amount of risks they take (i.e., choose the “quiet
life”) and so could earn lower returns.13 Other

n

A formal test w as conducted to determine if it w as appro­
priate to pool the Ohio and Pennsylvania banks. The calcu­
lated F-statistic w as roughly 0.50, which is well below the critical level,

and so pooling w as deemed acceptable.

12

13

For a discussion of expense-preference behavior, see
Edwards (19 77).
The possibility that managers might opt for the “quiet life" in
concentrated markets is explored in Heggestad (19 77).

Regression Results*
Independent variables
Equation

HS

MSS

-.000001
( 0.02)

(1 )

(2)

.00893
(1.89)

BSize

Mkt

MG

SLS

MBHC

Constant

-.00078
(-0.58)
R2
.025

.00028

-.00006
(-0.52)

-.00816
(-2.60)

.1205
(0.82)

1.156
(6.18)

-.00288
(-1.66)
R2

.00050
(2.70)
F
2.42

-.00005
(-0.42)

-.00627
(-1.99)

.1423
(0.97)

0.979
(6.77)

.00052
(2.85)
F

-.00003
(-0.26)

-.00666
(-2.14)

.1559
(1.08)

1.234
(6.66)

.043
(3)

-.000175
(-2.17)

.02063
(2.88)

-.00489
(-2.51)
R2
.062

(1.71)
F
1.79

2.79

*The dependent variable in each equation is bank return on assets averaged over the 1982-84 interval.
SOURCE: Author’s calculations, based o n Reports o f Incom e and Condition, Board o f Governors o f the Federal Reserve System; and on
Summary o f D eposit Data, FDIC.

TABLE

4

explanations exist.14 Additional research appears
necessary to explain this finding and is beyond
the scope of the present paper.

V. Summary and Conclusions
The empirical results obtained using this sample
of non-MSA banks do not support the
concentration-collusion hypothesis. That is, a
strong positive relationship between market con­
centration and bank profitability was not detected
using either type of statistical analysis. Instead,
the findings are in line with those reported in
Smirlock (1985). That is, bank market share was
found to be positively and significantly related to
bank profitability both when concentration was
included in the estimated regressions and when
it was not. In fact, in equations that included both
variables, the concentration variable had a nega­
tive, significant coefficient, rather than the
expected positive one. The fact that the results
closely mirror those of Smirlock, despite the
much smaller sample size and different time
period, with S&Ls excluded and included, lends
credence to the view that the efficient structure
hypothesis is the correct one.

14

See Smirlock (1985), p. 78, footnote 18.

The results suggest that high
market concentration is unlikely to lead to collu­
sion and monopoly profits, at least in states that
allow banks some freedom to branch. The im pli­
cation is that a purely structuralist antitrust policy
should be tempered with judgment, particularly
in the determination of critical tolerable concen­
tration levels.

APPENDIX

DEFINITION OF VARIABLES
HB: Herfindahl-Hirschman Index of market con­
centration, defined using commercial banks only.
HS: Herfindahl-Hirschman Index of market con­
centration, defined using both commercial banks
and S&Ls.
MSB: Bank share of commercial bank deposits in
the market.
MSS: Bank share of total bank and thrift deposits
in the market.
BSIZE: Bank total deposit size.
MKT: Total bank and thrift deposits in the market.

Heggestad, Arnold J. “Market Structure, Risk and
Profitability in Commercial Banking,”
vol. 32, no. 4 (September 1977),
pp. 1207-16.

Journal

oj Finance,

McCall, Alan S., and James R. McFadyen. “Bank­
ing Antitrust Policy: Keeping Pace With
Change,”
vol. 10,
no. 1 (Summer 1986), pp. 13-20.

Issues in Bank Regulation,

Morris, Charles S. “The Determinants of Banking
Market Structure,” Working Paper 86-07, Feder­
al Reserve Bank of Kansas City, September
1986.

SLS: S&L share of bank and thrift market deposits.

_________“The Competitive Effects of Interstate
Banking,”
Federal Reserve
Bank of Kansas City, vol. 69, no. 9 (November
1984), pp. 3-16.

MBHC: Dummy variable equal to one if a bank is
a member of a multibank holding company,
equal to zero otherwise. All variables, unless other­
wise noted, are calculated using June 1984 data.

Pozdena, Randall J. “Structure and Performance:
Some Evidence From California,”
Federal Reserve Bank of San Fran­
cisco, Winter 1986, no. 1, pp. 5-17.

MG: Percentage change in total market deposits,
1980-84.

REFERENCES
Baumol, W illiam J., John C. Panzar, and Robert
D. W illig.
New York: Harcourt,
Brace and Jovanovich, 1982.

Contestable Markets and the Theory
of Industry Structure,

Economic Review,

Review,

Economic

Rhoades, Stephen A. “Structure-Performance
Studies in Banking: An Updated Summary
and Evaluation,” Staff Study No. 119, Board of
Governors of the Federal Reserve System,
Washington, D.C., August 1982.

Brozen, Yale.
New York: Macmillan Publishing
Company, Inc., 1982.

Shaffer, S. “A Non-structural Test For Competi­
tion in Financial Markets,” Proceedings of a
Conference on Bank Structure and Com peti­
tion, Federal Reserve Bank of Chicago, April
12-14, 1982, pp. 225-43.

Burke, J., and S. Rhoades. “Profits, Potential
Com petition and ‘Contestability’ in Highly
Concentrated Banking Markets,” unpublished
manuscript, Board of Governors of the Fed­
eral Reserve System, 1985.

Sims, Joe, and Robert H. Lande. “The End of
Antitrust— Or a New Beginning?”
vol. XXXI, no. 2 (Summer 1986).

lic Policy,

Concentration, Mergers, and Pub­

Demsetz, Harold. “Two Systems of Belief About
Monopoly,” in H. Goldschmid, H. Michael
Mann and J. Fred Weston, eds.,
Boston:
Little, Brown and Co., 1974, pp. 164-84.

Industrial
Concentration: The New Learning,

Edwards, Franklin R. “Managerial Objectives in
Regulated Industries: Expense-Preference Be­
havior in Banking,”
vol. 85 (February' 1977).

Economy,

Journal of Political

Bulletin,

Antitrust

Smirlock, Michael, and David Brown. “C ollu­
sion, Efficiency and Pricing Behavior: Evi­
dence From the Banking Industry,”
vol. XXIV, no. 1 (January 1986), pp.
85-96.'

Inquiry,

Economic

Smirlock, Michael. “Evidence on the (Non)Relationship Between Concentration and Profitabil­
ity in Banking,”
vol. XVII, no. 1 (February 1985).

Banking,

Journal o j Money, Credit and

Gilbert, R. “Bank Market Structure and Com peti­
tion,”
vol. XVI, no. 4, pt. 2 (November 1984).

Smirlock, Michael, Thomas Gilligan, and W il­
liam Marshall. “Tobin’s q and the StructurePerformance Relationship,”
December 1984, pp. 1051-60.

Hannan, Timothy. “Limit Pricing and the Bank­
ing Industry7,”
vol XI, no. 4 (November 1979), pp.
438-46.

Whalen, Gary. “Competition and Bank Profitabil­
ity: Recent Evidence,”
Federal Reserve Bank of Cleveland,
November 1, 1986.

Journal oj Money, Credit and Banking

Banking

Journal of Money, Credit and

nomic Review,

tary,

American Eco­

Economic Commen­

The Effect of Regulation
on Ohio Electric Utilities
by Philip Israilevich
and K.J. Kowalewski
Philip Israilevich is an economist at
the Federal Reserve Bank of Chi­
cago. K .J . Kowalewski is an econ­
omist at the Federal Reserve Bank
of Cleveland. This article was com­
pleted while M r. Israilevich was an
economist at the Federal Reserve
Bank of Cleveland.

10

Introduction
ties operating under this constraint are not produc­
During the pioneering days of the electric utility
ing electricity as cheaply as they could. Virtually
industry, it was believed that utilities were natural
all empirical tests of regulatory bias to date have
monopolies, meaning that one utility could ser­
adopted the Averch and Johnson (A-J) model,
vice a geographic area more cheaply than any
and most have found an overcapitalization bias.2
combination of smaller utilities. More recently,
The major challenge to the A-J
the economic viability of transferring or wheeling
model concerns the nature of the regulatory envi­
electricity over long distances, the development
ronment. Im plicit in the A-J model is a regulator
of small-scale generators and efficient w indm ill
that constantly monitors capital returns and adjusts
and solar power, and the increased use of cogen­
electricity prices to keep capital returns at “fair”
eration have undermined the view of electric util­
levels. Joskow (1974) argues that regulators are
more concerned with nominal electricity prices
ities as natural monopolies. Nevertheless, electric
utilities continue to be monopolies because regu­ than with the rate of return on capital. As long as
nominal electricity prices do not increase, regula­
latory agencies, such as the Public Utilities Com­
mission of O hio (PUCO), give them exclusive
tors w ill not actively enforce the rate-of-return
constraint, thereby eliminating the source of the
rights to produce and distribute electricity in
A-J bias. Moreover, utilities face additional con­
designated markets.
straints, such as fuel-cost-adjustment clauses,
These regulatory agencies also
environmental regulations, strict rules about what
attempt to impose profit ceilings on electric utili­
ties in order to push the price and consumption
capital is allowed in the rate base, and the
requirement to meet all demand at given electric­
of electricity away from monopolistic levels and
ity prices. When these additional constraints are
toward competitive levels. This is accomplished
taken
into account, the net impact on a utility’s
by regulating the rate of return on capital of elec­
production decisions is not clear.
tric utilities. The regulator determines a “fair” rate
of return that is sufficient to allow a utility to
Atkinson and Halvorsen (1984) de­
cover its capital costs. With production costs and
veloped a generalized cost model that allows for
the demand for electricity, this “fair” rate deter­
the impact of additional regulatory constraints
mines the price of electricity.
and found empirical evidence of their impact on
The impact of this type of regulation
on the production decisions of regulated utilities
This interpretation of the A - J result is attributed to Baumol and
was first described byAverch and Johnson (1962).
Klevorick (1970).
They argued that this regulation gives utilities the
Courville (19 74 ), Spann (19 74 ), Petersen (19 75), Cowing (19 78 ),
incentive to overcapitalize, that is, to employ a
and Nelson and W ohar (1983), for example, test only for an over­
capital-labor ratio that is larger than one that m ini­
capitalization bias against an alternative hypothesis of no bias. O f these
mizes costs for a given output level.1 Thus, utili­
papers, only Nelson and W ohar do not find an overcapitalization bias.

