Full text of Review (Federal Reserve Bank of St. Louis) : July/August 1991

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R
Vol.

V
73,

No. 4




July/A ugust 1991

3 U.S. T rad e-R em ed y Laws: Do T h ey
Facilitate o r H ind er F ree T rad e?
19 T h e B ehavior of Retail G asoline
Prices: S y m m etric o r Not?
30 M o n e ta ry Policy an d th e F arm /N o n ­
fa rm Price Ratio: A C o m p ariso n
of Effects in A ltern ativ e M odels
47 T h e M ultiplier A p p ro a ch to th e
M oney Supply Process: A
P re c a u tio n a ry Note
65 M e asu rin g State E xports: Is T h e re
a B etter W ay?

THE
FEDERAL
RESERVE
RANK of
ST.IjOIIS

1

Federal Reserve Bank of St. Louis

R eview
July/August 1991

In This Issue . . .




Trade-remedy laws are intended to lessen the hardship on U.S. firms
resulting from the actions and policies of foreign firms and govern­
ments. Theoretically, these laws produce a “fair” and "free” trading en­
vironment. In practice, however, the concept of fair trade is often used
by special-interest groups to pursue their own agenda at the expense of
the national interest.
In the first article in this Review, "U.S. Trade-Remedy Laws: Do They
Facilitate or Hinder Trade?” Cletus C. Coughlin analyzes the primary
trade-remedy laws and concludes that their increasing use is hindering
free trade. These trade-remedy laws, says the author, are producing a
protectionist trading environment, which lowers economic well-being in
the United States, rather than a fair and free environment, which would
benefit the nation.
***
In the second article of this Review, "The Behavior of Retail Gasoline
Prices: Symmetric Or Not?” Jeffrey D. Karrenbrock examines the
behavior of retail gasoline prices. As the author first points out, oil pro­
ducers and refiners are not the only entities on the supply side who in­
fluence gasoline prices. Because retailers also play a role in determining
the retail price of gasoline, they could be equally responsible for any
price anomalies that occur in the industry—including the popular belief
that gasoline prices are increased more and reduced less in response to
rises and falls in the underlying price of crude oil.
Karrenbrock’s analysis finds little evidence to support this "pricegouging” hypothesis, however. He finds that retail gasoline prices res­
pond symmetrically to wholesale price increases and decreases in both
the timing and the amount of price pass-through. Karrenbrock does
note that the retail price adjustment lags are shorter for a wholesale
price increase than they are for a decrease.
***
During the early 1980s, while the Federal Reserve was pursuing an antiinflationary monetary policy, the U.S. farm sector also experienced one
of its worst downturns since the 1920s. Many observers linked the two
events, arguing that restrictive monetary policy hurt farm income
because it caused farm product prices to decline more quickly than
farm input prices; the result was a “cost-price squeeze” that, according
to this argument, caused farm income to fall.
In the third article of this issue, "Monetary Policy and the Farm/Non­
farm Price Ratio: A Comparison of Effects in Alternative Models,"
Michael T. Belongia reviews several models that have been used to link
monetary policy to the relative price of farm products. The author then
JULY/AUGUST 1991

2

attempts to synthesize the conflicting empirical evidence that has been
brought to bear on each. By using consistent measures of prices and
monetary actions and estimating each model over the same time period,
he finds that monetary actions have weak and short-lived effects on the
farm/nonfarm product price ratio.
*

*

*

In the fourth article in this issue, “The Multiplier Approach to the
Money Supply Process: A Precautionary Note," Michelle R. Garfinkel and
Daniel L. Thornton show that the money multiplier is not independent
of monetary policy actions as is commonly assumed. They note that, for
the multiplier to be independent of policy actions, movements in the
ratio of currency to checkable deposits (the most important determinant
of the multiplier) must be due to individuals simply adjusting their
holdings of currency and checkable deposits. Most of the movement in
this ratio, however, is due to policy-induced changes in checkable
deposits, say the authors; moreover, the influence of policy actions on
the multiplier has become more important since the implementation of
the Monetary Control Act of 1980. Since then, the relationship between
checkable deposits and reserves has become particularly close.
***
Reliable information about the amount of production a state ultimately
exports is essential for anyone interested in state economic develop­
ment. Unfortunately, no ideal measure of state export activity exists.
In the final article in this issue, “Measuring State Exports: Is There a
Better Way?” Cletus C. Coughlin and Thomas B. Mandelbaum contrast
the two currently available state export series. The most prominent defi­
ciency of both is that they are based on the value of export shipments
from firms within a state rather than on the value of economic activity
related to exports within a state. To address this deficiency, Coughlin
and Mandelbaum develop a third measure of state m anufactured ex­
ports. Comparisons between this and an existing measure of export ac­
tivity illuminate the shortcomings of the two available series and the ad­
vantages of the new series.
***


FEDERAL RESERVE BANK OF ST. LOUIS


3

Cletus C. Coughlin
Cletus C. Coughlin is a research officer at the Federal
Reserve Bank of St. Louis. Thomas A. Pollmann provided
research assistance. The author acknowledges the valuable
suggestions of Seth Kaplan.

U.S. Trade-Remedy Laws:
Do They Facilitate or Hinder
Free Trade?

"Our fair trade laws are the bedrock on which free trade stands.”
— Malcolm Baldrige

A N INCREASINGLY contentious issue in in­
ternational trade pertains to so-called "trade-

remedy laws.” These laws are intended to
remedy hardships for U.S. firms resulting from
the actions and policies of foreign firms and
governments. Allegedly, these laws produce a
“fair” and "free” trading environment. The
possibility exists, however, that the concept of
fair trade is simply a pretext used by interest
groups to pursue their own interests at the ex­
pense of the national interest. This can result in
a protectionist trading environment, which
lowers economic well-being in the United States,
rather than a fair and free one.
This paper provides an introduction to U.S.
trade-remedy laws. As background to under­
standing the justification and effects of these
laws, the concepts of fair trade, free trade and
protectionism are described. Next, an overview
of the primary laws is provided. This is follow­
ed by evidence on the increasing use of traderemedy laws. Finally, evidence on the adminis­

tration and effects of these laws is examined to
assess competing claims that these laws facilitate
or hinder free trade.
FAIR TRADE, FREE TRADE AND
PROTECTIONISM

To understand the controversy involving
trade-remedy laws, one must become familiar
with the basic concepts underlying the dispute.
The most elusive concept is that of fair trade.
On the surface, it is hard to argue against fair
trade; however, there are different interpreta­
tions of this term and, thus, its application in
concrete situations varies across individuals.
Two interpretations of fair trade are related
directly to differing impressions of reciprocity,
which is a concept of fairness used in interna­
tional trade negotiations.1 Before negotiations to
reduce trade barriers, two countries will gener­
ally have different levels and types of trade bar­

iSee Bhagwati and Irwin (1987) for a discussion of fair
trade and its relation to U.S. trade policy.




JULY/AUGUST 1991

4

riers. "First-difference” reciprocity means that a
fair outcome is characterized by reductions in
trade barriers such that the value received by
each country stemming from the other country’s
reduction in trade barriers is equal. Consequent­
ly, after the completion of negotiations, the two
countries may still retain different patterns of
trade barriers.
On the other hand, "full” reciprocity requires
that two countries allow identical access to their
respective markets, which implies identical
trade restrictions. Full reciprocity means that
reciprocity of access must be met for individual
sectors. This is known as a level playing field.
Negotiations under the auspices of the General
Agreement on Tariffs and Trade (GATT) use
first-difference reciprocity as a procedural de­
vice. Nonetheless, the implicit goal of GATT is
to generate a free trade environment, which im­
plies full reciprocity of market access. In such
an environment, certain actions, such as govern­
ment attempts to directly influence the pattern
of trade, are viewed as inappropriate and, thus,
can be counteracted.2
Even though actions taken to open foreign
markets and counteract inappropriate behavior
by foreign firms and governments can be justi­
fied in the name of fair and free trade, these
actions might not achieve their stated purpose.
If they do not, then the result is higher levels
of barriers with adverse consequences.
Trade restrictions tend to reduce the competi­
tion faced by domestic producers; this protec­
tion is at the expense of domestic consumers.
Empirical evidence shows clearly that the losses
suffered by consumers exceed the gains reaped
by domestic producers and government.3 Not
only are there inefficiencies associated with ex­
cessive domestic production and restricted con­
sumption, but there are costs associated with
both the enforcement of protectionist legislation
and attempts to influence trade policy. Empiri­
cal research also shows that the adverse effects
of protectionist policies persist because such
2Bhagwati (1988) characterizes GATT as a “ contractarian”
institution that regulates inappropriate actions. Political
pressures make it difficult to maintain a free trade stance
unilaterally, so GATT attempts to prevent those actions
that induce others to move away from free trade.
3See Coughlin et al. (1988) and Richardson (1989) for re­
cent surveys.
4Administered protection and procedural protectionism are
two other terms for contingent protection.


FEDERAL RESERVE BANK OF ST. LOUIS


policies generate relatively lower growth rates
than free trade policies.
THE BASICS OF
TRADE-REMEDY LAWS

The United States employs various traderemedy laws to provide relief from imports for
U.S. industries. These laws are frequently char­
acterized as "contingent protection” because the
import relief is provided only under certain
conditions.4 Table 1 lists the principal traderemedy laws and summarizes their key features.

The Escape Clause
The escape clause, contained in Section 201 of
the Trade Act of 1974, allows temporary import
barriers when rising imports can be shown to
injure a domestic industry seriously.5 The legis­
lation requires that the increase in imports con­
stitutes "a substantial cause” of serious injury.
While a substantial cause is not defined precise­
ly, a working definition is that the cause is im­
portant and no less important than any other
cause of serious injury.
Two primary justifications exist for escape
clauses. The first justification relies on the im­
portance of an “economic adjustment” goal.
Rapidly increasing imports can harm selected
groups, especially import-competing domestic
firms and their workers. Such firms must adjust
to rising imports by enhancing productivity or
by laying-off employees. Proponents of the es­
cape clause argue that the costs of this adjust­
ment can be reduced if the firm is provided
temporary relief from imports.
This argument, however, has some problems.
Foremost is that there are numerous circum­
stances in which firms are forced to make ad­
justments. Changes in consumer demand, en­
ergy price shocks and governmental changes in
spending, taxation and regulation necessitate ad­
justments. If rising imports justify governmental
intervention, then it can be argued that these
5Prior to 1974, escape clause legislation required that the
rising imports be due to a prior reduction of a trade bar­
rier. The elimination of this necessary relationship by the
Trade Act of 1974 appears to make U.S. law inconsistent
with GATT. See Jackson (1990) for a comparison of the
legal nuances of U.S. law with Article XIX of GATT.

5

Table 1
Principal U.S. Trade Law Provisions1
Statute

Focus

Criteria
for action

Response

Responsibility

Section 201:
Fair Trade
(escape clause)

Increasing
imports

Increasing imports
are substantial
cause of injury

Duties, quotas, tariffrate quotas, orderly
marketing
arrangements,
adjustment
assistance

President (ITC
recommendation)

Section 301:
Unfair Trade

Foreign practices
violating a trade
agreement or injurious
to U.S. trade

Unjustifiable, unreason­
able, or discriminatory
practices, burdensome
to U.S. commerce

All appropriate
and feasible action

U.S. trade
representative subject
to direction by
the president

Section 701:
Subsidized
Imports

Manufacturing,
production or export
subsidies

Material injury or
threat of material
injury2

Duties

ITC-lnjury determination
ITA-Subsidy
determination

Section 731:
Dumped Imports

Imports sold below
cost of production or
below foreign market
price

Material injury or
threat of material
injury

Duties

ITC-lnjury determination
ITA-Dumping
determination

'O rigin of current provisions: Tariff Act of 1930 (Smoot-Hawley), as amended; Trade Act of
1974, as amended; Trade Agreements Act of 1979, as amended; Trade and Tariff Act of 1984;
Omnibus Trade and Competitiveness Act of 1988.
2The material injury test is extended only to countries that fulfill certain conditions.
SOURCE: Council of Economic Advisers (1988, p. 152), modified by author.

other causes of adjustment costs should be miti­
gated as well. While there are cases other than
rising imports that do lead to governmental in­
tervention, where should the line be drawn?
Another facet of the adjustment argument
focuses on the fact that rising imports provide
benefits to many consumers and impose costs
on relatively few firms and workers. An equity
argument can therefore be made for shifting
some of the burden of adjustment from the
few who are harmed to the many who benefit
through the tax on consumers imposed by im­
port restrictions.
The second primary justification for escape
clauses relates to this argument. A relatively
small yet potentially well-organized group harmed
6Lande and VanGrasstek (1986) note that the escape
clause allows member countries to impose trade restric­
tions to mitigate the perceived adverse effects of rising im­
ports, while remaining cognizant of their obligations to




by rising imports could be a formidable force
for import restrictions. From a national perspec­
tive, it is much better to provide temporary and
limited protection for such a group not only to
mitigate the burdens of adjustment, but also to
reduce the political pressures for more perma­
nent import restrictions. Unfortunately, these
temporary measures often become long-lived.6
Petitions for relief can be filed by any one of
the following groups—individual firms, labor
unions, trade associations or selected govern­
ment bodies (such as the United States Trade
Representative, the House Ways and Means
Committee and the Senate Finance Committee).
The International Trade Commission (ITC) is a
six member, appointed body that assesses injury
fellow GATT members. This flexibility allows temporary
departures from trade obligations without full-fledged
repudiations.

JULY/AUGUST 1991

6

after the filing of petitions. Significant declines
in sales, production, profits, wages or employ­
ment are evidence of serious injury.
Negative ITC decisions require a majority of
the commissioners to reject the petition and ter­
minate the process. Affirmative ITC decisions
require either a tie or the majority of the com­
missioners to accept the petition; they are for­
warded to the president. A recommendation as
to the appropriate trade restriction and/or ad­
justment assistance to prevent or ameliorate the
injury is included. The president, however, is
not bound by the ITC’s injury finding or its sug­
gested relief. Nevertheless, the Trade Act of
1974 instructs the president to provide relief
(which can take the various forms identified in
table 1), unless such relief is deemed not to be
in the national interest.

U nfair T rad e L egislation
The escape clause allows a nation to restrain
imports regardless of w hether the imports have
been assisted by “unfair” practices. Examined
below are the three most prominent pieces of
U.S. legislation that address the issue of offset­
ting the effects of unfair actions: 1) Section 701,
which deals with governments subsidizing ex­
ports; 2) Section 731, which deals with dump­
ing, that is, with foreign firms selling their
goods at lower prices in the United States than
in their home markets; and 3) Section 301, which
deals with violations of trade commitments and
a wide range of other actions.
S ection 701: C ountervailing D uty
L egislation
The legal purpose of countervailing duty legis­
lation is to offset government-provided benefits
that assist the exports of foreign firms. These
benefits include export subsidies, such as direct
government payments, tax relief and subsidized
loans to a nation’s exporters and low-interest
loans to foreign buyers. By inducing additional
foreign export activity as U.S. consumers substi­
tute these goods for similar domestically pro­
duced goods, these subsidies can injure importcompeting U.S. industries. Assuming certain
provisions are met, U.S. trade law allows sub­
sidized exports to be counteracted with tariffs
term ed countervailing duties.
7Lande and VanGrasstek (1986) point out that an injury test
is used in countervailing duty cases only when the foreign
country meets certain conditions. When an injury test is
not required, the ITA has complete control.


FEDERAL RESERVE BANK OF ST. LOUIS


Even if one acknowledges that export subsi­
dies harm a domestic industry, it is possible to
question the wisdom of countervailing duties.
Many economists argue that if a foreign govern­
ment subsidizes exports, the importing coun­
try should accept the gift of cheaper goods.
Resources no longer needed by the importcompeting U.S. industries can be employed pro­
ductively in other sectors of the economy.
Countervailing duty legislation, however, fo­
cuses on the harm to these import-competing
industries and ignores the benefits reaped by
consumers and other producers.
Bhagwati (1988), while acknowledging the
economic validity of the preceding argument,
argues that a free trade regime might depend
on unfair trade legislation. Countries pursuing a
free trade policy find it difficult to resist the
demands for protection when the decline of an
industry is due to the market-determined advan­
tages of foreign producers. If the decline is due
to the use of export subsidies, demands for pro­
tection are heightened because issues of fairness
are stressed. A free trade regime that does not
counteract artificial advantages might find itself
unable to defend and perpetuate its free trade
stance.
The administration of countervailing duty
laws is the joint responsibility of the ITC and
the International Trade Administration (ITA) of
the Department of Commerce. The ITA deter­
mines the existence and magnitude of any sub­
sidy, negotiates agreements to offset any sub­
sidy, imposes duties, reviews the effectiveness
of the remedy and determines when the rem ­
edy is terminated. Concurrently, the ITC applies
an injury test to determine whether subsidized
exports have caused or will threaten material
injury to a domestic industry or have retarded
the establishment of a domestic industry.7
Material injury, as defined by the Tariff Act
of 1930, is "harm which is not inconsequential,
immaterial, or unimportant.” While this defini­
tion is far from clear, the law does require that
the ITC incorporate volume, price and impact
considerations in its determination of harm. In
examining the volume of imports, the ITC is
charged with determining w hether an increase
in that volume, either absolute or relative to
either U.S. consumption or production, is signif-

7

Figure 1
Statutory Timetable for Countervailing Duty Investigations (in days)_________
Total
Days

205

235

270

300

SOURCE: United States International Trade Commission (1987).

icant. With respect to the effect of imports on
prices, the ITC looks for significant price under­
cutting by the imports relative to domestically
produced goods and attempts to assess the re­
sulting price consequences. Finally, the impact
on the domestic industry is assessed by examin­
ing changes in production, employment, market
share, profits and wages.
Countervailing duty cases begin when a peti­
tion is filed with the IT A and the ITC by either
an interested party or the ITA itself. (A complete
timetable for countervailing duty investigations
is provided in figure 1.) If the ITA concludes
that an investigation is warranted, then the ITC
must reach a preliminary determination as to
whether a "reasonable indication” of material in­



jury exists. A negative ITC determination term i­
nates the proceedings, while a positive deter­
mination leads to additional investigation.
A preliminary affirmative ITA decision leads
to the announcement of a preliminary estimate
of the export subsidy and an order that import­
ers make a cash deposit or post a bond equal to
the estimate of the subsidy for each entry. If
the preliminary ITA decision is negative, no de­
posit or bond is posted; however, the ITA inves­
tigation continues until it reaches a final deci­
sion. If the final ITA decision is negative, then
the case is terminated; otherwise, the ITA must
determine the final subsidy margin.
An affirmative final determination by the ITA
leads to an ITC hearing in which all interested
JULY/AUGUST 1991

8

parties participate. If the ITC finds no material
injury, then the case ends. On the other hand, a
finding of material injury by the ITC leads to an
ITA order of countervailing duties against the
imported merchandise. Such an order continues
until revoked.
Section 731: A nti-dum ping L egislation
The legal purpose of anti-dumping legislation
is to prevent two unfair practices: 1) price dis­
crimination in which foreign firms sell in the
United States at prices lower than they charge
in their home markets, and 2) export sales in the
United States at prices below the average total
cost of production.8 Both practices tend to lower
the price of the good in the U.S. market causing
U.S. consumers to purchase less of similar do­
mestically produced goods. This decrease in de­
mand harms the domestic industry by reducing
profitability, sales, employment and other mea­
sures relative to a market without dumping.
Although domestic consumers do benefit from
the lower price, their interests are ignored by
this legislation.
Focusing on the first practice, if the IT A
determines that the product in question is being
sold in the United States at a price less than its
foreign market value, the case is referred to the
ITC. The ITC then investigates whether, as a
result of the dumping, a domestic industry is in­
jured, likely to be injured or prevented from be­
ing established.
The ITC’s assessment of material injury in
dumping cases is the same as in countervailing
duty cases. The ITA’s determination of the
dumping margin, however, differs from the
determination of the subsidy margin. The dump­
ing margin is simply the difference between the
home market sales price and the export sales
8Boltuck (1991) identifies international price discrimination,
promotional pricing, predatory pricing and hidden export
subsidies as instances of price dumping. Note that for
price dumping to be profitable, barriers must exist that
prevent the imported good from being resold in the ex­
porter’s market at the higher home market price. Interna­
tional price discrimination is profitable when an exporter
possesses more market power at home than in the United
States (that is, demand in the firm ’s home market is less
elastic than in the United States) and charges a higher
price in its home market than in the United States. Promo­
tional pricing arises when an exporter induces consumers
in a foreign market to try a product by introducing it at a
low price. Predatory pricing is a rarely used strategy in
which an exporter attempts to eliminate competitors by
reducing the export price below its rival’s costs and below
its own production costs. Once the competitors have ex­


FEDERAL RESERVE BANK OF ST. LOUIS


price. While the concept of a dumping margin
is simple, applying the concept to the real world
is complicated.9 Details on calculating the dump­
ing margin, which can affect the consequences
of this legislation, are highlighted later.
The administrative procedure for anti dumping
cases is virtually identical to that of countervail­
ing duty cases. The primary difference is that
while an injury test is automatic in anti-dumping
cases, it may not be required in certain counter­
vailing duty cases. In addition, the timetable for
dumping investigations (provided in figure 2) is
longer.
Section 301 a n d th e A u th o rity to R etaliate
Section 301 grants the United States Trade
Representative (USTR) authority (subject to any
directions from the president) to take all “appro­
priate and feasible action” to remove foreign
trade barriers that hinder U.S. exports and to
fight foreign subsidies that hinder U.S. exports
to third-country markets.10 This legislation is
primarily a Congressional response to dissatis­
faction with GATT’s ineffectiveness in resolving
trade disputes.11 Formally, Section 301 allows
the USTR to respond against any act, policy or
practice of a foreign country that is determined
to be: 1) inconsistent with the provisions of, or
otherwise denies benefits to the United States
under, any trade agreement; or 2) unjustifiable,
unreasonable, or discriminatory and burdens or
restricts U.S. commerce.
Unreasonable is broadly defined in the Trade
and Tariff Act of 1984 so that offending foreign
restrictions are not limited to violations of trade
agreements. The term includes, and goes be­
yond, any act, policy or practice that denies fair
and equitable opportunities to begin and oper­
ate a business. Unjustifiable, as well as discrimited the market, the exporter raises the price. Finally, hid­
den export subsidies are classified as dumping either
because there is no direct subsidization or the ITA is
unable to demonstrate the existence of a subsidy.
9See Jackson (1990) for details on this complexity.
10The President (rather than the USTR) had this authority
prior to the Omnibus Trade and Competitiveness Act of
1988.
"A lth ou gh GATT is frequently involved in dispute-settlement
proceedings, it has no authority to impose sanctions or en­
force its decisions. A GATT ruling favorable to the United
States simply justifies unilateral U.S. action when the other
party does not abide by the decision. In addition, Section
301 allows for the settlement of trade disputes with coun­
tries not belonging to GATT or when the issue is not
covered by GATT.

9

Figure 2
Statutory Timetable for Anti-dumping Investigations (in days)_______________
Total
Days

280

340

310

370

330

390

360

420

SOURCE: United States International Trade Commission (1987).




JULY/AUGUST 1991

10

Figure 3
A Representative Case for the Section 301 Process
Lesser of 12 months (18 months for trade
agreement cases) OR 30 days after pro­

register
notice

hearings

for public
hearings

hearings

register
notice

to stop
retaliation

SOURCE: Grinols (1989).

inatory, includes any act, policy or practice that
denies either national or most-favored-nation
treatment. In the context of the United States
and a specific foreign country, national treat­
ment focuses on whether U.S. firms operating
in that country are treated as favorably as the
firms of the foreign country are treated in the
United States. Most-favored-nation treatment
refers to the best treatm ent accorded to firms
from any other country operating in a specific
foreign country. Even though all foreign firms
(including U.S. firms) may be treated identically
in a specific country, Section 301 could still be
invoked if the treatm ent given were not as fav­
orable as the treatm ent given the foreign firms
in the United States.
Similar to other trade remedy proceedings,
U.S. firms may formally petition to initiate Sec­
tion 301 proceedings or the USTR may initiate
the case. A typical case in which the petition is
filed with the USTR is illustrated in figure 3.
The USTR’s role in Section 301 cases varies
from the roles of the ITA and ITC in other
trade-remedy cases. The USTR acts as both
judge and advocate, while, relatively speaking,
the ITA and ITC primarily judge on the basis of
the objective merits as defined by the relevant
12See G rinds (1989).


FEDERAL RESERVE BANK OF ST. LOUIS


statutes. The USTR's task is much more subjec­
tive because it must also devise and pursue a
negotiated settlement with a foreign
government.
If a negotiated settlement is not reached, the
USTR may: 1) suspend, withdraw, or prevent
the application of, or refrain from proclaiming,
benefits of trade agreement concessions to carry
out a trade agreement with the foreign party in­
volved; and 2) impose duties or other import
restrictions on the products of, and fees or
restrictions on the services of, such foreign par­
ty. This retaliation can be applied to all coun­
tries or to selected countries. Furthermore, the
retaliation can be applied to goods and services
other than those identified in the petition.
In addition to dissatisfaction with the GATT
dispute-settlement process, Congress has been
unhappy with the operation of Section 301.
Changes included in the Omnibus Trade and
Competitiveness Act of 1988 generally reduce
the president’s input into the process and en­
courage more frequent use of Section 301.12 A
particular provision of the legislation known as
Super 301 reflected Congress’ desire to “get
tough” with our foreign rivals and pry open

11

Table 2
U.S. Trade-Remedy Petitions: Number and Percentage of Total
Escape clause
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990

4
2
6
1
5
6
3
3
2
2
0
1

6.5%
5.4
11.3
0.5
6.2
4.5
2.7
2.9
7.7
3.2
0
1.8

Anti-dumping

Countervailing
duty

16
24
15
63
47
73
65
70
14
40
23
43

37
11
22
145
22
52
38
26
5
13
9
8

25.8%
64.9
28.3
29.2
58.0
54.9
58.6
66.7
53.8
64.5
56.1
76.8

59.7%
29.7
41.5
67.1
27.2
39.1
34.2
24.8
19.2
21.0
22.0
14.3

Section 301
5
0
10
7
7
2
5
6
5
7
9
4

8.1%
0
18.9
3.2
8.6
1.5
4.5
5.7
19.2
11.3
22.0
7.1

Total
petitions
62
37
53
216
81
133
111
105
26
62
41
56

SOURCE: Escape clause, anti-dumping and countervailing duty data for 1979-1988 are from
Messerlin (1990). The remaining years of data are from the Office of the United
States Trade Representative (1991). The data for Section 301, which includes peti­
tions as well as cases instigated by the President of the United States and the USTR,
is based on a listing from the Office of the United States Trade Representative.

their markets.13 Super 301 required the USTR
to name (by May 30 of each year) those nations
with the most restrictive barriers to U.S. ex­
ports and to identify the specific practices that
most hinder U.S. exports. The listed countries
faced retaliatory measures if no agreement on
removing the trade barriers was reached within
12 to 18 months.
THE FREQUENCY OF TRADEREMEDY PETITIONS

During the 1980s, import-competing firms
throughout the world increasingly used anti­
dumping actions rather than countervailing duty
and escape clause actions.14 As shown in table
2, this change has occurred in the United States
as well; however, this change is not as pro­
nounced in the United States as it is elsewhere.
From 1983 onward, anti-dumping actions in the
United States have exceeded 50 percent of the
total number of trade-remedy petitions. In fact,
the use of the escape clause mechanism has be­
13See Bhagwati and Patrick (1990), especially Chapter 1, for
an overview of the reasons for and the issues generated
by Super 301.
14See Messerlin (1990) for details.

come negligible. Since 1984, escape clause cases
have generally totaled less than 5 percent of the
petitions.
The total number of actions show a substan­
tial decline since 1987. However, to argue that
this decline indicates a sharp reduction in the
demand for protection would be erroneous. Be­
tween 1982 and 1986, the total number of ac­
tions exceeded 100 cases in every year but one;
however, approximately 200 cases involving
steel products were initiated prior to the volun­
tary steel agreement of October 1984.15 In addi­
tion, the duties imposed under anti-dumping and
countervailing duty actions can persist for some
time.16 Similarly, other non-tariff barriers that
were negotiated and imposed as a result of the
pressure represented by anti-dumping and coun­
tervailing duty actions also persist for some
time. Thus, many industries had their concerns
resolved (at least temporarily) which resulted in
a reduced number of new cases in the late
1980s.
16According to the Office of the United States Trade
Representative (1991), 71 countervailing duty orders were
in effect at year-end 1990. These 71 orders exceed the
total number of countervailing duty petitions (61) between
1986 and 1990.

,5See Ahearn et al. (1990).




JULY/AUGUST 1991

12

The trend since 1987 of declining actions,
however, is not exhibited by Section 301 peti­
tions. On the other hand, there is no clear up­
ward trend either. Recent legislative changes in
the Omnibus Trade and Competitiveness Act of
1988 suggest that this mechanism will assume
increasing importance in the future.
TRADE-REMEDY LAWS: THEIR
EFFECTS AND THE ROLE OF
ADMINISTRATIVE BIASES

To assess the consequences of trade-remedy
legislation, information on the frequency of these
petitions is supplemented with details of their
administration and outcome. The evidence pre­
sented below highlights biases in the administra­
tion of these laws and the proliferation of trade
barriers resulting from these cases. As a result,
it is highly unlikely that the legislation is facili­
tating free and fair trade.

