Full text of Economic Review (Federal Reserve Bank of Cleveland) : 1991 Quarter 2, Vol. 27, No. 2

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V o l. 2 7 , N o . 2

ECONOMIC REVIEW
1991 Quarter 2

The Demand for M2,
Opportunity Cost, and
Financial Change

2

by John B. Carlson and Sharon E. Parrott

Central-Bank Intervention:
Recent Literature,
Continuing Controversy

12

by Owen F. Humpage

A Regional Perspective
on the Credit View
by Katherine A. Samolyk




FEDERAL RESERVE BANK
OF CLEVELAND

27

ECONOMI C

REVI EW

1991 Quarter 2
Vol. 27, No. 2

i

The Demand for M2,
Opportunity Cost,
and Financial Change
by John B. Carlson and
Sharon E. Parrott
Weakness in M2 growth in recent years has been largely unex­
plained by estimated models of M2 demand. This paper examines
two hypotheses concerning the shortfall in this aggregate. The
results are consistent both with the theory that the restructuring of
the thrift industry has played a role in the recent weakness of M2
and with the belief that this restructuring will have only a minimal
effect on long-run velocity.

Central-Bank
Intervention:
Recent Literature,
Continuing
Controversy

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

Opinions stated in Economic
Review are those of the authors
and not necessarily those of the
Federal Reserve Bank of Cleveland
or of the Board of Governors of the
Federal Reserve System.

Over the past two decades, during which floating exchange rates
have been in effect, central banks have invested billions of dollars
in an attempt to influence the path of exchange rates or the
volatility of exchange rates around that path. The effectiveness of
these efforts remains a controversial topic among both academic
economists and policymakers. This review of recent literature on
the subject finds some qualified support for intervention, but noth­
ing to endorse the active interventionist policy undertaken in late
1985, m id -1987, and 1989.

27

by Katherine A. Samolyk
This paper develops a regional credit view to explain how regional
credit-market performance can affect local economic activity. The
existence of asymmetric information costs implies that a region’s
capability to fund local investments is related to the creditworthi­
ness of local borrowers. Imbalances in financial capacity across
regions can affect the mix of aggregate investment, causing capitalpoor regions to be underfunded. The author tests the empirical
relevance of this credit view for the period 1980 to 1986 using
state-level data and finds that reduced financial capacity is related
to economic activity in states that are experiencing low growth.



Coordinating Economist:
Randall W. Eberts

12

by Owen F. Humpage

A Regional
Perspective on
the Credit View

Economic Review is published
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of Cleveland. Copies of the Review
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Affairs and Bank Relations Depart­
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Material may be reprinted
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ISSN 0013-0281

The Demand for M2,
Opportunity Cost,
and Financial Change
by John B. Carlson and Sharon E. Parrott

Introduction
The role of money in the implementation of
monetary policy has waxed and waned over the
past 30 years. Policymakers’ attention to money
has been largely related to their confidence in
the stability of the relationship between meas­
ures of money and the ultimate objectives of
monetary policy, particularly the rate of inflation.
The link between inflation and money growth
has long been grounded in the quantity theory
of money. A key relationship in this link is the
demand for money. Indeed, in an influential re­
statement of the quantity theory, Milton Fried­
man (1966) argued that “the quantity theory is in
the first instance a theory of money demand.”
Friedman’s point was predicated on the empiri­
cal hypothesis that the demand for money is one
of the most stable relationships in the economy.1
Despite some unexplained behavior in the mid1970s, the money demand function was widely
perceived as reasonably stable and reliable
through the balance of that decade.2

John b. Carlson is an economist
and Sharon E. Parrott is a re­
search assistant at the Federal
Reserve Bank of Cleveland. The
authors gratefully acknowledge
helpful discussions with Michael
Bagshaw, W illiam Gavin, Jeffrey
Hallman, Gregory Hess, Kim
Kowalewski, and Yash Mehra.
They would also like to thank
Thomas Lee for identifying
a coding error that affected results
presented in an earlier draft.

The high-water mark for the role of money in
monetary policy was reached in the late 1970s,
when the Federal Reserve adopted a disinflation
strategy in which annual targets for monetary
growth played a key role. This strategy was
coupled with an operating procedure that auto­
matically reacted to deviations of money from
prespecified short-run paths. Although some
analysts criticized the procedure for not produc­
ing gradually slowing money growth, trend
money growth ultimately slowed, as did the in­
flation rate.3 Moreover, money markets reacted
systematically to announced changes in the
money supply, providing evidence that the shortrun financial market implications of the proce­
dure were widely understood and anticipated.
In 1980, Congress passed legislation authoriz­
ing significant changes in U.S. banking regula­
tions, including the elimination of most interestrate restrictions. Many analysts believed that such
deregulation would enable depository institutions
to pay higher yields on deposits and thereby

■ 2
■ 1

Hendry and Ericsson (1990) provide some evidence that a con­
stant, conditional money demand model cannot be inverted to obtain a
 constant model of prices for narrow measures of money. We do not pur­
http://fraser.stlouisfed.org/
sue this issue.

Federal Reserve Bank of St. Louis

For evidence on the breakdown of conventional money demand
models in the mid-1970s, see Goldfeld (1976) and Judd and Scadding
(1982).

■ 3

To appreciate the difficulty in choosing prespecified monetary tar­
gets to reduce the inflation rate, see Poole (1988).

claim a larger share of the household portfolio.
This, in turn, would affect the relationship be­
tween money measures (comprised largely of
deposits) and the level of economic activity.
Concerns about the impact of deregulation
on the stability of money demand appeared to
be warranted for the narrower money measures
such as M l. The introduction of interest-bearing
checking nationwide and new deposit instal­
ments such as money market deposit accounts
(MMDAs) greatly affected long-established
depositor behavior. Common specifications for
M l demand did not survive deregulation. And
while attempts have been made to rectify M l
demand in the short run, no consensus has yet
emerged on any particular empirical form.4
Research on M2 demand, however, has
yielded evidence of stable short-run specifica­
tions for this aggregate, at least in the post-World
War II period (see Moore, Porter, and Small
[1990], Hetzel and Mehra [1987], and Mehra
[1991]). But in 1989 and 1990, M2 grew more
slowly than these models had predicted. Two
hypotheses have been proposed to account for
this. The first is that at least part of the unex­
plained behavior is related to the mismeasurement of the opportunity cost of M2. The second
is that the restructuring of the savings and loan
(thrift) industry has affected M2 growth.
This paper presents a specification of M2 de­
mand that adopts the general framework used
by Moore, Porter, and Small (hereafter refened
to as MPS), but uses an alternative measure of
opportunity cost. We attempt to capture the ef­
fects of thrift restructuring on the adjustment of
M2 to its equilibrium level. The estimated regres­
sion remains stable throughout the period of
deregulation, and the results suggest that the
model’s performance can be improved by meas­
uring opportunity cost more precisely. Moreover,
our results are consistent with the hypothesis
that recent M2 weakness is partly related to the
thrift industry restructuring and is thus largely a
temporary phenomenon.

I. The ErrorCorrection
Framework

This research has generally found evidence of
inertia in the response of money demand to
changes in opportunity costs and spending.
Early postwar specifications attempted to cap­
ture this inertia as a partial-adjustment specifica­
tion. This approach was sometimes identified as
the conventional specification or the Goldfeld
equation (see Goldfeld [1973]). Alternatively, re­
searchers have handled the inertia by using a dis­
tributed lag (of either the levels or the first
differences of the levels) of the regressors.
MPS were among the first advocates of specify­
ing the inertia in an error-correction framework.
They noted two advantages to this approach.
First, error-correction regressors— entered as first
differences in the levels— are more likely to be sta­
tionary and are much less colinear than they
would be as undifferenced regressors. Second,
the long- and short-run money demand relation­
ships are clearly distinguished.
In addition, Hendry and Ericsson note that the
error-correction framework generalizes the con­
ventional partial-adjustment model in a way that
allows for separate rates of reaction to the vari­
ous determinants of money demand, reflecting
different costs of adjustment. They further argue
that the error-correction specification is related
to theories of money adjustment such as the
model developed by Miller and Orr (1966). In
these models, the short-run factors determine
money movements given desired bands, while
the long-run factors influence the levels of the
bands themselves.
We follow the approach of MPS, but specify
the long-mn money demand function as
(1)

where mt = log (M 2), yt = log ( nom inal GNP),
and 5 = log (opportunity cost).
Note that the unitary coefficient on nominal
GNP ensures that this expression also specifies
a relationship in which long-run velocity varies
only with opportunity cost.’ The second
component is a dynamic specification based on
an error-correction adjustment specification:
(2)

■

4 For a stable short-run specification of M1 demand, see Hendry

V m ( = a + bet_ j
II

V

+ X ci V m t - i + X di Vst-i

Empirical aggregate money-demand functions
estimated in the postwar period typically include
some measure of the opportunity cost of holding
money (most often a short-term interest rate)
and a scale variable such as income or spending.
 and Ericsson (1990). For an examination of long-run M1 demand, see
http://fraser.stlouisfed.org/
Hoffman and Rasche (1989).
Federal Reserve Bank of St. Louis

mt = a + yt + (3^ + et ,

1=1

1=0

w

+ Y u f iv yt-i
1=0

■

q

+

1 1

n

i= 1 7=0

SijWxu-j

+

5 MPS include a time index as a regressor to estimate any drift in
M2 velocity directly. Although they find the coefficient to be significant,
the drift is negligible— about 0.03 percent per year (see appendix).

where et _ x is the deviation of money from its
long-run equilibrium value (derived from equa­
tion [1]) and et is white noise. Adjustment speed
is determined by changes in the lagged values of
M2 and in the current and lagged values of
opportunity cost and the scale variable. The gen­
eral form of the model allows other variables,
xit, to affect adjustment speed (both current and
lagged values). These variables, which need not
affect equilibrium money balances, include any
factors that influence the adjustment process.
Equation (2) essentially specifies the shortrun convergence process of M2 to its equilib­
rium value. W hen the coefficient on the errorcorrection term is negative, convergence is
assured. Substituting (1) into (2) yields
(3)

V mt = a - b a - b

j
U

+ b (m t_ 1- y t_ 1) + ' £

c. V mt_ .

i = 16
V

w

+ X d i V s t - i + X f i v y t- i
i= 0
q

i= 1

n

+X ILgyVXi't-j +v
i = 1 7=0

We estimate a version of equation (3).

II. Measuring
Opportunity Cost
By definition, the opportunity cost of money is
the forgone interest income from holding a mon­
etary asset in lieu of some higher-yielding non­
monetary, but otherwise comparable, asset. A
common practice in the money demand litera­
ture has been to measure opportunity cost using
a market yield on some short-term security, such
as a Treasury bill (T-bill) or commercial paper.
This seemed appropriate for the narrow money
measures during much of the postwar period,
because holders of currency and demand depos­
its did not receive explicit interest payments on
these instruments.
However, many instruments in the broader
monetary aggregates, such as M2, have yielded
explicit interest. During regulation, yields
responded at least partially to market conditions
when interest-rate ceilings were not exceeded.
In principle, the forgone interest for each of
these instruments is the difference between its
yield and the yield on some close substitute.



An innovation of MPS was to measure the
opportunity cost of M2 as the difference between
the rate paid on M2 deposits and the rate earned
on a T-bill. The rate paid on M2, or its own rate,
is a weighted average of the rates paid on M2
components (which include small time deposits,
MMDAs, other checkable deposits, passbook
savings accounts, and repurchase agreements
[RPs]), where the weights are equal to the corre­
sponding component’s share of M2.
The three-month T-bill is generally considered
a close substitute for many M2 components
because, like most of these, it is of short maturity
and is relatively risk free. However, M2 compo­
nents vary in liquidity. Some deposits, such as
interest-bearing checking accounts, are available
on demand, while other components, such as
small time deposits, may not be accessible
without penalty for several years.
One hypothesis of this paper is that the
opportunity cost of M2 is more appropriately cal­
culated as a weighted average of the differences
between each M2 component and a market
instrument of comparable maturity. Such a meas­
ure would allow for the variation in M2 matur­
ities and hence would account for the interest
forgone by not holding more likely substitutes.
Unfortunately, data constraints make such a cal­
culation impossible before 1983.
Although it is not possible to match each M2
component perfectly with a market instrument of
equal maturity, one can achieve closer correspon­
dence between rates paid on these components
and on alternative assets than is realized by the
measure now employed. For example, a closer
approximation of the alternative asset rate can
be constmcted using a weighted average of the
three-month T-bill rate and the three-year Treas­
ury note (T-note) rate. The weight for the threeyear T-note rate is the small time deposit share
of M2, and the weight for the three-month T-bill
rate is the non-small time deposit share of M2.6
Some analysts have found that yield curve
steepness variables are statistically significant in
money demand models that use the conventional
opportunity cost measure. Using our measure of
opportunity cost, however, the yield curve vari­
able becomes insignificant. This suggests that
the M2 components’ relative shares are impor­
tant in calculating opportunity cost.

■

6 It should be emphasized that this alternative measure is still an ap­
proximation. We are not matching maturities, since our measure of the own
rate uses the interest rate paid on the six-month certificate of deposit (CD)
as the rate paid on all time deposits. Nevertheless, the introduction of the
longer-term T-note rate into the calculation appears to improve the model.

FI GURE

1

Change in Thrift Deposits,
1964 - 1990
Percent

NOTE: Percent changes are expressed as quarterly rates.
SOURCE: DRI/McGraw Hill.

III. The Thrift
Hypothesis
Over the past two years, many models of M2
demand have been consistently overpredicting
M2 growth. Some analysts have argued that the
unexplained weakness in this aggregate is
related to the ongoing restructuring of the sav­
ings and loan industry (see Furlong and Trehan
[1990]). As figure 1 shows, thrift deposits have
contracted significantly since 1988. Although
banks have acquired some of these funds, the
additional increase in bank deposits has only
partially offset the contraction at thrifts.
After the savings and loan industry’s problems
became evident, these institutions came under
increasing regulatory pressure. Regulators no
longer allowed thrifts to bid for funds above mar­
ket interest rates. And the closure of thrifts, as
Furlong and Trehan argue, led to changes in
deposit pricing strategy for the entire deposit
market. To the extent that institutions paying
above-market rates were eventually closed, their
competitors were able to offer lower interest
rates because they no longer had to compete
against the insolvent thrifts. Furthermore, when




the insolvent thrifts were closed and their assets
sold to other financial institutions, many con­
tracts were abrogated. When the assets were
absorbed, the interest-rate “contracts” were
renegotiated. This meant that the above-market
interest rates offered by the thrifts were no
longer available.
As Furlong and Trehan note, interest rates on
MMDAs and on small time deposit accounts
have recently been lower than one would have
expected prior to the thrift industry restructuring.
This has caused an increase in the opportunity
cost of M2 and has led many depositors to trans­
fer their funds, at least temporarily, out of M2.
While the thrift restructuring hypothesis ex­
plains why deposit rates may be unusually low,
it is unclear why the money demand function is
overpredicting M2 growth. If deposit rates are
lower than expected, then opportunity cost
should be higher than expected, which in turn
should imply lower money demand. That is, the
weakness in M2 growth should be explained by
higher opportunity cost.
One hypothesis for the shortfall in money
demand is that the changed pricing behavior is
not captured completely by the measured oppor­
tunity cost. Although refining the opportunity
cost measure by partially accounting for the vari­
ation in rates across maturities does improve the
model, the measurement of the M2 own rate also
presents problems for our analysis. As noted pre­
viously, the own rate is computed as a weighted
average of the rates paid on various M2 deposits.
Surveys conducted by the Federal Reserve and
the Office of Thrift Supervision (OTS) ask re­
spondent depositories to indicate the “most com­
mon rate paid” on various types of deposits.
The aggregated own rate computed from
these surveys masks the shape of the interest-rate
distribution in two ways. First, if depositories pay
different rates on accounts of the same type, then
the Federal Reserve uses the rate paid on the
largest number of deposits to compute the own
rate. For example, depositories may have many
levels, or tiers, of MMDAs, each requiring a dif­
ferent minimum balance and paying a different
interest rate. Second, the distribution of rates
across depositories could be skewed if a few in­
stitutions pay much lower or much higher rates
than average.
Depositories that need funds are more likely to
be on the high end of the interest-rate distribution
and are therefore likely to be the institutions most
responsible for the growth of deposits. Unfortu­
nately, the own rate now used will drown out the
rates reported by banks paying the “fringe” rates;
that is, those banks having the greatest effect on

FI GURE

2

hypothesis, we include both the lagged change
in thrift deposits and the lagged change in M2 in
the error-correction equation.8 Because the
thrift variable is largely a component of M2, we
would not expect it to add anything to the re­
gression unless it includes information not con­
tained in the lagged change in M2.9

Simulated and Actual M2:
Estimation Period =
1964:IQ - 1986:IVQ

B illio n s o f d o llars

3,300

IV. Empirical
Results

S im ulated
.