1

2

utility production decisions. However, no one has
formally tested the implications of Joskow’s view.
The purpose of this paper is to fill this gap by
estimating a modified version of Atkinson and
Halvorsen’s model. The modifications are of two
sorts. The first allows for different regulatory
impacts over time as argued byjoskow. The
second permits the use of panel data and the
estimation of total factor productivity and its
components to evaluate more accurately the
impact of regulation on the technical change
implemented by utilities.

The Short-Run Effect of Regulation on Utility Prices
Price

We find considerable circumstantial
evidence consistent with Joskow’s more general
regulatory mechanism. However, the estimation
results suggest that the impact of regulation in
O hio does not completely square with Joskow’s
expectation. In opposition to Joskow’s view, we
find that these utilities produce electricity with
their prevailing technologies more efficiently dur­
ing the years when Joskow expects regulatory
constraints to be more binding. In Joskow’s favor,
we find that regulation retards the rate of techni­
cal change implemented by these utilities to a
greater extent during the years when Joskow ex­
pects tighter regulatory constraints. To our knowl­
edge, this is the first paper to explicitly estimate a
regulators' impact on technical change in the
electric utility industry.4 Moreover, this type of
inefficiency7is surprisingly large in magnitude.
Thus, the emphasis regulators and economists
place on efficient production using a given capi­
tal stock appears to be misplaced; the retardation
of technical change implemented by these utili­
ties appears to be an important source of bias.
The first part of this paper reviews
the regulatory process and contrasts the A-J and
Joskow views. Next, the rate hearing experience
in O hio during the 1965 to 1982 period is dis­
cussed and is found to correspond quite well
with Joskow’s view of the regulatory mechanism.
The third section describes the empirical results.

I. The Regulatory Process
--- Demand
--- Marginal Revenue

Marginal Costs
Average Costs

SOURCE: Authors.

FIGURE

1

The data are a panel sample of the
seven major electric utilities in O hio over the
period 1965 to 1982.3 O hio utility data were used
because of general interest to most residents of
the Fourth Federal Reserve District. Also, because
these utilities are all privately owned, coalburning plants that are subject to the same regu­
lator, their technologies should be fairly similar.
Tli us, the estimation of a common cost structure
for these utilities should yield a smaller potential
for specification bias than is true of all previous
studies of electric utilities, whose samples
include utilities that employ varying technologies
and/or face different regulators.

3

It is useful to view the regulatory process in two
parts: 1) the
of setting a utility’s elec­
tricity price structure, and 2) the
that
initiate a rate hearing or a review of a utility’s
electricity price structure. There is little disagree­
ment among economists about the first part. Sim­
ply put, a regulatory agency such as PUCO
attempts to maintain a competitive price for elec­
tricity by regulating the rate of return on a utility’s
capital. It establishes a “fair” rate of return ( r),
taking into account all of a utility’s production
costs and the demand for its electricity, that is
consistent with a “fair” level of profit and that is
slightly higher than the utility’s cost of capital.
The “fair” return or profit on capital is then
7 rr=
where is the rate base or the book value of the
utility’s net capital stock. The basis for a rate
change and, hence, a change in the price of elec­
tricity, is the difference between this “fair” return
on capital and the utility’s accounting return on

mechanics

B

events

Br,

The seven major electric utilities in Ohio are Ohio Power; Cincin­
nati Gas and Electric; Cleveland Electric Illuminating; Columbus

and Southern Ohio Electric; Dayton Power and Light; Ohio Edison; and
Toledo Edison. Over the 1965 to 1982 period, they accounted for about
90 percent of electric power sales in Ohio.

4

Nelson and W ohar (1983) estimated the impact of a rate-of-return
constraint on

TFP and

calculated its impact on technical change

as a residual. Israilevich and Kowalewski (1987) argue that this residual
is an incorrect estimate of the regulatory impact on technical change.

capital ( 7r), which is the difference between the
utility’s operating revenues (/?) and its operating
costs
=
Electricity prices are set by the regulator to equate
with 7rr on the date of the hearing. If is less
than 7 rr, electricity prices are raised, while if is
greater than
electricity prices are decreased.
This mechanism is shown in figure
1, assuming there is only one utility serving the
market for electricity. If there were no regulation,
the utility would maximize profits (or minimize
costs) by equating marginal revenues with mar­
ginal costs, producing quantity
and charging a
price
Its profits would be
If
the utility was acting like a perfectly competitive
firm, it would maximize profits (and m inim ize so­
cial costs) by equating the market price,
to its
marginal costs and to its average costs and would
produce the quantity
In this case, its profits
w ould be zero. Note, however, that at both
and
production is efficient in the sense that
input-factor marginal products are equated to
their market prices. A regulator picks some price
that is less than
but greater than
giving
the utility a “fair” profit of
r) to cover
capital costs. At this point, production is inefficient.
This is a general description of the
price-adjustment mechanism of an electric utility
regulator. What brings a utility to a rate hearing
and what motivates a regulator are questions de­
bated by economists. The predominant answers to
these questions were influenced by Averch and
Johnson. They investigated the optimal response
of a cost-minimizing utility in static equilibrium
to a “fair” rate of return on capital regulatory con­
straint. They showed that when the rate of return
on capital constraint is binding, and when the
“fair” rate of return is larger than the cost of capi­
tal, a utility has the incentive to overcapitalize;
that is, to employ a capital-labor ratio that is larger
than the one that minimizes costs for the chosen
output level.6 This is called the
Implicit in the A-J model is the
assumption that the motivating factor behind
regulatory action is the rate of return on capital.
In the A-J model, the constraint on a utility’s
profit-maximization actions is that the actual rate
of return on capital earned by a utility is no
greater than the “fair” rate. Another assumption is
that an active regulator continually monitors util­
ity returns and pounds on a utility with a “visible
hand” to maintain the equality of a utility’s profits

(OC):5
n R'OC.

n

n

u

n r,

Qm
Qm(Pm~ ACm).

Pm.

Pc,

Qc.

Pm

Pc,

Pr

12

Pm

Qr(Pr- AC

Pc,

A-J bias.

with its “fair” profits. When a utility’s profits are
less than its “fair” level of profits, the regulator
calls a rate hearing to raise r and, hence, the utili­
ty’s price of electricity. When a utility’s profits are
above the “fair” level, the regulator calls a rate
hearing to lower rand the price of electricity.
With minor amendments, this view
of regulatory behavior predominates in the eco­
nomics literature, especially in empirical studies
of electric utility behavior, with the exception of
Joskow (1974).7 Joskow agrees that rate-of-return
regulation will give a utility the incentive to
employ an inefficient mix of input factors, but he
argues that the A-J bias may not always occur. In
Joskow’s view, regulators are political institutions
whose objective is to m inim ize “conflict and crit­
icism,” not to keep the rate of return on capital
equal to the “fair” rate.
One important source of conflict
and criticism is an increase in the nom inal price
of electricity. Consumers will agitate against
increases in electricity prices because they typi­
cally view these increases as price-gouging. If
electricity prices are not increasing, and especially
if they are falling, consumers are indifferent to
the profits earned by a utility. Thus, Joskow
argues that if utilities are able to adjust their pro­
duction and investment decisions to raise their
earned rates of return without raising electricity
prices, they will not be thwarted by the regulator.
In this case, there may be little A-J bias. O n the
other hand, Joskow argues that regulators do not
initiate any actions to raise the rate of return on a
utility’s capital when it is below the “fair” rate
unless requested to do so by the utility. Before a
rate increase is granted, the utility will earn a
return on capital below the “fair” return. In this
case, an A-J bias may appear.
Thus, in contrast to the active A-J
regulator, the Joskow regulator is passive, adjust­
ing the rate of return on a utility’s capital only
when requested to do so by a utility or by a con­
sumer advocate. As time passes, earned profits
may deviate from “fair” profits if input prices,
electricity demand, and other factors change, but
the regulator does not initiate a price change to
re-equate earned profits with “fair” profits until
the next rate hearing. In the meantime, a utility
can alter its production and investment decisions
in ways opposite to those predicted by the A-J
model. The “fair” rate of return is a means to an
end (uncontroversial electricity prices), not an
end in itself, in Joskow’s view. After reviewing the
regulatory experience across the U.S. between the
1950s and early 1970s, Joskow concludes that:
Contrary to the popular view, it

Operating costs include all noncapital costs of production.

6

does not ap-

Actually, Baumol and Klevorick (1970) argue that Averch and
Johnson did not prove this as a general result. Note that if there

are additional production factors, then the amount of capital relative to
these other inputs also will be higher than for the cost-minimizing firm.

7

A slight modification to the A - J regulatory process w as the intro­
duction of a "regulatory lag"; see, for example, Bailey and

Coleman (19 71) and Baumol and Klevorick (1970).

The Relationship of Electricity Prices
and Sales to the Frequency of Rate Hearings

initiated by

Electricity Prices (per kilowatt-hour)
Current dollars

Electricity Sales
Billions of kilowatt-hours

Rate Hearing Frequency
Percent of seven utilities

SOURCE: Public Utilities C om m ission o f O h io and Standard and Poor’s
Compustat Services, Inc., Utility Com pustat II.

FIGURE

This regulatory process is therefore ex­
tremely passive. Regulators take no action
regarding prices unless major increases or
structural changes are
the firms
under its jurisdiction. In short, it is the
firms themselves which trigger a regulatory
rate of return review. There is no “allowed”
rate of return that regulatory commissions
are continually monitoring and at some
specified point enforcing, (p. 298)
Because they work in a political en­
vironment, public utility commissions face other
sources of conflict and criticism, which have re­
sulted in two additional constraints on utility
behavior. First, when energy costs increased rapid­
ly in the mid- 1970s, utilities requested rate hear­
ings in greater numbers than in the past. This in­
creased caseload put a large burden on regulatory
agencies, who were accustomed to only a few
hearings per year. The time lag between the
request for a rate hearing and a change in elec­
tricity prices increased, and many utilities were
forced to request another rate hearing immediately
after their previous hearing. In order to shorten
this lag and to appease utilities, regulators insti­
tuted fuel-cost-adjustment clauses that permitted
utilities to pass on higher fuel costs to consumers
without the need for a formal rate hearing.
Second, the fossil-fuel generators
operating before the mid-1970s emitted a consid­
erable amount of pollution into the atmosphere.
Successful agitation by environmental advocates
forced public utility commissions to establish
limits on the amount of pollution that utilities
could emit. These additional constraints com pli­
cate the analysis of the impact of a rate-of-return
constraint on utility behavior.

