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Business
Review
Federal Reserve Bank of Philadelphia
january * February 199 4 __________________ISSN 0 0 0 7 -7 0 1 1
How Efficient Are Third District Banks?
Loretta J. M ester
New Indexes Track the State of the States
Business
Review
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JANUARY/FEBRUARY 1994
HOW EFFICIENT
ARE THIRD DISTRICT BANKS?
Loretta J. Mester
Although banks are still the main financial
interm ediaries in the United States,
whether they remain so in the face of
increased competition depends on how
efficiently they operate. How can we
measure a bank's efficiency? And what
specifically can we expect for banks oper
ating in the Third District? Loretta Mester
examines the issue of efficiency and ap
plies her findings to banks in the Third
Federal Reserve District.
NEW INDEXES TRACK
THE STATE OF THE STATES
Theodore M. Crone
For many years the Department of Com
merce has published an Index of Coinci
dent Indicators to track the U.S. economy.
Recently, two econom ists, one from
Harvard and one from Northwestern
University, developed a new index of
coincident indicators for the nation. To
gether these two indexes provide evi
dence of the direction of the national
economy. But what about regional econo
mies? They do not always follow the
national pattern, and there are no compa
rable indexes to track their progress. Ted
Crone discusses indexes available at the
national level, then details the develop
ment of indexes for the states in the Third
District.
FEDERAL RESERVE BANK OF PHILADELPHIA
How Efficient
Are Third District Banks?
Loretta ]. Mester *
I n recent years banks have had to operate in
an increasingly competitive environment. Com
petitors have come from both within and out
side the banking industry. Deregulation has
allowed commercial banks to expand beyond
their own state's borders; thus banks face com
petition from other commercial banks entering
their market for the first time. Investment
banks have also become competitors for some
of the commercial bank's most creditworthy
* Loretta J. Mester is a Research Officer in the Research
Department, Federal Reserve Bank of Philadelphia, and
Adjunct Assistant Professor of Finance, The Wharton School,
University of Pennsylvania.
customers, who have been able to turn to the
commercial paper market as a cheaper funding
source than bank loans. Similarly, savers have
been funneling their money into mutual funds
as opposed to bank deposits in a search for a
higher rate of return in the current low-depositrate environment. Although banks are still the
main financial intermediaries in the United
States, providing funding to firms and other
borrow ers and deposit services to savers,
whether they will remain dominant in the face
of increased competition depends on how effi
ciently they produce their outputs, that is, their
loans and other financial services. Efficient
banks will be able to offer more attractive loan
and deposit rates to their customers and still
3
BUSINESS REVIEW
make a normal rate of return, while inefficient
banks won't be able to follow suit and will,
therefore, lose business. Inefficiently run banks
will have to shape up, or they will be driven out
of the market or acquired by other banks; in
deed mergers between efficient and inefficient
banks have the potential for substantial social
gains via cost savings.1 And banks that operate
*
with lower costs can pass these savings on to
their borrowers and depositors.
What can we expect for banks operating in
the Third Federal Reserve District, which com
prises the eastern two-thirds of Pennsylvania,
the southern half of New Jersey, and Delaware?
Are they operating at a high level of efficiency,
or is there room for substantial improvement?
Can we expect a lot of restructuring in our
District as inefficient banks are driven from the
market? Measuring our banks' efficiency will
give us some indication of how they are likely
to fare in an increasingly competitive environ
ment.
WHAT DO WE MEAN BY EFFICIENCY?
When economists consider efficiency they
typically focus on scale and scope efficiency, which
concerns a bank's choice of outputs, and Xefficiency, which concerns a bank's use of in
puts. There has been substantially more study
of scale and scope efficiency in the banking and
financial services industry than of X-efficiency.
Scale Efficiency. Scale efficiency refers to
whether a firm is providing the most costefficient level of outputs. Let's consider a hypo
thetical example. Suppose firms are demand
ing $500 million of credit in total, and that it
1Although Berger and Humphrey (1992b) found little in
the way of cost-efficiency benefits on average from mergers
in the 1980s between banks with assets over $1 billion, as
they discuss, this is likely because the aim of such mergers
was to increase asset growth and geographic market exten
sion rather than to increase cost efficiency. This seems to be
changing in the 1990s, as merger participants have been
setting cost-cutting goals when announcing their mergers.
Digitized for 4
FRASER
JANUARY/FEBRUARY 1994
costs one bank $25 million to produce this
volume of loans and $10 million to produce
$250 million in loans. (Producing a loan in
volves the credit evaluation the bank must
perform to determine the credit quality of the
borrower along with funding the loan and
monitoring the loan over its length of matu
rity.) Then it is more efficient to supply the $500
million of credit to the market by having two
banks each produce $250 million of the loans
than by having one bank produce all $500
million—the average cost of production, that
is, the cost per dollar of loan, is less (4c versus
5c) when each bank produces $250 million of
loans than when one bank produces $500 mil
lion of loans. Society is better off with two
banks producing the output rather than one
bank, since the $5 million saved could be used
for some other productive activity. And a bank
trying to produce all $500 million of loans
would find itself at a competitive disadvantage
if another bank entered the market producing
only $250 million of loans, because the second
bank would be able to charge a lower interest
rate on its loans, since its per unit production
cost would be lower.
A bank is said to be producing with constant
returns to scale if, for a given mix of products, a
proportionate increase in all its outputs would
increase its costs in the same proportion; this is
also the point where the average cost of produc
tion is the least. A bank is operating with scale
economies if a proportionate increase in its out
puts would lead to a less than proportionate
increase in cost—the bank could produce more
efficiently by increasing its output level. A
bank is operating with scale diseconomies if a
proportionate decrease in its outputs would
lead to a more than proportionate decrease in
costs—the bank could produce more efficiently
by reducing its output level.
At output levels where there are scale econo
mies, an increase in outputs would reduce the
average cost of production, since it costs pro
portionately less to produce at a larger scale.
FEDERAL RESERVE BANK OF PHILADELPHIA
How Efficient Are Third District Banks?
Loretta /. Mester
One reason it might cost less per unit to pro
duce at a larger scale is that there may be large
fixed costs in the production technology that
are independent of the level of output pro
duced. For example, in banking, the cost of the
computers used to keep track of accounts can
be spread over a larger number of accounts as
the scale of operations increases, so that the per
unit cost of production falls. Another potential
source of scale economies is specialization.
Larger firms may permit employees to special
ize in one task, and this specialization may also
lead to more efficient production.2 In most
industries, including banking, firms find that at
a certain output volume (for a given mix of
products), average cost stops declining. For
example, at a large enough volume, the fixed
costs of production will become insignificant
relative to the cost of producing additional
units of output, so that scale economies are
exhausted. And if scale is increased beyond a
certain level, average costs begin to rise. Of
course, depending on the production technol
ogy, there may be a broad range of output levels
(for any product mix) that firms can produce at
minimum average cost. In other words, banks
of different asset sizes may be equally competi
tive with one another, since their average costs
are similar.
Scope Efficiency. Scope efficiency refers to
whether a firm is producing the most costefficient combination of products. Banks pro
duce more than one product—for example,
most commercial banks produce a variety of
different loans, like commercial and industrial
loans, commercial real estate loans, residential
mortgages, student loans, etc. To the extent
that different types of loans have different
default rates or other characteristics and to the
extent that they aren't used to fund the same
activities, they constitute different outputs of
the bank.3 Thus, in addition to choosing the
most cost-efficient scale of operations, the bank
must also choose the combination of products
it will produce. For a given level of outputs, the
per unit cost of production may be smaller if the
bank produces all of the products rather than
specializing in just a few of them, or it might be
more efficient to specialize. There are scope
economies if the cost of producing a given level
of outputs is lower when a bank produces all
the products than if the products are divided
up into specialized banks. There are scope
diseconomies if the costs are lower when special
ized banks produce the various outputs.
There are several potential sources of scope
economies.4* One is the sharing of inputs to
produce several outputs. For example, the
same group of tellers might handle both check
ing and savings accounts, or information on a
firm produced in a credit evaluation for a
mortgage can be used if the firm wants a busi
ness loan as well. Therefore, it would be cheaper
for the same bank to handle both types of
accounts and to extend both loans than to
duplicate the tellers and credit check at another
bank. Similarly, excess capacity on the bank's
computer may allow it to increase the scope of
products it produces as well as its scale. Thus,
there is an interconnection between scale and
scope economies—the fact that the bank is able
to process various types of loans on its com
puter (many products) enables it to increase its
scale and take advantage of any scale econo
mies. Of course, there may be a point at which
producing many products will increase the
bank's unit costs. For example, it may take a
more elaborate hierarchical management struc
ture to produce different product lines (some
2Mester (1987) discusses the sources of scale economies
in more detail.
4Mester (1987) describes the sources of scope economies
in more detail.
3Large banks also engage in many off-balance-sheet
activities, like underwriting, letters of credit, and loan guar
antees.
