Full text of Review (Federal Reserve Bank of Dallas) : Fourth Quarter 1997

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FEDERAL RESERVE BANK OF DALLAS
FOURTH QUARTER 1997

Has Long-Run Profitability
Risen in the 19905?
John V. Duca

Intellectual Property Rights
And Product Effectiveness
Stephen P. A. Brown and
William C. Gruben

Is the Business Cycle of
Argentina "Different"?
Finn E. Kydland and
Carlos E. j. M. Zarazaga

This publication was digitized and made available by the Federal Reserve Bank of Dallas' Historical Library (FedHistory@dal.frb.org)

Economic Review
Federal Re et

Bank of Dallas

Robert D. McTeer. Jr.
PI

and C

II

Helen E. Holcomb
CIl~ Opefaf IJ,l

Fir V PI.'

IQlr

Harvey Rosenblum
SenIOl Vice Ptes>dtmland D,

rr ~ Rese3rcl1

W. Michael Cox
VI", l'te$ldenj a.-.l WlJ11lt II!lv,soi

Senior Economists and
Assistant VIce Presidents
Stephen P A.. Brown
John Duca
Robert WGilmer
Evan F KoeOlg
Director, Center for latin AmerIcan
Economics, and Assistant Vice President
William C Gruben
Senior Economist and
Reseatch Officer
Mine K. VOcel
Economists
Rooert Formalrn
David M Gould
Joseph H Haslag
Kellh R Phillips
Stephen D Pro
Marci Rossell
Jason L Saving
Fiona O. Sigalla
Lori L Taylor
Lucmda Vargas
MarkA Wynne
Carlos E larazaga
Research Associates
Prolessor Nalilan S Balke
Ihem

Unt'/lll3llY

Professor Thomas B. Fomby
So"1 .n Mell101l

Un"

Pro essor Gregory W HuHman
hod~Un

Professor Finn E Kydland
Ca~14l
non U!ll l1ijy
Professor Roy J Rulfln
UIIIV"fSiIY 01 H<aJS1
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Contents

Has Long-Run Profitability
Risen in the 1990s?
John V. Duca

Page 2

Intellectual Property
Rights and Product
Effectiveness
Stephen P. A. Brown and William C. Gruben

Page 15

Is the Business Cycle of
Argentina" Different"?
Finn E. Kydland and Carlos E. J. M. Zarazaga

Page 21

This article analyzes the recent rebound in nonfinancial corporate profitability, as measured by after-tax profits as a share of
output. Virtually all the resurgence in corporate profitability during
the 1990s reflects a cyclical increase in profits and a decline in net
interest expense associated with deleveraging and lower interest
rates. In this sense, it is not clear that a long-lasting upward shift
in the economic returns to capital has occurred, after accounting
for short-run cyclical-related movements and for how deleveraging
and lower interest rates have shifted capital payments away from
debtholders toward equityholders.

Recent economic literature concludes that an inventionimporting country, where domestic invention is scarce or nonexistent, may reduce its welfare and, in some cases, world welfare, by
protecting intellectual property developed elsewhere. The analysis
presented in this article uses economic theory to show that such a
conclusion may not be fully warranted for a wide range of products, such as antibiotics, fungicides, herbicides, and pesticides,
whose effectiveness diminishes with cumulative use. Both developed and developing countries may find that protecting intellectual
property rights for these products will enhance welfare-even
when their invention is provided for free.

Despite the relative success of Real Business Cycle CRBC)
models to replicate key moments of the business cycles of the
United States and several European countries, economic research in
Latin America tends to take the more traditional view that monetary
factors play a predominant role in the economic fluctuations of
countries in that palt of the world. The different theoretical
approach is often justified on the grounds that business cycles in
Latin America are "different." However, few comparative studies
have analyzed the relevant differences between the business cycles
of Latin America and those of the United States and Europe.
In this article, Finn Kydland and Carlos Zarazaga present
business-cycle facts for Argentina, following as closely as possible
the empirical methodology and statistics other studies have used to
characterize U.S. and European business cycles. Overall, the
authors find no a priori evidence that dynamic general equilibrium
models, in which real shocks are the only source of economic fluctuations, cannot potentially account for as much of the Argentinean
business cycle as such models do for business cycles in the United
States and Europe.

Has Long-Run
Profitability Risen
In the 1990s?

After fluctuating in a high range during
the 1960s and 1970s, the profit share (the ratio
of after-tax profits to output) of nonfinancial
corporations moved within a lower range
through the early 1990s. In recent years, this
measure of profitability has rebounded somewhat (Figure 1 ), largely owing to strong growth
in corporate profits, which has both buoyed
optimism about the long-run prospects for
American companies—partly reflected by rising
stock prices—and spurred criticism that companies have profited at the expense of workers
(Bernstein 1995).
Whether the recent improvement is permanent or transitory is important for several
reasons. First, profitability affects the financial
strength of firms and has implications for their
ability to weather downturns. Second, because
retained earnings bolster investment, the permanence or impermanence of the recent
improvement will have implications for investment and thus the long-run growth of the U.S.
economy.1 Finally, profitability is a key determinant of stock prices, which are important not
only because they are indicators of future economic growth but also because they affect
wealth and, thereby, consumption and investment.2
This article analyzes the underlying sources
of the recent rebound in corporate profits. Contrary to popular perception, virtually all of the
resurgence in corporate profitability during the
1990s reflects a cyclical increase in profits and a
decline in net interest expense associated with
deleveraging and lower interest rates. In other

John V. Duca
Senior Economist and Assistant Vice President
Federal Reserve Bank of Dallas

T

his article analyzes the

underlying sources of the recent
rebound in corporate profits.
Contrary to popular perception,
virtually all of the resurgence
in corporate profitability
during the 1990s reflects a
cyclical increase in profits and
a decline in net interest expense
associated with deleveraging
and lower interest rates.

Figure 1

After-Tax Profit and Net Interest Shares of
U.S. Nonfinancial Corporations, 1953 – 96
Percent
12
Profit share
10

8

6

4

2

Net interest
share

0
’53 ’56 ’59 ’62 ’65 ’68 ’71 ’74 ’77 ’80 ’83 ’86 ’89 ’92 ’95

NOTES: Break-adjusted for revisions associated with the shift to
chain-weight GDP for the post-1958 period. The shaded
areas denote recessions.
SOURCE: U.S. Bureau of Economic Analysis.

2

from internal sources—which could stem from
deregulation—or from foreign firms—which
could stem from a rise in the real foreign
exchange value of the dollar. Equation 2 indicates that four factors affect profitability: cyclical, relative price, depreciation, and net interest
effects.
Cyclical factors. As real output growth
picks up or as the economy operates at a higher
level of capacity, the profit ratio should rise as
real fixed costs (F ) shrink as a share of output
(Y ) and if pricing power varies with the business cycle. Intuitively, profits tend to rise relative to output during economic recoveries and
expansions, because fixed costs are spread over
more output and perhaps also because producers may enjoy higher average profit margins when demand is high. Since the level of
fixed costs other than depreciation is difficult to
measure—in contrast to production—we can
only readily control for output-related swings in
the aggregate income share of fixed costs and in
overall profit margins. Such cyclical swings are
taken into account by including economywide
measures of output growth and capacity.
Relative price factors. Relative prices can
affect profits by altering the pricing power of
domestic firms and by affecting how other costs
vary with output. Swings in real exchange rates
can alter the profit margins of U.S. firms by
changing the relative competitiveness of foreign
products. For example, when the foreign exchange value of the dollar jumped in the mid1980s, many U.S. firms that produced traded
goods saw the demand for their products
decline because the high dollar made U.S.
exports more expensive overseas and imports
less expensive relative to U.S.-made goods to
Americans in dollars. As a result, U.S. manufacturers experienced sharp declines in profits as
demand for their output fell, reducing their
pricing power and profit margins.4 In terms of
equation 2, this relative price change affects the
extent to which prices exceed variable costs, as
reflected in the term [1 – (w + v)].
Another important relative price is the real
price of energy, which affects energy-using and
energy-producing firms in different ways. The
change in, rather than the level of, energy prices
is used because profits are more affected by
sudden changes in energy prices than by their
level for two reasons. First, when oil prices rise,
profits fall at energy-intensive firms because it
takes time for them to pass higher input costs to
their customers and because their production
efficiency will decline until they can shift
toward using more energy-efficient equipment

words, aside from cyclical movements in profitability, much of the recent improvement in
profit share reflects the compositional effects
of a shift in capital payments away from debtholders toward equityholders.
To establish these findings, the next section lays out a basic model of corporate profits,
describing ways of adjusting profits for swings
in net interest, the business cycle, oil prices,
exchange rates, and government regulatory
actions. The following sections discuss how
corporate profitability and its determinants are
measured and included in this model, and
present empirical results and corporate profit
measures adjusted for the aforementioned factors. The conclusion interprets the findings and
discusses their implications.

What determines profitability?
Profits equal revenues minus costs, where
revenues equal the product of prices (P ) and
the quantity of output (Y ). Costs include nominal fixed costs (PF ), which equal prices (P )
times real or inflation-adjusted fixed costs (F );
labor costs (WL ), which equal compensation
per hour (W ) times work hours (L ); nonlabor
variable costs (vPY ), which equal real variable
costs per unit of output (v < 1) times prices (P )
and real output (Y ); depreciation of capital in
nominal dollars (D ); and net interest payments
in nominal dollars (I ) to debtholders. In the
long-run, if the capital–labor ratio is fairly stable, hours worked generally move one-for-one
with output after adjusting for trend productivity
growth. Additionally, if real compensation
moves one-for-one with labor productivity, then
labor costs (W L ) are a constant share (w < 1)
of nominal output (PY ).3 Combining these details, the level of nominal profits (Π) can be
expressed as:
(1)

Π = (PY ) – PF – (wPY ) – (vPY ) – D – I
= (PY ) – PF – {[w + v ]PY } – D – I,

where w + v < 1 (otherwise profits would be
negative). Dividing both sides by nominal output (PY ) yields an expression for profits as a
share of output:
(2)

π = [1 – (w + v)] – F/Y – D/PY – I/PY,

where the profit share π = Π/PY and [1 – (w + v)]
< 1. The term [1 – (w + v)] reflects the pricing
power of firms because it depends on the extent
to which prices exceed average, short-run variable costs. The pricing power of firms falls
the greater the degree of competition, either

FEDERAL RESERVE BANK OF DALLAS

3

ECONOMIC REVIEW FOURTH QUARTER 1997

rates or when banks or thrifts book past loan
losses or capital gains). The other variables fall
into four categories: cyclical, relative price, regulation, and inflation. Many variables are from
related work by Duca and VanHoose (1996 and
forthcoming).
Profit share. The dependent variable is
the after-tax profit share (PRAT ) of nonfinancial
corporate output.6 An income share approach
to measuring profitability is used because of
difficulties in measuring the rate of return on
capital, as discussed in the box entitled
“Measuring Profitability: Income Share Versus
Rate of Return Approach.” After-tax, rather than
before-tax profits, are used to assess profitability
from a long-term perspective primarily because
of large, long-term shifts in direct corporate
taxation (see the box entitled “Should Profitability Be Measured on a Before- or After-Tax
Basis?”). The dependent variable excludes net
interest in its numerator, and net interest enters
the model as a right-side variable because of
tax and other differences (see the box entitled
“Net Interest: A Component or Determinant of
Profitability?”).
Depreciation. The depreciation ratio
(D/PY ) in equation 2 is measured by the ratio
of consumption of fixed capital to output
(DEPRAT ).
Net interest. The net interest ratio equals
the ratio of net interest payments to output
(INTRAT ).
Cyclical variables. To control for cyclical
effects, the models include the t through t -3 lags
of real GDP growth (GDP, GDP1, GDP2, and
GDP3, respectively) and the four-quarter lag of
year-over-year GDP growth (GDPyoy4 ).7 The
latter controls for medium-term effects of economic growth, while using fewer degrees of
freedom than would be the case if one used
four more lags of quarterly GDP growth. In
addition, the current and one-quarter lag of
the unemployment rate (Ut and Ut –1, respectively) are included to control for the effects of
capacity on profits discussed above.8 Both types
of variables are included because fast GDP
growth in the early stage of recovery from a
deep recession may not adequately reflect that
the level of fixed costs is high relative to output,
which may not have fully recovered from that
recession.
Relative price terms. Two types of relative
price terms, real exchange rates and real oil
prices, may have large effects on aggregate
profit measures. Real exchange rates, denoted
by RER, are measured using the Federal Reserve
Board’s series on the real trade-weighted value

and practices. In terms of equation 2, this effect
raises the variable cost of output (v ). Second,
the profitability of energy producers to some
degree reflects capital gains or losses on inventories and reserves stemming from changes in
oil prices. For example, following a jump in oil
prices, the energy-producing firms will book
the one-time capital gains on their oil holdings
as profits. It is thus unclear whether the negative impact of higher energy prices on profits at
energy-using firms theoretically outweighs the
positive impact of higher energy prices on
energy industry profits in practice. This empirical issue is relevant since most high-energyusing firms and oil firms are nonfinancial
corporations. Because cyclical variables are
taken into account, the inclusion of energy price
changes allows us to assess, after controlling for
the impact of energy on the business cycle,
whether profits tend to be higher or lower during recessions induced by rising oil prices.
Depreciation. According to equation 2,
profit share should be negatively related to the
depreciation ratio (D/PY ), which largely reflects
the obsolescence of prior investment.
Net interest. A higher net interest ratio
(I/PY ) also lowers the profit ratio. Net interest
would rise if firms borrow more to finance
inventories, if real interest rates rise, or if firms
shift from equity to debt financing.5 As for the
last factor, the shift from equity to debt in the
1980s and its reversal in the 1990s first lowered
and then boosted the profit ratio because the
profit ratio reflects equity returns and the net
interest ratio reflects debtholder returns.
Inflation. One omitted variable from this
model is inflation. Public finance economists,
such as Feldstein and Summers (1983), have
argued that inflation hurts profits because many
tax code provisions are not indexed for inflation. For example, higher inflation reduces
firms’ ability to depreciate capital for tax purposes because the nominal price of replacing
older capital rises with inflation, whereas the tax
write-offs for depreciation do not.

