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Vol. 26, No. 3

ECONOMIC REVIEW
1990 Quarter 3

A Reexamination of the
Relationship between Capacity
Utilization and Inflation
!

by Paul W. Bauer

• .> • -

• •

•

Financial Restructuring and
Regional Economic Activity

' vj f

by Brian A. Cromwell

The Short*Run Dynamics of
Long-Run Inflation Policy
by John B. Carlson,
William T. Gavin,
and Katherine A. Samolyk

FEDERAL RESERVE BANK
OF CLEVELAND

ECONOMI C

REVI EW

1990 Quarter III
Vol. 26, No. 3

A Reexamination of
the Relationship
between Capacity
Utilization and
Inflation

2
Economic Review is published
quarterly by the Research Depart
ment of the Federal Reserve Bank
of Cleveland. Copies of the Review
are available through our Public
Affairs and Bank Relations Depart
ment. 216/579-2157.

by Paul W. Bauer
This study presents new evidence on the relationship between capacity
utilization and inflation in order to provide a proper framework for under
standing the complexities involved. Because empirical results suggest that
capacity utilization and changes in inflation are jointly endogenous, much
of the previous work in this area may suffer from simultaneity bias. Using a
two-equation structural model, the author finds support for a “steady-state"
rate of capacity utilization of about 81.5 percent. While that figure is in line
with previous estimates, this model does not suffer from simultaneity bias
and appears to be stable over time.

Financial
Restructuring
and Regional
Economic Activity

Coordinating Economist:
Randall W. Eberts
Editors: TessFerg
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.

by Brian A. Cromwell
The relationship between the performance of the financial sector and
economic activity has received increasing attention from economists
during the past decade. Empirical studies generally support the view that
financial structure and stress can have real economic effects. This paper
explores the impact of financial restructuring on economic activity by using
an alternative data set that in some respects more completely measures
change in the local banking sector than do data used in previous research.
Results suggest that the deaths of midsized banks have a negative but
short-lived impact on economic activity.

The Short-Run
Dynamics of LongRun Inflation Policy

by John B. Carlson,
William F. Gavin,
and Katherine A. Samolyk
Currently, the Federal Reserve is being urged to adopt price stability or an
explicit price-index target as its primary long-term monetary policy objec
tive. The purpose of this paper is to ascertain the short- and long-term
implications of an inflation policy for real output. Inflation policy is defined
here in terms of a series of innovations that exclusively determine trend
inflation. To estimate this series, the authors adopt a recently developed
method that allows structural interpretation of a simple vector-autoregression and apply it to a macroeconomic system that includes real output and
inflation. Results suggest that the benefits of a monetary policy aimed at
achieving gradual disinflation would probably outweigh the costs.

Material may be reprinted
provided that the source is
credited. Please send copies of
reprinted material to the editor.

ISSN 0013-0281

A Reexamination of the
Relationship between
Capacity Utilization
and Inflation
by PaulW . Bauer

Paul W. Bauer is an economist at
the Federal Reserve Bank of
Cleveland. The author would like
to thank John B. Carlson, Randall
W. Eberts, Stephen E. Haynes,
and John A. Tatom for their help
ful comments. Thomas Kluth
provided valuable research assis
tance at the beginning of this
project.

Introduction

Though parsimonious, the model imposes no
explicit macroeconomic world view and yet pro
vides a reasonable fit for the movements of
capacity and inflation in the U.S. economy. In
addition, the full sample period from 1953 to
1989 can be employed, since no evidence of
stmctural change is found. Although the result
ing estimate of the steady-state capacity utiliza
tion rate is consistent with previous research,
the structural approach permits us to examine
the dynamics of the relationship, as revealed
through simulated aggregate demand and sup
ply shocks.

The Federal Reserve places a high priority on
controlling inflation and ensuring full employ
ment of economic resources. Thus, empirical
relationships that can better inform policymak
ers about the prospects of these two key eco
nomic variables are eagerly sought. One such
relationship that has received attention over the
years is that between capacity utilization and
inflation (McElhattan [1978. 1985]. Tatom [19-79l.
and Gittings [1989]). Although these authors
have employed various theoretical and empiri
cal methods, in general they all have found evi
dence for a “steady state" or “natural rate" of
capacity utilization of about 80 percent to 82
percent. Deviations from this rate are directly
related to changes in the inflation rate.
Most of these models posit a single equation
in which capacity utilization is assumed to be an
exogenous variable that explains changes in the
inflation rate. However, economic theory7and
Granger causality tests suggest that both capacity
utilization and inflation are endogenous. This
empirical finding is incorporated here by construction of a two-equation stmctural model
based on the work of Haynes and Stone (1985).

I. Theoretical
Foundations
Capacity utilization is defined as actual produc
tion divided by capacity (section II briefly
reviews the problem of adequately defining
these terms). The belief that high capacity
utilization levels lead to an accelerated rate of
inflation is based on the assumption that high
capacity utilization levels are related to increas
ing marginal costs of production in the short
run. In the long run, high capacity utilization

may prompt new investment, thereby expand
ing capacity and relieving price pressures.
McElhattan (1978) was the first to develop a
model linking inflation and capacity utilization.
The model is composed of two basic structural
equations, one that relates prices to a markup on
unit labor costs, and another that relates wage
changes to labor-market excess demand and
expected inflation. The markup equation can be
written as
(1)

I R i t ) = a u W i t ) - a l} T it)
+ f [ C U ( t )] ,

where IR is the inflation rate, W is the rate of
change of nominal wages, T is the growth rate
of labor productivity, and f [CUi t )} = [b0 +
b ! CU ( t )] is a measure of excess aggregate
demand that is an increasing function of
capacity utilization (CU).
The second equation relates the rate of
change in nominal wages to the expected infla
tion rate (IR*), the growth rate of labor produc
tivity [7X0] , and the excess demand in the
labor markets ( h [u ( t ) ] 1. It can be written as
(2)

W it) =

a2i IR* i t )
+ a2}Ti t ) - h [ u i t ) ] ,

where h ( u ) is a decreasing function in the
unemployment rate in ). With this specification,
inflation-adjusted wage changes ( W- a n IR* )
rise in proportion to labor productivity for a
given level of unemployment.
Substituting equation (2) into equation (1)
and simplifying yields
(3)

I R i t ) = a u a2l IR* i t )
* 13) T it)
- a u h [u i t )] +/ [CUi t ) ] .

A number of restrictions are imposed by
McElhattan in order to estimate equation (3).
First, given the high correlation between unem
ployment and capacity utilization rates, only
one of these variables is included.1 McElhattan
argues that retaining the capacity utilization rate
is preferable because the natural rate of unem
ployment may be affected by demographic
changes, whereas capacity utilization is not.

Next, a formulation for inflation expectations
must be imposed. As is common in pricemarkup models, inflation expectations are
modeled as a weighted average of past inflation.
McElhattan finds that only the one-year lag is
statistically significant, and that its estimated coef
ficient is close to one. Because this coefficient
must equal one for there to be no long-run Phil
lips curve type of trade-off between inflation
and capacity utilization, this constraint is
imposed as well.
Given these restrictions (and a few other
minor ones), the reduced-form equation in
McElhattan’s model can be rewritten as
(4)

C IR it) = a [CUit ) - CUe]
+ i'it),

a > 0,

where CIR i t ) is the change in the inflation rate,
CU i t ) is the capacity utilization rate, CUe is the
natural rate of capacity utilization, and v i t ) is
statistical noise. With this formulation, it is easy
to see that when CU i t ) is larger (smaller) than
C U e , the inflation rate will increase (decrease),
and that when CU( t ) is equal to CU the infla
tion rate will remain unchanged. This can be
viewed as an output-gap model, with capacity
utilization playing the role usually reserved for
the unemployment rate.2
Gittings (1989) basically follows McElhattan’s
approach, but argues informally that there are
two reasons for the existence of inflationary pres
sures when capacity utilization is high. First, as
capacity constraints are reached, firms are better
able to increase their prices in the face of strong
demand; however, these same firms’ customers
may find themselves in a similar position. The
second argument is that aggregate-demand
growth raises the demand and prices for new
capital goods, along with the costs of financing
those goods, relatively more when there is less
idle capital to employ. Thus, over the business
cycle, the rental price of capital rises relative to
that of labor.
An entirely different approach is taken by
Tatom (1979), who sets up a partial adjustment
model in which changes in capacity utilization
are the result of monetary surprises. This
relationship can be written as
(5)

d C U it) = a[C U e- CUit-1)]
+ b\ m ( i ) - EtA [mit)\ I ,

1 The correlation between unemployment and capacity utilization is
http://fraser.stlouisfed.org/
-0.875
using
yearly data from 1953 to 1989.
Federal Reserve Bank
of St.
Louis

■

2 See appendix B in Hallman, Porter, and Small (1989) for an over
view of output-gap and price-gap models.

F I G U R E

Short-Run Cost Curves

SOURCE: Author's calculations.

where dCU(O is the change in the capacity
utilization rate, m ( t ) is the actual rate of mone
tary growth, and E tA [m(t ) ] is the anticipated
rate of monetary growth in the previous period.
Here, capacity utilization adjusts to its equilib
rium level ( CUe) with a lag, and departures
from CUeoccur as a result of monetary surprises.
This model is fundamentally different from
those underlying McElhattan’s and Gittings’s
work. In Tatom’s model, money causes inflation,
and only monetary surprises cause changes in
capacity utilization. There is no structural link
between capacity utilization and inflation, and
the natural rate of capacity utilization is achieved
as a result of an absence of shocks.

II. Definition and
Measurement of
Capacity Utilization
At first glance, the concepts of capacity and cap
acity utilization are easily defined.3 Capacity is
the potential output that an economic unit (for
example, a plant, a firm, an industry, or an econ
omy) can produce during a given period, and
capacity utilization is simply actual output
divided by potential output. However, these
seemingly straightforward definitions gloss over
a number of problems, the greatest of which is
that they fail to take account of operating costs

■

3 For a general overview of the problem of defining capacity, see
Bauer and Deily (June 15,1988). For a more detailed treatment, see Klein
and Long (1973), Rasche and Tatom (1977), and Berndt, Morrison, and
http://fraser.stlouisfed.org/
Wood (1983).
Federal Reserve Bank of St. Louis

as output varies. Output can be increased by
employing workers and machines for longer
hours, but this results in overtime and higher
maintenance costs.
One alternative is to define capacity' as the
level of output at which short-run average cost
(AC), total cost divided by output, is minimized
(point A in figure 1) 4 This definition has the
somewhat peculiar property that an economic
unit might produce at a rate greater than “cap
acity7,’’ but it does result in a much more
informative measure of capacity utilization. At
output levels below capacity (to the left of point
A in figure 1), output can be increased without
a significant increase in marginal cost (MC), the
extra cost incurred to produce one more unit of
output. However, when output exceeds capac
ity (to the right of point A in figure 1), increases
in output are associated with more rapid
increases in MC. This definition of capacity links
capacity utilization with MC, and thus is one
conceivable microeconomic foundation for the
belief that a connection between price move
ments and capacity utilization exists.
Unfortunately, economic data do not fall like
manna from heaven, but must be painstakingly
compiled. In the case of capacity (and hence
capacity utilization), the usual data collection
and aggregation problems are aggravated
because capacity is essentially unobserved—
unlike actual output. Another complicating fac
tor is the lack of a generally accepted definition
of capacity, as noted above.
Most studies that attempt to relate inflation to
capacity utilization employ the Federal Reserve’s
capacity utilization series. In light of the empirical
results presented below, it would be useful to
have at least a cursory understanding of how this
series is constructed. (For a more detailed discus
sion, see Raddock [1985,1990] .)
The Federal Reserve's goal is to provide cap
acity utilization estimates that reflect the same
degree of “tightness" over time for a given rate.
No primary data is collected, as the Federal
Reserve relies instead on annual surveys pro
duced by McGraw-Hill and the Census Bureau,
and on various industry sources. Strangely,
McGraw-Hill offers no definition of capacity
utilization to its survey respondents. The Census
Bureau offers definitions for its two measures of

■

4 Other authors (for example, Tatom [1979] and Rasche and Tatom
[1977]) advocate defining capacity utilization as the level of output at
which short-run and long-run average costs are equal, so that a firm's
demand for any fixed inputs just equals the amount it actually possesses.
Although this definition has some theoretical advantages, no organization
that produces capacity estimates uses it. Thus, the relative merits of alter
native definitions are mentioned only briefly here.

capacity, but most respondents apparently
ignore them (see Bauer and Deily [July 1, 1988]).
After a preliminary end-of-year index of
industrial capacity is calculated, data are adjusted
to remove apparently excessive fluctuations and
short-term peak capacity. As a result, capacity

F I G U R E

figures reflect maximum “sustainable” capacity.
Monthly and quarterly estimates are generally
straight-line interpolations from past end-ofyear estimates and are based on projections of
capacity growth for the current year. Because
capacity is unlikely to grow7at a constant rate
throughout the year, the monthly and quarterly
estimates should be treated with more caution
than the yearly estimates.'1 For this reason, only
annual data are analyzed here.

Change in Inflation and Capacity
Utilization Rates, 1950-88
III. Empirical
Findings: Puzzles
and Possible
Explanations

Standardized units

NOTE: Series were standardized to have a mean of zero and a variance of
one.
SOURCES: U.S. Department of Labor. Bureau of Labor Statistics, and Board
of Governors o f the Federal Reserve System.