5
BUSINESS REVIEW
JANUARY/FEBRUARY 1994
times this is mandated by regulation—e.g.,
equities underwriting and commercial lending
must be done in separate subsidiaries of a bank
holding company), and this hierarchical struc
ture can increase production costs.5
X-Efficiency. If all firms in the industry are
producing the level and combination of out
puts that minimize the average cost of produc
tion, the total cost of producing the industry's
output is minimized, and the industry is pro
ducing an efficient combination and level of
products, provided each firm is using its inputs
efficiently. X-efficiency refers to whether a firm
is using its inputs, like labor and capital, in a
cost-effective manner— that is, for a given level
and mix of outputs, is a bank producing them
in the cheapest way possible? If not, the bank
is either wasting some of the inputs it has
purchased, or it is using the wrong combina
tion of inputs to produce its outputs. Technical
inefficiency refers to using proportionately too
much of all inputs and is just pure waste. For
example, the bank may be using too many
tellers and too many branches to produce its
products—it might be able to scale back its
inputs and produce the same amount of ser
vice. A bank that is technically inefficient is said
to be operating within its "production possibil
ity frontier." (The production possibility fron
tier indicates the maximum amount of output
that can be produced with a given amount of
inputs.) But wasting resources is not the only
way to inefficiently use inputs. A bank might be
able to produce a given amount of loans and
other financial services by combining its
inputs— including labor, physical capital, and
deposits— in different proportions than it cur
rently is doing. For example, a large bank might
be able to supply its output more cheaply by
substituting ATMs for tellers—while the fixed
costs of setting up an ATM are high, the cost per
transaction for an ATM is lower than that for a
human teller—so larger banks might benefit by
using more ATMs than tellers. Allocative ineffi
ciency refers to using the wrong combination of
inputs to produce a given output level and
product mix—an allocatively inefficient bank
is operating on its production possibility
frontier—that is, given the inputs it has chosen,
it is producing as much output as possible—but
the bank could lower its costs of producing that
output by selecting a different input mix. Of
course, a bank can be both technically and
allocatively inefficient.
A bank that is operating in an inefficient
manner might be doing so because its manager
isn't on top of things, but managerial inability
isn't the only source of X-inefficiency. It's
possible that a bank manager has goals that
differ from those of the bank's shareholders.
Shareholders want to maximize the stock mar
ket value of the bank, and so its long-run
profits. Thus, shareholders want the bank to
minimize its cost of production. But bank
managers might be interested in something
other than cost-minimization. For example,
managers might desire a larger staff because
they think it gives them more prestige within
the banking community. Thus, a bank might
use an inefficient combination of inputs (more
labor than is necessary) to produce its services.
Such "expense-preference" behavior on the part
of managers has been found in studies of com
mercial banks and savings and loans.6* The
bank's choice of the products it wishes to pro
duce might also be driven less by cost consid
erations than by managerial desires to run a
particular type of bank.
Survival. One might question how ineffi
cient banks are able to continue operating. In
the usual economic models of competitive
markets, competitive forces are thought to drive
5See Mester (1991) for further discussion of diseconomies
of scope in hierarchies.
6Mester (1989) discusses the conflicts between owners
and managers in financial firms and the empirical evidence.
6
FEDERAL RESERVE BANK OF PHILADELPHIA
How Efficient Are Third District Banks?
such inefficient banks out of the market. More
efficient banks are able to produce at a lower
cost. In a competitive market, the efficient bank
would share its cost savings with its customers
in the form of lower interest rates on loans and /
or higher deposit rates. This would attract
borrowers and depositors away from ineffi
cient banks, since the inefficient banks couldn't
match the lower prices without making a loss.
The inefficient banks would eventually be forced
out of the market.
But banking has been a regulated industry;
competitive pressures have not been as strong
as they might have been. For example, prior to
the 1980s, regulations restricted bank holding
companies from establishing banks in more
than one state, and there are still restrictions on
banks establishing branches across state lines.
Such restrictions reduce the number of poten
tial competitors, making it easier for inefficient
banks to survive.7 Similarly, laws that restrict
hostile takeovers make it less easy for more
efficient banks to gain control of their less
efficient counterparts. On the customer side,
there is empirical evidence that bank customers
have found it costly to switch banks (see Calem
and Mester, 1993); thus, it has been difficult for
efficient banks to attract customers with lower
prices.
But inefficient banks will find it less easy to
survive in the future as entry barriers fall.
States began passing laws in the 1980s that
authorize interstate banking for bank holding
companies. All but two states (Hawaii and
Montana) now allow bank holding companies
from at least some other states to acquire in
state banks. In April 1992, the Office of Thrift
Supervision adopted a rule allowing full na
tionwide branching for healthy federally char
tered savings and loans. According to the
A m erican B an ker (A u gu st 2, 1993) four
7Calem (1993) discusses the benefits of allowing banks
to branch across state lines.
Loretta f. Mester
states—New York, North Carolina, Oregon,
and Alaska—have passed reciprocal interstate
branching laws permitting a state-chartered
commercial bank that is not a member of the
Federal Reserve System to become a branch of
a bank in any other state that has an identical
law. Although interstate branching hasn't been
authorized for national banks as yet, Congress
has considered several proposals to permit it,
and the topic is likely to remain on the agenda.
In addition, the Federal Reserve has taken the
position that it will treat hostile bids no differ
ently from friendly bids in assessing whether to
permit a takeover. Nonbank competition is
also picking up. According to the flow of funds
accounts, banks' share of total U.S. financial
assets has shrunk to less than 25 percent from
over 35 percent in 1977.8 And foreign bank
competition is heating up too; the North Ameri
can Free Trade Agreement (NAFTA) should
also increase competition. Increased competi
tive pressures will make it more difficult for
inefficient banks to survive, as will anything
that reduces the costs customers face in switch
ing to low-cost banks. For example, the Truth
in Savings Act, part of the Federal Deposit
Insurance Corporation Improvement Act of
1991, requires banks to report the terms of their
deposit accounts in all advertisements for these
accounts, making it easier for customers to
shop for the best rates. Inefficient banks will
find it more difficult to keep well-informed
customers.
MEASURING EFFICIENCY:
THE METHODOLOGY
Outputs and Inputs. Studies of bank effi
ciency are based on an analysis of banks' cost
structure, that is, the relationship between
8The flow of funds accounts, published by the Board of
Governors of the Federal Reserve System, provide data on
financial assets and liabilities outstanding by sectors of the
economy and by type of transaction.
7
BUSINESS REVIEW
JANUARY/FEBRUARY 1994
banks' costs and their output levels, given the
input prices they face. Thus, the first step in
measuring efficiency in banking—scale and
scope efficiency and X-efficiency—is to deter
mine a bank's outputs and inputs. There is
some disagreement in the literature over what
a commercial bank is actually producing. Two
general approaches have been taken: the "pro
duction" approach and the "intermediation"
approach (also called the "asset" approach).
The production approach focuses on the bank's
operating costs, that is, the costs of labor (em
ployees) and physical capital (plant and equip
ment). The bank's outputs are measured by the
number of each type of account, like commercial
and industrial loans, mortgages, deposits, be
cause it is thought that most of the operating
costs are incurred by processing account docu
ments and debiting and crediting accounts;
inputs are labor and physical capital. The
intermediation approach considers a financial
firm's production process to be one of financial
intermediation (the borrowing of funds and the
subsequent lending of those funds). Thus, the
focus is on total costs, including both interest
and operating expenses. Outputs are measured
by the dollar volume of each of the bank's
different types of loans, and inputs are labor,
physical capital, and deposits and other bor
rowed funds.9*Luckily, the empirical results on
scale and scope efficiency do not seem to be
very sensitive to which approach is taken.
Theoretically, to compare one bank's effi
ciency to another's, we would like to compare
each bank's cost of producing the same outputs.
For banks, significant characteristics of loans
are their quality, which reflects the amount of
monitoring the bank does to keep the loan
performing, and their riskiness. Unless these
characteristics are controlled for, one might
conclude a bank was producing in a very effi
cient manner if it were spending far less to
produce a given output level, but its output
might be highly risky and of a lower quality
than that of another bank. It would be wrong
to say a bank was efficient if it were scrimping
on the credit evaluation needed to produce
sound loans. Although previous efficiency stud
ies have failed to compare the costs of produc
ing outputs of equal quality and risk, the study
of Third District banks described below does
so.
Scale and Scope Efficiency Studies. Most
of the studies interested in measuring scale and
scope efficiency for a particular sample of banks
have estimated an average practice cost function,
which relates a bank's cost to its output levels
and input prices. The technique implicitly
assumes that all banks in the sample are using
their inputs efficiently, that is, there is no Xinefficiency, and they are using the same pro
duction technology. Of course, it recognizes
that data are typically measured with error and
that there might have been unpredicted factors
9A slight variation on the intermediation approach,
which has been used in some studies, is to distinguish
between transactions deposits, which are treated as an
output, since they can serve as a measure of the amount of
transactions services the bank produces, and purchased or
borrowed funds (like federal funds or large CDs purchased
from another bank), which are treated as inputs, since the
bank does not produce services in obtaining these funds.
The strict intermediation approach would consider the
transactions services produced by the bank as an intermedi
ate output, something that must be produced along the way
toward the bank's final output of earning assets. Hughes
and Mester (1993) empirically tested whether deposits
should be treated as an input or output and found that they
should be treated as an input in their study.
Another approach that has been taken less often is the
"value-added" approach, which considers all liabilities and
assets of the bank to have at least some of the characteristics
of an output. See Berger and Humphrey (1992a) for further
discussion.
Still another approach, taken in Mester (1992), is to
consider the bank's output to be its loan origination and
loan monitoring services.
See Humphrey (1985) and Berger and Humphrey (1992a)
for further discussion of the different approaches to mea
suring bank output.
8
FEDERAL RESERVE BANK OF PHILADELPHIA
How Efficient Are Third District Banks?
that affected a bank's cost over the period when
the data were collected, like an unusually large
amount of computer down time or up time (bad
and good luck) or extraordinary sick leave.
Thus, no bank is expected to lie precisely on the
estimated cost function; instead the function
indicates what, on average, it costs a bank
facing a particular set of input prices to produce
a particular bundle of outputs. Some banks will
produce the given output at a slightly higher
cost and others at a slightly lower cost than is
indicated by the estimated cost function.
