Full text of Economic Review (Federal Reserve Bank of Cleveland) : 1993 Quarter 3, Vol. 29, No. 3

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Vol. 29, No. 3

ECONOMIC REVIEW




FEDERAL RESERVE BANK
OF CLEVELAND

ECONOMI C

REVIEW

1993 Quarter 3
Vol. 29, No. 3

Capital Requirements
and Shifts in Commercial
Bank Portfolios

2

by Joseph G. Haubrich and Paul Wachtel
Since 1989, U.S. commercial banks have shifted their portfolios away
from commercial loans toward government securities. Using data for in­
dividual banks, the authors document this shift and test for whether it
can be attributed to the imposition of risk-based capital requirements.
Their results indicate that these requirements may indeed account for
part of the portfolio shift.

FDICIA’s Emergency
Liquidity Provisions

16

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Coordinating Economist:
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by Walker F. Todd
The Federal Deposit Insurance Corporation Improvement Act of 1991
(FDICIA) made a potentially significant change in the standards for
Federal Reserve discount window access by nonbanks. In exploring
the background of this issue, the author contends that although most
of the legislation retrenched the federal financial safety net for under­
capitalized insured depository institutions, the provision effectively ex­
panded the safety net for uninsured nonbanks, irrespective of their
capital or net worth positions.

Efficiency and Technical
Progress in Check
Processing

24

by Paul W. Bauer
Cost functions can provide valuable insights into the efficiency
and technological constraints faced by firms. Using panel data for
47 Federal Reserve offices from 1983:IQ to 1990:IVQ, the author
examines the cost of providing check-processing services by esti­
mating a multiproduct cost function using an econometric frontier
approach. The article demonstrates how the Federal Reserve’s unit
cost measures of performance can be decomposed into separate
effects related to differences in cost efficiency, output mix, input
prices, and environmental variables to provide a much richer un­
derstanding of the sources of relative office performance. Estimates
of technical progress are also presented.




Advisory Board:
Jagadeesh Gokhale
Erica L. Groshen
Joseph G. Haubrich

Editors: Tess Ferg
Robin Ratliff
Design: Michael Galka
Typography: Liz Hanna

Opinions stated in Economic Re­
view are those of the authors and
not necessarily those of the Fed­
eral Reserve Bank of Cleveland or
of the Board of Governors ot the
Federal Reserve System.

Material may be reprinted pro­
vided that the source is credited.
Please send copies of reprinted
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ISSN 0013-0281

Capital Requirements and Shifts
in Commercial Bank Portfolios
by Joseph G. Haubrich and Paul Wachtel

Introduction
A dramatic and virtually unprecedented shift in
the portfolio structure of U.S. commercial banks
has taken place since 1989. Specifically, govern­
ment securities as a share of total loans has risen
from 15 percent in 1989 to more than 22 percent
today. This portfolio shift has coincided with an
important change in the financial regulatory struc­
ture. Bank regulators around the world agreed to
a common set of risk-based capital requirements
in mid-1988. These requirements were phased in
gradually in the United States and became fully
effective this year.
Some have suggested a connection between
the regulatory changes and the portfolio shift,
although this claim has not been substantiated.
In this paper, we will present some rather strong
evidence that the portfolio shift is consistent
with regulatory change, which has increased
the attractiveness of government securities as
an asset.1 The evidence comes from an exami­
nation of the quarterly “call report” data on
commercial banks from the Federal Financial
Institutions Examination Council (FFIEC).

Joseph G. Haubrich is an eco­
nomic advisor at the Federal Re­
serve Bank of Cleveland, and
Paul Wachtel is a research profes­
sor in the Department of Econom­
ics at the Leonard N. Stern School
of Business, New York University.
The authors thank Robert Avery,
Allen Berger, and James Thom­
son for helpful comments.

Though bankers and their regulators find
the portfolio shift interesting in itself, it also
has broader implications. Our results provide
evidence that regulation matters— a point of
considerable debate for capital requirements in
particular (Keeley [19881) and for public poli­
cies in general (Stigler [19751). The reason is
that bank portfolio risk strongly affects the
chance of financial collapse and an associated
government bailout. Concerns about this possi­
bility motivated the risk-based capital standards
in the first place. Furthermore, by altering the
credit available to businesses and consumers, a
shift in bank portfolios may slacken the pace
of economic recovery.
The new risk-based capital requirements
classify bank assets. Government securities are
deemed to be riskless and therefore have a
zero weight w hen the bank determines its re­
quired capital.2 Thus, a bank that finds it diffi­
cult to meet its capital requirements can do so
by shifting its asset portfolio away from loans
and other high-risk-weighted assets toward
government securities.
■

■

1 For some other interesting approaches to the same problem, see

 Furlong (1992), Jacklin (1993), and Hancock and Wilcox (1992).


2 U.S. government securities have a zero risk weight because there is
no default risk. However, they are subject to interest-rate risk, and the new
capital requirements have been criticized for ignoring this component.

FIGURE

1

Growth in Loans and Securities
for Commercial Banks, 1973-93
Billions of dollars

SOURCE: Board o f Governors o f the Federal Reserve System, statistical
release G.7.

FIGURE

2

Growth in Government Securities,
C&l Loans, and Real Estate Loans
for Commercial Banks, 1973-93
Billions of dollars

There are, of course, other plausible reasons
why bank portfolios have shifted toward gov­
ernment securities. First, the large loan losses
of the 1980s made business lending appear
more risky and less attractive. Second, the busi­
ness slowdown that coincided with the intro­
duction of risk-based capital requirements
weakened loan demand. The decline in loan
demand was exceptionally large in the recent
recession because of the boom in business and
consumer leverage in the mid-1980s. Thus, the
shift toward government securities could also
be the result of these factors.
We submit that the changes in portfolio com­
position are strongly related to the introduction
of risk-based capital requirements. Specifically,
banks with the largest increases in government
securities holdings tend to be those with the low­
est capital-asset ratios when the new capital re­
quirements were introduced. The conclusion is
unaffected when we control for the weakness of
the bank’s loan portfolio.3 Thus, the change in
bank portfolios does not seem to be the result of
this weakness.

I. Aggregate
Trends in Bank
Assets
The composition of commercial bank portfolios
has changed dramatically over the years. For
the first two postwar decades, banks reduced
the proportion of their assets in securities and
increased the proportion in loans. Part of the
reason was the need to liquidate large holdings
of government securities accumulated during
World War II. Moreover, the development of
highly liquid and active money markets as
sources of funds reduced the precautionary
need to hold both government securities and
cash assets (see Boyd and Gertler [19931).
These secular shifts in banks’ activities were
completed by the early 1980s.
Some dramatic changes have taken place
more recently, however. Figure 1 shows the
growth of total loans and total securities since
1973- After rapid gains beginning in 1973, the
outstanding stock of bank loans has been con­
stant for the last three years. Total securities hold­
ings expanded less rapidly through the 1980s
and began to speed up in the last three years.

SOURCE: Board o f Governors o f the Federal Reserve System, statistical
release G.7.




■ 3 It is more difficult to control for the influence of loan demand on
portfolio shifts because we lack any bank-specific measures of the strength
of demand. Still, while other factors may explain part of the portfolio shift,
they do not overturn the importance of the new capital requirements.

F I G U R E

3

Government Securities, C&l Loans,
and Real Estate Loans as a Share of
Total Loans and Securities, 1973-93
Percent

SOURCE: Board o f Governors o f the Federal Reserve System, statistical
release G.7.

More detail is provided in figure 2, which shows
three critical categories— government securities,
commercial and industrial (C&I) loans, and real
estate loans. The rapid increase in government se­
curities holdings since the late 1980s has clearly co­
incided with a substantial decline in the volume of
C&I loans outstanding.
Finally, figure 3 presents the proportions of
these three critical categories in total loans and
securities. The share of C&I loans began to de­
cline around 1984. Real estate loans as a percent
of the total began to increase in 1986 and then
leveled off around 1990. Most important, the pro­
portion of U.S. government securities in total
loans and securities rose dramatically in the three
years following 1989- The bank portfolio shifts of
the last decade thus occurred in two stages:
Banks initially turned from C&I loans to real es­
tate loans, but then shifted from loans to U.S.
government securities in recent years.

and secondary capital to assets. U.S. and for­
eign regulators agreed in 1988 to implement
risk-based capital requirements. The new re­
quirements were phased in gradually begin­
ning in 1990 and became fully effective at the
end of 1992 (see Saunders [19931).
U.S. commercial banks are now required to
have a minim um ratio of total (Tier 1 + Tier 2)
capital to risk-adjusted assets of 8 percent.4 In
order to calculate risk-adjusted assets, each as­
set is assigned to one of four risk categories
and given a weight of 0, 20, 50, or 100 percent.
U.S. government securities are in the first cate­
gory, with a risk weight of zero. C&I loans and
most real estate loans (except securitized mort­
gage pools and regular residential mortgage
loans) are assigned a weight of 100 percent.
Risk-adjusted assets are thus simply a weighted
average of the bank’s portfolio of assets. In ad­
dition, the entire portfolio faces a leverage re­
striction: Total capital must be 4 percent of
total assets (unweighted).5
Thus, a commercial bank that moves its as­
set holdings from loans with a full 8 percent
capital requirement to government securities
with no capital requirement eases the associ­
ated regulatory burden. Clearly, banks that are
inadequately capitalized have an incentive to
increase the proportion of their assets in gov­
ernment securities. Our central hypothesis is
simply that the large changes in bank balance
sheets observed in the last three years repre­
sent a response to these incentives.
One alternative hypothesis is that the shift
into government securities was an effort to
avoid risk as bank asset portfolios weakened
in general. Because banks found it more diffi­
cult to manage their risky asset portfolios, they
viewed government securities more favorably,
and there was a flight to quality. This hypothe­
sis is credible in light of the documented dete­
rioration in the condition of commercial bank
portfolios in the 1980s.

■

II. Changes in
Bank Portfolios
Regulation mandating commercial-bank capital
requirements has evolved over the years. In
1985, regulators established a required ratio of
book value of equity (primary capital) to assets
of 5.5 percent. There was also a total capital re­
 quirement of 6 percent for the ratio of primary


4 The minimum ratio of Tier 1 capital (primarily common stock
equity) to risk-adjusted assets is 4 percent. Tier 2 capital includes certain
types of preferred stock and subordinated debt. The details of the new rules
are published in the Federal Register, January 27,1989, pp. 4186-221.
■ 5 The Basel agreements themselves specify only Tier 1 risk-based
and total risk-based ratios. Outside the United States, banks face only
those capital requirements. U.S. banks have an additional constraint:
minimum leverage. While the capital guidelines implementing the Basel
Accord specified a constraint of 3 percent, the prompt corrective action
guidelines of the FDIC Improvement Act of 1991 (FDICIA) mandated a
constraint ot 4 percent, except for banks with a regulatory CAMEL rating
of 1. For a discussion, see Huber (1991), chapter 15, or Carnell (1992).

F I G U R E

4

Nonperforming Assets and Net
Charge-offs as a Share of Totals,
1983-92
Percent

This downtrend is illustrated in figure 4.
Nonperforming assets as a percent of total as­
sets and net charge-offs as a share of total
loans and leases both began to rise in the mid1980s. However, this portfolio deterioration
preceded the change in asset composition by
several years. The shift of assets into govern­
ment securities started in the late 1980s when
banks’ condition began to recover. The increas­
ing net charge-off rate and nonperforming loan
rates in 1990 and 1991 stemmed not from an
upturn in bad loans, but rather from a decline
in total loans outstanding.
As a final check, we control for the effects of
loan quality in section V. Though we cannot rule
out this factor, our results do indicate a risk-based
capital effect independent of loan quality.
An alternative explanation is that the change
in bank portfolios was related to loan demand
and overall economic conditions. Indeed, cycli­
cal changes in bank portfolio preferences are
quite common. For example, when monetary
policy eased in the mid-1970s and again at the
end of the 1980-82 recessions, the government
securities proportion of total loans and securi­
ties headed upward. The episode in the early
and mid-1980s is similar to the current situation.
Although monetary policy eased, banks were
reluctant to boost lending, and the proportion
of government securities in their portfolios in­
creased. At that time, the debt crisis in less

developed countries influenced bank behavior.
In both of the earlier cases, however, a grow­
ing economy generated loan demand and the
run-up in government securities holdings
lasted only about two years.
In the recent episode, the rise in government
securities holdings has continued for almost
four years without any sign of abatement, plac­
ing the proportion of these securities in com­
mercial bank portfolios at unprecedented
levels. This situation may be unique because
the recovery that began two years ago has
been particularly sluggish. Despite an expan­
sionary monetary policy, the persistently weak
economy has held dow n loan demand, and as
a result, banks continue to augment their hold­
ings of government securities. Although it is dif­
ficult to distinguish between the effects of weak
loan demand or risk-based capital requirements
on bank holdings of government securities, a
cyclical response to demand is unlikely to be
entirely responsible for the enormous portfolio
shifts observed.
A third alternative is that government securi­
ties became more profitable in the late 1980s.
The combination of a steep yield curve and a
large supply of government securities, driving
prices down, may have made banks eager cus­
tomers. This term-structure argument requires
more justification than is usually given: Many
bank loans are long term, and thus could also
be profitable for banks.
The story appears to rest on some shift not
in the term structure, but in the risk structure,
between Treasury bonds and bank loans. This
is less obvious than the initial statement, how ­
ever. Perhaps the explosion in government
debt drove down the price of Treasuries (though
this point itself is controversial). In either case,
such general factors should not affect individ­
ual banks differently. Therefore, our strategy of
comparing well-capitalized and weakly capital­
ized banks is not sensitive to this shift. The
lower prices for Treasuries may explain the
portfolio shift of well-capitalized banks.

III. Relationship
between Capital and
Portfolio Shifts
Our hypothesis indicates that a bank’s incentive
to satisfy the newly introduced risk-based capi­
tal requirements by adjusting its portfolio is
larger if the institution initially fails those new
requirements. That is, banks that will be capi­
tal constrained under the new standard if they

T A B L E

1

Asset Allocations for Commercial
Banks by Size Class, March 1990
(percent)
r»

,•

Size Class

C

Proportion of
Total Assets Held as:

1

2

3

4

5

6

Cash assets
Total securities
Treasuries (book value)

8
30
10

6
30
9

6
26
8

7
19
6

8
19
5

10
15
4

Total loans
C&I loans
Real estate loans
Mortgages (1-4 family)
Consumer loans

51
9
30
13
11

54
11
30
14
12

59
13
30
14
14

65
16
28
11
17

65
18
24

65
21
19
7
14

9
17

SOURCES: Federal Financial Institutions Examination Council, Quarterly
Reports on Income and Condition; and authors’ calculations.

TABLE

2

Capital Ratios by Size Class,
March 1990-September 1992
(percent)

do nothing will thus take greater actions to
comply. In this section, we present the data
used to examine the relationship between the
initial risk-based capital ratio of individual
banks (in either 1988 or 1990) and the banks’
portfolio changes. Our data source is the
quarterly call reports on all U.S. commercial
banks. In addition, we show that a bank’s in­
centive to hold government securities increases
even if it was not initially capital constrained.
Although the risk-based capital requirements
were announced in July 1988 and began to be
implemented in March 1989, the call reports
were not revised to reflect the new definitions
until March 1990. Prior to 1990, however, it is
possible to approximate the risk-based capital
ratio of the bank from the available data. In
both instances, we use algorithms developed
at the Federal Reserve Board by Avery and Ber­
ger (1991) to derive the risk-adjusted assets of
the bank. Thus, we will be able to look at the
changes in bank portfolios over two periods:
from June 1988 (when the new capital require­
ments were announced) to September 1992,
and from March 1990 (when the phase-in of
the new capital requirements began) to Sep­
tember 1992.
Our data set consists of 12,187 commercial
banks divided by asset size (as of March 1990)
as follows:6
1. Less than $50 million
2. $50-100 million
3. $100-500 million
4. $500 million-1 billion
5. $1-5 billion
6. More than $5 billion

Size Class
1

2

3

4

6

5

A. Capital Ratios and Changes
Total capital/
risk-adjusted assets,
March 1990

18.38 16.41 13.95

Change, 1990-92
Tier I capital/
risk-adjusted assets,
March 1990

0.12

8.67

1.41

1.45

2.71

17.20 15.27 12.77 10.04

9.38

6.79

1.28

2.37

Change, 1990-92

0.12

1.14

11.44 10.82

1.09

1.27

1.21

1.37

B. Distribution of Banks by Capital Class, March 1990
Capital Class
0-4%
4-8%
8-10%
10-14%
>14%

6.8
2.2
3.7
23.7
63.6

0.5
2.1
5.8
33.6
58.0

0.6
3.6
12.9
44.9
38.0

1.0
10.9
28.8
46.7
13.5

6,558
2,685
2,350
229
260
105

0.0
14.6
37.7
40.4

0.0
32.4

7.3

1.9

53.3
11.4

SOURCES: Federal Financial Institutions Examination Council, Quarterly

Table 1 shows that commercial bank asset
allocations differ according to bank size. For
example, the smallest banks had only 9 per­
cent of their assets in C&I loans, while the pro­
portion for the largest banks was 21 percent.
However, the asset allocation changes that oc­
curred over the two-and-a-half-year period be­
ginning in March 1990 were common to all
sizes of banks (the very smallest were some­
times an exception). Holdings of securities, par­
ticularly Treasury securities, rose and loans
(except for real estate loans) decreased.
The top part of table 2 shows the ratios of
capital to risk-adjusted assets at the start of the
period and the change for banks in each size
class over the sample period. O n average,

Reports on Income and Condition; and authors’ calculations.