TABL E

Correlation between the Change in
U.S. Inflation and Capacity Utilization
Rates and Lagged Capacity Utilization

cm ( t )

0 7 (0

c u itp

1980-89

0.885

1970-79

0.407

0.825

1960-69

0.461

0.090

1950-59

0.315

-0.439

SOURCE: Author's calculations.

0.530

Figure 2 plots changes in the inflation and the
capacity utilization rates from 1950 to 1988. It
appears that the two series are related— at least
indirectly— as manifested by the wray in which
they tend to move together. Gittings, who only
reports results for the 1971 to 1988 period,
asserts that there is a fairly uniform one-year lag
between the two series. Although this appears
to be true for most of the 1970s, it does not
seem to apply to the 1980s. Simple correlation
among changes in the inflation and the capacity
utilization rates confirms this (table 1). Note that
during the 1950s there was actually a negative
correlation between changes in the inflation rate
and the lagged value of capacity utilization. This
simple analysis reveals that, whatever the rela
tionship between capacity utilization and infla
tion, it appears to vary over time.
Figure 3 plots the change in the inflation rate
against the capacity utilization rate. Although a
straight line appears to fit these data well, clearly a
great deal of noise exists in the relationship. Table
2 reports estimates of the McElhattan model
(equation [3]) using various sample periods.
Although the point estimate of the noninflationary rate of capacity7utilization ranges only
from 80.0 percent to 81.9 percent, when the 95
percent confidence interval can be computed, it
suggests a range of from approximately 78 per
cent to 83 percent. The extent to which a change
in inflation is associated w7ith a given deviation
of capacity utilization from the natural rate (the
b coefficient) also appears to vary over time.
The "penalty” for a divergence from the equilib
rium rate of capacity utilization w7as 71 percent

■

5 Gittings (1989) attributes the failure to find any correlation between
the change in inflation rates and capacity utilization in the monthly series to
noise in the price series. However, the failure could also be a result of
problems in the monthly estimates of capacity utilization.

TABLE

OLS Results for the McElhattan
Model Using Various Time Periods
F ull
S am ple

V ariable

0.00154
(3.52)

1950-69
0.00127
(1.60)

1960-79
0.00106
(1.75)

1970-89
0.00217
(3.55)

Constant

-0.125
(-3.48)

-0.104
(-1.56)

-0.848
(-1.68)

-0.174
(-3.56)

CUe

81.4
[78.7, 83.6 f

81.9

80.0

80.3
[78.2. 82.5Î

0.246

0.125

0.145

0.411

a. 95 percent confidence intervals computed following McElhattan (1978).
h. The procedure fails when both parameter estimates are not statistically significant at the confidence level selected (see Scadding [19731).
NOTE: C IR (t) = a + b CU( t ) + e ( t ). T-statistics are indicated in parentheses.
SOURCE: Author's calculations.

F I G U R E

Change in Inflation Rate vs.
Capacity Utilization
Change in inflation, percent

75
80
85
Capacity' utilization, percent
NOTE: Yearly data.
SOURCES: U.S. Department of Labor, Bureau of Labor Statistics, and Board
of Governors of the Federal Reserve System.

higher in the 1970s and 1980s than in the 1950s
and 1960s, although the difference does not
appear to be statistically significant.
If capacity utilization is a true measure of
capacity constraints that result in higher infla
tion, then one would expect the Federal
Reserve's capacity utilization series (which cov
ers only manufacturing, mining, and utilities) to
predict more accurately the price pressure for
goods than for goods and services or just serv
ices. The empirical evidence for this conjecture
is mixed, however (see table 3).
As expected, the worst fit is found between
capacity utilization and the change in the infla
tion rate for services, although indirect effects
are observed. The better fit (at least as meas
ured by the R 2 coefficient and the statistical sig
nificance of the coefficients) between capacity
utilization and changes in the overall implicit
price deflator relative to goods only is unex
pected, however. Capacity utilization should be
more directly related to changes in the price of
goods than to changes in the prices for all goods
and services. This finding could be a result of the
variance of the goods implicit price deflator
being four times larger than the one for services.
At this point, it is appropriate to ask whether
capacity' utilization should be treated as an
exogenous variable. Models can easily be devel
oped in which both capacity Litilization and
changes in inflation are jointly endogenous.
Granger causality tests can then be employed to
examine this view, although the results must be
interpreted cautiously (see Conway, Swamy,
and Yanagida [1983]).

TABLE

OLS Estimates of
McElhattan’s Model Using
Various Measures of Inflation

V ariable

cn
Constant
CU e

G oods an d
Services

G oods

0.172
(4.24)

0.199
(3.21)

0.071
(1.91)

-14.0
(-4.21)

-16.2
(-3.21)

-5.76
(-1.90)

81.7
[78.8, 84.8]a

81.3
[70.6, 89.3f‘

81.4
[79.2, 83.4]a

Services

0.360

0.249

0.105

a. 95 percent confidence interval.
NOTE: Implicit price deflator = 31. T-statistics are indicated in parentheses.
SOURCE: Author's calculations.

T A B L E

Granger Causality Tests
H 0: C apacity u tiliz a tio n does n o t Granger-cause
changes in in fla tio n
Lag
Lengths

lnLu

lnLc

L ik e lih o o d
R atio 3

1
2
3
4
5
6

-50.34
-46.88
-42.43
-38.47
-32.39
-26.35

-56.21
-52.99
-49.34
-45.17
-40.55
-35.91

11.74
12.22
13.82
13.40
16.32
19.12

H (): C hanges in in fla tio n d o n o t Granger-cause
capacity u tiliz a tio n
Lag
Lengths

lnLu

lnLc

L ik e lih o o d
R a tio 0

1
2
3
4
5
6

-88.80
-82.39
-73.74
-65.63
-58.12
-53.13

-91.24
-84.84
-77.25
-69.77
-64.11
-58.99

4.88
4.90
7.07
8.28
11.98
11.72

For this study, the tests are performed as sug
gested by Guilkey and Salemi (1982), with both
a time trend and an equal number of lags of the
two right-side variables. The number of lags
was varied in order to check for robustness (see
table 4). Within the framework of the Granger
causality test, a variable x does not Grangercause y if the coefficients on the lags of x are
all not statistically different from zero. This
hypothesis can be easily examined using a
likelihood-ratio test.6
The second and third columns indicate the
value of the likelihood function of the uncon
strained model and the constrained model,
respectively (for the latter, coefficients of the lag
of the nondependent variable are constrained to
equal zero). The last column lists the value of
the test statistics and indicates whether the
hypothesis of no unidirectional Granger causal
ity can be rejected. These results suggest that
there is bidirectional causality between the two
time series, although the link from capacity
utilization to changes in the inflation rate
appears to be stronger (or at least easier to confirm statistically).
Bidirectional causality between capacity util
ization and changes in the inflation rate is more
consistent with Tatom's approach than with
those of McElhattan and Gittings. However, a
more complete analysis of the relationship can
be obtained through use of a more fully speci
fied structural model that relates the two time
series explicitly. The work of Haynes and Stone
(1985) provides the basis for one such model.

IV. A Structural
Approach
Haynes and Stone construct a model of aggre
gate demand and supply that is identified by its
dynamics: In the short run, quantity sold is
demand detemiined, but price is supply deter
mined. Given this assumption, shifts in aggre
gate demand trace out aggregate supply,
affecting output before prices and leading to an
inverse relationship between inflation and
lagged unemployment. Haynes and Stone's
aggregate supply equation can be written as
(6)

a. Statistically significant at the alpha = 0.01 level.

7 (0 = -1 /a U ( t - i ) - b / a d l ( t )
+ e (t),

i > 0,

b. Statistically significant at the alpha = 0.1 level.
SOURCE: Author’s calculations.

■

6 The test statistic 2(lnu - lnLc) is distributed chi-squared with k
degrees of freedom under the null hypothesis, where kis the number of
constraints placed on the model (the number of lags set to zero).

TABLE

Estimates of Aggregate
Supply and Demand

Param eter
a
b
c
d
A
B
C
Dl
D2
RHO l a
RHO 2a

E stim ate

Standard
E rro r

-0.108
0.866
0.0013
0.151
21.7
-89.0
0.716
104.4
-33.2
-0.265
-0.308

0.0306
0.0703
0.0004
0.0582
9.07
17.2
0.106
20.0
19.6
0.172
0.168

T-Statistic
-3.52
12.32
3.48
2.61
2.39
-5.17
6.79
5.22
-1.70
-1.54
-1.83

between money and capacity utilization and
inflation, along the lines of Tatom (1979).7
In both equations, lag lengths of one year for
capacity utilization and inflation yield the lowest
mean-squared error (the specification criterion
employed by Haynes and Stone). In the case of
M2 growth, allowing for a lag length of two
years in the supply equation and of both one
and two years in the demand equation yields the
lowest mean-squared error. Equations (8) and
(9) are estimated with an allowance for autocor
relation and cross-equation correlations. Results
are presented in table 5.
(8)

+ d g m 2 (t- 2 ) + e i t )
(9)

a. RHO 1 and RHO 2 are the autocorrelation coefficients in equations (8)

(7)

U it ) = c+ f l i t - j ) + g U i t - i )
+ v it), j > 0,

where c, / , and g are parameters, and demand
shocks enter the system through v it ). The
authors assume that the response of unemploy
ment to a supply shock occurs after the price
response, so unemployment is related only to
lagged inflation.
The Haynes-Stone framework is modified
here to use the capacity utilization rate rather
than the unemployment rate. It is also aug
mented to include the lagged value of M2
growth as an explanatory variable in each equa
tion. Inclusion of M2 growth allows for both a
varying monetary policy, certainly an important
influence on the inflation rate, and links

c u it)=

A + B irit- l) + C c u it- l)
+ D 1 gm 2 (7-1)

and (9).
NOTE: Value of the likelihood function = 30.05183SOURCE: Author's calculations.

where 7 ( 0 is the inflation rate, U is the unem
ployment rate, d is the difference operator, e
is an error term, and a and b are positive con
stants. U(t - i ) is a generalized delay of i peri
ods, and d l i t ) is an adaptive representation of
inflationary expectations. Supply shocks enter
the system through e it).
Similarly, short-run shifts in aggregate supply
trace out aggregate demand. These shifts affect
prices before output, leading to a direct relation
ship between unemployment and lagged infla
tion. Haynes and Stone model aggregate
demand as

i r i t ) = a + b ir it- l) + c c u it- l)

+ D 2 gm 2 it -2) + v i t )
Given the dynamic assumptions that identify
the model, the coefficient on lagged capacity
utilization should be positive in the supply
equation (8), and the coefficient on lagged infla
tion should be negative in the demand equation
(9). Both coefficients are of the expected sign
and are statistically significant, so reasonable
supply and demand relationships appear to
have been estimated.
In the short run, faster M2 growth leads to
higher inflation and higher capacity utilization
(possibly by stimulating aggregate demand, but
perhaps through monetary shocks, as suggested
by Tatom). However, solving this two-equation
system for the long-run steady state indicates
that the gain in steady-state capacity utilization
is quite modest. In fact, the system can be reesti
mated with the constraint that M2 growth not
affect steady-state capacity utilization by setting
(10)

D l + D 2 = - B d / i l - b).

This constraint cannot be rejected at any reason
able confidence level, and thus provides evidence
of a natural-rate hypothesis for capacity utiliza
tion.8 This suggests that the Federal Reserve is
fairly successful in ensuring that a given capac-

■

7 Although it is difficult to conceive of a rationale for including M2
growth in the supply equation, the coefficient is statistically significant.
Perhaps it influences the real cost of financing in the short run. Alterna
tively, this two-equation system could be reinterpreted as a VAR model.

■

8 This hypothesis was tested using a likelihood-ratio test that is dis
tributed chi-squared with one degree of freedom. The test statistic was
0.62, with a 1 percent critical value of 6.645 (the critical value for a 5 per
cent test was 3.84).

F I G U R E

steady-state inflation rate would mirror
increases in M2 growth.9 This property is
equivalent to imposing

Effect of a Supply-Side
Shock on Inflation

(11)

c = (1 - 0 ( 1 - b - d )/(B + D\ + D2)

Percent
10l---

Ol__________ I__________ I__________ L
0

20
Years

SOURCE: Author's calculations.