Most studies have focused on smaller banks,
with assets less than $1 billion. These studies,
others that included banks of all sizes, and
another study that included all banks with
assets greater than $100 million found that the
average cost curve is relatively flat, with scale
economies exhausted somewhere between $75
million and $300 million in assets.1 This is a
0
relatively small size when you consider the size
distribution of U.S. banks. While in 1992 about
90 percent of the 11,461 FDIC-insured commer
cial banks in the United States had less than
$300 million in assets, these banks held only 20
percent of total bank assets. Fifty-one banks
had assets over $10 billion, and the largest,
Citibank, had over $150 billion. Thus, the
studies of scale economies suggest that only
small banks are operating with unexploited
economies of scale and could become more
efficient producers by expanding their output
size. Moreover, the measured scale economies
for these small banks are usually fairly small: a
1 percent increase in all output levels typically
leads to about a 0.95 percent increase in total
cost, which means a 0.05 percent decrease in the
average cost of producing the bundle of out
puts. A handful of studies have focused solely
on large banks with assets over $1 billion. Some
10Berger and Humphrey (1992b), Evanof f and Israilevich
(1991), Clark (1988), and Mester (1987) summarize the re
sults of the studies.
Loretta ]. Mester
found scale economies at very large banks—the
minimum of the average cost curve usually was
found to lie between $2 billion and $10 billion in
assets. But here again, measured economies
were not very large. On the whole, these
studies concluded that there wasn't much in
the way of cost gains to be made by changing
the scale of operations at the typical bank.1
1
Similarly, although there are exceptions, most
studies have found little evidence of economies
or diseconomies of scope between the products
banks currently produce. Hence, there is little
evidence that changing the typical bank's prod
uct mix would significantly influence its cost of
production.1 1
2
3
X-Efficiency Studies. More recent studies
have focused on measuring not only scale and
scope economies but also the degree of Xinefficiency in banking. As with scale and
scope efficiency, we start with a set of banks
that are using the same production technology
for creating output. The technique is to esti
mate a best practice cost function—that is, the
predicted cost function of banks that are Xefficient—and then measure the degree of inef
ficiency relative to this best practice technol
ogy. Two common methodologies are data
envelopment analysis (DEA) and stochastic econo
metric cost frontier analysis.1.
3
11This isn't to say that banks operating at a significant
distance from optimal scale couldn't become more efficient
by changing their operating scale. See Evanof f and Israilevich
(1991) for more discussion on this point.
12This is not to say that deregulation that permits banks
to expand the types of products they can offer (e.g., equities
underwriting) could not enable banks to take advantage of
potential scope economies.
13There are other techniques for deriving efficiency mea
sures, including so-called "thick frontier" analysis and
"shadow price" models. Evanoff and Israilevich (1991)
describe these techniques.
A simpler method to compare the efficiency of banks is
to use peer-group analysis. Certain cost ratios are com-
9
BUSINESS REVIEW
DEA uses the data on costs, outputs, and
input prices for a sample of banks and deter
mines, for each output bundle and set of input
prices, the bank in the sample that spends the
least to produce the output bundle at the given
input prices— this is the "best practice" (that is,
most efficient) bank for that output /input price
combination. (If no bank in the sample pro
duces a particular combination, then a "best
practice" bank for the combination is approxi
mated based on "best practice" banks produc
ing similar combinations that do show up in the
sample.) A bank's relative inefficiency is then
measured by the ratio of its own cost compared
with the cost of the "best practice" bank that
faces the same input prices and produces the
same output bundle. The technique is called
data envelopment analysis because the data on
best practice banks "envelop" the data from the
rest of the banks in the sample. One benefit of
DEA is that it doesn't posit a particular func
tional form for the best practice banks' cost
function—it is more flexible. But a serious
drawback of the technique is that it does not
allow for any error in the data—banks that have
been lucky or w hose costs have been
undermeasured will be labeled as most effi
cient and other banks will look relatively less
efficient in comparison. Similarly any unfavor
able influence beyond the bank's control will be
attributed to inefficiency.
pared for banks that are considered to be similar in the types
of customers they serve and products they produce. These
ratios might include operating expenses per dollar volume
of assets, number of employees per dollar volume of loans,
or expenses attributed to commercial loans per volume of
commercial loans. The Functional Cost Analysis (FCA) data
collected by the Federal Reserve System permit such an
analysis. The drawback of the cost ratio approach is that it
cannot control for differences in banks' product mix or in the
input prices banks face, which influence bank costs, and it
cannot give an overall measure of efficiency. Also, the FCA
program is voluntary and the sample is skewed toward
smaller banks. And a bank's allocation of cost into various
lines of business may require some arbitrary division of
fixed or shared costs.
Digitized for10
FRASER
JANUARY/FEBRUARY 1994
Cost frontier analysis does not have to as
sume data are measured without error. In
stead, a bank is labeled as inefficient if: (1) its
costs are higher than the costs predicted for an
efficient bank producing the same outputs and
facing the same input prices and (2) the differ
ence cannot be explained by statistical noise,
e.g., measurement error or luck.1 To obtain the
4
cost frontier, that is, the relationship between
costs, outputs, and input prices for the efficient
banks, statistical techniques (that is, regression
analysis) are used to obtain the best fitting
curve through the data, just as they are used to
obtain the average practice cost function usu
ally employed in the scale and scope economies
studies. The difference is that the cost frontier
indicates what, on average, it costs an efficient
bank facing a particular set of input prices to
produce a particular bundle of outputs, while
the average practice function applies to all
banks. A particular bank's cost will deviate
from that predicted by the cost frontier for two
reasons: first, there will be statistical noise, or
unpredicted factors, that affected the bank's
costs—either positively or negatively—com
pared with an efficient bank's costs; second, the
bank may not be X-efficient—hence its costs
will be higher than those of efficient banks. The
statistical technique used to obtain the cost
frontier also provides information on these two
types of deviations in the sample. The second
deviation is always positive, since inefficient
banks' costs are always higher than efficient
banks' costs. This "one-sided" deviation can be
used to obtain measures of any particular bank's
inefficiency or the average level of inefficiency
in the sample of banks. (As with the average
practice cost function, no efficient bank is ex
pected to lie precisely on the estimated cost
frontier. Hence, the point estimate of ineffi
ciency for these banks will be small but not
14Again, the banks in the sample are assumed to be using
the same production technology in producing their outputs.
FEDERAL RESERVE BANK OF PHILADELPHIA
How Efficient Are Third District Banks?
Loretta /. Mester
zero.) Once the cost frontier is estimated, one estimates of inefficiency are higher from these
can also estimate scale and scope economies for studies—on the order of 20 to 50 percent. These
results suggest that there is substantial room
banks operating efficiently.1
5
One drawback of cost frontier analysis com for improvement at the average bank in the
pared with DEA is that it does require the United States, and the average bank will have
researcher to make more assumptions about to cut costs considerably or will have to leave
the form of the frontier and the errors; hence, it the industry via merger or failure as competi
is less flexible. However, this is a less serious tive pressures increase. Is the same true of
problem than DEA's inability to allow for any Third District banks?
noise in the data.1 Therefore, I use frontier
6
analysis to analyze efficiency of banks in the EFFICIENCY OF THIRD DISTRICT BANKS
I used the cost frontier approach to study the
Third Federal Reserve District. Another poten
tial problem with frontier analysis is that if the efficiency in 1992 of commercial banks operat
researcher misspecifies the cost function to be ing in the Third Federal Reserve District, which
estimated or omits factors that affect cost, this comprises the eastern two-thirds of Pennsylva
may be attributed incorrectly to inefficiency. nia, the southern half of New Jersey, and the
Current research is expanding on the method entire state of Delaware. Since I wanted to
ology by trying to actually model the ineffi estimate the cost frontier of standard commer
ciency rather than rely on deviations from the cial banks that are using the same production
frontier to capture inefficiency. This has great technology, some banks were omitted from the
7
potential, since it would more readily indicate sample.1 The sample of 214 banks included all
the causes of inefficiency. (SeeFaulhaber, 1993.) the Third District banks except the special pur
The handful of frontier studies (including pose banks in Delaware (legislated under the
stochastic econometric and thick frontier meth Financial Center Development Act and the
odologies), in general, used data from the 1970s Consumer Credit Bank Act—thus, we excluded
and 1980s, and have found X-inefficiency on Delaware's credit card banks), de novo banks
the average of about 20 to 30 percent in banking (that is, banks less than five years old, which
(see Evanoff and Israilevich, 1991). That is, have start-up costs that more mature banks do
elimination of X-inefficiency at the average not have), and three very large banks (which
bank could produce about a 20 to 30 percent very likely use different production techniques
8
cost savings, making this a much more serious than the other banks).1 The median asset size
source of inefficiency than scale and scope
inefficiency. Not surprisingly, since DEA at
tributes any statistical noise to inefficiency, the
17Since efficiency is measured relative to the cost fron
15A more technical explanation of the frontier methodol
ogy is contained in Mester (1994).
16Moreover, there are ways of relaxing some of the
maintained assumptions of stochastic frontier analysis and
achieving more flexibility, depending on the available data.
For example, using panel data— that is, data from several
periods (years, quarters, etc.) on the same sample of banks—
allows some of the assumptions regarding the error struc
ture to be relaxed. See Schmidt and Sickles (1984).
tier, it is important that all banks in the sample have access
to the same frontier; hence they should be using the same
technology. (Whether one technology is better than another
is a separate issue.) One advantage to restricting the sample
to the Third District rather than using a U.S. sample is that
banks in the Third District are likely to have more in com
mon with each other, thus making it more likely they are
using the same production technology. It should be remem
bered that the results presented below apply only to the
1992 period. Since branching restrictions have only recently
been eliminated in Pennsylvania—branching throughout
the state became totally unrestricted only on March 4,
1990—more years of data were not included in the study.
11
BUSINESS REVIEW
of banks included was $144 million, and the
average asset size was $325 million.
The intermediation approach was used to
determine bank outputs and inputs. Three
outputs were included: real estate loans, com
mercial and industrial plus other loans, and
loans to individuals. Each of these was mea
sured by the average dollar volume that the
bank held in 1992. These three outputs account
for just about all of a bank's nonsecurities
earning assets. The average volume of each of
these three outputs at banks in the sample was
about $120 million, $52 million, and $31 mil
lion, respectively. Thus, about 60 percent of the
average bank's loan portfolio is in real estate,
about 25 percent is business loans, and the rest
is loans to individuals.