Data and variables
This section describes how the dependent
and independent variables are constructed. The
profits, net interest, and depreciation variables
are based on data from the national income and
product accounts for nonfinancial corporations,
as financial corporate profits are sometimes
distorted by short-run shocks (for example,
weather-related insurance costs) or swings in
securities prices associated with unexpected
developments (for example, changes in interest

4

Measuring Profitability: Income Share Versus Rate of Return Approach
Profitability can be measured using an income share or rate of return approach. The former approach measures profits
as a share of output, whereas the latter expresses profits divided by the stock of capital. Each approach has relative
strengths and weaknesses.
In theory, the rate of return approach seems preferable. If returns and capital invested can be accurately measured,
then one can infer the rate of return. In such a case, a rate of return measure is superior to an income share variable
because, in principle, the return on capital can change even if the income share is constant. For example, if capital were
used more efficiently (that is, earned more per unit) and less were invested, then the income share of capital could be
unchanged or fall, even though the return to capital has actually risen.
However, in practice, the income share approach has two advantages over rate of return measures. First, official capital
stock measures for nonfinancial firms are available only on an annual basis, whereas quarterly income share variables are
available.1 The extra degrees of freedom (almost fourfold greater) allow for more rigorous and complete hypothesis testing.
Second, there are a number of practical difficulties in accurately measuring the capital stock. For example, if an economic
rather than a historical book-value approach to depreciation is used, the capital stock could plummet if the value of some
capital were quickly written off based on shifting asset prices. Indeed, the value of structures in the nonfinancial corporate
sector plunged in the early 1990s when government statisticians used market price data to downwardly adjust office building
values, even though vacancy rates in the early 1990s and mid-1980s were similar. Because profits are measured contemporaneously, whereas the capital stock reflects previous investment and depreciation, the measured return on capital in the
mid-1990s looks high largely because the measured rate of return jumped after the stock of office buildings was largely
written off. However, it is difficult to construct a rate of return measure that tracks the value of the capital originally invested
without distortions from large and uneven write-offs or capital gains.
Another source of measurement error arises with the shift from physical capital to human capital, the latter of which is
more difficult to measure. For example, companies that invest much in research and development (R&D) by hiring scientists
and engineers will appear to be less capital intensive than they really are according to a measure of the physical capital
stock. Since investment is increasingly done in the form of R&D, conventional measures may overstate the real rate of return
on capital by understating the stock of physical plus human capital. Thus, while measuring human capital raises problems for
the rate of return and income share measures of profitability, they likely pose more difficulty for the rate of return approach.
Overall, practical considerations favor using an income share approach, which necessitates using data from the
national income and product accounts. Table B.1 summarizes categories from these accounts and relates them to variables
used in this article.
1 In contrast, Nordhaus (1974) and Feldstein and Summers (1977) use annual rate of return data that provide few degrees of freedom and

limit hypothesis testing.

Table B.1

Nonfinancial Corporate Business Data
This table summarizes the national income and product accounts (NIPA) of the nonfinancial business sector and
relates NIPA categories to the variables used in the empirical model.
How this category affects
variables in the model

NIPA category
1. Gross domestic product of
nonfinancial corporate business*
(= lines 2+3)

Used as the denominator of
PRAT, DEPRAT, and INTRAT

2. Consumption of fixed capital

Used as the numerator of
the depreciation share, DEPRAT

3. Net domestic product
(= lines 4+5+6+12)

1996:4 level in
billions of dollars
4194.8

401.6
3793.2

4. Indirect taxes and net transfers

415.2

5. Compensation of employees

2788.2

6. Corporate profits with inventory
valuation and capital-consumption
adjustment (= lines 7+8+9+10+11)

484.5

7. Profits before tax

425.9

8. Profits tax liability

148.1
Used in the numerator of PRAT

9. Profits after tax
10. Inventory-valuation adjustment

277.8
– 9.2

11. Capital-consumption adjustment

67.8
Used in the numerator of INTRAT

12. Net interest

105.2

* Note that the cyclical variables GDP and GDPyoy in the model differ in that they are based on GDP data for all businesses (not just nonfinancial corporations) and are real, not nominal.

FEDERAL RESERVE BANK OF DALLAS

5

ECONOMIC REVIEW FOURTH QUARTER 1997

Should Profitability Be Measured on a Before- or After-Tax Basis?
Another measurement issue is whether to use before- or after-tax profits in
defining profitability. On the one hand, there are two reasons for using before-tax
profits. First, because net interest is measured on a before-tax basis in the national
income and product accounts, to be consistent, so should profits. Second, in the
short run, sluggish adjustment to swings in corporate taxation could actually result in
before-tax profit ratios being less volatile than after-tax ratios, which may absorb the
short-run impact of tax changes (see Feldstein and Summers 1977). On the other
hand, in the long run, shifts in direct corporate taxation will distort before-tax profitability but not after-tax profitability because competition will eventually force companies to pass on the changing costs of taxation to their customers and to yield an
equilibrium after-tax return to investors. This issue is important because direct corporate taxation has substantially fallen as a share of nonfinancial corporate output,
from roughly 7 percent in the 1960s to 5 percent in the 1970s and to 3 percent in
the 1980s and 1990s.1 More recently, tax changes passed in 1993 (specifically, the
alternative minimum corporate profits tax) increased this tax burden some, putting
pressure on firms to boost before-tax earnings to maintain after-tax earnings for
investors. Given the significance of these changes and because the analysis focuses
on long-run, rather than short-run, movements in profitability, the ratio of after-tax
profits to output (PRAT ) is used to assess whether there is a substantial and lasting
rise in profit share under way.
1

1968:1 level. This useful assumption, which
allows the regressions to start in the 1950s, is
reasonable on two grounds. First, the real value
of the dollar likely stayed in a narrow range
over this earlier period, as exchange rates were
fixed and inflation was low in the G –10 countries. Second, the inclusion of RER is economically significant largely because of the dollar’s
big rise and fall during the 1980s, which caused
sizable swings in the profitability of tradedgoods industries. Because exchange rates affect
traded-goods prices with a lag, they often affect
domestic profits with a lag. Accordingly, the
model includes the one- through four-quarter
lags of RER (denoted as RER1, RER2, RER3, and
RER4, respectively).
The other relative price term is the change
in the real price of energy (∆OIL), which controls for swings in energy prices. To distinguish
the relative price from the cyclical effects of
changing energy prices, the models include the
t through t -3 lags of ∆OIL and the four-quarter
lag of the year-over-year change in real energy
prices (∆OILyoy4 ). The real consumer price is
used because the impact of price controls on
wholesale and retail energy prices differed at

Auerbach and Poterba (1987) discuss tax code changes affecting corporations.

of the dollar, which is based on exchange rates
and consumer prices of the G–10 countries.9
Although there are broader measures of the real
value of the dollar, this one is used because it is
available over a longer sample period. Before
1968:1, when this series starts, RER equals the

Net Interest: A Component or Determinant of Profitability?
A third measurement issue concerns profits and net interest. At one level, both profits and net interest
are factor payments to capital, implying that profitability should be based on their sum, as in Feldstein and
Summers (1977). Indeed, the large shift from equity to debt in the 1980s implies that the profits were lowered then simply because of a shift from one form of capital to another. The high degree of substitutability
of debt and equity in the 1980s is further supported by the fact that much of the buildup in the 1980s in
corporate debt reflected stock repurchases and the issuance by highly leveraged firms of lower grade
bonds, whose risk profiles some analysts viewed as more like equity than traditional high-grade bonds. The
subsequent deleveraging of the 1990s bolstered profits and lowered net interest, as illustrated in Figure 1.
However, debt and equity are not perfect substitutes. For example, net interest was temporarily
boosted in the 1974 –75 inventory-related recession, when firms borrowed more to finance unintended
inventory buildups. In addition, the adoption of lean inventory techniques in the 1990s likely has reduced
the cost of financing inventories. Hence, some of the past swings in the net interest reflect swings or shifts
in (inventory) costs that negatively affect the economic returns to capital. Net interest payments also reflect
swings in real interest rates associated with fiscal policy and/or monetary policy, such as the short-lived
jumps during 1981– 82 and 1989 – 90. In addition, movements in net interest may also reflect how swings
in inflation affect corporate debt payments relative to income. For example, the net interest ratio may have
been bolstered in the 1981– 83 period when rapid and somewhat unanticipated disinflation slowed the
growth of nominal corporate revenues relative to the high interest rates on existing bonds that some corporations had issued during the high inflation of the late 1970s and early 1980s. The net interest and profit
measures used here also differ in that profits are measured on an after-tax basis, whereas net interest is
measured on a before-tax basis. For these reasons, it may not be appropriate to simply add net interest
and after-tax profits to measure the return to capital.1
Nevertheless, shifts in debt and equity financing imply that the analysis needs to control for movements
in net interest. With these considerations in mind, the ratio of net interest payments to output (INTRAT )
enters as a right-side variable. The more that debt and equity are substitutes, the closer the coefficient on
the net interest ratio should be to 1.
1

While adding net interest to before-tax profits avoids this problem, shifts in corporate taxation make it preferable to use
after-tax profit share in analyzing long-run profitability.

6

times, energy industry profits moved more
closely with consumer prices, and the speed at
which consumer energy prices react to wholesale energy prices has changed. From 1957 to
1996, the real price of energy is the ratio of CPI
energy prices to the CPI. Before 1957, real
energy prices are measured by the ratio of the
energy prices in the personal consumption
expenditures (PCE) deflator to the overall PCE
deflator, where overlapping ratios based on the
CPI and PCE in 1957:1 are used to break adjust
the two series.
Regulatory variables. Because equation 2
omits potentially significant regulatory or tax
actions, the empirical model assesses profit fluctuations stemming from two unusual government actions. One dummy, D534 (= 1 in 1953:4),
controls for the one-time plunge in profits during 1953:4. Firms booked profits out of that
quarter because it was announced in September
1953 that an excess-profits tax from the Korean
War would end in January 1954. Variables for
subsequent quarters are not needed because
firms apparently booked 1953 profits over several subsequent quarters, making it difficult to
construct dummy variables for the “payback”
effects.
Dummy variables are also tested to account
for the Nixon wage –price controls, during
which period price controls affected profit margins. Specifically, many firms were allowed to
increase prices in response to cost increases
only to the extent that their average profit margins did not exceed the average of the 1969 –70
period. However, profit margins tend to be low
during recessions, such as in the 1969 –70 recession. Thus, the price controls effectively capped
profit margins at low recessionary levels and
delayed a cyclical recovery in profits (see
Economic Report of the President, 1974, 91, and
1973, 65, respectively).10 Nine separate dummy
variables are used for each quarter when the
controls were in effect (1971:4 –73:4, denoted
D714 –D734 ) because a single dummy for
1971:4 –73:4 will not reflect how the different
phases of the controls and their ability to bind
changed in this economic recovery.11
Inflation. To assess the impact of inflation
on profitability, two inflation terms are tested:
the year-over-year CPI inflation rate (INFyoy )
and the four-quarter lag of this inflation measure (INFyoy4 ). Year-over-year measures are used
because inflation may show some short-run persistence, whereas including eight separate quarterly lags instead would use up many degrees of
freedom and make it difficult to observe any
persistent and significant effects of inflation.

FEDERAL RESERVE BANK OF DALLAS

Results
Empirical model. Based on equation 2 and
the discussion of possible regulatory and inflation effects on profits, the baseline empirical
model used is
PRAT = constant + δ1DEPRAT + δ 2INTRAT
+

3

∑ α GDP

+ α 4GDPyoy 4

t −i

i

i =0

+ β0U t + β1U t −1
+

3

∑ γ ∆OIL

+

t −i

i

i =0

(cyclical variables)
+ γ 4 ∆OILyoy 4

(relative energy price variables)

4

∑ ζ RER
i

i =1

+ φD 534t

t −i

(real exchange variable)
(regulatory variable),

where Greek letters denote estimated coefficients. The baseline model excludes the inflation and Nixon wage–price dummy variables,
as both are statistically insignificant. As a check
that potential simultaneity bias is not altering
the basic qualitative results, a version of the
baseline model is also used, which replaces
contemporaneous DEPRAT and INTRAT with
their one-quarter lags, drops contemporaneous
GDP and ∆OIL, and drops Ut but adds the twoquarter lag of U. Although some of the newly
lagged right-side variables became statistically
insignificant, the long-run trends in profitability
are similar when profits are adjusted for the
estimated effects of swings in the business
cycle, oil prices, and exchange rates. Results
from models with nonlagged variables are provided because profits are very sensitive to economic developments, and fits of models using
contemporaneous values are better than those
using lagged variables.
Regression results. Regression results are
presented in Table 1, in which models 1 and 2
include time-trend variables, unlike models 3
and 4, and in which models 2 and 4 include
inflation terms, unlike models 1 and 3. As this
table indicates, the after-tax profit ratio is very
sensitive to the business cycle in every model.
Faster GDP growth boosts the profit ratio, as
does a lower unemployment rate, which reflects
tighter capacity. The negative sign on Ut and
the positive sign on Ut –1 reflect that profits are
reduced by the current level of and change in
the unemployment rate. From a technical perspective, the negative sum of the coefficients on
the U lags reflects the negative effect of a lower
level of capacity on profits, while the positive
sign on the one-quarter lag actually reflects the
negative effect of a decrease in capacity.12
Turning to relative price effects, positive

7

ECONOMIC REVIEW FOURTH QUARTER 1997

Table 1

Regression Results for Models of After-Tax Profit Share
Variable

Model 3

Model 4

Model 5

constant

Model 1
.167**
(9.54)

Model 2
.160**
(8.92)

.167**
(9.34)

.159**
(8.40)

.168**
(9.30)

INTRAT

–.804**
(–4.17)

–.750**
(–3.92)

–.807**
(–3.95)

–.753**
(–3.65)

–.767**
(–3.76)

DEPRAT

–.248*
(–2.16)

–.306**
(–2.66)

–.247*
(–2.15)

–.302**
(–2.62)

–.248*
(–2.15)

GDP

.163**
(5.09)

.157**
(4.95)

.160**
(4.99)

.155**
(4.88)

.162**
(5.05)

GDP1

.136*
(4.02)

.129**
(3.79)

.134**
(3.91)

.129**
(3.72)

.133**
(3.85)

GDP2

.094**
(2.64)

.086*
(2.39)

.097**
(2.64)

.090*
(2.42)

.090*
(2.49)

GDP3

.093**
(2.83)

.085*
(2.56)

.098**
(2.92)

.092**
(2.66)

.091**
(2.73)

GDPyoy4

.042+
(1.92)

.038+
(1.70)

.051*
(2.18)

.047*
(1.98)

.042+
(1.90)

–.0032**
(–3.16)

–.0032**
(–3.24)

–.0033**
(–3.21)

–.0033**
(–3.29)

–.0032**
(–3.14)

Ut –1

.0024**
(2.65)

.0024**
(2.62)

.0024*
(2.47)

.0023*
(2.46)

.0023*
(2.39)

∆OIL

.032**
(2.88)

.031**
(2.86)

.033**
(2.75)

.032**
(2.70)

.034**
(2.88)

∆OIL1

.025*
(2.04)

.023+
(1.89)

.026*
(2.01)

.024+
(1.86)

.027*
(2.09)

∆OIL2

.058**
(4.21)

.055**
(4.00)

.059**
(3.75)

.056**
(3.57)

.062**
(3.93)

∆OIL3

.042**
(3.17)

.039**
(2.86)

.042**
(2.73)

.038*
(2.47)

.046**
(3.01)

∆OILyoy4

.026*
(2.56)

.024*
(2.36)

.021+
(1.76)

.019
(1.58)

.027 +
(2.59)

RER1

–.0001
(–1.07)

–.0000
(–.64)

–.0001
(–1.18)

–.0001
(–.77)

–.0001
(–1.14)

RER2

–.0001
(–1.35)

–.0001
(–1.04)

–.0001
(–1.41)

–.0001
(–1.11)

–.0001
(–1.36)

RER3

.0000
(.17)

.0000
(.40)

.0000
(.53)

.0000
(.15)

RER4

–.0003**
(– 3.67)

–.0002**
(– 3.07)

–.0003**
(– 3.54)

–.0002**
(– 2.98)

–.0003**
(– 3.68)

D534

–.016**
(–7.32)

–.016**
(–7.46)

–.016**
(–7.26)

–.016**
(–7.38)

–.016**
(–7.33)

Ut

t
t2

.00003
(.30)

.00026
(1.31)

.00025
(1.16)

.00029
(1.38)

–.0000016+
(–1.93)

–.0000017*
(–2.19)

–.0000018*
(–2.19)

INFyoy

–.023
(–.48)

–.019
(–.38)

INFyoy4

.047
(1.11)

.049
(1.12)

_
R2

.978

.977

.978

.977

ρ

.876

.952

.877

.952

–.027
(–.55)

.978
.882

D.W.