3,100

•

A ctual
The regression estimated in this paper as an
alternative to the MPS equation is given by

2,900
X*

ss• *

2,700

(4)

2,500
2,300 _
1985

...........
1986
1987

.....1............. L
1988
1989

1
1990

Vm, = -.053 - .009 5,_ j
(4.44) (4.60)
A 5 8 ( m t _ 1 - y t _ 1)

.007 Vs,
(3.32)

SOURCE: Simulations based on authors' model.

+ .186Vc,
(2.87)
the demand for deposits by drawing funds from
outside the depository sector.7
A key hypothesis of this paper is that the
change in thrift deposits is a proxy for deposit
pricing effects not captured by our measure of
opportunity cost. In other words, the effect of
thrift restructuring on M2 demand may be
viewed as another kind of measurement prob­
lem. Because deposit pricing has at times been
more aggressive at thrifts than at banks, thrift
deposit growth could incorporate information
about the skewness in the distribution of deposit
rates. The rates on the extreme end of the dis­
tribution might well account for a disproportion­
ate share of the change in thrift deposits. For
example, in the early to middle 1980s, some
thrifts expanded their market share of deposits
and other money market instruments by offer­
ing extremely attractive (and, more important,
unsustainable) rates.
Interest-rate skewness, while not sustainable
in the long run, might affect the adjustment of
M2 to its equilibrium level. To examine this

■

7 A second problem is that the Federal Reserve and the OTS neither
collect the same information on their surveys nor compute the aggregate
rate for M2 deposits in the same manner. The OTS computes an aggregate
rate for each type of deposit at thrift institutions. This is calculated by asking
for the “most common rate paid” on a given type of account and weighting
that rate by the total number of deposits at the entire institution, not by de­
posits in the given type of account. This method implicitly assumes that
every thrift has a similar distribution of deposits. The Federal Reserve, on
the other hand, weights its aggregate rates by the amount of deposits in the
given type of account— a more accurate method of computing weighted
 averages. However, the own rate is calculated using both OTS and Federal
http://fraser.stlouisfed.org/
Reserve data.

Federal Reserve Bank of St. Louis

+

(5.13)

,245Vm i_ 1
(3.08)

.007 V s ,
(3.39)
+ .214 V x t _ j
(3.30)

+ .031REGDUM + e,
(7.38)
Adj. R 1 = .74; est. period = 1964:IQ to 1986:IVQ,
where 5 is our alternative measure of opportu­
nity cost, c is personal consumption expendi­
tures, x is thrift deposits (including other
checkables, MMDAs, savings deposits, small and
large time deposits, and term RPs), and REGDUM
is a qualitative variable that equals zero in all
quarters except 1983:IQ, when it equals one.10
Because thrift restructuring has been ongoing
since 1988, and because we seek to avoid high
influence points given the substantial changes
in the industry since that time, equation (4) is
estimated before the thrift crisis (1964:IQ to
1986:IVQ) and simulated through 1990. All para­
meters are significant at the 5 percent level or
better.

■

8 We also looked at the thrift share of the deposit market as a proxy
for deposit pricing effects. Although this variable enters significantly in
some models of money demand, it is not significant here.
■

9 An underlying assumption is that pricing strategies persist over
several quarters. This persistence is reflected in the strength (weakness)
of thrift deposit growth relative to M2 growth, and accounts for the unique
information when both variables are included in the regression.

■

10 Following the practice of MPS, we present results that approxi­
mate s using a first-order Taylor series expansion (Taylog) when the op­
portunity cost is less than 0.5. We also estimate the model using the
simple log of opportunity cost. While the simple measure improves the
in-sample fit, out-of-sample simulations are less favorable. Nevertheless,
the usefulness of the Taylog transformation remains an open issue,
though beyond the scope of this study.

FI GURE

3

Simulated and Actual M2:
Estimation Period =
1964:IQ - 1989:IVQ

B illio n s o f d o llars

FI GURE

4

Simulation Residuals:
Estimation Period =
1964:IQ-1986:1VQ

Percent

Percent

SOURCE: Simulations based on authors’ and MPS’s models.




Figure 2 illustrates that although the model
overpredicts M2 growth in 1989, the gap nar­
rows in 1990. W hen we extend the estimation
period through 1989:IVQ, the adjusted R 2 in­
creases further, to 0.77, and the out-of-sample
simulation errors become smaller (see figure 3).
The measure of opportunity cost discussed
above appears superior to the measure calcu­
lated by MPS, improving fit and out-of-sample
simulation performance.11 Changes in the esti­
mates of the opportunity cost variable coeffi­
cients show that the choice of opportunity cost
measure is also important. When the alternative
rate is used, the coefficients for each of the
opportunity cost variables— the first difference,
the lagged difference, and the lagged level of
opportunity cost— increase in absolute value, in­
dicating that those variables explain a larger
share of the changes in M2.
The change in thrift deposits is also highly sig­
nificant, both before and after 1986. When this
variable is excluded from the model, out-ofsample simulation errors cumulate substantially
after 1988. Moreover, the lagged thrift variable
clearly adds something that the lagged change in
M2 does not explain. This suggests that the thrift
variable is capturing potential effects related to
the thrift restructuring, and is consistent with the
hypothesis that our thrift variable is capturing
part of the skewness of the own-rate distribution.
Our equation compares favorably with that
of MPS (see appendix). Improvement in fit is
substantial, from 0.68 to 0.74, and as figure 4
indicates, the bias in the MPS model appears to
be widening. More important, the stability of our
model does not rely on the inclusion of numer­
ous qualitative variables. Indeed, we account
only for the temporary effect caused by the
watershed of regulatory changes that occurred
in 1983. To test for stability before and after
1983, we employ a Chow test and reject the
hypothesis that the parameters have changed.
Figure 5 compares the simulation residuals
for the two models when the sample periods
are extended through 1989:IVQ. Note that both
models improve. The 1990 errors are negligible
in the alternative model, while the MPS model
continues to underpredict M2 growth.
To assess the robustness of our thrift proxy,
we examine the stability of its coefficient as the
sample size is varied. Figure 6A illustrates the
estimated value of the sample (bounded by two
standard deviations) as the sample size is in­
creased by one quarter, beginning with 1985:IQ

■

11 We estimate the model without the thrift variable for the period
1964:IQ to 1986.IVQ. Although the in-sample fit is only marginally better
for the MPS measure, the average out-of-sample bias is 57 percent higher.

FI GURE

5

Simulation Residuals:
Estimation Period =
1964:IQ- 1989:IVQ
Percent

Percent

SOURCE: Simulations based on authors’ and MPS’s models.

to 1989:IVQ and moving backward in time. Al­
though the coefficient does vary to some extent,
it tends to stabilize as the sample is increased
and is never statistically insignificant.
To see how important the most recent expe­
rience is, we repeat this experiment with an ini­
tial sample period of 1982:IQ to 1986:IVQ (see
figure 6B). The results, while not as favorable,
illustrate the relative stability of the thrift factor
as a determinant of money demand. However,
our findings also suggest that the influence of
the recent data is relatively substantial.
Finally, figure 6C shows the value of the coef­
ficient for an initial estimation period of 1964:IQ
to 1968:IVQ, and for each quarter forward. The
coefficient begins to stabilize in the early to mid­
dle 1970s. This finding is consistent with the
hypothesis that the thrift deposit change may
proxy for deposit pricing skewness. Prior to this
time, deposit-rate competition for funds was
largely constrained by Regulation Q. In 1973,
however, regulators began to erode this con­
straint by introducing exempt deposit instm
ments such as “wild card CDs.”12


V. Summary and
Conclusions
We investigate two hypotheses that may explain
the unexpected slowness in M2 growth. First, we
attempt to measure more accurately an ag­
gregate opportunity cost of M2. The results sug­
gest that some share of small time deposits is
more likely to be a substitute for instruments
with maturities of longer than three months.
Although the alternative measure is a crude
approximation of an “ideal” aggregate, it im­
proves the fit of the model substantially.
Second, we explore potential effects of the
change in thrift deposits on the adjustment of
money demand to its long-run equilibrium level.
Although the economic foundations of the latter
hypothesis may be unclear, our preliminary
analysis suggests that the inclusion of lagged
thrift deposit growth in the error-correction equa­
tion helps to account for the weakness in M2.
The thrift variable’s statistical significance in esti­
mation periods predating the recent restructur­
ing is surprising and needs to be explained.
We are encouraged by the out-of-sample per­
formance of our model and believe that further
improvements can be made in the measurement
of opportunity cost. Our results are consistent
both with the hypothesis that thrift restructuring
has played a role in the recent weakness of M2
and with the belief that the restmcturing will
have only a minimal effect on long-run velocity.
This study also highlights the difficulty that
policymakers face in choosing the appropriate
target for M2. Our findings suggest that desired
M2 growth should be conditioned on expecta­
tions concerning the continued effect of thrift
restructuring, as well as on future movements in
the tenn structure of interest rates. The analysis
does not address how to predict the behavior of
these conditioning factors, however.

Appendix:
The MPS Money
Demand Model
The MPS model, like the one described above, is
an error-correction model that assumes a longrun velocity following a constant, but nonzero,
trend.13 The regression and estimated coeffi­
cients are given in box 1 on page 10.

■

12 For an analysis of the competitive implications of this exempt
instrument, see Kane (1978).

■

13 Much of this discussion is based on Small and Porter (1989).

9

FI GURE

6

MPS specify the long-run equilibrium money
demand function as

Coefficient of the Thrift Variable

mt =
A. ESTIMATED FOR EACH PERIO D BACKW ARD FROM
1985:IQ TO 1989:IVQ

1964

1969

1974

1979

1984

B. ESTIMATED FOR EACH PERIO D BACKW ARD FROM
1982:IQ TO 1986:IVQ

1964

1968

1972

1976

1980

C. ESTIMATED FOR EACH PERIO D FORW ARD FROM
1964:IQ TO 1968:IVQ

___ Plus tw o standard deviations
--- M inus tw o stan dard de viatio ns
SOURCE: Simulations based on authors’ model.




a

+ yt + (35, + yTt + et ,

where mt = log (M2), yt = log ( nom inal GNP ),
s = log (opportunity cost ), and T = time. This
specification allows M2 velocity to drift over
time, although the estimated coefficient indi­
cates that this drift is negligible in the short run.
Since MMDA in the adjustment equation is
essentially an intercept shift variable, the statisti­
cal significance of its coefficient can be inter­
preted as a one-time downward shift in the M2
velocity trend.
In addition to imposing convergence of longrun equilibrium through the error-correction
term, MPS also impose a short-run “convergence”
restriction that requires the sum of the coeffi­
cients of Vlog (M 2t _ 1),Vlog (Consumpt ),
Vlog(Consumpt_ j), and Vlog (Consumpt _ 2 ) to
equal one.
There are a number of other differences
between the MPS model and ours. Aside from
differences in the measurement of opportunity
cost, MPS include variables that are not em­
ployed in our new specification of money
demand, specifically, DUM83Q2,
Vlog (Consum pt _ ^ ), Vlog (Consum pt _ 2),
Time, V CCDUM, and MMDA. We tested the
restriction on the sum of the coefficients on
consumption and M2 variables, but since the
restriction was not statistically justifiable, we
did not impose it in our specification. In addi­
tion, the coefficients on MMDA and Time be­
came statistically insignificant under our
specification. Thus, our results are consistent
with the hypothesis that, in the long run, M2
velocity depends only on its opportunity cost.
While both models fit reasonably well during
the estimation period, the new specification
behaves better out of sample, yielding smaller
forecast errors. In addition, our model is less
reliant on hard-to-predict dummy variables
whose effects are unlikely to be permanent with
respect to the growth rate of M2.

10

B O X I

MPS Model and
Estimated Coefficients
V lo g {M2t )

=

-.076
(5.55)

+

.508 Vlog(M2/_ 2)
(6.04)

-

.010 Taylog ( Opp t x)
(-6.25)

-

.185 [ log ( M 2 1 x )
(-5.60)

+ ,288 Vlog (Consum pt)
(3.89)

+

.120 Vlog (Consum p t j )
(1.64)

+ .085 Vlog ( Consump , 2 )
(1.37)
+ .0056 MMDA
(2.43)
+ .0271 DUM83Q1
(5.64)

-

-

-

.000077 Time
(-2.57)
-

log ( GNP t 2 )]

.0089 VTaylog (O pp.)
(-5.56)

.0103 VCCDUM
(-2.86)
-

.0075 DUM83Q2
(-1.36)

Adj. R 2 = .68; estimation period = 1964:IQ to 1986:IIQ.

Taylog :

The natural logarithm of values greater than 50 basis points and the linear approximation of
those values less than 50 basis points.

Tim e:

Time trend, which increases by one each quarter.

O pp:

Opportunity cost of M2.

C onsum p:

Personal consumption expenditure.

MMDA :

Dummy variable used to denote the permanent shift in money growth resulting from the intro­
duction of money market deposit accounts. It takes the value zero before 1983:IQ and one there­
after.

CCDUM :

Dummy variable to correct for credit controls in place in 1980:IIQ. It takes the value one in
1980:IIQ, zero otherwise.

DUM83QT.

Dummy variable to correct for the short-run shock to M2 caused by deregulation, which led to
the introduction of MMDAs and negotiable order of withdrawal (NOW ) accounts. It takes the
value one in 1983:IQ, zero otherwise.

DUM83Q2:

Dummy variable to correct for the second quarter in which N OW accounts were allowed. It
takes the value one in 1983:IIQ, zero otherwise.

NOTE: ^statistics are in parentheses.




References
Friedman, Milton. “Interest Rates and the De­
mand for Money,”Jo u rn a l o f Law a n d Eco­
nomics, vol. 9 (October 1966), pp. 71-86.

Furlong, Fred, and Bharat Trehan. “Interpret­
ing Recent Money Growth,” Federal Reserve
Bank of San Francisco, Weekly Letter, Sep­
tember 28, 1990.

Goldfeld, Stephen M. “The Demand for Money
Revisited,” Brookings Papers on Economic
Activity, vol. 3 (1973), pp. 577-638.
______ . “The Case of the Missing Money,”
Brookings Papers on Economic Activity, vol.
3 (1976), pp. 683-739.

Miller, Merton H., and Daniel Orr. “A Model
of the Demand for Money by Firms,” Q uar­
terlyJo u rn a l o f Economics, vol. 80, no. 3
(August 1966), pp. 413-35.

Moore, George R., Richard D. Porter, and
David H. Small. “Modeling the Disaggre­
gated Demands for M2 and M l: The U.S. Ex­
perience in the 1980s,” in Peter Hooper et al.,
eds., F inancial Sectors in Open Economies:
Em pirical Analysis a n d Policy Issues. Wash­
ington, D.C.: Board of Governors of the Fed­
eral Reserve System, 1990, pp. 21-105.

Poole, William. “Monetary Policy Lessons of Re­
cent Inflation and Disinflation,” Jo u rn a l o f
Economic Perspectives, vol. 2 (Summer
1988), pp. 73-100.

Small, David H., and Richard D. Porter. “Un­
Hendry, David F., and Neil R. Ericsson.
“Modeling the Demand for Narrow Money in
the United Kingdom and the United States,”
Board of Governors of the Federal Reserve
System, International Finance Discussion
Papers No. 383, July 1990.

Hetzel, Robert L., and Yash Mehra. “The Be­
havior of Money Demand in the 1980s,”
Federal Reserve Bank of Richmond, mimeo,
October 1987.

Hoffman, Dennis, and Robert H. Rasche.
“Long-Run Income and Interest Elasticities of
Money Demand in the United States,” Nation­
al Bureau of Economic Research, Working
Paper No. 2949, April 1989.

Judd, John P., and John L. Scadding. “The
Search for a Stable Money Demand Function:
A Survey of the Post-1973 Literature,”Jo u rn a l
o f Economic Literature, vol. 20, no. 3 (Sep­
tember 1982), pp. 993-1023.

Kane, Edward J. “Getting Along without Regula­
tion Q: Testing the Standard View of DepositRate Competition during the Wild-Card
Experience," Jo u rn a l o f Finance, vol. 33,
no. 3 (June 1978), pp. 921-32.

Mehra, Yash P. “An Error-Correction Model of
U.S. M2 Demand,” Federal Reserve Bank of
Richmond, mimeo, April 1991.




derstanding the Behavior of M2 and V2,” Fed­
eral Reserve Bulletin, vol. 75, no. 4 (April
1989), pp. 244-54.