The inputs (whose prices are used to esti
mate the cost frontier) are labor, physical capi
tal, and borrowed money (including deposits,
federal funds, and other borrowed money)
used to fund the outputs. To account for the
quality of the banks' outputs and bank risk (and
so to avoid labeling as efficient banks that are
not monitoring their loans), a bank's volume of
nonperforming loans and the volume of its
financial capital are included as arguments in
the cost fu n c tio n .1
1
8
9
The volum e of
nonperforming loans relative to the level of
18If the banks in the District are ordered by asset size, the
sizes grow relatively smoothly from about $13 million to
about $3.8 billion; then there is a jump to $7.8 billion, then to
$9.3 billion, and then to $16 billion. Since there is empirical
evidence that very large banks use a different production
technology than other banks (e.g., findings of scale econo
mies differ for small and large banks), and large banks also
produce different outputs from small banks (e.g., they have
more off-balance-sheet business), these three largest banks
were not included in the sample.
19The translog functional form was assumed for the cost
function; the two-sided error representing statistical noise
was assumed to have a normal distribution; the one-sided
error representing X-inefficiency was assumed to have a
half-normal distribution. Interested readers may consult
Mester (1994) for further details on the study's setup.
Digitized 12 FRASER
for
JANUARY/FEBRUARY 1994
bank output is inversely related to quality: the
higher the bank's nonperforming loans for a
given volume of loans, the less resources the
bank likely spent on monitoring its loan portfo
lio.2 The higher the bank's level of financial
0
capital relative to the level of output, the lower
the bank's probability of failure and so the
bank's interest costs. Financial capital is also
included because capital can be used as a fund
ing source for loans.
Scale and Scope Economies at Efficient
Third District Banks. The estimated average
cost frontier for Third District banks seems to
be quite flat. The efficient bank producing the
average level of each output and facing the
average input prices is producing with con
stant returns to scale. That is, a 1 percent
increase in the level of all outputs would lead to
about a 1 percent increase in costs. (See the
Table. The first line of the table's top panel,
Average Inefficiency Measures, shows the aver
age bank's point estimate of the scale econo
mies measure, indicating the percentage in
crease in cost from a 1 percent increase in all
outputs, holding quality and risk constant; it is
statistically insignificant from one.) Moreover,
over the entire size range of banks operating in
the District, efficient banks are operating with
constant returns to scale. The first line of the
table's middle panel, Scale and Scope Economies
over Different Sized Banks, shows the scale econo
mies measures for the average efficient bank in
each of four size categories. (Although the
point estimates suggest decreasing average
costs, the scale economies measures are suffi
ciently close to one that a flat average cost curve
cannot be ruled out statistically.) Therefore,
there do not seem to be many cost efficiency
20Nonperforming loans are loans that are 30 or more
days past due but still accruing interest plus loans that are
not accruing interest. While the macroeconomy can affect
nonperforming loans, the effect is felt equally across banks.
It is the differences in nonperforming loans across banks
that capture differences in quality across banks.
FEDERAL RESERVE BANK OF PHILADELPHIA
TABLE
Average Inefficiency Measures
Scale Economies3
Scope Economies1
5
X-Inefficiencyc
0.95%
0.37%
7.90%
Scale and Scope Economies over Different Sized Banks
Banks with
Assets Under
$72 Million
(53 banks)
Banks with Assets
Between $72 Million
and $144 Million
(54 banks)
Banks with Assets
Between $144 Million
and $280 Million
(53 banks)
Banks with
Assets Over
$280 million
(54 banks)
Scale
Economies3
0.89%
0.92%
0.94%
0.99%
Scope
Economies1
5
0.006%
0.22%
0.50%
1.10%
Bank-Specific X-Inefficiency Measuresd
Range of X-Inefficiency over All
Banks in Each Subsample
Average X-Inefficiency over All
Banks in Each Subsample6
Pennsylvania
(182 banks)
2.94% to 19.15%
7.74%
New Jersey
(26 banks)
3.71% to 22.97%
9.34%
3.69% to 8.58%
6.32%
Delaware
(6 banks)
aThe scale economies measure is (din C /dln y x
)+(dln C /dln y2)+(dln C /dln y3)+(dln C /dln k)+(dln C /dln q)
where C is the predicted cost of producing the average output bundle (in the specified bank size category) at the
average input prices, y. is the volume of output i, k is the level of financial capital, and q is the volume of
nonperforming loans. The measure indicates the percentage increase in costs from a 1 percent increase in each
output level, holding risk and quality constant. Constant returns to scale is indicated if the measure is insignificantly
different from one; decreasing returns to scale is indicated if the measure is significantly greater than one; increasing
returns to scale is indicated if the measure is significantly less than one.
None of the scale economies measures is significantly different from one, so there is no evidence of scale
economies or diseconomies; that is, there are constant returns to scale.
bThe scope economies measure is {[C(y1,y™y^4C(y1)y2,y3 C(y™ y2
n
V
'fy3)]-C(yJ,y2,y3)|/C(y1
,y2,y3) where y. is the
volume of output i, yj’is the least amount of output i produced by any bank in the sample, and C(») is the predicted
cost of producing an output bundle at the average input prices. The scope measure gives the percentage increase
in cost of dividing the bank's products among three banks, each of which is relatively specialized in one of the three
outputs. A statistically positive scope measure indicates there are economies of scope between the three outputs;
a statistically negative scope measure indicates there are diseconomies of scope between the three products.
None of the scope measures is significantly different from zero, so there is no evidence of scope economies or
diseconomies.
cThe X-inefficiency measure is significantly different from zero (at the 10 percent level). This measure is E(u.)
where u. is the one-sided component of the composed error term in the frontier regression. See Mester (forthcoming).
dThe bank-specific inefficiency measure is E(u. Ie.) where u. is the positive component of the composed error term
£. of the frontier regression. See Mester (forthcoming).
degression
http://fraser.stlouisfed.org/ results indicate that while the average point estimates differ across states, once bank characteristics
are
Federal Reservecontrolled for there is no statistical difference in inefficiency across states.
Bank of St. Louis
BUSINESS REVIEW
gains to be made from Third District banks'
changing their sizes, and these results are much
like those obtained in studies using U.S.
samples.2
1
The scope economies statistics give the per
centage increase in cost if the bank's three
outputs were divided up and produced in three
banks, each of which is relatively specialized in
one of the outputs.2 These measures indicate
2
that there is no evidence of economies or
diseconomies of scope at the average efficient
bank in the sample nor at banks in different size
categories, since the measures are statistically
insignificant from zero. (See the Table.) Thus,
there do not appear to be many cost efficiency
gains to be made by a bank's changing its loan
mix (which for the typical bank in the sample is
weighted toward real estate loans).
X-Inefficiency at Third District Banks. The
cost frontier technique allows one to estimate
the average level of X-inefficiency for the entire
sample of banks and also bank-specific levels of
inefficiency. The bank-specific measures can
then be averaged by state to indicate the aver
age level of inefficiency of banks in each of the
three states in the District. As shown in the
table's top panel, Average Inefficiency Measures,
and in the bottom panel, Bank-Specific X-lnefficiency Measures, X-inefficiency at banks in the
Third District runs in the 6 to 9 percent range.
In other words, given its particular output level
and output mix, if the average bank were to use
its inputs as efficiently as possible, it could
reduce its production cost by roughly 6 to 9
percent. The average annual cost of output
production at banks in the sample was about
$12 million, so a 6 percent reduction in cost
21The average scale measure for the sample indicates
that a 1 percent increase in output would yield a 0.95
percent increase in cost, which translates into a trivial
potential increase in the average bank's return on assets.
22This is the "within-sample degree of scope economies"
as defined in Mester (1991).
14
JANUARY/FEBRUARY 1994
could potentially add about $720,000 to bank
profits, which, given the average bank's size of
$325 million in assets, constitutes a potential
increase of 0.2 percent in before-tax return on
assets, or about 0.15 percent in after-tax ROA.
This isn't a trivial amount, as the average bank
in the District had an after-tax ROA of 1 percent
in 1992. In competitive markets not all of this
gain would be retained by the bank— the sav
ings would be passed on to customers in the
form of lower loan rates and higher deposit
rates. R egard less of who receiv es the
savings—banks or their customers—society
gains, since the savings created by increased
efficiency can be used for other productive
purposes.
Of course, not all banks are the "average"
bank. The figure, Third District Inefficiency
Distribution, indicates the number of banks in
the sample that fall into different inefficiency
ranges. As you can see, while the distribution
is weighted in the 6 to 9 percent range, some
banks are quite efficient but others show a good
deal of inefficiency (as high as 23 percent).
When compared with results of other studies
using U.S. samples that found average X-inef
ficiency on the order of 20 to 30 percent, Third
District banks seem to be performing better. It
is difficult to determine whether this is a statis
tically significant difference, however. It might
just reflect that the Third District study is based
on more recent data, or it might be because
banks in the U.S. samples are more diverse,
making efficiency measurement more difficult.2
3
In any case, as with U.S. banks in general, it
appears that many Third District banks have
room for improvement.
Characteristics of Inefficient Banks. Ulti
mately, we'd like to be able to say what banks
can do to increase their efficiency. For each
bank in the sample, the cost frontier analysis
23It might also be because Third District banks use a
different production technology than other U.S. banks.
FEDERAL RESERVE BANK OF PHILADELPHIA
Loretta ]. Mester
How Efficient Are Third District Banks?
