The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Economic
Review
Federal Reserve Bank
of San Francisco
Winter 1991
Number 1
Adrian W. Throop
Fiscal Policy in the Reagan Years:
A Burden on Future Generations?
Elizabeth S. Laderrnan,
Ronald H. Schmidt and
Gary C. Zimmerman
Location, Branching, and Bank Portfolio
Diversification: The Case of Agricultural Lending
Ramon Moreno
Explaining the U.S. Export Boom
Table of Contents
Fiscal Policy in the Reagan Years:
A Borden on Fotnre Generations? *„ „ 0<,<>0o<,0 „ 0„, 0„ „ *„ 0000<>„ o 0<>*o
3
A d ria n Wo T liro o p
Location, Branching and Bank Portfolio Diversification:
The Case of Agricultural Lending *. 0»0a 0„ a „ „ ., „ „ ** 0„ a 0**. o* o„ „ *„ 0„ 0a „ 24
Elizabeth S. Laderman, Ronald H. Schmidt and Gary C. Zimmerman
Explaining the U.S. Export Boom „ « „ „ „. **e *. 00<>„ 00„, *a, 0. a „ 00»00„ 39
Ramon Moreno
F ederal Reserve B ank o f San Francisco
1
Opinions expressed in the Economic Review do not neces
sarily reflect the views of the management of the Federal
Reserve Bank of San Francisco, or of the Board of Governors
of the Federal Reserve System.
The Federal Reserve Bank of San Francisco’s Economic Review
is published quarterly by the Bank’s Research Department under the
supervision of Jack H. Beebe, Senior Vice President and Director of
Research. The publication is edited by Judith Goff. Design, production,
and distribution are handled by the Public Information Department,
with the assistance of Karen Rusk and William Rosenthal.
For free copies of this and other Federal Reserve publicatons, write
or phone the Public Information Department, Federal Reserve Bank of
San Francisco, P.O. Box 7702, San Francisco, California 94120. Phone
(415) 974-2163.
2
E conom ic R eview / W inter 1991
Fiscal Policy in the Reagan Years:
A Burden on Future Generations?
Research Officer, Federal Reserve Bank of San Francisco. Editorial committee members were Reuven Glick
and Bharat Trehan. Research assistance by Panos Bazos
and Andrew Biehl is gratefully acknowledged.
This paper tests alternative views of the burden that
fiscal policy in the Reagan years placed on future generations. In the more conventional view, fiscal deficits substantially crowded out domestic capital formation and
increased net indebtedness toforeigners, thereby placing a
significant burden onfuture generations. In an alternative
view, this burden was reduced, or possibly even eliminated, by higher personal saving, an improved investment
climatefor business, and a "safe-haven" effect that stimulated capital inflows and increased the value ofthe dollar.
However, no significant support could be found for any of
the various aspects of this alternative view. The total economic burden that fiscal policy in the Reagan years placed
onfuture generations is estimated as equivalent to either a
lump sum payment equal to 9 percent of the nation's
current GNP or an annual payment equal to 0.4 percent.
Federal Reserve Bank of San Francisco
The national debt nearly tripled during the Reagan
Administration. This paper offers a quantitative assessment of the economic burden that may have been placed on
future generations by fiscal policy in the Reagan years.
One widely held view is that the extra spending that was
financed by the issuance of federal debt during the Reagan
years was generally used for consumption, rather than
investment, and as a result a burden was placed on future
generations. I This burden takes the form of a lower capital
stock, and therefore lower production and incomes in the
future, to the extent that the expenditures that were financed by the debt issue "crowded out" private capital
formation. Alternatively, it takes the form of increased
indebtedness to foreigners (without an offsetting increase
in the economy's capital stock) to the extent that
inflows were attracted from abroad. In this case, the
economy's capital stock, and hence production and incomes in the future, are not reduced, but the economy's
absorption of future output must decline in order to service
the debt to foreigners.
Both personal and corporate tax rates were cut in the
Reagan years. The cut in corporate rates encouraged
business investment by lowering its after-tax cost of capital. This would have tended to offset a crowding out of
business investment stemming from the pressure of budget
deficits on interest rates. As a result, it is likely that the
greater proportion of the burden from additional debt took
the form of a greater indebtedness to foreigners, rather than
a lower domestic capital stock. Indeed, U.S. external debt
grew very rapidly during this period.
An alternative view of the reason for rising indebtedness
to foreigners during the Reagan years is that investment
opportunities in the U.S. improved, not only because of the
tax cuts for business but also as a result of deregulation and
a reduced risk of government intervention.? Improved
investment opportunities in the U.S., in turn, led to greater
net capital inflows. To the extent that these inflows were
matched by increases in the domestic capital stock, they
would not have created a burden on future generations. The
stimulus to larger net capital inflows also may have been
reinforced by economic difficulties in many developing
3
countries and the election of socialist governments abroad,
which could have increased the relative safety of claims on
American capital.
In the alternative view, the budget deficits of the 1980s
had relatively benign effects. 3 Households are viewed as
far-sighted enough to foresee the taxes needed to service
the increased federal debt in the future. As a result, they
would tend to increase their saving, offsetting the increased dissaving of government. With relatively little
reduction in national saving, there would be no significant
decline in domestic capital formation, and hence no significant burden on future generations. Finally, to the extent
that lower marginal tax rates stimulated greater work
effort, potential GNP would rise. This would provide a
greater volume of national saving, tending to work against
the adverse effects of budget deficits and minimize the
burden they created for future generations.
This alternative view requires that major shifts occurred
in business investment, the exchange rate, consumption,
and potential output. Therefore Section I of this paper
examines the stability of these variables in relation to their
determinants in a mainline nee-Keynesian macroeconometric model of the U.S. economy" It finds that these
I.
of the Alternative
In the more conventional view, an expansive fiscal
policy was the primary source of higher interest rates, a
stronger dollar, greater net capital inflows, and larger trade
deficits in the Reagan years. The alternative view stresses
possible offsets to these fiscal effects through an increase
in saving and work effort. In addition, it points to the
possible importance of an improved investment climate in
the U. S., stemming not only from lower taxes on business
but also deregulation, a reduced risk of government intervention, and a safer haven for foreign investment in the
U.S. This stronger investment climate could have been an
independent source of the higher interest rates, stronger
dollar, greater capital inflows, and larger trade deficits.
The sections below examine the relevant
in a
structural macroeconometric model for evidence of these
two types of effects.
Consumption and Saving
We begin with the behavior of consumption and saving.
The consumption function in the macroeconometric model
that is used to simulate the effects of Reagan fiscal policy
follows in modified form the life-cycle theory of Modigliani and his colleagues." In this approach, households are
viewed as making a conscious attempt at achieving a
4
macroeconomic variables were not subject to statistically
significant instabilities in the 1980s, and that prediction
errors generally were not consistent with the patterns
called for by the alternative view.
Section II goes on to make a quantitative assessment of
the overall magnitude of the economic burden created for
future generations by federal fiscal policy in the Reagan
years. This is done by using the above macroeconometric
model to simulate the effects of fiscal changes. This
simulation provides a
measure
impact of
Reagan fiscal policy on
formation in the U.S.
compared to what it would have been with an unchanged
fiscal policy. It also gives an estimate of the contribution of
fiscal policy to the increase in net inflows of foreign capital
to the U.S.
The burden of fiscal
created
the Reagan
years can be expressed either in terms of (1) the lump sum
amount that would be required to restore the capital stock
and payoff the extra foreign debt, or (2) the annual loss of
future income due to the reduced capital stock and the
servicing of an increased amount of foreign debt. This is
done in Section III, which also contains a summary and
some policy conclusions.
preferred distribution of consumption over their lifetimes,
subject to the size of the economic resources expected to
accrue to them. Thus, total consumption of households is a
function of expected labor and property incomes plus the
current value of their wealth.
The formation of expectations of future income is crucial to the issue of fiscal effects. In the more conventional
view, there is too much uncertainty about the future for
household expectations to be very forward-looking. Instead, the best estimate that households can make of their
future income tends to be based on actual current and past
incomes. This adaptive approach to expectations formation is empirically implemented by making consumption a
function of a distributed
on actual current and
incomes. Thus, in the macroeconometric model consumption is, in part, a function of current and past disposable
income and the current value of stock market and non-stock
market wealth.
In a pure life-cycle model, a decline in the real market
rate of interest increases the amount
that is
consumed if substitution effects outweigh income effects.
The modification to the life-cycle model is that an important portion of households are liquidity constrained in the
sense that they cannot borrow all that they might like to
Economic Review / Winter 1991
against future income. 6 The aggregate size of this liquidity
constraint tends to be related to the unemployment rate and
the level of nominal interest rates. Therefore, in addition to
the variables mentioned above, the consumption function
in the macroeconometric model includes a weighted average of the real and nominal short-term interest rates, as
well as the unemployment rate.
The econometric model's consumption function, with
estimated t statistics in parentheses, is:
7
CON82 = -143.6 + k a, AGYD82 i + .165 NSW
i=O
(- 6.41)
(6.04)
+ .0146 SW - .00217 U' AGYD82
(2.22)
(2.88)
+
i=
bi
+ .89ge_ 1
(17.5)
7
k a = .491
i=O i
(8.12)
3
k b
i=O
=
I
.00150
(- 3.07)
where:
AGYD82
NSW
SW
U
P~
personal disposable income in 1982
dollars, adjusted for the reduction in real
value of government debt due to inflation.
real value of non-stock market wealth
real value of stock market wealth
civilian unemployment rate.
short-term interest rate.
short-term expectation of inflation.
Expected inflation enters with a weight of 0.5, implying
equal weights for real and nominal interest rates. The
positive weight for nominal interest rates and the effect of
the unemployment rate indicates the presence of liquidity
constraints. In addition, current consumption is estimated
to respond strongly and positively to disposable income
over the past two years, and also to non-stock market and
stock-market wealth.
A criticism of this type of consumption function is that
households maybe more forward-looking in forming their
expectations of income than assumed in the adaptive
expectations approach. Formal modeling of fiscal effects
under the assumption of forward-looking consumption
behavior has been done in a life-cycle context with overlapping generations by Auerbach and Kotlikoff (1987) and
Frenkel and Razin (1987), and on the assumption of an
infinite planning horizon for households with altruistic
bequest motives by Barro (1974).
Federal Reserve Bank of San Francisco
Over an infinite horizon, the government eventually
must payoff its debt -either explicitly with taxes or
implicitly by inflating it away. As a result, a public with
rational expectations and an infinite horizon could expect
deficit financing now to be matched by explicit or implicit
taxes of equal present value in the future. Therefore, the
consumption spending of (altruistic) households maximizing utility over an infinite horizon would be the same
whether current government expenditures are financed by
debt or taxation. If a shift to deficit financing does not
change household consumption, then household saving
would increase by enough to finance the increase in
the budget deficit; and there would be no potential for
a crowding out of domestic investment. This idea has
come to be known as the Ricardian equivalence of debt
and taxes."
In the less extreme case of planning only over a lifecycle, the saving response of households to fiscal deficits
would not be large enough to fully prevent a reduction in
capital formation, or increased indebtedness to foreigners,
because some of the expected taxes would fall on future
generations. However, Poterba and Summers (1986) have
shown that, under a variety of plausible fiscal scenarios a
substantial fraction of the deferred tax burden from deficit
financing is likely to fall on present generations. So even
with no altruistic bequest motive, a rational view of the
government's intertemporal budget constraint could lead
households to increase their saving by a substantial fraction of the increase in government's budget deficit.
Such a response could be considerably weakened, however, by liquidity constraints and by uncertainty about
taxes facing individuals. Evidence of liquidity constraints
that would make households relatively more responsive to
current income was discussed above. Uncertainty about
when and on whom taxes might be levied also is of
particular importance. If because of uncertainty taxes are
viewed by households as following a random walk, then the
current level of taxes is the best estimate of any future level
of taxes. So a reduction in current taxes would be interpreted by consumers as indicating a reduction in the
permanent level of taxes. They would raise their consumption spending accordingly, so that current taxation would
have a strong and immediate (Keynesian) effect on current
consumption. 8
Direct tests of Ricardian versus Keynesian views of
household saving behavior using a wide range of historical
data have not been fully conclusive. 9 A major difficulty has
been that until the 1980s there was relatively little variation
in government deficits independent of wars, cyclical fluctuations, and inflation, which might be expected to have a
5
systematic impact on national saving independent of the
effect of budget deficit. However, U.S. experience of a
sustained high level of deficits in the 1980s provides the
opportunity for a cleaner test.
.
We do this by examining the stability of the econometric
model's consumption function with adaptive expectations. First, the Quandt (1958, 1960) maximum likelihood
method is used to assess the most likely point (or points,
if about equally likely) in the estimation sample at which a
shift in the consumption function's coefficients may have
OOCUlTeIJ; and an F test then is used to assess the statistical
significance of the possible shift. 10 Second, the pattern and
direction of out-of-sample forecasting errors for the 1981 to
1988 period are examined. I1 This is the period over which
the effects of Reagan fiscal policy are later simulated.
The maximum likelihood ratio indicates most likely
break points in the consumption function at 1970:4 and
1981:1. But stability of the consumption function is accepted by the F test at a 5 percent level in both cases, as
shown in Table 1.12 The out-of-sample prediction errors for
the period 1981 to 1988 are shown in Chart lA. Up until
1984, there is some tendency towards negative errors,
meaning that actual consumption was less than predicted.
This would be consistent with a Ricardian type of response. But the size of these errors averages only around
one-sixth of the large $45 billion tax reductions, in 1982
dollars, that occurred in both 1982 and 1983. Furthermore,
rather than becoming more negative over time as the
budget deficit grew, and as the Ricardian response would
require, the prediction errors became less negative and
eventually as positive as they were negative before. This
pattern of errors appears to be related more to movements
in consumer confidence over the business cycle than to a
Ricardian response to changes in the budget deficit.
In summary, the errors in the consumption function
during the Reagan years were not atypically large, and they
appear to be more closely related to the business cycle than
to a Ricardian response to budget deficits. These results
are consistent with those of Summers and Carroll (1987),
who tested Ricardian
over the same
by
exammmg the out-of-sample predictive power of a number
of different models of national saving. If Ricardian equivalence holds and national saving has not been sharply
reduced by budget deficits, it should be possible to find
equations that do not consistently overpredict national
saving. But Summers and Carroll could not find any, and in
most cases the size of the errors was close to the size of the
budget deficit, suggesting the lack of even a partial Ricardian response.
6
Potential Output
The macroeconometric model that is later used for
the Reagan
simulating the effects of fiscal
years assumes a constant rate of
of rull-emnlovment, or potential, output. However, in the alternative view
of little or no burden from the debt, reductions in margmai
tax rates would have had a large impact on labor
and
thus potential output, as would increases in the rate of
investment. These forces would have tended
offset the
adverse effects of budget deficits on capital
and
indebtedness to foreigners. Therefore, we examine the
need for
the
of potential
effects.
In the macroeconometric model the rate of
potential output follows an Okun's law
As an
identity, output (GNP82) equals output per person hours of
labor services (q) times person hours of labor services.
The latter, in turn, can be expressed as the
of hours
per worker (h), the employment rate (e), the labor force
participation rate (l), and the civilian adult population
(N). Thus,
q .h .e . l .N
GNP82
or in terms of the civilian unemployment rate
q . h . (1- U) . l . N.
GNP82
In rate of change form this becomes
GNP82 ~
q + h sb + i + N.
Okun (1962) exploited systematic
between
these variables to estimate a reduced-form reianonstup
between changes in real GNP and
in the unemployment rate. The macroeconometric model
this
approach, with the modification of
for
the exogenous effect of population
since
quarterly data are used, changes in the
rate
depend upon a distributed lag on the rate of
of
real GNP.
Using annualized growth rates of real GNP and nomnation, the estimated equation for the
civilian unemployment rate is:
tiU =
.217 +
(2.59)
3
where:E a ~i
1=0
= (
.114
12.8)
is obtained
The estimated growth rate of potential
by setting the change in the unemployment rate to zero and
Economic Review / Winter 1991
Chart 1
Out-of-Sample Forecasting Errors*
1A
Consum
Billions of
1982 Dollars
18
Change in
Unemployment R
Percent
.6]
.4
81
83
87
85
81
1C
nvestment in Equipment
83
85
87
1D
Investment in Structures
Billions of
Billions of
1982 Dollars
1982 Dollars
24
16
8
o+--='r-I\--1-=--\;:.;'!:.\--It-----P!--8
61",
81
-16
i"','"
83
i'"
i'"
85
i'"
i"','"
87
Natural
Logarithm
-2 4 f-r-.-.--,-~,......,,..,...,~,......,,..,...,~_,_._........,...,...,...,..,
81
83
81
85
i
1E
Real Exchange Rate
.12
.08
.04
o -l-.\-PL4--4----:--t--I-\-.04
-.08
-. 12 +.--,........,..~-,-.-........,..~-,-.-........,..~-,-.-........,................,
*Actual less predicted
Federal Reserve Bank of San Francisco
81
83
85
81
7
solving for the corresponding growth rate of real GNP.
With population growth averaging slightly more than 1.0
percent, the equation requires an annual growth rate of 2.7
percent to hold the unemployment rate constant. Therefore, in the macroeconometric model the growth of potential output is constant at this rate, except for the small
variations due to population growth.
This equation has been quite stable. As shown in Table
1, the most likely breaks in structure occur at 1974:2 and
1984:1. But F tests reveal no significant shifts in the
equation's coefficients at these points. In addition, the
pattern of out-of-sample forecast errors over the 1981-88
period is not in the direction of unexpectedly high growth
in potential GNP. As shown in Chart IB, the actual change
in unemployment tends to be lower than predicted. But the
estimated growth rate in potential output equals the constant term (plus the contribution from population growth)
divided by the sum of the coefficients on real GNP growth.
So if these errors were due to downward shift in the
constant term, potential growth would be reduced. Alternatively, if they were caused by more highly negative
coefficients on real GNP growth, potential growth also
would be less.
Thus, there is no evidence of any significant speed-up in
the growth of potential output during the Reagan years.
Still, there may have been a small effect from the fiscal
8
changes that are known to have occurred. These include
possible effects of lower personal tax rates on labor supply,
of greater investment spending on labor productivity, and
of a more efficient allocation of capital on productivity.
Estimates of these specific effects are used to modify the
estimate of the burden of Reagan debt obtained from the
simulation of the macroeconometric model.
Hausman and Poterba (1987) have done a detailed study
of the effects of the 1981-83 and 1986 tax changes on labor
supply. They estimate that the 1981 Economic Recovery
and Tax Act raised the labor supply of primary earners by
0.4 percent and that of secondary workers by 1.2 percent,
in total labor
giving a weighted increase of 0.6
supply. The 1986 Tax Reform Act is estimated to
increased primary earners' labor supply by 0.9 percent and
secondary earners' supply
2.6
resulting in a
total weighted increase of 1.4 percent. Thus, Hausman and
Poterba estimate a total increase of 2 percent in labor
supply due to tax reductions in the Reagan years. An
alternative estimate can be derived from the work of
Fullerton (1982). Fullerton calculates an overall weighted
average elasticity of labor supply with respect to the real
wage of 0.15 percent. As shown in Table 2, from 1980 to
1988 the average marginal tax rate for households fell from
30 percent to 23 percent, meaning that the average after-tax
wage rose from 70 percent to 77 percent of the pre-tax
wage, or by 10 percent. Thus, with an elasticity of 0.15,
labor supply would rise by 1.5 percent-or close to the
estimate by Hausman and Poterba.P
Economic Review / Winter 1991
As a generous estimate of labor supply effects, we
assume in the simulation that labor supply was 2 percent
higher than it otherwise would have been over all eight
years of the Reagan administration, and that this increase
was fully incorporated into actual employment and output.