Central-Bank Intervention:
Recent Literature,
Continuing Controversy
by Owen F. Humpage

Introduction
Since the inception of floating exchange rates
nearly 20 years ago, governments have refused
to give private markets free reign in determining
the exchange values of their currencies. They
have instead bought and sold foreign exchange
in an attempt to influence the path of exchange
rates or to reduce the volatility of exchange
rates around that path. Nearly all governments
contend that intervention is effective. Less cer­
tain— and fundamental in the continuing debate
about intervention— is whether central banks
can separate intervention from their overall
monetary policies and have it remain an effec­
tive tool for influencing exchange rates.
If nations could successfully intervene with­
out altering their monetary bases (sterilized in­
tervention), then any country could manipulate
its exchange rate without jeopardizing price sta­
bility, and any group of countries could coordi­
nate its exchange-rate goals without sacrificing
monetary sovereignty. If, instead, intervention is
effective only wrhen it induces a change in the
monetary base or, possibly, when it signals



Owen F. Humpage is an eco­
nomic advisor at the Federal
Reserve Bank of Cleveland. The
author gratefully acknowledges
helpful comments and criticisms
on an earlier draft from Kathryn
Dominguez, Juann Hung, Michael
Klein, Michael Leahy, Bonnie
Loopesko, W illiam Osterberg, and
Edward Stevens. Diana Dumitru
provided research assistance.

future changes in monetary policies, then one
must weigh the merits of attempting to influence
exchange rates against the potential conflicts
with domestic monetary policy objectives.
This paper surveys theoretical arguments and
recent empirical literature bearing on this contro­
versy. Two conclusions emerge: First, the recent
literature offers some threads of evidence to sup­
port the view that intervention can sometimes in­
fluence market expectations and exchange rates.
Nevertheless, these threads cannot be woven
into a strong fabric of support for an active inter­
vention policy, whereby central banks acquire
huge portfolios, enter markets frequently, and
undertake large, sterilized transactions. I find lit­
tle evidence to support interventions of the type
that the Group of Three Countries (G 3)— West
Germany, Japan, and the United States —
undertook in late 1985, mid-1987, and 1989. In­
stead, the evidence suggests that under rather
specific and unusual circumstances, sterilized in­
tervention might temporarily influence exchange
rates. Second, I find that exchange-rate interven­
tion and price stability are not always incompat­
ible, but they can be difficult to combine.

I. Monetary Policy
and Intervention

B O X I

Sterilized and Nonsterilized
Intervention
M onetary A u th o rity ’s B alance Sheet
Assets
L iabilities
Net F oreign Assets
Gold
Foreign exchange
SDR
Net position in IMF
D om estic Assets
Government securities
Loans to depository institutions
Other

M onetary Base
Currency held by the public
Reserves

Net W o rth

The table above, which presents a stylized balance sheet for
a hypothetical central bank, helps to illustrate the important
distinction between sterilized and nonsterilized intervention.
O n the asset side of the ledger are net foreign assets (NFA ),
which consist of foreign reserves less liabilities to foreign offi­
cials, and domestic assets (DA), which consist primarily of
loans to depository institutions and government securities.
O n the liability side of the ledger is the monetary base (M B).
Assume that net worth is zero. Then both sides of the ledger
will balance such that
NFA + DA = MB.
When a central bank intervenes in the foreign exchange
market, it buys or sells foreign assets (NFA) in exchange for
its domestic currency. The transaction increases the nation’s
monetary base, in keeping with the balance-sheet identity:
A MB = A NFA.
The change in the monetary base leads to a multiple ex­
pansion of the nation’s money stock. This intervention is
nonsterilized. Notice that it is similar to a domestic openmarket transaction, except that it is undertaken with foreign
exchange rather than with government securities.
The monetary authority can offset the impact of this inter­
vention on the monetary base, or sterilize it, by undertaking
offsetting transactions with other assets. Typically, central
banks do this by selling government securities or by altering
their lending to depository institutions until
A MB = - A DA.
If nations undertake intervention in close consultation, all
governments could sterilize intervention in a similar manner.
The process of intervening, especially if that intervention
is completely sterilized, will change the mix of foreign and
domestic assets held by central banks. Correspondingly,
sterilized central-bank intervention must change the mix of
domestic and foreign governmental assets held by the public.




From an academic perspective, the distinction
between sterilized and nonsterilized intervention,
upon which this controversy ultimately focuses,
is straightforward, if not trivial. A central bank
can easily stabilize its monetary base, despite any
exchange-market activity, by undertaking coun­
tervailing transactions through open-market
operations or through other conventional mone­
tary policy instmments. Nonsterilized interven­
tion involves no monetary offset and differs from
a typical monetary policy transaction only in that
a central bank alters its monetary base through a
change in its foreign asset holdings rather than
through a change in its domestic asset holdings
(see box 1).
Despite its academic clarity, the practical dis­
tinction between sterilized and nonsterilized in­
tervention is neither obvious nor simple. Most
countries, including the United States, claim to
sterilize their intervention, but do so in the sense
of not allowing their foreign exchange transac­
tions to interfere with monetary policy goals,
which may include an exchange-rate objective.
When these countries factor exchange-rate tar­
gets into their monetary policy objectives, they
need not offset their intervention activities cur­
rency unit per currency unit to confonn with this
definition of sterilized intervention. Although
U.S. officials and many others accept this defini­
tion of sterilized intervention, it seems to violate
the spirit of the term, because it no longer offers
a means of pursuing independent exchange-rate
and domestic monetary policy objectives.
Throughout this paper, I define intervention as
central-bank actions to influence exchange rates,
and I define monetary policy only in terms of
domestic price stability.
None of the G3 countries completely divorces
its intervention activities from its domestic m on­
etary policies; these countries either occasionally
adopt exchange-rate targets for their monetary
policies, or they do not always completely steril­
ize their intervention. Pauls (1990, p. 901), for
example, observes, “During times when the dol­
lar’s exchange value raised particular concern —
1977-79, 1984-85, and 1987 — it became a sig­
nificant factor in Federal Reserve decisions
regarding monetary policy.” Furlong (1989) also
shows that FOMC directives from 1986 through
1988 gave substantial weight to exchange rates.
Although the United States routinely sterilizes its
intervention, in accordance with the definition
mentioned in the previous paragraph, it does

not completely separate its exchange-rate and
monetary policies.
In a recent article, Neumann and von Hagen
(1991) show that the Gemían Bundesbank has
often permitted deviations between actual
money growth and targeted money growth
because of exchange-rate considerations. Fol­
lowing von Hagen (1989), they also argue that
when the mark is strong against both the dollar
and the Exchange-Rate-Mechanism currencies,
the Bundesbank does not permanently sterilize
its intervention.1
The situation is similar for Japan. Hutchison
(1988) indicates that the Bank of Japan factored
an exchange-rate objective into its monetary
policy decisions between 1978 and 1985, and
Takagi (1989) shows that since late 1985, the
Bank of Japan has allowed intervention to affect
its monetary base.

II. Intervention and
Exchange Rates
Economic theory suggests three linkages be­
tween intervention and exchange rates, which
differ in their implications for sterilized interven­
tion. Only one of these, the portfolio-adjustment
channel, allows completely sterilized interven­
tion to affect exchange rates pennanently.
Through a second mechanism, the signaling
channel, intervention can influence market ex­
pectations and, thereby, exchange rates. The lit­
erature presents two versions of signaling.
According to the first, intervention might supple­
ment monetary policy by strengthening a central
bank’s credibility with respect to its stated mone­
tary policy objectives. According to the second
version, if exchange markets are not information­
ally efficient, intervention that improves the flow
of information might influence exchange rates.
Central banks could sterilize such intervention,
but the effect would be temporary. A third chan­
nel, the monetary channel, views intervention as
a type of open-market operation that, by defini­
tion, does not admit even temporary sterilization.

for example, increases the amount of publicly
held U.S. Treasury securities. Under certain cir­
cumstances, such changes in asset stocks affect
exchange rates.
According to the asset-market approach to
exchange-rate determination, risk-averse inves­
tors diversify their portfolios across assets
denominated in different currencies.2 At equi­
librium, the expected nominal returns on
domestic and foreign assets are equal. Equation
(1) represents this in logarithmic form:
(1)

rt = r* - f t +st ,

where rt and r * are one-period domestic and
foreign interest rates, respectively; f t is the cur­
rent forward exchange rate for delivery one
period ahead; and st is the current spot ex­
change rate (foreign currency units per domes­
tic currency units).
If investors form their expectations rationally
and view domestic and foreign assets as perfect
substitutes, the forward exchange rate will equal
the expected future exchange rate. If, however,
investors believe that domestic and foreign
assets have different risk characteristics, then the
forward exchange rate will differ from the future
expected exchange rate by a risk premium. Let
(2)

f t = E(st +1) - 0,

which defines the domestic asset as the relatively
risky asset. Under the assumption that domestic
and foreign assets are imperfect substitutes, the
equilibrium condition becomes
(3)

rt = r* - E (st +j ) + st + 0.

As equation (3) indicates, investors compare
the return on a domestic asset with the return on
a foreign asset, which includes the interest earn­
ings, the expected change in the exchange rate
over the holding period, and a risk premium, 0.
Rearranging equation (3), one can express the
risk premium in terms of the interest-rate differ­
ential and the expected change in the exchange
rate:

Portfolio Adjustment
(4)
By sterilizing intervention through open-market
transactions, central banks change the relative
supplies of publicly held government debt. A
sterilized intervention to depreciate the dollar,

■

1 See also Kahn and Jacobson (1989) and, for a somewhat dif­

ferent opinion, Obstfeld (1983).


0 = (r,- r * ) + [£(si+ j ) - ^ ] .

Although economists lack a widely accepted
theoretical model of the risk premium, most
express it, among other things, as a positive

■

2 Edison (1990), Humpage (1986), Obstfeld (1988), and Weber
(1986) discuss this channel.

function of relative asset supplies.3 When the
relative supply of a country’s assets increases, we
expect that the risk premium on those assets also
increases. Either a widening interest-rate differ­
ential, or a widening spread between the ex­
pected future exchange rate and the current spot
exchange rate, or both, can accommodate a rise
in the risk premium, as equation (4) indicates.
The exact mix of interest-rate and exchangerate adjustments associated with a change in the
risk premium would seem important for evalu­
ating sterilized intervention. That both interest
rates and the expected exchange rate could
change is entirely plausible (Obstfeld [1988]).
Many studies, however, assume that because
sterilized intervention leaves the monetary base
unchanged, it also does not affect interest rates
(Edison [1990]). Still others assume that the mar­
ket determines the expected future exchange
rate exogenously, so that sterilized intervention
affects only the spot exchange rate. Although
these are testable assumptions, no studies ex­
plicitly address them. A policy to depreciate the
dollar could conceivably put upward pressure
on domestic interest rates.4
Economists have not investigated the influ­
ence of intervention on the underlying compo­
nents of the risk premium, because generally
they have found little evidence that intervention
operates through this channel. Researchers typi­
cally conclude that risk premiums exist and that
they vary through time, but they have not suc­
ceeded in relating these changes to relative asset
supplies.3 With near unanimity, researchers
have found the relationship to be either statisti­
cally insignificant or quantitatively unimportant.
Three notable exceptions are Kearney and Mac­
Donald (1986), who study intervention in Great
Britain and attribute their findings in part to capi­
tal controls during the estimation period, Domin­
guez and Frankel (1989), and Dominguez
(1990a). The last two studies, which look at the
heavy intervention by West Germany, Japan, and
the United States during the 1980s, are particu­
larly interesting. Using a two-equation, simulta­
neous system (discussed later), Dominguez and
Frankel find statistical evidence of portfolio
effects, which could have practical relevance un­
der some conditions. Studying a similar period,

■ 3

See Hodrick (1987) for a comprehensive survey of the literature
and Osterberg (1989) for a theoretical model that explicitly includes
intervention.

■ 4

The results of Dominguez (1990b) raise interesting questions
with respect to this issue (see appendix).

 ■ 5 Edison (1990) presents an excellent annotated bibliography that
covers portfolio-adjustment models.


however, Humpage and Osterberg (1990) find
mixed evidence of portfolio effects, but none of
the coefficients seem large (see appendix).
In attempting to explain the empirical evi­
dence, many economists observe that interven­
tion volumes are too small relative to the
outstanding stock of publicly held assets to have
a perceptible impact on portfolio decisions and
exchange rates. The total stock of publicly held
U.S. government securities, for example, was
nearly $2.3 trillion at the end of 1989. U.S. inter­
vention amounted to $22 billion that year, a
record volume, but it was less than 1 percent of
the total stock of publicly held U.S. securities.
Even if dollar interventions of the other 10 major
industrial countries are included, the total
amount represents only about 3 percent of the
total stock of publicly held debt.6
Empirical research on risk premiums is sub­
ject to another qualification that is important for
understanding intervention. Studies of risk pre­
miums assume that exchange markets are ration­
al in the sense of using all available information
and of not making systematic forecast errors.
Under this assumption, the market’s failure to
exploit all profitable interest-arbitrage oppor­
tunities must reflect a risk premium, not market
inefficiencies. Recent work on expectations, dis­
cussed below, casts doubt on the validity of this
assumption. If exchange markets are not per­
fectly efficient, what empirical studies interpret
as a time-varying risk premium could instead
reflect market inefficiencies. This would open
another door through which intervention might
affect exchange rates.

Signaling
Without a portfolio-adjustment effect, sterilized
intervention will not affect exchange rates per­
manently. Nevertheless, central banks might
maintain some temporary leverage in the market
if they could improve the flow of information to
the market and influence market expectations.
Some economists have suggested that sterilized
intervention functions as such.
When making exchange-rate quotations, per­
fectly efficient traders incorporate all available
information, including their best guess about
future policy developments. Reflecting this proc­
ess, economists typically specify the exchange
rate as a function both of contemporaneous fun­
damentals and of the expected future change in
the exchange rate:

■

6 See Ghosh (1989), Hutchison (1984), and Loopesko (1984).

(5)

st = z t + p [£(5, +, - s,\Q.t )] ,

where st is the current spot exchange rate; zt
represents a linear combination of fundamentals;
[£(5, +j - 5,1 Q.t ) ] is the expected change in the
exchange rate conditional on all information
currently available, Q r; and P is the elasticity of
the current exchange rate with respect to expec­
tations. Solving equation (5) by successively sub­
stituting in future values of the spot exchange
rate, one obtains

(6)

sr

(1 + p ) - 1 £ (P/1 + P ) ‘ £ ( z , + I. I Q , ) ,
/= i

which shows the spot exchange rate as the dis­
counted sum of expected future values of the
fundamentals.
Defining the relevant set of fundamentals is
not an issue here. In general, economists employ
factors that influence the supply and demand of
domestic and foreign money. For the purposes
of this paper, equation (6) is important because
it highlights the role of new information and
expectations in determining exchange rates, and
illustrates that intervention can affect current
spot exchange rates if it provides information
about fundamentals. Two such scenarios seem
plausible: Through intervention, a central bank
could reveal priority information about unantici­
pated changes in monetary policy to an other­
wise perfectly efficient market.8 Or, ignoring
the possibility of priority information, a central
bank might enhance the informational efficiency
of the private sector through intervention, if it
enjoyed unique economies in the acquisition
and processing of publicly available informa­
tion. I consider both of these cases below.

New Information
According to many economists, if sterilized inter­
vention purchases (sales) of dollars create the
expectation that the Federal Reserve System will
tighten (ease) monetary policy, the dollar will
appreciate (depreciate) as a result. Two recent
studies focus directly on this mechanism and

■

7 For a recent discussion, see Meese (1990).

■

8 In most countries, the Treasury or the Ministry of Finance ulti­

mately controls exchange-market intervention. Conceivably, intervention
could then signal changes in fiscal policies. Given both the relative inflexi­
bility of fiscal policy and the uncertainty about the effects of fiscal policy on
 exchange rates, I discount this possibility and discuss only monetary
http://fraser.stlouisfed.org/
policy signals.

Federal Reserve Bank of St. Louis

cast doubt on its universal applicability. Dom in­
guez (1988), for example, reports evidence that
following the October 1979 change in Federal
Reserve operating procedures, the System sig­
naled its intention to offset unanticipated money
changes through intervention and that this inter­
vention subsequently influenced exchange
rates. Over two adjacent time periods, she found
no evidence for signaling. Studying a more
recent period, Klein and Rosengren (1991) con­
clude that neither the Federal Reserve nor the
Bundesbank used intervention as a signal of
policy changes. They did find, however, that
coordinated intervention significantly affected
daily exchange rates between the Group of Five
Countries’ (G5) Plaza meeting in September
1985 and the Group of Seven Countries’ (G7)
Tokyo meeting in May 1986, but at no later
period.9 Unilateral U.S. intervention also af­
fected the exchange rate between the Tokyo
meeting and the Louvre meeting in February
1987. The authors conclude that markets initial­
ly read intervention as a signal, but eventually
learned that it was not intended as such.
If intervention is to affect exchange rates in a
signaling context, it must provide new informa­
tion about credible changes in future monetary
policies. These studies suggest that intervention,
at best, has fulfilled this task only once since the
late 1970s. Perhaps we should expect this. Policy
changes are not exogenous. Officials react to the
state of the economy and to exchange markets
in broadly discernible fashions, and private mar­
kets offer rewards to those who learn to predict
those reactions accurately. When the market
learns how central banks react, the scope for sig­
naling diminishes. This limits the extent to which
central banks can signal with intervention.

Why Signal with
Intervention?
The signaling aspect of intervention is provoca­
tive not only for the possible channel of influ­
ence it portends, but also because of a question
it raises: What possible signaling advantage does
intervention have over a simple announcement
of future policy intentions? Often, as already indi­
cated, studies of intervention find a significant
relationship after the Plaza meeting of the G5.10
■ 9 The Group of Five Countries (G5) are France, West Germany,
Japan, the United Kingdom, and the United States. The Group of Seven
Countries (G7) are the G5 plus Canada and Italy.
■

10 See also Marston (1988) for a discussion of signaling and a
comparison of intervention after the Plaza period with intervention during
the 1978 Carter dollar-defense period.

