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FEDERAL RESERVE BANK OF ST. LOUIS

To our readers:
We begin the year 2000 with a new design for the Review. We adopted a new design five years ago to
improve readability and to enhance reproduction, particularly for classroom use. We think this new
design takes us a step further in that direction.
The Review continues to serve a diverse audience—as we have for decades—from the interested layman to
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for all of our readers. We will continue to publish a wide array of articles, ranging from essays on policy
issues for general readers to technical treatments of economic issues. As in the past, our editorial policy
is to place articles written for the general reader first in each issue.
We continue to appreciate hearing from readers with questions and comments on our articles. We also
welcome comments on our new design.
William T. Gavin
Editor
January 3, 2000

J A N UA RY /F E B RUA RY 2000

FEDERAL RESERVE BANK OF ST. LOUIS
Cletus C. Coughlin is a vice president and associate director of research and Patricia S. Pollard is an economist and research officer at the Federal
Reserve Bank of St. Louis. Heidi L. Beyer provided research assistance.

State Exports and
the Asian Crisis
Cletus C. Coughlin
and Patricia S. Pollard
INTRODUCTION

eal merchandise exports from the United States
to East Asia fell by 12 percent during 1998 as
the Asian crisis reduced demand in the region.1
These markets accounted for about 30 percent of
U.S. exports prior to the crisis. Given this market
share, the 12-percent drop in merchandise exports to
East Asia would have resulted in a 4-percent drop in
total U.S. merchandise exports, absent any changes in
export sales elsewhere. Because merchandise exports
account for 10 percent of U.S. output, the 4-percent
decline in exports would have resulted in a 0.4-percent decline in U.S. output. This decline did not occur
because exports to the rest of the world increased
and, more importantly, strong U.S. domestic demand
offset the negative effects of the trade shock.
Despite the overall benign effect on the U.S.
economy, the Asian crisis produced numerous
microeconomic effects. In a recent article in this
Review, Pollard and Coughlin (1999) estimated the
effects of the decline in exports to East Asia on an
industry-by-industry basis.2 Exports to East Asia fell
during 1998 for 25 of the 26 industries studied. These
declines ranged from 35 percent for the metallic ores
and concentrates industry to 3 percent for the food
and kindred products industry. The one industry
whose exports to East Asia rose was the transportation
industry, driven primarily by increases in aircraft
exports to China and Taiwan.
In the absence of offsetting effects, the relevance of
these declines in exports for the sales of each industry
depended on the importance of East Asian markets
for each industry’s production. Taking account of
this, Pollard and Coughlin (1999) calculated that the
nonelectrical machinery industry was the one
affected most adversely by the decline in exports to
East Asia. The growth rate of gross output in that
industry would have been 1.8 percentage points
higher if exports to East Asia had not declined. Of
those industries whose exports to East Asia fell

during 1998, the printing and publishing industry was
affected the least, primarily because East Asia was not
an important outlet for its production.
Because industry composition varies across states,
the different export effects across industries suggest
that individual states may have been affected to varying degrees by the Asian crisis. Furthermore, the
geographic pattern of trade varies across states indicating that those with a high proportion of exports going to East Asia were more likely to have been affected
by the crisis than others. Although the effects of the
Asian crisis on specific states have been addressed in
a number of studies, to date, no comprehensive study
of the export effects across states has been published.3
One study providing a state-by-state analysis was
released during early 1998 by the Department of
Commerce and the Treasury Department (1998).
The goal of that study, however, was to predict the
states that were most likely to be affected, rather
than to calculate the actual effects.
This article provides an additional perspective on
the microeconomic effects of the Asian crisis focusing
on manufacturing sectors of individual states. We
begin with an overview of state-level manufacturing
exports. Next, we examine the changes across states
in manufacturing exports to East Asia. We then
examine the importance of the East Asian markets to
each state. Combining estimates of the change in a
state’s exports to East Asia with the assessment of the
importance of these exports to the manufacturing
sector allows us to generate an estimate of the effect
of the trade shock on state manufacturing output.
The countries and industries driving these results
also are highlighted.
The Asian crisis affected the U.S. economy
through several channels, most notably, a direct
trade effect and indirect commodity price and interest
1

In this article, East Asia is defined as China, Hong Kong, Indonesia,
Japan, Korea, Malaysia, Philippines, Singapore, Taiwan, and Thailand.
These 10 countries are the ones most directly associated with the
Asian crisis. Focusing attention on a subset of the seven most affected countries does not change the nature of our results.

A microeconomic analysis of the Asian crisis focusing on firm-level
effects can be found in the article by Emmons and Schmid in this
issue of the Review.

For examples of analyses focusing on specific states, see Valletta
(1998) for a study of California, Oregon, and Washington; Duca et al.
(1998) for a study of Texas; and Gazel and Lamb (1998) for a study
of Colorado, Kansas, Missouri, Nebraska, New Mexico, Oklahoma,
and Wyoming.

J A N UA RY / F E B R UA RY 2 0 0 0

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Table 1

State Exports of Manufactured Goods (1997)
Total (millions of 1996 dollars)
East Asia (Rank)
All Countries (Rank)

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

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$49,333 (1)
15,327 (2)
14,418 (3)
7,697 (4)
6,329 (5)
6,259 (6)
5,385 (7)
4,655 (8)
4,086 (9)
3,754 (10)
3,218 (11)
3,188 (12)
3,024 (13)
2,760 (14)
2,759 (15)
2,754 (16)
2,555 (17)
2,545 (18)
2,066 (19)
2,036 (20)
2,018 (21)
1,797 (22)
1,715 (23)
1,518 (24)
1,419 (25)
1,375 (26)
1,345 (27)
1,334 (28)
1,247 (29)
1,097 (30)
984 (31)
977 (32)
960 (33)
957 (34)
800 (35)
760 (36)
707 (37)
705 (38)
685 (39)
461 (40)
459 (41)
328 (42)
316 (43)
269 (44)
244 (45)
240 (46)
219 (47)
164 (48)
126 (49)
70 (50)
35 (51)

$104,726 (1)
80,178 (2)
31,996 (5)
38,881 (3)
14,466 (13)
28,570 (6)
17,454 (9)
26,286 (8)
8,294 (21)
17,392 (10)
16,789 (11)
9,644 (20)
13,904 (14)
26,438 (7)
15,833 (12)
33,886 (4)
12,322 (16)
10,996 (17)
5,470 (28)
12,979 (15)
10,587 (18)
9,783 (19)
7,325 (24)
1,866 (38)
8,276 (23)
8,291 (22)
4,490 (30)
5,774 (27)
6,201 (26)
5,344 (29)
3,204 (32)
7,081 (25)
4,129 (31)
2,110 (37)
1,744 (39)
1,024 (44)
1,704 (40)
2,968 (33)
2,484 (35)
2,642 (34)
1,563 (42)
1,665 (41)
2,233 (36)
576 (47)
468 (49)
1,034 (43)
288 (51)
936 (45)
504 (48)
393 (50)
713 (46)

Per Capita (1996 dollars)
East Asia (Rank)
All Countries (Rank)
$1,533 (3)
791 (9)
2,568 (1)
424 (24)
1,390 (4)
522 (18)
881 (7)
416 (25)
1,260 (5)
313 (35)
433 (22)
680 (10)
404 (26)
188 (44)
342 (33)
282 (37)
379 (29)
585 (12)
531 (16)
347 (32)
388 (27)
334 (34)
525 (17)
881 (8)
363 (31)
363 (30)
517 (19)
262 (40)
288 (36)
384 (28)
477 (20)
181 (46)
1,632 (2)
577 (13)
661 (11)
1,246 (6)
569 (14)
212 (43)
271 (39)
169 (48)
253 (41)
280 (38)
430 (23)
561 (15)
460 (21)
243 (42)
184 (45)
98 (49)
171 (47)
80 (50)
54 (51)

$3,254 (5)
4,136 (3)
5,699 (2)
2,143 (17)
3,177 (6)
2,383 (11)
2,855 (8)
2,348 (12)
2,557 (9)
1,448 (30)
2,259 (13)
2,057 (19)
1,856 (23)
1,801 (26)
1,965 (21)
3,465 (4)
1,829 (24)
2,526 (10)
1,405 (34)
2,213 (15)
2,035 (20)
1,821 (25)
2,242 (14)
1,082 (41)
2,116 (18)
2,189 (16)
1,726 (27)
1,133 (39)
1,435 (32)
1,872 (22)
1,552 (29)
1,309 (36)
7,015 (1)
1,274 (37)
1,443 (31)
1,680 (28)
1,372 (35)
894 (45)
985 (43)
967 (44)
861 (47)
1,420 (33)
3,037 (7)
1,200 (38)
884 (46)
1,047 (42)
242 (51)
557 (49)
683 (48)
448 (50)
1,113 (40)

FEDERAL RESERVE BANK of ST. LOUIS

rate effects.4 While most of this article focuses on the
effect of the trade channel on state manufacturing
sectors, we attempt to provide some understanding
of the relative importance of this effect. To do so, we
examine both the strength of the trade shock and a
measure of the indirect effects (oil prices) as determinants of state manufacturing employment growth.
This analysis provides a rough estimate of the extent
to which the indirect effects of the Asian crisis may
have offset the direct effects of the crisis.

STATE MANUFACTURING EXPORTS
TO EAST ASIA
Across states, the levels of manufacturing exports
to all countries and manufacturing exports to East
Asia vary substantially.5 During 1997, California was
the leading state with manufacturing exports totaling
$104.7 billion, while Hawaii had the smallest amount
of manufacturing exports—$0.29 billion as shown in
Table 1.6 California also was the state with the largest
amount of manufacturing exports to East Asia—
$49.3 billion. North Dakota had the smallest amount
of manufacturing exports to East Asia—$0.04 billion.
Because larger states tend to have higher levels of
manufacturing exports, we also have presented manufacturing exports on a per-capita basis in Table 1.
On this basis, Vermont, Washington, Texas, Michigan,
and California were the five leading states. All of
these five states, except Vermont, also are among
the top five exporting states on a gross-dollar basis.
Substantial differences exist on a per-capita basis
between the 10 largest exporters and the 10 smallest
exporters. The average per-capita value of the
10 largest exporting states was $3,772, while the
average of the 10 smallest exporters was $757.
Turning to per-capita exports to East Asia, Washington, Vermont, California, Arizona, and Oregon
were the five leading states. Washington, California,
and Arizona were among the top five states in
exports to East Asia on a gross-dollar basis. Oregon
was in the top 10, but Vermont was much further
down the list. Once again, substantial differences
exist between the 10 largest and 10 smallest
exporters. The average per-capita value of the 10
largest exporters to East Asia was $1,286, while the
average of the 10 smallest exporters was $158.
In light of the national decline in exports to East
Asia from 1997 to 1998, it is not surprising that the
exports of most states declined, as shown in Table 2.
Ten of the 51 states (the District of Columbia is
treated as a state in this paper) experienced a rise in
exports to East Asia, while the remaining 41 states

experienced declines. Thirty-one of these latter
states had double-digit percentage declines in
exports to East Asia. Eight states—Alaska, Arizona,
the District of Columbia, Maryland, Montana,
Nevada, North Dakota, and Vermont—saw their
exports fall by more than 30 percent.
Examining state worldwide manufacturing exports
provides additional perspective on the Asian trade
shock. While only 10 of 51 states experienced a rise
in exports of manufactured goods to East Asia, 26
states saw their worldwide exports of manufactured
goods rise. Nevertheless, there is a high positive correlation between the changes in a state’s manufacturing exports to East Asia and the changes in its worldwide manufacturing exports.7 All 10 states with rising
manufacturing exports to East Asia were among the
26 states whose worldwide manufacturing exports
rose, while 25 of the 41 states whose manufacturing
exports to East Asia fell, also saw a decline in worldwide manufacturing exports.

THE ASIAN CRISIS TRADE SHOCK AND
STATE MANUFACTURING OUTPUT
The effect of the Asian trade shock on an
individual state can be separated into two factors:
1) the change in manufacturing exports to East Asia
and 2) the importance of those exports to the state’s
economy. As shown in Table 2, 10 states increased
their manufacturing exports to East Asia during
1998. These states, especially the five states with
double-digit increases, appear to have been immune
to the Asian crisis.8 Turning to the states whose manufacturing exports to East Asia fell during 1998, the
declines ranged from 0.7 percent in Arkansas to 56.7
percent in Montana. Despite being suggestive, these
data are not sufficient to conclude that the economy

See Noland et al. (1998) and McKibbin (1998) for discussions of
general equilibrium models and estimates indicating negligible
macroeconomic effects.

See the appendix for a discussion of the state export data as well as
all other data used in our study.

We use 1997 as the reference year for comparing exports across states
because the 1998 comparisons are affected by the Asian crisis.

The correlation coefficient is 0.76.

The increase in these states’ exports to East Asia does not mean that
they did not suffer trade effects from the Asian crisis. It is possible
that the increases in exports during 1998 were below what would
have occurred in the absence of the crisis. To examine such a hypothesis one needs to know the trend in exports to East Asia from these
states. Unfortunately, the MISER data prior to 1996 are not comparable
with the more recent data, making it difficult to calculate a trend.

J A N UA RY / F E B R UA RY 2 0 0 0

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Table 2

Change in Real State Exports
of Manufactured Goods
(Percent change 1997-98)

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

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East Asia
22.4
22.1
19.1
15.4
14.8
6.9
5.1
0.8
0.8
0.5
–0.7
–4.2
–4.9
–5.2
–5.8
–6.9
–7.7
–8.5
–9.6
–9.8
–10.4
–10.5
–10.9
–12.0
–12.7
–14.4
–15.0
–15.5
–16.4
–16.6
–16.7
–17.4
–18.5
–19.2
–19.4
–19.9
–20.0
–22.2
–24.8
–25.6
–27.9
–28.4
–29.6
–30.7
–33.0
–36.5
–40.1
–40.5
–41.6
–46.5
–56.7

All Countries
10.3
7.5
11.2
9.7
24.0
2.1
4.9
4.9
5.5
6.5
2.1
0.4
4.5
4.0
–0.2
–3.7
11.5
3.9
–4.3
–7.0
6.1
3.9
–2.4
–2.2
–10.9
7.0
–0.4
6.6
4.2
–0.1
8.1
–2.8
–1.6
1.3
5.2
–3.5
–8.6
2.7
–8.0
–16.4
1.4
–7.7
–13.7
–16.7
–23.7
–0.7
–10.6
–5.5
–33.2
–29.3
–17.6

of Montana was affected more severely by this trade
shock than the economy of Arkansas. To determine
how these declines in exports affect a state’s
economy, one must look at the importance of these
exports to output.
One clue to the importance of East Asian
exports for a state is the share of that state’s
exports going to the region. As shown in Table 3,
the geographic pattern of trade varies across states.
During 1997, East Asia was the destination for 81
percent of New Mexico’s manufacturing exports.
Alaska and Hawaii also were highly dependent on
the East Asian markets as both sent about 75
percent of their manufacturing exports to the
East Asian countries. The other states bordering
the Pacific Ocean—Oregon, California, and
Washington—also sent a sizeable share of their
manufacturing exports to East Asia. In contrast,
only 5 percent of the manufacturing exports of
North Dakota were shipped to East Asia. As a
general statement, states in the western United
States tend to ship a higher percentage of the manufacturing exports to East Asia than eastern states,
as shown in Figure 1. One notable exception is the
District of Columbia, which sent over 50 percent of
its manufacturing exports to East Asia during 1997.
A more precise measure of how important the
East Asian markets are to a state is given by a
state’s manufacturing exports to the region as a
share of its manufacturing output. Using this measure, Alaska’s manufacturing sector was the most
dependent on East Asia—one-quarter of its manufactured shipments were sent to this region.9 In
contrast, less than 1 percent of manufactured shipments from firms in North Dakota were sent to
East Asia. Thus, if exports to East Asia fell by the
same amount in Alaska and North Dakota, the
effect on the Alaskan economy would be 25 times
greater. The data in Table 4, as in Table 3, indicate
the relative dependence of western states on the
East Asian markets.
Multiplying a state’s change in manufacturing
exports to East Asia (Table 2) by the share of those
exports in manufacturing shipments (Table 4) provides an estimate of the trade effect of the Asian
crisis on a state’s economy as shown in Table 5.
More precisely, the data in Table 5 indicate the

A better measure of the effect of the change in exports on a state’s
economy would be the share of exports in a state’s gross product.
A state’s exports are measured, however, by the total dollar value of the
shipments while gross state product is based on value added.

FEDERAL RESERVE BANK of ST. LOUIS

Figure 1

Table 3

The Importance of East Asia for a State's
Manufacturing Exports 1997

East Asia’s Share of Real State Exports
of Manufactured Goods (1997)
State

35% and greater

Between 20% and 35%

20% or less

contribution of manufactured exports to East
Asia to a state’s manufacturing sector growth rate
during 1998. For example, the 26-percent decline
in Hawaii’s exports to East Asia reduced the growth
rate of manufacturing output in that state by 1.5
percentage points. While these estimates indicate
that the East Asian trade shock reduced the growth
rate of manufacturing output by less than 0.3 percentage points in most states, they also indicate
the diversity of effects across states even if one
considers only those states whose exports to East
Asia fell.
The data in Table 5 do not measure the overall
change in manufacturing output in the states during
1998. Rather, these data denote the contribution of
exports to the growth rate of manufacturing output.
For example, the 19-percent decline in Utah’s exports
to East Asia (Table 2) reduced the growth rate of
manufacturing output in that state by 1 percentage
point (Table 5). It is possible, however, that this
decrease in exports to East Asia was offset by either
an increase in exports to other foreign markets or
an increase in domestic sales. Overall, Utah’s manufacturing sector may have experienced no decline
in growth, depending on the strength of demand in
these other markets. The data in Table 2 indicate
that for most states a decline in exports to East Asia
was not offset by a rise in exports to other regions.
Whether domestic demand was strong enough to
overcome the trade effect will not be known until
the 1998 shipments data are released in late 2000.

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

Percent Share
81.4
76.0
74.2
52.1
49.3
47.1
46.8
45.9
45.3
45.1
43.8
41.5
37.8
33.1
30.9
30.7
30.0
29.4
27.6
25.0
23.7
23.4
23.3
23.2
23.1
23.1
21.9
21.7
21.6
20.7
20.5
20.1
19.8
19.7
19.2
19.1
19.1
18.4
17.8
17.7
17.5
17.4
17.4
17.1
16.6
15.7
14.2
13.8
10.4
8.1
4.9

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Table 4

Manufacturing Exports to East Asia as a
Share of Manufactured Shipments (1996)
State
Alaska
Washington
California
Arizona
Oregon
Wyoming
Hawaii
Utah
Massachusetts
Colorado
Texas
Idaho
New York
New Mexico
Vermont
Maryland
Minnesota
Nebraska
Illinois
District of Columbia
Florida
Connecticut
Louisiana
Virginia
New Jersey
Kansas
Maine
Georgia
West Virginia
South Carolina
Nevada
Ohio
Rhode Island
Alabama
Pennsylvania
Iowa
New Hampshire
North Carolina
Oklahoma
Tennessee
Wisconsin
Delaware
Indiana
Kentucky
Arkansas
Michigan
Montana
South Dakota
Missouri
Mississippi
North Dakota

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Export-Shipment Ratio
25.0
17.3
13.4
12.7
8.8
8.4
5.9
5.6
5.3
4.9
4.6
4.6
4.3
4.2
4.1
3.9
3.9
3.8
3.3
3.3
3.2
3.1
2.9
2.9
2.8
2.7
2.7
2.6
2.6
2.2
2.2
2.1
2.0
2.0
1.9
1.9
1.9
1.8
1.8
1.8
1.6
1.5
1.5
1.5
1.4
1.4
1.2
1.0
0.9
0.9
0.6

A CLOSER LOOK AT THE STATE EFFECTS
Country Detail
Examining the change in state manufacturing
exports to each country in East Asia, rather than
to the region as a whole, may provide some insight
into the different effects across states. At first
glance, differences in the states’ trading partners
do not appear to explain the variations in effects
across states. First, exports declined to nearly all
countries in the region, as underscored by the
data in Table 6. Only three states—Maine, New
Hampshire, and Washington—experienced
declines in exports to fewer than half of the East
Asian countries. Not surprisingly, these three
states were among those whose exports to East
Asia rose during 1998. In contrast, most states
had export declines to at least seven of the 10 East
Asian countries. Four—Maryland, Montana, North
Carolina, and Ohio—had declines in exports to
all 10 countries. As Table 2 shows, these states,
except for North Carolina, had export declines of
25 percent or more to the region.
Second, across states, the major trading partners
in East Asia do not vary substantially. Japan is the
most important export destination in East Asia for
most states, and South Korea is an important export
market for many. As a result, declines in exports to
these two countries typically accounted for a large
share of a state’s overall decline in exports to East
Asia in 1998.
Nevertheless, for some states, trading partners
mattered. The economy of Thailand, for example,
suffered one of the most severe contractions in
the region with output falling by 8 percent during
1998. Thailand was not an important export
market for most states, but it was for the District
of Columbia and Minnesota. In 1997, 48 percent
of D.C.’s exports of manufactured goods to East
Asia went to Thailand. In 1998, D.C.’s exports to
Thailand fell by 97 percent, accounting for nearly
all of the 47 percent decline in its exports of manufactured goods to East Asia. Meanwhile, Thailand
accounted for 13 percent of Minnesota’s manufacturing exports to East Asia during 1997. These
exports fell by half in 1998, accounting for 60
percent of Minnesota’s overall decline in manufacturing exports to East Asia. On the other hand,
China’s economy remained relatively robust during
the crisis. China is not a major export destination
for many states; however, Louisiana sent over 20
percent of its 1997 East Asian exports to China. A
rise in these exports during 1998 accounted for the

FEDERAL RESERVE BANK of ST. LOUIS

overall increase in Louisiana’s exports to
the region.

Table 5

The Effect of the Trade Shock on
Manufacturing Output Growth

Industry Detail
Disaggregating the manufacturing data from the
one-digit SIC level to the two-digit SIC level also provides
some insight into the different experiences of the states
during 1998. The chemical and allied products (SIC
28), industrial machinery and equipment (SIC 35), and
electronic and electrical products (SIC 36) industries
represented the highest shares of manufacturing exports to East Asia across the broadest range of states.
Each of these industries accounted for more than 10
percent of manufacturing exports to East Asia in more
than half of the states. Food and kindred products (SIC
20), transportation equipment (SIC 37), and instruments and related products (SIC 38) also were important industries for a number of states exporting to
East Asia. The importance of individual manufacturing
industries for exports varies across states, more so than
the destination of these exports, as discussed above.
The manufacturing exports of some states are
concentrated in a single industry. The electronic and
electrical products industry, for example, accounted
for 96 percent of New Mexico’s manufacturing
exports to East Asia during 1997, while chemicals
and allied products accounted for 99 percent of
Wyoming’s exports to the region. A sharp drop in
Wyoming’s chemical exports to East Asia, in
conjunction with the relative importance of these
markets for the state industry, was responsible for
most of the negative effect on the manufacturing
sector noted in Table 5. Wyoming’s experience is in
contrast with that of Washington. During 1997, 64
percent of Washington’s manufactured exports to
East Asia were transportation equipment, mostly aircraft. Sales of aircraft to East Asia, particularly China
and Taiwan, increased sharply during 1998. These
increases primarily are responsible for the increase
in Washington’s manufacturing exports to East Asia.10
The manufacturing exports of other states were
more diversified. For example, none of the 20 industries accounted for more than 20 percent of the
exports to East Asia from either Missouri, North Car-

It is likely that the increase in aircraft exports accounts for most of
the positive export boost in Washington’s manufacturing sector during 1998, listed in Table 5. Manufacturing shipments for
Washington’s transportation industry are not disclosed by the
Department of Commerce, however, to prevent disclosure of data relevant to one company.