Assuming an elasticity of substitution of 1.0, potential real
GNP would increase by labor's share in total output (.7)
times the 2 percent increase in labor supply, or by 1.4
percent. The average level of real potential GNP over the
was
,500 billion. So the average
increase in potential real GNP would be $3,500 X .014, or
$49 billion. Net private saving (including household saving in the form of consumer durables) averages 7 percent of
GNP. So the average addition to either the capital stock or
net investment abroad would be $3.4 billion (49 X .07)
per year. Over 8 years that comes to $27.2
in 1982
dollars. This amount will be added in to the simulated
impact of fiscal policy in the Reagan years.
Other possible effects on potential output come from
capital investment. First, to the extent that capital investment was increased, the productivity of labor would be
increased and potential output raised. But this effect would
not be captured by the Okun's law equation that assumes a
constant growth rate of potential output. The extent of the
required adjustment for this effect is examined
after
the results of the model's simulation on investment are
obtained. However, the overall size of this adjustment is
very much less than that for the effects on labor supply.
Lower taxes on business tended to raise business investment, but the higher interest rates due to larger budget
deficits tended to lower it, resulting in relatively little net
effect on investment from fiscal policy.
A second effect could have come through a change in the
efficiency of the allocation of
Capital is inefficiently allocated if the after-tax cost of capital differs
between different types of capital investments as the result
of differing tax treatment. Hendershott (l987a) has done
the most detailed study on the effects of tax
on the
efficiency of capital's allocation during the Reagan years.
He finds that the 1981-83 tax changes reduced the efficiency of capital's allocation within the corporate sector,
but increased
of its
between
owner-occupied housing and the
sector. Given
the large bias toward owner-occupied housing prior to
these tax changes, overall
probably allocated capital
more efficiently. But then, although the 1986 Tax Reform
Act narrowed the differences in the after-tax cost of
across corporate assets, it greatly increased the bias in
favor of owner-occupied housing. On balance, Hendershott
Federal Reserve Bank of San Francisco
estimates that the 1986 law returned the overall efficiency
of the allocation of capital to about that of the pre-1981law.
Also, since the size of the efficiency loss under the current
law is estimated at only 0.25 percent of GNP, any possible
changes in it would be of a very small order of magnitude. 14 Consequently, no adjustment is made to the results
of the simulation for any effect of fiscal changes on the
efficiency of the allocation of capital.
Business Investment
In the alternative view of the economy, in the Reagan
years investment opportunities improved not only because
of tax cuts for business but also as a result of deregulation
and a reduced risk of government intervention. An improved investment climate could have been an independent
source of greater capital investment, higher interest rates,
a stronger dollar, and larger capital inflows. Except for the
tax effects, an improved climate for investment would not
necessarily have been a part of the response to Reagan
fiscal policy. Nonetheless, this investment would have
increased the capital stock of future generations, and,
therefore, provided an offset to any burden created for
future generations by fiscal policy in the Reagan years.
Therefore, we examine the stability of business investment
in relation to its economic determinants.
The macroeconometric model used for the simulation
employs a standard neoclassical model of business fixed
investment, as refined by Hall and Jorgenson.P A firm's
desired capital stock is determined by the expected scale of
its output and relative factor prices. Given its expected
output, the desired capital stock varies inversely with the
real after-tax cost of capital. Because of an imperfect
secondary market for business capital goods, market prices
do not equate desired and actual capital stocks in the short
run. Instead, firms are assumed to eliminate some fraction
of the gap between desired and actual capital stocks in
the current period. This makes planned investment a function of sales, the rental cost of capital, and the lagged
capital stock.
Because the investment decision gives rise to a whole
stream of investment expenditures, investment spending
appears as a distributed lag on these variables, where the
lags are those between appropriations and expenditures. In
addition, expenditures may be modified after appropriations have been made. This effect is captured by adding a
"surprise" variable, equal to the difference between sales
lagged one quarter and a measure of expected sales.
The model's estimated equation for nonresidential fixed
investment in equipment is:
9
9
GIPD82 = -136.6
(-4.11)
+ .178k
u_ iGNS82_ i
l=2
(5.87)
9
- .0211 k U
i=2
(3.32)
i
RE __ i GNS82_ i
9
- .00529 _k u_ 1 KPD82
l=2
(-0.11)
+
.120 [GNS82
(5.35)
+
.88ge
(13.2)
9
where k U _
i=2
GlPD82
i
1
i
E(GNS82)]
= 1.0 and
nonresidential fixed investment in
equipment in 1982 dollars
final sales in 1982 dollars
rental cost of capital for equipment
capital stock of equipment, in 1982
dollars
expected final sales
GNS82
RE
KPD82
E(GNS82)
The equation for nonresidential investment in structures is
similar, except that a relatively short distributed lag on the
real price of oil (POlL), scaled by the size of the capital
stock in structures (KPS82), is included to account for
investment in oil drilling:
The Exchange Rate
9
GIS82
77.1 + .0674 k U iGNS82 i
i=2
(5.17) (3.70)
9
.00482 k U i RS __ i GNS82
i=2
( -1.63)
9
-
.106 ;=2
k U
(-2.90)
+
.0395 [GNS82
(3.14)
+
.016 POlL KPS82
(4.25)
;
KPS82
1 -
+ .016 POlL_I KPS82
(2.92)
E(GNS82)]
1
+ .879 e
I'
(17.9)
The real after-tax cost of capital has an important
influence on both types of investment. A one percentage
10
point increase in the real after-tax cost of capital is estimated to depress investment in equipment by 2.1 percent
and investment in structures by 2.6 percent. The expectations of inflation in the real cost of capital are formed
adaptively.
These investment equations exhibit a high degree of
stability. The most likely break-points occur in 1964:3 for
equipment and in 1976:1 and 1984:1 for structures. But
stability is accepted with an F test at the 5 percent level in
all cases (Table 1). Moreover, out-of-sample prediction
errors for the period 1981-88 do not show any patterns that
suggest a distinctly improved investment climate in the
Reagan years (Charts lC and ID). Investment in equipment
tends to be less than predicted in 1982 recession, but
greater than predicted in 1983, and again in 1987 and 1988,
when capacity utilization was relatively high. Thus, the
errors appear more closely related to business cycle effects
than to a permanent improvement in the investment climate. Also, while prediction errors for equipment are
generally positive, those for structures tend to be negative.
Thus, the view that the investment climate improved
independently of tax factors that are already included in the
model of business investment is not supported by the data.
Another recent study that examines the stability of a
standard neoclassical model of business fixed investment
in the 1980s is Corker, Evans, and Kenward (1989). It too
finds that such a conventional model can explain investment behavior quite well over this period and that evidence
of parameter instability is very limited.
In the alternative view of the economy in the Reagan
years, the emergence of a relatively safe haven for foreign
investment was an important factor in increasing the net
inflow of capital to the U. S. and driving up the value of the
dollar, tending to offset any crowding out of domestic
capital formation that would have been generated by budget deficits. Again, although a safe haven effect would not
necessarily have been a response to Reagan fiscal policy, it
could have provided an offset to the burden of fiscal policy
created in the Reagan years by increasing the capital stock
for future generations.
A safe haven effect would have produced instability in
the macroeconometric model's equation for the exchange
rate. This equation follows the asset theory of exchange
rates in which an open interest parity condition approximately holds. 16 Except for a risk premium, the current real
value of the exchange rate is assumed to be at the point
where the expected capital gains or losses from its expected
future return to long-run equilibrium just offset the dif-
Economic Review ! Winter 1991
ference between interest returns in the U.S. and abroad.
This implies that the current real value of the dollar equals
its expected future equilibrium value plus the difference in
real interest returns between U.S. and foreign assets, plus
the amount of any risk premium. A safe haven effect for the
dollar would make this risk premium more positive.
Real long-term interest rates in the model are assumed to
conform to the expectations theory of the term structure of
interest rates, where expectations offuture short-term rates
and future inflation are formed adaptively.'? Expectations
may be formed differently for interest rates than for inflation. So the real value of the dollar becomes a function of
separate distributed lags on the differences between U. S.
and foreign interest rates, and between U.S. and foreign
inflation. Also, the market's expectation of the equilibrium
real value of the dollar depends upon expected highemployment budget balances at home and abroad. Although the sign of these latter effects is theoretically
indeterminate, depending importantly on the market's effective time horizon, it is found that expectations of a larger
budget surplus depress the expected real value of a country's currency because of the expected reduction in the
government's demand for credit. Expectations of future
budget positions are assumed to be formed adaptively,
being based on the high-employment budgetary balance
over the previous year. [8
The econometric model's exchange rate equation is:
InEXCH =
3.38 +
(62.5)
17
2. a
i=O
17
i
iiL i
(is
..
+ i=O
2. b_(p-p*) .
1
- .0374B
(-2.46)
I
+
.0477B*
(1.68)
17
2.
i=O
+
.708e
(7.79)
17
a
. = .0908
-1
(6.90)
2. b -
i
1=0
- .0908
(- 6.80)
where EXCH
is' i';
p, p"
B, B*
real trade-weighted value of U.S.
dollar
short-term interest rate in the U.S.
and abroad, respectively.
inflation rate in the U.S. and abroad,
respectively.
= high employment budget balance as
percent of high employment GNP in
previous four quarters for U.S. and
foreign countries, respectively.
A sustained one percentage point increase in the differential between U.S. real short-term interest rates and the
trade-weighted foreign real rate is estimated to raise the
real trade-weighted value of the dollar by 9 percent. Also,
a one percentage point increase in the U.S. budget surplus,
as a percent of high employment GNP, lowers the real value
of the dollar by approximately 4 percent through its effect
on the expected equilibrium value of the dollar, while a like
change in the trade-weighted foreign budget balance appreciates the dollar by about 5 percent.
Turning to the stability of the exchange rate equation,
the most likely break point in its structure is found to be
1982:2. But an F test reveals no significant shift in its
coefficients at this point, as shown in Table 1. Also, the
out-of-sample prediction errors for the period 1981to 1988,
shown in Chart IE, indicate only a temporary safe-haven
effect at best. Up until 1985 the dollar's value is somewhat
stronger than predicted. But the size of this error averages
no more than 4 percent, and nearly equally large errors in
the opposite direction subsequently develop. Thus, even if
there was a small safe-haven effect acting to strengthen the
dollar by increasing the risk premium up until 1985, a
nearly equally large negative effect on the risk premium
occurred after 1985. Therefore there is no evidence of a
sustained safe-haven effect during the 1980s, which would
have raised U.S. domestic investment significantly by
attracting net capital inflows independently of the effect of
U.S. fiscal policy'?
H. Simulated Effects of Fiscal Policy
The previous section found no significant shifts in key
macroeconomic relationships that might either bias the
simulated effects of fiscal policy in the 1980s or create
an independent offset to the estimated burden of fiscal
policy on future generations. This section goes on to
simulate the effects of fiscal policy on U.S. capital formation and indebtedness to foreigners, using the mainline
nee-Keynesian macroeconometric model.
Most of the key relationships in this model have been
Federal Reserve Bank of San Francisco
described in the previous section. It is assumed that shortterm interest rates are determined either as matter of
Federal Reserve policy or, if money is being targeted,
through an equilibrium between the supply and demand for
money. Long -term interest rates basically follow the expectations theory of the term-structure of interest rates. Foreign central banks are assumed to partially respond to
changes in U.S. interest rates so as to stabilize their
economies.
11
Investment spending on consumer durables and housing
is importantly determined by nominal after-tax interest
rates because of the importance of liquidity constraints,
while investment spending on business plant and equipment responds to real after-tax interest rates. Net exports
are dependent upon the real exchange rate, which in tum is
a function of differences between real interest rates at
home and abroad, as well as expected budget deficits.
These elements of spending then combine with consumption spending and inventory investment to determine
the aggregate demand for output and the rate of unemployment. The inflation rate is determined by an expectationsaugmented Phillips curve, in which the inflation is a
function of the current unemployment rate and expected
inflation, with additional effects from the price of oil and
the exchange rate. Expectations in the Phillips curve are
formed adaptively, and there is no trade-off between inflation and unemployment in the long run. 20
Simulation Methodology
The effects of fiscal policy during the Reagan years were
estimated in two steps. First, the historical errors in each
equation of the macroeconometric model were added back
in to allow a simulation of the model to replicate history
exactly, or in other words to produce the historical baseline.
Second, with historical errors still in the equations, the
effect on the economy of holding the relevant fiscal policy
variables at their 1980 levels, instead of at their actual
historical values, was simulated. Then the difference between the economy's performance in the historical baseline
and in the counterfactual simulation with an unchanged
fiscal policy after 1980 can be attributed to the changes in
fiscal policy that occurred during the Reagan years.
Two aspects of this approach require further elaboration. The first is the measurement of an unchanged fiscal
policy, and the second is the assumption made with respect
to monetary policy. From a macroeconomic point of view,
there are two dimensions to the measurement of an unchanged fiscal policy. First, there should be no change
in federal marginal tax rates that would alter economic
incentives. For example, in the macroeconometric model
the average marginal tax rate for households affects their
after-tax mortgage rate and, therefore, influences expenditures on housing. Similarly, business taxes influence the
cost of capital for nonresidential investment and rental
housing. An unchanged fiscal policy is defined, in part, as
one that does not alter marginal tax rates that affect these
expenditures.
As shown in Table 2, the Economic Recovery and Tax
Act of 1981 and the Tax Reform Act of 1986 reduced the
average marginal federal tax rate on individual income
12
from 30 percent in 1980 to 23 percent in 1988. In the counterfactual simulation that keeps fiscal policy unchanged,
the average federal marginal tax rate for households is
therefore held constant at 30 percent from 1980 through
1988, instead of being allowed to fall. As a result, after-tax
interest rates for households are reduced, and their expenditures on durable items are raised, relative to actual expenditures in this period.
The Tax Act of 1981 also reduced effective tax rates on
business investment by shortening depreciable "tax lives"
and increasing the investment tax credit for purchases of
equipment. The Tax and Fiscal Responsibility Act of 1982
took back part, but by no means all, of these tax cuts for
business as a part of a package to reduce the size of the
federal budget deficit. Then, in 1986, the Tax Reform Act
reduced the corporate income tax rate from 46 percent to
34 percent, but at the same time eliminated the investment
tax credit for equipment and lengthened the tax lives for
residential and nonresidential structures. The net effects of
these changes are also shown in Table 2. 2 1 The effective tax
rate on investment in equipment dropped from 13 percent
in 1980 to only 1percent in 1985, but then rose to 14 percent
by 1988. The tax rates on investment in structures and
rental housing were cut by one third to one half in this
period. In the counterfactual simulation of an unchanged
Economic Review I Winter 1991
fiscal policy, these effective tax rates are held at their 1980
values, tending to reduce business investment spending
relative to actual business investment in the 1980s.
The second dimension of an unchanged fiscal policy
is that there should be no change in federal outlays
and receipts measured on a high employment basis. Unchanged receipts would prevent disposable income, and
hence consumption, from changing on account of fiscal
policy. With unchanged government receipts and outlays,
as well as unchanged marginal tax rates, there would
be no change in aggregate demand due to a change in
fiscal policy.
Federal Reserve Bank of San Francisco
As shown in Table 3, the federal high employment
budget deficit rose from 0.3 percent of high employment
GNP in 1980 to 4.0 percent in 1986, and then dropped back
to 2.4 percent of GNP by 1988. (In this calculation of the
fixed deficit, the erosion in the real value of the federal debt
due to inflation is counted as a receipt, as explained in Box
1). The most permanent contributor to the deficit's increase
was an increasing ratio of federal transfer payments to
GNP, which rose over 2 percentage points. In contrast,
purchases of goods and services as a proportion of high
employment GNP rose only a little more than one percentage point through 1985, but returned almost to their 1980
13
level by 1988. Although the ratio of income tax receipts to
GNP dropped two percentage points, a rise in Social
Security taxes approximately offset this decline.
In the counterfactual simulation of an unchanged fiscal
policy, the ratio of federal purchases of goods and services
to high-employment GNP is held at its 1980 value. In the
macroeconometric model, the impact of policy induced
changes in total federal receipts and transfer payments on
household disposable income, and hence consumption, is
14
captured by the ratio of cyclically adjusted federal taxes
less transfer payments to high employment GNP. So in the
counterfactual simulation of an unchanged fiscal
this ratio is also held at its 1980 value,
for an
adjustment for state and local taxes.
As shown in Table 3, the Reagan fiscal package included
a reduction in grants-in-aid to state and local governments. These governments were able to absorb the
reductions and maintain approximately the same level of
Economic Review / Winter 1991
services by raising taxes toward the end of the 1981-82 recession (see Weicher (1987)). Since the change in the ratio
of cyclically adjusted federal taxes less transfer payments
to high-employment GNP overstates the total reduction in
net taxes and transfers, in the counterfactual simulation
this change was adjusted for the increase in state and
local taxes.
Finally, the burden of fiscal policy during the Reagan
years would have been reduced to the extent that the
increase in federal debt financed greater capital formation
by the federal government. But as discussed in Box 2, the
federal government's capital formation as a percent of high
employment GNP was neither significantly higher nor
lower during the Reagan years than it was earlier. Therefore, in the counterfactual simulation of an unchanged
fiscal policy no change is made in the amount of public
investment.
The counterfactual simulation of an unchanged fiscal
policy requires an assumption to be made with respect to
the reaction of the Federal Reserve's monetary policy. The
goal of the Reagan Administration and the Federal Reserve
was to reduce the rate of inflation from near double digit to
more moderate levels. Monetary policy was successful in
achieving this objective. Inflation in the GNP price index
dropped from 9.3 percent in 1980 to 4.0 percent in 1984
and stayed in the 4 percent range through the end of the
decade. The demand for money became unstable in this
period, however, and the Federal Reserve shifted emphasis
in its short-run operating procedures from targeting money
to looking through to its ultimate economic objective of
controlling inflation. But because of the long lags between
monetary policy and its impact on inflation, an intermediate target was still needed.
One widely used approach for forecasting the dynamics
of inflation is the expectations-augmented Phillips curve
with adaptive expectations, as used in the macroeconometric model in this paper. In this framework, the unemployment rate is a logical intermediate target for monetary
policy. Unemployment has both a direct effect on the
inflation rate through current labor market pressures and
an indirect one operating through inflation expectations.
So any desired path for inflation requires a corresponding
path for the unemployment rate. In the basic counterfactual
simulation of an unchanged fiscal policy, it is therefore
assumed that the Federal Reserve used the unemployment
rate as an intermediate target and achieved the same
unemployment rate as occurred historically. 22
As is common, the expectations-augmented Phillips
curve in this macroeconometric model also contains an
effect on inflation from current and lagged changes in the
real value of the dollar. This effect operates through
Federal Reserve Bank of San Francisco
competitive pressures in the tradeable goods sector of the
economy. These effects are assumed to be regarded as onetime changes by market participants and therefore do not
feed through to inflation expectations. But an unchanged
fiscal policy would have produced a lower value for the
dollar than actually occurred, and consequently a higher
price level. Therefore, to achieve any price level, the
Federal Reserve would have had to conduct a tighter
monetary policy than otherwise. For an alternative reaction
of monetary policy, we therefore assume in the counterfactual simulation that the Federal Reserve achieved the
same level of prices by the end of the Reagan years as
actually occurred. This would imply higher interest rates
and higher unemployment than in the case of the counterfactual simulation that uses the unemployment rate as
a target.
The simulated effects of fiscal policy during the Reagan
years are most easily seen in chart form. These charts show
the results of the simulation on the assumption that the
Federal Reserve would have targeted the unemployment
rate. The results of the simulation under the alternative
target for monetary policy are discussed below. Although
nominal yields on long-term bonds generally declined in
the 1980s, real (or inflation adjusted) bond rates actually
rose quite substantially. Furthermore, as shown in Chart
2A, the rise in real bond rates was primarily due to the
effect of fiscal policy. It is estimated that with an unchanged fiscal policy the real bond rate still would have
shown considerable cyclical fluctuation, but would have
been 1 to 2 percentage points lower on average.
