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o

ECONOMIC

PERSPECTIVES
T U

n il




A review from
the Federal Reserve Bank
of Chicago

MARCH/APRIL 1987
Costs and c o m p e titio n in bank
c re d it cards
Econom ic events o f 1986—
A chron o lo g y
The m inim um w age: No m inor
m a tte r fo r teens
T echnical c o rrec tio n : The in fla tio n adjusted index o f th e d o llar

ECONOMIC PERSPECTIVES
March/April 1987

Volume XI, Issue 2

Karl A. Scheld, senior vice president
and director of research
Editorial direction

Edward G. Nash, editor
Herbert Baer, financial structure
and regulation
Steven Strongin, monetary policy
Anne Weaver, administration

Contents
Costs and competition in bank
credit cards

3

C hristine Pavel and Paula Binkley

If bank card operations are characterized
by increasing returns to scale, the industry
should undergo increasing concentration:
This seems to be the case.

Production

Roger Thryselius, graphics
Nancy Ahlstrom, typesetting
Rita Molloy, typesetter
Gloria Hull, editorial assistant

Economic Perspectives is
published by the R esearch D epart­
m ent o f the Federal R eserve Bank
o f C hicago. T h e view s expressed are
the authors’ and do not necessarily
reflect the view s o f the m anagem ent
o f the Federal R eserve Bank.
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vided source is credited and T he
Public Inform ation C enter is pro­
vided with a copy o f the published
m aterial.

ISSN 0164-0682




Economic events of 1986—
A chronology

14

G eorge W . C loos

Slow growth, moderate inflation, and
some significant developments.
The minimum wage: No minor
matter for teens

19

D onna C. V andenbrink

A lower minimum wage for teenagers
would dramatically increase teen
employment across the board: male,
female, black, white, suburban, and
center-city.
Technical correction: The inflationadjusted index of the dollar

29

Jack L. H ervey and W illiam A. Strauss

Costs and competition in bank credit cards
Christine Pavel and Paula Binkley
Last year consumers said “Charge it”
with Visa or MasterCard 76 times every sec­
ond, 4,500 times every minute, and nearly
275,000 times every hour of every day. Credit
cards, especially bank cards, are being used
more frequently both as a method of payment
and as a way of taking on consumer debt.
There are more than three times as many pur­
chases made with a bank card today than there
were ten years ago, and the number of trans­
actions per account is up from 18 per year in
1975 to 23 per year in 1985. Over 2 million
merchants accept MasterCard, Visa, or both
and about 3,000 institutions issue the cards.
In addition, bank card debt as a percent
of total consumer installment debt is up since
1975. Then bank cards accounted for only 6
percent of all installment debt outstanding; to­
day bank cards account for about 14 percent.
This article shows that the recent surge in
banks’ efforts to market bank credit cards are
a result of the existing cost structure of bank
card plans. It is shown that there were no
major changes in the cost structure of bank
card plans from 1975 to 1983, but bank card
operations were characterized during those
years by increasing returns to scale. That is,
as output increased, per unit costs fell. Sup­
pliers of bank cards, therefore, would want to
increase output in order to become low cost
producers. As long as the demand for bank
cards is not fully exhausted, suppliers would
probably concentrate on selling bank cards to
those who do not have them. But, as saturation
is reached, suppliers would have to increase
output by taking other suppliers’ market share
through lower overall prices, lower credit
standards, or greater product differentiation.
This article examines the cost functions
of bank card plans since 1975. The first section
briefly reviews the history of bank cards, and
the second section provides some institutional
detail on the credit card industry. In the third
section, the cost structure of the industry is ex­
amined. In this section, we show that there are
increasing returns to scale in bank card oper­
ations and that this cost structure can help ex­
plain the rapid growth in bank card activity.
There have been several references made to the
Federal Reserve Bank of Chicago




presence of economies of scale in credit card
operations, but to our knowledge this is the first
attempt to demonstrate their existence empir­
ically. The fourth section tests the notion that
credit cards can be used effectively as a mar­
keting tool to cross-sell ,other bank products.
Our analysis shows that other bank products,
such as demand deposits and retail CDs, are
better vehicles for cross-selling bank cards than
bank cards are for cross-selling these other
products. Finally, a summary and conclusions
are presented.
A brief history

The development of the modern bank
card occurred between 1950 and 1966. The
first bank card plan was produced by Franklin
National Bank of New York in 1951. But the
first bank cards resembled today’s travel and
entertainment cards, such as American Express
and Diners’ Club, although the early bank card
plans did not charge a membership fee. Rev­
enues were based on merchant discounts and
free credit was extended over the billing period,
usually 30 days. In 1958 the revolving credit
feature became a part of bank card plans. In
addition to offering banks an additional source
of revenue via interest charges, the extension
of credit beyond the usual free period gave
cardholders the advantage of being able to ex­
tend their repayments.
Until 1966, bank card plans were local
or regional in nature and almost all plans were
run independently, rather than jointly or by
associations. High start-up costs coupled with
the fact that in most cases bank cards were ac­
cepted only by merchants in the issuing bank’s
immediate area proved to be a major obstacle
to the rapid spread of bank credit card plans.
This hindrance was accentuated by the lack of
widespread branching systems due to state laws
limiting or prohibiting branching. Consumers
were not interested in bank cards unless they
were widely accepted, but for merchants to acChristine Pavel is an economist and Paula Binkley an as­
sociate economist at .the Federal Reserve Bank of Chicago.
The authors thank Herbert Baer and Douglas Evanoff for
helpful comments.
3

cept the cards, they must be in the hands of
many potential customers. To get around this
problem, banks would often send unsolicited
cards to consumers. This tactic led to huge
fraud and credit losses. Some banks discon­
tinued or sold their plans after a few years of
unsatisfactory performance.2
In response to these problems, the first
national bank card plan was started in 1966.
Bank of America began the nationwide clearing
of bank card sales slips and the nationwide li­
censing of banks to issue cards using the name
BankAmericard, later renamed Visa and
owned by Visa International. Several other
large banks formed the Interbank Card Asso­
ciation, later known as MasterCard Interna­
tional, and thus began a second national card
system. The advent of such nationwide systems
was a turning point in the development of bank
cards because it made bank credit cards ac­
ceptable to a significantly larger number of
merchants, and the cards became more attrac­
tive to consumers because local cards were
transformed into national cards.
The use of credit cards as a payments ve­
hicle and as a major source of unsecured credit
began to take off in the late seventies. From
1976 to 1979, the number of bank card ac­
counts rose 65 percent to 75 million, and the
number of transactions also rose 65 percent to
1.5 billion.3 Credit card loans outstanding at
banks more than doubled over this period and
accounted for about 16 percent of all consumer
installment debt held by banks in 1979.4
Just as bank card programs were showing
promise, however, soaring interest rates made
usury ceilings binding, bringing the growth in
bank cards to a halt. In addition, the credit
restraint measures of 1980 reduced the use of
bank cards. These setbacks, however, were
only temporary. The special credit restraints,
initiated in March 1980, were phased out be­
ginning in July of the same year.5 Usury ceil­
ings became less binding, either because they
were relaxed or because credit card operations
were shifted to states that did not have usury
ceilings. Since 1981, the growth in bank card
activity has been strong, particularly over the
1983-84 period when the number of bank card
transactions grew 34 percent.
While the recent surge in bank card ac­
tivity could be viewed as a natural progression
through the product life cycle, this paper sug­
gests yet another reason for the growth in bank
4




card activity. This explanation is rooted in the
cost structure of bank cards.
The mechanics of bank card
transactions

In the last 20 years, the bank card indus­
try has evolved into a complex network that
involves banks, merchants, cardholders, bank
card associations, and independent processors.
The mechanics of bank card transactions can
become quite complex, and they have impor­
tant consequences for the bank card industry’s
cost structure.
A credit card transaction cannot begin
until a bank customer receives a bank card
(usually a MasterCard or Visa credit card)
from an issuing or participating bank. An issu­
ing bank sets up its own card operation. It
obtains a license to use the Visa or MasterCard
logo, determines the nature and price of ser­
vices offered to the cardholder, establishes a
credit limit, sets annual fees, interest rates, and
payment and finance charge calculation proce­
dures, and arranges for or handles the process­
ing of credit card sales slips. A participating
bank is a bank that offers its customers the
bank card of an issuing bank.
A bank card transaction begins when the
cardholder uses his card as a means of pay­
ment. The merchant who accepts the card in
a transaction then sends the signed credit card
sales slip to his bank, a merchant bank, for pro­
cessing. A merchant bank is the bank that
maintains the account of a merchant who ac­
cepts bank cards as a means of payment.6
When a merchant deposits bank card sales slips
with its merchant bank, the bank credits the
merchant’s account for the amount on the slip
less the merchant discount, usually 2 to 5 per­
cent. The merchant bank then converts the
information on the slip—the cardholder’s ac­
count number, the merchant identifier number
and address, and the specific purchase
information—into machine readable form. This
transformation can be performed by the mer­
chant bank itself, an independent processor, or
another bank.
After the slips are put into machine read­
able form, the information is sent to the inter­
change facilities of MasterCard or Visa. The
interchange facilities act as clearing houses,
transferring the information on the sales slips
to the issuing banks. Visa or MasterCard send
Economic Perspectives

the merchant hank the amount of the trans­
action less an interchange fee based on the
dollar amount of the sales slip. Visa and
MasterCard also collect a per-item fee for this
clearing service. When a merchant bank uses
an independent processor, which has a re­
lationship with both the merchant and issuing
banks, Visa or MasterCard are sometimes by­
passed, and accounts are settled through the
transfer of funds between the issuing and mer­
chant banks. This situation is common among
large credit card processors.
The issuing bank can now bill the
cardholder. In cases where the merchant’s
bank is also the cardholders’s bank this entire
settlement process is simplified since no funds
have to be transferred between interchange fa­
cilities and banks. When a participating bank
is involved, the settlement between all parties
depends on the type of agreement between the
issuing and participating banks.
The cost structure

As is evident from the description of the
bank card mechanism, a bank’s credit card
operation consists primarily of two activities.
The first activity involves issuing the card, ex­
tending consumer credit, and providing a pay­
ments vehicle; the second activity involves
accepting and discounting merchant sales slips.
These activities generate four outputs: a
line of credit (i.e., a bank card account), loans,
billings, and merchant sales slips. Initially
when a customer applies for a bank card, the
issuing bank performs a credit evaluation. If
favorable, a card is issued to the customer and
a new account is established. At that time a
line of credit is established, but no loan is ac­
tually made. Only when the new cardholder
uses the card to make a purchase or receive a
cash advance does the bank make a loan to the
cardholder. This loan may be for a few days
or more depending on whether or not the
cardholder decides to pay off his balance in full
when billed or carry his balance over several
months. The issuing bank bills each active ac­
count. An active account is one that was used
to pay for a purchase, obtain a cash advance,
or pay a previous balance during the past
month. Each time a bank card is used a sales
slip is created, cleared through the system de­
scribed above, and the amount is debited to the
appropriate cardholder’s account.
Federal Reserve Bank ol Chicago




These outputs explain the cost structure
of a bank card operation, and the cost structure
of a bank card operation may help to explain
why interest in offering bank cards has in­
creased recently among banks and nonbanks.
If the underlying cost structure of a bank card
plan has changed due to some technological
advancement, such as improved automation,
then bank card services may be cheaper to
provide. Also, if there are economies of scale
in offering bank card services, then suppliers
would be expected to increase output in order
to become more efficient producers.
Using the Federal Reserve System’s
Functional Cost Analysis (FCA) data on 40
card-issuing banks that participated in FCA
from 1975 to 1979 and in 1981 and 1983, we
estimated a cost function for bank card plans.'
The FCA program is a cooperative venture
between the 12 Federal Reserve Banks and the
participating commercial banks. The program,
which develops individual bank income and
cost data for specific lines of business, is con­
ducted annually and covers a full calendar year
of operations. The program is voluntary and,
consequently, the sample of banks that partic­
ipate is small and not consistent from year to
year. For example, in 1983, 553 banks partic­
ipated, but in 1984, only 509 banks partic­
ipated. A commonly stated problem with the
FCA data is that large banks are underrepre­
sented. In 1983, the largest bank that partic­
ipated had $2.6 billion in assets, and only 12
of the more than 400 banks had more than $1
billion in assets.
A description of the sample of 40 banks
that participated in the FCA program and
were used in this study is presented in Table 1.
These banks were chosen for two reasons. First,
each of them participated in the Functional
Cost Analysis program from 1975 to 1983.
Second, each of these banks acted as an issuing
bank and as a merchant bank in credit card
transactions. The largest credit card issuers
were not included in this study because they
did not participate in the Functional Cost
Analysis program; however, our sample does
include banks among the top 30 percent of all
bank card issuers.8
The average bank in the sample had total
assets in 1983 of $266 million and about 10,000
active accounts. A bank with operations of this
magnitude would be ranked around 300th
based on number of active accounts according

Table 1
Sam ple o f 40 banks' c re d it card operations: Sum m ary statistics, 1983
Mean
Total assets

Median

Minimum
$27 mil.