■

6 Banks were removed from the sample it the data seemed to be
erroneous, if extreme outliers were present, or if the banks had greater
than 50 percent capital.

T A B L E

3

Bank Adjustment to Risk-Based Capital
Requirements: Portfolio Shifts, Growth,
and Raising Capital
Size Class
Capital
Class
0
1
2
3
4

2

1

4

3

5

6

Portfolio Shift P
-0.08
- 0.11
- 0.06
- 0.01
0.06

0.59
- 0.10
- 0.10
-0.03
0.01

- 0.20
-0.13
- 0.06
-0.03
0.01

—

—

—

-0.14
•-0.08
-0.02
0.07

- 0.10
-■0.08
-•0.06
0.30

-0.08
-0.07
0.00
- 0.10

Size Shift TA
0
1
2

0.87
0.09
0.24

3
4

0.25
0.31

0.70
0.01
0.38
0.28
0.19

- 0.20
0.15
0.20
0.22
0.19

—

0.04
0.19
0.16
0.36

—

0.17
0.20
0.20
0.10

—

0.00
0.10
0.27
0.28

Capital Shift C
0
1
2
3
4

48.55
0.89
0.47
0.32
0.24

12.01
0.46
0.48
0.31
0.20

2.42
0.48
0.30
0.28
0.21

—

0.35
0.31
0.20
0.24

—

0.56
0.30
0.22
0.15

—

0.34
0.30
0.30
0.29

SOURCES: Federal Financial Institutions Examination Council, Quarterly Re­
ports on Incom e and Condition; and authors’ calculations.

banks of all sizes were sufficiently well capital­
ized; the minimum total capital requirements
were 8 percent. Finally, in every size class,
banks augmented capital in this period.
To explore the relationship between portfo­
lio changes and capital requirements, we classi­
fied banks by total capital to risk-adjusted asset
groups at the start of the period. The capital re­
quirement classes and the distribution of banks
by size class are shown in the bottom part of ta­
ble 2. Most smaller banks had very high capitalasset ratios, although there were a significant
number of exceptions. As bank size increases, the
proportion of banks with capital ratios under 8
percent rises as well. When we reach the largest
size class, very few banks exceeded the minimum
capital requirement by a comfortable margin.
Under this classification scheme, banks that
are severely undercapitalized (0 to 4 percent
capital ratio) or moderately undercapitalized (4



to 8 percent capital ratio) must meet the new
requirements to stay in business. They may
downsize, raise new capital, or rebalance their
portfolios to take advantage of the different
risk weights. The explicitly undercapitalized
banks are not the only ones facing incentives
to increase their capital, however. Regulators
require banks to hold capital well in excess of
the minim um requirements in order to expand
or to be able to acquire new entities or busi­
nesses.7 A bank that just satisfies the 8 percent
minimum capital ratio and wishes to sell m u­
tual funds, for example, would probably need
to increase its capital ratio before obtaining
regulatory permission.
To assess how banks responded to the new
capital requirements, we explore the nature of
capital. Capital satisfies the following identity:
Capital = (capital/risk-weighted assets) x

(1 )

(risk-weighted assets/total assets) X total
assets.
In other words, C = R x P x TA, where C =
capital, R = the risk-weighted capital ratio, P =
the portfolio factor, and TA = total assets.
Using the standard circumflex notation for
a
AC
proportionate changes (C = -77-), we get

A

A

A

A

O

C =R +P + TA , or
(2)

A

r

-

A

A

A

c - P - TA.

Because the risk-adjusted capital requirements
are a constraint on R, we see that equation (2)
descriptively allocates the adjustment of banks
to three possible courses of action: raise capi­
tal (increase C), adjust the portfolio (lower P),
or shrink total assets (lower TA ). Table 3 re­
ports this breakdown.
Three patterns stand out in table 3- Banks did
shift their portfolios in a way that reduced their
capital requirements. Furthermore, this shift was
more pronounced for undercapitalized banks at
every size level. Banks likewise responded by
raising capital, although the well-capitalized
banks apparently raised more. Finally, on aver­
age, banks did not shrink, and in fact grew over
this period in every size and capital class. These
patterns confirm our primary emphasis on the
portfolio effects of the new capital requirements.

■

7 FDICIA directs bank regulators to use the risk-based capital re­
quirements in making supervisory decisions. The Act established five
categories based primarily on the bank’s capital position. To be consid­
ered well capitalized, a bank would have to exceed the minimum capital
requirements by a substantial margin. We caution the knowledgeable
reader that the capital classes we use are not FDICIA prompt-correctiveaction zones.

B O X I

Why ANOVA?
Though commonly used in many areas of statistics, analysis of
variance (ANOVA) is less popular among economists, who gen­
erally prefer regression analysis. For evaluating bank portfolio
shifts, however, ANOVA has several advantages.
First, it does not require assumptions about the nature of
the functional form of the statistical relation: In particular, it
does not impose a linear relation between capital and portfo­
lio shifts. A difference in the response of well-capitalized and
undercapitalized banks assumes a nonlinear response by defi­
nition. The different degrees of capital constraint (for exam­
ple, deeply undercapitalized, barely capitalized) coupled with
our ignorance about the correct form of the relation (linear,
logarithmic, quadratic) make the ANOVA specification particu­
larly attractive.
ANOVA might also be called “comparison of means.” It sta­
tistically estimates the effects due to various factors (here,
they are size and capital class) and then allows comparison
of those effects— analyzing how and why the cells of table 6
differ from each other.
ANOVA has a further advantage in that it facilitates the esti­
mation and interpretation of interaction effects. Our analysis con­
siders two m ain effects, size and capital. Accounting for each
one separately may not provide the whole story: The main ef­
fects may not be additive, and there may be interaction effects.
For example, undercapitalized large banks may receive more
scrutiny from the regulators or find it easier to invest in certain
markets, and so may adjust their portfolios differently.

Banks had another reason to adjust portfolio
shares. The new requirements changed the re­
turns on different types of investments. Relative
to business and commercial real estate loans,
government securities became more profitable
because they required less capital backing. A
simple calculation shows that the difference
can be substantial.
The standard way to approach these issues
is with a version of the Miller (1977) debt
model as extended to banks by Orgler and
Taggart (1983). Banks have two sources of
funds: deposits and equity. Deposits have a
tax advantage in that banks may deduct inter­
est paid as a business expense, but cannot de­
duct dividends paid on equity. Deposits have
an additional cost of reserve requirements, but
in general banks would prefer to raise funds
using debt. Banks cannot fund themselves ex­
clusively with deposits, however, because they
face a constraint on their funding, namely a
 capital requirement that the ratio of debt (for


example, deposits) to equity not exceed a limit
If we denote the return on deposits as rd
and the return on equity as re, the marginal
cost of raising funds, r, is given by

(3)

r=

re/ ( l - t) + ^ r a
1 + S (1 - P )

where t is the corporate tax rate and p is the
reserve requirement. The bank lends until the
return on the loan equals the cost of funds
needed to fund the loan.8
The capital requirements impose a different C,
on different assets, and thus induce a different
rate of return. As an example, consider a return
on equity re of 10 percent, a return on deposits
rd of 4 percent, a corporate tax rate t of 28 per­
cent, and required reserves p of 12 percent. A
U.S. Treasury bond has a £ of 24 (a zero risk
weighting and the 4 percent leverage require­
ment that becomes a debt-to-equity ratio of
0.96/0.04), while a C&I loan has a £ of 11.5 (a
100 percent risk weighting). In this case, the cost
of raising funds internally (r7) to buy a Treasury
bond is 4.9 percent, while the cost of raising
funds internally to make a loan is 5.4 percent.
The relative cost of loans has increased, making
their inclusion in a portfolio less attractive.9

IV. Analysis
of Variance
The relationship between portfolio changes and
the risk-adjusted capital ratio prior to the intro­
duction of risk-based capital requirements is ex­
amined with an analysis of variance (ANOVA,
detailed in box 1). We investigate the relation­
ship for the asset categories outlined in table 4.

■

8 A little more intuition on the exact form of equation (3) can be
gained as follows. Assume that the bank wishes to raise one dollar as
cheaply as possible. The bank would like to use debt, for which it pays rd ,
but it faces a capital constraint, so it can raise only a fraction of the funds us­
ing debt. It also must raise equity, and must pay more than rg because of
corporate income tax. This explains the first term in the numerator. Because
the bank raises money from two different sources, the actual cost is a
weighted average of the cost of funds from those sources, and a little algebra
shows that the 1/1 + C, and £ /1 + £ terms provide the proportion of equity
and debt to total assets. Finally, some of the debt must be invested in re­
quired reserves, so to invest one full dollar, the bank must raise slightly
more than that, which accounts for the p term in the denominator.

■

9 The general situation is more complicated, of course. For exam­
ple, some banks can meet their capital requirement by increasing their
Tier 2 capital. This includes subordinated debt, which despite being
more expensive than deposits avoids the corporate tax penalty of equity.

9

TABLE 4
Proportion of Total Assets
(percent)
March
1990
Cash assets
Total securities
Treasuries
Other securities

September
1992

7.3

6.0

28.7

31.7

9.3

10.3

19.3

21.4

Total loans

53.8

54.2

C&I loans

11.0

9.3

Mortgages (1-4 family)

13.2

14.5

29.6

30.4

Other loans (includes
other real estate)
SOURCE: Authors’ calculations.

TABLE 5
Analysis of Variance Results—
Probability that Observed Effect
Is Due to Chance
Difference across
Classes by:

Size and
Capital

Size

Capital

Asset Changes, 1988-92
Cash assets
Total securities
Treasuries
Other securities
Total loans
C&I loans
Mortgages
Other loans

0.003
0.0
0.0
0.0047
0.0
0.0
0.178
0.0

0.0017
0.0
0.0
0.0003
0.0
0.0
0.708
0.0

0.0384
0.8049
0.3661
0.7267
0.0072
0.0005
0.9200
0.0894

0.2578
0.0
0.0
0.006
0.0
0.0

0.9709
0.0
0.0
0.0
0.0
0.0
0.1272
0.0

0.0215
0.6090
0.0176
0.2811

Asset Changes, 1990-92
Cash assets
Total securities
Treasuries
Other securities
Total loans
C&I loans
Mortgages
Other loans

0.1553
0.0

0.0003
0.0017
0.6553
0.0270

The ANOVA was performed for the change in
the ratio to total assets for each category for two
time periods. The first period begins in June
1988, just before the risk-based capital require­
ments were announced, and the second one
starts in March 1990, the first available data after
the requirements were phased in. This process
shows whether the changes in the asset ratios dif­
fer significantly across size or capital classes.
The ANOVA F-tests for the effects of size
and capital class are summarized in table 5,
which presents the probabilities at which the
null hypothesis of no significant effect can be
rejected. That is, it gives the probability that all
effects of the given type are zero. The first col­
um n provides the overall test on all the effects
and interactions. The next two columns are
tests that depend on the ordering of the vari­
ables. The second column tests for the signifi­
cance of the size effects alone. This test is
based on the sum of squares, putting the size
effect in the estimation first. The third column
is a stringent test for the significance of the
capital class effects. It is based on the sum of
squares when the capital class is added last; it
tests the significance of the additional effect of
this variable, having already controlled for the
size and interaction effects.10
In most instances, there are significant differ­
ences in asset changes among banks of various
size classes. This reflects a wide divergence in
portfolio allocations between large and small
banks. More important, the differences across
capital classes are significant even at the 5 per­
cent level for only a handful of asset categories.
For the asset changes between 1988 and
1992, there are substantial differences across
risk-adjusted capital ratio classes for only cash
assets and C&I loans. The changes in Treasuryto-total-asset ratios do not vary much across
capital ratio classes (p = 0.3661). However,
when we examine changes from the introduc­
tion (rather than the announcement) of the
risk-based capital requirements, 1990 to 1992,
additional significant changes arise. For this
period, the changes in the Treasury-to-asset ra­
tios vary widely by capital ratio class ( p = 0.0176).
In addition, there are substantial differences at
the 5 percent level for cash assets, C&I loans,
mortgages, and other loans.
The ANOVA results indicate a strong rela­
tionship between the initial capital ratio and

SOURCE: Authors’ calculations.




■

10 For a theoretical background, see Searle (1971); lor a discus­
sion of the tests, see the SAS/STAT User's Guide (1990), chapters 9 and
24. The SAS system refers to the last two columns in table 5 as type I
and type III tests.

T A B L E

6

Change in Selected Asset Ratios,
1990-92
Size Class
Capital
Class

1

2

4

3

5

6

0.00
0.03
0.02

0.00
0.03
0.02
0.02

Government Securities
0-4%
0.04
0.04
4-8%
8-10%
0.03
10-14%
0.01
>14%
-0.01

0.03
0.03
0.02
0.02
0.00

0.09
0.03
0.02
0.02
0.01

0.00
0.02
0.03
0.02
-0.02

0.03
0.01

-0.03

Cash
0-4% '
4-8%
8-10%
10-14%
>14%

-0.01
-0.01
-0.01
-0.01
-0.01

0.00
--0.01
--0.01
--0.01
--0.01

0.01
-0.01
-0.01
-0.01
-0.01

_

_

_

0.00
-0.01
-0.02
-0.02

-0.01
-0.01
-0.01
-0.04

-0.01
-0.01
-0.01
-0.07

C&I Loans
0-4%
4-8%
8-10%
10-14%
>14%

-0.05
-0.03
-0.03
-0.02
-0.01

--0.02
--0.03
--0.03
--0.02
--0.01

-0.05
-0.05
-0.04
-0.03
-0.02

__
-0.04
-0.03
-0.03
-0.01

__
-0.04
-0.04
-0.03
-0.01

__
-0.02
-0.03
-0.02
-0.02

Mortgages
0-4%
-0.00
4-8%
0.01
8-10%
0.01
10-14%
0.01
>14%
0.01

0.02
0.02
0.01
0.01
0.01

-0.02
0.02
0.02
0.01
0.01

__
-0.03
0.01
0.02
0.01

_

__

0.00
0.02
0.01
0.01

0.02
0.01
0.03
0.00

SOURCES: Federal Financial Institutions Examination Council, Quarterly Re­
ports on Incom e and Condition; and authors’ calculations.




bank portfolio changes. In particular, the
changes emerge more clearly when the phasein of the new regulations began rather than at
the time they were announced. Two reasons
account for this delay: First, risk-based capital
requirements represented a radical change in
U.S. banking regulation, so a period of learn­
ing about their consequences is not surprising.
Second, if portfolio changes were made to im­
prove banks’ capital position, they were not
necessary until the phase-in began. In addition,
government security portfolios can be changed
quickly and easily.
The ANOVA significance tests suggest that
there are important differences across asset ra­
tio categories, but do not imply any particular
direction in the relationship. For the four asset
categories with significant differences across
capital-ratio classes, we show the actual mean
changes in each capital class for the two-and-ahalf-year period after the introduction of riskbased capital requirements in table 6.
The evidence is clear for both government
securities and C&I loans. The extent to which
the ratio of government bonds to assets in­
creased diminishes as the initial capital position
of the bank improves. In fact, in four of the six
size groups, the extremely well-capitalized
banks (capital ratios greater than 14 percent)
did not even boost their holdings of govern­
ment securities. The evidence for C&I loans is
equally compelling. Banks in all categories de­
creased their portfolio share in C&I loans. In
each size class, the fall in the C&I loan share
was larger for the poorly capitalized banks.
For banks with initial total-to-risk-adjusted
capital of less than 8 percent, the share of gov­
ernment securities in total assets increased on
average by 4 percentage points, and the share
of C&I loans in total assets decreased by 4 per­
centage points. Thus, there is a strong indica­
tion that poorly capitalized banks responded
to the new capital requirements by shifting
from C&I loans to government securities. Be­
cause the movement away from C&I loans is at
least partially due to the deteriorating quality
of loan portfolios, it is important to see if the
results are robust when we hold the quality of
the portfolio constant. The mortgage results
are more ambiguous, as expected. With a 50percent risk weight, they fall between commer­
cial loans and Treasury securities.
Tables 5 and 6 do not completely make the
case that a greater portfolio shift took place
among undercapitalized banks. The F-test sug­
gests that the means differ, and the means
themselves show greater portfolio shifts for un-

D
TABLE

7

Tukey Multiple Comparison Tests
for Differences in Means
A. Total Securities
Alpha = 0.05, Confidence = 0.05,
Degrees of Freedom = 10861,
Mean Square Error = 0.009487,
Critical Value of Studentized Range = 3.858.