F 1G U R E

Effect of a Supply-Side
Shock on Capacity Utilization
Percent

A likelihood-ratio test fails to reject this null
hypothesis at any reasonable level of signif
icance (the value of the test statistic is 0.32),
confirming expectations.10
One advantage of this model over the single
equation type represented by equation (4) is
that no significant structural change seems to
occur over the sample period. This hypothesis
was tested by dividing the sample into two peri
ods, 1953-71 and 1972-89, and reestimating the
model for each. A likelihood-ratio test was then
performed to see whether the null hypothesis of
no structural change could be rejected. The chisquared test statistic w ith 13 degrees of freedom
was 25.0, with a 1 percent critical value of 27.7.
At this level of significance, it was found that
the null hypothesis cannot be rejected.11
The long-term behavior of this tw o-equation
system can now be investigated by solving for
its steady-state solution. The steady-state capac
ity7utilization rate is 81.5 percent w’hen M2
grows at a yearly rate of 7 percent— an estimate
that is very close to those reported for the single
equation model (tables 2 and 3).12 The steadystate inflation rate is detenuined by the growth
rate of M2.
The dynamics of the two-equation model
can be illustrated through an examination of two
simulations. The first introduces a 2 percent sup
ply shock to the supply equation in the tenth
period. (This could represent a sudden increase
in the price of oil, for example.) The effects of
this shock on inflation and capacity utilization
are illustrated in figures 4 and 5. Initially, infla
tion increases wrhile capacity utilization remains
unaffected (because only the lags of variables

■

9 In the steady-state reduced form, inflation is equal to the M2
growth rate minus the growth rate of real output. The model’s estimate of
average real GNP growth over this period is 2.2 percent— a little less
than its 2.9 percent average annualized growth rate estimate.

Years
SOURCE: Author's calculations.

ity utilization rate does reflect the same degree
of “tightness" over time (unlike earlier research
based on models such as equation [3], where
the “penalty“ for deviations from the steadystate capacity utilization rate varied over time).
Given that M2 velocity is roughly constant in

the
http://fraser.stlouisfed.org/ long run, we would also expect that the
Federal Reserve Bank of St. Louis

■

10 An unfortunate feature of this model is that it is impossible to
impose simultaneously the constraints that 1) steady-state capacity
utilization not depend on M2 growth and 2) steady-state inflation
increase in tandem with M2 growth.

■

11 Although the null hypothesis would be rejected at the 5 percent
level (the critical value here is only 22.4), given the large number of
parameters that are allowed to vary and the extremely limited number of
observations, a relatively tight level of significance is justified.

■

12 See also McElhattan (1978,1985), Tatom (1979), and Gittings

(1989).

F I G U R E

appear on the right side of equations [8] and [9]).
In the next period, inflation begins to decline
toward its long-run steady state (in part because
the shock is no longer present), and capacity
utilization decreases because the preceding
period’s higher inflation reduces demand. Capac
ity utilization continues to decline for three
periods and then rebounds toward its long-run
steady state. Although the model does experi
ence some “overshooting,” the system is more
highly damped than many large macroeconomic
models.13
In the second simulation, inflation and
capacity utilization rates are examined as the
rate of M2 growth is reduced from 12 percent
to 7 percent in the tenth year (see figures 6 and
7). At the beginning, the steady-state inflation
and capacity utilization rates are 8.4 percent
and 80.1 percent, respectively. Given the lag
structures in equations (7) and (8), the initial
effect is felt as a reduction in capacity utiliza
tion the following year, a decline that contin
ues over the next three years. Inflation remains
unaffected until the second year after the
policy change, and then falls throughout each
of the next six years. Even though the system is
highly damped, there is still some overshooting
in both the inflation and capacity utilization
rates. Ultimately, the system reaches a new
steady state with nearly the same capacity utili
zation rate (81.5 percent— not a statistically sig
nificant difference), but with an inflation rate of
only 4.0 percent.
Figure 8 provides some insight into why the
McElhattan-type “misspecified” model (at least
in reference to the current one) yields reason
able results despite the apparent structural
change over time. The figure plots the change
in the inflation rate against the capacity utiliza
tion rate as the system returns to equilibrium fol
lowing a reduction in the growth rate of M2.
Clearly, a world with many such shocks could
easily generate a plot similar to that of figure 3A direct relationship between capacity utiliza
tion and changes in inflation would always be
found, but depending on the latest shocks to
the system, the actual estimated parameter

Effect of a Reduction in M2
Growth on Inflation
Percent
10

30
Years

SOURCE: Author's calculations.

FI GURE

Effect of a Reduction in M2 Growth
on Capacity Utilization
Percent

SOURCE: Author's calculations.

30
Years

■

13 Judd and Trehan’s (1989) approach also finds relatively damped
cycles, and is sim ilar in spirit to this study in that it does not subscribe to
any particular macroeconomic theory. The authors identify supply and
demand shocks for a five-variable VAR system (unemployment rate, real
GNP, nominal interest rate, labor supply, and foreign trade) using rather
uncontroversial restrictions. Their approach yields even shorter cycles
that exhibit much less overshooting. This could be the result of more
detailed modeling (the inclusion of five variables) or of the use of quar
terly rather than annual data. Because their study includes unemployment
data, it is not limited by the capacity utilization problems discussed
earlier.

F I G U R E

Simulated McElhattan-Type Plot
Percent

SOURCE: Author's calculations.

values of the regression line wrould change.
This is consistent writh results presented earlier.
In short, as a rough approximation, this rela
tively simple model tracks the U.S. economy’s
response to the major economic events of the
1970s and 1980s reasonably w^ell. It also pro
vides some insight into why the basic relation
ship between capacity utilization and inflation
appears to vary over time.

V. Conclusion
This paper examines the theoretical and empiri
cal relationship between capacity utilization
and inflation. Although there clearly is a connec
tion between these two time series, earlier
models suggest that stmctural changes occurred
in the relationship over the 1953-89 period.
Granger causality tests appear to confirm the
suspicion that there is bidirectional causality
between capacity utilization and a change in
the inflation rate. One implication of this finding
is that alternative models that treat both vari
ables as endogenous should be employed.
A relatively simple two-equation structural
model is developed here that is sufficient to
explain the relationship. The dynamics of sup
ply and demand relationships are employed to
identify the system following Haynes and Stone
(1985), with the model treating both capacity

utilization and inflation as endogenous vari
ables. No evidence of structural change is found
from 1953 to 1989, but because of the relatively
small number of yearly observations, only two
subperiods are investigated.
The natural rate of capacity utilization is
found to be about 81.5 percent and inde
pendent of the growth rate of M2. Although
faster monetary growth increases both capacity
utilization and inflation in the short mn, only
inflation is increased in the long run (moving in
tandem with M2 growth). As a rough approxi
mation, the model appears to track the real
economy’s reaction to supply and monetary
shocks reasonably well. However, development
of the proper framework for examining the
endogenous relationship between capacity
utilization and inflation is the most important
contribution of this study.

References
Bauer, Paul W. and Deily, Mary E., Measur
ing the Unseen: A Primer on Capacity Util
ization,” Economic Commentary', Federal
Reserve Bank of Cleveland, June 15, 1988.

Klein, Lawrence R. and Long, Virginia,
"Capacity Utilization: Concept, Measurement,
and Recent Estimates,” Brookings Papers on
Economic Activity, Washington, D.C.: The
Brookings Institution, 1973, 3, 743-56.

McElhattan, Rose, “Estimating a Stable___________ , "A User’s Guide to CapacityUtilization Measures,” Economic Commen
tary, Federal Reserve Bank of Cleveland,
July 1, 1988.

Berndt, Ernst R., Morrison, Catherine J.,
and Wood, David O., " The Modeling, Inter
pretation, and Measurement of Capacity
Utilization," contract requisition 63-3-2.
Washington, D.C.: U.S. Department of Com
merce, Bureau of the Census, U.S. Govern
ment Printing Office, May 1983.

Conway, Roger K., Swamy, P.A.V.B., and
Yanagida, John F., "The Impossibility of
Causality Testing," Special Studies Paper No.
178, Washington, D.C.: Board of Governors
of the Federal Reserve System, Division of
Research and Statistics, July 1983.

Gittings, Thomas A., “Capacity Utilization and
Inflation,” Economic Perspectives, Federal
Reserve Bank of Chicago, May/June

Inflation Capacity-Utilization Rate,”
Economic Review, Federal Reserve Bank of
San Francisco, Fall 1978, 20-30.
_________, “Inflation, Supply Shocks and the
Stable-Inflation Rate of Capacity Utilization,”
Economic Review, Federal Reserve Bank of
San Francisco, Winter 1985, 45-63-

Raddock, Richard D., “Revised Federal
Reserve Rates of Capacity Utilization,”
Federal Reserve Bulletin, October 1985, 71,
754-66.
___________ , "Recent Developments in Indus
trial Capacity and Utilization,” Federal
Reserve Bulletin, June 1990, 76, 411-35.

Rasche, H. Robert and Tatom, John A., “The
Effects of the New Energy Regime on
Economic Capacity, Production, and Prices,”
Economic Review, Federal Reserve Bank of
St. Louis, May 1977, 2-12.

1989, 2-9.

Scadding, John L., “The Sampling Distribution
Guilkey, David K. and Salemi, Michael K.,
“Small Sample Properties of Three Tests for
Granger-Causal Ordering in a Bivariate Sto
chastic System,” Review o f Economics a n d
Statistics, November 1982, 64, 668-80.

Hallman, Jeffry J., Porter, Richard D., and
Small, David H., M2 per Unit o f Potential
GNP as an A nchorfor the Price Level. Staff
Studies 157, Washington, D.C.: Board of
Governors of the Federal Reserve System,
April 1989.

Haynes, Stephen E. and Stone, Joe A., “A
Neglected Method of Separating Demand
and Supply in Time Series Regression," Jour
n al o f Business & Economic Statistics, July
1985,3, 238-43.

Judd, John P. and Trehan, Bharat, “Unemployment-Rate Dynamics: AggregateDemand and -Supply Interactions,”
Economic Review, Federal Reserve Bank of
San Francisco, Fall 1989, 20-37.

of the Liviatan Estimator of the Geometric
Distributed Lag Parameter,” Econometrica,
May 1973, 41, 503-508.

Shapiro, Matthew D., "Assessing the Federal
Reserve’s Measures of Capacity and Capacity
Utilization,” Brookings Papers on Economic
Activity, Washington, D.C.: The Brookings
Institution, 1, 1989, 181-225.

Tatom, John A., “The Meaning and Measure
ment of Potential Output: A Comment on Perloff and Wachter Results,” Three Aspects of
Policy and Policymaking: Knowledge, Data,
and Institutions, Camegie-Rochester Con
ference Series on Public Policy. New York:
North Holland Publishing Co., 1979, 10,
165-78.

Thornton, Daniel L. and Batten, Dallas S.,
"Lag-Length Selection and Granger
Causality,” Working Paper84-001, Federal
Reserve Bank of St. Louis, 1983.

Financial Restructuring
and Regional Economic
Activity
by Brian A. C rom well

Brian A. Cromwell IS an
economist at the Federal Reserve
Bank of Cleveland. The author
wishes to thank Randall Eberts,
Joseph Haubrich, Louis Jacob
son, Elizabeth Laderman,
Katherine Samolyk, and Gary
Whalen for helpful comments and
suggestions.

Introduction

more than half of these were permitted to offer
all banking services.1 Bank failures, which
averaged 75 per year in the 1970s, rose from
less than 50 per year in the early 1980s to more
than 200 per year by 1987. Financial-sector
restructuring in the fonn of bank mergers,
takeovers, or failures can affect investment and
consumption decisions by disrupting the links
between borrowers and creditors.
This paper explores the impact of financial
restructuring on economic activity using an alter
native data set that in some respects more com
pletely measures change in the local banking
sector than do data used in previous research.
Restructuring in the local banking sector is
measured by the birth, expansion, contraction,
and death of banks within a standard metropol
itan statistical area (SMSA), as estimated by the
Small Business Administration using Dun and
Bradstreet files.
These data, known as the U.S. Establishment
and Longitudinal Microdata (USELM), attempt to

The relationship between the perfonnance of
the financial sector and economic activity has
received increasing attention from economists in
the past decade. The “credit view” holds that var
iation in the supply of financial services not cap
tured in monetary aggregates can help to
explain real economic activity. Empirical studies
of the importance of the banking sector have
been conducted at both the macro and the
regional level and in general support the view
that financial structure and stress can have real
effects.
Interest in this view coincides with the dra
matic restructuring of the financial sector that
has accompanied both the advent of deregula
tion and interstate banking and the significant
increase in bank failures in the 1980s. By the
end of 1988, all but three states permitted some
form of interstate acquisition of their banks,
14,600 offices of banking organizations existed
outside of the organizations’ home state, and

■ 1 These figures come from a recent comprehensive review of inter
state banking by King, Tschinkel, and Whitehead (1989). Earlier surveys
include Whitehead (1983a, 1983b, and 1985), and Amel and Keane
(1986).

record the location and employment levels of
all establishments in all industries. For multi
establishment firms, ultimate ownership of each
establishment is tracked. With respect to bank
ing. an establishment equals a bank, a bank sub
sidiary, or branch office.2 (Although the USELM
establishment framework does not account for
the variations in bank organizations across states,
its advantage is that it is applied consistently.)
The USELM data are aptly suited for examin
ing the disruption of credit relationships, since
Dun and Bradstreet collects these data for the
purpose of recording the creditworthiness of
firms. A "death" is recorded if a firm fails or is
taken over by management sufficiently different
from existing management to warrant a reexam
ination of the firm’s credit. To the extent that
takeovers, management changes, and branch
closings affect local credit relations, the employ
ment effects from bank deaths can potentially
measure the dismption of credit links between
borrowers and lenders.
The empirical analysis presented here uses
employment changes resulting from the birth,
death, expansion, and contraction of small, m id
sized, and large banks as a proxy for restructur
ing in the banking industry. These measures are
linked to the local economic performance of
217 SMSAs in the periods 1980-82 and 1984-86.
If the credit view is supported, the impact of
employment changes from a bank death should
be negative and significantly greater in mag
nitude than the impact of a bank contraction,
since a bank closing should be more disruptive
to credit relations than simply a reduction in the
bank’s staff.
The results suggest that the deaths of mid
sized banks— those employing between 100 and
500 employees— have a negative but short-lived
impact on economic activity. Exploration of the
channels for this impact indicates that bank
deaths affect employment in other midsized
firms that presumably rely principally on local
banking markets and that are the most likely
customers of midsized banks. The results control
for overall financial restmcturing and lagged
economic activity, and are robust across several
specifications.