Next consider the investment sectors of the economy that
would have been significantly affected by the higher interest rates, starting with residential investment. As mentioned earlier, in the case of owner occupied housing, the
lower marginal income tax rates of the Reagan fiscal
program worked to discourage housing investment by
raising the after-tax cost of capital. But in rental housing
the effective tax rate on new investment went down. Of
course, both sectors were discouraged by higher interest
rates. Chart 2B shows that the net effect of fiscal
was to reduce total residential investment. Residential
investment was clearly crowded out by the fiscal expansion
in this period, consistent with the conventional view.
Chart 2C shows a similar story for household spending
on consumer durables. Both the tax effects and the interest
rate effects of fiscal policy in the Reagan years worked
to discourage consumer spending on durables, and the
simulation confirms that with an unchanged fiscal policy
consumer spending on durables generally would have
been higher.
There was a significant increase in tax incentives for
15
Chart 2
Simulated Effects of Fiscal Policy
During Reagan Years
2A
Real AAA Bond Rate
Percent
11
Reagan
10
Unchanged
Fiscal
Policy
9 Fiscal Policy
~
8
7
6
5
4+..........,..............,,..,...,.............,...,..,.... . . . . .,. . . . . ..................,...............,........,,...,.,
80
86
84
82
2B
Residential Investment
Billions of
1982 Dollars
240
Unchanged Fiscal
Policy ............
200
.
160
Reagan Fiscal
Policy
120
88
82
80
86
84
88
2C
Personal Consumption
Expenditures: Durables
Billions of
1982 Dollars
Billions of
1982 Dollars
450
600
Unchanged Fiscal
Policy
400
550
'" ,
350
300
Reagan Fiscal
Policy
500
....
450
Reagan Fiscal
Policy
250
400
~
Unchanged Fiscal
Policy
350
200-r.-......,.............,.....-......,..,......."..,....,....,.,.......-.-,......,.-...,.....-.....,..,........,
80
82
84
86
88
2E
Real
Trade-Weighted
Dollar
I nd ex
1973=100
300 -+-r-..,..,..,..,........,..,...,...,..,...,....,..,.,..,....,...."...,....,........,-.-.-.........,...,...,...":'T:'"""1
82
80
Billions of
1982 Dollars
84
86
88
2F
Net Exports
100
140
120
2D
Nonresidential
Fixed Investment
Reagan Fiscal
Policy ~
o
Unchanged Fiscal
Policy
100
A
80
60
16
-100
Unchanged
Fiscal
Policy
80
82
84
86
"
Reagan Fiscal
Policy
88
Economic Review / Winter 1991
business investment, however. Indeed, Chart 2D shows
that they were strong enough to outweigh the effects of
higher real interest rates to some extent. Thus, the fiscal
expansion in the Reagan years on balance acted to raise
business investment by a modest amount, or to crowd it in
rather than to crowd it out. But taking these three investment sectors together over the entire 1981 to 1988 period,
the reduction in the stock of housing and consumer durables exceeded the stimulus to nonresidential fixed capital
by $40.3 billion, as shown in Table 4. In addition, the
simulation shows a $13.6 billion reduction in the stock of
inventories, bringing the total simulated reduction in the
capital stock to $53.9 billion.
Other studies have been unable to find any significant
change in the rate of accumulation of fixed nonresidential
capital in the 1980s. For example, Oliner (1989) concludes
that the pace of accumulation of business capital in the
1980s continued to support a rate of capital deepening
(relative to the labor force) not much different from the
postwar average, suggesting that characterizations of capital formation in the 1980s as unusually weak or unusually
Federal Reserve Bank of San Francisco
strong are unwarranted. Another study by Englander and
Steindel (1989) also reaches the same conclusion.
Our simulated increase in fixed nonresidential capital
due to fiscal changes in the 1981 to 1988 period comes to
$47.2 billion in 1982 dollars, or 1.3 percent ofthe average
level of that stock in the 1980s; and under the alternative
assumption for monetary policy it is $65.6 billion, or 1.8
percent. This is equivalent to an increase in the annual
growth rate of the stock of fixed nonresidential capital of
around 0.2 percent during this period. Given the small size
of this number relative to the long-term growth trend of
about 3 percent, it is not surprising that other studies have
not been able to find a significant break in the rate of
accumulation of business capital in the 1980s.
Neither are the estimated effects on the capital stock
large enough to significantly alter
GNP. Assume
that all of the estimated $27.2 billion effect of a larger
labor supply on private saving was channeled into domestic
capital formation. Still, the total change in domestic capital stock (exclusive of consumer durables) comes to a
decline of$24.2 billion in the simulation where the Federal
Reserve targets the unemployment rate, and to an increase
of $9 A in the alternative simulation. As a result, the
average level of the capital stock as a percent of GNP would
have been 0.3 percent lower to 0.1 percent higher than
otherwise. Assuming a 20 percent gross rate of return on
investment, potential GNP therefore would have been .06
(.3 x .2) percent lower to .02 (.1 x .2) percent higher
because of the effects of fiscal policy on capital formation.
Since these estimates are small (equaling less than onetwentieth of the estimated effect of labor supply on potential output) and on average close to zero, no adjustment is
made for the effect of investment on potential output.
The remaining burden of fiscal policy during the Reagan
years stems from its effect on indebtedness to foreigners.
As we have seen, fiscal policy put upward pressure on real
interest rates in the United States. These, in tum, attracted
capital from abroad which was used either directly or indirectly to finance the higher level of government borrowing.
As foreign investors purchased
put upward
pressure on the real foreign exchange value of the dollar.
Chart 2E shows the effect of fiscal policy on the real
trade-weighted value of the dollar. The large volume of
foreign capital that the Reagan fiscal expansion attracted
put significant upward pressure on the dollar. It boosted
the real value of the dollar by a maximum of nearly 25 percent in 1985; and even by 1988, when the federal budget
deficit had been reduced somewhat, the real value of the
17
dollar was still 15 percent higher than it otherwise would
have been.
A by product of the stronger dollar was a large deterioration in our trade balance. For supply to equal demand in the
foreign exchange market, a dollar of extra capital inflow
must produce a dollar's worth of reduction in net exports.
So the reduction in net exports is also a measure of the net
increase in foreign capital inflows.23 As shown in Chart
2F, U.S. net exports would havedeclined-and net capital
inflows increased-even with an unchanged fiscal policy
because of the strong growth of the U.S. economy as it
pulled out of the 1982recession. But by 1988the change in
fiscal policy had reduced the value of net exports in 1982
dollars by over $90 billion. Thus, the effect of fiscal policy
in the Reagan years was to add about $90 billion dollars
indebtedness to foreigners in peak years, and lesser
amounts in other years, without increasing the domestic
capital stock to provide any more income to service this
debt. As shown in Table 4, by 1988foreign indebtedness is
estimated to have been $370.7 billion greater, in 1982
dollars, than it otherwise would have been with an unchanged fiscal policy.
The assumption that the Federal Reserve would have
targeted the price level, rather than the unemployment rate,
makes relatively little difference to the simulated effects of
fiscal policy, as shown in Table 4. On the assumption that
monetary policy targeted the unemployment rate, fiscal
policy in the Reagan years reduced the price level by 2
percent because of a stronger dollar. So targeting the price
level would have allowed a somewhat easier monetary
policy. However, this reduces the simulated increase in
short-term interest rates that is attributed to the effects of
fiscal changes in the Reagan years by only 15 basis points.
The estimated impact of fiscal policy on the total stock of
capital is reduced by $40.5 billion, and on indebtedness to
foreigners by $8.2 billion. The total estimated burden of
fiscal policy is reduced by only 12 percent.
Another intermediate target that the Federal Reserve
might have followed in this period is nominal GNP. But
targeting the unemployment rate is almost the same as
targeting real GNP, given the small supply-side effects of
fiscal policy on potential output. So a simulation assuming
nominal GNP targeting (or some combination of real GNP
and price level targeting) on the part of the Federal Reserve
would lie between the other two alternatives.
III. Summary and Conclusions
This paper has tested alternative views of the burden that
fiscal policy placed on future generations in the Reagan
years. The more conventional view is that fiscal deficits led
to a substantial crowding out of capital formation and net
exports, and as a result reduced the capital stock and
increased the indebtedness of future generations to foreigners. In the alternative view, there were important
offsetting responses to fiscal policy that reduced these
effects. One is a Ricardian response of private saving to the
budget deficits, and another is a positive response of
private saving, investment, and work effort to lower marginal tax rates. But no evidence of a Ricardian response in
consumption is found, and the estimated response of
to changes in the real interest rate is very small.
Similarly, the estimated effects of lower marginal tax rates
on labor supply, and hence potential output, provide only a
small offset to the burden. Also, while lower tax rates
stimulated domestic investment, higher real interest rates
discouraged it. As a result, no significant influence of
domestic investment on potential output is estimated.
Neither is it possible to find any evidence of an im-
18
provement in the investment climate of the U.S., which
could have independently boosted the stock of capital
for future generations. Although business investment responded positively to reductions in the effective rate of
taxation, it did not exhibit any unusual strength relative to
its usual economic determinants. Similarly, although there
is some evidence of a small "safe-haven" effect acting to
strengthen the dollar and net capital inflows up until 1985,
an equal and opposite effect on the dollar developed
afterward. Thus, there is no evidence of any sustained safehaven effect during the 1980s, which would have lowered
the cost of capital and raised U.S. domestic investment by
attracting net capital inflows from abroad independently
from the pull of U.S. fiscal policy.
The cumulative change in the U.S. high employment
budget deficit from 1981 to 1988 comes to $619.4 billion,
in 1982 dollars (Table 3). The longer-run tendency should
be for budget deficits to fully crowd out interest sensitive
private investment spending and net exports. But because
of lags in the responses of investment to interest rates, and
net exports to the exchange rate, the actual effect over any
EconomicReview / Winter 1991
finite period should be smaller. A simulation using a
mainline neo-Keynesian macroeconometric model estimates the reduction in the total domestic
stock due
to fiscal changes in the
years at $66.8
in
1982 dollars, when the
rate is assumed to be
the intermediate target of monetary policy. Alternatively,
the reduction comes to $26.8 billion if it is assumed that
the Federal Reserve targeted the
level.
the
largest estimated impact by far is on net exports, and
therefore on an increased indebtedness to
It is
estimated that fiscal policy in the
years increased
net indebtedness to foreigners by $410.3 billion, in 1982
dollars, if the
rate is assumed as an intermediate target for monetary
and
.6 billion if
the price level is assumed as the target.
It is interesting to compare these estimates with those
from other macroeconornetric models. Helliwell (1990)
has surveyed the consequences of an increase in debtfinanced U.S. government spending for ten multicountry
econometric models having alternative kinds of expectations. In almost all of them, there is complete or nearly
complete crowding out of real
spending and net
exports in the medium term; and the crowding out tends to
be divided about evenly between investment expenditures
and net exports.
There are two fundamental reasons why the simulation
in this paper produces a larger proportionate effect on net
exports, and smaller impact on investment, than in the
models surveyed by Helliwell. In the first place, the simulations surveyed by Helliwell assume only a simple change in
debt-financed government spending, and so do not capture
the full details of the kinds of fiscal changes that occurred
in the Reagan years. In
the
tax cuts for
business tended to shift crowding out from domestic investment to net exports. Second, a unique feature of the
present macroeconometric model is an expectational effect
of budget deficits on the exchange rate. Thus, the expectation of continued U.S. budget deficits raised the value of
the dollar independently from the budget's effect on interest rates. As a result, the dollar rose by more and interest
rates rose by less than would otherwise have been the case.
This shifted the crowding out even further on to net P'Yf)()rr<;:
and away from domestic investment. 24
The burden that fiscal policy
on future generations in the Reagan years can be
either in terms
of 0) the lump sum amount
would be
to
restore the capital stock and payoff the extra foreign
Federal Reserve Bank of San Francisco
or (2) the annual loss of future income due to the reduced
capital stock and the servicing of an increased amount of
foreign debt. Over the full eight years of the Reagan
Administration, the total burden
on
generations comes to a
sum amount of between
$361.6 and $410.3 billion, in 1982 dollars,
on
the assumption made for
This includes a
$27.2 billion offset from favorable labor supply
created by lower marginal tax rates. To put this total
burden in
it is
to about 9
of
nation's current output, or $2,706 in current dollars
every member of the adult population. This is what it would
cost to restore
lost
stock
lVl'-'l1",l1 debt incurred.
Alternatively, the burden would otherwise take the form
of an annual loss in income due to a lower capital stock and
the need to service the increased amount of
At a current 4 percent real bond rate, this comes to an
annual payment equal to 0.4 percent
current
or
$110 per year in today's dollars for every member of the
adult population, forever.
Of course, current generations benefitted in the Reazan
years by consuming more domestic and foreign goods than
they would have otherwise. But since omecnve mterpersonal welfare comparisons between different generations
cannot
a scientific assessment of the overall
effect of fiscal policy on the nation's economic
is
not possible. Still, the estimated
of the burden on
future generations is a good measure of the size of
intergenerational transfer that has occurred. If the burden
were paid off now, future generations would be relieved of
and the current generation would bear the full cost of its
current consumption. This would be an anpronnate
if we truly do not want to better our own welfare at
expense of future generations.
To correct this intergenerational
the Bush
Administration has proposed running budget surpluses
the mid-I 990s . 25 The broad outlines of
were
incorporated into the budget summit agreement of last
year, and a down
of about $40
in
reduction has been made for fiscal 1991. Such reductions in
the budget
along with resulting
in
interest rates and the value of the
would stimulate
private domestic investment and reduce net rrw'",wm
inflows. As a consequence, the burden on
generations from fiscal
in the
years would tend to
be eliminated.
lVl'-'lE,H
19
NOTES
oVQrnnlo
Modioliani
2. See
B. Friedman (1988), Gramlich (1989),
for statements of this view. For an
relevant collection of economists'
of the
debt, see Ferguson
, M. Friedman (1989), and Judd
3. See Barro (1
1989).
4. This model is
described in Throop (1
5. For elaboration of this
see Modigliani and
Ando and Modigliani (1963), Modigliani
C:tn;nrlni (1
).
(1
6.
detailed
C-hf-."iinrt the importance of 1I\.j'JlUlly
constraints is Wilcox
7.
David Ricardo was one of the first to discuss
the
he did not believe in the equivalence between
debt and
but like Adam Smith before him, argued
that taxes on households
reduce current conwhile internal borrowing tends to result in
reduced capital formation. Thus, the "Ricardian Equivalence Theorem" should be relabeled the "Non-Ricardian
Enuivalence Theorem" and Ricardo's doctrine relabeled
the "Ricardian Non-Equivalence Theorem." See Buiter
and Tobin
and O'Driscoll (1977). However, for ease
of
we have followed conventional usage.
8. As Blinder (1
it "When an individual has very
diffuse
over what
government policy will
it
me as
that his point estimates of
future
may have weak effects on his
current
is just the opposite of what
Barro and
and Wallace assume. If this is so, then
deep and weighty, may not
ornnirii,--QI importance."
recent survey of the theory and evidence on Rir'Qrrli",n equivalence, see Bernheim (1987).
Earlier surveys include Brunner (1986) and Tobin (1980,
Ch.
For this test all the variables were
1 See Chow (1
transformed
to the estimated serial correlation
coefficient for the
sample. The F test was then performed on the residuals from the estimated equations
these transformed variables. This procedure avoids
reiection of
because of
in the
as
to a shift in the structural equaerror
tion itself
11. Similar to the
for the F tests, the forecasting
equation was
for the period up until 1981 using
the serial correlation coefficient from the full sample period
1989.
12. Because the Quandt test was used to identify most
break
effective critical values would actually
be somewhat
than those reported for the F distribution alone in
1.
20
13. Other estimates in the same neighborhood have been
made by Hausman (1983) and Kendrick (1983)
14. See Hendershott (1987a).
15. The basic theory and its application are described in
Jorgenson (1963), Hall (1971) and Hall and
(1967).
16. The asset view of exchange markets was pioneered
by Dornbusch (1976) and Frankel (1979)
17. See Modigliani and Shiller (1973).
18. Earlier studies of this particular exchange rate equation are Hutchison and Throop (1985) and Throop (1989d,
198ge).
19. See also Throop (1989b, 1989c), in which it is argued
that movements in US and foreign monetary and fiscal
policies, rather than other factors such as safe-haven
effects, explain most of the fluctuation in the dollar's value
during the floating rate period.
20. Throop (1988) tests adaptive measures of expected
inflation against more "rational," or forward looking,
measures, but finds that the adaptive expectations have
provided a better representation of actual expectations
of inflation, even when monetary policy was changing
sharply as in the post-October 1979 period of disinflation.
See also Kaufman and Woglom (1984)
Two relationships in the model which were not examined in the previous section but which are potentially
subject to instabilities because of expectational effects
are the term structure of interest rates and the inflation
equation. Although the term structure equation does show
some evidence of instability during a temporary shift in the
"monetary regime" between 1979 and 1982, there is no
evidence of significant instability due to changes in fiscal
policy. In particular, expected budget deficits are not
found to enter significantly into the term-structure equation. See Throop (1988, 1989a) and Blanchard (1984) The
stability of the expectations-augmented Phillips curve that
is used to explain inflation in the model has been confirmed in a number of studies.
for example, Gordon
(1985), Perry (1983), and Blanchard (1984).
21. The effective tax rate on equity financed business
investment shown in Table 3 is calculated as
uz-k
1-u
where
u
z
k
corporate tax rate
present value of one dollar's worth of
depreciation allowance
investment tax credit
The Reagan program initially reduced the tax rate on
business investment by increasing the present value of
depreciation (z) and increasing the investment tax credit
(k). For the derivation of this
see Hall and Jorgenson (1967) or Throop (1989a).
Economic Review / Winter 1991
22. Because of a problem known as instrument instability,
this can only be done approximately See Holbrook (1972)
for a general discussion. Only a fraction of the total effect
of a change in interest rates on the unemployment rate
occurs contemporaneously Thus, if the targeted unemployment rate is hit exactly in a current period, in subsequent periods the lagged effects of the initial change in
interest rates have to be offset This can result in everlarger oscillations in interest rates. Therefore, a degree of
smoothing of interest rates is required. Still, the unemployment rate in the counterfactual simulation of an unchanged fiscal policy does not differ from the historical
unemployment rate by more than 0.1 percentage
in
any quarter.
23. The reduction in net exports is only an approximate
measure of the increase in net capital inflows. There are
two types of errors that tend to work in opposite directions.
Interest payments on foreign debt are not modeled explicitly in the macroeconometric model. Therefore, to the
extent that interest payments on debt to foreigners are
financed by further capital inflows, equating the change in
net indebtedness to the simulated change in net exports
understates the increase in indebtedness. On the other
hand, this procedure overstates the increase in indebtedness if the assumption in the simulation of a constant risk
premium in the foreign exchange market does not hold
exactly In this case, the accumulation of debt has the
effect of reducing the risk premium, and therefore the
value of the dollar. This would generate higher net exports
and smaller net capital inflows than in the simulation.
24. See Throop (1989d, 198ge) for a fuller discussion.
25. See Budget of the United States Government: Fiscal
Year 1991.
26. See Eisner (1986,1989), Blades and Sturm (1982) and
Throop (1980) for further discussion of this inflation tax.
The structural macroeconometric model that is used to
simulate the effects of Reagan fiscal policy subtracts the
Federal Reserve Bank of San Francisco
inflation tax on all government debt from the NIPA measure of disposable income. This inflation-adjusted measure
of income is consistent with households behaving rationally and generally saving (and reinvesting) inflation
premiums in the interest on government debt
Because of this behavior, the private saving rate as
conventionally measured should tend to rise and fall with
the inflation rate. This response of the private saving rate
to inflation is particularly evident in some European countries that have experienced sharp changes in inflation, but
it is somewhat obscured in U.S. data by movements in the
ratio of wealth to income, which influences the saving rate
in a life-cycle model of consumption. See Throop (1989a).