Capital
Classes
Com pared

Simultaneous
Lower
Confidence
Limit

Difference
betw een
Means

B. Treasury Book
Alpha = 0.05, Confidence = 0.95,
Degrees of Freedom = 10861,
Mean Square Error = 0.005965,
Critical Value of Studentized Range = 3.858.

Simultaneous
U pper
Confidence
Limit

Capital
Classes
Com pared

Simultaneous
Lower
Confidence
Limit

Difference
betw een
Means

Simultaneous
U pper
Confidence
Limit

0.044850
0.057620
0.063316
0.083068

0
0
0
0

1
2
3
4

-0.028772
-0.018189
-0.011794
0.003935

0.013774
0.023062
0.028919
0.044573

0.056321
0.064313
0.069632
0.0852113

0.008804
0.014403
0.020778
0.040624

0.062459
0.033228
0.037635a
0.0571923

1
1
1
1

0
2
3
4

-0.056321
-0.005640
0.001777
0.017660

-0.013774
0.009287
0.015145
0.030799

0.028772
0.024215
0.0285123
0.0439373

-0.033228
-0.057620
-0.004188
0.016125

-0.014403
-0.005599
0.006375
0.026220

0.004421
0.046423
0.016937
0.0363163

2
2
2
2

0
1
3
4

-0.064313
-0.024215
-0.002518
0.013506

-0.023062
-0.009287
0.005857
0.021511

0.018189
0.005640
0.014233
0.0295163

1
0
2
4

-0.037635
-0.063316
-0.016937
0.014214

-0.020778
-0.011973
-0.006375
0.019846

-0.0039203
0.039369
0.004188
0.0254773

3
3
3
3

0
1
2
4

-0.069632
-0.028512
-0.014233
0.011188

-0.028919
-0.015145
-0.005857
0.015654

0.011794
-0.0017773
0.002518
0.0201203

1
0
2
3

-0.057192
-0.083068
-0.036316
-0.025477

-0.040624
-0.031819
-0.026220
-0.019846

-0.024055
0.019429
-0.0161253
-0.0142143

4
4
4
4

0
1
2
3

-0.085211
-0.043937
-0.029516
-0.020120

-0.044573
-0.030799
-0.021511
-0.015654

-0.0039353
-0.0176603
-0.0135063
-0.0111883

0
0
0
0

1
2
3
4

-0.062459
-0.046423
-0.039369
-0.019429

-0.008804
0.005599
0.011973
0.031819

1
1
1
1

0
2
3
4

-0.044850
-0.004421
0.003920
0.024055

2
2
2
2

1
0
3
4

3
3
3
3
4
4
4
4

a. Significant at the 0.05 percent level.
SOURCE: Authors’ calculations.

dercapitalized banks, but neither approach in­
dicates which means differ from which other
means. To do so properly requires a multiple
comparison procedure, which introduces a
complication. The significance level (say 0.05)
of the standard t- and F-tests applies only to
that particular test, and not to a series of tests.
Thus, it would be inappropriate to use the
standard t-test to determine if the mean of capi­
tal class 1 a n d capital class 2 differed from the
mean of capital class 6. The standard statistic is
further inappropriate if the comparison is sug­
gested by the data, say comparing the highest


and the lowest means. For example, in compar­
ing the highest and lowest means, with six
classes the standard 5 percent test is in fact a
60 percent test (Neter and Wasserman [1974],
section 14.2). Table 7 corrects for these problems
by using the Tukey method for multiple compari­
son, which is based on the studentized range dis­
tribution (see Neter and Wasserman [1974],
section 14.3, and SAS/STAT User’s Guide [1990],
volume 2, chapter 24). For example, the first
line of table 7 compares the mean change in
the proportion of total securities for capital
class 0 with the same mean for capital class 1.

KB
TABLE

7 (CONT.

Tukey Multiple Comparison Tests
for Differences in Means
D. C&I Loans
Alpha = 0.05, Confidence = 0.95,
Degrees of Freedom = 10861,
Mean Square Error = 0.002579,
Critical Value of Studentized Range = 3-858,

C. Total Loans
Alpha = 0.05, Confidence = 0.95,
Degrees of Freedom = 10861,
Mean Square Error = 0.008611,
Critical Value of Studentized Range = 3-858.

Capital
Classes
C om pared

Simultaneous
Lower
C onfidence
Limit

Difference
betw een
Means

Simultaneous
U pper
C onfidence
Limit

Capital
Classes
C om pared

Simultaneous
Lower
Confidence
Limit

Difference
betw een
Means

Sim ultaneous
U ppe r
C onfidence
Limit

0
0
0
0

4
3
2
1

-0.103050
-0.071166
-0.046760
-0.038886

-0.054225
-0.022252
0.002801
0.012231

-0.0054013
0.026663
0.052363
0.063349

0
0
0
0

4
3
1
2

-0.0656967
-0.0514908
-0.0409112
-0.0398953

-0.0389768
-0.0247219
-0.0129366
-0.0127722

-0.01225693
0.0020471
0.0150380
0.0143508

1
1
1
1

4
3
0
2

-0.082242
-0.050543
-0.063349
-0.027364

-0.066457
-0.034483
-0.012231
-0.009430

-0.050672a
-0.0184233
0.038886
0.008504

1
1
1
1

4
3
2
0

-0.0346787
-0.0205744
-0.0096505
-0.0150380

-0.0260402
-0.0117853
0.0001644
0.0129366

-0.01740163
-0.00299613
0.0099792
0.0409112

2
2
2
2

4
3
0
1

-0.066644
-0.035116
-0.052363
-0.008504

-0.057027
-0.025053
-0.002801
0.009430

-0.0474093
-0.0149903
0.046760
0.027364

2
2
2
2

4
3
1
0

-0.0314679
-0.0174567
-0.0099792
-0.0143508

-0.0262045
-0.0119496
-0.0001644
0.0127722

-0.02094113
-0.00644263
0.0096505
0.0398953

3
3
3
3

4
0
2
1

-0.037339
-0.026663
0.014990
0.018423

-0.031974
0.022252
0.025053
0.034483

-0.0266083
0.071166
0.0351 l6 a
0.050543a

3
3
3
3

4
1
2
0

-0.0171911
0.0029961
0.0064426
-0.0020471

-0.0142549
0.0117853
0.0119496
0.0247219

0.01131873
0.02057443
0.01745673
0.0514908

4
4
4
4

3
0
2
1

0.026608
0.005401
0.047409
0.050672

0.031974
0.054225
0.057027
0.066457

0.037339a
0.1030503
0.0666443
0.0822423

4
4
4
4

3
1
2
0

0.0113187
0.0174016
0.0209411
0.0122569

0.0142549
0.0260402
0.0262045
0.0389768

0.01719Ha
0.03467873
0.03146793
0.06569673

a. Significant at the 0.05 percent level.
SOURCE: Authors’ calculations.

The difference between the means is positive,
but the confidence limits include 0, so we can­
not reject equality of the means.
The results in table 7 confirm the significance
of the portfolio change. The undercapitalized
banks shifted toward securities and away from
loans more than did the adequately capitalized
and well-capitalized banks.
But another possibility is yet unaccounted for.
Low-capitalized banks might have different
portfolio shifts even without a change in capi­
tal requirements. For example, suppose a bank
has low capital because of takedowns of loan




commitments that had been funded by pur­
chased money. That is, the bank ends up with
an unexpectedly high proportion of loans. Over
time, the bank might lower its loan level to re­
store the desired balance between loans and
securities. We wish to demonstrate that lowcapital banks do not normally increase their
securities holdings in the years following a
change in requirements.
To provide some evidence on this, we com­
pare the behavior of banks from 1988 to 1990
with their behavior from 1990 to 1992. Specifi­
cally, we compare the portfolio changes in low-

TABLE

most of the differences (even between nega­
tive and positive terms) are not statistically sig­
nificant, even at the 10 percent level.

8

ANOVA Comparison of Portfolio
Shifts between Periods
Asset Components
1990-92

1988-90

Capital Class

Cash
0-4%
4-8%
8-10%
10-14%
>14%

0.001
-0.015
-0.012
- 0.016
-0.019

0-4%
4-8%
8-10%
10-14%
>14%

-0.003
-0.003
-0.001
-0.004
-0.014

(0.044)
(0.047)
(0.035)
(0.045)
(0.050)

-0.003
-0.010
-0.010
-0.013
-0.015

(0.031)
(0.047)
(0.039)
(0.042)
(0.050)

Government Securities
(0.037)
(0.048)
(0.042)
(0.046)
(0.065)

0.046
0.032
0.032
0.017
0.001

(0.099)
(0.068)
(0.057)
(0.067)
(0.085)

C&I Loans
0-4%
4-8%
8-10%
10-14%
>14%

-0.038 (0.069)
-0.017(0.062)
-0.019 (0.059)
-0.012 (0.051)
-0.002 (0.040)

-0.047
-0.035
-0.035
-0.023
-0.008

(0.072)
(0.060)
(0.061)
(0.056)
(0.046)

Mortgages
0-4%
4-8%
8-10%
10-14%
>14%

0.013
0.007
0.007
0.005
0.007

(0.065)
(0.042)
(0.050)
(0.043)
(0.039)

-0.006
0.014
0.013
0.012
0.014

(0.062)
(0.067)
(0.065)
(0.053)
(0.051)

NOTE: Standard deviations are in parentheses.
SOURCE: Authors’ calculations.

capital banks from 1988 to 1990 with portfolio
changes in all other banks from 1990 to 1992
and with low-capital (as of 1990) banks from
1990 to 1992. By using this method, we control
for portfolio shifts due to both macroeconomic
effects and low capitalization.
Table 8 reports these results. Capital require­
ments certainly appear to have had an impact.
Across each capital class, banks reduced their
C&I loans more from 1990 to 1992 than from
1988 to 1990. Low-capital banks even de­
creased their bond holdings in the earlier pe­
riod, but raised them in response to capital
requirements from 1990 to 1992. A large caveat
goes along with this work, however, in that



V. Regression
Analysis
We examine the influence of deterioration in
the quality of the loan portfolio on bank portfo­
lio allocation changes with a regression model
that is a simple extension of the ANOVA frame­
work. The regression equation includes dummy
variables for each of the size and capital classes
and a measure of the quality of the i th bank’s
loan portfolio:
A asset ratiof = a + X (3; size dummiesf. + X yk
capital dummiesi + 5 loan quality,..
The charge-off ratio (as of March 1990) — the
ratio of net charge-offs to assets — is used to
measure loan quality.
A summary of the regression results for the
asset ratio changes between 1990 and 1992 for
each category is presented in table 9. The
charge-off rate has a significant influence on
each asset category. The largest effects of poor
loan quality are on the increase in Treasury se­
curities and on the decrease in real estate loans.
In both of these instances, a 0.5 percentagepoint increase in the charge-off ratio (which is
about equal to the increase in the aggregate ra­
tio over the 1980s, as shown in figure 4) re­
sults in an absolute change in the asset ratio of
about 0.01 percentage point. Significant differ­
ences between size classes and capital classes
appear in all but one category. Finally, the re­
gressions explain only a small proportion of
the interbank variation in asset ratios.
The bottom part of table 9 shows the esti­
mated coefficients for the capital dummies.
They represent differences from the omitted
category: banks with initial risk-adjusted capi­
tal ratios in excess of 14 percent. The relation­
ship between the initial capital position and
the extent to which the bank increased govern­
ment securities holdings and reduced loans is
still substantial. That is, even with the influ­
ence of the quality of the loan portfolio held
constant, poorly capitalized banks made large
portfolio adjustments away from both C&I and
real estate loans and toward holdings of gov­
ernment securities.

TABLE

9

VI.

Summary of Regression Results

Cash assets
Total securities
Treasuries
Other loans
Total loans
C&I loans
Mortgages
Other loans

Coefficient
and t-statistic

F-test Probability

Charge-Off
Ratio

Size
Capital
Dummies Dummies

0.051
(2.1)
4.60
(9.2)
2.78
(7.0)
1.83
(3.7)
-5.06
(10.6)
-0.86
(3.3)
-0.009
(3.4)
-0.03
(8.1)

0.3592

0.0499

R2
0.002

0

0

0.028

0

0

0.021

0.0002

0.6557

0.004

0

0

0.068

0

0

0.038

0.635
0

0.163
0

Conclusion

The evidence presented here strongly suggests
that bank portfolio changes since 1990 are at
least in part a response to the introduction of riskbased capital requirements. Qualitatively, at least,
the regulations succeeded. Comprehending the
changes improves our general understanding of
the effects of bank regulation. The particular ef­
fect of capital requirements on bank portfolios
merits special interest. The shift in bank portfo­
lios can affect their overall risk, and therefore the
risk of financial collapse and the liability of the
federal government acting as the lender of last re­
sort. O n the other hand, the reduction in loans
may (under the “credit view”) have macroeco­
nomic consequences and reflect on overall eco­
nomic growth, income, and unemployment.11

0.002
0.032

Capital Dummy Coefficients
(Difference from omitted category — Ratio >14%)
0-4%

4-8%

8-10%

10-14°/

0.01

0.00

0.00

0.00

Total securities
Treasuries
Other loans

0.03
0.04
-0.01

0.03
0.02
0.01,

0.02
0.02
0.00

0.02
0.01
0.00

Total loans
C&I loans
Mortgages
Other loans

-0.05
-0.04
-0.02
0.01

-0.05
-0.02
-0.02
-0.03

-0.04
-0.02
-0.00
-0.02

-0.03
-0.01
-0.02
-0.01

Cash assets

NOTE: Standard deviations are in parentheses.
SOURCE: Authors’ calculations.




■ 11 The credit view argues that changes in bank lending— and in
credit more generally— have an important effect on the aggregate econ­
omy above and beyond any effect on the money supply.

References
Avery, Robert B., and Allen N. Berger. “RiskBased Capital and Deposit Insurance Re­
form,” Jo u rn a l o f B anking a n d Finance,
vol. 15, nos. 4/5 (September 1991), pp.
847-74.
Boyd, John, and Mark Gertler. “U.S. Commer­
cial Banking: Trends, Cycles, and Policy,”
National Bureau of Economic Research,
Working Paper 4404, July 1993Camell, Richard Scott. “The FDIC Improve­
ment Act of 1991: Improving Incentives of
Depository Institutions’ Owners, Managers,
and Regulators,” Ohio State University,
working paper, August 1992.
Furlong, Frederick T. “Capital Regulation and
Bank Lending,” Federal Reserve Bank of
San Francisco, Economic Review, 1992 no. 3,
pp. 23-33.
Jacklin, Charles J. “Bank Capital Requirements
and Incentives for Lending,” Federal Re­
serve Bank of San Francisco, Working Pa­
per No. 93-07, February 1993Hancock, Diana, and James A. Wilcox. “Bank
Capital and Portfolio Composition,” Board
of Governors of the Federal Reserve System,
working paper, December 1992.
Huber, Stephen K. Bank Officer’s H andbook o f
Government Regulation, 2d ed., Cumulative
Supplement No. 1. Boston: Warren, Gorham,
& Lamont, 1991.
Keeley, Michael C. “Bank Capital Regulation in
the 1980s: Effective or Ineffective?” Federal
Reserve Bank of San Francisco, Economic
Review, 1988 no. 1, pp. 3-20.
Miller, Merton H. “Debt and Taxes,” Jo u rn a l o f
Finance, vol. 32, no. 2 (May 1977), pp.
261-75.
Neter, John, and William Wasserman. Applied
Linear Statistical Models-. Regression, A naly­
sis o f Variance, a n d Experimental Designs.
Homewood, 111.: Richard D. Irwin, 1974.