The Negligible E ffects o f Escape
Clause Legislation
The small number of escape clause petitions
in recent years suggests that this trade-remedy
legislation is having virtually no effect on the
pattern of trade. The underlying criteria for a
successful petition, plus the possibility of for­
eign retaliation unless other U.S. trade barriers
are reduced, have deterred the use of escape
clause petitions and induced industries to seek
protection using other trade-remedy avenues.17
Despite the “requirement” that anti-dumping and
countervailing duty actions can be invoked only
to counteract the specific unfair trade practices
of dumping and export subsidies, industries
have increasingly resorted to these traderemedy laws rather than use the escape clause
route. This apparent anomaly is explained when
the administrative details of these less-than-fair
value procedures are scrutinized.
17Article XIX of GATT allows trading partners affected
adversely by an escape clause action to retaliate by
withdrawing “ substantially equivalent concessions” affect­
ing the goods of the country invoking the escape clause.
An alternative is to provide compensation to these GATT
members by lowering trade barriers on their exports. See
Hamilton and Whalley (1990) and Lande and VanGrasstek
(1986) for additional details.
18See Finger and Murray (1990).
19Recent research has suggested another way that the ad­
ministration of this legislation could be biased. Moore (1990)
found that ITC anti-dumping decisions were biased toward
affirmative decisions when the complaining industry was


FEDERAL RESERVE BANK OF ST. LOUIS


Effects o f Countervailing Duty and
Anti-dumping Legislation and
Adm inistrative Biases
Countervailing duty and anti-dumping actions
are associated with rising trade barriers. For ex­
ample, of 774 countervailing duty and anti-dump­
ing cases completed in the United States be­
tween 1980 and 1988, 70 percent were resolved
to restrict trade in some way.18 W hether the
resulting duties, as well as the other negotiated
restrictions on trade, should be viewed as pro­
tectionist, however, requires additional
information.
Countervailing and anti-dumping duties can be
viewed as responses to actions taken by foreign
governments and firms. For example, if an in­
vestigation determines that dumping is occurr­
ing, then the U.S. response to impose a duty (a
dumping bond) equal to the dumping margin is
automatic. In this case, the effect of the duty is
to offset the injury to the domestic industry. If
each instance of dumping is counteracted, then
the net effect on trade would be zero in the
sense that the level of trade would return to
the same level prior to dumping. Many, how­
ever, do not feel that the actual workings of the
legislation are quite so benign.
A dm in istra tive B iases
Bias has been found to enter the administra­
tion of less-than-fair value statutes through their
interpretation by administrative agencies.19 The
administering agencies have discretion that is
sufficiently broad to allow for bias. For exam­
ple, Boltuck et al. (1991) conclude that the pro­
cedures used by the ITA in measuring subsidy
rates and dumping margins are biased toward
finding dumping and export subsidies.20
A foreign firm is found to be dumping when
the price of their good in the U.S. market is
located in the state of a senator on the Senate Finance
Committee.
20See Kaplan (1991) for a demonstration of the protectionist
bias in injury and causation determinations before the ITC.

13

below: 1) the price of the good in its home m ar­
ket; 2) the price of the good in a third market if
no home market exists; or 3) its production cost.
Such comparisons appear to be simple, but the
description below suggests otherwise. Similar­
ly, the measurement of subsidy rates appears
straightforward with the focus on devising ac­
counting rules to allocate government subsidies
across the volume of exports. Closer inspection,
however, indicates much complexity as well as
the potential for bias in these calculations.
The calculation of price dumping margins is
subject to error at four different stages: 1) in
identifying the home (domestic) market; 2) in
adjustments to make domestic and export prod­
ucts comparable; 3) in adjustments to calculate
the ex factory price (that is, the price as it
leaves the factory) of domestic and export prod­
ucts; and 4) in comparing the ex factory prices
of domestic and export products. Errors at the
first and last of these stages are illustrated.
The identification of the foreign firm’s home
market tends to produce the highest possible
fair value of the foreign good and, thus, the
largest possible dumping margin. To illustrate,
assume a firm occasionally charges a price be­
low its production cost in its home market. Such
sales are made in the "normal course of busi­
ness” for any firm in a competitive industry
that faces a demand for its product that varies
randomly.21 Below-cost sales, that occur when
demand falls below its average level, are bal­
anced by above-cost sales, that occur when de­
mand is above its average level, allowing the
firm to earn a competitive rate of return. The
21Fred Smith of the Competitive Enterprise Institute, as
quoted by Bovard (1987), notes, “ If the same antidumping
laws applied to U.S. companies, every after-Christmas sale
in the country would be banned.”
22To illustrate this particular bias, assume the foreign firm ’s
sales occur at the same ex factory price in both the United
States and its home market. Let the price be $10 in the
first half of the investigation period and $5 in the second
half. The firm sells five units both in the United States and
at home in the first half of the period and 15 units in the
United States and 10 at home in the second half. The
average home price is $6.67 because one-third of the
home sales occurred at $10 and two-thirds at $5. This
average price is compared to the individual sales prices in
the United States. Thus, each U.S. sale in the first half of
the period would show a negative dumping margin (that is,
dumping in the foreign firm ’s home market rather than in
the United States) of $3.33 because the sales price of $10
exceeds the average home price of $6.67; each sale in the
second half of the period would show a positive dumping
margin of $1.67 because the sales price of $5 is less than




ITA, however, excludes all below-cost sales from
its calculation which raises the fair value of the
product and creates an upward bias in the
dumping margin.
The comparison between the ex factory price
of individual sales in the foreign firm’s export
market (that is, the United States) to the average
ex factory price in its domestic market can pro­
duce bias as well. For example, if prices were
declining and exports increased relative to home
sales during the period used to assess the exis­
tence of dumping by the foreign firm, then a
positive dumping margin would be found. The
low-priced export sales at the end of the period
and the high-priced home m arket sales at the
beginning of the period would generate a posi­
tive margin. Note that dumping would be found
in this case even if home and export prices were
identical for every sale made on the same day.22
In theory, the preceding bias should also oper­
ate in the other direction to generate negative
margins. In practice, however, this does not oc­
cur because the ITC excludes the possibility of
negative dumping, not only on average, but on
each individual price comparison. Thus, all ex­
port sales below fair market value carry a posi­
tive dumping margin, while all export sales
above fair market value carry a margin of zero23
Consequently, a positive dumping margin will
be found even when all sales are made at the
same price on the same day and the weights on
each sale are identical.24 Thus, this procedure
punishes foreign firms for not price discriminat­
ing because the only way to avoid dumping
duties is to charge substantially more in the
United States than in other markets.
$6.67. One-fourth of the U.S. sales would have a negative
dumping margin of $3.33, while three-fourths of the sales
would have a positive dumping margin of $1.67. If this
were the only bias in the ITA’s calculations, a positive
dumping margin of $.42 would be calculated even though
no actual dumping occurred. Additional biases, however,
exacerbate the error further.
23ln the numerical example in the preceding footnote, the
one-fourth of the U.S. sales with the negative dumping
margin of $3.33 would be treated as having a zero dump­
ing margin. This increases the calculated dumping margin
to $1.25 even though no actual dumping occurred.
24Boltuck et al. (1991) show that the bias in the margin
equals approximately one standard deviation of the prices
from the mean. Thus, if a company charges an identical
price in the United States and other markets and its prices
generally vary within 10 percent of the average price
charged, the ITA will calculate a dumping margin of 10
percent. In other words, if the firm ’s average price is
$6.00, the bias in the dumping margin is about $.60.

JULY/AUGUST 1991

14

Similar bias exists in the ITA’s calculation of
subsidies in countervailing duty cases, especially
when the subsidy is not in the form of a direct
per unit export subsidy. Biased accounting
methods provide one route for finding inequity.
For example, assume a foreign firm produces
more than one product and that one of the pro­
ducts is allegedly subsidized. If the firm is
found to be subsidized, the ITA allocates the en­
tire subsidy to the specific product that is the
focus of the investigation irrespective of the
degree, if any, to which the product is
subsidized.25
Because of these sources of bias, virtually
every investigation that proceeds to a formal
determination finds in favor of positive dumping
margins and export subsidies.26 Thus, less-thanfair-value cases, similar to escape clause cases,
hinge on injury tests. Since the criteria for the
injury test are less stringent for less-than-fairvalue than they are for escape clause cases, the
infrequent usage of the escape clause is not sur­
prising.27
D uties a n d U ncertainty
The reasons for the protectionist conse­
quences of less-than-fair-value legislation are not
limited to biased administration. By law, any
dumping margin or export subsidy greater than
0.5 percent justifies an affirmative determina­
tion. While a small duty is unlikely to have a
large effect on competitiveness, the existence of
any anti-dumping and countervailing duties
creates costs of uncertainty that may have large
effects for the exporter. This possibility is
related to the fact that the importer (not the ex­
porter) assumes the risk of incurring higher
duties than those originally paid.
The bond initially posted on imports which
are subject to these duties is only an estimate of
the final duty. At the end of each year, the ITA
allows interested parties the right to request a
review of outstanding anti-dumping and counter­
25See Boltuck et al. (1991) for additional examples.
26See Finger and Murray (1990).
27The fact that the president cannot set aside affirmative
less-than-fair-value decisions but can set aside affirmative
escape clause decisions, and that less-than-fair-value
cases can be targeted against firms from specific coun­
tries while escape clause cases cannot, also explains the
relative use of less-than-fair-value cases.
28This problem is not so pronounced in the case of dump­
ing. An exporter that continued to dump would be sharing


FEDERAL RESERVE BANK OF ST. LOUIS


vailing duty orders. Such a review will typically
require three to four years before the final du­
ty rate is set. If there is no request for a review,
then the estimated deposit rate is equal to the
final duty rate.
The uncertainty over deposit rates means that
by importing under anti-dumping and counter­
vailing duty orders, importers are creating openended contingent liabilities for themselves. Find­
ings of underpayment require additional pay­
ments including interest to the government for
the imports. To assess the risk of underpay­
ment, an importer must have substantial infor­
mation. For example, importers of goods subject
to countervailing duties must be knowledgeable
about the various industrial programs in the ex­
porting country and be able to assess the bias
that exists in the calculation of duties.28
H arassm ent
Another line of argument suggesting the pro­
tectionist consequences of less-than-fair-value
legislation is known as the harassment thesis.
Gregory (1979) noted a barrage of administra­
tive complaints or court suits (35) filed by U.S.
electronic appliance and component manufac­
turers against Japanese competitors. He con­
cluded that even when these actions are not
directly successful, they impose lengthy and
costly delays on Japanese firms that indirectly
produce a protectionist result.
This harassment is not confined to Japanese
firms nor are the consequences limited to in­
creasing the cost of penetrating the U.S. m ar­
ket. Firms from virtually all developed countries
as well as many developing countries have been
subjected to less-than-fair-value petitions.29 While
these petitions directly increase the cost of pen­
etrating the U.S. market, the threat of a petition
also increases the risk of exporting to the United
States for actual and potential exporters. The
ultimate result is that foreign firms will reduce
their efforts to export to the United States.
revenue with the U.S. Treasury that it could have retained.
Nonetheless, an exporter more concerned with short-term
profits from price discrimination than with a long-term rela­
tionship with the importer might still find it profitable to
price discriminate.
29Finger and Murray (1990) note that before a firm files a
less-than-fair-value petition, it frequently makes inquiries
with law firms and holds informal discussions with the ITA,
neither of which are kept secret. In effect, the period of
harassment can begin before an official complaint is
lodged.

15

N on -tariff B arriers
As suggested by arguments discussing uncer­
tainty as well as harassment, less-than-fair-value
legislation can have protectionist consequences
apart from the actual duties resulting from
specific cases. Another route to protectionism is
that this legislation can result in the more fre­
quent use of non-tariff barriers. Finger (1981)
pointed out that less-than-fair-value mechanisms
can be used by a domestic industry to generate
public support for protection. Until all the stan­
dard means of seeking protection have been ex­
hausted, it is unlikely that there will be strong
political support for protection. Mechanisms
such as less-than-fair-value cases must be util­
ized prior to gaining access to more political
forms of protection. Many non-tariff barriers
originated as less-than-fair-value cases. For ex­
ample, Finger and Murray (1990) found that
nearly half of the less-than-fair-value cases in
the 1980s were superseded by some form of
negotiated export restraint.
The Use o f Less-Than-Fair-Value
L egislation in O ther C ountries
A final line of argument suggesting that lessthan-fair-value cases lead to protectionism is
that these cases induce other less-than-fair-value
cases. As indicated by Messerlin (1990), this
avenue of protection was not used by develop­
ing countries through 1985. The increased fre­
quency of these cases by developed countries
could have spurred their increased use by de­
veloping countries in the late 1980s. In 1988
more than 20 percent of anti-dumping actions
originated in developing countries. Some have
argued that use of this mechanism by develop­
ing countries is even more capricious than use
by developed countries because importers may
be subject to anti-dumping duties without either
due process or even formal notification.30 Thus,
the use of less-than-fair-value legislation in the
United States could backfire by generating addi­
tional inequities for U.S. producers—in this case,
exporters—and in subjecting international trade
to more barriers.31

S ection 301 a n d S u p er 301:
C o n tro versia l C o n sequ en ces

30This point is made by Powell (1990) based on an interview
with Robert McNeill, the executive vice chairman of the
Emergency Committee for American Trade.

petitor that is 48 percent British-owned. This new type of
trade complaint will likely cause some supporters of this
legislation to reconsider their positions as it becomes clear
that foreign-owned firms can benefit at the expense of
U.S. consumers.

31A related point is that the filing of anti-dumping petitions
in the United States is not limited to domestically owned
firms. As reported by Bradsher (1991), the American
manufacturing subsidiary of a Japanese company recently
asked the ITC to impose duties on the imports of a com-




Although Super 301 has generated much con­
troversy since its passage, it has produced only
a small number of offenders and practices. In
1989, for example, Brazil was cited for quantita­
tive restrictions involving her balance of pay­
ments; Japan was cited both for technical bar­
riers to trade hindering forest products and
government procurement practices involving
supercomputers and satellites; and India was
cited for barriers limiting trade in foreign in­
surance services and for trade-related invest­
ment measures that imposed export performance
requirements on foreign investors. It is note­
worthy that these priority practices were not
necessarily those with the greatest export poten­
tial and that they were similar to those gener­
ally handled under Section 301. Nonetheless,
these Super 301 actions generated protests from
our major trading partners.32
Barfield (1990) criticizes the Super 301 process
because it is ultimately controlled by the same
political judgments the United States criticizes
other countries for using in their trade policy
decisions. For example, the naming of India and
Brazil was in retaliation for their role as leaders
of a group of developing countries that opposed
U.S. goals in the Uruguay Round.
The politicization charge can be levied against
the Section 301 process in general. Powell (1990)
argues that voluntary export restraints on steel
in the 1980s were highly politicized. In the
course of the 1984 presidential campaign, Re­
publican political leaders bowed to steel in­
dustry pressure for protection. Finding no other
avenue available, the USTR threatened to file
Section 301 cases unless numerous countries
agreed to limit steel exports.
Other ways also exist to manipulate Section
301 cases. For example, in November 1987, the
USTR invited public comment to identify po­
tential Brazilian imports as targets for retalia­
tory tariffs in a computer piracy case. Represen­
tatives from various industries producing goods

32See Bhagwati and Patrick (1990), especially Part 3, for the
reactions to Section 301 by various U.S. trading partners.

JULY/AUGUST 1991

16

unrelated to computers, such as leather shoes
and dinner dishes, made appeals for retaliatory
tariffs of 100 percent to the USTR.
Barfield (1990) also criticizes Super 301 be­
cause it violates the fundamental premises of
GATT. GATT relies on negotiated reciprocal re­
ductions of trade barriers on a multilateral basis
across many industries. Actions in which coun­
tries unilaterally define unfair practices and
force bilateral negotiations under a retaliation
threat are antithetical to GATT. Since GATT is
the foundation for an orderly world trading sys­
tem, it is quite difficult to accept any argument
suggesting that use of this legislation by the
United States can facilitate free trade. It is more
likely that other countries will develop their
own versions of 301 legislation and that they
will be used to counteract the United States. In
such an environment, trade barriers will pro­
bably rise rather than decline.
A more fundamental criticism of Section 301
(in general) and Super 301 (specifically) is that
trade retaliation and retaliatory threats are inef­
fective in opening foreign markets. After study­
ing a large number of cases, Powell (1990) con­
cluded that this "crowbar” approach generally
fails and that markets are opened because of
domestic conditions rather than external ones.
From 1975 through March 1990, only 13 of 79
Section 301 cases that were filed led to market
openings.33 In many cases, countries have re­
sponded to retaliation by further closing their
markets.
Numerous reasons are offered to explain
the ineffectiveness and shortcomings of this ap­
proach to open foreign markets. First, nation­
alism in the target country is inspired by retali­
ation; a coercive attempt by a foreign govern­
ment tends to unite the target country against
the threat. Second, the target country reorients
its economy toward alternative suppliers and
markets. Firms and consumers in targeted coun­
tries can replace their transactions with the

United States by selling to and purchasing from
other countries. Third, the government's role in
the target country generally expands. This in­
tervention to manage the changes induced by
the retaliation involves trade-distorting policies,
many of which are difficult to eliminate once
they have been instituted. Fourth, the tougher
the sanctions, the larger the costs incurred by
the retaliating country in terms of higher con­
sumer and input prices.34
The preceding assessment, however, is not
shared by everyone. Ahearn et al. (1990) note a
congressional perception in recent years that
Section 301 and Super 301 are working. The
1989 Super 301 complaints against Japan and
Brazil were resolved in 1990. In addition, South
Korea and Taiwan, both frequently mentioned
as potential targets of Super 301, made advance
concessions to avoid being named as priority
countries.35 Nonetheless, even in situations
where this legislation is generating results, it is
far from clear that barriers to trade are actually
being reduced. For example, recent U.S.-Japan
discussions (known as the Structural Im­
pediments Initiative) were largely a Section 301
negotiation. It can be questioned whether the
U.S.-Japan agreement to reduce structural im­
pediments to trade, such as Japanese “conces­
sions” to review tax policies that favor agricul­
ture over new construction and American
"concessions” to reduce its budget deficit, will
promote a freer trading environment or even
reduce the U.S. bilateral trade deficit
with Japan.36

33Powell (1990) found that some openings for U.S. exports
to South Korea have led to new restrictions on imports
from other countries, especially those from Japan. Thus,
the South Korean market in not more open overall. While
the actions by South Korea have served the interests of
certain U.S. exporters, the actions reflect the fact that
political clout rather than economic efficiency is determin­
ing the pattern of trade.

1982, President Reagan ordered higher steel tariffs and
more restrictive import quotas as a result of a petition
charging European steel subsidies. While U.S. steel pro­
ducers undoubtedly benefited, American manufacturers
that required competitively-priced steel as an input were
harmed.

34Numerous examples are available to suggest the harm. In
1988, President Reagan imposed 100 percent tariffs on
Brazilian paper products, pharmaceuticals and consumer
electronics as a result of a Section 301 petition alleging in­
adequate Brazilian protection of pharmaceutical patents. In


FEDERAL RESERVE BANK OF ST. LOUIS


CONCLUSION

The alleged purpose of nearly all trade-remedy
laws is to ensure that international competition
is fair. Certain commercial practices, such as
dumping and export subsidies, are viewed as
unfair and, thus, should be counteracted. The
elimination of these unfair practices will pro­
duce an economic environment in which the

35The 1989 Super 301 complaints involving two practices in
India remain to be resolved. A review is to be conducted
after the conclusion of the Uruguay Round.
36See Butler (1991) for details on these negotiations as well
as for a general overview of U.S.-Japan trade.

17

success of firms and resource suppliers depend
on their own performance in a competitive
market rather than on their access to gov­
ernment subsidies or the use of questionable
practices.
Reality, however, bears little resemblance to
the alleged purpose. Less-than-fair-value trade
laws, especially anti-dumping laws, are the most
frequently used trade-remedy laws and provide
potential relief from all imports, whether they
are traded fairly or unfairly. In fact, the opera­
tions of these laws are biased toward findings
of dumping and export subsidies. Therefore,
they have become standard devices to protect
specific domestic producer interests at the ex­
pense of domestic consumer and other pro­
ducer interests. In addition, U.S. less-than-fairvalue trade laws explicitly instruct ad­
ministrators to protect specific domestic pro­
ducers, while ignoring the interests of domestic
consumers and other domestic producers.
The costs imposed by the increasing use of
less-than-fair-value trade laws are not restricted
to the consequences of the actual import duties.
The threat of such cases leads to “voluntary"
agreements to limit trade, agreements that harm
potential importers. Furthermore, the uncer­
tainty associated with the actual duties collected
functions as a type of non-tariff barrier. Finally,
there is evidence that the use of trade-remedy
laws tends to encourage protectionism in other
countries.
Despite some instances where Section 301
and Super 301 cases might have had positive ef­
fects in liberalizing foreign markets, there is
substantial evidence suggesting that the crowbar
approach generally fails. Domestic conditions
rather than external pressures provide the pri­
mary motivation for the liberalization of m ar­
kets. Nonetheless, there is a high probability
that this trade-remedy approach will be used
more frequently in the future. If so, then other
countries are likely to develop and use their
own versions of 301 legislation to counteract
the United States' actions. Similar to the world’s
experience with less-than-fair-value laws, protec­
tionism is a likely consequence.
Overall, the evidence is that trade-remedy
laws hinder rather than facilitate free trade.
U.S. fair trade laws can be more accurately
characterized as the bedrock for protectionism
rather than the bedrock for free trade. As such,
trade-remedy laws need to be remedied by elim­



inating the bias toward protection of domestic
producers.
REFERENCES
Ahearn, Raymond, Allan Mendelowitz, and Alfred Reifman.
Congress and U.S. Trade Policy in the Nineties, No. 663,
(National Bureau of Economic Research, October 5, 1990).
Barfield, Claude E. “ The Grand Inquisitor on Trade,”
Washington Post (May 1, 1990).
Bhagwati, Jagdish N. Protectionism (MIT Press, 1988).
Bhagwati, Jagdish N., and Douglas A. Irwin. “ The Return
of the Reciprocitarians— U.S. Trade Policy Today,” World
Economy (June 1987), pp. 109-30.
Bhagwati, Jagdish N., and Hugh T. Patrick, eds.
Aggressive Unilateralism: America’s 301 Trade Policy and the
World Trading System (University of Michigan Press, 1990).
Boltuck, Richard. ’’A ssessing the Effects on the Domestic
Industry of Price Dumping,” in P.K.M. Tharakan, ed., Policy
Implications of Antidumping Measures (North-Holland,
1991), pp. 99-141.
Boltuck, Richard, Joseph F. Francois, and Seth Kaplan.
“ The Economic Implications of the Current Administration
of the U.S. Unfair Trade Laws,” in Down in the Dumps: Ad­
ministration of the Unfair Trade Laws, Robert Litan, ed.,
(Brookings, forthcoming).
Bovard, James. “ U.S. Fair Trade Laws Are Anything But,”
Wall Street Journal, June 3, 1987.
Bradsher, Keith. ’’New Kind of U.S. Trade Complaint:
By Foreign Companies,” New York Times, April 19, 1991.
Butler, Alison. “ Bilateral Trade Balances and Economic
Theory: The Case for a U.S.-Japan Trade Deficit,” this
Review (March/April 1991), pp. 16-31.
Council of Economic Advisers. Economic Report of the
President (GPO, 1988).
Coughlin, Cletus C., K. Alec Chrystal, and Geoffrey E.
Wood. “ Protectionist Trade Policies: A Survey of Theory,
Evidence and Rationale,” this Review (January/February
1988), pp. 12-29.
Finger, J. Michael. “ The Industry-Country Incidence of ‘Less
than Fair Value’ Cases in U.S. Import Trade,” Quarterly
Review of Economics and Business (Summer 1981), pp.
260-79.
Finger, J. Michael and Tracy Murray. Policing Unfair
Imports: The U.S. Example, WPS 401, (The World Bank,
March 1990).
Gregory, Gene. “ The Profits of Harassment,” Far Eastern
Economic Review (October 26, 1979), pp. 74-79.
Grinols, Earl L. “ Procedural Protectionism: The American
Trade Bill and the New Interventionist Mode,” Weltwirtschaftliches Archiv (Heft 3, 1989), pp. 501-21.
Hamilton, Colleen, and John Whalley. “ Safeguards,”
Jeffrey J. Schott, ed., in Completing the Uruguay Round: A
Results-Oriented Approach to the GATT Trade Negotiations
(Institute for International Economics, September 1990), pp.
79-92.
Jackson, John H. The World Trading System (MIT Press,
1990).
Kaplan, Seth. “ Injury and Causation in USITC Antidumping
Determinations: Five Recent Approaches,” in P.K.M.
Tharakan, ed., Policy Implications of Antidumping Measures
(North-Holland, 1991), pp. 143-73.

JULY/AUGUST 1991

18

Lande, Stephen L., and Craig VanGrasstek. The Trade
and Tariff Act of 1984: Trade Policy in the Reagan
Administration (Lexington Books, 1986).
Messerlin, Patrick A. “Antidumping,” in Jeffrey J. Schott,
ed., Completing the Uruguay Round: A Results-Oriented Ap­
proach to the GATT Trade Negotiations (Institute for Interna­
tional Economics, September 1990), pp. 108-29.
Moore, Michael O. “ Rules or Politics?: An Empirical Analysis
of ITC Antidumping Decisions,” Working Paper (George
Washington University, 1990).


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Office of the United States Trade Representative.
1991 Trade Policy Agenda and 1990 Annual Report (GPO,
1991).
Powell, Jim. “ Why Trade Retaliation Closes Markets and
Impoverishes People,” Policy Analysis (Cato Institute,
November 30, 1990).
Richardson, J. David. “ Empirical Research on Trade
Liberalization with Imperfect Competition,” OECD Economic
Studies (Spring 1989), pp. 7-50.
U.S. International Trade Commission. Annual
Report 1986 (GPO, 1987).

19

Jeffrey D. K arrenbrock
Jeffrey D. Karrenbrock was an economist at the Federal
Reserve Bank of St. Louis when this paper was written. David
H. Kelly provided research assistance.

The Behavior of Retail
Gasoline Prices: Symmetric
or Not?

S in c e DEREGULATION in the early 1980s,
crude oil prices have been allowed to move
freely with market conditions. Because of oil
supply shocks and seasonal movements in gaso­
line demand, retail gasoline prices often fluctuate
more widely than consumer prices in general.
Some analysts and politicians have criticized
these retail gasoline price movements, alleging
that they do not respond symmetrically to price
changes at earlier stages of the marketing chain.
In particular, they believe that retail gasoline
prices do not reflect decreases in oil and whole­
sale gasoline prices as rapidly and fully as they
do price increases. The shaded insert on page
20 contains comments typical of this line of
criticism. The perceived asymmetry in retail
gasoline price movements is of special concern
to consumers who believe that they are being
“gouged" by the oil industry.
Much of the perception of possible asymmetry
focuses on the relationship between the price of
oil and the retail price of gasoline. This suggests

that oil producers or refineries are principally
responsible for the asymmetry. In fact, a survey
undertaken by the American Petroleum Institute
concluded that 80 percent of Americans be­
lieved that oil companies artificially raised the
price of gasoline after Iraq's invasion of Kuwait
on August 2, 1990.1 This statistic suggests that
many Americans believe retail gasoline stations
are owned and operated by the oil refiners. In
some cases this is true, but much of the gaso­
line sold at the retail level is sold through out­
lets that are not owned by the oil producers
and refiners. The fact that many retail outlets
are “independent" suggests that they have some
autonomy in setting the retail price. The role
these retailers play in the perceived asymmetry
is largely ignored, even though they are as
much a possible source of such an asymmetry
as are the oil producers and refiners.2 This arti­
cle analyzes the role that retailers may play in
the perceived asymmetric movement of retail
gasoline prices. Specifically, we test whether

1McKenzie (1991).
2An article by Solomon (1990), however, does point out the
potential role of retail outlets. See “ Gasoline Prices Resist
Crude Behavior.”