$864 mil.

6,970

1,081

61,945

8,715

1,413

91,058

326,942

163,255

16,512

1,450,574

15,235,420

7,452,227

1 , 1 0 0 ,0 0 0

60,073,198

6,031,868

2,926,202

364,515

33,031,000

380,983

0

0

4,307,000

300th

351st

398th

160th

$293 mil.

$289 mil.

Active accounts

13,985

Accounts

19,714

Sales slips
Volume ($)
Retail loans outstanding ($)
Cash advances outstanding ($)
Rank*

‘ Based on The Nilson Report ranking of bank card issuers by number of active accounts.
issuers according to this report. See The Nilson Report, Nos. 337 , 338 , 339 , and 340.

to The Nilson Report. The largest bank in the
sample had nearly $1 billion in assets in 1983
and about 54,000 active accounts. This insti­
tution would be ranked about 160th. The
smallest bank, with only $33 million in assets,
would be ranked 398th.
The activities of a credit card operation
can be measured several ways. For example,
the output of the lending/payment activity can
be measured as the total dollar volume of loans
outstanding, the number of accounts, the
number of active accounts, or the number of
times cardholders use their accounts (which is
not available from FCA). The output of the
processing activity can be measured as the dol­
lar volume of sales slips discounted or the
number of sales slips discounted.
To see if the cost structure could help ex­
plain why credit cards have become a popular
product among many banks, we estimated a
cost function based on bank card output. Ide­
ally, we would have specified total operating
costs as a function of new accounts opened,
billing volume (i.e., the number of times
cardholders use their accounts), and the num­
ber of sales slips. A new account causes the
bank to incur costs when opened, but since we
have two gaps in the data (1980 and 1982), this
measure was unavailable for two of the seven
years.9 The best measure of output associated
with the lending/payment activity would be the
number of times cardholders use their cards for
either cash advances or for purchases. This
measure, however, is unavailable from the FCA
program. As a proxy for this measure, there­
fore, we used the number of active accounts.
6




Maximum

There are approximately 1 5 0 0 bank card

An active account is one “with purchase activ­
ity, cash advance activity, unpaid balances, or
any combination of the above.” For the pro­
cessing activity, we used number of sales slips
rather than dollar volume of sales slips dis­
counted because the dollar volume is what
generates revenues, but the sales slips are what
are actually processed and, therefore, generate
costs.10
Thus, we estimated an equation for total
operating costs associated with a bank card
operation as a function of the number of active
accounts and the number of sales slips dis­
counted (see box). Bank card loans, i.e.,
receivables, were not included to measure the
lending/payment activity because the primary
cost associated with receivables is the cost of
funds, which was not included in operating
costs.
Since much of a bank card operation
consists of the transmission of information, ad­
vances in reader sorter and computer technol­
ogy over the last decade may have caused the
cost structure of bank card plans to have
changed, making them less expensive to oper­
ate. To test whether or not the underlying cost
structure of bank card operations had changed
since 1975, the cost equation was estimated for
each of the years 1975-79 and 1981 and 1983.
We tested separately the hypotheses that the
coefficients and intercept are each significantly
different from year to year and, therefore, that
the cost structure of bank card plans had
changed.
To test whether or not the intercept had
changed over time, all the years were pooled
Economic Perspectives

and the cost equation was estimated, using
dummy variables to control for changes in the
intercepts. The results revealed that the coef­
ficients on output were not different from year
to year, but that the intercept had changed.11
Figure 1 shows the total cost curves of a
bank card operation when the output mix is
held constant and output is increased propor­
tionately. That is, the ratio of active accounts
to sales slips is held constant while each grow
proportionately. The output mix shown in
Figure 1 is that of the median bank in 1975.
The figure shows that the cost curve shifted
downward in 1978, but the curve shifted back
up again in 1983. The shift represents a 12
percent difference in total costs.
Because the shift in the cost curve is only
temporary, a technological development is
probably not the reason for the change. There
are, however, other possible explanations. One
is that bank card costs are cyclical and tied to
default rates. We tested this hypothesis by ex­
cluding net loan losses from the dependent
variable, total operating costs, and reestimating the equation. The newly estimated
cost curve still shifted downward temporarily
in 1978.
A second possible explanation for the shift
in the cost curve is that, for the years in which
the curves changed, some important variable
was omitted from the equation. A likely canFigure 1
Ray to ta l c o s t as a fu n c tio n o f
a c tiv e a c c o u n ts and sa le s slip s
millions

NOTE: Ratio of active accounts to sales slips is held constant
at .04, the ratio for the median bank in the 4 0-b an k sample.

Federal Reserve Bank of Chicago




Figure 2
Bank ca rd a c tiv e a c c o u n ts and tra n s a c tio n s
trillions

millions

NOTE: These figures are for all banks from 1 9 7 5 to 1985. The
trends are comparable for the 4 0 banks in our sample.
SOURCE:

The Nilson Report,

various issues.

didate is billing volume, or the number of times
cardholders use their cards. As mentioned
above, this measure of output would perhaps
more accurately explain the operating costs as­
sociated with bank card plans because process­
ing costs are incurred by issuing banks every
time a cardholder uses his card. However, be­
cause this measure is not available from FCA
data, we could not test this hypothesis.
A third, and we believe most likely, ex­
planation is that actual output may have dif­
fered significantly from expected output levels.
As shown in Figure 2, bank card output fell
somewhat from 1979 to 1981. If a constant
growth path were expected such as that shown
by the dashed line in Figure 2, output would
have been greater than expected in 1978 and
1979 but less than expected in 1975 to 1977
and 1981 to 1983. Investments in plant and
equipment that were made with the expecta­
tion of higher output would have been under­
utilized from 1981 to 1983.
Thus, based on the 40 banks in this study,
changes in the cost structure do seem to have
occurred since 1975, but they have been only
temporary and do not seem to explain the
widening popularity of bank cards among
banks and other financial institutions.12 The
cost structure of bank card plans, however, can
still help to explain the rapid growth in bank
cards recently. If there are increasing returns
to scale in bank card operations, bank card
managers would feel pressure to increase out­
put, i.e., bank card activity. This would enable
7

the banks to move down the average cost curve
and become more efficient producers.
The estimated cost curves (shown in Fig­
ure 1) indicate that economies of scale in bank
card operations exist within the range of output
in our sample. The largest bank in our sample
had only 91,000 accounts, while the largest
supplier of bank cards in 1983, Bank of Amer­
ica, had over 5.5 million accounts.
Figure 3 shows the estimated relationship
between the average cost of a bank card oper­
ation and active accounts and sales slips when
the output mix is held constant and output is
increased proportionately. Within the range
of output for our sample, the curves exhibit
rapidly decreasing and then increasing returns
to scale, which may be explained by the
adoption of different operating procedures at
various output levels. For example, at very low
output levels, a firm may perform most of its
bank card activities in-house with a low
capital-to-labor ratio; at moderate output lev­
els, a firm may farm out tasks to outside ven­
dors; and at high output levels, a firm may
bring those activities back in-house but with a
high capital-to-labor ratio.
The humped average cost curve shown in
Figure 3 indicates that the output mix, i.e., the
ratio of active accounts to sales slips, is as im­
portant to a firm’s operating costs as is the level
of output. This is shown in Figure 4 above,
where scale economies are measured by S.13
Figure 3
Ray average c o s t as a fu n c tio n o f
a ctiv e a c c o u n ts and sa le s slip s
dollars per
sales slip active account

0

37,4 00
74,8 00
active accounts

112,200

NOTE: Ratio of active accounts to sales slips is held constant
at ,04, the ratio for the median bank in the 4 0-b a n k sample.

8




Figure 4
E co n o m ie s o f sca le vs. ratio o f a ctiv e
a c c o u n ts to sa le s slip s

s

ratio

When S is less than 1, there are increasing
returns to scale; when S is equal to 1, constant
returns to scale; and when S is greater than 1,
decreasing returns to scale. As a firm increases
its ratio of active accounts to sales slips, it
moves toward increasing returns to scale. In
our sample of 40 banks, 16 banks were operat­
ing in the range of increasing returns in 1983.
In other words, the elasticities of cost with re­
spect to the various output measures were less
than 1, indicating that there are increasing re­
turns to scale (see Figure 5). The point where
average costs begin to decrease depends on the
output mix (see box).
Thus, over the 1975-83 period three forces
seem to have been affecting bank card oper­
ations. First, for certain output prices, there
were increasing returns to scale. Second, the
cost curves were changing temporarily, and
third, output was decreasing over the 1979-81
period and did not pick up again until 1982.
These last two forces were often too strong to
allow the increasing returns to scale to keep
average costs on a downward path.
These results have important impli­
cations. As banks attempt to move down the
cost curves by increasing output, the prices
charged for bank card accounts and for sales
slip processing should fall. As low-cost pro­
ducers earn abnormal profits, they encourage
entry, which would then cause prices to fall.
As prices fall, those firms that are not
low-cost producers should be driven out of the
Economic Perspectives

Cost and cross-selling models for bank cards

To help explain why bank cards
have gained popularity among issuers re­
cently, we examined the cost structure of
bank cards and their usefulness as tools for
cross-selling other bank products. Two
separate models were estimated, using or­
dinary least squares regression.
Cost structure

To model the cost structure of a bank
card operation, we estimated a translog
cost function, using active accounts and
sales slips as output measures. A translog
cost function allows for the estimation of
a U-shaped average cost curve. The cost
function is expressed as follows:
InTC = a + b(lnSS) + c(lnA TV) +
1/2d(lnSS)2 + \l2e(lnATV)2 +
J\lnSS*lnA TV) + u
Where TC = total operating cost in 1984
dollars, including credit card
activity and franchise fees and
fraud losses but excluding the
cost of funds
55 — total number of sales slips de­
posited by merchants
ATV = total number of active ac­
counts, defined as “the number
of accounts with purchase ac­
tivity, cash advance activity,
unpaid balances, or any com­
bination of the above.”
u = error term
This equation was used to test
whether the cost structure of a bank card
operation had changed from year to year.
Such a change would be exhibited by co­
efficient changes and/or by changes in the
intercept term.
The above equation was estimated
separately for each of the years 1975 to
1979 and 1981 and 1983, using Ordinary
Least Squares.* F-tests were used to test

Reserve Bank of Chicago
Digitizedfederal
for FRASER


the hypothesis that the coefficients of the
cost function, excluding the intercept
term, had changed since 1975. If the Fstatistics were statistically significant, then
we could not reject the hypotheses and,
therefore, conclude that the cost structure
of bank card plans had changed since
1975. Such changes might indicate a
change in the technology of providing
bank cards. However, as discussed in the
text, the coefficients had not changed over
the 1975-83 period.
To test whether or not the intercepts
had changed over this time period, all the
years of data were pooled and the
equation was estimated using dummy
variables to control for changes in the in­
tercepts. The results were as follows.
1975-77 and 1983:
TC = 9.06 + 2.78(T TV) - .14(T7T)2
-2.24 (55) + .09(55)2 + M{ATV*SS)
1978-79 and 1981:
TC = 8.93 + 2.78^4 TV) - .14(,4 7T)2
-2.24(55) + .09(55)2 + .04(ATV*SS)
These equations explain 91 percent of the
variability in total operating costs, and
each of the variables was significant at the
1 percent level with the exception of the
interaction term, ATV*SS.
Cross-selling

To see if bank cards are useful tools
for cross-selling other bank products or if
other bank products are better tools for
cross-selling bank cards, we used a simple
causation model.** This model tests
whether variable A “causes” B, B
“causes” A, or A and B simultaneously af­
fect each other. This is necessary because
if A is a function of B, then A and B are
related in some way, but B does not nec­
essarily “cause” A in the Granger sense; A
may “cause” B, or the two variables may
be reinforcing.