Orgler, Yair E., and Robert A. Taggart, Jr. “Impli­
cations of Corporate Capital Structure The­
ory for Banking Institutions,” Jo u rn a l o f
Money, Credit, a n d Banking, vol. 15 (May
1983), pp. 212-21.
SAS/STAT User’s Guide. Version 6, 4th ed.
Cary, N.C.: SAS Institute, Inc., 1990.
Saunders, Anthony. M odem F in an cial Institu­
tions. Homewood, 111.: Richard D. Irwin,
1993 (forthcoming).
Searle, Shayle R. Linear Models. Toronto:
Wiley, 1971.
Stigler, George J. “The Tactics of Economic Re­
form,” in The Citizen a n d the State: Essays
on Regulation. Chicago: University of Chi­
cago Press, 1975, pp. 23-37.

FDICIA’s Emergency Liquidity Provisions
by Walker F. Todd

Is there any reason why the Am erican people
should be taxed to guarantee the debts o f banks,
any more than they should be taxed to g u a ra n ­
tee the debts o f other institutions, including the
merchants, the industries, a n d the mills o f the
country?
Senator Carter Glass (1933)1

Introduction
The Federal Reserve Banks’ discount window
advances to failing depository institutions have
become an increasingly controversial issue with­
in the last 20 years or so. This debate culminated
in congressionally mandated limitations on Re­
serve Banks’ advances to undercapitalized
banks in the Federal Deposit Insurance Corpo­
ration Improvement Act of 1991 (FDICIA), pre­
viously the subject of a Federal Reserve Bank
of Cleveland Economic Commentary.2
In a comparatively little-noticed amendment
of the Reserve Banks’ lending authority, FDICIA
made potentially significant revisions to the
emergency liquidity provisions of the Federal
Reserve Act. In particular, the Act now permits
 all nonbank firms — financial or otherwise
(called “nonbanks” here for simplicity) — to


Walker F. Todd is an assistant gen­
eral counsel and research officer at
the Federal Reserve Bank of Cleve­
land. Helpful comments and sug­
gestions were provided by Melvin
Burstein, Joseph Haubrich, Owen
Humpage, William Osterberg,
Robin Ratliff, Mark Sniderman,
James Thomson, and two anony­
mous referees.

borrow at the discount window for emergency
purposes under the same collateral terms
afforded to banks. Ironically, while the princi­
pal thrust of FDICIA was to limit or reduce the
size and scope of the federal financial safety
net, at least as applied to insured depository in­
stitutions, this provision effectively expanded
the safety net. This article describes the histori­
cal and theoretical backgrounds of the Reserve
Banks’ emergency lending authority for non­
banks and analyzes the changes made by
FDICIA that affect that authority.

■

1 See Smith and Beasley (1972), p. 357. Senator Glass offered
these remarks during the Senate debate on the Banking Act of June 20,
1933 (Glass—Steagall Act), which established, among other things, the
first plan of federal deposit insurance.

■

2 See Todd (1992a). FDICIA is Public Law No. 102-242 (Decem­
ber 19,1991). The provisions of FDICIA principally affecting Reserve
Banks’ discount window operations are Sections 131-133 (prompt cor­
rective action) and Sections 141-142 (least-cost resolutions, systemicrisk exceptions, and lending limitations). On prompt corrective action,
see Pike and Thomson (1992); on systemic risk, see Wall (1993); on
lending limitations, see Todd (1992a, 1993a).

I. Background of
Emergency Lending
Provisions for
Nonbanks
Since the creation of the first central banks in
Western Europe in the seventeenth century,
parliaments have often asked them to rescue
enterprises sponsored by the state or sover­
eign, favored private-sector enterprises, and
even, occasionally, the state itself.3 In the
United States, Congress understood quite early
that it should avoid the expediency of direct
funding of the Treasury by borrowings from
the central bank.4 This maxim of fiscal propri­
ety (“central banks should not undertake fiscal
activities”) also makes theoretical sense and
has been explained as follows regarding the
central banks of developing countries:
Fiscal activities [such as implementing
selective credit policies or recapitalizing insol­
vent financial institutions] involve expenditures
that reduce central bank profits and may even
produce losses. If central bank losses are not
met from government budget appropriations,
they must eventually lead to an expansion in
central bank money and the abandonment of
any monetary policy goal of price stability.5

Fiscal and monetary authorities in the United
States generally followed this view of the divi­
sion of their responsibilities during peacetime
from 1791 until sometime during 1931-33-6
The extension of governmental credit di­
rectly to nonbank enterprises historically has
been a fiscal operation in the United States,
not a monetary policy operation of the type or­
dinarily undertaken by a central bank.7 For ex­
ample, the original Federal Reserve Act of 1913
provided for the extension of Reserve Banks’
credit directly to member banks, but did not
allow for such credit to or for the account of
the Treasury, nonmember banks, or nonbanks.

Borrowing by member banks was governed by
the applicable sections of the Federal Reserve
Act (originally, Section 13), and borrowing by
other entities simply was not permitted. The
Federal Reserve Act was enacted in an era in
which peacetime federal budgets regularly
were in surplus, and it apparently was intended
that the Reserve Banks’ money-creating powers
should not be substituted for explicit congres­
sional decisions on the Treasury’s funding.
During the presidential election year of 1932,
economic pressures generated by the Great De­
pression caused President Herbert Hoover to
propose changing the previously indirect credit
relationship between Reserve Banks and non­
banks (the Reserve Banks could lend to banks,
but only banks could lend to nonbanks) to a
more direct one. Although he had vetoed a
prior version of the Emergency Relief and Con­
struction Act that summer because it would
have authorized the former Reconstruction Fi­
nance Corporation (RFC) to make loans di­
rectly to individuals,8 Hoover allowed Section
13 (3) to be added to the Federal Reserve Act
as part of a road construction measure de­
signed to relieve unemployment. Subject to cer­
tain restrictions, Section 13 (3) authorized
Reserve Banks, “in unusual and exigent circum­
stances,” to extend credit directly to “individu­
als, partnerships, and corporations.”9
Section 13 (3) proved to be so restricted that it
did not open the floodgates of Reserve Banks’

■

6 See Todd (1993b). Beginning in early October 1931, President
Hoover proposed that the Reserve Banks expand their lending authority
to include the rescue of insolvent banks during peacetime, but the princi­
pal proposals for use of the Reserve Banks' lending authority for fiscal
purposes were not enacted until the early months of the New Deal, after
March 4,1933. The most noteworthy of those proposals was the Thomas
Amendment to the Agricultural Adjustment Act of May 12,1933, revised
on May 27,1933, and on many subsequent occasions, added as Sec­
tions 14 (b)(3) and 14 (h) of the Federal Reserve Act (expired in 1981).
See Moley (1966), pp. 3 0 0 -0 3 ; and Hoover (1952), pp. 395-99.
■

7 See Todd (1992b) and Martin (1957), pp. 76 8 -6 9.

■

■

3 See, for example, Fry (1993), Bordo (1992), Todd (1988), and
Humphrey and Keleher (1984).

■ 4 Our Founding Fathers were well aware of the problems created by
Treasury borrowings from central banks. Alexander Hamilton, the first Secre­
tary of the Treasury, recommended, and Congress later passed, a bill provid­
ing that the First Bank of the United States, our first central bank, should be
prohibited from lending more than $50,000 to the Treasury or to any state or
foreign prince without the prior, explicit consent of Congress. When the Sec­
ond Bank of the United States was chartered in 1816, this limit was raised to
$500,000. See Hamilton (1967), pp. 31-32,34.


■ 5 See Fry (1993).


8 On the RFC, see generally Todd (1992b), Keeton (1992), and
Olson (1988). Strange as it may seem to modern readers, banks’ lending
to individuals (as opposed to farmers or business associations) before
1933 was commonly regarded as either a kind of speculation more appro­
priate for investment bankers than for commercial bankers or a charitable
act more appropriate for mutual savings banks or benevolent societies
than for commercial banks. For a colorful account of this phenomenon,
see Grant (1992), pp. 7 6 -9 5 ,2 6 7 -6 8 .
■

9 The text of the Emergency Relief and Construction Act of July 21,
1932, Public Law No. 72-302, is found in Federal Reserve Bulletin, vol.
18 (August 1932), pp. 520-27. Section 210 of that Act [Section 13 (3)] is
at p. 523. The Board's circular authorizing emergency discounts under
Section 13 (3) for six months beginning August 1,1932, is at ibid., pp.
518-20.

liquidity to the general public in 1932. At least
five members of the Board of Governors (the
“Board,” which then included six regularly ap­
pointed and two ex officio members) had to
vote affirmatively to find that “unusual and exi­
gent circumstances” warranting implementa­
tion of Section 13 (3) existed. The collateral
offered by borrowers had to consist of “real
bills” and certain Treasury obligations “of the
kinds and maturities made eligible for discount
for member banks under other provisions of
[the Federal Reserve] Act.”10 In essence, the
only acceptable collateral would have been
near substitutes for cash. The final statutory re­
striction required the Reserve Banks to find evi­
dence that the borrower was unable to “secure
adequate credit accommodations from other
banking institutions.”11
These restrictions made it unlikely that many
nonbanks could qualify for emergency advances
from Reserve Banks. In fact, due to these restric­
tions and the availability of credit elsewhere,12
the Reserve Banks “made loans to only 123 busi­
ness enterprises [from 1932 until 1936] aggregat­
ing only about $1.5 million [under Section 13
(3)]. The largest single loan was for $300,000.”13
In 1935, the Board requested, and Congress
approved, an amendment of Section 13 (3) in­
tended to make nonbanks’ borrowing somewhat
easier. Despite that statutory change, no such
loans actually have been made since the amend­
ment became effective in 1936.14 Prior to the
1935 amendment, a borrower had to satisfy two
relevant conditions: a satisfactory endorsement

■

10 Real bills, for the purposes discussed here, are “notes, drafts, and
bills of exchange arising out of actual commercial transactions,” with remain­
ing maturities of not more than 90 days [therefore, self-liquidating], “issued
or drawn for agricultural, industrial, or commercial purposes, or the pro­
ceeds of which have been used, or are to be used, for such purposes,” as
distinguished from “speculative,” investment, or working-capital pur­
poses. See Section 13 (2) of the Federal Reserve Act (12 U.S.C. Section
343) and Hackley (1973), pp. 37 and 129.
■ 11 See Hackley (1973), pp. 127-28. Under another provision of
the Federal Reserve Act, Section 13 (13)(12 U.S.C. Section 347c), added
in 1933, nonbanks may borrow directly from Reserve Banks without a
finding of financial emergency (“unusual and exigent circumstances") by
the Board, but only on the security of the U.S. government or (since
1968) U.S. government agency obligations.
■ 12 In particular, after 1934, the Federal Reserve was authorized to
mount a rival program to extend credit directly to individuals, partnerships,
and corporations “for working capital purposes” under former Section 13b
of the Federal Reserve Act (expired in 1958). However, the operations of the
RFC expanded greatly after 1933 and displaced the direct credit extension
role earlier foreseen for the Reserve Banks under Sections 13 (3), 13b, and
13 (13). Regarding former Section 13b, see discussions in Schwartz (1992),
pp. 61-62, and Hackley (1973), pp. 133-45.


http://fraser.stlouisfed.org/
■ 13 Hackley (1973), p. 130.
Federal Reserve Bank of St. Louis

(by either the borrower or a third-party surety)
on the borrower’s own note pledged to the Re­
serve Bank, a n d security (eligible collateral) for
the borrower’s discounted note or notes. After
the 1935 amendment, either an endorsement or
additional security for such notes was required.
This change made it easier for a borrower to dis­
count his own note.
After 1935, however, borrowers had a clear
choice between the distinct concepts of eligi­
ble collateral (what security could be pledged
to secure the Reserve Bank’s advance) and eli­
gible purpose (the use to which the Reserve
Bank’s advance would be put). That is, non­
banks could borrow for any purpose as long
as they pledged eligible collateral. Failing that,
they could borrow on their own notes against
any satisfactory collateral, including ineligible
collateral, as long as they had eligible purposes
for their borrowings.
Securities firms, mutual funds, and insurance
companies, the greater part of whose asset
portfolios included ineligible collateral, could not
be said to have eligible purposes for bonowing
to fund those particular assets. The payment of
an ordinary business firm’s general operating
expenses could qualify as an eligible purpose
for borrowing from a Reserve Bank, but eligi­
ble expenses normally included such things as
the payment of utility bills, regular taxes, pay­
roll, and the purchase of raw materials. Activi­
ties deemed speculative, such as the purchase
of a portfolio of common stocks or investment
securities generally (other than government se­
curities), or the financing of permanent fixed
investments with instruments maturing in more
than 90 days, were ineligible purposes.15 As
the principal historian of the subject explained
this point,

■ 14 The Board has reactivated Section 13 (3) rarely since the 1930s,
but this emergency lending authority has not actually been used since 1936.
It was activated for savings and loan associations, mutual savings banks,
and nonmember commercial banks in 1966 and 1969 (Hackley [1973], p.
130). Its use also was contemplated for assistance to New York City (said to
be a “municipal corporation") in 1975. The potential use of Section 13 (3)
for depository institutions became unnecessary when the Monetary Control
Act of 1980 added Section 19 (b)(7) to the Federal Reserve Act (12 U.S.C.
Section 461) to authorize routine advances of Reserve Banks’ credit to “any
depository institution in which transaction accounts or nonpersonal time de­
posits are held." Such routine advances are secured by any satisfactory as­
sets (not limited to eligible collateral) and are available at nonpenalty rates,
even for nonmember depository institutions. Thus, there has been no need
for emergency discounts for those institutions that could be secured only by
collateral that was a near substitute for cash.

■ 15 See generally Hackley (1973), pp. 34—38. At p. 129, he dis­
cusses the use of a borrower's own note under Section 13 (3).

[T]he reason why the Reserve Banks were
prohibited from extending credit on stocks
and bonds [under Section 131 was that the [Re­
serve] Banks were intended to assist commer­
cial banking and not investment banking.
Paper eligible for discount was confined to
self-liquidating paper arising out of commer­
cial rather than investment transactions.16

While securities firms and other nonbank fi­
nancial firms could borrow for the eligible pur­
pose of funding the types of current operating
expenses described above, their liabilities for
such expenses normally would constitute only
a small fraction of their balance sheets. In con­
trast, their loans to carry customers’ accounts
invested in securities (other than government
securities) are ineligible purposes but poten­
tially require much greater funding than the
proportion of their assets related to eligible pur­
poses. It apparently was the intent of Congress
to remove these ineligible collateral/ineligible
purpose restrictions on nonbanks’ borrowings
from Reserve Banks that underlay the 1991
amendment of Section 13 (3).

