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Bconomic
Review
Federal Reserve BaIllt
of San Francisco
Winter 1990
Brian Motley
Carolyn Sherwood-Call
Ramon Moreno
Elizabeth S. Laderman
Num.ber 1
Has There Been a Change in the
Natural Rate of Unemployment?
Assessing Regional Economic Stability:
A Portfolio Approach
External Shocks and Adjustment in
Four Asian Economies-1978-87
The Public Policy Implications of State Laws
Pertaining to Automated Teller Machines
Table of Contents
Has There Been a Change in the
Natural Rate of Unemployment? . . . . 0. . . . . . . . . . . . . . . . „ . . . » . » „ . . . . . . . . . . . . „ 3
Brian Motley
Assessing Regional Economic Stability:
A Portfoio Approach *. 00., 0„»0*„„ .«»„»„ 0*. 0„„„s »0<»000*„00„„0»0„0. *9 * 17
Carolyn Sherwood-Call
External Shocks and Adjustment in
Four Aslan Economies— 1978-87 0. . . . . . „. . „ . . . . . <>.... 00. . „ . . „ . . „. . . . . . „ 0„ 27
Ramon Moreno
The Pubic Policy Implications of State Laws
Ftertalnlng to Automated Teller Machines 0„„008000. <>0
. 0<>00„0. 00„*0000*„ 43
Elizabeth S. Laderman
F ederal Reserve Bank o f San Francisco
1
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2
E conom ic R eview / W inter 1990
Has There Been a Change in the Natural Rate of
Unemployment?
Brian Motley
Senior Economist, Federal Reserve Bank of San Francisco. Editorial committee members were Bharat Trehan,
John Judd, and Adrian Throop.
Many economists argue that inflation begins to pick up
when the unemployment rate falls below a critical level
known as the natural rate. Those who worry that there is a
risk offaster inflation argue that unemployment has fallen
below this level. Others suggest that the natural rate has
declined and hence that the inflation risk is less. The
results of this paper do not support the argument that the
natural rate has declined. The slow response ofinflation to
changes in unemployment can explain why the low rate of
unemployment since 1987 has not led to faster inflation.
However, this slow response of inflation to employment
suggests that if inflation is allowed to rise, it would take a
long period of slow growth to bring it down again.
Federal Reserve Bankof San Francisco
In the last two years, the rate of unemployment in the
U.S. has declined significantly. During 1989, unemployment averaged 5Y4 percent of the civilian labor forGe, ITlQre
than one percentage point below its level in the first half of
1987 and lower than at any time since 1974.
As the unemployment rate fell, concern increased that
the associated high level of economic activity would lead
to a pick-up in inflation. Many economists argue that the
unemployment rate is an indicator of the strength of
aggregate demand, and that inflation tends to increase
when unemployment is low. These economists hypothesize
that there is a critical level of the unemployment rate below
which wages and prices tend to accelerate and above which
they tend to decelerate. This critical level is called the
"natural rate of unemployment." Those who worry that
there is a risk that inflation will pick up argue that
unemployment has fallen below this natural rate.
Until recently, most estimates of the natural rate of
unemployment placed it around six percent. I With a natural rate in this range, inflation pressures should have begun
developing in the second half of 1987, when the unemployment rate fell to 5.9 percent from 6.4 percent in the first
half. Concerned that the risk of faster inflation was increasing, the Federal Reserve began to tighten policy in
mid-1987. In 1988, after a brief pause following the stock
market crash, the Federal Reserve continued the process of
policy tightening that it had begun in 1987.
Despite the low level of unemployment, however, the
increase in inflation since 1987 has been relatively modest.
This raises the possibility that the natural rate of unemployment may be less than six percent.? or even that there is no
critical level of unemployment below which inflation necessarily worsens. This article seeks to throw light on this
issue by estimating the natural rate and examining whether
it has changed in recent years.
The first section reviews the theory of the link between
inflation and unemployment. Particular attention is paid to
the role of "supply-side" or "relative-price" shocks in the
inflation process. Section II estimates four alternative
empirical equations linking inflation to the unemployment
rate and to relative-price shocks. The equations are estimated over a series of different sample periods to test
3
whether the natural rate of unemployment has changed
through time. Section III summarizes the empirical results
and discusses their implications for policy. The results of
this paper do not support the argument that the natural rate
has declined. The slow response of inflation to changes in
unemployment can explain why the low rate of unemployment since 1987 has not led to faster inflation. However,
this slow response of inflation to unemployment suggests
that if inflation is allowed to rise, it would take a long
period of slow growth to bring it down again.
I. Inflation and Unemployment: Theory
The notion that low levels of unemployment are associInflation Expectations
ated with high inflation is generally known as the "Phillips
3
curve." Although there is widespread agreement that
unemployment and inflation are related, at least in the
short run, the direction of causation is disputed. Most
economists argue that if unemployment falls below its
natural level, this causes inflation to increase. However,
some theorists maintain that causation runs from inflation
4
to unemployment. The empirical models in this paper are
based on the first hypothesis.
The theory that low unemployment causes faster inflation views the unemployment rate as an indicator of the
demand for labor relative to the supply. When the demand
for labor is strong, wages tend to be bid up. Because the
prices of goods and services are set as a mark-up over their
costs of production, most of which comprise wage costs,
wage inflation leads to overall price inflation.5
In the labor market, there always is a number of persons
who are searching for employment even when there are
jobs vacant. This is because individual jobs and workers
are unique, and matching unemployed persons to vacant
.jobs requires a process of search on both sides." In a
changing economy, persons continually are entering and
leaving the unemployment pool and firms are creating and
filling job vacancies. This observation means that even
when the supply of and demand for labor are in overall
equilibrium at going wages, there will be a certain remaining level of unemployment.
The "natural rate" of unemployment reflects this "equilibrium" quantity of joblessness. When unemployment is
below (or above) its natural rate, wages tend to rise more
(or less) rapidly. If the mark-up of prices over production
costs remains constant, (or, afortiori, if it increases when
aggregate demand is strong7), the rate of overall price
inflation also will be inversely related to the unemployment rate.
This inverse relation between inflation and unemployment may be represented algebraically as:
= RUt - UNAT)
(1)
where f' () < O. In this equation, 'ITt represents the inflation
'ITt
rate, and U, and UNAT represent the actual and the natural
rates of unemployment, respectively.
4
In negotiating wages, workers and employers pay attention not only to the state of labor supply and demand in
their own markets, but also to the rate of overall inflation
they expect in the future. If prices are expected to rise,
workers demand wage increases to maintain their real
incomes. Employers are willing to meet these demands,
because they expect to be able to pass their higher costs on
to their customers, and fear that, if they do not, they will
lose their best workers. Hence, an expectation that prices
and wages will rise tends to be self-fulfilling, even when
unemployment is at its natural rate.
This argument leads to the "expectations-augmented"
Phillips curve, which may be written as:
= Et- 1'ITt + RUt
- UNAT)
(2)
where E, _ 1'ITt represents the rate of inflation in period tthat
was expected in period t-l. When unemployment is below
(above) the natural rate, actual inflation will exceed (fall
short of) the rate that was expected.
Under simple assumptions about how price expectations
are formed, this model implies that inflation will increase
continually if unemployment remains below its natural
level. This will be the case, for example, under the common
assumption that people raise their expectations of future
inflation when current inflation is higher than they had
expected. This assumption about expectations may be
written as
'ITt
E,
1'ITt
= E,
2'ITt
1
+
g( 'ITt -
l -
E,
2'ITt- l )
(3)
where s'() > O. This assumption implies that the rate of
inflation expected for the current period is a function only
of past inflation. If this is a linear function, it may be
written:
Et-1'IT t
S
= S~lWS'
'ITt- s
(4)
The rate of inflation expected in the current period is a
weighted average of the rates of inflation experienced in
the past. 8 This is known as the "adaptive expectations
hypothesis." Substituting equation (4) into equation (2)
and assuming that the functionfO in equation (2) is linear
yields:
Economic Review / Winter 1990
1Tt
S
= sk,W s' 1Tt- s
+
I
i~oai' (Ut -
i -
UNAT)
(5)
It seems plausible that if inflation has been constant i~
the past and unemployment is at its naturalle~el,. the~ 11
will remain constant in the future. This assumptionimplies
that the weights (wsJ in equation (5) sum to one. In this
case a level of unemployment below the natural rate not
only'adds to current inflation, but also causes inflation to
continue rising in the future.
Even if unemploymentremains at its natural level, this
does not guarantee a low inflation rate. Any rate of inflation, if it is anticipated, is compatiblewith unemployment
at its natural level. Thus, although a decline in the unemployment rate below its natural rate will lead t~ fast~r
than-expected inflation, there is no long-run relationship
between the level of unemployment and the rate of inflation.
The assumption that agents form inflation expectations
adaptively implies that the only informa.tion th~y us~ in
forming expectations is the past behavior of inflation.
However, the theory of rational expectations suggeststhat,
since agents know inflationis related to the unemployment
rate, they should base their inflation expectations on t?eir
forecasts of unemployment rather than on past inflation,
Despite this reservation, models that assume adaptive
expectations appear to fit the data reasonably well.?
An alternativeexplanationwhy past inflationappearsto
affect current inflation is that wage contracts are made for
several years and are not all negotiated simultaneously!"
When a firm and its employees are negotiating a new
contract, they recognize that contracts made in the past in
related industries will be in effect for at least part of their
contract period. If those other contracts contained wage
increases, new contracts likely will call for similar increases even if economic conditions have changed. As a
result, inflation will tend to persist once it has begun.
Recently, some economistshavesuggested that chang~s
in the actual level of unemployment may affect the equilibrium rate at which inflation remains constant. According to this "hysteresis" theory, II although an increase in
unemploymentmaylowerinflationinitially, it later leadsto
a corresponding increase in the natural rate, as employers
and workers become accustomed to higher rates of unemployment. As a result, its effect on inflation is only
temporary. This hysteresis hypothesis may be tested by
examining whether the coefficients on the unemployment
rate sumto zero in equation (5).
Supply Shocks
Theaverageprice levelalso maybe influencedby shocks
that affectthe prices of particular commodities. Forexam-
Federal Reserve Bank of San Francisco
ple, a rise in the prices of imported raw materials adds
directly to costs of production and hence to the average
price level. As prices adjust to the higher level, there will
be an increase in the measuredinflationrate. This suggests
that equation (5) should be extended to include the effects
of such "relative-price" or "supply" shocks.
On several occasionsin the last two decades, changes in
the overall inflation rate have been attributed to changes
either in the real exchange valueof the dollar or in the real
price of oil. The real exchange rate measures the price of
U.S.-produced goods relative to foreign-produced goods.
A decline in the real exchange rate could add to U.S.
inflation, by raising the cost of imports and reducing the
. Iow.12
pressures on domesticproducersto keep thei
eir pnces
Thereal price of oil representsthe price of oil relativeto the
general level of prices in the U.S. A rise in the price of oil
adds directly to costs of production in the u.s. Thus, both
the real exchange rate and the real price of oil are prime
candidates for inclusion in equation (5).
This discussion suggests an estimating equation of the
form:
1Tt
S
= S;lws'1Tt-s
G
I
+ i~oai • (Ut -
g~iog . SHKOILt g
i -
UNAT)
+
H
+ h~ixh
. SHKEXt _ h (6)
where SHKOILt _ g represents the change in the real price
of oil and SHKEXt _ h the change in the real exchange
rate.P The distributed lags on these variables capture the
idea that the effects of supply shocks do not occur instantaneously. Several previous researchers'< have estimatedequationssimilar to equation (6) and havefoundthat
relative-price shocks have a significant impact on the
measured inflation rate.
Supply Shocks and Expectations
Equation (6) implicitly assumes that the impact of past
inflationon current inflationis the sameregardlesswhether
that past inflation was the result of excess demand or of
relative-price shocks. In particular, equation (6) implies
that a single relative-priceshock, unless offsetby a change
in the unemploymentrate, will lead to a permanent change
in the rate of inflation. This is because such a shock not
only has a direct impact on prices but also has an indirect
effect via expectations.15
This implication does not seem plausible. Economic
agents generally would recognize that the rise in pri~es
following an increase in the price of oil, for example, IS a
"one-time" effect, and so would not change their longerrun inflation expectations. Hence, although such a shock
would have a permanent effect on the level of prices, the
5
associated speed-up in inflation should be only temporary.
This suggests that equation (6) may not be a fully satisfactory model of inflation in periods (such as the 1970s
and 1980s) in which there were significant relative-price
shocks.
To incorporate the restriction that relative price shocks
do not affect the inflation rate in the long run, two alternative modifications of equation (6) will be estimated. The
first approach maintains the assumption that expected
inflation is a weighted average of past inflation, but imposes the restriction that the coefficients on the shock
variables in equation (6) sum to zero:
'iTt
S
= S~IWs'1Tt s
I
+ i~oai . (U,
N
n'focon . SHKOILt n
+
VNAT)
i -
+
M
~oCxm . SHKEXt _
m
(7)
where'v ICon = IC xm = O. This restriction implies that
although relative-price shocks affect the average level of
prices and so change the inflation rate temporarily, they
have no long-run effect on inflation.
The second approach modifies equation (4) so that
changes in inflation resulting from relative-price shocks do
not affect inflation expectations and so do not pass through
into future inflation:
J
S
E t - l1Tt = S~IWS [1T t s - (j~ioj . SHKOILt_ s_ j
K
+ k'fixk . SHKEXt
s-k))
(4')
where dOj and d X k represent the direct effects of relative
price shocks on inflation. This equation says that expected
inflation is a weighted average of past inflation, excluding
that part of past inflation that was due to relative-price
shocks. This model of expectations yields an estimating
equation of the form:
'iTt
S
J
= sIlws [1T t - s - (j~Odoj
K
+ k'fodXk . SHKEXt
s k)]
I
i~oai . (V t- i - VNAT)
+
. SHKOILt
s- j
+
J
+ j~OdOj
SHKOIL t_ j
K
k'fixk' SHKEXt_ k
(8)
where no restrictions are placed on the dOj and dX k
coefficients.
Equation (8) also may be written as follows:
6
S
I
1Tt = I Ws'1T t - s
s=1 .
+ 1=0
.I ai
J
'
(U,
i -
VNAT) +
S
j~Odof(SHKOILt-j - s~ows'SHKOILt_s)
K
+ k'flxk'(SHKEXt- k -
S
s~ows'SHKEXt-s-k) (9)
In this form, the sums of the coefficients on the shock
variables are Idoj . (1 - Iw s) and Id xk . (1 - Iw s)'
respectively. Since Iws = 1, these coefficient sums are
equal to zero. Thus, equations (7) and (9) are similar since
both specify that current inflation is influenced by a distributed lag of past relative-price shocks with coefficients
summing to zero. Equation (7) may be viewed as a generalization of equation (9) that puts fewer restrictions on the
shapes of the distributed lags on the shock variables. Thus,
the COn and CXm coefficients in equation (7) are combinations l7 of the underlying parameters (w sand doj , and w s
and dX k ' respectively), representing both the direct and
indirect effects of relative-price shocks.
One objection that may be raised to this approach is that
it is inconsistent to assume, as in equation (4'), that agents
can distinguish between price increases resulting from
supply shocks and those due to other factors, but cannot see
that future inflation also will be affected by the unemployment rate. This objection may be less serious in practice,
however, because relative-price shocks often have been
sufficiently large that the public probably was able to
recognize them as one-time events. This is particularly true
of the oil-price shocks in 1974 and 1979.
Demographic Shifts
The natural rate of unemployment may vary as conditions in the labor market change. Faster technological
change, for example, may lead to more job-changing and
hence a higher natural unemployment rate. Demographic
changes have similar effects. Because young persons have
fewer skills and less work experience than adults, and also
move in and out of the work force more often, they have a
higher unemployment rate. If the proportion of young
workers in the labor force declines, the measured unemployment rate will fall, but this does not imply an increase
in inflationary pressure, but rather a decline in the natural
rate. This argument implies that the unemployment measure used in estimating the Phillips relation should be
adjusted for demographic changes. 18
If lit represents the proportion of the labor force that is in
the ith population group at date t, and U it is the unemployment rate for that group, the total unemployment rate, VI'
may be decomposed as follows:
Economic Review I Winter 1990
unemployment rate. Conversely, the third component,
- iii)' shows how overallunemployment would
have changedas a resultof changesin thegroupunemployment rates, if the demographic structure of the labor force
had remained unchanged. The final term represents ambiguous "cross" effects, some of which also may be
demographic.'? Since their net contribution to overall
unemployment is small, these cross effects are grouped
with the demographic effects.
The "demographically-adjusted" unemployment rate is
constructed by subtracting the demographic and "cross"
effects from the measured unemployment rate. Thus:
I Li . (Uit
where Li is the average proportion of the labor force in the
ith groupovera periodof years, and iii is theaverage group
unemployment rate.
The first term on the right side of equation (10) is the
average unemployment rateovertheperiod. Theremaining
terms decompose the difference between the.actual rate at
date t and the average rate into three components. The
second term, I iii . (lit - LJ, measures how the overall
unemployment rate would have changed if the group
unemployment rates had remained constant and only the
structure of the population had changed. This component
measures the size of purely demographic effects on the
V;'=
I
u,
.I iii . (lit
1=1
I
- Li ) -
.I (Uit
1=1
- iii) . (lit - LJ
I
=
I
j;.• U·
i= 1
I
It
(11)
Chart 1A
Unemployment Rate:
Actual vs Adjusted
Percent
11
10
9
8
7
6
/
5
Demographically-Adjusted
Rate
4
3-h".--...-r-r-,...-...,....,,,.--...-r-r-,...-...,....,,,.--...-r-r-,...-...,....,,,
1963
1968
1973
1978
1983
1988
Chart 1B
Percent
Components of the Unemployment Rate
0.3
0.2
"Cross" effects
0.1
0.0
"
+:::::'::"_ _-:::::::::~~-T---":::::o.....=::.;:;.......:~..-::;::~~
-0.1
-0.2
....... Demographic Effects
-0.3
-0.4
-0.5 -h".--...-r....,-,...-,-".....,...-r....,-....,..,-".....,...-r-,-....,..,-"
1968
1963
1973
1978
1983
1988
Federal Reserve Bank of San Francisco
7
In constructing the data series of U ~ the population was
divided into eight age-sex groups: teens, young adults,
prime-age adults and seniors.20
Chart 1 shows the components of the unemployment
rate. The upper panel compares the actual unemployment
rate with the adjusted rate definedin equation(11). Mostof
the variation in the unemployment rate since1960has been
due to factors that affected all population groups rather
than to demographic changes and cross effects. However,
the lower panel shows that purely demographic factors did
have a significanteffect, raising the overalljobless rate by
almost 0.7 percentage point between 1960 and 1978 and
reducing it by 0.5 percentage point since then.
II. Empirical Analysis
The coefficients of the equations developed in the previous section were estimated using the demographicallyadjusted series (ut) constructedin equation(11). Thus, the
equations estimated were:
S
1ft
= S~lWs'11't
I
s
+ i~oai
.
(Ut
i-
UNAT*)
G
+ g~Obog
. SHKOILt_ g
H
+ h~obxh . SHKEXt_ h
S
1ft
(6a)
I
= S~lWs'11't-s + i~oai
.
(Ut-i - UNAF*)
N
+ n~ocon
. SHKOILt_ n
M
+ m~oCXm • SHKEXt_ m
S
1f
=
s~ lW s [11' t- s
(7a)
J
(j~ioj . SHKOILt_ s _ j
-
K
+ k~odxk . SHKEXt_ s
Full Sample Estimates
k)]
I
+ i=O
2 a· . (u*t . - UNAT*)
I
J
I
K
+ j~Odoj'SHKOILt_j + k~odxk'SHKEXt-k
(8a)
Thecoefficients on the relativepricevariables (SHKOIL
and SHKEX) are unrestricted in equation (6a), are constrained to sum to zero in equation (7a), and are subjectto
nonlinear restrictionsin equation (8a). Toassessthe role of
the shock variables, an equation that excludes these variables also was estimated. The equations were estimated
over a sample period from the first quarter of 1963 to the
fourth quarter of 1988, a total of 104 quarterly observations. Since the equations are nonlinear in UNAT*, and
equation (8a) includes restrictions on the relative-price
shock coefficients, nonlinear regression estimation was
employed.21
8
The measure of inflation used is the annualized quarterly growth rate of the fixed-weight GNP price index.
Unemployment is measuredby the civilian unemployment
rate. 22 The real price of oil is the ratio of the producers'
price of crude petroleum to the producer price index for
finished goods, and the real exchange rate is the Federal
Reserve's multilateral trade-weighted value of the dollar
deflatedby the ratio of a trade-weighted average of foreign
consumer price indexes to the U.S. fixed-weight GNP
price index. The equations also include dummy variables
(NIXON and NIXOFF) to capture the effects of the
imposition and removal of price controls by the Nixon
Administration in the early 1970s. 23
The estimates of UNAT* represent the demographically-adjusted natural rate. These are converted to estimates of the actual natural rate by adding the difference
betweenU,and U:, Fora givenestimate of UNAT;Chart 1
implies that the natural rate has fallen since 1978, as the
proportion of the population in groups that have higherthan-average unemployment rates has declined.