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

Percent
2.56
0.65
0.60
0.45
0.44
0.36
0.20
0.04
0.02
0.02
–0.01
–0.09
–0.11
–0.12
–0.12
–0.13
–0.18
–0.19
–0.19
–0.22
–0.25
–0.26
–0.26
–0.27
–0.28
–0.28
–0.31
–0.32
–0.33
–0.33
–0.33
–0.35
–0.37
–0.42
–0.45
–0.45
–0.59
–0.63
–0.71
–0.71
–0.72
–0.72
–1.08
–1.51
–1.51
–1.54
–1.58
–2.07
–2.48
–3.91
–10.40

Unweighted average

–0.62

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olina, or Nevada. Exports declined across a broad
range of the industries in all of these states.

Table 6

Declining Exports to East Asia
on a Country Basis (1998)
State
Alaska
Alabama
Arkansas
Arizona
California
Colorado
Connecticut
District of Columbia
Delaware
Florida
Georgia
Hawaii
Iowa
Idaho
Illinois
Indiana
Kansas
Kentucky
Louisiana
Massachusetts
Maryland
Maine
Michigan
Minnesota
Missouri
Mississippi
Montana
North Carolina
North Dakota
Nebraska
New Hampshire
New Jersey
New Mexico
Nevada
New York
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Virginia
Vermont
Washington
Wisconsin
West Virginia
Wyoming

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Number
8
7
7
7
9
6
8
8
7
6
9
7
8
7
8
9
8
7
7
7
10
3
9
5
6
9
10
10
9
5
3
7
7
8
6
10
7
6
7
5
9
6
7
8
7
7
6
4
9
9
8

THE ASIAN TRADE SHOCK AND STATE
MANUFACTURING EMPLOYMENT
The results in Table 5 indicate that the manufacturing sector in some states was subjected to large,
negative shocks, while many other states were affected
only slightly. We use regression analysis to assess
the importance of these trade effects on state manufacturing employment growth. Of course, the
estimated trade effects were not the only influence
on employment growth across states during 1998.
Consequently, additional variables are required for the
regression analysis. We use two additional variables,
one of which is related to the Asian crisis. Commodity
prices fell during 1998 partly as a result of decreased
demand in East Asia. Perhaps most important was
the fall in the price of oil. Those states with a high
concentration of manufacturing industries that use
petroleum products extensively as an input would
benefit relative to states that produce petroleum and
its related products. Employment trends across states,
which reflect the interaction of various other economic
factors affecting manufacturing employment, also
are likely to be important. For example, states where
manufacturing employment growth has been falling
recently may be those states with industries shedding
employment to remain competitive. Hence, employment changes are occurring regardless of the strength
of the Asian economies.
In sum, we can think of state-level manufacturing
employment growth during 1998 as being determined
by previous employment growth and two shocks:
the Asian trade shock, which was unfavorable to
employment growth in most states; and the oil
price shock, which was favorable to employment
growth in most states. These relationships can be
summarized by the following equation:
(1)

megti =b0+b1* tradei +b2* oili
+b3* megtrendi +ei

where the subscript i refers to individual states, megt
is the manufacturing employment growth rate in
each state, trade is the negative of the estimated trade
effect on the manufacturing sector in each state
(given in Table 5), oil is an estimate of the differential
effect of a decline in oil prices on each state (see the
appendix), megtrend is the average annual growth
rate of manufacturing employment from 1994 to

FEDERAL RESERVE BANK of ST. LOUIS

1997, and e is an error term. The betas indicate the
effect of each of these variables on manufacturing
employment growth.
We expect b1<0, and b2 and b3>0. First, a larger
Asian trade shock should result in a larger decline in
manufacturing employment growth. Specifically, states
such as Arizona and Alaska should experience larger
declines in manufacturing employment growth than
Arkansas or Tennessee. Second, a drop in the price of
oil should raise manufacturing employment growth in
energy-importing states. In other words, states with
industry compositions weighted toward users rather
than producers of energy should experience larger
increases in employment growth than others. Finally,
states that have experienced recent increases in manufacturing employment are likely to continue to do so.
Our estimation of equation 1 produced the
following results:
(2) megt = 0.23 – 0.03 * trade + 1.93 * oil
(0.99) (-0.24)
(3.03)
+ 0.57 * megtrend,
(6.23)
where the t-statistics are given in parentheses.11
These results indicate that the Asian trade shock had
a negative, but statistically insignificant, effect on
employment growth across states. That is, based
on the regression analysis, the Asian trade shock
was not a factor driving differences in state-level
manufacturing employment growth during 1998.
Meanwhile, we find the oil price shock to have a positive, statistically significant effect on manufacturing
employment growth. Thus, statistically speaking, the
differential effect of the Asian crisis on state employment may be more pronounced through its effect on
oil prices than through trade flows. Finally, prior
manufacturing employment growth was found to
have a positive, statistically significant effect on current (1998) manufacturing employment growth.12
Our results leave an important question
unanswered: Why didn’t the trade effect have a
noticeable effect on manufacturing employment
across states? One possibility is that the differential
shocks were not large enough to generate statistically
significant differences in employment growth. Despite
much variation across states in their percentage
declines of exports to East Asia, and the importance
of East Asia as an export destination, there is little
difference across many states in the estimated trade
shock. More than two-thirds of the estimates for the
individual states are between 0.04 percent and –0.72
percent, a range of only 0.76 percentage points.

Another possibility is that our measure of the trade
shock, because it relies on shipments data (both for
exports and output) rather than value-added data, is
deficient. Employment changes in a state are likely
to be related to a state’s value-added changes; however,
the shipments data might be a poor measure of the
production that occurred in a specific state, and thus,
are not directly related to employment.

CONCLUSION
The Asian crisis resulted in a decline in most states’
exports to East Asia, but the severity of the decline
varied across states. An assessment of the importance
of the decline in exports for a state’s economy depends
on the extent of the decline and the importance of
the East Asian markets for the state. In general, the
western states were more dependent on the East
Asian markets, and hence, were the hardest hit by the
trade shock. Of the states in which the decline in
exports to East Asia lowered the growth rate of manufacturing output by more than 1 percent, two-thirds
were western states. Some western states, most
notably Washington, however, were among those states
whose exports to East Asia rose despite the crisis.
Using these estimates of the trade shock’s effect
on each state’s manufacturing sector, we tested the
statistical relevance of the trade shock to explain
changes in a state’s manufacturing employment
during 1998. We found that the trade shock did not
explain differences in the employment experiences
of states during 1998. A factor that was statistically
relevant was the effect of the change in the price of
oil on a state’s economy. Thus, taking our results at
face value, the differential state employment effects
that resulted from the trade changes of the Asian crisis
mattered little. A stronger case can be made that the
oil price declines during late 1997 and 1998, some
portion of which can be attributed to the Asian crisis,
were more important than the trade effects in affecting
manufacturing employment at the state level.

–2
The R for the equation is .45.

12To

investigate whether the Asian crisis affected state employment in
specific industries, we estimated equation 1 for manufacturing industries at the two-digit level. Unfortunately, data limitations affected this
effort. Only 14 of the 20 SIC industries had complete data for at least
half of the states. For 11 of the 14 industries, the results were similar
to those for manufacturing as a whole—the Asian trade effect, even
though exhibiting the anticipated negative sign, was statistically
insignificant. Results for the employment effect of the oil price shock
also were not strong as the effect was statistically insignificant in 12
of the 14 industries.

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REFERENCES
Brown, Stephen P.A., and Mine K. Yücel. “Energy Prices
and State Economic Performance,” Federal Reserve Bank
of Dallas Economic Review (Second Quarter 1995),
pp. 13-23.
Coughlin, Cletus C., and Thomas B. Mandelbaum.
“Measuring State Exports: Is There a Better Way?”
this Review (July/August 1991), pp. 65-79.
Cronovich, Ron, and Ricardo Gazel. “How Reliable Are
the MISER Foreign Trade Data?” unpublished manuscript,
May 1999.
Duca, John V., David M. Gould, and Lori L. Taylor. “What
Does the Asian Crisis Mean for the U.S. Economy?”
Federal Reserve Bank of Dallas Southwest Economy
(March/April 1998), pp. 1-6.
Emmons, William R., and Frank A. Schmid. “The Asian
Crisis and the Exposure of Large U.S. Firms,” this Review
(January/February 2000). pp. 15-34.
Gazel, Ricardo C., and Russell L. Lamb. “Will the Tenth
District Catch the Asian Flu?” Federal Reserve Bank
of Kansas City Economic Review (Second Quarter 1998),
pp. 9-26.
McKibbin, Warwick. The Crisis in Asia: An Empirical
Assessment, Brookings Discussion Paper in International
Economics No. 136, 1998.
Noland, Marcus, Li-Gang Liu, Sherman Robinson, and Zhi
Wang. Global Economic Effects of the Asian Currency
Devaluations, Institute for International Economics, 1998.
Pollard, Patricia S., and Cletus C. Coughlin. “Going Down:
The Asian Crisis and U.S. Exports,” this Review
(March/April 1999), pp. 33-45.
U.S. Department of Commerce and U.S. Department
of Treasury. Treasury and Commerce Release Analysis
Showing Impact of Asian Crisis on Individual States,
March 24, 1998.
Valletta, Rob. “East Asia’s Impact on Twelfth District
Exports,” Federal Reserve Bank of San Francisco
Economic Letter (November 20, 1998).

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Appendix-Data Sources

State Export Data
The data on state manufacturing exports used
in this study are produced by the Massachusetts
Institute for Social and Economic Research (MISER)
at the University of Massachusetts. These data are
export shipments by the state of origin of movement.
The source of the data is the Shipper’s Export
Declaration (SED). This document identifies “the
state where the product began its journey to point
of export.” The Census Bureau collects these data,
which are adjusted by MISER to fill in missing
industry and state information.
The MISER export data have their weaknesses.
The identified export state may not be the state
of manufacture, but rather the state of a broker
(or wholesaler) or the state where a number of
shipments were consolidated. This problem is
more pronounced for exports of agricultural commodities than manufactured goods. Hence, our
study focuses on manufactured goods.
An alternative to the export data based on the
origin of movement is one based on exporter location.
These data, which had been available since 1993,
also are based on the Shipper’s Export Declaration.
Compared to the origin of movement series, the
identified state of the exporter in these data more
likely reflects the state of a broker or wholesaler, or
the headquarters of companies, rather than the state
of manufacture. A potentially better source of state
export data for identifying the state of production has
been produced by the U.S. Census as part of the
Annual Survey of Manufacturers; however, Exports
from Manufacturing Establishments was discontinued
after data for 1992 were published.
The data we used, despite their limitations, are
regarded as the best available on state exports.1
They are available quarterly with a three-month lag.
In addition to the state information, the data contain
information on one-and two-digit industry (SIC) code,
destination country, and dollar value and weight by
method of transportation.2

Additional discussion of the various issues involving state export data
can be found in Coughlin and Mandelbaum (1991) and Cronovich and
Gazel (1999).

Information on this database is available at
<www.umass.edu/miser/axes>.

FEDERAL RESERVE BANK of ST. LOUIS

Export Price Data
Real exports were calculated by deflating the 1997
export data by the change in the price of exports between 1996 and 1997. The 1998 exports were deflated
by the price change between 1996 and 1998. Export
price data are available from the Bureau of Labor Statistics. These data are not available by SIC code. Thus,
we started with an export price index that groups the
data based on the Standard International Trade Classification (SITC) system and matched these industries
with the appropriate SIC codes. When multiple SITC
codes fit one SIC category, a weighted average of the
price indices for those categories was constructed to
arrive at the price index on an SIC basis. For more
details see Pollard and Coughlin (1999).

and stimulate economic activity in energy-importing
states. The paper contains estimates for 1992 and
2000. The results reported in this paper use the estimates for the year 2000, but the results are similar
using the 1992 estimates.

Per-Capita Exports
Per-capita exports were calculated by dividing
real exports for each state by the population of the
state. The population data are available from the
Census Bureau.

Manufacturing Shipments Data
To measure manufacturing output at the state
level we use the value of industry shipments. These
data are from the Annual Survey of Manufacturing
conducted by the Census Bureau. The latest
available data were for 1996.

Employment Data
The state level employment data came from the
payroll employment survey conducted by the Bureau
of Labor Statistics. We used the average annual
employment data for 1997 and 1998 to calculate
the growth rate in employment during 1998. The
trend employment growth rate is calculated using
the average annual growth rate in state employment
between 1994 and 1997. Data at both the one-and
two-digit industry (SIC) code were used in the regression analysis.

Oil Price Effect Data
The oil price effect data are based on estimates by
Brown and Yücel (1995) of the effect of a 10-percent
change in the price of oil on a state’s nonagricultural
employment. Declining energy prices should retard
economic activity in energy-exporting states (i.e.,
those that produce more energy than they consume),
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FEDERAL RESERVE BANK OF ST. LOUIS
William R. Emmons is a economist and Frank A. Schmid is a senior economist at the Federal Reserve Bank of St. Louis. Judith Hazen, Robert
Webb, and Marcela Williams provided research assistance.

The Asian Crisis
and the Exposure
of Large U.S. Firms
William R. Emmons
and Frank A. Schmid
he financial and economic crises that ravaged
Thailand, Indonesia, South Korea, Malaysia, and
other Asian countries during 1997 and 1998 triggered one of the most abrupt and severe economic
slowdowns seen anywhere in the world during recent
decades. Financial-market volatility increased
around the globe soon after the Thai devaluation of
July 1997, reaching its high point in October 1998.
Many countries were not hit directly by this financial crisis; nonetheless, they felt significant repercussions. Worldwide economic growth slowed,
risk premiums in debt markets increased, stock
markets became more volatile, and confidence
indicators slumped in many countries (see
Economic Report of the President, 1999, pp. 227-51,
for an extensive discussion of the Asian crisis).
We examine how the Asian financial crisis
affected the sensitivity of large U.S. firms to U.S.
stock-market risk—that is, whether the economic
situation in Asia is related to changes in firms’ “betas.”
Following corporate finance theory, we define stockmarket exposure as the extent to which a firm’s
stock returns are correlated with overall market
returns. Exposure is summarized by the firm’s estimated beta, which, according to the Capital Asset
Pricing Model (CAPM), is simply the coefficient
estimated in a regression of the firm’s excess
returns—i.e., dividends plus price change less the
risk-free return—on market excess returns over
some specified period.1 If a firm’s beta rises, investors
demand higher excess returns for holding its stock.
This raises the firm’s cost of equity capital. Thus,
firms that saw their betas rise as a result of the
crisis would face higher equity financing costs.
If the Asian crisis mattered at all for U.S. firms’
stock-market risk, we would expect the largest
effects at firms with the highest relative sales expo-

sure to Asia. Similarly, we would speculate that the
betas of firms with relatively low sales exposure to
the Asian region would remain unchanged or decrease.
Betas are measures of a firm’s return sensitivity relative to the market, so some betas must go down if
others go up. In addition, we would expect firm
leverage (debt as a percentage of assets) to amplify the
effects of the Asian crisis on firms’ CAPM betas. This is
because contractually fixed payments owed to debtholders do not change even if underlying cash flows
are reduced. This is in contrast to the situation of
equity investors, who hold the residual cash flow rights.
The more highly leveraged a firm is, the larger the
income variability to which the equity holders are
exposed, everything else being equal. This income
variability is related directly to the amount of total
assets the firm controls or its sales, not to its
equity base.
Our analysis begins by identifying the S&P 100
firms that reported detailed regional breakdowns of
sales for 1996-98.2 We then estimate a model of each
firm’s weekly excess stock returns for the period
1997-98. We use the weekly excess return on the
S&P 500 as the relevant market factor because we
want a broad measure for the market. Our model
is based on the Capital Asset Pricing Model but
extends it to allow for changes in betas, perhaps
related to developments in Asia.3 Intuitively, betas
may change in response to the Asian crisis or other
shocks to the fundamentals of firms that are not
shared to the same degree by the market as a whole.
We run a second-stage regression using results
of the first-stage asset-pricing model as the dependent variable. We analyze the sensitivity of the
firms’ betas to the extent of their sales exposures
to Asia, where sales are weighted by the ratio of
long-term debt to total assets. We find that
leverage-weighted sales exposure to Asia exerts a
significant positive effect on a firm’s CAPM beta.
1

See Brealey and Myers (1996, pp. 160-64).

We attempted but were not able to obtain a breakdown of sales by
world regions for all of the S&P 100 firms. Only 39 firms had sufficiently detailed information to be included in our sample.

For a textbook presentation of the CAPM, see Brealey and Myers
(1996, pp. 173-203). Jagannathan and Wang (1996) and others have
developed the idea of a “conditional CAPM,” that is, a period-by-period
CAPM that allows betas to vary over time.

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Firms that had high sales exposure to Asia became
more sensitive to movements in the S&P 500 while
firms with low sales exposure to the Asian region
became less sensitive.

THE ASIAN CRISIS AND THE ROLE
OF INCREASED BUSINESS RISK
Following several decades of rapid economic
growth and increasing integration into global capital
markets, the economies of a number of Asian
countries suffered abrupt and severe contractions
during 1997 and 1998 (Economic Report of the
President, 1999, pp. 227-51). The proximate cause
of the crisis was a failed devaluation in Thailand,
but recent analyses of the period have pointed to
structural and especially financial-sector weaknesses
in many of the affected countries. At the same time,
a very large inflow of foreign capital during the early
and mid-1990s, and the subsequent rapid withdrawal
of many foreign investors during 1997 and 1998,
exacerbated the adjustment process.
The reverberations of the Asian crisis on the world
economy have been multifaceted. World economic
growth slowed as the shortfall in demand from the
Asian region caused both a severe regional recession
and a deterioration in the trade balances of important
trading partners such as the United States (Pollard
and Coughlin, 1999). Commodity prices weakened,
export competition increased in many sectors, and
interest rates fell in the world’s major economies.
Industrial production slowed in many countries
and corporate profits decelerated or declined.
Despite these disruptions, the U.S. economy grew
strongly throughout the 1997-98 period. This experience has led some observers to conclude that the
growth-enhancing consequences of the crisis for the
United States—primarily lower interest rates and lower
commodity prices—were simply more powerful
than the growth-reducing factors, which included
reduced demand for U.S. exports and financial
losses suffered by lenders and investors in the region.
Another impact of the financial crisis was an
increase in the observed volatility of financial markets and capital flows around the world. Figure 1
shows the sustained increase in stock-market volatility that occurred in the United States throughout
1997-98. The higher implied volatility of the S&P
100 index during this period indicates that investor
uncertainty about future stock-market returns had
increased. In addition, capital flows to emerging
markets collapsed while portfolio investments into

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the United States and other industrialized countries
increased. These shifts in capital flows may go some
way toward explaining the surge in U.S. and European stock-market price indexes during this period
despite increased uncertainty about global economic
growth and increased financial market volatility.
Figure 2 contrasts the divergent paths of
U.S. and Asian emerging-market financial returns
throughout this period. The cumulative total return
during the two-year period on the S&P 100 index
of large-capitalization U.S. firms was an astonishing 71 percent, nearly 60 percentage points better
than the risk-free return on three-month Treasury
bills. Meanwhile, the dollar-denominated total return during the two years on the FT/S&P Actuaries
Pacific Stock-Market Index (excluding Japan) was
about negative 42 percent. An investment in moneymarket instruments issued by Asian emerging-markets
borrowers that was continually rolled over during
1997 and 1998 would have earned about a 2 percent
total return in dollar terms, according to the J.P.
Morgan Emerging Local Markets Index Plus (Asia).
The surge in U.S. stock prices is even more
remarkable when one considers that after-tax
earnings per share of the S&P 500 companies actually fell during 1997 and 1998, and that corporate
bond yield spreads—that is, the extra yield that
corporate issuers were forced to pay to borrow in
comparison to U.S. Treasury yields—rose considerably (see Figure 3). Although alternative explanations may exist, a single risk-based explanation
of these phenomena—high stock returns and volatility, declining or flat profits, and increasing bond
yield spreads—is plausible. In a nutshell, the main
effect of the Asian crisis, which began in the second
half of 1997 and spread to Russia in August 1998,
may have been to increase the perceived riskiness
of corporate cash flows.
To understand how investor perceptions of increased business risk could be responsible for high
stock returns and volatility, as well as increased
corporate bond yield spreads—all while earnings
remain flat—we need to apply the “option-theoretic interpretation of the firm.”4 The key insight
of this approach is that the equity of a firm that
has issued debt (e.g., bonds) is identical to a call
option written on the assets of the firm, the ownership of which has been, in effect, transferred to the
4

Black and Scholes (1973) is the original source of this interpretation of
the corporate financial structure of every firm. Brealey and Myers
(1996, pp. 564-66) provide a textbook discussion of these ideas.

FEDERAL RESERVE BANK of ST. LOUIS

Figure 1

Chicago Board Options Exchange
S&P 100 Volatility Index
60
50
40
30
20
10
0
1992

1993

1994

1995

1996

1997

1998

Source: Created using data from Chicago Board Options
Exchange <http://www.cboe.com/tools/historical/vix.htm>.
Note: The vertical scale measures the implied annualized
volatility (standard deviation) of the S&P 100 derived from a
hypothetical 30-day call option written on the S&P 100 with
strike price at the current day’s market price, in percent.