The dollar, however, began to fall against the
mark and yen prior to the meeting, in anticipa­
tion of possible policy changes. Immediately
after the meeting, the dollar fell precipitously,
even before the major central banks began inter­
vening. Through the subsequent days and weeks,
as I indicate in a previous paper (Humpage
[1988]), the dollar’s day-to-day movements were
not correlated with day-to-day intervention. In­
stead, the dollar responded to expectations gen­
erated by policy announcements and not to
official currency transactions. When policymak­
ers no longer reinforced or validated expecta­
tions of policy changes to promote a dollar
depreciation, the dollar’s decline slowed.
In attempting to explain the signaling mecha­
nism, many economists have argued that the im­
portance of intervention centers not on its ability
to herald policy changes, but on its ability to ce­
ment governments’ commitment to those policy
changes.11 Even when governments announce
an optimal policy today, they can face incentives
to renege on that policy tomorrow. Markets, of
course, realize this and factor into their expecta­
tions the likelihood that policymakers will not fol­
low through on their pronouncements. Policies
allowing no opportunity for backing down, conse­
quently, can have very different effects than simi­
lar policies that permit reneging.
To understand the role that intervention might
play in cementing credibility, consider an exam­
ple in which the Federal Reserve System tightens
monetary policy to eliminate inflation and to pre­
vent a continuing dollar depreciation. Markets
recognize that political pressure will weigh on
the System if, even temporarily, real interest rates
rise and unemployment results. This possibility
will temper market expectations. Intervention,
however, increases the costs of reneging on an
announced monetary policy change. Through
intervention, the System acquires a short posi­
tion in foreign currencies and a long position in
dollars. Should it not subsequently tighten mon­
etary policy sufficiently to appreciate the dollar,
the dollar value of its foreign-currency debts will
rise relative to its dollar assets. The United States
will experience losses on its foreign-currency
portfolio, which could have budgetary implica­
tions and could prove politically embarrassing.12
The importance of intervention profits in in­
fluencing central-bank monetary policy seems

11 Dominguez distinguishes between signaling, as discussed
above, and targeting, the sending of false signals. Because intervention
leaves the monetary base unaffected, it allows central banks the opportu­
nity to renege on policy. Central banks could not renege in this way very

often without destroying their credibility, but in certain circumstances,
http://fraser.stlouisfed.org/
sending false signals could prove effective. See Dominguez (1990b).

related to their size. Table 1 lists the reported
Federal Reserve System profits from its foreignexchange operations since 1975.13 This table in­
cludes both realized profits, which reflect actual
currency transactions, and unrealized profits,
which result from currency swings that alter the
value of foreign-exchange inventories.
Judging from the pattern and size of past prof­
its, intervention probably has not significantly
influenced the costs of reneging on Federal
Reserve policy. Although on balance the System
has shown a profit, it reported losses for 10 of the
15 years listed in the table without obvious politi­
cal fallout. The reason is that balances associated
with intervention have typically been small rela­
tive to profits remitted to the Treasury (usually
less than 10 percent) and are only a trivial com­
ponent of overall federal budget receipts (typi­
cally less than 2.5 percent).
In recent years, however, the System’s portfo­
lio of foreign currencies has increased sharply.
To accommodate the rise, the Federal Open Mar­
ket Committee increased the System’s authoriza­
tion for holding a net open position in foreign
exchange to $25 billion in early 1990 from $12
billion in early 1989. This steep rise in holdings
of foreign currency has greatly increased the
chances of substantial unrealized losses should
the dollar appreciate sharply.14 The swings in
profits could reach levels at which their practical
significance might become important. As
Obstfeld (1988, p. 43) notes, when the federal
budget deficit is large, even marginal contribu­
tions become significant. The extent to which
such considerations might influence monetary
policy in the United States is unclear.

Signals and
International
Cooperation
Intervention might not provide a credible signal
of future monetary policy in a particular country,
but it could indicate to the market and to the
participating governments the willingness of

■

12 As stated in Henderson (1984, p. 391), “ ... losses on foreign
exchange positions can lead to significant political problems for the
authorities. Thus, if the authorities undertake an intervention policy which
would generate foreign exchange losses if their pronouncements about
future monetary policy were not put into effect, there might be more
reason for private agents to take these pronouncements seriously.”

■

Federal Reserve Bank of St. Louis

■

13 Table 1 contains published data. Leahy (1989) attempts to cap­
ture the opportunity costs of intervention profits more closely.
■

14 Leahy (1989) suggests how large portfolios make profits sensi­
tive to exchange-rate changes.

KB
TABLE

1

Federal Reserve Profits from Foreign
Exchange Operations and Their
Relationship to Treasury Receipts3
Federal
Reserve
Profitsb

Y ear
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985d
1986d
1987
1988
1989
a.
b.
c.
d.

$-241.8
-25.1
-146.4
-505.7
-3.7
96.1
-306.0
-149.6
-456.3
-454.8
1,210.0
1,970.0
1,804.3
-510.9
1,204.2

Paym ents
to T reasury
$ 5,382.1
5,870.5
5,937.1
7,005.8
9,278.6
11,706.4
14,023.7
15,204.6
14,228.8
16,054.1
17,796.5
17,803.5
17,738.9
17,364.3
21,646.4

R atio o f P rofits
to T reasury
Paym ents
-4.49%
-0.43
-2.47
-7.22
-0.04
0.82
-2.18
-0.98
-3.21
-2.83
6.80
11.07
10.17
-2.94
5.56

T otal
Receipts0

R atio o f
Paym ents to
T otal Receipts

$280,642
318,508
365,199
416,110
480,526
533,017
622,485
608,822
612,915
683,209
745,084
781,869
868,996
925,979
979,923

1.92%
1.84
1.63
1.68
1.93
2.20
2.25
2.50
2.32
2.35
2.39
2.28
2.04
1.88
2.21

Profits, payments, and receipts are expressed in millions o f dollars.
Includes realized and unrealized profits.
Total of off-budget and on-budget items.
Unrealized profits; total profits not reported as a separate item.

SOURCES: “Income and Expenses o f Federal Reserve Banks,” Board o f Governors o f the Federal Reserve System, A n nu al Report, years 19751989; and “On-budget and Off-budget Receipts by Source,” Table FFO-2, Department o f the Treasury, Treasury Bulletin, years 1975-1989-

countries to coordinate their macroeconomic
policy. Coordinated intervention could enhance
the credibility of an announced coordinated
monetary policy, because it might indicate that
other countries found the proposed policy
change appropriate and that they would not
attempt to offset the exchange-rate implications.
Indeed, some empirical results suggest that
coordinated intervention is more effective than
unilateral intervention. Dominguez (1988), for
example, provides evidence in favor of this
case. Moreover, Klein and Rosengren (1991)
find a larger effect from coordinated interven­
tion. Loopesko (1984), despite somewhat less
conclusive results, finds that coordinated West
German intervention had a significantly differ­
ent effect than noncoordinated intervention.
Humpage (1989) and Humpage and Osterberg
(1990), on the other hand, could not attach spe­
cial significance to coordination.
In a similar vein, intervention could provide a
quick, simple, and relatively inexpensive way
for countries to signal to one another their contin­
 uing willingness to coordinate macroeconomic


policies. Game theoreticians recognize that
players will often act in a cooperative manner,
even without a formal enforcement mechanism,
if each perceives cooperation to be to his ad­
vantage and if each believes that the others will
not revert to a noncooperative behavior. Formal
enforcement structures do not exist to ensure in­
ternational macroeconomic policy coordination.
One might then view intervention as a signal to
other countries, not of a future policy change,
but of an ongoing commitment to previously
agreed-upon policy changes; that is, a signal that
the intervening country will not revert to noncooperative behavior.
The G5 Plaza accord in September 1985, for
example, focused on eliminating current account
imbalances in West Germany, Japan, and the
United States, with the implication that these
countries would undertake appropriate macroeconomic policies. Given the lack of evidence in
support of prolonged sterilized intervention, one
might view the subsequent intervention, at least
in part, as a signal to do just that. By late October
of that year, however, the United States and

West Germany were not reinforcing the G5
agreement with additional policy changes, and
by November, both countries expressed concern
about the underlying implications of the G5 ini­
tiatives for their domestic monetary policies.
Joint intervention ended in early November,
and the United States refrained from intervening
until early 1987. During 1986, despite some joint
changes in discount rates, international policy
was not undertaken cooperatively. As Frankel
(1990, p. 24) notes,
[James] Baker was re­
peatedly quoted in the press as ‘talking the dol­
lar down,’ in large part as a weapon to induce
the trading partners to cut interest rates.”

Market Inefficiency
Economists characterize exchange markets as in­
formationally efficient, because traders face strong
incentives to consider all available information.
Nevertheless, a sufficient amount of anecdotal
and empirical evidence suggests that exchange
markets are not perfectly efficient. If central
banks enjoy an informational advantage, they
may intervene and improve market efficiency.
Grossman and Stiglitz (1980) argue that if in­
formation is costly to discover and to transmit,
exchange rates sometimes must reflect informa­
tional inefficiencies. These inefficiencies explain
the sizable expenditures and frequent large prof­
its of leading market participants. Hung (1991a)
contends that many market participants do not
base their trades on generally recognized eco­
nomic determinants of exchange rates. Instead,
so-called noise traders assess recent exchangerate trends or “psychological factors,” whose
long-term econom ic significance is not always
obvious. Hung states that because noise traders
use broadly similar techniques and often
respond to the same news, they can sometimes
dominate exchange markets, creating band­
wagon effects and moving the exchange rate
away from levels consistent with economic
fundamentals. Although such activities create
profit opportunities for those who trade on fun­
damentals, Hung notes that, in the short term at
least, the potential rewards might not be great
enough to justify the costs and the risks.
A number of empirical studies also suggest
that information inefficiencies do exist. Loopesko (1984), in an early study of daily intervention,
finds that lagged independent and dependent
variables help to explain day-to-day unexploited
arbitrage profits. This suggests inefficiency in
the processing of information. More recently, in
 an important study that questions the rationality


of exchange-market expectations, Frankel and
Froot (1987) find evidence that survey respon­
dents exhibit biased expectations and that
bandwagon effects exist, but are stabilizing. In
an extension of this work that uses moredetailed survey data, Ito (1990) determines that
individuals and industries hold dissimilar expec­
tations about future exchange-rate movements,
and that industrial groups exhibit “wishful think­
ing” with respect to forecasts. His results ques­
tion the assumption that expectations are
formed rationally and lend further support to
the view that bandwagon effects occur in the
short run. Also analogous in opening a role for
intervention, but not strictly the same, other in­
vestigators note the possible existence of multi­
ple exchange-rate equilibria, of exchange-rate
overshooting, and of bubbles, even allowing for
rational expectations.
The existence of temporary informational
inefficiencies could create an occasion when
central-bank intervention might improve the
functioning of exchange markets, even without
priority information about future monetary poli­
cies. Monetary authorities have long recognized
this possibility. According to the Jurgensen
Report (1983, p. 21), “The authorities in each of
the Summit countries at times undertook large
scale intervention when they judged that market
participants had not taken full account of funda­
mental factors, [or] had only reacted slowly to
changes in fundamentals....” Official exchange
transactions following the G5 meeting at the
Plaza in September 1985 adopted this view; dele­
gates characterized exchange rates as inconsis­
tent with underlying fundamentals.
For intervention to improve exchange mar­
kets by dampening or eliminating near-term
exchange-rate deviations from their equilibrium
paths, central banks must have timely and pre­
cise information about market fundamentals and
their relationship to exchange rates. Otherwise,
the central banks could not determine that
exchange-rate movements represented a devia­
tion from equilibrium rather than an adjustment
to a new equilibrium. As already noted, attempts
to relate market fundamentals to exchange rates
have not been very successful.
Although inefficiencies may exist in the short
mn, persistent deviations from equilibrium
eventually will create profit opportunities suffi­
cient enough to offset the risks for those who
trade on fundamentals. Little empirical evidence
exists to suggest that short-term inefficiencies
disrupt trade or investment flows. Many econo­
mists claim to have identified periods (such as
1984) when exchange rates departed from

fundamental levels and disrupted trade, but
such cases are exceptional.
Hung (1991a, 1991b) also notes that to offset
market inefficiencies, central banks must have
timely information about the trading strategies of
noise traders and should conduct their opera­
tions in secrecy. Humpage (1984) suggests that
knowledge of official intervention can have
destabilizing effects if the market interprets inter­
vention purchases of dollars, for example, as evi­
dence that the dollar is fundamentally weak. This
seems possible in the case of noise trading. Hung
theorizes that central banks undertake such inter­
vention in secrecy, because if they convince the
noise traders that private participants are affect­
ing the market trend, then the noise traders
might sustain the exchange-rate movement.
These comments imply that the occasions on
which a central bank might successfully exploit
market inefficiencies are probably rare. They do
not belie the possibility that intervention could
operate through such a channel. Indeed, some
preliminary papers by Dominguez and Frankel
(1989), Dominguez (1990a), and Hung (1991b)
offer tentative support. All of these papers incor­
porate survey data, which have shown informa­
tional inefficiencies in exchange markets, and
they all find some evidence that intervention can
significantly affect exchange rates.
Dominguez and Frankel estimate a twoequation simultaneous system that includes a
portfolio-adjustment equation. As noted previ­
ously, they also find a significant influence
through the portfolio channel. In evaluating the
quantitative significance of their results, they sug­
gest that this channel alone might not be impor­
tant, but when combined with an effect on
expectations, the magnitude of the influence
could become decisive.
Hung (1991b) regresses unexpected exchangerate changes on numerous “news” variables and
on U.S. intervention cumulated over the survey
horizon. After deriving expected volatility from
currency-option prices, she also regresses unex­
pected changes in exchange-rate volatility on
the news variables and on intervention. Hung’s
results are mixed, but do show significant
exchange-rate effects.

General
Observations on the
Empirical Evidence
An appendix to this paper briefly summarizes
recent literature covering G3 intervention. These
 studies lend some support to signaling, in the


sense that they all find periodic correlations
among the relevant variables. What they do not
find is a persistent relationship between inter­
vention and exchange rates across time periods.
As Meese (1990) notes, economists have en­
joyed little success in specifying a reliable model
of exchange-rate determination. This limits our
conclusions about the efficacy of intervention,
especially sterilized intervention. In addition, vir­
tually none of the work on intervention derives
from solid structural models, incorporating theo­
retical interactions among intervention, investors’
portfolios, central-bank monetary policies, or
expectations.13 The results are consistent with
many stories about how intervention works and
how failure to find an influence might reflect an
inadequate specification. The task of evaluating
intervention would be much easier if we had reli­
able guides to the equilibrium path of exchange
rates and to the formulation of expectations.16
The lack of a strong model increases the
danger that any observed relationship between
intervention and exchange rates could depend
on factors not directly measured in the experi­
ment: statements by officials, the degree of
market uncertainty, the state of the economy at
home and abroad, other domestic policies, or
international agreements on policy. This is par­
ticularly true with high-frequency data, since
most economic variables are not measured
more frequently than monthly. Often, these con­
ditions and events in themselves enhance the
credibility of policy announcements or convey
information. If other factors are sometimes cor­
related with intervention, one might easily ob­
serve periodic, short-lived effects on exchange
rates. Our ability to draw inferences about sig­
naling from such correlations is limited.

Nonsterilized
Intervention
Although sterilized intervention could tempo­
rarily affect exchange rates under some rather
unusual circumstances, central banks must link
their exchange-rate objectives with their mone­
tary policies in order to influence rates regularly
and permanently. Most central banks, including
the Federal Reserve System, at times seem to
operate in this fashion, either by not fully steril­
izing their intervention or by occasionally adopt­
ing exchange-rate objectives for their monetary

■

15 See Osterberg (1989) for a model of the risk premium that
specifically introduces intervention.

■

16 This paragraph reflects comments from Bonnie Loopesko.

21

policies. This section considers the possible con­
flicts that nonsterilized intervention can cause.17
Marston (1985) provides a comprehensive
review of stabilization policy, indicating how
different assumptions about the formulation of
expectations, allowances for wage indexing,
inclusions of wealth, and the extent of asset sub­
stitutability modify conclusions about exchangerate policies drawn from small open-economy
macroeconomic models. Although the qualifica­
tions and pemiutations are extensive, some gen­
eral conclusions pertain to discussions of the
appropriateness of nonsterilized intervention.
Most notably, Marston’s survey shows that
less exchange-rate flexibility promotes overall
price stability only when temporary, domestic
monetary (or financial) shocks predominate. In
this case, using nonsterilized intervention to
smooth exchange rates will not conflict with
price stability, because monetary shocks raise or
lower prices as they depreciate or appreciate a
nation’s currency.18
W hen real economic shocks predominate,
however, greater exchange-rate flexibility pro­
motes overall price stability, although the case
seems weaker for supply shocks than for
demand shocks.19 Under such circumstances,
attempting to smooth exchange rates might
actually increase the price movements necessary
to compensate for the shocks, because flexible
rates aid price movements in eliminating excess
supply or demand. Moreover, in responding to
real shocks, intervention might reduce the credi­
bility of a central bank’s long-term commitment
to price stability, by demonstrating that central
banks would compromise that objective.
Marston’s survey also weakens the argument
that floating exchange rates insulate an economy
from foreign shocks, by showing the large num ­
ber of possible ways that exchange-rate changes
might transmit these shocks. Nevertheless, his
survey does not argue that fixed rates and inter­
vention are superior to floating exchange rates
on this score.
Given that no single exchange-rate regime pro­
motes stability in all cases, a hybrid exchange-rate
regime, with the degree of intervention contin­
gent on the predominant nature of shocks,
might seem optimal. Such would indeed be the

■

17 For an interesting discussion of nonsterilized intervention,
expectations, and target zones, see Klein (1989) and Klein and Lewis
(1991).