1.97

2.02

1.94

2.00

1.97

Q (24)

22.49

15.51

24.12

16.86

22.28

F (t,t 2)

3.27**

3.23**

F (INFyoy,INFyoy4 )

.75

3.22**
.76

**(*,+ ) denotes statistical significance at the 99 percent (95 percent, 90 percent) confidence levels.

8

signs on ∆OILyoy4 and lags of ∆OILt – I suggest
that higher oil prices boost profits. However,
higher oil prices also lower output and thereby
profitability. For this reason, the energy coefficient estimates do not reflect that real oil price
increases probably hurt profits by inducing
recessions, effects that are picked up by the
cyclical variables. For this reason, it is best to
interpret the energy coefficients as indicating
that once the negative cyclical impact of higher
oil prices is taken into account, higher real oil
prices tend to boost profits. Put another way,
nonfinancial corporate profits have tended to
fall less in oil-induced recessions than in nonoil-induced recessions, once the overall magnitude of the recessions is taken into account by
cyclical variables. One plausible explanation is
that, after controlling for cyclical effects, energy
company profits are apparently bolstered by
capital gains on oil reserves by more than profits of oil-using companies are reduced, most
likely because oil companies book substantial
capital gains on oil reserves in the former type
of recession.
With respect to the other relative price variables, results also indicate that a real exchange
rate appreciation lowers the profitability ratio
with about a four-quarter lag, often through
reducing the competitiveness of traded-goods
industries. Because most of the statistical significance of the real exchange rate variable
reflects the large hump in the dollar’s value in
the mid-1980s, and because subsequent movements in the dollar’s value have been smaller in
size, the effects of exchange rates may be less
precisely estimated than the standard errors and
t ratios imply.
The estimated coefficients on the net interest ratio are worthy of more confidence, not
only because net interest changes have occurred
in several instances but also because the estimated coefficients are plausible. The fact that
the coefficients are under 1 (roughly 0.8) for the
after-tax profit share — coupled with results
from other regressions (not shown), which indicate that the net interest ratio has a 1–1 effect on
before-tax profit share—implies that after-tax
profits and net interest are not perfect substitutes largely because of tax differences.
Results for regulatory variables are mixed.
For example, the tax dummy for 1953:4 is
always statistically and economically significant,
indicating that the tax-cut announcement of
1953:4 cut the profit ratio by about 1.6 percentage points. On the other hand, the Nixon
price-control dummy variables are jointly insignificant, as indicated by F statistics testing the

FEDERAL RESERVE BANK OF DALLAS

joint significance of these dummies in models
corresponding to models 1– 4.13
In contrast to the price-control variables,
while the time terms (t and t 2) are jointly significant, their inclusion does not alter the qualitative regression results, as is reflected in Table 1,
and the general movement in the adjusted profit
ratio (not shown).
Perhaps at odds with the public finance
literature on inflation and corporate taxation,
the inflation variable is statistically insignificant
(see models 3 –5). This result also arises using
the shorter sample of 1953:1–79:4. In other runs
that omit statistically significant energy price
variables, inflation is significant but with a
counter-intuitive positive sign. This result likely
reflects that, in the absence of energy price variables, CPI inflation is spuriously picking up
changes in real energy prices stemming from
omitted variable bias. Another reason for the
lack of a negative and significant effect of inflation on profit share may be that changes in the
tax code (particularly in 1981 and 1986) render
the effect of inflation uneven over long samples.
As a result, it may be difficult to find a statistically significant effect of inflation on profits
without adjusting for tax code changes in some
way. In light of this plausible explanation, the
findings indicate that inflation may not add
information about the after-tax share of profits
in the presence of cyclical and relative price
terms and in the absence of accounting for tax
code changes.

Has the underlying trend in
profits changed much?
Using the regression results, one can
address the questions of whether and why the
underlying trend in after-tax profit share has
changed much in the 1990s. To do this, one can
adjust profit share by subtracting from it the
estimated impacts of the business cycle (GDP
growth and unemployment rate effects), real oil
prices, real exchange rates, regulatory variables,
and swings in net interest. As shown in Figure
2, this adjusted profit ratio fell from the late
1970s to mid-1980s and since then has fluctuated in a range that is noticeably below that of
prior decades.14 Interestingly, the profit performance of the 1970s differs little from that of the
1950s and 1960s on an adjusted basis, in contrast to the unadjusted data. This difference
mainly reflects that the cyclical performance of
the 1970s in terms of GDP growth and unemployment was noticeably worse than that of the
prior decades and that interest rates were higher
as well.

9

ECONOMIC REVIEW FOURTH QUARTER 1997

Table 2

Which Sectors Face Much Higher Competition Since the 1970s?
SIC Sector 1

Major Changes Boosting Competition in Particular Industries

Agriculture

———

Mining

Oil & Gas: Oil prices deregulated by a series of presidential executive
orders beginning in 1979, and natural gas prices deregulated in phases by
the Natural Gas Policy Act of 1978.

Construction

———

Manufacturing

Increased openness to trade, partly from GATT (1979, 1993),
Canada – U.S. Free Trade Agreement (1989), and NAFTA (1994).

Transportation

Trucking: Liberalization of truck rates in the late 1970s and the Motor
Carrier Reform Act (1980).
Airlines: The Airline Deregulation Act (1978) allowed entry in 1982 and
deregulated air fares in 1983.
Railroads: Deregulated by ICC liberalization of rail rates in the late 1970s
and the Staggers Rail Act (1981).

Communications

Telephones: Largely deregulated following the ATT court settlement of 1982.
Cable Television: Deregulated in a series of FCC rulings in the late 1970s
and by the Cable Television Deregulation Act (1984).
Telecommunications: Partly deregulated by Telecommunications Act (1996).

Utilities

Electricity: Wholesale deregulation enhanced by Federal Energy
Regulatory Commission rulings (1996).

Wholesale

———

Retail

Department Stores: Rise of discount store chains and electronic shopping.

FIRE (Finance, Insurance
and Real Estate)

Banking: Partly deregulated by the Depository Institution Deregulation
and Monetary Control Act (1980) and the Garn-St. Germain Depository
Institutions Act (1982).

Service

Health Care: Innovations in the form of HMOs and managed care in the
1990s.

1

Standard Industry Classification (SIC) of sectors at the 1-digit-level classification code.

SOURCES: Winston (1993) and author’s compilations.

in part, how the unexpected recession and
credit crunch of the early 1990s induced an
upward reassessment of the risks of leverage
and spurred a shift toward using stock swaps
and relatively less debt to finance takeovers and
mergers.
Because the impact of the recent net interest swing conceivably may persist for some
time, whereas cyclical swings appear to be more
short-lived, it is helpful to look at the after-tax
profit share adjusted only for net interest effects,
as shown in Figure 3.15 Consistent with the plot
of profit share adjusted for cyclical and net
interest swings in Figure 2, the net interestadjusted profit share has moved in a lower
range since the mid-1980s. Indeed, recent readings are well below the high points reached in
the later phases of prior business expansions,
such as in 1955, 1958, 1966, and the late 1970s.
The fact that adjusted profits have moved
in a lower range over the 1980s and 1990s raises
the question, Why did profitability fall after the

With respect to the late 1980s through
the mid-1990s, virtually all of the run-up in the
unadjusted profit ratio is due to swings in the
business cycle and net interest, as demonstrated by the flatness in the adjusted profit ratio
plotted in Figure 2. Much of the unadjusted rise
stems from a huge decline in net interest since
the late 1980s, which may largely be a longlasting effect if the deleveraging of the early
1990s does not reverse itself (and also if real
interest rates do not trend higher from the
levels of the mid-1990s).
To a large extent, swings in the use of
leverage reflect changing risk assessments and
the development of new financial markets. For
example, the rise of leverage in the 1980s partly
reflected the further development of the junk
bond market, more optimistic assessments of
the risk posed by increased leverage, and the
increased use of debt to finance an increase in
corporate takeovers and mergers. The subsequent deleveraging of the 1990s likely reflected,

10

1970s? One plausible, but unproven, explanation is that a number of factors have heightened
the degree of competition facing firms. In turn,
greater competition may have speeded up the
pace of capital depreciation as firms more
quickly replaced aging capital to match their
competitors. This possible effect of greater
competition may be reflected in the adjustedprofit ratio because the effect of depreciation
was not subtracted from the raw-profit ratio in
constructing the adjusted profit ratio in Figure 2.
In addition, lower pricing power could arguably
boost the shares of output going to other
factors, such as variable costs, as firms extract
less economic rent from other factors of production.16
What could have caused a rise in the
degree of goods market competition since the
1970s? One often mentioned reason is heightened global competition. While trade flows
imply that this should have been more of a
factor in the 1970s, when the ratio of imports
relative to GDP rose most prominently, import
penetration during the 1970s may have induced
U.S. firms to cut profit margins in the 1980s
after losing market share in key traded-goods
industries.
However, nontraded-goods industries have
arguably become more competitive as well. For
starters, a number of industries experienced
greater competition stemming from deregulation in the late 1970s and early 1980s, including

Figure 3

Adjusted and Net Interest-Adjusted
After-Tax Profit Shares of U.S. Nonfinancial
Corporations, 1953– 96
Percent
14

10

8
Actual
6

4

2
’53 ’56 ’59 ’62 ’65 ’68 ’71 ’74 ’77 ’80 ’83 ’86 ’89 ’92 ’95

NOTES: The net interest-adjusted profit share equals the actual
profit share minus the estimated effects of swings associated with net interest, as estimated by model 1 in Table
1. The shaded areas denote recessions.
SOURCES: U.S. Bureau of Economic Analysis and author’s
calculations.

13

the trucking, railroad, telephone, and airline
industries (Table 2 ). More recently, the development of new information technologies may
have enhanced competition in the information
processing industry and reduced the costs to
customers of shopping for the lowest prices. In
addition, the development of new health care
delivery organizations and changing cultural
attitudes have enabled health care restructuring
to open up the medical industry to more price
competition in the 1990s (see Frech 1996).17
It is not certain why the adjusted after-tax
profit share has not recovered to the range of
the 1950s –70s. Nevertheless, developments
suggest that increased goods market competition has played a role. For example, anecdotal
reports in Federal Reserve Beige books in the
1990s suggest that inflation has remained low,
in part because intense goods market competition has made it difficult for firms to raise prices
(see Duca and VanHoose 1996).

12

Conclusion

Figure 2

Adjusted and Actual After-Tax Profit Shares
Of U.S. Nonfinancial Corporations, 1953 – 96
Percent

Percent

11

17

10
16
9
8

15
Actual

7
14
6
5
4
Adjusted
3
2

This article finds that the rise in the aftertax profit share of nonfinancial corporations
during the 1990s largely stems from a cyclical
recovery in the U.S. economy and a decline
in the net interest ratio often attributed to
deleveraging and lower interest rates. In this
sense, it is not clear that a long-lasting increase
in the economic returns to capital has occurred,
after accounting for the returns to debtholders

11
’53 ’56 ’59 ’62 ’65 ’68 ’71 ’74 ’77 ’80 ’83 ’86 ’89 ’92 ’95

NOTES: The adjusted profit share equals the actual profit share
minus the estimated effects of swings associated with
the business cycle, the Korean War profits tax, real
exchange rates, real oil prices, and net interest, as estimated by model 1 in Table 1. The shaded areas denote
recessions.
SOURCES: U.S. Bureau of Economic Analysis and the author’s
calculations.

FEDERAL RESERVE BANK OF DALLAS

Net interestadjusted

12

11

ECONOMIC REVIEW FOURTH QUARTER 1997

and equityholders and short-run, cyclical-related
movements.
Although the financial strength of U.S.
firms may not appear to have improved much
based on the adjusted profit share variable, the
deleveraging of the 1990s, as reflected in a
lower net interest ratio, has improved the ability
of firms to meet debt payments in the event of
a downturn (see Bernanke, Campbell, and
Whited 1990). Findings also suggest that much
of the recent rise in the U.S. net investment rate
owes less to a permanent jump in profitability
and more to other potential factors, such as the
business cycle, transitory rises in profits, or less
crowding out of investment due to a lowering
of the U.S. budget deficit. As for stock prices,
the findings do not necessarily imply that stock
prices are over- or undervalued, because profitability is only one of the three key determinants of equity values, the others being interest
rates and risk preferences.18
Findings do, however, indicate that the
after-tax profit share of nonfinancial corporations is not directly affected by inflation,
although this result could stem from changes in
the tax code, which may have altered the effect
of inflation on profit share. Nevertheless, this
finding does not necessarily imply that the level
of profits is not hurt by inflation. In particular,
low and stable inflation may indirectly boost the
income going to each share of production, with
factor income equaling a factor’s income share
multiplied by output. By creating an environment of stable and sustainable growth, boom –
bust cycles in production are curtailed, which
keeps output closer, on average, to its sustainable path and indirectly curtails cyclical swings
in factor shares. Indeed, with respect to the
former channel, growth and inflation have been
smoother under the Federal Reserve’s forwardlooking low inflation policy in effect since the
early 1980s.