Figure 2

Total Return Indexes
(in U.S. Dollar Terms)
175
150
Jan. 3, 1997 = 100

debtholders. When issuing a bond, the owners of a
previously debt-free firm effectively sell the firm’s
assets to the bondholders and receive, in addition
to the cash proceeds of the bond issue, a call option
that gives them (the equityholders) the right—but
not the obligation—to buy back the firm’s assets by
paying off the debt in full with interest. Thus, the
bondholders will end up either with their money
back (plus interest) or the firm itself, whichever is
less valuable (because the choice is made by the
equityholders). In the case of bankruptcy, the owners
have decided that the firm is of less value to them
as a going concern than the cash required to buy it
back from its debtholders. The equityholders allow
their call option to expire unexercised and “walk
away” from the firm by virtue of limited liability.
How does this apply to the Asian crisis and the
conjunction of high stock returns and increased
corporate bond yield spreads during 1997 and
1998? One key determinant (among several) of
the value of any option is the volatility of the
underlying cash flows upon which the option is
written. In the present application, the U.S. corporate sector generates cash flows for which corporate
equityholders and corporate bondholders have
claims. Precisely because the owner of an option
has the right but not the obligation to exercise it, a
greater dispersion of likely outcomes—i.e., higher
risk—enhances the value of the option. The optionholder can capture all of the increased “upside”
while ignoring all of the “downside,” even if this
has increased as well. This explains why stock
prices could go up during the 1997-98 period even
while corporate earnings were flat. Investors may
have bid up stock prices because the range of
future earnings estimates had increased (even if
their expected level did not change). That is, the
value of a call option on the assets of a firm increases when fundamental business risk increases.
Is there any other evidence that increased risk,
rather than higher expected earnings, boosted
stock returns during the Asian crisis? The behavior
of corporate bond prices provides this evidence.
The “dual” or complementary approach to corporate financial valuation within the option-theoretic
interpretation of the firm can explain why corporate bond prices declined and yield spreads went
up at the same time that stock prices went up. The
dual approach points out the equivalence between
the call-option interpretation presented above and
a put-option interpretation that is relevant for corporate bondholders. In this interpretation, corporate

125
100
75
50
25
0
1997
ELMI+
3-mo. T-Bills

1998
FT/S&P Pacific Ex Japan

S&P 100

Data sources: The S&P 100 return data were purchased
from Standard & Poor’s Compustat. Data for calculating
the three-month T-Bill return are from various editions of
the Wall Street Journal. The Asian indices were purchased
from J.P. Morgan and the FT/S&P Actuaries, respectively.

bondholders effectively purchase an equivalent
default-risk-free bond (i.e., identical in maturity and
coupon rate) and underwrite a put option on the
assets of the firm that is given to the equityholders.
From the bondholders’ perspective, the equityholders
retain ownership of the firm’s assets and are given
the right to “put” (deliver) the assets of the firm to

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Figure 3

Quarterly Corporate Earnings Growth
and Corporate Bond Yield Spreads
20
15

Percent

10
5
0
-5
-10
I/97

II/97

III/97

IV/97

I/98

II/98

III/98

IV/98

Year-over-year percent change, S&P 500 after-tax
earnings per share
10 x spread between Moody’s Aaa corporate bond yield
and average of daily 10-year Treasury bond yield at
constant maturity
Data source: Haver Analytics, 1999.

the bondholders in lieu of cash payment in
satisfaction of the debt obligations.
Increased business risk means that the probability of default—situations when the assets are
worth less than the debt they secure—has increased. Thus, increased business risk means that
the put option underwritten by bondholders has
become more valuable and consequently the value
of the bonds has fallen. This implies that corporate bond yield spreads rise, as they did during the
Asian crisis. Together, these option-theoretic interpretations of corporate securities can explain why
high stock-market volatility, rising equity prices,
and falling corporate bond prices—all while expected earnings have not changed—are perfectly
consistent. The underlying shock was an increase
in the riskiness of the corporate sector and the
stock and bond price movements simply reflected
a redistribution of firm value among claimholders.5
We would not expect that all firms’ riskiness
went up at the same time by the same amount.
Instead, one would expect greater stock-price responses at firms with more exposure to the source
of increased risk. In addition, one would expect a
greater stock-price response at those firms that are
more highly leveraged; this is because they are
more likely to become bankrupt, all else being
equal, so the option to default is more valuable.
This article explores the firm-level changes in sys-

J A N UA RY / F E B R UA RY 2 0 0 0

tematic risk that may have been induced by the
Asian crisis. Systematic risk is measured as the
sensitivity of a firm to market-wide stock-price
movements. Risk is measured relative to the
market as a whole. Our goal is to isolate differences in firm responses to the Asian crisis rather
than to measure the overall impact on market risk.
The empirical analysis of the article uses two
total-return series as alternative proxies for the
evolution and severity of the Asian crisis. Although
they clearly reflect different asset and risk classes,
these two total-return series present a similar picture of the timing of the onset of the crisis, roughly
the middle of 1997. By way of contrast, the
money-market index indicates that recovery from
the crisis began in early 1998, while the stockmarket index did not turn upward decisively until
the third quarter of 1998.

THE ECONOMETRIC MODEL:
FIRST-STAGE REGRESSION
Our goal is to analyze the channel through
which the Asian crisis affected the risk positions of
U.S. firms during the period 1997-98.6 We use a
two-stage regression approach because it makes
interpretation of the results relatively straightforward.7 In the first-stage regression of our model,
we provide evidence for the influence of the Asian
crisis on the stock-market risk of the sample firms.
For this purpose we set up the asset-pricing model
with time-varying betas. We show that this model
can be rewritten as a two-factor model, with a
proxy for the Asian crisis appearing in the second
factor. We provide evidence that the Asian crisis
indeed affected the systematic risk of U.S. firms.

First-Stage Estimation Procedure
Our first-stage regression estimates a weekly
asset-pricing model for each firm i (i=1,...,39) for
the period 1997-98. This model follows the CAPM
but extends it in two ways to allow for time-varying
b-coefficients. First, we allow the firm-specific

Schmid (1999) provides a concise overview of the argument
made here.

All sample firms are headquartered in the United States with the
exception of Northern Telecom (NORTEL) of Canada.

Alternatively, we could estimate the model using a single-stage
approach, as a later section describes briefly.

FEDERAL RESERVE BANK of ST. LOUIS

parameters of the model to differ between 1997
and 1998.8 Second, we allow the CAPM beta of
each firm to depend on developments in Asia,
which we proxy by two alternative measures of the
excess dollar return on a portfolio invested in
Asian securities.
The first-stage regression model is the following:
(1)

(

Ri, t − R f , t = βi, j 1 + λi, j × CRISISt

(

)

× Rm,t − R f , t + ε i, t
with j = 1997 for t = 1,...,52 and j = 1998 for
t = 53,...,104 (weeks running from the beginning of
1997 to the end of 1998). The dependent variable,
Ri,t 2 Rf,t, is the excess log return in week t on firm
i’s stock in period t; Rf,t is the risk-free rate, the
one-week return on a three-month Treasury bill.
CRISISt is a variable that proxies for the state of the
Asian crisis in week t; Rm,t 2 Rf,t is the excess log
return during week t on the U.S. stock-market
portfolio (S&P 500); and εi,t is the error term. The
error term εi,t measures realizations of the firm’s
idiosyncratic risk, that is, movements in the firm’s
excess return that are not explained by its comovement with the market.
The parameters of interest are bi,j and λi,j. It is
important to recognize that it is the entire expression bi,j(1 1 λi,j 3 CRISISt) that corresponds to the
market beta of firm i in the standard CAPM framework (see Brealey and Myers, 1996, pp. 179-83). In
purely statistical terms, the standard CAPM beta—
as well as our more complicated expression for
beta—is the covariance of firm i’s excess log return
with the U.S. market excess log return, divided by
the variance of the market excess log return. In
interpreting our model, the parameter λi,j is meant
to capture the possible dependence of the CAPM
beta estimated for firm i on the economic situation in Asia. We measure the current state of the
Asian crisis as the weekly excess return on an
Asian portfolio of securities. This is because we
want to allow each firm’s CAPM beta to be timevarying and to depend on investors’ expectations
about Asia and the particular firm’s exposure to
these developments.
Our time-varying extension of the CAPM model
in equation 1 is nonlinear in the parameters. For
ease of estimation we remove the nonlinearity by
defining a new parameter, δi,j = bi,jλi,j, and
rewriting equation 1 as follows:

(2)

(

)

Ri, t − R f , t = βi, j Rm, t − R f , t + δ i, j

(

)

× CRISISt × Rm, t − R f , t + ε i, t

again with j = 1997 for t = 1,...,52 and j = 1998
for t = 53,...,104. The firm’s exposure to market
movements, its CAPM or market beta, can now be
seen to contain two components: An autonomous
(not crisis-related) component, bi,j, and a crisis-sensitive component, δi,j 3 CRISISt. The evolution of a
firm’s CAPM beta, therefore, is allowed to depend
on developments in Asia in an easily estimable way.9
It also is possible to interpret model 2 not solely
as a time-varying extension of the (one-factor) CAPM
but as a multifactor asset-pricing model (Merton,
1973; Ross, 1976; Fama and French, 1992; Campbell
and Cochrane, 1999).10 This interpretation is valid
only if the two “factors” represented by the U.S.
market excess return and the product of the Asian
and U.S. market excess returns essentially are
uncorrelated. It turns out in our sample that they
are: The pair-wise correlations of the two independent variables are -0.155 and -0.173 for the two
crisis proxies that we investigate. Thus, we also
can interpret our results as estimates of the sensitivity of firms’ excess returns to first, U.S. excess
market returns, and second, the product of Asian
and U.S. excess market returns. If the two factors
were completely uncorrelated, the estimated coefficient on the first (non-time varying component)
would, in fact, be the (unconditional) CAPM beta.
We use weekly observations of returns from
the beginning of 1997 through the end of 1998
to estimate the model. We examine two alternative
crisis variables, each of which is the excess log
return (in dollar terms) on an Asian portfolio,
8

We chose one-year windows because the firm characteristics of interest in our second-stage regression are observed annually. At the same
time, one-year windows allow for a sufficient number of weekly
observations for the estimation of the parameters of interest in the
asset-pricing model.

An increase in the firm’s exposure to market risk has two effects on
the parameter δi,j. First, investors demand higher future returns,
which causes δi,j to increase. Second, an increase in δi,j implies that,
for given expected future cash flows, the firm’s stock price will decline
initially. This reinforces the comovement of the firm’s stock with the
crisis variable.

Campbell and Cochrane (1999, p. 7) demonstrate the equivalence of a
one-factor conditional CAPM and an unconditional multifactor CAPM
in which the two factors are the excess market return and the product
of the excess market return and a time-varying predetermined financial variable (the log dividend-price ratio in their case). In our case,
the financial variable is the value of an Asian-crisis proxy.

J A N UA RY / F E B R UA RY 2 0 0 0

REVIEW

RAsia 2 Rf, where the risk-free return Rf, is the
weekly return on the three-month U.S. Treasury
bill. The first crisis variable we use is the excess
log return on the FT/S&P Actuaries Pacific StockMarket Index excluding Japan. The second crisis
variable is the excess log return on the J.P. Morgan
Emerging Local Markets Index Plus (Asia), an
index of money-market securities issued in Asian
emerging markets.11
We assume Cov[εi,t,εi,s] = 0 for t ° s, where
t,s = 1,...,T, that is, the error terms are serially
uncorrelated. We allow the error terms to be
heteroskedastic across equations (i.e., firms) but
not over time. This means that Var[εi,t] = si2
need not be identical for all i (i = 1,...,N) but they
are assumed to be identical for all t (t = 1,...,T).12
We adopt the seemingly unrelated regression
(SUR) approach but impose a diagonal covariance
matrix for the error terms. That is, for all t (t =
1,...,T), we impose the restriction Cov[εi,t,εj,t] =
0, i ° j (i, j = 1,...,N). This restriction is appropriate
because regressors are identical across the set of
firm-specific regression equations that are used
in the regression model 2.13
We test for serial correlation using the LjungBox (1978) test statistic with a standard lag length
of floor (4 (T/100)2/9), where floor means rounded
down to the nearest integer. The null hypothesis
of no serial correlation could not be rejected. We
used the heteroskedasticity-consistent covariance
matrix proposed by White (1980). Both the t-tests
and the Wald tests are based on heteroskedasticityconsistent standard errors.14 The White correction
procedure deals with heteroskedastic residuals in
the most general way. In estimating equation 2,
the procedure controls for any form of heteroskedasticity that goes beyond the cross-sectional differences in the variances si2 (i = 1,...,N) for which the
model controls directly.15

index; the FT/S&P Actuaries World Stock-Market
Index Pacific excluding Japan; and the J.P. Morgan
Emerging Local Markets Index Plus (Asia), a moneymarket return index.16 The cumulative total U.S.
dollar returns for these three risky portfolios and for
the riskless T-bill are plotted in Figure 2.
Estimates of the “autonomous” part of
each firm’s market sensitivity. The estimated
coefficients, b̂i,1997 and b̂i,1998, would retain their
interpretation from the traditional one-period CAPM
framework as measures of the comovement of the
excess return of firm i with the only relevant (priced)
factor—the excess return of the market (the S&P
500) in year j—only under the null hypothesis that
there is no crisis-related time variation in this factor
loading (i.e., estimated sensitivity). If there is some
systematic relationship between movements in our
proxies for the Asian crisis and the way the stockmarket prices our sample firms’ equity returns, however, then our beta coefficients will quantify only
the “autonomous component” of firms’ exposures
to market risk. Table 3, which appears on page 24,
displays our estimated autonomous b-coefficients,
where we have used the excess log returns on the
FT/S&P Asian stock-market index in the first two
columns and the excess log returns on the J.P.
Morgan money-market index in the last two
columns, respectively, as a proxy for the Asian crisis.
Estimated autonomous b-coefficients generally
are significant statistically in Table 3, with 73 of 78
coefficients significant at the 10-percent level when
using the Asian stock-market index. The Wald statistic for the overall fit of the 39-equation system of
estimated b-coefficients is significant statistically in
each year, as well. Significant Wald statistics for
tests of cross-sectional and intertemporal differences
indicate that there is a great deal of firm heterogeneity and intertemporal variation with respect

Results of First-Stage Regression

Although a larger sample size would have been
desirable, we were able to obtain reliable geographical
breakdowns of sales data for only 39 of the S&P 100
firms (see Tables 1 and 2 for the list of firms, their
sales exposures to Asia, their financial leverage, and
sample statistics for these variables). We obtained
total-return series and calculated weekly excess
returns (i.e., the return in excess of the return on a
three-month T-bill) for each firm’s stock and for the
following (all in U.S. dollar terms): the S&P 500

J A N UA RY / F E B R UA RY 2 0 0 0

For a list of the variables employed in this paper, see Appendix B.

We control for the effects of a possible violation of the assumption of
time-invariant variances by using White’s (1980) heteroskedasticityconsistent covariance matrix.

In this case, accounting for potential contemporaneous correlation
across equations offers no efficiency gain. See Greene (1997, p. 676).

See Greene (1997, p. 488) on how to implement covariance matrices
for nonspherical disturbances in Wald tests.

This implies that the White-corrected standard errors in the SUR
framework are identical to those that would result if the equations in
model 2 were White-corrected individually.

See Appendix A for a detailed data description.

FEDERAL RESERVE BANK of ST. LOUIS

Table 1
Sample Firms
Panel A: Industry Classifications

Firm
Aluminum Company of America
American Express Company
AMP Incorporated
Avon Products, Inc.
BankAmerica Corporation
Baxter International, Inc.
Boeing Company
Bristol-Myers Squibb Company
Champion International Corporation
Coca Cola Company
Computer Sciences Corporation
Delta Air Lines, Inc.
Eastman Kodak Company
Fluor Corporation
General Electric Company
General Motors Corporation
H.J. Heinz Company
Halliburton Company
Homestake Mining Company
Intel Corporation
International Business Machines Corporation
International Paper Company
Johnson & Johnson
Mallinckrodt, Inc.
McDonald’s Corporation
Merck & Co., Inc.
Merrill Lynch & Co., Inc.
Minnesota Mining & Manufacturing Company
Mobil Corporation
Monsanto Company
National Semiconductor Corporation
Northern Telecom Limited
Oracle Corporation
PepsiCo, Inc.
Pharmacia & Upjohn, Inc.
Ralston-Purina Company
Schlumberger Limited
United Technologies Corporation
Xerox Corporation

Ticker
Symbol

SIC
Code

AA
AXP
AMP
AVP
BAC
BAX
BA
BMY
CHA
KO
CSC
DAL
EK
FLR
GE
GM
HNZ
HAL
HM
INTC
IBM
IP
JNJ
MKG
MCD
MRK
MER
MMM
MOB
MTC
NSM
NT
ORCL
PEP
PNU
RAL
SLB
UTX
XRX

33
61
36
28
60
38
37
28
26
20
73
45
38
16
36
37
20
16
10
36
35
26
28
28
58
28
62
32
29
28
36
36
73
20
28
20
13
37
38

Primary Industry Classification
Primary Metal Industries
Nondepository Financial Institutions
Electronic & Electric Equipment
Chemicals & Related Products
Banks & Banking Services
Instruments & Related Products
Transportation Equipment
Chemicals & Related Products
Paper & Related Products
Food & Related Products
Business Services
Air Transportation
Instruments & Related Products
Heavy Construction
Electronic & Electric Equipment
Transportation Equipment
Food & Related Products
Heavy Construction
Metal Mining & Related Services
Electronic & Electric Equipment
Industrial Equipment & Machinery
Paper & Related Products
Chemicals & Related Products
Chemicals & Related Products
Dining & Drinking Places
Chemicals & Related Products
Security & Commodity Brokers
Stone, Clay & Glass Products
Petroleum & Coal Products
Chemicals & Related Products
Electronic & Electric Equipment
Electronic & Electric Equipment
Business Services
Food & Related Products
Chemicals & Related Products
Food & Related Products
Oil & Gas Production
Transportation Equipment
Instruments & Related Products

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REVIEW

Table 1
Sample Firms
Panel B: Values of the Variables SALES and DEBT

Firm

SALES
1996

SALES
1997

DEBT
1996

DEBT
1997

AluminumCompany
Co. of America
Aluminum
of America
AmericanExpress
ExpressCompany
Co.
American
AMPIncorporated
Inc.
AMP
AvonProducts,
Products,Inc.
Inc.
Avon
BankAmericaCorporation
Corp.
BankAmerica
BaxterInternational,
International Inc.
Baxter
BoeingCompany
Boeing
Bristol-MyersSquibb
SquibbCompany
Bristol-Myers
ChampionInternational
InternationalCorporation
Champion
CocaCola
ColaCompany
Company
Coca
ComputerSciences
SciencesCorporation
Corp.
Computer
DeltaAir
AirLines,
Lines Inc.
Delta
EastmanKodak
KodakCompany
Eastman
FluorCorporation
Corp
Fluor
GeneralElectric
ElectricCompany
General
GeneralMotors
MotorsCorporation
General
H.J.Heinz
HeinzCompany
H.J.
HalliburtonCompany
Co.
Halliburton
HomestakeMining
MiningCompany
Homestake
IntelCorporation
Corp.
Intel
InternationalBusiness
BusinessMachines
MachinesCorporation
International
InternationalPaper
PaperCompany
International
Johnson&&Johnson
Johnson
Johnson
Mallinckrodt Inc.
Mallinckrodt,
McDonald’sCorporation
Corp.
McDonald’s
Merck&&Co.,
Co.,Inc.
Inc.
Merck
MerrillLynch
Lynch&&Co.,
Co.,Inc.
Inc.
Merrill
Minn. Mining
& Manufacturing
Minnesota
Mining
& Manufacturing Company
MobilCorporation
Corp.
Mobil
MonsantoCompany
Company
Monsanto
NationalSemiconductor
SemiconductorCorporation
Corp.
National
NorthernTelecom
TelecomLimited
Northern
OracleCorporation
Corp.
Oracle
PepsiCo,Inc.
Inc.
PepsiCo,
Pharmacia&&Upjohn,
Upjohn,Inc.
Inc
Pharmacia
Ralston-PurinaCompany
Ralston-Purina
SchlumbergerLimited
Ltd.
Schlumberger
UnitedTechnologies
TechnologiesCorporation
United
XeroxCorporation
Corp.
Xerox

0.172
0.083
0.193
0.156
0.092
0.164
0.335
0.104
0.000
0.218
0.055
0.028
0.154
0.095
0.045
0.021
0.119
0.158
0.192
0.302
0.194
0.112
0.123
0.077
0.119
0.089
0.062
0.181
0.217
0.069
0.337
0.074
0.143
0.116
0.146
0.148
0.295
0.129
0.006

0.167
0.078
0.198
0.154
0.079
0.145
0.301
0.098
0.000
0.226
0.062
0.024
0.160
0.108
0.043
0.009
0.121
0.166
0.379
0.289
0.194
0.106
0.128
0.053
0.133
0.074
0.062
0.175
0.259
0.087
0.338
0.070
0.142
0.066
0.137
0.119
0.315
0.119
0.006

0.126
0.060
0.039
0.047
0.062
0.223
0.146
0.066
0.314
0.069
0.156
0.147
0.039
0.001
0.181
0.176
0.265
0.045
0.125
0.031
0.122
0.237
0.070
0.169
0.278
0.048
0.123
0.064
0.096
0.144
0.132
0.153
0.000*
0.344
0.051
0.300
0.062
0.086
0.314

0.111
0.066
0.033
0.045
0.054
0.303
0.161
0.085
0.351
0.047
0.176
0.116
0.045
0.064
0.153
0.186
0.271
0.096
0.202
0.016
0.168
0.267
0.052
0.182
0.265
0.052
0.147
0.077
0.084
0.184
0.111
0.125
0.065
0.246
0.038
0.392
0.088
0.076
0.317

* Zero value is due to rounding.

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FEDERAL RESERVE BANK of ST. LOUIS

Table 2
Summary Statistics for SALES and DEBT
1996
1997

Minimum

Median

Mean

Maximum

Standard
Deviation

SALES

0
0

0.123
0.121

0.136
0.138

0.337
0.379

0.083
0.094

DEBT

0
0.016

0.123
0.111

0.131
0.141

0.344
0.392

0.093
0.098

to market risk exposures of large firms. Results are
similar when excess log returns on the J.P. Morgan
money-market index are used as the crisis proxy.
We also tested the significance of firm-specific
intercepts in model 2. Wald test statistics of 37.60
(for the stock-market index) and 34.98 (for the
money-market index) indicate that firm-specific
intercepts are—as a group—not significant statistically,
and thus, are excluded from the regression equation.
Estimates of δ and the “crisis-sensitive”
components of firms’ market sensitivity. The
first-stage regression produces estimates of δ for
each firm i during each year, δ̂i,1997 and δ̂i,1998 (i =
1,...,39). It is important to note that our proxy variables for the Asian crisis are negative, on average,
during 1997 and 1998 for the FT/S&P stock-market
index and during 1997 for the money-market index.
Therefore, to translate the regression coefficients
into a measure of the influence of the Asian crisis
on the b-coefficients, we multiply δ̂i,1997 and δ̂i,1998
by the mean of the Asian crisis variable during the
respective year. The products of the means and the
respective regression coefficients are displayed in
Table 4. They are estimates of the “crisis-sensitive”
component of the firm’s CAPM beta.
Table 4 indicates that 23 of the 78 estimates of
a possible crisis-sensitive component of firm exposures to market risk were different from zero at the
10-percent confidence level when the Asian stockmarket index was used as the crisis proxy. This is
about three times as many as would be expected by
pure chance. When the Asian money-market index
was used as the crisis proxy, 20 of 78 estimates
were significant at the 10-percent level. Thus, we
conclude that it is possible to split large firms’
exposures to market risk (their CAPM betas) into
autonomous and crisis-sensitive components. As
in the systems of estimated autonomous betas discussed above, the Wald statistics all are significant

statistically in tests for the over-all fit of the crisissensitive components model, as well as for
cross-sectional and intertemporal differences in
crisis-sensitive components in firm returns.
By adding the crisis-sensitive component of a
firm’s beta from Table 4 to its corresponding
autonomous part, presented in Table 3, we obtain
the average total sensitivity of the firm to the S&P
500 return. This is the firm’s beta measured at the
mean of the respective crisis variable. Table 5,
which appears on page 26, provides summary
statistics that condense the information presented
in Tables 3 and 4.
It is important to note that the sensitivity of
the excess log return of firms we examine are
unlikely to increase (or decrease) all at the same
time. This is because a firm’s beta measures the
position of this firm relative to the index.17 For
example, if we had the entire set of S&P 500 firms
in our sample, some betas necessarily would
decrease if others increased. If greater sales exposure to Asia added to the firm’s sensitivity to
market risk, the betas of the firms that have a
higher-than-average fraction of their sales to Asia
would increase in response to the crisis while
betas of those firms with below-average sales to
Asia would have to decrease. The index average
corresponds to the S&P 500’s “average” firm.
Using the FT/S&P index returns in Tables 3
and 4, for example, we estimate the components
of Minnesota Mining and Manufacturing’s (3M)
average total U.S. market (i.e., S&P 500) sensitivity
during 1997 to be 0.497 and 0.111, for a total of

A common influence of the Asian crisis on all firms’ contributions to
market risk would not lead to changes in the firms’ betas. This is
because a common impact (by definition) would affect all firms equally,
leaving their positions relative to the market index unchanged.