Earlier estimates of inflation equations have assumed
that the distributed lag on past inflation is long. Gordon
and King.>' for example, used a twenty-four quarter lag
distributionconstrainedto lie on a fourth-order polynomial
with a zero end-pointconstraint. In a later paper, Gordon->
used four-quarter averages of the inflation rate extending
back six years (24 quarters).
To determine the appropriate lag lengths, equation (6a)
wasestimated using a series of alternative lag distributions
on past inflationfromfour to twenty-four quarters. In these
regressions, no restrictions were placed on any coefficients.w Table I shows the results of these regressions.
The estimates reported in this table represent the cumulative sums of the coefficients on lagged inflation out to
each indicated lag length.?" Regardless of the lag length
chosen, the estimated sum of all the coefficients (the last
figure reported in each column) is not significantly different from one. However, in each case, the sum of the
Economic Review / Winter1990
coefficients reaches unity by the fifth or sixth lag, suggesting that current inflation is not affected by inflation more
than five or six quarters in the past. Although some of the
coefficients for more distant lags are statistically significant when lags longer than twelve quarters are introduced,28 several of the later coefficients in these equations
take negative values-? which seems implausible. Column I
of Table 1 shows the result of estimating the equation
assuming a 24-quarter lag, but imposing the restriction
that this lag follows a polynomial. 30 This restriction
smoothes the estimated lag distribution and has the effect
of making it appear longer. However, the hypothesis that
the data satisfy this restriction may be rejected.
The results in Table 1 suggest that the finding of long
distributed lags in earlier research" may be due to the use
of overly restrictive polynomial specifications. Hence, the
equations in Tables 2 and 3 were estimated using a five-
Federal Reserve Bankof San Francisco
quarter lag on past inflation, with no polynomial restriction.
Table 2 shows the results of estimating the alternative
models over the 1963 to 1988 sample period. Model I
refersto an equation that excludes the relative-price shock
variables. Models II and III refer to equations (6a) and
(7a), which are identical except that Model III constrains
the coefficients on SHKOIL and SHKEX to sum to zero.
Model IV refers to equation (8a), which imposes additional
restrictions on the relative-price coefficients. F) is the
F-statistic.that tests the restriction that the coefficients on
lagged inflation sum to one. In all four equations, this
restriction may be accepted. 32, 33 F2 is an F-statistic that
tests for the presence of first- through fourth-order autocorrelation in the residuals.>' In every case, the hypothesis
that the residuals are autocorrelated may be rejected.
The coefficients on SHKOIL and SHKEX in Table 2
9
represent the estimated sums of the distributed lag coefficients on these variables. 35 The figures in parentheses
below these coefficients are the standard errors of these
sums and those in brackets are F-statistics that test the joint
hypothesis that the individual lag coefficients are all zero.
The alternative estimates of the demographicallyadjusted natural rate (UNAT*) range from 5.99 percent in
Model II to 6.20 percent in Model IV. In the fourth quarter
of 1988, the demographically-adjusted unemployment rate
(U*) exceeded the measured rate by 0.26 percentage
point, so these estimates of UNAT* imply actual values of
the natural rate between 5.73 and 5.94 percent. 36 These
estimates are similar to estimates developed in earlier
research by Gordon."? The finding that the results are not
sensitive to the model used to capture the effects of relative
price shocks adds to one's confidence in the estimate. On
the other hand, in all cases, the one-standard-error confidence interval on the estimated natural rate is nearly one
percentage point wide, which may be too wide for the
unemployment rate to be a useful signal to policy-makers.
The response of inflation to divergences between the
actual and natural unemployment rates depends on the
coefficients on lagged inflation and on the current and
lagged unemployment rate. Dynamic simulations of the
equations were used to estimate the response of inflation to
temporary and permanent divergences between the natural
unemployment rate and the actual rate. These simulations
showed that, in Model IV, for example, a one-percentagepoint decline in the unemployment rate that lasts for a
single quarter will raise the inflation rate by 0.11 percentage point. This increase in inflation will be permanent
unless it is later offset by an equal single-quarter rise
in unemployment. If the unemployment rate remains
one percentage point below the natural rate permanently,
the inflation rate will increase by 0.11 percentage point
every quarter. The other estimated models yielded similar
results.
The coefficients on current and lagged unemployment
are of opposite signs, implying that the initial impact on
inflation of a higher unemployment rate is less than its
long-run impact. The hysteresis hypothesis referred to
earlier suggests that the inflation rate changes only in
response to a change in the unemployment rate and that
inflation may remain constant at any level of unemployment. Although the coefficients on current and lagged
unemployment have different signs, they are not equal. The
estimated sum of these coefficients ranges from - 0.23 to
- 0.27 and is significantly different from zero. This means
that the level of unemployment does have a significant
impact on the inflation rate. Contrary to the hysteresis
hypothesis, an unemployment rate that remains constant
10
below the natural level does cause inflation to increase
continually.
In Model II, the hypotheses that the sums of the coefficients on SHKOIL and SHKEX are both zero may be
accepted, implying that relative-price shocks have only
temporary effects on inflation. Models III and IV impose
this restriction. In Model III, the hypothesis that all the
coefficients on SHKOIL are individually zero can be
rejected at the six percent level of significance (F = 2.88).
Moreover, the coefficient on the current value of SHKOIL
is positive and significant (t-statistic = 2.18), indicating
that changes in the real price of oil have a significant
temporary effect on the inflation rate. This conclusion is
confirmed by Model IV, which also shows a positive and
significant temporary impact of oil prices on inflation.
Economic Review / Winter 1990
Although the effects of changes in the exchange rate are
less significant, they go in the direction predicted by
theory. In Model III, the sum of the coefficients on the
current and twelve lagged values of SHKEX is constrained
to zero, but the estimated coefficient-sum out to the eighth
lag. is negative and significant at the five percent level,
implying that a real appreciation of the dollar reduces
inflation temporarily. However, the hypothesis that all the
coefficients on SHKEX are individually zero cannot be
rejected (F = 0.72). In the more restrictive Model IV, the
sUrtlofthecoefficients on SHKEX (which in this model
represent only the direct effect of exchange-rate changes)
is negative and significant at the 13-percent level (onetailed test) and the hypothesis that these coefficients are all
individually zero may be rejected at the 20-percent level of
significance (F = 1.41). Thus, there is only weak evidence
that changes in the real exchange rate have effects on
inflation.
Rolling Regressions
To examine whether the natural rate has changed over
time, the equations were estimated over fifteen overlapping
twelve-year sample periods, beginning with 1963.1 to
1974A and ending with 1977.1 to 1988A. The twelve-year
length of these sample periods was chosen to provide a
reasonable number of degrees of freedom and ensure that
even the earliest sample periods included a number of
observations after the shift to flexible exchange rates in
1971 and the first oil-price shock in 1974. The lag lengths
determined from the full 1963-88 sample period wereused
for these "rolling regressions." Table 3 shows the estimates of Model IV.38
This table suggests that the estimated natural rate tends
to be higher for samples that include the 1980s. The
estimates of UNAT* range around six percent for samples
ending before 1980,39 but are closer to seven percent for
samples ending between 1980 and 1988. The estimate of
UNAT* over the final period implies a natural rate in the
fourth quarter of 1988 of 6.8 percent, more than IV2
percentage points above the actual rate in the quarter and
% percentage point above the estimate from the full sample
period.
The estimates of the impact of relative-price shocks on
inflation are mixed. In all sample periods, the impact of
these shocks has the sign predicted by theory. On a onetailed test, the hypothesis that oil-price shocks initially
have a positive impact on inflation (that is, the coefficient
on the contemporaneous value of SHKOIL is positive) may
be accepted at the lO-percent level in nine sample periods.
The corresponding hypothesis that the sum of the coefficients on SHKEX is negative also may be accepted in nine
Federal Reserve Bankof San Francisco
sample periods. 40 Although relative-price shocks probably
have affected overall inflation in the directions expected
from theory, these effects have not been consistent.
11
Simulations
To provide a further test of the alternative models, outof-sample simulations of each of the four models were
conducted. For purposes of this simulation exercise, the
models were re-estimated" over the period from 1963.1 to
1984.4, and simulated to 1988.4.
Table 4 compares the error statistics for forecasts of the
quarterly change in inflation obtained from static and
dynamic simulations of the four models.V The static
simulations are out-of-sample fitted values of the estimated
equations. The dynamic simulations were begun in the first
quarter of 1963. In the early years of the simulations, the
simulated quarterly changes in inflation depend on actual
inflation before 1963 as well as on current and past values
of the unemployment rate and the shock variables. However, the effect of inflation before 1963 gradually dies out
and by 1980 is effectively zero.
The errors from the dynamic simulations are in most
cases smaller than those from the static simulations. In
the dynamic simulations, the simulated change in the
inflation rate depends only on its underlying determinants
(current and lagged unemployment and relative price
shocks) and is not affected by lagged actual inflation. The
results in Table 4 suggest that this may be a superior
forecasting procedure.
The errors from both the static and dynamic simulations
of Model III and Model IV are lower than those from
Model II. This result supports the argument that relativeprice changes should have no permanent effect on inflation. In addition, the errors from Model IV are lower than
those from Model III, suggesting that the Model IV
specification, in which changes in inflation associated with
relative-price shocks are explicitly "purged" from the
lagged dependent variable, is a better specification.
12
Economic Review / Winter 1990
However, Model I, which omits the relative-price
variables entirely, appears to predict inflation as well as
Model IV. This finding, together with the rolling regressionresults in Table 3, which showed that the effects of the
shock variables were sporadic, suggests that even the
direct impact of relative-price shocks on inflation is small,
except in periods when these shocks are unusually large (as
in the 1974 and 1979 oil shocks). Thus, Table 4 seems to
suggest that the role of relative-price shocks in the inflation
process probably has been over-emphasized in earlier
research •and in media discussions of the sources of
inflation.
The dynamic simulations make it possible to decompose
the change in the inflation rate into its underlying sources.
These decompositions are shown in Table 5. For each year
since 1985, the change in annual inflation is separated into
the portions due to past and present divergences of unem-
ployment from its natural level, to oil-price and exchangerate shocks, and to the cumulative simulation error.
Inflation declined by about one percentage point between 1984 and 1986. All four models attribute a significantportion of this decline to the high level of
unemployment relative to its natural rate. Conversely, the
models agree in attributing much of the 1.8-percentagepoint increase in inflation in 1986-88 to the low rate of
unemployment in those years.
As inTable 4, the simulation errors are larger for Model
Ilthan for the other three models, again suggesting that the
former is an inappropriate specification. The decompositions suggest that the errors in Models II, III, and IV are
largely due to the exchange rate variable. In every case, the
annual error is opposite in sign and of a similar magnitude
to the contribution of the exchange rate variable, implying
that the error would be reduced by omitting that variable.43
III. Summary and Conclusions
This paper has examined the link between inflation and
the rate of unemployment. The feature of the inflation
equations estimated in this paper that distinguishes them
from other equations in the literature is that relative-price
shocks are constrained to have only temporary effects on
the inflation rate. In addition, inflation expectations are
proxied by a distributed lag on past inflation that is much
shorter than in earlier studies.
As pointed out earlier, the failure of inflation to pick up
significantly since 1987, despite the decline in unemployment, has led some economists to lower their estimates of
the natural rate. However, apart from the effects of the
change in the age-sex structure of the labor force, the
results of this paper do not support the hypothesis that the
natural rate has declined. If anything, the results of the
rolling regressions in Table 3 suggest that the (demographically-adjusted) natural rate of unemployment has been
higher in the 1980s. However, the estimates of the natural
rate in these regressions are subject to an uncomfortably
wide margin of uncertainty.
This paper has not investigated the causes of any such
change in the natural rate. One possible cause is the
apparent increase in the rate of technological change
during the last decade, as a result of advances in computer
technology and the response of the economy to the oil-price
shocks of the 1970s. More rapid change either in methods
of production or in the types of goods and services being
produced would be expected to add to job-changing and
hence to the level of normal unemployment.
Federal Reserve Bank of San Francisco
A second important result of this paper concerns the role
of relative-price shocks. Commentary on inflation in the
media frequently focuses on the role of these shocks in
influencing the inflation rate, and earlier research generally has found their effect on inflation to be statistically
significant. The empirical estimates in this paper suggest
that these shocks may raise or lower the level of prices and
so cause temporary changes in measured inflation. However,these shocks do not influence the rate of inflation over
longer periods. In all the equations estimated, we can
accept the hypothesis that shocks have no long-run effect
on inflation.
Moreover, the estimates suggest that even the short-run
effects of relative-price changes have been sporadic. In
many sample periods, we cannot reject the hypothesis that
the shocks had no impact on inflation, even in the short run.
Also, in out-of-sample simulations, inclusion of these
shocks did not improve forecasts of the inflation rate. These
empirical results suggest that the role of these shocks in
causing inflation has been over-emphasized in earlier
research.
The results of this paper suggest that since the Fall of
1988, the gap between the actual unemployment rate and
the natural rate has ranged between 3f4 and 1Y2 percentage
points. 44 In view of this gap, why has inflation remained
relatively subdued? The primary reasorr" appears to be
that the increase in inflation in response to low unemployment occurs quite slowly. Simulations of the estimated
equations indicate that a permanent one-percentage point
13
gap between the natural and actual unemployment rates
would cause inflation to increase by 0.11 percentage point
per quarter or less than one-half percentage point per year.
Unemployment has been below its natural rate only
since mid-1987. Between the fourth quarters of 1986 and
1988, inflation increased by about lYz percentage points.
The dynamic simulations reported in Table 5 indicate that
the low rate of unemployment contributed between one
and 1Y4 percentage points of this increase. Thus, the
relatively modest pick-up in inflation may be explained
without invoking a decline in the natural rate.
At the same time, the estimated equations suggest that if
the unemployment rate were to remain permanently at its
present 5Y4 percentage level, inflation would continue to
increase by about Y2 percentage point each year. The slow
response of inflation to a rate of unemployment above or
below the natural rate means that the costs of low levels of
unemployment, in terms of rising inflation, are initially
small-and policy-makers may be tempted to ignore them.
However, once inflation has been allowed to increase to
"unacceptable" levels, this slow response means that
bringing it down again will require either a lengthy period
in which unemployment is held above the natural rate or a
shorter period of excessively high unemployment. The
high costs associated with either course suggest that it is
more prudent to move against rising inflation before it
reaches unacceptable levels.
NOTES
1. The estimate of "high-employment GNP" by the Commerce Department,for example, is computed as the level
of output that the economy would produce at six percent
unemployment, on the presumption that a lower jobless
rate would be associated with increasing inflation.
2. Commenting on the report of an unemployment rate of
5.3 percent in June 1989, Michael Boskin, the Chairman of
the President's Council of Economic Advisers, said, "I'm
pleased that unemployment remains low, and I don't see
the current level of unemployment as inflationary." The
Wall Street Journal, Monday, July 10,1989, page 2.
3. A.w. Phillips, "The Relationship between the Unemployment Rate and the Rate of Change in Money Wage
Rates in the United Kingdom, 1861-1957," Economica,
November 1957, pp 283-299.
4. These theorists argue that higher-than-expected inflation leads to a decline in the unemployment rate. These
alternative views of the theoretical underpinnings of the
Phillips curve are illustrated intwo popular macroeconomics textbooks. Dornbusch and Fischer develop a model of
unemployment and inflation in which causation runs from
the unemployment rate, which serves as a proxy for the
strength of demand in the economy, to the rateof inflation.
See Rudiger Dornbusch and Stanley Fischer, Macroeconomics, Fourth Edition, McGraw-Hili, 1987, Chapters
13-14. The causation runs in the opposite direction in the
"new classical" model presented by Hall and Taylor. In
this model, unexpected changes in the inflation rate lead
to changes in the unemployment rate. See Robert E. Hall
and John B. Taylor, Macroeconomics: Theory, Performance and Policy, Norton, 1986, Chapter 13.
5. This argument assumes implicitly that the excess demand for labor by firms is the result of an increase in
demand for their products. Clearly, if higher nominalwage
rates reflect increases in labor productivity, they will not
spill over into higher prices.
14
6. See Edmund S. Phelps, "Introduction: The New Microeconomics in Employment and Inflation Theory," in
Phelps (Editor), Microeconomic Foundations of Employment and Inflation Theory, w.w. Norton, New York, 1970.
7. Seethe discussion of price determination in FlintBrayton and Eileen Mauskopf, "The Federal Reserve Board
MPS Quarterly Econometric Model of the US Economy,"
Economic Modelling, July 1985, pp 202-203.
8. A special case arises when the function gO in equation
(3) is linear. In this case, the weights Ws in equation (4)
decline geometrically.
9. For a discussion of alternative models of inflation expectations, see Adrian Throop, "An Evaluation of Alternative Models of Expected Inflation," Economic Review,
Federal Reserve Bank of San Francisco, Summer 1988.
10. John B. Taylor, "Staggered Wage Setting in a Macro
Model," American Economic Review, Vol 69 (May 1979),
pp 108-113.
11. For an example of this approach, see Olivier J. Blanchard and Lawrence H. Summers, "Hysteresis and the
European Unemployment Problem," in Stanley Fischer
(Editor), NBER Macroeconomics Annual 1986, National
Bureau of Economic Research, 1986, and Robert J. Gordon, "Hysteresis in History: Was There Ever a Phillips
Curve?," American Economic Review, May 1989.
12. A decline in the real exchange rate also tends
to increase GNP growth by increasing exports and reducing imports. In the Phillips curve, these "aggregatedemand" effects of exchange rate changes will be captured through changes in the unemployment rate.
13. This specification implies that the direct effect of
relative-price shocks is on the level of prices. An increase
in the level of the price of imported oil, for example, adds
to the average level of prices in the U.S. Hence, the
average inflation rate, which is the dependent variable in
Economic Review / Winter 1990
equation (6), is influenced by the growth rate of oil prices.
14. Robert Gordon has contributed extensively to this
literature. See, for example, Robert J. Gordon, "Understanding Inflation in the 1980s," Brookings Papers on
Economic Activity, 1:1985, pp 263-302.
15. An arithmetic examplemayclarifytheargument. Consider a simplified version of equation (6):
'ITt
= 'ITt-1 + a . (Ut
-
UNAT)
+ bo' SHKOILt
+ bx . SHKEXt
(6')
Supposeunemployment is atthe natural rate (U, = UNAT)
and initially therearenoshocks(SHKOIL t = SHKEXt = 0).
This implies a constant inflation rate (TIt = TIt 1)' A onetime one percent increase in the price of oil means that
SHKOIL t rises from 0 to 1 for a single quarter and then
declinesback to O. The direct effectof thisshockisto raise
the inflation rate by b o percentage point. However, because of the presence of the lagged inflation term (with a
coefficient of one) in equation (6'), the inflation rate also
will be b o higher in the next quarter and in all future
quarters. Thus, a temporary shock leads to permanently
higher inflation. Similarly, suppose the exchange rate
begins to fall steadily at one percent per quarter, so that
SHKEX t decreases permanently from0to -1. The inflation
rate will rise by b x percent in the first quarter, by an
additionalbx percentage point in the second quarter, and
so on.Thus, a permanent shock leadsto continually rising
inflation.
16. In equation (7) different symbols are used for the
coefficients onthe shockvariables (con and CXm inplaceof
bOg and b xh) to signify that these coefficients are constrained to sum to zero, whereas thosein equation (6) are
not.
17. Inequation (9), the distributed lags onthe two relative
price variables run from 0 to S + J and S + K, respectively.
In equation (7) these lags run from 0 to Nand M, respectively. If equation (7) is interpreted as a generalization of
(9), this implies that N = S + J and M = S + K. In the empirical estimations, this implication was used as a guide in
choosing the lengths of the estimated distributed lags.
18. See Robert J. Gordon, "Inflation, Flexible Exchange
Rates, and the Natural Rate of Unemployment," in Martin
N. Baily, ed., Workers, Jobs and Inflation, Washington
D.C., The Brookings Institution, 1982.
19. In cases where changes in the size of individual
population groups are associated with unemploymentrate changes in the same direction, this term probably
capturesdemographicfactors, as increases inthesupply
of particular groups of workers lead to more unemployment. For both young adults and older workers, the correlation between unemployment and labor force share is
positiveoverthe1963 to 1988 period, suggesting a demographic effectof this kind. However, for primeagewomen
and for male teenagers, this correlation is strongly negative. In these cases, the causation may be reversed, with
strong labor demand leading both to lower jobless rates
and to greater labor force participation,
Federal Reserve Bankof San Francisco
20. A similar approach is used by the Congressional
Budget Office. See Appendix B, "Estimates of Potential
Output," in Congressional Budget Office, The Economic
and Budget Outlook: An Update, August 1987. However,
CBO estimates the natural rate from the raw data rather
tha.n fromthe adjusted data.
