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JULY/AUGUST 1995

PERSPECTIVES
A review from the
Federal Reserve Bank
of Chicago

Big emerging markets
and U.S. trade
Sectoral wage growth
and inflation




FEDERAL RESERVE BANK
OF CHICAGO

Contents
Big emerging markets
and U.S. trade................................................................................................2
Linda M . A g u ilar and M ik e A. Singer

Ten developing nations have been labeled as
important potential growth markets for U.S.
exports. The authors examine recent trends in
export sales to these nations from both a
national and Seventh District perspective.

Sectoral wage growth and inflation.......................................................... 16
Ellen R. Rissman

Econometric evidence suggests that only in manufac­
turing and retail trade do wages help predict inflation.
Inflation helps predict wage growth in other major
industry categories examined.

Ordering information for cassette tapes
for the 1995 Conference on Bank
Structure and Competition........................................................................ 1 5

ECONOMIC PERSPECTIVES

July/A ugust, 1995 Volum e XIX, Issue 4

President

ECONOMIC PERSPECTIVES is published by

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Microeconomic Policy

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ISSN 0164-0682

Big emerging markets
and U.S. trade

Linda M . A g u ila r and M ik e A . S in g er

"No nation was ever ruined
! by trade. ”
—Benjamin Franklin
The preceding quote by
Benjamin Franklin is as true today as it was 200
years ago. United States history is steeped in
trade and trade debate, from the pivotal role of
the Boston Tea Party in shaping the United
States as a nation, to the recent debate over the
merits of U.S. ratification of the present round
of the General Agreement on Tariffs and Trade
(GATT) negotiations.
The U.S. Department of Commerce is
actively involved in promoting exports. In
1993, President Clinton announced a National
Export Strategy for the United States, de­
scribed as “a comprehensive plan [that] up­
grades and coordinates the government’s
export promotion and export finance pro­
grams to help American firms compete in the
global marketplace.”1 In particular, the Na­
tional Export Strategy identifies past prob­
lems with U.S. trade promotion efforts and
recommends improvements to current ones.
This includes enhancing existing trade finance
ones such as the Exim Bank and the Overseas
Private Investment Corporation and creating a
Tied Aid Fund to help U.S. firms compete on
a level playing field. As an outcrop of this
initiative, Commerce identified ten foreign
nations as the big emerging markets (BEMs)
of the upcoming century, markets where the
potential for trade growth is the greatest.
It has long been recognized that exports
play an important role in the U.S. economy
because they support jobs and they represent a
significant component of gross domestic prod­
uct (GDP). Over the last few years, U.S. ex­
ports have contributed significantly to overall


2


GDP growth. But targeting emerging markets
is a new concept for the U.S. In the past, the
nation could expect trade to expand steadily
with its traditional trading partners—mainly
Europe, Canada, and more recently, Japan. As
the National Export Strategy was being devel­
oped, however, it became clear that the U.S.
could not rely on these partners as a source of
continued growth. In fact, trade with our tradi­
tional trading partners has been, and is project­
ed to continue to be, flat.2 The next logical
step was to determine where growth was likely
to occur. Thus was born the BEM initiative.
In addition to growth potential, the ten
BEMs have other traits in common. They are all
physically large with large populations, have
recently undergone some program of economic
reform, are politically important to their region
of the world, and are likely to spur growth within
their regions.3 Where are these markets? Geo­
graphically they represent several parts of the
world. In Asia they are China, Indonesia, India,
and South Korea; in Latin America they are
Mexico, Argentina, and Brazil; in Central and
Southern Europe they are Poland and Turkey; in
Africa it is South Africa.
Commerce estimates that the BEMs and
other less developed countries will be the fast­
est growing import markets through the year
2010. By then, the BEMs are expected to
account for 27 percent of total world imports,
three times their 1992 share.4 U.S. firms will
want to capture as much of that market as pos­
sible. With accurate knowledge and support
from all levels of government, they can realize
that goal; to some extent, they are already
Linda M. Aguilar is a regional economist and
Mike A. Singer is an agricultural economist at the
Federal Reserve Bank of Chicago.

ECONOMIC PERSPECTIVES

ahead of the curve. In 1987, U.S. commodity
exports to the BEMs accounted for nearly 15
percent of all U.S. exports. By 1994, the BEM
market had grown to 20 percent of all U.S.
exports—an increase of $65 billion. Total
exports to the BEMs increased 177 percent.
State governments also actively promote
exports and overseas business opportunities for
firms located in their state. In the Seventh
Federal Reserve District, which includes all of
Iowa and parts of Illinois, Indiana, Michigan,
and Wisconsin, efforts by state governments
may have helped exports to the BEMs grow
from 10 percent of all District exports in 1987
to 13 percent in 1994, an increase of $5.6 bil­
lion in goods.5 Total District exports to the
BEMs grew 152 percent over the period, with
those to Indonesia, Argentina, and Brazil expe­
riencing the largest growth (425 percent, 334
percent, and 249 percent, respectively).
This article will begin by examining the
import profiles of the BEMs as a group over the
1988-92 period. We then present U.S. and
Seventh District exports to these markets for
roughly the same time period. Next we examine
agricultural exports separately because of the
important role played by Seventh District states
in U.S. agricultural output and trade. We then
provide additional detail on U.S. trade with
several of the larger BEMs. The following
section examines current U.S. and District ex­
port promotion initiatives. Finally, we sum up
and conclude with an assessment of how well
U.S. exports are meeting the needs of the BEMs.
The data in this article represent the full
range of goods that can be bought and sold in the
marketplace, including agricultural goods, min­
erals, clothing, chemicals, metals, machinery,
scrap and waste, secondhand goods, and an­
tiques. They do not include services. We used
several data sources. Import data on the BEMs
came from United Nations data and cover the
1988-92 period. We chose 1988 as the base year
for import data since U.S. trade with the BEMs
has only recently started to expand. We chose
1987 as the base year for export data solely
because that was the start year of one of the data
series we used. Detailed Census data on U.S.
exports are more current and are available
through 1993, but to avoid confusion we used
those data only when discussing total U.S. ex­
ports or aspects of the BEMs unrelated to the
United Nations data. State export data, based on
Census data, came from the Massachusetts Insti­


FEDERAL RESERVE


RANK OF CHICAGO

tute for Social and Economic Research (MISER).
These data were available through 1994, but we
used them only for aspects unrelated to United
Nations import data.
One other note on the data. In reporting
imports for the BEMs, the United Nations uses
the Standard International Trade Classification
(SITC) system, a system originally developed
in 1950 by the United Nations so that all coun­
tries reporting trade statistics would use compa­
rable categories. However, for most purposes,
U.S. trade is reported on the basis of the Stan­
dard Industrial Classification (SIC) system that
was originally developed for analyses of do­
mestic commerce. These two systems (as well
as several other reporting systems) are not
generally comparable. Although the commodi­
ty or industry descriptions may sound similar,
the actual components that comprise them are
generally not the same.
The g ro w in g BEM m a rk e t

The BEMs’ share of world imports grew
from 7.7 percent in 1988 to 9.3 percent in
1992. In the latter year, the BEMs imported
$357 billion in commodities. The U.S. cap­
tured the largest share with nearly 22 percent,
up from 20 percent in 1988. Japan held sec­
ond place with approximately 14 percent,
down from 17 percent in 1988. Germany
captured nearly 9 percent, as it did throughout
the period (see figure 1). South Korea and
China are by far the largest of the BEMs in
terms of total imports. In 1992, each of those
two countries imported around $81 billion in
goods. Mexico was the next largest with near­
ly $48 billion.
Two things stand out about the types of
goods that the BEMs imported in 1992. First,
the single largest import commodity was petro­
leum and petroleum products (mostly crude
petroleum and fuel). Second, the next four
largest import commodities were all in machin­
ery and transportation equipment—electrical
machinery (such as household appliances and
switchgears), machines for special industries
(such as textile and leather machinery), road
vehicles, and general industrial machinery
(such as heating and cooling equipment).
Combined, these five commodity categories
accounted for $124 billion, or about 35 percent
of total BEM imports (see figure 2).

3

This collective import profile of the BEMs
shows an emphasis on production rather than
consumer goods, reflecting a desire to develop
the capacity to produce their own goods for
consumption or export. Given this desire, the


4


BEMs need machinery imports to
build an industrial structure or
upgrade an existing one. Thus
several of the Asian BEMs’ ma­
chinery imports are in the textile
and apparel industries. Road
vehicles, telecommunications, and
electronics and electrical machin­
ery are in demand in the Latin
American BEMs, and machinery
for special industries is in demand
in several others, for example,
industrial food processing ma­
chinery in Poland. To fuel these
industries (literally), petroleum
and petroleum products are need­
ed—for the factories, equipment,
workers’ homes, workers’ trans­
portation, and so on.
Individually, some of the
BEMs had quite different import
profiles than the group as a whole
(see table 1). For example, Chi­
na’s second-largest import commodity is textile
yarns, which in turn support two of their major
export industries—clothing and accessories,
and textile yarn and fabrics. Combined, these
two industries accounted for 30 percent of
China’s exports in 1992. India’s
only similarity with the BEMs’
collective import profile is that its
top import commodity is petro­
leum and petroleum products. Its
second-largest import commodity
is nonmetal minerals, which in­
clude precious and semiprecious
stones, primarily rough unset
diamonds. Diamonds accounted
for 15 percent of India’s exports
in 1992. Indonesia’s imports also
vary substantially from the
group's overall profile.
Another way in which the
BEMs differed from each other was
in who their largest sources of
imports were (see table 2). As
could be expected, several countries
had a neighboring country among
their top three sources. For exam­
ple, of all the goods that China
imports, Hong Kong was the single
largest supplier, capturing over 25
percent of the total. Of Argentina’s

ECONOMIC PERSPECTIVES

TABLE 1

Top commodities imported

by

selected BEMs,

1992

Value
($U.S. billions)
China

$8.3

Machines for special
industries

7.8

Textile yarns

4.9

Electrical machinery, NESa

4.5

Iron and steel

4.2

Plastic materials
35.6% of total imports

Indonesia

$2.7

Machines for special
industries

2.5

General industrial
machinery, NESa

2.1

Petroleum and products
Powergenerating
equipment

1.7
1.5

Iron and steel
38.3% of total imports

India

$6.6

Petroleum and products

2.8

Nonmetal mineral
(manufactures), NESa

0.9

Inorganic chemicals

0.9
0.8

Iron and steel
Fertilizers (manufactures)
59.1% of total imports

Note: SITC commodities imported from all countries, measured by
U.S. dollar value.
aNot elsewhere specified.
Source: United Nations (1993).

total imports, Brazil was the largest source, pro­
viding 23 percent. In turn, Argentina was Bra­
zil’s third-largest source, providing 8 percent of
the latter’s imports.
Total import growth for the BEMs over the
1988-92 period was nearly 59 percent. By
comparison, total world imports grew 32 per­
cent, and among the industrialized countries,
U.S. imports grew by 21 percent, Japan’s by 25
percent, and Germany’s by 63 percent. Germa­
ny’s spectacular increase can be attributed to
the country’s reunification and the increased
demand resulting from the effort to bring the
former East Germany up to par with the rest of
the country. (East Germany was not included
in the 1988 data). In addition, the BEMs as a
whole registered a higher average annual im­
port growth rate than did either the U.S. or
Japan, both of which have experienced recent


FEDERAL RESERVE


BANK OF CHICAGO

periods of economic slowdown.
However, Germany still outper­
formed the BEMs (on average)
for the reason noted above.
Individually, BEM import
growth ranged from a high of
179 percent for Argentina to a
low of 7 percent for South Afri­
ca. In addition to Argentina,
Mexico and Indonesia also had
above-average import growth,
rising 145 percent and 106 per­
cent, respectively. South Afri­
ca’s weaker gains were likely
due to its overall stagnant eco­
nomic growth that persisted
through the early 1990s.
To summarize, the import
profile of the BEMs over the last
few years indicates that they are
indeed growth markets. Import
growth in seven of the ten BEMs
exceeded world import growth,
the types of goods the BEMs
import are those most needed to
support growing economies, and
the major industrialized countries
of the world have recognized the
importance of serving these mar­
kets. The next section will
present in more detail the export
patterns of the U.S. and the Sev­
enth District in terms of meeting
the BEMs’ needs.