I. Local Economic
Effects of Financial
Stress
Restructuring due to financial stress— reflected
in bank failures, closings, and mergers— is poten
tially detrimental to local economic growth.3 In
the case of a bank failure, several types of eco
nomic agents may be affected. Bank share
holders and uninsured depositors, for example,
may suffer declines in wealth. For a local econ
omy, however, any wealth effect is likely to be
small. If the failed bank is merged with another
bank, as is commonly the case, uninsured
depositors may suffer no losses. Moreover, even
if a failed bank is closed, these depositors gen
erally recover (over time) a high percentage of
their funds when the bank’s assets are liquidated.
Another effect on local economic activity
takes place through reductions in bank employ
ment w7hen banks fail or are taken over. In addi
tion to this direct consequence for local employ
ment, unemployed bank workers suffering a
loss of personal income will also likely reduce
their consumption expenditures, sending a fur
ther negative ripple (or multiplier) effect
through the economy. The following section
presents direct measures of the employment
losses caused by bank deaths and looks for
employment effects in nonbank sectors. More
important, it examines a direct measure of
employment losses in banking due to bank con
tractions to see if the spillovers from bank
employment losses are different for failures
than for contractions.

Credit Disruptions
In addition to these two potential effects, bank
closings can disrupt credit relationships. The
credit-view literature holds that the principal
channel of a bank failure’s economic impact is
the dismption of borrower-lender relationships.
Each lender is assumed to have more informa
tion on its existing borrowers than do other
potential lenders.4 W hen bank failure results in
closure rather than reorganization, borrowers
are forced to seek credit from new sources. Dur
ing the period in wrhich a new long-term credit

■

3 This section in part follows Gilbert and Kochin’s (1990) presen

tation.

■

2 In practice, Dun and Bradstreet tracks all banking establishments
http://fraser.stlouisfed.org/
listed in telephone directories, including branch offices.
Federal Reserve Bank of St. Louis

■

4 Gertler (1988) surveys the literature on credit and aggregate
economic activity. Articles on the theory of financial intermediation
include Diamond (1984) and Campbell and Kracaw (1980).

relationship is established, borrowers are likely
to face higher costs of credit or credit rationing.
Even if the failed bank is merged into a sur
viving bank, borrowers may encounter new
loan policies and new senior management if
they apply for extensions of their credit. Again,
the terms of credit are likely to be less favorable
than those offered by the previous management.
The USELM data count bank takeovers and
mergers as bank deaths only if there is a
change in operating management sufficient for
Dun and Bradstreet to reexamine the new
organization’s credit rating. To the extent that
takeovers, management changes, and branch
closings affect local credit relations, USELM’s
measures of change in bank employment due
to bank deaths can be used to estimate the dis
ruption of credit links between borrowers and
lenders. Because we can also measure employ
ment changes due to bank contractions, we
use the difference between the impact of bank
contractions and bank deaths to distinguish
between the direct employment effects of a
bank death and the impact of credit dismptions.'1

Empirical Evidence
Empirical studies of the importance of the bank
ing sector at both the macro and the regional
level generally support the view that financial
structure and stress can affect economic activity.
In a study of the macro effects of financial
stress, Bemanke (1983) argues that extensive
bank runs and defaults in the 1930-33 financial
crisis reduced the efficiency of the financial sec
tor in performing its intermediation function,
and that this had adverse effects on real output
through other than monetary channels . He
examines the effect of the real value of the
change in deposits of failed banks on the
growth rate of industrial production. Using
regression analysis with monthly data for the
years 1919 through 1941, Bernanke finds that
bank failures have a negative and statistically
significant effect on industrial output. Samolyk
(1988) conducts a similar test on British data,
using corporate and noncorporate insolvencies
as proxies for the health of the financial sector,
and also finds that credit factors matter empiri
cally in explaining output. Using Canadian data,
Haubrich (1990) also determines that the credit

■ 5

We maintain the assumption that the local economic effect of a

bank contraction results solely from the multiplier effect of reduced
http://fraser.stlouisfed.org/
employment, rather than from credit disruptions.
Federal Reserve Bank of St. Louis

disruptions resulting from bank failures, as
opposed to expansions or contractions, affect
economic activity.
The credit view would also predict an impact
of stress in local banking markets on local econo
mies. Calomiris, Hubbard, and Stock (1986)
examine the impact of bank failures on real farm
output. Using annual state data for farm output,
they find that the number of bank failures lagged
one year has a negative and statistically signifi
cant effect. Gilbert and Kochin ( 1990) test the
hypothesis that bank failures have adverse
effects on sales subject to sales tax and on
county employment using rural county-level
data. They find that bank closings have a nega
tive impact on local sales and on nonagricultural
employment.

II. Alternative Data
on Financial
Restructuring: The
USELM File
Financial restructuring at the local level is ana
lyzed here by the birth, expansion, contraction,
and death of banking establishments at the
SMSA level, as measured in the USELM data for
the periods 1980-82 and 1984-86. The longitu
dinal establishment data files of the U.S. Estab
lishment and Enterprise Microdata (USEEM)
were constructed primarily from data in the Dun
and Bradstreet Duns Market Identifier Files
(DxMI). The Small Business Administration then
assembled more than 16 million establishment
records contained in the DMI files to construct
the USELM file. A team from the Brookings Insti
tution merged DMI data longitudinally and asso
ciated each establishment with its owners. The
longitudinal detail in the data makes it possible
to measure employment change of establish
ments in a given size class. It also allows employ
ment change to be decomposed by establish
ment birth or death, or by the growth (or con
traction) of continuing establishments, and
allows for the tracking of mergers, acquisitions,
and divestitures.
The data base includes employment figures
and industry classifications for all establishments
and enterprises, sales data for all enterprises and
subsidiaries, age of all nonbranch establish
ments, and organizational status and geographic
data for each establishment. In principle, every
establishment in the United States is covered,
except for federal agencies.
Dun and Bradstreet’s principal business is
provision of credit ratings, which must be

updated to reflect discontinuances of business
management. Among other activities, the firm
tracks court proceedings in bankruptcy cases in
order to record the creditworthiness of estab
lishments. A death is recorded if the establish
ment is closed or is taken over by management
that is sufficiently different to warrant reexamin
ing the firm’s credit.6
In the case of banking, a death could repre
sent the takeover of a bank by another bank
(recorded simultaneously as a death and an
expansion), a major change in ownership and
management (recorded simultaneously as a
death and a birth), or a tme failure (recorded
solely as a death). As such, it is an imperfect
measure of liquidations of financial institutions.
To the extent that takeovers and management
changes affect local credit relations, the USELM
measure of bank deaths can proxy for the dis
mption of credit links between borrowers and
lenders.

Advantages of the
USELM Data
In an exploration of the impact of bank struc
ture on regional development, Bauer and Crom
well (1989) show that the private banking sec
tor appears to be systematically related to firm
births. This study, however, is limited by the
inadequacy of data on the location of financial
institutions. Bank data were obtained from the
Federal Financial Institutions Examination
Council’s Reports on Condition and Income,
known as call reports, for 1980. For some finan
cial measures such as total loans, however, it is
not possible to detennine where the loans were
made, even though their dollar value is known.
For example, loans made by an Ohio bank to
firms in Florida and Ohio are counted in the
same wray. An additional measurement problem
is that a call report for a consolidated banking
unit may include data for branches not located
in the SMSA. In states that allow branch bank
ing, activity at the branches may be reported
solely in the headquarter’s SMSA.

■

6 In practice, a death is identified when a firm appearing in the Dun
and Bradstreet file in an initial year does not appear in an end-year file.
The reason could be either that the firm was actually closed or that its
identifying number was changed as a result of new management. Sim i
larly, a birth is identified by the appearance of a firm in the end-year file.
The Small Business Administration uses two-year intervals to track firms.

In principle, the LISELM data report the loca
tion and employment levels of banking estab
lishments with a greater degree of accuracy
than the call report data, which record bank
statistics at the firm (headquarters) level for
many establishments (subsidiaries or branches).
For example, a bank headquartered in Cincin
nati could report data for its Columbus and
Dayton branches in its call report, resulting in a
distortion of the measured banking activity in
Cincinnati. For purposes of the USELM data,
however, branches in Columbus and Dayton
are recorded as establishments in those SMSAs.
Out-of-state ownership of establishments is also
recorded, allowing examination of the impact
of interstate banking.

Disadvantages of
the USELM Data
The USELM data set, while having advantages
for this analysis, is not problem free. The major
reasons to question the validity of statistics
derived from DMI files stem from three charac
teristics of the underlying data-collection effort.
First, the employment figures are self-reported
by establishments, usually in telephone inter
views. Second, employment figures are not
routinely updated; updates are primarily a result
of requests for credit checks. Jacobson (1985)
reports that substantial lags can occur between
the date the file is extracted and the last time the
firm was surveyed. Employment statistics are
often more than two years out of date, which
may lead to infrequent reports for smaller and
slower-growing establishments with less need
for credit checks, and to delays in picking up
shutdowns and status changes for these firms.
Third, there are delays in recognizing the crea
tion of new establishments. A business may be
in existence three to five years before it is
recorded as a birth in the USELM data.
To deal with some of these shortcomings,
the Small Business Administration has modified
the Dun and Bradstreet data in several ways in
constructing the USELM file/ Establishments for
which employment data were missing were
assigned the state-level median employment for
organizations in their standard industrial classi
fication (SIC) code. Some 8.7 percent of estab
lishments in finance, insurance, and real estate
(FIRE) received estimated employment figures in

■ 7

See Harris (1983) and Armington and Odle (1983,1984).

TABLE

Bank Employment Share:
Small, Midsized, and Large Banks
B ank Size

1980

1982

1984

1986

Type 1:
0 to 100 employees

0.247
0.256
0.247
0.229
(0.277) (0.269) (0.260) (0.224)

Type 2:
100 to 500 employees

0.232
0.188
0.190
0.237
(0.282) (0.271) (0.206) (0.196)

0.480
0.486
Type 3:
0.511
0.519
More than 500 employees, (0.351) (0.344) (0.308) (0.294)
in-state HQ
Type 4:
More than 500
employees,
out-of-state HQ

0.062
0.046
0.035
0.035
(0.127) (0.120) (0.119) (0.145)

NOTE: Numbers are expressed as SMSA means. Standard deviations are in
parentheses.
SOURCE: Author's calculations based on USELM data.

1980 (Harris [1983]). Furthermore, when total
firm-level employment exceeded the aggregate
establishment-level employment data, it was
assumed that the number of branches had been
underreported. Branches were then imputed
from this excess employment according to the
average branch size for the particular industrysize classification. In 1980, 17 percent of the
establishments in FIRE were imputed. The
imputed branches lack geographic information
beyond the state, however.
Finally, records that were not updated
during the sample period were removed from
the data set. The remaining updated records
were then weighted to reflect the underlying
population. Weights were assigned on the basis
of the level of reporting in firm categories based
on size, organization type, and industry. Report
ing problems within the banking sector led
analysts at the Brookings Institution to separate
commercial banking from the rest of FIRE,
resulting in a much more accurately weighted
population in both parts of this sector. The level
of reporting problems is higher outside SMSAs
than within them (Armington and Odle [1983]),
which does not present a problem for the analy
sis where the SMSA is the unit of observation.
In general, geographic errors from nonreport
ing of branches appear to be more severe for
large firms with several establishments. Errors

resulting from records that are not routinely
updated and from inaccurate geographic distri
bution of weighting are more severe for small
firms that have infrequent credit checks under
the Dun and Bradstreet system. The employ
ment statistics and location of independent mid
sized firms with few establishments or branches
however, appear to be more accurately meas
ured. The midsized firms undergo more frequent
credit checks than small businesses and are less
likely to be widely distributed geographically.
The following estimation presents plausible and
significant results for the economic impact of
midsized banks on midsized firms, but weaker
results for small and large banks. Whether these
results are due to the true importance of mid
sized banks or to measurement error is uncertain,
so the findings should be interpreted with
caution.