21. The restrictions that the coefficients on lagged inflation sum to unity and that the coefficients on each of the
shock variables in equation (7a) sum to zero were imposed using a technique suggested by Scadding. See
John L. Scadding, "Simple Technique for Imposing Restrlctions on Sums of POL Coefficients," Appendix 1 in
RO$eMcl:lhattan,· "The:Response of Real Output and
Inflation to Monetary Policy," Economic Review, Federal
Reserve Bank of San Francisco, Summer 1981.
22, The demographic adjustments to the unemployment
rate data were made using the same 1963-88 sample
period as the regression estimates.
23. The definitions of these variables were adopted from
Robert J. Gordonand Stephen R. King, "TheOutputCost
of Disinflation in Traditional and Vector Autoregressive
Models," Brookings Papers on Economic Activity, 1:1982.
NIXON is defined as0.8 for the five quarters from 1971.3 to
1972.3 and NIXOFF is defined as 0.4 in 1974,2 and 1975.1
and as 1.6 in 1974.3 and 1974.4.
24. Gordon and King, cited in note 23.
25. Gordon, "Understanding Inflation in the 1980s." See
note 14for full citation.
26. These preliminary regressions also include a constantterm, the currentand four lagged values of SHKOIL,
the current and eight lagged values of SHKEX, and the
current and one lagged value of the unemployment rate
variable.
27. For estimation, the equations were transformed as
follows:
=
b1(TIt-1 - TIt-2) + b2 ( TIt-2 - TIt-3)
+ ... bT- 1(TIt-T+1 - 'ITt-T) + bTTIt-T + ...
In this form, b,
s
= k at. Thatis,theestimated coefficients
t~1
(bs ) arethe cumulative sums of the underlying parameters
(at).
28. Specifically, when more than 12 lagged values of
inflation are included in the equation, one can reject the
hypothesis that coefficients beyond the fifth lag are all
zero. Forexample, for the regression that includes24 lags
of the inflation rate, (column H) the F-statistic (with19and
63 degrees of freedom) for the hypothesis that the coefficientsfor lags beyond the fifth are all zero is 2.04, indicating that this hypothesis may be rejected with 97 percent
confidence. On the other hand, in the regression that
includesonly 12lags (column E), the f-statistic for the hypothesis that the coefficients on the sixth through twelfth
15
lags are all zero is only 0.83, implying that this hypothesis
may not be rejected.
29. In columns G, H and I of Table 1, the sum of coefficients out to lag 12 is less than that out to lag 8.
30. Specifically, the curnutatixe sums.of the coefficients
(that is, the figures reported in the table) are constrained
to follow a second-order polynomial. This restrictive specification was adopted because third-, fourth- and fifthorder polynomials yielded estimated lag distributions in
which the coefficient sums did not converge as the lag
length was extended.
31. See Gordon and •King, cited above, and Adrian
Throop, "A Macroeconomic Model of the U.S. Economy,"
Working Paper 88-06, Working Papers in Applied Economic Theory, Federal Reserve Bank of San Francisco,
1988.
32. F1 is less than onein all Cases, compared to its critical
value at the 5 percent level of 2.5.
33. However, it should be mentioned that the estimate of
UNAT* is sensitive to the impositionof this restriction. In
Model III, for example, the relaxation of the restriction
reduces the estimate of UNAT* by two standard errors
from 6.18 to 5.33 percent.
34. A.C. Harvey, The Econometric Analysis of Time Series, New York: John Wiley and Sons, 1981, pp 276-77.
35. In Model III, the equations include the current and two
lagged values of SHKOIL and the current and twelve
lagged values of SHKEX; that is, N = 2 and M = 12. In
Model IV, the direct effects of the shocks are represented
by the current value of SHKOIL and the current and eight
lagged values of SHKEX; that is J = 0 and K = 8. As
implied in note 17, J < Nand K < M.
36. The differences between these alternative estimates
are less than one standard error.
37. See, for example, the estimates by Gordon cited in
Appendix B, "Estimates of Potential Output," in Con-
16
gressional Budget Office, The Economic and Budget
Outlook: An Update, August 1987.
38. The estimates of Models I, II, and III are similar to
those from Model IV and are available from the author.
39. The estimates of UNAT*are unexpectedly low and
high in the 1963.,....74 and 1964-75 periods respectively.
These estimates may be biased by the effects of the
1973-74 oil shock.
40. However, the hypothesis that all the coefficients on
SHKEX are individually zero may be rejected with 90
percent confidence in only four sample periods.
41, The estimated coefficients over this sample period
are not significantly different from those reported in
Table 2.
42. In the static simulations, the errors in predicting the
change in inflation are the same as those in predicting its
level,because the equations include the lagged level of
inflation. In the dynamic simulations, the simulated level of
inflation depends on actual inflation in the quarters before
the simulation begins. This means that the error in predicting the level of inflation depends on the starting date of the
simulation. Hence, it is more appropriate to compare the
errors in predicting the change in inflation, which do not
depend on the starting date of the simulations.
43. Recall that the real exchange rate is only marginally
significant in the equations reported in Table 2.
44. After adjusting for demographic change, Models I, III,
and IV all imply a natural rate of six percent in the fourth
quarter of 1988, when estimated over the full sample
period. When estimated overthe 1977-1988 period, these
models imply a rate of 6% percent.
45. The results in Tables 4 and 5 do not show a preponderance of negative errors that would suggest that the
response of inflation to the unemployment rate in recent
years has been atypical.
Economic Review / Winter 1990
Assessing Regional Economic Stability:
A Portfolio Approach
Carolyn Sherwood-Call
Economist, Federal Reserve Bank of San Francisco. The
author wishes to thank Stephen Dean and Scott Gilbert for
their diligent and capable research assistance. The editorial committee, Gary Zimmerman, Jonathan Neuberger,
and Ronald Schmidt, provided many helpful insights.
This paper examines regional economic stability using
the analytical framework often used to study financial
portfolios. The analysis shows that industrial diversification reduces economic volatility, just as portfolio diversification reduces financial risk. However, because
the conditions that create a tradeoff between risk and
return in financial markets do not exist for regional
economies, regions do notface a tradeoffbetween stability
and growth.
Federal Reserve Bankof San Francisco
State and local government officials often want to improve economic performance by changing their region's
industry mix. For example, a state or local government
might offer tax abatements to relocating firms in an industry that is expected to enhance the region's economy.
However,it often is unclear just which industries improve a
region's economy. Specializing in a small number of fastgrowing industries, or targeting fast-growing industries as
promising sources of future growth, may make rapid
growth possible, but the region's economy may become
vulnerable to downturns in the industries in which it
specializes. Thus, a specialized regional economy may be
relatively volatile. If economic diversity reduces volatility,
a region wishing to reduce volatility might see a diverse industrial mix as a desirable goal of economic development.
Understanding the relationship between regional economic volatility and economic growth also provides useful
insights regarding a region's optimal industry mix. If, for
example, regional economies face a tradeoff between stability and growth, they may be willing to accept greater
instability to achieve more rapid growth. However, if no
such tradeoff exists, then stability would be a desirable
goal regardless of the region's aspirations regarding economic growth.
In a different context, the financial literature addresses
the relationships between diversity and volatility. Portfolio
theory suggests that diversification can reduce volatility, or
risk. The logic of diversification is compelling for regional
economies as well. Nevertheless, previous evidence regarding the relationship between regional economic diversity and regional economic instability is mixed. Conroy
(1975) and Kort (1981) concluded that the extent of industrial diversity explains a significant proportion ofthe interregional differences in economic instability, while Jackson
(1984), Steib and Rittenoure (1989), and Attaran (1986)
found little evidence to suggest a relationship between
diversity and instability. Others, including Brewer (1985),
assumed that economic diversity explains regional differences in economic stability, and looked for the diversity
measure that best captures this relationship. These studies
17
use a variety of measures to capture diversity and instability, but all suffer from a common conceptual problem: they examine the relationship between economic
diversity and total instability.
In contrast, the analogy with financial portfolios suggests that economic diversification should reduce only the
amount of regionaleconomic volatility that is diversifiable,
or nonsystematic. This result is derived from risk-spreading alone, and does not depend on restrictive assumptions
about the economic or statistical characteristics of the
region's industries. Since diversity is expected to be related to nonsystematic volatility, it is not surprising the
previous studies of the relationship between diversity and
total volatility have yielded conflicting results.
Carrying the analogy with financial portfolios a step
further also would suggest that the sensitivity of the
region's economy to systematic, or nondiversifiable, factors could be associated with the regionalanalog to higher
expected return, namely more rapid expected economic
growth. If this were the case, regions might choose to
accept more systematic sensitivity in exchange for higher
growth. This hypothesis, however, relies on the marketclearing assumptions of the Capital Asset Pricing Model
(CAPM), and those assumptions are quite tenuous for
regional economies. This suggests that accepting higher
systematic risk may not increase expected growth for a
regional economy.
.
This paper discusses these relationships conceptually
and tests them empirically. The analysis shows that there
is, in fact, a strong correlation between diversity and
nonsystematic volatility. However, systematic sensitivity is
not compensated with higher economic growth.
Thepaper is organizedas follows. SectionI presents the
analogy between financial market portfolios and regional
economies, alongwith its implications. SectionII explores
the meaning of "diversity" in the regional economics
context. SectionIII presentsthe data and variables usedfor
the analysis. Section IV discusses the empirical evidence
on the relationships among diversity, systematic and nonsystematic instability, and growth in regional economies.
Conclusions and implications are drawn in Section V.
I. Financial Portfolios and Regional Economies
The finance literature distinguishes between two kinds
of risk: "systematic" and "nonsystematic." Systematic
risk is associated with broad economic and financial market conditions. As a result, it is common to all assets and
cannot be diversified away. Nonsystematic risk, in contrast, is specific to a given asset and can be reduced
through portfolio diversification.
In a portfolio, diversification benefits investors by
spreading risk among various assets, where each asset's
"risk" is measured by the variance in its return. For
example, assume that an investor starts off with a single
asset with returnrI and variance VI' Adding a secondasset
to the portfolio makes the portfolio's variance Vp> where:
Vp = wyVI + W~V2 + 2W IW2C OV I,2
(1)
In equation (1), wI and w2 reflect the weights of assets 1
and 2, respectively, in the portfolio. Thus, 0 '5 WI'
o W2' and WI + w2 = 1.
The relationship between VI and Vp depends on: (a) the
magnitude of V2 relative to that of VI' (b) the relative
proportions of the assets in the portfolio, WI and w2' and
(c) the extent of covariance between the returns of the two
assets, Covl,2' IfV 2is verylargerelative to VI' Vp maybe
greater than VI' This is more likely if w2 is larger. Thus,
adding an asset to the portfolio mayor may not reduce the
portfolio's variance. However, as long as the covariance
among individual assets (Covl,2) is less than one, the
s:
18
variance of the portfolio is less than the weighted sum of
the variances of the individual assets. This property is
relatively easy to see in the case of uncorrelated returns,
that is, when Cov, 2 = O. In this case:
Vp = wyV I + W~V2
(2)
Since WI and w2 are between zero and one, wy<wl and
W~<W2' Thus, the variance of the portfoliois less than the
weighted sumof the variances of the individual assets, and
diversification reduces the risk associated with holding a
portfolio that includes assets 1 and 2. The lower is the
covariance between the returns of the two assets, the
greater are the benefits of diversification, since the covariance term in equation (1) is smaller. Thus, under a wide
range of circumstances, portfolio diversification reduces
risk. Note that the benefitsof diversification are associated
with the mathematical properties of variances, and do not
depend on restrictive assumptions about the market characteristics oreconomic propertiesof the assetsthemselves.
The benefits of diversification are even greater when the
returns of the two assets are negatively correlated. In fact,
the variance of the portfoliocan fall to zero in the case of
perfect negative correlation. However, in real-world markets the returns to most assets are correlated with general
economic and financial conditions, so the covariances
between the returns for most pairs of assets are positive.
Thus, investors cannot completely eliminate risk from
Economic Review / Winter 1990
their portfolios. The risk that cannot be diversified away is
referred to as systematic risk.
Not all assets or portfolios have the same degree of
systematic risk. According to the Capital Asset Pricing
Model (CAPM), investors who take on greater systematic
risk can expect to receive greater returns on their investments. Investors prefer the least possible risk at any given
level of return, so prices for assets that face little systematic
risk are bid up (thus reducing their returns) relative to
prices of assets that offer the same yield with more
systematic risk. Thus, the financial market bidding process
results in a tradeoff between systematic risk and return.
These principles suggest:
(1) Nonsystematic risk should fall with greater portfolio
diversification.
(2) There should be a trade-off between systematic risk
and return.
These expectations have been verified in the financial
literature (Fama and MacBeth, 1973; Black, Jensen, and
Scholes, 1972; and Gibbons, 1982).
The Analogy with Regional Economies
In the analogy with regional economies, industries play
the role of assets, and the region's industrial mix represents
the portfolio. The "return" becomes the economy's growth
rate, while its "risk" is the economy's volatility. In such an
analogy, systematic volatility is associated with general
economic conditions, such as fluctuations in the national
economy, and nonsystematic volatility is the regional variation that is not associated with national influences. Since
the relationship between portfolio diversity and nonsystematic risk depends on the mathematical properties of
variances and not on specific assumptions about the assets'
characteristics, the analogous relationship between a region's industrial diversity and its nonsystematic volatility
is likely to hold.'
In contrast, any relationship between systematic variability and growth would depend on a market-like mechanism. Under such a mechanism, risk-averse states would
accept greater variability only if they were compensated in
the form of stronger growth. However, several characteristics of regional economies make such a connection
unlikely.
First, although a financial asset earns the same return
regardless of whose portfolio it is in, a given industry may
perform differently depending on where it is located. For
Federal Reserve Bank of San Francisco
industries producing goods that are consumed in the same
locale in which they are produced, the health of the region's
economy affects the pace of activity, and this can differ
across regions.? For example, auto repair services are by
their nature provided in the same region where users of
those services live, and interregional differences in the
types of services provided are likely to be minor. Nevertheless, between 1980 and 1986, the real (inflation-adjusted)
value of auto repair services grew 48 percent in fastgrowing Arizona and only 10 percent in slower-growing
Oregon.
Another weakness of the analogy is that different regions have different attributes that favor production of
some goods over others. Natural resource endowments and
transportation infrastructure are the most obvious sources
of these regional differences in comparative advantage.
(See, for example, North, 1955; and Schmidt, 1989.) Even
when oil prices are high, residents of non-oil-producing
regions generally cannot change their industrial structures
to place more emphasis on oil production. Similarly, cities
with limited access to overseas transportation are unlikely
to become major transshipment points for international
trade. These kinds of differences in comparative advantage
limit the extent to which regions can (and should) diversify
their economies.
A final, and fundamental, problem with the analogy is
that a region's officials cannot "trade" in a "market" for
industries the way investors can trade in the market for
financial assets. Although state and local governments
often compete with each other to attract industries in order
to improve their regions' economies, using such tools as
tax incentives, infrastructure investments, and zoning variances, the "market" is thin and adjustments are slow.
Since any local jurisdiction is unlikely to have its desired
industry mix at a given point in time, equilibrium is not
observed. Moreover, no individual has the power to change
a region's industrial mix the wayan investor can alter
a portfolio. 3
These differences between assets in a portfolio and
industries in a regional economy suggest that a tradeoff
between systematic variability and growth may not be
observed for regional economies. These problems do not,
however, affect the extent to which diversification should
reduce nonsystematic volatility. This relationship is primarily a mathematical one, and does not rely on binding
assumptions about the character of regional economies.
19
II. What Is "Diversity?"
An investment portfolio that mimics the market portfolio in its composition (though not its size) is referred to as
a fully "diversified" portfolio. Thus, a portfolio of ten
different stocks would be somewhat diversified, but in a
market in which hundreds of stocks are traded, it would not
be completely diversified.
Financial economists have agreed-upon standards by
which to measure diversity.' Regional economists, in contrast, continue to debate what constitutes regional economic diversity. For the most part, this debate has been
framed as a measurement issue, in which the "best"
measure of diversity is the one that best explains regional
differences in economic volatility. (See, for example, Conroy, 1975; Kort, 1981; and especially Brewer, 1985.)
A "diversified" regional economy has been defined
variously as one in which (1)all industries are of equal size,
(2) the industry mix minimizes portfolio variance, or (3)
the region's industry mix is the same as the nation's.
Measures that define complete diversity as equal representation by all industries ("ogive" and "entropy" measures)
are particularly arbitrary, since they depend critically on
industry definitions. For example, an ogive or entropy
measure that uses two-digit SIC data implies that tobacco
manufacturing and health services would be equally important in a completely diversified regional economy.
The portfolio variance concept currently is the most
widely accepted measure of diversity, and it can be a
valuable tool if used appropriately (Gruben and Phillips,
1989a and 1989b). However, it should not be used to test
whether diversity reduces volatility (Conroy, 1975; and
Brewer, 1985) because it does not measure diversity independent of volatility. Examining the formula for the portfolio variance measure reveals why:
Vp =
t ,: wiwjVij
(3)
where Vp denotes portfolio variance, Vij denotes the variance (i = j) or covariance (i :f::.j) for each industry or pair of
industries, and Wi and wj are industry weights. Traditionally (Conroy), regional data are used to calculate the
industry weights, w, but due to data and computing con-
straints, or the particular task to which the measure is
tailored (Gruben and Phillips, 1989b), the industry variances and covariances, V, are calculated using national
data. As a general rule, if sufficient information and
computing resources are available, the portfolio variance
Vp should be calculated using regional variances and
covariances. If all of the data on the right-hand side of
equation (3) are consistent with each other, in terms of
regional coverage as well as the economic concept they are
measuring (employment, income, or gross product), the
right hand side of equation (3) is simply the decomposition
ofthe region's total variance.
Thus, the portfolio variance measure of diversity, correctly calculated, is exactly the same as the region's total
variance, which is a frequently-used measure of economic
instability. Therefore, the portfolio variance measure does
not measure diversity independent of volatility, and it is not
surprising that the portfolio variance measure tends to
"explain" differences in volatility better than other" diversity" measures do.
If the analogy with portfolio theory holds, regional
economic diversity should be defined in terms of the
"market" industrial mix. Ideally, this "market" industry
mix would reflect the comparative advantage of each
region. However, it is impossible to calculate an ideal
"diversified" industry mix that is different for each region
and that distinguishes between ideal and actual industry
structure. In view of these limitations, the national industry
mix provides a standard with which to gauge a region's
industry structure.
Such a standard implies that regions seeking to diversify
their economies should attempt to duplicate, to the extent
possible, the industrial structure of the United States. Of
course, no region could (or should) duplicate the U.S.
industrial structure precisely, since geographical differences in comparative advantage will determine the region's optimal industry structure to a significant extent.
Nevertheless, for most regions, the U.S. industrial structure provides a standard for diversity that is more reasonable than the available alternatives.
III. Data and Variables
The analogy between portfolio theory and regional
economic stability suggests two testable hypotheses. First,
regional economic diversity should reduce nonsystematic
volatility. Second, growth should be positively correlated
with systematic variations in the region's economy. Gross
State Product (GSP) data, released by the Bureau of
20
Economic Analysis.> were used to test these hypotheses.
These are annual data, adjusted for inflation, and disaggregated by state and by industry to the two-digit SIC level.6
They are available for the years 1963 through 1986. The
variables used in this analysis are defined below.
Economic Review / Winter 1990
systematic. Systematic volatility (SYSV), measured in
standard deviation terms, is therefore:
Variable Definitions
Diversity
Portfolio theory defines diversity as the extent to which a
portfolio's composition approximates the "market" portfolio. Similarly, regional economic diversity is defined
here as theextent to which a region's industrial structure
approximates that of the nation. This measure (DIV) is
derived using the following formula for each state and year.
J
D. :;;;: !,
It
)=1
(GSPj)t -GSPs)tF
GSP US)t
DIVj approaches infinity for states with economies that
resemble the industrial structure of the U.S. very closely,
and approaches zero for states with economies that deviate
substantially from the U.S. industrial structure.
Growth
AVGRGSPj measures the long-term growth rate in real
total GSP for state i. Annual percentage growth rates are
calculated for each state and year (GROWTHit) , and averaged across time periods t for each state i.
Total volatility
Total volatility, TOTSTDj, is measured as the standard
deviation over time in the state's annual percentage growth
rate, GROWTHir In order to decompose the variance into
its systematic and nonsystematic components, the variance
(TOTVARj :;;;: TOTSTD?) also is calculated.
Systematic and Nonsystematic Volatility
A simple univariate regression of state growth on national growth is used to divide total volatility into its
systematic and nonsystematic components:
:;;;: IX
+ 13 GROWTH u s t + e jt
(6)
The (unadjusted) R2 from this regression measures the
proportion of total variance in state i's growth rate that is
associated with contemporaneous variations in national
growth." This is the portion of the state's variance that is
Federal Reserve Bank of San Francisco
(7)
Nonsystematic volatility is the total volatility that is not
associated with variations in national economic growth. In
standard deviation terms:
NONSYSV j :;;;: vO - Ry) (TOTVARJ
(8)
Systematic Sensitivity
(4)
where GSP)t denotes the share of total GSP in industry j
during period t, i subscripts denote states, and US subscripts denote national figures. 7 After D it is calculated, its
reciprocal is taken, so that greater diversity is associated
with a higher value for the diversity measure," and the
measured is averaged over time within each state:
I 1986
I
DIV.:;;;: ~
(5)
I
24 t=1963 Djt
GROWTH jt
SYSVj :;;;: v(Rr) (TOTVARJ
The coefficient beta from regression (6) is analogous to
the beta coefficient often calculated for individual stocks,
and measures the region's sensitivity to national economic
conditions. This measure differs from that for systematic
volatility, described above. The beta measures the magnitude, and hence the sensitivity, of the response of state to
national changes. In contrast, systematic volatility measures the extent to which variations in the national economy
explain local fluctuations, regardless of the size of their
impact.