U .S . exp o rts to th e BEMs

Over the 1987-94 period, U.S. exports to
the BEMs grew $65 billion, or 177 percent, for
an average annual compound gain of 16 per­
cent. U.S. exports to the rest of the world grew
by 95 percent over the same period, for an
average annual compound gain of 10 percent.
With the exception of two industries—mining
of quarry nonmetal minerals (such as sand or
clay) and lumber and wood products—BEM
export growth by industry exceeded U.S. ex­
port growth to the rest of the world. The ma­
chinery industries did particularly well in terms
of absolute increase. Both electrical and non­
electrical machinery increased by over $11
billion each, and transportation equipment
increased by nearly $10 billion.
In terms of market share, the BEMs grew
from 15 percent of total U.S. exports in 1987 to

5

TABLE 2

BEMs’ largest import trading partners, 1992
BEM

Largest
partner

Im ports
($U.S. billions)

Im port
share
(percent)

Argentina

Brazil

Brazil

U.S.

China

Hong Kong

$3.3

22.5

5.4

23.2

20.5

25.5

India

U.S.

2.3

9.6

Indonesia

Japan

6.0

22.0

So. Korea

Japan

19.5

23.9

M exico

U.S.

30.1

62.9

Poland

G erm any

3.8

23.9

South Africa

G erm any

3.0

16.4

Turkey

G erm any

3.8

16.4

Note: This table should be read as follows: Brazil is Argentina's
single largest source of imports, supplying $3.3 billion worth of
goods, or 22.5 percent of Argentina's total imports.
Source: United Nations (1993).

20 percent in 1994. While all the BEMs had
positive growth over the period, Argentina,
Indonesia, and Mexico had the largest percent­
age increases, at 310 percent, 266 percent, and
247 percent, respectively. However, U.S. ex­
ports to Mexico in many ways stand out from
those to other BEMs because of certain charac­
teristics unique to Mexico. One major factor is
that Mexico is a free trade partner of the U.S.
The U.S., Mexico, and Canada have
a formal trade agreement that fosters
free and open trade among our
countries and includes rules and
agreements that go beyond GATT.
In addition, U.S. trade with Mexico
is augmented by their proximity to
each other. Thus, while U.S. export
growth to the combined BEMs has
outpaced export growth to the rest
of the world, the Mexican market is
especially significant.
While Mexico is by far the
largest BEM export market for the
U.S., South Korea, China, and
Brazil are also major markets for
the U.S. The South Korean market
is the largest of the three, nearly
double the size of the Chinese or
Brazilian markets in 1994. The top
export industries to South Korea in
1994 were electrical machinery,
nonelectrical machinery, and trans­


6


portation equipment. On a more
detailed basis, in 1993 (the latest
year for which such data are now
available), the top exports to
South Korea were semiconduc­
tors, aircraft, and meat products.
The top exports to China were
aircraft, motor vehicles, and
radio and TV equipment; those to
Brazil were data processing
equipment, aircraft, and industri­
al organic chemicals. (See figure
3 and table 3 for the top U.S.
goods exported to the BEMs as a
group and individually in 1993.)
Seventh D is tric t trade w ith
the BEM s

Exports to the BEMs from
the Seventh District states in­
creased by $5.6 billion, or 152
percent, over the 1987-94 period.
By contrast, exports to the rest of the world
grew 90 percent. Almost all industries had
positive export growth to the BEMs, with the
exception of forestry, scrap and waste, and the
two mining industries. Nonelectrical machin­
ery, electrical machinery, and chemicals had
the largest absolute increases, accounting for
60 percent of the District’s total export in­
crease to the BEMs over the period.

ECONOMIC PERSPECTIVES

FEDERAL RESERVE BANK OF CHICAGO

TABLE 3

Top five U.S. exports to the BEMS, 1993
(by U.S. dollar value)
1993 exports
(millions)
Argentina

$3,775.7

349.7
160.2
129.2
126.2
113.0
Brazil

India

7.7
7.6
5.0
3.8
3.8

Total

Total

23.2
7.4
3.8
3.3
3.1

Aircraft
Steam, gas, hydraulic turbines
Nitrogenous fertilizers
Aircraft parts
Industrial organic chemicals NEC3

Total

Total

24.1
5.1
4.1
3.9
3.6

Aircraft
Cotton
Petroleum refining
Soybeans
Oil field machinery & equipment

I Note: Throughout this table, total means total U.S. exports to that country.
1 aNot elsewhere classified.
Source: U.S. Departm ent of Commerce (1994b).




South Africa

South Korea

Turkey

Aircraft
Automated data processing machines
Low-value goods
Wheat
Industrial organic chemicals NEC3
Total

8.4
7.1
4.7
4.0
3.4

$3,428.9

758.8
292.0
154.2
153.0
136.9

Low-value goods
Aircraft
Corn
Oil field machinery and equipment
Chicken cuts
Total

12.4
5.9
4.3
3.9
3.3

$14,782.0

1,235.1
1,052.1
695.9
592.0
498.2

Motor vehicle parts, accessories
Low-value goods
Automated data processing machines
Electrical equipment— internal combustion engines
Electronic components NECa
Total

15.6
15.4
9.4
5.1
3.9

$2,188.4

272.2
129.1
94.9
85.7
72.6

SIC com m odity
Total

10.1
3.7
2.4
2.2
2.2

$911.6

142.4
140.4
85.8
46.4
35.1

Aircraft
Motor vehicles and car bodies
Radio, TV, & communication equipment
Nitrogenous fertilizers
Wheat

20.9
6.5
6.3
4.2
2.9

Poland

Percent
of total

$41,581.1

4,188.4
1,538.1
996.4
916.5
906.3

Automated data processing machines
Aircraft
Industrial organic chemicals NECa
Motor vehicles & car bodies
Metallurgical bituminous coal

$2,770.3

667.9
142.6
112.4
106.7
100.4

Mexico

Automated data processing machines
Aircraft
Low-value goods
Industrial organic chemicals NEC3
Motor vehicles & car bodies

$2,778.1

581.6
180.2
175.1
117.9
81.5
Indonesia

Total

$8,762.8

2,029.7
645.9
331.3
292.8
274.2

1993 exports
(millions)

SIC comm odity

9.3
4.2
3.4
3.3
3.0

$6,058.0

467.0
461.4
299.9
228.4
227.5
China

Percent
of total

Semiconductors, related devices
Aircraft
Meatpacking plants
Scrap and waste
Industrial organic chemicals NEC3
Total

22.1
8.5
4.5
4.5
4.0

Aircraft
Aircraft parts
Scrap and waste
Aircraft engines
Cigarettes

The BEMs’ share of Seventh District ex­
ports has also grown. In 1987, exports to the
BEMs comprised 10 percent of total District
exports; by 1994, that share had risen to 13
percent. The largest BEM export markets for
the District were Mexico, South Korea, and
China, which together comprised three-fourths
of the District’s exports to the BEMs in 1994.
However, as the fastest-growing markets, Indo­
nesia, Argentina, and Brazil had the largest
percentage increases over the period: 425 per­
cent, 334 percent, and 249 percent, respective­
ly. Like the U.S., exports to Mexico tended to
dominate the profile of District exports to the
BEMs as a group because of the large share
Mexico consumes—nearly half of all District
exports to the BEMs.
An interesting development in the District
between 1987 and 1994 was that transporta­
tion equipment declined as a share of total
District exports. This was true for total Dis­
trict exports as well as District exports to the
BEMs. In 1987, transportation equipment
exports comprised 38 percent of total District
exports; by 1994, their share had fallen to less
than 30 percent. While transportation was
still the top export industry for the District as
a whole in dollar value, other major industries
such as nonelectrical machinery, electrical
machinery, and chemicals were either gaining
or maintaining market share (see table 4).
District exports to the BEMs show an even
more pronounced pattern of change. In dollar
value, transportation equipment exports fell in
rank from first in 1987 to third in 1994. Also,
their market share fell from 32 percent of total
District exports to less than 17 percent. This
pattern was heavily driven by trade with Mexi­
co, where transportation exports (largely auto
parts) fell from 49 percent of the total to 21
percent. Another significant change occurred in
electrical machinery exports, which grew from
about 11 percent of total District exports to the
BEMs to almost 17 percent.
Several positive things can be said about
this change in the District’s export profile.
First, compared with the past, the fortunes of
the auto industry will have a smaller impact on
the District during both lean times and good
times. Second, less concentration of exports
along industry lines suggests that overall Dis­
trict export performance will not be so closely
tied to one or two industries. Finally, District
exports will tend to correspond—even more


8


than U.S. exports as a whole—to those indus­
tries in which BEM purchases are experiencing
significant growth.
U .S . agricultural exports to the BEM s