JULY/AUGUST 1991

20

What Goes Up Need Not Come Down?
“Those who are doing the gouging will hear
from the president.” —Treasury Secretary
Nicholas Brady. The Wall Street Journal,
(Shribman and McQueen) August 9, 1990.
“Retail (gasoline) prices go up much faster
than they come down.” —a spokesman for
the Automobile Association of America. The
Wall Street Journal, (Solomon) August 9, 1990.
“Pump prices are fast to respond to rising
prices but slower to fall when crude prices
fall.” —Antonio Szabo, oil consultant with
Bonner & Moore. The Wall Street Journal,
(Business Bulletin) August 3, 1989.
wholesale gasoline price increases are passed
along to the retail customer more fully and
rapidly than are wholesale gasoline price
decreases.
GASOLINE D ISTRIBUTIO N,
PR IC IN G A N D M ARGINS

The purchase of gasoline at the retail pump
is the end of a long and complicated marketing
chain. A simplified illustration of how oil, after
undergoing refining, reaches the consumer as
gasoline is shown in figure 1. From the oil fields,
oil is moved to the refineries either by tanker,
pipeline, or a combination of the two. The re­
finery receiving the oil may be owned by the
company that produced the oil or may be in­
dependent. On January 1, 1990, 205 U.S. refin­
eries, owned by over 100 companies, were in
operation.
At the refinery, oil is distilled into a variety of
products including gasoline, home heating oil,
diesel oil, jet fuels, asphalt, kerosene and lubri­
cants. One barrel of oil (42 U.S. gallons) yields
about 43 percent gasoline.3 Gasoline is trans­
ported from the refinery by truck, pipeline,
tanker or barge. Some is moved directly from
the refinery to retail outlets; some is moved
from the refinery to terminal storage areas
closer to final consumption. From these storage
3See Anderson (1984, p. 216).


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"Whenever oil prices fall, there is always this
stickiness in gasoline prices on the way down.
You never see this stickiness on the way up.”
—Ed Rothschild, energy expert at Citizen Ac­
tion. New York Times, (Wald) July 2, 1990.
“When crude prices go up, product prices
tend to rise with crude prices. But when crude
prices go down, product prices tend to lag—
they go down slowly.” —John Hilton, oil in­
dustry analyst for Argus Research Corp. St.
Louis Post-Dispatch, (Crudele) June 19, 1990.

areas, the gasoline is generally moved to the
point of final sale by truck. Once the gasoline
reaches its final destination before purchase, it
is usually stored in large underground tanks.
Refiners may sell gasoline directly to "end
users” such as large trucking firms, industrial
m anufacturers and utilities. They may also sell
directly to retail gasoline outlets. Retail gasoline
stations owned by the refining company are
classified as "end users.” Retail gasoline stations
not owned by refining companies are known as
“independents.” As figure 1 shows, sales to end
users accounted for about 19 percent of refin­
ers’ gasoline sales, by volume, in 1988, with 17
percent of the sales to company outlets and 2
percent to other end users.
The other 81 percent of refiners’ gasoline
sales are made to either “jobbers” or indepen­
dent retail outlets. Jobbers purchase gasoline
from the refiners which they in turn sell and
distribute to retail stations and large users. Gas­
oline sales made by refiners to the non-companyowned retail outlets and to jobbers are referred
to as “sales for resale.”
Several different entities are involved in the
pricing of gasoline as it is moved from the oil
field to the retail gasoline outlet. When oil is
sold to the refinery, the price for this transac­
tion is called the producer price. The price
charged for gasoline by the refiner or jobber to

21

Figure 1
Oil and Gasoline Distribution Channels

1. Based, in part, on information provided in Dougher and Jones (1990), p. 7.

the retail gasoline station is called the wholesale
price.4 The price the gasoline station charges
the consumer is called the retail price. The dif­
ferences between prices at various levels in the
marketing chain are called "margins.” The dif­
ference between the retail and wholesale price

is called the wholesale-retail margin. The dif­
ference between the wholesale price and pro­
ducer price is called the producer-wholesale
margin. The overall difference is called the
producer-retail margin.

4The price that the jobber pays the refiner is included in
the “ sales for resale” price series used in this study.




JULY/AUGUST 1991

22

DEFINITIONS OF ASYM M ETRIC
GASOLINE PRICE MOVEMENTS

Retail price movements are defined as asym­
metric if an increase in the wholesale price af­
fects the retail price differently than an equal­
sized decrease. Three types of asymmetry are
defined. The first deals with the length of time
in which a wholesale price change works its
way through to the retail level. For example, is
an increase in the wholesale price passed along
more quickly to the retail level than an equal­
sized wholesale price decrease?
The second type of asymmetry deals with the
amount of a wholesale price change that passes
through to the consumer. For example, does a
10-cent increase in the wholesale price lead to a
7.5-cent increase in the retail price, while a 10cent decrease in the wholesale price leads to
only a 5-cent decline in retail price?
The third type of asymmetry is a combination
of time and amount. The pattern of retail price
response may differ for wholesale price increases
and decreases. Although the retail price may ad­
just to a wholesale price increase and decrease
by an equal total amount and length of time,
the amount of adjustment in each period may
not be equal for price increases and decreases.
For example, in cases where the wholesale
price increases and decreases 10 cents per
gallon, retail prices may require two months to
completely respond to both wholesale price
changes. Assume the retail price increases and
decreases 9 cents per gallon in response to the
wholesale price increase and decrease, respec­
tively. In such a situation, symmetry exists with
respect to both the timing and amount of retail
price movements. The pattern of the retail price
response might be to increase (decrease) 7 cents
in the initial month and increase (decrease) 2
cents in the month following a wholesale price
increase (decrease). This pattern is symmetric.
However, the pattern could be such that the
retail price rises 7 cents and 2 cents in the first
two months for a wholesale price increase,
while the retail price falls only 3 cents in the
initial month and 6 cents in the second month
in response to a wholesale price decrease.
This pattern is not symmetric.
5Pick et al (1990), Kinnucan and Forker (1987), Ward
(1982), Heien (1980) and Hahn (1990) all find asymmetry
in the agricultural markets. Domowitz et al (1988), Bils
(1987) and Morrison (1988) find asymmetric markups in
the manufacturing sector.


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Since producers, wholesalers and retailers all
play a role in the determination of the retail
price of gasoline, the perceived asymmetric
price movements in the industry could be oc­
curring between the producer and wholesale
level or the wholesale and retail level. As noted
earlier, many discussions of the perceived price
asymmetry in the gasoline industry focus on the
producer-retail price margin. Such a focus on
the producer-retail margin tends to mask the
role that retailers play in determining the
producer-retail margin. Indeed, the perceived
asymmetry may as readily be due to retailer
behavior. In this case, simply observing the
producer-retail price margin would not allow
us to determine who is responsible for any
asymmetry.
Price-movement asymmetry has been found to
exist in several commodity markets, including
oranges, lemons, dairy products, some fresh
vegetables, pork and beef. In addition, the
markup of price over cost in durable and non­
durable manufacturing has been found to vary
over the business cycle.5 Thus, a finding that
price movement asymmetry exists in the retail
gasoline market would not be unique. Many of
the works cited above indicate the importance
of industry concentration as a factor in explain­
ing the existence of asymmetry in these m ar­
kets. Kinnucan and Forker (1987) note that
"because of industry concentration . . ., it is
commonly asserted that middlemen use market
power to employ pricing strategies which result
in complete and rapid pass-through of cost in­
creases but slower and less complete transmis­
sion of cost savings.”
GASOLINE PRICES AN D
CO NSUM PTIO N

The U.S. average retail and wholesale prices
of gasoline are shown in figure 2 for the period
examined (January 1983 to December 1990).
Several intervals of relatively large and rapid
wholesale price changes are shown in the fig­
ure. In early 1986, following the collapse of oil
prices, wholesale gasoline prices dropped sharp­
ly. In the spring of 1989, gasoline prices rose
sharply due "in part because of the temporary
closing of the port of Valdez, Alaska, at the ter­
minus of the Trans-Alaska pipeline, after the

23

Figure 2
U.S. Average Retail and Wholesale Gasoline Prices1
Cents per Gallon
160

Cents per Gallon
160

0
1983

84

85

86

1Retail prices include federal and state tax.

Exxon Valdez oil spill in March.”6 The jump and
subsequent decline in prices in late 1990 are
associated with an OPEC oil price increase
prompted by Iraq in late July 1990, the subse­
quent Iraqi invasion of Kuwait and the world
embargo of Iraq-Kuwait oil. In all instances, the
retail price appears to parallel the wholesale
price quite closely. A more detailed and system­
atic analysis is necessary to determine if there
is indeed a symmetric response in retail prices
to a wholesale price increase and decrease. Al­
though not shown in figure 2, the wholesale and
retail prices of different grades of gasoline (pre­
mium, unleaded regular and leaded regular) also

exhibit similar parallel movements with
wholesale price changes.
Since the analysis below examines asymmetry
for different gasoline grades, it is useful to note
the relative importance of these fuels. The mix
of different grades of gasoline has changed sub­
stantially during the last 30 years. Prior to 1975,
leaded gasoline accounted for over 50 percent
of all motor gasoline fuel sales. Leaded gaso­
line's market share began to decline, however,
after the enactment of environmental laws that
required automobiles to burn unleaded gasoline
and refiners to reduce the lead content of their

6See Wald (1990).




JULY/AUGUST 1991

24

gasoline. Today, leaded gasoline accounts for
only about 17 percent of total motor gasoline
consumption, while unleaded regular and pre­
mium gasoline account for 59 percent and 24
percent, respectively.7
TESTING THE W HOLESALER ETAIL M AR GIN FO R SYM M ETRY

The hypothesis considered is that movement
in the wholesale-retail margin in the gasoline
market is symmetric. We test to see if decreases
in wholesale gasoline prices are passed along to
consumers as rapidly and as fully as are whole­
sale gasoline price increases. We test only for
symmetry in the wholesale-retail price margin
because the model used for this test may be
best suited for this margin. The model assumes
a markup method is used to set the retail price
of gasoline.8
To test for symmetric movements in retail
prices, we use a model in which the current
retail gasoline price (Rt) is a function of the
wholesale gasoline price (W,); both prices are
measured in cents per gallon. This relationship
is summarized as
(1) R, = a0 + aiWt.
The effect of a change in the wholesale price
on the retail price is
(2) R , - R t_! = a j I W . - W n ) .

In order to examine how the affect of a
wholesale price increase differs from that of a
decrease, periods of wholesale price increases
and decreases must be separated.
Following an approach similar to that
developed by Wolfram (1971), this segmentation
can be achieved using the model
7Based on volumes of first sales of motor gasoline in the
Petroleum Marketing Annual [U.S. Department of Energy,
(1988)], p. 216.
8This approach seems to more accurately represent the
pricing behavior of retail outlets than oil refiners. Refiners
with several oil products are perhaps more likely to employ
a more sophisticated pricing mechanism than the retailer
with a narrower range of oil products. One could make the
argument, however, that retail outlets also have a multi­
product pricing function, especially if the station is
associated with a convenience store. Dougher and Jones
(1990) note suggestions that low margins on gasoline may
be offset by higher margins on convenience foods.
9W olfram’s procedure uses the level of the dependent vari­
able, while we use the first difference of the dependent


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Federal Reserve Bank of St. Louis

(3) ARt = a,W It + a2WD, + e t,
where
ARt = Rt - Rt_j,
WIt = Wt-W tM, if (W .-W ,.,) > 0,
and = 0 otherwise,
WDt = W .-W ,^, if (Wt-W t_j) < 0,
and = 0 otherwise,
et = a random error term .9
All WIt are positive or zero and all WD, are
negative or zero. If retail prices respond sym­
metrically to wholesale price increases and de­
creases, then one would expect to find a! = a2.
In order to allow for lags in adjustment time, a
more general specification is

where p and q are the specified num ber of lags
for the wholesale price increases and decreases,
respectively (p need not equal q). An intercept,
a^, could be positive, negative or zero and need
not be included on theoretical grounds. Follow­
ing Heien (1980) and Boyd and Brorsen (1988),
however, we include it to avoid biasing the co­
efficient estimates if the intercept is not truly
zero. This variable captures the average in­
fluence of all other factors besides raw material
price changes that influence the retail price.10
Differences in the timing of price pass-through
would be indicated by differences in the num ­
ber of lags for increases (p) and decreases (q).
The test of interest for the amount of pass­
through now becomes testing the equality
i?0ai,i = ,?0a2„. In other words, is the cumulative
variable. The model was also run for unleaded gasoline
using the natural logs of all variables. The results are
similar to those using the first-difference data.
10ln some studies, a variable to measure changes in other
major marketing margin cost components, such as labor,
transportation and packaging materials, has been included
in equation 4. Preliminary estimates for this study that in­
cluded transportation wages and/or service station wages
showed that neither variable was statistically significant.

25

effect of a wholesale price increase equivalent to
that of a wholesale price decrease? If wholesale
price changes are fully reflected in the retail
price, we would expect to see Z0ai,i = 1 and
Zfla2i = 1. Symmetry in the pattern of retail
price response cannot be rejected if p equal q
and all a j, = a2i.
D A T A A N D ESTIM ATION
PROCEDURE

January 1983 through December 1990, a pe­
riod of relatively little government intervention
in the gasoline market, was chosen as the period
of analysis. Honeycutt (1985) notes that a
". . . factor that influenced gasoline marketing,
beginning in August 1971 and continuing to Jan­
uary 1981, was extensive federal intervention in
the marketplace.”11Furthermore, he notes that
"statements by several major refiners that any
changes in gasoline marketing would be phased
in gradually suggest that not all important re­
sponses to decontrol had occurred by Septem­
ber 1981.”1Z In order to allow time for these
"important responses” to have little or no effect
on the results, the period studied here starts in
January 1983. During the period analyzed, the
number of months with price increases and
decreases for retail and wholesale prices was
roughly equal across all grades of gasoline.
11Honeycutt (1985), p. 108.
12lbid., p. 113.
13The unadjusted data are calculated by the U.S. Bureau of
Labor Statistics and reported in the U.S. Energy Informa­
tion Agency’s Monthly Energy Review. These prices in­
clude all federal, state and local taxes paid at the time of
sale. For the period 1978 forward, prices were collected
from a sample of service stations in 85 urban areas se­
lected to represent all urban consumers—about 80 percent
of the total U.S. population. Service stations are selected
initially, and on a replacement basis, in such a way that
they represent the purchasing habits of the Consumer
Price Index population. Service stations in the current
sample include those providing all types of service (i.e.,
full, mini and self-serve). See Monthly Energy Review,
February 1989, p. 106. Retail prices are collected at dif­
ferent stations during the month of estimation.
14Taxes were removed from the retail price using informa­
tion provided in the U.S. Department of Energy’s Petrol­
eum Marketing Monthly. Federal and state motor fuel taxes
are reported by the agency about twice a year (generally
those effective on January 1 and July 1).
,5Handling the tax rate changes in this manner could bias
the results because tax rate changes that occur between
reported tax rate changes are not accounted for until the
next reporting month.

The retail prices used are tax-adjusted U.S.
City Average Retail Prices of Motor Gasoline.13
The prices used were reduced by the sum of
the federal gasoline tax and a simple average of
the 50 states’ gasoline tax.14 No attempt was
made to interpolate tax rates between months
where tax rates were actually observed. The
most current reported tax rates were used until
new tax data became available.15
Wholesale prices are those from data referred
to as "Sales for Resale.”16 These are sales of re­
fined petroleum products to purchasers who
are "other-than-ultimate consumers.” This series
does not include refined petroleum product
sales made directly to end users, such as agri­
culture, industry and utility consumers or sales
made by refiners to company-operated retail
outlets. Wholesale prices are reported exclusive
of taxes.
RESULTS

Equation 4 was estimated for premium, un­
leaded regular and leaded regular gasoline. Pre­
liminary estimates of lag lengths were selected
using Akaike’s (1970) Final Prediction Error (FPE)
criterion.17 The FPE procedure used to estimate
the “best” lag length requires the user to specify
a maximum lag length. For our data, the lag
lengths selected by the FPE procedure were
sensitive to alternative maximum lag lengths.18
Information Agency by firms responding to two separate
surveys. The first survey, EIA-782A, “ Refiners'/Gas Plant
Operators’ Monthly Petroleum Product Sales Report,” is
sent to a census of about 200 refiners and gas plant
operators. The second survey, EIA-782B, “ Reseller/Re­
tailers’ Monthly Petroleum Product Sales Report,” is sent
to about 3,000 resellers and retailers. Some of the firms in
this survey are replaced on an annual basis. In both
surveys, firms are surveyed on a monthly basis and are
asked to report prices on a monthly volume-weighted
basis.
17Batten and Thornton (1984) note that the FPE criterion at­
tempts to balance the “ risk” due to bias when shorter lag
lengths are selected against the “ risk” due to the increase
in variance when longer lag lengths are chosen. Thornton
and Batten (1985) point out that the FPE procedure gives
relatively more importance to a lack of bias than efficiency.
They argue that the procedure is asymptotically inefficient
in that, on the average, it selects lags that are too long in
large samples.
18Maximum lag lengths of six, nine and 12 months were
specified in the FPE procedure. Results for the six-month
and nine-month maximum were identical, although the 12month maximum model chose longer Wl lags (10 months)
for premium and unleaded regular gasoline. Lag lengths
suggested by the six- and nine-month maximum lag length
models were used in estimating equation 4.

16See the U.S. Department of Energy's Petroleum Marketing
Annual and the Petroleum Marketing Monthly. This price
series is based on information provided to the Energy




JULY/AUGUST 1991

26

Table 1
Symmetry Tests For Different Grades of Gasoline from January 1983December 1990
Amount

Timing

Type of
gasoline

Number of
inonths lagged
Wl
WD

Price parameter
estimates
Increase
Decrease
la ,! = 0
I a 2.i = 0

t-value for
test of
£ a ij= Za2 i

t-value for
test of
£ a ij = i
l a 2,i = 1

R2

D.W.

Premium

1

1

.98*
(18.28)

.90*
(16.98)

.88

.34

1.79

.91

1.94

Unleaded
regular

1

1

1.03*
(18.64)

.99*
(17.64)

.46

.53

.22

.92

2.15

Leaded
regular

1

2

1.10*
(19.58)

1.05*
(16.93)

.49

1.70

.80

.92

2.29

Note: Numbers in parentheses are the absolute values of the t-statistics.
’ Indicates statistical significance at the 5 percent level.

After estimating the model with the lag lengths
suggested by the FPE procedure, F-tests and
t-tests were performed to see if any of the lags
(incrementally or as a group) could be elimi­
nated as statistically insignificant. Only the sig­
nificant lags are reported below.19 Significant
first-order autocorrelation was not present in
any of the estimated equations.

oline, however, wholesale price decreases affect
the retail price for three months. Thus, the hy­
pothesis that the length of time in which retail
prices completely respond to a wholesale price
change is symmetric cannot be rejected for
premium and unleaded regular gasoline but can
be for leaded regular gasoline.

T im ing S y m m e try

Since the impact of the wholesale price change
on the retail price is distributed over more than
one month, the test for symmetry in the
amount of pass-through examines w hether the
total response to a wholesale price increase is
equal to the total response topa wholesale price
decrease. In other words, is I a , i = I a 2i? The
results of this test are shown in table 1. For all
grades of gasoline, the cumulative response of
retail prices to a wholesale price increase is no
different from that to a wholesale price de­
crease. In addition, the hypotheses that
Z a1(i = 1 and Zoa2>i = 1 cannot be rejected for
any grade of gasoline. This implies that whole­
sale price decreases are fully passed along to

The ordinary least squares estimates for equa­
tion 4 are summarized in table 1. Lag lengths
used for periods of wholesale price increases
were the same across all grades of gasoline; lag
lengths used for periods of wholesale price
decreases were the same for premium and un­
leaded regular. Leaded regular gasoline had a
slightly longer lag length for wholesale price
decreases. These models suggest that wholesale
price increases affect retail prices for two
months (the initial month plus a lagged month).
Similarly, wholesale price decreases affect the
retail price of premium and unleaded regular
gasoline for two months. For leaded regular gas­
19The reported lag lengths are those suggested by the FPE
criterion except for premium’s Wl (for which the FPE pro­
cedure suggested a lag length of three months), and unleaded’s WD (for which the FPE procedure suggested a
lag length of two months).


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A m ou n t S y m m e try

1= 0

1

1= 0

*

27

Table 2
The Pattern of Retail Gasoline Price Response
Equation 4 Parameter Estimates
Wholesale
increases
a-1,0
Premium

Unleaded
regular

Leaded
regular

Wholesale
decreases
aV

32,0

32,1

a22

ao

.64*
(14.39)

.34*
(7.62)

.29*
(6.43)

.62*
(11.57)

-.1 6
(1.02)

.68*
(14.97)

.35*
(7.71)

.30*
(6.41)

.69*
(12.65)

__

- .0 8
( -50)

.76*
(17.21)

.34*
(7.69)

.23*
(5.13)

.68*
(12.85)

.14*
(3.05)

-.0 7
(0.41)

Note: Numbers in parentheses are the absolute values of the t-statistics.
‘ Indicates statistical significance at the 5 percent level.

consumers, as are wholesale price increases. In
short, the hypothesis that the amount of pass­
through in the retail gasoline market is sym­
metric cannot be rejected for the period of
investigation.

P a ttern S y m m e try

Even though the time it takes retail prices to
respond fully to wholesale price changes and
the total amount that retail prices respond to
wholesale price changes are symmetrical, there
is a difference in the pattern of response to
wholesale price increases and decreases. The
coefficient estimates for equation 4 are graphi­
cally shown, by grade of gasoline, in figure 3.
For wholesale price increases, the largest retail
response occurs in the current month for all
grades of gasoline. But, for wholesale price de­
clines, retail prices respond relatively little in
the first month, and make their largest adjust­
ment in the month following the wholesale
price decline.
Using the premium gasoline model as an ex­
ample, a direct interpretation of the coefficients,
as reported in table 2, is as follows: a 10-cent
increase in the wholesale price leads to a 6.4-

cent increase in the retail price during the in­
itial month, while a 10-cent wholesale price de­
cline leads to a 2.9-cent decline in the initial
period. For premium gasoline, there is about a
3.5-cent per gallon difference in the amount
that the retail price responds to a 10-cent whole­
sale price increase and decrease during the in­
itial month. For unleaded regular and leaded
regular, the difference is about 3.8 cents and
5.3 cents per gallon for every 10 cent change in
the wholesale price, respectively. Indeed, a test
for equality of the a10 and a20 coefficients is re­
jected for all grades of gasoline, indicating asym­
metry in the amount of price response during
the initial month of the wholesale price change.
Wholesale gasoline price increases are passed
along more fully in the initial month than are
wholesale price decreases. The amount of the
total retail adjustment occurring in the initial
month ranges from 65 percent to 69 percent
for wholesale price increases, and from 22 per­
cent to 32 percent for wholesale price
decreases.20
During the second month, between 31 percent
and 35 percent of the total retail adjustment oc­
curs for wholesale price increases, and between

20The initial month percent respons^for a wholesale price
increase was calculated as [ai,0/( 2EJai,i|) 1*100.




JULY/AUGUST 1991

28

Figure 3
Asymmetry in the Pattern of Retail
Price Response
(Estimated Coefficients for Equation 4)
Retail
Response
(Coefficient)

Premium Gasoline

Retail
Response
(Coefficient)

Response
(Coefficient)

Unleaded Regular Gasoline

Response
(Coefficient)

0.8

Retail
Response
(Coefficient)
0.8

0.8

Wholesale Price
Increase

Wholesale Price
Decrease

Leaded Regular Gasoline

Wholesale Price
Decrease

Wholesale Price
Increase


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t-2

Retail
Response
(Coefficient)

0.8

29

65 percent and 70 percent for wholesale price
decreases. The hypothesis that a^, = a21 is re­
jected for all grades of gasoline, indicating asym­
metry in the amount of price response during
the month following a wholesale price change.
For leaded gasoline, a third month is needed
before the impact of a wholesale price decline is
fully reflected in the retail price.
CONCLUSION

This paper has tested for symmetric retail
gasoline price responses to changes in wholesale
gasoline prices. The results show that the length
of time in which a wholesale price increase is
fully reflected in the retail gasoline price is the
same as that of a wholesale price decrease for
premium and unleaded regular gasoline. Whole­
sale gasoline price increases for leaded regular
gasoline are passed along to the consumer more
quickly than price decreases. Although the time
in which retail prices fully respond to increases
and decreases in wholesale prices is the same
for both premium and unleaded gasoline, the
pattern of retail price adjustment is such that
consumers will experience the bulk of a whole­
sale price change sooner for price increases
than they do for decreases. However, contrary
to the popular belief that consumers do not
benefit from wholesale gasoline price decreases,
wholesale gasoline price decreases are even­
tually passed along to consumers as fully as are
wholesale gasoline price increases.
REFERENCES

Domowitz, Ian, R. Glenn Hubbard, and Bruce C. Petersen.
“ Market Structure and Cyclical Fluctuations in U.S.
Manufacturing,” The Review of Economics and Statistics
(February 1988), pp. 55-66.
Dougher, Rayola S., and Russell O. Jones. Gasoline
Distribution and Service Station Margins: An Assessment of
EPA Assumptions and Implications for Methanol (American
Petroleum Institute, September 1990).
Fraser, R.W. “ Uncertainty and the Theory of Mark-up
Pricing,” Bulletin of Economic Research (1985), pp. 55-64.
Hahn, William F. “ Price Transmission Asymmetry in Pork
and Beef Markets,” The Journal of Agricultural Economics
Research (1990), pp. 21-30.
Heien, Dale M. “ Markup Pricing in a Dynamic Model of the
Food Industry,” American Journal of Agricultural Economics
(1980), pp. 11-18.
Honeycutt, T. Crawford. “ Competition in Controlled and
Uncontrolled Gasoline Markets,” Contemporary Policy
Issues (Spring 1985), pp. 105-18.
Kinnucan, Henry W., and Olan D. Forker. “Asymmetry in
Farm-Retail Price Transmission for Major Dairy Products,”
American Journal of Agricultural Economics (1987),
pp. 285-92.
McKenzie, Richard B. “ Did 'Big O il’ Gouge Prices?”
The Journal of Commerce, March 6, 1991, p. 8A.
Morrison, Catherine J. “ Markups in U.S. and Japanese
Manufacturing: A Short Run Econometric Analysis,” Work­
ing Paper Series No. 2799, National Bureau of Economic
Research, Inc. (1988).
Pick, Daniel H., Jeffrey D. Karrenbrock, and Hoy F. Carman.
“ Price Asymmetry and Marketing Margin Behavior: An
Example for California-Arizona Citrus,” Agribusiness,
Vol. 6, No. 1, (1990), pp. 75-84.
Shribman, David, and Michel McQueen. “ Office-Seekers
Revive 1970s Campaign Strategy of Bashing Oil Com­
panies Over Spike in Prices,” The Wall Street Journal,
August 9, 1990.
Solomon, Caleb. “ Gasoline Prices Resist Crude Behavior,”
The Wall Street Journal, May 2, 1990.
________“ Oil Companies Bend in Wake of Public Outcry,"
The Wall Street Journal, August 9, 1990.

Akaike, Hirotugu. “ Statistical Predictor Identification,” Annals
of the Institute of Statistical Mathematics (1970),
pp. 203-17.

Thornton, Daniel L., and Dallas S. Batten. “ Lag-Length
Selection and Tests of Granger Causality Between Money
and Income,” Journal of Money, Credit, and Banking (May
1985), pp. 164-78.

Anderson, Robert O. Fundamentals of the Petroleum
Industry (University of Oklahoma Press, 1984).

U.S. Department of Energy, Energy Information Agency.
Monthly Energy Review, Washington, D.C., various issues.

Batten, Dallas S., and Daniel L. Thornton. “ How Robust Are
the Policy Conclusions of the St. Louis Equation?: Some
Further Evidence,” this Review (June/July 1984),
pp. 26-32.

________Petroleum Marketing Annual, Washington D.C.,
various issues.
________Petroleum Marketing Monthly, Washington D.C.,
various issues.

Bils, Mark. “ The Cyclical Behavior of Marginal Cost and
Price,” The American Economic Review (December 1987),
pp. 838-55.

Wald, Matthew L. "Prices of Gasoline Fail to Reflect Drop
in Cost of Crude Oil,” New York Times, July 2, 1990.

Boyd, Milton S., and B. Wade Brorsen. “ Price Asymmetry
in the U.S. Pork Marketing Channel,” North Central Journal
of Agricultural Economics (1988), pp. 103-09.

Ward, Ronald W. “Asymmetry in Retail, Wholesale, and
Shipping Point Pricing for Fresh Vegetables,” American
Journal of Agricultural Economics (1982), pp. 205-12.

“ Business Bulletin,” The Wall Street Journal, August 3, 1989.

Wolfram, Rudolph. “ Positivistic Measures of Aggregate
Supply Elasticities: Some New Approaches—Some Critical
Notes,” American Journal of Agricultural Economics (May
1971), pp. 356-59.

Crudele, John. “ Gasoline Up, Oil Down in Price Paradox,”
St. Louis Post-Dispatch, June 19, 1990.




JULY/AUGUST 1991

30

Michael T. Belongia
Michael T. Belongia is an assistant vice president at the
Federal Reserve Bank of St. Louis. James A. Chalfant, Dennis
Jansen, Thomas Grennes, Richard A. King, John S. Lapp and
David Orden made many helpful comments on earlier drafts.
Dawn Peterson, Lora Holman and Lynn Dietrich provided
research assistance.