27. Because of adjustment costs, households tend to
respond to their perception of the permanent reduction in
real wealth due to the inflation tax. The inflation tax on
federal debt is therefore calculated as an eight-quarter
moving average of the inflation rate in consumer prices
times the stock of federal debt held by U.S. residents.
28. Algebraically, by definition GNP
C + I + G +
X
M, where C is private consumption, I is domestic
investment, G is government spending, and X
M is
exports less imports. But since GNP
C
S (private
saving) + T (taxes), then S + T - G = I + X - M. Thus,
given private saving (S), a reduction in the government
surplus (T
G) always decreases domestic investment
(I) or net foreign investment (equal to X - M).
29. These figures do not include matching grants to state
and local governments for state and local capital spending. In an accounting sense this capital does not belong to
the federal government, and in a behavioral sense the
prevailing empirical evidence is that grants do not build
up the stock of state and local capital because of a fiscal
substitution effect (see Gramlich (1978)). In any case,
federal grants to finance state and local capital projects
dropped by $7.5 billion in constant dollars between 1980
and 1988.
21
REFERENCES
Ando, Albert and Franco Modigliani. "The Life-Cycle
Hypothesis of Saving: Aggregate Implications and
Tests," American Economic Review, March 1963.
Barro, Robert J "Are Government Bonds Net Wealth?"
Journal of Political Economy, November/December
1974.
_ _ _ _ . "On the Determination of Public Debt" Journal of Political Economy, October 1979.
_ _ _ _ . "The Ricardian Approach to Budget Deficits,"
The Journal of Economic Perspectives, Spring 1989.
Bernheim, B. Douglas. "Ricardian Equivalence: An Evaluation of the Theory and Evidence" in NBER Macroeconomics Annual 1987. Cambridge, Mass: The MIT
Press, 1987.
Blades, Derek W. and Peter H. Sturm. "The Concept
and Measurement of Savings: The United States and
Other Industrial Countries," in Saving and Government Policy, Federal Reserve Bank of Boston, 1982.
Blanchard, Olivier J. "The Lucas Critique and the Volcker
Deflation," American Economic Review, May 1984.
Blinder, Alan S. "Ruminations on Karl Brunner's Reflections" in R.W. Hafer (ed.), The Monetary versus Fiscal
Policy Debate: Lessons from Two Decades. Totower,
N.J.: Rowmon and Allenfeld, 1986.
Brunner, Karl. "Fiscal Policy in Macro Theory: A Survey
and Evaluation" in RW. Hafer (ed.), The Monetary
versus Fiscal Debate: Lessons from Two Decades.
Totower, N.J.: Rowmon and Allenfeld, 1986.
Budget of the United States Government: Fiscal Year 1991.
Washington, D.C.: U.S. Government Printing Office,
1990.
Buiter, William H. and James Tobin. "Debt Neutrality:
A Brief Review of Doctrine and Evidence" in George
M. von Furstenberg (ed.) Social Security versus Private Saving. Cambridge, Massachusetts: Ballinger
Publishing Co., 1979.
Chow, G.C. "Tests of Equality Between Sets of Coefficients in Two Linear Regressions," Econometrica,
1960.
Congressional Budget Office. Trends in Public Investment, December 1987.
Corker, Robert and Owen Evans and Lloyd Kenward. "Tax
Policy and Business Investment in the United States:
Evidence from the 1980s," IMF Staff Papers, March
1989.
Darby, Michael R. "The Shaky Foundations of the Twin
Towers," U.S. Department of the Treasury, October
13,1988.
Dornbusch, Rudiger. "Expectations and Exchange Rate
Dynamics," Journal of Political Economy, December
1976.
Eisner, Robert. How Real is the Federal Deficit? New York:
The Free Press, 1986.
22
_ _ _ _ . "Budget Deficits: Rhetoric and Reality," The
Journal of Economic Perspectives, Spring 1989.
Englander, A Steven and Charles Steindel, "Evaluating
Recent Trends in Capital Formation," Quarterly Review, Federal Reserve Bank of New York, Autumn
1989.
Executive Office of the President. America's New Beginning: A Program for Economic Recovery, February 18,
1981.
Ferguson, James M. Public Debt and Future Generations.
Chapel Hill: University of North Carolina Press, 1964.
Frankel, Jeffrey. "On the Mark: A Theory of Floating
Exchange Rates Based on Real Interest Differentials,"
American Economic Review, September 1979.
Frenkel, Jacob A and Assaf Razin. Fiscal Policies and the
World Economy. Cambridge, Massachusetts: The MIT
Press, 1987.
Friedman, Benjamin M. Day of Reckoning. New York:
Random House, 1988.
Friedman, Milton. "Why the Twin Deficits Are a Blessing,"
Wall Street Journal, December 14,1988.
Fullerton, Don. "On the Possibility of an Inverse Relationship between Tax Rates and Government Revenues,"
Journal of Public Economics, October 1982.
Gordon, Robert A "Understanding Inflation in the 1980s,"
Brookings Papers on Economic Activity, No.1, 1985.
Gramlich, Edward M. "State and Local Budgets the Day
after It Rained: Why is the Surplus so High?" Brookings Papers on Economic Activity, No.1, 1978.
_ _ _ _ . "BUdget Deficits and National Saving: Are
Politicians Exogenous?" The Journal of Economic
Pe~pectives,Spring1989.
Hall, Robert E. "Application of the Theory of Optimum
Capital Accumulation" in Gary Fromm (ed.), Tax
Incentives and Investment Spending. Washington,
D.C.: Brookings Institution, 1971.
Hall, Robert E. and Dale W. Jorgenson. "Tax Policy and
Investment Behavior," American Economic Review,
June 1967.
Hausman, Jerry A and James M. Poterba. "Household
Behavior and the Tax Reform Act of 1986," Journal of
Economic Perspectives, Summer 1987.
Haveman, Robert. "How Much Have the Reagan Administration's Tax and Spending Policies Increased Work
Effort?" in Charles R. Hulten and Isabel V. Sawhill
(eds.), The Legacy of Reaganomics. Washington,
D.C.: Urban Institute Press, 1984.
Helliwell, John F. "Fiscal Policy and the External Deficit:
Siblings, but Not Twins," National Bureau of Economic Research Working Paper No. 3313, April 1990.
Hendershott, Patrie H. "Tax Changes and Capital Allocation in the 1980s." in Martin Feldstein (ed.), The
Economic Review / Winter 1991
Effects of Taxation on Capital Accumulation. Chicago: University of Chicago Press, 1987a.
_ _ _ _ . "Tax Reform and the Slope of the Playing
Field" in Martin Feldstein (ed.), Taxes and Capital
Formation. Chicago: University of Chicago Press,
1987b.
Holbrook, Robert. "Optimal Economic Policy and the
Problem of Instrument Instability," American Economic Review, March 1972.
Hutchison, Michael M. and Adrian W. Throop, "U.S.
Budget Deficits and the Real Value of the Dollar,"
Economic Review, Federal Reserve Bank of San Francisco, Fall 1985.
Jorgenson, Dale W. "Capital Theory and Investment Behavior," American Economic Review, May 1963.
Judd, John P. "Deficits: Twins or Distant Cousins?" Weekly
Letter, Federal Reserve Bank of San Francisco, October 6,1989.
Kaufman, Roger 1. and Geoffrey Woglom. "The Effects of
Expectations on Union Wages," American Economic
Review, June 1984.
Kendrick, John. "The Implications of Growth Accounting
Models," in Charles R. Hulten and Isabel V. Sawhill
(eds.), The Legacy of Reaganomics. Washington,
D.C.: Urban Institute Press, 1984.
Kotlikoff, Lawrence J. and Alan Auerbach. Dynamic Fiscal Policy. Cambridge: Cambridge University Press,
1987.
Modigliani, Franco. "Monetary Policy and Consumption:
Linkages via Interest Rate and Wealth Effects in the
FMP Model," in Consumer Spending and Monetary
Policy: The Linkages, Conference Series No 5, Federal Reserve Bank of Boston, 1971.
_ _ _ _ . "Reagan's Economic Policies: A Critique,"
Oxford Economic Papers, September 1988.
_ _ _ _ and R.E. Brumburg. "Utility Analysis and the
Consumption Function: An Interpretation of CrossSection Data," in K.K. Kurilance, ed., Post-Keynesian
Economics. New Brunswick, N.J.: Rutgers University
Press, 1954.
_ _ _ _ and Robert L. Shiller. "Inflation, Rational Expectations, and the Term Structure of Interest Rates,"
Economica, February 1973.
O'Driscoll, Gerald P. "The Ricardian Nonequivalence
Theorem," Journal of Political Economy, February
1977.
Okun, Arthur. "Potential GNP: Its Measurement and Significance," Proceedings of the Business and Economic Statistics Section of the American Statistical
Association, 1962.
Oliner, Stephen D. "The Formation of Private Business
Capital: Trends, Recent Developments, and Measurement Issues," Federal Reserve Bulletin, Board of
Governors of the Federal Reserve System, December
1989.
Federal Reserve Bank of San Francisco
Perry, George L. "What Have We Learned about Disinflation?" Brookings Papers on Economic Activity, No.2,
1983.
Poterba, James M. and Lawrence H. Summers. "Finite
Lifetimes and the Effects of BUdget Deficits on National Saving," Journal of Monetary Economics, Vol.
20,1987.
Quandt, Richard E. "The Estimation of the Parameters of a
Linear Regression System Obeying Two Separate
Regimes," Journal of the American Statistical Association, December 1958.
_ _ _ _ . "Tests of the Hypothesis that a Linear Regression System Obeys Two Separate Regimes," Journal
of the American Statistical Association, June 1960.
Steindel, Charles. "The Determinants of Private Saving"
in Public Policy and Capital Formation. Washington,
D.C. Board of Governors of the Federal Reserve
System, 1981
Summers, Lawrence and Chris Carroll, "Why is U.S.
National Saving So Low?" Brookings Papers on Economic Activity, No.2, 1987.
Throop, Adrian W. "Inflation Premiums, Budget Deficits,"
Weekly Letter, Federal Reserve Bank of San Francisco, March 14, 1980.
_ _ _ _ . "An Evaluation of Alternative Measures of
Expected Inflation," Economic Review, Federal Reserve Bank of San Francisco, Summer 1988.
_ _ _ _ . "A Macroeconometric Model of the U.S.
Economy," Working Papers in Applied Economic Theory, No. 89-01, Federal Reserve Bank of San Francisco, March 1989a (revised).
_ _ _ _ . "Fiscal Policies and Exchange Rates," Weekly
Letter, Federal Reserve Bank of San Francisco, September 15, 1989(b).
_ _ _ _ . "Monetary Policies and Exchange Rates,"
Weekly Letter, Federal Reserve Bank of San Francisco, September 22, 1989(c).
_ _ _ _ . "Fiscal Policy the Dollar, and International
Trade: A Synthesis of Two Views," Economic Review,
Federal Reserve Bank of San Francisco, Summer
1989(d).
_ _ _ _ . "Reagan Fiscal Policy and the Dollar," Economic Review, Federal Reserve Bank of San Francisco, Summer 1989(e).
Tobin, James. Asset Accumulation and Economic Activity.
Chicago: University of Chicago Press, 1980.
Weicher, John C. "The Domestic Budget after GrammRudman-and after Reagan," in Phillip Cagan, ed.,
Deficits, Taxes and Economic Adjustments. Washington, D.C.: American Enterprise Institute, 1987.
Wilcox, James A. "Liquidity Constraints on Consumption:
The Real Effects of Real Lending Policies," Economic
Review, Federal Reserve Bank of San Francisco, Fall
1989.
23
Location, Branching, and Bank Portfolio
Diversification: The Case of Agricultural Lending
Elizabeth S. Laderman,
Ronald H. Schmidt,
and
Gary C. Zimmerman
Economist, Senior Economist, and Economist, respectively, at the Federal Reserve Bank of San Francisco. The
authors are grateful to the editorial committee, Frederick
Furlong, Jonathan Neuberger, and Carolyn SherwoodCall, for many helpful comments. We also would like to
thank Paul Cheng and Deborah Martin for their research
assistance.
In this paper, we hypothesize that loan monitoring costs
increase with distance from the borrower, and, thus, that
bank loan portfolio choice depends on the bank's location.
A corollary of our hypothesis is that branching increases
bank loan portfolio diversification. To empirically test our
hypothesis, we focus on banks' choice between agricultural and nonagricultural loans. We find that, even
after controlling for a variety ofother factors, rural banks
devote a significantly larger proportion of their loan
portfolio to agricultural loans than do urban banks. Moreover, we find that, when statewide branching is permitted,
rural banks hold higher nonagricultural loan portfolio
shares, and urban banks hold higher agricultural loan
portfolio shares, than when branching is restricted. Thus,
we conclude that branching enhances bank loan portfolio
diversification.
24
Banking economists have given considerable attention
to the special nature of commercial bank lending. Leland
and Pyle (1977) and Diamond (1984), among others, argue
that bank lending differs from other forms of lending, such
as the purchase of debt that is directly issued by companies, because of the extensive information gathering and
monitoring functions that banks perform. These authors
argue that, to a greater extent than other lenders, banks
gather their own detailed information on loan projects and
monitor borrowers' conditions and adherence to loan covenants. Thus, although all lenders attempt to monitor their
loans and enforce loan or debt covenants, banks may
specialize in lending to borrowers who are particularly
costly to monitor.
One implication of the importance of bank monitoring is
that a bank's location may be a significant determinant of
its choice of borrowers. It is reasonable to suppose that
monitoring is more difficult and more costly from a distance, so banks would tend to favor local borrowers over
distant borrowers, all other things equal.
Support for this view comes from the work of Black
(1975), who suggests that deposit relationships with borrowers enhance a bank's ability to monitor.' Black argues
that since bank borrowers often are depositors as well, the
bank has a low-cost ongoing history of financial information. If deposit markets are local, as some evidence shows,
then this effect would strengthen the tie between banks and
local borrowers by reducing monitoring costs."
The dependence of monitoring costs on distance implies
constraints on a bank's ability to expand beyond the
local headquarters area through branching may directly
affect its loan portfolio choice and perhaps its ability to
diversify assets. This is because branching restrictions may
impinge on the ability of banks to locate offices near
different types of borrowers and thus efficiently monitor
their loans. The effect on diversification is important
because, in many situations, diversification across assets
can reduce expected bankruptcy costs and the probability
of bank failure.
In this article, we present evidence supporting the
Economic Review / Winter 1991
hypothesis that location affects the types of loans that
banks choose, and, consequently, that branching enhances
diversification. We focus on banks' choice between agricultural and nonagricultural loans. This choice is wellsuited to our study because, by its nature, agriculture is
location-specific and concentrated in rural areas. 3
Unlike earlier related work, which was limited to a study
of different types of rural banks, our analysis includes
institutions headquartered in both urban and rural locations in restricted and unrestricted branching states. Our
results indicate that rural banks have a significantly higher
share of agricultural loans than urban banks, even after
controlling for a variety of other factors. Moreover, we find
that rural banks increase the share of nonagricultural loans
I.
Portfolio Model
In this paper, we hypothesize that monitoring costs
increase with distance from the borrower, and, thus, that
a bank's location affects its relative monitoring costs
for different types of loans. Our hypothesis is based on
reasoning that bank personnel are more familiar with local
borrowers and local market conditions, and therefore can
more easily monitor local borrowers than distant ones." In
addition, personnel should be better able to keep a close
watch on a local loan project's progress.
We assume that monitoring costs are important for
banks, so that differences in relative monitoring costs for
different types of loans should influence a bank's loan
portfolio choices. Thus, our hypothesis implies that location affects a bank's loan portfolio choices. To test our
hypothesis, we focus on a bank's choice between agricultural and non-agricultural loans. Agricultural borrowers,
by their nature, are assumed to be located in rural areas,
while most nonagricultural loans are assumed to be for
projects in urban areas. Thus, our hypothesis predicts that,
at least when branching is restricted, banks located in rural
areas will have lower monitoring costs for agricultural
loans than will banks located in urban areas and will
therefore devote a larger proportion of their portfolio to
farm-related lending.
If branching is unrestricted, it may still be the case
that agricultural shares will be higher for rural-headquartered banks, but we expect that the difference between
rural and urban banks' agricultural shares will be smaller
than when branching is restricted. This is because branching should decrease differences in monitoring costs between agricultural and nonagricultural loans. This would
encourage banks to take advantage of the benefits of
diversification, thereby lowering rural banks' agricultural
Federal Reserve Bank of San Francisco
in their portfolios when they are allowed to branch statewide, and that urban banks increase the share of agricultural loans in their portfolio when they are allowed to
branch statewide. This offers support for the hypothesis
that branching enhances diversification, and
support than can be obtained from a study of rural banks alone.
A theoretical model demonstrating the effects of location and branching laws on portfolio choice is presented in
Section 1. In Section II, we empirically test the implications of the theory, examining differences in agricultural
loan shares across a wide selection of commercial banks
over the period 1981-86. Results from the estimation are
described in Section
with
remarks in
Section IV.
loan portfolio shares and raising urban banks' agricultural portfolio shares. 5
Previous researchers have already conducted some investigation of these topics. Gilbert and Belongia (1988)
study how rural bank portfolios are affected
tory structure by examining whether affiliation with large
multi-bank bank holding companies affects the proportion
of loans devoted to agriculture in rural banks' portfolios.
In some states, multi-bank bank
are
not permitted.
They find that rural banks which are subsidiaries of bank
holding companies with assets greater than $1 billion have
a lower agricultural loan ratio than other banks in the same
counties. They attribute this to the greater ability that such
banks have to diversify their loan portfolios away from
agriculture. Thus, Gilbert and Belongia's results implicitly
suggest that laws that restrict the geographic dispersion of
bank affiliates or even bank offices also restrict the diversification of rural bank portfolios out of agriculture,
White (1984) suggests that geographic restrictions prevented loan diversification at small rural banks in the
1930s, increasing their failure rate, but he does not empirically test this hypothesis. In their empirical examination of the effect of bank credit on farm
Calomiris,
Hubbard, and Stock (1986) argue that
restrictions, by impairing the ability of small rural banks to
diversify assets, may contribute to bank
the depletion of bank credit, and a decline in farm
6
though, they do not empirically test this hvpothesis.
The Model
The effect of location and branching laws on portfolio
choice can be seen using a portfolio model. 7 For simplicity,
25
we assume that the amount of monitoring required per
dollar lent is fixed." However, following the above discussion, the cost per dollar of producing the required monitoring depends on the bank's location vis-a-vis the borrower,
or distance from the borrower. Thus, the monitoring cost
for an agricultural loan of a given size will be higher for an
urban bank than a rural bank. We also assume the bank has
a fixed stock of loanable funds to allocate among the two
types of loans, agricultural and nonagricultural.
In the following discussion, we will introduce several
variables that are not choice variables for the bank. Some of
these variables, namely monitoring costs, interest rates,
and risk-related variables, depend on bank-specific exogenous factors, such as location, that enter into our
empirical analysis. We will explicitly introduce these
exogenous factors in the next section, but, for the sake of
notational simplicity, we will suppress these factors in the
following formulas.
The explication of the theoretical model proceeds in two
steps, first without uncertainty, and then with uncertainty
added. In the absence of uncertainty, the bank would
allocate all funds to the project yielding the highest return
net of monitoring costs. Since the volume of total loans is
fixed, we can normalize the volume to one. Mathematically, the decision is simple:
Maximize tt
=
6iA+(1-6)iN-6CA-(1-6)CN-rd· (1)
The bank chooses 6, the share of loans made to agricultural projects, so as to maximize profits, rr, net of the
cost of obtaining loanable funds, rd. The interest rates on
agricultural and nonagricultural loans, iA and iN' respectively, and the monitoring costs per dollar for agricultural
and nonagricultural loans, C A and CN' respectively, determine the optimal allocation. In this simple case, the bank
allocates all credit to the types of projects that pay the
highest interest rate net of monitoring costs.