3

4

5

6

7

8

Notes

1

2

9

I would like to thank, without implicating, Jean Zhang,
Jeremy Nalewaik, and Justin Marion for providing
research assistance and Mike Cox, Stephen Prowse,
and Evan Koenig for making helpful suggestions.
See Meyer and Kuh (1957) and Fazzari, Hubbard, and
Petersen (1988).
The Standard & Poor’s 500-stock index is a component
of the index of leading economic indicators, based on
evidence that stock prices are indicators of future economic growth — for example, see Bosworth (1975) and
Duffee and Prowse (1996). Stock market wealth has
been shown to affect consumption (see Mishkin 1977)

10

11

12

and is used in many econometric models of consumption and investment, such as those used by the
Federal Reserve Board and DRI/McGraw-Hill.
When compensation is deflated by an overall price
index for consumption and business goods, rather
than simply for consumption goods, real labor costs
trend with productivity, as shown in the 1996 Economic
Report of the President, 60 – 61.
Several studies of how exchange rates affect traded
goods, such as Mann (1986), have found a role for
imperfect competition in which exchange rates and
pricing power are related.
Crabbe, Pickering, and Prowse (1990) analyze the
shift in the 1980s from equity to debt.
Profits exclude capital consumption and inventoryvaluation adjustments. Results are similar using beforetax profits — see Duca and VanHoose (forthcoming).
To include as many business cycles in the sample
as possible, the after-tax profit ratio was created
by splicing two series: one based on data revisions
associated with the shift to chain-weighted GDP data
affecting data starting in 1959:1 and the other based
on earlier data not affected by this rebenchmarking.
Both real and nominal data on corporate profits before
1959 will be revised when pre-1959 estimates of real
chain-weighted GDP are released. Using overlapping
data over 1959:1–4, a break-adjustment ratio was
calculated. Multiplying the earlier data by this ratio
eliminates a small level shift between 1958:4 and
1959:1 when the two series are spliced together.
A similar procedure was used to create break-adjusted
series for the depreciation ratio (DEPRAT ) and the net
interest ratio (NETINT ).
For values of GDP variables involving pre-1959 data,
growth rates were based on 1987 GDP because pre1959 chain-weighted GDP data are not available. This
splicing was done to include as many business cycles
as possible to help estimate cyclical effects.
By including both lags, the model effectively includes
current unemployment and the change in unemployment. To control for the 1994 change in the employment survey, which boosted the unemployment rate
by 0.2, 0.2 is added to the unemployment rate before
1994:1.
The G –10, or Group of Ten countries, includes
Belgium, Canada, France, Germany, Italy, Japan, the
Netherlands, Sweden, the United Kingdom, and the
United States.
Gordon (1972, 411, Table 5, line 5e) estimates that the
controls reduced the before-tax profit ratio by 1.25
percentage points for nonfinancial corporations over
1971:4 – 72:2.
By contrast, the Korean War wage and price controls
did not limit price rises based on profit margins, were
imposed in an expansion, and did not cap profit
margins at recessionary levels. By stabilizing the gap
between prices and costs at its prewar, expansionary

12

13

14

15

16

17

18

level, these controls stabilized profit margins at levels
typical of an economic expansion. Hence, unlike the
Nixon price controls, dummies for the Korean War
price controls are not needed.
Denoting the coefficients on Ut and Ut –1 as β1 and β2,
respectively, the unemployment effects can be reexpressed as (β1 – β2)Ut + (β2 Ut – β2 Ut –1) ≡ (β1 – β2)Ut
+ β2 ∆Ut .
The controls had a larger, but still jointly insignificant,
effect on before-tax profit share in regressions not
reported in the tables. The coefficients on the control
variables in these regressions indicate that the controls depressed the before-tax ratios by about 1.25
percentage points, near Gordon’s (1972) estimate for
the effects in the first three quarters. The inclusion of
the price-control variables is qualitatively significant in
the before-tax runs because it affects the estimated
cyclical effects. Specifically, the unemployment rate
variables are only marginally significant when one
excludes the price-control dummy variables from the
model but very significant otherwise. However, this
result is not too surprising, given that there is good
reason to believe that the controls forestalled a cyclical
recovery in profit ratios in the early 1970s.
Two different scales are used in Figure 2 because estimated effects of selected regressors reflect the means
of these variables and because the model is estimated
with a constant. For our purposes, it is the trend of
adjusted profitability that matters.
Unlike the trend in the income share of before-tax
profits plus net interest, the trend in the share of aftertax profits adjusted for estimated net interest effects
(shown here) is less susceptible to being distorted
by the big declines in direct corporate taxation since
the 1960s.
Flatness in labor share suggests that the coefficient w
in Equation 2 has not risen.
For example, health maintenance organizations
(HMOs) are less vulnerable to malpractice suits than
traditional health providers and are thus more able to
adopt cost-saving practices. The increase in health
insurance premiums in the 1980s induced firms to
curb medical costs. Given the sensitivities surrounding
health care and time needed to reform practices, the
rise of HMOs lagged this need. The impact of HMOs
on corporate competition is limited to the extent that
health services are delivered by proprietorships and
partnerships.
In theory, stock prices equal the present value of future
earnings minus a “risk premium” to pay investors for
risk, where the present value formula adjusts earnings
for interest that could be earned from bonds. Thus,
stock prices should rise when earnings’ forecasts rise,
interest rates decline, or risk premiums fall. The risk
premium equals the price of the risk times the amount
of risk, and seems to have declined since the 1970s
(see Blanchard 1993). Aside from whether the price

FEDERAL RESERVE BANK OF DALLAS

of risk has fallen, one explanation for the apparent
decline of the risk premium is that the volatility of stock
returns — a measure of risk— has dropped since the
1970s (see Davis and White 1987 and Hickok 1996).
It is unclear whether the risk premium has fallen
enough to justify equity prices, because it is hard to
disentangle the market price of risk from the actual
degree of investment risk and to accurately measure
each of these elements.

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Have Corporate Tax Revenues Declined?” in Tax Policy
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(1990), “U.S. Corporate Leverage: Developments in 1987
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——— and ——— (1996), “Has Greater Competition
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Forecast Business Cycles and Test the Q-Theory of
Investment,” Federal Reserve Bank of Dallas Research
Department Working Paper no. 9610 (Dallas, August).

Economic Report of the President (1996) (Washington,
D.C.: U. S. Government Printing Office).

13

ECONOMIC REVIEW FOURTH QUARTER 1997

Economic Report of the President (1974) (Washington,
D.C.: U. S. Government Printing Office).

Hickok, Susan (1996), “Declining Economic Volatility
and Financial Markets,” Prudential Economics 12
(November): 8.

Economic Report of the President (1973) (Washington,
D.C.: U. S. Government Printing Office).

Mann, Catherine (1986), “Prices, Profit Margins, and
Exchange Rates,” Federal Reserve Bulletin 72 (June):
366 – 79.

Economic Report of the President (1954) (Washington,
D.C.: U. S. Government Printing Office).

Meyer, John R., and Edwin Kuh (1957), The Investment
Decision: An Empirical Study (Cambridge, Mass.:
Harvard University Press).

Fazzari, Steven M., R. Glenn Hubbard, and Bruce C.
Petersen (1988), “Financing Constraints and Corporate
Investment,” Brookings Papers on Economic Activity,
no. 1: 141– 95.

Mishkin, Frederick S. (1977), “What Depressed the
Consumer? The Household Balance Sheet and the
1973 –75 Recession,” Brookings Papers on Economic
Activity, no. 1: 123–64.

Feldstein, Martin, and Lawrence Summers (1977), “Is the
Rate of Profit Falling?” Brookings Papers on Economic
Activity, no. 1: 211– 27.

Nordhaus, William D. (1974), “The Falling Share of
Profits,” Brookings Papers on Economic Activity, no. 1:
169 – 208.

——— and ——— (1983), “Inflation and the Taxation of
Capital Income in the Corporate Sector,” National Tax
Journal 32 (4): 445 –70.

Winston, Clifford (1993), “Economic Deregulation: Days
of Reckoning for Microeconomists,” Journal of Economic
Literature 31 (September): 1263 – 89.

Frech III, H. E. (1996), Competition and Monopoly in
Medical Care (Washington, D.C.: American Enterprise
Institute for Public Policy Research).
Gordon, Robert J. (1972), “Wage –Price Controls and the
Shifting Phillips Curve,” Brookings Papers on Economic
Activity, no. 2: 385 – 421.

14

In the Uruguay round of multilateral trade
negotiations, industrialized nations focused on
using the General Agreement on Tariffs and
Trade (GATT) to increase the protection of intellectual property rights in developing countries.
Representatives of developed countries have
long claimed that the protection of intellectual
property in developing countries raises welfare
for all parties (Vishwasrao 1994). In contrast,
recent theoretical literature (Chin and Grossman
1988, Diwan and Rodrik 1991, Deardorff 1992,
and Helpman 1993) argues that in an inventionimporting country, where domestic invention is
scarce or nonexistent, the protection of intellectual property can reduce the country’s welfare
and, in some cases, world welfare.
An important assumption of this literature
is that the only return to society from protecting
intellectual property is that it stimulates inventors to invent. In the developing world,
however, human capital is assumed to be insufficient to produce many inventions. In addition,
markets in the industrialized countries can be
large enough that offering protection for intellectual property in developing countries adds
little incentive for invention in the industrialized
world.1 Therefore, intellectual property protection is likely to imply monopoly costs to
consumers in developing countries, without
providing much stimulus for either local or
foreign invention.2 Using this approach, Nogués
(1993) contributes empirical evidence that
patent protection for pharmaceuticals can
reduce welfare in developing countries.
Yet both developed and developing
countries may have another reason to protect
intellectual property — preserving product effectiveness. This reason for protection has not
been addressed in previous literature. For a
wide range of products, such as antibiotics,
fungicides, herbicides, and pesticides, effectiveness diminishes with cumulative use. Ironically,
such products have been among the least likely
to receive intellectual property protection in
nonindustrial countries (Butler 1990). Furthermore, developed countries generally let the protection of intellectual property rights for all
products expire after a set period. Neither
course is optimal.
To highlight the importance of preserving
product effectiveness, we present a model in
which the entire world shares a characteristic
that most analysts ascribe only to developing
countries: invention is not motivated by financial gain. Invention is costlessly bestowed
through divine intervention, pure altruism, or
dumb luck. Nonetheless, we show that protec-

Intellectual Property
Rights and Product
Effectiveness
Stephen P. A. Brown
Senior Economist and Assistant Vice President
Federal Reserve Bank of Dallas
William C. Gruben
Director, Center for Latin American Economics,
and Assistant Vice President
Federal Reserve Bank of Dallas

Y

et both developed and

developing countries may have
another reason to protect
intellectual property—
preserving product effectiveness.

FEDERAL RESERVE BANK OF DALLAS

15

ECONOMIC REVIEW FOURTH QUARTER 1997

pesticides lose effectiveness through cumulative
use. Effective use of such a product can destroy
all or most of the target population of bacteria,
fungus, weeds, or pests in a given ecological
niche. In some cases, small numbers of the target population survive; these are strains that are
resistant to the product in use. With the ecological niche cleared of competing members of the
target population, resistant strains multiply and
fill the niche. Eventually, resistant strains take
over the niche and spread to other environments. As this happens, the antibiotic, fungicide, herbicide, or pesticide in use loses its
effectiveness. Low-value uses accelerate the
process in which a product loses effectiveness.
We simplify the process by assuming that
product effectiveness at any moment in time is
a decreasing function of cumulative consumption to date, X t :

tion of intellectual property remains necessary
to optimize social welfare.
In the model, the absence of intellectual
property protection permits a competitive
market to develop for a product whose effectiveness diminishes with cumulative use. The
deterioration of product effectiveness yields an
externality cost that neither consumers nor producers take into account. As a consequence,
product effectiveness is depleted at a faster than
optimal rate, as resistant strains of bacteria,
fungi, weeds, and pests develop.
In contrast, a monopoly producer, who
owns the intellectual property right to such a
product, has an economic incentive to preserve
product effectiveness. The monopolist takes
into account how one individual’s use affects
future effectiveness and consequent product
demand. In doing so, the monopolist internalizes the externality and acts to preserve the
product’s effectiveness for future use. These
findings have important implications for domestic patent protection, as well as for trade
negotiations, which increasingly involve intellectual property rights.

(2)

where ∂E t /∂Xt < 0.
At any moment in time, cumulative consumption to date is defined:
X t ≡ ∫ 0 Qτ ∂τ,
t

An analytical model
The model characterizes the market for a
product whose effectiveness diminishes with
cumulative use. Invention is costlessly bestowed
under two types of policy regimes: one without
intellectual property protection and one with it.
In the regime without intellectual property protection, all producers have equal claim on the
invention, and they produce in a competitive
market. In the regime with intellectual property
protection, the invention is bestowed on a
single producer, who gains a monopoly.
We begin by presenting demand and supply conditions for the product. We next develop
the social-welfare-maximizing conditions for the
market. We then compare these optimality conditions with the conditions that would prevail in
a competitive market (with no intellectual property protection) and a monopolized market
(with intellectual property protection). Finally,
we conclude by comparing the competitive and
monopolistic cases.
Demand. The quantity demanded at any
moment in time (Q t ) is a function of price (Pt )
and product effectiveness (E t ):
(1)

E t = E (Xt ),

where τ is a dummy of integration for t (time),
and Q τ is the time derivative (rate of change)
of X t.
For analytical convenience, we rewrite
demand as an inverse function, incorporating
E (X t ) in place of E t :
(3)

Pt = D (Q t , Xt ),

where ∂Pt /∂Q t < 0, and ∂Pt /∂Xt < 0.
Supply. Production occurs in n identical
plants so the total quantity produced at any time
(Q t ) is the number of plants (n) times the quantity produced in each plant (q ):
(4)

Q t = nqt .

For an individual plant, the total cost of
production (c) is a function of output (q ):
(5)

c t = c (qt ),

where marginal cost is positive —that is, ∂c /∂q
> 0. If output is distributed efficiently across all
n plants, the aggregate total cost of production (C t ) can be written as a function of either
Q or q :4

Q t = Q (Pt ,E t ),

where ∂Q t /∂Pt < 0 and ∂Q t /∂E t > 0.3
Natural selection drives the process by
which antibiotics, fungicides, herbicides, and

(6)

16

C t = C (Q t ) = n •c (qt ).

(11)

Social welfare maximization. The optimality conditions for social welfare maximization serve as a benchmark against which
competition and monopoly can be compared.
Social welfare is the present discounted value of
the sum of consumer and producer surplus,
evaluated over time:
(7)

∞

P = D (Q,X ).

For each firm, profit-maximizing conditions are obtained at the output where the firm’s
marginal cost equals the market price:
(12)

PVSW = ∫ 0 e –r t ∫ 0 [D (χ, X ) – C χ) ∂χ∂t ,
Q

P = cq .

With n identical firms, market-clearing conditions require that the quantity demanded
(Q ) equals the total quantity produced (n •q ) at
the market-clearing price (P ). Given the cost
function, equation 6, and Q = nq, it can be
shown that CQ equals cq . Therefore, competition yields the familiar case in which price
equals marginal cost:

where r is the interest rate, χ is a dummy of
integration for quantity (Q ), and C χ is defined
as ∂C/∂χ. (To simplify notation, we drop the
time subscript; it is implicit.)
Pontryagin’s maximum principle (and some
manipulation) yields the optimality condition
for social welfare maximization:

(13)
(8)

This familiar case is not optimal, however.
With consumers and producers ignoring the
externality effects that consumption has on
future effectiveness, the user cost found in
equation 8 does not arise. Figure 1 illustrates the
effect for a given demand curve at any moment
in time. P * and Q * are the socially optimal price
and quantity, respectively. For the given
demand curve, the competitive market will yield
a lower price, PC , and a higher quantity, QC ,
than is socially optimal.
Comparing the dynamics of the competitive case with those of the socially optimal case
is more complicated. Because the competitive
market produces above the socially optimal
rate, the demand curve shifts inward more
rapidly than in the optimal case. At some point
in time, demand in the competitive case will
have shifted inward enough more that output,

Price (P ) equals marginal cost (CQ ) plus a user
cost (λ).5
The user cost represents the marginal
value of preserving effectiveness for future periods as follows:
(9)

∞

λ = –e r t ∫ t (PX e – r τ) ∂τ,

where PX is defined as ∂P/∂X. If cumulative use
reduces effectiveness, the price consumers are
willing to pay for the product falls with cumulative production, PX < 0, and the user cost is
positive. If cumulative consumption does not
alter effectiveness, then PX = 0, the user cost is
zero, and equation 8 becomes the familiar optimality condition in which price equals marginal
cost.
The optimality conditions also indicate
that the user cost can increase or decrease in
value over time. In particular,
(10)

P = CQ .