U.S. agricultural exports make an impor­
tant contribution to farm income as well as to
our nation’s trade balance. The U.S. Depart­
ment of Agriculture (USDA) reported that 17
percent of the value of U.S. agricultural produc­
tion was exported last year, accounting for a
tenth of the value of all U.S. exports and gener­
ating a major positive contribution to the mer­
chandise trade balance.6 Furthermore, current
developments suggest that foreign markets will
become even more important to U.S. agricul­
ture. The budget constraints so prominent in
the 1995 farm bill debate and the trend towards
greater market orientation portend a decrease in
the level of federal spending on programs that
support farm prices and income. Slow popula­
tion growth in the U.S. will continue to be a
significant constraint on future gains in domes­
tic food demand. Moreover, biogenetic re­
search promises to augment strides in agricul­
tural productivity. Given these factors, farmers
and agribusinesses must increasingly look to
foreign markets as an outlet for continued gains
in output and as a vehicle to maintain or im­
prove income levels.
The states of the Seventh District make an
important contribution to both agricultural
output and trade. Farms in these states account
for a substantial share of the nation’s domestic
livestock, milk, corn, and soybean production.
The high level of output propelled District
states into an 18 percent share of U.S. farm
commodity receipts in 1993 and also provided
raw material to a sizable food processing sec­
tor. District states also play an important role
in international agricultural trade. The USDA
estimates that the five states together accounted
for over a fifth of the value of U.S. agricultural
exports in 1993.7
The BEMs represent a major market for
U.S. agriculture. From 1987 through 1994,
their share of foreign sales of U.S. agricultural
products rose from 14 percent to 20 percent.
Moreover, the potential for future gains is
significant, as rising incomes and international
agreements that liberalize trade are expected to
boost purchases of U.S. agricultural products.
Among the BEMs, the top three buyers of U.S.
agricultural products are Mexico, South Korea,

ECONOMIC PERSPECTIVES

TABLE 4

A. Top five District export industries to the world, 1987 and 1994
Ranked by
1987 value

1987
value
(billions)

Transportation
equipment
Nonelectrical
machinery

Industry
market share3

Ranked by
1994 value

(percent)

$14.0

38.1

1994
value

Industry
market share3

(billions)

(percent)

Transportation
equipment
Nonelectrical
machinery

$21.4

29.6

7.8

21.2

15.8

21.9

Electrical machinery

2.9

8.0

Electrical machinery

8.6

12.0

Chemicals

2.9

7.8

Chemicals

6.4

8.9

Fabricated metals

2.1

5.7

Measuring instruments

3.4

4.7

B. Top five District export industries to the BEMs, 1987 and 1994
Ranked by
1987 value

1987
value
(billions)

Transportation
equipment
Nonelectrical
machinery

Industry
market share3

Ranked by
1994 value

1994
value

Industry
market share3

(percent)

(billions)

(percent)

$1.2

32.1

Nonelectrical
machinery

$2.4

26.0

1.6

16.8

1.5

16.5

Chemicals

1.1

11.4

Food & kindred products

0.5

5.9

24.2

Electrical machinery

0.9
0.4

Electrical
machinery

10.8

Transportation equipment

Chemicals

0.4

Measuring instruments

0.2

9.6
5.0

industry market share is that industry's share of total District exports.
Source: Massachusetts Institute for Social and Economic Research (1992 and 1995).

and China. These three nations accounted for
over 80 percent of total U.S. agricultural ex­
ports to the BEMs from 1987 through 1994.
Sales to Mexico increased nearly four times
during this period, while those to China tri­
pled. But the most rapid growth rates in U.S.
agricultural sales were to the relatively smaller
markets of Argentina, Brazil, and Indonesia.
(Agricultural exports to South Africa also rose
quickly, but this was due to a severe drought
in that nation.)
Much of the growth in the value of agricul­
tural exports to the BEMs stemmed from rising
sales of value-added processed products, a
trend that is reflected in agricultural exports to
other nations as well. Since 1985, the share of
U.S. agricultural exports made up of these
products has been growing.8 Processed prod­
ucts include meat, poultry, dairy products, fats
and oils, beverages, and a wide variety of other
consumer food products. Foreign sales of
processed products have actually exceeded the
export value of bulk agricultural commodities
(such as wheat, cotton, and other crops) since
1991. In general, bulk exports have suffered as
the effects of more favorable exchange rates


FEDERAL RESERVE


BANK OF CHICAGO

have been offset by greater competition from
other nations as well as weakened foreign
demand. In contrast, U.S. sales of processed
products have benefited from reduced trade
barriers, income growth in many developing
nations, a growing taste for Western foods, and
the convenience offered by processed foods.
Furthermore, the transport of perishable food
items has been aided by advancements in tech­
nology that improved cost-effectiveness and
reduced the potential for spoilage.9
From 1987 through 1994, the processed
share of U.S. agricultural exports to the BEMs
rose from a third to nearly half. The major
processed exports are red meat and poultry,
which together accounted for a fifth of the
value of U.S. agricultural sales to the BEMs
from 1989 through 1993, the latest year for
which individual industry data are available.
Mexico and South Korea are by far the largest
buyers. But while exports of red meat to the
BEMs tended to rise from 1989 to 1992, a
sharp drop in 1993 pushed the value back down
to the level of five years earlier. In comparison,
the value of U.S. poultry exports made brisk
gains—particularly to Mexico, China, and

9

Poland—and continued to climb even as sales
of red meat faltered.
A host of other processed products export­
ed to the BEMs made only modest individual
contributions to total sales, yet together ac­
counted for 21 percent of the aggregate figure
from 1989 through 1993. The most important
are soybean oil, animal fats and oils, milled
corn products, and milk powder. Those prod­
ucts experiencing the most rapid export growth
include soft drinks, ice cream and cheese, pota­
to chips and snacks, and breakfast foods. Over
the period, the BEMs increased their purchases
of all processed products other than red meat
and poultry by a remarkable 50 percent. In
comparison, purchases of red meat and poultry
rose by a more modest 20 percent.
Among the major bulk commodities, sales
of wheat and cotton to the BEMs generally
declined from 1989 through 1993. The drop in
wheat exports was largely attributable to Chi­
na, which reduced its purchases by roughly 75
percent. Cotton export sales not only declined
overall but shifted away from South Korea and
China toward Mexico and Brazil. The value of
U.S. corn exports to the BEMs also suffered a
serious decline from $1.2 billion to $288 mil­
lion. This stemmed mostly from a steady de­
cline in sales to South Korea and Mexico.
China supplanted the U.S. as South Korea’s
major supplier, but China’s recent switch from
corn exporter to importer will give the U.S. an
opportunity to recapture market share. U.S.
sales of corn to Mexico suffered partly because
of past Mexican policy that encouraged domes­
tic production and erected trade barriers insu­
lating Mexican producers from foreign compe­
tition. But reform of those policies and the
implementation of the North American Free
Trade Agreement (NAFTA) helped revive U.S.
corn exports to Mexico last year. In contrast to
wheat, cotton, and corn, the value of soybean
exports fared much better, rising by over onethird. Most of it went to Mexico and South
Korea, though sales to Indonesia also regis­
tered strong gains.
What share of agricultural exports to the
BEMs is produced within Seventh District states?
Though data on state-level exports to the BEMs
are available, they must be interpreted with cau­
tion for two reasons. First, the data are aggregated
along broad product categories rather than by
individual commodities. More importantly,
exporters may assemble commodities at a central


10


location (such as a major port) and then report
that site as the point of origin of shipments.1
0
Consequently, the data on agricultural exports
originating from District states tend to be under­
stated, while those from states with major ports
are likely inflated. Nevertheless, some insight
may be gained regarding District agricultural
exports to the BEMs by examining the trends in
these data.
From 1987 through 1994, the value of
District agricultural exports to the BEMs tri­
pled, a much faster increase than sales to the
rest of the world. Nearly all the gain in District
exports to the BEMs stemmed from crops and
processed products rather than forestry prod­
ucts, fish, or live animals. However, there was
considerable difference between the sales pat­
tern of bulk commodities and that of processed
products. While the export value of processed
products to the BEMs generally gained steadily
from year to year, District crop exports experi­
enced wide swings. As an example, China’s
displacement of the U.S. as the primary corn
supplier to South Korea was likely responsible
for the sharp decline in District crop exports to
the BEMs in 1991.
A closer look at the larger BEM s

It should be clear by now that the BEMs
are not a homogeneous group. While they
have some similarities, such as in the types of
goods they import, individually they appear to
present unique challenges for U.S. export pro­
motion and market strategies. Collectively
they exhibit considerable growth potential, but
several of them already are large export mar­
kets for U.S. goods, namely Mexico, China,
South Korea, and Brazil. Following is a closer
look at these four markets.
Mexico

One clear signal of Mexico’s economic
reform efforts was its becoming a participant in
GATT in 1986. Since then, the country has
made significant strides in opening its economy
by lowering tariffs (which in some cases were
as high as 100 percent), by privatizing many of
its state-owned industries, and by reducing
barriers to foreign investment. Between 1986
and 1992, Mexico’s total imports rose an aver­
age of 25 percent per year. Road vehicles and
machinery (including electrical, general indus­
trial, and machines for special industries) are
Mexico’s largest import items. Machinery
imports cover a broad spectrum including

ECONOMIC PERSPECTIVES

telecommunications equipment, metalworking
machinery, textile and leather machinery, and
civil engineering equipment such as shovels
and excavating equipment.
U.S. exports to Mexico have increased 247
percent over 1987-94, the third-largest per­
centage increase of the BEMs. The U.S. is
Mexico’s largest trading partner, with approxi­
mately 70 percent of all imports coming from
the U.S. and approximately 80 percent of all
exports going to the U.S. Not suprisingly, our
exports to Mexico are in the industries from
which Mexico imports the most—electrical and
nonelectrical machinery, and transportation
equipment. Nearly half of all U.S. exports to
Mexico are in these three industries.
In 1993. the U.S., Canada, and Mexico
became signatories to NAFTA, which further
reduced tariffs between them. As a result, in
1994 U.S. exports to Mexico increased by 22
percent, or $9 billion from the prior year. The
horizon has been clouded, however, by the
peso devaluation in late 1994.
South Korea

In terms of imports, South Korea is the
largest of the BEMs, importing approximately
$81 billion in goods in 1992. Yet import re­
strictions still impede trade with South Korea.
Policies to reduce barriers have resulted in less
formal barriers including still-high tariffs, par­
ticularly on agricultural products, as well as
emergency tariffs and adjustment tariffs." An­
other major barrier is a restriction to import on
credit. U.S. exporters estimate they could in­
crease exports to South Korea by nearly onethird if this restriction were not in place.1
2
Between 1987 and 1994, U.S. exports to
South Korea grew by 123 percent. Over that
period, exports from all industries except agri­
cultural crops increased. Electrical and non­
electrical machinery exports increased by over
$2 billion each, while transportation equip­
ment exports grew by $1.5 billion. The top
two U.S. exports to South Korea in 1993 were
semiconductors and aircraft, accounting for
over 15 percent of all U.S. exports to South
Korea in that year.
China

U.S. exporters have historically found it
difficult to trade with China. In 1991, China’s
import licensing system covered about half of
their imports (by volume), including consumer
goods, raw materials, and production equip­