2

pear that regulatory agencies have been con­
cerned with regulating rates of return per
se. The primary concern of regulatory com­
missions has been to keep
Firms which can increase
their earned rates of return without raising
prices or by lowering prices (depending
on changing cost and demand characteris­
tics) have been permitted to earn virtually
any rate of return that they can.

from increasing.

nominal prices

Formal
regulatory action in the form of rate of
return review is primarily triggered by firms
attempting to raise the level of their rates or
to make major changes in the structure of
their rates. The rate of return is then used

to establish a new set of ceiling prices
which the firm must live with until another
regulatory hearing is triggered. General
price
do not trigger regulatory
review, but are routinely approved without
formal rate of return review.

reductions

II. Rate Hearings and Average Costs
of O hio Utilities: 1965 to 1982
Some evidence consistent with Joskow’s view of
the regulatory mechanism is found in the history
of rate hearings in O hio between 1965 and 1982.
To put this evidence into perspective, first con­
sider the behavior of the average price per
kilowatt-hour of electricity charged, and the quan­
tity of kilowatt-hours sold, by the seven major
O hio electric utilities (figure 2).
For the purposes of this discussion,
three distinct periods of different electricity price
and consumption behavior can be seen: 1965 to
1968, 1969 to 1975, and 1976 to 1982.8 W ithin

8

Note that the average price shown in figure 2 is not the regulated
price, but the ratio of average total revenue for the seven utilities

to their average total sales. In general, different consumers face different

regulated price schedules, and utilities serving different geographic
markets m ay be allowed to charge different prices for the same category
of consumer.

1 3

each period, the directions of change in price and
quantity were the same for each utility in the sam­
ple. During the 1965 to 1968 period, the average
price of electricity changed very little and electric­
ity sales rose considerably. During the 1969 to
1975 period, the average annual growth rate of
electricity sales slowed, while that of prices
increased greatly. Between 1976 and 1982, the
electricity sales declined for the first time in
O hio’s history, while prices increased at their fast­
est average annual percentage rate.
It is important to note that the
average price shown in figure 2 is also the aver­
age cost of electricity. All regulators, including the
PUCO, define the price of capital to be divided
by
hence equating operating revenues with
operating costs. The neoclassical economist’s
measure of average cost uses a market price of
capital and, hence, the neoclassical measure of
average costs can differ from the PUCO’s defini­
tion. Bemdt and Fuss (1986) argue that a capital
price measure such as that used by the PUCO is
more appropriate because it is a rental price or
user cost of capital and because it controls for
changes in capacity utilization. For these reasons,
and because it is the measure the PUCO uses and
to which utilities respond, the rental price of cap­
ital is employed in this paper.
Figure 2 also shows the percentage
of the seven utilities requesting rate hearings in
each year. In the first period, utilities rarely re­
quested rate hearings, and their average costs were
falling. This behavior corresponds with Joskow’s
first proposition: “During periods of falling average
cost we expect to observe virtually no regulatory
rate of return reviews” (p. 299). The average price
of electricity was also falling during this period,
consistent with Joskow’s second proposition: “Dur­
ing periods of falling average costs we expect to
observe constant or falling prices charged by reg­
ulated firms” (p. 299). Given that there were few
rate hearings in this period, it is plausible that
utility returns on capital were greater than or
equal to the “fair” returns the PUCO would have
defined had they been requested to do so.9 Ac­
cording to Joskow, if actual returns were lower
than the “fair” return, then the utilities would
have asked for price increases. Hence Joskow’s
third proposition: “During periods of falling aver­
age costs we expect to observe rising or constant
(profit maximizing) rates of return” (p. 299).

n

B,

1 4

During the 1969 to 1975 period,
average costs increased slightly, triggering a
modest increase in the frequency of hearings,
while during the 1976 to 1982 period, average

9

It can never be known whether earned returns were greater than
“fair" returns because there were no rate hearings for all firms

during these years.

costs increased tremendously. Production costs in­
creased in the late 1960s because of inflation stimu­
lated by economic policies; they increased very
quickly and unexpectedly in the mid- 1970s be­
cause of inflation engendered by worldwide food
shortages and by the Arab oil embargo. For a given
electricity price, such increases in operating costs
drove utility profits below their “fair” levels. Utili­
ties promptly responded to these cost increases
by requesting electricity price increases that, in
most cases, were granted by the PUCO. The fre­
quency of hearings increased sharply as utilities
had trouble keeping up with the effects of the
rapid rise in costs. Viewing the 1969 to 1975
period as a transition from a period of falling
average costs to one of rising average costs, the
modest increase in rate hearings during this period
is consistent with Joskow’s fifth proposition:
The transition from a period of falling
average costs to one of rising average costs
for a particular regulated industry w ill at
first yield no observable increase in the
number of rate of return reviews filed by
the regulatory agency, but as cost increases
continue more and more rate of return
reviews are triggered as firms seek price
increases to keep their earned rates of
return at least at the level that they expect
the commission will allow in a formal reg­
ulatory hearing, (p. 300)
For estimation purposes, the 1965 to
1982 interval was divided into two periods: 1965
to 1973 and 1974 to 1982. Testable hypotheses of
the A-J and Joskow views deal with the absolute
and relative production inefficiencies of the utili­
ties in these two periods. The near absence of
regulatory hearings in the first period would sug­
gest, to both Joskow and A-J, that earned rates of
return of these utilities were at least as great as
“fair” rates of return. Averch and Johnson would
argue that earned rates of return were lower than
monopoly rates of return and, hence, that the A-J
bias should exist in the first period. O n the other
hand, Joskow would argue that earned rates of
return may have been close to monopoly rates. If
this were true, then because m onopoly rates are
consistent with efficient production, there may
have been very little A-J bias in the first period.
Indeed, as Joskow argues in his seventh proposi­
tion, production may have been very efficient in
the first period because reducing costs would
have contributed to higher earned rates of return
that were not taken away by regulators:
During periods of falling or constant nom ­
inal average costs firms have an incentive
to produce efficiently since all profits may
be kept as long as prices stay below the
level established by the regulatory7com­
mission in the last formal rate of return
review, (p. 303)

The high frequency of hearings in
the 1974 to 1982 period suggests that earned
rates of return for these utilities were lower than
“fair” rates of return for most of the period; that
is, tt
Because these earned rates were even
further away from monopolistic rates of return,
Joskow would argue that it is more likely there
are inefficiencies of the A-J type in the second
period. His proposition eight says: "During peri­
ods of rising average costs A-J type biases may
begin to become important” (p. 304). He does
not exclude the possibility that firms may con­
tinue to try to be as efficient as they were in the
first period in order to earn greater than “fair”
rates of return. However, he argues:
Unless the direction of the cost path can
be changed, however, the continuous inter­
action of firms and regulators in formal regu­
latory hearings, resulting from the necessity
to raise output prices, is exactly the situa­
tion for which the A-J type model (with
some modifications) would hold. I would
therefore expect that it is under this situa­
tion of continuously rising output prices,
triggering rate of return reviews that the A-J
type models and the associated results are
most useful, (p. 304)
Thus, Joskow would argue that
utilities would try to organize their production
more efficiently in the first period than in the
second period. His concept of production effi­
ciency includes the static notion of employing
currently available production inputs in the leastcost way for any given level of output (that is,
employing the least-cost combination of inputs
along a given isoquant) and the dynamic notion
of investing in more productive capital over time
(that is, investing in productive capital to push
the family of isoquants toward the origin). Averch
and Johnson deal only with the static notion of
productive inefficiency because their model ana­
lyzes a static equilibrium. They would argue that
the amounts of this static inefficiency are the
same in both periods because they assume a reg­
ulator who maintains the earned rate of return on
capital at its “fair” rate.
The distinction between the static
and dynamic notions of production efficiency is
important. When a public utility commission
conducts a rate hearing, it pays attention only to
the static notion of production efficiency. Indeed,
most models of regulatory impact deal only with
the static notion. However, it is conceivable that
regulation also affects the rate of technical change
implemented by utilities; if regulation biases the
amount of capital employed by a utility, it may
also bias the type of capital employed. Regulatory
impacts on overall inefficiency' and on the rate of
technical change are estimated below.

< n r.

III. Empirical Evidence About
the A-J and Joskow Views
The A-J and Joskow views are examined using a
modified version of the Atkinson and Halvorsen
(1984) generalized long-run cost-function ap­
proach with capital ( ), labor
and fuel (
as inputs.10 Atkinson and Halvorsen argued that
the long-run neoclassical cost-function approach
is incorrect for a regulated firm because it
assumes the firm is m inim izing costs in a per­
fectly competitive world constrained only to pro­
duce a given level of output.11 When a firm is
subject to a number of regulatory constraints, as
is generally true today, firms view all input prices
differently from their market or rental prices. The
exact specification of these nonmarket or
“shadow” prices depends on the exact form of
the additional constraints. Atkinson and Hal­
vorsen approximated these shadow prices by
simple proportional relationships with market
prices; that is, the shadow price of input
*=
where
is its market price and
is a con­
stant. Thus, the generalized cost function is
simply the neoclassical cost function with
substituted for
Instead of m inim izing longrun actual costs, a utility is assumed to minimize
long-run
costs by equating the
marginal cost of each input with the amount of
the input used.
The modifications made to the
Atkinson and Halvorsen approach are 1) the
inclusion of time variables to accommodate panel
data and to permit the estimation of total factor
productivity (
and its returns to scale and
pure technical change components, and 2) the
distinction between the 1965 to 1973 and 1974 to
1982 time periods.
is measured as the change in
the cost of production not due to changes in in­
put prices, and reflects the overall productivity of
all inputs rather than the productivity of a single
input such as labor. The neoclassical approach to
the measurement of
assumes an optimal dis­
tribution of production resources in a firm, which
is an inappropriate assumption for regulated
electric utilities. The generalized-cost-function
approach yields an estimate of
that is con­
sistent with regulated behavior. The most impor­
tant variable for examining Joskow’s view on
productivity behavior is the pure technical
change component of
Gollop and Roberts
(1981), among others, argue that this component
is a better measure of productivity than

K

kiPi,

(L),

Pt

F)

kt

i: P

P*

Pt.

shadow

shadow

TFP)

TFP

TFP

TFP

TFP.

TFP.

See Israilevich and Kowalewski (1987) for complete details
about the data, the specification and estimation of the
shadow-cost model, and the results.

n

utilities.