P = CQ + λ.

⋅
λ = r λ + PX ,

Figure 1

Supply and Demand

which, given that PX < 0, indicates that the user
cost grows more slowly than the interest rate
and declines if PX is sufficiently negative.
The optimality condition expressed in
equation 8 serves as a benchmark against which
we compare the competitive and monopolistic
cases.
Competitive case. In a purely competitive
case, product effectiveness influences demand,
but individual consumers and producers ignore
the effect that individual consumption has on
future effectiveness.
In the competitive case, inverse demand
remains

FEDERAL RESERVE BANK OF DALLAS

Price
Marginal cost
plus user cost

PM
P*

Marginal
cost

MRM
PC

Demand

Marginal
revenue

QM

17

Q*

ECONOMIC REVIEW FOURTH QUARTER 1997

QC

Quantity

demand in the monopolistic case will have
shifted inward enough less that output, Q , will
be higher than if use of the product had always
been managed in a socially optimal fashion.
This condition will be maintained thereafter
until product effectiveness goes to zero. Nevertheless, on the monopolistic time path, cumulative consumption to date, X, will always be
lower and the price will always be higher than
on the socially optimal time path.
One way to encourage the monopolist
to allocate the product in a socially optimal
manner is to establish a government-mandated
price path in which the market-clearing price in
each period is set equal to marginal cost plus
user cost. With a set price path, the monopolist
faces a perfectly elastic demand, and the incentive to restrict output disappears. Setting such
a price path requires considerable information about demand and true production costs.
In addition, such a policy could be rife with
political influence because the monopolist
would have an incentive to lobby government
officials to raise the regulated price above the
optimal level.
A more politic approach is to offer the
monopolist a production subsidy equal to –PQ Q.
The government can avoid making a transfer to
the monopolist by auctioning off permanent
rights to monopolize the product’s market with
the government subsidy in place. Under competitive bidding, the monopoly rents and subsidies would be recaptured by the government.
This policy can solve the allocation problem
only if the government commitment to honor
the contract is credible. We do not address time
inconsistency problems here.

Q, will be lower than if use of the product had
always been optimal. This condition continues
thereafter until product effectiveness goes to
zero. Nevertheless, on the competitive time
path, the cumulative consumption to date, X,
will always be greater and the price will be
lower than on the socially optimal time path.
Although intellectual property protection
optimizes social welfare, the no-protection,
competitive case can be made socially optimal
by imposing a tax equal to the user cost or
by identifying and banning low-value uses. The
trouble with these solutions is that political time
horizons and pressures may render political
actors unwilling or unable to optimally defer
product use either through higher prices or by
proscribing low-value uses.
Monopolistic case. In the monopolistic
case, the single seller has an incentive to consider how current consumption affects future
effectiveness because the loss in effectiveness
will be reflected in future sales. At the same
time, however, a monopolist has the incentive
to earn monopolistic rents by restricting output.
The monopolist’s profit is described as
(14)

∞

Π = ∫ 0 e –r t [P (Q , X ) •Q – C (Q )] ∂t .

Pontryagin’s maximum principle (and
some manipulation) yields the monopolist’s
profit-maximizing condition as
(15)

P + PQ •Q = CQ + λ.

Marginal revenue (P + PQ Q ) equals marginal
cost (CQ ) plus the user cost (λ), where PQ is the
reduction in price required to sell the marginal
unit. Equations 9 and 10 describe the user cost.
The presence of the user cost in equation
15 shows that the monopolist takes into account
how current consumption affects future effectiveness. At the same time, however, the
monopolist restricts output to obtain a monopoly rent. Figure 1 illustrates monopolistic
behavior for a given demand curve at any
moment in time. P * and Q * remain the socially
optimal price and quantity, respectively. For the
given demand curve, the monopolist sets a
higher price, PM , and sells a smaller quantity,
Q M , than is optimal. (The monopolist obtains a
marginal revenue of MRM .)
Comparing the dynamics of the monopolistic case with those of the socially optimal one
is more complicated. Because the monopolist
produces below the socially optimal rate, the
demand curve shifts inward less rapidly than in
the optimal case. At some point in time,

Conclusion: Competition versus monopoly
As shown above, neither competition nor
monopoly is consistent with social welfare
maximization when a product’s effectiveness
declines with cumulative use. A competitive
industry would charge too low a price and
deplete the product’s effectiveness too rapidly.
A monopolist would charge too high a price
and produce too little of the product.
Our results are broadly consistent with
those of Chin and Grossman, Diwan and
Rodrik, Deardorff, and Helpman. They find that
a competitive industry would provide too little
invention, and a monopoly too little output, to
maximize social welfare. But in their analyses,
competition is preferable to monopoly when
the welfare cost of the lost stimulus to invent is
less than the welfare cost of restricted output.
In our analysis, competition is preferable

18

to monopoly when the welfare cost of failing to
protect product effectiveness is less than the
welfare cost of restricted output. Monopoly is
preferable to competition when the welfare cost
of failing to protect product effectiveness is
more than the welfare cost of restricted output.
We are unable to put prior values on these costs
other than to say they depend on the elasticity
of demand and the rate at which product effectiveness is depleted through cumulative use. In
some cases, a monopoly that protects intellectual property may be preferable to competition,
even when invention is costlessly provided.
If we simultaneously consider both the
incentive to invent and the depletion of product
effectiveness, competition will result in too little
invention and too rapid depletion of product
effectiveness. A monopolist will produce too
little of the product.6 In addition, Vishwasrao
shows that the gains to developed countries in
avoiding monopoly pricing through patent
infringement may be limited.7 As a consequence, the case for protecting intellectual
property rights is substantially stronger for
products whose effectiveness is depleted with
cumulative use. Products with this characteristic
— antibiotics, fungicides, herbicides, and pesticides —have been among the least likely to
receive patent protection in developing countries.

6

7

References
Baumol, William J., John C. Panzar, and Robert D. Willig
(1988), Contestable Markets and the Theory of Industry
Structure, rev. ed. (Austin, Texas: Harcourt, Brace, and
Jovanovich Publishers).
———, and Robert D. Willig (1981), “Fixed Cost, Sunk
Cost, Entry Barriers and Sustainability of Monopoly,”
Quarterly Journal of Economics 96 (August): 405 – 31.
Butler, Alison (1990), “The Trade Related Aspects of
Intellectual Property Rights: What Is at Stake,” Federal
Reserve Bank of St. Louis Review, November/December,
34 – 46.

Notes

1

2

3

4

5

Chin, Judith C., and Gene M. Grossman (1988),
“Intellectual Property Rights and North–South Trade,”
NBER Working Paper Series, no. 2769 (Cambridge,
Mass.: National Bureau of Economic Research,
November).

The authors would like to thank Zsolt Becsi, Alan V.
Deardorff, Evan Koenig, Roy Ruffin, and Carlos Zarazaga for helpful comments on earlier drafts, without
implicating them in any shortcomings of the analysis.
Taylor (1994) underscores the importance of incentives
by showing that an invention-importing country can
slow technological progress and make both itself and
the world worse off when its failure to protect intellectual property developed elsewhere reduces the incentive to invent elsewhere.
Diwan and Rodrik (1991) and Frischstak (1990) find
that developing countries can improve their welfare by
protecting intellectual property when they have a
strong demand for a product that is not particularly
useful in industrialized countries.
Over some ranges of effectiveness, consumers may
increase their use of an antibiotic, fungicide, herbicide,
or pesticide to offset reduced effectiveness. We
abstract from this case by assuming that they would
do so only at a reduced price. Therefore, at a given
price, consumption falls with effectiveness.
For simplicity, we assume the same number of plants
in all three cases. This assumption simplifies the analysis without affecting the results.
This optimality condition should be familiar to those
who are versed in the economics of exhaustible nat-

FEDERAL RESERVE BANK OF DALLAS

ural resources. See Dasgupta and Heal (1979).
The monopolist’s incentive to restrict output may be
limited, however, by the potential entry of competing
inventions. The extent of competition may depend on
the breadth of patent protection and the cost of imitation. See Baumol and Willig (1981), Baumol, Panzar,
and Willig (1988), Gallini (1992), Gilbert and Shapiro
(1990), and Klemperer (1990).
Vishwasrao shows that a lack of patent protection can
adversely affect the licensing of low-cost technologies
to developing countries and that strategic behavior on
the part of firms in developed countries can erode the
gains developing countries reap through patent
infringement.

Dasgupta, P. S., and G. M. Heal (1979), Economic
Theory and Exhaustible Resources (Cambridge: Cambridge University Press).
Deardorff, Alan V. (1992), “Welfare Effects of Global
Patent Protection,” Economica 59 (February): 35 – 51.
Diwan, Ishac, and Dani Rodrik (1991), “Patents, Appropriate Technology, and North – South Trade,” Journal of
International Economics 30 (February): 27– 48.
Frischstak, Claudio R. (1990), “The Protection of
Intellectual Property Rights and Industrial Technological
Development,” in Intellectual Property Rights in Science
and Technology and Economic Performance, ed. Francis
W. Rushing and Carole Ganz Brown (Boulder, Colo.:
Westview Press), 61– 98.
Gallini, Nancy T. (1992), “Patent Policy and Costly
Imitation,” The RAND Journal of Economics 23 (Spring):
52 – 63.

19

ECONOMIC REVIEW FOURTH QUARTER 1997

Gilbert, Richard, and Carl Shapiro (1990), “Optimal
Patent Length and Breadth,” The RAND Journal of
Economics 21 (Spring): 106 –12.

Nogués, Julio J. (1993), “Social Costs and Benefits of
Introducing Patent Protection for Pharmaceutical Drugs in
Developing Countries,” The Developing Economies 31
(March): 24 – 53.

Helpman, Elhanan (1993), “Innovation, Imitation and
Intellectual Property Rights,” Econometrica 61
(November): 1247– 80.

Taylor, M. Scott (1994), “TRIPs, Trade and Growth,”
International Economic Review 35 (May): 361– 81.

Klemperer, Paul (1990), “How Broad Should the Scope of
Patent Protection Be?” The RAND Journal of Economics
21 (Spring): 113 – 30.

Vishwasrao, Sharmila (1994), “Intellectual Property
Rights and the Mode of Technology Transfer,” Journal
of Development Economics 44 (August): 381– 402.

20

Is the Business
Cycle of Argentina
“Different”?

Societies would prefer a steady growth
path for their national income of, say, 3 percent
every year to one that delivers a 3 percent
growth rate on average, but with zigzags from,
say, 12 percent one year to –6 percent the next.
Consequently, they typically demand that
policymakers eliminate undesired economic
fluctuations.1 It is not surprising, then, that the
understanding of business cycles has always
captured the interest of economists and has
inspired some of their best work.
The work of John Maynard Keynes and
Milton Friedman went a long way in defining
the terms and identifying the issues that a successful theory of economic fluctuations ought to
address. Despite the much-advertised difference
between the schools of thought inspired by
these scholars, their work agrees on something
very important: nominal factors, such as the
money supply, interest rates, and price rigidities, play the most important role in explaining
economic fluctuations.
As is well known, the 1970s were not kind
to the Keynesian interpretation of business cycles.
This interpretation predicts that the rising inflation rates of that decade should have been
associated with declining unemployment rates,
not with the rising rates actually observed.
Empirical and theoretical research did not treat
the “rival” school much better. Sims (1980), for
example, showed evidence that seems to contradict some versions of the monetarist theory.
Initially, the theoretical developments
inspired by these failures kept nominal factors
as the paramount force behind economic fluctuations. In fact, in Lucas (1972), the first and
perhaps most celebrated application of the
novel approach to macroeconomic analysis for
which Robert Lucas received the 1995 Nobel
Prize, the money supply still plays a crucial role
for the business cycle. Thus, economists were
surprised when Kydland and Prescott (1982)
showed that one could account for two-thirds of
the U.S. economic fluctuations with a dynamic
stochastic general equilibrium model from
which nominal variables were totally absent—
that is, a model without any money in it.
Kydland and Prescott obtained this result
using a variation of the same basic theoretical
model economists had been using time and
again to study economic growth issues.
Unifying theories —that is, theories that can
simultaneously explain seemingly unrelated
phenomena—are usually welcome in science.
What many economists found attractive about
the Real Business Cycle (RBC) theory proposed
by Kydland and Prescott was that, for the first

Finn E. Kydland
Professor
Carnegie Mellon University
and
Research Associate
Federal Reserve Bank of Dallas
Carlos E. J. M. Zarazaga
Senior Economist and Executive Director
Center for Latin American Economics
Federal Reserve Bank of Dallas

N

ominal factors do not seem

to be able to account for any
significant fraction of the
business cycles of Latin
American countries in general,
and of Argentina in particular.
Perhaps for this reason it is time
to give real factors their fair
chance to do the job.

FEDERAL RESERVE BANK OF DALLAS

21

ECONOMIC REVIEW FOURTH QUARTER 1997

time, a business-cycle theory pointed to the
possibility that the same analytical tools used to
address economic growth issues could be used
to address business-cycle questions as well. This
may explain why these economists regarded
Kydland and Prescott’s findings persuasive
enough to begin seriously exploring the
hypothesis that “real” factors, rather than nominal ones, are a prevalent driving force behind
economic fluctuations.2 Although real or supplyside factors, such as the amount of resources
used by the government, tax policies, technological changes, government regulations, modifications of financial intermediation rules, and
even political shocks signaling possible changes
in property rights, may appear to be the obvious candidates to explain business cycles, this
was not that clear a short while ago.
The process of verifying, sharpening, or
refuting the real-shock account of business
cycles has generated a large body of theoretical
and empirical research concentrated, so far,
on developed countries. This is unfortunate, because the evidence suggests that economic fluctuations are particularly severe in developing
countries. Understanding why this occurs could
lead to ways to make the business cycles of
these countries at least as smooth as those of
developed ones. What makes the study of Latin
American countries’ business cycles particularly
interesting is the claim that economic fluctuations in those countries have been driven by
nominal factors. Science makes progress precisely when it encounters observations that the
prevailing paradigm cannot explain. Therefore,
there seems to be a compelling need to confirm
the alleged anomalies by answering the question, Are business-cycle regularities in Latin
America really all that different from those in
the United States and in Organization for Economic Cooperation and Development (OECD)
and other European countries?
This article focuses this question on
Argentina, with the hope of making a modest
contribution to the understanding of the business cycles of Latin American countries in
general. For example, if Argentina’s businesscycle regularities are similar to those of the
United States or Europe, then the business
cycles of all these countries may be manifestations of essentially the same phenomenon.
Therefore, real factors could play an important
role in accounting for Argentina’s business
cycles, just as, according to recent research,
they do in the United States and Europe.
By contrast, if Argentina’s business cycles
show important anomalies with respect to the

evidence available for other countries, then the
possibility of real factors playing an important
role in its business cycle diminishes. In this
case, existing interpretations emphasizing the
role of nominal variables in Latin America may
regain the prominence they had in businesscycle theories until the 1970s. Allowing for
comparisons with the empirical evidence for
other countries, this article examines the
Argentinean business-cycle regularities with
the same methodological approach used in
previous studies for the United States and several European countries.
In the following section, we present the
evidence other authors have used to support the
contention that nominal factors have driven the
business cycles in Latin America and provide
reasons to doubt the robustness of those findings. We also suggest that the data require further systematic scrutiny before economists can
conclude with some confidence that business
cycles in Latin American countries, and particularly in Argentina, differ in nature from those
observed in the United States and in OECD and
other European countries. Next, we undertake
one such systematic study by presenting, as the
availability of data permits, the Argentinean
counterpart of the statistics researchers have
used to describe the business cycles of the
United States and several European countries.
We then compare the statistics for Argentina
with those of other countries and state the
implications that result from analysis of crosscountry similarities and differences. The last
section summarizes our conclusions.