III. Model
Specification
This paper assumes that banking employment
losses due to bank deaths, as measured in the
USELM data, are a reasonable proxy of credit
disruptions during restructuring in local bank
ing markets. To control for the direct effects of
the job losses of bank employees and the credit
effects of restructuring, I use the difference
betw een the impact of bank contractions and
bank deaths on local economic activity to iden
tify real economic effects resulting from disrup
tion of credit channels. Data were collected for
217 SMSAs for the periods 1980-82 and 198486. The sample was limited to those SMSAs for
which complete information was available.
Four types of establishments are identified
through an extract of the USELM data base. Type
1 establishments belong to independent firms
with fewer than 100 employees— typically single
establishment small businesses. Type 2 establish
ments belong to independent finns with 100 to
500 employees— midsized finns that may have
more than one establishment. Type 3 establish
ments belong to firms with greater than 500
employees headquartered within the same state.
Type 4 establishments belong to firms with greater
than 500 employees headquartered out of state.
The percentage of bank employees in the
four types of firm categories used in this study
(averaged across SMSAs) is given in table 1. The

■

8 The Small Business Administration’s time-series construction oí
the files compels the use of two-year periods.

TABLE

Bank Employment
as a Proxy for
Financial Structure

Distribution of Bank Employment
by Asset Category, 1980
Asset Class
($ m illio n s )

N um b e r
o f Banks

0 to 5
5 to 10
10 to 25
25 to 50
50 to 100
100 to 300
300 to 500
500 to 1,000
1,000 to 5,000
5,000 and more

874

N um ber o f
Em ployees
3,113
16.282
80,604
125,868
144,710
220,047
79,216

1.937
4,662
3,553
1,972
1,156
198
158

112,331
280,302
419,908

157
37

E m ployees
p e r B ank
3.6
8.4
17.3
35.4
73.4
190.4
400.1
711.0
1,785.4
11.348.9

SOURCE: Federal Deposit Insurance Corporation, A nnual Report. 1980.

TABLE

Bank Employment by Asset
Category over Time
Asset Class
($ m illio n s )
Oto 25
25 to 100
100 to 1,000
1,000 and more

E m ployees p e r B ank
1980

1986

13.4
49.0
272.2

11.3
33.3
150.1

3,609.3

2,710.3

Percent
C hange
13.2
32.1
44.9
24.9

SOURCES: Federal Deposit Insurance Corporation, A n nual Report. 1980,
and Statistics on Banking , 1986.

impact of financial restructuring is suggested by
changes in these employment categories over
time. Employment in small banks declined from
an average 24.7 percent in 1980 to 22.9 percent in
1986; employment in midsized banks declined
from 23.7 percent to 19-0 percent. Employment in
large in-state banks, however, increased from 48.0
percent to 51.9 percent, while employment in outof-state banks almost doubled, from 3-5 percent to
6.2 percent, reflecting the growing importance of
interstate banking.

Before concluding that changes in bank
employment represent changes in financial
structure, one must assess the validity of bank
employment as a proxy for bank size, the
impact of labor productivity in the banking sec
tor, and changes in the size distribution of banks.
Table 2 reports the distribution of bank
employment in 1980 across banks categorized
by asset size. In general, the standard definition
of a midsized firm as having 100 to 500 employ
ees matches up well with the standard definition
of a midsized bank as having assets between
$100 million and $1 billion. Average employ
ment per bank ranges from 73 employees for
banks in the $50 million to $100 million category
to 711 employees for banks in the $500 million
to $1 billion category. Similarly, our measures of
small and large firms match up with standard
definitions of small and large banks.
The data suggest large improvements in
labor productivity (measured as employees per
bank, controlling for assets) for all bank size
categories over the 1980-86 period. As shown in
table 3, average employment per bank declined
over the period for banks in all asset categories.
Decreases ranged from 13 and 32 percent for
small banks in the $0 to $25 million and $25 mil
lion to $100 million categories, respectively, to
45 percent for midsized banks in the $100 mil
lion to $1 billion category. Productivity gains for
large banks with assets of greater than $ 1 billion
averaged only 25 percent. Again, our definition
of a midsized firm is consistent with the defini
tion of midsized banks, which averaged 150
employees per bank in 1986.
Table 4 reports shifts in the relative impor
tance of small, midsized, and large banks over
the 1980-86 period. In general, small banks
declined in both number and relative impor
tance, midsized banks were little changed, and
large banks increased in importance.
Small banks with less than $25 million in
assets constituted 50.8 percent of all banks in 1980
but held only 5.1 percent of all assets. By 1986,
they had declined to 34 percent of all banks
and their share of assets stood at 2.4 percent.
Declines in the share of assets also occurred in
banks in the $25 million to $50 million and $50
million to $100 million categories. The number
and share of assets of midsized banks were little
changed over the 1980-86 period. As a percentage
of all banks, those in the $100 million to $300 mil
lion category increased from 7.9 percent to 13.4

B L E

Distribution of Banks
by Asset Category
Asset Class
($ m illio n s )

N um ber
o f Banks

1980:
0 to 25
25 to 50
50 to 100
100 to 300
300 to 500
500 to 1,000
1,000 and more
Total

Percent
o f Banks

Percent
o f Assets

7,473
3.553
1,972

50.8
24.2
13.4

1,156
198
158
194

7.9
1.3
1.1
1.3

7.3
9.8
4.1
5.8
61.1

14,704

100.0

4,823
3,685
2,899
1,903
334
216
340

34.0
26.0
20.4
13.4
2.4

2.4

1.5
2.4

4.3
5.1
66.5

14,200

100.0

5.1
6.8

1986:
0 to 25
25 to 50
50 to 100
100 to 300
300 to 500
500 to 1,000
1,000 and more
Total

4.5
6.8
10.4

percent, but their share of assets rose only from
9.8 percent to 10.4 percent. Banks in the $300
million to $500 million category increased their
share of assets slightly, while those in the $500
million to SI billion range showed a small
decline in asset share. Banks with assets of
greater than $1 billion constituted only 1.3 per
cent of all banks in 1980 but accounted for 6 l.l
percent of assets. By 1986, the asset share of
large banks rose to 66.5 percent.
In general, the changes in bank structure sug
gested by the employment shifts in table 1
reflect transformations observed in the distribu
tion of banks by asset size. Small banks declined
in importance, wTiile large banks gained. The
drop of employment in the midsized banks,
however, is most likely the result of strong labor
productivity gains, which exceeded those of
both the small and large banks, rather than a
decline in their importance in terms of assets.
Bank employment at any particular time, how
ever, does appear to track closely with asset size.
Thus, the use of bank employment losses due to
bank deaths appears to be a reasonable proxy of
credit disruptions. In addition, the definitions of
small, midsized, and large firms used here cor
respond with standard definitions of small, mid
sized, and large banks.

SOURCES: Federal Deposit Insurance Corporation, A nnual Report. 1980,
and Statistics on Banking. 1986.

Dependent Variable
and Specification

B L E

1 County-level employment data from the Bureau

Nonbank Employment Rates:
Small, Midsized, and Large Firms
F irm Size

1980

1982

1984

1986

Total employment

0.390
0.411
0.385
0.385
(0.064) (0.066) (0.065) (0.072)

Type 1:
0 to 100 employees

0.138
0.130
0.136
0.133
(0.027) (0.030) (0.026) (0.025)

Type 2:
100 to 500 employees

0.056
0.053
0.053
0.053
(0.017) (0.016) (0.015) (0.014)

Type 3:
More than 500
employees, in-state HQ

0.080
0.080
0.077
0.077
(0.045) (0.044) (0.044) (0.048)

Type 4:
More than 500
employees,
out-of-state HQ

0.112
0.114
0.110
0.119
(0.050) (0.046) (0.046) (0.046)

NOTE: Numbers are expressed as SMSA means. Standard deviations are in
parentheses.
SOURCE: Author's calculations based on USELM data.

of Labor Statistics aggregated to the SMSA level
are used to measure local economic activity.
Bank employment, as reported in the USELM
data, is subtracted from the aggregate employ
ment. An alternative proxy for output (personal
income) yielded qualitatively similar results to
those reported here, but in order to compare
total employment rates with the employment
rates in firms of various size classes, it is not
used. Thus, specifications are also estimated
with employment in small, midsized, and large
firms (nonbank) as dependent variables. These
average employment rates (employment
divided by population) are reported in table 5.
Total employment rose over the period, which
began in recession, from 38.5 percent in 1980 to
41.1 percent in 1986. Employment in small and
midsized firms was essentially flat. Employment
in large firms headquartered within the state
changed little over the period, as did employ
ment in out-of-state firms.
The effects of bank deaths on local eco
nomic activity are estimated using regression

TABLE

Summary Statistics:
Independent Variables
WAGE

9.256

CONTRACT 1

(1.875)
TAX

0.404

0.011
(0.035)

CONTRACTI

(0.038)

0.014
(0.047)

EXPAND 1

0.033
(0.073)

CONTRACTi

0.034
(0.075)

EXPAND 2

0.020

CONTRACT 4

0.005
(0.034)

(0.055)
EXPAND 3

0.057
(0.151)

DEATHI

0.020
(0.056)

EXPAND 4

0.009
(0.101)

D EAT HI

0.011
(0.046)

BIRTHI

0.015
(0.032)

DEATH 3

0.040
(0.110)

B IRT H I

0.025
(0.068)

DEATH A

0.001
(0.004)

BIRTH 5

0.099
(0.302)

BIRTH A

0.024
(0.173)

NOTE: Numbers are expressed as SMSA means. Standard deviations are in
parentheses.
SOURCE: Author's calculations based on USELM data.

analysis. The dependent variable is the employ
ment rate at the end of the period. (Using
employment levels and including population as
an independent variable yielded similar results,
as did using changes in employment and
changes in employment rates.)
The following measures potentially affecting
employment growth are included as independ
ent variables:
1) LWAGE = average (log) wage of
production workers in the SMSA as measured
by the Census of Manufacturers.
2) TAX = effective corporate tax rate for
the state.
3) DUM 86 = a dummy variable equaling
1 for the 1984-86 period.
4) BIRTH 1-4 = percent change in banking
employment due to the births of banks of types
1 through 4.

5) EXPAND 1-4 = percent change in bank
ing employment due to the expansion of banks
of types 1 through 4.
6) CONTRACT 1-4 = percent change in
banking employment due to the contraction of
banks of types 1 through 4.
7) DEATH 1-4 = percent change in bank
ing employment due to the deaths of banks of
types 1 through 4.
8) lagged employment rates.
The means and standard deviations for these
variables are given in table 6. Financial restmcturing is measured by the percent change in
bank employment due to births, expansions,
contractions, and deaths (BIRTHi, EXPANDi,
CONTRACTi, and DEATHi; i = 1, ...4 ) for banks
of types 1 through 4. Note that the credit-disruption hypothesis cannot explain wrhy an expan
sion or birth of a bank would have an effect on
nonbank employment. All components of
change in banking are included for complete
ness, however. In particular, the expansion and
contraction variables appear separately in case
the multiplier effect of changes in bank employ
ment on local economies is not symmetric. Dif
ferences in the estimated coefficients of CON
TRACT and DEATH variables are meant to
measure the impact of credit disruptions. O n
average, 2.0, 1.1, 4.0, and 0.1 percent of SMSA
bank employment is lost over a two-year
period, due to deaths of bank types 1 through
4, respectively.
As suggested in the literature on firm loca
tion, w^ages and tax rates are included to control
for their impact on economic growth. A dummy
variable for the 1984-86 period controls for any
fixed effect associated with this period of
economic expansion.
Following previous empirical studies, the
specification includes lagged values of the
dependent variables as independent variables to
control for the possibility of a spurious rela
tionship between bank deaths and employment.
Suppose the causality between bank deaths and
employment actually runs from employment to
bank deaths. Banks tend to close after periods of
relatively slow regional economic growth. If the
lagged values of the dependent variables were
not included as independent variables, the coeffi
cients on the bank death variables would tend to
be negative and significant even if bank deaths
had no true effect on employment. The regres
sion results thus indicate whether lagged bank
deaths explain employment after accounting for
employment in the past year.
Timing problems in the data make it impos
sible to entirely discount a spurious correlation.

The dependent variables are 1) overall employ
ment data for the SMSA— an average rate for the
ending year— and 2) employment rates for
various-sized firms reported in the USELM data,
based on end-of-calendar-year employment.
The bank death data are employment changes
due to deaths that occur in the two-year period
from December of the beginning year to
December of the ending year. The lagged
dependent variables are employment rates at
the beginning and middle of the two-year
period. Bank deaths at the end of the two-year
period (part of our independent variable) are
thus potentially caused by adverse economic
conditions at the end of the period (our depend
ent variable).
To explore the simultaneity problem further,
however, we examine the effect of bank deaths
on economic activity in the year following the
tw-o-year period. We also examine the impact of
economic conditions at the beginning of the
period on bank deaths. Finally, we test whether
our results are driven by adverse shocks occur
ring in the oil states. The results, while not con
clusive, suggest that our measure of the impact
of banks deaths on regional activity is not being
driven by simultaneous-equation bias.