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Table 3
Estimates of the Autonomous Component of CAPM Betas, bi,j
Firm
Co. of America
Aluminum Company
of America
Co.
American Express Company
Inc.
AMP Incorporated
Avon Products, Inc.
Corp.
BankAmerica Corporation
International Inc.
Baxter International,
Inc.
Boeing Company
Bristol-Myers Squibb Company
Champion International Corporation
Coca Cola Company
Corp.
Computer Sciences Corporation
Lines Inc.
Delta Air Lines,
Eastman Kodak Company
Corp
Fluor Corporation
General Electric Company
General Motors Corporation
H.J. Heinz Company
Co.
Halliburton Company
Homestake Mining Company
Corp.
Intel Corporation
International Business Machines Corporation
International Paper Company
Johnson & Johnson
Mallinckrodt Inc.
Mallinckrodt,
Inc.
Corp.
McDonald’s Corporation
Merck & Co., Inc.
Merrill Lynch & Co., Inc.
Minn. Mining
& Manufacturing
Minnesota
Mining
& Manufacturing Company
Corp.
Mobil Corporation
Monsanto Company
Corp.
National Semiconductor Corporation
Northern Telecom Limited
Oracle Corporation
Corp.
PepsiCo, Inc.
Inc
Pharmacia & Upjohn, Inc.
Ralston-Purina Company
Ltd.
Schlumberger Limited
United Technologies Corporation
Corp.
Xerox Corporation
Wald statistic
Wald statistic (cross-sectional differences)
Wald statistic (intertemporal differences)
Observations per firm

Asian Stock-Market Index
1997
1998
0.810***
1.229***
1.152***
0.733**
0.817***
1.113***
0.307*
1.181***
0.783***
1.381***
1.097***
1.033***
0.721**
0.949***
1.111***
0.827***
0.892***
1.051***
0.261
1.074***
0.897***
0.901***
1.035***
0.290*
0.614***
1.188***
1.928***
0.497***
0.916***
0.829***
1.467***
1.450***
0.774*
0.785***
0.981***
0.857***
1.065***
0.803***
0.973***
1122***
119.2***

J A N UA RY / F E B R UA RY 2 0 0 0

0.764***
1.297***
0.850***
1.004***
1.057***
1.041***
0.252
1.241***
0.814***
1.377***
0.667*
0.925***
0.555*
1.070***
1.242***
0.813***
0.592*
0.921***
0.273
0.988***
0.729***
1.147***
1.218***
0.391**
0.589***
1.135***
1.990***
0.578***
1.011***
0.727**
1.252***
1.348***
0.383
0.698**
0.924***
0.803***
0.745***
0.746***
0.728***
770.1***
92.80***

74.86***
52

*/**/*** significant at 10/5/1 percent levels (t-tests are two-tailed).

0.640***
1.778***
0.095
1.397***
1.245***
0.541***
1.187***
0.945***
1.336***
1.085***
0.788**
1.203***
0.077
1.198***
1.351***
1.112***
1.818***
0.508***
0.353
0.642**
0.816***
0.753***
0.485***
0.807***
0.941***
0.858***
2.055***
0.650***
0.680***
0.789
1.039**
1.604***
1.844***
0.367*
0.807***
0.787***
1.563***
1.160***
0.981***
916.2***
139.5***

Asian Money-Market Index
1997
1998

0.659***
1.785***
0.124
1.384***
1.232***
0.523***
1.215***
0.946***
1.333***
1.083***
0.781**
1.184***
0.087
1.184***
1.334***
1.117***
1.858***
0.505***
0.408
0.637***
0.828***
0.764***
0.470***
0.810***
0.953***
0.866***
2.052***
0.635***
0.712***
0.781
1.077**
1.602***
1.885***
0.386**
0.806***
0.799***
1.588***
1.161***
0.988***
930.1***
136.7***
66.49***
52

FEDERAL RESERVE BANK of ST. LOUIS

Table 4
Estimates of the Crisis-Sensitive Component of CAPM Betas, δi,j 3 CRISISt,
at Annual Means of CRISIS
Firm
Company
of America
Aluminum Co.
of America
Company
American Express Co.
Incorporated
AMP Inc.
Avon Products, Inc.
Corporation
BankAmerica Corp.
International,Inc.
Inc.
Baxter International
Boeing Company
Bristol-Myers Squibb Company
Champion International Corporation
Coca Cola Company
Computer Sciences Corp.
Corporation
Delta Air Lines
Lines, Inc.
Eastman Kodak Company
Fluor Corp
Corporation
General Electric Company
General Motors Corporation
H.J. Heinz Company
Halliburton Co.
Company
Homestake Mining Company
Corporation
Intel Corp.
International Business Machines Corporation
International Paper Company
Johnson & Johnson
Mallinckrodt,Inc.
Inc.
Mallinckrodt
Corporation
McDonald’s Corp.
Merck & Co., Inc.
Merrill Lynch & Co., Inc.
Minnesota
Mining
& Manufacturing Company
Minn.
Mining
& Manufacturing
Corporation
Mobil Corp.
Monsanto Company
Corporation
National Semiconductor Corp.
Northern Telecom Limited
Corporation
Oracle Corp.
PepsiCo, Inc.
Inc.
Pharmacia & Upjohn, Inc
Ralston-Purina Company
Limited
Schlumberger Ltd.
United Technologies Corporation
Corporation
Xerox Corp.
Wald statistic
Wald statistic (cross-sectional differences)
Wald statistic (intertemporal differences)
Observations per firm

Asian Stock-Market Index
1997
1998
0.043
–0.097
0.100
0.112
0.057
0.074*
0.171***
–0.104**
0.150
–0.010
–0.045
–0.169**
0.044
0.027
0.017
–0.009
–0.062
–0.074
0.077
0.108*
0.155**
0.124
0.052
–0.023
0.080*
–0.026
–0.066**
0.111***
–0.057***
–0.060
0.136
0.010
0.340
0.043
0.002
–0.027
–0.023
0.070
0.026
91.21***
87.13***

Asian Money-Market Index
1997
1998

0.012**
0.005
0.001
–0.007
0.020
–0.012***
0.026**
0.002
0.008
0.008
–0.002
–0.009*
0.019*
–0.010*
–0.011**
–0.007
0.004
0.000
0.019
–0.007
–0.001
0.006**
–0.010*
–0.001
0.005
0.002
0.019**
–0.008*
0.013**
0.025
0.016
–0.011
0.013
0.010
0.000
0.012
0.006
0.002
0.010*
72.72***
71.95***

0.046
–0.085
0.211***
–0.088
–0.099*
0.075*
0.114
–0.084
0.059
–0.003
0.206**
–0.027
0.110
–0.051
–0.061*
0.003
0.128
0.032
0.032
0.100
0.167**
–0.068
–0.071
–0.065*
0.053
0.016
–0.066
0.013
–0.079***
0.024
0.182
0.059
0.378
0.068
0.031
0.015
0.158**
0.065
0.143**
77.04***
76.89***

91.04***
52

–0.031
–0.011
–0.032
0.020
–0.001
0.029**
–0.051*
–0.003
–0.004
–0.004
0.009
0.029**
–0.025
0.024
0.028*
0.000
–0.048
0.003
–0.076*
0.010
–0.012
–0.018*
0.024*
–0.003
–0.017
–0.011
–0.012
0.023**
–0.046***
–0.011
–0.055
0.011
–0.055
–0.029*
0.001
–0.023
–0.032
–0.002
–0.016
67.03***
65.37***
64.42***
52

*/**/*** significant at 10/5/1 percent levels (t-tests are two-tailed); tests refer to the actual regression coefficients, not to the displayed
products between the regression coefficients and the annual means of the crisis variable.

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REVIEW

Table 5
Summary of Results from First-Stage Regressions
1997
1998
Autonomous
component of
CAPM beta, bi,j

Crisis-sensitive
component of
CAPM beta, δi,j 3
CRISISt , (at annual
means of crisis
index)

Asian-Crisis
Index

Minimum

Maximum

Standard
Deviation

1.78 3 10–1
1.35 3 10–1

9.18 3 10–1
8.86 3 10–1

9.09 3 10–1
9.49 3 10–1

1.72
1.97

3.17 3 10–1
4.45 3 10–1

Money market

2.13 3 10–1
1.50 3 10–1

8.42 3 10–1
8.87 3 10–1

8.82 3 10–1
9.55 3 10–1

1.81
1.98

3.51 3 10–1
4.45 3 10–1

Stock market

–1.49 3 10–1
–1.35 3 10–2

2.09 3 10–2
2.47 3 10–3

1.86 3 10–2
3.77 3 10–3

2.92 3 10–1
2.60 3 10–2

8.25 3 10–2
1.05 3 10–2

Money market

–1.15 3 10–1
–7.88 3 10–2

2.17 3 10–2
–6.19 3 10–3

2.20 3 10–2
–9.02 3 10–3

3.54 3 10–1
3.24 3 10–2

9.90 3 10–2
2.55 3 10–2

THE ECONOMETRIC MODEL:
SECOND-STAGE REGRESSIONS
We attempt to identify the link between the
Asian crisis and changes in firms’ exposures to U.S.
stock-market risk—their CAPM betas—in a secondstage regression. To this end, we gathered firmspecific data on sales to the Asian region as a proportion of total sales, on (the book values of)
long-term debt and total assets, and on industry
participation. In the first-stage regression approach
discussed above, we used two different excessreturn measures as proxies for the Asian crisis, one
based on the FT/S&P stock-market index and the
other derived from the J.P. Morgan money-market
index. We run the second-stage regression using
the results of each set of first-stage regressions as
dependent variables.

Outline of Second-Stage Estimation Procedure
This section details the second step in our estimation procedure, which examines the channel by
which the Asian crisis affected the sample firms’
stock-market betas. We do this by regressing the
coefficients δ̂i,1997 and δ̂i,1998 obtained from the
asset-pricing model on a set of firm characteristics.
These firm attributes are sales to the Asian region,

J A N UA RY / F E B R UA RY 2 0 0 0

Mean

Stock market

0.608. The comparable estimates for 1998 are
0.650 and -0.008, for a total of 0.642. As shown
in the tables, all of these component estimates
for 3M were statistically significant in the first
stage regression.

Median

weighted by the firm’s leverage, and the firm’s primary industry affiliation.
Sales exposure to Asia and the sensitivity
of CAPM betas to the Asian crisis. The estimated
crisis-sensitive coefficients from equation 2, δ̂ i,1997
and δ̂i,1998, serve as the dependent variables in
the second-stage regression. We have one estimate
for each of the 39 firms in each year, for a total
of 78 observations. A standard pooled-time-series
cross-section approach can be applied because
the stochastic nature of the dependent variable is
controlled for by the error term of the regression
equation.
The explanatory variable of interest in this
regression is SALES 3 DEBT, the product of the
firm’s ratio of sales to Asia (SALES) and its ratio of
book value of long-term debt to total assets (DEBT).
The reason for weighting SALES with DEBT is that
an equity holder’s actual returns are influenced by
the firm’s capital structure, that is, the relative
amounts of debt and equity the firm has. The key
point is that leverage (i.e., debt) amplifies the risk of
the firm’s underlying cash flows as perceived by the
equity holders. See the insert on the next page for
more details.
To avoid simultaneity problems in the regression model, the observations for SALES and DEBT
were lagged by one calendar year. There also are
indicator variables in the regression corresponding
to the two-digit SIC codes of the sample firms’ main
lines of business. No intercept is included and one
of the 19 industry indicator variables is excluded
(SIC 73). This is appropriate because we do not
assume a common shift in the stock-market

FEDERAL RESERVE BANK of ST. LOUIS

LEVERAGE EFFECTS ON BETA
To illustrate the effect of leverage on an equityholder’s returns more formally, let the exposure
of a firm’s underlying cash flows to variations in
the cash flows of all firms in the market be denoted the firm’s “assets beta,” or b assets , and let the
firm’s stock-market risk exposure, or “equity
beta,” be written as b equity . Finally, let the market
comovement of returns on the firm’s debt be
denoted its “debt beta,” or b debt . Then, if D is the
book value of the firm’s debt and E is the book
betas due to the Asian crisis for the reason
discussed above.18
The second-stage regression equation reads:
(3)

δˆ i , j = θ × SALESi , j −1 × DEBTi , j −1
K

+ ∑ φ k Ii , k + ω i ,t
k =1

with j = 1997 for t = 1,...,52 and j = 1998 for t =
53,...,104. The regressor Ii,k is an industry indicator
variable that takes on unit value if the corresponding firm belongs to industry k (k = 1,...,K; K = 18),
and is equal to zero otherwise.
The SALES variable is measured with error
because firms reported regional sales on a nonstandardized basis during our sample period.19
Thus, we used an instrumental-variables (IV)
approach, which entails ranks of the leverageweighted sales numbers as instruments for the
actual leverage-weighted sales numbers.20 We
tested for the presence of serial correlation using
the Ljung-Box (1978) test statistic with unit lag
length.21 The t-values and the Wald test statistics
are based on White (1980) corrected standard
errors.22 We also provide bootstrapped Student’s t
intervals and bootstrap-t intervals. While the first
type of bootstrap intervals makes use of the Central
Limit Theorem, the latter is distribution-free.23

Results of Second-Stage Regressions
Second-stage regression results are presented in
Tables 6 and 7. Our most important finding is that
firms’ leverage-weighted sales exposure to Asia is significantly related to changes in their CAPM betas.
Sales exposure to Asia and the sensitivity of
CAPM betas to the Asian crisis. Tables 6 and 7

value of equity, it is possible to express the firm’s
equity beta as follows (see Brealey and Myers,
1996, pp. 213-17):

β equity =

D+E
D
β assets − β debt
E
E

If the firm had no debt, its asset beta and its
equity beta would be identical. Given positive
amounts of debt in its capital structure, however,
a firm’s equity beta is larger than its asset beta.
provide the results from our second-stage regression.
We regress the crisis-sensitive coefficients δ̂i,1997 and
δ̂i,1998, (i = 1,...,39) on firm characteristics. The
first two columns of Table 6 show results using the
Asian stock-market index as the crisis proxy, while
the results in the last two columns were generated
using the money-market index. The table indicates
that the impact of the Asian crisis on the crisis-sensitive component of the firm’s CAPM beta was
associated significantly with leverage-weighted
sales exposure to the region. To interpret the negative coefficients, recall that the average excess
returns on the stock-market index during both
1997 and 1998, as well as the average moneymarket excess return during 1997, were negative.
The negative coefficients on the variable SALES 3
DEBT, when multiplied by the average excess return
on the crisis proxy, indicate that a worsening of the
crisis increased the crisis-sensitive component of a
firm’s beta, the greater the firm’s leverage-weighted

We tested for the statistical significance of the intercept and found it
insignificant for both crisis variables. The t-values were equal to
–1.243 (stock-market index) and –1.309 (money-market index).

For details on the collection of the sales data, see Appendix A.

On the use of ranks as instrumental variables in regressions, see
Greene (1997, pp. 440-42).

We use the Ljung-Box test because it is related to the heteroskedasticity and autocorrelation consistent covariance matrix proposed by
Newey-West (1987). This correction would be particularly important
if serial correlation were a problem, although we did not find any evidence of serial correlation for either Asian excess-return series.

A White (1980) test is not well-defined for our regression because the
number of regressors in the matrix needed for the test exceeds the
number of observations. This also is true after eliminating linearly
dependent regressors.

For details on these two bootstrapping procedures, see Efron and
Tibshirani (1993, pp. 158-62).

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REVIEW

Table 6
Effects on Crisis-Sensitive Coefficients, δi,j
Independent Variable
SALES 3 DEBT
SIC 10
SIC 13
SIC 16
SIC 20
SIC 26
SIC 28
SIC 29
SIC 32
SIC 33
SIC 35
SIC 36
SIC 37
SIC 38
SIC 45
SIC 58
SIC 60
SIC 61
SIC 62
R2
Wald statistic (SIC codes)
Number of observations

Asian Stock-Market Index
Coefficient
t-Statistic
–1.813 3 102
1.153 3 10
4.170
–5.752 3 10–1
4.903
–5.986
2.996 3 10–1
–2.940 3 10–1
2.172
–8.886 3 10–1
–2.534
1.008
5.544 3 10–1
7.761 3 10–1
3.247
7.534
–5.784 3 10–1
–3.488
–3.452

–2.620***
3.268***
2.165**
–0.239
1.689*
–1.499
0.169
–0.055
0.448
–0.625
–1.203
0.477
0.288
0.190
2.128**
3.076***
–0.136
–0.693
–1.403

0.147
325.8***
78

Asian Money-Market Index
Coefficient
t-Statistic
–2.563 3 102
–3.442
–1.127 3 10
–4.323
5.941
–6.951
–4.842 3 10–1
7.087
7.452
–2.249
–4.263
–2.855 3 10–1
8.932
–3.833
–1.549 3 10
1.401 3 10
–1.728
1.102 3 10
–4.371

–2.729***
–0.351
–1.406
–0.969
1.590
–1.486
–0.142
2.076**
1.527
–1.191
–0.987
–0.081
1.913*
–0.750
–1.190
2.767***
–0.254
2.980***
–2.095**

0.179
85.96***
78

*/**/*** significant at 10/5/1 percent levels (t-tests are two-tailed).

sales exposure to Asia. Similarly, we would expect
a greater decline in the crisis-sensitive component
of a firm’s beta, the greater its leverage-weighted
sales exposure, if the change in the crisis proxy
were positive.24 We control for industry effects in
both regressions by including SIC code indicator
variables representing each firm’s primary industry
classification.
Significance tests that are more reliable than
the t-values displayed in Table 6 are shown in Table
7. For the regressor of interest, SALES 3 DEBT, we
provide results from two alternative bootstrapping
procedures that confirm our previous findings. A
firm’s leverage-weighted sales exposure significantly affected the impact of the Asian financial
crisis on the firm’s CAPM beta.
It is interesting to note that the two SIC codes
identified by Pollard and Coughlin (1999, p. 39) as
suffering the largest declines in real exports to East

J A N UA RY / F E B R UA RY 2 0 0 0

Asia during 1998—metallic ores and concentrates
(SIC 10) and crude oil and natural gas (SIC 13)—
were identified in our regression (using the stockmarket index) as experiencing large and significant
increases in the crisis-sensitive component of their
market betas. For both crisis indexes, significant
Wald statistics indicate that there were important
industry effects even after controlling for the effect
of leverage-weighted sales exposure to Asia on
betas. We do not wish to emphasize these industry
results, however, because the one-stage approach
described in the next section does not find significant industry effects.

The partial derivative of the expression (1 1 λ i,j 3 CRISISt ) from equation 1 with respect to δi,j is CRISISt /bi,j, where bi,j is the autonomous
(i.e., crisis-insensitive) part of firm i’s CAPM beta in year j. As Table 3
shows, all bi,j (i = 1,...,39; j = 1997, 1998) were positive.

FEDERAL RESERVE BANK of ST. LOUIS

Table 7
Effects on Crisis-Sensitive Coefficients, δi,j (Bootstrapped Standard Errors)
Asian-Crisis
Index

Regression
Coefficient

Bootstrapped Student’s t
Interval

Bootstrap-t
Interval

Stock market

–1.813 3 102

1/21.654 3 102

–1.382 3 102; 1.399 3 102

Money market

–2.563 3 102

1/22.133 3 102

–1.790 3 102; 1.818 3 102

95-percent confidence intervals, based on 2,500 draws.

A ONE-STAGE REGRESSION
APPROACH
The two-stage regression procedure applied
in this article can be aggregated into a one-stage procedure. Inserting equation 3 into equation 2 results
in:

(

( 4 ) Ri ,t − R f ,t = βi , j Rm,t − R f ,t

)

+ θ˜ × SALESi , j −1 × DEBTi , j −1

(

× CRISISt × Rm,t − R f ,t
K

)

(

+ ∑ ( φ k Ii , k ) × CRISISt × Rm,t − R f ,t
k =1

)

+ ε˜ i ,t

with j = 1997 for t = 1,...,52 and j = 1998 for
t = 53,...,104. We must drop one firm from the sample
(Champion International) because it has zero values
for the variable SALES (see Table 1, Panel B).
We estimated model 4 with a SUR approach
that is similar to the estimation procedure we
applied to model 2. In contrast to model 2 however,
the regressors in model 4 are not identical across
equations, which means that the efficiency of the
regression can be improved by accounting for contemporaneous correlation across the equations in a
SUR model.25 Thus, we relaxed the previously
imposed restriction of no contemporaneous correlation, that is, Cov[ε i,t ,ε j,t] = 0, i ° j (i, j = 1,...,N),
in favor of Cov[εi,t,εj,t] = si,j, (i, j = 1,...,N). We then
applied an iteration procedure in the estimation of
the cross-equation covariance matrix.
The important results from this one-stage
regression approach were similar to those of
our preferred two-stage approach (not reported).
Both approaches indicate that the leverage-ad-

justed sales exposure of our sample firms was
significantly associated with changes in the
firms’ exposure to stock-market risk. Industry
effects were not robust across the two approaches, however.