The state of the business-cycle
debate in Latin America
The understanding of the Latin American
business cycles has not escaped the view that
nominal shocks are the predominant cause of
economic fluctuations. This view still influences
the thinking on many Latin American economic
problems. This thinking is particularly noticeable in the inflation stabilization literature.
One of the most serious economic problems many Latin American countries have faced
in past decades has been persistent, high inflation.3 Therefore, the quest to find the best antiinflation policies has inspired a large body of
research on this problem. The monetarist influence in that literature is evident in its contention
that nominal factors (such as changes in the
nominal exchange rate regime) were the only
systematic force driving economic fluctuations
around the time the stabilization programs were
implemented. For example, the conventional

22

monetarist approach: the dynamics of output
immediately after the announcement of an ERBS
program were mere continuations of upswings or
downturns that had begun earlier. In these four
cases, forces other than the adoption of a fixed
or pegged exchange rate were already driving
the business cycle when the ERBS programs
began. But such conclusions from the casual
reading of two-dimensional plots would be premature.7 We are more persuaded, instead, by
the more thorough empirical effort of Rebelo
and Végh (1995), who conclude that monetaristinspired theoretical models of ERBS programs
are quantitatively incapable of replicating any
significant fraction of the economic fluctuations
associated with such programs.
The evidence on ERBS programs, both
from casual plot readings and from the work of
Rebelo and Végh, poses a serious challenge to
monetarist theories of Latin American business
cycles: if nominal exchange rate shocks in Latin
America seem to have failed to produce the
noticeable and consistent effects on consumption and other real variables predicted by monetarist-inspired theories precisely when they
were given the best shot at it, how could they
have significant real effects at other times? 8
A natural next step in the research agenda
is to pay more attention to real shocks as a
potentially important source of the economic
fluctuations observed in Latin American countries, including fluctuations observed during
inflation stabilization programs.9 In principle,
there is no reason the assessment of the quantitative importance of such shocks in Latin
America could not be accomplished with the
same kind of dynamic stochastic general equilibrium models the RBC tradition has used to
that effect for the United States and other developed countries.
But such a research program must start by
describing the data with a systematic, atheoretical methodology.10 The remaining sections of
this article make a modest attempt in that direction by describing the business-cycle regularities
of Argentina without imposing theoretical priors
to the data.11

wisdom in Latin America is that anti-inflation
programs using the exchange rate as a nominal
anchor (exchange-rate-based stabilization, or
ERBS, programs) have been able to reduce the
inflation rate without causing the initial output
losses associated with programs that use some
monetary aggregate as a nominal anchor
(money-based stabilization programs).4
Of course, a theory for stabilization programs is not the same as a theory for the
business cycle. But there should be some consistency among them. For example, a finding
that nominal shocks do not have important real
effects during Latin American stabilization programs would make it harder to maintain the
monetarist view that such factors may have
been important at any other point of the business cycle. And this is precisely what we find
problematic: a reexamination of the evidence
on ERBS programs shows that it is far from clear
that the adoption of the exchange rate as a
nominal anchor has been responsible, as the
literature claims, for the economic fluctuations
observed during those programs.
Figure 1 illustrates the consumption
growth rates for the ten ERBS programs studied
by Végh (1992). The vertical line indicates the
year or quarter in which the ERBS program
started.5 Casual inspection of the plots suggests
that only in the first four cases did consumption
experience the upward jump that theory predicts should occur upon announcement of ERBS
programs.6 However, this theoretical prediction
did not materialize in the remaining six cases. In
particular, in none of these six did consumption
grow faster than in the immediately preceding
period. Instead, in four of the six cases, consumption growth was basically the same immediately before and immediately after the
announcement of the corresponding ERBS program. In two of the four, the so-called consumption boom preceded the announcement.
In the other two, there was no consumption
boom whatsoever: consumption continued
falling at approximately the same rate as before
the ERBS programs began. Furthermore, in the
last two cases, the ERBS program was followed
instead by a consumption bust.
Therefore, the timing, intensity, or direction of consumption growth for the countries in
Végh’s study, after most ERBS programs began,
appears to differ from that implied by the ERBS
theory.
In this sense, at least four of the plots in
Figure 1 (Chile, February 1978; Argentina, December 1978; Argentina, June 1985; and Israel,
July 1985) could be interpreted using the non-

FEDERAL RESERVE BANK OF DALLAS

Business-cycle regularities for Argentina
Some caveats about the data. National
account data in Latin America are not as reliable
as their U.S. and OECD counterparts.12 In fact,
because of frequent methodological changes
and corrections of previous errors, the reported
series may change substantially from one
national account estimate to the next. This is
indeed the case for Argentina. For example,

23

ECONOMIC REVIEW FOURTH QUARTER 1997

Figure 1

ERBS Programs
Uruguay, June 1968
10

Real private consumption*

Real private consumption*

Argentina, March 1967
12
10
8
6
4
2
0
–2
–4
–6
’62

’63

’64

’65

’66

’67

’68

’69

’70

’71

8
6
4
2
0
–2
–4

’72

’67

SOURCE: Table 5 in Végh (1992).

’68

’69

Uruguay, October 1978
Real private consumption*

Real GDP*

10
5
0
–5
–10
–15
’79:1

’80:1

’81:1

’82:1

’83:1

SOURCE: Table 10 in Végh (1992).

15
10
5
0
–5
–10
’84:1

’85:1

’79:1

’80:1

’81:1

’82:1

’83:1

Real private consumption*

20
15
10
5
0
–5
–10
–15
–20
’78:1

’79:1

15
10
5
0
–5
–10
’86:1

’87:1

’88:1

SOURCE: Table 11 in Végh (1992).

25
20
15
10
5
0
–5
–10
–15
’84:1

15
10
5
0
–5
–10
’65

’85:1

’86:1

’87:1

’88:1

’89:1

’90:1

Mexico, December 1987
Real private consumption*

Real private consumption*

Brazil, March 1964

’64

’82:1

SOURCE: Table 13 in Végh (1992).

20

’63

’81:1

Israel, July 1985
Real private consumption*

Real total consumption*

Argentina, June 1985

’62

’80:1

SOURCE: Table 8 in Végh (1992).

20

’85:1

’87:1

Argentina, December 1978

SOURCE: Table 9 in Végh (1992).

–15
’84:1

’86:1

SOURCE: Table 12 in Végh (1992).

Real total consumption*
’78:1

’72

20

Chile, February 1978
20
15
10
5
0
–5
–10
–15
–20
’77:1

’71

Brazil, February 1986

15

–20
’78:1

’70

SOURCE: Table 7 in Végh (1992).

’66

’67

’68

’69

’70

SOURCE: Table 6 in Végh (1992).

8
6
4
2
0
–2
–4
’87:1

’88:1

’89:1

SOURCE: Table 14 in Végh (1992).

* Growth in percent with respect to the same period of the previous year.

24

’90:1

’91:1

’73

volatility of consumption relative to output is
substantially lower in the national accounts estimate at 1986 prices (released at the end of 1996)
than in the previous estimates at 1970 prices.
The example above emphasizes that in
dealing with countries such as Argentina,
researchers should heed the usual warning to
appropriately weigh the quality of the data
before taking for puzzles anomalies that in
reality may be mere statistical artifacts. For that
reason, we report the business-cycle regularities
obtained from using two different estimates of
GDP and its components. The comparison of
the results from each data set will eventually
give some idea of the confidence one should
place on the business-cycle regularities of Argentina reported here or elsewhere (for examples,
see Kaufman and Sturzenegger 1996 and Carrera,
Féliz, and Panigo 1996).
One estimate (the “old” estimate), in constant prices of 1970, covers the 1970:1 – 90:4
period and was prepared by the Central Bank of
Argentina. We obtained this estimate from the
FIEL (Fundación de Investigaciones Económicas
Latinoamericanas) data bank. The other estimate (the “new” estimate), in constant prices of
1986, covers the 1980:1–95:4 period. The figures
for this estimate were taken from the publication Oferta y Demanda Globales, 1980 –1995,
prepared by the Dirección Nacional de Cuentas
Nacionales. Notice that these two estimates
overlap only during the 1980:1– 90:4 period.13
Methodology. We characterize the business-cycle regularities of Argentina using
Kydland and Prescott (1990) as a guide. Their
procedure is inspired by Lucas (1977), who
defines the business-cycle component of a variable as its deviation from trend. Kydland and
Prescott define the trend of a variable as that
which results from applying the Hodrick –
Prescott filter (HP filter) to the raw data.
Informally, this filter produces trends that are
“close to the one that students of business
cycles and growth would draw through a time
plot” (Kydland and Prescott 1990).14 Application
of the HP filter to Argentinean GDP, for example, produces the trend represented by the
smoother curves in Figure 2.15,16
Except for net exports, all variables in the
tables of this article are expressed in natural
logarithms, as is standard in the business-cycle
literature.17 Since it is not possible to compute
the logarithm of negative values, variables that
can take on such negative values, such as net
exports, were expressed instead as ratios to
GDP. All the variables were seasonally adjusted
using the X-11 procedure.

FEDERAL RESERVE BANK OF DALLAS

Figure 2

Real GDP, Old and New Estimates
Thousands of 1986 pesos (log scale)*
9.5

9.4

Real GDP old
Trend real GDP old
Real GDP new
Trend real GDP new

9.3

9.2

9.1

9

8.9
’70:1 ’72:1 ’74:1 ’76:1 ’78:1 ’80:1 ’82:1 ’84:1 ’86:1 ’88:1 ’90:1 ’92:1 ’94:1

* For visual effect, the old estimates have been rescaled so that
their level is the same as for the new estimates in 1980:1.
SOURCES: Dirección Nacional de Cuentas Nacionales for new
estimates; FIEL for old ones; authors’ calculations for
trends.

The tables report statistics that measure (1)
the direction of the movements of a variable
compared with that of real GDP (procyclical, in
the same direction; countercyclical, in the opposite direction; acyclical, when there is no clear
pattern); (2) the degree to which the variable
follows the movements of real GDP (contemporaneous correlation); (3) the amplitude of fluctuations (volatility or relative volatility); and (4)
the phase shift—that is, whether a variable
changes before or after real GDP does (leads or
lags the cycle, respectively.)
The statistics volatility corresponds to the
standard deviation of the percentage by which
the cyclical component of a variable deviates
from trend. The statistics relative volatility is the
ratio between the volatility of the variable of reference and the volatility of real GDP.

Real facts for Argentina
Output and its components: GDP. Table 1
reports statistics for real GDP and its major
components. The first striking feature of the
table is the high volatility of real GDP.
According to the new national account estimates, the percentage standard deviation from
trend of Argentina’s real GDP is roughly 2.5
times larger than for the United States. Real GDP
volatility is also high in the old national account
estimates, but within the range observed in
European countries such as Greece (2.85),
Portugal (3.05), and Luxembourg (3.2).18,19
Total consumption. An important caveat in
interpreting the consumption evidence is that in
Argentina’s national account, consumption is
computed as a residual, which casts consider-

25

ECONOMIC REVIEW FOURTH QUARTER 1997

Table 1

Cyclical Behavior of Real GDP and Its Main Components in Argentina and Other Countries
Argentina
(new national
account estimates)
1980:1– 95:4

Argentina
(old national
account estimates)
1970:1– 90:4

United
States1

OECD, G –7,
and other
European
countries2

4.59

3.06

1.71

.90 to 3.20

Total consumption
Contemporaneous correlation
Relative volatility 4
Phase shift

Procyclical
.96
1.19
Coincidental

Procyclical
.84
1.69
Coincidental

Procyclical
.82
.73
Coincidental

Procyclical
.1 to .83
.66 to 1.46
Coincidental 5

Gross fixed investment
Contemporaneous correlation
Relative volatility 4
Phase shift

Procyclical
.94
2.90
Coincidental

Procyclical
.71
3.44
Coincidental

Procyclical
.90
3.15
Coincidental

Procyclical
.15 to .90
2.30 to 5.63
Coincidental

Acyclical6
.206
3.196
Lagging6

Acyclical7
.247
4.437
Lagging7

Acyclical
.05
1.21
Lagging

Acyclical
–.23 to .27
.36 to 1.28
—

Net exports 8
Contemporaneous correlation
Volatility 3
Phase shift

Countercyclical
–.84
2.28
Coincidental

Countercyclical
–.62
3.27
Coincidental

Acyclical
–.28
.45
Leading

Acyclical/countercyclical
–.01 to –.68
.5 to 1.33
—

Imports
Contemporaneous correlation
Relative volatility 4
Phase shift

Procyclical
.81
4.05
Coincidental

Procyclical
.71
5.61
Coincidental

Procyclical
.71
2.88
Coincidental

—
—
—
—

Exports
Contemporaneous correlation
Relative volatility 4
Phase shift

Countercyclical
–.61
1.68
Coincidental

Countercyclical
–.21
3.21
Coincidental

Procyclical
.34
3.23
Lagging

—
—
—
—

Real GDP volatility 3

Government consumption indicator
Contemporaneous correlation
Relative volatility 4
Phase shift

1
2
3
4
5
6
7
8

Statistics are from Kydland and Prescott (1990).
Statistics are from Backus, Kehoe, and Kydland (1995) and Christodoulakis, Dimelis, and Kollintzas (1995).
Percent standard deviation from trend.
Ratio of volatility of the variable and the volatility of real GDP.
Except in France, where, according to Christodoulakis, Dimelis, and Kollintzas (1995), it leads the cycle.
For the period 1980:1– 89:4.
For the period 1970:1– 89:4.
Trade balance as percentage of GDP.