FEDERAL RESERVE


BANK OF CHICAGO

ment.1 China also restricts imports by means
3
of quotas, embargoes on certain consumer
goods, and stricter quality standards and testing
for imports versus domestic products.
In 1992, China’s imports topped $80.5
billion, up $25 billion from 1988.1 The coun­
4
try is the second-largest import market of the
BEMs, led only by South Korea. Its largest
import commodities in 1992 were machinery
for special industries such as textile and leather
manufacturing, and machinery related to weav­
ing and felt manufacturing. Textile machinery
and textile yarns accounted for nearly 20 per­
cent of its imports.
U.S. commodity exports to China grew
by 166 percent over the 1987-94 period, with
transportation equipment, nonelectrical ma­
chinery, and chemicals the largest export
industries in the latter year. At a more de­
tailed level, the top U.S. export to China in
1993 was aircraft, accounting for nearly onefourth of all exports to China in that year.
Motor vehicles and car bodies were the next
largest export, accounting for over 7 percent
of total exports to that country.
Despite the considerable growth in U.S.
exports to China in recent years, they com­
prised less than 2 percent of all U.S. exports in
1994. In an effort to broaden market access for
U.S. exports, especially in telecommunications,
insurance, and agriculture, the United States
and China agreed in March 1995 to an eightpoint plan to open the latter’s market to U.S.
goods. The agreement included U.S. support
of China’s accession to the newly formed
World Trade Organization.
Brazil

Until 1990, Brazil’s trade policy in regard
to imports was highly restrictive. From 1980
to 1992, annual import growth was nil, and
import tariffs averaged 78 percent.1 Howev­
5
er, economic reforms begun in 1989 have
helped expand trade. In 1993, imports in­
creased by over $5 billion, or 25 percent over
the prior year. Average tariffs have been re­
duced to 14 percent.1
6
As a result, between 1987 and 1994, U.S.
exports to Brazil increased by 101 percent.
According to various newspaper reports, Bra­
zil offers several key market opportunities to
U.S. companies, particularly in the computer
and textile manufacturing industries. With a
population of 155 million, the country’s com­
puter market is expected to quadruple from

11

2.5 million units in 1994 to 10 million by the
end of the decade.1 Another growth industry
7
for U.S. exports will be textiles and textile
manufacturing equipment. In the city of
Fortaleza alone, 45 new textile and clothing
companies are expected to open.1 U.S. cotton
8
exports to Brazil have already increased dra­
matically, from $5 million to $85 million over
the 1989-93 period.

inghouse for advocacy requests.1 These advo­
9
cacy efforts represent a coordinated interagen­
cy initiative by the federal government to help
American firms compete and win major con­
tracts such as infrastructure projects with BEM
governments or joint ventures with BEM firms.
The center maintains information on major
projects and procurement opportunities world­
wide and tracks advocacy requests.2
0
Export promotion efforts at the state level
U .S . export prom otion in itia tive s:
are similar to federal efforts but provide more
Advocacy and assistance
one-on-one support and are geared more toward
Various government agencies provide
helping small and medium-sized businesses.
export assistance to U.S. firms in search of
Most states also have overseas trade offices in
foreign sales. To date, these efforts have tend­
key markets to help facilitate the process at the
ed to be fragmented and confusing to users.
other end, as well as to generate new trade leads,
For example, certain programs are available
host trade shows, and promote their states’
only to small businesses or new businesses but
exports. Table 5 lists the overseas offices of the
not to large or established ones, and vice versa;
Seventh District states. Note that most of the
other programs are available only to specific
states have at least two offices in the BEMs.
industries or for purposes of job creation. To
The USDA also operates several agricul­
address this problem, the U.S. Department of
tural export promotion programs. The two
Commerce opened export assistance centers in
largest and best-known are the Export En­
1994 in Chicago, Baltimore, Los Angeles, and
hancement Program (EEP) and an export credit
Miami. These are “one-stop shops” that pro­
guarantee program. The EEP offers “bonus”
vide exporters and potential exporters with
payments to U.S. exporters that enable them to
information to help them enter new markets or
meet the subsidized prices offered by other
build on existing ones. The centers provide
nations, particularly the European Union. Over
trade leads, information on overseas-related
time, implementation of GATT will reduce the
trade shows, and information on major project
amount of direct subsidies that member nations
and procurement opportunities abroad. In
may use to promote agricultural exports. The
addition, they offer information and assistance
export credit guarantee program provides fed­
on the various trade finance programs available
eral guarantees to private lenders involved in
at the federal level, help exporters determine
financing purchases of U.S. agricultural com­
the right program for them, assist with paper­
modities from abroad. Unlike the EEP, there is
work, and provide ongoing support. Nearly a
no specific outlay unless a borrower defaults
dozen more such centers are scheduled to open
and the lender incurs a loss. Moreover, this
in 1995.
program is not affected by GATT. Finally, the
Another recent effort by Commerce was to
USDA also operates separate programs to sup­
open an in-house information center and clear­
port exports of soybean oil, cot­
tonseed oil, and dairy products,
TABLE 5
and to promote the sale of pro­
cessed products in general.
Seventh District overseas trade offices, 1994
Illinois

Indiana

Io w a

M ichigan

W isconsin

Belgium

Canada3

Germany

Belgium

Canada3

Hong Kong

China

Japan

Canada

Germany

Hungary

Japan

Hong Kong

Hong Kong

Japan

Mexico

Japan

Japan

Mexico

Netherlands

Mexico

Mexico

Poland

So. Korea

South Africa So. Korea

Taiwan
Indiana, Wisconsin, and Pennsylvania share a Canadian trade
office in Toronto.

12



Summary

This article examined the
recent U.S. experience in export
sales to the ten nations identified
by the Department of Commerce
as potential growth markets. Spe­
cifically, we assessed the current
size and growth potential of the
ten BEMs as export markets, and
we put the current U.S. presence

ECONOMIC PERSPECTIVES

in these markets into perspective. We also ex­
amined the role played by Seventh District firms
in supplying these markets. A separate discus­
sion of U.S agricultural exports to the BEMs
was included because of agriculture’s important
contribution to the U.S. trade balance and be­
cause of the large share of U.S. agricultural
production held by Seventh District states.
The ten BEMs clearly represent an impor­
tant outlet for many types of U.S. products.
Recognizing this, U.S. exporters have already
made inroads into these markets. U.S. export
sales to the BEMs have posted significant gains
in recent years, accounting for an ever-larger
share of total U.S. exports. Most industries
have increased their sales to the BEMs, though
they have not shared equally in the overall gain.
Furthermore, the rise in U.S. exports to the
BEMs has generally outpaced the increase in
exports to the rest of the world. In addition,
the U.S. share of BEM imports indicates that
American exporters are holding their own
against tough competitors from nations such as
Japan and Germany. This is true despite the
fact that the U.S. is the leading supplier to only
three of the BEMs.
In 1994, of all U.S. industries, the nonelec­
trical, electrical, and transportation equipment
industries registered the largest sales to the
BEMs. These industries also accounted for half
of the export sales gain to the BEMs from 1987
through 1994. However, several other indus­
tries experienced even more rapid growth over
this period. This underscores two important
points. First, the U.S. is responding to the
BEMs’ current requirements, which are char­
acteristic of developing nations. As the econ­
omies of these nations grow and evolve, their
needs and wants will change. The challenge
to U.S. industry is to anticipate and respond to

these potential shifts in demand. To a large
extent, this will determine whether we can
maintain or increase current levels of export
sales to the BEMs. Second, the rapid growth
of these markets holds promise for smaller
firms, as more opportunities are available in
rapidly expanding markets.2
1
Exports from the Seventh District states to
the BEMs also rose more quickly than those to
the rest of the world from 1987 through 1994.
However, the growth of Seventh District ex­
ports tended to lag that of the U.S. in general.
While Mexico, South Korea, and China were
the major customers for Seventh District prod­
ucts, sales to Indonesia, Argentina, and Brazil
experienced the fastest growth. Furthermore,
of total District export sales to the BEMs, pro­
cessed food products moved into the top five
industries, reflecting rising incomes and the
growing demand for U.S. agricultural products
in these nations.
Among the industries exporting agricultur­
al products to the BEMs, processed products
have showed the steadiest growth in recent
years and seem better positioned to achieve
future gains than bulk agricultural commodi­
ties. This is true because the factors driving
foreign demand for processed products are
more lasting than the year-to-year production
and price variations that tend to exert a rela­
tively greater influence over imports of bulk
commodities.
In conclusion, it is clear that there are many
opportunities for U.S. exporters in the emerging
markets. While several industries have made
substantial inroads into these markets, consider­
able potential for future growth appears to lie in
other industries as well.2
2

NOTES
'U.S. Department of Commerce (1994a).

sGreene (1994).

2“The big emerging markets” (1994).

9Tse (1993).

’Ibid.

'"Coughlin and Mandelbaum (1991).

“Ibid.

"U.S. Department of State ( 1994b).

'Coughlin and Cartwright (1987) found evidence that
state export promotion expenditures support manufactur­
ing export levels.

l2Ibid.

6Capehart (1994) and Carter (1994).

"This section uses United Nations data as the source of
China’s imports and excludes the province of Taiwan.

7U.S. Department of Agriculture (1994).


FEDERAL RESERVE


BASK OF CHICAGO

"U.S. Department of State (1994a).

"Brooke (1994b).

13

Ih
lbid.

:oU.S. Department of Commerce (1993).

l7Brooke (1994c).

2lLyon (1995).

l8Brooke (1994a).

“ Firms that are considering entering these markets may
receive further information by contacting a U.S. export
assistance center.

I9U.S. Department of Commerce (1994a).

REFERENCES

“The big emerging markets,” Business Ameri­
ca., March 1994, pp. 4-6.

Brooke, James, “Denim and jeans pour out of
northeastern Brazil,” New York Times, April 7,
1994a, sec. D, p. 7.
____________, “U.S. business flocking to
Brazilian ventures,” New York Times, May 9,
1994b, sec. D, p. 1.

“India: Reforms spawn superb business oppor­
tunities,” Business America, January 1995,
pp. 11-14.

Lyon, Carmi, “Target marketing consumer
food products,” Agricultural Trade Products,
May 1995, pp. 7-9.

Massachusetts Institute for Social and Eco­
nomic Research. “Exports from all states to all
countries by 2 digit SIC, 1987,” diskette, 1992.

____________, “Brazil luring computer com­
panies,” New York Times, August 6, 1994c,
sec. 1, p. 33.

___________ , “Exports from all states to all
countries by 2 digit SIC, 1994,” diskette, 1995.

Burns, John F., “U.S. ends a $4 billion visit to

Sciolino, Elaine, “Clinton is stern with Indone­

India,” New York Times, January 18, 1995, sec.
D, p. 3.

sia on rights but gleeful on trade,” New York
Times, November 17, 1994, sec. A, p. 1.

Capehart, Tom, “Exports as a share of agri­
cultural production,” Agricultural Outlook,
August 1994, pp. 20-22.

Tse, Robert, “Grain and meal shipments in the

Carter, Ernest, “Agriculture’s trade balance
retains second place among eleven major U.S.
industries in 1993,” Agricultural Trade High­
lights, April 1994, pp. 5-6.