■

18 Glick and Hutchison (1990) provide an easy-to-read exposition,
which uses a simple model.


http://fraser.stlouisfed.org/
■ 19 See Glick and Hutchison (1990).
Federal Reserve Bank of St. Louis

case in a world where the central bank had per­
fect information about the nature of economic
disturbances. Unfortunately, economists dis­
agree on whether monetary or real shocks have
been primarily responsible for the variation in
real and nominal exchange rates since the early
1970s. Even in cases where monetary shocks
predominate, the proper intervention response
is not clear. Central banks should smooth
exchange-rate movements in some cases and
accentuate them in others.
The richness of Marston’s survey suggests,
whether intentionally or not, that economists
do not agree on a specific variant of the openeconomy macroeconomic model.20 Conse­
quently, one cannot reach an unequivocal
conclusion about the benefits of targeting ex­
change rates with monetary policy. At best, the
literature offers a qualified recommendation for
nonsterilized intervention when a domestically
produced disturbance is clearly monetary in na­
ture. Such instances do occur and are some­
times readily discernible. In the 1977-79 period,
for example, the dollar depreciated sharply as
U.S. inflation accelerated relative to inflation
abroad and as markets lost confidence in our
willingness to eliminate it. A monetary contrac­
tion would have promoted a stronger dollar and
stable prices.

III. The Implications
for Policy
Economists have offered various theoretical argu­
ments in support of sterilized intervention. Some
researchers have found statistically significant
and, at times, quantitatively important relation­
ships between intervention and exchange rates.
I have argued that this evidence does not en­
dorse an active intervention policy, as the G3
countries have often conducted in recent years.
The empirical evidence generally does not find
an economically significant relationship between
the risk premium and intervention, as required by
the portfolio-adjustment theory. This finding sug­
gests that intervention, at least in volumes typically
observed, cannot permanently alter exchange
rates, independent of monetary policy. Central
banks must weigh exchange-rate objectives in tan­
dem with their inflation objectives.
Similarly, I question the idea that centralbank intervention provides a credible market
signal of future policy intentions. Central banks
do not generally seem to operate in this man­
ner, and intervention does not have an obvious

■

20 See also Frankel and Rockett (1988).

comparative advantage over other methods of
ensuring monetary policy credibility. Most im­
portant, such intervention cannot remain steril­
ized and effective; it does not constitute an
independent policy instmment.
One might interpret the portfolio-adjustment
and the monetary-signal arguments for interven­
tion as requiring much larger magnitudes of
intervention. The United States, for example,
has built up its foreign-exchange reserves since
the early 1980s to approximately $42 billion.
While this might enhance our ability to intervene
through these rather questionable channels, it
also greatly increases our exposure to foreignexchange losses.
Intervention, however, might play a role in in­
ternational macroeconomic policy coordination,
serving as a signal of continuing cooperation.
Countries may not even intend such intervention
primarily to influence exchange rates, although
such an effect could be a desired side benefit.
Although I know of no research specifically
directed at this issue, the hypothesis is not incon­
sistent with recent patterns of intervention. It
also may explain the interest in coordinated in­
tervention, which other theories of intervention
do not require, except to reduce the overall costs.
A recent body of literature suggests that
foreign-exchange markets are at times informa­
tionally inefficient and that intervention, by im ­
proving market efficiency, could influence
exchange rates. Indeed, some of the most inter­
esting recent empirical support is consistent
with this explanation. Nevertheless, how impor­
tant are these inefficiencies? Do they obviously
disrupt international commerce? Do central
banks regularly meet the informational require­
ments to exploit this situation successfully? At
best, this literature seems to support relatively
small, secretive interventions under conditions
of extreme market disorder.
Although the scope for affecting exchange
rates through sterilized intervention seems nar­
row, nearly all economists recognize that
countries can influence nominal exchange rates
through their monetary policies. The literature
indicates, however, that nonsterilized interven­
tion can conflict with domestic price stability.
Only when domestic monetary shocks create
exchange-market disturbances will intervention
remain consistent with price stability. Although
this observation justifies targeting exchange
rates with monetary policy under certain cir­
cumstances, it does not justify pursuing that
policy through cunency purchases in the ex­
change market, rather than through typical
 open-market operations. A small country


lacking well-developed secondary markets in
government bonds might find such intervention
useful for conducting its monetary policy. The
Swiss have traditionally conducted monetary
policy through foreign-exchange purchases.
Nevertheless, the benefits to larger countries,
such as West Germany, Japan, and the United
States, are not apparent.

Appendix: Studies
of Recent G3
Intervention
This appendix summarizes recent empirical stud­
ies of U.S., West German, and Japanese interven­
tion. One can interpret the results as broadly
relating to a signaling approach, either because
they incorporate high-frequency data or because
their methodology suggests this approach. Edi­
son (1990) presents a comprehensive survey of
intervention literature, including earlier papers,
research on the portfolio-adjustment mechanism,
and research on intervention profits.
Humpage (1984) investigates dollar-mark
interventions by the United States and other
major developed countries for a one-year period
following President Carter’s November 1, 1978,
intervention efforts. Using simultaneous BoxJenkins techniques, he finds that both unantici­
pated U.S. intervention against marks and
unanticipated foreign intervention against dollars
were significantly correlated with the closing
exchange rate. The results, however, suggest that
official dollar purchases resulted in a dollar de­
preciation. The coefficients were economically
insignificant. The reaction function suggests that
central banks attempted to smooth exchangerate movements, or leaned against the wind.
Loopesko (1984) finds that lagged, cumulative
intervention was related to changes in ex-post
arbitrage profits in 11 out of 24 cases. The stron­
gest evidence is for Canadian dollars, West Ger­
man marks, Japanese yen, and French francs in
sample periods extending from late 1978 through
1981. This supports the portfolio-adjustment
channel. Moreover, lagged exchange rates or
profits were significant in about 21 of the cases,
implying less-than-perfect market efficiency. In
some cases for West Germany (when passive in­
tervention was eliminated from the data), the ef­
fect of coordinated intervention was different
from the effect of uncoordinated intervention.
In a unique and interesting paper, D om in­
guez (1988) studies the ability of intervention to
signal monetary policy intentions between
January 1977 and February 1981. She regresses

23

intervention on unanticipated money, which
she calculates using survey data, and also
regresses exchange-rate changes on interven­
tion. Following the Volcker shift in operating
procedures in October 1979, intervention sig­
naled the Fed’s intention to offset unanticipated
fluctuations in money. This intervention bore a
significant and correctly signed relationship to
the exchange rate, suggesting that the market
believed the signal. The results for the CarterMiller anti-inflation period, beginning in Novem­
ber 1978, and for the Carter-Volcker credit
control period, beginning in March 1980, do not
support the signaling hypothesis.
Humpage (1989) looks at U.S. intervention,
measured with dummy variables, from August
1984 through August 1987 and finds only three
instances when intervention clearly affected
exchange rates. In all cases, the association was
with the first official transaction after a period of
no intervention and followed an unusual event
or announcement. This intervention also tended
to lean unth the wind. The impact seemed short­
lived and not associated with subsequent official
transactions following the initial intervention.
Using actual intervention data instead of dummy
variables over similar time periods, Humpage
(1989) reexamines these findings. The only dif­
ference is that initial intervention sometimes ap­
peared significant even if not associated with an
unusual event or policy announcement. A dis­
tinction between coordinated and unilateral in­
tervention was not important. These coefficient
estimates could contain a simultaneity bias.
Dominguez and Frankel (1989) estimate a
two-equation simultaneous system that con­
siders both signaling and portfolio-adjustment
channels over two subperiods: November 1982
to October 1984 and October 1984 to December
1987. The models use survey data for values of
expected future exchange rates. The portfolio
equation considers intervention both in absolute
terms and relative to wealth. The researchers
either cumulated intervention over the expecta­
tions horizon or from the start of the sample, or
entered the individual intervention prior to the
survey measure. The evidence offers support to
the portfolio channel.
A second equation models expectations as
extrapolations from past exchange-rate changes,
but includes a dummy variable for reported
“news” of any official actions to affect the
exchange rate and a measure of reported inter­
vention (the intervention series times the news
dummy). Both of these intervention variables
often prove significant, but the news dummy
 does so more often. The authors’ quantitative


analysis of the results suggests that intervention
that has only a portfolio effect is quantitatively
insignificant, but intervention that also alters ex­
pectations can be quantitatively significant.
Dominguez and Frankel focus on U.S. and
West German intervention to affect dollar-mark
exchange rates, because Japanese intervention
data were not available. Dominguez (1990a) ex­
tends this work by including U.S. and Japanese
intervention to affect the dollar-yen exchange
rate from January 1985 to December 1988. The
results were broadly similar.
Dominguez (1990b) investigates intervention
and ex-post arbitrage profits from January 1985
to December 1987. Various subperiods show
different results with respect to the significance
and the sign of the coefficients for unilateral
and coordinated intervention. Overall, how ­
ever, coordinated intervention is more apt to
show a significant and correctly signed coeffi­
cient than is unilateral intervention. Sometimes,
notably in the G5 period (September through
December 1985), the coefficient on coordinated
intervention appears to exert an economically
significant effect.
Although these conclusions hold for over­
night transactions, they appear more often over
one-month and three-month investment
horizons. W hen the results hold only for the
longer horizons, intervention dollar sales (pur­
chases) must raise (lower) domestic interest
rates, lower (raise) foreign interest rates, or ap­
preciate (depreciate) the future exchange rate,
but do not affect the spot exchange rate.
Humpage and Osterberg (1990), using a
generalized autoregressive conditional heteroscedasticity (GARCH) model, examine the ef­
fects of daily U.S., West German, and Japanese
intervention on ex-post arbitrage profits from
January 3, 1983, to February 19, 1990. Follow­
ing Loopesko (1984), they find cumulative inter­
vention associated with a very small, significant
increase in the mark-dollar risk premium, but
find cumulative intervention in the yen—dollar
market to be insignificant. The variance equa­
tion does not include cumulative intervention.
Following Dominguez (1988), they differentiate
between coordinated and unilateral interven­
tion, and do not cumulate the data. Coordinated
intervention was not significant in any mean or
variance equations, nor was unilateral West Ger­
man intervention. Unilateral Japanese interven­
tion was significant in the mean with the wrong
sign, and in the variance with a positive coeffi­
cient. Unilateral U.S. intervention was not sig­
nificant in the mark-dollar equations, but was

significant in the yen-dollar, conditionalvariance equation with a negative coefficient.
Building on theoretical arguments for inter­
vention when noise trading persists, Hung
(1991b) investigates the impact of U.S. interven­
tion on both the level and volatility of exchange
rates. She regresses unexpected exchange-rate
changes on net intervention cumulative up to
the realization of the expectation, and on four
common news variables: unanticipated trade
deficits, unemployment results, producer-price
inflation, and changes in interest-rate differen­
tials. Hung measures volatility by the standard
deviation of exchange rates over two-week in­
tervals and regresses unexpected exchange-rate
volatility on the news variables and on cumula­
tive gross intervention. (Hung estimates ex­
pected exchange-rate volatility from option
prices.) The tests span two subperiods: Decem­
ber 1984 to December 1986, and January 1987
to December 1989- The results are mixed. U.S.
intervention affects the yen exchange rate in
both subperiods and influences the mark in the
second period. U.S. intervention lowers
exchange-rate volatility in the first period, but
otherwise raises volatility. Hung interprets the
disparate results as indicating that the effective­
ness of intervention depends on market condi­
tions and on the skill of those intervening.
Klein and Rosengren (1991) consider inter­
vention from the September 1985 Plaza agree­
ment to the October 1979 stock-market crash,
proxying official transactions with dummies
based on newspaper accounts. Interventions
did not precede monetary policy changes with
sufficient frequency to suggest that the United
States or West Germany intended them as a sig­
nal of future monetary policy changes. Never­
theless, coordinated intervention did have a
statistically significant and correctly signed im­
pact on daily exchange-rate changes between
the Plaza and Tokyo (May 1986) summits. Uni­
lateral U.S. intervention influenced the exchange
rate between the Tokyo and Louvre summits.
Klein and Rosengren conclude that the market
initially thought of intervention as a policy sig­
nal, but soon learned that central banks were
not using it as such.




References
Dominguez, Kathryn M. “The Informational
Role of Official Foreign Exchange Interven­
tion Operations: An Empirical Investigation,”
Harvard University and National Bureau of
Economic Research, unpublished m anu­
script, November 1988.
______ . “Have Recent Central Bank Foreign Ex­
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the Yen?” Harvard University and National
Bureau of Economic Research, unpublished
manuscript, August 1990a.
______ . “Market Responses to Coordinated
Central Bank Intervention,” CamegieRochester Conference Series on Public
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______, and Jeffrey Frankel. “Does Foreign Ex­
change Intervention Matter? Disentangling
the Portfolio and Expectations Effects for the
Mark,” Harvard University and National
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manuscript, December 1989-

Edison, Hali. “Foreign Currency Operations: An
Annotated Bibliography,” Board of Gover­
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Frankel, Jeffrey A. “The Making of Exchange
Rate Policy in the 1980s,” National Bureau of
Economic Research, Working Paper No.
3539, December 1990.

______, and Kenneth A. Froot. “Using Survey
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Exchange Rate Expectations,” American
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1987), pp. 133-53.

Frankel, Jeffrey A., and Katharine E. Rockett.
“International Macroeconomic Policy Coordi­
nation W hen Policymakers Do Not Agree on
the True Model,” Am erican Economic Re­
view, vol. 78, no. 3 (June 1988), pp. 318-40.

Furlong, Frederick T. “International Dimen­
sions of U.S. Economic Policy in the 1980s,”
Federal Reserve Bank of San Francisco,
Economic Review, Spring 1989, pp. 3-13-

Ghosh, Atish R. “Is It Signalling? Exchange
Intervention and the Dollar-Deutschemark
Rate,” Princeton University, unpublished
manuscript, September 1989-

______, and William P. Osterberg. “Interven­
tion and the Foreign Exchange Risk Premium:
An Empirical Investigation of Daily Effects,”
Federal Reserve Bank of Cleveland, unpub­
lished manuscript, October 1990.

Glick, Reuven, and Michael Hutchison. “Ex­
change Rates and Monetary Policy,” Federal
Reserve Bank of San Francisco, Economic
Revieiv, Spring 1990, pp. 17-29.

Hung, Juann H. “Noise Trading and the Effec­
tiveness of Sterilized Foreign Exchange Inter­
vention,” Federal Reserve Bank of New York,
Research Paper No. 9111, March 1991a.

Grossman, Sanford J., and Joseph E. Stiglitz.
“O n the Impossibility of Informationally Effi­
cient Markets,” Am erican Economic Review,
vol. 70, no. 3 (June 1980), pp. 393-408.

Hagen, Jurgen von. “Monetary Targeting with
Exchange Rate Constraints: The Bundesbank
in the 1980s,” Federal Reserve Bank of St.
Louis, Review, September/October 1989, pp.
53-69.

Henderson, Dale W. “Exchange Market Inter­
vention Operations: Their Role in Financial
Policy and Their Effects,” in John F.O. Bilson
and Richard C. Marston, eds., Exchange Rate
Theory a n d Practice. Chicago: University of
Chicago Press, 1984.

______ . “The Effectiveness of Sterilized U.S. For­
eign Exchange Intervention— An Empirical
Study Based on the Noise Trading Approach,”
Federal Reserve Bank of New York, Working
Paper No. 9118, May 1991b.

Hutchison, Michael M. “Intervention, Deficit
Finance and Real Exchange Rates: The Case
of Japan,” Federal Reserve Bank of San Fran­
cisco, Economic Review, Winter 1984, pp.
27-44.
______ . “Monetary Control with an Exchange
Rate Objective: The Bank of Japan, 197386,vJo u rn a l o f International Money a n d
Finance, vol. 7, no. 3 (September 1988), pp.
261-71.

Hodrick, Robert J. The Em pirical Evidence on
the Efficiency o f Forward a n d Futures
Foreign Exchange Markets. Chur, Switzer­
land: Harwood Academic Publishers, 1987.