IV. Estimation
Results
The model was estimated on the pooled sample
of 434 observations using ordinary least
squares, which are presented in table 7.
Model 1 (in column D, which uses the total
employment rate as the dependent variable,
suggests that high wages have a negative
impact on total employment, while taxes have
no significant effect. Lagged employment rates
have a significant effect, as does the 1984-86
dummy variable.
The expansion and births of large in-state
banks (EXPAND 3 and BIRTH 3) have a small
but statistically significant effect on nonbank
employment. The coefficient on EXPAND 3 is
0.012 and is significantly different from zero at
the 95 percent confidence level, which indicates
that a 10 percent increase in bank employment
from the expansion of large banks raises the
nonbank employment rate by 0.12 percentage
point. A 10 percent increase from the birth of
large banks raises the employment rate by 0.07
percentage point.
The contraction of bank employment
( CONTRACTI through CONTRACT4) does not
have a statistically significant effect on nonbank

employment for any of the bank types. The
death of midsized banks (D EA T H I), however,
has a statistically significant negative effect on
the nonbank employment rate. The estimated
coefficient is -0.053 with a standard error of
0.020, suggesting that a 10 percent decrease in
bank employment from the death of midsized
banks reduces the nonbank employment rate
by 0.53 percentage point. Given that the aver
age employment rate in the sample is 39.8 per
cent, this represents a drop in nonbank employ
ment of 1.3 percent. The estimated coefficient
for midsized bank contraction ( CONTRACTI) is
-0.004 and is statistically not significantly dif
ferent from zero. The difference between the
estimated coefficients of DEA TH I and CON
TRACTI suggests that the effect of midsized
bank deaths on employment is almost entirely
due to credit disruptions as opposed to the mul
tiplier effect of lost bank jobs. An F-test rejects
the hypothesis that the coefficients are identical
at the 90 percent confidence level.
The coefficients for DEATH3 and DEATH4
are negative, and the coefficient for DEATH 1 is
positive, but all are statistically insignificant.
These insignificant effects for deaths of types 1,
2, and 3 banks could result from the relative
importance of midsized banks to local econo
mies, from a difference in the type of deaths
they represent (for example, small-bank deaths
being takeovers rather than true failures), or
from the relative accuracy of the midsized bank
data previously discussed. Results should thus
be taken as positive evidence for the impor
tance of midsized banks, rather than as
evidence for the lack of importance of small
and large banks.
To investigate further the local economic
impact of bank deaths, the model was reesti
mated with the small-, midsized-, and large-firm
employment rates as dependent variables in
models 2, 3, and 4, respectively (shown in
columns 2, 3, and 4 of table 7). In model 2, the
expansion of midsized banks has a positive
effect on small-business employment, while the
contraction of small banks has a negative effect.
Bank deaths do not have a significant effect on
small businesses, which is contrary to the com
mon view that small firms are the first to suffer
the effects of a credit crunch. It is possible, how
ever, that these firms, many of which are rela
tively new or small “mom-and-pop” operations,
rely more on infonnal sources of capital— such
as loans from friends and relatives, retained
earnings, and personal savings— than on funds
from commercial banks. This would make small
firms less likely to be affected by bank deaths. It

Regression Results: Impact
of Financial Restructuring
on Employment Rates

(1)

(2 )

(3 )

T otal
E m p lo y m e n t Rate

Sm all-Firm
E m p lo y m e n t Rate

M idsized-Firm
E m p lo y m e n t Rate

LWAGE

-0.013a
(0.005)

0.001
(0.001)

-0.001
(0.001)

0.005
(0.004)

TAX

0.029
(0.024)

0.003
(0.007)

0.0002
(0.0060)

0.038a
(0.018)

EXPAND 1

0.009
(0.013)
0.002
(0.017)

0.0002
(0.0038)

-0.005
(0.003)

0.001
(0.010)

-0.003
(0.004)

0.011
(0.013)
0.004
(0.005)

C oefficient

Large-Fim
E m p lo y m e n t

EXPAND 3

0.012a
(0.006)

0.010a
(0.005)
0.002
(0.002)

EXPAND 4

-0.009
(0.009)

0.002
(0.003)

-0.0002
(0.0015)
0.006a
(0.002)

BIRTH I

-0.032
(0.031)
-0.001
(0.014)

-0.006
(0.009)

-0.002
(0.008)

-0.009
(0.023)

0.003
(0.004)

-0.001
(0.003)

-0.004
(0.010)

0.007a
(0.003)
-0.002
(0.005)

0.001
(0.001)

0.0005
(0.0008)

0.004b
(0.002)

-0.002
(0.002)

0.001
(0.001)

0.003
(0.004)

CONTRACT1

-0.030
(0.027)

-0.013b
(0.008)

-0.009
(0.007)

-0.016
(0.020)

CONTRACT 2

-0.004
(0.019)
0.007
(0.012)

-0.002
(0.006)

-0.025b
(0.014)

0.002
(0.004)

-0.004
(0.005)
-0.004
(0.003)

CONTRACTA

0.017
(0.028)

-0.001
(0.008)

-0.0003
(0.0070)

DEATH1

0.022
(0.017)

-0.001
(0.004)

DEATH 2

-0.053a
(0.020)

0.002
(0.005)
-0.006
(0.006)

EXPAND 2

BIRTH 2
BIRTH 3
BIRTH A

CONTRACT 5

is also possible that these results are driven by
the errors in the data for small firms discussed
above.
The impact of financial restructuring on m id
sized firms is measured in model 3- Midsized
firms are more likely to rely on local commer
cial banks than on national credit markets (such
as with large firms) or on informal sources of
capital (such as with small firms); see Elliehausen and W oken (1990). They are thus more
likely to be affected by stress in the financial sec

tor. The results support this conclusion. The

-0.017a
(0.005)

-0.007
(0.007)

0.003
(0.009)
0.011
(0.021)
0.005
(0.013)
0.001
(0.015)

estimated coefficient for DEATH 2 is -0.017 with
a t-statistic of 3.40, indicating that a 10 percent
decrease in bank employment from bank
deaths reduces the employment rate of mid
sized firms by 0.17 percentage point. With an
average employment rate of 5.3 percent, this
represents a 3-2 percent drop in midsized firms’
employment. Again, an F-test rejects at the 90
percent confidence level the hypothesis that the
coefficient for DEATH2 equals the coefficient
for CONTRACT2.

TABLE

n t i n u e d

Regression Results: Impact
of Financial Restructuring
on Employment Rates

(2)

(3)

T otal
E m p lo y m e n t Rate

Sm all-Firm
E m p lo y m e n t Rate

M idsized-Firm
E m p lo y m e n t Rate

Large-Firm
E m p lo y m e n t Rate

DEATHS

-0.002
(0.009)

0.003
(0.002)

-0.003
(0.002)

-0.005
(0.006)

DEATH A

-0.008
(0.254)

0.020
(0.074)

CONSTANT

0.0363
(0.016)

0.026
(0.189)
-0.040a
(0.012)

DUM 86

0.010a
(0.002)

-0.002
(0.005)
-0.002a
(0.001)

-0.062
(0.063)
0.002
(0.004)
0.002a
(0.001)

0.007a
(0.002)

EMPRTB

1.624a
(0.088)

0.308a
(0.026)

0.047a
(0.022)

0.001
(0.066)

EMPRTA

-0.697a
(0.092)

-0.291a
(0.027)

-0.036
(0.023)

0.031
(0.069)

C oefficient

SEMPRTA
MEMPRTA

—
—

0.975a
(0.013)
—

GEMPRTA

—
—

Log likelihood

—

1122.7

1661.8

—

—
—

0.885a
(0.019)
—

—
0.970a
(0.017)

0.933

0.965

1726.3
0.912

1250.9
0.912

Mean of the

0.398

0.137

0.053

0.079

dependent variable
Number of
observations

434

a. Significant at the 95 percent confidence level.
b. Significant at the 90 percent confidence level.
NOTE: Numbers are expressed as estimated coefficients. Standard errors are in parentheses.
SOURCE: Author's calculations.

The impact of midsized bank deaths on mid
sized firms is small but statistically significant.
This result is plausible because these firms are
most likely to rely on local banks and because
midsized commercial banks are more likely to
concentrate on lending to such firms. This find
ing controls for lagged total employment rates
and for the lagged midsized-firm employment
rate. A spurious correlation is still possible, how
ever, if these lagged measures are inadequate
controls due to timing problems in the data.
Model 4 measures the impact of financial
restructuring on large firms. These firms are more

likely to have access to national credit markets

and thus may be less affected by local restructur
ing. The results suggest that the expansion or
birth of large banks has a positive impact on
large-firm employment, while the contraction of
midsized banks has a negative impact. These
findings are significant at the 90 percent confi
dence level, but not at the 95 percent level,
implying that changes in local bank structure do
not have powerful effects on large firms.
To explore the robustness of the results and
the potential for simultaneous-equations bias,
other specifications of the model were also
tested. First, the employment rate in the year fol
lowing the two-year period used in the USELM

file was used as a dependent variable, with
lagged employment rates for the beginning,
middle, and end of the period included as
independent variables. The estimated coeffi
cient on DEATH 2 was -0.013 (compared with
an estimated coefficent of -0.053 in model 1)
with a t-statistic of 1.024. This smaller (and statis
tically insignificant) effect suggests that either
the effect of bank deaths dampens out quickly
over time, or that the original result was driven
by simultaneity bias. This specification, how
ever, allows bank deaths to have an effect up to
three years after they occur. Such long-term
influences of credit disruptions are unlikely if
banking markets are competitive.
Second, the impact of local economic condi
tions on bank deaths was explored by using
midsized bank deaths as a dependent variable
and the beginning-of-period economic condi
tions as an independent variable. The economic
conditions (total employment rates and employ
ment rate of midsized firms) had no explanatory
effect on DEATH2. (T-statistics of the economic
variables in various specifications were never
larger than 0.50.) This suggests that bank deaths
were not statistically driven by our measures of
local economic conditions.
Finally, robustness of the results was tested
by including geographic dummy variables to
control for effects from economic distress in oilproducing states, which had an especially large
number of bank failures. In particular, we tested
whether the findings were solely due to bank
failures in Oklahoma, Louisiana, and Texas
between 1984 and 1986. Controlling for these
states did not affect the results. The coefficient
on DEATH2 in model 1 remained above -0.040
in the various specifications tested, and the
t-statistic did not fall below 2.60.
In sum, the structure of the data in this
experiment does not permit standard tests of
Granger causality or simultaneity bias. The alter
native specifications tested, however, suggest
that the results are not being driven by the
occurrence of bank deaths in economically dis
tressed states or by any obvious feedback of
economic conditions on bank deaths.

V. Conclusion
Restructuring of the financial sector in the form
of bank mergers, failures, or takeovers poten
tially affects investment and consumption deci
sions by disnipting the links between borrowers
and creditors. Empirical evidence at both the
macro and regional levels has shown that finan
cial structure and stress can have real economic
effects.
This paper further explores the impact of
financial restructuring on local economies using
a data set that measures change in the local
banking sector by the birth, expansion, contrac
tion, and deaths of banks at the metropolitan
level.
The empirical analysis suggests that, control
ling for overall financial restructuring and
lagged economic activity, the deaths of m id
sized banks— employing between 100 and 500
employees— have a negative but short-lived
impact on local economic activity. Furthermore,
employment in other midsized firms appears to
be most directly affected by these deaths. These
firms presumably rely on local banking markets
and are the most likely customers of midsized
banks. The results are robust across several
specifications.
The strongest effects of bank deaths are
found for midsized banks and firms, but this
should not be interpreted to mean that the
deaths of small and large banks have no effect.
Measurement problems, which exist for all size
categories, are least severe for midsized banks
and firms, w hich can account for the statistical
significance of these results and the statistical
insignificance of the results for the other two
types. Nonetheless, the results suggest that mid
sized banks are an important source of funds
for midsized firms and that a disruption of this
link through financial restructuring can have
negative short-mn local economic effects.
The effects of credit disruption appear to be
short run. In particular, the impact of bank
deaths on employment rates appears to die out
after two years. One would expect such a result
if banking markets were competitive and con
testable. In such markets, firms that found their
source of credit disrupted by a bank death
would quickly be able to establish credit rela
tions with another institution. Thus, the results
presented here, while consistent with the creditview theory that disruption in financial markets
can have real effects, also suggest that these
effects are short-lived, as one would expect in a
competitive environment.

References
Amel, Dean F. and Keane, Daniel G., "State
Laws Affecting Commercial Bank Branching,
Multibank Holding Company Expansion,
and Interstate Banking,” Issues in Bank
Regulation, Autumn 1986, 10, 30—40.

Armington, Catherine and Odle, Marjorie,
“Weighting the 1976-1980 and 1978-1980
USEEM Files for Dynamic Analysis of
Employment Growth,” Business Microdata
Project. Washington, D.C.: The Brookings
Institution, April 1983________, “U.S. Establishment Longitudinal
Microdata (USELM), The Weighted Inte
grated USEEM 1976-1982 Sample,” Business
Microdata Project. Washington, D.C.: The
Brookings Institution, June 1984.

Gertler, Mark, “Financial Structure and Aggre
gate Economic Activity: An Overview,”Jour
n al o f Money, Credit, a n d Banking, August
1988 (Part Two), 20, 559-88.

Gilbert, Alton R. and Kochin, Levis A., “Local
Economic Effects of Bank Failures,”Jo u rn a l
o f F inancial Services Research, 1990 (forth
coming).

Harris, Candee S., "USEEM, U.S. Establishment
and Enterprise Microdata, Version 3,” Busi
ness Microdata Project. Washington, D.C.:
The Brookings Institution, April 1983-

Haubrich, Joseph G., "Nonmonetary Effects of
Financial Crises: Lessons from the Great
Depression in Canada / J o u r n a l o f Monetary
Economics, March 1990, 25, 223-52.

Jacobson, Louis, “Analysis of the Accuracy of
Bauer, Paul W. and Cromwell, Brian A., "The
Effect of Bank Structure and Profits on Firm
Openings,” Economic Revieu\ Federal
Reserve Bank of Cleveland, 1989 Quarter 4,
25, 29-39.