9

Accordingly, the following equations
were estimated:
0

^8 3 =

^8 1 +

^7 9 +

^7 8 +

Q l +

C 79 +

C 78

2) C83 = C81 + C79 + C78 + Pgi + P79 + P78
Where P = the number of other product
accounts. P takes on values for
the number of demand deposit
accounts, retail (less than
$100,000) time deposits ac­
counts less retail certificate of
deposit accounts, retail certif­
icate of deposit accounts, and
consumer installment loan ac­
counts.
C = the number of bank card ac­
counts (total or active).
An F-test was used to test whether
C81, C79, andC78 in the first equation are
equal to zero (C81 = C79 = C78 = 0). If not,
then the number of bank card accounts
market. So if the bank card industry is char­
acterized by increasing returns to scale, the in­
dustry should be experiencing significant
consolidation. This is, in fact, what seems to
be happening. Since 1980, the share of bank
card loans held by the ten largest issuers of
bank cards has increased 21 percentage points
to 58 percent in 1985.14
There is further evidence that cost con­
siderations are important. Rather than have
each of 15,000 banks issue their own card,
about 12,000 participating banks provide bank
card services through 3,000 other issuing banks.
The Independent Bankers Association recently
formed an organization intended to issue bank
cards for its members and, utilizing the result­
ing scale economies, provide bank card services
at a lower cost than would be possible if mem­
bers acted as independent issuers.10 In addition,
several banks are attempting to differentiate
their bank cards and increase their market
shares through “affinity-group marketing.”
Such strategies have various organizations co­
operating with a bank to help promote its card
10




“causes” the number of P accounts. If
C81 = C79 = C78 = 0, then the number of
bank card accounts does not affect the
number of P accounts; i.e., there would be
no benefits to cross-selling P accounts
through bank cards. Similarly, an F-test
was used to test whether PBIj P795 and P78
in the second equation are equal to zero
(P81 = P79 = P78 = 0) . If not, then the
number of P accounts “causes” the num­
ber of bank card accounts; i.e., there
would be benefits to cross-selling bank
cards through P accounts.
The results are shown in Table 2 of
the text. They suggest that the other bank
products tested would be better for cross­
selling bank cards than bank cards are for
cross-selling other bank products.*
*Before the equation was estimated, we tested for
heteroskedasticity and found none.
**C.W. J. Granger, “Investigating Causal Relations
by Econometric Models and Cross-Spectral
Methods,” Econometrica, Vol. 37, No. 3 (July 1969),
p. 431.

by appealing to the potential cardholders’ loy­
alty to a particular group or association.16
Bank cards and cross-selling

In addition to increasing returns to scale,
another possible reason that bank cards have
become popular products for banks and other
financial institutions to offer to their customers
is that bank cards may help banks to generate
profits in other product lines, such as time de­
posits, auto loans, and other consumer loans,
through cross-selling. Such cross-selling is usu­
ally achieved by including ads in monthly bill­
ing statements. Information from customers’
bank card activity can also be used to cross-sell
products by targeting products.
A bank would want to cross-sell products
if it were more profitable than traditional sell­
ing techniques. Cross-selling might save ad­
vertising and marketing costs because
advertising would be consolidated with regular
mailings rather than sent to customers sepa­
rately. Cross-selling may also reduce the need
Economic Perspectives

T able 2
Bank cards as cross-selling too ls
Other bank products as a function of bank cards
(C 81 = C 79 = C 78 = 0)
Total
accounts

Active
accounts
( -------- F-statistics -------- ;

Number of

Demand deposits

2.07

0.14

Time deposits

2.40

0.96

Retail CD s

1 .0 2

3.61*

Consumer installment
loans

0.14

0.92

Bank cards as a function of other bank products
ff*81 = ^ 7 9 =

Number of

P 78

= 0)

Total
accounts

Active
accounts

( -------- F -sta tistics----------)
Demand deposits

3.81

6.03*

Time deposits

4.45*

5.68*

Retail CD s

5.71*

4.40*

Consumer installment
loans

2.60

0.57

rectly test these ideas; however, we were able
to see if there is any relationship between bank
card accounts and a few other bank products.
By estimating one equation in which other
bank products are a function of bank card ac­
counts or active accounts, however, we cannot
tell whether the other bank products influence
the number of bank card accounts or vice
versa. To overcome this problem, we used a
simple test of causation, first developed by C.
W. J. Granger (see box). In this model, other
bank products are specified first as a function
of the number of other bank products in previ­
ous years as well as the number of bank card
accounts, and second, the number of bank card
accounts is specified as a function of the num­
ber of bank card accounts in previous years as
well as the number of other bank products.
Figure 5

C o st e la s tic it y w ith re sp e ct to
a c tiv e a c c o u n ts and s a le s s lip s
elasticity

'S ig n ific a n t at the 5 percent level.

for some personnel, especially if bank card cus­
tomers open accounts for other products by
mail. A bank may also want to cross-sell other
bank products with bank cards in order to
penetrate out-of-state markets or to prepare for
electronic banking. Of course, a bank might
have to lower the prices on some accounts
and/or raise the interest rates paid on some
deposit accounts in order to get customers to
bank by mail. However, if customers place a
premium on “one-stop shopping,” the oppor­
tunity to consolidate bank accounts might al­
low a bank actually to charge higher prices.
Some banks clearly use their credit cards
to cross-sell deposit accounts, consumer loans,
and insurance. Citibank Visa and MasterCard,
for example, are vehicles for soliciting retail
CD, student loan, and life insurance customers.
Similarly, Sears uses its new Discover card to
attract savings deposits for its nonbank bank,
Greenwood Trust.
Because FCA data are not broken down
sufficiently by product lines, we could not di­
Federal Reserve Bank oi Chicago




elasticity

77

The bank products tested as cross-selling vehi­
cles were demand deposits, time deposits, retail
certificates of deposit, and consumer install­
ment loans.
The results indicate that, for smaller
banks, credit cards are of limited use as vehicles
for cross-selling certain bank products (see Ta­
ble 2 and box). The number of demand de­
posits and the number of time deposits
excluding CDs are not influenced by the num­
ber of total or active bank card accounts, but
the number of bank card accounts is affected
by the number of demand deposit accounts and
the number of time deposit accounts excluding
CDs. Also, the number of bank card accounts
does not seem to affect the number of retail
CDs, although the number of active bank card
accounts seems to influence them. The number
of CD accounts does seem to influence the
number of bank card accounts, both active and
total. The number of consumer installment
loan accounts, excluding bank card accounts,
does not seem affected by the number of total
or active bank card accounts, and bank card
accounts do not seem affected by consumer in­
stallment loans. Thus, demand deposits and
time deposits probably are good vehicles for
cross-selling bank cards, but bank cards are not
a good tool for increasing the number of de­
mand deposit accounts and time deposits.
However, there seem to be some reinforcing
effects between the sale of bank cards and retail
CDs.
Conclusions

The growth in credit card activity has
been quite rapid recently. This growth can be
explained, at least in part, by the induced de­
mand generated by suppliers attempting to
utilize cost economies present in the structure
of a bank card operation. Our results do not
suggest, however, that bank cards can be used
effectively as a tool to cross-sell other bank
products. Bank cards seem to be a good vehicle
for selling retail CDs but not other types of time
deposits or consumer installment loans.
The cost structure of bank card plans at
small banks seems to have changed over the
1975-83 period for relatively small to medium­
sized issuers of bank cards. These changes,
however, were only temporary and so do not
seem to lend any support to the hypothesis that
technology has enabled bank cards to be of­
72




fered more cheaply. However, the cost struc­
ture of bank card operations can still help to
explain the recent surge in bank card activity
because there are increasing returns to scale,
at least for small to medium-size banks. Bank
card managers, therefore, should want to in­
crease output, e.g., active accounts and sales
slips, in order to become more efficient and
profitable producers.
1 See D ennis B. Fitzpatrick, “An A nalysis o f Bank
Card Profits,” Journal of Bank Research, V ol. 7, (N o.
3, A utum n 1986), pp. 199-205, and Lewis M andell
and N eil B. M urphy, Bank Cards, A m erican Insti­
tute o f Banking, 1976, p. 87.
2 T hom as Russell, The Economics of Bank Credit
Cards, N ew York, 1975, p. 5.
5 The Nilson Report, various issues.
4 Federal Reserve Bulletin and The Nilson Report, vari­
ous issues.
3 See Federal Reserve Bulletin, O ctober 1979, April
1980, and J u ly 1980.
6 If a m erchant bank accepts M asterC ard and/or
V isa transactions but does not directly or indirectly
offer bank cards to consum ers, an issuing bank
sponsors its m em bership in the V isa and/or
M asterC ard system s. T hus, these m erchant banks
can utilize the system s’ settlem ent facilities.
7 1980 and 1982 data as well as data after 1984 for
individual banks were not available to us.
8 The Nilson Report, N o. 337, A ugust 1984, pp. 4-5
J For the years 1976 through 1979, w e estim ated a
translog costs function that included the variables:
active accounts; active accounts squared; sales slips;
sales slips squared; new accounts; and new accounts
squared. It also included the three interaction
terms: active accounts tim es new accounts; sales
slips tim es new accounts; and sales slips tim es active
accounts. All variables excep t the interaction terms
and new accounts squared were significant at the
5 percent level.
10 M andell and M urphy, pp. 86-87.
11 An alternative m ethod o f testing for tech nolog­
ical change is to use a tim e trend variable. W e also
tried this m ethod and found sim ilar results. See
W illiam C. H unter and Stephen G. T im m e,
“T echn ological C hange, O rgan izational Form , and
the Structure o f Bank P roduction,” Journal of
Money, Credit and Banking, V ol. 18 (N o. 2, 1986),
152-66.
12 As previously m entioned, our sam ple o f 40 banks
represents relatively sm all bank card issuers. T ech ­
Economic Perspectives

nological advances may have altered the cost
structure of bank card plans for the largest 100 or
so issuers. See “Interest Rate Controls on Credit
Cards—An Economic Analysis,” Lexicon Inc., Oc­
tober 1985.
13
S ln(TC) S\n(TC)
dln(ATV) <5ln(SS)

Federal Reserve Bank ol Chicago




14 Board of Governors of the Federal Reserve Sys­
tem, Reports of Condition, 1980 and 1985.
lo American Banker, October 13, 1986, p. 2.
16 American Banker, November 10, 1986, p. 11.

13

Economic events o f 1986—A chronology

George W. Cloos
1986 was a year of slow growth and
moderate inflation, though both were slightly
less than had been expected. However, the year
contained an unusually large number of mo­
mentous events and developments, at home
and abroad.
Oil prices declined sharply before staging
a partial recovery as OPEC reasserted its
power. The apartheid uproar caused most large
U.S. corporations to pull out of South Africa.
Governments changed hands in the Philippines
and Haiti, and deep unrest was evident else­
where. The Iraq-Iran war escalated. American
aircraft attacked Libya. Hopes for a
U.S.-Soviet arms pact were dashed as the
Iceland Summit broke up.
The Democrats regained control of the
Senate. The Administration’s influence was
undermined by the arms-for-hostages deal with
Iran. The Chernobyl disaster deepened the
shadow over nuclear energy. The U.S. space
program was jolted by the Challenger tragedy.
Deficits in the federal budget and the
nation’s balance of trade set records. The dol­
Jan 1 Social Security benefit payments rise by 3.1%.
Tax base rises to $42,000. Tax rate rises to 7.15%.
(Benefits rise 1.3%, tax base rises to $43,800, and tax
rate remains unchanged on Ja n 1, 1987.)
Jan 1 Regulatory minimum deposits eliminated for

lar declined further against other leading cur­
rencies. Interest rates declined to the lowest
levels in eight years. The stock market soared.
Comprehensive tax overhaul was enacted. The
six-year program to deregulate bank deposits
was completed. Turnover on the Federal Re­
serve Board was unusually high.
Large mergers made headlines, promi­
nently in banking, the airlines, and communi­
cations. Financial restructurings, layoffs, and
plant closings altered the profiles of large cor­
porations. Bankruptcy filings were frequent.
Revelations of insider stock trades brought
action by regulatory authorities. Problem
credits in agriculture and energy threatened
many financial institutions. Labor unrest was
highlighted by lengthy shutdowns at USX and
Deere. Consumers continued to support the
economy, while business investment slowed.
Motor vehicle sales set a record, aided by costly
sales incentives.
Despite the barrage of unsettling events,
of a sort that preceded past recessions, the na­
tional economy plowed ahead with surprising
aplomb. An informed consensus held that it
could duplicate the feat in 1987, extending one
of the longest expansions in history.
Ja n 17 AT& T will close Teletype manufacturing facility
in Skokie, IL.
Jan 17 Monsanto and Searle, merged in Oct 1985, will

consolidate pharmaceutical research.

Super NOW accounts, money market deposit accounts,
and 7- to 31 -day time deposits. (See Apr 1.)

Jan 22 Dow Jon es industrial stock average closes at

Jan 1 Spain and Portugal join European Economic

Ja n 22 Supreme Court rules against Federal Reserve
Board's attempt to stop spread of limited service banks.