Now let returns to the two types of projects be randomly
distributed. The random return variables for agricultural
and nonagricultural projects are denoted by rA and rN'
respectively. These are the returns to the project owners
themselves. The expected return and variance may differ
between agricultural and nonagricultural project pools, but
we assume that individual project returns within a given
loan pool are drawn from the same distribution. 9
The bank is assumed to be risk-neutral, in the sense that
its objective is simply to maximize expected profits. Portfolio variance enters the bank's objective function through
bankruptcy costs; we assume that if the bank cannot payoff
its own liabilities, it will face bankruptcy costs. 10 Because
26
the probability of bankruptcy increases as the variance of
the portfolio increases, holding interest rates constant,
expected profits decrease with increased variance. In this
way, uncertainty enters into the allocation decision of
the bank.
The variances of individual project returns will affect
expected profits through the portfolio variance and, independently, through a separate channel. The separate, independent influence is due to the effect of project return
variance on the probability of borrower default. As the
variance of a project's return increases, holding the loan
rate and the expected value of the return constant, the
probability of the borrower defaulting increases. Because
the highest return that the bank can receive is the contracted loan rate, the bank is not compensated on the high
end for the increase in the probability of default. Therefore,
the increase in variance lowers the expected return to the
bank from that particular loan.
The introduction of uncertainty into the model gives the
bank an incentive to diversify its portfolio and hold some of
both types of loans.'! The principle of diversification says
that by making loans to different types of borrowers, the
risk to the lender's portfolio can be reduced in most cases.
Realizations of future events that cause some projects to be
successful can cause others to fail. Therefore, by combining different types of loans into the same portfolio, these
offsetting risks cancel to some extent, thus reducing overall portfolio variance. Diversification is therefore defined,
in this paper, as an adjustment of portfolio shares in such a
way as to reduce portfolio variance. 12
The decision problem for the bank now becomes the
following:
Maximize
Tr
+ (1 O)iN - 6CA - (1- 6)CN
- [3var(6rA +(1 O)rN) - 6a Avar(rA)
- (1- 6)aNvar(rN)
r d,
(2)
= f3i A
where Tr is now expected profits. The effect of portfolio
variance on bankruptcy is captured in the fifth term of (2),
while the independent effects of agricultural and nonagricultural loan defaults are captured in the sixth and
seventh terms of (2), respectively. Here, [3var(6rA +
(1 6)rN) is the expected cost of bankruptcy, which we
assume rises linearly with the portfolio's variance, and
6a Avar (rA) and (1- 6)aNvar (rN) represent the expected
costs associated with the default of agricultural and nonagricultural loan projects, respectively.
The optimal portfolio for the bank can be determined by
maximizing (2) with respect to 6 and solving for the
equilibrium value of 6:
Economic Review / Winter 1991
e* =
Equation (3) shows that three types of factors affect the
proportion of a bank's portfolio that is devoted to agriculture: interest rate spreads, relative monitoring costs and
relative risk. Holding all other factors constant, an increase
in the interest rate on agricultural loans (iA) relative to the
rate on nonagricultural loans (iN) will increase agriculture's portfolio share. On the other hand, an increase in
monitoring costs for agricultural loans (CA) relative to
monitoring costs on nonagricultural loans (eN) will decrease agriculture's portfolio share. Finally, an increase in
the relative variance of agricultural loan projects or in the
relative cost of agricultural loan project defaults (which
depends on relative variances and the relative sizes of the
parameters (XA and (XN) will decrease agriculture's portfolio share.
We also can use equation (3) to see the effect of
differences in relative monitoring costs on diversification.
For simplicity, set the interest rates and project return
variances equal on the two types of projects, and set the
covariance between project returns equal to zero. Then,
equation (3) tells us that, in the absence of differences in
monitoring costs between the two types of loans, the
optimal proportion of the portfolio devoted to agriculture is
one-half. Since there are no differences in interest rates and
no differences in monitoring costs to keep the bank from
choosing a perfectly diversified portfolio, this must be the
portfolio of minimum variance. An increase in monitoring
costs for agricultural loans, for example, would decrease e
below one-half. This move away from the minimum variance portfolio, and into nonagricultural loans, would
decrease portfolio diversification, as we have defined diversification.P Similarly, an increase in monitoring costs
for nonagricultural loans would decrease diversification by
decreasing the proportion of the portfolio devoted to nonagricultural loans.
As seen in equation (3), exogenous variables that affect
relative monitoring costs, interest rate spreads, and relative
risk, will, in tum, affect the share of a bank's portfolio that
is devoted to agriculture. In the next section we will
empirically examine how observable variables that should
affect these three types of factors actually influence agricultural portfolio shares. Among the factors we will be
examining are:
• Location and branching restrictions. These should affect
relative monitoring costs. For example, banks that are
located in urban areas and are prohibited from setting up
Federal Reserve Bank of San Francisco
branches in rural areas will have relatively higher monitoring costs for agricultural projects, and, therefore,
lower agricultural loan portfolio shares. In addition, it
may be the case that even urban headquartered banks that
can branch have a comparative disadvantage in agriculturallending, relative to rural banks that can branch. 14
However, we expect that, for a given bank, branching
reduces differences in monitoring costs between agricultural and nonagricultural loans, thereby encouraging diversificaion and narrowing the difference in agricultural
loan portfolio shares between rural and urban banks.
• Competition in the agricultural loan market. If competition from other lenders in agriculture increases and
forces agricultural interest rates downward, the bank will
shift its portfolio away from agriculture.
• Government subsidies or guarantees for crops. An increase in government agricultural support, which stabilizes farm income, should decrease the relative risk of
agricultural lending and increase bank willingness to
lend to agriculture.
Graphical Solution
The bank's portfolio choice can be depicted graphically.
This helps to illustrate the effect of location and branching
restrictions on loan portfolio choice.
Figure 1 breaks total profits into its two component
parts. The vertical axis measures expected profits, while
the horizontal axis measures agriculture's share of the
Figure 1
f/)
:;:
o
10.
D.
-
"0
CI)
<CI)>
Q.
><
W
a*
Share of Agricultural Loans
27
Figure 3
Figure 2
'
.. .,
f/)
:;::
0
1-
,.,
e,
Q)
JtN
e
Q)
0-
X
W
8*U
8*
8*R
Share of Agricultural Loans
portfolio, ranging from 0 to 100 percent. The curve labeled
'IT A denotes total expected profits from agricultural lending. Expected profits from that source rise as more loans
are made to agriculture, but the marginal profits begin to
diminish as the benefits of diversification are lost. Similarly, the curve labeled 'ITN measures expected profits from
nonagricultural lending , which fall as more of the portfolio
is shifted into agriculture. The total profits for the bank are
the vertical sum of the 'ITA and 'ITN curves. Expected total
profits, 'IT, are maximized at e* .15
Differences in bank locations can result in different
optimal portfolios. Figure 2 compares two stylized banks,
one urban (denoted with a U superscript) and the other
rural (denoted with an R superscript). The rural bank is
assumed to have lower monitoring costs for agricultural
loans, while the urban bank has lower costs for nonagricultural loans. The effect of this assumption is to yield
an expected agricultural profit function for the rural bank
8*'
Share of Agricultural Loans
that lies above that of the urban bank ('IT~ > 'IT;';), while the
nonagricultural loan profit function of the urban bank
exceeds that of the rural bank ('IT};; > 'IT~ ) at any given level
of e. As shown in Figure 2, these differences result in the
urban bank lending less to agriculture.
Changes in monitoring costs (or other key variables) can
change a bank's portfolio. Consider the case of changes in
relative monitoring costs due to liberalization of branching
restrictions. Figure 3 depicts the situation facing an urban
bank that is suddenly permitted to open or acquire a rural
branch. Monitoring costs fall for agricultural loans, because the bank now has a monitoring presence in an
agricultural area.
The drop in monitoring costs pushes 'ITA up to 'IT~ and
raises the total profit function to 'IT' from 'IT. The optimal
allocation of credit, therefore, shifts in the direction of
greater diversification, which, for the urban bank, corresponds to more agricultural lending (e*' > e*).
H. The Data and the Empirical Model
In Section I we suggest that differences in location and
branching restrictions, among other factors, are likely to
help explain differences in bank portfolios. In this section,
we choose empirical counterparts for these factors and
present an empirical model of differences in commercial
banks' agricultural production loans as a proportion of
total Ioans.!s The model seeks to explain deviations in
banks' agricultural portfolio shares from the average for
the sample.
We model bank agricultural production loan portfolio
shares as functions of exogenous factors which influence
relative monitoring costs for the two types of loans, or
28
interest rate spreads, or the relative amount and cost of risk
for the two loan types.
Our empirical model is similar to Gilbert and Belongia's
model in that, like these authors, we are modeling the
relationship between the geographic dispersion of bank
offices and bank loan portfolio choice. Three important
factors distinguish our model from Gilbert and Belongia's
model, however. First, we include urban banks in our
study, while Gilbert and Belongia do not. If we find that
urban banks respond to branching opportunities by holding more agricultural loans, we can better argue that it is
the benefits of diversification that drive the results than if
Economic Review / Winter 1991
we only have evidence on the response of rural banks to
branching opportunities.
The second difference between the two models is that
Gilbert and Belongia do not place their model in the context
of a bank portfolio choice model. Thus, we include some
important explanatory variables that are not included in the
Gilbert and Belongia model.'? The third difference between the two models is that we look at the effects of
branching laws per se, while Gilbert and Belongia look at
the effects of affiliation with a multi-bank bank holding
company.
The Variables
The explanatory variables of greatest interest to us
involve the location of the bank and the branching laws in
the state in which the bank operates. As discussed above,
whether a bank is located in a metropolitan or rural area
should influence its cost of monitoring agricultural loans
and thus its agricultural production loan share.
Holding other factors constant, it is expected that a
metropolitan bank will have a lower agricultural loan share
than a rural bank, because it will find it relatively more
difficult to monitor agricultural loans. However, if the
metropolitan bank is located in a statewide branching state,
we expect that it will have a higher agricultural loan share
than if it is located in a limited branching or unit banking
state. Likewise, we expect that a rural bank will have a
lower agricultural loan share in a statewide branching state
than in a restricted branching state.18
Thus, we include three interaction terms, one indicating
whether the bank is a metropolitan bank in a restricted
branching state, one indicating whether the bank is a rural
bank in a restricted branching state, and one indicating
whether the bank is a metropolitan bank in a statewide
branching state. These are all thought to influence relative
monitoring costs and thereby the proportion of the loan
portfolio devoted to agriculture. (This leaves rural banks in
statewide branching states as the control group.)
We also include the percent of gross state product
accounted for by agriculture, bank size and average farm
size in the regression. All three variables may influence
relative monitoring costs. The first is an important additionallocation variable; banks that are located in "farm
states" should have lower monitoring costs for agricultural
loans and thus higher agricultural loan shares than banks
located in nonfarm states.
Bank size and average farm size are included to allow for
the possibility that there are economies of scale in monitoring.'? Figures reported by "The Survey of Terms of Bank
Lending" indicate that there may be consistently large
Federal Reserve Bank of San Francisco
differences in average loan sizes between commercial and
agricultural loans, and that this difference is more pronounced for large banks than for small banks. 20 This
means that if large loans have lower monitoring costs per
dollar than small loans, then, all other things equal, large
banks would have a comparative advantage in commercial
loans and would devote a smaller proportion of their
portfolio to agricultural loans than would small banks.
In addition, if large farms require large loans, then an
increase in average farm size may lower monitoring costs
on agricultural loans relative to nonagricultural loans,
thereby increasing agriculture's portfolio share.
On the other hand, it is possible that an increase in
average farm size would decrease agriculture's portfolio
share through its influence on the demand for bank loans.
As stated in the introduction, banks may have a comparative advantage in lending to borrowers who are especially
costly to monitor. To the extent that farm size is positively
correlated with ease of monitoring, large farm borrowers
may have less need for banks' special monitoring capabilities. They may have greater access to other types of lenders
and may therefore have less of a demand for bank loans.
The remaining variables in our regression should influence either interest rate spreads or relative risk factors
between the two types of loans. We include one variable
that may influence interest rate spreads: competition from
nonbank agricultural lenders, specifically, the Farm Credit
System (FCS). In a survey of several California banks'
agricultural lending, respondents noted that they face vigorous competition from the government-sponsored FCS.21
(See Box for a brief description of the types of agricultural
lenders, including the FCS.)
Such competition lowers the relative return that banks
receive on agricultural loans. As pointed out by Gray,
Woolridge, and Ferrara (1982), the FCS has some advantages over commercial banks in lending to agriculture.
Its advantages help it to be an effective competitor with
banks, thereby lowering equilibrium rates of return on
agricultural loans. These include access to the national
money markets through a government-sponsored entity,
favorable tax treatment, and the absence of the loans-toone-borrower limits that are imposed on nationally chartered commercial banks. 22
The FCS's competitive disadvantages include strict eligibility restrictions for FCS loans to ensure that it remains
only an agricultural lender, an obligation to serve all
agricultural areas during all economic times and an inability to provide the full range of services provided by
commercial banks. 23
We measure the degree of competition from the FCS by
29
the percent of total agricultural production loans outstanding in the state that were held by the PCS in the previous
year. We expect this variable to have a negative coefficient.
We use the lagged PCS market share rather than the
contemporaneous market share because the contemporaneous share likely is a function of the dependent variable
in the regression. Moreover, it is in the nature of lending
relationships that the short-term price elasticity of demand
would be relatively low, so the lagged PCS share should be
strongly positively correlated with current competition
facing banks. 24
Our risk-related variables are the share of government
payments in farm net income and the bank's deposit-toloan ratio. An increase in the share of government guaran-
30
tees (through price supports or export subsidies) should
decrease the level of risk in agricultural lending and should
increase agriculture's portfolio share.
The deposit-to-loan ratio is included to capture firmspecific differences in attitudes toward risk. Such differences may depend on management's goals concerning, for
example, firm growth. Generally, the more "aggressive"
the bank, the more it depends on borrowed funds, rather
than just deposits, for loan funding. We consider such
aggressiveness to be a sign that, given the variances of
project returns, a relatively low cost is assigned to overall
portfolio risk.
other words, the parameter 13 in our
theoretical model is relatively low.)
Economic Review / Winter 1991
A decrease in the expected cost of bankruptcy, holding
variances constant, would induce a bank to invest more in
the projects with higher risk and higher contract interest
rates.s> Therefore, if agricultural loans tend to have higher
interest rates, an increase in the deposit-to-loan ratio,
corresponding to an increase in the cost assigned to bankruptcy, would decrease the agricultural loan portfolio
share. If, on the other hand, commercial loans tend to have
higher risk and higher interest rates, an increase in the
deposit-to-loan ratio would increase the agricultural loan
portfolio share. 26 We do not predict the sign of the coefficient for the deposit-to-loan ratio.
One variable not included explicitly in the model is the
interest rate spread. This variable is excluded because of
data limitations. The appropriate variable to include is
bank-specific and not directly obtainable. The relevant
spread depends on the bank's alternatives to agricultural
loans-be they commercial, real estate or consumer loans.
We do not have this information, nor do we have the
relevant interest rates for each type of loan for each bank.
We would like to point out that the narrow categorization
of several of the variables in the regression is mainly for the
sake of exposition. Specifically, bank size, the deposit-toloan ratio and average farm size may work through any or
all of the monitoring costs, interest rate spread, or risk
channels to influence the agricultural portfolio share. For
example, examination of several years of data from the
Survey of Terms of Bank Lending reveals that large farm
loans tend to carry lower interest rates than small loans .27
Therefore, if farm size is positively correlated with loan
size, then farm size may be negatively correlated with
interest rates on farm loans.
This caveat means that the coefficients on the bank
size, deposit-to-loan ratio and average farm size variables
should be interpreted with caution. These are reducedform coefficients, not structural coefficients. Most important, their interpretation does not affect the interpretation
of coefficients on the main variables of interest, the location and branching law interaction terms, and agriculture's
share of gross state product.
with some banks reporting data throughout the sample
period and other banks reporting only once or twice.
There are 1069 observations in our sample. A bank was
included in our sample for a particular year if it reported
having outstanding fixed or variable rate agricultural production loans on the FR2028b in at least one quarter of that
year. In total, banks in 33 states are represented.?" (The
remaining states were not represented because they either
had-less' than 2 percent of their gross state product in
agriculture, or they had no banks surveyed in the sample.)
Forpurp()ses of our analysis it is important that the sampleofbanks be fairly evenly divided between banks with
their head offices in metropolitan areas (565 observations)
andth()selocated in rural areas (504 observations). The
breakdown between banks located in restricted branching
states and statewide branching states is 811 and 258,
respectively.
Our dependent variable, the share of agricultural production loans in total loans, bank assets, and the depositto-loan ratio were an obtained from data reported on
The Data
We examine a subset of a sample of commercial banks
that the Federal Reserve's Board of Governors has determined are representative of banks making farm production
loans. 28 This sample consists of the banks that were
surveyed on the quarterly FR 2028b, the Survey of Terms
of Bank Lending to Agriculture, between 1981and 1986. 29
The FR2028b surveys between 168 and 188 banks in each
quarter. The set of banks can differ from survey to survey,
Federal Reserve Bank of San Francisco
31
Figure 4
State Branching Laws in 1986
o
o
Statewide
the quarterly "Report of Condition and Income (Call
Report)." These items were averaged for the entire year to
generate annual figures. The branching law variables were
obtained from various editions of the Annual Statistical
Digest, published by the Board of Governors of the Federal
Reserve System.
The percent of gross state product in agriculture was
obtained from the Bureau of Economic Analysis of the
U. S. Department of Labor. Average farm size and the
percent of state farm income from government payments
were obtained from Agricultural Statistics, published by
the U. S. Department of Agriculture. The Farm Credit
System market share was obtained from editions of Agricultural Finance Statistics. Both of these publications are
published annually by the U. S. Department of Agriculture.
Figure 4 shows the branching laws for all the states in
1986. (Unit and limited branching states are considered
restricted branching states.) Table I presents the mean
values for the agricultural production loan share and for the
continuous independent variables. Means are given for the
entire sample and for subsamples broken down by bank
headquarters location and branching law status. Note the
large differences in agricultural production loan shares
between rural and urban banks. Also, urban banks in the
32
Limited
Unit
sample are significantly larger than rural banks, especially
in statewide branching states. We control for this difference in the regression.
The Empirical Model
The dependent variable in our regression is the difference between the bank's agricultural production loan
portfolio share and the mean value of this variable for all
banks in the sample for that year. All explanatory variables
except the location and the branching law interaction terms
also are expressed as deviations from sample means.
Expressing variables as deviations from means helps to
control for macroeconomic effects such as agricultural
business cycles and government policy cycles for which
we have inadequate empirical measures.