Monetary Policy and the
Farm/Nonfarm Price Ratio: A
Com parison of Effects in
Alternative Models

S i n c e 1974, f o l lo w in g p u b lic a tio n of
Schuh’s "The Macroeconomics of Agriculture/’
much research effort has been devoted to deter­
mining whether and how monetary policy af­
fects the farm sector. One of the more active
areas of interest has been the question of wheth­
er changes in the money stock affect the farm/
nonfarm product relative price ratio. The reason
for this particular interest, as described by
Tweeten (1980), is that declines in the relative
price ratio represent a "cost-price squeeze” for
farmers; thus, he suggests, if contractionary
monetary policy causes farm prices to adjust
downward more quickly than farm input prices,
farm income will decline as well. Penson and
Gardner (1988), surveying the relevant literature,
conclude that the agricultural sector has borne
the brunt of adjustment costs whenever slower
money growth has occurred.
’ Chambers (1984); Starleaf, Meyers and Womack (1985);
Falk, Devadoss and Meyers (1986); Taylor and Spriggs
(1989); and Tegene (1990) found similar results. Doo Bong
Han, Jansen and Penson (1990) reaffirm the significance
of this linkage by reporting that the conditional means and
variances of agricultural prices are more closely related to


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These conclusions received considerable atten­
tion in policy discussions during the mid-1980s
when real farm incomes, exports and asset values
were falling sharply. As those discussions inten­
sified, additional research reported that the
quantitative effect of monetary actions on rela­
tive farm prices was not only large but persis­
tent, if not permanent.
Among recent studies, Devadoss and Meyers
(1987), for example, report that negative money
supply shocks . . . "harm farmers because farm
product prices decrease relatively more than
nonfarm product prices” (p. 842). Many other
studies found similar results.1
In sum, the notion that contractionary mone­
tary policy affects agricultural prices and in­
come differently than comparable measures in
the nonfarm sector has become one of the
the conditional means and variances of M1 than those of
industrial prices. Orden (1986a), Lapp (1990), Gardner
(1981) and Grennes and Lapp (1986), in contrast, did not
find the relative price of farm products to be related to
nominal macroeconomic variables.

31

stylized facts" of agricultural economics.2
Because neoclassical theory implies that changes
in money growth have no real consequences in
the long run, however, the large and sometimes
permanent effects of monetary actions on
agricultural prices reported in the literature
seem to present an anomaly. Recent episodes,
moreover, seem to run counter to the view that
contractionary monetary policy selectively hurts
farmers. First, real farm income rose during the
late 1980s, a period some analysts would charac­
terize as one of substantial monetary contrac­
tion.3 Second, although the dollar's decline since
early 1985 would help expand U.S. farm ex­
ports, all other things the same, the exchange
rate depreciation has occurred at an odd time:
when monetary policy has been contractionary
and federal budget deficits have been expanding.
Although the conventional wisdom links both
factors to lower farm sector prices and income,
this result is supposed to be transmitted through
a rising value of the dollar.4
This article reviews the previous literature
linking monetary actions to the relative price of
farm products and attempts to reconcile the
conflicting theoretical and empirical approaches
that have been applied to this issue. Because
previous studies derive their empirical models
from a variety of generally noncomparable theo­
retical models, this paper highlights cases in
which the direction or significance of a particu­
lar variable’s impact differs across models. By
estimating each model with the same data and
testing each model’s implications directly, we
can better assess monetary policy’s effects on
relative farm prices and the agricultural sector.
A REVIEW OF THE LITER ATUR E

Table 1 lists the important features of studies
that examined the effects of monetary actions
on the farm/nonfarm relative price ratio. The
most common measure of farm prices used in
these studies is the index of prices received by
farmers. The relative price issue is typically in­
2Chambers (1985).
3Between IV/1986 and IV/1990, for example, the 12-quarter
moving average growth rate of M1 declined from 10.3 per­
cent to 3.0 percent. A “ trend” growth rate of M1 this low
has not been seen in nearly three decades.
“ See, for example, Belongia and Stone (1985).
5See, for example, Granger and Newbold (1974), Plosser
and Schwert (1978) and Dickey and Fuller (1979, 1981).

vestigated by dividing this index by another in­
dex of either the aggregate price level or the
prices of some commodity bundle composed of
nonfarm products; in some instances, farm and
nonfarm nominal price indexes have been re­
gressed on a monetary measure individually to
identify different speeds of adjustment and
thereby infer the net impact of changes in mon­
ey growth on the selected relative price ratio.
Ml has been used almost uniformly as the in­
dicator of monetary actions.
Annual, quarterly and monthly data have been
used to estimate the empirical relationship be­
tween Ml and the relative price measure. Most
studies specify this relationship as one between
the natural logarithms of the two series, some­
thing which, in view of the more recent litera­
ture on common trends in data and spurious
regression relationships, may have given rise to
significant associations where none actually
existed.5
With the exceptions of Lapp (1990), and Grennes and Lapp (1986), these studies found Ml to
have short-run effects on the farm/nonfarm
relative price ratio. Unfortunately, in many
cases, it is not easy to categorize the significance,
magnitude or persistence of these effects. Where
tested, the verdict seems about evenly split be­
tween those studies that find monetary actions
to be neutral in their effect on the long-run
relative price ratio and those that find the effects
to be permanent. The only general conclusion
that emerges from the studies summarized in
table 1 is that the wide diversity among sample
periods, relative price measures, variable specifi­
cations and results makes it difficult to tell
whether and by how much monetary actions af­
fect the farm/nonfarm relative price ratio.
A R EVIEW OF THE D A T A

Figure 1 shows quarterly values for the an­
nualized percentage changes in the indexes of
prices received and prices paid by farmers since
1976.6 These indexes are based on the bundle
qualitative conclusions of this section. The plot starts in
1976 to avoid the price volatility associated both with
OPEC and U.S. farm policies in the 1973-74 period.
Moreover, the empirical work to follow begins after the
system of flexible exchange rates was adopted and most
of the one-time adjustments to new exchange rate
levels—especially trade flows—are presumed to have
taken place.

of

6Use
monthly data or producer price indexes for farm
and industrial (nonfarm) commodities does not affect the




JULY/AUGUST 1991

32

Table 1
A Summary of Results from Studies of the Monetary Policy-Relative Farm
Price Question
Author(s)

Relative
price measure

Monetary
policy
indicator

Sample
period

Data
frequency

Specification
of
variables

M1

1976.05-1982.05

Monthly

Logs

Not tested

Inflation
rate

1930-83;
1930-53;
1954-70;
1971-83

Annual

Percentage
changes

Not tested

M1

1960.01-1985.12

Monthly

Logs

Long-run neutrality
of money

“ Im portant” Monetary Effects

Chambers (1984)

Starleaf, et al. (1985)

Devadoss and Meyers
(1987)

CPI-food
CPI-nonfood
Prices received,
Prices paid

Prices received

Non-neutral

Index of Industrial
Prooduct Prices

Taylor and Spriggs (1989)

Index of Canadian
Farm Product Prices

M1 (Canada)

1959.1-1984.4

Quarterly

A Logs

M1 had largest effect of domestic
variables, but all foreign
variables had larger effects.

Tegene (1990)

PPI farm output,
PPI nonfarm output

M1

1934-87

Annual

Logs

In the short run, a change in M1
affects farm prices more quickly
than manufacturing prices.

Index of Farm Prices

M1

1960.1-1985.4

Quarterly

ALogs

M1

1951-81

Annual

Logs; A logs

Neutral

M1

1951-85

Quarterly

A Logs

Neutral

Han, et al. (1990)

Conditional mean and variance of
farm prices are more sensitive to
changes in M1 than are the
conditional mean and variance of
industrial prices.

“ Sm all” Monetary Effects

Grennes and Lapp (1986)

Lapp (1990)

Prices received
CPI
Prices received
PPI or CPI

Im portance of Monetary Effects is Subject to Interpretation

Orden (1986)

Prices received
GNP deflator

M1;
Interest rates

1960.1-1984.3

Quarterly

Logs

Monetary effects are small if
represented by M1, but larger if
represented by interest rates.

Orden and Fackler (1989)

Prices received
GNP deflator

M1;
Interest rates

1975.1-1988.1

Quarterly

Logs

Small, but significant, effect
over four quarter period; longrun neutrality.

New Zealand farm
output prices,
Manufacturing
prices

New Zealand
M1

1963.1-1987.1

Quarterly

Logs

Clear short-run effect over four
quarters; long-run neutrality
holds.

Robertson and Orden
(1990)

of farm products that farmers produce and sell
and commodities that farmers purchase as pro­
duction inputs, respectively.7 Quarterly values
for the growth rate of Ml also are shown in
this figure. Data for the individual series are
summarized in table 2. As the figure and table
show, farm product prices have been more vol­
7The prices received measure is a weighted index of about
112 farm product prices; the prices paid measure is a
weighted index of about 450 farm input prices. For more

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atile than farm input prices. A test of the equali­
ty of variances, for example, produces an Fstatistic of 7.62 against a 5 percent critical value
of 1.53.
Figure 2 shows changes in the ratio of the in­
dexes plotted against changes in M l growth. In
detail, see Handbook #365, U.S. Department of
Agriculture (1970).

33

Figure 1
Growth of M1, Prices Paid by Farmers, and
Prices Received by Farmers

Table 2
Descriptive Statistics for Farm and Nonfarm Prices,
I/1976-IV/1990 (annualized first differences of logarithms,
quarterly data)__________________________________________
Mean
Prices received by farmers
Prices paid by farmers
Prices received/prices paid
M1




Standard
deviation

Minimum

Maximum

2.22%
4.74
-2 .5 2
7.03

14.30%
5.18
12.38
4.49

- 24.75%
-4 .9 6
- 26.35
-4 .0 9

46.74%
15.92
32.18
16.85

JULY/AUGUST 1991

34

Figure 2
First Difference of the Ratio of Prices Received to Prices
Paid and the Change in M1 Growth
20

20

16

16

12

12

-4

-4

-8

-8

-12

-12

1976

77

78

79

80

81

82

very simple terms, these series represent the
logic in much of the literature that links mone­
tary policy to the relative price of farm pro­
ducts. For example, accelerations in money
growth are thought to be associated with in­
creases in the farm/nonfarm product price ratio.
Over this sample period, however, the simple
correlation coefficient for these series, 0.13, is
not significantly different from zero at the 5
percent significance level.
Finally, it is interesting to abstract from the
short-run volatility in these series and examine
the data for longer-run trends. Since 1976, the
average growth rate of prices received by farm­
ers has been about one-half that of farm input
prices; as a consequence, the relative farm price
ratio has fallen at an annual rate of more than
8See Meltzer (1990) for a thorough review of U.S. monetary
policy since the mid-1960s, with special emphasis on its
tendency to produce increasing rates of money growth.


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83

84

85

86

87

88

89

1990

2.5 percent. Conversely, Ml has grown over the
sample period at an annual average rate of 7.03
percent. From a long-run perspective, the down­
ward trend in the relative price ratio is consis­
tent with what Tweeten has called a “cost-price
squeeze” for farmers. The origin of this squeeze,
however, does not seem to be related to the
relatively expansionary long-run course of
monetary policy.8
MONEY G R O W T H A N D R ELATIVE
PRICES: ALT E R N A T IV E
THEO RETICAL RESULTS

The research attempting to link monetary ac­
tions to relative farm price changes has not
questioned whether changes in money growth

35

Figure 3
Graphical Representation of a Barro-type Model
MODEL I.

Farm Commodities Market

Non-farm Commodities Market

Qf

q nf

Model Assumptions and Predicted Result:An unanticipated decrease in money growth causes the demands
for both farm and nonfarm commodities to fall. Because the income elasticity of farm commodity demand
is assumed to be lower than that for nonfarm products, the decrease in farm product demand is smaller.
Assuming identical supply elasticities in the two markets, APF < APN and (PF /PN rises.
F
F)

affect the farm/nonfarm price ratio; instead, the
direction, size and persistence of this effect have
been its primary focus. Because alternative theo­
retical models produce different empirical speci­
fications and, quite possibly, different results,
some attempt must be made to distinguish
among these alternatives. For guidance on these
issues, the testable implications of three models
used to investigate the money-relative price
question are developed below.

M odel 1: A Change in M on ey
G row th as a S h o ck to A ggregate
D em an d

Equilibrium “Barro-type” models assume that
anticipated changes in the money stock affect

all nominal prices equi-proportionally and there­
fore leave relative prices unchanged.9 Relative
prices are affected in these models only by an
unexpected change in the money stock. In model
#1, illustrated in figure 3, an unanticipated
decline in the money stock produces a negative
shock to aggregate demand as people find
themselves with a shortage of real money bal­
ances and an excess supply of goods. Their col­
lective actions to restore equilibrium by reduced
spending shifts aggregate demand to the left.
This shift lowers output and income temporarily
and the price level permanently.
If supply elasticities in the farm and nonfarm
sectors are identical, this demand shift will af­
fect relative prices only if the income elasticities

9See, for example, Barro (1976).




JULY/AUGUST 1991

36

of demand for farm and nonfarm products dif­
fer. If the income elasticity for farm products is
lower than that for nonfarm products, an unex­
pected decrease in the money stock would in­
crease temporarily the relative price of farm
products.10 This interpretation of the model,
therefore, predicts a response that is contrary
to the story embedded in the "stylized facts” of
agricultural economics. Because the direction of
relative price change will vary with the par­
ticular assumptions about shifts in supply and
demand across markets, the “sign” on this effect
in a regression equation offers a direct way to
test the implications of this one interpretation
of the model.
The predictions of this model, however, deny
that monetary contractions are a source of longlasting harm to the farm sector. In this case, as
in the other examples that follow, the real in­
come effect is a short-run phenomenon. When
people realize that the real demand for in­
dividual products has not changed fundamental­
ly but that, instead, the monetary contraction
caused a general decline in aggregate demand,
the aggregate price level will fall to restore the
original equilibrium and relative price ratio.
Thus, because the decrease in the money stock
is reflected only in a lower aggregate price level
in the long run, the neutrality of monetary ac­
tions is preserved. In this model, as well as in
model #2 that follows, whether the relative price
ratio rises or falls and whether it returns to its
original value in the long run are the model's
testable hypotheses. As shown in table 1,
however, the long-run neutrality proposition has
not been tested in many previous studies or,
when violated, often has not been discussed.11

M odel 2: R e la tive P rice C hanges
C aused b y D iffere n t E la sticities o f
S u p p ly

A slightly different variant of the equilibrium
Barro model, which predicts a relative price
change in the opposite direction from the pre­
vious discussion, is based on different assump­
tions about the structure of the farm and nonfarm goods markets. In this model, illustrated in
figure 4, the short-run elasticity of supply of
farm products is argued to be less than that of
’ “ Historically, this assumption has been supported by the
data with estimates of the income elasticity for food de­
mand near 0.2 and higher estimates for nonfood items;
see King (1979) for a review of this literature.
"E xceptions are Bessler (1984) and Robertson and Orden
(1990).


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nonfarm products because of differences in the
production processes.12 With long lags between
planting and breeding decisions and product
marketings, the ability to adjust farm output in
the short run is assumed to be limited. Other
things the same, this characteristic of farm pro­
duction would cause the farm/nonfarm relative
price ratio initially to fall in response to a
negative aggregate demand shock. Again, the ex­
istence of long-run neutrality is a testable pro­
position and the length of the adjustment pro­
cess must be determined empirically. This
model’s predictions, however, are consistent
with the argument that the relative price of
farm products will fall under a contractionary
monetary policy.

M odel 3: P rice S tick in ess an d
“O ve rsh o o tin g ”
So far, prices in both the farm output and in­
put markets were assumed to be flexible in
response to changes in other variables that af­
fect them. Thus, changes in the relative price
ratio depended on the relative magnitudes of
shifts in supply and demand and the slopes of
those curves; they were not influenced by dif­
ferent speeds of adjustment in the two markets.
Another approach to this question has relied on
some degree of price-stickiness in nonfarm
prices to explain changes in the relative price
ratio.
By adapting the overshooting model from the
exchange rate literature, as illustrated in figure
5, this analysis assumes that prices in the flexi­
ble price (farm) sector adjust to a monetary
change more quickly than other prices in the
fixed price (nonfarm) sector.13 So, for example,
while long-term contracts prevent nonfarm
prices from adjusting downward immediately in
response to a monetary contraction (as they
would in the Barro-type model), the auction
market characteristics of the determination of
farm prices force them to fall quickly and, con­
sequently, temporarily reduce the farm/nonfarm
relative price ratio as well. Thus, again in the
short run, a negative monetary change causes a
temporary reduction in farm prices relative to
nonfarm prices.
12See, for example, Starleaf (1982).
13See, for example, Frankel (1986) or Rausser (1985).

37

Figure 4
Graphical Representation of Differing Supply
Elasticities Model

As in Model I, an unanticipated decrease in money growth decreases the demands both for farm and
nonfarm products; here, however, the decreases are assumed to be equal. Under these conditions, a lower
elasticity of supply for farm products will cause APP > APN and (PF /PN ) will fall. Note that combining the
F
F
results of Model I with Model n produces an ambiguous result because differences in the sizes of demand
shifts may be large enough to cause an increase, a decrease, or no change in (PF F
/PN ).

Although this predicted direction of relative
price change is the same as in model #2, the
mechanics of price stickiness allows the
possibility that fully anticipated monetary
changes (as well as u n expected changes) can af­
fect the relative price ratio. Thus, testing for
the significance of expected monetary changes
on the relative price ratio provides a direct way
to discriminate between the two models. Unfor­
tunately, the converse is not true: failing to find
significant effects from anticipated monetary
changes does not necessarily reject an over­
shooting type of model because its mechanics
can be set in motion solely by monetary sur­
prises as in the previous cases.
RECONCILING ALTE R N A TIV E
THEORIES

The foregoing discussion showed that farm
prices are significantly more variable than non­



farm prices, and that the farm/nonfarm relative
price ratio has declined persistently over time.
Unfortunately, the implications of our three
models differed considerably in terms of the ex­
p ected direction of change in the relative p rice
of farm products to nonfarm products as well
as the mechanism by which a monetary action
influenced this ratio. To resolve this conundrum,
each model was estimated using identical data
sets to see which one’s implications are best
supported by the results.
These estimations are intended to provide
evidence on three aspects of the possible mone­
tary influences on the farm/nonfarm relative
price ratio. The first piece of evidence is the
direction of relative price change; this will
discriminate between model vs. models *2
and #3. The second piece of evidence is the
statistical significance of the relationship, where
the significance of a variable can be viewed as
evidence for or against a particular model. The
JULY/AUGUST 1991

38

Figure 5
The Overshooting Model1

Schedule EE shows all possible equilibrium relationships
between PF and PN prior to the change in monetary
F
policy. Slower money growth, which is disinflationary,
shifts EE to E'E' where farm (flex) prices (PF decrease
)
but nonfarm (fix) prices (PN ) do not adjust; thus, PF
F
“ o v e rs h o o ts ” fro m p o in t A to p o in t B. A s fix -p ric e
markets adjust, (P f/P Nf) gradually returns to the long-run

equilibrium at point C.
’ A dopted from R. D ornbusch, Open Econom y M acroeconom ics, Fig. 11-8, p. 208.

third piece of evidence is the magnitude of the
impact that a given monetary change has on the
relative price ratio; here, it is recognized that
monetary effects may be statistically significant
and yet still be quantitatively unimportant.

VAR E stim ation

As a first step in the investigation, a vector
autoregressive (VAR) model was estimated. A
VAR, which can be used to determine the

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amount of variation in the relative price ratio
that one might attribute to monetary shocks, is
useful in gauging the strength of the hypothe­
sized relationship. It has the additional advan­
tage of not requiring the specification of any
particular functional form among the variables
included in the model. The VAR and other
equations that follow were estimated with quar­
terly, seasonally adjusted data over the 1/1976IV/1990 sample interval. Thus, by way of in­

39

Table 3
Single-Equation Results for the VAR Estimation, I/1976-IV/1990
Explanatory Variables
Dependent
variable

Relative
prices

M1

Exchange
rate

Industrial
production

Relative price

0.206
(1.089)

0.031
(0.064)

-0 .2 3 3
(1.735)

M1

0.015
(0.257)

0.626
(4.192)

Exchange rate

0.451
(1.874)

Industrial production

0.131
(1.690)

R1

DW

-0 .1 4 7
(0.413)

.12

1.98

-0 .0 2 0
(0.488)

-0 .2 5 7
(2.340)

.28

1.96

-0 .0 4 6
(0.075)

0.499
(2.929)

-0 .1 3 7
(0.302)

.09

2.04

0.344
(1.737)

0.003
(0.063)

0.515
(3.541)

.29

2.01

NOTE: t-statistics in parentheses apply to sums of lag coefficients and apply to the null hypothesis
that the sum is equal to zero.

troduction to the more specific testing to follow,
the VAR can offer some insights to the strength
of the money-relative price link.
The VAR model included four variables: the
farm/nonfarm price ratio (as measured by the
ratio of the indexes of prices received by farm­
ers to the producer price index), Ml, the index
of industrial production and the real tradeweighted exchange rate. Variables other than
Ml were included because observed changes in
the relative price ratio may have other origins.
For example, technological changes in the non­
farm sector (which would affect industrial
prices) or export demand (which could have
varying effects across the farm and nonfarm
sectors) could affect the relative price ratio in
isolation from monetary changes.14 While these
other measures do not exhaust the list of in­

fluences on the relative price ratio, they do cap­
ture other influences affecting prices in the
farm and nonfarm product markets so that the
remaining variation can be explained by
changes in Ml growth and the past history of
the relative price ratio itself. These variables
also were chosen because they have been used
in previous work and our interest is in stressing
comparability with other studies. All variables
were specified as first differences of logarithms.15
Sums of lagged coefficients and t-statistics for
these sums for the single equation estimation
are reported in table 3. The results of interest
indicate that Ml growth is not related signifi­
cantly to changes in the relative price of farm
products.16 One possible explanation for this
result is that flows from farm inventories, which
were historically large over most of the sample

14Evaluating the impact of the industrial production measure
here also will serve as an additional check on the over­
shooting model, which includes an output measure as an
explanatory variable and predicts a positive relationship
with the relative price ratio.

of prices received by farmers divided by either the index
of input prices paid by farmers or the all-item CPI. The
possible effects of exports on relative prices also was in­
vestigated by replacing the real exchange rate index with
the real quantity of
farm exports. In no case,
however, were the qualitative conclusions discussed in the
text affected by this change: M1 growth never had a
significant effect on the relative price ratio and the effects
of trade flows were significant only if significance levels
beyond the standard 5 percent level were used.

15A likelihood ratio test suggested by Sims (1980) was
employed to select a single lag length for all variables in
each of the four equations in the VAR representation. This
test indicated a choice of two quarters.

U.S.

16The estimations reported in tables 3 and 4 also were per­
formed using relative price measures defined as the index




JULY/AUGUST 1991

40

Table 4
VAR Variance Decomposition: Four-, Eight- and 12-QuarterAhead Forecast Error Variances
Innovations Series
Dependent
variable
Relative price

Relative
prices

M1

Exchange
rate

Industrial
production

81.62
81.28
81.28

0.72
0.93
0.93

8.11
8.15
8.15

9.54
9.64
9.65

M1

5.42
6.87
6.90

79.14
76.11
76.04

2.81
2.75
2.75

12.63
14.27
14.30

Exchange rate

6.30
6.47
6.48

7.14
7.33
7.33

84.78
84.24
84.20

1.79
1.96
1.98

29.59
28.92
28.93

13.51
15.10
15.12

2.75
3.05
3.05

54.16
52.93
52.91

Industrial production

NOTE: Row 1 = Four-quarter-ahead forecast error
Row 2 = Eight-quarter-ahead forecast error
Row 3 = 12-quarter-ahead forecast error

period, offset any relative price change caused
by an aggregate shock.
Only the real exchange rate, which has a
marginally significant and negative coefficient, is
statistically associated with the relative price
ratio. While this result is consistent with many
of the arguments raised by agricultural econo­
mists about how restrictive monetary policy
could raise the exchange rate, reduce exports
and depress farm prices, this line of reasoning
is not shown by line 3 of table 3, which indi­
cates no statistically significant relationship be­
tween Ml and the real exchange rate. Thus,
these reduced-form estimates suggest that
monetary changes have little, if any, effect on
the relative price ratio.

V ariance D eco m p o sitio n

Further evidence about the effect of monetary
shocks on relative prices is found in table 4,
which presents the percentage of four-, eight17Because
variables
tains the
potential

VAR results are sensitive to the ordering of
[e.g., Cooley and LeRoy(1985)], the table con­
results from the ordering that gives the largest
influence for M1.


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and 12-quarter-ahead forecast error variances
explained by past innovations in the relative
price ratio and the other variables in the
model.17
Monetary shocks explain less than 1 percent
of the relative price forecast error variance,
while about 81 percent is attributable to past in­
novations in the relative price series itself.
These findings are generally consistent with
those reported by Chambers (1984) and Orden
(1986b), who found less than 10 percent of the
error variance could be attributed to monetary
shocks and more than half could be attributed
to past behavior of the relative price ratio.18
Moreover, both the real exchange rate and in­
dustrial production explain substantially more of
the variation in the relative price ratio than
does Ml.
As noted earlier, other analysts (most notably
Schuh) have argued monetary effects are trans18Devadoss and Meyers (1987), who reported large and
quite persistent monetary effects on relative prices, did not
report a variance decomposition, so their results are not
directly comparable.

41

mitted to agriculture through the real exchange
rate and its impact on farm exports. Some in­
sight into this notion is found in table 4, which
shows that innovations in the real exchange
rate series account for about 8 percent of the
variance in relative farm prices. Moreover, mon­
etary shocks apparently explain only about 7
percent of the variance in real exchange rate
movements, a result consistent with the small
or non-significant effects of monetary shocks on
the real exchange rate reported by Batten and
Belongia (1986). Thus, all things and potential
avenues of influence considered, these results
indicate a statistically weak and numerically
small relationship between monetary shocks and
movements in the farm/nonfarm price ratio.
The shaded insert on page 42 discusses possible
changes in these relationships if the thrust of
monetary policy is measured by different
indicators.

E stim a tes F rom a B a rro -T yp e
M odel

While the foregoing results suggest a fairly
weak relationship between monetary shocks
and relative price changes, the VAR method is
not appropriate for testing the relevant struc­
tural hypotheses that characterize the models
discussed above. In a model that treats a mone­
tary shock as a shock to aggregate demand,
assuming a lower income elasticity for farm
products would imply that, in the short run, the
farm/nonfarm relative price ratio is inversely
related to innovations in Ml. Moreover, because
neoclassical models of this nature recognize that
nominal shocks affect real or relative magni­
tudes only in the short run, the sum of the
coefficients for lagged innovations in Ml should
not be significantly different from zero. The
persistence of any short-run nonneutralities,
however, remains to be determined.
The basic predictions of this model can be ex­
amined by estimating an equation of the form:
P
(1) A(p^) = a + i I 0 bj E(m),_j + j I= 0 c, [m-E(m)]t ;
*NF
=
P

+

Q

£ t<

where E(m) is the expected growth rate of Ml,
[m - E(m)] is the unexpected component of Ml
19Equation 1, in many respects, is the one Devadoss and
Meyers (1987), among others, estimated after placing zero
restrictions on the bj coefficients. If, however, their results
are explained by a fix-price/flex-price (overshooting)
economic structure, fully anticipated changes in the money
stock could affect the relative price ratio and there is no
justification for the restrictions (see equations 2 and 3




growth, and a, b( and c, are coefficients to be
estimated over undetermined lag lengths p and
q, respectively. Under the assumptions about
market structure discussed earlier, the b, coeffi­
cients should be zero and the c, coefficients for
the initial lags of unexpected changes in money
growth should take negative values; the model’s
general prediction about the long-run neutrality
of monetary shocks implies that the sum of c,
coefficients should be zero. Lapp (1990), who
recently discussed and reported results from a
model of this form, found monetary actions to
have small, short-lived effects on the relative
price ratio.19
Before equation 1 can be estimated, the re­
quisite values for the aggregate demand shock
(the unexpected component of Ml growth) must
be obtained. An autoregressive model was fit to
the first differences of logarithms of Ml and in­
spection of the autocorrelation functions in­
dicated an AR(6) was an adequate representa­
tion of this series. The null hypothesis that the
residuals from this representation were white
noise could not be rejected. These residuals
were employed in equation 1 as the measure of
monetary shocks; the fitted values were used to
represent anticipated money growth.
A final prediction error (FPE) criterion sug­
gested estimating a model with contem­
poraneous and three lagged values for the
unanticipated component of money growth and
excluding the anticipated portion of money
growth entirely. Before estimating equation 1 in
this form, it first was estimated using contem­
poraneous and three lagged values for both
monetary variables in order to test more di­
rectly whether anticipated money growth had
any effect on relative prices. As the first row of
table 5 indicates, neither component of money
growth is related significantly to the farm/nonfarm product ratio. The second row of the
table, which reports the results of the model
chosen by the FPE criterion, shows that
monetary shocks have no permanent effects on
the relative price ratio. Moreover, none of the
individual lag coefficients (not reported) is
significantly different from zero indicating the
below); rather, the significance of those coefficients is a
key hypothesis to be tested. Moreover, by failing to specify
a theoretical model, Devadoss and Meyers also miss the
chance to rule out a Barro-type model as an explanation
for relative price behavior on the basis of “ wrong”
(positive) signs for the Cj coefficients.