A Look at the Variables
Table 1 presents the value of each variable calculated for
each state. DIV exhibits a wide range of values across
states, suggesting that states differ significantly from
each other in their degree of diversity. According to this
measure, Washington, D.C. is the nation's least diverse
economy, while Illinois is its most diverse. The rankings
implied by these values are not surprising. The District of
Columbia's economy is strongly oriented toward government, and Illinois has a large and diverse economy. Moreover, the measures for Alaska's economy, which is quite
specialized, and for California's economy, which is very
diverse, appear reasonable. However, a few DIV values are
somewhat surprising. For example, DIV values for Missouri and Colorado are higher than one might expect.
Nevertheless, the overall rankings appear to be plausible.
Average GSP growth (AVGRGSP) also varies considerably from state to state. Between 1963 and 1986, Alaska
was the fastest-growing state, at an 8.1 percent average
annual rate. The District of Columbia experienced the
slowest GSP growth, at only 1.5 percent per year. Other
fast-growing states included Arizona and Florida, while
West Virginia, Pennsylvania, and Illinois were among the
nation's slowest growing states.
Considerable variation also is apparent in the values for
the coefficient beta from equation (6), which measures systematic sensitivity. The strongest measured responses to
national changes occur in the industrial states of Michigan
21
Nonsystematic
Volatility
(NONSYSV)
LIO
3.14
2.96
1.94
3.43
2.55
2.18
2.87
0.48
22
10.28
2.86
1.60
1.32
1.95
1.76
3.26
2.20
2.23
1.13
3.33
3.34
0.97
I.l2
2.70
I.l5
1.32
4.04
1.71
1.22
1.71
2.17
1.25
2.03
I.ll
3.17
2.41
3.25
2.27
1.49
2.29
1.59
I.l4
5.43
0.71
2.58
2.34
0.82
1.75
1.33
3.24
I.l7
1.81
2.50
1.97
I.l5
2.76
1.88
0.77
5.94
Economic Review / Winter 1990
and Ohio. In contrast, the weakest responses are found in
the energy-dependent states of Wyoming and Oklahoma.
A look at the standard deviation of the annual growth
rates reveals that Alaska's was by far the most volatile state
economy in the nation during this period. Other relatively
volatile economies included Wyoming and North Dakota.
At the other end of the spectrum, the nation's most stable
economies during this period included Kansas, the District
of Columbia, California, and Colorado.
Changes in the national economy affect different states
in different ways, as reflected in the R2S for equation (6),
which are listed in column 6 of Table 1. National influences
are relatively unimportant for Hawaii, Wyoming, and
North Dakota, but they explain more than 90 percent of the
total variations in the economies of Illinois, Indiana, Ohio,
Pennsylvania, and Wisconsin.
The remaining columns in Table 1 decompose the total
volatility into that explained by national fluctuations
(SYSV) and that which is nonsystematic (NONSYSV).
Nonsystematic volatility is highest for states with a combination of a high standard deviation and relatively low R 2,
such as Alaska and Wyoming. Nonsystematic volatility is
low for states that exhibit only moderate variation, most of
which is explained by national movements. Wisconsin and
Pennsylvania. fall into this category.
IV. Empirical Results
This section presents the results of tests of the following
two hypotheses:
(1) Nonsystematic volatility should be lower in states
with more diverse economies.
(2) Growth should be positively correlated with systematic sensitivity, as measured by the beta coefficient
calculated in equation (6).
Note that the discussion of the analogy between portfolios and regional economies suggests that the first hypothesis is more likely to be corroborated than is the
second.
Diversity and Volatility
Correlations between diversity and volatility are summarized in Table 2. 10 The correlation coefficient between
diversity and nonsystematic volatility is significantly different from zero at the 99.8 percent level, with a magnitude
of -0.425. The extremely high level of statistical significance is particularly noteworthy. Thus, as expected, states
with more diverse economies tend to experience less
nonsystematic volatility. This suggests that risk spreading
is applicable to regional economies.
To get a sense of how important the components of
volatility are to this hypothesis test, Table 2 also presents
correlations between diversity and both systematic and
total volatility. Results suggest that no correlation between
diversity and systematic volatility exists. The correlation
coefficient is 0.087, and is significant only at the 45.4
percent level." The correlation coefficient between diversity and total volatility is -0.284, and is significant at the
95.6 percent level. This relationship is slightly weaker than
that between diversity and nonsystematic volatility, although it is somewhat stronger than most other measured
relationships between national average diversity and total
volatility. 12
Federal Reserve Bank of San Francisco
Systematic Sensitivity and Growth
The .relationship between systematic volatility and
growth is measured as the "security market line" relationship in the financial literature. (See, for example, Sharpe,
1985.) The equation estimating this relationship is:
AVGRGSP =
4.00 - 0.85 BETA
(15.00) (3.37)
R2 = .172
Numbers in parentheses are t-statistics, Note that the
coefficient, which the portfolio analogy predicts should be
positive, is in fact negative and statistically significant.
However, Alaska's summary statistics in Table 1 suggest
that the state may be an outlier. If Alaska is omitted from
the sample, the coefficient becomes positive, but statistically insignificant:
AVGRGSP = 2.98
(7.72)
+ 0.20
BETA
R2
=-
.015
(0.51)
23
The lack of a significant positive relationship between
beta and growth strongly suggests that there is no mechanism in regional economies that generates a tradeoff
between systematic sensitivity and growth.
In fact, a negative relationship between systematic
sensitivity and growth is consistent with previous work
by Sherwood-Call (1988) and with Schmidt's 1989 work
on resource industries during the 1963-1986 period.
Schmidt found that resource-dependent states tended to
grow more rapidly during this period than did states that
did not depend heavily on natural resource industries.
Sherwood-Call found that resource dependence tended to
be negatively correlated with the extent of linkage to the
national economy. Taken together, these results suggest
that resource-dependent states may have weaker associations with movements in the national economy than most
states do, which could translate into smaller beta coefficients, while at the same time these states experienced
relatively rapid growth during the period under study.
SUmmary of Empirical Results
These empirical results suggest that regions maybe able
to improve the stability of theireconomies by diversifying
them. 13 •Regional economic diversity is negatively correlated•• withthenonsystematic.component of volatility in an
extremely significant way. However, regionsdonotseemto
be compensated for accepting more systematic sensitivity
through higher growth rates.
v. Conclusions and Implications
Previous studies of the relationship between regional
economic diversity and economic volatility have yielded
mixedresults. These studies focussed onmeasurement and
econometric issues in seeking to explain the conflicting
results. These measurement and econometric issues are
serious ones, but this paper has focused on a fundamental
conceptual problem with the previous studies. Most researchers have looked for a relationship between diversity
and total volatility, whereas the portfolio analogy suggests
that the relationship is between diversity and nonsystematic volatility.
In this paper, simplestatistical tests have shown that the
expectedrelationship between diversity andnonsystematic
volatility does exist and is extremely strong. These observations, which parallel those in the portfolio literature,
reflect the risk-spreading that occurs as regional economies diversify.
However, there is no correlation between systematic
sensitivity and growth, although the portfolio analogy
seemsto suggest thatsucha relationship should exist. This
result is not surprising, since the mechanism by whichthe
tradeoff occurs in financial markets does not exist for
regional economies. The financial market relationship
between systematic risk and return in portfolios occurs
becauserisk-averse investors willnotholdhigh-risk assets
unless they expect to be rewarded with higher returns.
24
Regional economies, in contrast, lack a singleomnipotent
decision-maker, and the "market" for industries is illiquid
and slow to adjust.
The implications for regional policy makers are relatively straightforward: greater economic diversity improves the stability of a region's economy. Thus, other
thingsequal,regional development officials should be able
to improve their region's economic stability by making
their regional economies more diverse.v' However, the
instability that is associated with fluctuations in the national economy remains significant source of instability
formoststates,and it is notcompensated by highergrowth
rates as the analogy with portfolio theory suggests it
should be.
While this study has focussed on issues of regional
economic stability, it is important to notethat regions may
pursueothereconomic goals, suchas rapidgrowth, instead
of or in addition to seeking economic stability. Fora region
that has a natural resource, or an agglomeration of activity
that provides it with a comparative advantage in a particular industry, pursuing that advantage may be a more
effective overall strategy than a diversification strategy
would be. At the same time, a region that develops an
industry mix that yields strong growthneed not "pay" for
that rapid growth by accepting greater instability.
a
Economic Review / Winter 1990
NOTES
1. However, because an industry is made up of many
firms, small states may have more volatile economies
even if they have diversified industrial mixes. Since differentfirms in a particular industry mayexperience different
fortunes, diversification across firms within an industry
probably has benefits as well. These issues are not addressed in this paper.
2.:. The differences. among regions' industries are even
greaterthanthe data usedin this study indicate, because
industry detail. is available only to the two-digit SIC level.
Thus, for example, the transportation equipment category
does not distinguish between motor vehicle manufacturing, which is important in Michigan, and aerospace production, which is important in California.
3. Even if local officials had control over their region's
industry mix, the community's residents and politicians
are likely to disagree about what industry mixthe region
should movetoward. While some may preferto maximize
economic growth, others might prefer slower growth if it
allows them to maintain the community's character.
4. The most commonly used measures include a representative "marketbasket"of securities, suchasthe stocks
included in the Dow Jones or S&P 500 index. These
measures do not. however, include bonds, real estate, or
other non-security assets.
5. Most previous studies of the relationship between
economic diversity and economic stability have used
employment data. While the employment data have the
advantage of being monthly, they provide a less comprehensive measure of economic activitythan GSP does,
and also suffer from a large number of missing values.
6. Most industries are disaggregated to the 2-digit level.
A few, including construction and retail trade, are disaggregated only to the 1-digit level.
7. U.S. production for each industry was calculated by
summing GSP across states.
8. The reciprocal istaken onlyso thata highermeasure is
associated with greater diversity, making results easier to
interpret. It does not materially affect the results.
9. An alternative measure of the relative contribution of
national changes to regional economic fluctuations was
developed in Sherwood-Call (1988). That linkage measure accounted for lags in the transmission of economic
changes from the national to the state level. However, the
R4 measure parallels work done in the portfolio literature.
10. The data presented in Table 1 suggest that Alaska is
anoutlier, whichmaybiasthe results presented inTable 2.
To. determine whether this is the case, all of the empirical
estimates wererecalculated usinga sample thatexcludes
Alaska. The results indicate that the calculations presentedin Table 2 are not driven solely by Alaska.
11. The positive sign on the correlation coefficient may
be due to a spurious correlation that results from the way
the diversity variable is constructed. The most "diverse"
economies are those with industrial structures that most
closely resemble the national economy. If each industry
exhibits similar fluctuations over time in various regions of
the country, then the states that haveindustry mixes that
mostclosely resemble the U.S. industry mixalsoare likely
to experience economic fluctuations in concert with national economic fluctuations.
12. The differences between these results andthe results
of other studies that used national average diversity
measures may be due to differences in the geographical
or industrial coverage. Most previous studies looked at
metropolitan areas rather than states, and examined only
manufacturing activity.
13. The empirical work presented hereexamines a static
measure of diversity overa cross-section ofstates. Thus, it
does not explicitly examine the benefits that a particular
state would gain from diversifying its own economy. Gruben and Phillips (1989a) address that issue directly.
14. Gruben and Phillips (1989a) suggest that regions
interested in reducing total volatility target industries
that have small or negative covariances with existing
industries.
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Barth, James, John Kraft, and Philip Wiest. "A Portfolio
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Black, F., M.C. Jensen, and M. Scholes. "The Capital
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NewYork: Frederick A. Praeger, Inc., 1972.
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Brealey, Richard and Stewart Myers. Principles of Corporate Finance, 2nd ed. New York: McGraw Hill, 1984.
Brewer, H.L. "Measures of Diversification: Predictors of
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Conroy, Michael E. "The Concept and Measurement of
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Schmidt, Ronald H. "Natural Resources and Regional
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Economic Review / Winter 1990
External Shocks and Adjustment
in Four Asian Economies-1978-87
Ramon Moreno
Economist, Federal Reserve Bank of San Francisco.
Editorial committee members were Gary Zimmerman,
Hang-Sheng Cheng, and Brian Motley. Research assistance by Judy Horowitz is acknowledged.
This paper presents a small open-economy model that
illustrates thepossible equilibrium real exchange rate and
current accountresponses to changes in the world rate of
interest and the terms of trade. The model provides a
simple framework for assessing whether theadjustment to
external shocks in four Asian economies in 1978-87 was
roughly consistent with equilibrium, and the implications
for economic performance.
Overall, the ability of these four economies to prevent
real exchange rate overvaluation in the face of adverse
external shock appears to have contributed to their very
successful economic performance in 1978-87. However,
thediscussion provides somesupportfor theview thatreal
exchange rates may have become undervalued in some
Asian economies, particularly after 1985, and thatopportunities for increasing investment and consumption in
these economies may have been missed.
Federal Reserve Bank of San Francisco
Since the second half of. the 1970s, the developing
economies of easternAsia have experienced faster growth
and lower inflation than other developing countries as a
group have. This economic performance is remarkable in
light of the significant disturbances to the world economy
duringthis period-a runupin oil pricesandin world rates
of intereststartingin the late 1970s, a slumpin commodity
prices and a slowdown in the growth of industrial economies in the early 1980s, sharp changes in the value of the
U.S. dollar, and the LDC debt crisis.
The success of Asian economies generally is attributed
to soundeconomic management. One oftencited theme is
that these Asianeconomies responded more appropriately
to external disturbances than did other developing economies,' thereby successfully preventing overvaluation in
their real exchange rates. However, not everyone believes
that the policy responses of Asian countries have been
appropriate. Critics argue that Asian economies have carried their adjustment efforts too far, and that as a result,
their exchange rates have become undervalued.
This paper attempts to examine the appropriateness of
economic policies in Asian economies in greaterdetail by
addressing two questions. The first is whether adjustment
in Asianeconomies in response to external shocks appears
to be broadly consistent with long-run equilibrium. The
second question is how shocks and the response to them
may have influenced economic performance in the economies of the region. The four Asian economies examined
here may be loosely divided into two groups following
roughly comparable economic policies: Korea and Thailand, and Malaysia and Singapore.
This paper is organized as follows. Section I develops
a theoretical framework for assessing whether adjustmentin Asianeconomies wasconsistent withequilibrium.
Section II describes the equilibrium response to external
shocks and provides definitions of real exchange rate
misalignment. Section III describes the experiences of
Asianeconomies in theperiods from1978 to 1982 andfrom
1983 to 1987. External shocks and their impact on the
region are described, and the possible role of policy
responses in explaining variations in economic performance is discussed. Some conclusions on the appropriateness of policy responses in each of the four Asian
economies are also offered.
27
I. External Shocks and Equilibrium Adjustment
Two main types of external shocks affected Asian economies in the ten years from 1978to 1987:sharp fluctuations
in world rates of interest, and changes in the terms of trade.
External shocks during the 1978-82 period generally had
adverse effects on the Asian economies. After declining
sharply from 1974 to 1977, the world rate of interest, as
represented by the 3-month London Interbank Offer Rate,
rose between two and three percentage points every year
between 1978and 1981, to peak at a level of over 18percent
in 1981. The run-up in interest rates tended to reduce the
disposable income of the debtor countries in the region,
although the vulnerability of each of the four countries
varied considerably. Over the period 1978-82, Korea and
Thailand averaged higher ratios of external debt to GNP
(respectively, 45 and 28 percent), and consequently were
more vulnerable to increases in world interest rates than
were Singapore and Malaysia (with external debt-to-GNP
ratios, respectively, averaging 18 and 23 percent).
Other major shocks affected the terms of trade of these
Asian economies. Double-digit increases in oil prices each
year between 1979 and 1981 adversely affected the terms of
trade of three of the four Asian economies, with only Singapore the exception since it exports petroleum products.
And although oil prices declined to still-high levels in
1982, the favorable impact on the terms of trade was offset
by a slowdown in the growth of industrial countries.
Among other things, the slowdown in growth contributed
to a decline in non-oil commodity prices, which particularly affected Thailand and Malaysia.
Finally, the dollar began a steady appreciation against
the currencies of other industrial countries after 1979. This
increased the competitiveness of industrial countries in
U.S. markets, potentially at the expense of the four Asian
economies, particularly those pegging to the U.S. dollar.
The world economic environment changed significantly
in the second period, 1983-1987. The nominal world
interest rate declined an average of 1.3 percentage points a
year, and by 1986 interest rates were close to their lows of
ten years earlier.? Earlier shocks affecting the terms of
trade of Asian economies also were reversed. Oil prices
declined further, and the growth of industrial economies
recovered, producing an erratic recovery in world commodity prices and a moderate and sustained expansion not
seen since the 1960s. After 1985, the dollar's depreciation
apparently increased the competitiveness of Asian economies in the U. S. market as the cost of Japanese and European goods rose. Against this largely favorable background
was the interruption in voluntary lending to heavily indebted developing countries after 1982, which had a rela-
28
tively small direct impact on the economies discussed in
this paper, but may have encouraged them to seek to limit
the growth in their external debt.
To examine how these interest-rate and terms-of-trade
disturbances may have affected the equilibrium real exchange rates and external balances of Asian economies, a
theoretical model of a small, open economy is developed
below.
Aggregate Supply and Demand
Consider an economy with three goods, non-tradable,
exportable, and importable, that are all produced and
demanded by domestic residents. The full-employment
aggregate supply of goods (S) in the economy is given
exogenously by endowments. Domestic prices are assumed to be flexible, the nominal exchange rate is fixed by
the government, and world prices of importable and exportable goods are determined in world markets. Total
supply can be expressed as the sum of the supply of each of
the three types of goods produced in the economy:
S = PNS N + PMS M + PxS x
= PNSNS
+ PMSMS + PxsxS
(1)
where PN, PM' and P x, respectively, refer to the domestic
currency prices of non-tradable, importable, and exportable goods, S, (i = N, M, and X) is the supply of each
good, and Sj is the share of each good in total supply. The
Appendix provides a full list of variable definitions.
Aggregate demand, D, is the sum of the demand for
non-tradable, exportable, and importable goods:
D = PNDN + PMDM + PxD x
= PNdND
+ PMdMD + PxdxD
(2)
where D, is total demand for each good and d, is the share of
each good in aggregate demand.
Aggregate demand is the sum of equilibrium private
demand, De, and government demand, Ds. Equilibrium
private demand, in turn, is a function of consumer demand
and investment demand. Equilibrium consumer demand is
the result of intertemporal utility maximization subject to
the economy's intertemporal budget constraint, while investment demand is determined by the optimizing condition that the world cost of funds (i*) should equal the
marginal product of capital (MPK). The solution to these
two conditions yields the equilibrium demand of the private sector.3
The equilibrium private demand De is increasing in the
Economic Review / Winter 1990
private sector's disposable permanent income, PI. Permanent income, in turn, depends on world interest rates and
the terms of trade. If the country is a net debtor, a
permanent increase in world interest rates increases the
volume of exports needed to service a given stock of debt
outstanding, thus reducing the permanent income available for domestic consumption. A permanent decline in the
terms of trade also reduces permanent income by increasing the volume of exports needed to purchase a given
volume of imports.
Aggregate demand in the economy also can be directly
influenced by government policy (Ds), which can help or
hinder the private sector in achieving an optimal spending
path." These relationships may be summarized as follows:
D = De[PI(i*, Px/P m), i* - MPK]
+-
+ Dg
+
Equilibrium in:
Non-tradable goods market
e = PN I EP;
(3)
+
N°
Figure 1
Equilibrium in Non-Traded Goods
It is assumed that the respective shares of non-traded
goods in total supply and demand depend on the relative
price of traded and non-traded goods, e, here defined as5
PN
e =
Non-tradable
goods
Importable goods market
(4)
E(PT *)
where E is the nominal exchange rate (in units of domestic
currency per unit of foreign currency), PT is the price of
tradable goods, defined as a suitably weighted average of
exportable and importable goods prices," and the * refers
to prices expressed in foreign currency. The relative price,
e, is often called the "real exchange rate" in the literature,
since it reflects the country's profitability, or competitiveness, of production in the traded goods sector. (Moreover,
when non-traded goods and world prices adjust sluggishly,
changes in e reflect changes in the nominal exchange
rate. 7)
Thus, a rise in e increases the share, SN' of non-traded
goods in total supply (SN is rising in e) because the
increased profitability of non-traded goods production
shifts resources to the non-traded goods sector. At the same
time, a rise in e lowers the share, dN, of non-traded goods in
total demand (dN is falling in e).