II. Amendments
of Section 13 (3)
in FDICIA
Section 13 (3) has been discussed very little
since the 1930s, so it might seem unusual to
find Section 473, amending Section 13 (3), in­
serted in the final stages of the congressional
deliberations on FDICIA in November 1991.
Increasingly, however, since the stock market
crash of October 1987, some policymakers had
been discussing the potential use of the Re­
serve Banks’ discount windows to relieve non­
bank financial firms’ liquidity crises directly.
Procedurally, there were enough obstacles to
such use of the discount window to discour­
age financial firms from relying on Section 13
(3) to rescue them in a liquidity crisis: The pro­
cedural starting point always was an emer­
gency declaration approved by at least five
members of the Board. Also, the practical ob­
stacles appeared insurmountable: For borrow­
ings secured by eligible collateral, nonbank
financial firms typically held comparatively few
unpledged assets that would qualify, and bor-

■

16 Hackley (1973), p. 38. Depository institutions may, however,
obtain extensions of Reserve Bank credit under Section 10B (12 U.S.C.
Section 347b) even on ineligible stock or bond collateral (“any satisfac­
tory assets"), but the amounts available might be limited under Section
 11 (m)(12 U.S.C. Section 248 [ml), added in 1916.



rowings said to be for eligible purposes typi­
cally would be quite limited.
Another issue that was not, but probably
should have been, raised explicitly during con­
gressional deliberations on FDICIA was that any
consideration of altering the Reserve Banks' col­
lateral or purpose of borrowing standards to ac­
commodate nonbanks’ asset portfolios under
Section 13 (3) clearly would shift a portion of the
risk of loss previously borne by the nonbanks’
creditors onto the Reserve Banks and, thus, indi­
rectly onto the taxpayer.17 One of the poten­
tially troublesome aspects of the FDICIA
amendment of Section 13 (3) is that it appears
to reflect a motive or spirit that contradicts that
of the FDICIA provisions intended both to
limit Reserve Banks’ loans to undercapitalized
depository institutions and to make it more dif­
ficult for the Federal Reserve to treat an institu­
tion as too big to fail. If the amendment was
intended to provide a vehicle for possible Fed­
eral Reserve treatment of a failing securities
firm as too big to fail, then it arguably consti­
tutes a contradictory extension of the same fed­
eral safety net that was retrenched in other
parts of FDICIA and apparently enlarges the
moral hazard problem of deposit insurance.
O f the issues just identified regarding the
amendment of Section 13 (3), only restrictions
based on the types of collateral that nonbank
borrowers could offer were discussed explicitly
during the congressional deliberations on FDICIA
in 1991. It appears that, having satisfied itself that
the risks from expanding the collateral limits
were minimal and that it might prove helpful to
provide the Reserve Banks with this additional,
liquidity-maximizing policy tool for a financial
emergency, Congress adopted the revisions of
Section 13 (3) as Section 473 of FDICIA without
extensive discussion or debate, leaving a rather
sketchy legislative history for this statute. How­
ever, by altering the collateral standards explic­
itly, FDICIA implicitly rendered Section 13 (3)’s
purpose of borrowing restrictions largely super­
fluous because the prior standards for eligible
purposes were binding only on nonbanks that
could not pledge eligible collateral.

■ 17 The Reserve Banks’ operations create an indirect gain or loss
for the taxpayer because the operating profits are rebated to the Treasury
as a miscellaneous receipt offsetting part of the federal government’s op­
erating expenses. In fiscal year 1992, those receipts were $27.1 billion, of
which the Reserve Banks contributed $22.9 billion (Council of Economic
Advisers [1993], p. 437). Losses incurred on Reserve Banks’ operations
would reduce those receipts. While material losses for Reserve Banks
have been rare since World War II, they are not inconceivable for central
banks that attempt to subsidize fiscal operations on their balance sheets.
See Fry (1993).

The actual statutory language change made by
Section 473 of FDICIA was comparatively minor.
The restrictive phrase in quotation marks below
was deleted from the part of Section 13 (3) that
described the collateral acceptable for emergency
discounts for nonbanks. Prior to the change, a
Federal Reserve Bank could discount for any in­
dividual, partnership, or corporation any notes,
drafts, and bills of exchange when these instru­
ments were endorsed or otherwise secured to the
satisfaction of the Reserve Bank and, when en­
dorsed, were “of the kinds and maturities made eli­
gible for discount for member banks under other
provisions of this Act ....’’ It generally was under­
stood that this reference was primarily to the types
of financial instruments meeting the eligible pur­
pose standards as illustrated in Section 13 (2), but
also included instruments described in other parts
of Sections 13 and 14 of the Federal Reserve Act.
Since FDICIA, Reserve Banks’ emergency ad­
vances to nonbanks may be based on the
types of collateral acceptable for depository in­
stitutions under an entirely different provision
of the Federal Reserve Act, Section 10B, which
permits “advances ... secured to the satisfaction
of ... [the] Federal Reserve Bank,” or “any satis­
factory assets.”18 Because nonbanks’ emer­
gency borrowings need not be secured by
eligible collateral, eligibility of purpose of bor­
rowing has become moot. The only collateral
test remaining under revised Section 13 (3) is
“satisfactory security,” the same test that ap­
plies to borrowings by depository institutions
under Section 10B.

III. Analysis of
Potential
Ram ifications
The changes made by FDICIA expanded emer­
gency discount window access for nonbanks of
all types, not merely securities firms, because any
satisfactory assets (not just marketable securities,
for example) may be pledged to secure the bor­
rower’s own note. Whether these changes will
have practical consequences is an open question.
After all, Section 13 (3) is an emergency lending
provision that has been and presumably will con­
tinue to be invoked very rarely and that requires
the affirmative vote of five Federal Reserve Board
governors. It is important to keep in mind that
nonbanks’ behavior depends in part on howr they
expect the Federal Reserve to manage its emer­
gency lending powers.
 ■ 18 See Hackley (1973), pp. 109-12, and Eccles (1951), pp. 171-73.


The few, scattered public statements regarding
congressional intent with respect to Section 473
of FDICIA do indicate that the intended benefi­
ciaries were securities firms, and no other type of
nonbank was mentioned explicitly.19 Although a
brief reference was made during the FDICIA delib­
erations to the absence of any discounts under Sec­
tion 13 (3) since 1936, the potentially increased
taxpayer risk from alteration of the collateral and
purpose standards was not discussed.20
How could a new element of taxpayer risk
arise? One possible source is derived from the
m oral hazard aspects of the increased availa­
bility of Reserve Banks’ loans to nonbanks dur­
ing financial emergencies. Nonbanks lacking
eligible collateral or eligible purposes for bor­
rowing must manage their affairs and conduct
their relations with creditors and clients so as
to be able to survive financial market emergen­
cies. Now, with increased potential for assis­
tance during emergencies, nonbanks’ managers
might have less incentive to avoid recourse to
the Federal Reserve. Although nonbanks still
have strong incentives to run their firms pru­
dently, their managers now have potential ac­
cess to another funding source during financial
crises. Whether this potential access alters non­
banks’ business decisions — so as to make
their calling upon that funding source more
likely — remains to be seen.
More troubling, however, are the macro im­
plications of these incentive changes. The ex­
tension of the federal financial safety net to
nonbanks may increase the probability of mar­
ket liquidity crises that appear to require Fed­
eral Reserve emergency lending. This could
happen during periods of market stress if the
costs of risky investment and funding strategies
are not fully borne by the managers and share­
holders of nonbank firms, but instead are per­
ceived as being partially or fully underwritten

■ 19 During the floor debate in the Senate on the version of FDICIA
that was enacted, Senator Christopher Dodd of Connecticut spoke as fol­
lows in support of the bill:
It [FDICIA] also includes a provision I offered to give the Federal Re­
serve greater flexibility to respond in instances in which the overall
financial system threatens to collapse. My provision allows the Fed
more power to provide liquidity, by enabling it to make fully secured
loans to securities firms in instances similar to the 1987 stock mar­
ket crash.
See Congressional Record (1991), p. S18619. For similar legal interpre­
tations of Section 473 of FDICIA, see FDICIA (1992), pp. 37 and 92. See
also Holland (1991).

■ 20 See U.S. Senate Report No. 102-167 (October 1,1991),
pp. 2 0 2 -0 3.

by U.S. taxpayers.21 Self-correcting market
forces that help to insulate financial markets
from macroeconomic shocks could be eroded
by what nonbanks regard as implicit taxpayer
guarantees of nonbank losses and, thereby, in­
crease the probability that a real-sector shock
would become translated into a financial crisis.
A certain amount of adverse selection also
might compound the Federal Reserve’s difficul­
ties: It becomes increasingly likely that bettercapitalized firms would remain outside the
Reserve Banks’ lending net (in order to avoid
the perceived stigma of borrowing). It also is
likely that only the worst-capitalized firms
could not raise adequate funds during financial
market emergencies.
The other main source of taxpayer risk from
the revision of Section 13 (3) is derived from
the accounting principles that would be used in
evaluating the collateral offered for emergency
loans. Nonbanks’ previously ineligible assets,
including corporate equity securities and mort­
gages on real estate in the case of securities
firms and institutional investors, tend to be illiq­
uid under the market emergency conditions that
would conceivably give rise to the Board's
authorization of Section 13 (3) loans. In an emer­
gency, whatever market value satisfactory (but
formerly ineligible) assets that nonbanks already
had could undergo severe downward market
pressures, triggering wide gaps between par and
market collateral valuations. Although all dis­
count window advances are expected to be ex­
tended against collateral that is thought to be
both sound and ample, there is reason to be con­
cerned about accurate valuation of nonbanks’ as­
sets in periods of intense financial distress.
The expansion of the collateral limits for Re­
serve Banks’ extensions of credit under Section
13 (3) might appear to be somewhat at odds
with the principal thrust of the other discount
window provisions of FDICIA, Sections 141 and
142, which, together with the prompt corrective
action provisions, Sections 131-33, were in­
tended to reduce taxpayers’ potential risk of
loss due to loans to insured banks. The lending
criteria applicable to undercapitalized depository
institutions were tightened, and more exacting
and publicly accountable procedures for such
lending decisions were established. In Section
141 of FDICIA, provision for a “systemic risk” ex­
ception to normal supervisory intervention and
closing requirements was limited to circumstances
■ 21 Comparable perverse incentives tor insured depository institu­
tions’ behavior are described in the deposit insurance literature. See
 Barth and Brumbaugh (1992), pp. 7-12; National Commission (1993),
pp. 62-68; and Kane (1989), pp. 95-114.
http://fraser.stlouisfed.org/

Federal Reserve Bank of St. Louis

in which both two-thirds of the Board and
two-thirds of the FDIC’s Board of Directors ap­
proved the exception, with the further concur­
rence of the Secretary of the Treasury, after
consultation with the President.22 The clear
objective of that provision was limiting the tax­
payer’s potential exposure to loss through in­
creased procedural hurdles that had to be over­
come to invoke the exception.

IV. Conclusion
The removal of the collateral barriers for Re­
serve Banks’ extensions of credit under Section
13 (3) seems to conflict with the spirit of the
other discount window provisions of FDICIA,
Sections 141 and 142. These provisions, along
with the Act’s prompt corrective action provi­
sions, Sections 131-133, were intended to less­
en taxpayers' potential exposure to loss
resulting from loans to insured banks.
In contrast, Section 473, by removing the eli­
gible collateral threshold, may have marginally
increased taxpayers’ potential risk of loss. This
risk could arise from the moral hazard associated
with the perceived availability of the equivalent
of a federal guarantee for nonbanks. Conse­
quently, increased access to the discount window
by nonbanks carries with it some of the same
kinds of risks that arose during the savings and
loan debacle: Adverse selection and misaligned
agency incentives could increase, together with
the probability of use of the emergency lending
facility and the implicit underwriting of nonbank
losses by taxpayers.
The increased degree of discount window
access for nonbanks was not accompanied by
some of the safeguards normally applicable to
discount window access, such as annual exami­
nations by the federal bank supervisory authori­
ties, maintenance of required reserves and clear­
ing balances at Reserve Banks, and requirements
to meet minimum regulatory capital adequacy
standards. Moreover, by extending a component
of the federal safety net, the Reserve Banks’ dis­
count windows, to nonbanks without limitations
on too-big-to-fail rescues, Section 473 of FDICIA
contradicts the spirit of the limitations on the toobig-to-fail doctrine enacted for depository institu­
tions in FDICIA.

■

22 See Todd (1992a). Systemic risk, as described in Section 141
of FDICIA, is a condition in which the closing of an insured institution,
without redemption of uninsured claims at par, “would have serious ad­
verse effects on economic conditions or financial stability.” The connec­
tion between systemic risk for banks and for securities firms is made
strikingly and explicitly in Wall (1993), p. 10.

Finally, it is unclear that there was a real (as
opposed to a perceived) need for revision of
Section 13 (3). Section 473 of FDICIA appar­
ently was intended to deal primarily with situ­
ations like the aftermath of the stock market
crash of October 19, 1987, in which securities
firms, mutual funds, and other nonbank hold­
ers of large investment portfolios consisting of
ineligible collateral would have found it help­
ful to obtain credit from Reserve Banks instead
of from banks, insurance companies, invest­
ment banks, and other usual providers of
funds to nonbank financial firms.
Normally, financial markets treat eligible col­
lateral as high-quality instruments that are
close substitutes for cash. Firms holding large,
unpledged amounts of such collateral ordinar­
ily could be expected to be able to obtain suffi­
cient extensions of credit without having
recourse to direct loans from Reserve Banks,
even during market conditions approximating
financial emergencies, as long as financial mar­
kets had adequate supplies of liquidity that the
Federal Reserve could ensure through openmarket operations. In fact, aggressive use of
open-market operations in October 1987 pro­
vided sufficient aggregate liquidity to prevent
the stock market crash from generating sub­
stantive harm to the economy.
The changes effected by Section 473 of
FDICIA should prove quite harmless if the stat­
ute is implemented in a straightforward, riskaverse manner. However, perverse incentives,
continued observance of a too-big-to-fail doc­
trine (in this case, for nonbanks), and the ab­
sence of adequate procedural safeguards could
increase Reserve Banks’ and, ultimately, taxpay­
ers’ losses from Section 13 (3) lending activities
in the future. Furthermore, greater potential ac­
cess to the federal financial safety net could
boost the risk-taking incentives for nonbanks,
thereby increasing the probabilities that they
will request discount window lending during
financial emergencies.




References
Barth, James R., and R. Dan Brumbaugh, J r . “De­
pository Institution Failures and Failure Costs:
The Role of Moral-Hazard and Agency Prob­
lems,” in Peter Dickson, ed., Rebuilding Public
Confidence Through Financial Reform. Ohio
State University, College of Business, Confer­
ence Proceedings, June 25, 1992, pp. 3-19Bordo, Michael D. “The Lender of Last Resort:
Some Insights from History,” in George G.
Kaufman, ed., Research in F in an cial Serv­
ices: Private a n d Public Policy, vol. 4.
Greenwich, Conn.: JAI Press, Inc., 1992,
pp. 1-20.
Congressional Record, vol. 137, no. 37 (March
5, 1991), 102nd Congress, 1st Session.
Council of Economic Advisers. Economic Re­
port to the President, 1993• Washington,
D.C.: U.S. Government Printing Office, 1993.
Eccles, MarrinerS. Beckoning Frontiers: Public
a n d Personal Recollections, Sidney Hyman,
ed. New York: Alfred A. Knopf, 1951.
FDICIA. Legal symposium publication, The Fed­
eral Deposit Insurance Corporation Improve­
ment Act o f 1991, William P. Bowden, Jr.,
et al., eds. Englewood Cliffs, N.J.: Prentice
Hall Law & Business, 1992.
Fry, Maxwell J. “The Fiscal Abuse of Central
Banks.” International Monetary Fund, Work­
ing Paper No. 93-58, July 1993Grant, James. Money o f the M ind: Borrowing
a n d Lending in America from the Civil W ar
to M ichael Milken. New York: Farrar Straus
Giroux, 1992.
Hackley, Howard H. Lending Functions o f the
Federal Reserve Banks: A History. Washing­
ton, D.C.: Board of Governors of the Fed­
eral Reserve System, 1973.

Hamilton, Alexander. “Report on a National
Bank (December 13, 1790),” in M. St. Clair
Clarke and D.A. Hall, eds., Legislative a n d
Docum entary History o f the B ank o f the
United States (1832). New York: Augustus
M. Kelley, 1967 (reprint), pp. 15-35.
Holland, Kelley. “Limits on Fed’s Discount Loans
Prompt Fears,” Am erican Banker, Decem­
ber 31, 1991, p. 1.

Smith, Rixey, and Norman Beasley. Carter Glass:
A Biography (1939). New York: Da Capo
Press, 1972 (reprint).
Todd, Walker F. “Lessons of the Past and Pros­
pects for the Future in Lender of Last Resort
Theory,” Proceedings o f a Conference on
Bank Structure a n d Competition, Federal
Reserve Bank of Chicago, May 11-13, 1988,
pp. 533-77.

Hoover, Herbert. The Great Depression: 19291941 (vol. 3 of Hoover’s memoirs). New
York: Macmillan Co., 1952.