Ito, Takatoshi. “Foreign Exchange Rate Expec­
tations: Micro Survey Data,” Am erican Eco­
nom ic Review, vol. 80, no. 3 (June 1990),
pp. 434-49.

Humpage, Owen F. “Dollar Intervention and
the Deutschemark-Dollar Exchange Rate: A
Daily Time-Series Model,” Federal Reserve
Bank of Cleveland, Working Paper 8404, Sep­
tember 1984.

Jurgensen, Philippe (Chairman). “Report of
the Working Group on Exchange Market In­
tervention,” processed March 1983.

Kahn, George A., and Kristina Jacobson. “Les­
______ . “Exchange-Market Intervention: The
Channels of Influence,” Federal Reserve Bank
of Cleveland, Economic Review, vol. 22, no.
3 (1986 Quarter 3), pp. 2-13-

sons from West German Monetary Policy,”
Federal Reserve Bank of Kansas City, Eco­
nom ic Review, vol. 74, no. 4 (April 1989),
pp. 18-35.

______ . “Intervention and the Dollar’s Decline,”
Federal Reserve Bank of Cleveland, Eco­
nom ic Review, vol. 24, no. 2 (1988 Quarter
2), pp. 2-16.

Kearney, Colm, and Ronald MacDonald. “In­

______ . “O n the Effectiveness of ExchangeMarket Intervention,” Federal Reserve Bank
of Cleveland, unpublished manuscript, May
1989.




tervention and Sterilisation under Floating
Exchange Rates: The UK 1973 -1983,” Euro­
pean Economic Review, vol. 30 (1986), pp.
345-64.

Klein, Michael W. “Big Effects of Small Interven­
tions: The Informational Role of Intervention
in Exchange Rate Policy,” Clark University,
Working Paper No. 89-16, November 1989-

______, and Karen K. Lewis. “Learning About
Intervention Target Zones,” unpublished
manuscript, January 1991-

Pauls, B. Dianne. “U.S. Exchange Rate Policy:
Bretton Woods to Present,” Federal Reserve
Bulletin, vol. 76, no. 11 (November 1990),
pp. 891- 908.

Klein, Michael W., and Eric Rosengren. “For­
eign Exchange Intervention as a Signal of
Monetary Policy,” Federal Reserve Bank of
Boston, New England Economic Review,
forthcoming, May/June 1991.

Takagi, Shinji. “Foreign Exchange Market Inter­
vention and Domestic Monetary Control in
Japan, 1973-89,” International Monetary
Fund Research Department, Working Paper
WP/89/101, December 1989.

Leahy, Michael P. “The Profitability of U.S. In­
tervention,” Board of Governors of the Fed­
eral Reserve System, International Finance
Discussion Papers No. 343, February 1989-

Loopesko, Bonnie E. “Relationships among Ex­
change Rates, Intervention, and Interest
Rates: An Empirical Investigation,”Jo u rn a l o f
International Money a n d Finance, vol. 3
(December 1984), pp. 257-77.

Marston, Richard C. “Stabilization Policies in
Open Economies,” in Ronald W. Jones and
Peter B. Kenen, eds., Handbook o f Interna­
tional Economics, Vol. II. Amsterdam: Else­
vier Science Publishers, 1985, pp. 859-916.
_______. “Exchange Rate Coordination,” in Mar­
tin Feldstein, ed., International Economic
Cooperation. Chicago: University of Chicago
Press, 1988, pp. 79-136.

Meese, Richard. “Currency Fluctuations in the
Post-Bretton Woods Era "Jo u rn a l o f Eco­
nom ic Perspectives, vol. 4, no. 1 (Winter
1990), pp. 117-34.

Neumann, Manfred J.M., and Jurgen von
Hagen. “Monetary Policy in Germany,” in
Michele Fratianni and Dominik Salvatore,
eds., H andbook on Monetary Policy. Green­
wood Press, forthcoming, 1991.

Obstfeld, Maurice. “Exchange Rates, Inflation,
and the Sterilization Problem,” European Eco­
nom ic Review, vol. 21 (1983), pp. 161-89.
______ . “The Effectiveness of Foreign-Exchange
Intervention: Recent Experience,” National
Bureau of Economic Research, Working
Paper No. 2796, December 1988.

Osterberg, William P. “Intervention and the
Risk Premium in Foreign Exchange Rates,”
Federal Reserve Bank of Cleveland, Working
Paper 8908, August 1989.



Weber, Warren E. “Do Sterilized Interventions
Affect Exchange Rates?” Federal Reserve Bank
of Minneapolis, Quarterly Review, vol. 10
(Summer 1986), pp. 14-23.

A Regional Perspective
on the Credit View
by Katherine A. Samolyk

Introduction
Although the last decade ushered in the longest
peacetime expansion of the modern era, it also
saw a precipitous rise in the number of bank
failures. More than half of the banks that have •
been declared insolvent since the Federal De­
posit Insurance Corporation was founded in 1933
failed during the 1980s. Given the current trend
toward deregulation, the structure of the finan­
cial services industry has come under intense
scmtiny. More recently, the Federal Reserve Sys­
tem has been concerned about how the poor
health of the banking industry may be affecting
the supply of credit and thereby depressing eco­
nomic activity.
Concerns about a credit crunch are paralleled
by macroeconomists’ increasing interest in un­
derstanding the relationship between the finan­
cial sector and the real sector. The notion that
credit-market activity may affect real economic
activity has come to be known as the credit view.
According to this view, credit markets are impor­
tant in determining the allocation of resources in
an economy for two simple reasons. First, indi­
viduals with profitable investment projects may
 not have the financial resources to fund their
http://fraser.stlouisfed.org/
ventures themselves. Investors with financial
Federal Reserve Bank of St. Louis

Katherine A. Samolyk is an econo­
mist at the Federal Reserve Bank of
Cleveland. The author gratefully
acknowledges helpful comments
and suggestions from Ben Bernanke, Randall Eberts, William
Gavin, Joseph Haubrich, and
James Thomson. Rebecca
Wetmore provided valuable
research assistance.

capital do not have complete information about
these investment projects and face costs asso­
ciated with monitoring their performance. Con­
sequently, investors will impose more stringent
credit terms, such as higher interest rates or
higher collateral requirements, on less credit­
worthy borrowers to compensate for expected
monitoring costs.
Second, this view also posits that financial
intermediaries (hereafter referred to as banks)
improve the efficiency of credit markets by iden­
tifying, funding, and monitoring the perform­
ance of profitable investment projects. However,
much of the information produced by banks is
confidential, so they must be monitored as well.
This implies that the ability to fund risky ven­
tures is affected by the creditworthiness of
banks, as measured by the financial health of
their balance sheets. Because a less creditwor­
thy bank is more likely to require monitoring,
depositors will (and regulators should) impose
more stringent credit requirements on the insti­
tution. Thus, the credit view posits that financial
factors, such as the health of bank balance
sheets, can affect the allocation of resources
and the level of real economic activity.
The credit view may have important implica­
tions for nations that are characterized by

FI GURE

1

The Growth Rate of Real GSP
Minus the Growth Rate of Real GNP

Percent

Percent
_ _

S o u th A tlantic

— — East S o u th C e ntral
____

s.

W e st S o u th C entral

— — M o u n ta in
^

—

1

—

• • . P n rifir

••••••.

V

- <¿2

y
/

\ n> ^

•.

/

v
^

^

\ ^

.*

"

■
___r »
s . •*
'
— r
^
/_______ —

X

^

—

.

1980

!

1981

...l _

1982

1

1983

1

1984

1

1

1985

1986

SOURCE: U.S. Department of Commerce, Bureau o f Economic
Analysis.

diverse regional economies, such as the United
States. W hen it is more costly to monitor the per­
formance of risky ventures in regions outside
the local sphere, credit markets may segment
along regional dimensions. Thus, because local
banks play an important role in funding local
bonowers, the health of a region’s banking sec­
tor may affect its ability to intermediate credit to
local projects. In addition, features of state and

federal financial regulatory systems, such as


interstate banking restrictions, tend to magnify
the effect of factors that impede the inter­
regional flow of funds.
The regional dimensions of credit flows may
be important in assessing the performance of
regional economies. Credit markets may be a
channel by which regional economic conditions
can be propagated into the future. This credit
view suggests that regional recessions may be
prolonged because of the effect of poor eco­
nomic performance on the creditworthiness of
both local banks and nonbank borrowers and,
hence, on the region’s ability to attract the exter­
nal finance needed to fund local investment
activity. For example, capital-poor Boston banks
may be unable to lend to a profitable-but-risky
local biotechnology firm, while healthy Cleve­
land banks may choose not to invest in it
because monitoring the firm is too costly. In the
extreme case, the venture is not undertaken at
all. Instead, resources in Cleveland are chan­
neled to local investment projects with lower
real returns — albeit projects with lower informa­
tion costs.
Regional disparities in economic performance
have been stark in recent years. Figure 1 depicts
the difference between the growth rate of real
gross state product (GSP) and the growth rate of
real gross national product (GNP) for nine
regions from 1980 to 1986. Likewise, credit
problems, especially those impacting on the
banking industry, have also varied considerably
across regions. Failures of depository institutions
have been concentrated in economically dis­
tressed areas. The most stark examples are the
depressed fa mi belt and oil-producing regions
in the mid-1980s, and more recently the North­
east. The credit view suggests that financial
problems associated with regional recessions
may make it more difficult for these areas to
fund a recovery.
Despite the sharp disparities in regional eco­
nomic conditions, most empirical studies have
looked for a link between credit and economic
activity at the national level. A significant credit
channel at the regional level, however, may be
obscured in tests that aggregate data across
regions. Specifically, regional information costs
may cause the relationship between financialsector conditions and economic activity to be
different for states experiencing economic dif­
ficulties than for those in an economic boom;
thus, a cross-sectional approach may be better
suited to testing for a credit channel in the
United States.
This paper provides a first step in testing for
whether there is a link between regional credit

29

markets and regional economic performance.
State-level data between 1980 and 1986 are
used to examine the relationship between state
output growth (relative to national output
growth) and several measures of regional credit
health, including failed business liabilities, com­
mercial bank loan-loss reserves, and the return
on commercial bank equity. A pooled crosssectional time-series approach is used to exam­
ine whether the relationship between financial
conditions and economic performance differs
for low-growth versus high-growth states.
The results yield evidence of a regional credit
channel. Regional bank balance-sheet conditions
are significantly related to the performance of
regional economies. Moreover, there is a differ­
ent relationship between credit health and eco­
nomic growth in states experiencing slow output
growth compared with those that are doing well.

I. A Regional
Credit View
The regional credit view presented here ex­
amines the implications of an asymmetric distri­
bution of information among investors and
entrepreneurs for a regional economy.1 It
assumes that investors with financial capital do
not have good information about entrepreneurs
seeking funding. Thus, the creditworthiness of
these borrowers — as measured by their col­
lateral, the underlying project risks, and the
costs of monitoring their contracts — affects the
terms of credit and subsequently the mix of in­
vestments that are funded.2 The credit view also
assumes that banks improve the efficiency of
capital markets by reducing the information costs
associated with credit flows. Banks specialize in
identifying and monitoring investment projects.
They also diversify across many projects, thus
reducing the costs that depositors must incur to
monitor bank portfolios (in an unregulated finan­
cial system).3 However, when banks cannot
completely diversify portfolio risks that are costly
to monitor, the creditworthiness of these institu­
tions — as measured by their equity capital and

■

1 See Gertler (1988) for a review of asymmetric information
models of credit markets.
2 The information costs associated with credit risks may even lead
to the credit rationing of borrowers with profitable investment projects
(see Williamson [1986]).

the credit quality of their loan portfolios —
affects their ability to fund risky investments.4
An important implication of this view is that
changes in bank creditworthiness can affect eco­
nomic activity. Specifically, a deterioration of the
internal wealth of banks (bank equity capital)
can make it more costly for them to fund projects
and thus can depress investment activity.
In a previous paper, Samolyk (1989), I present
a formal model of how imperfect information
can underlie a regional credit channel between
local credit conditions and local investment ac­
tivity. The model emphasizes the role of banks
in funding investments and assumes that banks
possess a specialized information technology
that allows them to identify and monitor invest­
ment projects more efficiently than other individ­
uals in credit markets. Unlike much theoretical
literature that uses imperfect information to moti­
vate financial structure, however, this analysis
assumes that the economy is made up of regional
economies that have different production tech­
nologies. The local production technologies
have a random return, and the distribution of
returns on local investment activity is assumed
to exhibit diminishing marginal returns.
In each productive sector there are two types
of individuals: bankers and lenders. Bankers
possess an information technology for locating
and monitoring specific real investment projects;
lenders do not. Bankers obtain external finance
to fund their portfolios of projects, produce
information in locating and monitoring projects,
and provide lenders with access to additional
investment opportunities. As explained in Bernanke and Gertler (1987), local banks cannot
perfectly diversify portfolio risk because the
scale of an individual bank project is large rela­
tive to the size of a bank’s portfolio. Therefore,
the ability of banks to fund local investments is
related to their financial health.
The model also assumes that monitoring
costs are lower for local investments than for
investments in other regions.3 Thus, credit mar­
kets are regional because banks can use their
technology most efficiently in making local
investments. Banks can invest in other regions,
but they face higher monitoring costs in doing
so. These conditions imply that the cost of credit
to local banks depends on their relative credit-

■

■

3 In the extreme, when a bank can completely diversify individual
credit risks, the amount of the bank's capital and the dispersion of its in­
 dividual asset returns do not affect the ability to fund its portfolio (see
http://fraser.stlouisfed.org/
Diamond [1984]).

Federal Reserve Bank of St. Louis

■

4 Bernanke and Gertler (1987) formally model the relationship be­
tween bank creditworthiness and the funding of specialized investment
projects.

■

5 These costs include the cost of monitoring both the ex ante dis­
tribution of investments and the ex post returns to projects undertaken.

30

worthiness as well as on the profitability of their
investment projects.
Since expected monitoring costs rise as lever­
aged investment increases, while expected proj­
ect returns exhibit diminishing marginal returns,
an upper bound exists on the capacity of a
region to fund risky investments externally,
given its stock of internal financial capital.
Regional balance-sheet conditions and the dis­
tribution of investment opportunities are there­
fore related to a region’s financial capacity.
In this model, disparities in regional economic
performance can be exacerbated by the impact
of regional economic conditions on the credit­
worthiness of local banks. In areas experiencing
a local recession, the resulting erosion in bank
capital can prevent banks from funding profit­
able, albeit privately monitored, local projects
that would be financed if information were cost­
less. For example, consider an economy com­
prised of regions with independent but identical
production possibilities. If half of the regions
receive a poor investment return while the other
half receive an above-average return, banks in
ailing regions may find it more difficult to attract
external finance to fund profitable new invest­
ment projects, even though banks in other
regions are flush with funds. Thus, poor regional
economic performance can be propagated into
the future as the associated decline in creditwor­
thiness hinders the ability of banks to fund a
recovery.6 This occurs because poor regional
credit health precludes the use of local informa­
tion about profitable investment opportunities.
Moreover, capital-rich sectors will invest in
lower-yielding local projects as long as the
return is greater than the cost-adjusted return as­
sociated with funding capital-poor regions. As a
result, although national bank capital may not
have changed, disparate regional credit health
can cause the return from investment activity in
the overall economy to be lower.7 The impact
of regional disparities in bank capital is greater
than the impact of regional differences in other
sources of funds because of banks’ comparative
efficiency in producing information about local

■

6 See Bernanke and Gertler (1989) for a theoretical model in which
credit effects are strongest in distressed economies.
■

7 In Samolyk (1989), I demonstrate how, when there is short-run
im m obility in information technology, regional imbalances both in entre­
preneurial wealth and in the distribution of investment opportunities can
affect the aggregate allocation of credit and aggregate future output rela­
 tive to the allocation that would be feasible if regional information asym­
http://fraser.stlouisfed.org/
metries did not exist.

Federal Reserve Bank of St. Louis

investments.8 Thus, this credit view also sug­
gests that a link between credit conditions and
economic activity at the regional level could be
obscured in examining data aggregated at the
national level.