Given the above assumptions, the equilibrium in the
non-traded goods sector is determined by the requirement:
PNsN(e)S = PNdN(e)D
E/ P~
.
..
..
M o- ---.....
Importable
goods
Figure 2
Exportable goods market
E/P N
(5)
+
In equation (5), aggregate domestic supply S is determined exogenously by endowments, while aggregate
domestic demand D is determined in equation (3) by
....x o...
Exportable
goods
Figure 3
Federal Reserve Bank of San Francisco
29
variables that also are exogenous to the model. Since
nominal exchange rates (E) and the world prices of exports
and imports are exogenously determined, the price of nontraded goods will adjust to satisfy equation (5), resulting in
an equilibrium real exchange rate that clears the nontraded goods market. The short-run equilibrium real exchange rate is represented as eO in Figure 1.
excess demand for importable goods, resulting in total
imports MO.
The level of exports can be determined in a similar
fashion, that is,
PxS x = Pxsx(e, Px/PM)S
(8)
+
and
External Sector
PxD x = Pxdx(e, Px/PM)D
To see the relationship between the equilibrium real
exchange rate and the external sector, consider the market
for imports of goods and services. The shares of importable
goods in total domestic supply and demand are functions of
the real exchange rate as well as of the relative price of
exportable and importable goods. Thus, a rise in the real
exchange rate or in the price of exportable versus importable goods (the terms of trade) will reduce the domestic
supply of importable goods, while increasing the domestic
demand for such goods. That is,
PMSM
= PMsM(e, P/PM)S
(6)
and
PMDM = PMdM(e, P/PM)D
+
(7)
+
Figure 2 illustrates the domestic supply and demand
schedules for importable goods in the economy. Since the
schedules are defined in value terms, changes in export or
import prices are reflected in shifts in the supply and
demand curves. On the other hand, changes in the nominal
exchange rate, or in the price of non-traded goods (shown
in the vertical axis) will be reflected in movements along
the curves. Given a nominal exchange rate set by the
government, the price of non-traded goods pg.that clears
the non-traded goods market (which corresponds to the real
exchange rate eO in Figure 1), is associated with an
(9)
+
In this case, the price of non-traded goods, ~, is associated with an excess domestic supply of exportable
goods, corresponding to the equilibrium level of exports
XO (Figure 3).
Since the non-traded goods market always clears
(SN= D N), the difference between the aggregate supply
and demand in the economy equals the current account
balance (CA), or the excess supply of goods and services in
the traded-goods sector:
S - D = CA = Px(Sx-Dx) - PM(DM-S M)
= PxX - PMM
(10)
Suppose there is no government, so D in equation (10)
equals the equilibrium demand of the private sector, De, in
equation (3). Given eO, equation (10) then defines an
equilibrium current account, or level of external lending or
borrowing, that is consistent with private intertemporal
utility maximization.
In equilibrium, current account deficits (and borrowing)
are expected in a developing country for two reasons. First,
per capita incomes are lower than they are expected to be in
the future, so it is welfare-enhancing to borrow in order to
smooth consumption. Second, due to the relative scarcity
of installed capital, the marginal product of capital is likely
to be higher than that of industrial countries, making
borrowing to finance investment profitable. 8
II. External Shocks and Misalignment
This model can be used to determine how an economy
would adjust to two kinds of external shocks. First, an
interest-rate shock is considered, and then a terms-of-trade
shock is evaluated. Assume that a country is initially at an
equilibrium e" that clears the non-traded goods market and
that corresponds to an equilibrium current account deficit.
A permanent rise in the cost of funds (world rate of
interest) reduces permanent disposable income for countries that are net debtors and raises the cost of funds above
the existing marginal product of capital. The reduction in
30
permanent income leads to a fall in consumer demand, and
the rise in the cost of funds reduces investment demand.
The result is a fall in aggregate demand D in equation (3),
shifting the demand schedule for non-tradable goods to the
left, as illustrated in Figure 4. In order to clear the nontraded goods market, the real exchange rate must depreciate to e'. If nominal exchange rates are fixed and prices are
flexible, the depreciation will be accomplished through a
reduction in the price of non-traded goods.
As can be seen in Figure 5, the reduction in permanent
Economic Review I Winter 1990
An adverse external shock reduces aggregate demand and leads to:
A reduction in the demand for non-tradables
and real exchange depreciation
e
= PN / EP;
Non-tradable
goods
A fall In the equilibrium level of imports
E/ P~
E/ P~
""'..-r; •••• c
.;
,,""'
~M/
- MoFigure 5
~
Importable
goods
and an increase in the equilibrium level of exports
E/P N
E/P~
..
E/p~I···
·;....:
income and aggregate demand shift the domestic demand
schedule for importables to the left, which, in combination
with the decline in the equilibrium price of non-traded
goods, results in a reduction in the equilibrium amount of
imports. The corresponding fall in the domestic demand
for exportable goods leads to an increase in the level of
exports (Figure 6), and a decline in the equilibrium current
account deficit. 9
The analysis of terms-of-trade shocks is very similar to
that of interest-rate shocks. Suppose a supply-side disturbance, .such as a rise in imported oil prices, leads to a
worsening in the terms of trade. The resultant decline in
permanent income lowers the demand for non-tradable
goods,thereal exchange rate depreciates, imports fall, and
exports and the current account balance tend to rise, as
illustrated in Figures 4 to 6. 10 In the present discussion,
shifts in supply have been ignored to simplify the illustration,but they can be incorporated by assuming that factor
productivity depends on imported inputs. In this case, a
rise in import prices may lead to a reduction in aggregate
supply which is associated with a real exchange rate
appreciation, and a deterioration in the current account
balance in the short-run." These effects will tend to be
reversed as domestic demand contracts in response to the
reduction in permanent income.
In addition to being subject to supply-side disturbances,
the terms of trade may be affected by changes in foreign
demand and in third country exchange rates. The terms of
trade may tend to improve if foreign trading partners grow
faster, as demand abroad rises.P On the other hand, the
impact of changes in third country exchange rates on the
terms of trade is ambiguous. For example, if Korea exports
mainly to the US. and imports from Japan, a dollar
appreciation against the yen can lower the price of imports
relative to exports, thus improving the terms of trade.P
However, the appreciation also can induce a substitution in
US. demand awayfrom Korean exports in favorof cheaper
Japanese exports, which may adversely affect Korea's
terms of trade. The appropriate policy response will depend on which effect appears to be dominant. In particular,
if the substitution effect is stronger, it is appropriate for
Korea to weaken its currency against the US. dollar, in
order to offset the incipient substitution toward Japanese
goods.
Misalignment and Nominal Exchange Rates
...xo......
- - - X1
Exportable
goods
•
Figure 6
Federal Reserve Bankof San Francisco
An increase in world interest rates or adverse movements in the terms of trade may produce two different kinds
of real exchange rate misalignment, which may be illustrated by reference to Figure 4. First, price rigidity in
31
non-traded goods may prevent an adjustment in the real
exchange rate from e" to e' in Figure 4. The real exchange
rate will be overvalued in the sense that there is an excess
supply of non-traded goods in the short-run. Equilibrium
in the non-traded goods sector can only be achieved by
contracting output, resulting in unemployment, which
over time deflates the price of non-traded goods and
eliminates the misalignment. One way of avoiding unemployment under these conditions is to depreciate the nominal exchange rate, speeding the adjustment to e'. 14
A second kind of misalignment results when the government seeks to offset the contractionary impact of external
shocks by adopting expansionary fiscal and monetary
policies to prevent unemployment, thus maintaining the
short run equilibrium at eO. In this case, the demand
schedule will not shift to the left (demand will still be
represented by the schedule D~) in Figure 4, and the levels
of imports and exports will remain at MO and XO in Figures
5 and 6, respectively. The current account balance will also
remain unchanged. Since a domestic demand contraction
is the equilibrium response, the real exchange rate at eO
will now be overvalued in the sense that a country is
borrowing more than is optimal, or perhaps sustainable, in
the long-run. 15
In this model, nominal exchange rate policy cannot
prevent the real exchange rate overvaluation that results
from expansionary domestic demand policies. Ifthe nominal exchange rate depreciates, e may temporarily fall
below the equilibrium eO in Figure 1. Over time, however,
the resulting excess demand for non-traded goods leads to
inflation, and a real appreciation back to e".
By the same token, an increase in permanent income
due to a decline in world interest rates or an improvement
in the terms of trade can lead to misalignment if prices
adjust slowly and the real exchange rate fails to appreciate,
resulting in excess demand in the non-traded goods sector
and inflationary pressures. Alternatively, misalignment
can arise if domestic demand fails to expand in response to
the increase in permanent income. In this case, the country
is borrowing less than is optimal, at the cost of missed
opportunities for increasing present consumption or investment in a manner that will maximize intertemporal
utility. In both of these examples, the real exchange rate is
undervalued.
Qualifications
Adjustment to external shocks in developing economies
frequently differs from the preceding description of the
equilibrium, at least initially. Three factors may account for
the discrepancy.
32
First, adjustment takes time. A rise in the world rate of
interest, or adverse movements in the terms of trade,
initially may be associated with an increase (rather than a
decrease) in the current account deficit, because interest
payments to foreigners rise or export revenues fall. In
addition, if adverse terms-of-trade shocks lead to a contraction in domestic supply, domestic prices may rise more
than the prices of traded goods, leading to real exchange
rate appreciation, rather than depreciation. However, initial increases in the current account deficit and real exchange rate appreciation may not be inconsistent with
adjustment to the long-run equilibrium suggested by the
model if they are reversed over time.
Second, the equilibrium pattern of adjustment depends
on whether external shocks are permanent or temporary. In
the preceding discussion, and in what follows, it is assumed that shocks are perceived as permanent at the time
that they occur. (This is plausible, as the shocks were large
and generally were not reversed for several years.) However, if an adverse terms-of-trade shock is perceived as
temporary, an increase in external borrowing to smooth
consumption will enhance welfare, and expansionary policies (or government guarantees for private external borrowing) may be consistent with equilibrium adjustment if
domestic residents are unable to borrow abroad.
Third, the equilibrium pattern of adjustment is influenced by a country's previous borrowing. A country that
has borrowed little in the past may adopt expansionary
policies that offset the contractionary impact of external
shocks without producing disequilibrium. On the other
hand, a country whose exchange rate has been systematically overvalued in the past, and has been borrowing more
than the optimal amount probably will have to respond
more vigorously to depreciate the exchange rate and reduce current account deficits. In cases where past borrowing has been excessive, countries may even seek to
generate current account surpluses to reduce external debt
to manageable levels. This may be a deliberate choice on
the part of policy makers, or may be compelled by the
interruption of voluntary lending by creditors.
Equilibrium Responses
To sum up, in equilibrium, a permanent increase in the
world rate of interest or a permanent decline in the terms of
trade is likely to be associated with a depreciation in the
equilibrium real exchange rate (or an initial appreciation in
the real exchange rate that is later reversed) and a decline in
domestic demand and in the equilibrium current account
deficit. Policies to facilitate this type of adjustment (a
currency devaluation if domestic prices are rigid, or pal-
Economic Review / Winter 1990
icies to reduce domestic demand and external borrowing)
would tend to prevent real exchange rate misalignment. On
the other hand, policies that hinder adjustment to the
external shock may lead to an overvalued real exchange
rate, resulting in declining growth or in unemployment, or
alternatively in higher than optimal external borrowing.
Conversely, when a country experiences favorableexternal
shocks, policies that facilitate the equilibrium real appreciation and increases in external borrowing would prevent
misalignment. A number of factors, notably adjustment
lags and debt management considerations, may influence
the observed pattern of adjustment.
III. External Shocks and Their Impact
The responses of the four east Asian economies to
external shocks in the ten years from 1978 to 1987 can be
evaluated against the equilibrium suggested by the model
presented in the preceding section. Since the external
shocks in the first half of this period largely were adverse
and the shocks in the second half generally were favorable,
the analysis proceeds by evaluating the four economies'
adjustment first in the 1978-82 period and then in the
1983-87 period.
Table 1illustrates the impact of changes in world interest
rates and in the terms of trade in the four Asian economies
during the 1978-82 period. To represent the impact of
changes in world interest rates on income, the product of
the change in the U.S. dollar London Interbank Offer Rate
and the ratio of gross external debt to nominal GNP was
multiplied by -1for each country. The impact of changes in
the terms of trade was calculated as the product of the
percentage change in the commodity terms of trade in each
country and the corresponding ratios of real imports to
real GNP.
Korea was most adversely affected by external shocks.
The rise in Korea's estimated interest burden, as measured
by the ratio of estimated interest liabilities to GNP,16
reduced disposable income by an average of 0.5 percentage points a year in the period 1978-82, while adverse
movements in the terms of trade had an average annual
impact of 1.2 percent of GNP over the period.'? Due to
their status as commodity exporters, both Thailand and
Malaysia experienced declines in the terms of trade somewhat later than did Korea, although the average impact of
external shocks appeared to be smaller, especially in
Malaysia.
In contrast, Singapore appears to have benefited from
external shocks over the period, as improvements in the
terms of trade, magnified by the openness of Singapore's
economy, had a very strong impact, 6.6 percentage points a
year. Moreover, the increase in Singapore's estimated
interest burden was relatively small (about 0.2 percentage
points a year). Although the reasons Singapore's terms of
trade improved over this period are not clear from the
aggregate data, 18 it appears that Singapore was less vulnerable to external interest rate shocks because historically
Federal Reserve Bank of San Francisco
very large current account deficits were financed by private
direct investment rather than external borrowing. This
meant that any capital losses from a rise in world interest
rates would be borne by foreign investors, rather than by
Singapore residents. 19
Given the estimated effects of the external shocks during
the period 1978-82, the earlier discussion suggests that
Korea, Thailand, and to a lesser degree, Malaysia should
have adjusted by reducing current account deficits and
allowing their real exchange rates to depreciate. Moreover,
the adjustment should have been more pronounced in
Korea, which suffered the largest external shocks, and
relatively moderate in Malaysia. In contrast, Singapore
appears to have benefited from the external environment
33
over this period, so real exchange rate appreciation and
domestic demand expansion would be consistent with
equilibrium.
Details of the actual pattern of adjustment and economic
performance in these Asian economies are provided in
Table 2. The table reviews trends in the current account
(as an indicator of relative domestic demand stimulus),
the trade-weighted real and nominal exchange rates, 20
inflation, and real GNP. To evaluate the possible role
of debt-management considerations in influencing policy
responses, the debt-to-GNP ratio also is included.
In the case of Korea, current account deficits quadrupled
to nearly nine percent of GNP in 1980, and the real
exchange rate also appreciated over this period. Korea's
34
expansionary policies contributed to rising inflation, while
failing to prevent a slowdown in GNP growth. Real exchange rate appreciation exacerbated adverse external and
domestic shocks, culminating in a recession in 1980.
Efforts to correct the economic difficulties of Korea
began in earnest in 1980-82, when Korea adjusted in
textbook fashion. Domestic demand restraint produced a
more than 50 percent drop in current account deficits as a
proportion of GNP, and real exchange rate appreciation
virtually ceased as inflation dropped sharply. As the rate of
real exchange rate appreciation slowed, GNP growth rebounded (from a rate of decline of three percent in 1980 to
increasesof7.4and 5.7 percent in 1981 and 1982, respectively), in spite of slower growth in industrial economies
anclcontractionary domestic demand policies in Korea.21
The rate of increase in the debt-to-GNP ratio slowed after
1980,although at 54 percent, it was still above the average
for the four Asian economies.
It is worth highlighting that contractionary demand
management policies in Korea were consistent with rapid
growth and, as suggested by the discussion in the preceding section, appeared to be crucial in ensuring the effectiveness of nominal exchange rate depreciation. Korea's
nominal exchange rate depreciated sharply in 1980 and
1981,but this was not fully reflected in real exchange rate
depreciation, due to high inflation associated partly with
past expansionary domestic demand policies. The appreciation of Korea's real exchange rate was interrupted only
after contractionary demand management policies took
effect and inflation fell. 22
In Thailand, as in Korea, sharp increases in the current
account deficit were reversed, and real exchange rate
appreciation slowed late in the 1978-82 period. In contrast to Korea, however, Thailand's nominal exchange rate
drifted upward over the period due to its link to an
appreciating U. S. dollar.P While nominal appreciation
apparently led to more moderate inflation in Thailand, it
may also have contributed to the declining (and somewhat
erratic) trend in Thailand's GNP growth over the 1978-82
period. In particular, Thailand's GNP growth fell significantly below the average for the three other Asian economies in 1982, because of the contractionary combination of
declining terms of trade, domestic demand restraint (as
reflected in the decline in the current account deficit) and
nominal exchange rate appreciation. Over the period,
Thailand's debt-to-GNP ratio rose 14 points to 35 percent.
In Malaysia. and Singapore, current account deficits
increased and the real exchange rate appreciated strongly,
and, in contrast to Korea and Thailand, there was no
significant reversal in these trends over the period. To an
even greater degree than in Thailand, the appreciation in
Economic Review / Winter 1990
Malaysia's and Singapore's real exchange rate was largely
attributable to rapid nominal exchange rate appreciation.
Given the relatively benignexternal environment facing
bothMalaysia and Singapore, the combination of expansionarydomestic demand and nominal exchange rate appreciation appears to have been quite effective; inflation
was relatively moderate in botheconomies over the entire
period,while real GNP growth rates were consistently
high. •Malaysia's expansionary policies were associated
with largeincreases in its external debt over the period,
from 21percent to 32 percent of GNP. In contrast, because
Singapore's largecurrentaccount deficits were toa significant extentfinanced by foreign direct investment, Singapore's debt-to-GNP ratio did not increase over the period.
Our review indicates that Korea and Thailand, which
were more adversely affected by external shocks, experienced slower real exchange rate appreciation anda reductionin currentaccount deficits latein theperiod 1978-82.
On the other hand, Malaysia and Singapore, which were
lessadversely affected or benefited from external developments, andwere less indebted, experienced sustained real
exchange rate appreciation and increases in current account deficits. The pattern of adjustment is thus roughly
consistent withequilibrium as described by the model and
may partlyexplain the impressive economic performance
of the fourAsian economies in 1978-82.24
Nevertheless, the adjustment over this period in some
cases had adverse effects. Thailand's policy of linking its
currency to an appreciating U.S. dollar was probably too
deflationary, while in Malaysia and Singapore, the steep
realexchange rateappreciation posedtheriskofa contraction when domestic demand stimulus ended.P Furthermore, in Korea, Thailand, and Malaysia, external debt
grew significantly over the period (in spite of eventual
reductions in current account deficits in the first two
economies), increasing the importance of debt managementconsiderations in future adjustment.
tively, thedecline in the interest burden averaged 0.5 and
0.6 percent annually, while in Singapore, the annual decline averaged only 0.2 percent.
Table 3 also shows thatin this period, Korea, Thailand,
and Malaysia on average benefited from improvements in
theterms of trade, although only in Korea was there a
consistentimprovement. In Thailand and Malaysia, the
terms oftrade declined up to about 1985, before rising
sharply in1985-87. In the case of Singapore, the terms of
tradedecIined overmost.of the period.
'rhemodelsuggests thatin1983-87, declines in interest
rates andimprovements in the terms of trade would be
consistent with larger current account deficits and real
exchange rate appreciation in Korea, and somewhat later,
in Thailand and Malaysia. On the other hand, the equilit>riumresponsefor Singapore would be a reduction in
currentaccount deficits and real exchange rate depreciation.Table A suggests .that.in all Asian economies, the
actualpathsofreal exchange rates and of current account
balances over this period differed from the equilibrium
paths predicted by the model.
In the cases of Korea and Thailand, current account
deficits fell sharply over the 1983-87 period, turning to
1983-87
Theperiod 1983-87reversed manyoftheshocks experienced in 1978-82. As a result, Korea, Thailand, and
Malaysia, which had been adversely affected by external
shocks in the earlierperiod, benefited from changes in the
external environment in 1983-87. In contrast, Singapore,
which had been favored in 1978-82, was adversely affected by external shocks.
Table 3 shows that the decline in world rates of interest
was associated with an average annual decline in the
estimated interest burdenof Korea of about 0.7 percent of
GNPfrom 1983 to 1987. InThailand andMalaysia, respec-
Federal Reserve Bankof San Francisco
35
large surpluses in Korea by the end of the period. At the
same time, there was largely uninterrupted, and accelerating, real exchange rate depreciation in both economies,
reinforced by sharp nominal exchange rate depreciation
starting in 1984-85. Partly as a result of the domestic
demand restraint reflected in rising current account balances, inflation rates were moderate. However, inflation
rose after 1985 and recent reports of accelerating wage
demands in Korea and of supply-side bottlenecks in Thailand, as well as the sharp acceleration in growth in both
economies, suggest that the inflation rates in Table 4 may
understate underlying inflationary pressures. Rising current account balances also contributed to a decline in the
external debt-to-GNP ratio from 53 percent to 34 percent
36
inKorea, and moderated the rise in the debt-to-GNP ratio
in Thailand {the latter rose from 35 to 44 percent).