Bernanke, Ben S., “Nonmonetary Effects of the
Financial Crisis in the Propagation of the
Great Depression,” American Economic
Review, June 1983. 73, 257-76.

SBA’s Small Business Data Base,” Working
Paper 1958, Center for Naval Analyses,
Alexandria, Va., October 1985.

King, B. Frank, Tschinkel, Sheila L., and
Whitehead, David D., "F.Y.I.: Interstate
Banking Developments in the 1980s,”
Economic Review, Federal Reserve Bank of
Atlanta, May/June 1989, 71, 32-51.

Samolyk, Katherine A., "In Search of the
Calomiris, Charles W., Hubbard, R. Glenn,
and Stock, James H., "The Fann Debt Crisis
and Public Policy,” Brookings Papers on
Economic Activity, Washington, D.C.: The
Brookings Institution, 1986, 2, 441-79.

Elusive Credit View: Testing for a Credit
Channel in Modern Great Britain," Economic
Review, Federal Reserve Bank of Cleveland,
1990 Quarter 2, 26, 16-28.

Whitehead, David D., “Interstate Banking:
Campbell, Tim S. and Kracaw, William A.,
“Information Production, Market Signalling,
and the Theory of Financial Intermediation,”
Jo u rn al o f Finance, September 1980, 35,

865 82
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Diamond, Douglas W., "Financial Intermedia
tion and Delegated Monitoring,” Review o j
Economic Studies, July 1984, 51, 393—‘414.

Elliehausen, Gregory E. and Wolken, John
D., B anking Markets a n d the Use o f F inan
cial Services by Sm all a n d Medium-Sized
Businesses. Staff Studies 160, Washington.
D.C.: Board of Governors of the Federal
Reserve System, 1990.

Taking Inventory," Economic Review, Fed
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_______________, "Interstate Banking: Probability
or Reality?” Economic Review, Federal
Reserve Bank of Atlanta, March 1985, 6-19.

The Short-Run
Dynamics of LongRun Inflation Policy
by John B. Carlson,
W illia m T. Gavin,
and Katherine A. Sam olyk

John B. Carlson and Katherine A.
Samolyk are economists and
W illiam T. Gavin is an assistant
vice-president and economist at
the Federal Reserve Bank of
Cleveland. The authors gratefully
acknowledge helpful suggestions
from David Altig, Richard Baillie,
Dennis Hoffman, and Richard
Jefferis.

Introduction

growth rates of the monetary aggregates, or the
ultimate policy objectives mandated by Con
gress through the Humphrey-Hawkins Act.
Our purpose in this paper is to ascertain the
short- and long-temi implications of an inflation
policy for real output. Although a complete costbenefit analysis is not provided, we do present
one basis for assessing the short-mn costs of
lowering trend inflation, based on the estimated
dynamic relationship between output and infla
tion. The simple framework developed here
abstracts from issues concerning the implementa
tion of monetary policy. We do not specify a pol
icy reaction function, nor do we include interest
rates or the money supply, variables normally
associated with monetary policy. Rather, an infla
tion policy is defined in temis of a disturbance
that exclusively determines trend inflation.
In order to isolate this permanent inflation dis
turbance, we adopt an identification method
developed by Blanchard and Quah (1989). Using
a model of output growth and unemployment,
they identify two independent disturbances that
they interpret as shocks to aggregate demand

Currently, some economists, legislators, and
policymakers are recommending that the
Federal Reserve adopt price stability or an
explicit price-index target as its primary long
term monetary policy objective.1 This recom
mendation is based on three ideas. First, high
and uncertain inflation leads to an inefficient
allocation of resources. Second, inflation is ulti
mately a monetary phenomenon, so controlling
inflation over the long term would be the sen
sible goal for monetary policy. Finally, a posi
tive inflation trend provides no net long-run
benefit to the economy.
The dynamic effects of monetary policy are
difficult to understand for many reasons, the
most important of which is simply that the term
“monetary policy" can be interpreted in different
w a y s . It may be defined in terms of the short-run
interest-rate policy of the Federal Reserve, the

■ 1 See House Joint Resolution 409, introduced by U.S. Congres

sional Representative Stephen L. Neal of North Carolina, and testimony in
http://fraser.stlouisfed.org/
support of the resolution by Hoskins (1990).
Federal Reserve Bank of St. Louis

and aggregate supply. Their identifying assump
tion is that only the supply shock has perma
nent effects on output, while neither supply nor
demand disturbances affect the trend rate of
unemployment. Using a model of inflation and
output, we identify two independent distur
bances that we interpret as innovations to infla
tion policy and real output. Our identifying
assumption is that only the inflation shock affects
trend inflation. Innovations to real output may
affect the path of inflation in the short run, but
the inflation shock alone determines the infla
tion trend.
Sims (1986) discusses how estimated distur
bances can be interpreted as reflecting govern
ment policy choices in the context of a VAR
model. Policy actions are associated with predic
tion errors in the corresponding policy variables.
We interpret the shocks that drive the inflation
trend as reflecting innovations accommodated
by policy. We then compute the impulseresponse functions and variance decompositions
for real output in order to determine howr these
inflation-policy shocks contribute to real GNP
fluctuations.
To understand our purposes, consider an
economy characterized by the classical dichot
omy. In such an economy, the processes driving
inflation and output could be identified by
restricting inflation so that it would have no
effect on real output. The estimated nominal dis
turbances would then unambiguously reflect
monetary policy actions, albeit irrelevant ones
for real economic activity.
Similarly, this study attempts to disentangle
the disturbances associated with inflation policy
from the real disturbances driving the macro
economy. It does not, however, require adher
ence to the classical dichotomy in either the
short or the long run. Our specification allows
both estimated shocks to influence output and
inflation in the short run.
To impose our assumption that only the infla
tion shock determines the inflation trend, we
constrain the model so that the output distur
bance has only a transitory effect on inflation.
Inflation shocks are interpreted as policy innova
tions that can affect both the short- and long-run
dynamics of the system. Thus, our key identify
ing assumption is consistent with a large variety
of economic structures, including all of the
major macroeconomic theories.

In examining the dynamic consequences of
these two fundamental shocks, we expect that
innovations in the inflation trend will have an
effect— although not a substantial one— on out
put. One reason for this is that the failure to
index taxes on capital gains in the United States
creates a situation in w'hich raising the inflation
trend wrould increase the marginal tax rate on
capital. Thus, a higher inflation trend creates an
incentive to substitute current consumption for
capital accumulation.2
Our results indicate that inflation-policy
shocks have small effects on real GNP over both
long and short horizons. In the long run, the
effect is negative. Thus, a policy action that
w^ould reduce the inflation trend wrould be asso
ciated with a long-run increase in the level of
real output but with only negligible short-run
costs.
We recognize the preliminary nature of
these results and discuss some qualifications
below; for example, the zero restriction that we
place on the long-run impact of real output
shocks on inflation is not strongly supported by
the data. To investigate the sensitivity of our
results to this restriction, we compare them to
the findings obtained in two recursive VAR sys
tems that do not restrict the long-run relation
ship between inflation and output. Even though
both of these systems show that output shocks
have a small positive impact on the inflation
trend, the estimated effect of inflation on real
output is essentially the same as in our model.

I. Framework for
Identification
To identify the innovations to real output and
the inflation trend, we apply an approach devel
oped by Blanchard and Quah (1989) and Shapiro
and Watson (1988) to a simple two-variable sys
tem that includes inflation and output. It is
assumed that there are twro fundamental distur
bances in the system— ep , an inflation shock,
and e , an output shock— and that they are
uncorrelated at all leads and lags. The system is

■ 2

In a general-equilibrium framework, we would also expect work
efforf to be substituted for capital in the production process. Thus, labor
productivity would fall, hours worked would rise, and the net effect on
output would be ambiguous. See Jarrett and Selody (1982) and Bryan
(1990).

TABLE

Unit Root Descriptive Statistics
A ug m ented D ickey—F ulle r T-Statisticsa
W ith a
T im e T rend

W ith o u t a
T im e T rend

-V/
dp.
dy,
ddpt

-0.22
-3.08
-4.23b
-3.99b

-2.16
-2.47
-4.24b
-4.00b

Residual v
Residual dp

-3.17
-3.12

-1.07
-3.14b

Series

a. The Dickey-Fuller t-statistics were calculated from a regression that
included six lags o f the differenced data. All regressions included a constant,
and there were 144 observations. See Fuller (1976. p. 373) for a tabulation of
the distribution of this statistic.
b. The null hypothesis of a unit root is rejected at the 10 percent significance
level.
NOTE: The series "residual y ” is the residual from a regression o f y on a
constant and dpt . The series “residual dp" is the residual from a regression of
dpt on a constant and yf . If both yt and dpt are /(1) and the residual con
tains a unit root, then y and dp cannot be cointegrated.
SOURCE: Authors' calculations.

identified by imposing the restriction that only the
inflation shock may affect the inflation rate in the
long run. The output disturbance is a composite
of real supply and real demand shocks that may
affect inflation only in the short run.3
Let dp denote the inflation rate and y
denote the log level of output. In vector nota
tion, let X be (dp, y) and e be O x\.). We
assume that there is some n for which X fol
low's a stationary7process, given by
(1)

(1 - L ) " X ( t ) = ,4(0) e ( t )
+ ,4(1) e (t- 1) + ...
= A ( L ) e(t),

where A (L ) is a matrix of polynomials in lag
operators.
Results of unit root tests that use the aug
mented Dickey-Fuller procedure are presented
in table 1. These statistics do not reject the null
hypothesis that the elements of X are /(l); that

■

3 As Blanchard and Quah show in the appendix to their 1989 paper,
there are some common identification problems in low-dimension
dynamic systems. For example, if the aggregate shocks are composites of
many different types of disturbances, as is the case here, then our decom
position may be invalid. We intend to address this issue in subsequent
work by adding more economic structure (and more variables) to the
http://fraser.stlouisfed.org/
basic framework.
Federal Reserve Bank of St. Louis

is, the inflation rate and output each contain
one unit root. Therefore, we difference the infla
tion and output series once before estimating
the model.4 Tests for cointegration of dp and y
suggest that these tw o variables do not share a
common trend.
Under our restriction that the long-run
impact of e on inflation is zero, the sum of the
coefficients in the upper-right polynomial in
A (L ) must equal zero. Under our assumptions,
the variance (e ) = I and the contemporaneous
effect of e on X is given by A (0). Thus, our
framework allows for bidirectional causality,
even though the effect of an output innovation
on inflation must dissipate in the long run.
Because e is not observable, A (L ) cannot
be estimated directly. In practice, A (0) can be
identified in a variety of ways.’ We use the
instrumental variables approach described by
Shapiro and Watson (1988), wrhich allows the
system in equation (1) to be estimated directly
in autoregressive form:
(2)

B ( L ) ( l - L) X ( t ) = u ( t ),

where, in general, the us are combinations of
structural disturbances that, by construction,
may be correlated contemporaneously but not
across time. We estimate ep and ey by impos
ing our assumptions on this autoregressive
form. Because the matrix of long-run multipliers
is assumed to be lowrer triangular, the sum of
the coefficients in the upper-right polynomial in
B (L ) must also equal zero. In practice, this
restriction is imposed by including first differ
ences of the current value and n -1 lags of out
put growth as regressors:

■

4 King et al. (1989) and Shapiro and Watson (1988) find a unit root
in inflation. We could not reject the null hypothesis of a unit root in infla
tion using the Dickey-Fuller t-statistic, but other tests, including the
Dickey-Fuller normalized bias, the Phillips—Perron normalized bias, and
the Phillips—Perron t-statistic, did reject the null hypothesis. See Phillips
and Perron (1988), Said and Dickey (1985), and Schwert (1987) for argu
ments in favor of using the normalized bias t-statistic. We assume the
existence of a unit root because the particular constraint that we impose
to achieve identification requires that the model be specified in first dif
ferences of inflation and output. We could have specified the model in
output growth and inflation, but doing so would have required policy
shocks to be defined as the sole determinant of the price level. Prelimi
nary work with this specification resulted in a time series of policy shocks
that had extremely large negative effects on real GNP: An increase in the
price level led to an implausibly large decline in real GNP. We suspect
that any problems caused by possible overdifferencing in our model are
small relative to the difficulties that would be induced by the alternative
specification.

■

5 See Blanchard and Quah (1989), Shapiro and Watson (1988). Bernanke (1986), Sims (1986), Litterman and Weiss (1985), Judd and
Trehan (1989), and Boschen and M ills (1989).

F I G U R E

1
(2')

Innovations to Output and Inflation:
Model Imposing Long-Run
Constraint on Output Shocks

ddpt = cx+ £ b ln ddpt_ .
/= i
n- 1
+ £ 8 / 2ddyt_ t+ ept
i= 0
n

A. E stim ated In n o v a tio n s to the In fla tio n Rate

dy, = c2 + Y M ^d d P ,- i
i= 1

Standard deviations

n
+ X&22 dyt- i+ b20%t+eyt’
i= 1
where the long-mn constraint has been incor
porated by replacing
n

n- 1

£¿> 12dyt-i with £ 5 / 2ddy,_i
i=0
i=0

B. E stim ated In n o v a tio n s to R eal O u tp u t
Standard deviations
3,----------------------------

in the ddp equation.
Contemporaneous effects of the inflationpolicy shock are allowed to enter the equation
for output growth.6 Because the ddp equation
includes a current value of the change in real
output, we use an instrumental variables esti
mator. The instrument list includes six lagged
values of ddp and dy, as well as the contem
poraneous and six lagged values of the relative
oil price; the price of oil is assumed to be exoge
nous in this model. Essentially, this two-stage
procedure replaces dyt with the ordinary least
squares projection of this variable on the list of
instruments. Then, by including the residual
from the price equation in the output equation,
the real output shock can be identified.