Community (E E C ), raising membership to 12.
Jan 7 International Harvester, now exclusively a truck

manufacturer, changes name to Navistar.
Jan 7 Executive order bans trade with, and travel to,

Libya.
Jan 10 Federal Reserve Board applies margin require­

ments to certain "junk" bonds.
Jan 10 McLean Trucking, nation's fifth largest motor

carrier, files for bankruptcy.
Jan 13 Yield on 20-year Treasury bonds (constant

maturity index) rises to 9.86%, high for the year. (See
Aug 29.)
Jan 13 FH LM C begins purchase of second mortgages.
Jan 14 Three-month Treasury bills yield 7.48% (co u ­

pon equivalent), high for the year. (See Oct 8 .)
Jan 15 Union Carbide buys back 55% of its own stock

for $3.3 billion to prevent takeover.

74




1502, low for the year. (See Dec 2.)

Jan 23 Oil prices hit lowest level in 6 years, $18 per

barrel, after 2-month decline. (See Apr 1.)
Jan 27 Global Marine, offshore driller, files for bank­

ruptcy.
Jan 28 Space shuttle Challenger explodes, killing all

seven aboard.
J a n 29 Bank of Japan cuts its discount rate from 5 to
4.5%.
J a n 29 Richard Lyng named to succeed Joh n Block
as Secretary of Agriculture.
Feb 4 Administration's budget for fiscal 1987 shows
deficit of $143.6 billion, just under $144 billion limit set
by Gramm-Rudman.

Feb 6 Economic Report of the President projects 4%
rise in G N P fourth quarter 1985 to fourth quarter 1986,
and 3.8% rise in price deflator. (Both were somewhat
high.)
Economic Perspectives

Feb 7 Federal district court rules key section of
Gramm-Rudman deficit reduction law to be unconsti­
tutional because of authority given to Comptroller
General. (Confirmed by Supreme Court Ju l 7.)
Feb 7 Haitian dictator Jean-C laude Duvalier departs
following unrest.
Feb 7 Wayne Angell and Manuel Johnson sworn in as
members of Federal Reserve Board.
Feb 11 Kodak will cut employment by 10%. (One of
many such announcements by large corporations in
1986.)
Feb 11 United Airlines takes over Pan American's
Pacific routes.
Feb 11 United States Steel buys Texas Oil & Gas for
$3.0 billion in stock.
Feb 12 Farmers Home Administration ends two-year
moratorium on foreclosure of farm loans, telling debtors
to pay up or arrange new loans.
Feb 13 Japan will keep 2.3 million unit quota on auto
exports to the U.S. for year starting Apr 1.
Feb 18 Eastern Airlines agrees to merger with Texas
Air to create the nation's largest airline.
Feb 18 Farm Credit System reports $2.7 billion loss for
1985 because of farm loan write-offs.
Feb 19 Federal Reserve Chairman Paul Volcker testifies
on monetary targets for 1986: M2 and M3, 6-9%; M1,
3-8%. (See Ju l 18.)
Feb 20 Alabama passes interstate banking law. (West
Virginia follows. Mar 17; Minnesota, Mar 19; New
Jersey, Mar 28; Mississippi, Apr 14; Missouri, Apr 30;
Wisconsin, Apr 30; Oklahoma, May 7; Pennsylvania,
Ju n 25; Louisiana, Ju l 2; Texas, Sep 23; California, Sep
26.)
Feb 25 Corazon Aquino becomes Philippine president
after flight of Ferdinand Marcos.
Feb 28 Olaf Palme, Swedish Prime Minister, assassi­
nated in Stockholm.
M ar 3 President's Commission on Organized Crime
calls drug traffic "the most serious problem." (Second
report, Mar 6 , criticizes Teamsters Union.)
M ar 6 Fox Television buys 6 TV stations from
Metromedia for $1.5 billion in cash and stock.
M ar 7 Federal Reserve cuts discount rate from 7.5 to
7%, following similar cuts by Germany and Japan.
Major U.S. banks cut prime rates from 9.5 to 9%. (See
Apr 18, Ju l 10, Aug 20.)
M ar 13 VA mortgage rate reduced from 10.5 to 9.5%,
lowest since 1979. (See Nov 24.)
M ar 16 Reagan appeals for more aid to Contras fighting
Sandinista government in Nicaragua.
M ar 16 French election shifts power to alliance of right
wing parties. The presidency, held by socialist Francois
Mitterrand, is not affected.
M ar 19 Federal Reserve Board issues Regulation D
amendments preserving current treatment of money
market deposit accounts and revising early withdrawal
penalties to distinguish between transaction and time
deposits for reserve requirement purposes.

Federal Reserve Bank of Chicago



M ar 21 Preston Martin, Vice Chairman of Federal Re­
serve Board, resigns, effective Apr 30.
M ar 21 Union Carbide agrees to pay $350 million in
claims resulting from 1984 poison gas disaster in
Bhopal, India.
M ar 24 U.S. Navy sinks two Libyan patrol boats.
M ar 25 Jo h n Deere, largest farm equipment manufac­
turer, will reduce employment further.
M ar 25 Maytag will buy M agic Chef.
M ar 25 Turner Broadcasting buys M GM /UA for $1.3
billion in cash and stock.
M ar 27 Federal Reserve Board implements limitations
on daylight overdrafts.
M ar 27 Regulatory agencies relax capital and loan
write-off standards for troubled agricultural and energy
banks.
M ar 31 Bank One, Columbus, OH, issues first security
backed by credit card receivables.
A p r 1 Regulatory deposit rate ceilings on passbook
savings eliminated, ending 6 -year deposit deregulation
process. (See Ja n 1.)
A p r 1 Public debt of the U.S. surpasses $2 trillion.
A p r 1 Milk Termination Program requires participating
farmers to slaughter or export their dairy herds.
A p r 1 Oil prices drop below $1 0 , low for the year, and
down from $28 in Nov 1985.
A p r 1 Occidental Petroleum buys MidCon gas pipe­
lines for $2 .6 billion in cash and stock.
A p r 9 Caterpillar announces plan to cut costs an addi­
tional 5%.
A p r 10 Halley's comet, awaited 76 years, makes closest
approach to earth, and proves to be a virtual no-show.
A p r 15 U.S. aircraft from bases in the United Kingdom
and Navy carriers bomb Libyan bases.
A p r 16 Kohlberg Kravis Roberts buys Beatrice Cos. for
$6 .2 billion in cash and stock.
A p r 18 Federal Reserve cuts discount rate from 7 to
6.5%. (See Mar 7, Ju l 10, Aug 20.)
A p r 18 Titan rocket, rumored to carry a spy satellite,
blows up shortly after liftoff, repeating failure of previ­
ous launch in Aug 1985.
A p r 19 Bank of Japan cuts its discount rate from 4 to
3.5%.
A p r 21 Dollar falls to 171 Japanese yen, low since
World War II. (Dollar hits 1 52 yen, low for the year, on
Sep 19.)
A p r 21 Consumer price index declined in both Feb and
Mar, first 2-month decline since 1965. (Third consec­
utive drop in Apr was unmatched since 1949.)
A p r 21 Prime rate falls from 9 to 8.5%.
A p r 25 G M A C cuts auto loan rate to 5.9%. (See Aug
28.)
A p r 26 Fire and explosion at Chernobyl U S S R nuclear
power plant spreads radioactive fallout over large area
of central Europe.

75

A pr 29 Cleveland Electric merges with Toledo Edison,
becomes Centerior Energy.
A p r 29 Exxon offers early retirement plan to reduce
employment and cut costs. (One of many such an­
nouncements by large firms.)
A p r 29 Aancor Holdings buys National Gypsum for
$1.6 billion in cash and debentures.
A p r 29 Federal Reserve Board approves merger of
Wells Fargo and Crocker National, creating 10th largest
commercial bank.
M ay 3 Delta rocket, carrying weather satellite, blows
up shortly after lift-off.
M ay 7
Senate
and
House
heavily
reject
Administration-proposed sale of arms to Saudi Arabia.
(Reagan's veto of the resolution was upheld by one
vote in Senate.)

J u l 10 Federal Reserve reduces discount rate from 6.5
to 6 %, lowest since Ja n 1978. Prime rate falls to 8 %.
(See Mar 7, Apr 18, Aug 20.)
J u l 14 Comptroller of the Currency closes First Na­
tional Bank and Trust Co. of Oklahoma City, second
largest U.S. bank failure in history.
J u l 14 Hughes count of operating oil and gas rigs falls
to 663, 43-year low and low for 1986, down from a
peak of 4530 at the end of 1981.
J u l 17 Bank of America reports $640 million loss for
second quarter.
J u l 17 LTV Corp, second largest steel company, files
for bankruptcy, largest industrial company ever to do so.
J u l 18 Federal Reserve releases mid-year HumphreyHawkins report: M2 and M3 targets for 1 986 retained;
M1 above target "acceptable." (See Feb 19.)

M ay 9 Case-IH will close three farm equipment plants.

J u l 26 Southeast suffers extensive drought damage to
crops, livestock, and poultry.

M ay 9 Mobil Oil freezes pay and hiring, plans staff
cuts.

J u l 27 Interstate Commerce Commission rejects
merger of Santa Fe and Southern Pacific railroads.

M ay 19 Federal Reserve Board announces new policy
to deal with large discount window borrowings arising
from computer and other operational problems.

J u l 30 U.S. Treasury announces all its new marketable
securities will be in book entry form.

M ay 21 U.S. and Canada begin negotiations on free
trade agreement.
M ay 27 Supreme Court rejects appeal by Northern
Indiana Public Service Co to recover cost of abandoned
Bailly nuclear power plant.
Ju n 1 A T& T strike begins. (Settled Ju n 26.)
Ju n 5 Dennis Levine, a director of the acquisitions d i­
vision of Drexel Burnham Lambert, pleads guilty to
perjury, tax evasion, and securities fraud, and agrees to
make restitution. Four others plead guilty to similar
charges. (See Nov 14.)
Ju n 6 Capital Cities Communicatons buys A B C broad­
casting for $3.5 billion in cash.

A u g 1 Manuel Johnson confirmed as Vice Chairman of
Federal Reserve Board. (See Feb 7, Mar 21.)
A u g 1 Administration offers to subsidize wheat exports
to U SSR . (Offer was refused.)
A u g 1 United Steelworkers strike U SX, first time since
1959. (No settlement by Dec 31.)
A u g 6 House upholds veto of bill to limit textile and
shoe imports. (But new agreements to limit textile im­
ports are soon negotiated with several Pacific-rim na­
tions.)
A u g 7 LTV Corp announces steel plant shutdowns and
layoffs.
A u g 8 Caterpillar-UAW strike begins.
28.)

(Settled Aug

Ju n 9 General Electric buys R C A for $6.4 billion in
cash.

A u g 12 Northwest Airlines buys Republic Airlines for
$0.9 billion in cash.

Ju n 24 Hunt brothers sue 23 banks, charging a con­
spiracy against the family.

A u g 13 General Motors and Volvo White will combine
heavy truck units.

Ju n 24 Hawkeye Bancorp, Des Moines, and creditors
announce repayment agreement which includes sale of
17 banks.

A u g 16 House-Senate conferees approve draft of Tax
Reform Act of 1986, cutting tax rates and curbing de­
ductions. (Reagan signs bill Oct 22.)

Ju n 27 U.S. trade deficit for May includes first agri­
cultural deficit in 2 0 years.

A u g 19 Robert Heller sworn in as member of Federal
Reserve Board.

Ju n 30 New York Financial Control Board, set up to
monitor New York City finances in 1975, ends its sur­
veillance.

A u g 20 Federal Reserve Board cuts discount rate from
6 to 5.5%, lowest in nine years. (See Mar 7, Apr 18,
Ju l 10.)

Ju n 30 Ralston Purina buys battery division from U n­
ion Carbide for $1.4 billion in cash.

A u g 21 Federal debt ceiling raised from $2.08 trillion
to $2.11 trillion. (See Oct 21.)

J u l 2 Supreme Court upholds affirmative action, 6-3,
rebuffing Administration.

A u g 21 First Commodity Corp of Boston expelled from
futures trading and fined for fraudulent activities at the
Mid-America Commodity Exchange.

J u l 4 100th anniversary of the Statue of Liberty, re­
stored and rebuilt, celebrated in massive ceremonies.
J u l 5 Alcoa strike ends after five weeks with wage
freeze and benefit cuts.
J u l 7 United States Steel changes name to USX.

76



A u g 23 United Auto Workers strike three Jo h n Deere
plants. (On Aug 25, Deere closes 10 additional plants
in response to strike.)
A u g 26 Prime rate falls from 8 to 7.5%, lowest in almost
nine years.