Nevertheless, some authors, for example Gollop and Roberts
(19 8 1, 1983), use the neoclassical approach to study electric

15

The distinction between the two
periods is made by estimating separate coeffi­
cients for them. This allows the production deci­
sions, as well as the degree of regulatory con­
straint, to differ between the two periods.12
The
coefficients measure the
degree to which the neoclassical first-order con­
ditions are not satisfied and, hence, serve to test
for production input biases. If all
equal one,
then shadow prices equal market and rental prices,
and regulation does not affect production deci­
sions; actual, not shadow, long-run costs are m in­
imized. If the
for all inputs except capital
equal one, then there is only an overcapitaliza­
tion bias. If any other
do not equal one,
regardless of the
value for capital, then the A-J
view is rejected.
The results of estimating the
model over the 1965 to 1982 period show that
both
and
are separately and jointly statisti­
cally different from one at better than a 5 percent
significance level in both periods.13 Thus, pro­
duction efficiency is rejected, and the neoclassical
cost-function approach for regulated firms
employed by Gollop and Roberts (1981) and
others is inappropriate for this sample. Moreover,
these results reject the A-J view over the whole
sample; regulation affects the efficient utilization
of all production inputs by these utilities.
Another test of the A-J view, and a
test of the implications of Joskow’s view, is
whether production inefficiencies resulting from
regulation differ in the 1965 to 1973 and 1974 to
1982 periods. The A-J view is that the inefficiencies
should be the same in each period, while the
Joskow view is that there should be greater ineffi­
ciencies in the second period than in the first.
Two approaches are taken here. In the first, the
differences in
and
are examined. The A-J
view is not rejected if the difference in
between the two periods is insignificantly differ­
ent from zero and both
equal one. If the
suggest greater inefficiencies in the second
period than in the first, then the Joskow view is
not rejected.
The test results show that the A-J
view is rejected at better than 0.5 percent, and the
differences in the
and
coefficients between
the two periods are significantly different from
zero at better than 5 percent. However, the Jos­
kow view is also rejected, because the differences
and
coefficients, second period
in the

kt

kt

kt

kt

kt

kK

16

kF

kK

kF

kK

kF

ki

kK

kK

12
-1

O

JL ^

kF

kK

kF

kF

kK

A test of similar production behavior in the two periods was
convincingly rejected.
For technical reasons, only two of the three
mated. The

to one, and only
estimated.

kF

minus the first, are significantly negative; to not
reject Joskow, this difference should have been
positive. Unfortunately, due to technical reasons
related to the specification of the cost function,
the sources of the differences in these coeffi­
cients cannot be identified.
The second approach examines
estimates of the differences in total and dynamic
inefficiency due to regulation between the two
periods. The full cost of regulation and, hence,
the magnitude of the inefficiencies created by
regulation, cannot be estimated because there is
no evidence to suggest how the utilities would
have organized their production had regulation
not existed over the sample period. O f course, it
is impossible to know how these utilities would
have behaved without regulatory constraints. For
example, the activities of production and distribu­
tion might have been separated, different
amounts of capital might have been employed,
and different technologies might have been
chosen.14 Hence, it is impossible to know what
these firms’ cost functions and associated returns
to scale and productivities would have been.
“Instantaneous” inefficiency esti­
mates can be computed, however. A total ineffi­
ciency measure compares actual utility costs pre­
dicted by the estimated model with the actual
costs predicted by the model, but with
and
set equal to one in both periods. That is, cur­
rent production costs for actual levels of output,
which are generated by current capital, labor, and
fuel inputs; production techniques; and regulatory
constraints, are compared with the costs generated
with the same input levels and production tech­
niques and for the same actual output levels, but
without the regulatory constraints. This estimate,
also examined by Atkinson and Halvorsen, mea­
sures the shift in the cost curve due to regulation.
An estimate of the dynamic notion
of inefficiency can be obtained by examining the
technical change experienced by these utilities
with and without regulation. Technical change is
defined here as the negative of the derivative of
total costs with respect to time, holding all other
factors constant. It is a function of a constant
term, shadow input prices, output (returns to
scale), and time, and it shifts the position of a
firm’s average cost curve over time. As above,
technical change with regulation is that im plied
by the estimated model; technical change with­
out regulation is that im plied by the estimated
model, but with
and
set equal to one.
The difference does not have a real-world coun-

kK

k,

and

kt

can be esti-

coefficient on the price of labor is normalized

k F,

for capital and fuel, respectively, are

"|

/

Under the current regulatory environment, the production and

_L

x.

distribution of electricity must be handled by each utility.

Moreover, the transferal of electric power across state lines is also
impeded.

terpart or explanation, but it does indicate the
direction of regulatory bias.
These inefficiency estimates provide
mixed evidence about Joskow’s view. The differ­
ence in the total inefficiency measure between
the two periods is the opposite of Joskow’s expec­
tation. Instead of greater total inefficiency in the
second period, when Joskow expects regulatory
constraints to be binding, our estimates show
greater total inefficiency in the first period, when
Joskow expects regulatory constraints to be less
binding. In the first period, the total inefficiency
varies between 26 percent and 49 percent and
averages 36 percent. In the second period, it var­
ies between 16 percent and 19 percent and aver­
ages 17 percent. This difference in total ineffi­
and
ciency squares with the differences in
between the two periods described earlier.
An interesting feature of these total
inefficiency estimates is their large magnitude in
the first period. Atkinson and Halvorsen find
much smaller inefficiency losses (9.0 percent) in

kK

kF

Estimated Technical Change
Technical change
in percentage points, average
over firms

Year

1965
1966
1967
1968
1969
1970
1971
1972

0.3
-0.1
-0.3
-0.6
-1.0
-1.2
-1.4
-1.7
-2.0
-3.4
-3.6
-3-6
-3.8
-3.8
-4.0
-4.2
-4.4
-4.6

1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
Average over year

1965-1973
1974-1982

-0.9
-3-9

their cross-section sample, which includes two of
our firms.15 However, the Atkinson and Hal­
vorsen result captures only the static portion of
total inefficiency costs because the authors do not
use time variables in their cost equation. Our
estimates include the dynamic inefficiency costs
and, hence, are more representative of the total
costs of regulation.
The difference between the Atkin­
son and Halvorsen result and ours suggests that
the dynamic inefficiency may be quite large.
Indeed, we find that regulation retarded the
growth of technical change, on average, by 0.3
percentage point per year in the first period and
by 0.4 percentage point per year in the second.
This is an important result, and one that has been
neglected by economists and regulators alike.
Regulation not only affects the efficient utilization
of existing production inputs, but it also affects
the implementation of efficient capital and man­
agement techniques over time. Unlike our total
inefficiency estimates, our dynamic inefficiency
estimates support Joskow’s view.
The behavior of technical change
over time also confirms Joskow’s view. Table 1
shows the technical-change estimates over the
whole period, averaged over all firms for each
year. As Joskow argues, the rate of technical
change is lower in the second period, when he ex­
pects regulatory constraints to be more binding.
The most notable characteristic
about these technical-change estimates is their
strong downward trend.16 Starting at 0.3 percent
in 1965, the annual average rate of technical
change drops steadily each year to -4.6 percent in
1982. This rather uniform decline, except around
1973 and 1974, when period one ends and
period two begins, is due to dominant estimated
time trends in each period. That shadow input
prices have little influence on technical change is
not surprising, because electricity production
offers little opportunity for input substitution in
the short and medium runs. The time trend cap­
tures the effects of pure technical change embod­
ied in the capital investments of these utilities
and may be additional evidence in favor of Jos­
kow’s seventh proposition. Although this is not
conclusive proof of Joskow’s seventh proposition,
because we do not know the nature of the capital
investments made in these and earlier periods, it
at least does not contradict it.

SOURCE: Authors.

*1
JL

4T

-1 Z '

A strong downward trend in the rates of technical change

1

experienced by utilities also w as found by Nelson and Wohar

0

I * is likely that our estimates are more accurate for Ohio

(1983), Gollop and Roberts (19 8 1), and Gollop and Jorgenson (1980), all

because our sample includes only Ohio firms, which are fairly

of whom used samples that ended in the 1970s. Thus, the results report­

similar in a number of important respects, as mentioned earlier.

ed here confirm these earlier findings for the late 1970s and early 1980s.

17

18

IV. Summary and Conclusions
Electric utility regulators attempt to maintain a
competitive price for electricity by adjusting the
rate of return on a utility’s capital. At first blush,
this price-setting scheme appears sensible. It
seems reasonably efficient to allow utilities to
pass along operating costs and cover their cost of
capital. However, potentially serious problems
with this type of regulation relate to consumer
reactions to price increases and to the types of
incentives given to utilities. First, price increases
may lower the consumption of electricity, which
may reduce earned rates of return below “fair”
rates and trigger a price increase, which in turn
may lower consumption and trigger another price
increase, and so on. That is, the proper response
to falling utility profits because of lower demand
may not be to raise prices.
Second, utilities may be able to ef­
fect price increases by overcapitalizing, which
inflates their rate base. Indeed, rate increases
lower the risk of capital investment below the
risk level of unregulated industries, clearly giving
utilities the incentive to overcapitalize. This poten­
tial bias was recognized by Averch and Johnson,
and many empirical studies that adopted their
model found an overcapitalization bias.
Finally, the ability to pass along
operating cost increases that originated from pro­
ductivity declines suggests that utilities may not
have the incentive to raise productivity. This
dynamic source of inefficiency was recognized by
Joskow, who also argued that the regulatory7
mechanism is more complicated than that
assumed by Averch and Johnson.
This paper is, to our knowledge,
the first to test the A-J view against Joskow’s more
general view. Using a modified version of the
generalized long-run cost function derived by
Atkinson and Halvorsen (1984) and a sample of
the seven major electric utilities in O hio over the
1965 to 1982 period, substantial evidence is
found against the A-J view. However, the evi­
dence is not wholly in agreement with Joskow’s
view, either. The circumstantial rate hearing evi­
dence is consistent with Joskow’s view of the
regulatory mechanism, but our estimation results
do not wholly confirm the implications Joskow
draws from his regulatory mechanism. Two sets
of results imply that regulatory constraints were
more binding during the years in which Joskow
expects them to be less binding. Nevertheless, in
accordance with Joskow’s view, we find that regu­
lation substantially retards the rate of technical
change experienced by these utilities, and the
retardation is greater when Joskow expects regu­
lation to be more binding. This is the first dem­
onstration of a regulatory impact on technical
change. It clearly suggests that regulators ought to

pay closer attention to the incentives they give
utilities to innovate.17
A reconciliation of these findings is
difficult. They may suggest that the circumstantial
rate hearing evidence is not closely correlated with
the degree of regulatory constraint. Utilities may
have been constrained in the 1965 to 1973 period
by the possibility or fear of a rate hearing that
would eliminate the above “fair” returns they were
currently earning. Another possibility is that fre­
quent rate hearings in the 1974 to 1982 period pre­
vented utilities from artificially fattening their rate
bases. That is, given the incentive to overcapital­
ize, utilities were prevented from taking advantage
of the regulatory system by frequent and accurate
regulatory review. In this case, the price of elec­
tricity may have remained close to competitive
levels, where production, though different from
monopolistic levels, nonetheless is efficient. The
poor technical-change performance between 1974
and 1982 may be the primary cause of the greater
rate-hearing frequency, and not the reverse.
Or Joskow may be correct, and utili
ties were simply lax about maintaining efficient
production in the first period, or they anticipated
future regulatory constraints and took actions to
fatten their rate bases while they had the oppor­
tunity.18 Clearly, there is much to learn about the
impact of regulation on utility performance.