Adjustment in Malaysia and Singapore in 1983-87 may
be divided into two distinct phases. In the first phase, from
1983to early 1985, current account deficits fell sharply as a
result of reductions in investment spending to apparently
more sustainable levels. 26 At the same time real exchange
rates continued to appreciate or at the very least, did
not. fall, partly because nominal exchange rates were
still appreciating. These contractionary policies, during a
period when Singapore in particular experienced declines
in terms of trade, produced strong reductions in inflation
and growth, culminating in recessions in both economies
in 1985.
In the second phase, from 1985to 1987, current account
deficits in Singapore and Malaysia turned to surpluses, and
real. exchange rates depreciated sharply, due to strong
nominal exchange rate depreciation. This was associated
with a recovery in growth and a gradual increase in
inflation in both economies. In spite of the turnaround in
current account balances, Malaysia's external debt-toGNP ratio rose from 42 to 65 percent over the period
1983-87. The rapid growth in external debt in Malaysia
was a major source of concern for domestic policy makers
and external creditors, and increases in current balances
reflected efforts to limit this growth.
To sum up, in spite of recessions in Malaysia and
Singapore that were quickly reversed, economic performance in all four Asian economies in 1983-87 was again
better than the average for all developing countries as a
whole. Nevertheless, the earlier discussion suggests that
the real exchange rate depreciation and the associated
tendency toward current account surpluses may have been
carried too far, given the decline in world interest rates and
the improvement in the terms of trade in most of these
economies.
In terms of the model, exchange rate undervaluation is
suggested by large current account surpluses in Korea and
Malaysia, and by the indications of excess demand in
Korea and Thailand cited earlier. It could also be argued
that current account surpluses indicate that Singapore
exceeded the depreciation required to respond to adverse
movements in terms of trade and to correct an apparent
currency overvaluation. In addition to fostering resentment abroad, current account surpluses suggest that opportunities for consumption and investment in these rapidly
growing economies are not being fully exploited.
One possible justification for current account surpluses
inAsian economies is external debt management. Current
surpluses or declining current deficits have significantly
EconomicReview / Winter 1990
reduced the external debt of Korea, while slowing the
growth of external debt in Thailand, and to a lesser degree,
Malaysia, Prudent debt management permitted the Asian
region to escape largely unscathed from the debt crisis of
the early 1980s and reduces the vulnerability of these
economies to external shocks in the future, Nevertheless,
debt-to-GNP ratios are low in the four Asian economies
in comparison to other developing countries, and debtmanagement does not appear to be a consideration in the
case of Singapore. Furthermore, given the decline in world
rates of interest, it can be argued that Korea in particular
might have earned a higher return by stepping up investment spending rather than reducing its gross external debt
tnrouen current account surpluses in 1985-87,
Even if it were desirable to prevent further increases in
debt-to-GNP ratios in Asian economies, it can be argued
that current account surpluses (and the sacrifice of present
investment and consumption opportunities) may not be
required to accomplish this, The experience of Singapore
suggests thatforeign private direct investment can finance
increased consumption and investment with little or no
accumulation of external debt.s? Given the outstanding
economic performance of the region, debt reduction could
also be achieved easily by converting foreign debt into
foreign equity,
In conclusion, the ability of the four economies of
the region to prevent real exchange rate overvaluation
in the face of adverse external shocks appears to have
contributed to their very successful economic performal1CeiI11978--87, However, the discussion provides some
support for the view that real exchange rates may have
become undervalued in some Asian economies, particularly after 1985, and that opportunities for increasing
inve!;tmentand consumption in some of these economies
may have been missed,
NOTES
1. Notably Latin America and Africa. For discussions of
economic performance and policy responses to external
shocks, see Dornbusch (1985), Balassa (1986), Balassa
and Williamson (1987), Edwards (1988), Khan (1986), and
Sachs and Sundberg (1988).
2. Because of disinflation, however, real rates in the 1980s
still were higher than they were in the mid-1970s.
3. For a rigorous discussion of this type of optimization
problem with a focus on private consumption demand,
see Ostry (1988). For an example of this type of analysis
applied to Asian economies, with a discussion of investment demand, see Alesina (1987). Sen and Turnovsky
(1989) also analyze investment demand in an intertemporal framework.
4. If capital markets are imperfect, so that the private
sector cannot borrow abroad while the government can,
government policy can be designed to create a spending
and borrowing path that is consistent with intertemporal
utility maximization. For a discussion, see Alesina (1987).
On the other hand, government policy often ignores such
intertemporal concerns, leading to suboptimal spending
paths.
5. For convenience, the definition of the real exchange
rate that follows is the reciprocal of the standard definition.
6. A proxy for the traded goods price is described in
note 20.
See Dornbusch (1980).
8. To correspond to an equilibrium, equation (10) must
satisfy certain constraints. In particular, in the absence of
government intervention, we know that De is given in
equation (3), S is given by endowments and that the
Federal Reserve Bank of San Francisco
elasticities of supply of each good are given by technology. The price elasticities of demand between nontradable, importable, and exportable goods must then be
such that, given the overall demand and supply, the
equilibrium real exchange rate that clears the non-traded
goods market is also consistent with achieving an equilibrium current account and rate of external borrowing.
9. It is implicitly assumed here that the reduction in
permanent income in the long-run reduces the current
account deficit by more than the increased interest payments initially increase the current account deficit. The
reason is that the reduction in permanent income, as well
as the higher cost of current consumption reflected in the
increase in world interest rates, reduces the equilibrium
level of external borrowing.
10. It is implicitly assumed that the contraction in demand
more than fully offsets the tendency for the higher import
price to decrease the current account balance. The prediction that a worsening of the terms of trade tends to
increase the equilibrium current account balance would
contradict the so-called Laursen-Metzler effect. In Laursen and Metzler's 1950 framework, adverse movements in
the terms of trade cause a rise in the export value of
expenditure, a decline in saving, and a current account
deficit. Obstfeld (1982) notes that this prediction results
from Laursen and Metzler's assumption that individuals
reduce their saving when experiencing a decline in real
income, and shows that this assumption may be invalid if
individuals maximize intertemporal utility, and the rate of
discount is increasing in utility. (The latter condition is
required for stability in the stationary state. See Svensson
and Razin, 1983.) In an expanded optimizing framework
37
which includes investment, Sen and Turnovsky (1989) cite
conditions under which an adverse terms of trade shock,
leads to a decline in the steady-state equilibrium capital
stock, so that current investment spending and the current account deficit fall, contradicting the Laursen-Metzler
hypothesis,
A related literature focuses on the proposition adopted
in the text that adverse movements in the terms of trade,
due, say, to a rise in import prices, are associated with a
depreciation of the equilibrium real exchange rate, Edwards and Van Wijnbergen (1988) note that this standard
proposition may be questioned, because it implies that
income effects (which lead to a reduction in the demand
for non-traded goods and a tendency toward real exchange rate depreciation) dominate substitution effects
(which lead to an increase in the demand for non-traded
goods due to the rise in import prices, and a corresponding tendency toward real exchange rate appreciation),
Although they concede that it is quite possible for income
effects to dominate substitution effects, they note that
such a result is generally considered an anomaly (presumably because it contradicts traditional assumptions
made in the literature), One way of addressing this objection is to note that in many small open economies imported
goods are often not good substitutes for non-traded
goods, so that income effects may dominate substitution
effects, (In certain important cases, such as oil imports,
imports and non-traded goods may be complements.) In
the discussion in the text, the ambiguity between income
and substitution effects does not arise because it is implicitly assumed that changes in relative prices that are
not accompanied by shifts in the sectoral demand or
supply schedules lead to excess demand or supply in the
short-run but do not affect the long-run equilibrium real
exchange rate,
11, A rise in (oil) import prices will tend to shift the supply schedule in Figure 4 to the left, producing a real exchange rate appreciation and an associated increase in
the price of non-traded goods, In Figure 5, the import
supply schedule also will shift to the left, increasing the
total level of imports, Similarly, the level of exports will fall,
as will the current account balance,
12, For the terms of trade to improve, the expansion in
world economic activity must increase export prices more
than import prices, This will occur, for example, if the world
supply of exportable goods is less elastic than the supply
of importable goods, Thus we might expect commodity
exporters to be more affected by fluctuations in the growth
of industrial countries,
13, See Lipschitz (1979). This effect does not depend
on whether a country pegs its currency to the dollar or
the yen.
14, Alternatively, a mix of commercial policies may be
used, See Edwards (1988). Note that in theoretical discussions it is generally assumed that a nominal exchange rate
depreciation has adverse effects on the terms of trade,
which may further reduce demand. This assumes that
importers and exporters pass-through the full impact
38
of exchange rate changes to their respective markets,
so that import prices rise and export prices remain unchanged in domestic currency. However, the terms of
trade may not worsen in response to a nominal depreciationifexporters price-to-market (which some argue is
the case for Asian exporters), as export prices in domestic currency can then rise with a nominal depreciation,
without adversely affecting export volume. Such nominal
eXChange rate effects do not change the direction of
equilibrium adjustment and should not prevent the attainment of a new equilibrium.
t5..Fordefinitions of real exchange rate misalignment,
see Edwards (1988, 1989).
16. Note that the estimate does not reflect actual interest
payments, which in some cases were deferred into the
future. Furthermore, the use of gross, rather than net,
external debt statistics tends to overstate the debt burden
ofthe Asian economies. This is likely to be true of Singapore for most of the period, and for Korea after 1985,
Nevertheless, the figure presented in the table gives a fair
picture of the burden faced by these economies during a
period when world rates were rising.
The extent to which a change in world interest rates
affects demand and the real exchange rate also depends
on the gap between the marginal product of capital and
the cost of funds produced by the change in interest rates.
Due to lack of available data, no attempt was made to
estimate this effect.
17. The estimates show the impact of terms-of-trade
changes in all Asian economies in 1978-82, except for
Singapore, where the data correspond to 1979-1982, For
a more precise specification of the effect of changes in the
terms of trade on disposable income, as a function of the
share of imports, see Ostry (1988).
18. One possibility is that the sharp rise in crude oil prices
starting in 1979 contributed to the improvement in the
terms of trade, since Singapore was a net exporter of
petroleum products. The assumption is that Singapore's
profit margins from processing petroleum products increased with the rise in oil prices. However, the relationship between oil prices and profit margins is quite
unstable, Furthermore, a case can be made that profit
margins may rise more strongly when crude oil prices fall,
if refiners do not fully pass on the savings to consumers.
19. The analysis in Section II assumed that external deficits are financed through borrowing, so that changes in
interest rates affect the permanent income of domestic
residents. Since Singapore financed its current account
deficits by foreign direct investment, the rise in world
interest rates did not automatically lead to an increase in
the flow of payments to owners of foreign capital (as it
would to foreign creditors). Instead, through arbitrage, the
market value of the Singapore capital owned by foreigners may have fallen. In contrast to the case where
current account deficits are financed by external borrowing, the rise in world interest rates meant that foreigners,
rather than domestic residents, took a loss. Although the
rise in interest rates still would tend to lower equilibrium
Economic Review / Winter 1990
investment spending in Singapore, theeffectondomestic
permanent income and consumption would be smaller.
20. The weighted average CPls of major industrial countries, adjusted for bilateral exchange rates, were taken to
represent traded goods prices (or factors that would
heavily influence such prices), while the domestic CPI
was taken to represent non-traded goods prices for each
Asian economy. In an effort to focus on those economies
whose prices and currencies are most likely to influence
theworld prices of traded goods,theU.S., Japanese, and
EC CPols, adjustedfor bilateral exchange rates, were used
to denve a measure of traded goods prices for the Asian
economies (the German deutschemark served asa proxy
forthe European currencies). Weights were based on the
1980 bilateral trade of each Asian economy with the U.S.,
Japan, and the EC.
. ~ith the exception of Malaysia, these indices give
~Imllar results to broader real exchange rate indices that
Include the currencies of a larger number of industrial
countrvtradino partners as well as newly-industrializing
economies. In the case of Malaysia, the fact that Singapore is one of its larger trading partners affects the
r~sUlt~. Both the Singapore dollar and the Malaysian
Ringgit strongly appreciated on a trade-weighted basis
against the currencies of the major industrial countries in
1978-80. However, since the Singapore dollar appreciated by more, Malaysia's real exchange rate appears to
appreciate more strongly in the three-currency basket,
because the Singapore dollar is excluded.
21. Two points are worth making here. First, the sharp
contraction in output and the sharp rebound thatfollowed
were p~rtly the result ofthe1980 contraction in agricultural
output In Korea, and are thus not entirely attributable to
economic policies. Second, the decline in oil prices after
1980 contributed to disinflation in the more indebted
economies. Nevertheless, this was notthe onlyfactor. The
rapid increases in inflation in othereconomies after 1980
(notably Latin America) suggests that declining oil prices
are not sufficientto guarantee a decline in inflation.
~2. Inthe absence of more detailed empirical analysis, it
IS difficult to determine the "equilibrium" real exchange
Federal Reserve Bank of San Francisco
rate (for a description of an attempt, see Edwards, 1988)
and to make precise statements about whether real exchange rate adjustment was sufficient to correct overvaluation. One reason is that, aside from the magnitude of
the shocks, the.extent of equilibrium real exchange rate
adjustment to contractionary external shocks dependson
additional factors that are not easily measured, such as
the price elasticities of demand and supply between
traded and non-traded goods sectors. The smaller these
elasticities, the larger is the required depreciation.
23.. Thepeg to a rising dollar nullified the impact of a 10
percent depreciation of the Thai Baht against the U.S.
dollar in 1981.
24. Although Malaysia responded to adverse shocks with
expansionary policies, its relatively low external debt at
the time suggests that there was scope for increasing
domestic spending. See discussion in Section II.
25. The composition of domestic demand also posed
potential problems. InbothSingapore and Malaysia, stimulusto domesticdemand was reflected in increases ofthe
investment to GNP ratio of up to 10 percentage points over
the. period. It is not clear whether it was appropriate to
stimulate investment demand sharply at a time when
w~)fld rates of interest rates were rising. In particular, such
stimulus may have prevented these two economies from
increasing investment to exploitthe decline in world interest rates in 1983-87.
26. In Malaysia, a desire to limit increases in external
debt appears to have been an additional motivation. See
below.
27. There is some evidence thatthisstrategy isnowbeing
p.ursued by Thailand and, to a lesser degree, by Malaysia. In 1988 Thailand's current account deficit increased
sharply, while in Malaysia the current surplus declined. A
five-fold increase in foreign direct investment partly financed Thailand's growing current account deficit, while
foreign direct investment in Malaysia increased 54 percent. In the case of Korea, foreign direct investment
increased 72 percent in 1988, but this was accompanied
by an increase in the current account surplus.
39
Appendix
Variable Definitions and Data Sources
Variables
i*
M
MPK
N
PI
P*M
P*x
P*T
PM
PN
Px
PXPM
S
totaldemand
equilibrium demand by private sector in the absence of govemmentintervention, basedon
intertemporalutilitymaximization
(exogenous}f.lggregate demand resultingfromgovernment intervention
thetotal demand for eachgood,i""N,M, and X
the shareof each good in aggregate demand
real exchange rate
nominal exchange rate (unitsof domestic currency per unit of foreign currency), set by the
government
world rate of interest, set abroad
importable goods
marginal product of capital
non-tradablegoods
pennarient income = the discounted presentvalue of disposable income
(exogenous) foreign currency priceof importable goods
(exogenous) foreign currency priceof exportable goods
(exogenous) weighted average foreign currency price of importable and exportable goods
domestic currency priceof importable goods = EP*M
the price of non-tradable goods, set endogenously
domestic currency price of exportable goods = EP*x
commodity termsof trade
the (exogenous) total supply of goods in the economy
theshare of each good in aggregate supply, i = N, M, and X
the total supply of each good
exportable goods
Data sources.
Three-month LIBOR, exportand importunit values, nominal exchange rates, current account, GNP and CPI series are from the IMF,
International Financial Statistics, various issues. Trade weights were constructed using IMF Direction of Trade statistics. Debt/GNP ratios
are from World Bank, World Debt Tables 1988-89.
40
Economic Review / Winter 1990
REFERENCES
Aghevli, Bijan B. "Experiences of Asian Countries with
Various ExchangeRatePolicies," in JohnWilliamson,
ed., Exchange Rate Rules: The Theory, Performance
and Prospects of the Crawling Peg. New York: St.
Martin's. 1981, pp 298-318.
Alesina, Alberto. "Optimal Borrowing Policies for Developing Countries: the Cases of Korea, the Philippines and Thailand, 1965-1983," Working Papers
WP/87/41 , International Monetary Fund. June 10,
1987.
Balassa, Bela. "Policy Responses to Exogenous Shocks
in Developing Countries," American Economic Review. Vol 76, NO.2. May 1986.
Balassa, Bela and JohnWilliamson. Adjusting toSuccess:
Balance of Payments in the Far East Asian NICs.
Washington, D.C.: Institute for International Economics.1987.
Dornbusch, Rudiger. Open Economy Macroeconomics.
New York: Basic Books. 1980.
____ . "Policy and Performance Links between LDC
Debtors and Industrial Nations," Brookings Papers on
Economic Activity, 2:1985.
Edwards, Sebastian. "Real Exchange Rates in the Developing Countries: Concepts and Measurement,"
NBER Working Paper, No. 2950, April 1989.
____ . "Exchange Rate Misalignment in Developing
Countries," Occasional Paper Number2/New Series.
Baltimore and London: Johns Hopkins, 1988.
Edwards, Sebastian and Sweder Van Wijnbergen. "Tariffs, the Real Exchange Rate and the Terms of Trade:
On Two Popular Propositions in International Economics," Oxford Economic Papers. Vol. 39, 1987, pp
458-464.
Khan, Mohsin, S. "Developing Country Exchange Rate
Policy Responses to Exogenous Shocks," American
Economic Review. Vol 76, No.2, May1986, pp 85-87.
Federal Reserve Bankof San Francisco
Khan, Mohsin S. and Malcolm D. Knight. "Determinants
of Current Account Balances of Non-Oil Developing
Countries in the 1970s: An Empirical Analysis," Staff
Papers, 30, International Monetary Fund. Washington, December 1983, pp. 819-42.
Laursen, S. and L. A. Metzler(1950). "Flexible Exchange
Rates and the Theory of Employment," Review of
Economics and Statistics. 32, 281-99.
Lipschitz, Leslie. "Exchange Rate Policy for a Small Developing Country and the Selectionof an Appropriate
Standard, StaffPapers, International Monetary Fund.
Washington, September 1979, pp. 423-449.
Obstfeld, Maurice. "Aggregate Spending and the Terms
of Trade: Is There a Laursen-Metzler Effect?," Quarterly Journal of Economics. 97, 1982. pp. 251-70.
Ostry, Jonathan D. "The BalanceofTrade, Terms ofTrade,
and Real Exchange Rate: An Intertemporal Optimizing Framework," StaffPapers, International Monetary
Fund. Washington, 1988, pp 541-573.
Sachs, Jeffrey D. and Mark W. Sundberg. "International
Payments Imbalances of the East Asian Developing
Economies," in Norman S. Fieleke, ed., International
Payments Imbalances in the 1980s. Proceedings of a
Conference Sponsored by the Federal Reserve Bank
of Boston, October 1988.
Sen, Partha and Stephen Turnovsky. "Deterioration of the
Terms of Trade and Capital Accumulation: A Reexamination of the Laursen-Metzler Effect," Journal
of International Economics. 26, No. 3/4, 1989. pp.
227-250.
Svensson, Lars E.O. and Assaf Razim. "The Terms of
Trade and the Current Account: The HarbergerLaursen-Metzler Effect," Journalof Political Economy.
Vol. 91, No.1, 1983. pp. 97-125.
41
42
Economic Review / Winter 1990
The Public Policy Implications of State Laws
Pertaining to Automated Teller Machines
Elizabeth S. Laderman
Economist, Federal Reserve Bank of San Francisco. I
would like to thank Rachel Long and Deborah Martin for
research assistance, and the members of the editorial
committee, Randall Pozdena, Gary Zimmerman, and Fred
Furlong, for many helpful comments.
In the early 1970s, as automated teller machines (ATMs)
were beginning to grow in popularity, some states instituted mandatory sharing laws, whereby ATM-owning
banks were required to share their ATMs with any other
bank that wished to do so. It was perceived that ATM
technology was subject to significant economies of scale,
and it was thought that these laws would increase small
bank customers' access to ATM services. Empirical tests
in this paper reject the hypothesis that mandatory sharing
increases the level of ATM services for small bank customers and show that mandatory sharing may in some
cases decrease the level of ATM services for all bank
customers. It also is shown that, under certain conditions,
branching restrictions may have negative effects on the
supply ofATM services.
Federal Reserve Bank of San Francisco
In the early 1970s the automated teller machine (ATM)
was introduced, enabling people to perform banking transactions such as cash withdrawals, deposits, balance inquiries, and interaccount transfers without the aid of a
human teller. By the mid-1970s, banks had started sharing
ATMs, allowing other banks' customers access to their
machines.' Beginning at this time, too, certain states
instituted mandatory sharing laws, which required that any
ATM-owning institution share its off-premise machines for
a "reasonable fee" with any other financial institution in
the state that wanted to share. The intent of such laws was
to ensure that customers of small banks would have access
to ATMs, despite ATM systems being subject to significant economies of scale. In this paper, I will investigate
whether there is any empirical evidence that mandatory
sharing laws have been successful in this regard.