Economic Perspectives

A u g 28 General Motors announces 2.9% auto financing
and rebates to move excess inventories. (Other com­
panies offer similar incentives.)
A ug 29 Yield on 20-year Treasury bonds falls to 7.12%,
lowest since Oct 12, 1973, and low for 1986. (See Jan
13.)
A u g 29 Hunt brothers' Placid Oil Co files for bank­
ruptcy.
Sep 4 Chicago & Northwestern and Illinois Central
railroads will reduce staff.
S ep 7 Whirlpool will close plants in St. Joseph, Ml.
S ep 11 Dow Jones industrial stock average drops a
record 87 points to 1793. (Drop associated with pro­
gram trading in stock futures and options.)

O c t 6 Sales of cars and trucks in model year 1986,
domestic and foreign, totaled record 16 million.
O c t 8 Three-month Treasury bills yield 5.18% (coupon
equivalent), low for the year and lowest since Ju l 1,
1977. (See Ja n 14.)
O c t 12 Iceland summit meeting between Reagan and
Soviet Premier Gorbachev ends with failure to achieve
arms agreement.
O ct 16 U.S. announces 15% tariff on Canadian con­
struction lumber. (Later replaced by Canadian export
tax.)
O c t 16 F D IC plans to sell to the public 30% of its 80%
ownership of Continental Illinois Corp.
O ct 20 IBM and General Motors will pull out of South
Africa. (See Dec 30.)

S e p 11 Government survey projects 2.5% decline in
real capital spending by business for 1986. (See Dec
18.)

O c t 20 Union Pacific buys Overnite Transportation
trucklines for $1.2 billion in cash.

S e p 15 Government data show the U.S. ended 1985 a
net international debtor, first time since 1919.

O c t 21 Federal debt limit temporarily raised to $2.3
trillion through May 1 5, 1987. (See Aug 21.)

S ep 15 Heavy rains and flooding cause extensive crop
losses in Michigan.

O c t 22 Budget Reconciliation Bill for 1987 permits
Farm Credit System to amortize loan writeoffs over 20
years.

S e p 16 Burroughs buys Sperry for $4.8 billion in cash
and stock in largest computer-electronics merger.
(Consolidated company named Unisys.)

O c t 22 General Motors reports operating loss in third
quarter, partly reflecting cost of sales incentives.

S e p 16 Textron buys E X -C E L L - 0 machine tools for
$1.0 billion in cash.

O c t 23 In fiscal 1986, federal outlays totaled $990
billion, revenues $769 billion with record $221 billion
deficit. Farm programs hit record $26 billion.

S e p 17 Senate approves William Rehnquist as Chief
Justice and Antonin Scalia as Associate Ju stice of S u ­
preme Court.

O ct 27 Ahmed Yamani, long-time Saudi Arabian oil
minister, removed from office.

S e p 24 Farmers Home Administration moves to sell part
of its loan portfolio, a first for a government agency.
S e p 25 Federal judge dismisses Senator Joh n
Melcher's suit challenging Federal Reserve Bank
presidents' right to vote on Federal Open Market Com ­
mittee.
S e p 29 Coca-Cola buys bottling companies from J T L
for $1.2 billion in cash.
Se p 29 U.S. journalist Nicholas Daniloff freed in com ­
plex deal with U SS R .
S ep 29 Wieboldt Department Stores files for bank­
ruptcy.
O ct 1 Burroughs to move up to 300 executives from
Michigan to Blue Bell, PA, following Sep 22 merger.

O ct 27 London security markets deregulated in the
“Big Bang."
O ct 29 J . Henry Schroder Bank, subsidiary of Indus­
trial Bank of Japan, buys Aubrey G. Lanston & Co.
O c t 30 St. Louis Globe-Democrat ceases publication.
O ct 31 U SD A reports soybean prices lowest in 10
years, corn lowest in 14 years.
N ov 1 Minimum rate on EE Savings Bonds held five
years falls from 7.5 to 6 %.
Nov 1 Fire at Sw iss warehouse results in spill of
chemicals into the Rhine River causing massive fish kill.
N ov 3 U.S. agricultural exports in fiscal 1986 were
lowest in 9 years, 40% below 1981 peak.

O ct 1 PepsiCo buys Kentucky Fried Chicken for $0.8
billion in cash.

N ov 4 Election transfers control of Senate to the Dem­
ocrats, 55-45. Democratic majority in House increases
to 259-176. G O P gains eight governorships.

O ct 2 Emmett Rice resigns from Federal Reserve Board,
effective Dec 31.

N ov 6 Amoco begins to phase out leaded gas com ­
pletely.

O ct 2 Senate overrides Reagan's veto of bill to impose
economic sanctions on South Africa to protest
apartheid. (Follow s similar House action.)

N ov 6 General Motors plans to close 11 plants in the
Midwest, 7 in Michigan.

O c t 4 Flood waters crest after weeks of heavy rains in
a band stretching from Oklahoma to Michigan.
O c t 6 May Department Stores buys Associated Dry
Goods stores for $2.4 billion in stock.
O ct 6 N H T SA reduces C A FE (corporate average fuel
economy) requirement from 27.5 to 26 miles per gallon
for auto model years 1987 and 1988.

Federal Reserve Bank of Chicago




N ov 6 Immigration Reform Act imposes heavy fines on
those who hire illegal aliens.
N ov 7 Canada imposes duty on "heavily-subsidized"
U.S. corn.
N ov 10 International Paper buys Hammermill Paper for
$1.1 billion in cash.
Nov 11 IBM will close Greencastle, IN, parts distrib­
ution center.

77

IMov 13 Reagan reports on controversial arms sales to
Iran. (On Nov 25 he states he was not fully informed.)
(See Dec 4.)

D ec 16 RepublicBank of Dallas plans to acquire Interfirst of Dallas, to become 11th largest commercial bank.

Nov 14 Ford ends C O LA s for salaried workers, follow ­
ing GM and Chrysler.

D ec 18 Commerce Department survey projects busi­
ness plant and equipment spending in 1987 to equal
1986. (See Sep 11.)

Nov 14 Ivan Boesky pleads guilty to S E C charges of
insider trading, agrees to pay $100 million in fines. (See
Ju n 5.)

D ec 18 A T& T announces $3.2 billion pre-tax charge for
fourth quarter.

Nov 17 Omnibus Water Resources Development Act
increases user fees and cost sharing with state and local
governments.
Nov 18 House Majority Leader Jim Wright predicts
comprehensive trade legislation in 1987.
Nov 20 Japan agrees to limit exports of machine tools
to the U.S. to its 1981 market share.
Nov 24 VA reduces home mortgage rate from 9.5 to 9%,
lowest since June 1 978.
Nov 24 Kohlberg Kravis Roberts buys Safeway Stores
for $4.3 billion in cash, debentures, and stock.
N ov 30 Goodyear halts Sir Jam es Goldsmith's takeover
threat by paying him $619 million for Ifis shares.
D ec 1 U.S. imposes 0.22% "user fee" on all imports.
D ec 1 H. Ross Perot agrees to sell back holdings of
General Motors stock and resigns from its board.
D ec 1 Chesebrough-Pond's agrees to purchase offer
by Unilever N.V. of $3.1 billion.
D ec 2 Dow Jones industrial stock average closes at
1956, high for the year, up 30% from Ja n 22 low. (See
Jan 22.)
D ec 3 MCI Communications will reduce labor force by

15% .

D ec 4 Baxter Travenol, following merger with American
Hospital Supply, will cut 5,000 jobs, mainly in northern
Illinois.
D ec 4 House and Senate create separate committees to
investigate Iran arms deal. (See Nov 13.)

D ec 18 Delta Air Lines buys Western Air Lines for $0.9
billion in cash and stock.
D ec 21 O P EC nations agree to cut oil output and sell
at $18 per barrel.
D ec 22 Interstate Oil Compact Commission (29 states)
urges tariff on crude oil and products.
D ec 22 U SD A reports hog inventory lowest in 10 years,
cattle inventory lowest in 2 2 years.
D ec 23 Fruehauf Holdings, investor group, buys
Fruehauf truck trailers for $1.1 billion in cash and stock.
D ec 23 Federal Appeals Court rules that commercial
paper operation of Bankers Trust is lawful, overruling
district court which held such activities constitute se­
curity underwriting.
D ec 23 Honeywell buys Sperry Aerospace from Unisys
for $1.0 billion in cash.
D ec 24 U A L Inc will buy Hilton International hotel
chain for 1 billion.
D ec 30 Exxon sells Reliance Electric for $1.4 billion in
cash.
D ec 30 Auto sales surge late in 1986 to beat loss of
sales tax deductions and slower depreciation starting
Ja n 1.
D ec 30 New York State Banking Dept rules that statechartered banks can underwrite corporate securities.
D ec 30 Exxon becomes 81st U.S. company to pull out
of South Africa since 1984. (See Oct 20.)

D ec 5 Unisys will close plants in Tennessee and
Wisconsin. (See Sep 16.)

D ec 30 Administration will impose heavy tariffs on EEC
luxury exports if U.S. is not compensated for grain ex­
port loss resulting from Spain and Portugal entering
EEC. (See Jan 1.)

D ec 5 GM 's Electro-Motive Division halts locomotive
output in M cCook, IL, because of low orders.

D ec 31 Campeau buys Allied Stores for $3.5 billion in
cash.

D ec 10 Trade deficit hit record $37.7 billion in third
quarter.
D ec 11 Federal Reserve Bank of New York adds five
U.S. government securities firms, including two owned
by Japanese banks, to its list of primary dealers.
D ec 15 Chemical New York Corp plans to acquire
Texas Commerce Bancshares of Houston for $1.2
billion, to become 4th largest commercial bank.
D ec 15 Henry Wallich resigns from Federal Reserve
Board due to ill health, effective immediately.




D ec 31 Fire at DuPont Plaza Hotel in San Juan, PR,
causes 96 deaths.
D ec 31 Dollar falls to low for year against the German
mark.

NOTE: These events were gathered from many sources
including: Fortune magazine. The Wall Street Journal,
World Almanac, Information Please Almanac and other
newspapers and trade journals.

The m in im u m wage: No m inor m atter
for teens
Donna C. Vandenbrink
“One of the Nation’s most serious and longstanding
problems is providing adequate employment for our
young people. ...The restricted job opportunities for
youth, especially minority youth, due to the minimum
wage have contributed to the growing consensus on the
value of a lower minimum wagefor youths as a means
of expanding their employment.”
Presidential Message to Congress,
May 17, 1984
“ The record does not justify the establishment of a
youth differential [minimum wage].”
Minimum Wage Study Commission,
Report of the Commission, 1980.
Whether teenagers should receive special
treatment under the federal minimum wage
law has been a matter of controversy for some
years. Bills to introduce a special lower mini­
mum wage for teenagers have been proposed
in the last two sessions of Congress. Advocates
contend that the minimum wage has a signif­
icant negative impact on job opportunities of
low-skilled youth. But some research suggests
that the employment gains from a differential
minimum wage might be quite modest. The
1980 Congressional Minimum Wage Study
Commission concluded that a differential of 25
percent less than the adult minimum wage
would likely increase youth employment by at
most 5 percent.
In this paper, I look at the effects of such
special treatment on teenage employment in
the states of the Seventh Federal Reserve Dis­
trict. I find a much larger effect on youth em­
ployment than earlier time-series studies based
on aggregate data. This study analyzes indi­
vidual wages and personal characteristics
rather than the average wages and population
characteristics. Another study using data on
individual adults finds similar results. The re­
search also shows that the positive employment
effect of a lower youth minimum wage is
roughly the same across racial groups and ge­
ographic areas.
Federal Reserve Bank of Chicago




Minimum wage legislation

A federal minimum wage, intended to
ensure all workers a “living wage,” was estab­
lished in 1938 by the Fair Labor Standards Act
(FLSA). The minimum has been raised over
the years from the original level of $0.25 per
hour to $3.35 in 1981. Initially, the federal
minimum covered 43 percent of all nonsupervisory and salaried workers. Today, the cov­
erage rate is over 80 per cent. Currently, the
FLSA exempts low-volume retail establish­
ments, trade and service establishments, sea­
sonal amusement establishments, and certain
other establishments from paying the minimum
wage. There were about 22 million exempt
workers in the private sector in 1980. (This
included 13 million executive, administrative,
and professional workers who already earn well
above the minimum wage.)
The employment effects of
wage regulation