-I “ “7
1

The poor technical-change performance also m ay be due to

/

increased investment in nuclear power plants. M any of these

plants were cancelled after the mid-1970s, but they diverted managerial
attention and funds aw ay from conventional power-generation capital
investments.
-1

Q

JLO

Jo sk o w can also be defended by arguing that our inefficiency
measures are incorrect. A s w as mentioned earlier, it can

never be known how utilities would have behaved without regulation.
Without this knowledge, any inefficiency measure can be faulted. Never­
theless, the estimated change in

kF

and

is hard evidence against Jo sk o w ’s view .

kK

between the two periods

REFERENCES
Atkinson, Scott E., and Robert Halvorsen. “Para­
metric Efficiency Tests, Economies of Scale,
and Input Demand in U.S. Electric Power
Generation,”
vol. 25, no. 3 (October 1984), pp. 647-62.

International Economic Review,

Gollop, Frank M., and MarkJ. Roberts. “Environ­
mental Regulations and Productivity Growth:
The Case of Fossil-Fueled Electric Power Gen­
eration,”
vol. 91,
no. 4 (August 1983), pp. 654-74.

Journal of Political Economy,

Averch, Harvey, and Leland L Johnson. “Behavior
of the Firm under Regulatory Constraint,”
vol. 52, no. 5
(December 1962), pp. 1052-69.

__________“The Sources of Economic Growth in
the U.S. Electric Power Industry,” in T.C. Cow­
ing and R.E. Stevenson, Eds.,
New York:
Academic Press, 1981.

Bailey, Elizabeth E., and Roger D. Coleman. “The
Effect of Lagged Regulation in an AverchJohnson Model,”
vol. 2, no. 1
(Spring 1971), pp. 278-92.

Israilevich, Philip, and K. J. Kowalewski. “A Test of
Two Views of the Regulatory Mechanism:
Averch-Johnson and Joskow,” Federal Reserve
Bank of Cleveland,
(forthcoming).

Baumol, W illiam J., and Alvin K. Klevorick. “Input
Choices and Rate-of-Return Regulation: An
Overview of the Discussion,”
vol. 1,
no. 2 (Autumn 1970), pp. 162-90.

Joskow, Paul L. “Inflation and Environmental
Concern: Structural Change in the Process of
Public Utility Price Regulation,”
vol. 17 (October 1974),
pp. 291-327.

Berndt, Ernst R., and Melvyn A. Fuss. “Productivity
Measurement with Adjustments for Variations
in Capacity Utilization and Other Forms of
Temporary Equilibrium,”
vol. 33, no. 1 / 2 (October/November
1986), pp. 7-30.

Nelson, Randy A., and Mark E. Wohar. “Regula­
tion, Scale Economies, and Productivity in
Steam-Electric Generation,”
vol. 24, no. 1 (February

American Economic Review,

The BellJournal oj Econom­
ics and Management Science,

The BellJournal
of Economics and Management Science,

metrics,

Journal of Econo­

Christensen, Laurits R., and Dale W. Jorgenson.
“U.S. Real Product and Real Factor Input, 19291967,”
ser­
ies 16, no. 1 (March 1970), pp. 19-50.

The Review of Income and Wealth,

Courville, Leon. “Regulation and Efficiency in the
Electric Utility Industry,”
vol. 5,
no. 1 (Spring 1974), pp. 53-74.

The BellJournal of
Economics and Management Science,

Cowing, Thomas G. “The Effectiveness of Rate-ofReturn Regulation: An Empirical Test Using
Profit Functions,” in M. Fuss and D. McFadden,
Eds.,
Amsterdam: North
Holland Publishing Company, 1978.

Production Economics: A Dual Approach
to Theory and Application,

Fare, Rolf, and James Logan. “Shephard’s Lemma
and Rate of Return Regulation,”
vol. 12 (1983), pp. 121-25.

ters,

Economics Let­

Gollop, Frank M., and Dale W. Jorgenson. “U.S.
Productivity Growth by Industry, 1947-1973,”
in J.W. Kendrick and B.N. Vaccara, Eds.,
National Bureau of
Economic Research, Chicago: University of
Chicago Press, 1980.

in Income and Wealth,

Studies

Productivity Mea­
surement in Regulated Industries,

Working Paper

Law atid Economics,

Economic Review,

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1983), pp. 57-79.
Petersen, H. Craig. “An Empirical Test of Regula­
tory Effects,”
vol. 6, no. 1 (Spring
1975), pp. 111-26.

The Bellfoum al of Economics
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Spann, R.M. “Rate of Return Regulation and Effi­
ciency in Production: An Empirical Test of the
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The BellJournal of
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1 9

Views from the Ohio
Manufacturing Index
by Michael F. Bryan
and Ralph L. Day
Michael F. Bryan is an economist
and Ralph L . Day is an economic
analyst at the Federal Reserve Bank
of Cleveland. The authors gratefully
acknowledge the assistance of
Diane Smith, Nannette Thompson,
and Frances Davis of the Federal
Reserve Bank of Cleveland's Data
Services Department, w ho compiled
the electric power consumption data
used in this study.

20

A Preview
Economists and other observers are closely exam­
ining the manufacturing sector these days, fearing
that America’s industrial base is disappearing. Cer­
tainly, the steady decline in the proportion of
total jobs in manufacturing, as shown in figure 1,
supports this view. However, a more careful look
reveals that manufacturing’s overall share of real
national output has remained essentially
unchanged since 1950.1

Ohio and U.S. Manufacturing Employment
Thousands, seasonally adjusted

A more reasonable worry, it would
seem, is the uneven regional distribution of manu­
facturing growth that is obscured by nationally
aggregated data. Unfortunately, the information
used by analysts to evaluate regional manufactur­
ing output has been limited to quinquennial cen­
sus data and, when available, annual survey data.
Lack of timely regional data
prompted the establishment of regionally based
production indexes by the Federal Reserve Banks
of Atlanta, Boston, Dallas, and San Francisco.2 The
Federal Reserve Bank of Cleveland has recently
developed a monthly manufacturing production
index for the state of O hio— the O hio Manufac­
turing Index (OM I).
The OMI is an experimental index
of real output by O hio manufacturers that is
derived from state-level manufacturing employ­
ment and electric power consumption data. The
OMI tracks manufacturing output at the two-digit
standard industrial classification (SIC) level of
aggregation, beginning in January 1979 and end­
ing in December 1986. The methodology and pro­
cedures used to develop the index are outlined
in the technical appendix that follows this article.

I

For an overview of developments in the U .S . manufacturing sec­
tor, see Michael F . Bryan, "Is Manufacturing Disappearing?"

Economic Commentary,

Federal Reserve Bank of Cleveland, Ju ly 15,

1985; and Patricia E. Beeson and Michael F . Bryan, “ The Emerging Ser­
vice Econom y,"

Economic Commentary,

Federal Reserve Bank of Cleve­

land, June 15, 1986.

2

Regional production indexes produced by the Federal Reserve
Banks of Boston and Atlanta have been discontinued, primarily

due to budget reductions.

In 1984, O hio firms represented 6.3
percent of the nation’s manufacturing output,
making O hio the third-largest manufacturing state,
trailing only California (11.0 percent) and New
York (7.4 percent) in manufacturing prominence.3

Manufacturing Output
Index, 1982 = 100

SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the
Federal Reserve System.

FIGURE

2

Despite this size, the cyclical pat­
terns of O hio’s manufacturing output remain large
lv unseen and are often thought to mirror national
manufacturing trends. Yet, evidence from the
OMI suggests that important differences exist
between U.S. and O hio manufacturers, particularly
within individual industries. In this article, we

Distribution of Manufacturing Output by State, 1984
(ten largest manufacturing states, nominal dollars)
Distribution
o f Output
State

United States
1. California
2. New York
3. O H IO
4. Texas
5. Illinois
6. Michigan
7. Pennsylvania
8. N. Carolina
9. New Jersey
10. Indiana

Value A dded
(m illion s $ )

Share o f
Nation

Durable

N ondurable

(%)*

(%)

983,560

__
11.0
7.4

57.6
68.1
53.7

42.4

108,373
72,361
62,346
55,556
55,246
53,069
51,725
36,682
36,543
33,762

6.3
5.6
5.6
5.4
5.3
3.7
3.7
3.4

68.3
49.9
56.1
75.8
56.2
38.7
43-3
70.3

31.9
46.3
31.7
50.1
43.9
24.2
43.8
61.3
56.7
29.7

*Durable-goods manufacturing is defined to include SICs 24, 25, and 32-39.
SOURCE: 1984 Annual Survey o f Manufactures, Bureau o f the Census.

introduce the OMI and discuss the new perspec­
tive it provides of manufacturing trends in Ohio.