NOTE: Seemingly anomalous statistics are in bold type.
SOURCES: Authors’ calculations, using the sources reported in the text.

able doubt on the nature of the anomalous
behavior of consumption that we discuss below.
The volatility of real GDP and the relative
one for consumption imply that the volatility of
this real GDP component is higher than that for
the United States or European countries. But
this anomaly is not all that remarkable because
it results directly from the reported high volatility of real GDP and the fact that consumption
and GDP are highly correlated.
Perhaps what is remarkable is that the
volatility of consumption is larger than that of
output. Although theoretically the opposite
should hold, this excess relative consumption
volatility is within the ranges observed in Japan
and some European countries.20 More specifically, according to the new national account

estimates in Table 1, Argentinean consumption
is 19 percent more volatile than GDP. This is not
uncommon by international standards. Backus,
Kehoe, and Kydland (1995) report that the corresponding figure is 14 percent for Austria and
15 percent for Japan. According to Christodoulakis, Dimelis, and Kollintzas (1995), it is as
high as 46 percent for the Netherlands.21
By contrast, relative consumption volatility
does exceed international standards for the old
national account estimates. A consumption
volatility 70 percent larger than that of output
is indeed hard to explain. Some studies have
attributed this excess volatility to the presence
of credit constraints.22 However, there are reasons to be skeptical about this explanation
because in models with credit constraints, con-

26

sumption is not as smooth as it would be
otherwise, but it is still typically smoother than
income.23
In considering the correlation between
output and consumption, it is the figure for the
old national account estimates that is normal
and the one for the new national account estimates that is abnormal. The correlation of 0.84
for the old national account estimates is about
the same as the 0.83 correlation reported for
Canada—the highest correlation among the
countries reported in Backus, Kehoe, and
Kydland (1995) and Christodoulakis, Dimelis,
and Kollintzas (1995). This means that the 0.96
correlation between deviations from trend of
consumption and GDP reported for the new
national account estimates is unusually high by
international standards. It seems to be high even
by Latin American standards, as that correlation
is 0.91 for Mexico (our own estimates for the
1980:1–95:4 period) and 0.88 for Uruguay (for
the 1976:1–93:4 period; see Kamil Saúl 1997).
Theory predicts that such correlation
should be higher the more permanent the
shocks are to income. Therefore, the high correlation observed for Argentina might be an
indication that its business cycle is indeed different in the sense that shocks are more permanent there than in other countries. We suspect,
however, that most business-cycle models,
monetarist or real, will have a hard time
accounting for this high correlation without, at
the same time, failing to accommodate other
key regularities of the Argentinean business
cycle. Nonetheless, there are reasons to be
cautious about the magnitude of the contemporaneous correlation between detrended consumption and GDP in Argentina. One reason, of
course, is that the significant discrepancy
between the correlations obtained with the two
national account estimates points to the possibility of important measurement errors. This
possibility becomes even more apparent when
we recall that consumption in Argentina, as in
many developing countries, is calculated as
a residual. This residual includes government
consumption —for which Argentina produces
no separate quarterly estimates—and, in the
case of the new national accounts estimate,
changes in inventories, for which there also is
no separate estimate.
An additional methodological source of
spurious correlation between consumption and
output is the way output in Argentina is allocated between consumption and investment.
Many goods— such as automobiles, electronics,
furniture, computers, and telecommunications

FEDERAL RESERVE BANK OF DALLAS

equipment—may be used for consumption or
investment purposes. Unfortunately, Argentina
does not have the information necessary to
determine the categories in which these goods
are being applied. To circumvent this problem,
the production of many items is imputed to
both consumption and investment according to
fixed coefficients constructed with information
available only for the base year. For example, 80
percent of automobile production is always
imputed to consumption and 20 percent to
investment. The same procedure is applied to
imports and to the output of many other industries that produce goods that can be used for
both investment and consumption purposes.24
Of course, the proportions in which many
goods are purchased for consumption or investment purposes change over the cycle. As a
result, the fixed-proportion methodology used
for Argentina’s national account estimates will
distort the true underlying features of the business cycles. In particular, with this imputation
method, part of the investment booms will
show up misleadingly in the data as consumption booms.25 Because investment is highly correlated with output, the fixed coefficients
method of imputation can artificially increase
the measured correlation between consumption
and GDP. This problem could be especially
serious in the new national account estimates
that include the unusual investment boom of
the 1990s (Figure 3 ).
In summary, there are reasons to be cautious about the interpretation of the high correlation between consumption and output for
Figure 3

Real Gross Fixed Investment,
Old and New Estimates
Thousands of 1986 pesos (log scale)*
8.1
7.9
7.7
7.5
7.3
7.1
6.9
6.7

Real investment old
Trend real investment old
Real investment new
Trend real investment new

6.5
’70:1 ’72:1 ’74:1 ’76:1 ’78:1 ’80:1 ’82:1 ’84:1 ’86:1 ’88:1 ’90:1 ’92:1 ’94:1

* For visual effect, the old estimates have been rescaled so that
their level is the same as for the new estimates in 1980:1.
SOURCES: Dirección Nacional de Cuentas Nacionales for new
estimates; FIEL for old ones; authors’ calculations for
trends.

27

ECONOMIC REVIEW FOURTH QUARTER 1997

Table 2

Cyclical Behavior of Argentinean and U.S. Labor Inputs and Productivity
Argentina
(new national
account estimates)
1980:1– 90:4

Argentina
(old national
account estimates)
1970:1– 90:4

United
States1

5.57

5.84

4.18

Real GDP
Contemporaneous correlation
Relative volatility 3
Phase shift

Procyclical
.95
.69
Coincidental

Procyclical
.90
.52
Coincidental

Procyclical
.86
.36
Coincidental

Total hours
Contemporaneous correlation
Relative volatility 3
Phase shift

Procyclical
.76
.89
Coincidental

Procyclical
.77
.70
Coincidental

Procyclical
.86
.73
Coincidental

Employment
Contemporaneous correlation
Relative volatility 3
Phase shift

Procyclical
.55
.66
Lagging

Procyclical
.49
.56
Lagging

Procyclical
.79
.60
Lagging

Hours per worker
Contemporaneous correlation
Relative volatility 3
Phase shift

Procyclical
.70
.43
Coincidental

Procyclical
.68
.38
Coincidental

Procyclical
.77
.20
Coincidental

Productivity
Contemporaneous correlation
Relative volatility 3
Phase shift

Procyclical
.48
.66
Coincidental

Procyclical
.72
.65
Coincidental

Procyclical
.71
.52
Coincidental

Industrial real GDP volatility 2

1

2
3

The statistics correspond to the 1959:3 – 94:4 period and were constructed by the authors using a series of value added by the
manufacturing sector and a corresponding series of employment and hours worked in that sector published by the Bureau of Labor
Statistics (BLS) until 1994. The quarterly measure of industrial value added was taken from CITIBASE and corresponds to the
“fixed-weighted gross product originating” series for manufacturing produced by the BLS (see Gullickson 1995 for details).
Percent standard deviation from trend.
Ratio of volatility of the variable and the volatility of real industrial GDP.

NOTE: Seemingly anomalous statistics are in bold type.
SOURCES: Authors’ calculations based on sources in the text for national account estimates and on FIEL for labor market data.

the new national account estimates reported in
Table 1. Better data are needed before one can
confidently establish that this unusually high
correlation is indeed an anomaly by international standards.
Gross fixed domestic investment. The magnitude and sign of the statistics for this component (plotted in Figure 3) are in line with
those observed in other countries. It is particularly noteworthy that the relative volatility of
this real GDP component is close to that for the
United States.
Government consumption. As stated, Argentina does not have separate quarterly national
account estimates for government consumption.
The disorganization of public accounts in combination with the high inflation rates that prevailed during the period have made estimation
of such a series very difficult.
However, the same high inflation that prevents the construction of reliable government
consumption estimates also suggests that fiscal
policies may have played an important role in

the Argentinean economy. Therefore, we believe
it is important to report statistics— albeit partial—for an indicator that shows the government consumption contribution to GDP at
quarterly frequencies. Figures for treasury payroll payments are available on a monthly basis
for the 1970 –89 period, so we choose this variable as a potential indicator of fiscal policy. We
must emphasize, however, that these disbursements represent only a fraction of all such payments in the Argentinean public administration.
The statistics in Table 1 show that the
relative volatility of our real government consumption indicator is well above international
standards. It is also acyclical, a feature that
characterizes government purchases in the
United States as well. This acyclicality seems
to be anomalous by Latin American standards
(see Talvi and Végh 1996).
Trade balance. Some of the statistics for
Argentinean net exports (trade balance as a percentage of GDP) are in line with the international evidence: net exports are countercyclical,

28

as in several OECD countries, although the
Argentinean contemporaneous correlation with
output is on the high end of the range. By contrast, the volatility of this component seems to
be abnormally high by international standards.
A similar situation arises with imports: they are
procyclical, as in the United States, but exhibit a
much higher volatility relative to output. Finally,
almost all of the statistics for exports are out of
line with those for the United States.
One caveat in analyzing the trade balance
components of GDP is that Argentinean imports
and exports are subject to considerable measurement errors because Argentina used open
or hidden forms of exchange rate controls during substantial portions of the period under
analysis. During these periods, the private
sector had incentives to understate exports and
overstate imports in order to exploit the differential (which eventually became large) between
the often multiple official exchange rates and
the higher exchange rate usually prevailing in
the black market.
Labor inputs. Table 2 presents facts on
aggregate production and labor input for the
old and new national account estimates.
Because we are trying to follow the methodological approach in Kydland and Prescott
(1990) as closely as possible, we would like to
replicate in our Table 2 all the statistics those
authors report in their Table 1. Unfortunately,
lack of data has prevented us from achieving
the same results so far: there are no reliable
quarterly estimates of capital input. And information on employment and hours worked is
available only for the manufacturing sector,
whose value added represents a 25 percent
average of total GDP in the 1980 –95 period.
For these reasons, we report in Table 2 the
correlation and relative volatility of labor inputs
with respect to real industrial GDP, rather than
aggregate overall real GDP, used in Tables 1 and
3. We also construct similar measures for the
United States. To give some idea of how well
these series eventually approximate the relationship between labor inputs and real GDP for
the whole Argentinean economy, we report the
correlation and relative volatility of aggregate
and real industrial GDP.
Another serious limitation of the data is
that there are no reliable estimates of average
worker compensation. Also, the relevant series
for labor markets have not been updated since
1990. Thus, these series overlap the new GDP
estimates only during the 1980:1–90:4 period.
With these caveats about the data in mind,
Table 2 suggests that total hours worked,

FEDERAL RESERVE BANK OF DALLAS

Figure 4

Argentina, Productivity Is Procyclical
Percent standard deviation from trend
9
6
3
0
–3
–6

Deviations
industrial
productivity

–9
–12

Deviations
real industrial GDP

–15
’80:1 ’81:1 ’82:1 ’83:1 ’84:1 ’85:1 ’86:1 ’87:1 ’88:1 ’89:1 ’90:1

SOURCES: Authors' calculations using new national account
estimates from Dirección Nacional de Cuentas
Nacionales and index of total hours worked in the
manufacturing sector from FIEL.

employment, and hours per worker are strongly
procyclical. The statistics for those variables are
similar across the different national account estimates. Except for employment, this similarity
extends also to the correlations for the United
States for both periods.
The correlation of employment in the
industrial sector with real industrial GDP is
lower in Argentina than in the United States.
This finding is not surprising given the much
more stringent labor market regulations in
Argentina. Because of high firing costs, firms
will postpone hiring and firing decisions. So
changes in employment will not trace changes
in output as closely as they would in the
absence of labor market restrictions.
Relative volatilities are remarkably similar
across the countries, although volatility tends to
be higher in Argentina for the number of hours
per worker. This finding, again, likely reflects
the labor market restrictions: when confronted
with the high costs of firing workers, firms tend
to expand or contract the labor hours of those
already employed, rather than hire or lay off
more workers.
Finally, it is worth noting that productivity
in the Argentinean industrial sector is procyclical (Figure 4 ), with correlations and relative
volatilities on the same order of magnitude as
those for the United States.
Overall, the business-cycle features of
Argentinean labor inputs are reasonably similar
to those in the United States.

Nominal facts for Argentina
Table 3 summarizes the statistical properties of the business-cycle component of several

29

ECONOMIC REVIEW FOURTH QUARTER 1997

Table 3

Cyclical Behavior of Monetary Aggregates and Price Level Indices in Argentina and Other Countries
Argentina
(new national
account estimates)
1980:1– 95:3

Argentina
(old national
account estimates)
1970:1– 90:4

United
States1

OECD, G –7, and other
European countries2

M1
Contemporaneous correlation
Relative volatility 3
Phase shift

Countercyclical
–.36
15.13
Lagging

Acyclical
–.09
15.68
No clear pattern

Procyclical
.31
1.00
Leading

Acyclical/procyclical
–.06 to .42
.49 to 2.93
Leading (when countercyclical)

M2
Contemporaneous correlation
Relative volatility 3
Phase shift

Countercyclical
–.40
12.51
Lagging

Acyclical
–.07
13.08
No clear pattern

Procyclical
.46
.88
Leading

Acyclical/procyclical
–.034 to .39
.59 to 5.56
No clear pattern

M2 – M1
Contemporaneous correlation
Relative volatility 3
Phase shift

Acyclical
–.23
11.42
No clear pattern

Acyclical
.01
13.76
Leading

Procyclical
.40
1.12
No clear pattern

—
—
—
—

Velocity M1
Contemporaneous correlation
Relative volatility 3
Phase shift

Countercyclical
–.46
3.20
Leading

Countercyclical
–.26
4.58
Leading

Procyclical
.31
1.18
Coincidental

—
—
—
—

Velocity M2
Contemporaneous correlation
Relative volatility 3
Phase shift

Countercyclical
–.37
5.06
Lagging

Acyclical
–.24
7.08
Lagging

Acyclical
.24
1.08
Lagging

—
—
—
—

CPI
Contemporaneous correlation
Relative volatility 3
Phase shift

Countercyclical
–.47
16.92
Lagging

Acyclical
–.20
17.54
No clear pattern

Countercyclical
–.57
.82
Leading

Acyclical/countercyclical
–.55 to –.03
.18 to 1.82
Leading

ER
Contemporaneous correlation
Relative volatility 3
Phase shift

Countercyclical
–.61
16.04
Lagging

Countercyclical
–.49
18.29
Lagging

—
—
—
—

—
—
—
—

1
2
3
4

From Kydland and Prescott (1990).
From Christodoulakis, Dimelis, and Kollintzas (1995).
Ratio of volatility of the variable and the volatility of real GDP reported in Table 1.
Only Spain exhibits a large negative correlation (–.30).

NOTE: Seemingly anomalous statistics are in bold type.
SOURCES: Authors’ calculations, based on sources reported in the text for national accounts and on FIEL for monetary aggregates and price level indices.

nominal and monetary aggregate series. This
table presents information analogous to that in
Table 4 of Kydland and Prescott (1990), with the
necessary modifications to incorporate some
idiosyncracies of the Argentinean economy.
First, we do not report statistics for the
monetary base. Because of the frequent and
cumbersome changes in financial regime that
Argentina experienced in the period under
analysis, the concept of monetary base does not
have the meaning it has in the United States or
in the OECD and European countries we use for
comparison in this article.26
Second, the implementation of different
forms of price controls during the analysis
period may have distorted the true businesscycle price features. Therefore, as proxy for the
true underlying nominal price level, we also

report statistics for the exchange rate in the
black market.
The intense inflationary process that
Argentina experienced in the 1970s and 1980s
is responsible for the unusual high volatility of
all variables in Table 3. However, to correctly
interpret this volatility and other statistics in the
table, it is important to stress that monetary
policy in Argentina during most of the 1970 –95
period was not monetary policy in the sense
that it is in the United States, but rather a form
of implementing fiscal policies financed with
money creation.27
One striking similarity with international
evidence stands out from the table: whether
measured by the consumer price index or the
black market exchange rate, the price level has
been countercyclical (Figure 5 ), as it is in the

30

acyclical. This is in contrast with the United
States, where, according to Kydland and
Prescott (1990), this monetary aggregate is procyclical and leads the cycle. But it would be
wrong to conclude that this evidence suggests
that credit arrangements could play a more significant role in U.S. business cycles than in
those of Argentina, because during most of the
analysis period, there was a considerable
degree of financial repression in the latter
country. As a result, part of the credit market
was channeled through the informal financial
sector, whose transactions by its very nature are
not captured by the official monetary statistics.
Finally, velocity of all monetary aggregates, whether using the consumer price index
(reported in Table 3) or the exchange rate
(not reported) as a deflator, is countercyclical,
whereas Kydland and Prescott (1990) reported it
is procyclical for the United States.