A teenage minimum wage differential is
intended to ease a problem created when gov­
ernment sets a legal minimum on wages. Eco­
nomic theory suggests that a minimum wage
reduces the demand for low-skilled labor.1 In a
competitive market, with no regulation, the
wage a worker is paid reflects the value of his
time in the marketplace. Other things equal,
the more skilled or productive a worker, the
higher the market wage he can command.
When a minimum wage is introduced, it raises
the cost of employing workers whose market
wage is below the legal floor.
Faced with a minimum wage, employers
have several options. They can bring workers
previously paid below the minimum up to the
minimum, offsetting the added cost by reducing
Donna C. Vandenbrink was an economist at the Federal
Reserve Bank of Chicago until July 1986. She thanks
Herbert Baer, Gary Koppenhaver, and Gordon Phillips for
their help.
19

nonwage compensation or requiring greater ef­
fort. Or, they can choose to employ only those
workers whose hourly contribution to output
exceeds the minimum wage. When employers
choose the latter course, the institution of a
minimum wage (or an increase in the level of
the minimum) reduces employment. This out­
come has been substantiated in a number of
empirical studies of the effects of the federal
minimum wage.2
The side effects of minimum wage regu­
lation may be felt particularly by teenagers
who, because of inexperience and lack of skills,
tend to have low market wages. If the mini­
mum wage set by the FLSA were higher than
the market value of most teenage workers, the
regulation would make teens too costly to hire
and thereby foster teenage unemployment. A
survey of empirical research on the minimum
wage concluded that the federal minimum
wage has indeed reduced teenage employment,
in the range of 1 to 3 percent for a 10 percent
increase in the minimum.3
Youth joblessness is of considerable con­
cern to policymakers. Whether induced by the
minimum wage or caused by other factors,
youth joblessness may have long-range conse­
quences for individuals and society. Research
has shown that although early periods of un­
employment are not associated with later re­
curring periods of unemployment, the effect of
lost work experience on a young worker’s wage
level persists as he gets older.4 Furthermore,
teenage joblessness may be associated with
crime and other antisocial behavior.5
Permitting employers to pay teens less
than the adult minimum wage would make
more teenagers more employable. Minimum
wage differentials—lower minimums that apply
to certain types of workers—have been used in
the past. For example, the FLSA permits au­
thorized employers to pay below-minimum
wages to some students and entry-level workers.
Until recently, such differentials were not an
important feature of federal minimum wage
policy. However, since 1972 Congress has
considered a number of proposals for a youth
differential minimum wage. And, while failing
to pass such a broad-based differential, it has
greatly expanded the full-time student submin­
imum program.6
Proponents of a minimum wage differen­
tial for youth believe that by increasing teenage
employment such a policy would encourage the
20



development of positive work attitudes and the
accumulation of job-related skills among youth.
Critics of a differential object to singling out
teenagers for special treatment. A minimum
wage, they point out, makes employment of
any low-skilled worker less attractive, regard­
less of age. According to Linneman (1982) al­
most 10 percent of the U.S. adult population
did not have the characteristics to earn a wage
above the minimum wage in 1974. Moreover,
a subminimum wage for teenagers would en­
courage employers to substitute the cheaper
teens for very low-skilled adult workers, in­
creasing the unemployment problem in the
adult population. These important issues are
beyond the scope of this study.
Overview

The purpose of this study is to estimate
the effect of a special minimum wage for teen­
agers on the level of teenage employment in the
Seventh Federal Reserve District. Two alter­
native youth minimum wages are
analyzed—one 25 percent below and one 15
percent below the adult minimum wage level.
These translate into teenage minimum wage
levels of $2.33 and $2.64, respectively, given
the adult minimum wage of $3.10 in 1980, the
year for which employment estimates are
made.
A lower minimum wage for youth is ex­
pected to increase teenage employment, but the
size of the increase depends on the distribution
of market wages among teenagers and on indi­
vidual teens’ employment decisions. The wage
distribution indicates how many teens have
market wages between the existing adult mini­
mum and the new youth minimum, and hence,
how many teens would be available for hire as
the legal minimum wage is lowered. However,
not all of these teens would be willing to work
even when employers were permitted to offer
them their market wage. For some, employ­
ment at their market wage is not as attractive
as alternative uses of their time. How many
teens would choose to work depends on each
individual’s employment decision.
This study develops expected market
wage distributions specifically for the popu­
lation of teenagers in the Seventh District
States and predicts aggregate employment us­
ing individual survey data. If there is signif­
icant individual variance in the distribution of
Economic Perspectives

wages or in employment rates, then this ap­
proach will be more accurate in measuring the
total change in employment than one based on
aggregate data and population averages.

a teenage minimum at $2.64, slightly under
one-third of teens would remain in the belowminimum group. Under this scenario only 16
percent would become newly eligible for em­
ployment.

The distribution o f market wages

As a starting point, it is useful to look at
the distribution of wages for teenagers in the
Seventh Federal Reserve District. The coeffi­
cients of a wage equation estimated on data
from a national survey of youth (see Box), to­
gether with socio-demographic data for indi­
vidual teenagers from the Public-Use Micro
Samples (PUMS) of the 1980 Census of Popu­
lation, were used to calculate expected market
wages for individual teenagers in each of the
five District states. The resulting wage distri­
bution is shown in Figure 1. According to this
measure, just under half of the teens in the five
states could expect market wages below the
1980 federal minimum wage level of $3.10.
The characteristics of the teens in the
District with expected wages above the $3.10
minimum differ considerably from those of the
teens with expected wages below the minimum.
The above-minimum teens are older, averaging
just under 18 years of age. They have about
one and one-half more years of education.
Only slightly more than one-fourth of the
above-minimum group is female, but young
women comprise over three-fourths of the
below-minimum group. Overwhelmingly, the
below-minimum group is still enrolled in
school. All five states exhibit similar aboveand below- minimum differences by race, mar­
ital status, and motherhood status, but the av­
erage level of these characteristics differs among
the states.
A youth differential would have its great­
est effect on teens whose expected wage was
between the youth minimum and the current
minimum. In order to estimate the size of this
group, I compared the proportion of teens
having expected wages under $3.10 with the
proportions below the alternative minimum
wage levels of $2.33 and $2.64.7
These proportions are given in Table 1.
According to the table, setting a teen minimum
at $2.33 would reduce the proportion of teens
below the minimum from approximately 47
percent to about 20 percent. Under this sce­
nario, roughly 27 percent of teenagers would
become newly eligible for employment. With
Federal Reserve Bank of Chicago




Employment probabilities

In order to measure the employment ef­
fect of lowering the minimum wage we also
need to understand what determines whether
a teen will decide to work if he is given the
opportunity to earn his market wage. After
we have developed a model of the probability
of employment given an expected market wage
and the level of the minimum wage, we can
calculate how the rate of teenage employment
would change under different minimum wage
levels.
The employment decision

An individual will choose to work if the
value of his wage income exceeds the value of
time spent in school, homemaking, or other
activities. But, when a minimum wage is in
place, some individuals—those with market
wages below the minimum—will not be able to
work even if they choose to. So whether or not
a person works depends not simply on his
market wage, but on the relation of the market
wage to the level of the minimum wage.
Thus, in a teenager’s employment deci­
sion, the probability of his employment de­
pends on opportunities for work in the locale
where he resides, the nonmarket activities he
engages in, and the probability of his market
wage lying below the minimum wage. This
latter probability captures the effect of the
minimum wage on his ability to find work as
well as the effect of his market wage on his de­
cision to seek work. It varies with individual
characteristics as well as with the level of the
minimum wage.
Table 2 shows the specific variables used
to predict employment along with the coeffi­
cients generated by the analysis. The sample
was composed of all 16-to-19-year-olds in the
five District states. Conventional statistical
techniques are not appropriate for predicting
“yes/no” decisions. The employment decision
is an example of this, since people either have
a job or they don’t. A special statistical tech­
nique known as probit analysis was used to
21

The wage equation

The distribution of wages for teen­
agers in the Seventh District used in this
analysis was based on imputed hourly
wage rates. I describe here the rationale
for that imputation and the details of its
derivation.
Gaps in employment information
make hourly wage rates constructed from
the Census of Population unreliable.
Rather than use the Census data to mea­
sure wage rates, we derived wage rates for
the teenagers in the Seventh District cen­
sus samples by estimating a wage equation
on another data set and applying those
coefficients to the personal characteristics
reported in the Census samples. The other
source of data was the 1980 interview
wave of the Second Youth Cohort of the
National Longitudinal Survey (NLS).
The questions on this survey were designed
specially to provide information on the la­
bor market behavior of youth in the gen­
eral population.*
The wage rate of interest is one
which indicates the true market value of
an individual worker. However, mini­
mum wage regulation may distort actual
observed wages from this true market
wage when it does not cover all workers in
the economy. If workers excluded from
employment in the covered sector seek
work in the uncovered sector, the addi­
tional supply of labor will push wages
there below their value in an unregulated
labor market. As a practical matter, this
means that when a teenager on the NLS
survey reports a wage less than the federal
minimum, we cannot be sure what his true
market wage would be. On the other
hand, actual wages which are higher than
the federal minimum should be relatively
unaffected by the regulation and should
indeed represent unconstrained market
wages. Consequently, I used only those
NLS observations with reported wages
above $3.10 (the federal adult minimum

22




wage in 1980) as the sample for the wage
equation.
Having thus excluded from the wage
equation teens who were not employed
and those who were employed but with
below-minimum wages, I needed to adopt
an appropriate estimating technique.
Standard ordinary least squares (OLS) es­
timates of the wage equation would be bi­
ased if the chance of inclusion in the
sample (here, the chance of employment
at an above-minimum wage) were system­
atically correlated with the personal char­
acteristics that determine the market
wage. I adopted Heckman’s solution,
controlling for the potential bias by adding
to the wage equation a variable (lambda)
whose value depends on the probability of
being included in the sample (here, the
probability of being employed at a wage
above the minimum).**
Table A reports the estimates of the
wage equation. The explanatory power
of the equation is reasonable, with an R2
of .15. The wage structure is consistent
with our expectations about the relation­
ship of various personal characteristics and
their market value. More education,
greater age (and presumably more experi­
ence), and married family status all garner
higher wages while being female, being
black, and being enrolled in school reduce
an individual’s wage, other things equal.
The second column of Table A shows
the OLS estimates of the wage equation
without controlling for the probability of
employment with a higher than minimum
wage. Comparing these results with those
in the first column we can see that includ­
ing lambda in the wage equation shifts the
intercept without having much effect on
the coefficients of the other variables. This
suggests that average wage levels differ
with the probability of employment, al­
though the slope of the wage structure

Economic Perspectives

T a b le B
C o m p a riso n o f a ctu a l and p red icted
w a g e d istrib u tio n
N L S sam p le

T a b le A
W a g e and e m p lo ym en t a n a ly sis
o f N L S y o u th sam p le
Adjusted
0 LS wage

O LS
wage

Intercept

0.52
(.323)

0.77
(.059)

Highest grade

0.02
(.005)

0.02
(.003)

Employment
probit
-8 .6 8
(1.103)
-

Education
9-12 years

-

-

0.49
(.058)

Education
over 12

-

-

0.60
(073)

Female

-0 .1 8
(0 2 1 )

-0 .17
(0 1 1 )

-0 .29
(.028)

Age

0.04
(.008)

0.03
(.004)

0.75
(.117)

Age 2

-

-

-0 .0 2
(.003)

Enrolled

-0.11
(.039)

-0 .8 0
(01 3 )

-0.57
(.036)

Black

-0 .0 5
(.025)

-0 .0 3
(.013)

-0 .37
(.033)

Married

0.02
(.026)

0.04
(0.17)

-0.31
(.050)

Lambda

0.24
(3 1 6 )

-

-

F

91,061

106.121

-

.1491/.1475

.1475

-

fl2/*2
log likelihood
ratio

-5477.99

-

n

3645

3645

Standard error

.3553

.3094

9819

with respect to personal characteristics
does not. In any case, this NLS youth
sample does not suffer from conventional
selection bias, since the coefficient on
lambda is not significant.
Table B compares the distribution of
wages actually reported on the NLS with
the distribution constructed from the pa­
rameters of the wage equation in Table
A. The predicted distribution is con­
structed by taking into account not only

Federal Reserve Bank ot Chicago




Actual
(workers only)
$0.01 - 2.32

8.9%

Predicted
(workers
+ nonworkers)
15.9%

2.33 - 3.09
3.10 - 4.11

14.0
43.7

23.2

4.12 - 5.00
5.01 - 8.00
8.01 and over

12.5

27.9
15.4

16.3
4.6

15.9
1.7

each individual’s expected wage level de­
rived from the wage equation but also the
variance in this predicted value. (The
variance arises because a person’s wage is
influenced by many unobserved factors
and by variables not included in the wage
equation.) The actual distribution in Ta­
ble B is more concentrated above $3.10
than the predicted distribution. Of course,
this is as it should be since the minimum
wage law prohibits many employers from
paying wages under $3.10.*
♦ The NLS Youth Cohort is a sample of 5,700 young
men and 5,700 young women who were interviewed
annually between 1979 and 1984. At the time of the
1980 interview they ranged in age from 15 to 23 years
old.
**The value of lambda is computed from a probit
estimate of employment status. The employment
states were: “employed with a wage higher than the
minimum wage” and “other.” The results of this
probit are shown in the third column of Table A.
This lambda differs slightly from the conventional
“Heckman lambda” which controls only for potential
bias due to censoring the sample by employment
probability. These results also are consistent with
expectations. Being female, enrolled in school, black,
or married makes one less likely to be employed.
Greater age increases the probability of employment,
with a diminishing effect as one gets older (agesquared is negative). Individuals with a high school
education are more likely to be employed than those
completing eighth grade or less (the omitted category)
and those with education beyond high school are even
jytpre likely to be employed.