CONCLUSION
The Asian crisis affected consumers, investors,
firms, and national economies around the world in
many ways. Our analysis highlights one specific
channel of influence on a sample of large U.S.-based
firms. We find that the Asian crisis changed many
of these firms’ sensitivity to U.S. stock-market
movements, that is, their CAPM betas. We find evidence that the link connecting the Asian crisis and
changes in the stock-market risk exposure of our
sample firms is the firms’ leverage-weighted sales
exposures to the crisis region.
As a firm’s beta rises in response to a financial
crisis, investors demand higher excess returns. This
raises the firm’s cost of equity capital. A firm could
respond to this by reducing the share of its total
sales that go to the crisis region or by decreasing
its leverage. As a mitigating effect, some shrinkage
in the ratio of Asian to total sales occurs automatically in the wake of such a crisis because the
falling dollar value of sales constitutes a smaller
fraction of total revenues. Some firms also may
find it desirable to take actions to accelerate their
shift away from the region or to reduce leverage.
Another mitigating effect in the stock market is
the decrease in the weights of firms with increasing
market betas. Even if the firm’s expected earnings
do not decrease, the firm’s stock price must drop
initially to generate the higher future returns investors demand due to the increase in the firm’s
25

See Greene (1997, pp. 674-76).

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REVIEW

beta. A reduction in the share price reduces the
firm’s market capitalization, and therefore, its
weight in the market index. This effect may be
reinforced by a depression of the firm’s expected
earnings due to a decline in sales revenues from
the crisis region.

Ljung, Greta M., and George E.P. Box. “On a Measure of
Lack of Fit in Time Series Models,” Biometrica (June 1978),
pp. 297-303.
Merton, Robert C. “An Intertemporal Capital Asset Pricing
Model,” Econometrica (September 1973), pp. 867-87.

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Econometrica (May 1980), pp. 817-38.

FEDERAL RESERVE BANK OF ST. LOUIS

Appendix A: DATASET

We analyze 39 of the 100 firms in the “Standard
& Poor’s 100” stock-market index for calendar years
1997 and 1998. We were limited to this subset of
the S&P 100 because the other firms did not provide a sufficiently detailed breakdown of international sales. Our criterion for inclusion in the
sample was that the firm’s reports must allow us
to calculate the ratio of sales to customers in Asia
to worldwide sales (where both items are stated in
U.S. dollar terms).
Sales data and balance-sheet information
(long-term debt and total assets) were taken from
each firm’s annual reports. Data on consolidated
sales (including all subsidiaries) for each firm were
obtained from the income statement. Data on
sales to customers in Asia were taken from the
notes to the consolidated financial statements. For
firms whose fiscal years did not coincide with the
calendar year, the dollar amounts of sales and the
end-of-fiscal year items were interpolated linearly
to determine corresponding calendar-year values.

Construction of the SALES variable
and lines of business
Data on international sales are provided in the
notes to the consolidated financial statements in
annual reports of U.S. corporations. Unfortunately,
current reporting of international sales is not standardized. Many companies provide almost no
detail beyond a breakdown into domestic and foreign sales. Only 39 of the S&P 100 firms provided
enough country or area detail to allow us to calculate sales to the Asia/Pacific region. For example,
nine companies included sales data for areas outside Asia as part of their totals for Asia/Pacific. We
assumed that these firms combined sales totals
from regions where they do relatively little business with regions where they are more active.
We believe the non-Asian sales included in the
Asia/Pacific totals are insubstantial.
A second problem is that firms that distinguish
between sales to other departments or divisions
within the organization (intracompany sales) and
sales to unaffiliated customers (third-party sales)
do not always do so on a consistent basis when
presenting geographic breakdowns. In four cases,
firms did not distinguish between intracompany
and third-party sales on a geographic basis. For

these four firms we know the total amount of intracompany sales on a consolidated (world wide)
basis, but we do not know what percentage of sales
to Asia were intracompany. In these four instances,
the sum of sales totals to the various world regions
will be greater than total sales (because intracompany sales have not been eliminated). Fortunately,
intracompany sales averaged only 5.4 percent of
third-party sales for these four firms.
The SIC two-digit code for each firm’s main line
of business was taken from the 10-K filings found on
the Securities and Exchange Commission’s EDGAR
website <http://www.sec.gov/edgarhp.htm>; 1998.

Financial Markets Data
Weekly total-return data for our 39 sample companies and the S&P 500 index for 1997 and 1998
were purchased from Standard & Poor’s Compustat
and DRI respectively. Weekly returns were calculated
from Friday to Friday (closing prices), with adjustment
for stock splits and dividends.
The risk-free rate was proxied by a strategy
of investing in three-month Treasury bills each
week, holding the bill one week, then rolling over
into the new three-month T-bill. We collected
the “on-the-run” three-month yield as well as the
“off-the-run” yield of the bill issued the previous
week for each week during 1997 and 1998 from
the Wall Street Journal. Using these yields, we
calculated prices. Three-month T-bill log returns
were calculated as follows:
 Poff , t

Log 
r ,
 Pon, t - 1 t −1 
where Poff,t is the price of the “off-the-run” issue at
time t; Pon,t-1 is the price of the “on-the run” issue
at time t-1; rt-1 is the value of the return index at
time t-1 (t 2 1 . 0); and r0 equals 100.
We used the log returns on two indexes of
Asian securities to measure the economic situation
in Asia. These were the FT/S&P Actuaries World
Indices-Pacific Excluding Japan, a Pacific-region
stock-markets index, and the J.P. Morgan Emerging
Local Markets Index Plus (ELMI1)2Asia, a Pacificregion emerging-markets money-market index.
The FT/S&P World Indices are owned by FTSE,

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International Limited, Goldman, Sachs & Co., and
Standard & Poor’s. These indices are compiled by
FTSE International and Standard & Poor’s in
conjunction with the Faculty of Actuaries and the
Institute of Actuaries. More information on the J.P.
Morgan Emerging Local Markets Index Plus can be
found in J.P. Morgan’s Emerging Markets Bond Index
Monitor. Both indexes are stated in U.S. dollar
terms based on current exchange rates. Further
details are provided below.
At year-end 1998, the FT/S&P Actuaries World
Indices-Pacific Excluding Japan index included Australia (75 companies), Hong Kong (66), Indonesia
(26), New Zealand (18), The Philippines (22), Singapore (41), and Thailand (35). Malaysia (106 companies as of September 25, 1998) was removed
from the index on October 1, 1998, following the
Malaysian government’s introduction of investment exchange controls on September 1, 1998.
According to the ground rules for the construction and maintenance of the FT/S&P Actuaries
World Indices, criteria for inclusion in the World
Indices are the following:
• Direct equity investment by non-nationals
must be permitted;
• Accurate and timely data must be available;
• No significant exchange controls should exist
that would prevent the timely repatriation
of capital or dividends;
• Significant international investor interest
in the local equity market must have
been demonstrated;
• Adequate liquidity must exist.
Companies whose business is that of holding
equity stakes in other firms or other investments
are not excluded necessarily. Equity-holding firms
that are excluded include split-capital investment
trusts and companies whose share price is a direct
derivation of the values of underlying holdings,
such as mutual funds. Only shares listed on a
stock exchange are eligible for inclusion. Where a
company does not list all its shares in an eligible
class, or does not list an entire class, these partially
listed or unlisted shares are not eligible. All securities
comprising the bottom 5 percent of a country’s
market capitalization are excluded from the indices.

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A security is totally excluded if foreign investors are
barred from ownership. Calculation of the U.S.
dollar version of this index is explained in detail in
the FT/S&P Actuaries World Indices Ground Rules at
<http://www.ftse.com>.
The second financial-market index we used was
the J.P. Morgan Emerging Local Markets Index Plus
(ELMI1) (U.S. Dollar Index)-Asia. The ELMI1 tracks
total returns for local-currency-denominated moneymarket instruments in 24 emerging markets. It is
predominantly non-Latin America weighted and
includes four regional composites, Asia (48 percent
target weight as of January 29, 1999), Europe (18.26),
Latin America (23.07), and Middle East/Africa (10.67).
The ELMI1 employs a liquidity-sensitive weighting
system, which uses exports plus imports as a base.
Its portfolio consists of FX forwards, wherever
possible, to represent a country’s money markets.
A country is selected if it has been identified as
an emerging market with an economy large enough
to support significant capital flows, and has
accessible liquid local-currency-denominated
money-market instruments, either on- or offshore.
The Asian regional sub-index was used for this
analysis. The countries included in the Asian
regional composite are China (4.167 percent of the
total index), Hong Kong (20.833), India (4.167),
Indonesia (16.67), The Philippines (4.167), Singapore
(20.833), South Korea (4.167), Taiwan (4.167), and
Thailand (20.833). A special August month-end
rebalancing was performed to account for Russia’s
removal from the index, and a special September 8,
1998, rebalancing was performed to account for
Malaysia’s removal.
Target weights are derived by applying a series
of caps to the three-year, rolling, trade-weighted allocation for each country. Specifically, for countries
with convertible currencies, the weight per country
is limited to no more than 10 percent of the total
index. For countries with nonconvertible currencies
or impediments to investing onshore, the weight per
country is limited to no more than 2 percent of the
index. For each country subindex, a ladder of three
instruments was constructed, by initially investing in
one-, two-, and three-month instruments. Each
month, the proceeds of the maturing instrument is
reinvested in a new three-month instrument.

FEDERAL RESERVE BANK OF ST. LOUIS

Appendix B: DEFINITION OF VARIABLES

Dependent Variables
The dependent variable in the asset-pricing
regressions is each sample firm’s weekly excess
return, defined as the difference between the log
total return on the firm’s stock and the risk-free
return. A second-stage regression uses parameter
estimates from the first-stage regression as the
dependent variable. This parameter is the crisis-sensitive component of each firm’s estimated market
beta, δ̂i,j (i = 1,...,39; j = 1997,1998).

RAsia:

Firm-specific variables include the following:
SALESi:

Ratio of dollar amount of sales to
customers in Asia to dollar amount of
consolidated sales of firm i (annual);

DEBTi:

Ratio of book value of long-term debt of
firm i to its total assets (end of calendar
year; annual);

SICxi:

Indicator variable: 1 if the firm’s main
line of business falls into the two-digit SIC
division x, 0 otherwise (annual).

Independent Variables
We used four total-return indexes with each
return denoted as follows:
Ri:

Log change in total-return index of firm i
(weekly);

Rm:

Log change in S&P 500 total-return index
(weekly);

Rf:

Log change in three-month T-bill totalreturn index (weekly);

Alternatively defined as log change in the
FT/S&P total-return index or in the J.P.M.
total-return index (weekly).

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FEDERAL RESERVE BANK OF ST. LOUIS
Daniel L. Thornton is a vice president and economist at the Federal Reserve Bank of St. Louis. The author thanks John Duffy, Kevin
Hoover, David Laidler, Alvin Marty, and Bennett McCallum for helpful comments. Jonathon Ahlbrecht and Stephen Majesky provided
research assistance.

Money in a Theory
of Exchange
Daniel L. Thornton
“In primitive traffic the economic man is
awaking but very gradually to an understanding of the economic advantages to be
gained by exploitation of existing opportunities of exchange...Consider how seldom it is
the case, that a commodity owned by somebody is of less value in use than another
commodity owned by someone else! And for
the latter just the opposite relation is the case.
But how much more seldom does it happen
that these two bodies meet!...Even in the relatively simple and so often recurring case,
where an economic unit, A, requires a commodity possessed by B, and B requires one
possessed by C, while C wants one that is
owned by A—even here, under a rule of mere
barter, the exchange of the goods in question
would as a rule be of necessity left undone.”
— Carl Menger, “On the Origin of Money,”
The Economic Journal (June 1892), p. 242.
“Money, I consider, is a device which facilitates the working of markets.” Sir John Hicks,
A Market Theory of Money (1989), p. 2.
major problem in monetary economics has
been to introduce money into the economy in
a way that: (1) explains how money arises endogenously, (2) explains why money is preferred to other
methods of exchange, and (3) identifies the welfare
gains associated with money’s use. Money has been
introduced by including it as an argument in consumers’ utility functions or producers’ production
functions, assuming the existence of a welfare-reducing cash-in-advance constraint, assuming that it is
a vehicle for making intergeneration transfers with
no role in exchange, or simply assuming that money
exists—although it is given no specific role to play.1
This paper develops a framework for assessing
money’s role and the welfare gains associated with
its use. This framework shows how money reduces

the resources necessary for exchange, thereby increasing both consumption and leisure. The welfare
gains from trade are enhanced because the use of
money promotes further trade and greater specialization. For expository purposes the analysis is
linear; however, it is more correct to think of trade,
money, and specialization as essentially evolving
simultaneously, with the development of each reinforcing the development of the others. Nevertheless,
I argue if there were no trade, there would be no need
for money. To understand the role of money in an
exchange economy it is not necessary to know the
precipitous event that set off this evolutionary chain.
By showing how the use of money economizes
on scarce resources, expands trade and promotes
specialization, the analysis makes explicit Laidler’s
claim (1990, p. 47) that “...there is something of the
nature of a public good about money” so that “...we
should be very wary of treating the sum of its private products as its social product.” In so doing,
I show that to determine the welfare gains from
money it is necessary to compare a monetary economy with economies that use alternative methods of
exchange. The analysis has micro foundations, but
contrary to the trend in macroeconomics, no formal,
explicit general-equilibrium model of money is
developed. Indeed, the analysis suggests that the
obstacles to the formulation of such a model of
money are considerable.
The analysis is presented in four sections. The
first section develops a model of an autarkic economy and derives a measure of social welfare as a
function of economic resources. In the second
section, individuals are permitted to trade and several fundamental consequences of costly trade are
derived and discussed. The third section argues
that there only are three possible methods of effecting exchange: barter (simple and sequential), credit
(simple and sequential), and money. Because economic agents have an incentive to choose the least
costly method of effecting exchange, an analysis of
the relative cost of each of these methods reveals
why the world has been dominated by monetary,
rather than barter or credit economies. The analyses in the first three sections provide the spring1

See Hoover (1995) for a discussion of these and other approaches.

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Figure 1

U
U
U*

δi R

δi R*

board for the fourth, a discussion of several interesting and difficult questions in monetary theory.

individual produces these commodities via the following production functions,

AN AUTARKIC ECONOMY
Many monetary analyses begin by modeling
an economy with money and discuss money’s
implications. Since the purpose of this article is
to better understand why money exists and how
its use enhances welfare, it seems natural to start
with an economy where there is no money. Moreover, since I argue that money’s existence depends
on trade, the natural starting place would seem to
be a model of an autarkic economy. Consider an
economy with N individuals and Q commodities.
Each individual is endowed with a quantity of a
non-depletable resource δiR*, where δi is the i th
individual’s proportionate share, 0 # δi # 1, of
the total economy-wide resource, R, which is fixed
and given at R*. Individuals are self-sufficient and
maximize utility, where the i th individual’s utility
function is
(1)

U i ( C i1 , C i2 , . . ., C iQ , li ).

Cji, j=1, 2,..., Q, denotes the quantities of the Q
commodities consumed by the i th individual and l
denotes the amount of time devoted to leisure. Each

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(2)

[

]

C ij = f ji  δ i R * , Lij 


j
j = 1, 2 , . . ., Q ,
and i = 1, 2 , . . ., N ,

where [δiR*] denotes the physical quantity of the
resource devoted to the production of the j th commodity by the i th individual and Lji, denotes the
amount of the i th individual’s time devoted to the
production of the j th commodity. The i th individual
maximizes 1 subject to 2 and to the constraints
Q

(3)

*
*
∑ (δ i R ) j = δ i R

j =1

and
Q

(4)

i
i
∑ Lj + l = Γ .

j =1

to maximize utility, each individual must allocate
resources, δiR*, among the production of the Q
consumption goods and the total available time, Γ,
among the productions of consumption goods and
leisure. The solution to this optimization problem

FEDERAL RESERVE BANK of ST. LOUIS

Figure 2

I
I
I*

results in the first-order conditions,

∂f ji / ∂( δ i R* ) j

(

∂fki / ∂ δ i R*

)

U ki
U ij

j , k , = 1, ..., Q , j ≠ k

and

(

∂f ji / ∂Lij

)

U li
U ij

j = 1, ..., Q.

These conditions are familiar. The first set requires
individuals to allocate resources, δiR*, between the
production of the goods that they consume by equating the ratio of the marginal utilities with the marginal rate of technical substitution for each pair of
commodities consumed. The second set requires
individuals to allocate time between the production
of the goods that they consume and leisure by
equating the marginal product of each good with
respect to the labor devoted to its production to the
ratio of the marginal utility of leisure to the marginal
utility of that good.
Let Uji, (C1*, C2*, ..., CQ*, l*) denote the solution
to the optimization problem for the i th individual.
Assume that utility is measured ordinally, i.e., each

individual assigns a real number, n, to a particular
level of utility such that U ′ . U, if n ′ . n. Under
the usual assumptions about preferences and production, the i th individual’s maximum utility can
be put into a monotonic relationship with that individual’s resources, as illustrated in Figure 1. The
point of interest is where U intersects the vertical
line at δiR*. This is the point where the i th individual maximizes his well-being given the state of
technology, the available resources and time.
The simple aggregation of the ordinal utility
measures over the N individuals yields an index
of maximum utility for society as a whole, I,
shown in Figure 2. Since this index is a linear
combination of monotonically increasing functions in R, for a given distribution of resources,
i.e., a given set of δ ’s, it is a monotonically increasing function in R as well. Society’s well being
is maximized given the state of technology, the
available resources and time at the point where I
intersects society’s resource constraint, R*. This
is the point of maximum social welfare for the
autarkic economy.

AN EXCHANGE ECONOMY
The purpose of this section is to illustrate the
effects of trade and to show how transactions costs

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Figure 3

Good 1

A
B

u
u1 2

−λ
y2

reduce the benefits from trade, thereby limiting the
extent of trade. The intent is not to develop a complete model of costly trade, explain the degree of
specialization that one observes, or to specify the
precise benefits from trade. For simplicity, leisure
is omitted as an argument in the utility function
and exchange and production require only time,
not additional resources, R.
Figure 3 illustrates the autarkic optimum and
the gains from trade. The point A is the autarkic,
no-trade optimum for individual i. At point A the
slope of the production frontier 2f2′ / f1′ is equal to
the slope of the indifference curve, 2U1/U2. The
point marked B is the trade optimum, given the
exchange ratio, λ. By producing more of good 1
and less of good 2, relative to autarky, the individual is able to reach a level of consumption that
was infeasible without trade, thereby, increasing
utility from u1 to u2. With trade the individual
produces y1 units of good 1 and y2 of good 2 and
consumes C1 and C2 units of good 1 and good 2,
respectively.
Now consider the effects of costly trade. The
analysis is kept simple by assuming that individual
1 wishes to maximize the utility function U(C1,C2)
and can produce these goods with the following
technologies:

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Good 2

y1 = f1 ( L1 )
and

y 2 = f 2 ( L2 ) ,

where y1and y2 are the quantities of the two goods
produced and L1and L2 are the amounts of labor
time devoted to the production of each of the two
goods. Assume that the production technologies
are linear, so that the marginal rate of technical
substitution, f2′ / f1′, is constant. Further assume
that individual 1 specialized in the production of
good 1, which can be traded for good 2. Note that
if exchange is costless, trade will be advantageous
for any exchange ratio λ . f2′ / f1′.
Assume that the transaction cost, i.e., the
amount of time needed to trade, is fixed at Ω and
does not vary with the volume of trade. Given this
assumption and the others, the Lagrangian, L, can
be written as
(5)

L = U (C1 , C2 )

+ µ (C2 − λ ( f (Γ − Ω ) − C1 )) .

Differentiating and solving the usual first-order

FEDERAL RESERVE BANK of ST. LOUIS

Figure 4

Good 2

c'2
−λ
A

c1
c'1

λ≥

conditions, yields
(6)

Good 1

U1
U2

= λ.

Equation 6 is the familiar condition that the marginal rate of substitution equals the exchange ratio.
The effect of costly trade on optimal consumption is illustrated in Figure 4. The autarkic, no-trade
optimum is denoted by A. The costless trade optimum, for a given exchange ratio, λ, is denoted by
B. The fixed-cost trade optimum for the same exchange ratio is denoted by C. Costly trade reduces
the welfare gains from trade, as the consumption
of both goods is smaller when trade is costly. Note
that the volume of trade, T2, is smaller when trade
is costly than when trade is costless, T1. The reason is that trade draws resources, in this specific
example time, away from production. Indeed, if
the cost of trade, Ω, is large enough, no trade will
take place—the autarkic optimum will dominate
the trade optimum.
The most important thing to notice, however, is
that the exchange ratio that is required to achieve
the costless trade outcome is larger when trade is
costly. This is seen by noting that to a first-order
approximation,

f 2′
+ ( f1 ( Γ ) − C1A ) −1 Ω ,
f1′

where C1A,is the optimum consumption of good 1
in the autarkic economy (see the appendix for
details). When trade is costly (Ω . 0), the individual must be compensated for the cost per unit of y1
that must be given up to make the trade.
This effect of costly trade is more apparent
when exchange costs vary with the volume of
trade. The exact outcome depends on the assumption made about the nature of these trading costs,
however, the basic effect of costly trade will be
invariant to their nature. Hence, for simplicity,
assume that the exchange cost, Le, the time that is
required to trade, is proportionate to the volume of
trade, i.e.,
Le = α ( f ( L1 ) − C1 ) , α > 0. 2
2

Note that given this specification, the marginal exchange costs of
using one more unit of time for exchange rather than production, i.e.,
dLe /dL1 = af ′, depends on the marginal product of labor. The greater
the marginal product of labor, the larger is the marginal cost of
exchange. This fact may help explain why some individuals specialize
in production and others specialize in marketing or exchange. For
example, the stereotypical western storekeeper is someone who cannot hunt, rope, ride, or steal.

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Figure 5

Good 2

−λ

−λ
1 +α f ′

c'2
A

c'1
c1

T3
T2

Again, the individual is assumed to specialize in the
production of good 1. Moreover, the total amount
of time, Γ, is used either in the production of good
1, L1, or exchange, Le, i.e., Γ = L1 + Le. The
LaGrangian for this maximization problem is
(7)

L = U (C1 , λ ( f ( L1 )
+ µ( Γ

−

C1 ))

− L1 − α ( f ( L1 ) − C1 )).

The first-order conditions are:
U 1 − λU 2 +

λU 2 f ′ −

µ −

µα = 0
αµf ′ = 0

Γ − L1 − α (C1 − f ( L1)) = 0
Solving the first-order condition yields,
(8)

U1
λ
=
.
(1 + αf ′ )
U2

If a = 0, this condition reduces to the previous
one. When there are no exchange costs, an
individual who specializes in the production of

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Good 1

good 1 is better off trading whenever the exchange
ratio, λ, is greater than or equal to the individual’s
fixed marginal rate of technical substitution, i.e., λ
. f2′ / f1′. When exchange costs vary with the volume
of trade, however, the marginal condition for trade
becomes λ . (11af1′ )(f2′ / f1′ ).3
The effect of marginal exchange costs is
illustrated in Figure 5. The point marked C is the
same as that in Figure 4; namely, it is the optimal
point assuming that the exchange costs are fixed.
D denotes the optimum when exchange costs vary
with the volume of trade. The effect of variable
trade costs is to reduce the effective exchange rate
for a given exchange ratio, λ. The gains from trade
are smaller than when exchange costs are fixed
and there is a corresponding reduction in the
volume of trade.4 All other things being the same,
the volume of trade falls from T2 to T3. The trade
optimum, for a given λ, is pushed closer to the
autarkic no-trade optimum, which again is denoted
by A. If the exchange costs are sufficiently high,
3

The strict inequality is due to the fact that an individual must be
compensated for the total amount of y1 that must be given up to
make the trade.