The regression equation that we estimate is:
AGRICULTURAL PRODUCTION LOAN SHARE
Bl*ASSETS +
B2*DEPOSIT-TO-LOAN RATIO +
B3*AGRICULTURE'S SHARE OF GROSS STATE
PRODUCT +
B4*AVERAGE FARM SIZE +
B5*GOVERNMENT SUPPORT +
Economic Review / Winter 1991
B6*LAGGED FARM CREDIT SYSTEM SHARE OF
FARM LOANS +
B7*RESTRICTED BRANCHING, RURAL +
B8*RESTRICTED BRANCHING, URBAN +
B9*STATEWIDE BRANCHING, URBAN +
E,
where AGRICULTURAL PRODUCTION WAN SHARE
= percent of total loans outstanding in agricultural
production loans;
ASSETS = bank assets, in billions of dollars;
DEPOSIT- TO-LOAN RATIO = the ratio of total deposits to total loans outstanding, in percent (positively
correlated with the cost of risk);
AGRICULTURE'S SHARE OF GROSS STATE PRODUCT = for the state in which the bank is located, the
percent of gross state product that is accounted for by
agriculture;
AVERAGE FARM SIZE = average farm size in the
state in 1978, in acres;
GOVERNMENT SUPPORT
the share of government payments in total state farm net income, in percent;
LAGGED FARM CREDIT SYSTEM SHARE OF FARM
WANS
the percent of total agricultural production
loans outstanding in the state held by the FCS in the
previous year;
RESTRICTED BRANCHING, RURAL = I if the
bank's main office is not in a Metropolitan Statistical
Area and if it is in a unit banking or limited branching
state, 0 otherwise;"
RESTRICTED BRANCHING, URBAN = I if the
bank's main office is in a Metropolitan Statistical Area
and if it is in a unit banking or limited branching state, 0
otherwise;
STATEWIDEBRANCHING, URBAN = 1if the bank's
main office is in a Metropolitan Statistical Area and if it
is in a statewide branching state, 0 otherwise;
and E is an error term.
Our method of estimation was ordinary least squares.
Because of the sample composition, we did not have a
"panel" data set giving a consistent time series for each
bank.V Therefore, we could not perform the usual corrections for heteroskedasticity and autocorrelation that are
done for time-series, cross-section regressions.
III. Regression Results
The regression results are reported in Table 2. In general, the results provide strong evidence to support the
importance of location in explaining differences in bank
portfolios. As indicated by the adjusted R 2 , the equation
explains 64 percent of the variation in agricultural loan
portfolio shares.
Coefficients on the three interactive dummies indicate
Federal Reserve Bank of San Francisco
the importance of location and restrictions on branching.
All three are highly significant with the predicted signs.
Results in Table 2 are consistent with the hypothesis
that location, through its influence on relative monitoring costs, is an important determinant of bank portfolio
choice, even when branching is permitted. Urban banks
have significantly smaller portfolio shares in agricultural
33
loans than rural banks, ranging from 5.3 percentage points
smaller in statewide branching states to 19.3 percentage
points smaller in restricted branching states.
Branching restrictions work in the expected direction.
To the extent that branching allows urban banks to reduce
the costs of monitoring agricultural loans and rural banks
to lower monitoring costs for nonagricultural loans, unrestricted urban banks would be expected to have greater
agricultural loan portfolio shares than restricted urban
banks, and unrestricted rural banks would have smaller
agricultural shares than restricted rural banks. As shown in
Table 2, holding other factors constant, unrestricted urban
banks would hold 4.3 percentage points more of their portfolio in agricultural loans than restricted urban banks. 33
Likewise, unrestricted rural banks would hold 9.7 percentage points less of their portfolio in agricultural loans than
restricted rural banks. These effects are responsible for the
considerably smaller difference in agricultural shares between urban and rural unrestricted banks than between
urban and rural restricted banks.
As discussed above, monitoring costs for agricultural
loans should be influenced not only by the location of the
bank within the state, be it urban or rural, but also by the
agricultural orientation of the state's economy as a whole.
The regression results in Table 2 show that, as expected,
AGRICULTURE'S SHARE OF GROSS STATE PRODUCT has a positive and highly significant coefficient.
Other factors besides bank location and branching laws
may affect farm production loan portfolio shares. Although
the coefficient on ASSETS is insignificant, indicating that
bank size does not appear to affect relative monitoring
costs in such a way as to significantly influence agricultural
portfolio shares, AVERAGE FARM SIZE has a significant
negative coefficient. This sign is consistent with the
hypothesis that large farm borrowers may demand fewer
bank loans.
One of the risk-related variables, GOVERNMENT SUPPORT, has a statistically significant coefficient. As expected' the sign is positive, indicating that such payments
decrease the relative risk of agricultural loans, thereby
making them more attractive investments. The other riskrelated variable, the DEPOSIT-TO-LDAN RATIO, has an
insignificant coefficient.
As discussed above, the interest rates on agricultural
loans relative to nonagricultural loans for commercial
banks should be negatively correlated with the lagged
Farm Credit Share of the agricultural loan market. As
expected, the regression results do show a negative and
significant coefficient for LAGGED FARM CREDIT
SYSTEM SHARE OF FARM LDANS.
V. Conclusion
In this paper, we present empirical evidence to support
the hypothesis that location, through its effect on relative
monitoring costs, affects bank loan portfolio choice. We
also present evidence that branching restrictions, by confining the location of bank offices to a relatively small area,
inhibits bank loan portfolio diversification.
Specifically, we find that rural banks devote a larger
proportion of their loan portfolio to agricultural loans than
do urban banks. Moreover, we find that, when branching is
unrestricted, rural banks hold higher nonagricultural loan
portfolio shares, and urban banks hold higher agricultural
loan portfolio shares. As a result, the allocation of loan
portfolios across agricultural and nonagricultural loans is
more similar for urban and rural banks that are not constrained in their ability to branch than it is for constrained
urban and rural banks.
Within the context of our theoretical model, our empirical results indicate that a move to statewide branching
causes banks to diversify their loan portfolios. By permitting banks to locate branches near both agricultural and
nonagricultural borrowers, statewide branching narrows
the difference in monitoring costs between agricultural and
nonagricultural loans for a given bank. As demonstrated in
(
34
the theoretical model, differences in monitoring costs
cause rural banks to concentrate more on agricultural loans
and urban banks to concentrate more on nonagricultural
loans than they would were their portfolio perfectly diversified. Therefore, the convergence of relative monitoring costs increases rural bank lending to nonagricultural
projects and urban bank lending to agricultural projects,
thereby increasing diversification. Given this interpretation, we can say that the benefits of intrastate branching
liberalization would include the benefits that accompany
asset diversification. Among these are a decrease in the
risk of credit disruption as a result of bank failure and a
decrease in the expected withdrawals from the deposit
insurance fund.
Although our results are broadly consistent with those
found by Gilbert and Belongia, our inclusion of urban
banks in the study has enabled us to provide stronger
confirmation of the hypothesis that branching restrictions constrain asset diversification. Previous authors
made this conjecture, but did not provide any strong
empirical evidence.
We also find evidence supporting the general conclusions of the theoretical model regarding the effect of
Economic Review / Winter 1991
relative rates of return and relative risks on bank loan
portfolio choice. Specifically, we find that factors that
presumably decrease the relative rate of return on agricultural loans, such as an increase in the Farm Credit
System's competitiveness, have a statistically significant
negative effect on agricultural loan portfolio shares. In
addition, an increase in government agricultural supports,
which likely is associated with a decrease in the relative
riskiness of agricultural loans, has a statistically significant
positive effect on a bank's agricultural lending. These
results lend support to our theoretical model and, thus, our
interpretation of the effects of location and branching laws
on bank portfolio diversification.
NOTES
1. Slack's work also suggests that banks would have a
comparative advantage, all other things equal, over other
financial intermediaries in the credit evaluation and monitoringprocess. For further discussion and evidence on
banks' comparative advantage in monitoring, see Fama
(1985) and James (1987).
2. See Keeley and Zimmerman (1985) and Neuberger
and Zimmerman (1990) for evidence on the extent of
geographic markets for different types of deposits.
3. Throughout this paper, in both the theoretical discussion and in the empirical work, we equate rural areas with
agricultural areas. See endnote 18for a discussion of how,
ideally, one might deal with this issue.
4. As stated in the introduction, this may be especially
true if borrowers tend to be depositors, and if deposit
markets are local.
5. We have some evidence that metropolitan banks with
branches in rural areas are quite active in agricultural
lending in some states. California is an example. Zimmerman (1989) reports that although the proportion of large
metropolitan California banks' loans in agriculture is quite
small, these banks held almost 88 percent of the commercial bank total of $2.6 billion in outstanding agricultural
production loans in the state in 1989.
6. Smith (1987) finds empirical evidence that banks in
restricted branching states are generally at greater risk of
closure than are banks in statewide-branching states.
However, the link between branching laws and diversification is not strongly drawn.
7. The model presented in this section is very similar to
the model of bank loan portfolio choice presented in
Gruben, Neuberger and Schmidt (1990).
8. An alternative would be to have the level of monitoring
be a decision variable for the bank, with increases in
monitoring imposing costs, but also yielding benefits in
the form of decreased project return variances. Such a
treatment is beyond the scope of this paper.
9. It is important to note that the returns under discussion
here are the returns to project owners, as opposed to
returns to the bank. Projects may yield returns to their
owners that exceed the contract loan rate, but the most
the bank can receive, net of costs, is the contract loan
rate.
10. For example, managers may face some sort of reputational penalty should their bank fail.
Federal Reserve Bank of San Francisco
11. It may be argued that although diversification theory
applies to investors, it does not apply to individual firms,
such as banks. According to this view, bank equity holders are the decision-making agents in the bank, and their
objective is to have the bank make loan allocations that
yield the maximum risk-adjusted expected return on their
entire portfolio. Because these investors can be expected
to hold more than the stock of the one bank in their
portfolios, the argument goes, their objectives will not
necessarily be consistent with having the bank maximize
the risk-adjusted return on the bank portfolio in isolation.
For this reason, those who model bank behavior sometimes assume that the bank should properly have a riskneutral objective function, and thus should maximize
expected return without any concern for risk. It is assumed
that if investors are risk-averse, they can adequately
hedge any risk in one bank's stock returns with investments in other firms.
However, several arguments have been made explaining why risk may indeed enter into the bank's asset choice
decision. For example, if the bank would face bankruptcy
costs should it turn out that its net worth is negative, then
an increase in the variance of the bank's portfolio will
actually lower its expected return. In this case, diversification within the bank's portfolio again becomes important.
(See Santomero (1984), for a more detailed discussion of
this issue.) In this paper, we will assume that this sort of
mechanism is at work.
12. Even if loan returns are not negatively correlated,
diversification can often reduce portfolio risk. As long as
the returns on new and existing loans are not perfectly
positively correlated, then, given the distribution of the
returns on new loans, and their covariance with the return
on the existing portfolio, there exists a set of non-zero
weights to attach to new and existing loans such that the
variance of a combined portfolio is less than the variance
of the existing portfolio.
13. It must be emphasized, that, under different assumptions for relative interest rates, monitoring costs, and
covariances, portfolio variance would not necessarily be
minimized by devoting exactly one-half of the portfolio to
agriculture.
14. A bank may have centralized credit policies or credit
approval processes that make the location of the bank
headquarters important.
35
15. The solution depends on the relative curvature of the
two individual profit functions. A major factor causing the
functions to be concave is the importance of bankruptcy
costs, 13. As 13 increases, the functions become more
concave, making it more likely that diversification will
take place.
16. The other type of agricultural loan is an agricultural loan secured by real estate, which typically has a
much longer maturity than an agricultural production loan
(about 15 years versus about one year). We focus on
agricultural production loans because they are more comparable, in maturity, with the commercial loans that we
envision as the alternative asset. In addition, commercial
banks are more involved in agricultural production lending than in agricultural real estate lending, as measured
by market share. Over the years 1981-1986 (the years
which we study), an average of 9.35 percent of total
agricultural real estate loans were held by commercial
banks. The corresponding figure for agricultural production loans was 41.7 percent. (Source: Sullivan, 1990.)
17. Gilbert and Belongia's explanatory variables are limited to variables related to bank holding company size
and the length of time that a bank has been affiliated with a
bank holding company.
18. Implicitly, we are equating rural areas with agricultural
areas. Ideally, we would use county-level information on,
for example, agriculture's share of total personal income,
to refine our definition of an agricultural area. However, we
do not have such information for every county in our study.
On the surface, an alternative may be to use the entire
state's share of agriculture in gross state product to
measure the degree to which rural areas in the state are in
fact engaged in agriculture. However, this is not likely to
be a good indicator of agricultural activity in rural areas.
This is because a state is likely to have a low agricultural
gross state product share not because its rural areas are
not engaged in agriculture, but because the contribution
of industry to the state's economy is more important than
the contribution of agriculture. California, with approximately a 2 percent share of agriculture in gross state
product, is an example of such a state.
19. This notion was not incorporated into the theoretical
model. There, a change in the proportion of funds devoted
to agricultural loans, holding loan size constant, did not
affect monitoring costs per dollar for agricultural loans.
Likewise, a change in average agricultural loan size,
holding the total proportion of the portfolio devoted to
agriculture constant, did not affect monitoring costs per
dollar for agricultural loans. Allowing for such effects in the
theoretical model would have unnecessarily complicated
the model, given that the main focus is on the relationship
between monitoring costs and location.
20. For example, figures for loans made during one week
in August in each of the years 1981 to 1986 reveal the
following: Averaged over all six years, for the 48 large
banks surveyed, the average size of short-term commercial and industrial loans was $1.433 million, the average
size of long-term commercial and industrial loans was
36
$1.093 million, and the average size of farm loans was
$73,000. For small and medium sized banks, the corresponding numbers were $68,000, $62,000 and $12,000.
Moreover, the pattern was consistent over all six years.
(Source: Survey of Terms of Bank Lending.)
21. Source: Informal survey conducted by Federal Reserve Bank of San Francisco of six major commercial bank
agricultural lenders in the Twelfth Federal Reserve District, March 1990.
22. Nationally chartered banks may lend no more than
the value of 10 percent of their capital to anyone borrower.
23. In addition, the FCS has a requirement that borrowers
purchase stock in the organization. (See Box.) Under
certain circumstances this too can be detrimental to its
competitiveness. If farmers fear substantial losses on any
FCSbank stock, they may "run" on the bank, rushing to
payoff loans and redeem their stock at full price. This
effort is most feasible for the financially strongest borrowers, so any exodus would leave behind the most
troubled borrowers, exacerbating bank losses. Commercial banks do not face the possibility of runs by their
borrowers, and deposit insurance protects them from
runs by their depositors. Also, until recently the FCS has
followed the practice of setting its loan rate based on
its historical average cost of funds. This meant that, in
periods of falling interest rates, the FCS was less competitive with commercial banks, who are more apt to price on a
marginal cost basis.
24. According to the theory of financial intermediation
outlined in the introduction to this paper, banks provide
credit to borrowers who are unable to obtain funds by
issuing their own debt. A bank is willing to lend to such a
borrower because it has special credit evaluation and
monitoring capabilities that are specific to that borrower.
A relatively low short-term interest rate elasticity of demand is consistent with this theory; a borrower could
expect that although another lender may offer a lower
interest rate, other terms of the contract may be less
favorable due to the new lender being less familiar with the
borrower. For example, a borrower may rationally have
loyalty to his lender born of experience that shows that the
lender "stands by" the borrower in difficult times. A lender
that has not had a long-term relationship with the borrower
would not be expected to be as accommodating. Agricultural lending relationships seem to be particularly stable;
an official of one commercial bank involved in agricultural
lending stated that in order to win over a customer from
another lender you often have to call on the customer for
three or four years.
25. We assume that investing in the higher interest rate
projects also adds to portfolio risk and/or raises the probability of project defaults. If it did not, then the bank would
already have invested its entire portfolio in the projects
with the highest interest rates, and changing the cost
assigned to bankruptcy would not affect its portfolio.
26. Another variable that may affect the cost of risk to the
bank is the capital-to-asset ratio. We included this variable
in some versions of our regression, but this did not signifi-
Economic Review / Winter 1991
cantly affect the results we report here. Therefore, we
report only the version of the regression that excludes the
capital-to-asset ratio.
27. For example, for farm loans made by large banks
during the week of August 4, 1986, weighted average
interest rates for six size classes decreased monotonically from 10.57 percent for $1 ,000 to $3,000 loans to 8.94
percent for loans of at least $250,000. (Source: Survey of
Terms of Bank Lending, August 4-8, 1986.)
28. By restricting our sample to defined "agricultural
lenders,": we may be introducing selectivity bias into
our regression estimation. However, we believe that our
model is more applicable to banks that do some agriculturallending than it is to banks that do none at all, and
that the determination of whether a bank does agricultural
lending can be separated from the determination of how
much agricultural lending it does.
29. These banks account for about one-third of total
commercial bank agricultural lending nationwide.
30. It may be noted that agricultural loan market conditions experienced a severe downturn during our sample
Federal Reserve Bank of San Francisco
period due to a significant decrease in the trend of
expected earnings and a consequent plunge in the value
of farmland. (See Melichar (1986) and Melichar (1987)
for discussions of this period of financial stress in agriculture.) However, we do not believe that this biases
our results.
31 .• Metropolitan Statistical Area is a designation assigned to counties or areas of contiguous counties by the
Census Bureau.
32. Because some banks appear more than once in our
dataset, we do not have completely independent observations.However, because bank size and the deposit-toloan ratio should be fairly constant over time for each
bank, the inclusion of these variables in the regression
should help to control for firm-specific effects.
33. The difference between the coeffients on the restricted branching, urban location variable and the statewide branching, urban location variable is statistically
significant.
37
REFERENCES
Board of Governors of the Federal Reserve System.
Annual Statistical Digest, Washington, D.C., various
years.
_ _ _ _ . Survey of Terms of Bank Lending (FR2028b),
Washington, D.C.,various years. (Both are published
in Statistical Release E.2.)
Black, Fischer. "Bank Funds Management in an Efficient
Market," Jouma/of Financial Economics, volume 2
(1975), pp.323-339.
Blank,Dennis."NewCompetitorsin Farm Loans," The
New York Times, April 24, 1990.
Calomiris, Charles W., R. Glenn Hubbard and James H.
Stock. "The Farm Debt Crisis and Public Policy,"
Brookings Papers on Economic Activity, volume 2
(1986), pp. 441-485.
The Conference of State Bank Supervisors. A Profile of
State-Chartered Banking, Washington, D.C., 1983,
1984 and 1986.
Diamond, Douglas W. "Financial Intermediation and Delegated Monitoring," Review of Economic Studies, volume 51 (1984), pp. 393-414.
Fama, Eugene F. "What's Different About Banks?,"
Journal of Monetary Economics, volume 15 (1985),
pp.29-39.
Federal Financial Institutions Examination Council. Reports of Condition and Income by All Insured Banks
(FFIEC 031-034), Washington, D.C., various years.
Gilbert, R. Alton and Michael T. Belongia. "The Effects of
Affiliation with Large Bank Holding Companies on
Commercial Bank Lending to Agriculture," American
Journal of Agricultural Economics, February 1988.
Gray, Gary, J. Randall Woolridge and Steven Ferrara.
"Competition in Agricultural Lending: Some Recent
Developments," The Journal of Commercial Bank
Lending, August 1982.
Gruben, William C., Jonathan A. Neuberger and Ronald H.
Schmidt. "Imperfect Information and the Community
Reinvestment Act," Economic Review, Federal Reserve Bank of San Francisco, Summer 1990.
James, Christopher. "Some Evidence on the Uniqueness
of Bank Loans," Journal of Financial Economics, volume 19 (1987), pp.217-235.
38
Keeley, Michae] C. and Gary C. Zimmerman. "Determining. Geographic. Markets for Deposit Competition in
Banking," Economic Review,Federal Reserve Bank
of San Francisco, Summer 1985.
Leland, Hayne E.and David H. Pyle. "Informational Asymmetries, Financial Structure, and Financiallntermediation," The Journal of Finance, May 1977.
Melichar, Emmanuel. "Agricultural Sanks Under Stress,"
Federal Reserve Bulletin, Board of Governors of the
Federal Reserve System,.July1986.
_ _ _ _ . "Turning the Corner on Troubled Farm
Federal. Reserve Bulletin, Board of Governors of the
Federal HeserveSystem, JUly 1987.
Neuberger, Jonathan A. and Gary C. Zimmerman. "Bank
Pricing of Retail Deposit Accounts and 'The California
Rate Mystery'.' Economic Review, Federal Reserve
Bank of San Francisco, Spring 1990.