JULY/AUGUST 1991

42

Is M l The "Right’' Measure Of Monetary Actions?
Investigations of how monetary actions af­
fect economic activity have been influenced
in the 1980s by financial deregulation and in­
novation. Most notable among these financial
changes was the introduction of interestbearing checkable deposits, which, many
economists believe, has distorted the behavior
of Ml since 1981.1 Other research has argued
that another measure of Federal Reserve
actions—the federal funds rate—is more close­
ly related to economic activity than money
growth; indeed, Orden and Fackler (1989), in
a similar study of monetary actions and the
relative price of farm products, speculate
about whether interest rates are a better
gauge of monetary actions than money
growth.2

To investigate these issues, the analysis
reported in tables 3 and 4 was repeated
replacing M l growth with the growth rate of
MIA and the first difference of the federal
funds rate. MIA, which is Ml less interestbearing checkable deposits, presumably
deletes idle savings-type balances from what
is intended to be a transactions-based
measure of the money stock. Indeed, Darby,
et al. (1989) found MIA to be a better em­
pirical measure of monetary actions in the
1980s than Ml. Using the federal funds rate
can be defended by arguing that it examines
the influence of an interest rate that is direct­
ly under the control of the Federal Reserve.
The relevant results of these estimations are
reported in the table below.3

Revised VAR Estimates Using M1A and the Federal Funds Rate
As Monetary Indicators: I/1976-IV/1990_____________________
Revised Reduced-Form Estimates
Relative
price

Monetary
indicator

Exchange
rate

Industrial
production

Relative price

0.215
(1.124)

0.0191
(0.036)

-0 .2 3 7
(1.488)

Relative price

0.213
(1.148)

-2 .7 1 8 2
(1.404)

-0 .2 1 0
(1.694)

Dependent
variable

R2

DW

-0 .1 6 7
(0.471)

.12

1.97

0.034
(0.091)

.15

1.96

Revised Variance Decompositions
Dependent
variable

Relative
price

Monetary
indicator

Exchange
rate

Industrial
production

Forecast
horizon

Relative price

81.70
81.05
81.04

1.48’
2.21
2.21

7.53
7.47
7.47

9.29
9.27
9.27

4 Qtr.
8 Qtr.
12 Qtr.

Relative price

78.19
77.81
77.81

8.942
9.09
9.09

3.60
3.62
3.62

9.28
9.47
9.47

4 Qtr.
8 Qtr.
12 Qtr.

’ Monetary indicator is A In M1A.
2Monetary indicator is A fed funds rate.

1See, for example, Belongia and Chalfant (1990) for a
review of some of the issues.
2See Friedman and Kuttner (1990) for arguments and
evidence on the federal funds rate as an indicator of
monetary policy.


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3Complete results are available from the author,

43

The top portion of the table, which reports
the revised reduced-form equations using the
growth rate of MIA and changes in the
federal funds rate as monetary indicators,
shows no qualitative change to the results in
table 3: neither indicator of monetary actions
is related significantly to movements in the
absence of short-run effects as well. If monetary
actions have any effect on the farm/nonfarm
price ratio, the results in table 5 reject the no­
tion that they are transmitted through the mech­
anism described in figure 3.

The O versh o o tin g M odel

The implications of the overshooting model,
derived in Frankel (1986), can be stated in a
straightforward manner. The testable hypotheses
implied by the model shown in equation 2 are
that a change in the log level of the money
stock (Am,) or in the expected growth rate of
money (A^t) will have larger-than-proportional
effects on farm prices. That is, in a regression
of the form,
(2) APFt = c0 + Cj Am, + c2 A^t + £„
where APFt is the change in the log level of the
index of prices received by farmers, the ex­
pected results are that Cj > 1 and c2 > 1.
Thus, “overshooting” occurs because farm
prices respond initially by a larger percentage
than either the actual level of the money stock
or the expected rate of money growth. The
standard interpretation of this model, which

relative price ratio. Similarly, the bottom por­
tion of the table indicates that neither
monetary indicator explains more than 10
percent of the forecast error variance and
that movements in the relative price ratio
continue to be dominated by past shocks to
the ratio itself.
assumes that nonfarm prices are fixed in the
short run, also would imply that contractionary
monetary policy would temporarily depress the
farm/nonfarm relative price ratio, a result op­
posite to that from model
Finally, some analysts have tried to account
for business cycle effects on farm prices by ad­
ding the change in the log level of real output
(Ay,) to equation 2. This gives equation 3:
(3) APf, = c„ + c, Am, + c2A^, + c3 Ay, +
Note that equation 3 maintains the two original
overshooting hypotheses implied by equation 2
(c, > 1; c2 > 1).
The hypotheses embodied in equations 2 and
3 were tested over the same periods reported
earlier. Real output was measured by industrial
production. The change in the expected money
growth rate, h\x, was calculated as the first dif­
ference of fitted values from the money growth
autoregression discussed earlier. The equations
first were fit only with contemporaneous values
for right-hand-side variables and then, again,
allowing for lags. Results of the estimations are
reported in table 6.

Table 5
Effects of Monetary Shocks on Relative Prices in a Barro Model
Intercept

Expected M

Unexpected M

H1

DW

-2 5 .8 6 2
(1.604)

3.371
(1.473)
0-3

-2 .2 9 8
(0.909)
0-3

- .0 3

1.51

-2.211
(1.237)

_

0.493
(0.535)
0-3

-.0 1

1.48

NOTE: The third line of numbers in each row of the table indicates lags estimated. The numbers in
parentheses are t-statistics for the sum of the lag coefficients.




JULY/AUGUST 1991

44

Table 6
Results from Overshooting Models, I/1976-IV/1990
R2

DW

-.0 1

1.56

Intercept

Am

-0.07 1
(0.20)

0.350
(0.80)

0.381
(0.49)

-1 .6 6 2
(0.47)

0.248
(0.59)

0.283
(0.38)

0.793
(2.58)

.08

1.60

5.26
(1.36)

- 0.485
(1.01)
0-1

0.120
(0.17)
0

0.685
(1.77)
0-1

.23

1.38

Am

Ay

NOTE: The third line of numbers in the bottom row of the table indicates lags estimated. Numbers
in parentheses for bottom regression are t-statistics for the sums of the lag coefficients.

Although none of the results for the restricted
model shows any effects from either monetary
variable, the more general form of the over­
shooting model indicates a significant contem­
poraneous relationship between the growth rate
of industrial production and index of prices
received by farmers; the sum of this effect and
the coefficient for the lagged effect, however, is
not significantly different from zero.
The crucial question for the overshooting
model, however, is whether the coefficients
associated with the growth rate of Ml and the
change in the expected growth rate of Ml are
significantly greater than one. For Am, its coeffi­
cient in each of the three regressions is num er­
ically less than one and is significantly less than
one in the last regression. This rejects a predic­
tion of the overshooting model. Similarly, the
coefficient associated with A^, the change in the
expected growth rate of money, is numerically
less than one in each case and significantly so
in the last equation. The implication is a rejec­
tion of the overshooting model.
CONCLUSIONS

Because many studies have found monetary
shocks to have positive and persistent effects on
the farm/nonfarm relative price ratio, the pur­
suit of a contractionary monetary policy to
reduce inflation has been blamed for causing
widespread financial distress in agriculture.
Although an understanding of this literature is

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certainly important to the debate about whether
farm programs can or should be used to cushion
the sector from changes in macroeconomic
policies, the evidence on the response of farm
prices and income to monetary policy actually
has been widely mixed. In part, this diversity
has been due to the different theoretical models
and empirical techniques that have been
employed.
Following a research strategy suggested by
King (1979) to distinguish among alternative
models and empirical results, a revised set of
"stylized facts” emerges on the relationship bet­
ween monetary actions and the relative price of
farm products:
• Farm prices are significantly more variable
than nonfarm prices.
• VAR results consistently show that monetary
innovations explain less than 10 percent of
the forecast error variance of the farm/non­
farm price ratio, whereas past innovations in
the relative price ratio itself explain 80 per­
cent or more of the error variance. Thus,
while monetary effects may be statistically
significant, they are economically
unimportant.
• Although the flex-price/fix-price model is
widely asserted to represent the economic
structure generating the farm/nonfarm price
series, its main hypotheses are rejected by
the data. The standard interpretation of a
Barro-type model also is rejected.

45

• Tests find the behavior of farm prices to be
consistent with the neoclassical prediction of
long-run neutrality; the "long run” for ad­
justments in the farm/nonfarm price ratio to
a monetary change is less than one year.

Falk, Barry, S. Devadoss and William H. Meyers. “ Money,
Inflation, and Relative Prices: Implications for U.S.
Agriculture,” Center for Agriculture and Rural Development,
Department of Economics Working Paper Number 86-WP1
(Iowa State University, 1986).
Frankel, Jeffrey A. “ Expectations and Commodity Price
Dynamics: The Overshooting Model,” American Journal of
Agricultural Economics (May 1986), pp. 344-48.
Friedman, Benjamin M., and Kenneth Kuttner. “ Money, In­
come, Prices and Interest Rates After The 1980s,” Federal
Reserve Bank of Chicago Working Paper 90-11 (July 1990).

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Batten, Dallas S., and Michael T. Belongia. “ Monetary
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King, Richard A. ’’Choices and Consequences,” American
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Batten, Dallas S., and Daniel L. Thornton. “ Lag Length
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and Income,” Journal of Money, Credit and Banking (May
1985), p. 164-78.
Belongia, Michael T., and James A. Chalfant. “Alternative
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1990), pp. 20-33.
Belongia, Michael T., and Courtenay C. Stone. “ Would
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Bessler, David A. “ Relative Prices and Money: A Vector
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Agricultural Economics (February 1984), pp. 25-30.
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Economics (May 1985), pp. 390-95.
_______ . “Agricultural and Financial Market Interdependence
in the Short Run,” American Journal of Agricultural
Economics (February 1984), pp. 12-24.
Cooley, Thomas F., and Stephen F. LeRoy. “Atheoretical
Macroeconometrics: A Critique,” Journal of Monetary
Economics (November 1985), pp. 283-308.
Darby, Michael R., Angelo R. Mascaro, and Michael L.
Marlow. “ The Empirical Reliability of Monetary Aggregates
as Indicators: 1983-87,” Economic Inquiry (October 1989),
pp. 555-85.
Dickey, D. A., and W. A. Fuller. “ Distribution of the
Estimators for Autoregressive Time Series with a Unit
Root,” Journal of the American Statistical Association (June
1979), pp. 427-31.

111- 20 .

Lapp, John S. “ Relative Agricultural Prices and Monetary
Policy,” American Journal of Agricultural Economics (August
1990), pp. 622-30.
Meltzer, Allan H. “ The Fed at Seventy-Five,” in, Michael T.
Belongia, ed., Monetary Policy on the 75th Anniversary of
the Federal Reserve System (Kluwer Academic Publishers,
1990).
Obstfeld, Maurice. “ Overshooting Agricultural Commodity
Markets and Public Policy: Discussion,” American Journal
of Agricultural Economics (May 1986), pp. 420-21.
Orden, David. “ Money and Agriculture: The Dynamics of
Money-Financial Market-Agricultural Trade Linkages,”
Agricultural Economics Research (Summer 1986a), pp.
14-28.
_______ . "Agriculture, Trade, and Macroeconomics: The
U.S. Case,” Journal of Policy Modeling Spring (1986b), pp.
27-51.
Orden, David, and Paul L. Fackler. “ Identifying Monetary Im­
pacts on Agricultural Prices in VAR Models,” American
Journal of Agricultural Economics (May 1989), pp. 495-502.
Penson, John B., and Bruce L. Gardner. “ Implications of the
Macroeconomic Outlook for Agriculture,” American Journal
of Agricultural Economics (December 1988), pp. 1013-22.
Plosser, C.I., and G.W. Schwert. “ Money, Income and
Sunspots: Measuring Economic Relationships and the Ef­
fects of Differencing,” Journal of Monetary Economics
(November 1978), pp. 637-60.
Rausser, Gordon C. “ Macroeconomics and U.S. Agricultural
Policy,” U.S. Agricultural Policy: The 1985 Farm Legislation,
ed. Bruce L. Gardner (American Enterprise Institute for
Public Policy Research, 1985), pp. 207-52.

_______ . “ The Likelihood Ratio Statistics for Autoregressive
Time Series with a Unit Root,” Econometrica (July 1981),
pp. 1057-72.

Rausser, Gordon C., James A. Chalfant, H. Alan Love, and
Kostas G. Stamoulis. “ Macroeconomic Linkages, Taxes,
and Subsidies in the U.S. Agricultural Sector,” American
Journal of Agricultural Economics (May 1986), pp. 399-412.

Devadoss, S., and William H. Meyers. “ Relative Prices and
Money: Further Results for the United States,” American
Journal of Agricultural Economics (November 1987), pp.
838-42.

Robertson, John C., and David Orden. “ Monetary Impacts on
Prices in the Short and Long Run: Some Evidence from
New Zealand,” American Journal of Agricultural Economics
(February 1990), pp. 160-71.




JULY/AUGUST 1991

46

Schuh, G. Edward. “ The Exchange Rate and U.S.
Agriculture,” American Journal of Agricultural Economics
(February 1974), pp. 1-13.

Taylor, J. S., and J. Spriggs. “ Effects of the Monetary
Macro-economy on Canadian Agricultural Prices,” Cana­
dian Journal of Economics (May 1989), pp. 278-89.

Sims, Christopher. “ Macroeconomics and Reality,”
Econometrica (January 1980), pp. 1-48.

Tegene, Abebayehu. “ The Impact of Macrovariables on the
Farm Sector: Some Further Evidence,” Southern Journal of
Agricultural Economics (July 1990), pp. 77-85.

Starleaf, Dennis R. “ Macroeconomic Policies and Their Im­
pact Upon the Farm Sector,” American Journal of
Agricultural Economics (December 1982), pp. 854-60.
Starleaf, Dennis R., William H. Meyers and Abner W.
Womack. “ The Impact of Inflation on the Real Income of
U.S. Farmers,” American Journal of Agricultural Economics
(May 1985), pp. 384-89.


FEDERAL
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Tweeten, Luther G. "Macroeconomics in Crisis: Agriculture
in an Underachieving Economy,” American Journal of
Agricultural Economics (December 1980), pp. 853-65.
_______ . “ Impact of Federal Fiscal-Monetary Policy on
Farm Structure,” Southern Journal of Agricultural Economics
(July 1983), pp. 61-68.
U.S. Department of Agriculture. “ Major Statistical Series of
the U.S. Department of Agriculture: How They are Used
and Constructed,” Handbook #365 (1970).

47

Michelle R. Garfinkel and
Daniel L. Thornton
Michelle R. Garfinkel, an assistant professor at the University
of California at Irvine, was a senior economist at the Federal
Reserve Bank of St. Louis while this paper was written. Daniel
L. Thornton is an assistant vice president at the Federal
Reserve Bank of St. Louis. Richard I. Jako and Scott Leitz pro­
vided research assistance.

The Multiplier Approach to
the Money Supply Process:
A Precautionary Note

T T h E MULTIPLIER MODEL of the money
supply, originally developed by Brunner (1961)
and Brunner and Meltzer (1964), has become
the standard paradigm in macroeconomics and
money and banking textbooks to explain how
the policy actions of the Federal Reserve in­
fluence the money stock. It also has been used
in empirical analyses of money stock control
and the impact of monetary policy actions on
other economic variables.
One important feature of this model is that it
decomposes movements in the money supply in­
to the part that is due directly to Federal Re­
serve policy actions (the adjusted monetary base)
and the part that is due to changes in technology
and the tastes and preferences of depository in­
stitutions and the public (the money multiplier).
In this decomposition, the multiplier is assumed
to be independent of the policy actions of the
central bank. The independence is implicitly
predicated on the assumptions that the demands
1The notion that the multiplier is independent of Federal
Reserve actions— implicit in the work of Brunner and
Meltzer (1964, 1968) and, more recently, in Plosser (1991)
— has never been demonstrated rigorously with micro-eco­




for both checkable deposits and currency are
determined by the same factors, and that indi­
viduals can quickly and costlessly alter their
holdings of currency and checkable deposits to
achieve the desired proposition of the two alter­
native forms of money.1 Open market purchases,
for example, increase reserves and consequently
checkable deposits; but the public simply shifts
from checkable deposits to currency until the
(unchanged) desired ratio of currency relative to
checkable deposits is once again achieved. Be­
cause policy actions have no impact on the pub­
lic’s holdings of currency relative to checkable
deposits, the multiplier does not depend directly
on the policy actions of the Fed.
This article investigates the theoretical and
empirical validity of the key feature of the mul­
tiplier approach. In theory, the multiplier is in­
dependent of the policy actions of the Federal
Reserve only if the demands for currency and
checkable deposits are determined by identical
nomic principles. The argument presented here that would
suggest such independence is implicit in works as early as
Fisher (1911).

JULY/AUGUST 1991

48

factors and if, conditional on these factors, these
demands are strictly proportional. From an em­
pirical perspective, this condition is necessary
but not sufficient; the degree to which the mul­
tiplier is influenced by policy actions also de­
pends on the strength of the relationship be­
tween policy actions and checkable deposits.
An empirical analysis shows that most of the
variability of the observed ratio of currency to
checkable deposits is due to variation in check­
able deposits, and thereby suggests that the de­
mand for currency is not strictly proportional
to the demand for checkable deposits. Prior to
the Monetary Control Act of 1980 (MCA), how­
ever, the link between reserves and checkable
deposits was quite loose—so much so, that the
notion that the multiplier is independent of pol­
icy actions was operationally valid. Nevertheless,
the empirical relevance of this notion has weak­
ened considerably since the implementation of
the MCA in the early 1980s. Since then, the re­
lationship between Fed policy actions and
checkable deposits and, thus, the multiplier has
tightened markedly.
The evidence presented here, that the multi­
plier is not independent of Federal Reserve ac­
tions in the post-MCA period, raises some ques­
tions about the appropriateness of using the
monetary base as an indicator of the effects of
policy actions on the money stock. More impor­
tant from a policy perspective, it also suggests a
modification of the standard approach to money
stock control that might yield substantial im­
provements in effective monetary aggregate
targeting.
THE M ONEY M ULTIPLIE R
A P P R O A C H : A SIMPLE EXAM PLE

As a starting point for understanding the de­
composition of the money supply into the mone­
tary base and the multiplier, note that the nar­
row money stock, Ml, is defined as
(1) Ml, = TCD, + C„
where TCD denotes total checkable deposits and
C denotes the currency held by the nonbank
public. The monetary base (MB), not adjusted
for changes in reserve requirements, is simply
the sum of currency and reserves (including
2Since the Fed eliminated reserve requirements on all non­
transaction deposits in December 1990, this representation
approximates the current system. For convenience of ex­


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cash in the vaults of depository institutions) in
the banking system, R:
(2) MBt= Ct+ Rt.
Currency, supplied by the Federal Reserve on
demand, reflects the portfolio decisions of the
public rather than monetary policy actions. Re­
serves, in contrast, can be affected directly by
the Fed’s sales or purchases of government se­
curities in the open market.
For simplicity, assume that the Federal Re­
serve has a simple system of reserve require­
ments, with required reserves, RR, given by
(3) RR,= rTCD,, 0 < r < 1,
where r denotes the ratio of reserves that must
be held against TCD.2 A change in the reserve
requirement ratio, r, also would constitute a
monetary policy action by the Fed.
Furthermore, for simplicity, assume that ac­
tual reserves always equal required reserves so
that excess reserves are identically zero. With
this simplifying assumption, equation 3 can be
rewritten as
(4) R, = rTCD,.
The model is completed by assuming that cur­
rency is held in some proportion, k, of TCD.
That is,
(5) C, = kTCD,,
where the proportion k, hereafter called the kratio, is the public’s desired ratio of currency to
TCD holdings.
Combining equations 1, 2, 4 and 5 produces
the monetary base-multiplier representation of
the money supply:
(6) M lt = m MB,,
where m, the money multiplier, is given by
(7) m = (1 +k)/(r + k).
According to this representation, a policy action
that increases R by one dollar, through open
position, the discussion to follow abstracts from reserves
that depository institutions must hold on government and
foreign transactions balances.

49

market purchases of government securities, in­
creases MB by one dollar and the money stock
by m dollars.3
In this representation, policy actions are
reflected not only in MB, through changes in R,
but in m, through changes in r. With a simple
adjustment to MB, however, the effects of pol­
icy actions on the money supply can be isolated
in one measure. This alternative measure of the
monetary base, called the adjusted monetary
base, AMB, reflects both changes in R and r. It
is constructed by calculating the hypothetical
level of reserves that would have been required
under the reserve requirements in existence dur­
ing a chosen base period for the current (actual)
level of reservable deposits. With the chosen
base period, changes in required reserves due
to changes in reserve requirements, r, are
added to the monetary base.4
Specifically, the AMB is given by
(8) AMB, = MB, + RAM,,
where the reserve adjustment magnitude, RAM,
is defined as
(9) RAM, = (r*-r)TCD,.
This adjustment measures the reserves released
or absorbed by changes in r relative to r*, the
required reserve ratio during the base period.
In the base period, RAM is zero and AMB = MB.
A decrease in r from its base-period level (r*)
releases reserves into the banking system and
thereby increases RAM and AMB. Conversely,
an increase in r reflects the reserve drain by
reducing RAM and AMB.
Combining equations 1, 4, 5, 8 and 9 yields
the following decomposition of Ml,
3Note that because r< 1 , m > 1 . If the assumption that ex­
cess reserves are not held were replaced by the assump­
tion that they are held in a fixed proportion, e, of TCD,
then the denominator of the multiplier would include e as
well, so that the multiplier would be smaller than that
shown in equation 7.
4See Tatom (1980), for example, who discusses the issue
of choosing the appropriate base period in light of changes
in the structure of reserve requirements. This theoretic
discussion focuses on the measure of the adjusted mone­
tary base constructed at the Federal Reserve Bank of St.
Louis. See Garfinkel and Thornton (1991) for a more de­
tailed discussion of this measure and a similar one con­
structed by the Federal Reserve Board.

(10) Ml, = m* AMB,,
where
(11) m* = (l + k)/(r*+k).
In this characterization of the money supply pro­
cess, all changes in monetary policy, through
changes in r or R, are reflected in the AMB.
Changes in the multiplier reflect only changes
in the public’s desire to hold currency relative
to checkable deposits, changes in the k-ratio.5
Because, in this model, the k-ratio is not directly
influenced by the policy actions of the Fed, the
multiplier is independent of policy.
THE D EM AND FOR CURRENCY,
CHECKABLE DEPO SITS AN D
NEAR-MONIES: W H A T IS
THE k-RATIO?

Interest in the currency-deposit ratio dates
back to Fisher (1911), who was concerned that
the two forms of money had different income
velocities. He realized that these two monies are
imperfect substitutes: currency is especially use­
ful for making small, "face-to-face" transactions,
while checkable deposits provide a convenient
means for making large, “out-of-town”
transactions.
Fisher reasoned, however, that individuals
achieve an "equilibrium” in their holdings of the
two forms of money. The notion of a desired or
optimal k-ratio is based on the assumption that
individuals decide how much of their money
holdings they will allocate between currency
and checkable deposits, based on both the rela­
tive advantages of each in undertaking an indiv­
idual’s planned transactions and their relative
holding cost. This ratio was assumed to be a
ment and foreign transaction balances, m * = (1 + k)/
(r*(1 + g + f) + k), where g and f denote the ratios of gov­
ernment and foreign transactions accounts to TCD, re­
spectively. If, in addition, excess reserves were held, as
described in footnote 2, m* = (1 +k)/(r*(1 + g + f) + k + e).
These complications can be ignored, however, because
movements in the observed ratio of currency to TCD ex­
plain most of the movements in the multiplier, as will be
discussed shortly.

5ln a slightly more realistic model, which allows for the fact
that depository institutions must hold reserves on govern­




JULY/AUGUST 1991

50

function of a num ber of economic and social
variables.6
Given these variables, the demands for cur­
rency and checkable deposits were thought to
be strictly proportional to each other. More­
over, because individuals are free to adjust their
holdings of the two monies quickly and costless­
ly, it was assumed that the actual currencydeposit ratio would deviate from the desired
ratio for only a short period of time.7 According
to this line of reasoning, all changes in the
observed currency-to-deposit ratio, denoted here
by the K-ratio, are to be interpreted as changes
in the desired ratio caused by one of these fac­
tors. While not numerically constant, as it was
assumed to be in the previous analysis, the kratio was viewed as not being directly affected
by monetary policy actions.
The following discussion, supported by subse­
quent empirical analysis, suggests, however, that
the observed ratio of currency to checkable
deposits can be and has been affected directly
by the policy actions of the Federal Reserve.8
This effect can emerge without changing the
relative advantages of currency and checkable
deposits or their relative holding cost.

S u b stitu ta b ility , H oldin g C osts a n d
th e O ptim al k-R atio
There are a num ber of reasons why one
might question the assumption that changes in
the observed currency-to-deposit ratio necessar­
6Fisher assumed that the optimal k-ratio depended on real
income or wealth, the degree of development of the
business sector, population density, relative holding costs
and custom and habit. (Checkable deposits were thought
to be “ superior” to currency, although money in any form
was a superior good.) Cagan (1958) extends the list of
determinants of the k-ratio considerably. Both he and Hess
(1971) attempt to quantify the effects of such factors.
7Cagan (1958) recognized that, at times, restrictions might
prevent the adjustment of the currency ratio. He explicitly
considered the case of financial crises where banks sus­
pended convertibility. He noted, “ At these times individ­
uals could not exchange deposits for currency, and the
desired currency ratio undoubtedly exceeded the actual
ratio...” Without such restrictions, however, individuals are
free to adjust their currency holdings to the desired level
very quickly and at a low cost.
The assumption that there exists a desired currency-todeposit ratio and that individuals adjust their actual hold­
ings of currency to their desired level was made opera­
tional for models of the money supply process by Karl
Brunner and Allan Meltzer in a series of articles. See
Brunner (1961) and Brunner and Meltzer (1964, 1968).
8lt has long been recognized that policy actions can have
an indirect effect on the multiplier through the presumed


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ily reflect changes in the optimal k-ratio—that
is, changes in the relative holding cost and ad­
vantages of currency and checkable deposits.
First and perhaps foremost among these is that
the demand for either of these forms of money
might depend on a number of special factors
that are unrelated to the demand for the other.
Thus, changes in the relative advantages of
these two forms of money might not be empiri­
cally important in explaining changes in the
ratio of currency to checkable deposits.
For example, many believe that currency has
no rival for illegal transactions. The same is
true for foreign demand for U.S. currency by
countries that need “hard currencies” for their
domestic transactions.9 To the extent that cur­
rency is held for these reasons, independent of
factors that determine the demand for check­
able deposits, policy actions can induce changes
in TCD without affecting currency demand. Con­
sequently, policy can alter the ratio of currency
to TCD and, hence, the multiplier.10
One might also argue that changes in the rela­
tive holding cost of the two monies are not es­
pecially relevant for explaining observed changes
in the currency-to-deposit ratio. The relative
holding cost of the alternative monies is given
by the difference between the rates of return
on the two forms of money.11 The return on
holding currency is zero.12 Although non-interest-bearing checking accounts (demand deposits)
have an explicit return of zero, they can yield a
effect of policy actions on economic variables such as real
income or interest rates. It is argued that such variables
influence the k-ratio or the other ratios that make it up—
particularly, the ratio of excess reserves to total checkable
deposits. See Mishkin (1989) for a more detailed discus­
sion of this indirect effect.
9Because it is difficult to account for a relatively large
amount of the total stock of U.S. currency outstanding,
one should not be too surprised to find that, in the ag­
gregate, demand for currency and checkable deposits are
not closely related. See, for example, Avery, Elliehausen,
Kennickell and Spindt (1986, 1987).
10This potential influence is illustrated below with an exam­
ple. To be sure, longer-run movements in the K-ratio might
be attributable to some factors that affect the relative ad­
vantages for the two forms of money.
11The discussion to follow focuses on the nonbank public’s
perspective. The relative holding costs to depository in­
stitutions generally will differ.
12Adjusted for inflation, it is minus the expected rate of infla­
tion. Note that currency used for illegal transactions yields
a greater return because of the tax avoidance. For foreign­
ers, currency can yield a return that differs from zero due
to the appreciation or depreciation of their home currency
relative to the U.S. dollar.

51

positive implicit return—for example, free toast­
ers for new customers, subsidized accounting
and payment services, etc. The return on hold­
ing interest-bearing checking accounts is the net
interest paid on these accounts plus free pay­
ment services.13
The relative holding cost of currency and
demand deposits, however, is unresponsive to
movements in market interest rates because the
explicit returns to both assets are identically
zero. Surprisingly, the same seems to be true
for currency and interest-bearing checking ac­
counts, even since the elimination of Regulation
Q ceiling rates in 1986. Interest rates paid on
interest-bearing checkable deposits included in
TCD have been unresponsive to movements in
short-term interest rates.14 Despite the fact that
the explicit holding cost of currency relative to
that of checkable deposits has changed little, the
observed ratio of currency to checkable depos­
its exhibited sharp swings during the 1980s.
Thus, it is unlikely that changes in the public’s
holding of currency and checkable deposits are
due primarily to changes in their relative hold­
ing cost.