II.

-3 ,[ I 1 LI I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I 1

1951 1955 1959 1963 1967 1971 1975 1979 1983 1987

NOTE: Shaded bars indicate National Bureau of Economic Research busi
ness cycles.

Results

In this paper, inflation is measured as the change
in the logarithm of the Consumer Price Index
(CPI ), and output is measured as the logarithm
of real GNP. Monthly CPI data were averaged to
determine the quarterly series, and data from the
sample period 1951 :IQ to 1987:IIQ were used to
estimate equation (2'). The estimated series for
ep and ey are shown in figure 1. A cursory look
at these two series reveals an output shock that
exhibits its largest negative values in the midst of

SOURCE: Authors' calculations.

■

6 This program was written in Regression Analysis Time Series
(RATS) Version 3.10. We thank Mark Watson for sending us the data and
program used to identify supply shocks in Shapiro and Watson (1988).

F I G U R E

Impulse-Response Functions

A. R esponse o f th e In fla tio n Rate to In n o v a tio n s in :

B. R esponse o f R eal O u tp u t to In n o v a tio n s in :
Percent (quarterly rate)

SOURCE: Authors' calculations.

recessions; however, a clear cyclical pattern is
not evident for the inflation-policy shock. On
average, the inflation-policy shock was positive
during the 1973-75 recession and negative
during the 1981-82 recession.
Impulse-response functions for inflation,
shown in figure 2A, indicate the response of
inflation to a one-standard-deviation shock in
the error vector. Taken at face value, our results
suggest that the output shock has small shortrun effects on inflation when the long-run effect
is constrained to be zero. A standard-deviation
shock to inflation (about 2 percent at an annual
rate [a.r.]) raises the rate slightly more than 1
percent (a.r. ) in the long run.
The impulse-response functions for output
are shown in figure 2B. A one-standarddeviation shock to output results in increased
output throughout the first year. In the long run,
output rises by more than one and one-half
times the initial shock, a gain that is nearly com
plete after the first year.
Inflation-policy shocks have a small but posi
tive short-run effect on real GNP. After the sec
ond quarter, the sign becomes negative and
remains that wray. Thus, a policy action that
lowers the inflation trend would initially have a
negative effect on real output, but would raise
the level in the long run.
Interpreting our results structurally, a
decline in the inflation trend from 4 percent to
zero would have a negligible damping effect on
output in the first two quarters— less than
2/10ths of 1 percent. After the second quarter,
the effect would become positive, and in the
long run (after about two years), the output
level would have increased about 2.5 percent.7
Another way to examine the dynamic effects
of inflation shocks on output is to decompose
the variance of output into the part caused by
variation in the separate shocks. The variance
decompositions for different time horizons are
shown in the top section of table 2. Note that
most of the variance in the two series, inflation
and output, is explained by their own independ
ent shocks. The output shock never explains
more than 2 percent of the variance in the infla
tion rate, and the inflation shock explains
almost none of the variance in output (in the
long run, it accounts for only about 3 percent).
Although raising the inflation trend reduces the

■ 7

Note that this long-run effect is qualitatively and quantitatively
similar to that produced by the failure to index capital-gains income for
tax purposes. See Altig and Carlstrom (1990).

TABLE

Variance Decompositions
Percent o f
O u tp u t V ariance
E x p la in e d
b y S hock to:

Q uarte r

R eal
O u tp u t

In fla tio n
Rate

Percent o f
In fla tio n V ariance
E x p la in e d
b y S hock to:
R eal
O u tp u t

In fla tio n
Rate

Decomposition with Long-Run Constraint on Output Shocks
1
4
8
12

100.0
100.0

0.0
0.0

98.7
98.0

1.3
2.0

20
36

97.5
97.2

97.1

0.5
0.3
1.2

99.5
99.7
98.8

2.5
2.8

0.9
0.6
0.4

99.1
99.4
99.6

2.9

0.3

99.7

Choleski Decomposition with Inflation as the Lead Equatio
1
4
8
12
20
36
49

99.7
99.5
97.3
96.3
95.7
95.4
95.2

0.3
0.5
2.7
3.7
4.3
4.6
4.8

0.0
3.4
10.6

100.0
96.6
89.4

12.7
14.9
16.7
17.4

87.3
85.1
83.3
82.6

Choleski Decomposition with Output as the Lead Equation
1
4
8
12
20
36
49

100.0
99.9
98.5
97.8
97.4
97.2
97.1

SOURCE: Authors’ calculations.

0.0
0.1
1.5
2.2
2.6
2.8
2.9

0.3
2.3
8.5
10.1
12.0
13.4
13.9

99.7
97.7
91.5
89.9
88.0
86.6
86.1

level of output, the increase explains little of the
variation in the series.
Other research suggests that these results are
not dependent on the small size of our model.
King et al. (1989) also find that the permanent
inflation shock never explains more than 3 per
cent of output variance over any horizon.8

III. Some Caveats
There are at least three potentially important
caveats that may limit the validity of our findings.
First, our identifying restriction— that the long-run
impact of an output disturbance on inflation must
be zero— is only weakly supported by the data.
Second, the real shock is clearly an amalgamation
of supply and demand shocks. Third, it may not
be inappropriate to difference the inflation rate.
Differencing may wash out some important shorttenn relationships between output and inflation.
The second and third problems will be addressed
in future research.
As noted above, our identifying restriction is
not strongly rejected by the data. The likelihoodratio statistic for our restricted model is 3.12,
w'hich implies that this hypothesis is rejected at
the 7.7 percent significance level. Evidence
provided below indicates that our empirical
results do not depend critically on this identify
ing assumption.
To examine the implications of this restric
tion, we contrast our results against those
obtained using a standard VAR approach; that
is, we estimate equation (2) with B (0) equal to
the identity matrix. The contemporaneous
relationships between output growth and the
change in inflation are thereby captured in the
variance-covariance matrix of the estimated
residuals. These residuals are then transformed
into orthogonal series in order to examine the
dynamic consequences of independent distur
bances to output and inflation.
The conventional method of orthogonalization (based on the Choleski decomposition of
the variance-covariance matrix) restricts the
transformation matrix to be lower triangular.
While this decomposition achieves an orthogonalization of the residuals, it also imposes a
recursive structure on the system. In contrast to

■

8 See table 8b, King et al. (1989, p. 25). It should be noted that one
of their identifying assumptions is that the permanent inflation shock
does not affect output in the long run.

FI GURE

Impulse-Response Functions:
Recursive System with Inflation
as Exogenous Shock

A. R esponse o f th e In fla tio n Rate to In n o v a tio n s in :
Percent (quarterly rate)

B. R esponse o f R eal O u tp u t to In n o v a tio n s in :

SOURCE: Authors' calculations.

our method of identification, this decomposi
tion places no long-run restrictions on the
model. In this study, we have only two vari
ables and hence only two potential orderings.
Each alternative decomposition corresponds to
an alternative ordering of the variables.
We then compare the structural implications
of the two alternative recursive systems (table 2)
to our restricted model. In the first specification,
we assume that the first-stage residual ( u xin
matrix equation [2]) in the ddp equation is the
structural disturbance ep . Any correlation
between
and u2 is assumed to be caused by
uv the inflation-policy shock. The output shock,
e , is defined as the variation in the first-stage
VAR residual,
that is not correlated with ep .
In the second specification, the order of the
equations, and hence the assumption about the
direction of causation among the contempo
raneous errors, are reversed.
Figures 3 and 4 show the impulse-response
functions for these two specifications. The major
difference is that real shocks now affect the trend
inflation rate. One explanation for this result
could be that monetary policy actions are not
aimed exclusively at achieving a specific inflation
trend. Suppose that the Federal Reserve were fol
lowing a strict money growth rule. Under such a
rule, higher real output growth would result in
lower inflation. However, the positive relation
ship in figure 3A is probably the consequence of
a monetary policy that tries to smooth money
market interest rates in the absence of an explicit
inflation target.
For example, whenever the investment—
demand function shifts to the right, the econ
omy experiences a transitory period of capital
accumulation and relatively higher real returns.
In order to prevent an increase in the federal
funds rate, the Federal Reserve automatically
increases the money growth rate and thereby
raises the inflation rate. Unless it consciously
reverses this accommodative money growth,
the long-mn inflation trend will be positively
related to output shocks.
Although the real shock affects long-run
inflation in the unrestricted model, the esti
mated effect of an inflation shock on output is
essentially the same as in our model. Lowering
the 4 percent inflation trend to zero would raise
long-run output more than 3 percent in the first

F I GU RE

Impulse-Response Functions:
Recursive System with Output
as Exogenous Shock

A. R esponse o f th e In fla tio n Rate to In n o v a tio n s in :
Percent (quarterly rate)

run benefits of eliminating inflation outweigh
the estimated short-run costs.
In some sense, it should not be surprising that
simple time-series models of output and infla
tion would exhibit an inverse long-run relation
ship when estimated over the post-WWII period.
After all, income growth was higher and infla
tion was lower before 1965. If, however, output
has a substantial random-walk component, out
put growth could vary significantly by chance.
Thus, the estimated inverse link between output
and inflation could be spurious. Moreover, our
model does not include other variables that
might account for the productivity (and hence
output) slowdown.

IV. Conclusion

B. Response o f R eal O u tp u t to In n o v a tio n s in :

SOURCE: Authors' calculations.

We assume that two types of disturbances gen
erate inflation and output dynamics— an infla
tion shock and an output shock— both of which
are defined by identification restrictions. The
inflation shock is allowed to have transitory and
permanent effects on both output and inflation.
Although the output shock may have only tran
sitory effects on inflation, it may have both tran
sitory and permanent effects on output.
We interpret the inflation shock to be a con
sequence of monetary policy given our restric
tion that it alone determines the inflation trend.
Under this interpretation, the estimated policy
shocks have minimal real effects. Although the
results concerning the impact of inflation policy
on real output are produced in a small and very
simple model, we suspect that they will hold up
in future extensions of this work. One indication
can be found in King et al. (1989), who find
similar results using a larger and theoretically
richer model.
The policy implications of our findings are
encouraging. Not only would a policy aimed at
lowering the inflation trend raise the output
level in the long run, but a structural interpreta
tion of our VAR indicates that the short-run out
put loss associated with such a policy may be
negligible. Our results thus suggest that there is
a sequence of feasible policy actions that could
lower trend inflation in such a way that the
benefits would outweigh the costs. This seems

to be the case on the margin— that is, where
policy might succeed in offsetting inflation
shocks marginally more than it did on average
over the estimation period.
This study does not address the question of
how such a policy would be implemented, how
ever. Specifically, we do not consider how
policymakers would control the inflation shock
or to what extent they should offset it. Since the
inflation-policy innovations are estimated over a
period in which monetary policy largely accom
modates quarterly disturbances to inflation, it is
questionable whether our findings would apply
in those circumstances where policy largely off
sets inflation shocks. Conventional econometric
evidence suggests that the underlying structure
of the economy is unlikely to remain invariant to
a monetary policy procedure that does not
accommodate a large part of inflation shocks.
In light of this evidence, extreme policy
measures could lead to greater output losses
than our results suggest. Any attempt to largely
offset a positive inflation shock within a quarter,
however, would seem to be infeasible from a
practical standpoint, if not from a technical one.
O n the other hand, the experience of the 1970s
suggests that policymakers can also be too timid
in implementing a strategy to combat inflation
shocks. O n balance, these results suggest that
the benefits of a monetary policy aimed at
achieving gradual disinflation would probably
outweigh the costs. One avenue for future
research would be to extend the framework
presented here to include variables more closely
associated with policy actions so that policy7
implementation issues might be investigated.

References
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“Inflation and the Personal Tax Code:
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9006, Federal Reserve Bank of Cleveland,
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Litterman, Robert B. and Weiss, Laurence,
“Money, Real Interest Rates, and Output: A
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Phillips, Peter C. B. and Perron, Pierre, “Test
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Bernanke, Ben S., “Alternative Explanations of
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Blanchard, Olivier Jean and Quah, Danny,
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Boschen, John F. and Mills, Leonard O., "Real
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Sims, Christopher A., “Are Forecasting Models
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Fuller, Wayne A., Introduction to Statistical
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Hoskins, W. Lee, “A Monetary Policy for the
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Jarrett, J. Peter and Selody, Jack G., “The
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Judd, John P. and Trehan, Bharat,
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Usable for Policy Analysis?” Q uarterly
Review, Federal Reserve Bank of Min
neapolis, Winter 1986, 2-15.

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■ 9006
inflation and the
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Assessing Indexation

■ 9008
Sticky Prices, Money,
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tions

■ 9010
Consumption and
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Old and New Anomalies

by David A ltig

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Intervention and the
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