United Nations, Department of Economic and

Confedera^ao Nacional da Industria (Bra­
sil), Departmento de Comercio Exterior e Investimentos, Doing Business with Brazil, 1994.

Coughlin, Cletus C., and Phillip A. Cart­
wright, “An examination of state foreign ex­

form of meat are on the rise,” Agricultural
Trade Highlights, September 1993, pp. 6-7.
Social Development, 1992 International Trade
Statistics Yearbook, New York, 1993.

U.S. Department of Agriculture, Foreign
Agricultural Trade of the United States,
March/April 1994.
U.S. Department of Commerce, Competing to
Win in a Global Economy, September 1994a.

port promotion and manufacturing exports,”
Journal of Regional Science, August 1987,
pp. 439-449.

_____________, International Trade Adminis­
tration, Implementation of the National Export
Strategy, December 1993.

Coughlin, Cletus C., and Thomas B. Mandelbaum, “Measuring state exports: Is there a

_____________, Bureau of the Census, U.S.
Exports History: Historical Summary, 19891993, CD-ROM, July 1994b.

better way?” Economic Review, Federal Re­
serve Bank of St. Louis, July/August 1991, pp.
65-79.

Greene, Joel, “High-value food products boost
agricultural exports,” FoodReview, SeptemberDecember 1994, pp. 18-22.




U.S. Department of State, China: Economic
Policy and Trade Practices, Country Reports
on Economic Policy and Trade Practices, CDROM, May 10, 1994a.
___________ , South Korea: Economic Policy
and Trade Practices, Country Reports on Eco­
nomic Policy and Trade Practices, CD-ROM,
May 10, 1994b.

ECONOMIC PERSPECTIVES

Cassette tape s (ire a v a ila b le fo r
the 3 1 s t A n n u a l Conference
on B a n k Stru ctu re a n d C o m p e titio n

T

H

E

M ay 1 1 & 12, 1995

s e t
ASSESSING INNOVATIONS IN BANKING

Thursday Sessions

Friday Sessions

(M a y 1 1 ,1 9 9 5 )

(M a y 12, 1 9 9 5)

FRB500
W elcom ing R em arks (Michael H. Moskow)
Keynote Address (Alan Greenspan)

FRB505
Session A:
M o rtg ag e Financing and Comm unity Development

FRB501

(Moderator: Lewis M . Segal).Implementing CRA: W hat
Is the Target? (Glenn B. Conner and W ayne Passmore);
South Shore Bank: Is It the Model of Success for Community
Development? (Benjamin C. Esty); Discrimination, Default,
and Loss in FHA M ortgage Lending (James A. Berkovec,
Glenn B. Conner, Stuart A. Gabriel, and Timothy H. Hannan)

The N ew Tool Set: Assessing Innovations in Banking
(Moderator: W illiam C. Hunter) Derivatives— Are We Better
O ff as a Result of the Growth in this Industry? (Warren G.
Heller); The Impact of Expanding Bank Product Powers
(Richard S. Cornell); Assessing Innovations in Banking
(Frank L. G entry); Innovations in the Financial Services
Industry: Securitization of Small Business Loans (Cynthia A.
Glassman); W h a t Is the Value Added Large U.S. Banks
Find in O ffering Mutual Funds? (Edward J. Kane)

FRB502
Luncheon Presentation (Ricki Tigert Heifer)
FRB503
D erivatives an d Risk M a n a g e m e n t
(Moderator: Douglas D. Evanoff) John F Marshall,
.
G ay H. Evans, Brian Quinn, and Susan M. Phillips

FRB504
Lessons fro m Financial Crises
(Moderator: G eorge G. Kaufman) Lessons from Financial
Crises— Evidence from Japan (Thomas F C argill, Takatoshi
.
Ito, and Michael M. Hutchison); Contagion and Bank Failures
During the G reat Depression: The June 1932 Chicago
Banking Panic (Charles W. Calomiris and Joseph R. Mason);
The Effects of the N orw egian Banking Crises on N orw e­
gian Bank and N onbank Stocks (Fred R. Kaen and Dag E.
Michalsen); Lessons from Financial Crises— Evidence from
Venezuela (Ruth de Krivoy)

To order tapes at $ 12.00 each,
please contact
FRB Cassettes
c/o Teach'em
160 E. Illinois Street
Chicago, Illinois 60611

To order by phone
Toll-free (outside Illinois) 800-225-3775
In Illinois 312-467-0424



FRB506
Session B: Responding to B ank Regulations
(Moderator: M ark S. Carey) Regulatory Risk and Hedge
Accounting Standards in Financial Institutions: The Case of
Franklin Savings (Suzanne M. Holifield, Michael B. Madaris,
and W illiam H. Sackley); The Effects of Fair Value Accounting
on Investment Portfolio Management (Anne Beatty); Acquirer
Gains in FDIC-Assisted Bank Mergers: The Influence of Bidder
Competition and FDIC Resolution Policies (Matthew T. Billett,
John P O'Keefe, and Jane F Coburn)
.
.

FRB507
Session C: E xpanding B ank Product Powers
(Moderator: James T. Moser) The Role of Firewalls in
Universal Banks: Evidence from Commercial Bank Securities
Activities Before the Glass-Steagall Act (Randall S. Kroszner
and Raghuram G. Rajan); Bank U nderwriting of Debt
Securities: Modern Evidence (Amar Gande, Manju Puri,
Anthony Saunders, and Ingo Walter); Commercial Bank
Mutual Fund Activities: Implications for Bank Risk and Return
(Vincent P Apilado, John G. Gallo, and James W. Kolari)
.

FRB508
Session D: Interstate B ank Activity
(Moderator: Larry A. Frieder) Diversification and Interstate
Banking (Peter S. Rose); The Effects of Interstate Branching
on Small Business Lending (Joe Peek and Eric S. Rosengren);
The Efficiency of Bank Branches (Allen N. Berger, John H.
Leusner, and John J. M ingo)

FRB509
Luncheon Panel:
Strategies fo r Utilizing the N e w Tool Set in Banking
(Introductory Comments: M ichael H. Moskow)
Frank V. Cahouet, David W . Fox, and G a ry Allen

Sectoral wage growth
and inflation

Ellen R. Rissm an

Most mainstream macroecon­
omists believe that the price of
forcing the unemployment
rate permanently below the
natural rate of unemployment
for a prolonged period of time is ever-increas­
ing inflation. If this overheating occurs, the
conventional wisdom is that the inflation rate
can be reduced to more acceptable levels only
if one endures a difficult recessionary period
during which the unemployment rate exceeds
the natural rate. In the parlance of economists,
there is a vertical long-run Phillips curve that
limits the ability of policymakers to indepen­
dently affect both the rate of inflation and the
unemployment rate. In the short run, the Fed­
eral Reserve may be able to reduce the unem­
ployment rate below the natural rate, but in the
long run the economy would revert to produc­
ing at its equilibrium level. The only lasting
legacy of the Fed’s actions would be to raise
the level of inflation.
The Federal Reserve has raised the feder­
al funds target rate seven times since February
1994 in the hopes of keeping the economy
from “overheating.” In doing so, the Fed has
been attempting to walk the fine line of the
long-run Phillips curve. This is no easy feat.
It is more akin to a walk in the dark with
policymakers feeling their way than to a stroll
down a well-marked street. The main obsta­
cle is the measurement of the natural rate of
unemployment itself. If we could know the
natural rate with certainty, the Fed’s course of
action would be clear: If the unemployment


16


rate fell below the natural rate, the Federal
Reserve would conduct a more restrictive
policy; if the unemployment rate rose above
the natural rate, the Fed would conduct a
more stimulative policy.
Unfortunately, the natural rate is not
known and therefore must be estimated. There
are as many different estimates of the natural
rate as there are econometricians who estimate
it. Furthermore, because these are just esti­
mates, there is some uncertainty with respect
to how confident we can be of these esti­
mates. For example, a point estimate of the
natural rate of 6 percent may easily have con­
fidence intervals of ±1 percent—an uncom­
fortably large spread if one is trying to imple­
ment policy.
To complicate matters further, the natural
rate hypothesis is typically stated as a knifeedge phenomenon, that is, if unemployment is
above the natural rate, the inflation rate would
decline, while if unemployment falls below
the natural rate, inflation would spiral out of
control. In fact, both scenarios appear unlike­
ly and simplistic. One can well imagine that
as the unemployment rate slips below the
natural rate, some industries, although not all,
will experience difficulties in obtaining pro­
duction inputs, including labor, at existing
prices. As these shortages become more re­
strictive, input prices will be bid up and infla­
tion will result. Only when these shortages
Ellen R. Rissman is an econom ist w ith the Federal
Reserve Bank of Chicago.

ECONOMIC PERSPECTIVES

become widespread at the existing price level
will inflation result. The presence or absence
of these shortages tells us a great deal about
whether our assessment of the natural rate is
accurate. If we believe the rate of unem­
ployment is below the natural rate, then we
should expect to see shortages and ensuing
price pressures. If such shortages are absent,
then our original assessment of the natural
rate must be flawed. Absent such corrobo­
rating statistical evidence, we must reexam­
ine our estimates of the natural rate.
It is tempting to argue that rising wages
in specific sectors are a precursor to wide­
spread inflation. However, an analysis re­
quires more than simply identifying the indus­
tries in which nominal wage growth is accel­
erating. Wages can increase for reasons other
than inflationary pressures. For example, as
workers become more productive, their wages
naturally rise. Wages respond to sector-spe­
cific as well as aggregate factors. Wages in
one industry may be increasing relative to
another because of changes in the composition
of product demand unrelated to inflation.
Further complicating matters, even if some
industries have high nominal wage growth
unrelated to productivity growth, this does not
necessarily foretell future inflation. According
to economic theory, nominal wages adjusted
for productivity should grow at the same rate
as inflation in the long run. In the short run
there may be deviations from this equilibrium
relation, but the two tend to grow at the same
rate over long periods. A recent article by
Campbell and Rissman (1994) suggests that the
direction of Granger-causality in aggregate
wages is from inflation to wage growth and not
the opposite. If this result holds true at the
industry level, then high adjusted nominal
wage growth need not have any implications
for future inflation. Nominal wage growth
may simply be “catching up” to past inflation.
In the remainder of this article, I attempt
to document the relationship between a mea­
sure of aggregate inflation and unit labor costs
in a number of one-digit industries. Specifical­
ly, I assess whether changes in nominal wage
growth in one industry have any implications
for future inflation. A positive finding would
indicate that a more disaggregated approach
would aid policymakers in further assessing
estimates of the natural rate.