23

Figure 1
D istrib u tio n o f e xp e cte d m arket w a g e s o f 15-19
year o ld s in S e v e n th D is tr ic t sta te s

T a b le 1
E ffe c t o f a lte rn a tive m in im u m w a g e s
on the p ro p o rtio n o f te e n a g e rs w ith b elo w m inim um w a g e s

16 %

Proportion of teens
with wages below
$3.10
$2.33
$2.64

market wage ranges

predict employment status. The dependent
variable is individual employment status. The
variable PROBSUB is the probability that the
individual’s expected wage is below the mini­
mum wage of $3.10. The other explanatory
variables include four state dummies, two indi­
cators of local labor market opportunities—a
local unemployment rate for teens and local per
capita income—and three measures of nonmar­
ket alternatives—school enrollment and marital
and motherhood status.
According to the coefficients on the state
dummies, the average probability of employ­
ment, other things equal, is higher in Indiana,
Iowa, Michigan, and Wisconsin than in Illinois
(the omitted category). The difference between
Illinois and Indiana is not statistically signif­
icant, however. Teens living in counties with
a higher per capita income are more likely to
be employed while those in areas with a higher
proportion of unemployed teens are themselves
less likely to be employed. Being enrolled in
school, being married, or being a mother all
reduce the probability of being employed, al­
though the effect of marital status is not statis­
tically significant. As expected, the higher the
probability of having a market wage below the
minimum wage, the lower the probability of
being employed.8
Figure 2 demonstrates the relative im­
portance of each of the independent variables
by showing the change in employment proba24




47.40%
20.80
31.36

bility that results from a 10 percent increase in
the mean value of each explanatory variable.
In these terms, the below-minimum
status—PROBSUB—is the most important de­
terminant of employment probability.
Figure 3 illustrates how changes in the
probability of earning a subminimum wage af­
fect the probability of employment. As the
figure shows, lowering the minimum wage has
its biggest impact on individuals who already
have a 50-50 chance of being employed. The
impact on individuals with extremely high or
extremely low probabilities of employment will
be much smaller.
Increase in employment with
teenage differential

We can calculate the effect of a youth
minimum wage on teenage employment in the
Seventh District by combining our under­
standing of the determinants of individuals’
market wages with our analysis of the determi­
nants of employment. From the wage equation
we can determine the probability of a teen’s
T a b le 2
E m p lo ym en t eq u a tio n fo r 7G S ta te s
Probit
coefficient
Intercept
Indiana
Iowa
Michigan
Wisconsin
Enrolled
Married
Mother
Teen unemployment
rate
PROBSUB
Per capita income
log likelihood ratio
n

Standard errors
per probit

0.8316145
0.00579456
0.04991053
0.12036113
0.09386675

.037
.012
.014
.010
.012

-0.36047791
-.0.00818144
-0.72281929

.010
.018
.021

-0.03103838
-1.37434363
.02998966
-83,903.2
129,623

.001
.032
.003

Economic Perspectives

market wage lying below the alternative mini­
mum wages of $2.33 and $2.64.
These new values of PROBSUB can be
used to recompute each individual’s probability
of employment using the employment model
from Table 2. These in turn can be used to
generate an aggregate employment rate for
teenagers. Comparing these new employment
rates with the baseline rate gives the effect of
the new policy.
Table 3 shows the employment rates cal­
culated in this way for the five states of the
Seventh District. The expected baseline em­
ployment rates with the minimum at $3.10
range from 39.3 per cent in Indiana to 46.7
percent in Wisconsin. Under a $2.33 minimum
wage, estimated teenage employment rates
stand above 50 percent in all five states, and
with a $2.64 minimum, estimated employment
rates range between 47 and 55 percent.
These predicted employment rates suggest
that reducing the minimum wage by 25 percent
(to $2.33) would raise the teenage employment
rate by fourteen percentage points. In the
District states this would translate into a 30 to
36 percent increase. Lowering the minimum
by 15 percent would increase employment by
18 to 21 percent. By comparison, the Mini­
mum Wage Study Commission determined
from a review of previous research that we
might expect a 2.5 to 5 percent increase in
teenage employment for a 25 percent youth
minimum wage differential.
Figure 2
C h a n g e in p ro b a b ility o f e m p lo ym en t w ith a 10
p e rce n t in cre a se in in d e p e n d en t v a ria b le s
3

2

1

percent
-0 +

1

2

Figure 3
W hen the p ro b a b ility o f h a vin g a su b m in im u m
w a g e d e cre a se s by 10 p e rce n ta g e p o in ts, the
p ro b a b ility o f a c tu a lly h a vin g a jo b in cre a se s.
Th e e ffe c t is g re a te st w hen th e p ro b a b ility of
em p lo ym en t is n early even

change in probability of employment (percent)

6 [~

initial probability of employment (percent)

Some insight into the greater employment
responsiveness of our results can be gained by
looking back at Figure 3. In that figure, which
is based on our employment probability model,
reductions in the minimum wage have their
greatest effect on employment when initial em­
ployment rates are between 25 and 50 percent.
This is exactly the range of teen employment
rates obtained for the District states under the
baseline, $3.10, minimum wage assumption
(see Table 3).
Our estimates of the responsiveness of
employment to changes in the minimum wage
are consistent with the results of one other
study. Linneman investigated changes in adult
employment following the 1974 increase in the
minimum wage from $1.60 to $2.00. He cal­
culated employment rates of 64 percent and 51
T a b le 3
E xp e cte d e m p lo ym en t rates am ong
D is tr ic t te e n a g e rs under
a lte rn a tive m in im u m w a g e levels
Percent
employed
with a
minimum
wage of: Illinois Indiana
$3.10

Federal Reserve Bank ol Chicago




Iowa

Michigan

Wisconsin

46.6

39.6

46.7

42.6

39.3

$2.64

50.8

47.4

55.1

47.8

55.1

$2.33

56.4

53.0

60.6

53.3

60.6

25

Ta b le 4
P re d icte d in cre a se in te e n a g e e m p lo ym en t under a lte rn a tive m in im u m w a g e d iffe re n tia ls
By age, sex, race, employment status, and location for Seventh District states
(Percentage point difference from predicted employment rate with $3.10 minimum wage)
Change in employment rate
when minimum lowered to:

Illinois
$2.33
$2.64

Indiana
$2.33 $2.64

Iowa
$2.33 $2.64

Michigan
$2.33 $2.64

Wisconsin
$2.33 $2.64

AM 15 to 19 yr. olds

13.8

8.2

13.7

8.1

14.0

8.5

13.7

8.2

13.9

8.4

By age
15 yr.
16 yr.
17 yr.
18 yr.
19 yr.

15.5
15.3
14.5
12.7
11.0

8.7
8.9
8.7
7.9
7.1

15.2
15.1
14.3
12.8
11.1

8.5
8.7
8.6
7.9
7.1

16.0
15.6
14.8
13.0
10.8

9.1
9.2
9.0
8.1
6.9

15.1
14.9
14.3
12.7
11.2

8.4
8.6
8.6
7.9
7.2

16.0
15.6
14.6
12.8
10.7

9.1
9.2
8.8
8.0
7.0

By sex
Males
Females

12.6
15.0

7.9
8.6

12.6
14.7

7.9
8.5

12.6
15.4

7.9
8.9

12.6
14.6

7.8
8.4

12.5
15.3

7.9
8.8

By race
Black
Other

13.9
13.7

8.1
8.3

13.6
13.7

7.8
8.2

14.8
14.0

8.7
8.5

13.3
13.7

7.7
8.2

14.5
13.9

8.5
8.4

By employment
Unemployed
Not in labor force

12.3
14.2

7.7
8.5

12.2
14.1

7.6
8.4

11.9
14.4

7.5
8.1

12.3
13.9

7.6
8.3

12.0
14.4

7.5
8.7

By location
Center city
SMSA outside center city
Non SMSA

13.8
13.8
13.6

8.2
8.3
8.1

13.0
13.9
13.5

7.6
8.3
8.0

13.9
13.8
14.1

8.4
8.4
8.5

15.0
13.9
13.5

9.6
8.4
8.0

16.6
14.0
13.9

8.3
8.4
8.4

olds
olds
olds
olds
olds

percent before and after the change in policy,
respectively, for those adults who had belowminimum wages in 1974. In other words,
Linneman found that the 25 percent increase
in the minimum wage resulted in a thirteen
percentage point decline in the employment
rate for this group. This result is quite close to
our own estimate of a fourteen percentage
point change for teenagers. Significantly,
Linneman’s work, like ours, is based on the
analysis of data on individuals, not on aggre­
gate employment statistics such as were used in
most other studies.

younger than for older youth, for females than
for males, and for those currently not in the
labor force than for the unemployed.
Noteworthy is the fact that the increment
to the employment rate for nonblacks is as
large as it is for blacks. Also, the gain in the
employment rate of teens living in suburban
areas is on a par with that of center city teens.
Thus, a youth differential would not appear to
benefit primarily blacks or primarily center city
youth. Its benefits would be felt across all ra­
cial groups and geographic areas.
Conclusion

The distribution of employment benefits

By using the employment equation in
Table 2 to predict unemployment rates for dif­
ferent demographic groups, we can get a better
idea of who will benefit most from a lowering
of the teenage minimum wage. Table 4 shows
the percentage point increase over the pre­
dicted baseline employment rate by age, sex,
race, current employment status, and residen­
tial location.
Lowering the minimum wage generates
larger increases in employment rates for
26



This study used survey data on individual
teenagers to investigate the effect of a youth
minimum wage differential on teenage em­
ployment in the Seventh Federal Reserve Dis­
trict. The study found that allowing employers
to pay teenagers a minimum wage 25 percent
below the adult level would likely increase
teenage employment rates by about one third.
This is a substantially greater increase in youth
employment than many observers, including
the Minimum Wage Study Commission, have
predicted. This study also showed that the
Economic Perspectives

youth differential would draw new teen workers
from outside the labor force as well as from the
unemployed, from all racial groups, and from
all geographic locations. Thus, a youth differ­
ential minimum wage should not be considered
a job program for the inner city, minority,
hardcore-unemployed youth. Rather, it would
be a broadbased youth employment program.
^ee George J. Stigler (1948) for the classic analysis
of the economic impact of minimum wage legis­
lation.
2See Brown, Gilroy and Kohen, (1982), for a review
of this literature.
3Brown, Gilroy and Kohen (1982), p. 505.
4 See Ellwood (1982) and Meyer and Wise (1982).
5See Albert Rees (1986) for discussion of the prob­
lem of youth joblessness and public policy.
bBefore 1975 student employment under the pro­
gram never exceeded 79,000 but it has fluctuated
between 250,000 and 500,000 thousand annually
since the changes initiated in 1974. Richard B.
Freeman, Wayne Gray and Casey E. Ichniowski.
“Low-Cost Student Labor: The Use and Effects of
the Subminimum Wage Provisions for Full-Time
Students,” Vol. 5. Minimum Wage Study Com­
mission. 1981, Table 3.
71 first calculate the probability that each
individual’s expected market wage is belowminimum under the three assumptions about the
minimum wage level. The mean of this probability
for each state sample indicates the expected pro­
portion of teens in the state with a wage below the
assumed minimum.
8The following table shows the results of ordinary
least squares (OLS) estimates of an employment
status equation similar to the one in Table 2. The
regression on the left includes the variable

PROBSUB, while the one on the right does not.
Comparing these results, we can see that including
the probability of a below-minimum wage increases
the explanatory power of the model.
O LS employment results

Model 1
Model 2
Standard
Standard
Coefficients errors
Coefficients errors
.014
.801869
.014
.685833
.000115832
.005
.001 94259
.005
.01 6953" .005
.019478* .005
.050854* .004
.043907* .004
.034730* 004
035551 * .004
-.225064* .003 -0.138086* .004
.007 -.0077382
.001073928
.007
-0.308383* .007
-.252184* .007
-0.012438* .000
-.011211* .000

Intercept
Indiana
Iowa
Michigan
Wisconsin
Enrolled
Married
Mother
Teen
unemployment
rate
PROBSUB
Per capita
.008595258*
income
129,623
"r 2
.0620
F
952.2
"Significant at 1%.
—

.001

-0.512270*
.011697*
129,623
.0756
1061.0

.012
.001

However, even the OLS version of the model
with PROBSUB accounts for less than 8 percent of
the variation in employment among the sample of
Seventh District teens. The remaining variation
must be explained by other factors not included in
the model and their influence on individual em­
ployment decisions. (One of the factors omitted
from the employment model is the existence of
programs, like the full-time student certification
program, which do permit some employers to pay
below-minimum wages.) Since the employment
model accounts for only a small percentage of
variation in employment, it does not predict accu­
rately whether a particular individual will be em­
ployed. But, since the coefficient on PROBSUB is
significant, as long as factors omitted from the
model are not correlated with PROBSUB, the
model captures fully the effect of a change in the
probability of below-minimum market wages on
the probability of employment.