FIGURE
Third District Inefficiency Distribution
(214 Banks in the Sample)
Number of Banks in Inefficiency Range
100
Inefficiency Range
provides a point estimate of its level of Xinefficiency. Perhaps the best way to determine
what banks should do to raise efficiency is to go
on site to the banks that are identified as most
efficient in the study and see what they are
doing differently from the banks that are least
efficient. A simpler first step is to see if there are
any aspects of the banks that seem to be related
to their degree of inefficiency. (Of course, a
relationship need not imply causality. That is,
we are not saying these characteristics cause
24Another reason to interpret the results as providing
information on correlation only instead of causality is that
there may be some endogeneity, since the characteristics are
for the same period as the inefficiency measures. Causality
may run from inefficiency to the characteristics instead of
the other way around. For example, inefficient firms may
choose to invest in real estate rather than investing in real
estate leading to inefficiency.
inefficiency, only that they seem to be more
prevalent in inefficient banks.24) Simple corre
lations between the inefficiency measures and
characteristics of the banks can be calculated,
and the inefficiency measures can be regressed
on bank characteristics to get an idea of how the
inefficient and efficient banks in the sample
differ.2 *
5
25The regression involved estimating a logistic equation
relating the bank-specific inefficiency measure to the fol
lowing regressors: charter type (federal vs. state), holding
company status (member of a holding company or not),
member of the Federal Reserve System or not, number of
branches, total assets, location in Pennsylvania, location in
New Jersey, location in Delaware, total qualifying capital/
assets, return on assets, volume of uninsured deposits/total
deposits, construction and land development loans/total
loans, real estate loans/total loans, loans to individuals/
total loans, and year opened. See Mester (1994) for further
details.
15
BUSINESS REVIEW
The simple correlation, which does not hold
constant the other characteristics, and the re
gression results, which do hold constant other
characteristics of the bank, indicate that ineffi
cient banks in the District tend to be younger
than more efficient banks. This might be evi
dence that banking involves "learning by do
ing," or it might indicate that more efficient
banks are more likely to survive. (Recall that
the de novo banks were not included in the
sample, so the result probably doesn't merely
reflect younger banks' higher start-up costs, for
example, the costs of establishing customer
relationships.)
Even though the point estimates show dif
ferences in inefficiency among banks in the
three states, once other bank characteristics are
controlled for, there is no statistically signifi
cant difference in inefficiency across the states.2
6
Similarly, there is no evidence that larger banks
are more or less X-efficient than smaller banks.
This result, coupled with our results on scale
economies, suggests that banks of all sizes in
our District can be equally competitive when it
comes to cost efficiency.
Among the statistically significant relation
ships, one of the more interesting is the nega
tive relationship between inefficiency and the
26The simple correlation coefficient indicates that being
located in New Jersey is significantly related to being inef
ficient, but this is because the New Jersey banks in the
sample tend to have lower capital ratios than Pennsylvania
and Delaware banks in the sample. Once capital ratio is
controlled for (as in the regression), being located in New
Jersey is not significantly related to inefficiency.
27There are a few other statistically significant relation
ships. For example, inefficient banks tend to have a higher
percentage of their loans in construction and land develop
ment; national banks appear to be less efficient than state
banks that are members of the Federal Reserve System but
seem to have the same level of efficiency as state nonmember
banks. (Note: all nationally chartered banks are Fed mem
ber banks, but their primary regulator is the Office of the
Comptroller of the Currency, not the Fed.)
16
JANUARY/FEBRUARY 1994
capital-asset ratio.27 This result should not be
interpreted as saying that if a bank increases its
capital-asset ratio then its efficiency will in
crease. But it may be an indication that higher
capital ratios may prevent "moral hazard." As
is often cited in discussions of the thrift crisis, as
an institution's capital level decreases it has an
increasing incentive to "bet the bank," since it
stands to gain if the risk pays off and tends to
lose only the amount of capital it has invested
in the bank if the bet loses. Similarly, the
managers of banks with lower capital levels
might have more of an incentive to engage in
perk-taking, and they face less shareholder
scrutiny than banks with higher capital ratios.
(If the owners' stake, that is, capital, is low,
owners have less incentive to make sure the
bank is run efficiently.28) Therefore, higher
bank capital may not only provide a cushion for
the deposit insurance fund, it might also pro
vide appropriate incentives to bank managers
to avoid waste. The capital-asset ratio might
also be significantly related to inefficiency be
cause inefficient banks have lower profits, which
might lead to lower capital-asset ratios in the
future.2
9
CONCLUSION
Banks in the Third District appear to be
operating at cost-efficient output sizes and prod
uct mixes, but there appears to be a significant
level of X-inefficiency at our banks. Some
banks apparently are not using their labor,
plant and equipment, and funds in the most
efficient way possible, and case studies that
focus on the more efficient banks in the District
28Mester (1990) discusses the incentive effects of bank
capital in mitigating bank risk-taking.
29But this is probably not the entire reason, since the
relationship between capital assets and inefficiency holds
even when return-on-assets is held constant, and while
return-on-assets and capital assets are correlated, they are
not collinear.
FEDERAL RESERVE BANK OF PHILADELPHIA
How Efficient Are Third District Banks?
might shed light on how greater efficiency can
be achieved. Theoretical advances may enable
us to better identify the sources of the ineffi
ciencies and verify that measured differences
in inefficiency are true differences and do not
result just from omitting factors that affect cost
or misspecifying the cost function.
In terms of coping with the increased com
petitive pressures, inefficientbanks in the Third
Loretta ]. Mester
District have more to fear from banks that are
efficient producers than from banks that are
producing a particular output volume or prod
uct mix. There is less to be gained in terms of
cost savings from changing output size or mix
than from using inputs more cost-effectively.
Inefficient banks will have to get costs under
control or else be prepared to be driven from an
increasingly competitive marketplace.
Berger, Allen, and David Humphrey. "Measurement and Efficiency Issues in Commercial
Banking," in Z. Griliches, ed., Measurement Issues in the Service Sectors. Chicago:
National Bureau of Economic Research and Chicago University Press, 1992a.
Berger, Allen, and David Humphrey. "Megamergers in Banking and the Use of Cost
Efficiency as an Antitrust Defense," Antitrust Bulletin, 37 (Fall 1992b), pp. 541-600.
Calem, Paul S. and Loretta J. Mester. "Search, Switching Costs, and the Stickiness of Credit
Card Interest Rates," Working Paper 92-24/R, Federal Reserve Bank of Philadelphia,
revised January 1993.
Calem, Paul S. "The Proconsumer Argument for Interstate Branching," this Business Review
(May/June 1993), pp. 15-29.
Clark, Jeffrey A. "Economies of Scale and Scope at Depository Financial Institutions: A
Review of the Literature," Economic Review, Federal Reserve Bank of Kansas City
(September/October 1988), pp. 16-33.
Evanoff, Douglas D., and Philip R. Israilevich. "Productive Efficiency in Banking," Economic
Perspectives, Federal Reserve Bank of Chicago, (July/August 1991), pp. 11-32.
Faulhaber, Gerald R. "Profitability and Bank Size: An Empirical Analysis," The Wharton
School, University of Pennsylvania, mimeo, 1993.
Hughes, Joseph P., and Loretta J. Mester. "A Quality and Risk-Adjusted Cost Function for
Banks: Evidence on the 'Too-Big-To-FaiT Doctrine," Journal of Productivity Analysis,
4 (September 1993), pp. 293-315.
Humphrey, David. "Costs and Scale Economies in Bank Intermediation," in R. Aspinwall
and R. Eisenbeis, eds., Handbookfor Banking Strategy. New York: Wiley and Sons, 1985.
Kraus, James R. "The Whole World Is Watching Asset Quality, Rate of Return," American
Banker, July 29,1993, p. 2A.
Mester, Loretta J. "Efficient Production of Financial Services: Scale and Scope Economies,"
this Business Review (January/February 1987), pp. 15-25.
17
SUGGESTED READINGS
BUSINESS REVIEW
JANUARY/FEBRUARY 1994
Mester, Loretta J. "Owners Versus Managers: Who Controls the Bank?" this Business Review
(May/June 1989), pp. 13-23.
Mester, Loretta J. "Curing Our Ailing Deposit-Insurance System," this Business Review
(September/October 1990), pp. 13-24.
Mester, Loretta J. "Agency Costs Among Savings and Loans," Journal of Financial Interme
diation, 3 (June 1991), pp. 257-78.
Mester, Loretta J. "Traditional and Nontraditional Banking: An Information-Theoretic
Approach," Journal of Banking and Finance, 16 (June 1992), pp. 545-66.
Mester, Loretta J. "Efficiency of Banks in the Third Federal Reserve District," Federal Reserve
Bank of Philadelphia Working Paper 94-1 (1994).
O'Hara, Terrence. "Interstate Branching Yet to Sound Alarms," American Banker, August 2,
1993.
Schmidt, Peter, and Robin C. Sickles. "Production Frontiers and Panel Data," Journal of
Business and Economic Statistics, 2 (October 1984), pp. 367-74.
18
FEDERAL RESERVE BANK OF PHILADELPHIA
New Indexes Track the State
Of the States
Theodore M. Crone*
H a v e recessions lasted longer in Pennsylvania than in the nation? When did the most
recent recession begin in New Jersey? Did
Delaware avoid the recessions of the early
1980s altogether? Questions about how busi
ness cycles differ from state to state are raised
frequently in the popular press and in business
commentaries. The answers are seldom clear,
but the questions are not idle ones. Some
industries, such as construction and retail trade,
*Ted Crone is Assistant Vice President in charge of the
Regional Economics section in the Philadelphia Fed's Re
search Department. Ted would like to thank James Stock
and Mark Watson for a copy of the programs that estimate
their new composite index of coincident indicators. He also
thanks Keith Sill for help in adjusting Stock and Watson's
programs to calculate the new state indexes.
are particularly sensitive to the local business
cycle. Since people tend to live close to their
jobs and shop close to where they live, sales of
new homes, cars, and many consumer items
depend on the prospects for jobs and income in
the local area. If the local economy is weaken
ing, these prospects are poor; if the economy is
strengthening, the prospects are better. So
knowledge about where the region is in the
business cycle can be critical for managers in
many businesses. But economic data are often
ambiguous and sometimes contradictory; one
indicator may be showing improvement while
another shows decline. For example, the unem
ployment rate may be up at the same time job
levels are increasing. Composite indexes, con
structed from a number of individual indica
tors, can help clear up the ambiguity. There are
19
BUSINESS REVIEW
JANUARY/FEBRUARY 1994
by a recovery phase in which economic mea
sures return to their previous peaks and then an
expansion phase in which the measures reach
new peaks. The description by Bums and
Mitchell mentions three criteria for dating the
various phases of the business cycle. The con
tractions and expansions must be broad-based,
that is, they must occur in many sectors and be
reflected in several indicators (diffusion). They
must last a sufficient length of time (duration).