II. Identifying
a Financial
Transmission
Mechanism through
Disaggregation
The notion that the financial system propagates
economic fluctuations depends on how finan­
cial structure affects the allocation of resources.
The imperfect-information view of a credit chan­
nel suggests that changes in the costs of supply­
ing credit-market services can affect investment
expenditures and output; thus, financial-sector
performance can feed back to the real sector
and exacerbate output fluctuations. However,
empirical tests for a macroeconomic link be­
tween financial structure and economic activity
have yielded inconclusive evidence of the exis­
tence of such a channel.9
The mixed evidence of the importance of
financial performance for business fluctuations
in studies using national-level data may reflect
the difficulties inherent in finding proxies for
financial services associated with the informa­
tion costs that underlie the credit view. Tests for
a credit channel to output often use credit flows
and interest-rate measures to proxy for financial
performance. These measures, however, are a
reflection of financial capacity as well as expec­
tations about future economic activity and
hence about the profitability of real investment
opportunities. Expectations about the distribu­
tion of future investment opportunities would
affect credit flows even in a world of perfect in­
formation, where financial structure is irrelevant
to the level and mix of investment activity.
Thus, concluding that these variables help to
predict economic activity does not imply that
they also cause economic activity. For example,
the determination that lower growth in bank
lending tends to precede a decline in economic
activity may merely reflect a decrease in the prof­
itability of investment opportunities (and, hence,
a decrease in loan demand). Likewise, evidence
of an increase in perceived credit risks, in the

■

8 This result generalizes to any firm that produces information in
funding local ventures, including other types of financial intermediaries
as well as local entrepreneurs who have access to direct credit markets.
■

9 See Gertler (1988) for a survey of these studies.

form of larger default-risk premiums, is not
unambiguously indicative of financial-market
frictions, because it is difficult to identify
whether the premium is associated with higher
monitoring costs or with a change in the under­
lying distributions of returns on risky real invest­
ment projects.
In a previous paper (Samolyk [1990]), I argue
that it is the higher cost of finance associated
with credit failures and reduced internal capital
for future financing that may “cause” real activity
to the extent that it magnifies output fluctuations.
Debt default, because it reduces the entrepre­
neurial capital of both primary borrowers and
financial intermediaries, may be a relevant chan­
nel by which financial-market performance can
feed back and affect economic activity. I find
that, controlling for monetary conditions and
lagged economic activity, past insolvencies are
significantly related to real output.10
The regional credit view presented here has
implications for empirically testing for a credit
channel. First, because it implies that the alloca­
tion of credit is affected by a region’s creditwor­
thiness, it recommends the use of variables
related to the health of regional financial balance
sheets (such as debt in default) as financial prox­
ies. In addition, because a region’s relative credit­
worthiness can affect its access to funds, the
regional credit view suggests that there may be
an asymmetric relationship between credit condi­
tions and economic activity for creditworthy
regions versus those that are poor credit risks;
regional credit problems may constrain regional
growth more than healthy credit markets may
stimulate it. Thus, regional credit conditions may
be significantly related to differences in regional
economic performance in a way that would be
obscured in examining data aggregated at the
national level. To the extent that credit markets
may be regional— whether a vestige of regulation
or a feature of optimal industrial organization —
a cross-sectional time-series approach may be
better suited in testing for a credit channel.11

The Empirical Model
I estimate two types of reduced-form models of
relative state output growth using annual statelevel data from 1979 to 1986 (which span the
■

10 This study is related to Bernanke’s (1983) study of the Great
Depression.

■

11 Although this asymmetry implies that regional credit imbalance
could be a drag on aggregate economic growth, this paper tests for a

regional credit channel rather than for whether regional credit imbalances
http://fraser.stlouisfed.org/
can, in fact, help to explain aggregate economic activity.

Federal Reserve Bank of St. Louis

most recent business cycle). Relative state out­
put growth, yt , the difference between the
growth rate of real GSP and that of real GNP, is
regressed on its own lagged value and on var­
ious lagged measures of state credit conditions.
The first type of model specifies a log-linear
relationship between credit conditions and rela­
tive state output growth of the general form
(1)

y itt = BoVi, t -1 + X Bi CREDIT; , . j +e,
i= 1

where all explanatory variables are lagged one
year and CREDIT is the set of proxies for state
credit conditions included in the regression.
The second type of model includes interactive
dummy variables for all explanatory variables to
test whether there is a significantly different rela­
tionship between credit conditions and output in
low-growth versus high-growth states. Regres­
sions of this type are of the general form
G)

y, ,- c .,(H o>y:.

1

+ X Ct W /> CREDIT,
i= 1
+ D o ^ y i, t -1
+ £ D i (Li) CREDIT' t . 1 + e,
i= 1

where H 0 and H j are dummy variables that
equal one when yt t is positive, and D Q and D t
are dummy variables that equal one when y i t
is strictly negative. This specification effectively
splits the pooled sample into low-growth and
high-growth observations.

The Credit Variables
The credit view suggests that credit flows— while
inherently reflecting expectations about the prof­
itability of current real investment opportunities
— may also be affected by the information costs
associated with supplying external finance. I
have not identified whether agency costs or un­
derlying investment fundamentals drive financial
flows. However, I attempt to control for expecta­
tions about future economic performance by in­
cluding the growth rate of the constant-dollar
volume of bank lending ( GLOAN) as a credit
proxy that is relatively forward-looking, as op­
posed to balance-sheet measures that capture the
quality of existing credit. State-level data on the
nominal stock of end-of-year bank loans out­
standing were obtained from the Federal Finan-

32

FI GURE

2

Ratio of Failed Business
Liabilities to GSP (Scaled
by the National Ratio)
R atio

R atio
4,---

Ol_______ I_______ I_______ I_______ I_______ I_______ I___
1980 1981 1982 1983 1984 1985 1986

SOURCES: D un and Bradstreet Corporation; and U.S. Department of
Commerce, Bureau o f Economic Analysis.

cial Institutions Examination Council’s Reports
of Condition and Income (call reports) and
were deflated by the GSP deflator.
Other financial proxies, more directly related
to the ex post creditworthiness of both bank
and nonbank business borrowers as a result of
past financial decisions, are included in each
specification. I include these proxies to test
 whether, w^hen controlling for real loan grow th
http://fraser.stlouisfed.org/
(expectations about the future) and past relative
Federal Reserve Bank of St. Louis

output growth, they significantly help to explain
the relative growth of state output.
I use Dun and Bradstreet state-level annual
data on the volume of failed liabilities associated
with business failures to measure the overall
creditworthiness of business bonowers. Busi­
ness failures that occur in a given year are re­
lated to the flow of credit in default; thus, their
numbers are related both to bankruptcy costs
and to changes in the stock of entrepreneurial
capital. The business failure series does not in­
clude firms that voluntarily discontinued opera­
tions with no loss to creditors, but only those
that are legally insolvent.12 A higher level of
business liabilities in default should increase the
cost of credit to entrepreneurs and reduce future
economic activity.
To control for differences in the size of state
economies, the volume of failed business
liabilities was scaled by GSP.13 The log of this
ratio was included in all regressions. Figure 2
illustrates the regional differences in this vari­
able, depicting the ratio of failed business
liabilities to GSP (deflated by the national ratio)
for nine regions. During the 1980s, the volume
of bad credit relative to income increased for
the U.S. economy as a whole. For regions such
as the oil-producing states, however, credit
problems were reflected in a substantially
greater deterioration in business balance sheets.
A regional credit view that emphasizes the
role of banks in funding local projects implies
that bank equity capital reflects bank creditwor­
thiness because it is the buffer between the
performance of bank loan portfolios and insol­
vency. However, bank equity capital is a poor
proxy for bank creditworthiness because, like
capital on any corporate balance sheet, it is not a
market valuation of the present value of firm
ownership. Such a valuation would reflect
expectations about the quality of the current
loan portfolio, including the return on the loan
portfolio, the volume of loans in default, and the
degree of default on bad loans. Instead, the
measures of the creditworthiness of local bank­
ing sectors came from state-level call report data
on loan quality and bank profitability. These
include loan loss reserves, nonperfonning loans
(defined as loans 90 days past due and still accru­
ing, plus nonaccming loans) as a share of total
loans, and the ratio of net income to bank equity
■

12 It should be noted, however, that legal insolvency— the inabil­
ity to service debt lia b ilities— may reflect balance-sheet illiquidity rather
than economic insolvency. The Inability to obtain credit may reflect expec­
tations about the profitability of future local investment opportunities.
■

13 This ratio measures the flow of bad debt relative to the flow of
income available for debt service.

33

FI GURE

3

Ratio of Nonperforming
Loans to Total Loans
R atio

R atio

the current portfolio and thus on the credit
quality of current loans outstanding. Given the
promised yields on existing loans, larger loan
loss reserves correspond to a lower return on
existing assets. Therefore, the growth rate of
loan loss reserves deflated by the GSP deflator
( GLOANLOSS ) was included in the regressions
as a proxy for expected default losses on exist­
ing loan portfolios.
Nonperforming loans should reflect the share
of the loan portfolio that is currently in default,
but do not necessarily indicate the degree of
default. Nonperforming loans, like failed busi­
ness liabilities, reflect realizations of credit per­
formance rather than expectations about future
loan perfomiance. The log of the ratio of non­
performing loans to total loans (SNONPERF)
was included in some specifications as a proxy
for the default rate on bank loans. Unfortunately,
data on nonperforming loans are available only
after 1981, so regressions using this series span
only 1983 to 1986.
These problem-loan variables are important
because they are related to the market values of
both bank assets and bank capital— and hence
to the creditworthiness of banks as borrowers
today. The credit view presented here suggests
that these variables may affect the characteristics
of future credit extended and the use of inter­
mediation technology in making new loans. In
addition, bad loans can also cause banks to
abrogate existing credit relationships; therefore,
banks must expend resources in seeking out
new investment opportunities. The disparities in
bank-credit problems across regions are shown
in figure 3, which depicts the share of nonper­
forming loans to total loans by region, as well
as the national share. Although the national
share was flat over the sample period of 1983 to
1986, there were substantial differences in both
the level and the trend across regions.

SOURCE: Board of Governors of the Federal Reserve System.

III. Empirical
Evidence
capital. The latter measure — the ex post return
on bank equity (ROE) — is positively related to
bank creditworthiness, as it represents the poten­
tial growth of internally generated bank capital.u
Provisions for loan loss reserves should
reflect assessments of the degree of default on

Results were derived from pooled regressions
using cross-sectional state data over the sample
period of 1980 to 1986. Regressions including
the nonperforming loan series had a sample
period of 1983 to 1986.15 The variables are
■

■

14 In choosing measures of financial distress for a state’s banking
sector, obvious choices are data on the number of and liabilities of failed
banks. However, because many failed banks are merged, their balance
 sheets are included in call report data. Data on bank failures will be in ­
http://fraser.stlouisfed.org/
cluded in future extensions of this study.

Federal Reserve Bank of St. Louis

15 The pooled cross-sectional time-series regressions were es­
timated using the Shazam statistical package, with the autocorrelation
coefficient, rho, constrained to be zero for all states. Pooled regressions
that did not restrict the autocorrelation coefficient to be zero (but also did
not adjust estimates for the inclusion of lagged dependent variables)
yielded near-zero estimates of rho and no significant difference in the
results.

34

Notes on Statistical Tables3

GSPDIF : The growth rate of real gross state product minus
the growth rate of real gross national product.
LGSPDIF : GSPDIF ( - 1) if GSPDIF< 0.
HGSPDIF : GSPDIF (-1) if GSPDIF> 0.
GLOAN : The growth rate of commercial bank loans deflated
by the GSP deflator.
LGLOAN: GLOAN(-1) if GSPDIF< 0.
H G LOAN-. GLOAN (-1) if GSPDIF > 0.
FLIAB : The log of the ratio of failed business liabilities to
GSP.
LFLIAB : FLIAB (-1 ) if GSPDIF < 0.
HFUAB : FLIAB (-1) if GSPDIF> 0.
GLOANLOSS : The growth rate of loan loss reserves deflated
by the GSP deflator.
LGLOANLOSS : GLOANLOSS (-1) if GSPDIF< 0.
HGLOANLOSS : GLOANLOSS (-1) if GSPDIF> 0.
ROE : The ratio of net income to equity capital of commer­
cial banks.
LROE : RO E(-1) if GSPDIF< 0.
HROE : ROE(-l) if GSPDIF> 0.
SNONPERF : The log of the ratio of nonperforming loans to
total loans for commercial banks.
LSNONPERF : SNONPERF (-1 ) if GSPDIF < 0.
HSNONPERF : SNONPERF(-1) if GSPDIF> 0.
a. The term (-1) indicates a one-year lag.

defined in box 1. The relative growth rate of
real GSP was regressed on its own lagged value
and on the lagged values of the proxies for state
balance-sheet conditions. To control somewhat
for expectations about the profitability of local
investment opportunities, all regressions in­
clude the lagged value of the growth rate of
bank loans. Also, dummy variables testing for
economywide fixed effects by year were in­
cluded in each regression specification.
Estimates of equation (1) are presented in
panel A of table 1. In these regressions, the rela­
tionship between credit conditions and output is
restricted to be the same for low-growth and
high-growth observations.
Panel B of table 1 presents the results for sym­
metric regressions including interactive dummies
that allow the coefficients on the explanatory
variables to be different for states experiencing
low growth and high growth. These dummies
 split the sample and help to detemiine whether


there is a structurally different relationship
between lagged credit conditions and current
relative output growth for low-growth and highgrowth observations.
In all regressions, the lagged dependent vari­
able explains most of the current relative growth
of state output. Interestingly, controlling for
lagged credit conditions, the relationship is not
significantly different for low-growth versus highgrowth observations in any of the specifications
estimated.
Alternatively, there is a significantly different
relationship between lagged credit conditions
and current relative output growth in every
specification. This indicates that the financial
balance-sheet conditions inherited from the
past are related to real economic activity differ­
ently for states experiencing a relative boom
than for those experiencing relatively low
growth. Thus, comparing the results in panel A
with those in panel B indicates that restricting
the relationship between financial factors and
economic activity to be the same across states
independent of relative conditions — a restric­
tion implicitly imposed in tests using macroeconomic data — may obscure a significant link
between credit and output.
The split sample results yield some evidence
that financial factors matter in a way that is con­
sistent with the credit view discussed here. The
structural differences are in the relationship of
output to the lagged variables that proxy for in­
herited financial balance-sheet conditions.
The ratio of failed business liabilities to state
output is a significant predictor of negative out­
put growth primarily in low-growth states (table
1, specifications 2.A and 2.A' ). However, when
nonperforming loans and the return on bank
equity capital are included, this asymmetric rela­
tionship is no longer evident. At the same time,
reverse causality tests (table 2) indicate that there
is a different relationship among failed business
liabilities, the return on bank equity capital, and
nonperforming loans in low- versus high-growth
states. These results suggest that bank credit
quality and bank earnings may reflect the impact
of broader business financial conditions in lowgrowth states.
Lagged loan loss reserves are negatively
related to output growth only in low-growth
states. The coefficients on loan loss reserves are
generally insignificant in high-growth states.
This can be interpreted to indicate that past
provisions for loan losses may be constraining
credit availability in states experiencing low rela­
tive growth. Interestingly, reverse causality tests
between loan loss reserves and the return on

35

TABLE

1

Results for Regressions Explaining
Relative State Output Growth—
Dependent Variable: GSPDIF
P a n e l A:

P o o le d S a m p le R e s u lts

( 1 .A )

(l.B )

(1 .A ' )

(l.B ' )

(l.C )

(1.D)

No. of observations

336

336

192

192

192

192

R2

.3705

.3744

.4296

.3864

.3882

Log of likelihood
function

GSPDIF (-1)
GLOAN(-1)
FLLAB(-1)
GLOANLOSS(-1)
ROE(-1)
SNONPERF(-1)

846.925

847.081

.4345
500.824

.515
(11.06)a
.008
(2.29)b
-.006
(-4.97)a
.003
(0.37)
-

.513
(10.82)a
.008
(2.18)b
-.006
(-4.86)a
.002
(0.30)

.429
(8.09)a
.013
(2.65)a
-.006
(- 6.99)a
-.006
(-1.38)

--

Year dummies

Y80-Y863

.011

--

(0.58)
-

-

Y80 -Y86a

Y83-Y86a
P a n e l B:

500.479

497.797

498.195

.433
(7.98)a
.013
(2.42)a
-.005
(-5.81)3
-.007
(-1.45)
.022
(0.89)
-

.449
(7.92)a
.013
(1.83)b
-.005
(-4.55)a
-.006
(-0.80)

-.001
(- 0.36)
Y83 -Y86a

.442
(7.73)a
.012
(1.64)
-.005
(-4.49)a
-.006
(-0.71)
.027
(0.87)
-.0003
(-0.01)
Y83 -Y86a

Y83-Y863

--

S p lit S a m p le R e s u lts

( 2 .A )

( 2 .B )

(2 .A ' )

( 2 .B ' )

( 2 .C )

( 2 .D )

No. of observations

336

336

192

192

192

192

R2

.5096

.6960

.6255

.7388

.7310

537.563

560.402

560.040

.293
(6.55)a
.002
(0.57)
-.003
(-2.45)a
.0005
(0.06)
.081
(4.48)a

.349
(5.73)a
-.004
(-0.33)
.001
(1.06)

.252
(4.81)a
-.009
(-0.70)
-.001
(-0.45)
.001
(0.07)
.133
(5.23)a

.270
(5.28)a
-.027
(-2.02)b
-.001
(-1.14)
-.003
(-0.47)

.260
(5.03)a
-.023
(-1.72)b
-.001
(-1.08)
.003
(0.45)
.026
(0.76)
-.009
(-2.80)a
.432
(5.73)a'd
.004
(0.53)d
.003
(1.33)d
-.021
(-1.75)b
-.037
(-1.42)
-.001
(-0.28)°
Y83 -Y86a

886.669

946.573

HGSPDIF

.476
(8.81)a
-.001
(-0.30)
.0003
(0.09)
.025
(2.84)a

HGLOAN
HFLLAB
HGLOANLOSS

.011

(1.29)

HROE

-

HSNONPERF

--

--

--

--

.349
(4.87)a
-.005
(-0.59)
-.008
(-5.46)a,c
-.048
(-3.05)a'c

.272
(4.63)a
-.008
(-0.96)
-.005
(-3.52)a
-.018
(-1.40)
-.114
(-6.01)a’c

.349
(4.06)a
-.021
(-3.46)a
-.010
(-6.10)a,c
-.040
(-3.40)a,c

.419
(5.69)ad
-.004
(-0.54)
-.002
(-0.87)
-.022
(-1.87)b
-.082
(-3.94)ax

LGSPDIF
LGLOAN
LFUAB
LGLOANLOSS

-

LROE

-

LSNONPERF

--

--

--

--

Y80-Y86a

Y80 -Y86b

Y83 -Y86a

Y83-Y86b

Year dummies

.7392

554.651

Log of likelihood
function

--

—

-.010
(-3.81)a
.429
(5.66)ad
.005
(0.70)d
.003
(1.64)d
-.016
(-1.40)
--

.001
(0.46)c
Y83-Y863

a. Coefficient (or sum o f coefficients) is significant at the 1 percent level.
b. Coefficient (or sum o f coefficients) is significant at the 5 percent level.
c. Slope coefficients are significantly different for GSPDIF < 0 and GSPDIF > 0 at the 1 percent significance level.
d. Slope coefficients are significantly different for GSPDIF< 0 and GSPDIF > 0 at the 5 percent significance level.
NOTE: ^statistics are in parentheses. The sample period is indicated by the year dummies. The means o f the dependent variable are .003 and
-.002 for the sample periods 1980-86 and 1983-86, respectively.