Santomero, Anthony M. "Modeling the Banking Firm: A
Survey," Journal of Money, Credit and Banking, November 1984, Part 2.
Smith, Hilary H. "Agricultural Lending: Bank Closures and
Branch Banking," Economic Review, Federal Reserve
Bank of Dallas, September 1987.
Sullivan, Gene D. "Changes in the Agricultural Credit
Delivery System," Economic Review, Federal Reserve
Bank of Atlanta, January/February 1990.
Todd, Richard M. "Taking Stock of the Farm Credit System: Riskier for Farm Borrowers," Quarterly Review,
Federal Reserve Bank of Minneapolis, Fall 1985.
United States Department of Agriculture, Economic Research Service. Agricultural Finance Statistics, Washington, D.C., various years.
_ _ _ _ . Agricultural Statistics, Washington, D.C., various years.
United States Department of Labor, Bureau of Economic
Analysis, gross state product data, Washington, D.C.,
various years.
White, EN "A Reinterpretation of the Banking Crisis of
1930," Journal of Economic History, March 1984.
Zimmerman, Gary C. "Agricultural Lending in the West,"
Weekly Letter, Federal Reserve Bank of San Francisco, December 22, 1989.
Economic Review / Winter 1991
Explaining the U.S. Export Boom
Economist, Federal Reserve Bank of San Francisco. The
author thanks, without implicating, members of the editorial committee, Chan Huh, Bharat Trehan and Liz Laderman, for helpful comments. The research assistance of
Judy Horowitz is gratefully acknowledged.
This paper assesses the performance of us. exports in
the later part of the 1980s and finds that it cannot be fully
explained by key variables that are generally believed
to determine the demand for us. exports: the nominal
trade-weighted dollar, relative inflation, and foreign GNP
growth.
The unexpectedly robust performance of us. exports
partly reflects improvements in the competitiveness of us.
exporters that are not captured by the trends in inflation in
the us. and abroad. 1n particular, US. export price
increases in the 1980s fell below the rate ofinflation in the
US., apparently as a result of a change in the pricing
behavior of us. exporters.
Federal Reserve Bank of San Francisco
For much of the 1980s, there was widespread pessimism
about the outlook for US. exports. US. exports declined
over the period 1980-85. Given the widespread perception
of lagging productivity growth and a lack of competitiveness in U.S. manufacturing,' dramatic improvements in
US. export performance were not expected.
As a result, the robust performance of U.S. exports at
the end of the last decade surprised a number of observers.
Real US. exports of goods and services grew at a compound annual rate of 12.5 percent between 1985-89, well
above the 8.1 percent average growth of the 1970s. Furthermore, the growth in exports was not confined to the period
of dollar depreciation between 1985 and 1987. Exports
grew nearly 14 percent in 1988, and 10 percent in 1989,
even as the dollar appreciated between early 1988 and the
third quarter of 1989.
This paper assesses the performance of U.S. exports up
to 1989 and finds that it cannot be fully explained by key
variables that are generally believed to determine the
demand for US. exports: the nominal trade-weighted
dollar, relative inflation, and foreign GNP growth. Three
possible explanations for the tendency to underpredict
exports are examined. First, exports of services may have
grown unusually fast in relation to exports of goods.
Second, recent efforts by Japan, Taiwan and South Korea
to increase access to their markets have contributed to an
increase in US. exports to these economies that is not
captured by the standard determinants of export demand.
Third, there has been a tendency to understate the competitiveness of US. exporters, because of changes in their
pricing behavior.
The paper is organized as follows. Section I reviews the
determinants of US. export demand and assesses their
ability to predict exports in recent years. Section II evaluates the role of services exports in explaining the behavior
of total exports. Section III examines whether recent
efforts by rapidly growing Asian economies to liberalize
imports may have contributed to the inability to explain the
growth in US. exports. Section IV discusses the possibility that pricing behavior in the US. export sector may have
changed, and Section V examines the implications of the
pattern of export pricing for US. competitiveness and
the ability to predict exports. Section VI offers some
conclusions.
39
I. The Determinants of U.S. Exports
Two main factors are generally believed to determine
the change in the demand for U.S. exports: the competitiveness of U.S. exporters, which is influenced by the
U.S. dollar and relative inflation rates, and the overall
demand for goods abroad, which is influenced by the GNP
growth of major U.S. trading partners. Table 1 shows the
behavior of these determinants of export demand in the
1970s and 1980s.
Table 1 suggests that in the first half of the 1980s, U.S.
export growth was limited by the sharp appreciation of the
dollar and a slowdown in foreign GNP growth in comparison to the 1970s. These trends were largely reversed in
the second half of the 1980s. In particular, the growth of
U.S. exports in recent years appears to be partly the result
of the lagged effects of the depreciation of the dollar
between 1985 and 1987 and of an acceleration in the
growth of U.S. trading partners since 1985. It may also be
noted that in the first half of the 1980s, U.S. inflation
remained on average below foreign inflation, contributing
to U.S. export competitiveness. In contrast, an acceleration in U.S. inflation above inflation abroad adversely
affected the competitiveness of U.S. exporters in the
second half of the 1980s.
While Table 1 highlights some of the factors that may
have contributed to recent U.S. export performance, it
cannot tell us whether these factors fully account for
recent export growth. To shed some light on this question,
the demand for U.S. exports of goods and services was
modeled as a function of the exchange rate-adjusted ratio
of U.S. to foreign prices, or the real exchange rate (as a
proxy for U.S. competitiveness) and to foreign GNP (as a
proxy for foreign demand). This model of export volume
was expressed in log first-difference form, with the (one
quarter) lagged levels of the explanatory variables and the
respective dependent variables on the right-hand side of
each equation. This representation, also known as an
"error-correction" specification, is shown in equation (1):
m
JiXGS
+
NXR
Pus
TWFCPl
FGNP
Nominal trade-weighted dollar
U.S. fixed-weight GNP price index
= trade-weighted CPI of 10-major industrial
countries
= trade-weighted GNP of 10 major industrial
countries
=
=
The error-correction specification used in equation (1)
has three desirable features: (1) it avoids the possibility of
spurious correlation among strongly trended variables; (2)
long-run relationships which may be lost by expressing the
data in differences are captured by including the lagged
levels of the variables on the right hand side; and (3) the
specification can distinguish between short-run (first differences) and long-run (lagged levels) effects.
To test the ability of competitiveness and demand factors to explain recent export behavior, equation (1) was
estimated from 1972:4 through 1987:4 and an out-ofsample simulation was performed for the period 1988:1 to
1989:4. The sample was broken in 1987:4 because the
dollar reached its most recent trough in that quarter.? The
coefficients and summary statistics from the estimation of
equation (1) are reported in a later section. We focus on the
m
= ex + 1=0
.L 13i MXR 1 t-i + 1=0
.L "Ii MGNP t _ i
113 XGS t _
(1)
1
where
XGS
=
real exports of goods and services, NIPA
RXR1
=
NXR xp
basis
real exchange rate = TWFC;/
40
Economic Review I Winter 1991
Chart 1
Billions U.S. Real Exports of Goods and Services
1982 Dollars
Actual vs. Predicted
635
620
Actual
605
590
575
560
"..---'
8801 88Q2 88Q3 88Q4 89Q1 89Q2 89Q3 89Q4
results of the simulation here. Chart 1, which compares the
path of actual and predicted exports, shows that the export
equation did not fully anticipate the robust performance of
the U.S. export sector in 1988 and 1989. Over that period,
there was a systematic and growing underprediction of the
level of real exports of goods and services, so that by
1989, the out-of-sample forecast was outside the 95 percent confidence range. Thus, factors other than changes in
the dollar, relative inflation, and growth abroad appear
to have contributed to export growth in the latter part of
the 1980s. 3
Three explanations may be offered for the tendency of
equation (1) to underpredict exports of goods and services
over that period. First, exports of services, which are
included in the left-hand-side of equation (1), may have
grown faster than expected in response to variables (such
as rising interest rates abroad) other than the real exchange
rate and foreign GNP.
Second, recent efforts by Japan, Taiwan and South
Korea to increase access to their markets have contributed
to an increase in U.S. exports to these economies. As a
result, the coefficients on foreign GNP in equation (1) may
be unstable.
Third, the improved competitiveness of U.S. exporters
may not have been fully reflected in movements in the
dollar or in U.S. inflation, which are the basis for the
competitiveness measure used in equation (1).
Exports of Services, Not ljoomu
A possible explanation for the underprediction of exports towards the end of the 1980s is that in Section I, a
single export equation is used to forecast exports of both
goods and services. Since factor incomes or services may
respond to variables other than the real exchange rate and
foreign GNP (notably foreign interest rates), their behavior
may account for the underprediction of total exports. The
plausibility of this hypothesis may be examined in two
ways. First, if exports of services explain the underprediction observed in Chart 1 they must have grown unusually
fast in comparison to merchandise exports. Second, if
exports of services contributed to the underprediction of
total exports, the out-of-sample forecast of exports should
improve when services are excluded.
To check the first possibility, Table 2 compares the
growth in the components of real exports of goods and
services. Table 2 shows that U.S. merchandise exports
grew faster than U.S. exports of services in the 1980s,
Federal Reserve Bank of San Francisco
reversing the pattern of the 1970s, when exports of services
grew faster. (As a result, the real share of U.S. merchandise exports in total exports, which had fallen from nearly
68 percent in 1970 to 62 percent in 1980, rose to nearly 66
41
Chart 2
Errors in Predicting US Exports
Percent
(Actuet-Predlctedj/gctual
16
Exports of Goods and Services
Less Factor Income
12
\
8
"Exports of Goods
4
0-+----,---,-----,----.-----..,.----.---.,..--....,
8801 8802 8803 8804 8901 8902 8903 8904
percent in 1989.) Thus, Table 2 does not support the
hypothesis that unusually rapid growth in services accounts for the remarkable growth in exports at the end of
the 1980s. 4
To check the second possibility, equation (1) was reestimated respectively using (i) exports of goods and
services net of factor income and (ii) exports of goods as
the dependent variable. Inspection of the errors, illustrated
in Chart 2, indicates that the systematic and rising tendency to underpredict exports still occurs when factor
incomes or services are excluded. Thus, the underprediction of exports does not appear to be the result of any
unusual pattern in exports of services. In the discussion
that follows, we will therefore continue to focus on total
exports of goods and services.
III. Growing Access to Foreign Markets
In the 1980s, a number of highly successful Asian
economies sought to liberalize their commercial policies
and improve access to their domestic markets. The cases of
Japan, Taiwan and South Korea have drawn particular
attention, as all three economies experienced large trade
surpluses over extended periods in the 1980s. In the case of
Japan, where tariffs are low, and formal nontariff barriers
are quite limited, efforts have focused on eliminating
impediments to agricultural imports (for example, by
eliminating prohibitions on beef and citrus imports), and
lifting so-called "intangible" barriers to trade that have
tended to discourage imports. In the cases of Taiwan and
South Korea, steps have been taken to eliminate nontariff
barriers or to replace them by tariff barriers (thus enhancing the transparency of protection, which facilitates trade),
and also to lower tariff barriers.
For example, South Korea increased the percentage of
goods approved for import licenses from 64 percent in 1978
to 95 percent by the late 1980s. It also adopted a plan to
reduce average tariff rates progressively. Tariffs have fallen
from an average of nearly 24 percent in 1983 to 19 percent
in 1987 and to under 13 percent in 1989. Assuming no
reversals, they are projected to fall to 7 percent by 1993.
42
Taiwan's trade liberalization efforts have been even more
extensive. In early 1989, 98 percent of the products could
be freely imported. Average tariff rates, which had remained at around 31 percent from 1980 to 1984 fell to
around 20 percent in 1987 and to 6.3 percent in 1989. Tariff
rates are to fall to 3.5 percent by 1993.
Economic Review / Winter 1991
These efforts by major Asian economies to improve
access to their markets appear to have benefited U.S.
exporters. U. S. nominal exports to Japan, South Korea and
Taiwan grew at an unprecedented rate in the later part of
the 1980s. As shown in Table 3, annual U.S. export growth
between 1987 and 1989 respectively averaged 26 percent to
Japan, 53 percent to Taiwan and 29 percent to Korea. This
is well above historical averages. While real bilateral
export data are not available, it is likely that real export
growth follows a similar pattern. The rapid growth of U.S.
exports to these economies implies that they accounted for
a significant proportion of total U.S. export growth in the
of the 1980s.
later
If the acceleration of U.S. exports to Japan, Taiwan and
South Korea is due to their efforts to improve access to
their economies, the explanatory power of equation may
be adversely affected. In particular, it may be argued that
greater openness in these markets will tend to increase the
responsiveness of the demand for U.S. exports to foreign
GNP. To investigate this possibility, equation (1) was
estimated over the period 1972-1989, with slope dummies
for the foreign GNP variable beginning in 1988:1. The
results are summarized in Table 4. It is apparent that there
IV.
Change
Exporter Pricing Behavior?
Another possible explanation for the tendency to underpredict exports at the end of the 1980s is that the improvements in the competitiveness of U.S. exporters may not be
fully reflected in the measure of competitiveness used in
equation (1). The competitiveness of U.S. exporters may
be measured in two different ways. One approach is to take
the exchange rate-adjusted ratio of a domestic U.S. price
(such as the U.S. fixed-weight GNP price) and tradeweighted
prices (such as foreign CPIs).5 In this
case we obtain the measure of U.S. competitiveness, or the
real exchange rate, used in estimating equation (1):
RXRl =
NXRXP us
TWFCPl
(2)
where an increase in RXR j corresponds to a real appreciation, or a decline in external competitiveness. An
alternative approach is to construct an exchange rateadjusted index of the price of U.S. exports relative to tradeweighted foreign prices, that is:
NXRxPX
TWFCPl
(3)
where PX is the (fixed-weight) export deflator.
Although RXR 2 is a more direct measure of the competitiveness of U.S. exporters, RXR j , which is based on a
domestic U.S. price, is often used as a proxy for U.S.
Federal Reserve Bank of San Francisco
has been no statistically significant change in the response
of exports to foreign GNP. Thus, a larger marginal propensity to import abroad does not explain the underprediction
of U.S. exports over the past two years.
competitiveness for a number of reasons. First,
reflects the overall competitiveness of all goods produced
in the U.S. rather than of the goods that are currently
produced in the export sector. A broad measure of U. s.
competitiveness, such as RXR j , accounts for the
bility that if domestic prices are sufficiently competitive, certain U. S. producers may begin
for the
U.S. export sector even if they do not do so currently.
RXR 2 , which is based on the export
of current
exporters, does not explicitly take this possibility into
account. Second, RXR j reflects the plausible view that in
the long run, the competitiveness of U.S. exporters will
largely be determined by domestic costs of production, as
represented
a
U.S.
the use of
RXR j is consistent with the traditional conventional wisdom regarding the market conditions that face U.S. exporters." According to this view, substitutes for
in world markets historically were not readily available and
exports had a limited impact on total profitability. As a
result, U.S. exporters were relatively less concerned about
their external competitiveness, and export prices were set
primarily on the basis of domestic costs of production,
rather than on conditions prevailing in export markets. In
this environment, there would be a stable relationship
between the U.S. export price (used in RXR 2 ) and the
fixed-weight GNP price (used in RXR j ) , and the two
43
measures RXRj and RXR2 would give the same overall
picture of competitiveness. The GNP price in RXRj can
then be interpreted as a proxy for the export price that is
used directly in RXR2 .
However, RXRj will give a misleading picture of the
competitiveness of US. exporters if the relationship between the export price and the fixed-weight GNP price is
not stable because of a change in the pricing behavior of
exporters.
To assess whether the relationship appears to be stable,
Chart 3 shows the ratio of these two prices between 1970
and 1989. I call this ratio the relative export price. As can
be seen from equations (2) and (3) the relative export price
is equivalent to dividing RXR2 by RXRj , and thus indicates
whether the two measures of competitiveness behave in a
in the ratio is flat,
and RXR2
similar way. If
give the same measure of competitiveness. If the ratio
declines, exporters are more competitive than suggested
by RXRj ; the reverse is true if the ratio rises. As a
reference, the chart also shows the path of the nominal
trade-weighted dollar.
The interpretation of Chart 3 is facilitated if we think of
the relative export price as an indicator of the aggregate
profit margin of the export sector. 7 The chart suggests that
there was no trend in export profit margins in the 1970s, as
there was little net change in the relative export price
between the early and late 1970s. In contrast, a pronounced
decline in the relative export price occurred between 1980
and 1985, and was not reversed subsequently. 8
The decline in the relative export price in the early 1980s
may have been partly the result of a contraction in world
economic activity that reduced demand for US. exports
and thus prompted a (cyclical) reduction in US. export
prices. An alternative explanation, which we focus on
here, is that US. producers may have been attempting to
price more competitively in US. export markets. This
explanation is suggested by the fact that a lower relative
export price persisted after world economic activity recovered in 1983 and particularly after the dollar depreciation between 1985 and 1987 sharply reduced the foreign
currency price of US. exports.
Such a change in pricing behavior would be consistent
with growing competitive pressures caused by the entry of
producers from Japan, and later the newly industrializing
Asian economies in world markets previously dominated
by US. producers, such as capital goods and electronics,
beginning in the 1970s. These pressures probably
sified in the 1980s because the sharp appreciation of the
dollar (see Chart 3) in the first half of the 1980s increased
the price of U.S. products in foreign currencies, paving the
way for further entry by Asian producers in US. and world
markets. Furthermore, the debt crisis that began in 1982
led to stagnation in traditional US. exports of manufactures to Latin America, which required US. producers to
seek out new markets.
Testing for Stability in Export Pricing
The discussion in the preceding section raises the question of whether the decline in the US. relative export price
can be detected as a change in exporter pricing behavior.
We may attempt to test more formally for such a change
and attempt to identify the sources of any such change at
the aggregate level, by using a model of export pricing.
Following the literature on this subject, assume that in
setting the prices of traded goods, suppliers add a markup
over their costs of production. The markup is in turn a
function of competing goods prices, which are influenced
by the exchange rate and foreign prices. The export price
Chart 3
Relative Export Price
Index
1982::100
140
Trade-Weighted
Exchange Rate --....
130
,
120
Relative Export Price
110
100
90
80
70 -+-r,.,........,.,.........,.,..........,..........,.,....,,.,..,..,..,,.,..,..,..,.,..,.......,..,......,..,....,.,..,......,............,...................,,.,.....,.,...................,..,..,..,.,
72
44
74
76
78
80
82
84
86
88
90
Economic Review ! Winter 1991
can then be expressed as a function of the domestic GNP
price (to represent domestic costs of production), and the
exchange rate-adjusted foreign price (to represent foreign
competition). In error-correction form, this relationship
may be expressed as follows:
m
D.PXt
Ko
m
TWFCPI
+ i~of.LiD.PUSt-i + i~O viLl [ NXR
]t-i
m
+ j='k1 ~.'1
(4)
In the long run the export price will tend to rise in
response to an increase in the domestic GNP price, which
raises the costs of production. The export price will also
tend to rise in response to an increase in the foreign price or
a dollar depreciation, to the extent that U.S. producers
respond to export market conditions in setting the export
price.? The long-run coefficients in equation (4) (based on
'T I' 'T2) are thus expected to be positive. The signs on the
short-run coefficients (f.Li' Vi' ~j) depend on the precise
pattern of adjustment.
To verify whether the response of export prices to its
determinants has changed, equation (4) was estimated
between 1972:1 and 1985:1, and the equation was simulated out-of-sample from 1985:2 to 1989:4. The sample
was broken in 1985:1, when the U.S. dollar peaked,
because in the period that followed, cyclical and exchange
rate factors would tend to put upward pressure on U.S.
export prices. A moderate export price response to these
upward pressures, as indicated by a systematic tendency to
overpredict export prices after 1985:1, would thus suggest
more competitive pricing behavior on the part of U.S.
exporters.