The H oldin g o f C u rren cy,
C h ecka ble D ep o sits a n d O th er
F inancial A ssets
Thus, it would not appear that individuals
simply shift their money holdings between cur­
rency and checkable deposits in response to
variations in their relative advantages or holding
cost. This conjecture would be reinforced by
the fact that currency and especially checkable
deposits are substitutes for other “near-money”
stores of wealth, for example, money market
mutual funds. From this broader perspective,
the demands for currency and checkable depos­
13Net interest is interest net of service charges. For a dis­
cussion of these, see Carraro and Thornton (1986). This
explicit return also could be adjusted for inflation.
'■•Indeed, interest rates on the interest-bearing portion of
TCD, called other checkable deposits (OCD), have changed
little during the 1980s. The rate on OCD fluctuated be­
tween 5 percent and just over 5.5 percent during our sam­
ple period.
15This consideration raises a fundamental question— namely,
what constitutes an appropriate monetary aggregate? In
theory, monetary aggregation requires the “ monetary” ag­
gregate to be “ weakly separable.” That is to say, it must
behave as a fundamental commodity with respect to con­
sumption and other financial assets. There can be
substitution between assets that compose the aggregate,
but not between those that compose the aggregate and




its are seen as being determined simultaneously
with the demand for near-money assets.15
An important part of the determination of the
ratio of currency to checkable deposits, there­
fore, is the degree of substitutability between
currency and demand deposits on the one hand
and between each of these money assets and
near-money assets on the other. Although the
explicit rates paid on TCD are relatively unre­
sponsive to changes in market interest rates,
rates paid on near-money assets can vary mark­
edly with variations in other market interest
rates. The effect of these variations on the pro­
portion of Ml held in the form of currency, of
course, depends on the degree of substitutabili­
ty between near-money assets and the two forms
of money. If currency is a relatively poor substi­
tute for such assets while TCD is a relatively
good one, the ratio of currency to TCD will
change with changes in rates paid on such nearmoney assets because of changes in TCD.
The relevance of this substitutability between
TCD and other near-money assets appears to
have been heightened by the nationwide intro­
duction of interest-bearing checking deposits in
January 1981. Since then, the cross-price or in­
terest elasticity of the demand for checkable
deposits has increased. This increase is hardly
surprising because the payment of interest on
checkable deposits has made them closer substi­
tutes for interest-bearing time and savings de­
posits. Indeed, some evidence suggests that in­
dividuals have shifted a significant portion of
their "savings” balances into interest-bearing
checking accounts.16 Because these saving bal­
ances are substitutes for savings and money
market accounts that have higher explicit re­
turns, the interest elasticity of the demand for
checkable deposits should have risen, while the
interest elasticity of currency demand should
not have changed.17
those that do not. Some evidence suggests that, while cur­
rency and demand deposits satisfy this condition for weak
separability, these two assets plus interest-bearing transac­
tion balances do not. See, Fisher (1989), for example.
Belongia and Chalfant (1989), among others, however, find
that the data are consistent with the notion that the assets
included in M1 and a grouping broader than M1 (currency
and total checkable deposits) satisfy the weak separability
condition. Thus, the empirical results in this line of
research are not conclusive.
16See Sill (1990).
17See Thornton and Stone (1991) for a derivation of this
result. These results are borne out empirically by simple
linear regressions of the monthly change in both currency
and other checkable deposits on a scale measure and the
three-month T-bill rate.

JULY/AUGUST 1991

52

Thus, changes in interest rates, whether pol­
icy induced or not, can have an asymmetric ef­
fect on the demands for currency and checkable
deposits, with a direct effect on the proportion
in which the alternative monies are held.18 Al­
though this asymmetric effect is likely to have
played a larger role since the introduction of
interest-bearing checking accounts in generating
fluctuations in the ratio of currency to TCD,
policy has induced changes in this ratio more
directly since the MCA (as discussed below).
D EPO SIT SU BSTITU T IO N AN D
THE M ONEY M ULTIPLIE R

Provided that the demands for currency and
checkable deposits are determined by factors
that are independent of one another, monetary
policy actions can have a direct influence on the
relative holdings of each and, thus, the multi­
plier.19 The channel of influence is most easily
illustrated in the extreme case where the de­
mand for currency is completely independent
of the demand for checkable deposits. That is,
equation 5 is replaced with
(5') C, = C,
where C is a constant. Equation 1 also can be
rewritten as
(10 Ml, = (1 + K,)TCDt,
where, as defined previously, Kt = (C/TCD)t is
the observed ratio of currency to TCD.20
Using equations 1' and 5' in place of 1 and 5,
the money supply can be written as
(12) M l, = m ,' AMB,,
where m*' = [(l + K,)/(r*+K,)].
The crucial difference between this expression
and equation 10 is that, here, policy actions af18The same would be expected for changes in the level of
income. Indeed, Hess (1971) presents estimates indicating
asymmetric effects of both changes in interest rates and
income on the demands for currency and checkable
deposits. It should be emphasized that this effect of
changes in interest rates on the k-ratio is not the same as
that which was alluded to earlier-i.e., through the relative
holding cost of currency and checkable deposits (see foot­
note 8).
19Many researchers who have estimated currency demand
equations have abstracted from the relationship of curren­
cy to TCD. For example, see Hess (1971) and Dotsey
(1988).


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feet both the adjusted monetary base and the
money multiplier. To see why, consider a policy
action involving the purchase of T-bills in the
market by the Fed. This policy action increases
the stock of reserves and, assuming zero excess
reserves, TCD. In the earlier formulation of his
model, the K-ratio was assumed to be unchang­
ed; the increase in TCD would be accompanied
by a proportionate increase in currency, so that
the observed ratio of currency to TCD, K, would
not change. Thus, the effect of this policy action
on the money stock would be isolated in the
monetary base—the multiplier would be
unaffected.
In the modified model, however, TCD increases
while currency is constant. Consequently, the Kratio falls and the multiplier, m*', rises. In this
instance, the change in monetary policy is re­
flected both in the adjusted monetary base, be­
cause of a change in R, and in the multiplier
because of a policy-induced change in K. Al­
though this argument is made in terms of a stat­
ic model, the main point, that policy can in­
fluence the multiplier, would carry over into a
more realistic dynamic model. Two of the more
salient features of the longer-run consequences
of this analysis are taken up in the shaded in­
sert on page 54.
THE RECENT BE H A V IO R
OF THE K -R ATIO

Figure 1 shows the K-ratio and the observed
adjusted monetary base multiplier, Ml/AMB,
from January 1970 to November 1990. Note that
the multiplier is essentially the m irror image of
the K-ratio; the K-ratio accounts for much of
the multiplier’s short-run (month-to-month)
variability and for the significant shifts in its
longer-run “trends.” Indeed, as shown in table
1, changes in the K-ratio alone explain over 80
percent of the month-to-month variability in
20Because k is meaningless in this formulation, K, will not
equal k. More generally, currency demand can be thought
of as having two components, one related to TCD as em­
bodied in the k-ratio and the other unrelated to TCD. That
is, C, = C + kTCD,. In this more general formulation, the
k-ratio is determined solely by the relative holding cost of
currency and TCD and the substitutability between them
as discussed above.
In this case, K , = —

Q

+ k. The restriction in (5’),

that k = 0, is imposed only for illustrative purposes.

53

Figure 1
The K-Ratio and the M1 Multiplier
January 1970-November 1990
Ratio

R a tio

Table 1
Regression Estimates of Changes in the Multiplier on Changes
in the K-ratio
Period

Constant

K-ratio

SEE

R2

D.W.

1/1970-12/1980

.000
(0.35)

-4 .3 5 5 *
(13.94)

.007

.598

2.47

1/1981-11/1990

.001
(1.84)

-3 .7 1 4 *
(23.02)

.005

.818

2.49

3/1984-11/1990

.001
(1.35)

-3 .5 0 4 *
(21.66)

.004

.854

3.02

* indicates statistical significance at the 5 percent level.
Absolute values of t-statistics are in parentheses.




JULY/AUGUST 1991

54

The Long-Run Multiplier?
That the K-ratio does not appear to be sta­
tionary (in the sense of being mean-reverting)
raises an interesting question of how the
magnitude of the multiplier is determined in
the long run. Two examples are presented to
show that the multiplier is not invariant to
monetary policy and the "long-run multiplier”
is critically dependent on the specifications of
the demands for currency and TCD.
The first example assumes that the demand
for real currency is determined solely by real
income, while the demand for real TCD is
determined both by real income and the nom­
inal interest rate, where TCD is inversely
related to the latter. This example captures
the notion that the demand for TCD is deter­
mined simultaneously with the demands for
other near-monies. For simplicity, these
demands are assumed to be linear in natural
logs, and the income elasticities of the
demands for currency and TCD are assumed
to be equal. Under these assumptions, the
natural log of the K-ratio, InK, depends solely
on the natural log of the nominal interest
rate and is positively related to it. By influ­
encing the nominal interest rate, which
equals the real rate plus a premium for ex­
pected future inflation, monetary policy
would affect the long-run multiplier.
To see why, assume that a change in policy
raises reserve growth permanently. If this in­
crease results in a permanent increase in the
actual and anticipated rates of inflation, the
nominal interest rate will rise. The perman­
ently higher level of the nominal interest rate
will increase the level of the K-ratio causing a
permanent reduction in the multiplier.
The second example assumes that the de­
mand for currency is driven largely by forces
external to the domestic economy, say, for­
eign demand for U.S. currency. It also as­
sumes that the domestic demand for curren­
cy is determined solely by the relative
holding cost of currency vs. TCD and that
this cost is constant. Again, these relation­
ships are assumed to be linear in the natural
'The parameter h might be thought of as the log of the op­
timal k-ratio, reflecting only the relative advantages and
costs of currency and TCD.


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logs. If the foreign demand grows at a con­
stant rate, b, then the log of the demand for
currency is given by
InC, = b t + h lnTCDt,
where t denotes a time trend and h is a con­
stant.1 What happens to the the K-ratio in the
long run is determined by the relative growth
rates of foreign and domestic demands for
currency. For example, assume that the de­
mand for nominal TCD is determined solely
by nominal income and that nominal income
grows at a constant rate, d—at least in the
long run. If d is less than or equal to b/(l-h),
the K-ratio will rise without bound and the
multiplier will approach unity. If d is greater
than b/(l-h), the K-ratio will approach zero
and the multiplier will approach 1/r*, where
r* is the base-period reserve requirement (see
the text for more details).
Note, however, that, in this example, the
long-run multiplier is not independent of pol­
icy actions. For example, assume that
d>b/(l-h) so that the multiplier is ap­
proaching 1/r*. Now assume that a change in
policy reduces the growth rate of TCD and,
thus, the rate of inflation and the growth
rate of nominal income. At the very least,
this policy change would cause the multiplier
to approach its long-run equilibrium value
more slowly, as it drives down d. Indeed, if
the growth rate of TCD slowed to the point
where d<b/(l-h), the long-run multiplier
could converge to 1 rather than 1/r*.
Of course, there are a number of other in­
teresting possibilities. The major points that,
even in the long run, the multiplier depends
on monetary policy and that the exact value
of the long-run multiplier between 1 and 1/r*
depends critically on the specifications of the
demands for currency and TCD are nonethe­
less valid. Before a meaningful “long-run”
representation of the multiplier can be ob­
tained, it is necessary to specify carefully
both the demand for currency and the de­
mand for TCD.2
2Note that, because the multiplier is bounded, M1 and AMB
must be cointegrated.

55

Figure 2
The K-Ratio, Currency and TCD
Ratio

changes in the multiplier since the implementa­
tion of the MCA.21 The MCA tightened the link
between the K-ratio and the multiplier by
reducing or eliminating other sources of varia­
tion in the multiplier.22 While the MCA was im­
plemented in a series of steps from November
1980 to September 1987, its major features
were almost fully implemented by February
1984.23 Since then, changes in the K-ratio alone
explain over 85 percent of changes in the
multiplier.
21The Durbin-Watson statistic for each of the equations in­
dicates significant, negative first-order serial correlation.
Because we are primarily interested in the explanatory
power of changes in the K-ratio as measured by the ad­
justed R-square, however, maximum likelihood estimates
of the equations adjusted for serial correlation are not
reported here. Nonetheless, there are no substantive dif­
ferences between the maximum likelihood estimates and
those reported in table 1.

B illio ns o f D ollars

The R ela tio n sh ip B etw een T otal
C h ecka ble D ep o sits a n d the
K -R atio
Figure 2 shows the K-ratio, currency and
TCD. The behavior of these series suggests that
changes in the trend of the K-ratio are associ­
ated more closely with changes in the trend of
TCD than with changes in the trend of curren­
cy growth. For example, the sharp rise in the
23The MCA was first implemented in November 1980 and
was fully phased-in by September 1987. The empirically
significant features of the act were completed with the
Fed’s adoption of contemporaneous reserve requirements
in February 1984, so the sample was broken at this point.
See Garfinkel and Thornton (1989) for a discussion of
these changes and their effect on the multiplier.

“ See Garfinkel and Thornton (1989) for details.




JULY/AUGUST 1991

56

Figure 3

Deviations of the Growth Rates of the K-Ratio
and Currency from Their Means
January 1970 thru November 1990
Currency

K -R a tio
40

40

30

K-Ratio Growth Rate

20
10

-10

-2 0
-3 0

-3 0
1970

72

74

76

78

K-ratio in the early 1970s is associated with a
slowing in the growth of TCD. The decline in
the K-ratio in the early 1980s and its subse­
quent rise are clearly associated with a sharp
acceleration in the growth of TCD followed by
a sharp deceleration in its growth.
That TCD accounts for much of the shortrun variation in the K-ratio also is evidenced by
figures 3 and 4, which show, respectively, devi­
ations of the growth rate of the K-ratio from its
mean and deviations of the growth rates of cur­
rency and TCD from their respective means. As

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80

82

84

86

88

1990

shown in the figures, the month-to-month vari­
ability in the growth of TCD is considerably
larger than that of currency. The variability of
TCD more closely matches the variability of the
K-ratio than does the variability of currency.
While the growth rates of the K-ratio and TCD
are highly, inversely related, there is little
positive association between the growth rate of
the K-ratio and the growth rate of currency.
This observation is verified in table 2, which
shows the simple correlations between the
monthly annualized growth rates for currency

57

Figure 4
Deviations of the Growth Rates of the K-Ratio
and Total Checkable Deposits From Their Means
January 1970 thru Novem ber 1990
K -R a tio

40

and the K-ratio and for TCD and the K-ratio for
four periods of roughly equal length between
January 1970 and November 1990. If variation in
the K-ratio were simply due to shifts between
currency and TCD, its variation would be equally
attributable to variation in currency and TCD.
This is not the case, however. The growth
rates of currency and the K-ratio were positive­
ly correlated during only two of the four peri­
ods. They were negatively correlated in the
other two, although the correlations are not sig­
nificantly different from zero. In contrast, there
is a strong, consistent negative correlation be­
tween the growth rate of TCD and the K-ratio
during all four of the periods. Figures 3 and 4
and the correlations reported in table 2 clearly
suggest that month-to-month variability in the



Table 2
Correlations Between the Monthly
Growth Rate of the K-Ratio and the
Monthly Growth Rates of Currency and
TCD
Period
1/1970-12/1974
1/1975-12/1979
1/1980-12/1984
1/1985-11/1990

K-ratio
and currency
.368*
-.0 1 6
-.1 1 2
.265*

K-ratio
and TCD
-.9 0 1 *
-.9 1 1 *
- .955*
- .951 *

* indicates statistical significance at the 5 percent level.

JULY/AUGUST 1991

58

K-ratio is driven largely by movements in TCD.
Finally, as shown in figure 4, periods of per­
sistent deviations in the growth rate of TCD
above (below) its mean are associated with per­
sistent deviations of the growth rate of the Kratio below (above) its mean. Consequently, both
the short and long-run movements of the Kratio are associated with movements in TCD
rather than currency. The apparent importance
of TCD in influencing the K-ratio suggests that
K-ratio changes have not occurred simply
because of variations in the relative advantages
and holding cost of currency and TCD. That is
to say, changes in the K-ratio have not been a
simple result of the public’s desire to shift the
composition of Ml between currency and
checkable deposits.

The L ink B etw een T otal C h ecka ble
D ep o sits a n d R e se rv e s
Movements in the multiplier appear to be deter­
mined primarily by movements in the K-ratio,
which, in turn, appear to be determined pri­
marily by changes in TCD. The question that re­
mains is what determines the stock of TCD out­
standing? The models of the money supply pre­
sented above provide a simple answer: given
the reserve requirement ratio, TCD is deter­
mined solely by the amount of reserves supplied
by the Federal Reserve. This strong link arises
in this model because reserves are assumed to
be held only to support checkable deposits.24
Prior to the MCA, commercial member banks
were required to hold reserves against all time
24ln reality, of course, depository institutions hold excess
reserves and are required to hold reserves on transaction
deposits other than those included in M1.
25The total reserves measure used here is total reserves ad­
justed for reserve requirement changes, prepared by the
Federal Reserve Board.
26The Durbin-Watson statistic for the first time period sug­
gests that there is significant first-order positive serial cor­
relation, as would be expected given the likelihood of
misspecification (see the appendix). Maximum likelihood
estimates of this equation adjusting for first-order serial
correlation confirm this result. The estimated coefficient of
first-order serial correlation is -.314 with a t-statistic in ab­
solute value of 3.29. Nevertheless, the parameter
estimates after adjusting for serial correlation are generally
close to those reported in table 3, and they are statistically
significant. More important, the adjusted R-square only in­
creases to .147; hence, the dramatic rise in the adjusted
R-square in the 1980s is not due to the fact that total
reserve captures the autoregressive part of TCD.


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and saving deposits, while non-member banks
and other depository institutions were not re­
quired to hold reserves against their transaction
deposits in Ml. Because of these factors, the
link between TCD and reserves was not particu­
larly strong. In reducing or eliminating reserve
requirements on a num ber of non-transaction
accounts and extending reserve requirements to
all depository institutions, however, the MCA
significantly strengthened the relationship be­
tween TCD and reserves.
The effect of the MCA is illustrated in table 3,
which shows the results of simple linear regres­
sions of changes in TCD on changes in total re­
serves, TR, for several periods between January
1970 and November 1990.25 The regression equa­
tions in this table (and in subsequent ones) are
intended to be illustrative and should not be in­
terpreted as alternative models for the money
supply process. (See the appendix for details.) In
all cases but the initial phase-in of the MCA,
there is a statistically significant relationship
between changes in TCD and TR. The strength
of the relationship, as measured by the adjusted
R-square, however, increases after the imple­
mentation of the MCA.26 The adjusted R-square
increases from .06 before the MCA to .67 after
the MCA. All of this improvement emerges in
the period after February 1984, when the ad­
justed R-square increases further to .83.27 More­
over, the reciprocal of the estimated coefficient
on TR is .124, very close to the marginal re­
serve requirement of .12 during the latter peri­
od. Indeed, the null hypothesis that this coeffi­
cient is equal to 1/.12 cannot be rejected at the
5 percent significance level (the t-statistic
is 0.62).
27The switch from lagged to contemporaneous reserve ac­
counting in February 1984 might explain some of this ap­
parent improvement. To account for this possibility, the
change in TCD was regressed on both the contemporan­
eous and lagged change in TR. In no case was the coeffi­
cient on lagged TR statistically significant from zero at the
5 percent level. Indeed, the results differed little from
those reported in table 3. That the switch from lagged to
contemporaneous reserve requirements is of no significant
consequence is consistent with the conjecture of Thornton
(1983) and the empirical evidence presented by Garfinkel
and Thornton (1989). The relationship between TR and
TCD will likely become even stronger given the recent
elimination of reserve requirements on all time and sav­
ings deposits.

59

Table 3

Regression of Changes in TCD on Changes in Total Reserves
Period

Constant

Total
reserves

SEE

R2

D.W.

1/1970-12/1980

.795*
(5.72)

1.870*
(3.10)

1.418

.062

1.35

1/1981-11/1990

.742*
(3.42)

7.264*
(15.64)

2.081

.674

2.23

1/1981-2/1984

1.765*
(3.81)

2.704
(1.92)

2.414

.067

2.06

3/1984-11/1990

.441*
(2.09)

8.082*
(19.90)

1.676

.832

1.94

* indicates statistical significance at the 5 percent level.
Absolute values of t-statistics are in parentheses.

Table 4

Regression Estimates of Changes in the Multiplier on Changes
in Total Reserves
Period

Constant

Total
reserves

SEE

R*

D.W.

1/1970-12/1980

- .003*
(2.86)

-.0 0 3
(0.67)

.011

.004

1.70

1/1981-11/1990

- .004*
(3.28)

.016*
(6.77)

.011

.275

2.12

1/1 981 -2/19 84

.002
(0.77)

-.0 0 3
(0.42)

.014

- .0 2 3

1.82

3/1984-11/1990

-.0 0 6 *
(6.05)

.020’
(11.07)

.007

.603

2.31

* indicates statistical significance at the 5 percent level.
Absolute values of t-statistics are in parentheses.

The E ffec ts o f P o lic y A ction s on
the M u ltiplier a n d th e M o n ey S to ck
The above analysis suggests that policy actions
could exert a strong effect on the multiplier in
the 1980s. Table 4 shows that this is the case.
Changes in TR account for 60 percent of the
variation in the multiplier since March of 1984.
The table also shows that, because of the loose
link between reserves and total checkable de­



posits, the assumption that policy actions had
no effect on the multiplier was a reasonable
working assumption before the adoption of the
MCA. Indeed, changes in the multiplier are uncorrelated with changes in TR during the pe­
riod ending December 1980.
These results suggest that there should be
a dramatic change in the relationship between
Ml and TR in the 1980s. Simple regressions of
changes in M l on changes in TR and changes in
JULY/AUGUST 1991

60

Table 5

Regression Estimates of the Change in M1 on the Change on
Total Reserves and the Change in the Adjusted Monetary Base

Period

Constant

Total
reserves

1/1970-12/1980

1.328*
(8.86)

Adjusted
monetary
base

2.079*
(3.20)

-.1 8 5
(0.74)
1/1981-11/1990

1.868*
(8.34)
-.2 8 0
(0.53)

1 /1 981 -2/19 84

2.613*
(5.33)
1.113
(1.51)

3/1984-11/1990

1.696*
(7.73)

1.839*
(3.20)
7.995*
(18.89)

-1 .0 2 3
(1.43)

3.158*
(7.48)

1.28

1.312

.312

1.54

.659

2.13

2.939

.361

1.39

.070

2.00

2.369

.200

1.86

1.746

2.910
(1.95)

.066

2.554

2.773*
(8.23)

D.W.

2.149

7.251 *
(15.12)

R!

1.529

2.513*
(7.73)

SEE

.817

1.73

3.138

.407

1.33

* indicates statistical significance at the 5 percent level.
Absolute values of t-statistics are in parentheses.

the AMB, reported in table 5, bear this out.28TR
explains a relatively small amount of the varia­
tion in changes in Ml before MCA and over 80
percent of the variation of changes in Ml since
early 1984. The table also shows that the ex­
planatory power of the monetary base has in­
creased since the MCA, as would be expected.29
Nonetheless, the explanatory power of the AMB
declined significantly relative to that of TR.
IM PLIC ATIO N S FOR
M O NE TAR Y PO LICY

Prior to the MCA, when it appeared that the
multiplier was independent of policy actions,
28Again, the Durbin-Watson statistics indicate significant
serial correlation, especially when the AMB is used as the
independent variable. In no case did an adjustment for
seriai correlation using a maximum likelihood technique
alter any of the substantive results presented in table 5.
That is, these results too suggest that there is a marked
increase in the explanatory power of TR in the 1980s and


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a rather simple, straightforward approach to
money stock control was implied—namely, to
target the level of the adjusted monetary base
consistent with a money-stock target conditional
on a forecast of the multiplier, where the multi­
plier forecast was not conditional on the target
setting for the monetary base. This notion also
implied that the adjusted monetary base is the
best indicator of the effects of policy actions on
the money stock.
The realization that the multiplier is not in­
dependent of policy actions suggests that the
monetary base might not be the best indicator
of policy actions on the money stock and that
revising the simple empirical models of the
that changes in TR explain much more of the variation in
changes in M1 in the 1980s than do changes in the AMB,
even allowing for significant first-order serial correlation.
29See Garfinkel and Thornton (1989) for a discussion of this
point.

61

money supply process to account for the effects
of policy actions on the multiplier could result
in improved money stock control. These issues
are discussed briefly in this section.

The A d ju ste d M o n eta ry B ase as an
In d ica to r o f P o lic y A ction s on the
M o n ey S to ck
The adjusted monetary base continues to
reflect all policy actions—changes in both re­
serves and reserve requirements; however, it
does not fully capture the effects of these ac­
tions on the money stock. Indeed, changes in
Ml are now more closely linked to changes in
TR than to changes in the AMB. Consequently,
it now appears that total reserves, adjusted for
reserve requirement changes, is a better indica­
tor of the effects of monetary policy actions on
the money supply than is the adjusted monetary
base.
Furthermore, the quantity of currency out­
standing is demand-determined. Consequently,
unlike adjusted reserves, the adjusted monetary
base can give misleading signals of the course of
monetary policy when there are exogenous shifts
in the demand for currency.
To take a concrete example, currency growth
accelerated markedly beginning about December
1989.30 This acceleration was accompanied by
a sharp acceleration in the growth of the ad­
justed monetary base from 3.4 percent in 1989
to 8.4 percent in 1990. Such a sharp rise in
base growth would tend to indicate that mone­
tary policy had eased. But the growth of ad­
justed reserves and, thus, TCD indicate a sub­
stantially weaker easing of policy. TCD increas­
ed at a 1.2 percent rate in 1990 compared with
a -1.3 percent rate in 1989. Of course, the ap­
parent exogenous increase in the demand for
30While the exact cause of this acceleration remains un­
clear, many attribute it (at least in part) to currency exports
to South American and Eastern bloc countries.
31An equally interesting, but less frequently discussed, epi­
sode occurred during 1989 when, after remaining fairly
constant, currency growth slowed abruptly. During this
period, the K-ratio rose rather than fell, as one might ex­
pect given the apparent shift in the demand for currency.
The increase in the K-ratio was driven by negative growth
in reserves and, hence, TCD during this period.
32See Balbach (1981), Hafer, Hein and Kool (1983), and
Johannes and Rasche (1979, 1987) for a discussion of this
approach and for alternative methods that have been used
to forecast the money multiplier.
33Note that the multiplier approach can be more difficult to
implement. Most notably, the control problem becomes




currency caused the K-ratio to rise and the mul­
tiplier to fall, so that Ml grew slowly relative to
the monetary base during the period.31 Because
there is now a closer link between TR and Ml
than between the AMB and Ml and because TR
is less likely to give misleading signals, TR is
likely to be a better indicator of both monetary
policy and the effects of policy changes on the
money stock.

The M u ltiplier A p p ro a ch to
M on ey S to ck C on trol
That the multiplier is not independent of
policy actions also has important implications
for the multiplier approach to money stock con­
trol. Taking this approach, the target level of
Ml is achieved by forecasting the multiplier,
then supplying the amount of the adjusted mon­
etary base necessary to hit the desired Ml tar­
get.32 If, however, the multiplier is a function of
open market operations, policymakers must also
predict the effect of their actions on the multi­
plier. That is to say, the multiplier approach to
money control should be modified to take ac­
count of the effects of policy actions on the
multiplier. Taking account of such effects un­
doubtedly will improve money control over the
simple approach that assumes independence be­
tween the multiplier and policy actions. The
magnitude of this improvement depends on how
accurately the effects of policy actions on the
multiplier can be forecast. To the extent that
variations in the multiplier are largely explained
by variations in the k-ratio and these variations
are, in turn, largely influenced by policy (espe­
cially in the post-MCA period), such a modifica­
tion could produce a substantial improvement in
money stock control.33
nonlinear. One alternative approach would be to simply
forecast the level of currency, then supply the reserves
necessary to hit a target level of TCD. The target level of
TCD would have to be consistent with both the M1 target
and the forecast level of currency. Whether this or the
multiplier approach, suitably modified to account for the
effect of policy actions on the multiplier, would provide
greater monetary control is an empirical issue well beyond
the scope of this paper. Both approaches will produce
forecast errors when there are unexpected shifts in the de­
mand for currency. The real issue is whether better esti­
mates of the K-ratio can be obtained by estimating the
numerator and denominators separately than estimating
them together. This is an empirical issue. Nevertheless,
this alternative approach could be simpler to implement
and might provide superior control if reasonably accurate
forecasts of currency can be made.