________ . “FDICIA’s Discount Window Provi­
sions,” Federal Reserve Bank of Cleveland,
Economic Commentary, December 15, 1992a.

Humphrey, Thomas M., and Robert E. Keleher.
“The Lender of Last Resort: A Historical Per­
spective,” Cato Journal, vol. 4, no. 1 (Spring/
Summer 1984), pp. 275-318.

________ . “History of and Rationales for the Re­
construction Finance Corporation,” Federal
Reserve Bank of Cleveland, Economic Re­
view, 1992b Quarter 4, pp. 22-35.

Kane, Edward J. The S&L Insurance Mess: How
D id It Happen? Washington, D.C.: Urban In­
stitute Press, 1989.

________ . “New Discount W indow Policy Is
Important Element of FDICIA,” B anking Pol­
icy Report, vol. 12, no. 5 (March 1, 1993a),
pp. 1, 11-17.

Keeton, William R. “The Reconstruction Finance
Corporation: Would It Work Today?” Federal
Reserve Bank of Kansas City, Economic Re­
view, First Quarter 1992, pp. 33-54.
Martin, W illiam McChesney, Jr. “Problem of
Small Business Financing,” Federal Reserve
Bulletin, vol. 43, no. 7 (July 1957), pp.
767-69.
Moley, Raymond. The First New Deal. New
York: Harcourt, Brace & World, Inc., 1966.
National Commission on Financial Institution Re­
form, Recovery, and Enforcement. Origins
an d Causes o f the S&L Debacle: A Blueprintfo r
Reform, A Report to the President and Con­
gress of the United States. Washington, D.C.:
U.S. Government Printing Office, July 1993Olson, James S. Saving Capitalism: The Recon­
struction Finance Corporation a n d the New
Deal, 1933-1940. Princeton, N.J.: Princeton
University Press, 1988.
Pike, Christopher J., and James B. Thomson.
“FDICIA’s Prompt Corrective Action Provi­
sions,” Federal Reserve Bank of Cleveland,
Economic Commentary, September 1, 1992.
Schwartz, AnnaJ. “The Misuse of the Fed’s Dis­
count W indow ,” Federal Reserve Bank of St.
Louis, Review, vol. 74, no. 5 (September/
October 1992), pp. 58-69.



________ . “The Federal Reserve Board before
Marriner Eccles (1931-1934).” Paper pre­
sented at Western Economic Association In­
ternational Conference, South Lake Tahoe,
Nev., June 23, 1993b.
United States Senate Report No. 102-167. “Com­
prehensive Deposit Insurance Reform and
Taxpayer Protection Act of 1991,” Report of
the Committee on Banking, Housing, and Ur­
ban Affairs, U.S. Senate, to accompany S. 543
(October 1, 1991), Calendar No. 245. 102nd
Congress, 1st Session. Washington, D.C.: U.S.
Government Printing Office.
Wall, Larry D. “Too-Big-to-Fail after FDICIA,”
Federal Reserve Bank of Atlanta, Economic
Review, vol. 78, no. 1 (January/February
1993), pp. 1-14.

Efficiency and Technical Progress
in Check Processing
by Paul W. Bauer

paui w gauer ¡s an economist at
the Federal Reserve Bank ot Cleve­
land. For their comments, sugges­
tions, and encouragement, the
author would like to thank Randall
Eberts and Mark Sniderman.

Introduction
By lowering the transaction costs associated with
barter, a payments system greatly facilitates the
exchange of goods and services.1 Although
vastly improved over the years, the process of
transferring funds remains costly, and the evolu­
tion of the payments system has been at least
partially determined by efforts to trim these costs
further.2 Increasing the productivity of the pay­
ments system improves economic welfare both
by releasing resources to other sectors of the
economy and by lowering the effective purchase
price of goods and services.
In addition to its roles as the nation’s central
bank and as the primary federal regulator of state
member banks and bank holding companies,
the Federal Reserve System is also a major pro­
vider of payment services. Ordered by the Fed­
eral Reserve Act of 1913 to ensure the efficiency
of the payments system, the central bank has
■ 1 The payments system refers to such activities as the provision of
currency and coin, processing and clearing of checks, providing for set­
tlement of checks and other types of payments, and wire transfers of
funds. See Board of Governors of the Federal Reserve System (1984).


■ 2 See Garbade and Silber (1979), Niehans (1971), and Brunner
http://fraser.stlouisfed.org/
and Meltzer (1971).
Federal Reserve Bank of St. Louis

directly participated in the market since its in­
ception. Initially, it provided a national mech­
anism for clearing and settling checks — two
major components of payment services — and
instituted regulations that eliminated the incen­
tive for the circuitous routing of checks.3
Prior to passage of the Depository Institu­
tions Deregulation and Monetary Control Act
(MCA) of 1980, the Federal Reserve did not
charge fees for its payment services and pro­
vided them only to member banks. Conse­
quently, it faced little competition from private
providers serving nonmember financial institu­
tions. Starting in 1981, the MCA required the
Federal Reserve to make its services available
to all depository institutions and to charge fees
that would recover its costs. The goal was to
foster a more efficient payments system by giv­
ing private providers of payment services the
opportunity to compete.
Given this new competitive environment, it
became even more important for the Federal
Reserve to be able to track the performance of
its various offices. Over the years, an extensive
accounting system has been developed to iden­
tify costs associated with each of its services.

■ 3 See Garbade and Silber (1979) and Humphrey (1980).

This has allowed unit cost performance meas­
ures (total service costs divided by service vol­
ume) to be calculated for each service offered.
This article examines the costs of providing
check-processing services at 47 Federal Re­
serve offices (District Banks, branch offices,
and regional check-processing centers) from
1983:IQ to 1990:IVQ by estimating a multiprod­
uct cost function using an econometric frontier
approach. After briefly discussing the advantages
and disadvantages of the Federal Reserve’s unit
cost measures, I demonstrate how they can be de­
composed into separate effects related to differ­
ences in cost efficiency, output mix, input prices,
and environmental variables (these control for
various site-specific characteristics) using estimates
derived from the cost function.4 The cost-function
approach provides much more complete informa­
tion about the sources of office perfonnance than
do unit cost measures, but it is more difficult and
time-consuming to calculate.
In order to explore how the cost frontier may
have shifted over time in response to technologi­
cal and regulatory changes, the article also pre­
sents estimates of technical progress, as measured
by whether the cost of producing a given level
of output declines over time. This technique pro­
vides valuable insights into the technological con­
straints faced by the Federal Reserve.
It should be remembered, however, that re­
search such as this is a continuing process and
that a more complete understanding of the pro­
duction and cost efficiencies associated with
check processing will require multiple investiga­
tions. Consequently, the numerical estimates pre­
sented here must be interpreted with caution,
understood in the context of stated caveats, and
viewed as only a partial effort to model one as­
pect of the payments system.
Section I describes the central bank’s provision
of check-processing services and summarizes
some previous studies of the payments system.
Section II then discusses how the econometric
frontier approach is used to estimate the multi­
product cost function, and explains how a unit
cost measure of performance can be decom­
posed into its various components. After describ­
ing the data employed in the study, I analyze

■

4 Output mix includes the effects of scale economies, whether aver­
age cost rises or falls as output expands, and the effects of the relative
production of the various outputs. Cost efficiency determines how closely
firms operate to the cost frontier.

■

5 Under same-day settlement, banks will have access to funds on
the same day they are deposited, as long as the checks are presented be­
fore 8:00 a.m.- Electronic check truncation refers to sending only an elec­
 tronic image of the check, rather than the check itself, through the
http://fraser.stlouisfed.org/
settlement process.

Federal Reserve Bank of St. Louis

estimates of cost efficiency, scale economies, and
technical change. Unit costs are decomposed for
each office using the estimated multiproduct cost
frontier. The final section considers the future of
Federal Reserve check processing in light of new
technologies, such as same-day settlement and
electronic check truncation.'’

I. Background
Description of
Check Processing
Check processing is, in some ways, a fairly
straightforward operation: A payor writes a check
to a payee, who deposits it at his bank or other
depository institution. This is all most of us ever
think about, and if the payor and the payee are
customers of the same bank (which occurs about
30 percent of the time), this is almost the end of
the story. For these “on-us” items, the only step
left is for the bank to debit the payor’s account
and credit the payee’s account. But if both parties
have accounts at different banks, then the payee’s
bank must forward the check to the payor’s bank
— a situation that occurs roughly 45 billion times
a year. For these items, a bank can send checks .
directly to the payor’s institution or route them in­
directly through a local clearinghouse, a corre­
spondent institution, or a Federal Reserve office.
The Fed processes about 35 percent of these
interbank checks.
In the relatively rare event that the check is re­
turned for insufficient funds (less than 1 percent
of checks), the process repeats itself, only in re­
verse. The return process is more labor intensive
and costly. In contrast to forward volumes, the
Federal Reserve handles the vast majority of pay­
ment system return items. This lack of privatesector competition suggests that the Fed’s prices
for handling returned checks may be too low, a
subject discussed in more detail below.
Thus, the central bank provides two types
of check-processing services, forward items
and return items, and has a separate price
schedule for each. Although the end result is
the same for all checks, this description fails to
reveal the myriad products offered by a typical
processing center. Items can be differentiated
by the location of the payor bank, the times of
presentment and settlement, and the amount
of presorting performed by the institution sub­
mitting the checks.
Costs can vary significantly as a result of
these product characteristics. Fine-sort items, for
instance, are fully presorted by the submitting

m

FI GURE

1

Check-Processing Volumes
Index, 1981:IIQ = 100
180
140
100

60
20

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

SOURCE: Author’s calculations.

While not changing the physical process of check
clearing in the United States, the MCA altered
the institutional environment profoundly. Fed­
eral Reserve payment services prior to passage
were available at no charge, but only to mem­
ber banks. The MCA required the Fed to begin
charging for its services and to offer them to
all depository institutions, including those that
are not members of the System.6 Based on guide­
lines established by the Board of Governors, prices

for each payment service are designed to re­
cover direct and indirect costs as well as a
markup (known as the Private Sector Adjustment
Factor [PSAF]) that imputes other costs typically
incuned in the private sector. In check process­
ing, each Federal Reserve District offers a slightly
different mix of products, and District Banks
have some flexibility in pricing.
Although the MCA increased the number of
institutions that could employ the Federal Re­
serve’s payment services, a large drop in volume
was expected because fees were imposed on
previously “free” services. When pricing was
implemented, the Fed’s share of interbank check
processing fell from approximately 45 percent in
1981 to 38 percent in 1982. Cunently, the System
processes about 35 percent of all interbank items.
The drop in volume that immediately fol­
lowed pricing can be seen in figure 1. In the
first year, systemwide and Fourth District proc­
essing volumes plunged 15 and 18 percent, re­
spectively. However, not all Fourth District
offices experienced similar declines: In Pitts­
burgh, check volume dived almost 40 percent
and has grown relatively little since, yet in Co­
lumbus, check volume recovered within the
first year and expanded rapidly thereafter.7
Even with this overall drop in Federal Re­
serve volume following the onset of pricing,
the national allocation of resources improved
because banks that already owned their own
reader-sorters frequently found it less expen­
sive to process more of their own checks and
even to offer the service to others. Pricing
boosted the efficiency of resource allocation in

■

■

institution and use only the Federal Reserve’s
transportation, settlement, and adjustment serv­
ices, meaning that they cost very little to handle.
At the other extreme, an item can be submitted
without any presorting during the peak period of
check processing (in Cleveland, from 10:00 p.m.
to 1:00 a.m., but this varies significantly across
offices), when the check reader-sorters are oper­
ated at close to maximum capacity. The incre­
mental costs of these items are much higher.
Sorting checks and forwarding them to payor
institutions (or returning them to depositing insti­
tutions) involves a variety of resources, or inputs.
Transit (transportation and communication) is re­
quired to get the items to the processing site and
on to their final destination once sorted. At the
processing site, which must meet certain security
standards, labor employs a variety of capital
goods (mainly high-speed sorters and computers)
to sort the checks and keep track of the settle­
ment operation.

Monetary Control Act

6 These firms include savings and loans, credit unions, and foreign

banks.


7 Unusual local economic factors accounted for much of the check
volume growth in Columbus.

FI GURE

2

GLS Estimates of Ray
Average Cost
Cents per u n it o f aggregate outp ut

a. Millions of items per quarter.
SOURCE: Author’s calculations.

another way. Humphrey (1981) estimates scale
economies in check processing at 36 Federal
Reserve Banks and branches for the 1974-76
period. He concludes that 78 percent of checks
deposited with the Fed during that time were
processed at offices with significant scale dis­
economies, a finding he attributes to a lack of
market competition.
The increase in competition after pricing be­
gan led to greater cost-control incentives and im­
proved resource allocation. By 1982, constant
(rather than decreasing) returns to scale were the
mle in Federal Reserve check-processing opera­
tions (see Humphrey [1980,19851 ).

II. Frontier
Estimation and Unit
Cost Decomposition
The cost function, C (y,w,z), for a firm simply
yields the minimum cost of producing any
specified level of outputs (y) given technologi­
cal constraints, input prices (w), and environ­
mental effects (z). Foreshadowing our results
somewhat, figure 2 plots the generalized least
squares (GLS) estimates of the ray average cost
function for check processing using the Sys­
tem’s averages for output mix, input prices,
and site-specific characteristics.8 The curve in­
dicates the lowest ray average .cost that can be
achieved for a given level of output, provided
the site is operated efficiently.9
The concept of a frontier is quite natural in
the context of a cost function. Even allowing
for random events that may lead to temporar­
ily lower or higher unit costs, we would expect


most offices to operate on or above the cost
function. In the context of this theoretical con­
struct, there are many ways for things to go
wrong and only one way to get them exactly
right. Thus, observed costs will tend to be above
the corresponding ray average cost curve.
The cost function is a particularly useful con­
cept because many characteristics of the techno­
logical constraints facing the firm can be derived
from it. For example, from figure 2 we can see
that for low levels of output, check-processing
services face scale economies— that is, ray aver­
age costs fall as both outputs are increased pro­
portionally. For the average mix of outputs, the
advantages of running a larger operation are al­
most exhausted after about 105 million aggregate
items per quarter. Increasing the level of output
from about 76 million items per quarter (the
mean value from 1983 to 1990) to the level re­
quired for scale efficiency (holding the output
mix constant) lowers ray average costs only 2.6
percent. Once these levels of output are reached,
we will see that cost efficiency (the ratio of the
cost on the frontier to observed cost) becomes a
more important consideration.

■

8 Ray average cost is defined as
C (ky, w,

M
z) / X = C (y, w, z) / £ y t .
/= 1

Although the denominator appears to be arbitrarily summing over the
various outputs, since the output mix is held constant for this calculation,
the rather arbitrary output aggregator function imposes no additional re­
strictions.

■

9 Holding the mix of outputs constant by increasing them proportion­
ally is extremely restrictive. In the results section, I demonstrate how the
scale-efficient level of output depends crucially on the mix of outputs.