And the change from peak to trough or from
COMPOSITE INDEXES CAN HELP TRACK trough to peak must be sufficiently large (ampli
tude). All three criteria must be satisfied in
BUSINESS CYCLES
Between 1970 and 1990 real output in the order to define a contraction or expansion. A
U.S. grew at an average annual rate of 2.7 sharp decline in one sector, such as agriculture,
percent, and employment increased 2.2 percent would not qualify as a recession if it did not spill
a year. But output and employment fluctuated over into other sectors of the economy; the
widely around these trends as the economy decline would not be broad enough. On the
went through several business cycles. Eco other hand, a broad-based decline that was
nomic trends vary from region to region, but all very brief, one quarter, for example, probably
regions are affected by national business cycles, would not qualify as a recession; it would be too
and some regions have exhibited cycles of their short. Likewise, two quarters of 0.1 percent
decline in output might not qualify as a reces
own.
Almost 50 years ago, Arthur Burns and sion; the decline would not be deep enough.
Since business cycles are broad-based, they
Wesley Mitchell fashioned the commonly ac
tend to generate their own momentum. Down
cepted description of a business cycle:
turns in the economy can be set in motion by a
Business cycles are a type o f fluctuation found
variety of factors, such as a sharp increase in the
in the aggregate economic activity o f nations
price of a major resource like oil or a sudden
that organize their work mainly in business
large reduction in government spending. Once
enterprises: a cycle consists o f expansions
a general downturn begins, firms begin to lay
occurring at about the same time in many
off workers. This loss of jobs as well as the
economic activities,followed by similarly gen
uncertainty among people who are still em
eral recessions, contractions, and revivals
ployed leads consumers to cancel or postpone
which merge into the expansion phase o f the
purchases, which results in more layoffs. Not
next cycle...; in duration business cycles vary
all sectors of the economy are equally vulner
from more than one year to ten or twelve years;
able to a downturn; consumers may be reluc
they are not divisible into shorter cycles . . .
tant to delay seeing a doctor if they are ill, but
[that exhibit swings in economic activity o f
they might readily put off the purchase of a new
similar] amplitudes}
A complete cycle from one peak to the next car. In general, manufacturing industries are
consists of a recession or contraction followed1 more sensitive to business cycles than service
*
industries. A downward spiral in the economy
might be halted by a change in consumer expec
1Arthur F. Bums and Wesley C. Mitchell, Measuring
tations that raises confidence, by an increase in
Business Cycles (National Bureau of Economic Research,
disposable income through a reduction in taxes,
1946).
commonly accepted composite indexes for the
national economy, such as the index of leading
indicators, but composite indexes are not readily
available at the regional level, making it more
difficult to track regional business cycles. This
article introduces new composite indexes for
the three states in the Third Federal Reserve
District that make use of statistical techniques
previously used for national indexes but not
regional ones.
Digitized 20 FRASER
for
FEDERAL RESERVE BANK OF PHILADELPHIA
New Indexes Track the State of the States
Theodore M. Crone
or by a rise in government spending for goods have been 18 business-cycle turning points in
and services— any of which would increase the U.S. economy. With four exceptions, the
demand. When this increased demand be highest and lowest levels of the Commerce
comes broad enough it creates its own momen Department's index during each cycle have
tum toward further expansion. Thus, knowl been within three months of the official dates of
edge of whether the economy is entering a the business-cycle peaks and troughs.3 Al
recession or beginning a recovery is important though the NBER dating committee considers
the coincident index when it sets the dates for
to local businessmen.
Dating business cycles using the Burns and business cycles, it is not obligated to set the
Mitchell criteria is not always straightforward; dates at or near the turning points of the index.
it involves some personal judgment. In prac Therefore, the close correspondence between
tice, the official dating of recessions and expan the o fficia l dates and the C om m erce
sions is done by the Business Cycle Dating Department's index suggests that the index is
Committee of the National Bureau of Economic coincident with the business cycle.
The Commerce Department's Composite
Research (NBER), which is composed of pro
fessional economists. To avoid any possibility Index of Coincident Indicators is constructed
of political considerations in setting these dates, from four monthly data series—the number of
none of these economists is a government offi jobs in nonagricultural establishments, personal
cial. The committee considers a number of income (minus transfer payments) adjusted for
economic indicators in dating business cycles. inflation, the index of industrial production,
Composite indexes, however, play a special and manufacturing and trade sales adjusted for
role in these decisions because they combine inflation. Month-to-month percent changes are
information from several sources to indicate calculated for each of these series, and the
the general state of the economy. They include changes are standardized based on the longnot only data for the overall economy, such as run average absolute monthly change in the
employment and personal income, but also series. For example, the average absolute per
data from individual sectors, such as retail centage change in monthly employment be
sales or industrial production.
tween 1948 and 1985 was 0.32 percent. Thus, if
The Department of Commerce publishes the change in nonfarm employment were 0.64
three com posite indexes for the national percent this month, the standardized change
economy—indexes of leading, lagging, and co for this indicator would be 2 (i.e., 0.64 / 0.32).
incident indicators.2 Of these three, the com A preliminary coincident index is formed based
posite index of coincident indicators is the most on the average of the standardized changes in
important for dating business cycles. A good the components that make up the index. To
index of coincident indicators should decline at obtain the Department's official composite in
or near the beginning of recessions and should dex, this preliminary index is adjusted to grow
rise at or near the end. In the last 45 years there over time at the same rate as real gross national
2This article was prepared before the most recent revisions of the Commerce Department's composite indexes.
All references in this article refer to the unrevised indexes.
SeeGeorgeR. Green and Barry A. Beckman, "Business Cycle
Indicators: Upcoming Revision of the Composite Indexes,"
Survey o f Current Business, Vol. 73,10 (Oct. 1993), pp. 44-51.
3A s the name suggests, an index of leading indicators
should peak several months before the economy goes into
recession and should reach its cyclical low before the recession ends. The timing should be reversed for an index of
lagging indicators. The leads and lags in the Commerce
Department's leading and lagging indexes have varied
from as long as 23 months to as short as 1 month.
21
BUSINESS REVIEW
product and is set to 100 in 1982.4 Except for the
adjustment to account for differences in the
average monthly changes in the four indica
tors, each indicator is given the same weight in
forming the composite index. But some indica
tors like the total number of jobs may better
reflect the overall state of the economy than
other indicators like manufacturing and trade
sales. Thus, while the Commerce Department's
index has tracked national business cycles very
well, it has been criticized for not being derived
from a formal m athem atical or statistical
model.5
In order to support the theory of business
cycles and aid in the dating of recessions and
expansions, James Stock of Harvard University
and Mark Watson of Northwestern University
have constructed a new index of coincident
indicators.6Using time-series econometric tech
niques, they formalized the notion that the
business cycle is best measured by the common
movements across several economic data se
ries. Each monthly indicator is thought of as
having two components. The first is the general
"state of the economy," which affects all the
monthly indicators. It is not observed directly
but only in the common movement of the indi
cators that are observed. The second compo
nent is an idiosyncratic element that might
cause any one indicator to move in ways not
associated with the general state of the economy.
Stock and Watson's coincident index is an esti
4See "Composite Indexes of Leading, Coincident, and
Lagging Indicators," Survey o f Current Business, U.S. Depart
ment of Commerce, November 1987.
5The entire attempt to define business cycles was criti
cized from the beginning as an exercise in measurement
without theory. See Tjalling C. Koopmans, "Measurement
Without Theory," Review ofEconomics and S tatistics, 29 (1947),
pp. 161-72.
6James H. Stock and Mark W. Watson, "New Indexes of
Coincident and Leading Economic Indicators," NBER
Macroeconomics Annual (1989), pp.351-94.
22
JANUARY/FEBRUARY 1994
mate of the common component. The move
ment of this unobserved state of the economy is
reflected in varying degrees in each of the
published monthly series used to estimate the
composite index. Moreover, for some series,
changes in the general economy could be re
flected not only in the current month but also in
succeeding months, and for other series, changes
in the general economy could be foreshadowed
in preceding months (see A Formal Model o f the
New Coincident Index). In effect, the Stock and
Watson index is a weighted average of current
and past values of the individual indicators,
with the weights determined by the degree of
common movement in the indicators.
In constructing their coincident index Stock
and Watson used the same data series as the
Department of Commerce, with one exception:
they su bstitu ted em ployee h ou rs in
nonagricultural establishments for the number
of nonagricultural jobs because economic out
put depends not only on how many people are
working but also on how long they work. Stock
and Watson's new index is available from 1959,
and over that period it has coincided with the
official business cycles even more closely than
has the Commerce Department's Index of Co
incident Indicators. The cyclical highs and
lows in the Stock and Watson index coincide
exactly with the official business-cycle turning
points except in 1969 when the new index peaks
two months prior to the official turning point.7
CONSTRUCTING STATE INDEXES
The success of the Stock and Watson method
in constructing a national coincident index that
tracks the official business cycles so closely
suggests that this method could be used suc
cessfully to construct an index for state econo
mies. But the construction of a comparable
7Of course, in developing their index Stock and Watson
were attempting to trace the official business cycles prior to
1990, and the NBER dating committee had the new Stock
and Watson Index when it dated the most recent recession.