Figure 5 is drawn on the assumption that the total cost of trade in this
example is exactly equal to the fixed costs of trade in the previous
one, i.e., a( f( L1)2C1) = Ω.

FEDERAL RESERVE BANK of ST. LOUIS

Figure 6

Good 2

β′ > β

−λ  α = β′

−λ  α = β

−λ  α = 0
Good 1

the autarkic optimum will dominate the trade
optimum for a given λ.
Costly trade not only reduces the gains
from trade, but more importantly, it increases
the minimum exchange ratio that is required for
the individual to benefit from trade. Define the
reservation exchange ratio, rer, to be the minimum
exchange ratio required for an individual to acquire
the same level of utility that would be acquired
under autarky. If exchange is costless, rer is simply
an individual’s marginal rate of technical substitution. Figure 6 shows rer for three assumptions
about exchange costs: There is no cost of
exchange, i.e., a = 0, and two cases where
exchange costs are positive, a = b . 0 and
a = b ′, b ′ . b. Figure 6 illustrates that the higher
the exchange cost, the larger is rer, i.e., the more of
good 2 that an individual who specializes in the
production of good 1 must get to compensate for
the cost of trade. Trade is advantageous only when
the terms of trade are sufficiently favorable, i.e., λ
is sufficiently large to compensate for the cost
of trade.
All of the above conclusions were predicated
on the assumption that the individual specializes in
the production of good 1. Hence, it is important to
see how costly trade affects the potential exchange
between individuals where the comparative advan-

tage is well defined. Assume that individuals 1 and
2 are able to produce both goods 1 and 2. Again,
the production technologies are assumed to be
linear and the solid gray and black lines, respectively, in Figure 7 denote their production frontiers.
Individual 1 has a comparative advantage in the
production of good 1; individual 2 has a comparative advantage in the production of good 2. The
exchange ratio at which trade can take place, λ,
must be between the slopes of the solid gray and
black lines which, in the case of costless trade, represent individual 1’s and 2’s rer, respectively. The
points A and B are optimal if each individual is selfsufficient. Point A′ denotes a trading possibility
where all of the gains from trade accrue to individual 2, while B ′ denotes a trade possibility where
all of the benefits from trade accrue to individual 1.
Now assume that both individuals have marginal exchange costs, i.e., a1 f1′ and a2 f2′ , which are positive but not necessarily equal. The effect of costly
exchange is to raise the rers for both individuals.
The solid light blue and dark blue lines represent
the rers for individuals 1 and 2, respectively, when
trade is costly. The dashed dark blue line is parallel
to the solid light blue line and, hence, denotes the
maximum benefits from trade when trade is costly
if all the benefits from trade accrue to individual 2.
Likewise, the dashed light blue line is parallel to the

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Figure 7

Individual 2

Good 1

Good 2

A′

B′

A
Good 1
Good 2

Individual 1

solid dark blue line and, hence, denotes the maximum benefits from trade when trade is costly if all
the benefits accrue to individual 1. The maximum
benefits from trade are clearly less when trade is
costly and diminish as the cost of trade increase.
The important thing to note is that while the
precise gains from trade for the two individuals
depend on the respective size of the transactions
costs, costly trade reduces the range of exchange
ratios where trade is mutually advantageous and,
therefore, the volume of trade. Moreover, the larger
the exchange costs, the smaller the region where
trade is mutually beneficial. Indeed, if the slopes
of the solid light blue and dark blue lines were sufficiently large for either individual, no exchange
ratio would exist where trade would be mutually
advantageous—no trade would take place.5 Costly
trade reduces the feasible set of opportunities
where trade is mutually advantageous.
The exchange ratio at which individuals trade
and how much each benefits from trade depends
on the relative costs of trade for both individuals,
which in turn depends on strategic considerations
that go well beyond the scope of this inquiry. For
example, the above analysis assumed that the costs
were borne by both traders and that there were no
social arrangements for sharing the costs. Moreover,
there is nothing in this analysis that ensures these

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individuals trade or that more trade takes place if
the exchange costs are reduced. The conclusion
that trade increases is inferred from noting that individuals have an incentive to engage in mutually
advantageous trade up to the point where the marginal resource cost-of-trade equals the utility gains
from trade. Anything that reduces exchange costs
gives rise to this potential by expanding the feasible
set of mutually beneficial trades.
The existence of exchange costs and the desire
to reduce them has implications for the development of markets and, more generally, for their
structure. Anything that reduces transactions cost
encourages greater trade and specialization. At the
same time, the benefits from specialization and
trade encourage the use of the most efficient method
of exchange. Of course, the catalyst for all of this is
the heterogeneity that makes trade mutually advantageous. Reducing the cost of trade enhances welfare by (a) reducing the amount of resources that
5

Since trading costs are positively related to the real volume of the
goods traded, the rers will vary with the level of trade. This makes
determining the exact amount of trade in costly trade environments
very difficult. Moreover, it has been assumed that the trade costs are
proportional to the volume of trade, but this need not be the case.
Trading costs also may vary across individuals or goods. Regardless
of how exchange costs are treated, the same fundamental conclusion
emerges: The larger the exchange costs the less trade will take place.

FEDERAL RESERVE BANK of ST. LOUIS

Figure 8

U′′
U′

D
C

U2
U1

T- L e

must be devoted to exchange, freeing up resources
for production (and/or leisure) and by (b) increasing
the amount of trade that takes place, i.e., increasing
the extent of the market.
A host of mechanisms have evolved to reduce
the cost of exchange: bazaars, trading posts, retail
establishments, brokers, agents, dealers, and other
specialists and, most especially, money. Some of
these have given way to more efficient methods of
exchange. Others have not—at least not yet.
The effect of innovations, such as money,
which reduce the cost of exchange, is illustrated in
Figure 8. Point A denotes the level of utility for an
individual who is self-sufficient. All time is spent
in production or leisure. Point B denotes the level
of utility associated with costly trade. The utility
level is higher than for autarky despite the fact that
some time, Le, is used for exchange. Money reduces
exchange costs, so that fewer resources are devoted
to exchange, and there is more time for production (or
leisure). This gain can be seen by comparing points
B and C. In addition, the use of money increases
welfare by expanding the set of feasible transactions
and, thereby, increasing the volume of trade. This
is illustrated by the difference between points C and
D. The total gains from reducing exchange cost are
illustrated by the difference in utility levels associated
with points B and D. Applying the same aggregation

Time

analysis as before yields the implication that any
reduction in exchange cost is welfare enhancing
for society as a whole. This analysis makes it
clear that by reducing the transaction cost, money
expands the set of exchange ratios where trade
is mutually advantageous. To this extent, the
use of money expands the feasible set of
transactions.6

The Implications of Costly Trade
The objective of the above analysis was to
illustrate a role of money and the benefits from
money’s use, not to construct a general theory of
trade. Indeed, the analysis says nothing about who
trades with whom or how much trade takes place.
Rather, it illustrates that trade is welfare enhancing
and that costly trade reduces welfare (relative to
costless trade), both by drawing resources from
production or leisure and by limiting the extent of
the markets. Money increases economic welfare
by mitigating some of these costs. Moreover, welfare is enhanced even though money does not appear in the utility or production functions or is a
prerequisite for trade through a cash-in-advance

For the view that money does not increase the set of feasible transactions, see Ostroy, (1973), pp. 608-9.

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constraint; nor are there legal restrictions requiring
the use of money. Furthermore, there is nothing to
rule out the possibility that some transactions are
achieved using barter or credit. The analysis confirms Brunner and Meltzer’s argument (1971, p.
804) that “...the private and social productivity of
money are a direct consequence of the saving in
resources that the use of money permits and of the
extension of the market system that occurs because
of the reduction in the cost of making exchanges.”7
The conclusion that money enhances economic
welfare by reducing exchange costs is independent of
the market structure, so long as there is some degree
of decentralization.8 Of course, the exact nature and
extent of the benefits from innovations that reduce
exchange costs depend on such factors. Consequently,
any attempt to quantify the benefits associated with
innovations that reduce exchange costs is necessarily
stylized: Specific results will depend on the assumptions made about the structure of markets, production
technologies, the nature and extent of the exchange
costs, and who bears them. Since my purpose is to
gain insight into how money ameliorates exchange
costs, it is essential to deal with these costs very
generally. Before turning attention to the issue of
exchange cost, however, several other implications
of the above analysis for money are noted.
First, innovations that reduce the exchange cost
of one individual can benefit others. This implication is clear from Figure 7. Instead of both individuals experiencing a reduction in exchange cost,
assume that only one individual does. The effect
still is to increase the feasible region of exchange
ratios where exchange could take place. Consequently, an innovation that reduces the exchange
cost of one individual can raise the utility of both.
Second, individuals have an incentive to use
the least-costly method of exchange. The fact that
some form of money has evolved in every society
suggests that money is efficient relative to other
methods of effecting trade. Moreover, that similar
assets have functioned as money in very different
societies suggests that certain assets seem to have
a distinct advantage in reducing exchange costs.
Third, exchange costs limit the extent of trade and,
hence, specialization and the use of money reduces
these costs. Consequently, it is not surprising that
the division and specialization of labor appear to
have evolved simultaneously with the use of money.
Finally, and perhaps most importantly, the welfare gains from money can be obtained only by
comparing a monetary economy with an economy

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that uses an alternative method of exchange. Moreover, the welfare gains from money will change as
markets develop and economies become increasingly specialized. Consequently, the more highly specialized the economy and the greater the extent of
trade, the larger the likely effects of disruptions to the
supply of money will be. As we will see, this point
has implications for the welfare costs of inflation.
It has been recognized for some time that economies with a medium of exchange are better off
than if no such medium of exchange exists. Indeed,
McCallum (1983a, p. 24) uses “the traditional presumption that an economy with a medium of exchange
is more productive than it would be if no medium
of exchange existed” to show that overlapping generations models of money (e.g., Wallace, 1983)
have no role for money as a medium of exchange.
The above analysis refines McCallum’s point by
explicitly showing how money necessarily enhances
welfare by facilitating trade. Models that do not
explicitly recognize this role of money are unlikely
to capture money’s essential feature.9
7

Despite the large amount of resources devoted to market activity
(bringing buyers and sellers together) economic analyses have focused
on production and consumption. For an exception, see Hirshleifer
(1973).

Ostroy (1973) was one of the first to observe that the Walrasian general
equilibrium market had no role for money because no trade takes
place until the equilibrium set of accounting prices Patinkin (1965), is
determined. This is why Meltzer (1995), Hicks (1989), and others
argue that such models may be of limited use in understanding the
role of money in the economy.

Even models that explicitly capture the medium-of-exchange function
of money do not necessarily capture the welfare enhancing properties
of money noted here. For example, the shopping-time model of
McCallum and Goodfriend (1987) or the money-in-exchange models
of Dornbusch and Frenkel (1973), Benhabib and Bull (1983), and
Fischer (1986) explicitly recognize the medium-of-exchange function.
In the latter models, however, the metric for measuring aggregate welfare provides no motive for exchange. In these models welfare is
measured by aggregate consumption, which is given by,

C =

[

]

f (k ) 1 − v(m) ;
0 ≤ v < 1, v ′ < 0 ,

where v(m) is the proportion of total output, f(k), that is used in
exchange. Since C for v . 0 is always less than C for v = 0, exchange
does not enhance economic welfare. Hence, there is no motive for
exchange and, consequently, no motive for money as a medium of
exchange. The problem is analogous to that of Tobin (1965) and
Mundell (1971), where per-capita output is maximized when money
holdings are zero.
This is not true of the search-theoretic models of money (e.g.,
Jones, 1976; Oh, 1989; Kiyotaki and Wright, 1989, 1991, 1993; Trejos
and Wright, 1993; Johri, 1994) that explicitly model money’s role as a
medium of exchange. In these models, money facilitates exchange by
ameliorating the search costs associated with the double coincidence of
wants essential for barter. Implications of some of these models have
been born out experimentally (e.g., Duffy, 1998).

FEDERAL RESERVE BANK of ST. LOUIS

Before discussing why money exists and why
money dominates barter and credit as a medium of
exchange, one final point should be made. Specifically, the welfare gains from money discussed above
are those associated with the real stock of money,
i.e., money’s purchasing power. If money is held
solely to facilitate transactions, a larger real money
stock means that more transactions are facilitated
and, hence, the welfare gains are larger relative to
the next best method of exchange; therefore, society’s welfare should increase with the equilibrium
stock of real money. Changes in the nominal stock
of money, however, do not necessarily result in an
increase in the equilibrium stock of real money.
Given classical neutrality and Archibald and Lipsey’s
(1958) invariance principle, ceteris paribus, increases
in the stock of nominal money may have no significant effect on economic welfare.10

EXCHANGE COSTS WITH ALTERNATIVE
METHODS OF EXCHANGE
The previous section showed why anything
that reduces the exchange cost is welfare enhancing.
Such innovations enhance welfare by reducing the
quantity of society’s scarce resources devoted to
exchange, freeing up resources, time for production,
or time for leisure, and by enabling society to achieve
a greater extent of specialization and trade. I inferred that money is one such innovation without
carefully defining what money is. This section takes
up this issue. Specifically, money is defined as a
commodity that is a generally acceptable medium
of exchange.
The essential feature of an exchange economy
is that individuals trade the commodity that they
have, commodity j, for one that they want, commodity k. The essential point is that there are only
three possible methods of exchange: barter, money,
or credit. In an exchange economy, trade must
take place with one of these methods.11 Which
of these methods is used depends on their relative
costs in effecting exchange. Hence, the analysis of
money necessarily requires an analysis of the relative costs of barter, credit, and money in exchange.
In discussing the relative costs of these alternative methods of exchange, it is important to distinguish between simple barter (trading commodity j
for commodity k) and multistage or sequential
barter (trading commodity j for commodity h and
trading commodity h for commodity k). Money
always entails a sequential transaction (trading

commodity j for m and trading m for commodity k).
Hence, a monetary transaction can be thought of
as a sequential barter transaction that involves a
particular commodity, m. When m becomes generally acceptable, it is money.12
It also is important to distinguish between simple
and sequential credit transactions. A simple credit
transaction involves trading commodity j for a promise of some commodity (k, j, m or some other
commodity) in the future. Hence, a simple credit
transaction is just an intertemporal barter transaction.
A sequential credit transaction involves trading
commodity j for an IOU and trading the IOU for
commodity k or perhaps another IOU.13 According
to this definition, a sequential credit transaction is
a particular form of a sequential barter transaction
where the intermediate commodity is an IOU. If a
particular IOU were generally acceptable, it would
be money. Simple credit transactions have been used
to effect exchange for a long time, e.g., trade credit.
IOUs have even circulated as a form of local currency
for relatively short periods of time. There are a number
of reasons, however, why money initially took the
form of tangible commodities and not IOUs. Indeed,
I will argue later that the use of credit for the purpose
of facilitating trade is due to the existence of money.
Hence, while credit can supplement money in effecting
exchange, it will not supplant it. In this section,
however, I only will consider the question of how
well credit can facilitate exchange. For this
purpose, sequential credit is required.
It is important to distinguish between costs that
are independent of the method of exchange and those
10

See Bullard (1999) for a survey of the evidence for monetary neutrality and superneutrality.

McCallum (1985) has also made this point.

In the search literature on money, general acceptability is achieved in
various ways. For example, in Oh’s (1989) model where individuals
search randomly, a dominant medium of exchange emerges due to
the assumption that one commodity has the largest subjective probability of trade. He shows that if traders try to minimize the number of
encounters that result in the desired trade, e.g., commodity j for commodity k, this commodity will emerge as the dominant medium of
exchange. Money and barter coexist because barter occurs when an
individual who has commodity j and wants commodity k just happens to meet an individual with commodity k and who wants commodity j.

Brunner and Meltzer (1971) consider what they termed a barter-credit
economy. In their discussions, credit is simply bartered for goods.
That is, one person gives the other an IOU for the goods that the former wants. But this implicitly assumes that the latter person wants
the IOU and not some other commodity that is desired for current
consumption. The case where credit is used in barter transactions is
discussed later.

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that vary with the method of exchange. That is, it is
essential to differentiate between costs that money
can ameliorate and those that it cannot.14 To this
end, exchange costs, i.e., all costs associated with
making the exchanges of two or more commodities
between two or more individuals, are categorized as
either information costs or noninformation costs.
Noninformation costs are the packaging, handling,
and other assorted costs associated with getting a commodity from the point of final production to the point
of final consumption. Such costs are independent of
whether the transaction is achieved with barter, credit,
or money. Hence, while such costs are essential for
determining the extent of and the benefits from
trade, they are immaterial for the broader question
of why money is used and for determining the welfare gains from its use.15
Information costs vary with the method of
exchange. Information costs are divided into
assurance costs and shopping costs. Comparisons
of the assurance costs associated with money,
barter, and credit explain why money dominates
sequential barter or sequential credit in exchange.
Comparisons of the shopping costs associated with
money, barter, and credit add to this explanation.

Assurance Costs
Consider first the case of multistage transactions. This is when individuals trade a commodity
they have for one that they currently do not. This
process will continue until they obtain the desired
commodity. Such multistage transactions require
that individuals obtain assurance that they will be
able to obtain the desired commodity, k. Broadly
speaking there are two distinct, although not mutually exclusive, categories of costs associated with
obtaining this assurance. The first of these I term
verification costs. Verification costs, which are discussed extensively by Brunner and Meltzer (1971)
and Alchian (1977), are the costs of verifying the
characteristics and attributes of the good received.
These costs include the costs of inspection, measuring, perfecting property rights, etc.16
The second category of costs I call value-determination costs. Value-determination costs are the
costs associated with determining the value or
worth of the commodity received. The value of the
commodity is the number of units of it that must
be traded for x units of the desired commodity, k.
Clearly, a poor-quality commodity is less valuable
than a high-quality commodity. Nevertheless, know-

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ing the quality of the product does not necessarily
mean that you know its exchange value, hence, it is
useful to treat these information costs as separate
and distinct.
A sequence of barter transactions that ultimately leads to the acquisition of commodity k,
requires verification costs at each stage in the sequence.17 Hence, the verification costs associated
with sequential barter could be considerable, especially if a large number of intermediate transactions
are required. Money economizes on verification
costs relative to sequential barter for two reasons.
First, money has relatively low verification costs.
Indeed, other things being the same, the commodity with the lowest verification cost will emerge as
money (Jevons,1875).18 Second, the use of money
means at most two transactions, j for m and m for k,
are required.
The verification costs of sequential credit transactions are likely to be high. If one person exchanges
commodity j for an IOU of Mr. Smith, he has no
difficulty in verifying that this is the IOU of Mr. Smith;
after all, he watched Mr. Smith write it.19 When he
attempts to trade Mr. Smith’s IOU for commodity k,
however, the verification costs for the next person
are likely to be significant. There may be considerable difficulty in verifying that this IOU is the promise of a particular Mr. Smith.20 Moreover, even if

In this context, it is somewhat arbitrary to assume where production
ends and exchange begins; however, Hirshleifer (1973) suggests treating transportation costs as part of production costs.

Of course, there could be second-order effects. Specifically, an innovation to the method of exchange could significantly increase the
extent of the markets, which may in turn reduce transportation costs
if there were economies of scale in transportation. Such innovations
also could foster innovations in the transportation industry.

Alchian (1977, p. 134) argues that it is the low verification costs alone
that make a commodity money.

See Jones (1976), Oh (1989), Kiyotaki and Wright (1993) and Trejos
and Wright (1993).

Jevons’ observation is explicitly modeled by Jones (1976) and Oh
(1989), who assume that one good is more in demand than other
goods. Specifically, they take the “subjective expected transaction
costs”—the time spent searching for complimentary trading partners—to be the number of encounters one anticipates before completing a single trade.

Search-theoretic models abstract from the problem of credit by
assuming that chance meetings of individuals have a Poisson distribution, so that the probability of the same individuals meeting twice is
infinitesimally small relative to the probability of meeting once.

Of course, methods have been developed to deal with such verification costs, but the costs still may be rather high relative to the verification costs of money.

FEDERAL RESERVE BANK of ST. LOUIS

one knew it was the IOU of a particular Mr. Smith,
one might not know Mr. Smith’s willingness and
ability to honor the obligation. Even if the IOU is
collateralized, all subsequent holders of the IOU
must evaluate and perfect their interest in the collateral. Because of the costs associated with such
activities, it seems likely that the verification costs
of sequential barter in IOUs will be higher than the
verification costs of sequential barter in commodities. Consequently, if money has lower verification
costs than sequential barter, it must have lower verification costs than sequential credit as well.
The individual also must determine the value
of the commodity, i.e., the number of units of the
commodity k (or h) that they can get for x-units of
the commodity j. The value-determination costs
of sequential barter are high because it requires
that the individual know up to Q(Q 2 1)/2 relative
prices. The problem associated with a multitude
of prices is exacerbated if credit is used to effect
trade. Credit instruments can be denominated
in any one of the Q commodities for any one of
the N individuals. Hence, the credit price of a
commodity can vary across goods and individuals.
Credit prices also can differ in other dimensions,
such as the maturity of the contract, whether
there is collateral and the nature and extent of
the collateral. Given problems associated with
asymmetric information, it seems that the cost
of determining the value of IOUs is so high that it
eliminates the possibility that a credit instrument—
denominated in a nonmoney asset—could serve
as an effective, generally acceptable medium of
exchange, i.e., it could serve as money.21 Money
has significantly lower value-determination costs
than either sequential barter or sequential credit
because traders are required to know at most
Q 2 1 money prices.
Value-determination costs also depend on the
variability of the value of money. All other things
being the same, money serves best as a medium of
exchange when its value remains relatively stable.
Because of the difficulty in determining the relative
value of commodities, it would be surprising to
find that a commodity whose value fluctuates considerably—relative to other commodities—serves
as a generally accepted medium of exchange, i.e.,
serves as money.
Maintaining the stability of money’s value over
long periods of time is important for what Jevons
(1875, pp. 5-6) called the standard of value function
of money. Because of money’s role as a medium of

exchange, and hence a source of generalized purchasing power, it is convenient to denominate credit
contracts in terms of money. That is, the existence of
money facilitates the use of credit. Indeed, as Hoover
(1988) has noted, and I will argue later in more detail,
money appears to be essential for credit. Variation in
the value of money can have a significant, detrimental
effect on money’s standard of value function.22 I
will argue later that this effect may be larger than
the effect of variation in the value of money on
money’s function as a medium of exchange.