Given this demonstration of the usefulness
of cost functions, there is one problem— these
functions must be estimated from data generated
by sites in operation. A number of empirical
techniques have been developed to estimate
frontier cost functions. Generally, they can be
divided into two classes: 1) estimators based on
econometric techniques, such as maximum likeli­
hood estimation and panel data estimation, or 2)
estimators based on linear programming tech­
niques, such as data envelopment analysis.10 In
this paper, I report only estimates derived from
the GLS approach.11

Econometric
Techniques
Broadly speaking, econometric techniques em­
ploy a specific (although flexible), functional
form for the cost function and impose some ad­
ditional assumptions about the statistical prop­
erties of the inefficiency terms. As a category,
these techniques assume a compound error
term that comprises both cost inefficiencies and
statistical noise. Within the category, the tech­
niques differ in the assumptions used to de­
compose this error term to obtain estimates of
cost efficiency.
All of the econometric techniques impose an
explicit functional form for the cost function.
The translog functional form is employed be­
cause it is a second-order approximation to any
cost function about a point of approximation
(here, the sample mean). Essentially, this means
that it can model many different possible relation­
ships among outputs, inputs, and environmental
factors, depending on its parameter values. The
translog cost function can be written as

M
(1)

ln C (, = p0+ £




$ J n y nut

L
m= 1
1990
+ X

^ j ° j t + U i + Vit’

j = 1984

where y is a vector of M outputs, w is a vector
of K input prices, z is a vector of L environ­
mental variables, D is a set of T -1 dummy
variables (one for every year except the first),
u ( u > 0 ) measures cost inefficiency, and v
represents statistical noise.
Estimation of this function involves finding
the values of the parameters that best fit the
observed data given the imposed assumptions.
Equation (1) is estimated, along with the corre­
sponding equations for input shares, imposing
the usual mathematical restrictions of symme­
try and linear homogeneity in input prices. The
symmetry constraints come from assuming that
the cost function is twice differentiable, so that

d Wjd Wj

diV jdw i

' and
d 2C _
dy^yj

d 2C
d y ^ y ,'

This forces 8 k[ = 8 lk and (3 kl = (3 lk, for every k
and /. Linear homogeneity in input prices,
t ■C(y, w) =
t ■w), stems from defining
the cost function as yielding the minimum cost
of producing a given output level when faced
with a particular set of input prices. Propor­
tional changes in input prices affect only the
cost level, not the cost-minimizing input bun­
dle. This property imposes constraints on all
parameters related to the \nwkit's-.

m=l

M

+1/2X
m=

(3 )

M

X

I

Y „= 1
k

PmMVnuM’/U
and

1 1=1

K
I

+ X i k lnwkit
k= 1

M

9 - = I
k

S t( = 0 , V / , m .

k

K

+X
I
Qmklnym:t[nWkit
m= 1 k=1

K
+ 1 /2 X
k=l

K
X
1=1

b k !l n W kUl n W IU

■ 10 A more detailed description of the techniques employed in this
paper can be found in Bauer and Hancock (1993). For a thorough treat­
ment of these two classes of techniques, see Greene (1993) and Ali and
Seiford (1993).

■

11 Bauer and Hancock (1993) report estimates using a variety of
econometric and linear programming techniques. Here, I choose to con­
centrate on one set of results in order to provide a sharper focus.

29

The use of longitudinal data often allows us
to avoid assuming a specific distribution for
the inefficiency terms. Repeated observations
over time identified site-specific, time-invariant
inefficiencies.12
For the GLS technique, the inefficiency terms
are calculated by using the average of the residu­
als by site, a ( . The most efficient site in the sam­
ple is taken to be the best estimate of where the
cost frontier lies and is thus assumed to be fully
efficient. The inefficiency of the i th site is meas­
ured by the proportionate increase in predicted
costs over the predicted costs of the most effi­
cient site. An index bounded by zero (costs are
inclined, but no output is produced) and one
(a site on the cost frontier) can be calculated as
r/\

W

A

exp (min a

(5)

In [ ( C / y u ) / ( C / y , ) ]
T

= 1/

I n [C(yit,wit, z it)

t£

t= i
exp (Uj+ v it)/y ut)
T

N

- 1 / 77 V ] T ] T I n [C(yit,wit,zit)
T= 1 i = l

exp (u i + vi() / y li(] .

Equation (5) can be rearranged to
(6)

In [(C / y () /( C / y ) ] = { ut- u 1

A

- a / ).

m= 1
The GLS technique mns an iterative, seemingly
unrelated regression (ITSUR) on the system of
cost and K -1 input share equations using panel
data. One of the share equations, which are de­
rived using Shephard’s lemma, must be dropped
in order to avoid singularity of the system.13 How­
ever, since the estimates are obtained using ITSUR,
the numerical estimates are the same no matter
which one is dropped.

M

M

+
1
/
2
XX
P
m
/ lnyu[nymi
n
j
/
)
m= 1

1=1

+ Inyu - In V,

X yk(lnw k i~ [nwk)
k= i

Unit Cost
Decomposition
For the moment, assume that only one output
is produced. In this case, unit cost is just C/y,
where C is observed cost and y is observed
output. If w e wanted to compare one site to
the average of all sites, we could do so by tak­
ing the ratio of that site’s unit costs to the over­
all average. This would readily tell us whether
a site’s costs were above or below average,
but we would not know why.
Using the definition of the cost function and
the error specification developed for the GLS
estimation technique, we can rewrite the ratio
of a site’s average unit costs to the overall aver­
age unit costs as follows in order to derive a
more informative set of measures.14

12 See Schmidt and Sickles (1984) for further explanation. Berger
(1993) contains some possible extensions.

K

K

+ 1/2 X
k=i
M

X

dk/(lnw kilnw n - ln w klnw ,)

1=1
K

+1 X
X emk([nyn,:lnu;ki- ln^
m= 1 k= 1

ln ^ )

m —1

where the expressions in braces can be defined
as effects resulting from differing cost efficiencies,
outputs, input prices, the interaction of outputs
and input prices, and environmental effects.15

■

■

13 For more details on the treatment of the share equations, see
Bauer, Ferrier, and Lovell (1987).


■ 14 Although any two observations could be chosen to compare
http://fraser.stlouisfed.org/
unit costs, comparing the sample mean for the ithsite to the overall sam­
Federal Reserve Bank of St. Louis
ple mean causes the term involving statistical noise,

v, to drop out.

■ 15 I derive the decomposition for the general case when there are
M outputs and arbitrarily use the first output (forward items) as the denominator
in the construction of unit costs. Empirically, the resulting measure of unit cost
is highly correlated with the Federal Reserve's measure because forward proc­
essing appears to account for more than 80 percent of the costs of processing
services, but has the advantage that this specification can be exactly decom­
posed into the various effects described below.

While these are logarithmic differences, as long
as the numerical values are close to zero, they
can be roughly interpreted as the percentage
difference in costs stemming from these vari­
ous effects.16
Clearly, unit costs provide a useful measure
of a site’s relative ability to produce a given
level of output at the lowest possible cost, be­
cause it summarizes the overall effect of a variety
of cost factors. Once the trouble and expense of
collecting the data have been incurred, the unit
cost measures are easy to calculate. O n the
other hand, the cost-function approach imposes
greater structure and requires more effort to
calculate, but it also provides a much more de­
tailed set of information.
Now one must explicitly consider the com­
plications posed by the presence of multiple
outputs. The Federal Reserve constructs unit
cost measures for each of its services and then
weights them by cost shares to obtain an over­
all measure of performance across service lines.
A potential problem is that the accounting rules
employed to allocate the costs of joint inputs
(those used to produce more than one service,
like computer systems) may not accurately re­
flect the flow of services from these inputs to
the various services. This will cause the calcu­
lated unit cost measures to be biased up or
down, depending on whether the service in
question receives more or less of its share of
costs associated with the joint inputs. In the
case of some joint inputs, there may be no
simple accounting mle that could accurately al­
locate their costs because of nonlinear techno­
logical relationships among the various outputs
and inputs.
Rather than relying on arbitrary accounting
rules, the cost-function approach allows the
data (combined with the imposed assumptions)
to allocate costs to the various outputs by find­
ing the parameters that best fit the cost model.
Marginal costs for each of the outputs can then
be readily calculated by differentiating the esti­
mated cost function. For pricing and output
decisions, marginal costs should be more rele­
vant than unit costs.

III. Data
Construction
Quarterly data for the 1983-90 period were col­
lected on total costs, check volume, input prices,
and environmental variables for 47 Federal Re­
serve check-processing sites.17 The primary data
 source was annual functional cost accounting re­


ports, which are prepared by the Federal Re­
serve via its Planning and Control System to
monitor costs and improve resource allocation
within the System. These data were supple­
mented by other cost and revenue figures, infor­
mation from occasional Federal Reserve surveys,
price index data from the Commerce Depart­
ment’s Bureau of Economic Analysis and the La­
bor Department’s Bureau of Labor Statistics, and
pricing data from industry sources.
Production costs for forward items, return
items, and adjustments were included in total
costs, but certain overhead expenses, such as
special District projects, were excluded. The
two measures of output were the total number
of forward items and return items processed at
each site. Reflecting the earlier discussion of
the vast array of products offered by the vari­
ous offices, this measure is at best an approxi­
mation. Some of the environmental variables
discussed below attempt to adjust for the differ­
ent product mixes across offices. Inputs to the
check-processing function fall into the catego­
ries of buildings, materials, transit, and labor.
Labor expenditures— salaries, retirement, and
other benefits— accounted for 47.1 percent of
total costs in 1990:IVQ.
Buildings’ total cost share was only 5.6 per­
cent in 1990:IVQ, in part because the interest
expenses associated with the acquisition of build­
ings are not represented in the cost-accounting
framework (these are included in the PSAF rather
than in direct and indirect costs).
Expenditures for materials (office equip­
ment and supplies, printing and duplicating,
data processing, computers, and check readersorters) accounted for 29.8 percent of total
costs in 1990:IVQ. Transit expenditures— the
expenses associated with data and other com­
munications, shipping, and travel— made up
just over 17.5 percent.
Environmental variables, which control for a
variety of site-specific characteristics, include the
item-pass ratio, the number of endpoints, the
machine error rate, and the type of machine
used. The item-pass ratio, defined as the aver­
age number of times a check must pass through
a reader-sorter, is a measure of the exogenous
check-sort pattern and has been found in pre­
vious studies to influence costs significantly. The
number of endpoints is the number of locations

■ 16 For the exact percentage difference, one must take the antilog
minus one.

■

17 For complete details, see Bauer and Hancock (1993). The New
York check-processing operation was omitted because it was closed in
1988.

m

FI GURE

3

M1 Locus
Millions of return items per quarter
4 .0
3 .5
3 .0
2 .5

2.0
1 .5

1.0
0 .5

0.0
0

20

40

60

80

100

120

140

160

180

200

220

Millions of forward items per quarter
SOURCE: Author’s calculations.

to which checks must be sorted and delivered.
The machine enor rate is the number of incom­
ing enors per 100,000 checks at each office
and is largely a matter of poor MICR (magnetic
ink character recognition) encoding. The last
environmental variable indicates whether the
site used IBM or Unisys machines and allows
for differences in maintenance expenses, fail­
ure rates, and downtime.

IV. Empirical
Results
Scale Efficiency
A scale-efficient office operates at the output
level at which ray average costs are minimized
for its output mix or, equivalently, at the out­
put level at which cost elasticity equals one (that
is, a 1 percent increase in output would cause
costs to rise by 1 percent).18 Conversely, a
scale-inefficient office operates at an output level
larger or smaller than the scale-efficient level. If
the office processes less than the scale-efficient
volume, the cost elasticity is less than one (a 1
percent increase in output would raise costs by
less than that amount), meaning that the office
could achieve lower unit costs by boosting out­
put. Alternatively, a scale-inefficient office that
processes more than the scale-efficient volume
has a cost elasticity greater than one, and unit
costs can be lowered by reducing output. Thus,
■

18 Cost elasticity is defined as 9 lnC(ky, w,z)/dlnXlx = 1 .

This turns out to be identical to the sum of the cost elasticities with re­

spect to each output.


estimates of cost elasticities yield direct esti­
mates of scale efficiency.
When multiple outputs are produced, the mix
of outputs must also be considered when exam­
ining scale efficiency. The M locus (see figure 3)
is defined as the set of all outputs with unitary
cost elasticities.19 For any level of forward items,
the M locus reveals the conesponding level of
return items required to achieve scale efficiency.
A site operating below (above) the M locus ex­
periences scale economies (diseconomies). The
estimated M locus indicates that a site process­
ing a large number of return items relative to for­
ward items reaches scale efficiency at a lower
level of forward items.
It may not be possible for every office to
achieve scale efficiency, despite the best efforts
of managers. The volume of checks and return
items processed at an office depends on the size
of the market and the prices charged. The eco­
nomic size of managers’ payments markets is out­
side their control, and although managers may
have some authority over prices, their need to re­
cover costs may prevent them from setting a
price low enough to attract a scale-efficient vol­
ume of output. In short, even the best-run office
will be scale inefficient if it is in a market too
small to achieve scale efficiency.
Figure 2 demonstrated that the ray average
cost curve for check and return processing was
U-shaped (meaning that at low levels of aggre­
gate output, ray average cost falls as outputs are
increased proportionally, but that scale econo■ 19 The M locus in figure 3 is drawn with the input prices equal to
their values at the sample mean. Unfortunately, the estimated M locus be­
comes increasingly speculative as it moves away from the output ratio
found at the sample mean.

mies are exhausted at some point, so further in­
creases result in higher ray average costs). In
table 1, we present estimates of cost elasticities
using site-specific characteristics for 1990JVQ.
Most sites are fairly close to achieving scale effi­
ciency— given their individual output mixes, in­
put prices, and environmental variables. Even
so, these estimates suggest that the average of­
fice could lower its costs about 12 percent if it
could generate scale-efficient volumes. Full scale
efficiency would require the average office to
increase its scale of operations significantly.
However, as revealed by the ray average cost
function in figure 2, most of the gains occur be­
fore 76 million items per quarter are processed.
Although some smaller offices appear to be
operating in the output range where further scale
economies could be exploited in the future, addi­
tional float costs that are not incorporated in our
model may make this infeasible. As items from
more-distant banks are processed, additional
shipping costs and delays will be incuned that
may outweigh the associated cost savings.
Estimates of marginal cost, or the incremental
cost of processing one more item, can provide
additional information for pricing. One of the
beneficial outcomes of competitive markets is
that competition forces prices to be set equal to
marginal costs. In other words, the price that con­
sumers pay for a good or service equals a firm’s
incremental cost of producing it. If the Federal
Reserve set its actual prices for forward and re­
turn items in 1990:IVQ to equal estimated mar­
ginal costs, those prices would have averaged
$0,009 and $0,643 per item, respectively (see ta­
ble 1). In practice, prices are based on account­
ing data, and the Federal Reserve’s calculated
unit costs for forward and return items averaged
$0.0135 and $0,159, respectively.
Neither unit costs nor marginal costs can be
directly used for pricing because they fail to ac­
count for several characteristics (such as the time
the checks are submitted for processing), yet
to the extent that pricing is based on unit costs,
the estimated marginal costs imply that forward
items could be regarded as overpriced, whereas
return items could be regarded as underpriced.
Even though the Federal Reserve sets prices to re­
cover costs, econometric estimates indicate that
the accounting data appear to assign too much
of the costs to forward items and too little to re­
turn items. Market conditions are consistent with
econometric estimates, since there are no entry
barriers into either market, yet the Federal Re­
serve faces little competition for return items,
and there are many private-sector competitors



for forward items. Clearly, this is an issue that
requires further study.

Cost Efficiency
Cost inefficiencies appear to raise costs more
than does scale inefficiency. If all offices could
be operated on the cost frontier, costs could
be lowered by about 23.5 percent. Table 2
compares GLS estimates of the cost efficiencies
calculated using the multiproduct cost function
employed here with the single-product esti­
mates reported in Bauer and Hancock (1993).
Overall, the results change remarkably little
when return items are treated as a separate out­
put: O n average, estimated cost efficiency rises
only 3 5 percent. However, one site with a rela­
tively large number of return items (FR27) saw
its estimated efficiency increase by 16.3 per­
centage points.
Many of the top-ranked offices were in the
same Federal Reserve District, indicating that
management differences may be important.
Aside from superior managerial skill, estimated
cost efficiency could vary across sites because
some Districts may focus on cost performance
while others may stress customer service, which
is largely uncontrolled for in this study. For ex­
ample, one District may specify precisely how
checks must be submitted and refuse to accept
them otherwise, while another may accept
checks in any form but charge higher fees for
packages that require more attention. The for­
mer District will appear to be more efficient than
the latter, other things being equal, because it re­
ceives the checks exactly as it wants them. How­
ever, the latter District receives a higher fee by
providing a service desired by its customers.

Unit Cost
Decomposition
The average unit cost measure for each of the 47
offices over the 1983-90 period relative to the
overall sample mean is presented in table 3,
along with estimates of each of the component
effects. Unit costs vary substantially across of­
fices, from -0.388 to 0.309, or from about a third
below to a third above the overall average. The
largest single component appears to be the costefficiency effect, with a conelation between it
and unit cost of more than 80 percent.