JULY/AUGUST 1991

62

SUM M AR Y

This article has examined closely the standard
multiplier model of the money supply process,
specifically questioning the view that the ad­
justed monetary base multiplier is independent
of the policy actions of the central bank. Be­
cause the demand for currency depends on a
number of factors that are unrelated to the de­
mand for checkable deposits (and vice versa) and
because the stock of checkable deposits has
been more closely tied to the quantity of re­
serves supplied by the Federal Reserve since the
implementation of the MCA, changes in mone­
tary policy result in changes in the ratio of cur­
rency to checkable deposits and, consequently,
changes in the multiplier. Hence, the Federal
Reserve’s monetary policy actions are reflected
both in the adjusted monetary base and the
money multiplier.
Theoretical considerations suggest that the
multiplier has never been independent of policy.
The elimination of reserve requirements on
some non-transaction accounts and the exten­
sion of Federal Reserve reserve requirements to
all depository institutions has greatly increased
the association between checkable deposits and
reserves. These changes have increased signifi­
cantly the association between changes in mone­
tary policy actions and changes in the multiplier.
That the multiplier is affected by policy actions
suggests that money stock control using the
multiplier model would be enhanced by taking
the effect of policy actions on the multiplier into
account. How much improvement can be expec­
ted with this modified approach and how effec­
tive alternative approaches to monetary control
can be is left as a topic for further research.
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Avery, Robert B., George E. Elliehausen, Arthur B.
Kennickell, and Paul A. Spindt. "The Use of Cash and
Transaction Accounts by American Families.” Federal
Reserve Bulletin (February 1986), pp. 87-108.
_____ . “ Changes in the Use of Transactions Accounts and
Cash for 1984 to 1986.” Federal Reserve Bulletin (March
1987), pp. 179-96.
Balbach, Anatol B. “ How Controllable is Money Growth?,”
this Review (April 1981), pp. 3-12.
Belongia, Michael T., and James A. Chalfant. “ The Chang­
ing Empirical Definition of Money: Some Estimates from a
Model of the Demand for Money Substitutes,” Journal of
Political Economy (April 1989), pp. 387-97.
Brunner, Karl. “A Schema for the Supply Theory of Money,”
International Economic Review (January 1961), pp. 79-109.


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Brunner, Karl, and Allan H. Meltzer. “ Some Further
Investigations of Demand and Supply Functions for
Money,” Journal of Finance (May 1964), pp. 240-83.
______ “ Liquidity Traps for Money, Bank Credit, and Interest
Rates,” Journal of Political Economy (January/February
1968), pp. 1-37.
_____ . “ Money Supply,” in B. M. Friedman and F. H. Hahn
eds., Handbook of Monetary Economics (Elsevier Science
Publishers, 1990).
Cagan, Phillip. “ The Demand for Currency Relative to the
Total Money Supply,” Journal of Political Economy (August
1958), pp. 303-28.
Carraro, Kenneth C., and Daniel L. Thornton. “ The Cost of
Checkable Deposits in the United States,” this Review
(April 1986), pp. 19-27.
Dotsey, Michael. “ The Demand for Currency in the United
States,” Journal of Money, Credit and Banking (February
1988), pp. 22-40.
Fisher, Douglas. Money Demand and Monetary Policy (The
University of Michigan Press, 1989).
Fisher, Irving. The Purchasing Power of Money (Augustus M.
Kelley, 1911).
Garfinkel, Michelle R., and Daniel L. Thornton. “ The Link
Between M1 and the Monetary Base in the 1980s,” this
Review (September/October 1989), pp. 35-52.
_____ . “ Measuring the Monetary Base,” mimeo (February
1991).
Hafer, R. W., Scott E. Hein and Clemens J. M. Kool. “ Fore­
casting the Money Multiplier: Implications for Money Stock
Control and Economic Activity,” this Review (October 1983),
pp. 22-33.
Hess, Alan C. “An Explanation of Short-Run Fluctuations
in the Ratio of Currency to Demand Deposits,” Journal of
Money, Credit and Banking (August 1971), pp. 666-79.
Johannes, James M., and Robert H. Rasche. "Predicting the
Money Multiplier,” Journal of Monetary Economics (July
1979), pp. 301-25.
_____ . Controlling the Growth of Monetary Aggregates
(Kluwer Academic, 1987).
Mishkin, Fredric S. The Economics of Money, Banking and
Financial Markets, 3rd. ed. (Little, Brown and Company,
1989).
Plosser, Charles I. "Money and Business Cycles: A Real
Business Cycle Interpretation,” in Michael T. Belongia, ed.,
Monetary Policy on the 75th Anniversary of the Federal
Reserve System (Klumer Academic Publishers, 1991).
Sill, Keith. “An Empirical Examination of Money Demand in
an Intertemporal Optimizing Framework,” unpublished
paper (University of Virginia, 1990).
Stone, Courtenay C., and Daniel L. Thornton. “ Solving the
1980s’ Velocity Puzzle: A Progress Report,” this Review
(August/September 1987), pp. 5-23.
Tatom, John A. “ Issues in Measuring An Adjusted Monetary
Base,” this Review (December 1980), pp. 11-29.
Thornton, Daniel L. ’’Simple Analytics of the Money Supply
Process and Monetary Control,” this Review (October
1982), pp. 22-39.
_____ . “ Lagged and Contemporaneous Reserve Accoun­
ting: An Alternative View,” this Review (November 1983),
pp. 26-33.
Thornton, Daniel L., and Courtenay C. Stone. “ Financial
Innovations: Causes and Consequences,” in Kevin Dowd
and Mearyn K. Lewis, eds., Current Issues in Monetary
Analysis and Policy, (MacMillan Publishers, 1991).

63

Appendix
A Model of the Money Supply Process
One might be tempted to interpret the regres­
sion equations in the text as representing alter­
native models of the money supply process; how­
ever, the reader is cautioned not to do so. In­
deed, as the article suggests, some existing mod­
els of the money supply process are misspecified. This appendix illustrates the bias of some
of the regression equations estimated in the
article.
The discussion in the paper suggests that, since
the MCA, there is a very simple linear relation­
ship between TCD and TR of the form
TCD, = a + bTR, + et,
where the coefficients a and b are constants
and e is a residual error that is assumed to be
white noise. The error term arises because some
reserves are held against transaction deposits
not included in TCD and because depository in­
stitutions hold excess reserves. The constant
term, a, enters the equation because a lower
reserve requirement for a tranche of checkable
deposits exists and because some of the vari­
ables omitted from this equation might have
non-zero means. If TR is correctly adjusted for
changes in reserve requirements, including the
annual change in the deposit tranche, then the
coefficients a and b should be constant, where
b is the reciprocal of the marginal reserve
requirement—that is, b = 1/.12 = 8.33. The
discussion and the empirical evidence in the
paper further suggest that currency holdings
are independent of TCD, so that C, is simply ex­
ogenous from the perspective of money stock
control.
If this representation is true, then a regres­
sion of Ml on the monetary base is misspecified, because it imposes a restriction that is in­
consistent with the process generating the data.
To see this, consider the following regression
specification:

(A.2) C, + TCD, = g + hTR, + jC, + q,.
With the restriction h = j, equation A.2 is iden­
tical to equation A.I. The above analysis, how­
ever, suggests that the coefficient h should
equal 8.33 and the coefficient j should equal 1.
If this is the case, imposing the restriction that
these coefficients are equal will be resoundingly
rejected by the data.
To test this hypothesis, first-difference specifi­
cations of equations A.l and A.2 are estimated
using monthly data for the period from March
1984 through November 1990. These estimates
use Federal Reserve Board data for the adjusted
monetary base and total reserves, adjusted for
changes in reserve requirements. These data
come close to satisfying the identity that the
monetary base is equal to the currency compo­
nent of the money supply plus total reserves.
These estimates are presented in table A.l. In
the unrestricted version of the equation, neither
the null hypothesis that h = 8.33 nor the null
hypothesis that j = l can be rejected at the 5
percent significance level. The t-statistic for the
test that h = 8.33 is .59 and the t-statistic for the
test that j = l is .25. Hence, it is not surprising
that the restriction that h = j is soundly rejected
by the data.
It is interesting to note that imposing this
restriction biases the coefficient of the mone­
tary base multiplier away from its true value.
The estimated multiplier of 4.005 is nearly 50
percent larger than its average value during this
period. This bias emerges because of an omitted
variable.
To see this, note that equation A.2 could be
rewritten as either
(A.3) Ml, = g + hMB, + (j-h)C, + q,
or

(A.l) M l, = g + sMB, + q,.

(A.4) Ml, = g + jMB, + (h-j)TR, + q,.

Given the definitions of Ml and MB, this equa­
tion can be rewritten as

Hence, equation A.l can be obtained by omit­
ting C,from equation A.3 or TR, from equation




JULY/AUGUST 1991

64

Table A.1

Estimates of Equations A.1 and A.2
Constant

AAMB

-2 .0 9 9 *
(2.49)

4.005*
(7.48)

.590
(1.21)

ATR

AC

D.W.

.407

1.10

1.690

.908*
(2.51)

R2

3.138

8.090*
(19.67)

SEE

.828

1.93

'indicates statistical significance at the 5 percent level.
Absolute values of t-statistics are in parentheses.

Table A.2

Estimates of Regression of ATCD on ATR and AC:
March 1984 - November 1990
Constant
.581
(1.19)

ATR
8.069*
(19.67)

AC

SEE

R*

D.W.

-.1 1 5
(0.32)

1.685

.830

1.93

'indicates statistical significance at the 5 percent level.
Absolute values of t-statistics are in parentheses.

A.4. In the former case the estimate of h is bias­
ed downward (4.005 vs. 8.33); in the latter case
the estimate of j is biased upward (4.005 vs. 1).
Furthermore, the equation exhibits serial cor­
relation, a common indicator of misspecification.
These results are not too surprising given that
the demand for currency appears to be indepen­
dent of the demand for TCD, as illustrated in
table A.2, which shows the results of a regres­
sion of changes in TCD on changes in TR and
changes in C. The coefficient on the change in
C is negative, indicating a substitution between


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TCD and currency, but is not significantly dif­
ferent from zero. Given this independence, it is
hardly surprising that regressions of changes in
Ml on TR and changes in TCD on TR produce
nearly identical results. Comparing the results
in table A.l with those in table 5 shows that the
coefficient is biased downward slightly when
Ml is regressed on TR. This occurs because Ct
is omitted from the right-hand side of the equa­
tion and because of the weak negative associa­
tion between changes in both Ct and TCDt and,
hence, changes in TRt.

65

Cletus C. Coughlin and
Thomas B. M andelbaum
Cletus C. Coughlin is a research officer and Thomas B.
Mandelbaum is an economist at the Federal Reserve Bank of
St. Louis. Thomas A. Pollmann provided research assistance.

Measuring State Exports: Is
There a Better Way?

Te
„

RISING LEVEL OF U.S. EXPORTS in re­
cent years has caused jobs and incomes in
many states to become more closely tied to ex­
ports. To assess the economic effects of state
exports, it is essential to have reliable informa­
tion on the level of export activity by firms with­
in the individual states. Such information is
essential for numerous other purposes as well.
For example, policymakers and others interested
in state economic development require export
data to assess the effectiveness of programs de­
signed to stimulate export activity; they also re­
quire such data to assess the effects of trade
policy changes, such as the proposed free trade
agreement with Mexico.1 Unfortunately, no ideal
measure of state export activity currently exists.
This article describes the two available state
export series and compares their estimates of
manufactured exports. Such a comparison was
1As reported in Business America (1991), state governments
engage in a wide variety of activities to promote exports.
These activities include overseas trade missions, technical
assistance (such as seminars on the legal and financial
aspects of trade), and the dissemination of trade leads.
Seven states have export finance programs; 41 states
maintain offices in 24 countries to promote trade. These
promotional activities raise the issue of whether interna­
tional exports by firms within a state generate different
economic results than domestic exports (or exports to
other states by firms within a state). Empirical evidence to




not possible until recently because the two se­
ries were not available for the same year. Our
comparison for 1987 reveals that the two series
provide conflicting information about export ac­
tivity in many states.
The most prominent deficiency of both mea­
sures is that they are based on the value of ex­
port shipments by firms within a state rather
than on the value of goods produced within a
state that are exported. While this distinction
may sound arcane, the discussion below indicates
that it is not. Moreover, income and employ­
m ent in a state a re dep en d en t on the latter
measure, not on the value of export shipments.
To address this deficiency, a third estimate of
state manufactured exports is developed in this
article. Comparisons show the differences be­
tween this new measure and one of the existing
measures of export activity. Such a comparison
further illuminates the shortcomings of the two
assess whether such a distinction is meaningful in an
economic sense is scarce. See Webster et al. (1990) for
evidence that the employment effects of international ex­
ports exceed those of domestic exports for many
industries.

JULY/AUGUST 1991

66

available series and the advantages of a series
like that developed here.
EXISTING MEASURES OF
STATE EXPORTS

Historically, the focus of U.S. trade data has
been on country-to-country trade flows (that
is, U.S. exports to and imports from individual
countries). Recently, increasing attention has
been focused on trade flows involving individual
states. Exports of Boeing aircraft from Wash­
ington and imports of foreign cars by Missouri
residents are just two examples of traded goods
that have attracted attention to the fact that
state jobs and incomes are related to the inter­
national economy. Our focus is restricted to ex­
port activity at the state level. To date, those in­
terested in the magnitude of these flows have
relied on two data sources published by the U.S.
Census Bureau: Exports From Manufacturing
Establishments (EME) and the Origin o f Move­
ment o f Commodities (OMC).

E x po rts F rom M anu factu ring
E sta b lish m en ts
Approximately 56,000 of 220,000 manufactur­
ing establishments are asked in the Annual Sur­
vey of Manufactures to report the total value of
products shipped for export.2 Since many estab­
lishments do not know the final destination of
their products, the reported exports understate
the value of all manufacturing export shipments.
To compensate, the total amounts reported are
adjusted to include estimates of exports by other
distributors, such as wholesalers.
2At five-year intervals, a more comprehensive coverage of
manufacturing establishments occurs with the Census of
Manufactures. See appendix A of U.S. Census Bureau
(1991) for details on the 1987 Census of Manufactures and
the 1986 Annual Survey of Manufactures.
3For details, see appendix C of the U.S. Census Bureau
(1991).
4A Shipper’s Export Declaration must be filed for all export
shipments except for those going to Canada. Effective
November 30, 1990, this document was no longer required
for Canadian shipments because of a decision to
substitute Canadian import statistics for U.S. export
statistics. See Ott (1988) for an explanation why Canadian
import data are considered more accurate than U.S. ex­
port data.
5Processed food, forestry, petroleum and coal products that
originate in these primary sectors are included as
manufactured exports.
6Such decisions are also complicated by the fact that, until
1987, export data were generally unavailable at the three-


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Differences between the directly reported
values and the national total derived from Ship­
per’s Export Declarations are allocated to states.3
A Shipper’s Export Declaration is a document
that exporters must file which includes the
value of each export shipment.4 The allocation
procedure is complicated slightly because the in­
dustry classification scheme used in Shipper’s
Export Declarations differs from that used in the
Annual Survey of Manufactures. An additional
complication is that the value of export ship­
ments in Shipper’s Export Declarations includes
freight and wholesale margins. Since the value
of export shipments in the EME is reported as
shipments leave the plant, the costs associated
with transportation and wholesaler activity must
be removed from the values reported in Ship­
per’s Export Declarations.
EME was first produced in 1960 as the Origin
o f Exports o f Manufactured Products. It was pro­
duced at varying intervals until it became an
annual report in 1983. This series possesses
some significant shortcomings. First, the series
is restricted to manufactured exports. It pro­
vides no information for establishments engaged
in exporting services or unprocessed commodi­
ties produced by the agricultural, mining, fores­
try and fishing sectors.5
Second, this series is available with a two-year
or more delay. For example, data for 1985 and
1986 became available in early 1989 and data
for 1987 became available in 1991. Many ana­
lysts view these data as having only historical
value because information on recent activity is
not available for use in current decisions, such
as those involving targeting export promotion
expenditures.6
digit Standard Industrial Classification (SIC) level. The SIC
is the standard by which establishment-based U.S. govern­
ment economic statistics are classified by industry. For
details, see U.S. Office of Management and Budget
(1987). For manufacturing, 20 industries are identified at
the two-digit SIC level. The industry becomes more nar­
rowly defined as the number of digits for an SIC level in­
creases. Prior to 1987, the export data were presented at
the two-digit SIC level, or only for broad industries. An ex­
ample of the disaggregation offered by the use of threedigit SIC codes is chemicals and allied products (SIC 28)
which has eight industry groups: industrial inorganic
chemicals (SIC 281); plastics materials and synthetics (SIC
282); drugs (SIC 283); soaps, cleaners and toilet goods
(SIC 284); paints and allied products (SIC 285); industrial
organic chemicals (SIC 286); agricultural chemicals (SIC
287) and miscellaneous chemical products (SIC 289).

67

The final and most important shortcoming is
that this series reports the value of shipments
instead of what is termed "value added.” Value
added is the value of a firm's sales minus the
value of the goods and services it purchases
from other firms to make its products. As the
term implies, value added measures the dollar
value a firm adds to the value of purchased in­
puts in its production process.
One way to calculate the market value of
final goods and services produced during a year
is to sum the value added at each stage of pro­
duction by the firms in an economy.7 To illus­
trate, assume an automobile producer had total
sales of $18 billion, of which $10 billion reflect
the value of steel, tires, plastics, electricity and
other inputs used by the producer to make auto­
mobiles. The cost of these intermediate inputs is
subtracted from the producer’s revenue to cal­
culate value added, so the automobile pro­
ducer’s value added was $8 billion. This pro­
cedure is repeated for each firm in the econ­
omy; the sum of all firms’ value added equals
the total value of production within an
economy.
Using the value of export shipments rather
than the value added related to exports might
be a misleading indicator of export activity in a
state. Some manufactured products are not ex­
ported directly, but are combined as inputs with
other resources to produce an export. If these
inputs were produced in one state and trans­
ported to another for final processing, the value
of export shipments from the latter manufactur­
ing establishment in the exporting state would
overstate the value added that actually occurred
in that state.8 The value of export shipments in­
cludes value added in both states.
A state’s value of export shipments will exceed
its export value added if its exporting firms rely
heavily on inputs produced elsewhere or if its
TThe value added approach is one of three standard
methods for calculating the market value of production.
The other methods focus on income and expenditures.
The income approach sums the incomes derived from
economic activity, which are primarily wage, profit and in­
terest incomes from employment of labor and capital
resources. The value added in an establishment is the in­
come generated by the establishment’s activity. The ex­
penditure approach sums four general categories of spen­
ding on goods and services: consumption, investment,
government and net exports (that is, exports minus im­
ports). The income and expenditure approaches are used
more extensively in the United States than the value add­
ed approach. In the European Community, however, the
value added approach is used extensively in the ad­
ministration of taxes.




firms produce relatively few inputs used by ex­
porting firms in other states. On the other hand,
the value of export shipments from a state will
fall short of its export value added if its export­
ing firms produce more inputs that are used by
exporting firms in other states than its firms
purchase from elsewhere. Export value added
and the value of export shipments will only be
the same if the value of shipments used to pro­
duce exports in other states exactly offsets the
value of inputs from other states that are used
to produce exports. Overall, a state’s value of
export shipments may overstate, understate or
equal the value added that actually occurred in
the state. The empirical importance of this dif­
ference is examined below.

O rigin o f M o vem en t o f
C o m m o d ities
Prompted by a request from the transporta­
tion industry, a second export series, the OMC,
began in 1987. The goal of this series is to iden­
tify where merchandise begins its export jour­
ney so that it can be tracked to its port. In the
case of a manufactured good, the so-called “point
of origin” does not require that the location of
production of all component parts be identified,
but rather where a completed manufactured
good began its export journey. According to the
instructions that accompany the Shipper’s Ex­
port Declaration, the point of origin could be
any of the following: 1) the state in which the
merchandise actually began its journey to the
port of export (indicated by the two-digit U.S.
Postal Service abbreviation); 2) the state of
origin of the commodity with the greatest share
of value in a bundle of exports; or 3) the state
of consolidation (the state where goods are con­
solidated by an intermediary for overseas ship­
ment). In practice, the ports from which goods
are shipped overseas are frequently used to iden­
tify the point of origin. This discretion in identi8Although such intermediate products are identified by the
state of production as “ supporting exports” in the EME,
the state from which they are ultimately exported is not in­
dicated. In addition, adding a state’s supporting exports to
its final shipments would result in some “ double counting”
of exports and overstate the value added associated with
manufactured exports. Note that the national value of ex­
port shipments is a theoretically appropriate measure of
value added because the sum of export shipments across
all establishments does measure the market value of these
manufactured exports. At the national level, there is no
double counting. The shipments of intermediate inputs used
for the exports are already included in the value of export
shipments and are not added again in the calculation of
manufactured exports.

JULY/AUGUST 1991

68

fying the point of origin reflects the fact that
determining the location of production is not a
primary objective of this data series.9
Origin of movement totals are determined by
sorting Shipper's Export Declarations by the
state where a commodity became an export. A
problem, however, is that Declarations for many
shipments contain no point of origin. For exam­
ple, in 1987 about 25 percent of the 9.7 million
Declarations for shipments contained no state
code. To make the data more useful, the Census
Bureau contracted with the Massachusetts In­
stitute for Social and Economic Research to de­
velop estimates for the origin of shipments lack­
ing state codes.10 This expanded series is used
in the following discussion and is referred to as
the OMC series.
This newer series has some desirable charac­
teristics relative to the export data provided di­
rectly by manufacturing establishments in the
EME, although the older series is generally
viewed as the more reliable of the two series.11
One attractive feature of the OMC is that the
data are available with a lag of months rather
than years. In addition to manufactured ex­
ports, shipments data on nonmanufactured m er­
chandise exports are provided. The initial for­
eign destination of these goods is provided as
well. Consequently, information about state-tocountry export flows is available for the first
time. Like the EME, however, this series does
not approximate the extent of value added in a
state resulting from manufactured exports.
A C O M PAR ISO N OF THE
T W O SERIES

While there are several reasons why the two
export series might differ, it is possible that the
actual differences are small enough to allow the
data to be used interchangeably. No comparison
of the two series has been possible previously
because 1987 was the first year for which the
9Smith (1989, 1990) notes that identifying the production
locations of exported goods is especially difficult for
agricultural and mined commodities. Small shipments of
these commodities are often combined at storage facilities
prior to reaching their port of embarkation. Shippers tend
to report either the state of consolidation or the port as the
state of origin.
10Details on the methods to generate these estimates can
be found in Lerch (1990).


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data in the OMC were available and the 1987
EME was just released in April 1991. First, we
compare each state's level of exports as in­
dicated in the EME and the OMC. Next, we in­
vestigate whether a state’s rank differs between
the two measures. To complete the analysis, a
particular facet of the linear relationship be­
tween the two export series is examined. See
appendix A for details on the three methods
used to compare the two 1987 series, as well as
the two 1986 export series discussed later. If
the two series are closely related, then the OMC
data, which are available after a considerably
shorter lag, could be used in place of the EME
data.

C om parin g Levels: 1 98 7 E xport
S eries
Table 1 shows the value of 1987 manufac­
tured exports according to the two series for
the 51 "states” (50 states and the District of Co­
lumbia) and the total of the states. One reason
the two series differ is that the data in the OMC
include transportation costs and wholesale m ar­
gins, while the data in the EME are the value of
exports at the producing plant. This accounts
for the bulk of the $22.3 billion excess of the
OMC state total over the EME state total in
table l.12
If these items were the only source of dif­
ference, the export value in the OMC for each
state would be higher than the value in the
EME. Also, the difference would be greater for
those states farthest from major ports or a for­
eign border, reflecting the higher transportation
costs. Table 1 shows that exports according to
the OMC are higher than the level according to
the EME in just 20 of the 51 states. This is in
sharp contrast to the expectation that the OMC
measure should be higher based on differences
in its coverage and on the difference in the state
totals. This discrepancy occurs primarily because
of the OMC’s focus on where merchandise began
its export journey. Since this location is often
"S e e Farrell and Radspieler (1990) and Little (1990).
,2Appendix B of the Exports from Manufacturing
Establishments: 1987 shows the difference between the
value of exports at the port of export and the estimated
plant value to be $28 billion.




69

T a b le 1

Manufactured Exports by State for 1987
1987 E xports'
EME
OMC

State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Total

Differences
Levels2
Percentage3

Rank
EME

OMC

$2,138.6
889.6
2,086.0
1,353.0
22,996.1
1,818.4
4,741.1
519.7
100.7
4,803.0
3,561.1
175.3
765.8
8,687.8
5,001.1
2,552.6
1,858.2
2,906.4
3,408.7
779.7
1,927.9
6,347.9
12,412.0
4,733.3
1,712.1
5,148.6
135.7
768.7
170.3
1,160.4
3,982.5
206.0
11,824.0
5,670.6
222.6
13,041.1
1,355.4
2,121.1
6,717.3
691.9
3,234.7
270.3
3,567.2
14,046.3
589.1
598.6
3,656.6
10,841.7
1,126.2
4,108.5
39.5

$1,896.8
1,516.0
2,772.7
636.1
30,448.7
1,623.4
3,096.9
842.0
265.1
9,602.8
3,380.1
153.5
462.0
8,471.5
4,102.9
1,756.3
1,448.2
1,930.9
5,865.2
636.1
1,881.1
8,093.9
17,618.2
3,850.1
1,122.3
2,851.0
167.0
693.5
356.8
835.0
6,347.6
148.9
17,614.8
4,898.0
217.4
8,991.1
991.3
2,294.6
5,734.5
408.5
2,159.6
56.0
2,309.3
22,662.3
757.0
704.3
4,750.8
11,793.9
920.0
3,500.4
227.2

$ -2 4 1 .8
626.4
686.7
-7 1 7 .0
7,452.6
-1 9 5 .0
- 1,644.2
322.3
164.4
4,799.8
-1 8 1 .0
-2 1 .8
-3 0 3 .8
-2 1 6 .3
-8 9 8 .2
- 796.3
-410.1
-9 7 5 .5
2,456.5
-1 4 3 .6
-4 6 .8
1,746.0
5,206.2
- 883.2
-5 8 9 .8
-2,297.6
31.3
-7 5 .2
186.5
-3 2 5 .4
2,365.1
-57 .1
5,790.8
- 772.6
- 5 .2
-4,0 5 0 .0
-364.1
173.5
-9 8 2 .8
-2 8 3 .4
-1,075.1
-2 1 4 .3
- 1,257.9
8,616.0
167.9
105.7
1,094.2
952.2
- 206.2
-608.1
187.7

-1 1 .3 %
70.4
32.9
-5 3 .0
32.4
-1 0 .7
-3 4 .7
62.0
163.3
99.9
-5 .1
-1 2 .4
-3 9 .7
- 2 .5
-1 8 .0
-3 1 .2
-22.1
-3 3 .6
72.1
-1 8 .4
- 2 .4
27.5
41.9
-1 8 .7
-3 4 .4
-4 4 .6
23.0
- 9 .8
109.5
-2 8 .0
59.4
-2 7 .7
49.0
-1 3 .6
- 2 .3
-3 1 .1
-2 6 .9
8.2
- 14.6
-4 1 .0
-3 3 .2
-7 9 .3
-3 5 .3
61.3
28.5
17.7
29.9
8.8
-1 8 .3
- 14.8
475.2

25
36
27
33
1
30
14
43
50
13
20
47
39
7
12
24
29
23
21
37
28
9
4
15
31
11
49
38
48
34
17
46
5
10
45
3
32
26
8
40
22
44
19
2
42
41
18
6
35
16
51

26
30
21
41
1
29
19
35
45
6
18
49
42
8
15
28
31
25
11
40
27
9
3
16
32
20
48
39
44
36
10
50
4
13
47
7
33
23
12
43
24
51
22
2
37
38
14
5
34
17
46

$193,571.0

$215,863.5

$22,292.5

11.5

—

—

Sources: EME\ U.S. Department of Commerce, Bureau of the Census, Exports from Manufacturing
Establishments: 1987 (GPO, 1991). OMC: Massachusetts Institute for Social and Economic
Research, University of Massachusetts, "U.S. Exports by State of Origin of Movement,”
data tape (1990).
’ Millions of dollars.
2OMC value minus EME value.
H(OMC - EME)!EME)'\ 00.

JULY/AUGUST 1991

70

identified as a port, the OMC estimates of ex­
ports are more concentrated in states that con­
tain, or are near, major ports. Also, in some
states where transportation costs might be ex­
pected to be relatively high, such as Nebraska
and Kansas, the export value in the OMC is
lower than the value in the EME, again con­
trary to the expectations based on transporta­
tion costs alone.
The importance of ports in the OMC data is
further illustrated in figure 1, which plots the
level of exports in each series. If each state's ex­
ports were identical in both series, all points
would fall on the line labeled "line of equality.”
The points below the line of equality indicate
that states’ exports in the OMC often are lower
than reported in the EME. In seven of the states
labeled in figure 1— California (CA), Florida (FL),
Louisiana (LA), Michigan (MI), New Jersey (NJ),
New York (NY) and Texas (TX)—the value using
the OMC is much higher than the value using
the EME. In these states, total exports using the
form er measure exceed exports using the latter
measure by almost $37 billion. This pattern is
consistent with the fact that data in the EME in­
dicate the value of exports shipped from a state’s
manufacturers, while the data in the OMC are
more likely to indicate the value of exports ship­
ped from the state of consolidation or the port.
Therefore, using the value in the OMC as a
measure of a state’s export activity can be
misleading.
As table 1 shows, the percentage differences
are also considerable for many states. For exam­
ple, the value of exports from Wyoming mea­
sured in the OMC is nearly six times higher than
that reported in the EME, while the OMC esti­
mate for South Dakota is 79.3 percent lower.
On average, the absolute value of the difference
for a state is 44 percent; excluding Wyoming
reduces the average difference to 35.3 percent.
The overall correspondence between the dol­
lar levels of the two series is highlighted by cal­
culating the simple correlation between them.
This measure ranges from negative one to posi­
tive one and equals one when the two measures
are perfectly correlated. In the present case, the
correlation of .95 is high. Thus, when a state’s
OMC export value is higher than the average
OMC export value using all states, the state’s
EME export value also tends to be higher than
the average EME export value.
Figure 1 shows this general correspondence
by plotting the states’ 1987 exports as indicated

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by the two series. Most observations cluster
around the line of equality. Still, the substantial
difference between the two series for several
states indicates that the two measures are not
identical.