I. A View of the Forest
Manufacturing employment in O hio reached a
peak of 1.4 m illion workers in March 1979. At that
time, manufacturing industries employed more
than 30 percent of the state’s workers. Since 1979,
however, manufacturing employment in O hio
has fallen by more than 20 percent. In recent
months, it was roughly 1.1 m illion workers, or
about 20 percent of O hio’s civilian work force
(figure 1). As in the nation, O hio’s manufacturing
sector has failed to register significant employ­
ment growth in nearly three years.
However, because the relationship
between employment and output is not constant
over time, due to changes in productivity and to
the substitution of capital for labor, inferences
about the manufacturing sector drawn exclusively
from a labor perspective can be misleading.
Unlike employment, real manufac­
turing output in Ohio, as measured by the OMI,
has been rising throughout most of the current
economic expansion (figure 2). Between the recessionary trough occurring in the fourth quarter
of 1982 and the fourth quarter of 1986, real m anu­
facturing output in the state rose 34.7 percent.
Manufacturing output at the national level grew at
a slower pace over the period, 30.4 percent.4
Differences between U.S. and O hio
manufacturing output trends arise principally
from two related sources. First, the level of real
output per worker (labor productivity) and the
growth rate of labor productivity are greater in
O hio than in the rest of the country. Furthermore,
the O hio manufacturing business cycle tends to
be more sharp than the national cycle, a conse­
quence of the state’s concentration of durablegoods manufacturing.
For example, 1984 census data show
that O hio workers produced roughly 8 percent
more real manufacturing output per worker than
is produced nationally. Between 1982 and 1984,
the rate of growth in labor productivity for O hio
manufacturers was roughly 20 percent, compared
with only a 16 percent gain for the nation.5 More-

3

Output estimates are based on value added.

4

comparable because of differences in methodology. How ever,

The U .S . and Ohio manufacturing indexes m ay not be perfectly

many of the data sources and the fundamental structure of the indexes
are the same.

5

These productivity estimates are based on real value added per
worker. Value added and employment data come from the

Survey of Manufactures. Nominal value-added estimates were deflated
using national price deflators supplied by the U .S . Department of
Commerce.

2

1

over, evidence from the OMI indicates that
O hio’s leading growth industries generally have
above-average labor productivity. As a result,
slightly slower rates of growth in total manufac­
turing employment since 1982 generated some­
what greater real manufacturing output gains for
O hio manufacturers than for U.S. manufacturers.

Distribution of the Ohio Manufacturing Sector by
Industry, 1984
(durable-goods industries in CAPITALS)
Industry Importance
Industry (SIC)____________

Qhio share

in

To Ohio (% ) To U.S. ( % ) of U.S. ( % )

the U.S.

1. TRANSPORTATION
17.8
EQUIPMENT (37)
2. FABRICATED
12.6
METALS (34)
3. NONELECTRICAL
11.5
MACHINERY (35)
4. PRIMARY
9.7
METALS (33)
5. Chemicals and
8.9
Allied Products (28)
6. ELECTRICAL
8.8
MACHINERY (36)
7. Food and Kindred
7.7
Products (20)
8. Rubber and
5.5
Plastics (30)
9. Printing and
4.9
Publishing (27)
10. STONE, CLAY,
3.6
AND GLASS (32)
11. Paper and Allied
2.6
Products (26)
12. INSTRUMENTS
1.7
Remaining
Manufacturers

4.7

11.6

9.7

3

6.9

11.6

1

11.4

6.4

3

4.3

14.3

1

9.6

5.9

5

11.2

4.9

5

10.0

4.9

6

3.5

10.0

1

6.8

4.6

6

2.8

8.1

2

4.2

4.0

8

4.1

2.6

17

13.6

2.2

—

SOURCE: 1984 Annual Survey o f Manufactures, Bureau o f the Census.

TABLE

2

O hio’s manufacturing recovery was
also preceded by a contraction that occurred ear­
lier and was more severe than that experienced
nationally. To illustrate, O hio’s last manufacturing
recession may be more accurately viewed as a
combination of two recessions. Between the first
quarter of 1979 and the third quarter of 1980,
manufacturing output in O hio declined by
slightly over 15 percent—about three times the
percentage drop felt at the national level (5.2
percent). O hio’s second manufacturing contrac­
tion began in the third quarter of 1981, and by
the fourth quarter of 1982, manufacturing produc­
tion had fallen 12.6 percent, compared with a
10.7 percent decline over the same period for all
U.S. manufacturers.

The relatively sharp business cycle
experienced by O hio manufacturers reflects the
state’s industrial composition (table 1). In the
latest survey year, 1984, durable-goods manufac­
turing represented 68.3 percent of the state’s total
manufacturing output. O hio is not the most
durable-goods-intensive state of the 10 largest
manufacturing states— Michigan’s durable-goods
share was 75.8 percent in 1984 and Indiana’s
share was 70.3 percent. However, the relative size
of durable-goods manufacturing is considerably
greater in O hio than is the case nationally, where
durable-goods manufacturing accounted for only
57.6 percent of the 1984 total.
Michigan’s dependence on durablegoods production is primarily a consequence of
the automobile industry’s dominance in that state
(representing about 36 percent of its manufactur­
ing output in 1984), while O hio’s durable-goods
sector is more broad-based. For example, in 1984,
O hio’s manufacturing output was distributed
among five important durable-goods and one
nondurable-goods industry (table 2). The state’s
largest manufacturing industry was transportation
equipment, representing 17.8 percent of its over­
all manufacturing production, compared with a
contribution of only 11.6 percent at the national
level. Following transportation equipment were
the fabricated metals (12.6 percent), nonelectrical
machinery (11.5 percent), primary metals (9.7
percent), chemicals (8.9 percent), and electrical
machinery (8.8 percent) industries.
In 1984, O hio led all states in out­
put for two durable-goods industries, fabricated
metals and primary metals, and for one
nondurable-goods industry, rubber and plastics.
In addition, O hio manufacturers were the
second-leading producers of stone, clay, and glass
products and the third-leading producers of
transportation equipment and nonelectrical
machinery, all durable-goods industries.
Historically, durable-goods pro­
ducers have suffered more pronounced businesscycle swings than nondurable-goods producers; a
phenomenon, it would seem, that is not yet clearly
understood (figure 3)- One view is that changes
in the economic climate, which are accompanied
by fluctuations in income and interest rates, result
in intertemporal substitutions by consumers.
Because durable goods, by definition, involve a
longer consumption horizon than nondurable
goods, these intertemporal substitutions are more
keenly felt in the consumer durables market.
A possibly complementary view,
from the perspective of the firm, is that changes
in the desired capital stock, such as those arising
from changes in consumer demand, generate
exaggerated swings in net investment. This
“acceleration principle” implies that the more
“durable” the capital stock, the more pronounced

Ohio Durable and Nondurable Goods
Index, 1982 =100

generates roughly a 1.0 percent decrease in
manufacturing output.6
Indeed, the 6 percent plunge in the
value of the dollar between June and September
1986 was probably welcomed by O hio’s manufac­
turers, as the OMI showed five consecutive month­
ly advances between July and December 1986, and
increased 2.3 percent in the final quarter, com­
pared with only a 0.8 percent increase nationally.
From the broad perspective, then,
O hio’s manufacturing economy seems to be char­
acterized by a rather pronounced cycle, resulting
from the combined influence of a large concentra­
tion of durable-goods manufacturers and a relative­
ly high and growing level of productivity.

II. A View of the Trees

SOURCE: Federal Reserve Bank o f Cleveland.

FIGURE

3

the production cycle for capital goods.
Beyond its business-cycle implica­
tions, O hio’s industrial mix probably makes the
state’s manufacturing sector more vulnerable to
pressure from foreign rivals, and implies that
O hio’s manufacturing economy is more sensitive
to international trade fluctuations than is the
national manufacturing economy. A recent analy­
sis of the impact of exchange-rate movements on
manufacturing revealed that a 10 percent increase
in the value of the dollar generates about a 0.8
percent decrease in U.S. manufacturing output,
whereas in Ohio, a similar exchange-rate increase

At the industry7level, differences between the
O hio and national manufacturing economies are
more striking. In some industries, the perfor­
mance of O hio’s manufacturers between 1979
and 1986 exceeded national growth rates, and in
a few cases, such as chemicals and fabricated
metals, O hio’s growth has been impressive. Other
industries, including paper, printing, electrical
machinery, and stone, clay, and glass manufactur­
ing, have lost ground relative to the rest of the
country since 1979.
It is not the intention of this analy­
sis to discuss each industry7in detail, and only the
state’s largest industries have been singled out for
comment. Industries that are not expressly consid­
ered in this section are presented in figures 4h
through 4o at the end of the article.

• Transportation Equipment
Transportation Equipment
Index, 1982 = 100

SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the
Federal Reserve System.

Transportation equipment manufacturing, tradi­
tionally a pivotal industry7in the national business
cycle, was hit particularly hard by the recessions
of the 1980s. The ensuing expansions, however,
allowed transportation manufacturers in the U.S.
and O hio to surpass the output peaks established
in 1979 (figure 4a).
Over the expansionary7period span­
ning the fourth quarter of 1982 and the fourth
quarter of 1986, transportation equipment output
in the U.S. grew 48.2 percent. Over the same
period, this industry’s growth rate in O hio was
50.4 percent, making transportation equipment
production one of O hio’s fastest-growing man­
ufacturing industries in recent years. Indeed, evi­
dence from the OMI suggests that transportation

6

See C B O Staff Working Paper, "The Dollar in Foreign Exchange
and U .S . Industrial Production,” December 1985; and A m y Durrell,

Philip Israilevich, and K .J . Kowalewski, "Will the Dollar's Decline Help

Ohio Manufacturers?"

Economic Commentary,

Cleveland, August 15, 1986.

Federal Reserve Bank of

2

3

Fabricated Metals
Index, 1982 = 100

SOURCE. Federal Reserve Bank o f Cleveland and Board o f Governors o f the
Federal Reserve System.

FIGURE

24

4 B

equipment production has generated about 25 per­
cent of the state’s manufacturing output growth
since 1982 and may currently represent more
than 20 percent of its manufacturing economy.
There are a number of reasons that
O hio’s transportation equipment producers have
expanded rapidly since 1982. For one, motor ve­
hicle production, the fastest-growing component
in the transportation field in this decade, repre­
sents a larger share of transportation equipment
output in O hio (about 70 percent) than it does
nationally (about 48 percent). It would seem that
motor vehicle production also contributed to

Nonelectrical Machinery
Index, 1982 = 100

SOURCE: Federal Reserve Bank o f Cleveland and Board o f Governors o f the
Federal Reserve System.