The results of the regression are reported in column I of
Table 5. As can be seen, equation (4) produces a satisfactory fit and the hypothesis that there is no serial correlation cannot be rejected. In line with conventional wisdom,
the
results suggest that U.S. exporters priced
mainly on the basis of domestic costs of production, and
ignored the exchange rate-adjusted foreign price up to the
first half of the 1980s.
Chart 4 illustrates the results of the simulation from
1985:2 to 1989:4. As can be seen, there was a tendency to
overpredict the export price in the second half of the 1980s,
which supports the view that exporters were pricing more
competitively.
To identify the sources of this apparent change in pricing
behavior, equation (4) was re-estimated over the period
1974:4-1989:4. Several regressions were then performed,
Federal Reserve Bank of San Francisco
45
the long-run response to U. S.
and to the
of the dependent variable may have changed. However,
was difficult to isolate the precise nature of the \A1<U1i:S"".
The slope dummy coefficients on the
and changes
of the U. S. GNP
and the exchange rate-adjusted
foreign price were not
1 and 2
above). The results of the
are
not
because
are very similar to the results
shown in column II.
However, there is some weak evidence that the rate at
which exporters adjust their
in response to
deviations from
the desired
of
may have
As shown in column
III of Table 5 the coefficient on the slope
variable
for the lagged level of the
variable
3
is
at the 10 percent
To sum up, the U. S. export
fell in
to
the GNP price as the dollar appreciated in the
1980s.
This relative decline persisted even after the dollar appreciation was fully reversed over the period 1985-1987.
decline in the relative export
appears to reflect a
change in the pricing behavior of exporters, but the nr,,',,"'"
nature of the change was not easy to identify or interpret.
Further research is required to clarify the process govern. In "",rri,.,., I ",.
ing the pricing behavior
U. S.
studies of export pricing at the
level may be
necessary, as recent research of the U. S.
suggests that aggregation problems may
model aggregate pricing
12
with slope dummy variables for the period 1985:2-1989:4
on the following variables:
1) the first differences of the domestic U.S. price, the
exchange-rate adjusted foreign price, and the lagged
dependent variable;
2) the lagged levels of the domestic U.S. price and the
exchange-rate adjusted foreign price, with and without
a slope dummy on the lagged dependent variable; and
3) the lagged level of the dependent variable only.
A negative slope dummy coefficient on the domestic
U. S. price variable would suggest that exporters were
adjusting their export prices by less in response to changes
in their costs of production, which would be consistent
with growing competitive pressures. 10
A positive slope dummy coefficient on the foreign
variable would suggest that U. S. exporters were responding to external competitive pressures after 1985, whereas
they had not done so in the past. 11
A change in the response to the lagged dependent
variable is more difficult to interpret. However, a negative
coefficient on the slope dummy indicates that the increase
in the U.S. export price associated with its past value has
fallen, which is consistent with more moderation in the
pricing behavior of U. S. exporters or a change in the
desired level of U. S. export prices.
Column II in Table 5 reports the results of the regression
over the period 1972:4-1989:4 without any slope dummy
variables. A comparison of columns I and II suggests that
Chart 4
Index
vs. P
Export
1982=100
130
~
125
120
95% Confidence
Interval
115
\
110
105
100
46
1985
1986
1987
1988
1989
Economic Review / Winter 1991
Chart 5
US and Fnrj:!linn
Index
1980=100
1
1
70
72
74
76
18
80
82
84
86
88
90
'Expressed in foreign currency by multiplying with the Federal Reserve Board's
nominal trade-weighted index of the dollar.
Competitiveness and Export Performance
The preceding discussion suggests that a measure of
U.S.
based on the export price may give a
markedly different picture of U.S. competitiveness than
does a measure based on the U. S. GNP
This can be
seen in Chart 5, which compares the respective paths of the
U.S. GNP
and the U.S. export price, both in foreign
currency, to the trade-weighted foreign Cl'I over the period
Note that fluctuations in the U.S. GNP price
and U.S.
price now reflect changes in the dollar
exchange rate.
The U.S. GNP
measure suggests that after adjustU.S. inflation on average exceeded
for exchange
foreign inttauon, so that U.S. exporters were still relatively
uncompetitive at the end of the 1980s. In contrast, the
measure suggests that U.S. exporters at
end of the 1980s were better positioned to face foreign
competition than they had been at any other time during the
preceding twenty years.
After rising in the first half of the 1980s, the U. S. export
price in foreign currency fell sharply in 1985. Even though
the drop in the U.S. export price was reversed starting in
1988, the level of the export price was still below the level
of the trade-weighted foreign CPI in 1989. Of course,
comparisons of indices can be sensitive to the choice of
base period (Chart 5 uses 1980 as the base year), but the
conclusion that U. S. exporters are still competitive relative
to their trading partners is fairly robust; any base year
between 1970 and 1985 yields the same conclusion. 13
Chart 5 suggests that the post-1985 increase in U.S.
inflation relative to inflation abroad (recall Table 1) was not
U
1982 Dollars
t
95% Confidence
Interval
8801 8802 8803 8804 8901 8902 8903 8904
Federal Reserve Bank of San Francisco
47
fully reflected in export prices. As a result, the measure of
competitiveness based on the U.S. GNP price (RXR1) used
in equation (1) tends to understate U.S. competitiveness,
while RXR2 may give a more realistic picture of U.S.
competitiveness in the 1980s. Changes in U.S. competitiveness not captured by RXR1 may thus explain the
tendency for equation (1) to underpredict U.S. exports.
To verify this last hypothesis, equation (1) was estimated
over the period 1972:4-1987:4, replacing RXR1 by RXR2 .
An out-of-sample dynamic simulation was then performed
for the period 1988:1-1989:4.
Table 6 compares the results of the regressions using
RXR1 and RXR2 while Table 7 compares the out-of-sample
forecasting performance over the period 1988:1-1989:4.
Taken together the tables show that the in-sample performance of either measure of competitiveness over the period
1972:4-1987:4 is roughly comparable. However, when
RXR2 is used, the mean square error of the out-of-sample
forecast in the last two years of the 1980s falls by 60 percent
in comparison to the forecast using RXRI: Furthermore,
Chart 6 shows that using RXR2 eliminates the systematic
underprediction of U.S. exports after 1988, and that the
path of actual exports now tends to remain within the 95
percent confidence band of the forecast. 14
The ability of RXR2 to improve the forecast of exports,
in comparison to the forecast based on RXRl ' suggests that
the rapid growth in exports towards the end of the 1980s
was partly the result of changes in the competitiveness
of U.S. exporters. This change in competitiveness was in
tum apparently attributable to changes in their pricing
behavior. 15
48
Economic Review I Winter 1991
VI. Conclusion
The rapid growth of U.S. exports of goods and services
in 1988 and 1989 is not fully explained by a standard model
of export demand that accounts for trends in the dollar,
relative inflation rates in the U.S. and abroad and robust
growth among U.S. trading partners. U.S. exports grew
rapidly in 1988 and 1989 in spite of an appreciating dollar
and an increase in U.S. inflation in comparison to inflation
abroad.
The unexpectedly robust performance of U.S. exports
partly reflects improvements in the competitiveness of
U.S.
are not captured by the
in
intlation in the U.S. and abroad. In the 1980s, U.S. export
prices increased by less than inflation in the U.S. or (after
adjusting for exchange rates) in major foreign industrial
countries. Thus, the relative rate of U.S. inflation has
tended to understate the competitiveness of the U.S. export
sector. The empirical tests reported in this paper suggest
that the deviation between export price increases and U.S.
Federal Reserve Bank of San Francisco
inflation in the 1980s may in tum have been caused by a
change in pricing behavior on the part of U.S. exporters.
However, further research at the industry level is required
to confirm this hypothesis.
The findings of this paper underscore the fragility of the
boom in U.S. exports that began in the late 1980s. While
the relative slowdown in the rise of the U.S. export price
offset the adverse impact of rising U.S. inflation on U.S.
competitiveness, this offset cannot persist indefinitely.
Export price increases can remain below the U.S. rate of
inflation in the
run
if the
of the
export sector consistently exceeds productivity
the U.S. domestic sector. There appears to be no evidence
that this is occurring, and in the absence of further U.S.
dollar depreciation, continued gains in U.S. competitiveness will require a reduction in U.S. inflation below the
rate of inflation of its trading partners.
49
NOTES
1. For a recent discussion of the poor productivity performance of the U.S. manufacturing sector and the possible contribution of lagging innovation, see Baily and
Chakrabarti (1988). A more optimistic interpretation of
trends in U.S. productivity is offered by Baumol, Blackman
and Wolff (1989).
2. Furthermore, the discussion of Chart 5 later in the
text suggests that in contrast to previous episodes of dollar appreciation, U.S. exporters remained competitive in
comparison to foreign producers during the dollar appreciation of 1988-89, Out-of-sample simulations of equation
(1) for the period 1985:1-1989:4 also suggest that there
was no systematic tendency to underpredict until 1988,
These out-of-sample simulations were performed after the
break-point was selected.
3. A similar conclusion is reached when the simulations
are based on the export equation of the structural model of
the Federal Reserve Bank of San Francisco, which uses a
quadratic POL specification. See Throop (1989). A POL
specification is also used in the export equations of the
MPS model of the U.S. economy maintained by the Board
of Governors of the Federal Reserve System.
4. The nominal data convey a different impression. While
the growth of merchandise and services exports were
roughly the same in the 1970s, in the 1980s, the value of
services exports grew more rapidly than did the value
of merchandise exports. As a result, the nominal share of
services in U.S. exports grew from 35 percent in 1970 to
36 percent in 1980 and to 41 percent in 1989. This rising share reflects the more rapid rate of inflation in the
services sector.
5. This approach is followed in the FRBSF structural
model as well as the Federal Reserve Board's MPS model.
The latter model uses the nonfarm business fixed-weight
deflator net of indirect business taxes, in lieu of the export
price, in measuring the competitiveness of the U.S export
sector. See Brayton and Mauskopf (1985), Section VII. In
contrast, the Board's MCM model uses the export price in
measuring U.S. export competitiveness, as in equation
(3). See Helkie and Hooper (1988), Table 2-3.
6. These market conditions are discussed in Hooper and
Mann (1987). Another reason the use of RXR 1 is appealing
is that it eliminates the need to estimate an export price
separately
same is true on the
side). This can
be useful in forecasting, particularly since specifying a
stable price equation can be difficult.
7. For related measures see Hooper and Mann (1987)
and Moreno (1989b),
8. Note that there also seems to be a decline in the
relative export price if other price indices are used. See
Moreno (1989b), which compares the nonagricultural export price to the PPI. A comparison of export unit values
and the PPIyields a similar conclusion, although it may be
argued that this may reflect a shift in the composition of
exports towards high-productivity and low-price sectors,
such as computers.
50
9. For an analogous equation, see the export equation of
the Federal Reserve Board's Multicountry Model (MCM),
described in Helkie and Hooper (1988). However, Helkie
and Hooper use the non-agricultural export price on the
left-hand-side and a specially constructed price index to
represent domestic costs of production on the right-handside. Note that as in Helkie and Hooper, equation (4)
assumes that exporters respond in exactly the same way
to changes in the exchange rate that they do to changes in
the foreign price, on the assumption that the response to
changes in the exchange rate is motivated purely by the
effect it may have on competitiveness in foreign markets.
10. One possible interpretation of such a result is that
increases in productivity in the export sector have recently
exceeded increases in overall U.S, productivity, and that
exporters are passing on these gains to their customers,
An informal examination of some of the industry data
provides no clear indication of whether productivity gains
among exporters in the 1980s have in fact exceeded
productivity gains for U.S. producers as a whole. For
example, in the capital goods industry-one of the most
dynamic U.S. export sectors-labor productivity growth
in the 1980s in semiconductors, computers and nonelectrical machinery-exceeded the growth of labor productivity in manufacturing as a whole, On the other hand,
labor productivity growth was below average in a number
of historically important U.S. export sectors, such as construction machinery, ball bearings, machine tools and
pump and compressors. For a more detailed discussion,
see Orr (1989).
11. Using data at the four-digit SIC level, Hooper and
Mann (1987) found some indications that U,S. producers
tend to price more competitively relative to foreign producers in industries where exposure to export markets is
rising or where there is strong competition for market
share because close substitutes for U,S. products are
available abroad (for example, in semiconductors)
12. See Melick (1990). Melick has performed a battery of
econometric tests to characterize U,S. import pricing
behavior. His results highlight the difficulties that arise
when using aggregate data to model pricing behavior,
Econometric tests rejected the restrictions suggested by
three widely used models of import pricing behavior: (i)
perfect competition; (ii) Nash imperfect competition; (iii)
the mark-up model (as in Hooper and Mann (1989)).
Melick attributes this rejection to aggregation problems,
In particular, all three types of market structure may
be present at the aggregate level. Other tests suggested
that the widely used POL specification with correction for
serial correlation may produce spurious instability, but
appropriate alternative specifications are not obvious.
Additional tests using similar recursive econometric techniques may verify whether the apparent instability in export pricing behavior suggested by Chart 4 is robust to
changes in specification and clarify its sources,
Economic Review / Winter 1991
13. For a related discussion, see Moreno (1989b). The
reader should recall that the trade-weighted foreign CPI
measure covers only major industrial countries. A measure that includes the CPls of a number of developing
countries-notably the Asian newly industrializing economies-might indicate a less robust improvement in the
competitiveness of U.S. exporters. However, it would still
be true that U.S. exporters are more competitive in relation
to their industrial country trading partners.
14. Similar results are obtained when using a PDL specification for the export equation, as in the FRBSF structural
model. One issue that has not been directly addressed in
the paper is whether the single equation estimation techniques used here-which are commonly used in the
literature-may account for the tendency to underpredict
export volume observed in Chart 1. Since single equation
estimates are correct if the elasticity of supply is infinite (or
the demand function is stable while the supply function
shifts around), one way of justifying the use of single
equation techniques is to note that the U.S. domestic
production sector is very large in comparison to the export
sector, and that supply can therefore shift to the export
sector quite easily.
Furthermore, it does not appear that simultaneous
equation bias would produce the underprediction of exports obtained in this paper. As pointed out by Goldstein
and Khan (1985), single equation estimates can produce
weighted averages of demand and supply elasticities and
may therefore be biased downward. Consider now the
demand function estimated in Table 6. Assuming this
function was stable over the period 1988-1989, the U.S.
dollar appreciation over much of this period would tend to
reduce the demand for U.S. exports. However, if the
estimates were subject to simultaneous equation bias,
there would be a tendency to understate the impact of
dollar appreciation in out-of-sample simulations, that is, a
tendency to overpredict exports. Thus, the underprediction in Chart 1 does not appear to be the result of simultaneous equation bias.
15. The preceding results permit us to rule out another
explanation for the tendency to underpredict exports, the
Federal Reserve Bank of San Francisco
phenomenon of "hysteresis." Hysteresis is a situation
where a phenomenon (for example, large export volume)
persists even when the disturbance that produced it (for
example, dollar depreciation) is removed. As applied to
the present case, hysteresis would imply that sharp gains
in U.S. competitiveness after 1985 produced persistent
effects on U.S. exports that are not readily captured in
equation (1).
To understand how hysteresis in export markets may
arise, suppose that entry and exit from world export
markets is characterized by relatively high fixed costs.
One implication is that large swings in prices may encourage entry or force exit, while small swings may not. Small
swings in US. competitiveness (such as those observed
up to the early 1980s) would be characterized by changes
in export demand that are well-captured by equation (1).
However, large swings in U.S. competitiveness (such as
the dollar appreciation between 1980-85 and the depreciation that immediately followed) would be accompanied by entry or exit decisions that are not easily
explained by equation (1).
Consider the trends revealed in Chart 5. The Chart
suggests that the depreciation of the dollar after 1985, in
combination with the tendency to restrain increases in the
export price, resulted in a net competitive gain for U.S.
exporters over the decade, in comparison to their industrial country trading partners. In particular, the sharp gains
in U.S. competitiveness after 1985 were probably sufficiently large to prompt the exit or deter the entry of foreign
competitors (specifically, competitors in industrial countries). Foreign producers may have been dissuaded from
entering export markets to compete with U.S. producers
even when the dollar appreciated between 1988 and
1989, because the gains in competitiveness U.S. exporters achieved earlier were not entirely eliminated. Such a
situation, where U.S. export volume remains high even if
the competitiveness of U.S. exporters is being eroded, fits
the definition of hysteresis.
However, the effects of hysteresis in explaining robust
export growth, if any, are not very strong. Otherwise, the
forecast using RXR 2 should also show a persistent tendency to underpredict exports in recent years.
51
REFERENCES
Baily, Martin Neil and Alok K. Chakrabarti. Innovation and
the Productivity Crisis. Washington, D.C.: Brookings
Institution, 1988.
Balassa, Bela and John Williamson. Adjusting to Success:
Balance of Payments Policy in the East Asian NICS.
Institute for International Economics. Policy Analyses
in International Economics, No. 17. Washington, April
1990.
Baldwin, Richard and Paul Krugman. "Persistent Trade
Effects of Large Exchange Rate Shocks," The Quarterly Journal of Economics, Vol. CIV, Issue 4, November1989, pp.635-654.
Baldwin, Richard. "Some Empirical Evidence on Hysteresis in Aggregate U.S. Import Prices." NBER Working Paper, No. 2483, January 1988.
Baumol, William J., Sue Anne Batey Blackman and Edward N. Wolff. Productivity and American Leadership.
Cambridge, Ma.: MIT Press, 1989.
Brayton, Flint and Eileen Mauskopf. "The MPS Model of
the United States Economy," Board of Governors of
the Federal Reserve System, February 1985.
Dollar, David and Edward N. Wolff. "Convergence of
Industry Labor Productivity Among Advanced Economies, 1963-1982," C.V. Starr Center for Applied Economics, Economic research report No. 88-19, 1988.
Goldstein, M. and M.S. Khan. "Income and Price Effects in
Foreign Trade." In Jones and Kenen (eds.), Handbook of International Economics, Volume 2. Amsterdam: North Holland, 1985.
Helkie, William L. and Peter Hooper. "An Empirical Analysis of the External Deficit, 1980-86." In Bryant, Holtham and Hooper (eds.), External Deficits and the
52
Dollar: The Pit and the Pendulum. Washington, D.C.:
The Brookings Institution, 1988.
Hendry, David F. "Predictive Failure and Econometric
Modelling in Macroeconomics: The Transactions Demand for Money." In P. Omerod, ed., Modelling the
Economy. London: Heinemann Educational Books,
1979.
Hooper, Peter and Catherine L Mann. "The U.S. External
Deficit: Its Causes and Persistence," Board of Governors of the Federal Reserve System, International
Finance Discussion Papers No. 316, November 1987.
_ _ _ _ . "Exchange Rate Pass-Through in the 1980s:
The Case of U.S. Manufactures," Brookings Papers
on Economic Activity, 1: 1989.
Melick, William R. "Estimating Pass-Through: Structure
and Stability," Board of Governors of the Federal
Reserve System, International Finance Discussion
Papers, No. 387, September 1990.
Moreno, Ramon. "Exchange Rates and Trade Adjustment
in Taiwan and South Korea," Federal Reserve Bank
of San Francisco, Economic Review, Spring 1989a,
NO.2.
Moreno, Ramon. "A Fading Export Boom?" Federal Reserve Bank of San Francisco Weekly Letter, December
15,1989b.
Orr, James. "The Performance of the US. Capital Goods
Industry: Implications for Trade Adjustment," Federal
Reserve Bank of New York Quarterly Review, WinterSpring 1989, pp. 69-82.
Throop, Adrian. "A Macroeconometric Model of the U.S.
Economy," Federal Reserve Bank of San Francisco
Working Paper 89-01, March 1989.
Economic Review ! Winter 1991
@FedFRASER