C om parin g R an ks: 1 987 E x po rt
S eries
Another useful comparison between the two
series involves ranking the states to see if states
with larger (smaller) export values using one
measure also have larger (smaller) export values
using the other measure. Ranks are often used
as a summary measure of a state’s relative ex­
port performance. If each state had the same
or, at least, a similar rank among the 51 states
using either series, then the more current data
in the OMC could be used to rank the states in
a more recent year.
In view of the high simple correlation between
the two series, it is no surprise that table 1 indi­
cates a general similarity between a state’s ex­
port ranks using the two measures. California
and Texas, for instance, rank first and second
in both series. This general impression is cor­
roborated by the calculation of a Spearman rank
correlation that allows for pairwise comparisons
of the alternative proxies. This coefficient
ranges from negative one to positive one; it
equals one when measures yield identical rank­
ings and minus one when the rankings are iden­
tically inversely related. The correlation be­
tween the two series’ export ranks is .96, which
is very close to one.
Although this high correlation indicates a
close overall correspondence between the rank­
ings according to the two series, policymakers
or researchers who rely on the more current
ranking available from the OMC as an indicator
of the relative scope of export activity in a
specific state can easily be misled. The ranking
of each state in the OMC is not identical to the
more reliable ranking in the EME. Florida and
Louisiana, for example, rank considerably
higher according to the OMC, due to the major
ports in those states from which a large volume
of merchandise is shipped. Missouri, on the
other hand, ranks only 20th using the more
current OMC measure, but is 11th according to
the EME.

71

Figure 1

A Graphical Comparison of Two
State Export Series: 1987
Billions of Dollars

\
\

s '

✓
Actual Linear
/
*
Association y
TX
•
✓

\

/

/

/

/

Line o f
Equality

\

FL
•

l a . n j ✓/ /

NY Ml /
• • /
/
/
/
>

/

/

•

OH

/

&

•V*
0

••

5

10

15

20

25

Exports from Manufacturing Establishments (EME)

A C loser L o o k a t th e L in ear
A sso cia tio n B etw een th e 1987
E x po rt S eries
A more rigorous criterion to assess the inter­
changeability of the two measures reveals a
substantial difference between the two series.
This criterion, termed difference preservation,
requires that the two export series differ by no
more than some constant across states. If this



criterion is met, one export series could be
reliably used as an index for the other.
If the OMC data preserved the difference in
the EME data, the association between the two
series could be illustrated by a line indicating
equality of a state’s exports, give or take some
constant. In figure 1, such a line would be par­
allel to the line of equality. This is not the case,
however. The dashed line, based on the actual
linear association between the two series, is
clearly not parallel to the line of equality. Con­
JULY/AUGUST 1991

72

sequently, one measure is not interchangeable
for the other. This means that researchers and
other users of state export data in statistical
studies should not use one measure as a proxy
for the other because the results can vary de­
pending upon which measure is used. In prac­
tice, this finding applies to the use of the more
timely OMC-based measure as a proxy for the
EME-based measure.

A NEW STATE EXPORT MEASURE
RASED ON VALUE ADDED
Existing state export series indicate the value
of export shipments rather than export value
added. As such, they reflect both the value add­
ed in a state’s factories as well as the value add­
ed embodied in intermediate goods which may
have been produced in other states. For exam­
ple, an airplane assembled and exported from
the state of Washington may have components
manufactured in California and Texas. Conse­
quently, these series fail to identify the true
amount of state economic activity used to pro­
duce manufactured exports.
To address this problem, we estimate a mea­
sure of each state’s value added associated with
manufactured exports. In conjunction with the
EME, the Census Bureau provides data for each
state regarding the number of manufacturing
workers producing manufactured exports in
each industry as well as the number of nonmanufacturing workers in jobs related to the
production of manufactured exports. In fact, ap­
proximately the same number of nonmanufac­
turing jobs as manufacturing jobs are related to
manufactured exports. This reflects the fact
that manufacturing requires the productive ef­
forts of workers (such as lawyers, accountants
and transportation and communication workers)
from various nonmanufacturing industries.
Unlike the value of export shipments, the
level of export-related employment is directly
related to the value added of exports in a state;
such employees directly generate the value add­
ed. This employment information is used to esti­
13This is consistent with the Washington State input-output
model for 1982 (Bourque, 1987). For example, in
W ashington’s largest export sector, aerospace, inputs from
other states equal 56.2 percent of the sector’s shipments.


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Federal Reserve Bank of St. Louis

mate state export value added. The estimates
are based on the assumption that the productiv­
ity (output) of each export-related employee is no
different than the average w orker’s productivity
in that industry and state. Consequently, export
value added in a state is equal to the sum over
all industries of the num ber of export-related
employees in a state multiplied by their
productivity.
One data series necessary for such estimates,
gross state product—the market value of the
goods and services produced within a state dur­
ing a year—is not currently available for 1987.
Since this precludes calculating export value
added for 1987, our measure of exports is
estimated for 1986. Appendix B provides a
detailed discussion of the methodology used in
estimating state export value added.

C om parin g E x po rt Value A d d e d Fs.
EME
Figure 2 and table 2 compare the value of the
newly constructed series of manufactured ex­
ports, export value added, with the value of
state export shipments from the EME. Sum­
ming over all states, the export value added total
($196,656.2 million) exceeds the total in the EME
($159,374.5 million) by $37,281.7 million. Threefourths of this difference is due to transporta­
tion costs and trade margins that are included
in our calculations, but are not in the EME
total.
The differences between the two measures at
the level of individual states, however, reflect
much more than transportation costs and trade
margins. Rather, they reflect the fundamental
distinction between value added and value of
shipments accounts. The state of Washington is
especially noteworthy as the level of export
value added is approximately one-half the level
of exports in the EME. This suggests that manu­
facturing export shipments from Washington
contain a large percentage of intermediate in­
puts produced elsewhere.13 Using the export
shipments value as a measure of this state's ex­
port activity is clearly misleading.

73

Figure 2
A Graphical Comparison of Two
State Export Series: 1986
30

Billions of Dollars
CA
•

Actual Linear
Association /

/
s

/
T3

20

*

a
>
T3
■
a
(0

a)
D
n
>
ro

NY
•
/

a
x

/

in

/

/

/ '•TX
OH /

/

/

*

✓

/

s

/

Line of
Equality

/ •

10

NJ

PA* /

/

/

• WA

5

10

15

20

Exports from Manufacturing Establishments (EME)

Using export shipments values also can cause
inaccurate inferences in terms of understating a
state’s export activity. For example, the export
value added in 12 states exceeds the values in
the EME by more than 50 percent. Wyoming,
with an export value added that is more than
nine times its E/WE-based export value, is by far
the most extreme example. The primary reason
is that firms in Wyoming process large quanti­
ties of oil and coal that are shipped to other
states for use in manufactured exports.
While Wyoming is a small exporter regardless
of the measure used, the large percentage dif­
ferences are not restricted to relatively small ex­



porters. California, the nation’s leading exporter
by both measures, is estimated to export 53.5
percent more on the basis of value added than
it does on the basis of shipment data. Thus,
California firms are supplying large amounts of
goods and services ultimately exported in the
form of manufactured exports from other states.
These differences for many states between
export value added and the EME-based measure
of state exports raise the issue of the general
association across all states between the mea­
sures. As was done above, the ranking of states'
export value added was compared with the
ranking of exports reported in the EME to
JULY/AUGUST 1991

74

T a b le 2

Manufactured Exports by State for 1986______________
1986 E xports'
Value
EME
Added

State

Levels2

Rank

Percentage3

EME

Value
Added

29
38
27
33
1
30
13
42
50
16
20
45
40
7
11
24
26
23
18
36
28
9
3
14
31
12
49
37
48
35
15
47
6
10
44
4
32
25
8
41
22
46
19
2
39
43
21
5
34
17
51

22
43
24
33
1
28
13
39
49
14
19
51
42
6
11
29
31
26
23
38
27
9
5
15
32
17
48
37
47
34
8
44
2
10
45
4
30
25
7
40
21
46
20
3
36
41
18
12
35
16
50

-

—

$1,684.9
712.9
1,755.8
1,065.4
17,216.4
1,477.7
3,996.4
429.5
91.0
3,372.6
2,826.7
214.3
502.6
7,209.2
4,787.4
1,932.4
1,835.0
1,939.8
3,020.3
800.6
1,740.5
5,513.8
10,878.0
3,691.9
1,337.1
4,267.9
101.2
753.3
167.1
892.6
3,548.1
177.7
9,412.4
5,260.8
214.7
10,653.0
1,084.6
1,862.7
6,026.6
481.9
2,398.0
212.7
2,910.4
10,981.5
668.5
384.0
2,704.0
9,862.8
983.2
3,313.5
19.1

$2,427.1
545.8
2,283.7
1,299.2
26,421.4
2,087.0
4,968.0
794.5
194.3
4,966.2
3,685.0
160.8
548.1
10,107.3
5,352.8
1,893.0
1,690.3
2,191.2
2,374.9
799.6
2,162.7
7,139.8
10,273.9
4,327.7
1,401.7
3,896.3
232.8
808.0
243.5
1,193.0
7,248.5
335.4
15,660.5
5,916.7
289.0
11,561.7
1,819.6
2,264.5
9,373.2
748.7
2,451.8
250.8
3,212.1
13,195.1
944.0
651.3
3,701.8
5,176.1
1,049.1
4,163.6
173.2

$742.2
-167.1
527.9
233.8
9,205.0
609.3
971.6
365.0
103.3
1,594.0
858.3
-5 3 .5
45.4
2,898.0
565.4
-3 9 .4
-1 4 4 .7
251.4
-6 4 5 .4
-1 .1
422.2
1,626.0
-604.1
635.8
64.5
- 371.6
131.6
54.7
76.4
300.4
3,700.0
157.7
6,248.0
655.9
74.3
908.7
735.0
401.8
3,347.0
266.8
53.8
38.1
301.7
2,214.0
275.5
267.3
997.8
-4,6 8 7 .0
65.9
850.1
154.1

44.1%
-2 3 .4
30.1
21.9
53.5
41.2
24.3
85.0
113.5
47.3
30.4
-2 5 .0
9.0
40.2
11.8
- 2 .0
- 7 .9
13.0
-2 1 .4
-0 .1
24.3
29.5
- 5 .6
17.2
4.8
- 8 .7
130.1
7.3
45.7
33.7
104.3
88.7
66.4
12.5
34.6
8.5
67.8
21.6
55.5
55.4
2.2
17.9
10.4
20.2
41.2
69.6
36.9
-4 7 .5
6.7
25.7
806.9

$159,374.5

$196,656.2

$37,281.7

23.4

Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Total

Differences

Sources: EME: U.S. Department of Commerce, Bureau of the Census, Exports from Manufacturing
Establishments: 1986 (GPO, 1989). Export Value Added: Authors' calculations.
'Millions of dollars.
2Export Value Added minus EME value.
3((Export Value Added - EME)IEME)100.


FEDERAL RESERVE BANK OF ST. LOUIS


75

determine whether states with larger (smaller)
export values using one measure in 1986 also
had larger (smaller) export values using the
other measure in the same year. The two
measures yield a general similarity between a
state's export ranks. The Spearman rank cor­
relation is .98, which is virtually one.
The ranks of a number of states, however,
differ substantially across the two measures.
Nine states have ranks that differ by five places
or more. The largest changes involve Washing­
ton, which drops from fifth place using export
shipments to twelfth place using export value
added, and New Jersey and Alabama, both of
which moved up seven places (New Jersey from
15 to 8 and Alabama from 29 to 22) when using
value added.
The overall correspondence between these two
measures is also indicated by the simple correla­
tion between the two measures. The simple cor­
relation is .95. This close correspondence is evi­
dent when the two series for each state are
plotted as in figure 2. Many observations cluster
around the line of equality reflecting the linear
association between the two measures.
This strong association, however, does not
mean that the two measures are interchange­
able. In terms of figure 2, the actual linear asso­
ciation (identified by the dashed line) varies sig­
nificantly in a statistical sense from the line of
equality.14 Consequently, one measure is not a
reliable proxy for the other when used in
statistical studies.

CONCLUSION
Despite some improvement in available infor­
mation on state export activity in recent years,
the two existing state export series are deficient
in several ways. Their most important limitation
is that they both measure the value of shipments
and not the extent of a state’s economic activity
(value added) related to manufactured exports.
Nonetheless, as general indicators of export ac­
tivity across all states, the two measures provide
similar information. Despite this overall similari­
ty, the two series can lead to substantially dif­
ferent conclusions when used for some states.
Furthermore, the OMC series, which is available
on a more timely basis, is not a satisfactory

proxy for the more accurate EME data on ex­
port shipments according to the criterion used
in this article.
The estimate of a state’s export value added
generated in this article is inherently superior
to the existing measures available for assessing
state export performance. This new measure
can produce different conclusions than shipments-based data when used for some states or
when used in statistical studies. Consequently,
users should reconsider their use of the existing
export series when they desire an accurate mea­
sure of a state’s value added related to manufac­
tured exports.
The evidence presented here on export value
added and its deviation from the EME-based ex­
port shipments measures is for one year only,
however. The behavior of this discrepancy over
time is unknown. This reinforces the importance
of developing historical data on state export
value added for analyses involving state export
activity.

REFERENCES
Bourque, Philip J. The Washington State Input-Output Study
for 1982, Graduate School of Business Administration,
University of Washington (March 1987).
Coughlin, Cletus C., and Phillip A. Cartwright. “An Examina­
tion of State Foreign Exports and Manufacturing Employ­
ment,” Economic Development Quarterly (No. 3, 1987), pp.
257-67.
Farrell, Michael G., and Anthony Radspieler. “ Origin of
Merchandise Exports Data,” unpublished manuscript,
Foreign Trade Division, Census Bureau, U.S. Department of
Commerce (March 20, 1990).
Jackson, John D., and James A. Dunlevy. “ The Interchange­
ability of Alternative Measures of Permanent Income in
Regression Analysis,” Applied Economics (October 1982),
pp. 455-68.
_______ . “ The Orthogonal Least Squares Slope Estimator:
Interval Estimation and Hypothesis Testing Under the
Assumption of Bivariate Normality,” Proceedings of the
Business and Economic Statistics Section of the American
Statistical Association (1981), pp. 294-97.
Lerch, Stephen C. ‘‘State of Origin Export Data: An Interpre­
tive View,” unpublished manuscript, Office of Financial
Management, State of Washington (1990).
Little, Jane Sneddon. “ New England’s Links to the World
Economy,” Federal Reserve Bank of Boston New England
Economic Review (November/December 1990), pp. 33-50.
Malinvaud, Edmond. Statistical Methods of Econometrics
(North-Holland, 1980).

14The dashed line indicates that a state’s value added tends
to be higher than its shipments. In particular, each dollar
increase in state export shipments in 1986 was associated
with a $1.36 rise in export value added, on average.




JULY/AUGUST 1991

76

Ott, Mack. "Have U.S. Exports Been Larger Than
Reported?” this Review (September/October 1988),
pp. 3-23.
Smith, Tim R. “ Regional Export Growth: Lessons from the
State-Level Foreign Trade Data,” Regional Science Perspec­
tives (No. 1, 1990), pp. 21-38.
_______ . “ Regional Exports of Manufactured Products,”
Federal Reserve Bank of Kansas City Economic Review
(January 1989), pp. 21-31.
U.S. Department of Commerce, Bureau of the Census.
Exports from Manufacturing Establishments: 1985 and 1986
(GPO, January 1989).

________Exports from Manufacturing Establishments: 1987
(GPO, February 1991).
________“ State Offices That Provide Export Assistance,”
Business America (No. 2, 1991) pp. 24-25.
U.S. Office of Management and Budget. Standard Industrial
Classification Manual 1987.
Webster, Elaine, Edward J. Mathis, and Charles E. Zech.
“ The Case for State-Level Export Promotion Assistance: A
Comparison of Foreign and Domestic Export Employment
Multipliers,” Economic Development Quarterly (August
1990), pp. 203-10.

Appendix A
Interchangeability of Alternative Measures
The existence of alternative export measures
raises the issue of the extent to which the
measures are interchangeable. In other words,
do these measures provide virtually identical in­
formation about state export performance? Dif­
ferent criteria exist for assessing this issue.
Three criteria are used here: 1) rank correla­
tion criterion; 2) simple correlation criterion;
and 3) orthogonal regression criterion. The rank
correlation criterion focuses on the degree to
which measures have identical rankings for cor­
responding observations. As a first step, states
(including the District of Columbia) are ranked
from 1 (the state with the largest value of ex­
ports) to 51 (the state with the smallest value of
exports) for each export measure. To make pair­
wise comparisons of the rank-order, a Spearman
rank correlation coefficient, Rs is calculated as
follows:
(1) Rs = 1 - [6/N(N2- 1)] I (Oj-Ai)2,
where i is a subscript denoting specific states
(i = l, 2,. . ., 51); 0j is the rank of the ith state
using one measure; Aj is the rank of the ith
state using the alternative measure; and N is the
sample size.
If the rank-orders of the two measures are
identical, then Rs= l. For example, using one
measure and assuming that three states—A, B

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and C—had export values of 300, 200 and 100,
the states would be ranked 1, 2 and 3. If, using
the other measure, states A, B and C had export
values of 5, 4 and 1, then an ordering of the
states based on the two export measures would
be identical and the Spearman rank correlation
coefficient is one. A rank-order of zero means
that the rank-orders of the two measures are
not related. A rank-order of minus one means
that the rank-orders are the reverse of each
other. Thus, the closer Rs is to one, the more
similar the rank-orders and the more inter­
changeable the measures for ranking purposes.
As indicated in the text, the Spearman rank
correlation coefficient was approximately one
for the comparison of the 1987 export measures
in EME and OMC (.96) and the 1986 measures
in EME and our export value added (.98). Conse­
quently, the measures provide highly correlated
rankings. These results suggest that, as a sum­
mary indicator of states' relative export perfor­
mance, the measures provide roughly identical
information. Satisfying this criterion, however,
does not preclude large differences in a specific
state’s rank across the measures. For example,
recall that the state of Washington dropped
from fifth place using the shipments-based data
in the EME for 1986 to 12th place using our ex­
port value added measure.
A stronger condition than rank correlation in­
volves the simple correlation of the levels of the

77

alternative measures. The simple correlation
coefficient provides information concerning the
extent of a linear relationship between the alter­
native measures. The simple correlation coeffi­
cient is calculated as follows:
(2) r = Z (x-x)(y-y)/V (2 (x -x )2 X(y-y)2),
where x and y are the sample means of the
alternative export series, x and y.
For any given state, if the value of exports
using one measure exceeds the mean of this
measure based on all states by a certain amount
and the value of exports using the other measure
also exceeds (or falls below) its series mean by a
set amount, then a perfect linear correlation
exists. The value of the correlation coefficient
will equal one (or minus one in the event of a
negative relationship). For example, as in the
numerical example above, assume states A, B
and C had export values of 300, 200 and 100
using one measure. Using the other measure,
assume states A, B and C had export values of
450, 250 and 50. Thus, A’s exports exceed the
first series mean of 200 by 100 using one
measure, and exceeds the second series mean of
250 by 200 using the other measure. For a per­
fect linear correlation, C's exports (which are
100 less than the first series mean) must be 200
less than the second series mean (that is, equal
50). Since this is the case, the correlation equals
one. A correlation coefficient of zero means
that no linear relationship between the
measures exists.
As indicated in the text, the linear relationship
between both sets of measures is strong. For
the 1987 measures in EME and OMC, the cor­
relation coefficient is .95. For the 1986 measures
in EME and of export value added, the correla­
tion coefficient is also .95. Since these coeffi­
cients are virtually one, the measures can be
viewed as interchangeable using this criterion.
When using a more stringent criterion, how­
ever, this is not the case. This criterion for inter­
changeability requires that the measures are not
only highly correlated, but that they consistently
differ by a constant, possibly zero. Once again,
11n contrast to simple regression, the fitted line in or­
thogonal regression is the one that minimizes the mean
square of the perpendicular (rather than the vertical) devia­
tion of the sample points from the fitted line. See Malinvaud
(1980), for a thorough discussion of the differences between
orthogonal and simple regression.




assume states A, B and C had export values of
300, 200 and 100 using one measure. Using the
other measure, assume state A had exports of
350. For “difference preservation,” states B and
C's exports must be 250 and 150. In this case,
the two measures differ by 50 for each state.
This difference preservation is known as an or­
thogonal regression criterion.1
Jackson and Dunlevy (1982) illustrate this
criterion, in a time-series context, by a simple
example of estimating a consumption function
with different perm anent income measures.
Assume perfect correlation between two income
measures yj and y2, so that:
(3) y, = a + by2,
where a is the intercept and b is the slope.
Suppose the following consumption function is
estimated:
(4) c = d + eyi + z,
where d is the intercept, e is the slope and z is
the random disturbance term. The slope is called
the marginal propensity to consume and is the
change in consumption associated with each $1
change in income. If y2 is used rather than y,,
however, the consumption function becomes:
(5) c = (d + ea) + eby2 + z.
The two measures of income will yield the same
estimate of the marginal propensity to consume
only if b equals one.
Using orthogonal regression, we generated
estimates of b for the alternative export mea­
sures discussed in the text. Specifically, we
estimated two equations similar to equation 3.
In one regression, the 1987 measures of state
exports in OMC and EME were used as y and
y , respectively. In the other regression, the
1986 measures of state exports based on our
calculations of export value added and in EME
were used as y, and y2, respectively.
The orthogonal regression criterion is not
satisfied by alternative export measures. For the
1987 measures in EME and OMC, the or-

78

thogonal least squares estimate of b equals 1.37.
A t-statistic can be used to test the null hypoth­
esis that b equals one.2 The critical t-value for a
5 percent significance level with 49 degrees of
freedom is 2.01, which is far below the actual
t-value of 7.03. Consequently, the null hypothesis
is rejected. Similarly, the 1986 measures in EME
and of export value added produce an ortho­
gonal least squares estimate of b (1.36), which
yields a rejection of the null hypothesis that b
equals one; the critical t-value of 2.01 at the
2Because of random variation in the data it is unlikely that
b exactly equals one. Therefore, the t-statistic is used to
test whether we can reasonably infer that the estimated
value of b equals one. See Jackson and Dunlevy (1981)
for additional details on hypothesis tests involving the or­
thogonal least squares slope estimator.
3A related issue involves whether the two measures are
consistently proportional to one another, that is, whether
they tend to differ by a given percentage. This is investi­
gated by testing whether the orthogonal least square slope
estimator between the logarithms of the two measures
significantly differs from one. Using the logarithms of the
1987 measures in EME and OMC, the slope estimate
equals 1.01. The associated t-statistic is 0.109, which is
less than the critical t-value of 2.01 (5 percent significance
level). Consequently, the null hypothesis that the slope

5 percent level of significance is far less than
the actual t-value of 6.35.3
The implication of this analysis is that the
levels of alternative export measures are not in­
terchangeable using this criterion and their use
would generate different regression results in
otherwise identical estimations. Specifically, the
coefficient estimates for the impact of a change
in state exports on some variable of interest,
say state economic growth, would differ depen­
ding on the measure used.4
equals unity cannot be rejected. These results suggest
that the logarithmic forms of the two 1987 export
measures in EME and OMC are interchangeable. On the
other hand, the logarithmic forms of the 1986 measures in
EME and of export value added yield an orthogonal least
squares slope estimate of 0.892. Because the associated tstatistic of 2.95 exceeds the 2.01 critical value, the null
hypothesis is rejected, suggesting that the two measures
of 1987 exports are not interchangeable.
4See Coughlin and Cartwright (1987) for an empirical ex­
amination of the effect of manufacturing exports on
employment for individual states. This is an example of a
study where the regression results could be altered by us­
ing different export series.

Appendix B
Estimating Value Added Related to Manufactured
Exports by State
In this appendix, we identify the data and
methodology used to calculate the value added
related to manufactured exports by state. We
begin by identifying the variables used in the
calculations and the data sources.
Various employment, shipments and gross state
product data are essential for our calculations.
Manufacturing employment (ME), export-related
manufacturing employment (XME) and exportrelated nonmanufacturing employment (XNME)
for each state are published in Exports from
Manufacturing Establishments: 1985 and 1986,
1For a detailed explanation of how these data were
developed, see this publication’s Introduction and Appen­
dix C.


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U.S. Department of Commerce (1989).1 ME is
reported by respondents in the U.S. Census
Bureau’s Annual Survey of Manufactures, while
XME and XNME are calculated by the Census
Bureau.
Three other series are used. The first is un­
published data from the U.S. Department of
Commerce (1991) on nonmanufacturing employ­
ment (NME). The second data series, which is
published in Exports from Manufacturing
Establishments: 1985 and 1986, is total state
employment. The third data series, which is
published by the U.S. Department of Commerce
(1989), is gross state product (GSP). GSP is the

79

market value of the goods and services pro­
duced within a state during a year and is the
state analog of U.S. gross domestic product. GSP
data for individual manufacturing and nonmanufacturing industries were used.

M eth od olo gy: C alculating E x po rt
Value A d d ed
To calculate total value added related to
manufactured exports in state s (XTVS), we
summed estimates of value added within the
state's manufacturing sector (XMVS) and value
added in nonmanufacturing sectors related to
the export of manufactured goods (XNMVJ.
That is,
(1) XTVS = XMVS + XNMVS.
Because identical data were not available for
each manufacturing sector, the components of
XMVS were calculated in one of two ways.2 For
industries in which export-related data are
published, XMVS was estimated by applying the
following equation:
(2) XMVsi = (GSPsi/MEsi)(XMEsi).
As defined above, GSP is gross state product,
ME is manufacturing employment and XME is
export-related manufacturing employment. The
subscript i designates the different SIC manufac­
turing industry groups. In our calculations, we
used the two-digit manufacturing industry
group so i ranged from SIC 20 to SIC 39. This
method implicitly assumes, for each industry,
that output per worker in the production of ex­
port goods (XMVsi/XMEsi) is the same as output
per worker in the production of all goods
(GSPsi/MEsi). Equation 2 was applied using data
for individual industries rather than for total
manufacturing, because this assumption is more
plausible for each industry than for manufac­
turing as a whole.
For those manufacturing sectors with no pub­
lished export-related employment at the two­
2ln some states, export-related manufacturing employment
data were not published for certain industries either to
avoid disclosing data for individual companies or because
the estimate did not meet publication standards. Summing
over all states, unpublished export-related manufacturing
employment accounts for 1.8 percent of total 1986 exportrelated manufacturing employment.




digit industry level, XMVS was estimated using
the following equation:
(3) XMVsm = (GSPsm/MEsm)(XMEsm),
where the m subscript refers to the total of
those sectors not reported. For example, to
compute a state’s total unreported export-related manufacturing employment (XMEsm), we
simply subtracted the amount reported from
the total export-related manufacturing employ­
ment. Consequently, XMVS is the sum of the
estimates for the reported industries (XMVsj)
plus the single estimate for the missing in­
dustries (XMVsm).
To compute a state’s value added in its non­
manufacturing sectors related to manufactured
exports (XNMVS), we first estimated the follow­
ing measure for each of a state's four nonmanu­
facturing industries [where j = 1, trade; j = 2,
business services; j = 3, transportation, com­
munication and utilities; and j = 4, other];
(4) XNMVsj = (GSPsj/NMEsj) (XNMEsj),
where GSP for the "other” sector is calculated
as total state GSP minus manufacturing and
minus the three nonmanufacturing industries,
(j = 1...3); NMEsj is nonmanufacturing employ­
ment in industry j; and XNMEsj is export-related
nonmanufacturing employment in sector j.3
“Other” employment is total state employment
minus employment in manufacturing, trade,
business services and transportation, com­
munication and utilities.
The state total for the value added in these
nonmanufacturing sectors, XNMVS, is simply the
sum of the value added in the four nonmanu­
facturing sectors. The accuracy of this calcula­
tion, similar to the calculation for the manufac­
turing sectors, rests on the degree to which the
productivity of export-related workers in sector
j (XNMVsj/XNMES is equal to the productivity of
j)
all workers in that sector (GSPsj/NMEsj).
3Export-related nonmanufacturing employment in the
“ other” sector accounts for 31.8 percent of the 1986 national total for such employment,

JULY/AUGUST 1991

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