SOURCE:
Author’s calculations.


36

TABLE

2

Reverse Causality Tests
for Credit Variables

( 3 .A )

( 3 .B )

( 4 .A )

( 4 .B )

( 5 .A )

( 5 .B )

( 6 .A )

336

192

336

192

336

192

192

.6663
-149.546

.6249
417.654

FLIAB

FLIAB

GLOANLOSS

GLOANLOSS

HGSPDIF

-5.332
(-3-67)a

-2.651
(-1.54)

-.199
(-1.11)

-.471
(-2.20)b

HGLOAN

-.397
(-1,84)b

.020
(0.62)

.039
(0.55)

HFL1AB

.599
(11.49)a
.174
(0.62)

.827
(1.31)
.528
(6.79)a
-.118
(-0.26)

-.001
(-0.24)

.005
(0.6 1 )

-.001
(-1.07)

.222
(3.44)a

.341
(4.20)a

-4.231
(-2.95)a
.180
(1.16)

.228
(2.54)a

.541
(3.l4)a

.292
(1.55)d
-.066
(-1,79)b'd
-.0001
(-0.01)

.188
(0.65)d

.151
(2.46)a

-.109
(-2.99)ad
-.001
(-0.06)

-.020
(-1.47)d

.375
(4.56)ad

.317
(2.80)a

-.006
(-3.43)a’c
.012
(0.74)c

.157
(1.62)

.363
(1.78)b

.803
(22.90)a-c

.011

—

No. of
observations

R2
Log of
likelihood
function
Dependent
variable

HGLOANLOSS
HROE
HSNONPERF

.6660
-293.820

-.974
(-1.49)
—

LGSPDIF

-1.228
(- 0 .6 l)d

-3.589
(-1.94)b

LGLOAN

.264
(0.51)
.580
(10.27)a

.644
(1.02)

LFLIAB
LGLOANLOSS
LROE
RSNONPERF
Year dummies

.339

.676
(8.98)a
.610

(0.7 0)

(1.0 7)

-1.044
(-1.70)b

-.475
(-0.47)d

—

Y80-Y86a

.383
(2.64)a,t
Y83-Y86a

—

—

Y80-Y86a

.3578
225.174

.023
(1.00)

(0.49)
Y83 -Y86a

.7810
834.776

.8671
470.437

.9219
111.726

ROE

ROE

.131
(2.49)a
.010
(0.96)

-.044
(-3.78)a

.156
(2.09)b
-.022
(-0.89)
-.002
(-0.91)
-.048
(-3.17)a

.860
(26.l4)a

.824
(13.76)a

-.083
(-0.24)

-.027
(—5 56)a

1.009
(26.39)a
-1.211
(-3.18)a

—

.126
(2.29)b
.016
(0.82)
-.011

(—4.15)ac

SNONPERF
-1.235
(-2.58)a
.333
(1.79)b
-.009
(-0.60)
.188
(1.84)b

-.140
(-1.15)d
.041
(2.79)a,c

-.003
(-0.13)
.652
(7.92)ad

.183
(1.62)

-0.33
(-6.22)a

1.037
(29.76)a

Y80-Y86a

Y83-Y86a

.961
(3.87)a,c

Y83-Y86a

a. Coefficient (or sum of coefficients) is significant at the 1 percent level.
b. Coefficient (or sum o f coefficients) is significant at the 5 percent level.
c. Slope coefficients are significantly different for GSPDIF< 0 and GSPD1F > 0 at the 1 percent significance level.
d. Slope coefficients are significantly different for GSPDIF< 0 and GSPDIF > 0 at the 5 percent significance level.
NOTE: ^statistics are in parentheses. The sample period is indicated by the year dummies.
SOURCE: Author’s calculations.

bank equity capital (table 2) yield evidence that
banks in high-growth states may be using loan
loss reserves to smooth income. These results
are consistent with the notion that financial
capacity is more important for ailing economies
than for healthy ones.
A somewhat puzzling result is that, in the
regressions that exclude nonperforming loans,

the lagged return on bank equity capital is posi­
http://fraser.stlouisfed.org/
related to output growth in high-growth
Federal Reserve Bank oftively
St. Louis

states, but negatively related to output growth
in low-growth states. The credit-health view
implies that if the return on bank capital merely
captures the potential flow of internally gener­
ated funds and hence increased financial capac­
ity, it should be positively related to local relative
growth. Alternatively, there is evidence that the
negative relationship between bank profitability
and output may be capturing tighter lending
practices by loan officers. Thus, the asymmetry

between low- and high-growth states is consis­
tent with the notion that creditworthiness may af­
fect credit availability and economic activity.16
W hen nonperforming loans are included (table
1, specification 2.D), the lagged return on equity
is no longer significantly related to output
growth for either group. In addition, reverse
causality tests (table 2, specification 6.A) yield
evidence in favor of the tighter-lending interpre­
tation of the asymmetry; in states experiencing
low growth, the lagged return on equity is posi­
tively related to the share of nonperforming
loans, while this credit-quality variable nega­
tively impacts future profitability.
Finally, although lagged loan growth is sig­
nificantly related to relative output growth in
the pooled sample regressions (table 1, panel
A), the split sample regressions (table 1, panel
B) yield little evidence that real loan growth is
positively related to output growth when state
financial balance-sheet conditions are included
as explanatory variables.
These results are not meant to be interpreted
as identifying the exact nature or magnitude of a
regional credit channel. As with all tests of
whether financial variables cause real variables,
the fact that lagged financial variables “Granger
cause” economic activity does not mean that in­
herently forward-looking financial decisions do
not reflect expectations about future economic
conditions. Thus, decisions to extend credit as
well as to default or to mark down the valuation
of bank assets reflect, to some degree, the
present valuation of the expected payoff on
financial claims as related to expectations about
future economic conditions. The evidence that
financial factors may exacerbate output fluctua­
tions is the significantly different relationship be­
tween inherited credit conditions and economic
performance in healthy regions versus those
experiencing poor relative economic growth.

IV. Conclusion
Cunent concerns about financial-market fragil­
ity are forcing policymakers to face the issue of
whether monetary policy should be used to con­
front credit-quality problems in the financial sec­
tor. However, opinions and interpretations
differ on what the evidence of a credit channel
implies for policymakers. The regional dimen­
sion of current financial conditions further com­
plicates the problem, because its solution

■

16 In addition, states with a lower capital-to-asset ratio (and hence

lower bank creditworthiness) will have higher returns on equity, all else
http://fraser.stlouisfed.org/
equal.
Federal Reserve Bankbeing
of St.
Louis

depends on what is causing credit markets to be
regional, as well as on the sources of regional
credit disparities.
This paper presents evidence that regional
economic performance is related to regional
creditworthiness. State financial balance-sheet
conditions, inherited from the past, have a sig­
nificant relationship to current state output
growth for states that are experiencing low rela­
tive growth; the relationship is consistent with
the credit-health view and is significantly dif­
ferent from the relationship in states experienc­
ing high growth. The empirical tests presented
here, however, are a joint test of whether bank­
ing markets are regional and whether there is a
credit link between these markets and the rela­
tive performance of state economies. Thus, the
implications of these results for policymakers
depend on why credit markets are regional.
The model of regional credit markets dis­
cussed here captures some of the features of
banking in an economy that is regional because
of information costs. To the extent that entre­
preneurs must rely on regional credit markets to
originate specialized investments, the health of
these borrowers and of the local banking sector
that provides intermediation services can affect
regional economic activity when there is asym­
metric information between borrowers and in­
vestors supplying external finance.
The regional nature of U.S. credit markets may
also be a reflection of the historically unique
regulatory structure of the banking industry. Reg­
ulations, such as interstate branching restrictions,
limit the ability of banks to diversify across
regions. If credit markets are regional because
regulations are binding, then the benefits of reg­
ulation should be weighed against the costs of
less diversification. W hen it is costly to monitor
borrowers — whether financial or nonfinancial
— the ability to diversify is related to the ability
to avoid bad outcomes that can make it more
costly to obtain credit in the future. Likewise,
limits on the scale of banks that impede their
ability to raise capital may exacerbate regional
output fluctuations, as poor bank profitability
may constrain future lending when local real
economic conditions improve.
Because the regional dimensions of credit
markets in an economy that is inherently
regional are not likely to be merely artifacts of
regulatory policies, the implications of a
regional credit view for the conduct of stabiliza­
tion policy will not disappear with deregulation.
Thus, even in a deregulated environment, it is
likely that financial flows will be sensitive to the
health of regional entrepreneurs to the degree

that these borrowers write contracts that are not
fully contingent on the random return on their
investments. But the credit view recognizes that
this financial structure may also be the most effi­
cient way of dealing with information costs in­
herent in financial contracts. Currently, we do
not observe large banks divesting themselves of
what can be defined as “aggregate risks.” This
may be the result of disincentives in the current
regulatory environment. Alternatively, bank con­
tracting may reflect the highly specialized char­
acteristics of bank investments that make these
risks difficult to assess, but at the same time may
reflect one reason that financial intermediaries
exist: to fund portfolios of specialized invest­
ment opportunities (Fama [1980, 1985]).
To the extent that information costs make
financial markets inherently regional, financial
conditions may be an unavoidable propagation
mechanism to relative regional performance. In
this scenario, it is hard to argue from a pure ef­
ficiency criterion that policymakers should “do
anything” in response to a regional credit im ­
balance, such as that plaguing Newr England,
because the malaise may be an unavoidable out­
come of the market mechanism, however infor­
mation intensive. General stabilization policies
aimed at alleviating a regional credit problem
are likely to have redistributional effects that are
not justifiable according to a pure efficiency
standard. Indeed, an expectation of this policy
response — to the extent that it amounts to a
monetary bailout — may distort the incentives
to diversify ex ante and may exacerbate the
potential problem.
In assessing the policy implications of the
events of the past decade, it is therefore impor­
tant to distinguish between microeconomic pol­
icies affecting financial market structure and
macroeconomic policies aimed at promoting
economic stability and growth. The interdepen­
dence of structural policies and stabilization pol­
icies allows the distinction to be easily blurred.
The unfortunate outcome is often that macroeconomic tools are used to try to remedy the ills
that result from microeconomic banking regula­
tions and structural changes in the financial sec­
tor. If the current regional financial crisis is to
some degree the result of regulatory policies,
then the crisis represents an opportunity to fos­
ter a sentiment for regulatory change. To use
regional financial fragility as a rationale for a
general macroeconomic easing without address­
ing whether regulatory policies are part of the
problem may mean losing an opportunity for
structural reform that could ameliorate the prob­
 lem in the long run.


References
Bernanke, Ben S. “Nonmonetary Effects of the
Financial Crisis in the Propagation of the
Great Depression,” Am erican Economic Re­
view, vol. 73, no. 3 (June 1983), pp. 257-76.

______, and Mark Gertler. “Banking and Mac­
roeconomic Equilibrium,” in William A. Bar­
nett and Kenneth J. Singleton, eds., New
Approaches to Monetary Economics. New
York: Cambridge University Press, 1987,
pp. 89-111.

______ , and _______ . “Agency Costs, Net
Worth, and Business Fluctuations,” Am erican
Economic Review, vol. 79, no. 1 (March
1989), pp. 14-31.

Diamond, Douglas W. “Financial Intermedia­
tion and Delegated Monitoring,” Review o f
Economic Studies, vol. 51, no. 3 (July 1984),
pp. 393-414.

Fama, Eugene F. “Banking in the Theory of Fi­
nance,” Journal o f Monetary Economics, vol.
6(1980), pp. 39-57.
______ . “What's Different about Banks?” Jo u r­
n al o f Monetary Economics, vol. 15(1985),
pp. 29-39.

Gertler, Mark. “Financial Structure and Aggre­
gate Economic Activity: An Overview, ”/o«rn al o f Money, Credit a n d Banking, vol. 20,
no. 3 (August 1988), pp. 559-88.

Samolyk, Katherine A. “The Role of Banks in
Influencing Regional Flows of Funds,”
Federal Reserve Bank of Cleveland, Working
Paper 8914, November 1989.
______ . “In Search of the Elusive Credit View:
Testing for a Credit Channel in Modem Great
Britain,” Federal Reserve Bank of Cleveland,
Economic Review, vol. 26, no. 2 (1990
Quarter 2), pp. 16-28.

Williamson, Stephen D. “Costly Monitoring, Fi­
nancial Intermediation, and Equilibrium Credit
Rationing,”Jo u rn a l o f Monetary Economics,
vol. 18, no. 2 (September 1986), pp. 159-79-

39

Price Stability
Conference Proceedings Offered
The papers in this special issue
of the Journal of Money, Credit,
and Banking were presented and
discussed at a conference on

“Price Stability” held at the Federal Reserve Bank of Cleveland
on November 9 -1 0 ,1 9 9 0 . The
purpose of the conference was

to encourage research and discussion on the costs and benefits
of adopting a policy to achieve
and maintain price stability.

Contents:
■ The Genesis of
■ The Welfare Costs of
Inflation and the Costs of Moderate Inflations
Disinflation
by Thomas F. Cooley

Panel Discussion on
Monetary Policy: What
Should the Fed Do?

by Laurence Ball

and Gary D. Hansen

Comments: Dennis W. Carlton
and Peter Howitt

Comments: Roland Benabou
and Randall Wright

■ The Goal of Price
Stability: The Debate
in Canada

■ Seigniorage as a
Tax: A Quantitative
Evaluation

■ Optimal Fiscal and
Monetary Policy: Some
Recent Results

by C. Freedman

by Ayse Imrohoroglu
and Edward C. Prescott

by V.V. Chari,
Lawrence J. Christiano,
and Patrick J. Kehoe

Comments: Stephen G. Cecchetti
and Herschel I. Grossman

■ Inflation, Personal
Taxes, and Real
Output: A Dynamic
Analysis
by David Altig
and Charles T. Carlstrom
Comments: Alan J. Auerbach
and Finn E. Kydland




by J. Huston McCulloch

Comments: B. Douglas Bernheim
and R. Anton Braun

■ How Should LongTerm Monetary Policy
Be Determined?

■ The Sustainability
of Budget Deficits with
Lump-Sum and with
Income-Based Taxation

by Lawrence Summers

by Henning Bohn
Comments: Timothy S. Fuerst
and James D. Hamilton

To order a copy of the conference proceedings, please send
$8.00 (U.S.) in a check or money order drawn on a U.S. bank.
Make checks payable to the Federal Reserve Bank of Cleveland.
Complete and detach the form and mail to:
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Research Department
P.O. Box 6387
Cleveland, Ohio 44101

■ An Error-Correction
Mechanism for LongRun Price Stability

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Second Quarter
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■ 9019
Tastes and Technology
in a Two-Country Model
of the Business Cycle:
Explaining International
Co-Movements

■ 9106
Principal-Agent
Problems in
Commercial-Bank
Failure Decisions

■ 9108
Bracket Creep in the
Age of Indexing: Have
We Solved the Problem?
by David Altig and
Charles T. Carlstrom

by Ash Demirgüç-Kunt

by Alan C. Stockman and
Linda L. Tesar

■ 9105
Magnification Effects
and Acyclical Real
Wages
by Charles T. Carlstrom and
Edward N. Gamber

■ 9107
Generational Accounting:
A New Approach for
Understanding the
Effects of Fiscal Policy
on Saving
by Alan J. Auerbach,
Jagadeesh Gokhale, and
Laurence J. Kotlikoff

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