O hio’s relatively severe decline in real transporta­
tion equipment output between 1979 and 1982.
Despite some strength since 1983,
production of aircraft, railroads, and ships
changed little between 1980 and 1985. These
industries are significantly less important to the
state’s manufacturing economy than they are to
the national economy.
In addition, real output per worker
in transportation equipment production is
roughly 15 percent greater in O hio than in the
U.S., and the rate of growth in labor productivity
for transportation equipment workers between
1982 and 1984 was about 28 percent, compared
with 19 percent nationally.
Another contributing factor to
O hio’s recovering transportation equipment
industry7has been the establishment of a Japanese
auto plant, and its supporting suppliers, in the
state. Honda, which began producing in O hio in
1982, currently assembles more than 145,000 cars
there annually, generating roughly $650 m illion
in annual manufacturing output.7

• Fabricated Metals
Fabricated metals has been a growth industry in
O hio’s manufacturing economy (figure 4b).
Although the state’s fabricated metals manufac­
turers experienced approximately the same con­
traction as national manufacturers did over the 16
quarters between 1979 first quarter and 1982
fourth quarter (-25.6 percent versus -26.5 percent
nationally), the recovery of fabricated metals pro­
duction in O hio has been stronger than the pace
set nationally (40.0 percent over the 16 quarters
ending in 1986 fourth quarter, compared with
32.3 percent for the nation).
Again, some of O hio’s improve­
ment in fabricated metals production can be
traced to a decided productivity advantage for the
state. In 1984, real output per worker in fabri­
cated metals was about 21 percent greater in
O hio than in the U.S., and the state’s growth rate
of productivity in this industry exceeded the U.S.
rate (roughly 22 percent versus 14 percent).
Industrial mix also appears to be a
contributing factor to O hio’s success in the fabri­
cated metals area. About one-third of the state’s
fabricated metals production occurs in the forging
and stampings field, whereas nationally this indus­
try7represents only about 18 percent of the fabri-

7

These estimates assume domestic content of 50.0 percent, on an
average 1985 new-car cost of $8,845. Not all of the U .S . content

is captured in Ohio, as some domestic suppliers are located outside the
state. See Michael F. Bryan and Michael W . Dvorak, “American A u to ­
mobile Manufacturing: It's Turning Japanese,"

Economic Commentary,

Federal Reserve Bank of Cleveland, March 1 ,1 9 8 6 .

Primary Metals
Index, 1982 = 100
1801---------

O hio manufacturers rely heavily on the produc
tion of metalworking machinery, an industry
dependent on durable-goods demand and one
that has been under pressure in recent years from
foreign competition. Approximately 20 percent of
O hio’s nonelectrical machinery involves the pro­
duction of metalworking machinery, more than
twice the national incidence.
Surprisingly enough, the national
nonelectrical machinery industry is heavily dom i­
nated by computer manufacturing, which gener­
ates roughly 25 percent of the nation’s nonelec­
trical machinery output, but which accounts for
only about 7 percent of the nonelectrical
machinery output in Ohio. Computer production,
which set a blistering pace early in this decade,
has slowed appreciably since 1984.

• Primary Metals

sol—
I— I— I— i— I— I— l—-I
1979 1980 1981 1982 1983 1984 1985 1986
SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the
Federal Reserve System.

FIGURE

4D

cated metals output. The forging and stampings
industry generates much of its demand from pro­
duction of consumer durables, particularly motor
vehicles which, as stated earlier, have been
important contributors to the current economic
expansion.
At the national level, the fabricated
metals industry has been dominated by the pro­
duction of structural metals, which are used
primarily in construction—an industry that has
not fared as well as consumer durables during
the recovery to date.

• Nonelectrical Machinery
Although the recovery in O hio’s nonelectrical
machinery industry' has been slightly greater than
that experienced nationally (figure 4c), produc­
tion of nonelectrical machinery in the state suf­
fered a sharper decline during the recessions of
1980 to 1982. Between 1979 first quarter and
1982 fourth quarter, O hio nonelectrical machin­
ery production was off 27.8 percent versus a
decline of only 8.6 percent nationally.
In this industry, at least, differences
in productivity and productivity growth rates are
not a major factor in industrial growth rate differ­
ences between the U.S. and Ohio. Here, the differ­
ences in national and Ohio industry performance
are probably related to the mix of industries
within the nonelectrical machinery category.

O hio is the largest producer of primary7metals in
the nation, as a result of its heavy concentration
of steel and iron makers. And, as is true nation­
ally, the performance in O hio’s primary metals
industry7has failed to regain the ground lost since
1979 (figure 4d). Data from the OMI indicate that
at year-end 1986, O hio primary7metals makers
w7ere producing at only about 68 percent of their
average 1979 output.
O hio’s experience in the primary7
metals area has been virtually identical to the
nation’s, even though real output per wrorker in
this industry is apparently greater in O hio than in
the U.S. (about 23 percent more in 1984).

• Chemicals and Allied Products
In the U.S., the chemicals and allied products
industry7means drugs ( more than 22 percent
compared with 5 percent in O hio), but in O hio it
means soaps (34 percent versus 18 percent
nationally). The patterns outlined by the OMI
suggest that, despite similar performances
between 1979 and 1985, O hio chemicals produc­
ers substantially outpaced the nation last year
(figure 4e). During the current expansion (end­
ing in the fourth quarter of 1986), the growth rate
of the chemicals industry7nationally w7as 28.5 per­
cent, w7hich is wrell below the 45.2 percent
advance registered for Ohio.
Differences in productivity
between O hio and U.S. manufacturers are also
influential in this industry7; real output per w7orker
in O hio was 19 percent greater than for workers
nationally, and the growth rate of productivity in
O hio between 1982 and 1984 exceeded the
nation’s (33 percent versus 25 percent).

25

Chemicals and Allied Products
Index, 1982 = 100

ufacture of communications equipment. This com­
pares with only about a 12 percent share in Ohio.
Moreover, electrical components used in the
production of computers, namely semiconduc­
tors, are much more important to national electri­
cal machinery manufacturing than to manufactur­
ing in O hio (about 26 percent versus 9 percent).
O hio’s electrical machinery7m anu­
facturing industry relies primarily on the manufac­
ture of appliances. Although the household appli­
ance industry has been relatively healthy in
recent years, its growth pales in comparison to
the gains felt in the communications and com pu­
ter fields.

• Rubber and Plastics

SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the
Federal Reserve System.

FIGURE

4 E

• Electrical Machinery
At the national level, electrical machinery7produc­
tion enjoyed a boom between 1982 fourth quar­
ter and 1984 third quarter because of an enor­
mous increase in the output of communications
equipment and electronic components (figure
40- These industries manufacture products essen­
tial to the skyrocketing telecommunications field.
But O hio’s experience in electronic equipment
manufacturing has been unimpressive, rising only
to its pre-recession levels.
At the national level, one-third of
the electrical machinery industry involves the man­

Electrical Machinery
Index, 1982 = 100

SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the
Federal Reserve System.

Plastics has supplanted rubber as the dominant
component of the rubber and plastics industry in
Ohio, and the OMI appears to reflect this transi­
tion (figure 4g).
The rubber and plastics industry7
has enjoyed growth in both O hio and the nation
over the present expansion, but O hio’s expe­
rience has been more volatile. The sharp cycle
here is probably a result of O hio’s rubber-makers,
whose production follows the often-turbulent for­
tunes of the transportation equipment industry.
O hio seems to be shedding its
dependence on rubber production. In 1977,
O hio’s rubber and plastics industry was dom i­
nated by rubber-makers (54 percent versus 46
percent in plastics). Yet, within six years the roles
were reversed, as rubber-makers accounted for
only 39 percent of the state’s output in the rubber
and plastics industry.

III. An Overview
The OMI and its subindexes are a product of
ongoing research at the Federal Reserve Bank of
Cleveland. It is therefore important to emphasize
that these indexes are experimental and may not
be wholly7reliable from month to month, or
within some industries. The structure of the
indexes and the data used in their construction
are subject to revisions. Future revisions may be
especially large between 1984 and 1986, over
which period the productivity assumptions were
intentionally conservative.
With these caveats noted, the pat­
terns traced by the index make sense in light of
O hio’s manufacturing mix and differences in pro­
ductivity levels and growth rates. The state’s m anu­
facturing cycle tends to be sharper than that expe­
rienced at the national level.
Industry-level data show7that O hio
manufacturers are recovering the transportation
equipment output lost in the last recession, as a
result of the state’s active motor vehicles industry7.

Rubber and Plastics
Index, 1982 = 100

SOURCE: Federal Reserve Bank o f Cleveland and Board o f Governors o f the
Federal Reserve System.

FIGURE

4G

Indeed, the demand for consumer durables in
this decade probably accounts for much of the
growth experienced by O hio manufacturers since
1982, such as that experienced by O hio’s fabri­
cated metals producers.
In addition, many of these recover­
ing industries are characterized by relatively high
and rising productivity levels, which in part
explains why the growth of O hio manufacturing
production since 1982 exceeds the national expe­
rience, despite slightly more modest gains in
manufacturing employment.
Unfortunately, not all manufactur­
ing industries in the state have improved their
position relative to the rest of the country. O hio
manufacturing growth in recent years appears to
be most prominent in industries w7hose futures
are regarded by many as uncertain. However,
O hio has lost ground in manufacturing fields that
are considered growth industries nationally, such
as printing and publishing, and electrical
machinery manufacturing.

Technical Appendix —
Methodology for the Ohio
Manufacturing Index (O M I)
A number of production index methodologies
have been proposed. The procedure chosen for
the construction of the O hio Manufacturing Index
(O M I) involves a m inim um of time to produce
and has been showTi to be relatively accurate for
the Texas economy (see Fomby [1986]). The
OMI is structurally similar to the regional produc­
tion indexes produced at other Federal Reserve
Banks and is virtually identical to that produced
by the Federal Reserve Bank of Atlanta (see
Stroebel [1978]).1
We begin by assuming that O hio
manufacturers are profit maximizers who operate
in a competitive market. If we further assume that
O hio manufacturers are subject to a two-factor
(labor and capital) linear homogeneous produc­
tion function (constant returns to scale), we can
use Euler’s theorem to show7that:
(1)
l ) + k
where
is manufacturing output measured by
value added,
and
are the unit price of labor
and capital inputs, respectively, and and
are
the industry’s employment of labor and capital.
Equation 1 can be algebraically
manipulated to yield the more complex, but eas­
ily estimable, time series:
(2)
(
(
+
( k
) (
t) for =
where
are the factor shares for labor (Z ) and
capital ( ) inputs,
are the output ratios for
inputs in period
and
represents the level of
inputs in period
The O hio Manufacturing Index
uses fixed shares of labor and capital, but allows
for monthly productivity increases by a factor
Specifically, the output ratios are adjusted
monthly such that:
(3)
+ C,
where
represents the number of months that
have elapsed since the last survey of O hio manu­
facturers. The productivity factor is defined by:

VA = (P L {P K \
VA
PL PK

L

K

VAt = P,L/VA) VA/L)t Lt
P K/VA VA/K)t K,
= X (S i Oiti
i L,K,
Si
K
Ol t
t,
it
t.