United States and in most European countries
(Christodoulakis, Dimelis, and Kollintzas 1995).
The countercyclicality of prices for the United
States was pointed out in Kydland and Prescott
(1990) at a time when economists commonly
held the opposite view. Not surprisingly, this
finding created considerable debate because it
went against the predictions of most Keynesian
or monetarist-inspired theories of business
cycles.28
For nominal M1, however, the comparison
with other countries is not that clear cut. The
pattern of correlation for this monetary aggregate depends in an important way on the
national account estimates used. For the old
estimates, M1 is acyclical and all correlations
are similar in sign and magnitude to those
reported for the Netherlands in Christodoulakis,
Dimelis, and Kollintzas (1995). By contrast,
according to the new national account estimates, M1 is countercyclical, whereas in the
United States and the European countries in
Christodoulakis, Dimelis, and Kollintzas (1995),
it is acyclical or procyclical.
The differences between the two national
account estimates should serve as a note of
caution to researchers working with nominal
monetary aggregates for Argentina. It is possible
that some of the regularities taken for granted in
the past were derived using the old estimates,
but now those regularities have disappeared or
become less obvious with the new national
account estimates.
In any case, both national account estimates suggest that the monetary aggregate of
savings accounts and time deposits (M2 –M1) is

Conclusion
Is the business cycle of Argentina really
different from that of other countries? We hope
this article shows other researchers how difficult
it is to answer this simple question. One reason
for this difficulty is that the business-cycle features of Argentina can change substantially from
one national account estimate to the next. As
we indicate, the commonly held view that
absolute volatility of output is abnormally high
in Argentina is a myth by the old national
account estimates but a fact by the new ones.
Similarly, the correlation of the cyclical
component of real total consumption with that
of real GDP is within the range observed in
other countries, according to the old national
account estimates, but unusually high by the
new ones. We have given reasons, however, to
consider this last feature as partly a figment of
the data.
The statistics related to production inputs
(labor and investment), which play a crucial
role in RBC models, display remarkable similarities with the international evidence. In particular, except for absolute volatilities, all the
statistics for investment, labor inputs, and productivity are within the range observed in the
United States or European countries.
Based on these statistics, the only challenge for an RBC model of Argentina would be
to explain the larger volatility of output. But a
study by Mendoza (1995) suggests that an RBC
model could accomplish that if properly
adapted to deal with the idiosyncracies of the
Argentinean economic environment. By that, we
do not mean a model that incorporates only

Figure 5

Argentina, Prices Are Countercyclical
Percent standard deviation from trend
200

12
Price index deviations

150

10
8

100

6
4

50

2
0

0
–2

–50

–4
–100

–6

–150
–200
’80:1

–8

Real GDP
deviations

–10
–12

’82:1

’84:1

’86:1

’88:1

’90:1

’92:1

’94:1

SOURCES: Authors' calculations using new national account
estimates from Dirección Nacional de Cuentas
Nacionales and the consumer price index from
Instituto Nacional de Estadísticas y Censos (INDEC)
as reported by FIEL.

FEDERAL RESERVE BANK OF DALLAS

31

ECONOMIC REVIEW FOURTH QUARTER 1997

general, and of Argentina in particular. Perhaps
for this reason it is time to give real factors their
fair chance to do the job. Therefore, it is essential that a research agenda first specify the
empirical regularities that real factors must
account for.
To that end, we have presented the facts
about the Argentinean business cycle, following
a well-defined, systematic approach that does
not impose on the data any strong a priori
belief on a particular theory of business cycles.
We hope that our atheoretical description of
empirical regularities will motivate further empirical and theoretical work that will ultimately
lead to a better understanding of the economic
fluctuations and of the real effects of inflation
stabilization programs in Latin American countries in general, and in Argentina in particular.

technology shocks, but one that uses other real
factors or economic policies whose effects can
be captured through the aggregate production
function of the economy. More specifically,
Mendoza’s study adds terms-of-trade shocks to
an RBC model with technology shocks and
shows that such a model can replicate about the
same proportion of GDP variability—50 percent
for G–7 and developing countries—even if the
absolute volatility of GDP is substantially larger
in the developing countries. Interestingly,
according to the Mendoza study, the variability
of Argentina’s terms of trade is twice that for the
United States, which is the order of magnitude
by which the variability of Argentina’s GDP
exceeds that of U.S. GDP (using the new
national account estimates).29
A host of other empirical studies confirm
the potential of RBC models to mimic a large
fraction of the economic fluctuations observed
in Latin American countries. For example, using
a structural vector autoregression model (VAR),
Hoffmaister and Roldós (1997) find that supply
shocks are, even in the short run, the main
source of the output fluctuations in these
countries. Sturzenegger (1989) also reports VAR
estimates, according to which supply shocks
account for 90 percent of the Argentinean output fluctuations.
The results in Table 3 are unfavorable to
the hypothesis that nominal factors play the
most important role in economic fluctuations. In
particular, the price level is countercyclical.
Monetary theories of business cycles have had a
hard time accommodating this empirical regularity within an empirically successful (by some
measure) dynamic stochastic general equilibrium model. Furthermore, the Argentinean
monetary aggregates display, in general, a very
different cyclical (countercyclical) pattern than
those of the United States and Europe (procyclical). Yet, these differences do not seem to
translate to the relative volatilities and other features of real variables, which behave more similarly in Argentina and these other countries.30
In addition, our analysis of the businesscycle debate in Latin America suggests that
nominal exchange rate shocks, even during
ERBS programs, do not seem to have had the
clear real effects the literature has alleged. In
fact, the evidence we have presented— circumstantial as it may be — and the few available
studies that have attempted to analyze it in a
more systematic way all point in the same direction: nominal factors do not seem to be able
to account for any significant fraction of the
business cycles of Latin American countries in

Notes

1

2

3
4

5

32

The authors are grateful to David Gould, Carlos Végh,
and Mark Wynne for substantive and useful comments. We are also thankful to Anne Coursey, whose
editorial suggestions contributed to a clearer exposition of our ideas.
This distaste for economic fluctuations is implied by
the assumption that economic agents have concave
preferences. An old joke illustrates the meaning of this
economic jargon. An economist is informed that a
fellow citizen, with one leg freezing in ice and the
other boiling in hot water, is in pain. “Why?” the
economist asks. “On average, he is OK.” Actually,
this joke doesn’t do justice to the economics profession, whose members know very well that the citizen
has concave preferences: he would prefer to have
both feet in lukewarm water. Likewise, economists
know that consumers would prefer an economy in
which output and consumption grow at the same
steady rate, quarter after quarter, to one whose growth
is the same on average but varies from high (a hot
economy) in some quarters to slow (a cold economy)
in others.
So much so that a prominent monetarist like Lucas
himself recently asserted, “Monetary shocks just aren’t
that important. That’s the view I’ve been driven to….
There’s no question, that’s a retreat in my views.”
(The New Yorker, December 1996, 55.)
For an excellent summary, see Végh (1992).
For details, see Kiguel and Liviatan (1992), Végh
(1992), Calvo and Végh (1993), and citations therein.
The vertical line is drawn on the tick corresponding
to the period in which the program was announced,
unless the announcement was made in the first third
of the period. In this case, the vertical line is drawn on
the tick corresponding to the immediately preceding
period. The implicit assumption is that the real effects
of ERBS programs did not have time to show up in the

6

7

8

9

10

11

period of the announcement if it came too late in the
period.
This prediction arises from the intertemporal substitution effect originally emphasized by Calvo (1986): the
temporary (by assumption) reduction of the devaluation rate translates into a temporary reduction in the
nominal interest rate that increases the demand of current tradable goods relative to future tradable goods.
The empirical relevance of this mechanism, however,
has been questioned by Reinhart and Végh (1995a).
“Witty” analysis of plots is a valid and widely used
method of analyzing economic evidence, especially in
the early stages of a theoretical development. However, this casual empiricism presents serious problems
(see Easterly 1996). To avoid ambiguities and imprecisions, plot analysis should be complemented with
more formal quantitative methods whenever possible.
In our case, it would be important to construct measures establishing whether the consumption growth
rate immediately after the announcement of ERBS
programs was significantly different (by some criteria)
than immediately before. The ERBS literature has yet
to provide such a measure. The few formal quantitative
studies in that literature that have attempted to go
beyond the plot analysis (Reinhart and Végh 1994,
1995b, and Hoffmaister and Végh 1996) are concerned, instead, with the dynamics of real variables
within different inflation stabilization programs.
It is true that nominal factors deliver important real
effects in the nominal wage rigidity version of the
monetarist-inspired models examined by Rebelo
and Végh (1995). However, that success is achieved
at the expense of generating countercyclical real
wages, which goes against the available evidence.
For example, Carrera, Féliz, and Panigo (1996) report
that real wages in Argentina and Brazil are procyclical.
In fact, none of the stabilization programs reported in
the literature has been a “pure” monetary experiment.
They were always associated with other policy measures, such as financial liberalization, changes in taxes
and tariffs, and so on, all factors that would fall in
the category of “real” in the analytical framework of
real-business-cycle theory. The omission of these
factors from the analysis may lead to serious misinterpretations of the evidence on stabilization programs.
For example, as pointed out by Calvo (1986), “…if
expected to be temporary, a banking liberalization
policy will tend to have effects similar to the type of
exchange rate policies analyzed above [in reference
to ERBS programs].”
In this sense, we enthusiastically agree with Calvo
and Végh (forthcoming, 14) that “too little empirical
work — relative to theoretical work — has been done
in the area.”
This methodology is “theory free” in the sense that it
does not take any stand with respect to the causes of
economic fluctuations.

FEDERAL RESERVE BANK OF DALLAS

12

13

14

15

16

17

18

19

20

21

22

33

Heston (1994) provides a very thorough discussion of
all the measurement problems typical of the national
accounts of developing countries like Argentina.
The change in the base year is not the only difference
between the two series. There were also important
methodological modifications and other adjustments in
the new estimates. The magnitude of the corrections
should be apparent from the fact that the level of
annual real GDP for 1980 is 36 percent higher in the
new estimates than in the old estimates. Jumps of this
size in the level of GDP between subsequent national
account estimates are not unusual in European countries as well (see Maddison 1995, 124).
A technical presentation of the HP filter can be found
in Hodrick and Prescott (1997).
Because we are dealing with quarterly data, we follow
Kydland and Prescott (1990) in setting the “smoothing
parameter” λ = 1600.
We acknowledge that the statistical properties of the
detrended components measured with the HP filter
remain somewhat controversial (see, for example, King
and Rebelo 1993). But it is important to keep in mind
that our main goal is to compare the business-cycle
regularities of Argentina with those of the United States
and Europe. Several recent studies for such countries
have indeed detrended the data with the HP filter as
well. Moreover, no detrending technique is free from
criticism.
The reason for this transformation of the data is that
the business-cycle literature is concerned with percentage (rather than absolute) deviations from trend in
growing series.
As an exercise, we extended the GDP series from
each national account estimate to the entire 1970:1– 95:4
period by applying to each estimate the growth rates
of the other during the nonoverlapping period. The
cyclical volatility of GDP from the series constructed
this way is 3.9 for the new estimates and 3.65 for the
old ones.
See table A2 in Christodoulakis, Dimelis, and
Kollintzas (1995).
According to the permanent income hypothesis, the
series for consumption should be smoother than that
for income (or GDP). However, this prediction is valid
only for consumption of nondurable goods, and the
series for consumption typically includes durable
goods.
The conjecture that the excess volatility of consumption relative to that of output most likely reflects a mismeasurement problem, as hypothesized in note 20,
is reinforced by the finding in Backus, Kehoe, and
Kydland (1995) that consumption volatility is indeed
lower than that of GDP in the U.K. once expenditures
on consumption durables are excluded from aggregate consumption.
See, for example, “Overcoming Volatility,” Inter-American
Development Bank (1995, 191).

ECONOMIC REVIEW FOURTH QUARTER 1997

23

24

25

26

27

28

Intuitively, in an economy incapable of transferring
wealth between periods, economic agents will use up
all they produce in every period — that is, consumption
will be exactly equal to income period after period.
Although there is absolutely no credit in this economy,
the volatility of consumption cannot exceed that of
output (or income).
Heston (1994, 43) discusses a concrete case in which
allocating imports between consumption and investment, with procedures analogous to the one outlined
above, may lead to significant errors in consumption.
The new national account estimates used information
from the National Economic Census of 1985 to impute
imports as consumption or investment goods, and
data from the National Economic Census of 1973 for
the same imputation of domestically produced goods.
For more details, see CEPAL/ ECLA, final report, 1991.
The particular example in the text about the allocation
of automobiles between consumption and investment
was provided in an interview with staff members from
the Subsecretaría de Programación Económica del
Ministerio de Economía of Argentina.
This may have serious implications for the prolific
literature inspired by reported consumption booms in
Latin American countries: it may well be the case that
these booms, or at least a part of them, are in reality
capturing mismeasured investment booms.
For example, in July 1982 all Argentinean deposits
were “nationalized”— that is, from that month on, all
deposits in financial institutions were considered
deposits at the central bank. Since these deposits are
by definition part of the money base, this base
became almost identical to M2 and therefore experienced an increase equal to the difference between
these two monetary aggregates previous to the reform.
Almost all of the resulting jump in the money base that
month was, then, an artifact of accounting procedures
rather than the result of a change in monetary policy.
For these and other details on the institutional features
of the Argentinean financial system over the 1900 – 95
period, see Zarazaga (1996).
Monetary policy in the United States is closer to what
economists would regard as “pure” monetary policy. In
particular, U.S. monetary policy is carried out through
open-market operations that exchange one form of
government debt (fiat money) for another (government
bonds), leaving the overall level of outstanding government debt unchanged. In Argentina, by contrast,
the typical monetary policy consisted of handing over
fiat money directly to the treasury, which used it to
finance its deficit and not to retire other forms of government debt as in the United States. Thus, monetary
policy in Argentina has typically increased the overall
government debt by expanding the money base. It is
in this sense that Argentina’s monetary policy has
really been a hidden form of fiscal policy.
Abel and Bernanke (1992) provide an excellent, bal-

anced discussion of the business-cycle facts and their
consistency with RBC or Keynesian theories (see
especially Sections 11.2, 12.4, and 12.5).
29

30

A recent paper by Crucini and Kahn (1996) shows
that tariffs can have a larger impact on GDP than
generally believed. This is relevant in the light that
substantial implicit or explicit changes in tariffs were
a usual ingredient of the many stabilization programs
implemented in Argentina during the sample period.
Gavin and Kydland (1996) have recently reported a
related finding for the United States. They found that
real variables in that country seemed to have been
invariant to the changes in the cyclical behavior
observed in the nominal variables after 1979. They
showed that these observations can be generated by
a business-cycle model with impulses to technology in
which monetary policy affects the cyclical behavior of
nominal variables but not that of real variables.

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