TABLE

1

Estimates of Marginal Costs
and Cost Elasticities, 1990MVQ

Cost Elasticities

Office

FR1
FR2
FR3
FR4
FR5
FR6
FR7
FR8
FR9
FR10
FR11
FR12
FR13
FR14
FR15
FR16
FR17
FR18
FR19
FR20
FR21
FR22
FR23
FR24
FR25
FR26
FR27
FR28
FR29
FR30
FR31
FR32
FR33
FR34
FR35
FR36
FR37
FR38
FR39
FR40
FR41
FR42
FR43
FR44
FR45
FR46
FR47
Average

Marginal Costs

Return
Items

Overall

Forward
Items

Return
Items

0.381
0.400
0.416
0.392
0.464
0.483
0.405
0.359
0.444
0.418
0.469
0.450
0.391
0.382
0.412
0.429
0.396
0.533
0.489
0.439
0.436
0.381
0.407
0.439
• 0.509
0.492
0.375
0.493
0.502
0.393
0.452
0.371
0.427
0.413
0.453
0.473
0.366
0.485
0.473
0.415
0.480
0.448
0.403
0.442
0.450
0.421
0.414

0.568
0.568
0.439
0.565
0.333
0.240
0.501
0.670
0.436
0.443
0.424
0.388
0.590
0.629
0.506
0.421
0.526
0.219
0.162
0.556
0.495
0.548
0.519
0.500
0.198
0.303
0.708
0.402
0.362
0.519
0.130
0.528
0.400
0.470
0.430
0.341
0.604
0.382
0.312
0.463
0.378
0.467
0.576
0.445
0.491
0.378
0.447

0.949
0.968
0.855
0.958
0.797
0.723
0.906
1.029
0.880
0.860
0.893
0.838
0.982
1.011
0.918
0.850
0.922
0.752
0.651
0.996
0.932
0.929
0.926
0.939
0.706
0.795
1.083
0.896
0.864
0.912
0.582
0.899
0.827
0.882
0.883
0.814
0.971
0.867
0.784
0.878
0.859
0.915
0.979
0.887
0.941
0.799
0.861

0.007
0.010
0.007
0.011
0.010
0.009
0.007
0.009
0.009
0.009
0.012
0.007
0.010
0.011
0.009
0.008
0.008
0.015
0.010
0.011
0.009
0.006
0.012
0.012
0.006
0.008
0.011
0.011
0.013
0.005
0.006
0.009
0.006
0.006
0.010
0.010
0.010
0.013
0.008
0.009
0.010
0.009
0.009
0.007
0.013
0.006
0.009

0.738
0.797
0.655
1.011
0.618
0.520
0.657
0.865
0.584
0.817
0.525
0.486
0.907
0.902
0.700
0.631
0.767
0.433
0.561
0.466
0.473
0.662
1.077
0.673
0.296
0.331
0.653
0.346
0.389
0.531
0.601
1.258
0.570
0.531
0.547
0.534
1.131
0.496
0.480
0.805
0.457
0.481
0.631
0.475
0.601
0.671
0.895

0.433

0.446

0.880

0.009

0.643

Forward
Items

SOURCE: Author’s calculations.




The output and input price effects can also
exert a significant influence on some offices’
unit costs, but the correlations with unit costs
are much lower. In fact, the correlation be­
tween unit costs and the input price effect is
negative, hinting that some input quality may
vary across sites and that higher-priced inputs
may be more productive. By construction, in­
put prices and estimates of cost efficiency are
uncorrelated, so in this case, the unit cost
measures have revealed an issue that requires
further study.
The environmental effects tend to be mini­
mal across all sites except FR25, which serves
an unusually small number of endpoints. The
interactive effect is slight for all offices, with
the largest estimated effect shifting relative unit
costs only about 6.8 percent.

Productivity Growth
Including year dummies in the cost function al­
lows us to estimate whether it shifts down (or
up) over time as a result of changes in technol­
ogy or in the regulatory environment. Estimates
of a technical change index are presented in
figure 4. For 1983, the index equals 100; for
later years, it rises or falls depending on the be­
havior of the estimated cost function. As of
1990, costs had risen about 8.7 percent. Most of
the upward shift that occurred in 1989 appears
to have stemmed from transitory costs related
to the implementation of regulations designed
to post checks more quickly, since costs fell
sharply in 1990.
Measured productivity growth in check proc­
essing has been anemic for two main reasons:
1) some of the cost savings have been plowed
back into producing a higher-quality product
(such as expedited funds availability), and 2)
even though prices for computer equipment
and other office machinery have fallen precipi­
tously over the last 10 years, the price of high­
speed check reader-sorters has remained
roughly unchanged in real terms. Apparently,
the limit of how quickly paper checks can be
read and sorted has nearly been reached, and
further advances will have to await the in­
creased use of electronics in collecting checks.

Bal
TABLE

2

GLS Cost Efficiency Estimates,
1983-90 Average

Single Product

Multiproduct

Office

GLS

Rank

GLS

Rank

FR1
FR2
FR3
FR4
FR5
FR6
FR7
FR8
FR9
FRIO
FR11
FR12
FR13
FR14
FR15
FR16
FR17
FR18
FR19
FR20
FR21
FR22
FR23
FR24
FR25
FR26
FR27
FR28
FR29
FR30
FR31
FR32
FR33
FR34
FR35
FR36
FR37
FR38
FR39
FR40
FR41
FR42
FR43
FR44
FR45
FR46
FR47

0.864
0.535
0.997
0.587
0.625
0.669
0.634
0.633
0.656
0.629
0.581
0.765
0.717
0.713
0.715
0.714
0.754
0.647
0.645
0.707
0.683
0.919
0.530
0.612
0.802
0.880
0.627
0.738
0.615
0.969
0.693
0.630
0.939
1.000
0.711
0.660
0.574
0.610
0.792
0.557
0.747
0.742
0.668
0.843
0.610
0.630
0.689

7
46
2
42
37
25
31
32
28
35
43
11
16
19
17
18
12
29
30
21
24
5
47
39
9
6
36
15
38
3
22
34
4
1
20
27
44
40
10
45
13
14
26
8
41
33
23

0.884
0.604
0.998
0.615
0.708
0.687
0.689
0.696
0.673
0.632
0.668
0.861
0.736
0.718
0.737
0.721
0.728
0.645
0.679
0.770
0.694
0.928
0.585
0.661
0.837
0.927
0.790
0.798
0.665
0.961
0.720
0.636
0.939
1.000
0.756
0.685
0.656
0.644
0.815
0.567
0.820
0.721
0.745
0.914
0.659
0.685
0.638

8
45
2
44
25
29
28
26
33
43
34
9
19
24
18
22
20
39
32
15
27
5
46
36
10
6
14
13
35
3
23
42
4
1
16
31
38
40
12
47
11
21
17
7
37
30
41

Average

0.708

SOURCE: Author’s calculations.




0.742

Change in
Efficiency
0.020
0.069
0.002
0.028
0.083
0.018
0.056
0.063
0.017
0.003
0.086
0.095
0.019
0.005
0.022
0.007
-0.026
-0.002
0.034
0.063
0.011
0.009
0.055
0.048
0.035
0.047
0.163
0.060
0.050
-0.008
0.027
0.006
0.000
0.000
0.045
0.025
0.082
0.034
0.023
0.011
0.074
-0.021
0.078
0.071
0.049
0.055
-0.051
0.035

Chang
in Ran
1
-1
0
2
-12
4
-3
-6
5
8
-9
-2
3
5
1
4
8
10
2
-6
3
0
-1
-3
1
0
-22
-2
-3
0
1
8
0
0
-4
4
-6
0
2
2
-2
7
-9
-1
-4
-3
18

Unit Cost Decomposition3
Logarithm ic Differences from Sample Means, 1983-90

Office

Unit
Cost ($)

FR1
FR2
FR3
FR4
FR5
FR6
FR7
FR8
FR9
FRIO
FR11
FR12
FR13
FR14
FR15
FR16
FR17
FR18
FR19
FR20
FR21
FR22
FR23
FR24
FR25
FR26
FR27
FR28
FR29
FR30
FR31
FR32
FR33
FR34
FR35
FR36
FR37
FR38
FR39
FR40
FR41
FR42
FR43
FR44
FR45
FR46
FR47

0.015
0.021
0.013
0.023
0.018
0.018
0.016
0.022
0.019
0.020
0.020
0.014
0.018
0.018
0.017
0.015
0.018
0.024
0.021
0.018
0.016
0.012
0.022
0.022
0.014
0.016
0.020
0.020
0.024
0.012
0.019
0.020
0.014
0.013
0.018
0.021
0.021
0.021
0.016
0.019
0.017
0.017
0.019
0.013
0.023
0.016
0.017

U nit
Cost

-0.179
0.176
-0.349
0.242
0.022
0.028
-0.087
0.212
0.047
0.133
0.131
-0.221
0.023
0.029
-0.036
-0.145
-0.017
0.304
0.182
-0.007
-0.112
-0.376
0.193
0.219
-0.217
-0.124
0.120
0.134
0.309
-0.388
0.042
0.122
-0.269
-0.352
0.025
0.155
0.162
0.141
-0.086
0.038
-0.051
-0.036
0.046
-0.288
0.259
-0.104
-0.049

a. Office mean relative to overall sample mean.
SOURCE: Author’s calculations.




Cost
Efficiency

-0.185
0.195
-0.307
0.178
0.037
0.067
0.064
0.054
0.087
0.151
0.096
-0.159
-0.002
0.022
-0.003
0.019
0.009
0.130
0.078
-0.047
0.057
-0.234
0.227
0.106
-0.130
-0.232
-0.073
-0.083
0.100
-0.269
0.019
0.145
-0.245
-0.308
-0.029
0.070
0.113
0.132
-0.103
0.258
-0.111
0.018
-0.014
-0.218
0.109
0.069
0.142

Total
O u tp u t

-0.054
0.001
-0.110
-0.163
-0.136
0.010
-0.133
-0.122
0.018
-0.119
0.174
-0.038
-0.148
0.053
-0.125
-0.085
-0.155
0.278
0.054
0.149
0.005
-0.191
-0.081
0.188
0.134
0.149
0.357
0.356
0.375
-0.193
-0.027
-0.288
-0.165
-0.179
0.117
0.105
-0.093
0.029
0.017
-0.126
0.102
0.187
0.152
0.001
0.133
-0.252
-0.162

Direct
In p u t Price

0.017
0.061
0.038
0.225
0.213
0.012
0.068
0.211
-0.046
0.086
-0.044
0.083
0.223
-0.167
-0.029
-0.084
0.073
-0.159
0.076
-0.183
-0.156
0.078
0.113
-0.167
0.404
-0.136
-0.233
-0.270
-0.275
0.065
0.024
0.219
0.101
0.124
-0.189
-0.151
0.034
0.076
-0.004
0.031
-0.058
-0.338
-0.150
-0.005
-0.130
0.217
0.101

Interactive
Effect

-0.006
0.003
0.005
0.008
-0.022
-0.006
0.000
0.019
0.006
0.002
0.004
-0.002
0.001
-0.023
0.001
0.000
0.000
0.030
-0.029
0.004
0.002
0.002
-0.001
-0.002
-0.068
0.020
-0.047
0.020
0.028
0.002
-0.003
0.012
0.003
0.001
0.008
0.016
0.001
-0.004
0.004
0.001
0.008
0.019
-0.018
0.003
0.002
-0.007
0.003

Environm ental
Effect

0.048
-0.084
0.024
-0.005
-0.070
-0.054
-0.086
0.050
-0.018
0.013
-0.099
-0.106
-0.051
0.143
0.119
0.005
0.055
0.026
0.002
0.070
-0.020
-0.031
-0.066
0.094
-0.556
0.075
0.117
0.111
0.080
0.008
0.029
0.034
0.038
0.010
0.119
0.115
0.107
-0.092
0.001
-0.126
0.008
0.077
0.076
-0.069
0.144
-0.132
-0.133

fia

FIGURE

4

Technical Change Index
Index, 1983 = 100
140
120
100

80

60
40
20

0

1983

1984

1985

1986

1987

1988

1989

1990

SOURCE: Author’s calculations.

V. Conclusions
and Prospects for
the Future
This study finds scale economies to be sufficiently
large to enable most offices to proportionally in­
crease their forward and return volumes, yet still
lower their ray average costs by roughly 12 per­
cent. Costs appear to increase much more rapidly
as more return items are processed than as more
forward items are processed. Although there ap­
pear to be opportunities for most offices to im­
prove their performance by further exploiting
scale economies, costs could be lowered even
more (up to 23-5 percent overall) if all offices
could operate closer to the cost frontier.
It is necessary to keep in mind three impor­
tant caveats. First, some offices may be located in
areas where it may not be possible to expand
output enough to achieve scale efficiency. Sec­
ond, the cost-efficiency measure is relative to the
most efficient office observed in the sample.
Third, although I use the concise term ’‘cost effi­
ciency,” the concept is more fully described as
“once factors included in the cost function are
controlled for, there remain unexplained cost dif­
ferences across processing sites.” Every effort has
been made to control for the factors that affect
the costs of check-processing offices, but no one
can hope to account for every factor that might
significantly affect costs. Future research will ex­
tend the analysis by trying to control for product
quality in a more detailed way.20
The multiproduct cost-efficiency estimates for
the 47 offices covered here are highly conelated
with earlier single-product estimates presented in
Bauer and Hancock (1993). O n average, cost effi­




ciency rose only 3.8 percentage points when
returns were treated as a separate output. How­
ever, one office that processed an atypically
high level of returns had its cost-efficiency in­
dex increase by 16.3 percentage points. The
overall level of cost efficiency is roughly the
same as that found for private financial institu­
tions, using similar estimation techniques.21
In the single-product setting, unit cost meas­
ures provide an easily calculated overall indicator
of relative office performance. Unfortunately,
they do not reveal the sources of superior or
inferior performance. In the multiproduct set­
ting, unit cost measures could be biased if the
costs of joint inputs are misallocated across
services. The cost-function approach over­
comes both of these drawbacks, but requires
imposing a number of potentially restrictive as­
sumptions. The decomposition of unit costs re­
veals that, for this sample, cost efficiency tends
to be the largest single component, but consid­
erable office-specific variation results from the
other components. Only the interaction effect
between output levels and input prices is con­
sistently small in magnitude for all offices.

■

20 Product quality can affect cost efficiency measures because it is
expensive to provide higher quality. If output is not adjusted for product
quality, sites providing lower-quality output will, other things being
equal, appear to be more cost efficient.

■

21 For example, see Bauer, Berger, and Humphrey (1993), Ferrier
and Lovell (1990), Fried, Lovell, and Vanden Eeckaut (1993), and Mester
(1993), to name just a few. While these studies examine the cost efficiency of
producing outputs other than check-processing services, their estimated effi­
ciency levels suggest that the Federal Reserve pursues its behavioral goal
about as well as private financial institutions pursue theirs.

The net effect of technological and regulatory
changes seems to have shifted the multiproduct
cost frontier up slightly over time, a finding that
supports the prevailing view that much greater
use of new technologies, such as check trunca­
tion and imaging, will be required to achieve sig­
nificant technical change in check processing.
This finding is also consistent with earlier work
by Bauer and Hancock (1993).
In the coming years, check processing at the
Federal Reserve will face a number of new
challenges, since volume is likely to rise less
rapidly, and may even fall. One cause is merg­
ers and acquisitions in the financial service sec­
tor, which have resulted in more on-us items
that can be cleared internally. Other causes in­
clude bilateral agreements among banks to swap
checks directly, the emergence of private nation­
wide check processors, same-day settlement, and
technological advances such as electronic check
presentment and the shift to electronic payments.
The introduction of pricing, the evolution of
technology, and the consolidation of the banking
industry during the past few years have led to
many changes in the check-processing market.
Moreover, increased competition between bank­
ers and nonbank providers of financial services,
along with more competition between checks
and other payment media, indicates that more
changes will follow. In the future, market forces
will largely determine the number and location
of check-processing sites across the country. Re­
search studies can contribute to a more complete
understanding of developments in this dynamic
payment service.




References
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367-74.




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1992 Quarter 3

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Comparing Central Banks’ Rulebooks
by E.J. Stevens

Generational Accounts and Lifetime Tax Rates,
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by Alan J. Auerbach, Jagadeesh Gokhale,
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Forbearance, Subordinated Debt, and the Cost
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1993 Quarter 1

Examining the Microfoundations of Market
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White- and Blue-Collar Jobs in the
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W ho’s Singing the Blues?
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Assessing the Impact of Income Tax,
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H

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Using Bracket Creep to Raise Revenue:
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Cyclical Movements of the Labor Input
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