FEDERAL RESERVE BANK OF PHILADELPHIA
New Indexes Track the State of the States
Z
0)
rz
£
5O
Theodore M. Crone
The basic notion that a change in a monthly indicator reflects a change in the underlying
state of the economy is captured in the following equation:
AIf = a + b ASt+ut
(1)
where:
AIt = the change in the observed monthly indicator between time t-1 and time t, and
ASt= the change in the unobserved state of the economy between time t-1 and time t.a
Since the purpose of this model is to form a composite index, this equation is applied to a
number of monthly indicators. For example, Stock and Watson use four monthly indicators
so there are four equations similar to equation (1) in their model. The coefficients (a and b)
will vary with each equation, but the unobserved variable (ASt) is the same. In addition, the
error term inequation (1) and the unobserved variable are assumed to follow an autoregressive
process, so that
Ut = gl Ut-l + g 2 Ut-2 + et
( 2)
and
ASt = c + f,AStl + f2ASt 2 + zt
(3)
where et and zt are error terms. Equations (2) and (3) are the transition equations in the
system.
This system of equations (1) through (3) can be estimated using maximum likelihood
techniques to produce an estimate of the change in the unobserved state of the economy
(ASt).b If we then index the unobserved variable St to equal 100 at some point in time, we can
construct a time-series of the so-called "state of the economy," or a coincident index.c
aIf the monthly indicator also reflects prior changes in the state of the economy, the estimating equation
becomes AI = a + bQ + bj AS( ] . . . + u . If the monthly indicator partially foreshadows a change in the
AS
general state of the economy, the lagged values of the unobserved state of the economy are replaced by
leads.
bIn the actual estimating equations, Stock and Watson use the log difference of the monthly indicators.
The change in the log of the monthly indicator is normalized by subtracting the historical mean and
dividing by the standard deviation. Thus the constants a and c do not have to be estimated, and the
unobserved variable that is estimated is the normalized change in the log of S .
cStock and Watson set their national index at 100 in July 1967, and we set our state indexes at 100 in
July 1987.
state index is not a simple matter of estimating
Stock and Watson's model using state data.
The monthly indicators used by Stock and
Watson are not available at the state level.
Moreover, there is no direct way to determine
whether a composite index using other indica
tors at the state level would coincide with the
business cycle because there are no official
dates for state business cycles. Indeed, this was
one reason for developing state indexes. To
address the problem of finding an appropriate
set of indicators to construct state indexes we
identified a set of monthly indicators that are
available at both the national and state levels.
We selected those variables that were useful in
dating national business cycles and assumed
they would also be useful in identifying cycles
in the state economies.
23
BUSINESS REVIEW
JANUARY/FEBRUARY 1994
This indirect method resulted in identifying
four variables to be used in our state indexes of
monthly indicators— the total number of jobs in
nonagricultural establishments, real retail sales,
average weekly hours in manufacturing, and
the unemployment rate.8 These variables differ
somewhat from those used in other national
ind exes. The to tal n um ber of jo bs in
nonagricultural firms is used in the Commerce
Department's Index of Coincident Indicators
and was used in an earlier version of the Stock
and Watson index.9 The sales data used in our
indexes are less comprehensive than those used
by the Commerce Department and by Stock
8The first three variables enter our model in log differ
ence form. The unemployment rate enters in first difference
form and is modeled to reflect the current value and three
lags of the variable that reflects the state of the economy.
9James H. Stock and Mark W. Watson, "A Probability
Model of the Coincident Economic Indicators," in Geoffrey
Moore and K. Lahiri, eds., The Leading Economic Indicators:
New Approaches and Forecasting Records (Cambridge Univer
sity Press, 1990).
and Watson. Both use a series that includes
sales by manufacturers and wholesalers as well
as by retailers. Two of the variables we selected
to construct our indexes, average hours in
manufacturing and the unemployment rate,
have not traditionally been counted among the
coincident indicators. But we included them in
our index because doing so improved the cor
respondence between the index and the official
business cycles, compared to an index using
only employment and retail sales.
Using these four variables we developed a
national index and examined how closely it
coincides with the official dates of national
business cycles and with other composite in
dexes for the nation. Based on data since 1972,
the pattern of the new national index follows
closely the p attern of the C om m erce
Department's coincident index and Stock and
Watson's coincident index (Figure l).1 * There
0
10We started the index in 1972 because some of the data
series used in the index are not available at the state level
prior to 1972.
FIGURE 1
Coincident Indicators-
Index duly 1987=100)
-u.s.
120
110
New National Index
1Stock & Watson
1Commerce Departme
100
90
80
70
The shaded areas represent the official recessions as determined by the NBER Dating Committee.
New Indexes Track the State of the States
Theodore M. Crone
have been four national business cycles since
1972. Only the Stock and Watson index coin
cides precisely with the official peaks and
troughs of all of them. But the new national
index developed with data series that are also
available at the state level traces the four na
tional business cycles closely. With two excep
tions the peaks and troughs of this new com
posite index are within one month of the official
peaks and troughs of the U.S. business cycles
since the early 1970s (Table l).1 The Commerce
1
Department's Composite Index of Coincident
Indicators was also off by several months at the
same two turning points. Thus, the timing of
the new index compares favorably with the
11The two exceptions are the peak preceding the 1980
recession when the index led the economy by seven months
and the trough of the most recent recession when the index
lagged the economy by 15 months. Prior to the 1980 reces
sion, the Stock and W atson index, the Commerce
Department's index, and the new national index were basi
cally flat for almost a year. Although the new index and the
Commerce Department's index peaked several months be
fore the beginning of that recession, they changed very little
in the intervening months. After the official end of the most
recent recession in March 1991, the cyclical lows for the
Commerce Department's index and the new national index
lagged by several months. The steep declines in the two
indexes ended, however, about the same time as the official
end of the recession, and the two indexes improved tempo
rarily shortly after the official end of the recession.
timing of the Commerce Department's index,
and it can be considered a coincident index.1
2
Using the same monthly indicators as in the
new national index, we constructed coincident
indexes for each of the three states in the Third
Federal Reserve District — Pennsylvania, New
Jersey, and Delaware (see New National and
State Indexes). Since retail sales data are not
available for Delaware, that state's index in
cluded only three of the four indicators.1 These
3
12The average monthly increase in the new national
index between 1972 and 1992 was 0.13 percent, compared
with 0.16 percent for the Commerce Department's index
and the 0.19 percent for the Stock and Watson index. The
variance in the monthly change for the new index is also
smaller than the variance for the other two indexes. The
correlation between monthly changes in the new index and
the Commerce Department's index is 0.54, and the correla
tion between the new index and the Stock and Watson index
is 0.55. Both correlation coefficients are significantly differ
ent from 0 and from 1 at the 0.01 level. The correlation
between the Commerce Department and the Stock and
Watson indexes is considerably higher at 0.93, because with
one minor exception these two indexes are constructed from
the same monthly indicators.
13A national index constructed from the three variables
used in the Delaware index tracks the national business
cycles slightly less accurately than the new national index
constructed from all four variables. In some cases, e.g., the
1981-82 recession, the timing of the peaks and troughs of the
two national indexes are identical.
TABLE 1
Leads and Lags of the New National Index
at Business Cycle Peaks and Troughs
(leads and lags in months)
BUSINESS CYCLE
PEAKS
November 1973
January 1980
July 1981
July 1990
lead (+)/
lag (-)
0
+7
0
+1
BUSINESS CYCLE
TROUGHS
March 1975
July 1980
November 1982
March 1991
lead (+)/
lag (-)
-1
0
-1
-15
25
JANUARY/FEBRUARY 1994
BUSINESS REVIEW
New National and State Indexes
Except for Delaware there are four measurement equations in each system used to estimate the
new national and state indexes:
(1) Aempt=Pe ASt + ute
(2) Ahrst = PhAS, + uft
(3) Arst = pr AS, +
(4) AURt = Pu AS, + Pu AS,, + pu AS, 2 + Pu ASt.3 + u,u
0
l
2
3
where
Aemp = the standardized change in the log of nonfarm employment
Ahrs = the standardized change in the log of average hours worked in manufacturing
Ars = the standardized change in the log of real retail sales
AUR = the standardized change in the unemployment rate.
Since retail sales are not available for Delaware, equation (3) is omitted in the system of equations
for the Delaware index. Lagged values of the unobserved state of the economy are entered in the
unemployment rate equation because including the lags produced a national index that coincided
better with the official NBER recession dates. Moreover, the unemployment rate is often a lagging
indicator reflecting the state of the economy in previous months. The estimated coefficients for each
of the four systems is given in the following table:
Estimates of Coefficients Used to Construct
Indexes of Coincident Indicators
US
INDEX
PA
INDEX
NJ
INDEX
DE
INDEX
0.715
(.051)
0.530
(.081)
0.823
(.065)
0.701
(.134)
0.175
(.032)
0.175
(.041)
0.159
(.050)
0.185
(.053)
0.156
(.026)
0.128
(.034)
0.046
(.049)
-0.428
(.052)
-0.213
(.052)
0.033
(.045)
0.026
(.045)
-0.044
(.092)
-0.240
(.102)
-0.161
(.103)
0.217
(.110)
-0.202
(.058)
-0.102
(.058)
-0.003
(.061)
-0.010
(.055)
EMPLOYMENT EQ
Pe
HOURS EQ
Ph
RETAIL SALES EQ
Pr
UNEMPLOYMENT EQ
P U0
Pul
Pua
Pu3
-0.637
(.130)
-0.136
(.088)
0.071
(.089)
-0.010
(.063)
( ) = standard error of the estimate
26
FEDERAL RESERVE BANK OF PHILADELPHIA
Theodore M. Crone
Neiv Indexes Track the State of the States
models produced estimates of an unobserved
"state of the economy," or a coincident index,
for each of the three states.