Shopping Costs
If an individual who has commodity j and
wants commodity k runs into an individual who
has commodity k and wants commodity j, is the
result necessarily barter? Some insights into the
answer to this question come from considering
shopping costs. Shopping costs are of particular
interest because these are the costs that money is
ordinarily thought to ameliorate relative to simple
barter. Shopping costs encompass a wide variety
of costs, including costs associated with locating a
seller or buyer, haggling over price, budgeting, and
planning expenditures. Shopping costs that are of
particular interest are those associated with the
lack of a double coincidence of wants and those
associated with not having a common unit of
account. While the medium of exchange and unit
of account functions are separable, the fact that most
often the same good has performed both functions
suggests that this arrangement is efficient.
Trade is a planned activity. Sellers seek buyers
and buyers seek sellers. Trade that occurs as the
result of chance meetings is rare.23 Indeed, special-

Note that if credit were to supplant money, credit contracts would
have to be denominated in something other than money. Moreover,
it should be clear from this discussion that credit is less likely to arise
as a general method of trading goods in primitive economies, where
the costs of acquiring information are relatively high. Credit is more
likely to exist as the primary method of exchange in advanced societies where information costs are relatively low.

Jevons (1875, p. 6 and p. 12).

In the search-theoretic literature, e.g., Jones (1976), Oh (1989),
Kiyotaki and Wright (1993), and Trejos and Wright (1993), trade
results from chance encounters. In such settings, money arises
because it minimizes the costs of transactions due to a double coincidence of wants. Traders bump into each other randomly and engage
in simple barter if there is a double coincidence of wants, they trade
goods for money if one of the traders has money, or they do nothing.
Recently, search models of money have allowed for the development
of longer-term relationships, see Corbae and Ritter (1998).

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ists who are particularly efficient at verifying and
determining the value of particular products arise.
In some cases, these specialists make markets for
specific commodities by dealing in them. Matching buy and sell orders and managing inventories
is difficult if a whole host of commodities is traded
for the commodity(s) that the specialist deals in.
Hence, the specialist’s function is more efficient if
transactions are carried out in a single commodity.
Efficiency is further enhanced if bid (offers to buy)
and ask (offers to sell) prices are quoted in this
same commodity.
The efficiency of the market is increased if
all participants agree to use the same medium
of exchange and if this commodity also serves
as the unit of account.24 The elimination of the
double coincidence of wants reduces the time
for buyers and sellers to locate each other. Money
also reduces the time spent haggling over price
if everyone agrees to quote prices in the same
commodity and if that commodity is accepted
generally in exchange. For analogous reasons,
planning and budgeting are made easier if a single
commodity is used as both the medium of exchange
and the unit of account.
Let us now reconsider the intriguing question
that began this section: If an individual who has
commodity j and wants commodity k runs into an
individual who has commodity k and wants commodity j, is the result necessarily barter? In the
search-theoretic literature of money (e.g., Jones,
1976; Oh, 1989; Kiyotaki and Wright, 1993; and
Trejos and Wright, 1993), the answer is unequivocal. Yes! In these models, money and barter
coexist because chance encounters sometimes
result in a double coincidence of wants. The
scarcity of barter is related inversely to the probability of such encounters. In economies where
monetary exchange is well established, however,
barter is scarce because seeking a double coincidence of wants becomes increasingly inefficient
with the increased use of money and specialization
(Jevons, 1875, p. 3). Individuals who possibly
could barter might bump into each other and never
know it. Furthermore, they would not care. In
highly specialized monetary economies, barter is
motivated more by tax considerations or thin markets for peculiar goods (such as second-hand
goods—goods become more heterogeneous as they
get used—e.g., trading a used car in on the
purchase of a new car) rather than by chance
encounters.

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Why Is Money Held?
The above analysis explains why money is the
most efficient method of effecting transactions, it
does not explain why money is held. The classical
explanation of why money is held deals with the
lack of synchronization of receipts and expenditures. Brunner and Meltzer (1971, p. 785, fn. 4)
challenged this view, arguing
It is easy to see why a “lack of
synchronization” does not imply that
money is used and held. Consider an
economy that has neither a medium of
exchange nor money. If there are no costs
of acquiring information, differences in the
timing of receipts and payments are adjusted
by issuing verbal promises in exchange for
goods and, later, delivering goods. More
generally, in a barter-credit economy, commitments or promises to pay bridge the gap
between receipts and payments.
Brunner and Meltzer are correct that the lack
of synchronization does not explain why money is
held and they also are correct in suggesting that
costly information explains money’s dominance in
exchange. Money is held for only one reason—by
its very nature the process of exchange takes time
so that anything that functions as a medium of
exchange must be held. This is so obvious that
Brunner and Meltzer (1971, p. 804) refer to it as
“trivial.” Trivial though it is, this fact alone is sufficient to explain why money is held. While economists have dealt with time in a number of ways,
it is convention to refer to things that are measured
as a point in time as stocks and things that are measured through time as flows. Out of necessity,
money is a stock. If the stock of money facilitates
the flow of trade, it must exist before and after the
trade takes place. In a barter economy, the goods that
are traded in the interval from t to t 1 1 must exist
at time t. If, hypothetically, trade and consumption
are permitted to occur simultaneously, consumption
goods bartered at time t do not exist at t 1 1. In
the case of money (or sequential barter or credit),

Niehans (1978) has argued that the medium-of-exchange and unit-ofaccount functions are wedded because money cannot ameliorate the
pricing problem noted above unless money also is the unit of account.
The wedding of the medium-of-exchange and unit-of-account functions enhances market efficiency also has been noted by White (1984,
p. 711).

FEDERAL RESERVE BANK of ST. LOUIS

however, this is not the case. The nominal quantity
of money held at t 1 1 must be the same as that
held at t. Hence, although money is continuously
changing hands, it always is being held by someone
—it is never consumed. This is not solely a characteristic of money; it is true of any asset.25 Assets
traded at time t exist at both t and at t 1 1.
What then distinguishes money from any other
asset? Ipso facto every asset is a store of wealth. To
say money is a store of wealth is tautological! Being
a store of wealth is not a defining characteristic of
money or any other asset.26 Money can be distinguished only from other assets by applying
another criterion.27 The important criterion for
separating money from other assets is that money
is an asset (or group of assets) that is generally
acceptable as the means of trading goods—other
assets are not. This characteristic distinguishes
money from other assets and has a long tradition
in classical monetary economics (e.g., Menger, 1892;
Jevons, 1875; Brunner and Meltzer, 1971; and
McCallum, 1983a, b, 1985). At different times and
under different circumstances various assets have
served as money. Nevertheless, some assets appear
to have characteristics that have made them the
predominant forms of money.
Money is distinguished from other assets by
the function it performs; this is illustrated by a
simple story from Jevons (1875):
When Mr. Wallace was traveling in the
Malay Archipelago, he seems to have suffered rather from the scarcity than the superabundance of provisions. In his most interesting account of his travels, he tells us that
in some of the islands, where there was no
proper currency, he could not procure supplies for dinner without special bargain,
and much chaffering upon each occasion.
If the vendor of fish or other coveted eatables did not meet with the sort of exchange
desired, he would pass on, and Mr. Wallace
and his party had to go without their dinner.
It therefore became very desirable to keep
on hand a supply of articles, such as knives,
pieces of cloth, arrack, or sago cakes, to
multiply the chance that one or other article
would suit the itinerant merchant. (pp. 2-3).

but the party’s members would normally not have
chosen to store their wealth in this form. Rather,
these particular articles were held because they
facilitated trade, i.e., because they reduced transaction costs. The Wallace party used these articles as
a form of local currency. Whether an asset can be
used as money depends solely on whether it generally is held to facilitate exchange.
Finally, it is worth emphasizing that money is
unique among assets not solely because it facilitates the exchange of goods for consumption.
Money also facilitates the exchange of other nonmoney assets as well. Individuals typically do not
exchange shares of stock for acres of land even
when the person who has land wants stock and
vice versa. Rather, land is traded for money and
the money for stock. More importantly, it is generally convenient to denominate credit contracts in
units of money rather than bushels of wheat, acres
of land, or other commodities or assets. Generally
speaking, assets are held for the myriad of reasons
that individuals accumulate wealth. In contrast,
money is primarily held because of its low cost in
effecting transactions. Money is distinguished from
other assets in that it is the only asset that is a generally held medium of exchange. Because of this, it
also is the standard of value.

ISSUES IN MONETARY THEORY
The above analysis has implications for several
interesting issues in monetary theory such as the
origins of fiat money, the asset demand for money,
the relationship between money and credit, the
buffer-stock notion of money demand, the welfare
benefits of money, and the welfare costs of inflation.
I will now discuss each of these issues in turn.

Fiat Money
Until now, money implicitly has been a tangible
real commodity, i.e., a commodity money, or a claim
to such. In modern monetary economies, however,
money is typically paper currency with no intrinsic
value. A question that has troubled monetary econ-

Assuming, of course, that assets do not depreciate.

The Wallace party undoubtedly kept a cache
of articles that were most highly demanded by the
natives. These articles are clearly stores of wealth,

Hicks (1989, p.42) also has made this point.

There are several criteria for differentiating among assets, e.g.,
whether they are tangible or intangible, financial or real, liquid or illliquid, etc. These are not defining characteristics of money, however.

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omists is why do people hold an asset that is valueless except in exchange? The answer suggested
here is that money is the only asset that provides
exchange services that other assets cannot provide.28
A better question is why have all monetary
economies evolved into fiat money economies?
The answer to this question arises naturally from
the framework presented here. In the analysis presented above, I implicitly assumed that commodity
money is costless to produce and/or to maintain.
This is not the case. Commodity monies require
that resources be used in their production and to
maintain the stock. In the case of commodity
monies like precious metals, most of the costs are
production costs (the maintenance costs, i.e., depreciation, are fairly low). In the case of more abstract
commodity monies, like checkable deposits, the
costs are on going, and are related to the extent
of their use.29 Indeed, even paper currency is not
costless to produce and maintain.30
Because the production/maintenance of money
requires the use of economic resources, the welfare
gains associated with the use of resource-using money
are necessarily smaller than if money were costless.
If money production requires resources, augmenting
the stock means that resources will be drawn, at least
temporarily, from other uses. The nominal money
stock (e.g., tons of gold or silver) will increase as long
as the marginal exchange value of the last unit produced exceeds its marginal production cost. If there
are maintenance costs, (e.g., the rate of physical
depreciation is positive), then resource-using money
will be less welfare enhancing because of higher
maintenance costs. Because the verification costs
are likely to be higher the more rapidly and less
predictably an asset depreciates, there is an incentive to choose as money commodities that have a
low, perhaps negligible, rate of depreciation. That
is, all other things being the same, the asset with
the lowest maintenance cost will serve as money.
For these reasons, society has an incentive to
replace high-cost commodity money with lower
cost money. One step in this evolution was to
replace commodity money with lower cost representative money. A further step is to replace representative money with even lower cost fiat money.
Because fiat money requires fewer resources for
production and maintenance, its use is welfare
enhancing. In addition, if fiat money were to further reduce transactions costs, social welfare could
be further enhanced if specialization and trade
were encouraged.

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Despite its advantages over resource-using
money, fiat money evolved slowly over a considerable period of time. Money’s evolution was undoubtedly affected by wars, other political events, and
difficulties associated with regulating the supply of
various commodity monies. Nevertheless, the fact
that fiat money increases society’s welfare relative
to commodity money suggests the evolution to fiat
money is the result of economic forces rather than
the happenstance of a number of noneconomic
events (Russell, 1991).
Governments issue fiat money because private
fiat money issuers have an incentive to issue money
as long as the marginal value of the last nominal
unit issued is greater than its production cost. Consequently, it would be difficult for private money
issuers to make a credible commitment not to over
issue fiat money, so as to make it “worthless” (e.g.,
Ritter, 1995).31 Moreover, only the government can
credibly commit to distribute the seigniorage revenue
from money’s creation. Seigniorage arises when
the exchange value of the money issued exceeds
money’s production cost.
The existence of a money whose value in exchange exceeds its production cost has given rise
to the notion that society’s wealth exceeds the stock
of tangible assets by the real value of fiat money
held. As Tobin (1965, p. 676) put it:
...as viewed by the inhabitants of the nation
individually, wealth exceeds the tangible
capital stock by the size of what we might
call the fiduciary issue. This is an illusion,
but only one of the many fallacies of composition which are basic to any economy
or any society.
Many economists accept Tobin’s claim that the
“wealth” associated with the real stock of fiat money
is illusionary, but this proposition is erroneous.
Tobin’s error comes from viewing a monetary econ28

See Tobin (1992, p. 774) for the traditional answer to this question.

Thornton (1983) shows that the relevant issue for determining whether
“inside money” is part of a society’s stock of net wealth is whether
there are resource costs involved in its production and maintenance.

The United States is issuing another in a series of dollar coins. The
purpose of these coins is to reduce the cost of maintaining the stock
of currency, since coins depreciate less rapidly than paper money.

Goodhart (1998) suggests that gold’s role as a medium of exchange
was greatly enhanced by government’s use of gold to pay tributes or
tariffs to avoid feuds.

FEDERAL RESERVE BANK of ST. LOUIS

omy as simply a barter economy with money. In
so doing, he fails to recognize the private and public
benefits that accrue from money’s use. The benefits
from the use of money naturally accrue to fiat money
when society shifts from using a more costly commodity money to a less costly (or, ideally, completely
costless) fiat money. Hypothetically, if resourceusing money were replaced unit-for-unit with fiat
money, the real value of the stock of fiat money
would reflect the welfare benefits associated with
the previously held stock of commodity money.
Hence, the benefits of commodity money are embodied fully in the same real quantity of fiat money.32
Furthermore, the fact that all of the benefits
from the previous stock of money would be obtained
at lower cost guarantees that welfare is enhanced
by the switch, even if there is no further reduction
in the marginal transaction cost and, consequently,
no further increase in trade and specialization.
Because of the existence of positive externalities associated with money’s use, it is inappropriate
to equate the welfare benefits of money with the
real value of the money stock, i.e., M/P, as is frequently done. Nevertheless, it is clear that including the
real value of the “fiduciary issue” as part of society’s
net wealth is not an illusion. Some time ago, Clower
(1967) pointed out the dangers of treating monetary
economies as if they were analytically equivalent
to barter economies. In a similar vein, Coase (1960)
argues that when a comparison of economies with
alternative social arrangements is made, it is essential to consider the total effect. The true benefits of
fiat money only can be obtained by comparing a
fiat money economy with a commodity money
economy or with barter or credit economies. The
conclusion that the wealth associated with the real
quantity of fiat money is illusionary emerges from
a naive comparison of a fiat money economy with
an economy where all of a sudden no one uses or
holds money but nothing else changes.

with credit than with cash or checks. Given the
large and increasing use of credit in effecting transactions, how can one reasonably argue that this is
a monetary economy and not a credit economy?
Let us begin this discussion by trying to answer
the intriguing question: Could there be a pure credit
economy with no medium of exchange? A pure
credit economy may have been what Brunner and
Meltzer (1971) had in mind when they argued that
the problem of synchronizing payments and receipts
could be achieved by making verbal promises. To
see what such a world might look like, I will assume
that not only is there perfect information, but that
all individuals’ promises are fully credible, i.e., no
person makes a promise that cannot be kept.33 In
such a world, individual A could give individual B
commodity j in exchange for a promise to receive
commodity j or some other commodity at a later
date. This world would be very complicated. For
example, assume that individual A sells his labor
services to Firm F for the promise from F to pay a
certain quantity of commodity j at week’s end.
Individual A then buys the goods that he needs by
promising to deliver j or some other commodity
at some point in the future or by transferring part
of Firm F’s promise to deliver commodity j. Of
course, it is not necessary that these promises change
hands per se, it could be that some centralized
accountant keeps track of all promises made to and
from all parties, or everyone could simply have a
perfect memory.34
If promises were denominated in all possible
commodities, quantities, and future dates, the problem
of calculating the prices in this economy would be
extremely difficult. The pricing problem could be
significantly reduced (and the accounting simplified) if individuals agree to denominate all credit

Money and Credit as Media of Exchange

The world is dominated by monetary economies;
however, this does not mean that transactions are
not carried out using barter or credit. In monetary
economies, all three methods of effecting exchange
are used. Indeed, money may not be used to initiate most transactions. For example, when one
considers every extension of trade credit or the
transfer of goods by credit card, it is arguably the
case that more transactions are carried out initially

This point was initially made by Johnson (1969), p. 38, who recognized
that there were utility or output gains associated with the use of money.

There are a number of similarities between the no-money world I am
about to describe and that described by Fama (1980, 1983). Others
who have suggested that transactions could be carried out without the
use of money are Black (1970), and Greenfield and Yeager (1983). See
McCallum (1985) and White (1984) for analyses of these models.

Kocherlakota (1998) suggests that fiat money is “merely a physical
way of maintaining this balance sheet.” Hence, he suggests that
money is merely memory. He even suggests that his approach “represents an advance over the usual justifications for the existence of
money: Money is a store of value, money is a medium of exchange
and/or money is a unit account...After all, money does not allow society to transfer resources over time. Money does not reduce the cost of
transferring resources from one person to another.”

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contracts in the same commodity. This would give
rise to this commodity being a medium of exchange,
however. For example, if all credits are denominated
in m, it must be the case that a credit instrument
worth z units of commodity m today must trade for
z units of m itself. This means that individuals
with m could simply trade it for the commodities
they desire just as well as they could trade credit
instruments denominated in m. If a credit
instrument denominated in m facilitates trade, then
so too must commodity m—m would be money.35
It could be that m is bulky, like a barrel of oil,
so that it could not circulate hand-to-hand.36 The
promises themselves would be inconvenient, however, because longer-term contracts would have to
be discounted relative to shorter-term contracts.
This difficulty could be overcome by issuing noninterest-bearing sight drafts denominated in the
common unit of account, i.e., currency.37 In this
case, a credit economy would give rise to money.
It could be, however, that the commodity is
completely abstract, like a quark. Hence, we would
have a pure credit, nonmonetary, exchange economy where all credit contracts are denominated in
a unit of account, whose only function is to determine the price level (Fama, 1983). People, however,
only would accept promises denominated in something abstract or something that they did care to
hold if they were certain that they would be able to
exchange these promises for the commodities they
desire. Hence, that would demand that credit contracts be denominated in things that they value or
are certain that they would be easily converted into
other commodities.
Note the similarity between the world I have
just described and the one that exists today. Our
money is called the dollar. Congress adopted the
dollar (and the decimal system) as our unit of currency in 1785. Alexander Hamilton’s coinage recommendation establishing the U.S. dollar as 270 grains,
11/12 fine of gold or 416 grains, 0.89242 fine of
silver was not adopted until April 1792.38 Because
of the inconvenience of carrying gold or silver, sight
drafts were issued in convenient denominations.
These claims on the U.S. stocks of gold and silver
circulated in lieu of the commodities themselves.
Over the years the dollar has been redefined. U.S.
currency now is just a claim on the same quantity
of U.S. currency. That is, we now have a pure paper
currency standard. People are willing to hold intrinsically useless pieces of paper and claims that are
denominated in intrinsically useless pieces of paper

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because they are certain that other individuals will
accept the same. Collectively, the people agree to
maintain the paper’s value by limiting its issuance
and to share the seigniorage.39
The above analysis also reinforces why it is
efficient to have credit contracts denominated in
the same commodity, and better still if this commodity is money. Jevons (1875) termed this the
standard of value of money. The point to emphasize is that money facilitates the use of credit just
as it facilitates the trade of consumable commodities and tangible assets, (e.g., savings deposits are
exchanged for dollars that are used to purchase
bonds). Consequently, while credit figures prominently in many transactions, the analysis presented
above makes it clear why credit almost never is used
sequentially for other transactions and why the adoption of a commodity medium of exchange has tended
to precede credit arrangements, and not the other
way around.40
Can credit instruments function as money? The
answer is yes. They can and they have. Checkable
deposits (or electronic transfers of funds) are the
liabilities of the entities who hold the balances.41
As such, they are promises to pay dollars upon demand. Such balances are included in measures of
transactions money not only because they facilitate
exchange, but also because financial institutions
are committed to exchanging these deposits for
cash immediately and at a fixed one-to-one ratio.
This is what Pesek and Saving (1967) termed the

Hoover (1988) has made this point in a similar fashion to argue
against Fama’s (1980) “new monetary economics.”

Fama (1980, 1983) eliminated the possibility that what he termed the
“numeraire—unit of account” would circulate as money by assuming
that it was a “barrel of oil.” Note that this was not a necessary consequence of his model, but assumed. Hence, Fama did not establish
that there would be no commodity that would circulate as a medium
of exchange, rather he assumed it.

In addition, there may be a problem with the denominations of such
contracts. Indeed, Russell (1991) notes that both of these problems
were drawbacks to bills of exchange circulating as currency during
the seventeenth and eighteenth centuries in England.

The mint began to coin silver in October 1794 and gold in July 1795,
but a mistake by the first mint director resulted in coins of 9/10 fine.
See Studenski and Krooss (1952) for more details.

See McCallum (1985) for other ways of achieving price-level determinacy under a currency standard.

Bagehot (1873) makes this point with respect to the origins of bank
credit.

See Goodfriend (1991) for a good discussion of the evolution of
bank money.

FEDERAL RESERVE BANK of ST. LOUIS

instant repurchase clause. As long as the commitment is fully credible, such deposits and currency
substitute perfectly. In this case, it is sensible to
add such commitments to the stock of cash and
call the sum the stock of money. Indeed, this is
what is done.
Finally, it is worth noting that the increased use
and availability of credit might mitigate the effects
of disruptions to the supply of money, at least in
the short run. In economies where the credit market is not well developed, a negative shock to the
money supply may have a more immediate effect
on output and/or prices than in an economy where
individuals and businesses can not only readily
borrow against their future income but can make
transactions without having money immediately
available. In addition, as more transactions are initiated with the use of credit, the stock of money
necessary to support a given level of commodity
transactions could diminish, i.e., the velocity of
money could rise. It should be remembered, however, that financial transactions also require the use
of money. In any event, it is reasonable to speculate
that the relationship between money and output
and money and prices is likely to change as financial markets develop and mature.