References

Brown, Charles, Curtis Gilroy, and Andrew Kohen.
“The Effect of the Minimum Wage on Em­
ployment and Unemployment.” Journal of
Economic Literature. Vol. 22 (June 1982): pp.
487-528.
Center for Human Resource Research. The Na­
tional Longitudinal Surveys Handbook.
Columbus, Ohio: Ohio State University.
Revised 1983.
Cotterill, Philip. “Differential Legal Minimum
Wages.” In Simon Rottenberg, ed. The Eco­
Federal Reserve Bank of Chicago




nomics of Legal Minimum Wages. Washington,
D.C.: American Enterprise Institute, 1981.
Ehrenberg, Ronald G. and Alan J. Marcus.
“Minimum Wages and Teenagers’
Enrollment-Employment Outcomes: A
multinomial Logit Model.” Journal of Human
Resources Vol. 17 (1982): pp. 39-58.
Ehrenberg, Ronald G. and Robert S. Smith.
Modem Labor Economics: Theory and Public Pol­
icy. Glenview, IL: Scott, Foresman and Co.,
1985. 2 ed.
27

Ellwood, David T. “Teenager Unemployment
Permanent Scars or Temporary Blemishes?”
In Richard B. Freeman and David A. Wise,
eds., The Youth Labor Market Problem: Its Na­
ture, Causes, and Consequences. Chicago: Uni­
versity of Chicago Press, 1982.
Freeman, Richard B., Wayne Gray, and Casey E.
Ichniowski. “Low-Cost Student Labor: The
Use and Effects of the Subminimum Wage
Provisions for Full-Time Students,” Effects of
the Minimum Wage on Employment and Unem­
ployment. Vol. 5. Washington, D.C.: Mini­
mum Wage Study Commission, 1981.
Gilroy, Curtis. “A Demographic Profile of Mini­
mum Wage Workers,” Evolution of the FLSA
and A Profile of Minimum Wage Workers Vol.
2. Washington, D.C.: Minimum Wage Study
Commission, 1981.
Greene, William H. “Sample Selection Bias as a
Specification Error: Comment.” Econometrica.
Vol. 49 (May 1981): pp.795-798.
Heckman, James J. “Sample Selection Bias as a
Specification Error.” Econometrica. Vol. 47
(January 1979): pp. 153-161.
Hoffman, Saul D. and Charles R. Link. “Selectiv­
ity Bias in Male Wage Equations: BlackWhite Comparisons.” Review of Economics and
Statistics. Vol. 66 (May 1984): pp. 320-324.
Linneman, Peter. “The Economic Impacts of
Minimum Wage Laws: A New Look at an

28




Old Question.” Journal of Political Economy.
Vol. 90 (1982): pp. 443-469.
Meyer, Robert H. and David A. Wise. “The Ef­
fects of the Minimum Wage on the Employ­
ment and Earnings of Youth.” Journal of
Labor Economics. Vol. 1 (1983): pp. 66-100.
Meyer, Robert H. and David A. Wise. “High
School Preparation and Early Labor Force
Experience.” In Richard B. Freeman and
David A. Wise, eds. The Youth Labor Market
Problem: Its Nature, Causes, and Consequences.
Chicago: University of Chicago Press, 1982.
Rees, Albert. “An Essay on Youth Joblessness,”
Journal of Economic Literature, Vol. 24 (June
1986): pp. 613-628.
Stigler, George J. “The Economics of Minimum
Wage Legislation.” American Economic Review,
Vol. 36 (June 1946): pp. 358-367.
Quester, Aline O. “State Minimum Wage Laws,
1950-1980.” Evolution of the FLSA and A Profile
of Minimum Wage Workers. Vol. 2
Washington, D.C. Minimum Wage Study
Commission, 1981.
Welch, Finis and James Cunningham. “Effects of
Minimum Wages on the Level and Age
Composition of Youth Employment.” Review
of Economics and Statistics Vol. 60 (February
1978): pp. 140-145.
“Youth Subminimum Wage Proposal”. Congres­
sional Digest. Vol. 64 (April 1985).

Economic Perspectives

Technical correction: The inflation-adjusted
index o f the dollar
The article, “The international value of
the dollar: An inflation-adjusted index” in the
January/February 1987 issue of Economic Per­
spectives1 contained an error in the formulation
of the equation that specified the inflationadjusted aggregate exchange rate of the dollar.
As formula (1) is specified on page 21, the rel­
ative prices term is inverted. The correction is
as follows: The ratio of the CPI for the United
States to the CPI for country i is used to
measure the relative movement of prices in the
United States as compared with the movement
of prices in country i. The corrected equation
(1) for the calculation of the Chicago real
trade-weighted dollar follows:
16

i= l

where
7-Grt = the Chicago real trade-weighted dollar
in quarter t.
An equivalent formulation for applying
the deflator to nominal exchange rates is:
16

i= l

In the example at the bottom of page 19
the real DM/$ exchange rate in q should be
2.47 DM/S with the real appreciation between
t0 and q being 23.4 percent as compared with
the 20 percent nominal appreciation. The
third sentence of the final paragraph of that
example should read: “However, the dollar
cost in terms of the claim on U.S. real resources
necessary to acquire that product at a real ex­
change rate of 2.47 mark/dollar would be
$42.51—less than in time t0 and less than indi­
cated by the nominal exchange rate. The rel­
ative increase in U.S. prices contributed to a
boost in the real appreciation of the dollar
above that of the nominal appreciation.”
Recall that during the period of inquiry,
1971-1986, U.S. prices relative to price trends in
the countries included in the 7-G indexes per­
federal Reserve Bank of Chicago




formed as follows: During 1971-1977, U.S.
prices declined; during 1978-1980, U.S. prices
increased; during 1981-1983, U.S. prices de­
clined; and during 1984-1986 U.S. prices re­
mained stable. The relative price movements
in conjunction with nominal dollar exchange
rate trends exerted the following modifying in­
fluences during the 16-year period under study:
(1) During 1971-1977 the nominal value
of the dollar declined. Falling relative
U.S. prices exacerbated the decline.
Thus, the relative-price adjusted ex­
change rate declined more than the
nominal exchange rate. The real com­
petitive position of the dollar improved
more during that period than is reflected
by a nominal measure of the aggregate
value of the dollar.
(2) During 1978-1980 the nominal value
of the dollar continued to decline.
However, the trend in U.S. relative
prices turned upward in 1978. As a re­
sult, the impact of U.S. inflation began
to offset the continued decline in the
nominal aggregate value of the dollar.
Consequently, in real terms the compet­
itive position of the dollar began to de­
teriorate in the first-quarter of 1979.
This was well before the turn-around
indicated by the nominal indexes, which
indicate that the competitive position of
the dollar began to deteriorate in the
fourth quarter of 1980.
(3) During 1981 to mid-1983 the nomi­
nal value of the dollar increased. U.S.
relative prices declined. Thus, the real
deterioration in the dollar’s competitive
position during this period was some­
what less than indicated by the nominal
index.
(4) From mid-1983 into 1986 there was
virtually no change in U.S. relative
prices, consequently, during this period
the nominal aggregate index is a near
29

perfect proxy for the relative-price ad­
justed index.
Due to the correction in the relative price
term we will restate, with some modification,
our initial conclusions.
First, the trend of the 7-Gr real dollar in­
dex, as corrected here, was negative during the
1970s (rather than positive as initially re­
ported). (The 7-Gr index, of course, remained
unchanged.) Indeed, because U.S. prices were
declining during this period, relative to other
index-country prices, the real dollar index de­
clined more (the dollar became more compet­
itive in international markets) than the
nominal index.
However, the turn-around in the trend,
and thus the beginning of the deterioration in
the competitive position of the dollar, com­
menced substantially earlier than indicated by
nominal indexes. The 7-Gr index, as corrected
here, “bottomed out” in the fourth quarter of
1978 and had increased 8 percent by the time
the nominal indexes—the 7-Gn and the
FRB-TWD—reached their low points in the
third quarter of 1980.
Second, the initial formulation of the real
index indicated a somewhat surprising conver­
gence of the index level during three periods
of current account balance—1971, 1974, and
1979-1980. Evidence of such a convergence
Th e 7 -G r and th e F R B tra d e -w e ig h te d
d o lla r in d exes

disappears in the corrected index. Index levels
during 1971, when fixed exchange rates re­
mained in force during much of the year, were
far outside the index range in 1974 and
1979-1980 (this is also true for the nominal in­
dexes). Indeed, this is less surprising than was
the initial finding, given the apparent overval­
uation of the dollar that led to its devaluation
at the end of 1971, again in 1973, and the
subsequent floating of the dollar. Evidence of
convergence is also much weaker in the latter
two periods. While the range of the 7-Gr index
as corrected here (98.0 for the year 1974 and
90.4 for the two years 1979-1980) remains nar­
rower than the range observed for the
FRB-TWD we do not consider this to be an
especially interesting property of the index.
Third, the conclusion concerning the
magnitude in the decline of the dollar and its
longer term “recovery ratio” remains fully
supported, indeed, is strengthened. The de­
cline in the dollar since early 1985, as measured
by the more broadly based 7-G nominal and
price-adjusted indexes, has been more moder­
ate than suggested by the more narrowly based
indexes such as the FRB-TWD. Both 7-G in­
dexes indicated a depreciation of about 23.5
percent between the first quarter of 1985 and
the third quarter of 1986. By comparison, both
the FRB-TWD index and a FRB-TWD index
modified to account for relative price changes
indicated a depreciation of about 37 percent.
However, placed in a longer term per­
spective the recovery ratio, which sets the

index*

7 -G r real tra d e -w e ig h te d dollar*

*The 7-Gr index is constructed to have a common base with
the Federal Reserve Board's trade-weighted dollar as of the
first quarter of 1973, which equals 104.8.

30




Year

Q1

Q2

Q3

Q4

1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986

120.9
110.9
104.8
100.0
95.0
97.0
95.9
91.4
89.3
93.3
95.1
103.4
106.9
112.2
125.9
108.4

120.2
109.5
100.4
95.9
94.6
96.7
95.4
91.1
90.9
93.4
99.7
106.2
108.7
113.4
123.3
103.2

118.8
108.7
97.9
98.2
97.9
96.4
95.0
88.2
90.4
90.7
104.9
110.3
111.8
118.9
119.1
99.7

114.6
108.7
99.2
97.8
98.0
95.8
93.4
87.9
92.3
92.5
101.2
110.3
111.7
120.9
113.1
—

Annual
average
118.6
109.5
100.6
98.0
96.4
96.5
94.9
89.7
90.7
92.5
100.2
107.6
109.8
116.4
120.4
—

'The 7-Gr index is constructed to have a common base with
the Federal Reserve Board's trade-weighted dollar as of the
first quarter of 1973.

Economic Perspectives

magnitude of the dollar’s depreciation since the
first quarter of 1985 in relation to the magni­
tude of the dollar’s appreciation (since the late
1970s when the real indexes turned up and
1980 when the nominal indexes turned up),
indicates that the decline in the dollar relative
to its earlier appreciation for the nominal and
real comparisons and for the 7-G and FRB
comparisons are virtually identical. As of the
third quarter of 1986 the recovery ratios for the
corrected 7-Gr index and the relative-price ad­
justed formulation of the FRB-TWD were 0.69

Federal Reserve Bank of Chicago




and 0.70, respectively. The recovery ratios for
the nominal indexes were nearly identical to
those of the relative-price adjusted indexes.
The 7-Gn index and the FRB-TWD index both
recorded recovery ratios of 0.68.
---------Jack L. Hervey and William A. Strauss
1 Vol X I, issue 1, pp. 17-28, Federal Reserve Bank
of Chicago.

31

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