Cr

Oi t = Oit n(\
n

cf =

(4)

m

r v * j i mI

LVAo /‘ o J

n),

i<!>

4>

w7here
and o are tw7o survey years and is the
monthly interval separating the tw7o surveys. Input
productivity factors since 1984, for w7hich data do
not yet exist, wrere assumed to be equal to the av­
erage productivity factor between 1978 and 1984.2

I

The Sixth District Manufacturing Production Index uses man-hours
to measure labor inputs, while the OM I uses employment levels. In
addition, the Sixth District Index seasonally adjusts the computed indexes,

while the OM I seasonally adjusts the factor inputs prior to index
construction.

27

Percentage Share of Labor and Capital For Ohio Manufacturers
Labor
(% )

Capital
(% )

Manufacturing

40.3

59.7

Durable-Goods Manufacturing
Nondurable-Goods Manufacturing

44.0
31.9

56.0
68.1

Food and Kindred Products (20)
Apparel and Other
Textile Products (23)
Lumber and W ood Products (24)
Furniture and Fixtures (25)
Paper and Allied Products (26)
Printing and Publishing (27)
Chemicals and
Allied Products (28)
Rubber and Miscellaneous
Plastic Products (30)
Stone, Clay, and
Glass Products (32)
Primary Metals Industries (33)
Fabricated Metal Products (34)
Machinery, Except Electrical (35)
Electric and Electronic
Equipment (36)
Transportation Equipment (37)
Instruments and
Related Products (38)

24.9

75.1

43.2
44.0
46.2
46.1
41.5

56.8
56.0
53.8
53.9
58.5

19.7

80.3

45.2

54.9

43.2
43.8
45.5
50.1

56.8
56.2
54.5
49.9

38.0
40.9

62.0
59.1

44.6

55.4

Industry (SIC)________________________

( n - 32),

APPENDIX

The fixed factor shares (5 ,) were
estimated using Ohio manufacturing data from
the 1984 Survey of Manufactures. The share of
labor ( L) was calculated as the ratio of the total
manufacturing payroll to the value added in
manufacturing in nominal dollars. The share of
capital (
was derived by:
(5)
The factor shares are reported in table 1 of this
technical appendix.
The output ratios were calculated
for the survey years 1978, 1983, and 1984 and for
the census year 1982. The labor output ratio (
is real value added to total employment. The cap­
ital output ratio (
is similarly constructed,
using electric power consumption as a proxy for
the employment of capital.3

S

SK)
SK = 1 - St.

Ol)

Ok)

2

(n -

Description of the Data and Procedures

SOURCE: 1984 Annual Survey o f Manufactures, Bureau o f the Census.

TABLE 1

The OMI was produced for 15 twodigit SIC industries and for the durable-goods,
nondurable-goods, and total manufacturing
aggregates (appendix table 1). Five manufactur­
ing industries are not reported because of con­
straints on the data: tobacco products (21), textile
mill products (22), petroleum and coal products
(29), leather and leather products (31), and other
miscellaneous manufacturing (39). Fortunately,
these five industries are relatively small contribu­
tors to the O hio economy, representing only about
2 percent of this state’s value added in 1984.
The OMI and components are
available monthly
96) and quarterly
both seasonally adjusted and nonseasonally adjusted. Index values are reported on a
1982 = 100 basis.

• The O hio Manufacturing Index
and the durable- and nondurable-goods aggre­
gates represent a summation of the industry-level
indexes, weighted according to share of real
value added in 1984.
• O hio manufacturing value
added and payroll data are available for the cen­
sus year 1982 and for the survey years 1978, 1983,
and 1984.
• Value added was deflated using
national price deflators for these two-digit indus­
tries, supplied by the U.S. Department of
Commerce.
• Monthly employment data in
O hio by two-digit industrial classifications were
supplied by the U.S. Bureau of Labor Statistics and
the O hio Bureau of Employment Services.
• O hio electric power, measured
in kilowatt-hours, is used as a proxy' for capital
use. Electric power data were collected by twodigit SIC codes by the Data Services Department
of the Federal Reserve Bank of Cleveland.4 The
data include self-generated electric power. The
monthly timing of electric power consumption
data is not exact and tends to overlap between
months. For this reason, electric power data are
entered into the OMI as a three-month moving
average.
• The input series are indepen­
dently seasonally adjusted using the X-11 ARIMA
adjustment procedure.

In many industries, this period is associated with little or no growth
in factor productivity. Consequently, this assumption may be unrea-

listically low. Without firm data to the contrary, however, a conservative
approach seemed appropriate.

3

Virtually all regional and national industrial production indexes
employ electnc power data to approximate capital usage. See

Moody (1974) for a justification of this procedure.

4

A short description of electrical consumption data sources used in
this study is available from the authors upon request.

H. Food Production Index

I. Apparel and Other Textiles

Index, 1982 = 100

Index, 1982 = 100

J. Lumber and Wood Products

K. Furniture and Fixtures

Index, 1982 = 100

Index, 1982 = 100

L. Paper and Allied Products

M. Printing and Publishing

Index, 1982 = 100

Index, 1982 = 100

SOURCE: Federal Reserve Bank o f Cleveland and Board o f G overnors o f the Federal Reserve System.

N. Stone, Clay, and Glass

O. Instruments and Related Products

Index, 1982 = 100

Index, 1982 = 100

160

170
160 —
—

140

140

120

J O h io
•

120 —

A\
N \

~

_

130 —

_____

130 _

/s _ /

150

/ United States

\ V "\

.

110 —

/

100 —

J

8 0 __
/

O h io

V

A

/
________ _— ■
United States

\
-------- p \

90 —

\ v

A

jT

/
/

7 0 — __ /
60 —

90

........... L ...

1 .........L ......... 1.......

1

1

........._L...... .

50

1979 1980 1981 1982 1983 1984 1985 1986

____ 1

1

........1

SOURCE: Federal Reserve Bank o f Cleveland and Board o f Governors o f the Federal Reserve System.

FIGURES 4N.0

Technical Appendix References
Fomby, Thomas B. “A Comparison of Forecast­
ing Accuracies of Alternative Regional Pro­
duction Index Methodologies,”
vol. 4, no.
2 (April 1986), pp. 177-186.

Journal of
Business and Economic Statistics,

Moody, Carlisle E. “The Measurement of Capi­
tal Services by Electrical Energy7,”
vol. 36,
no. 1 (February71974), pp. 45-52.

Oxford
Bulletin of Economics and Statistics,

Stroebel, F.R. “Sixth District Manufacturing
Index, Technical Note and Statistical Supple­
ment,” Federal Reserve Bank of Atlanta, 1978.
Sullivan, Brian P. "Manufacturing Capacity7New Texas Index Assesses Utilization,”
Federal Reserve Bank of Dal­
las, September 1975.

iness Review,

Bus­

1 ..........1

1

!...

.....

1979 1980 1981 1982 1983 1984 1985 1986

Economic Commentary

Alternative Methods for Assessing Risk-Based
Deposit-Insurance Premiums
James B. Thomson
9/15/86
Monetarism and the M l Target
William T. Gavin
10 / 1/8 6

Debt Growth and the Financial System
John B. Carlson
10/15/86
Competition and Bank Profitability:
Recent Evidence
Gary Whalen
1 1 / 1/86

Is the Consumer Overextended?
K.J. Kowalewski
11/15/86
Labor Cost Differentials:
Causes and Consequences
Randall W. Eberts
and Joe A. Stone

12/ 1/86
The Thrift Industry: Reconstruction
in Progress
Thomas M. Buynak
6/ 1/86
The Emerging Service Economy
Patricia E. Beeson
and Michael F. Bryan
6/15/86
Domestic Nonfinancial Debt: After
Three Years o f Monitoring
John Carlson
7/1/86
Equity, Efficiency, and
Mispriced Deposit Guarantees
James B. Thomson
7/15/86
Target Zones for Exchange Rates?
Owen F. Humpage
and Nicholas V. Karamouzis
8/ 1/86
Will the Dollar’s Decline Help
Ohio Manufacturers?
Amy Durrell,
Philip Israilevich,
and KJ. Kowalewski
8/15/86
Implications o f a Tariff on Oil Imports
Gerald H. Anderson
and KJ. Kowalewski
9 / 1/86

The Changing Nature o f Our Financial
Structure: Where Are W e Headed?
Where Do W e Want To Go?
Karen N. Horn
12/15/86
Loan-Quality Differences:
Evidence from Ohio Banks
Paul R. Watro
1/1/87
Is the U.S. Pension-Insurance
System Going Broke?
Thomas M. Buynak
1/15/87
Should W e Intervene in Exchange Markets?
Owen F. Humpage
2/1/87
The Decline in U.S. Agricultural Exports
Gerald H. Anderson
2/15/87
The Japanese Edge in Investment:
The Financial Side
William Osterberg
3/1/87
Debt-Deflation and Corporate Finance
Jerome S. Fons
3/15/87
Requirements for Eliminating
the Trade Deficit
Owen F. Humpage
4/1/87

3 1

Economic Review

3

2

Quarter I 1986
The Impact of Regional Difference in Unionism
on Employment

Quarter III 1986
Exchange-Market Intervention:
The Channels of Influence

by Edward Montgomery

by Owen F. Humpage

The Changing Nature of Regional Wage Differ­
entials from 1975 to 1983

Comparing Inflation Expectations
of Households and Economists: Is
a Little Knowledge a Dangerous Thing?

by Lorie D. Jackson

Labor Market Conditions in Ohio Versus the
Rest of the United States: 1973-1984
by James L Medoff

by Michael F. Bryan
and W illiam T. Gavin

Aggressive Uses of Chapter 11
of the Federal Bankruptcy Code
by Walker F. Todd

Quarter II 1986
Metropolitan Wage Differentials:
Can Cleveland Still Compete?
by Randall W. Eberts
and Joe A Stone

The Effects of Supplemental Income
and Labor Productivity on Metropolitan
Labor Cost Differentials

Quarter IV 1986
Disinflation, Equity Valuation,
and Investor Rationality
by Jerome S. Fons
and W illiam P. Osterberg

by Thomas F. Luce

The Collapse in Gold Prices:
A New Perspective

Reducing Risk in Wire Transfer Systems

by Eric Kades

by EJ. Stevens

“Don’t Panic”: A Primer
on Airline Deregulation
by Paul W. Bauer