The Asset Demand for Money
The asset demand for money has been associated with two literatures. The first deals with demand
for money as an asset and focuses on the interest
elasticity of the demand for money. The second
focuses on whether money should be defined to
include non-medium-of-exchange assets. Money’s
essential function is to facilitate transactions. Hence,
while it is appropriate to consider the effect of close
substitutes for money on its demand, it is inappropriate to define money to include such non-mediumof-exchange assets.42
The asset demand for money focused attention
on holding money for asset purposes, just like you
hold any other asset. I will argue, however, that
the asset demand for money is inconsequential.43
The asset demand for money has its origins with
Lavington (1968), but was most influentially advanced by Keynes. If money was held primarily
as an asset, its demand should be quite sensitive
to changes in interest rates, because the nominal
return to holding money is zero.
If money is primarily a medium of exchange,
however, the interest elasticity of money demand

might be quite low. To see why, I note that Brunner
and Meltzer (1971) begin their seminal work on
money by noting that money remains in circulation even during periods of high and accelerating
inflation. They argue that this fact “calls into question the relevance of treating money as an asset that
provides little or no return.”44 The analysis of why
individuals continue to use money during periods
of high and accelerating inflation presented here is
complementary with theirs. Money continues to
function as a medium of exchange even under conditions of severe or hyperinflation because it enjoys
a significant cost advantage over both barter and
credit as a medium of exchange. Indeed, this advantage is likely to be so large that it would take an extreme increase in the holding cost to induce individuals to shift to the widespread use of either barter
or credit to facilitate exchange. Moreover, the cost
advantage of money increases as economies become
increasingly specialized and dependent on exchange.
The advantage also increases as payments practices
become increasingly institutionalized.45
The point is that a large discontinuity exists between money and the next best alternative for
exchange. Economists normally think of continuous
functions where small changes induce individuals
to switch from one alternative to another. No such
continuum of media of exchange exists, however.
Money so dominates barter and credit as a medium of
exchange that it continues to serve as a medium of
exchange despite very large increases in the cost of
holding it. Jevons (1875, p. 6) was aware of this, stating:
...even if the medium of exchange varied
considerably in value, people would go on
making their payments in terms of it, as if
there had been no variation, some gaining
at the expense of others.

See Mason (1976) for an excellent critique of this approach to defining money.

McCallum and Goodfriend (1987) also have suggested the asset demand
for money should be relatively inconsequential, stating that money “will
also serve as a store of value, of course, but may be of minor importance
to the economy in that capacity.” They do not elaborate on why this
should be so, however.

Brunner and Meltzer (1971, p. 784).

Wallace (1983) has emphasized one of these institutional features;
namely, the legal restriction that currency is legal tender. Overlapping
generations models focus on the store of value function of money, i.e.,
money’s function as an asset (e.g., McCallum, 1983; and McCallum
and Goodfriend, 1987).

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The discontinuity between money and other
means of exchange suggests that the demand for
the medium of exchange may be rather insensitive
to changes in its holding cost.
The key observation is the degree of substitutability between money and other assets is that
the substitution is unidirectional: While money is
an asset (or group of assets) that provides a particular function that other assets do not provide, at
times, money may be held for the same reasons
that other assets are held—the asset that normally
serves as money also is now being held as a store
of wealth. It is never the case, however, that other
assets are held for the reason that money is primarily held. This means that when rapid and
accelerating inflation significantly increases the
cost of holding money it will not be a simple matter
for other assets to substitute for it, i.e., become
money. The most individuals can do is to economize
on their money holdings along the lines suggested
by Baumol (1952) and Tobin (1956). Since other
assets dominate money in their ability to transfer
wealth through time, however, individuals have a
strong incentive to economize on their holdings of
money for transactions purposes even when the
returns to other stores of wealth are low.
Nevertheless, it is possible to envision circumstances where the return on real assets is so low
that some individuals choose to hold money for
the same reason they normally hold other assets.
Indeed, classical economists, including Keynes,
were concerned about the consequences of hoarding money. Given the observed stickiness of prices,
they argued that hoarding money would have significant consequences for the real economy.
Hoarding money by individuals seems more
likely, however, in economies with relatively poorly
developed financial markets.46 If few alternatives
to holding wealth are readily available, more individuals may opt to hoard money, especially during
times of economic or financial uncertainty. The
more sophisticated and well developed the financial
system becomes, however, the less likely it is that individuals will choose to hold money as an asset, even
when nominal interest rates are extremely low.47
Keynes’ notion of the asset demand for money
focused the attention of monetary economists on
the interest sensitivity of money demand. The interest sensitivity of money demand has been extensively investigated, with a wide array of results
(e.g., Goldfeld and Sichel, 1990; and Laidler, 1993).
The amount of money held for transactions

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purposes depends on the planned volume of transactions. This, in turn, depends on the timing of
receipts and payments, which are affected by the
degree of specialization and the structure of the
markets, as well as the size, extent, and activity in
credit markets, etc. Changes in the opportunity
cost of holding money will induce individuals to
economize on their holdings of money balances,
but the degree to which they do this depends on
the size of the gain relative to the marginal cost of
the economizing activity. Given that money holdings are typically a small part of an individuals’
wealth and that individuals have a strong incentive
to minimize their holding of money at any nonzero
nominal interest rate, it would not be surprising to
find a relatively low interest responsiveness of
money demand. Indeed, empirical investigations
of currency demand (e.g., Hess, 1971; and Dotsey,
1988), which has a zero nominal return and is held
primarily for transactions purposes, suggest that
the interest elasticity of currency demand is zero.48
Other mediums of exchange that pay an implicit or
explicit interest may be held, in part, for the same
reasons individuals hold other assets, so that the
demand for them is likely to be more sensitive to
changes in their relative holding cost.

The Buffer-Stock Notion of the Demand for Money
The idea that there is no close substitute for
money as a medium of exchange is complementary with the buffer-stock notion of money demand.
In the buffer-stock theory (Laidler, 1984, 1987), holdings of real balances substitute for costly information and uncertainty. Individuals absorb shocks
to their real money holdings due to a shock to their
nominal money balances. Over time, nominal
money holdings are adjusted to a level more consistent with individuals’ demand for real money balances,
given the level of nominal interest rates, the level
46

Unfortunately, Keynes attempted to rationalize hoarding at a time
when financial markets were well developed. Hence, it was difficult to
explain why individuals held money when there were assets that had
all of the same risk characteristics of money but yielded a positive
rate of return (Barro and Fischer, 1976).

It is usually assumed that zero is a lower bound for the nominal interest rate because individuals could simply hold money that bears a
zero nominal return. This analysis too ignores the costs of acquiring
and storing money. See Thornton (1999).

Furthermore, most studies find a remarkably low substitutability
between currency and transactions deposits, suggesting that these
alternative media of exchange are held for quite different reasons.

FEDERAL RESERVE BANK of ST. LOUIS

and pattern of current income and expenditures and
expectations of future nominal interest rates, income
and expenditures, etc. The buffer-stock notion implies that individuals will not change their holdings
of real money balances immediately when nominal
interest rates, real income, or prices change.
Because other assets cannot perform money’s
function as a medium of exchange, I speculate that
individuals respond more quickly to reductions in
the real money balances due to negative nominal
money shocks (or positive price-level shocks) than
they do to increases caused by positive money shocks
(or negative price-level shocks). For example, when
there is a positive aggregate nominal money shock,
individuals may hold these balances temporarily
rather than spending them for goods and services
or purchasing other assets. If this were to happen,
there would be no immediate adjustment of
output, employment, prices, or interest rates. On
the other hand, since individuals cannot substitute
for money, negative aggregate shocks may affect economic behavior directly and more quickly.

The Welfare Benefits of Money
The usual approach to assessing the welfare
benefits of money is to assume that money is like
other assets; for instance, shoes or cars. In the case
of these assets, the benefits accrue only to the consumer so the welfare gains can be obtained by simply
summing up the so-called Harberger triangles. It
is well known that this approach fails when there
are significant social externalities. Since I have argued that there are significant social benefits from
money—because of the role it plays in expanding
the size and extent of the markets for goods and
credit, and the degree of specialization—this approach cannot possibly work. Indeed, it seems
reasonable to speculate that the social benefits
of money could eclipse its private benefits.
Unlike many innovations, it is virtually impossible to internalize the benefits from using money.
This further enhances the idea that there is significant social benefit to money. Indeed, once the
usefulness of money is recognized, the one who
recognizes it has an incentive to share the insight
with others, as my parable of the trader illustrates.

The Parable of the Trader
There was a producer who once every period
loaded some of his produce on a wagon and

went to a destination where he and other producers would meet to trade their wares. One
day, the producer noticed that there was
one good, g, that nearly everyone wanted and
would exchange goods for g. Realizing that
he can buy virtually any good he desired
using g, he offers to take g for the goods he
was trying to trade. Initially he does this
only when the double coincidence of wants
necessary for barter is lacking. He soon
discovers, however, that trading in g is much
faster and easier than searching out barter
opportunities, so he stops seeking barter
opportunities and his barter transactions
become increasingly infrequent. By trading his wares for g, and g for the goods that
he desires, this producer discovers that he
can accomplish the desired trading in a fraction of the time that he had previously spent.
Now he could attempt to internalize the
gain from his private knowledge (no one
else has made this observation yet) by
offering to tell others how they could save
trading time for a fee. He realizes, however,
that no one would pay for this information
because all they have to do is observe him
and they, too, would know the secret. More
important, he realizes that he could further
shorten his trading time if the others behaved as he. Hence, rather than keeping
this information private and attempting to
internalize the benefit from his superior
information, the trader has an incentive to
make the information public. In so doing,
however, not only does he gain by shortening the transactions time, but others do
as well.
As Laidler (1990, p. 48) puts it, “one agent’s
cash balances produce services not just for that
agent then but for all other agents with whom his
market activities bring him into contact.” The use
of money that facilitates the trade of one agent
facilitates the trade of all agents. In addition, the
reduction in individuals’ exchange cost associated
with money’s use causes markets to flourish. Increased trade promotes greater specialization, greater
dependence on trade, and a greater need for and
use of money, and so on, and so forth.
The synergy among trade, money, and specialization makes isolating the welfare benefits of

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money extremely difficult, if not futile. The welfare
benefits of money can be ascertained only by comparing monetary economies with economies that
have alternative arrangements for exchange, i.e.,
only by comparing the total welfare of a monetary
economy with that of a nonmonetary economy.49

The Welfare Costs of Inflation
The main implication of the discontinuity
between money and barter or credit as a medium
of exchange is that money will continue to be
used even at very high rates of inflation. This
implies that the welfare costs of inflation, which
are associated with the reduced reliance on money
as a medium of exchange, may be relatively small.
This is particularly likely at relatively modest rates
of inflation. Hence, it is not surprising that estimates suggest that the cost of inflation is large only
at relatively high inflation rates (e.g., Bruno and
Easterly, 1996).
Furthermore, not only is it inappropriate to
estimate the welfare gains from the use of money
by adding up Harberger triangles, it is equally
inappropriate to measure the welfare costs of
inflation this way, as is frequently done.50 Since
money will continue to circulate as a medium
of exchange and since the ability to economize
further on money holdings is likely to be small,
so, too, is the cost of inflation from holding money
balances. This is important because many discussions about inflation assume that its principal
cost is the private shoe leather cost associated
with economizing on the use of money as a medium of exchange. If the externalities associated
with money are important and significant, such
analyses understate the welfare costs of inflation,
perhaps significantly.
Most economists would argue that if an economy were just starting, the optimal rate of inflation
would be zero. Nevertheless, many argue that once
inflation is underway, society is better off tolerating
some inflation rather than to suffer the output loss
they believe would be associated with reducing
inflation to zero. This idea is called Howitt’s (1990)
Rule. The effects of inflation on the institutional
arrangements of trade are likely to be extremely
important, however, and these costs are missed
completely by estimates that ignore the externalities associated with money’s roles as a medium of
exchange and a standard of value. Consequently,
Tobin’s often cited dictum that “it takes a heap of

J A N UA RY / F E B R UA RY 2 0 0 0

Harberger triangles to fill an Okun gap,” which
underlies such analyses, is simply irrelevant if
there are significant social costs of inflation.51
The third consequence of the discontinuity
between money and other methods of exchange is
that it may be inflation uncertainty, rather than
inflation per se, that produces the most significant
welfare cost. Here it is important to distinguish
between the medium of exchange and standard of
value functions of money. An important benefit of
money is that it reduces shopping costs—gathering
information about relative prices, planning, budgeting, etc. Uncertainty interferes with the shopping function by distorting price signals that enhance
market efficiency. Price-level uncertainty makes
distinguishing between absolute and relative prices
and between permanent or transitory changes in
the price level difficult. Distortions to the pricing
mechanism affect the efficiency of markets that
affect investment (e.g., DeLong and Summers,
1991; and Barro, 1995), financial markets, and relative input prices (e.g., Easterly, 1993). Inflation also
reduces efficiency by encouraging the development of alternative market structures that would
not exist in a world with a stable price level. Because uncertainty about the future level of prices
increases with the average rate of inflation, these
costs are likely to be small at relatively low rates of
inflation but increase with the rate of inflation.
It could be, however, that the most deleterious
effects of inflation on economic welfare may come
from the effect of inflation on the efficient function
of the credit market. Both the rate of inflation and

This may have implications for how money is modeled. For example,
it is frequently the case that money is modeled in the context of one
good economy where exchange is implied but not explicitly modeled.
Given the possibility that there are large externalities associated with
money, this practice may not be useful for some issues. It also may
have implications for other models. For example, Lucas (1980, p. 145)
states, “When we apply theories of barter economies to problems in,
say, public finance or labor economics, it is not our intent to obtain
results applicable only to primitive or prehistoric societies. We apply
this body of theory to money-using economies such as our own
because we believe that for many problems the fact that money is
used in attaining equilibrium can be abstracted from, or that the theoretical barter economy is a tractable, idealized model which approximates well (is well-approximated by) the actual monetary economy.
If this practice is sound, then we want monetary theories which rationalize it or at least do not radically conflict with it.”

For example, Bailey (1956), Friedman (1969) and Lucas (1994).

For a critique of some other limitations of Howitt’s Rule,
see Thornton (1996). Also see Marty and Thornton (1995) for
a discussion of some other arguments for the desirability of
moderate inflation.

FEDERAL RESERVE BANK of ST. LOUIS

inflation uncertainty are detrimental to denominating credit contracts in terms of fixed units of
money. Consequently, while high, accelerating,
and especially uncertain inflation may have a relatively small effect on money’s medium-of-exchange
function, they may have a significant effect on
financial markets. It is not easy to replace money
as the standard of value. Recently, credit contracts
have been denominated in variable units of money, so
that the value of the contract varies with a measure
of the actual inflation experience during periods of
inflation uncertainty. For reasons that are not well
understood, however, this practice has been relatively
limited, especially at relatively moderate inflation
rates. Long-term debt markets tend to dry up during
periods of rapid inflation and, as a consequence,
the rate of capital formation slows. While far from
definitive, the evidence suggests that the covariance
between inflation and the rate of economic growth
is negative (e.g., Bruno and Easterly, 1996).
While inflation potentially has a significant
effect on the rate of economic growth, its potential
to affect the level of output may be modest. To the
extent that high and accelerating inflation reduces
the reliance on money as either a medium of exchange or a standard of value, resources are drawn
from one use to another. The result is that the level
of measured output may change relatively little
between high and low inflation states, but the distribution of output may be significantly different
and the level of economic welfare may be significantly lower in higher inflation environments.
This may account for the fact that economists have
not found a statistically significant relationship
between the rate of inflation and the level of output,
at least for relatively moderate rates of inflation.

SUMMARY AND CONCLUSION
I have argued that money is a social arrangement resulting from a complicated evolutionary
process. Money exists because it facilitates exchange
by reducing the cost of trade. Seen in this point of
view, money is but one of several institutional arrangements designed to reduce the costs of exchange.
By reducing the cost of exchange, money reduces
the reservation relative price where trade is mutually advantageous thereby encouraging more trade
and greater specialization. Because of their strategic
complementary, it is not surprising that money,
trade, and specialization have tended to evolve
simultaneously.

I argue that there are only three methods of
effecting trade: simple and sequential barter, simple
and sequential credit, and money. I then explain
why the information and shopping costs of sequential barter and/or sequential credit are likely to be
high relative to those of money. It is not surprising
that the world is populated with monetary economies and not barter or credit economies.
I also have argued that money has a significant
cost advantage relative to simple barter and credit
and this advantage helps explain why the same
good has served most often as both the medium
of exchange and the unit of account, and why the
development and widespread use of money tends to
make simple barter scarce.
The use of money promotes specialization and
trade by reducing exchange costs. The reduction
in exchange costs associated with money cannot
benefit one individual without benefiting others.
Indeed, it is virtually impossible to internalize the
benefits from money. Consequently, there are significant externalities associated with the use of
money. Money is a social arrangement whose benefits can be calculated correctly only by comparing
monetary economies with barter or credit economies.
I speculate that the social gains from the use of money
are likely to be large relative to the private opportunity cost of holding it. Furthermore, these benefits
extend to nonresource-using fiat money. Indeed, the
fact that nonresource-using money frees resources
for production and/or leisure necessarily implies that,
other things being the same, the transition from
commodity to fiat money is welfare enhancing.
I argue that money enjoys an enormous cost
advantage over barter or credit as a medium of
exchange. Because of this, inflation is not likely to
result in a large-scale substitution away from money
as a medium exchange. Hence, money continues
to circulate as a medium of exchange even during
periods of hyperinflation. Significant costs of inflation could be associated with the effects of inflation
uncertainty on the efficiency of the goods, labor,
and financial markets, most especially the efficiency of the credit market because of the deterioration of money’s function as a standard of value.
The fact that there are significant externalities
associated with the use of money and that inflation
increases the costs of using money gives rise to the
possibility that the welfare costs of inflation are significant. Because money dominates barter and credit
as a medium of exchange, the welfare costs of inflation due to a reduction in money’s role as a medium

J A N UA RY / F E B R UA RY 2 0 0 0

REVIEW

of exchange are likely to be small, relative to those
associated with its function as a standard of value.

Dornbusch, R., and Jacob Frenkel. “Inflation and Growth:
Alternative Approaches,” Journal of Money, Credit and
Banking (February 1973), pp. 141-56.

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FEDERAL RESERVE BANK of ST. LOUIS

Appendix

The Effect of
Exchange Costs of the
Exchange Ratio
Necessary for Trade

for individuals specializing in good 2.
The optimality of (c1T,c2T) with trade implies
that the individual equates the ratio of the
marginal utilities of the two goods to the exchange
ratio, λ, so that

Robert D. Dittmar
and Daniel L. Thornton

is satisfied.
Equation A.4, equation A.1, and one of the two
budget constraints above implicitly determine the
consumption bundle in the case of specialization
and the terms of trade that are necessary to compensate the individual for trading when there is a
fixed transaction cost, Ω.
In principle these equations can be solved to
determine the effect of Ω on λ when an individual
specializes in the production of either of the two
goods. A closed-form solution cannot be obtained,
however, without making explicit assumptions about
functional forms. Linear approximations to these
functions that will be accurate predictors of the effects
of small transaction costs can be made, however.
These linear approximations are obtained by
implicitly differentiating the equations and evaluating the resulting expressions at Ω=0. Note that
if Ω=0, λ = f2′ / f1′ if an individual is to be indifferent
between trading and autarky. Furthermore, at
these terms of trade, the individual must be indifferent between specializing in the production of
good 1 or good 2. Consequently, either of the
budget constraints above can be used as the starting
point of the approximation. Finally, note that optimization requires the individual to equate the ratio
of the marginal utilities of the two goods to terms
of trade. In the absence of exchange costs, or
under autarky, the condition is

The text argues that costly trade reduces the
feasible range of exchange ratios where trade is
mutually advantageous. The question that arises is
by how much must the exchange ratio change to
compensate an individual for the costs of exchange
if there are fixed exchange costs? The question is
not answered easily because the relevant comparison is the utility levels obtained with costly trade
and with autarky. What must the terms of trade be
to compensate an individual for fixed transactions
costs? Some intuition about this can be obtained
by considering the effect of a small change in fixed
costs in the borderline case when the individual is
indifferent between the autarkic consumption
bundle and the consumption bundle obtained by
paying a fixed cost and trading.
Let (c1A,c2A ) denote the individual’s consumption bundle under autarky, and (c1T,c2T) denote the
individual’s consumption bundle with fixed exchange
costs. Indifference implies that
(A.1)

U ( c1T ,c 2T ) = U ( c1A ,c 2A ).

When production technologies are linear and the
terms of trade, λ, are such that λ ≠ f2′ / f1′, individuals
specialize in the production of one of the goods,
good 1 or good 2. The budget constraint

(A.4)

(A.5)
(A.2)

λc1T + c 2T = λf1 ( Γ ) − Ω

is satisfied for individuals specializing in good 1,
and
(A.3)

λc1T + c 2T = f 2 ( Γ ) − Ω

U1 (c1T , c2T ) = λU2 (c1T , c2T )

U 1 ( c1A ,c 2A )
f2′
=
.
U 2 ( c1A ,c 2A )
f1′

To economize on notation, we use an overdot
to represent differentiation with respect to the
fixed cost of exchange, Ω, i.e., λ̇ =dλ /dΩ. Regardless of whether the individual specializes in good 1
or 2, we can implicitly differentiate Equation A.4
to obtain,

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REVIEW

U 1 ( c1T ,c 2T )c˙1T + U 2 ( c1T ,c 2T )c˙ 2T = 0 .

(A.6)

Evaluating this expression at Ω=0 yields
f2′ T
c˙1 ( 0 ) + c˙ 2T ( 0 ) = 0 .
′
f1

(A.7)

Implicitly differentiating Equation A.2, on
the assumption the individual specializes in
good 1, yields

λ˙ c1T + λc˙1T + c˙ 2T = λ˙ f1 ( Γ ) − 1.

(A.8)

Evaluating Equation A.12 at Ω=0, as before, yields,

(A.13)

Combining Equations A.7 and A.13, yields
(A.14)

(A.15)
f ′
λ˙ ( 0 )c1A + 2 c˙1T ( 0 )
f1′
+ c˙ 2T ( 0 ) = λ̇ ( 0 ) f1 ( Γ ) − 1.
Combining Equations A.7 and A.9 and solving for
λ̇ (0) yields

λ̇ ( 0 ) =

(A.10)

1
.
f1 ( Γ ) − c1A

Equation A.10 shows the effect on λ of a small
fixed transaction cost, evaluated at the point Ω=0.
Hence, an individual will be induced to specialize
in good 1 and trade only if the terms of trade are
approximately
(A.11)

λ(Ω ) ≈


f2′ 
1
+
Ω
A
f1′  f1 ( Γ ) − c1 

or larger. This result is intuitive. The quantity
f1(Γ ) 2 c1A is approximately the amount of good 1
that the individual must give up to trade. Hence,
Ω/(f1(Γ ) 2 c1A) is the extra amount of good 2, per
unit of good 1 exchanged, that the individual must
obtain to be compensated for the fixed cost of
entering the market.
A similar analysis applies to individuals that
specialize in the production of good 2. In this case,
Equation A.3 is differentiated to obtain
(A.12)

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λ˙ c1T + λc˙1T + c˙ 2T = −1.

λ̇ ( 0 ) = −

1
.
c1A

Hence, an individual will be induced to specialize
in good 2 and trade only if the terms of trade are

Evaluating this expression at Ω=0, yields
(A.9)

f ′
λ˙ ( 0 )c1A + 2 c˙1T ( 0 ) + c˙ 2T ( 0 ) = −1.
f1′

λ(Ω ) ≈

f2′
1
− AΩ
f1′ c1

or smaller. The quantity 2Ω/c1A is the discount per
unit of good 1 purchased required to compensate
the individual for the fixed cost of trade.

Full text of Review (Federal Reserve Bank of St. Louis) : January/February 2000, Vol. 82, No. 1

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