I will begin with a general discussion of the market for
ATM services, economies of scale in ATM networks, and
the legal environment surrounding ATMs before proceeding to the empirical analysis.
43
I. The Demand for and Supply of ATM Services
The Demand for ATM Services
According to one estimate, 137.7 million ATM cards
were outstanding in July 1988. 2 Estimates of the percentage of households thatownat least one ATM card run from
45 percent" to 54 percent. 4 The group that reports the 54
percent figure states that, as a comparison, 76 percent of
households have at least one credit card." ATM use has
been increasing over time, and now, forthe first time, more
than 50 percent of all cardholders use their cards at least
once a month.6
Whether a customerof a particular bank will choose to
obtain transactions services from an ATM or a teller partially will depend on the direct charges the customer faces
when using the two procedures. For example, a customer
may face a choice between paying 25 cents to use the
bank's ATM or paying 10 cents to cash a check through a
teller.7
In addition to the levels of direct ATM and teller use
charges, the convenience of using an ATM versus a teller
will influence a bank customer's decision. Whether a
particular customer will choose an ATM or a teller will
depend on such factors as the value of the time and the
effort that the customerneeds to contributein order to get
to and use an ATM or a teller. The decision also will
depend on the customer's attitudes and tastes regarding,
for example, computers versus human interaction.
Apparently, age and income are determining factors in
the choice between ATMs and human tellers. A typical
ATMuser is under 40 and uses an ATM three to fourtimes
a month, on average. Very heavy ATM users, those who
use the machines as often as three times a week or more
are apt to be between 18 and 24 years of age." A 1986
survey revealed that the percentage of families with less
than $10,000 in yearly income that owned ATM access
cards was 32 percent. This percentage increased with
income,up to 60 percentforthoseearning$50,000or more
a year. However, the survey also revealed that those families in the lowest and highest income categories were the
most frequent users of ATMs, at least for the purpose of
withdrawing cash.?
The Supply of ATM Services
ATM industryobservers have cited at leasttworeasonsa
bank might chooseto offerATM servicesto its customers.
First, banks mayintroduce ATMs to increasemarketshare.
In a market where ATMs are not prevalent, or ATM
networks are not extensive, a bank may be able to differentiate its services from those of other banks and thereby
44
attract new customers. For example, one East Coast bank
attributes the increase in its statewide share of checking
andNOW accounts from16percentin 1984 to 19 percentin
1988 to its extensive ATM network. An executive vice
president of the bank claims that the ATM network is one
of the two factors peoplemention most often as reasons for
banking with that bank. 10
This bank's experience raises the possibility that some
banks may adopt ATMs even if the per transaction cost is
higherwith ATMs than with tellers. ATMs raise the value
of transactions servicesby, forinstance, lowering the" time
tax" that customers face whentheycarry out bank transactions. If this attracts new customers, it can lead to economies of scale in some other aspect of bank operations
besidestransactions services. However, giventhe ubiquity
of ATMs, it seems unlikely that this is the primary means
by which ATMs increase profitability for most banks.
A second, and more important, reason banks might
choose to install ATMs is that, above a certain level of
operations, the cost of a single transaction performed at an
ATM potentially is less than the cost of a transaction
conductedat a teller window. II This is because ATMs are
capableof handlingmoretransactions per unitof time than
are tellers.
However, Allen Bergerhas found that the costper dollar
withdrawn is significantly higher for ATM withdrawals
than for withdrawals conducted at a teller window. 12 This
is because ATMs are sufficiently more convenient than
te~lers that customers tend to make more frequent trips and
withdraw smaller amounts each time than they would if
they had to use tellers. Despite this, ATMs still are
attractive to banks as long as the price per transaction is
lowerand banks are able to cover the cost of transactions
by charging transactions fees.
The per-transaction cost of ATMs apparently is subject
to significant economies of scale, due to the relatively high
fixed cost of installing and operating an ATM system.
Purchasing and installing an ATM costs about $25,000
to $30,000. Moreover, armored car services and data
processing can add $200,000 a yearto theoperating cost of
an ATM system.P In light of these high fixed costs,
Walker (1980) estimated that economies of scale associated with ATM transactions in a network of ATMs are
realized up to at least 43,600 transactions per month per
ATM.14 This number should be interpreted with some
caution, however, because Walker's cost data were from
early 1974, a time when sharing of ATMs was not very
prevalent. Therefore, this may be a better measure of
Economic Review / Winter 1990
economies of scale for proprietary ATM networks that are
used by only one bank's customers than for shared ATM
systems.
Shared ATM Networks
Some have claimed that the economies of scale in ATM
systems help to explain the rise of shared ATM networks.
A shared network is a collection of ATMs that are owned
by different banks but can be used by any customer of any
bank in the network. IS By spreading the fixed cost associated with ATMs over transactions initiated by customers of
many different banks, a shared network can take advantage
of economies of scale.
Shared networks also may be attractive because they
increase the convenience of ATMs by enabling a given
bank's customers to carry out banking transactions over a
wider geographic area than would be possible with a
proprietary network." This factor may be particularly
attractive to banks in states that place geographic restrictions on branching and the placement of ATMs.
Statistics show that ATMs more often than not are
shared, and that customers take advantage of shared machines. In 1987, 75 percent of the banks that operated
ATMs shared them with other institutions,'? and in 1988,
90 percent of the ATM terminals in the U. S. were shared
with at least one other institution. IS A 1986 survey found
that about 28 percent of families with ATM cards used
another institution's ATMs.19 ATM sharing also has been
growing over time, as shown in Table 1.20
It is important to note, moreover, that banks form shared
Federal Reserve Bank of San Francisco
networks even in states that do not require sharing. In 1983,
23 states had mandatory sharing laws, yet every state had
banks or other financial institutions that belonged to shared
networks.
Banks that participate in a shared network pay fees to the
network owners to cover the various costs of the network's
operation. These costs include the costs of transferring
"foreign" transactions, those transactions that are carried
out by one bank's customers on another bank's ATMs.
Such transactions are commonly sent through a central
"switch," in which case the ATM-owning institution pays
a "switch fee" to the network. In addition, "interchange
fees" are paid by the card-issuing bank to the ATMowning bank.
The fees charged by a given network depend on two
countervailing factors. On the one hand, economies of
scale associated with high transactions volume should help
to keep network fees low. There appears to be some
evidence that networks do, in fact, pass on cost savings
resulting from economies of scale. For instance, the American Banker recently reported that increased transaction
volume at many of the nation's largest regional networks
has enabled them to reduce fees to network members. Last
year, interchange fees averaged 15 to 20 cents per transaction, but are now running around five to 10 cents."
On the other hand, network costs may rise as the number
of network members rises, and this could partially offset
cost savings from economies of scale in transaction volume. As the number of network members rises, costly
telecommunications technology is needed, and negotiat-
45
ing costs and the costs associated with settling accounts
among institutions also rise. Given that networks with
high transaction volume also may have many members, it
is not surprising that some fees apparently do not vary
much with transactions volume. As Table 2 shows, the
average switch fee across five different volume classes is
very close to 20 cents a transaction.
These observations suggest that the marginal cost of
adding a small bank to a shared network could outweigh
the marginal benefit this bank would contribute by way of
increased transaction volume. As a result, even with the
existence of sharing, small banks are less likely than are
large banks to own and operate ATMs. Table 3 shows that,
indeed, relatively few small banks own and operate ATMs,
and the small banks that do own ATMs own fewer terminals, on average, than do larger banks.
46
1,000
or More
99.0%
.8
Economic Review / Winter 1990
II. Mandatory Sharing Laws and ATM Branching Laws
The discussion in the preceding section suggests that the
economics of shared ATM networks discourages the participation of smaller banks. The perception that smaller
banks have more limited access to shared ATM networks
may explain why a number of states have adopted mandatory sharing laws. Table 4 shows that as of September
1983, 23 states had instituted some sort of mandatory
sharing, whereby banks must share their ATMs with any
other in-state financial institution that wishes to do so and
is willing to pay a reasonable fee. 22
Many of the mandatory sharing statutes do not specify
the level of payments which may "reasonably" be required
of banks wishing to join a network. This is an important
issue because it has implications not only for whether small
Federal Reserve Bankof San Francisco
banks join networks, but also for whether the incentives to
form networks diminish with mandatory sharing.
Where legally imposed sharing requirements exist in
other institutional contexts, courts have ruled that new
members had to be admitted on the same terms applicable
to the preexisting members, and that "open admissions"
and "equal treatment" are called for.> Since many mandatory sharing laws predate the widespread formation of
shared networks, the open admissions and equal treatment
provisions are the most relevant of the three principles.
Unfortunately, these provisions have been defined only
vaguely by the courts. Nevertheless, they seem to imply
that sharing requirements preclude discriminatory fees and
fee schedules.
47
Thus, the "reasonable fee" clause in mandatory sharing
statutes may prohibit shared ATM networks from imposing
surcharges on banks that contribute too few transactions to
the network. Coupled with the open admission provision,
this pricing approach likely would increase the number of
small banks with access to ATM machines. However, the
addition of these banks likely would decrease efficiency
and increase network fees for all members, large and
small, since the marginal cost of these banks' membership
would outweigh the marginal benefits they contribute.
There is another problem with the "reasonable fee"
provision; that is, it is difficult to determine what rate of
return on risk taking in shared networks ought to be
incorporated into the reasonable fee. Baxter, Cootner, and
Scott (1977) argue that regulators are likely to underestimate the degree of risk faced ex ante by network founders,
and are thus likely to underestimate the appropriate rate of
retum.>' Ex post, the successful networks to which new
members will wish to gain access will appear to the
regulator not to have faced extraordinary risk, these critics
maintain. Therefore, according to this argument, rates of
return and compensating fees will be set too low. In
expectation of this outcome, banks in mandatory sharing
states will be discouraged from forming shared ATM
networks.
The Justice Department's view on mandatory sharing is
consistent with this line of reasoning. The Department
argues that mandatory sharing "undercuts in advance any
incentive to innovate, creating a 'free rider' problem with
respect to initial risk-taking."25 Other observers note that
mandatory sharing may introduce an additional free rider
problem simply by allowing banks to join in after the
initial capital costs have been borne by the original ATM
installing bank or network members. 26
Other state laws pertaining to ATMs include those that
set the geographic limits for off-premise ATMs within
states. A list of these statutes can be found in Table 4. Note
that all of the states that have constraints on ATM placement also constrain the geographic expansion of traditional branches. However, not all unit banking or limited
branching states limit ATM placement.
III. A Model of the Market for ATM Transactions
As noted in the preceding section, mandatory sharing
may increase small bank customers' access to ATM services, but also may make sharing more costly for all network
participants, thereby decreasing the level of ATM services
for all customers. To empirically test whether mandatory
sharing laws have increased the supply of ATM services to
small bank customers, I develop the following model of the
supply of ATM transactions, which includes mandatory
sharing as an explanatory variable.
The supply of ATM transactions will depend on the cost
of ATM transactions and the price of ATM transactions. It
may also depend on the banking market structure in the
sense that a less competitive banking market will yield a
lower supply of ATM transactions.
The aggregate supply of ATM transactions thus is
given by:
SUPPLY = s(BANKS, COST OF ATM
TRANSACTIONS, STRUCTURE, PRICE
OF ATM TRANSACTIONS),
(1)
where s is a continuous function, BANKS is the number of
banks, and STRUCTURE indicates the bank market structure. The aggregate supply of ATM transactions depends
positively on the number of banks and negatively on the
cost to each of those banks of providing ATM transactions.
It depends positively on the price of ATM transactions.
48
The cost of ATM transactions, in turn, is given by:
COST = c(BANK SIZE, ATM LAWS,
BRANCHING LAWS),
(2)
where BANK SIZE is the size of the bank in terms of
number of depositors, ATM LAWS are laws governing
ATMs, including mandatory sharing laws, and BRANCHING LAWS are laws governing traditional branching. 27
As bank size decreases, the cost of ATM transactions rises.
For any given bank, however, this may be modified by the
existence of mandatory sharing laws, other ATM laws or
branching laws. I will test the hypothesis that mandatory
sharing mitigates the negative effects of a decrease in bank
size. The possible effects of other ATM laws and of
branching laws will be discussed in more detail in the next
section.
The aggregate demand for ATM transactions should
depend on the population, its income, and its age. It should
also depend on the price of ATM transactions and on the
number of traditional branches and main bank offices
available. The aggregate demand for ATM transactions is
given by:
DEMAND = d(POP, PER CAPITA INCOME,
AGE, OFFICES, PRICE OF
ATM TRANSACTIONS),
(3)
Economic Review / Winter 1990
where POP is population, AGE is the mean age of the
population and OFFICES is the number of bank offices
(main offices plus branches). Aggregate demand will
depend positively on population and per capita income and
negatively on the mean age. It also will depend negatively
on the number of bank offices, since these are a substitute
forATMs, and negatively on the price of using an ATM.
This model was given a log-linear specification, and the
resulting reduced form, derived in the Appendix, is:
ATM transactions = Bl + B2*POP + B3*PCINC +
B4*BANKS + B5*BRANCHES
+ B6*MAND + B7*ATMLIM
+ B8*(MAND)(BANKS)
+ B9*(ATMLIM)(BANKS)
+ BlO*(UNIT)(BANKS)
+ Bll*(LIM)(BANKS)
+ B12*CONC + B13*UNIT
+ BI4*LIM + Z
(4)
where,
POP = population
PCINC = per capita income,
BANKS = number of banks,
BRANCHES = number of bank branches,
MAND is a binary variable indicating the presence or
absence of mandatory sharing,
ATMLIM is a binary variable indicating the presence or
absence of limitations on "branching by ATM,"
UNIT is a binary variable indicating the presence or
absence of unit banking,
LIM is a binary variable indicating the presence or
absence of limited branching,
CONC = the degree of concentration of the statewide
banking market,
Z is a normally distributed error term with mean
zero, and
Bl - B14 are coefficients to be estimated.
Data and Regression Specification
To determine the effect of mandatory sharing laws, I
estimate the reduced form given in equation (4) for a crosssection of 50 states, using two different proxies for the
number of ATM transactions. Data on monthly transaction
volumes by state are not available. Although data on
monthly transaction volumes are available for each network, these data are not useful for measuring the effects of
mandatory sharing laws. Sharing laws affect ATM transactions initiated by customers of only the banks within a
given state, while transactions on shared networks frequently involve banks that are located outside the state in
Federal Reserve Bank of San Francisco
question. As a result, I tried two different proxies for
transaction volume, the total number of ATM debit cards in
each state in 1987,28and the number of ATMs in each state
in 1987. 29
The first regression uses the number of ATM cards as the
dependent variable. Population, per capita income, the
number of banks , and the number of bank branches in 1987
are all included in the regression as explanatory variables.P? Increases in population and per capita income
should increase the aggregate demand for ATM cards.
Increases in the number of bank branches should decrease
demand for ATM cards, since traditional branches are to
some extent substitutes for ATMs.
Variations in the number of banks, holding population
and per capita income constant, should be negatively
related to variations in the average size of banks, in terms
of number of depositors. It is expected that states with
banks that are larger on average, in terms of number of
depositors, will have more ATM cards because larger
banks are more likely to have ATM programs. Therefore,
states with fewer banks, holding all other factors constant,
should have more ATM cards. However, a decrease in the
number of banks also may decrease the aggregate supply
of ATM transactions by decreasing the number of suppliers. (See the Appendix for more detail.)
I also include a statewide concentration ratio on the
right-hand side of the regression.:" This controls for the
competitive effects of bank market structure. It is possible
that a less competitive banking market would lead to a
lower supply of ATM cards. However, since bank services
are a multi-dimensional "good," with many different
characteristics, it is not obvious a priori that a decrease in
competition would decrease the supply of ATM services or
ATM cards in particular.
A binary variable for mandatory sharing enters the
regression by itself and in an interaction term with the
number of banks. The mandatory sharing binary takes a
value of one if a state has mandatory sharing between like
institutions and takes a value of zero otherwise. 32 Whether
there is mandatory sharing between unlike institutions is
not considered.
The interaction term is the product of the mandatory
sharing binary and the natural logarithm of the number of
banks. As such, it allows the effects of an increase in the
number of banks to be modified by the binary, and it allows
the effects of a change in the binary variable from zero to
one to be modified by the number of banks. The interaction
term is included on theoretical grounds. If mandatory
sharing is working, it may modify the depressing effect that
an increase in the number of banks, and thus a decrease in
49
their average size in terms of depositors, would haveon the
number of ATM cards.
A binary variable indicating the presence of ATM
branching limitations is also included in the regression, by
itself and in an interaction term with the number of
banks.P ATM branching limitations should decrease the
profitability of an ATM program, and may also exacerbate
the effect of a decrease in bank size. For example, if large
banks from metropolitan areas are prohibited from placing
ATMs in communities with small banks that find ATMs
too costly, those communities may have no access to ATMs
at all.
Unit banking and limited branching laws also may have
some negative effects on the number of ATM cards.
Studies have shown that barriers to entry in the form of
branching restrictions decrease competition in local banking markets. 34 Therefore, unit banking and limited branching binaries are included as indicators of the level of
competition in the local market, in addition to the statewide concentration measure.
The unit banking and limited branching binaries also
appear in interaction terms with the number of banks.
There are two reasons for including these interaction
terms. First, there is likely to be more dispersion in bank
size in a unit or limited branching state than in a statewide
branching state, all other factors equal. This is because
under statewide branching, banks are freer to seek the
most efficient scale of operations, unconstrained by geographic limitations. The greater size dispersion in states
with narrower branching provisions may mean that average bank size is a less useful measure of the overall scale of
banking operations in the state. Second, if unit banks are
relatively small, on average, then they may want to take
advantage of the "branching" opportunities ATMs can
provide.
The data for all of the state law binary variables are as of
1983 and are reported in Table 4. 35
Regression Results with ATM Cards
The regression results are presented in Table 5. The
population coefficient has the expected positive sign and
is highly significant. The coefficient on the number of
branches has the expected negative sign, and it is highly
significant. The per capita income coefficient is positive,
but significant only at the 10 percent level. The coefficient
on the number of banks is insignificant.
The coefficient on mandatory sharing is positive and
significant at the 10 percent level, while the coefficient on
the mandatory sharing-bank interaction term is negative
and significant at the five percent level. This means that the
50
overall effect of mandatory sharing will be negative whenever the number of banks is sufficiently large to cause the
interaction term to outweigh the constant positive effect.
To aid interpretation of the mandatory sharing coefficients, Chart 1compares the predicted effect of an increase
in the number of banks in a mandatory sharing state with
that in a state that does not have mandatory sharing,
holding constant the other explanatory variables at their
sample means and the other legal variables at zero.
The point estimates in Chart 1 show that mandatory
sharing is associated with a decrease in the number of
ATM cards when there are many banks. 36 This decrease is
significant beyond about 270 banks.I? There are nine
mandatory sharing states in the sample with at least 270
banks. At 342 banks, the mean number of banks in
mandatory sharing states, mandatory sharing reduces the
number of ATM cards by about 36 percent.
Three possible explanations can be given for the negative effect of mandatory sharing. First, for the reasons
Economic Review / Winter 1990
discussed above, mandatory sharing may increase the cost
of ATM services. Thus, the growing strength of the
negative effect as the number of banks increases may be
because of member-related network costs, which would be
higherthe smaller are the banks in the state.
Second, mandatory sharing may mostly encourage twowaysharing, thereby eliminating the need for a customer to
hold more than one institution's card in order to use more
than one institution's ATMs. Thus, mandatory sharing
simply may discourage customers from establishing secondarytransaction accounts and obtaining multiple cards,
which they otherwise would do. This argument assumes
that the major reason bank customers hold more than one
transaction account is to obtain relatively small amounts of
cash-at multiple locations. Available evidence indicates,
however, the secondary checking accounts are typically
used for large expenditures that constitute a significant
proportionofa family's spending-" This argument also
implies that an increase in ATM sharing would significantly reduce the use of secondary checking accounts.
However, between 1984 and 1986 the percent of ATMs
shared increased from 46 percent to 76 percent, and the
proportion of families with secondary checking accounts
increased, from 20 to 22 percent. 39
Third, the existence of mandatory sharing laws may not
cause a reduction in the number of ATM cards, but may
instead be indicative of the existence of other factors, not
included in the regression, that inhibit the establishment
and growth of ATM systems. States with mandatory
sharing laws may have passed them because they knew
their banks would have difficulty supplying ATM services.
The negative coefficient on the mandatory sharing-bank
interaction term may merely be an indication that mandatory sharing did not succeed in overcoming whatever other
forces were depressing the level of ATM services.