FEDERAL RESERVE


BANK OF CHICAGO

This article is divided into four sections.
In the first section, I present a simple twosector model of a profit-maximizing firm that
employs two different types of labor. The
implications for long-run equilibrium behavior
are analyzed. The data are presented in the
second section, with particular emphasis on the
time-series properties. An empirical model of
wage growth and inflation is developed in the
third section. Conclusions and discussion of
further research are found in the last section.
To summarize the results, the evidence
suggests that the direction of causality for most
industries is from prices to wages and not the
reverse. Only in manufacturing and retail trade
is there strong evidence for the hypothesis that
wages Granger-cause inflation. The results for
manufacturing depend upon the measurement
of productivity employed in the analysis.
A simple model

Suppose that there are two different sec­
tors (x and y), that each produce a single good
using two types of labor (1 and 2).' Let the
price of good i be denoted P, where i = x, y.
Output in sector i is produced according to the
production function f(L \, L\), where Li is
employment of type j labor in industry i. The
superscript i on the production function indi­
cates the industry to which this technology
applies. It is assumed that/'(Lf, L') = 0 if
U = 0 for any i,j, that is, both labor inputs
are needed to produce any output; d f/d L ' > 0
and d2f ‘ d(L.)2< 0, that is, adding additional
/
labor input increases output but at a decreasing
rate. Furthermore, d2
f'/d{L\)2d2f i d (L i)2/
1
d2
f'/[d{L\)d{L\)] > 0 states that the production
function is concave in its inputs, guaranteeing
a local maximum. The representative firm in
each industry is assumed to take the wage rate
for each type of labor as given, that is, the
firm’s actions do not affect the wage rate for
either type of labor input. Similarly, the firm
is assumed to be too small to influence the
price of its output. Thus, the profit function of
the representative firm is given by

n = P f ‘(L[,L<2) - W

[ L \-W ^ ,

where /7 is the profit of the firm in sector i,
and W\ and V are the wage rates paid respec­
F;
tively to type 1 and type 2 labor in industry i.

17

The firm’s problem is to select the amounts of
L\ and L' given P , Wj, and W) so as to maxi­
mize profits. The firm’s first-order conditions
are given by
(1) P fj(L i,L ‘) = Wj,
where/j(Lj, L\) = df'/dL'r the marginal prod­
uct of type j labor in industry /, can be thought
of as the extra output the firm in sector / would
produce if it hired an additional unit of type j
labor but held the amount of the other type of
labor unchanged (/ = x, y, and j = 1, 2). The
profit-maximizing firm chooses inputs of la­
bor, L\ and L;, so as to equate the value of the
marginal product of each type of labor to the
wage rate for that labor input. If the value of
the firm’s marginal product of labor exceeds
the wage rate for a particular type of labor,
then the firm will not be profit-maximizing.
This is because the firm could increase its
revenues more than its costs by hiring addition­
al labor. Conversely, if the value of the firm’s
marginal product of labor were less than the
wage rate for that particular type of labor, then
the firm could increase profits by reducing its
employment of the labor input.
Until this point, the model has not ad­
dressed how labor is allocated across indus­
tries. The representative firm takes the wage
rate as outside its control and hires all the labor
input it requires at the existing wage. How
wages and the allocation of labor across indus­
tries are determined depends upon assumptions
concerning resource flows and supplies. To
address these issues in a simple way, it is as­
sumed that labor resources flow freely across
industries. In fact, if a particular type of labor
could earn more in one industry than in the
other, labor would flow to the industry that
pays the highest wage.2 Thus, we would ex­
pect that in the long run, W'. = W2= W for ally.
.
The first-order conditions imply that in equi­
librium, the value of the marginal product of
labor (VMP; ) must be equated across industries for both labor inputs.
Suppose that the wage rate paid to type 1
labor is higher in industry x than in industry y.
Then from the first-order conditions, the value
of the firm’s marginal product of labor in in­
dustry jc exceeds the marginal value product for
the same type of labor in industry y, that is,
VMP) > VM Pl. Type 1 labor sees the wage
differential and flows to industry x. In the

18



process, wages are reduced there and the mar­
ginal value product is lowered. At the same
time, the outflow of labor from industry y
causes the wage rate and marginal value prod­
uct to rise in that industry. This adjustment
continues until the wage rate and the VMP are
equilibrated across sectors. In equilibrium,
VMP) = VMP) = W.
Of course, the average wage rate paid in a
sector can differ across industries. For one
thing, firms use labor inputs in different com­
binations. Those industries employing more
professional workers, for example, will typical­
ly pay higher wages than those that require
lower-skilled workers. However, in the long
run, professionals (lower-skilled workers)
should earn the same regardless of the industry
in which they are employed.
In an economy populated by many small
firms such as those described above, the price
of output always equals the productivity-adjusted wage rate in equilibrium. Firms’ profitmaximizing behavior constrains the price’s
growth rate as well as the growth rate of pro­
ductivity-adjusted wages. To see this, take
logarithms of equation 1 and subtract it from
itself across adjacent time periods, t and t- 1
(2) Avr/O - Azj(t) = Ap.(t),
where Aw (t) = lnVT(r) - InW. (7-1) is the growth
rate of nominal wages for type j labor; Az%t) =
In [/;(£;(/),L'(r»] - In[/j(L ;(f-l),L '(/-/» ] is
the growth rate of type j's marginal physical
product of labor in industry/; and Ap.(t) =
In P.(t) - In P.(f-l) is the growth rate of the
price of output in industry /.
It can be shown that equation 2 can be
aggregated so that
(3) Aw.(t) - Az.(r) = Ap.(t),
where Aw.(t) is the growth rate of nominal
wages in sector /, Az.(f) is the growth rate of
total factor productivity in sector /, and as
before, Ap.(t) is the growth rate of prices in
industry /. Market discipline ensures that,
given industry productivity growth, the growth
rate of nominal wages cannot deviate from the
growth rate of output prices in equilibrium.
The model examined above overlooks
some potentially interesting questions about
labor market and product market behavior.

ECONOMIC PERSPECTIVES

For example, firms and workers may not be
price-takers as assumed above. Instead, they
may be monopolists and monopsonists, exert­
ing some degree of control over prices and
wages respectively. Although this modifies the
tight connection between productivity-adjusted
wages and price growth described above, as
long as the wage and price markups do not
deviate from a constant mean for prolonged
periods of time, productivity-adjusted wages
and prices must move in tandem.
Second, the model expounded above does
not take into consideration how the participants
adjust to changing economic conditions. For
example, suppose that the firm incurs substan­
tial hiring and firing costs when adjusting its
labor input. In the interests of profit-maximi­
zation, the firm must assess how its current
hiring and firing decisions affect its future
production. By increasing its level of employ­
ment, the firm incurs not only the direct cost of
wages, but also an additional adjustment cost
that depends on the change in the level of em­
ployment. If the firm’s level of employment is
nearly optimal, then adjustment costs will be
relatively small and the equilibrium condition
of equation 3 will hold reasonably well. How­
ever, adjustment costs can be substantial, with
significant short-run deviations from the equi­
librium occurring.
Similarly, workers may not be completely
mobile. For example, suppose that an individ­
ual is currently employed in one industry but
wages are higher for the same type of worker
in another industry. The worker will not nec­
essarily switch industries as this may require
moving costs, both pecuniary and nonpecuniary. Only over time are workers likely to
switch industries. Again, the equilibrium con­
dition relating the growth of wages, productivi­
ty, and prices would hold only in the long run.
The data

The theory of the profit-maximizing firm
presented above suggests that productivityadjusted wages in an industry must grow at the
same rate as the industry output price in the
long run. However, the model is not particu­
larly informative on the subject of short-term
dynamics. Short-term deviations from equilib­
rium may occur, but economic theory suggests
that there is a tendency for these variables to
converge to their equilibrium relationship as
described in equation 3.


FEDERAL RESERVE


BANK OF CHICAGO

In the analysis that follows, the pricewage-productivity linkages are examined in
ten one-digit industries for the nonfarm non­
government sector. These industries include
construction (CON); mining (MIN); manufac­
turing (MFG); durable manufacturing
(MFGD); nondurable manufacturing (MFGN);
finance, insurance, and real estate (FIR); ser­
vices (SRV); retail trade (RT); wholesale trade
(WT); and transportation and public utilities
(TPU). The agriculture and government sec­
tors are omitted from the discussion because of
the difficulty in imputing wages in the former
and the noncompetitive nature of the latter.
While nominal wage information is avail­
able for each of these industries, productivity
data are unfortunately available for only a
subset including manufacturing and its durable
and nondurable components. Let Z be produc­
tivity in industry /, with Z defined as
7 = (W
'
(W

’

where Y is nominal output in that industry, P
is an appropriate price index, L is the number
of workers in the industry, and h: is the average
number of hours worked. Thus, productivity in
any given industry is defined as real output per
man-hour.
For the nominal output for each sector, I
used national income in the relevant industry
as reported quarterly by the Department of
Commerce in its National Income and Product
Accounts. Employment is reported for each of
these sectors by the Bureau of Labor Statistics
in its monthly publication. Employment and
Earnings. Hours, also reported monthly, are
measured by the Index of Average Weekly
Hours for each of these sectors with the excep­
tion of retail and wholesale trade. It was as­
sumed that for these two industries the relevant
hours index is that for the combined trade
sector. Nominal wages are given for the differ­
ent sectors and are also reported monthly by
the Bureau of Labor Statistics. All monthly
data have been converted to quarterly averages
for the period from 1964:Q 1 to 1994:Q4.
The selection of an appropriate price index
to use in constructing productivity is not a
straightforward matter. There are a number of
candidates from which to select. In the econo­
metric work that follows, I examined ten dif­
ferent price indices, all of which have been

19

FIGURE 1

Selected price indices
A. Selected consumer price indices
index, 1987=100

B. Selected consumer price indices
index, 1987=100

C. Selected producer price indices
index, 1987=100

Note: Shaded areas indicate recessions.
Source: U.S. Department of Labor.