BUSINESS CYCLES IN THE STATES
The coincident indexes for the three states in
the Third Federal Reserve District define busi
ness cycles that correspond generally to the
four national business cycles since 1972. But
the cycles in each state have differed in their
timing and duration. These differences can be
seen by comparing the peaks and troughs of the
state indexes with the official NBER dates and
with the peaks and troughs of the new national
index (Figures 2 through 4). Since there are
clear differences between state and national
business cycles, we need to apply some crite
rion to the new indexes to identify recessions
and expansions at the state level. The experi
ence of the NBER dating committee illustrates
that there is no simple rule that will always
identify peaks and troughs in the business
cycle, but there should be some minimum de
cline in the index in order to characterize a
given period as a recession. We found that a
cumulative decline four times the average ab
solute monthly change in the index clearly
defined four recessions in the new national
index since 1972, and these recessions corre
sponded closely with the four officially recog
nized national recessions over that time period.
We used the same rule of thumb to identify
recessions at the state level. The peak of the
cycle can be dated by the high point in the index
just prior to the cumulative decline. Likewise,
the trough of a cycle can be dated by the low
point in the index prior to a cumulative increase
that is four times the average absolute monthly
change.1 This identification of recessions at the
4
state level allows us to compare cycles in the
Third District states to national cycles. The
peaks and troughs of the state indexes are
shown in Table 2.
14Other simple rules could be used to date the beginning
and end of a recession, such as three or four consecutive
decreases or increases in the index. While the use of such
consecutive decrease or increase rules would move the
peak or trough closer to the NBER date for some recessions,
for other recessions they would move the peak or trough
further away from the official dates. These rules were not
clearly superior to using the absolute high point and low
point of the index as the business cycle turning points, and
they have no compelling theoretical justification.
TABLE 2
Peaks and Troughs of State Indexes
PA INDEX
NJ INDEX
DE INDEX
PEAK
TROUGH
November 1973
May 1975
November 1973
May 1975
February 1973
April 1975
PEAK
TROUGH
June 1979
September 1980
February 1980
July 1980
February 1980
April 1980
PEAK
TROUGH
March 1981
February 1983
September 1981
November 1982
July 1981
January 1982
PEAK
TROUGH
March 1990
July 1991
March 1989
September 1992
June 1990
April 1991
27
BUSINESS REVIEW
JANUARY/FEBRUARY 1994
Pennsylvania's economy comprises some
what less than 5 percent of the U.S. economy as
measured by the number of jobs in the state and
by gross product. In terms of the mix of
industries, the cyclically sensitive manufactur
ing sector represents a larger percentage of the
Pennsylvania economy than it does of the na
tional economy, so one might expect Pennsyl
vania to suffer more recessions or longer reces
sions than the nation.1 While there have not
5
been more recessions in the state, recessions
have generally lasted longer in Pennsylvania
than in the nation (Figure 2). In every recession
since 1972 the new coincident index for Penn
sylvania has recorded a longer downturn than
indicated by the official dates for the national
15Over the period 1972 to 1992 manufacturing employ
ment has averaged 25 percent of Pennsylvania's total em
ployment but only 21 percent of U.S. employment.
recession. And except for the last recession, the
declines in the Pennsylvania index have also
lasted longer than the declines in the compa
rable national index. And generally recoveries
in Pennsylvania have been less vigorous than in
the nation as a whole. The current recovery is
a striking example. At the end of the 1990-91
recession the Pennsylvania index technically
reached its cyclical low 11 months before the
new national index, but the state's index was
little changed for more than a year after reach
ing that low point and was not signaling a
recovery. The index reflected the popular im
pression of a lingering recession in the state.
New Jersey's economy represents slightly
more than 3 percent of the U.S. economy in
terms of jobs and gross product. The structure
of the New Jersey economy has changed over
the past 20 years from a greater than average
dependence on manufacturing to a less than
average dependence. Financial and business
FIGURE 2
Index of Monthly Indicators— PA
Index (July 1987=100)
72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93
The shaded areas represent the official recessions as determined by the NBER Dating Committee.
The U.S. index is the new national index constructed from variables also available at the state level.
28
FEDERAL RESERVE BANK OF PHILADELPHIA
Theodore M. Crone
New Indexes Track the State of the States
services have become a more important part of
the state's economy. According to the new
coincident index for New Jersey, some reces
sions in the state have been longer than the U.S.
average (1973-75,1990-91), and some have been
shorter (1980, 1981-82). That pattern holds
whether we measure national recessions by the
official NBER dates or by the comparable na
tional index (Figure 3). The most recent reces
sion in New Jersey has been especially pro
longed in part because this recession affected
the service-producing sectors more than previ
ous ones. Based on the peak and trough in the
state's coincident index, the latest recession in
New Jersey lasted from early 1989 to mid-1992,
much longer than it did in the other states of the
Third Federal Reserve District. There were
some temporary improvements in the index
over this three-year period, but none of the
improvements were strong enough to qualify
as a recovery.1
6
Delaware's economy is less than one-half of
1 percent of the U.S. economy in terms of jobs
in the state and in terms of gross state product.
The state's economy is more heavily concen
trated in manufacturing than the U.S. economy,
a fact that should tend to make it more cyclical.
The very rapid growth in financial and business
services since the early 1980s, however, has
helped the state weather the last few recessions
relatively well. Clear counterparts to three of
the four national recessions since 1972 are ap
parent in the history of Delaware's new coinci
dent index (Figure 4). The one national reces
sion that has no counterpart in the Delaware
index is the short-lived one in 1980. The decline
from peak to trough in Delaware's monthly
16That is, the total increase in the index during these
temporary improvements did not equal four times the aver
age monthly change.
FIGURE 3
Index of Monthly Indicators— NJ
Index (July 1987=100)
The shaded areas represent the official recessions as determined by the NBER Dating Committee.
The U.S. index is the new national index constructed from variables also available at the state level.
29
BUSINESS REVIEW
JANUARY/FEBRUARY 1994
FIGURE 4
Index of Monthly Indicators— DE
Index (July 1987=100)
Delaware
U.S.
The shaded areas represent the official recessions as determined by the NBER Dating Committee.
The U.S. index is the new national index constructed from variables also available at the state level for many states.
index in the first half of 1980 was very brief (two
months), and the total change in the index in
those two months was less than three times the
average monthly change. Based on the normal
criteria for national recessions the brief 1980
downturn in Delaware would not qualify as a
recession. The subsequent recession in 1981-82
is clearly discernible in the Delaware index,
which registered a cumulative decline well
over four times the monthly average, but this
recession ended much earlier in the state than
in the nation. Passage of legislation in 1981
encouraging the establishment of credit card
banks in the state aided Delaware's economy.
While the new index indicates that Delaware
weathered recessions much better than the
nation in the 1980s, it also indicates that the
state suffered more in the 1970s. The 1973-75
recession began much earlier in Delaware than
in the nation or in the other two states in the
30
Third District. Moreover, the new coincident
index suggests that Delaware suffered a local
recession between February 1976 and February
1977—a downturn not matched at the national
level. The state index declined a total of 5.9
percent, more than six times the average
monthly change. The weakness in the state's
economy was concentrated in the manufactur
ing and construction industries.
GETTING ANSWERS ABOUT
STATE BUSINESS CYCLES
The ability to construct a composite index of
monthly indicators that are available at the
state level helps answer some of the questions
frequently raised about regional business cycles.
The new coincident index for Pennsylvania
indicates that recessions have generally lasted
longer in that state than in the nation as a whole.
A set of indexes for all 50 states would undoubt
FEDERAL RESERVE BANK OF PHILADELPHIA
New Indexes Track the State of the States
edly uncover other states that tend to have
longer recessions and help us identify some
reasons. The new index for New Jersey indi
cates that the most recent downturn in that
state began more than a year before the onset of
the national recession and continued for more
than a year after the end of the national reces
sion. The index confirms that this recession
was much longer in New Jersey than in the
other states in the region. The new index for
Delaware indicates that the state suffered only
one recession in the early 1980s, and that one
was briefer than the national downturn. But
Delaware suffered a more extended recession
than the nation in the early 1970s. Moreover,
Theodore M. Crone
Delaware's index provides evidence of a local
recession in the second half of the 1970s. The
expansion of financial and business services in
Delaware seems to have made the state's
economy less cyclical.
These new composite indexes for the states
provide another tool to monitor and analyze a
region's economy. They can help us compare
the timing of business cycles among the states
and between any state and the nation. A full set
of such indexes for all the states would help
answer even more questions about regional
business cycles and the structure of regional
economies.
31
January/February
Leonard I. Nakamura, “Information Externalities: Why Lending May Sometimes Need a Jump Start'
D. Keith Sill, “Predicting Stock-Market Volatility"
March/April
Laurence Ball, “What Causes Inflation?"
Satyajit Chatterjee, “Leaning Against the Wind: Is There a Case for Seasonal Smoothing of Interest
Rates?
May/June
Edward G. Boehne, “Testimony on the Third District Economy and Monetary Policy"
Paul S. Calem, “The Proconsumer Argument for Interstate Branching"
July/August
Francis X. Diebolcf, “Are Long Expansions Followed by Short Contractions?"
Shaghil Ahmed, "Does Money Affect Output?"
September/October
B. Douglas Bernheim and John Karl Scholz, “Do Americans Save Too Little?"
Gerald A. Carlino, “Highways and Education: The Road to Productivity?"
November/December
Dean Croushore, “Introducing: The Survey of Professional Forecasters"
Laurence Ball, “How Costly Is Disinflation? The Historical Evidence"
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