A likely left-out factor is some aspect of bank size that
has not been considered. Table 6 shows the results of a
regression of mandatory sharing on a constant and the
number of banks and population. The significant positive
coefficient on the number of banks and the significant
negative coefficient on population indicate a positive
correlation between mandatory sharing laws and small
banks.v'
Chart 1
Cards
(in thousands)
The Effect of Mandatory Sharing
on the Number of ATM Cards
12,000
10,000
Mandatory Sharing
8,000
6,000
4,000
2,000
0'---"---"-------..1-_ _...1..-_ _..1-_---'
20
54.6
148.4
403.4
1096.6
Note: Calculations assume mean values for demographic
and structural variables.
Federal Reserve Bank of San Francisco
Banks
(Log Scale)
51
I hadpresumably controlled forbanksize, but it may be
thatthe numberof.banksandpopulation donotadequately
control for therelevant aspects of bank size. Forinstance,
although thenum\)erofiJanks andpopulation should pretty
closelydetermineJhe .average .number of depositors per
bank ina state, they do not determine the distribution of
bank sizes within a state. If mandatory sharing is correlatedwith particular.sizedistributions in addition tobeing
correlated with particular average sizes, and if size distributionsinfluenqetheJevel ofATM services, thenmandatorysharingmaysimplybereflecting this correlation
and may have no causal effect on the supply of ATM
transactions.
$ome observers havesuggested thatmandatory sharing
laws were passed under pressure from. small rural banks
hoping to protect their. markets from larger metropolitan
banks.41 If so,.andif small.rural banks supply lower levels
ofATM services,.then mandatory sharing would be correlated with decreases in the number of ATM cards. There
are several possible reasons small rural banks may be
especially likely to supply lower levels of ATM services.
One is that they are small and distant from large metropolitan banks, so sharing is more costly. Another is that
they may have a protected monopoly market and may thus
supplylower levels of services thanwould banks in a more
competitive market. Athird reason may be associated with
the low population density. Evenif a given rural bank has
the same number of depositors as a metropolitan bank, it
will be more costly for it to provide ATMs with the same
level of locational convenience, since its depositors will
be more geographically dispersed than the metropolitan
bank's customers.
Totestwhether mandatory sharing is associated withthe
influences of small rural banks, I reestimated the regression reported in Table 6 with an additional explanatory
variable, the percent of the population in metropolitan
areas. The coefficient on this variable turned out to be
insignificant, while population •and the number of banks
remained significant. Although this evidence does not
completely dismiss the rural bank argument, it does cast
some doubt.
The effects of unit banking are shown in Chart 2.·Unit
banking ·has a significant negativeeffectup·toabout 284
banks.P This may be a consequence of reduced competition in local banking markets in unit banking states. All
otherthings equal, local competition would be lowerin
stateswith fewer banks. Thiswould help explain whythe
negative effects become stronger as the number ofbanks
decreases.
Thepositive effects of unit banking, as seenin Chart2,
become significant beyond about 1,540 banks.v' Texas, a
unit banking state with 1,765 banks in 1987, is the only
state in the sample with at least 1,540 banks. However,
there areno statesin the sample withthis manybanks, unit
banking, andnoATM placement constraints. Asexplained
below and as seenin Chart 2, ATM placement constraints
eliminate the positive effects of unit banking.
Theeffects of ATM limitations in eitherunit or limited
branching statesare negative and significant beyond about
395 banks.44 Theeffects of unit bankingand ATM placementconstraints together arenegative andsignificant upto
about 395 banks. It is interesting to note that, once ATM
constraints are added, the positive effects of unit banking
disappear. This may be because ATM placement con-
Chart 2
The Effect of Unit Banking and
ATM Constraints on the Number of ATM Cards
Cards
(in thousands)
16,000
14,000
12,000
Unit Banking
Alone "
10,000
8,000
6,000
Statewide Branching
and no ATM Constraints
~
4,000
2,000
o
Unit Banking and
ATM Constraints"
20
54.6
148.4
403.4
1096.6
Banks
(Log Scale)
Note: Calculations assume mean values for demographic
and structural variables.
52
Economic Review / Winter 1990
straints foreclose any opportunity that unit banks would
have to "branch by ATM."
Several alternative specifications of the model were
estimated. When regressions without the interaction terms
were estimated, the coefficient estimate for the mandatory
sharing dummy variable alone was insignificant. This
indicates that bank size does play a role in helping to
explain the effect of mandatory sharing. A regression
using 1987 data for the legal variables also was estimated,
and all ofthe legal variables were found to be insignificant.
This suggests that the effects of regulation work with a lag.
Regression Results with ATM Machines
We have seen that mandatory sharing is associated with
decreases in the number of ATM cards for states with
relatively small banks. However, the number of ATMcards
is only a proxy for the number of ATM transactions.
Below, I have estimated a second regression, this time
with the number of ATM machines as a proxy for ATM
transactions.
I have estimated a regression of roughly the same form
as the ATM cards regression. The explanatory variables in
the regression are defined as before.
The results are presented in Table 7. As before, and
as expected, population and per capita income have positive and highly significant coefficients. The number of
branches has a significant negative coefficient. This time,
though, the coefficients relating to mandatory sharing are
insignificant.
This result makes it difficult to draw inferences regarding the effect that mandatory sharing may have on the
Chart 3
The Effect of Unit Banking
on the Number of ATMs
ATMs
7,000
6,000
5,000
Unit Banking
4,000
3,000
r----
Statewide Branching
~:::::::"~~-_oL-_...l--_==:::::L=-_..J.--_.....l-_---I
2,000
1,000 ~
20
54.6
148.4
403.4
1096.6
Banks
(Log Scale)
Note: Calculations assume mean values for demographic
and structural variables.
Federal Reserve Bank of San Francisco
53
number of ATM transactions. We already know that mandatory sharing has a significant negative effect on the
numberof ATM cards, and weassume thatATM cards
transactions volume are. positively correlated. We also
know that, across networks at least, ATM machines and
monthly transactions are very strongly correlated."
One possibility is that the numberof ATMs in a state is
not a verygood measure of the numberof transactions in a
state. There may be more uniformity in the relationship
between growth in ATMs and growth in transactions
within networks than withinstates because networks seek
an efficient level of operations across state lines. Thus,
the close relationship between machines and transactions
within networks may not hold within states.
Alternatively, changesin the numberof cardsmay more
ana
sensitively measure changes in the number of transactions than do changes in the number of ATMs. Any
given percent change in transactions volume is likely
to be represented by a percent change in cards that is
greaterthantheconcomitant percentchangeinthe number
of ATMs.
The effects of unit banking are shown in Chart 3. Unit
bankinghas a significantly negative effect on the number
of ATMs up to about 254 banks and has a significantly
positive effect beyond about1,236 banks. Again, Texas is
theonly state in the sample withat least this manybanks.
The positive effect may be due to ATMs serving as a
substitute for traditional branches in states with small
unit banks.
IV. Conclusion
As of September 1983, 23 states had instituted mandatory sharing statutes that required banks to share their
ATMs with any other banks that wished to do so. The
purposeof theselaws was to ensurethatsmallbankswould
be able to offer their customers access to ATM systems.
Since ATM systems were perceived to be subject to
significant economies of scale, small banks feared that
without mandatory sharing, only large banks would be
able to participate in proprietary or shared ATM networks.
There is some evidence that the cost structures of both
sharedandproprietary ATM systems dopossess characteristics that make it difficult for small banks to gain access
to ATMs.
However, mandatory sharing does not appearto accomplish its goal. It either directly decreases the number
of ATM access cards in the hands of depositors, or it
simply does not sufficiently counteract negative independent forces that were left out of the regression and with
which mandatory sharing is correlated. If the "reasonable
fee" clause in mandatory sharing statutes does not in
fact constrain the fee-setting behavior of shared network
owners in mandatory sharing states, then there must be
some such independent factor, correlated with mandatory
sharing, that reduces the number of ATM cards.
One such independent factor may be the presence of a
large number of rural banks. However, there was no
statistical supportforthis possibility. Although mandatory
54
sharing is correlated with the presence of banks with few
depositors, it is notcorrelated withthe degreeof urbanization of the population. Moreover, bank size, as measured
by number of depositors, was controlled for in the ATM
cardsregression. Therefore, thenegative effects of mandatory sharing do not appear to be due to rural banks.
Although mandatory sharing does not have a significant
effecton the numberof ATMs, it does reduce the number
of cards, suggesting that mandatory sharing may be increasing the costand priceof ATM transactions, or may be
associated with such an increase. Thus, it is possible that
mandatory sharing does give small bank customers access
to ATM machines, but only at a significantly higherprice
for all customers. Mandatory sharing does not appear to
be able to legislate away the higher ATM costs faced by
small banks.
Unit banking has a significantly negative effect on the
numberof ATM cardsin stateswith banksof largeaverage
size, anda positive effectin thepresenceofrelatively small
banks, butthe positive effectis reducedto insignificance if
there are also ATM placement constraints. These results
are consistent withthe view that unit banking is associated
with reduced competition, higher prices, and lower service. The fact that unit banking has a significant negative effect on the number of ATM machines in states
with banks of large average size also is consistent with
this view.
Economic Review / Winter 1990
NOTES
1. Commercial banks, savings and loans, and credit
unions all have ATM programs. However, most ATMs are
owned by banks.
2. Source: Kutler (July 22, 1988). The number of access
cards is one measure of the scale of long-run demand for
ATM services. The Electronic Funds Transfer Act, passed
inNovember 1978 as an addition to the Consumer Credit
Protection Act, states that a financial institution may issue
a validated access card to a consumer only in response to
an<oral or written request or application for the card.
(Source: Regulation E, 12 C.F.R., Section 205.5(a)(1)).
Invalidated cards may be distributed unsolicited, but the
customer has to sign and return a form in order for the card
to be validated for use. Therefore, the number of cards
provides a better measure of the number of people that
expect to use an ATM at least once than if validated cards
could be distributed unsolicited.
3. American Banker (1988).
4. Kutler (September 30,1988).
5. The group that reports the 54 percent figure states that
out of all ATM cardholders, 13 percent never use ATMs, 46
percent use them less than once a week and 41 percent
use them at least once a week. In contrast, two thirds of
cardholders over 54 years of age report either never using
ATMs or using them less than once a month. Only 22
percent of the total population use ATMs at least once a
week.
6. BankNetwork News (November 10, 1988).
7. Informal surveys indicate that consumer demand for
ATM transaction services is fairly insensitive to direct fees.
Although studies of actual ATM use are not available, in a
1988survey of customers who use ATM cards, 35 percent
of those who pay fees said the fees caused them to cut
back on their use of ATMs and 43 percent said fees did not
do so, while 19 percent said they had always paid fees
and 3 percent were unsure whether they paid fees.
(Source: Kutler, September 30, 1988.) Another survey
concluded that customers were not too price sensitive
around a charge of about 30 cents. (Source: Herscher,
1988.)
8. American Banker (1988).
9. Avery, et a/. (1987).
10. Source: Kutler (September 30, 1988).
11. See, for example, Kantrow (1989), Herscher (1988),
and ABA Banking Journal (1988).
12. Berger (1985).
13. American Banker (December 12, 1988).
14. "Electronic Funds Transfer Cost Models and Pricing
Strategies," David A. Walker, Journal of Economics and
Business, Fall 1980, pp. 61-65.
15. The network logos on the back of a customer's ATM
card tell the customer that he has access to machines
displaying those logos, as well as his own bank's machines. Different customers of the same bank may have
Federal Reserve Bankof San Francisco
different logos on their cards. It is nonetheless a fair
generalization that there is universal access for all cardholding customers of all banks in the network.
Also, it should be pointed out that a bank need not
necessarily own any ATMs itself in order to belong to a
shared network.
16. Many shared networks operate across state lines.
17. American Bankers Association (1987), p. 21.
18. Bank Network News (November 24, 1988).
19. Avery, et a/. (1987), p. 186.
20. TransData Corporation (1987).
21.. Cox (1989).
22-. Source: Conference of State Bank Supervisors (1984).
More recent data are available, and some changes in
state laws have occurred since 1983, but these are the
data that were used for the regressions.
Mandatory sharing laws are not completely uniform.
Some states require sharing between like institutions, for
example, banks with banks, but not between unlike institutions, for example, banks with savings and loans. All
states that address the topic, however, at least permit
sharing between like institutions. Furthermore, Nebraska
is the only state that explicitly prohibits sharing between
unlike institutions, though it allows third parties to own,
operate, and maintain shared systems between unlike
institutions.
Most states that have mandatory sharing do not require
sharing with out-of-state banks which request it. Those
states that do not explicitly restrict mandatory sharing to
in-state banks appear to be those which, under separate
statutes, prohibit customers of out-of-state banks from
using ATMs belonging to in-state state chartered banks.
23. The information on case law is from Baxter, et a/.
(1977), pp. 138-140.
24. Baxter, eta/. (1977), pp. 141-143.
25. Einhorn (1988), p. 44.
26. However, as far as development costs go, initial
owners of ATMs or participants in a network can expect
the courts to uphold their right to demand some compensation for these expenses from any new members. See
Baxter et a/. (1977), p. 141.
27. This measure of bank size differs from the traditional
measures that use assets or deposits.
28. The figures for ATM cards do not include credit cards
that may be able to access a line of credit for cash. The
card data were obtained from a private consulting firm.
29. Network-level data on both the number of ATMs and
transaction volume are available. These data show a
strong positive relationship between the number of machines and transaction volume. In a regression of the log
of ATM transactions on a constant and the log of the
number of ATMs, the adjusted R2 was .8, and the coefficient on the number of ATMs was estimated to be 1.01,
55
with a t-statistic of 19.98. Assuming this relationship holds
at the level of individual states, it appears that the number
of machines is a good proxy for transaction volume.
30. The number of banks and number of branches were
obtained from the year-end 1987 Reports of Condition and
Income (Federal Financial Institutions Examination Council (1987». Population and percapita income in thousands
in 1987 were obtained from the U.S. Bureau ofthe Census
(1989).
Neither the mean age of the population nor variables
indicating the age distribution of the population were
found to be significant in preliminary regressions. Therefore, age variables were excluded from the final reported
regression.
31. The concentration ratio is the total share of deposits
held by the four largest banking organizations in the state.
It was obtained from the Board of Governors of the Federal
Reserve System (1988).
32. I classified New Jersey as a mandatory sharing state,
even though it was classified as a non-mandatory sharing
state in the data source. I did so, because, as noted in a
footnote in that source, sharing may be required by the
New Jersey Banking Commissioner if the institution requesting to share maintains a principal, branch, or minibranch office within 5 miles of the proposed terminal
location.
33. The limited ATM placement dummy variable takes a
value of one if ATMs are not allowed to be placed statewide and zero otherwise. If the state has no statute or a
silent statute regarding this topic, this dummy was given a
value of zero. Louisiana, which allows statewide placement of ATMs only if they are shared, was assigned a
value of one for this dummy.
.
34. For a review of these types of studies, see McCall
(1980).
35. Data for mandatory sharing laws and ATM branching
laws were obtained from the Conference of State Bank
Supervisors (1984). Data for traditional branching laws
were obtained from the Board of Governors of the Federal
Reserve System (1984).
56
36. Because of the log-linear specification of the regression, proportional changes in predicted values matter, not
arithmetic differences in predicted values. This should be
kept in mind when viewing Chart 1. Statistical tests reveal
that the positive effects of mandatory sharing that appear
in Chart 1 are insignificant.
37. The linear combination of coefficient estimates, B6 +
B8*ln(banks),wastested for sign and significance at
values ofln(banks) between 1 and 8 (banks between
about 3 and 2980).
All positive values of B6 + B8*ln(banks) were found to
be insignificant. Negative values of B6 + B8*ln(banks)
were found to be significant at a 5 percent level at and
beyondln(banks) = 5.6 (banks = 270).
The sample range for the number of banks is from 11
(Alaska) to 1,765(Texas).
38. Averyetal. (1986).
39. Avery et a/. (1987).
40. The high correlation between mandatory sharing and
small banks may help to explain why the coefficient on
BANKS is insignificant. There may not be enough states
with both small banks and no mandatory sharing to obtain
a good estimate of the coefficient on BANKS.
41. Baxter et a/. (1977), p. 139.
42. Montana, North Dakota, West Virginia, and Wyoming
are the unit banking states in the sample with fewer than
284 banks.
43. At 602 banks, the mean number of banks in unit
banking states, the effects of unit banking are insignificant.
44. The specification of the model assumes that the effects of ATM placement constraints in statewide branching states also would be negative and significant beyond
about 395 banks. However, because there are no such
states, this result is doubtful.
45. See note 29.
Economic Review / Winter 1990
APPENDIX
Derivation of Reduced Form
of the ATM'ftansactions Model
Assume that all variables arein logform. Theaggregate
supply of ATM transactions in a state is given by:
S = al + a2*BANKS + a3*SIZE + a4*MAND +
a5*ATMLIM + a6*MANDSIZE +
a7*ATMLSIZE + a8*UNITSIZE +
a9*LIMSIZE + alO*CONC +all*UNIT
aI2*LIM + a13*PRICE + e.
(1)
where BANKS is the number of banks,
SIZE is the average size of a bank, in terms of
number of depositors,
MANDisa binary variable indicating thepresence
or absenceof mandatory sharing,
ATMLIM is a binary variable indicating the presence or absence of limitations on "branching by
ATM,"
UNITis a binaryvariable indicating the presence or
absence of unit banking,
LIM is a binary variable indicating the presence or
absence of limited branching,
CONC = thedegree ofconcentration of thestatewide banking market
MANDSIZE = (MAND)(SIZE),
ATMLSIZE = (ATMLIM)(SIZE),
UNITSIZE = (UNIT)(SIZE),
LIMSIZE = (LIM)(SIZE), and
PRICE = priceof an ATM transaction.
The errorterm e is assumed to be normally distributed with
mean zero.
All the right-hand side variables except for PRICE are
assumed to be exogenous. The variables UNIT and LIM
are included as indicators of local market structure, in
addition to the measure of statewide market structure,
CONC. The bank size interaction terms are included
because the effect of changes in bank size maydepend on
whether or not laws regarding branching and ATM placement are in place.
The signs of many of the coefficients are uncertain a
priori. However, a2, a3 and a13 should all be positive.
The aggregate demand for ATM transactions is given
by:
D = bl + b2*POP + b3*PCINC + b4*AGE +
b5*OFFICES + b6*PRICE + n,
(2)
where POP = state population,
PCINC = per capita income
Federal Reserve Bank of San Francisco
AGE = mean age, and
OFFICES = total bank offices (main bank offices plus branches).
Theerrorterm n is assumed tobe normally distributed with
mean zero. All of the right-hand-side variables in (2)
except for PRICE are assumed to be exogenous. The
coefficients b2 and b3 should be positive, while b4, b5,
and b6 should be negative.
Setting S in (1) equalto D in (2) allows us to solve for
PRICE. Substituting this solution back into (2), we eliminate PRICE from the. equation for ATM transactions.Two
further assumptions are madein order to arrive at the final
reduced form that-is estimated. First, the. SIZE variable
should depend negatively on BANKS and positively on
POP-. It is.assumed that SIZE is a non-stochastic function
of BANKS and POP:
SIZE = kl *BANKS + k2*POP,
(3)
where kl is negative and k2 is positive. Second,
OFFICES
= BANKS
+ BRANCHES,
(4)
by definition.
Substituting from (3) and (4) into (2), and simplifying,
we arrive at:
ATM transactions = Al + A2*POP + A3*PCINC +
A4*AGE + A5*BANKS + A6*BRANCHES +
A7*MAND + A8*ATMLIM +
A9*(MAND)(BANKS) + AI0*(MAND)(POP) +
All *(ATMLIM)(BANKS) + AI2*(ATMLIM)(POP) +
AI3*(UNIT)(BANKS) + AI4*(UNIT)(POP) +
AI5*(LIM)(BANKS) + AI6*(LIM)(POP) +
AI7*CONC + AI8*UNIT + A19* LIM +W,
(5)
where W is an error term.
A regression of this form was estimated, and AGEand
all of the population interaction terms were found to
be insignificant. Eliminating them did not significantly
change either the size or significance of the remaining
variables' coefficients, so these variables were dropped
from the final regressions.
The final reduced form is then:
ATM transactions = Bl + B2*POP +B3*PCINC
B4*BANKS + B5*BRANCHES + B6*MAND .+
B7*ATMLIM + B8*(MAND)(BANKS) +
B9*(ATMLIM)(BANKS) + BlO*(UNIT)(BANKS)
Bll*(LIM)(BANKS) + BI2*CONC + B13*UNIT
BI4*LIM + Z,
+
+
+
(6)
where Z is an error term.
57
The coefficients BI through Bl4 are functions of al
through a13, bl through b6 and kl and k2. Given the
assumptions about the signs of a2, a3, alO, a13 and b2
through b6, B2 and B3 should be positive, and B5 should
be negative. The sign of B4 is ambiguous because of the
coexistence of a positive direct effect of an increase in the
number of banks on the aggregate supply of ATM transac-
tions and a negative effect of an increase in the number of
banks on the size of banks.
The coefficients B6, B7, B12, B13 and Bl4 will have the
same signs as a4, as, alO, all and a12, respectively. The
coefficients B8, B9, BlO and Bll will have signs opposite
from those of a6, a7, a8 and a9, respectively.
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@FedFRASER