20


indexed to 1987=100. These
include seven from the Consumer
Price Index (CPI): commodities
(CPICOM), durables (CPID), fuel
and other utilities (CPIFOU),
nondurables (CPIND), services
(CPISRV), transportation
(CPITRN), and urban workers
(CPIU). In addition, I examined
the Producer Price Index (PPI) for
consumer durables (PPICD),
finished consumer goods
(PPICG), and finished goods
(PPIFG). I then measured pro­
ductivity for each of the industries
using each of the possible ten
different price indices, which
yields 100(= 10x10) different
productivity measures.
Some price indices are clear­
ly more appropriate for con­
structing measures of industry
productivity in specific sectors
than others. For example, a price
index measuring service prices is
probably not a good deflator of
manufacturing output. Services
output should not be deflated by
a price index for durable goods
for a similar reason. However, I
report results for all of the pro­
ductivity measures constructed to
assess how important the price
index is in the analysis.
The price indices are shown
in figures 1A-C, and their
growth rates are shown in figures
2A-C. Growth rates are calcu­
lated as four-quarter log differ­
ences. There are several points
to make concerning the timeseries pattern exhibited. All of
these price series show quite
similar behavior. All have been
trending upward, with a slow­
down in the growth rate occur­
ring in the early 1980s. There is
of course some difference in the
growth rate across sectors. Since
1987, service prices have grown
most rapidly. Durables prices
have grown more slowly, as has

ECONOMIC PERSPECTIVES


FEDERAL RESERVE


the CPI index for fuel and other
utilities. From figure 2 it is clear
that price inflation accelerated
through the 1970s and slowed
markedly in the early 1980s.
This characterization of rising
then falling inflation is true for
all of the series examined. It is
also worth noting that inflation is
highly persistent, in that high
inflation today usually means
high inflation tomorrow.
As shown in figures 3A-C,
nominal wages across the various
industries exhibit behavior over
the same period that is quite
similar to that of prices. Corre­
sponding growth rates for the
wage series are shown in figure
4.3 Growth rates are calculated
as four-quarter log differences.
Again, the time-series behavior
of the different wage series is
quite similar across industries.
As with prices, wages seem to be
trending upward, and a kink
occurs in the series in the early
1980s that corresponds to a de­
crease in the growth rate of wag­
es. This decline in nominal wage
growth is exhibited quite clearly
in figure 4. Prior to the early
1980s, wage growth was trending
upward. At some point in the
early 1980s, wage growth fell
abruptly and has shown little
acceleration or deceleration
since. As with prices, nominal
wage growth is highly persistent.
The model described in the
previous section suggests that the
gap between productivity-adjust­
ed wage growth and inflation
{GAP.{t) = Avv (0 - Az.(f) A/?.(/)) reflects deviations away
from long-run equilibrium,
where Az.(/) = AlnZ(f). In terms
of its time-series properties,
theory suggests that the gap
should exhibit some positive
serial correlation and should
revert to its mean over time.

BANK OF CHICAGO

21

2
2


The disequilibrium term is
shown for the ten industry cate­
gories in figure 5A-C. Produc­
tivity has been constructed using
the CPI for urban workers. Infla­
tion is also measured as the
growth rate of that index. I have
normalized the gap in each in­
dustry by subtracting the industry
mean and dividing by the indus­
try standard error. The evidence
in figure 5 clearly supports the
time-series interpretation.
Do wages cause prices?

In developing an economet­
ric specification of the joint be­
havior of productivity-adjusted
wages and prices, one needs to
account for the long-run restric­
tion that productivity-adjusted
wages and prices move in tan­
dem. The error corrections mod­
el is one such framework.4 The
advantage of using such a frame­
work is that it imposes the longrun restriction that the gap be­
tween productivity-adjusted
nominal wage growth and infla­
tion disappears in the long run,
while at the same time the frame­
work permits the short-term
dynamics to be estimated from
the data.
At its simplest, let o)u = Awh A zn be the growth rate of produc­
tivity-adjusted nominal wages at
time t. Furthermore, let p: = A/?; be
the inflation rate at time t. The
error corrections model is then
(4) Ap, =

-p,_,] + e;

Aw f = a 2[a> t , - p; ,] + e2 ,
i
n
where e\ and e 2t are random error
terms assumed to be normally
distributed with zero mean.
These error terms may be corre­
lated with each other but are
independent over time. This

ECONOMIC PERSPECTIVES

FIGURE 4

Nominal wage growth by industry
A. Growth rates of nominal wages
percent

B. Growth rates of nominal wages
percent

model is quite clear in its implica­
tions for short-run and long-run
behavior. The gap affects short­
term behavior, which in turn af­
fects the gap. If there were no
further disturbances, these short­
term adjustments would eliminate
the gap in the long run. However,
because the error terms change
each period, the gap is never elim­
inated completely; rather, it fluctu­
ates around zero. If a' < 0 and a 2
> 0, then the gap is closed by price
inflation decreasing and wage
inflation increasing. Alternatively,
if a' > 0 and a 2< 0, the gap is
closed by increasing inflation and
decreasing wage growth.
In the error corrections model
of equation 4, only the most recent
wage-price gap is useful for con­
structing forecasts of wage and
price inflation. A less restrictive
error corrections model that per­
mits more complex short-term
dynamics while leaving the longrun restriction on the wage-price
gap intact is
(5) Ap; =
- p,_,] +
E* \.y'Ap - j.+ £*, X' Acu.- . + £'
j= i j r t
j= l
.
u
ij

C. Growth rates of nominal wages

ii j

Aw , = a 2[o)jl \ — ] +
pM
I * \. y2 r , - j + E*. X2 A cqj . + e2 .
Ap
j= < ]
j -1
,,a
ij

percent

Note: Growth rates are calculated as four-quarter log differences.
Shaded areas indicate recessions.
Source: U.S. Department of Labor.


FEDERAL RESERVE


BANK OF CHICAGO

In this system of equations,
the wage-price gap and k lags of
changes in price and wage growth
are incorporated into forecasts.
The parameters of the model
(namely, a \ y 5 and Xs., where
.,
.
s = 1,2;j= 1, ..., k) can be esti­
mated by ordinary least squares
for each i = 1,..., I.
Whether wage growth is
useful for forecasting price infla­
tion depends upon the estimated
parameters and their variancecovariance matrix. If either
a 1* 0 or A ^ 0 for some j,' then
,!.
ij

j

23


24


wage inflation in industry / helps
forecast price inflation. If this is
not true, then knowing wage
growth in a particular industry
does not add any additional in­
formation to inflation forecasts.
A test of the null hypothesis
that wage inflation in industry i
does not help forecast price infla­
tion can be stated as

(6) H0

a' = 0
A1 =0
,
A'ik = 0 .

A simple F-test can be used
to test this hypothesis. Two
equations are estimated. The
first one estimates equation 5
without any constraints. The
second equation reestimates
equation 5 imposing the con­
straints of the hypothesis by
eliminating lagged changes in
adjusted wage growth and the
gap from the equation. If the
first equation fits the data better
than the second, then the hypoth­
esis is rejected.
Fit in this case is measured
using an F-test that compares the
estimated standard error of £'f
from the original unconstrained
equation, G'u, to that estimated
from the restricted equation, o)r.
The smaller the standard error, the
more accurate the equation fore­
casts. If <7'( is much smaller than
<7'., then including data on wage
inflation produces more accurate
inflation forecasts. In this case
the F-statistic will be large, pro­
viding evidence against the hy­
pothesis that industry wage infla­
tion does not help forecast price
inflation. Unfortunately, test
statistics can be large for another
reason: random variation. Some
of the time we may obtain large
test statistics even though the

ECONOMIC PERSPECTIVES

TABLE 1

Causality tests from wages to prices and from prices to wages
CON

MIN

Commodities
Fuel and other
Services
Transportation
Consumer durables
Finished consumer
goods
Finished goods
Urban workers
Durables
Nondurables

W->P

0.90
0.64
0.82
0.58
1.43

2 .7 2 ***
2.4 5**
2.2 0**
2 .6 0 ***
2 .7 6 ***

1.19
1.04
1.07
1.07
1.13

1.45
1.62
1.74*
1.82*
1.54

2 .5 9 ***
1.18**
2.15**
2.34**
3 .1 7 ***

1.47
1.13
1.33
1.33
1.55

1.06
1.09
0.87
1.03
0.67

2 .6 3 ***
2 .6 1 ***
2 .5 9 ***
2 .4 9 ***
3 .3 0 ***

1.14
1.15
1.14
1.10
1.22

1.35
1.43
1.59
1.44
1.30

3 .1 6 ***
3 .0 1 ***
2 .5 9 ***
2.16**
2.38**

1.36
1.32
1.49
1.21
1.42

MFGD

P->W

Commodities
Fuel and other
Services
Transportation
Consumer durables
Finished consumer
goods
Finished goods
Urban workers
Durables
Nondurables

P->W

W->P

3 .1 0 *“
1.49
2 .6 0*“
2 .8 4 ***
3 .8 4 ***

2 .5 1 ***
2.0 8**
2.27**
2 .8 6 ***
2 .39**

1.83*
0.86
1.39
1.45
1.82*

4 .0 4 ***
3 .8 9 ***
3 .1 2 ***
2 .6 4 ***
3 .2 2 ***

2 .04**
2.06**
2 .5 3 ***
1.83*
2.46**

2.17**
2.00**
1.70*
1.03
1.48

RT

Commodities
Fuel and other
Services
Transportation
Consumer durables
Finished consumer
goods
Finished goods
Urban workers
Durables
Nondurables

TPU
P-»W

W->P

P - aW

1.24
1.37
1.40
0.79
1.20

1.48
1.27
1.00
1.41
1.31

1.88*
2 .5 3 ***
1.62
2 .05**
2 .34**

0.89
0.93
1.35
2.08**
0.96

1.50
1.50
1.32
1.09
1.47

1.98**
2.1 6**
1.91*
1.77*
1.72*

WT

W->P

Price index

P-»W

2 .9 2 ***
1.28
1.81*
2 .5 6 ***
5 .0 0 ***
3 .2 1 ***
3 .2 0 ***
2.31**
2 .7 0 ***
2 .13**

P->W

W->P

MFGN

W—
>P

Price index

MFG

P->W

W->P

Price index

SRV

W->P

P->W

2.02**
1.98**
1.54
1.39
2.13**

0.73
0.81
0.50
1.01
1.52

2.12**
2 .6 3 ***
2 .5 1 ***
2.00**
2.30**

1.72*
1.97**
1.99**
2.04**
1.97

1.30
1.08
0.61
0.70
0.66

2.30**
2.43**
2.59**
2.01**
2.46**

FIR
P->W

W->P

P->W

1.06
0.90
0.54
0.77
2.48

1.10
2.22**
1.50
1.45
2 .8 1 ***

1.58
1.76*
1.45
1.39
0.98

1.30
1.73*
1.35
2.15**
1.92*

2.59
2.29
0.77
0.80
1.14

1.07
1.18
1.11
1.62
1.25

1.47
1.38
1.49
0.85
1.76*

1.56
1.62
1.30
1.98**
2.04**

W->P

N ote: N u m b e rs are F-statistics. The eq u ations w e re estim a ted using 8 lags. The n o tatio n W -» P indicates a test of th e
hyp oth esis in e q u atio n 6 w h ile P -» W indicates a test o f th e hyp oth esis in eq u atio n 7. See page 19 fo r d efin itio n s of
in dustry ab b re v ia tio n s .
‘ S ig n ifican t at .10 level.
“ S ig n ifican t at .05 level.
‘ “ S ig n ific a n t at .01 level.

hypothesis is true. Recognizing this, one can
compare the test statistic to some standard criti­
cal value in order to determine whether the
former is big enough to warrant rejecting the
null hypothesis with some degree of confidence.


FEDERAL RESERVE


BANK OF CHICAGO

Table l presents F-statistics that test the
null hypothesis of equation 6 and those testing
the converse hypothesis for the second equa­
tion of the model of equation 5, that price
inflation does not help forecast industry pro-

25

ductivity-adjusted wage growth. This second
hypothesis is stated as

(7) H0

a 2 =0
n=o
Yl=

0•

Although I used 4, 8, and 12 lags of
changes in price and wage growth as regressors
in both the restricted and unrestricted equa­
tions, for the sake of brevity I report only the
results for 8 lags.5 The entire data sample from
1964 through 1994 was used for the estima­
tion. Growth rates are calculated as the fourquarter log differences. Since the maximum
lag length is 12, this leaves us with 106 obser­
vations for most of the models estimated. Be­
cause the durables and nondurables price indi­
ces are available for a shorter time span, re­
gression estimates using these variables to
construct productivity measures have only 98
observations. Inflation was measured as the
four-quarter log differences in the CPI index
for urban workers.
As noted above, the error corrections mod­
el imposes the long-run restriction that produc­
tivity-adjusted wage growth and prices move
together in the long run. Therefore, it cannot
be the case that both a 1= 0 and a 2= 0. Other­
wise, neither variable would respond to the
wage-price gap. Thus, it is impossible for the
hypotheses in both equations 6 and 7 to be true
simultaneously, even though separately testing
these hypotheses can in principle lead to the
result that both hypotheses cannot be rejected.
In practice, this was an issue for some of the
price indices used in constructing productivity
in mining; nondurable manufacturing; services;
and finance, insurance, and real estate.
Causality from wages to prices and from
prices to wages varies depending upon the
industry. In general, the direction of causation
in construction was from prices to wages, with
no evidence that construction wages help pre­
dict prices. This held true for all the different
price indices used to construct productivity
measurements. In mining, the number of lags
used was critical for causality inference from
prices to wages. For none of the lags or price
indices did mining wages Granger-cause infla­
tion. Exclusion tests suggest that a lag length
of 12 quarters is appropriate. Results regard­
ing the hypothesis in equation 7 are mixed

6
2


depending upon the price index. However,
they generally support the idea that prices
Granger-cause wages in mining.
Results are somewhat different for manu­
facturing. In that industry, wages clearly show
causality running from wages to prices rather
than vice versa for most price indices. Howev­
er, manufacturing’s durable and nondurable
components behave differently. The durable
component typically shows joint causality, that
is, prices cause wages and wages cause prices,
although results on prices causing wages de­
pend upon the lag length, with longer lags not
as clear on statistical inference. Nondurable
manufacturing exhibits some evidence suggest­
ing that nominal productivity-adjusted wage
growth causes inflation. In general, while the
hypothesis of equation 6 cannot be rejected at
conventional confidence levels using 8 lags of
data, it can be rejected using other lag lengths.
Evidence as to whether price inflation causes
wage inflation is mixed for this sector, varying
both with lag length and price index.
For transportation and public utilities, the
hypothesis that wages do not Granger-cause
prices is accepted for all price indices when 8
lags of the data are included. However, when
only 4 lags are employed, the hypothesis is
typically accepted, with the notable exception
of the transportation price index. Prices clearly
Granger-cause wages in this industry. Retail
trade shows direction of causality going both
ways, that is, from wages to prices and from
prices to wages. The evidence for causality
from wages to prices is much stronger than that
for the opposite direction, since the former
holds true at essentially all lag lengths. The
latter seems to be true for only the intermediate
length of 8 quarters. Wholesale trade is some­
what different, with a lag length of 12 quarters
supporting the idea that wages cause prices,
while the results for shorter lag lengths suggest
the opposite. The data show fairly clearly that
prices Granger-cause wages for most lag
lengths and price indices.
The results of the hypothesis of equation 6
depend considerably on the price index em­
ployed to construct services productivity.
When nominal income is deflated by the vari­
ous PPI measures, wages strongly Grangercause prices. However, this result does not
hold for the various CPI measures. Inflation
does not consistently Granger-cause wage

ECONOMIC PERSPECTIVES

growth in services. The results of tests of hy­
pothesis 7 depend upon the lag length as well as
the price index. It is interesting to note that the
price index for services shows Granger-causality
when the lag length is 4 but not longer. For
finance, insurance, and real estate, the direction
of causality does not run from wages to prices.
There is, however, mixed evidence that prices
cause wages in this sector.
Alternate m anufacturing productivity
measures

The results discussed above are based on
productivity measures that have been con­
structed from national income data by industry.
For manufacturing, an alternative source for
productivity is available. The Bureau of Labor
Statistics (BLS) reports quarterly productivity
indices for manufacturing and its durable and
nondurable components separately. The corre­
lation between the BLS productivity measures
and the various constructed productivity mea­
sures is quite high. For example, the correla­
tion between BLS manufacturing productivity
and productivity constructed using the CPI
index for durables is .96. For durable manu­
facturing, the correlation is .93 for productivity
constructed from the same price index. Non­
durable manufacturing exhibits a correlation of
.95 with productivity constructed using the CPI
index for nondurables.
However, it is the growth rate of produc­
tivity that is important for constructing esti­
mates of the wage-price gap and for estimat­
ing the error corrections model. For manufac­
turing as a whole, the growth rates of the
constructed productivity measures tend to
lead productivity growth as measured by the
BLS. For durable manufacturing, the results
depend on the price index employed in the
construction of productivity measures. For
example, when national income in nondura­
bles is deflated by the CPI index for commod­
ities, the constructed growth rate tends to lead
that reported by the BLS. However, using the
CPI index for durables changes the result,
with BLS productivity growth tending to lead
constructed productivity growth. Results in
nondurables also hinge on the measure of
constructed productivity.
I reestimated the error corrections model
using the BLS productivity measures for manu­
facturing, durable manufacturing, and nondu­
rable manufacturing. Granger-causality tests


FEDERAL RESERVE


BANK OF CHICAGO

for 4, 8, and 12 lags of the data show that the
null hypothesis that wages do not enter the
inflation equation cannot be rejected at stan­
dard confidence levels. Similarly, tests of
whether prices enter the wage inflation equa­
tion cannot be rejected at standard confidence
levels.
Clearly, the measurement of productivity
is important in the analysis. The two measures
presented here differ in the measure of real
output used to construct the index. The BLS
adjusts annual data based on the National In­
come and Product Accounts to form a quarterly
series. The form of the adjustment comes from
the Federal Reserve Board’s index of manufac­
turing production. Thus, the quarterly pattern
is imputed from another source. This appears
to be the main cause of discrepancy between
the two measures.
Summary

There are various ways to construct pro­
ductivity measures. In the evidence presented
above, national income by industry was deflat­
ed by a number of price indices to construct
productivity measures. The causality results
are quite robust across the various price indices
employed. Judging from the similar time
series exhibited amongst these price indices,
such a result is to be expected. In most of the
industries examined, the direction of causality
runs from prices to wages rather than wages to
prices. Only in manufacturing and retail trade
does productivity-adjusted wage growth appear
to help forecast inflation.
This finding has a variety of implications.
First, if one is attempting to find corroborating
evidence that the unemployment rate is below
or above the natural rate, observing wage
growth in a variety of sectors is apt to be mis­
leading. High wage growth today may simply
be a natural response to high past inflation and
in most industries does not presage impending
inflation. Manufacturing and retail trade are
the anomalies in that the wage-price gap
appears to be narrowed not only by move­
ments in prices but by movements in wages as
well. In short, high productivity-adjusted
wage growth in these sectors helps predict
future inflation.
It has frequently been argued that the way
in which our gross domestic product has been
produced has shifted away from goods pro­
duction towards service production. However,

27

the statistics that are collected to gauge the
health of our economy disproportionately repre­
sent the now smaller goods-producing sector. Is
our perception of the economy’s performance
somehow skewed by the narrow focus of these
measures? The above empirical work suggests
that for the purposes of forecasting inflation, it
is not necessary to have data on wages in a wide
variety of industries, as wages in these sectors
do not Granger-cause inflation. Only in manu­
facturing and retail trade is any value added to
our forecasts of inflation.

These results hinge heavily on the mea­
surement of productivity. Only in manufactur­
ing can statistics be found to independently test
the hypothesis that wages Granger-cause infla­
tion. The results based upon BLS productivity
measures do not support the hypothesis that
wages Granger-cause inflation. The extent to
which this is due to the imputation of quarterly
patterns in the measurement of real manufac­
turing output is a question for further study.

NOTES
'In the discussion that follows, the introduction of two
distinct types of labor is not essential. However, it is
included to motivate an understanding of why wages
differ across industries.
2Of course, if workers were performing hazardous work in
one industry relative to another, then they would need to
receive a compensating wage differential to make them
indifferent to the hazard.

REFERENCES
Barth, James R., and James T. Bennett,
“Cost-push versus demand-pull inflation,”
Journal of Money, Credit, and Banking, Vol. 7,
No. 3, August 1975, pp. 391-397.
Campbell, Jeffrey R., and Ellen R. Rissman,
“Long-run labor market dynamics and short-run
inflation,” Federal Reserve Bank of Chicago,
Economic Perspectives, Vol. 18, No. 2, March/
April 1994, pp. 15-27.
Engle, Robert F., and Clive W. J. Granger,
“Co-integration and error correction: Represen­
tation, estimation, and testing,” Econometrica,
Vol. 55, No. 2, March 1987, pp. 251-276.
Friedman, Milton, “The role of monetary poli­
cy,” American Economic Review, Vol. 58, No.
1, March 1968, pp. 1-17.
Gordon, Robert J., “The role of wages in the
inflation process,” American Economic Review,
Vol. 78, No. 2, May 1988, pp. 276-283.
______________ , “Understanding inflation in
the 1980s,” Brookings Papers on Economic
Activity, Vol. 85, No. 1, 1985, pp. 263-299.


28


3I have indexed wages to equal 100 in 1987 for ease of
comparison.
4See Engle and Granger (1987) for a full discussion of the
error corrections model.
5Results for 12 lags in the specification are qualitatively
the same except as noted. Tests of the hypothesis that
lags of greater than length k enter with a zero coefficient
suggest that 8 to 12 lags is a proper specification.

____________ . “Price inflation and policy
ineffectiveness in the United States, 1890—
1980,” Journal of Political Economy, Vol. 90,
No. 6, December 1982, pp. 1087-1117.
______________ , “Can the inflation of the
1970s be explained?” Brookings Papers on
Economic Activity, Vol. 77, No. 1, 1977,
pp. 253-277.
Mehra, Yash P., “Unit labor costs and the price
level,” Federal Reserve Bank of Richmond,
Economic Quarterly, Vol. 79, No. 4, Fall 1993,
pp. 35-51.
Perry, George L., “Inflation in theory and
practice,” Brookings Papers on Economic Activ­
ity, Vol. 80, No. 1, 1980, pp. 207-241.
______________ , “Slowing the wage-price
spiral: The macroeconomic view,” in Curing
Chronic Inflation, Arthur M. Okun and George
L. Perry (eds.), Washington: Brookings Institu­
tion, 1978, pp. 269-291.
U.S. Department of Commerce, Bureau of
Economic Analysis, National Income and Prod­
uct Accounts, electronic database, Washington,
DC, March 1995.

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