The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
S E P T E M B E R /O C T O B E R 1997
CONTENTS
39
Chain-Weighted GDP: Has
Our Perspective Changed?
Michael R. Pakko
Our perspective on the U.S. economy's recent performance has been
challenged recently by changes in the
methodology used to adjust the
National Income and Product
Accounts for inflation. Michael R.
Pakko surveys the changes embodied
in the revised data, examining the
question of whether or not the revisions alter our view of the overall pattern of economic fluctuations known
collectively as the business cycle.
Volume 79, Number 5
3
Economic Models of
Employee Motivation
Joseph A. Ritter and
Lowell J. Taylor
Workers present employers with a
range of tricky problems. They can be
crooked, subversive, surly, or indolent, even if they are paid on time.
Joseph A. Ritter and Lowell J. Taylor
explore economists’ main theories of
how compensation is used to address
employee motivation and how these
models help to explain puzzling features of labor markets. Although
these theories are often regarded as
competitors, the authors treat them as
complementary tools in understanding how employers deal with the complex problem of motivating workers.
23
51
The Demand for Divisia
Money in a Core Monetary
Union
Katrin Wesche
Proponents of an aggregation theoretic
approach to money demand argue that
simple-sum measures do not capture
the theoretical notion of money, especially for broad monetary aggregates.
European monetary aggregation,
which uses indices for monetary services,
seems attractive because these indices
can account for the imperfect substitutability between different currencies.
In this article, Katrin Wesche applies
the aggregation theoretic framework
to money holdings of European residents
and compares the resulting index to
simple-sum M3. She concludes that
the Divisia index of European monetary
services may provide additional insight
into money demand during the period
of transition to monetary union.
Technical Analysis in the
Foreign Exchange Market:
A Layman’s Guide
Christopher J. Neely
Economists have traditionally been skeptical of the value of technical analysis, the
use of past price behavior to guide trading decisions in asset markets. Instead,
they have relied on the logic of the efficient markets hypothesis. Christopher J.
Neely briefly explains the fundamentals
of technical analysis and the efficient
markets hypothesis as applied to the foreign exchange market, evaluates the
profitability of simple trading rules, and
reviews recent ideas that might justify
extrapolative technical analysis.
The Business Cycle and
61
Working Paper Series
SEPTEMBER/OCTOBER 1997
Joseph A. Ritter is a research officer at the Federal Reserve Bank of St. Louis. Lowell J. Taylor is an associate professor at the Heinz School of
Public Policy and Management, Carnegie Mellon University. Eran Segev and Joshua D. Feldman provided research assistance.
Economic
Models of
Employee
Motivation
use the terms “wage” and “compensation”
interchangeably throughout the article)
high enough to deter undesirable behavior
by making a job too good to lose are said
to pay efficiency wages.
It is fairly easy to see whether a firm
is using some sort of piece rate plan. There
is quite a bit of controversy, however, about
whether firms that do not use piece rates
adopt efficiency-wage or performancebonding plans. We follow our overview
with a discussion of the nature of the evidence supporting the different models.
Joseph A. Ritter
Lowell J. Taylor
T
o most people it is a common sense
proposition that hiring workers is a
trickier problem than buying ballpoint
pens. It is often difficult to find the right
worker to hire, and workers who have
already been hired can quit, steal, be hung
over, refuse to cooperate with other workers, or simply not work very hard. In some
workplaces some of these problems are relatively easy to solve, either by direct
supervision or by directly linking pay to
production. In general, however, things
like ability, effort, and honesty are difficult
to verify and consequently present special
problems for personnel managers and economic theorists. The ways firms solve the
problems of selecting, motivating, and
retaining employees are potentially interesting to a wide cross-section of economists because they can affect how labor
markets function and, therefore, how the
entire economy operates.
This article presents an overview of
economists’ main hypotheses about the
compensation strategies businesses use to
address these kinds of problems. Broadly
speaking, these solutions fall into three
categories (with considerable diversity
within each): piece rates, performance
bonding, and efficiency wages. Piece rates
link pay directly to workers’ output. Performance bonding uses a combination of
up-front payments from workers and conditional repayments to guarantee workers’
performance. Firms that pay wages (we
SIMPLE SUPPLY-ANDDEMAND MODELS OF
LABOR MARKETS
On one level, economists can analyze
labor markets using the same supply-anddemand model they might apply to, say,
wheat. Supply increases as the price (wage)
received by the supplier increases. Demand
increases as the price paid decreases. Equilibrium occurs where supply equals demand.
For many purposes it is important to
recognize that workers are not perfectly
interchangeable; most nurses are not economists. This complication is easily handled
by treating the markets for nurses and
economists separately, each with its own
supply and demand curves. Similarly,
workers within the same profession are not
typically interchangeable. An important
dimension along which different kinds of
workers can be distinguished is the collection of applicable knowledge and skills
that economists call human capital. Levels
of human capital vary not only across individuals, but also over time for a given
individual. As an employee accumulates
human capital, or as existing human capital
deteriorates, the employee’s compensation
can be expected to change.
A worker’s willingness to accept a
particular job will be affected by agreeable
and disagreeable facets of the job. Workers
require a higher wage to accept a hazardous
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
3
SEPTEMBER/OCTOBER 1997
job than a safe one. They may accept lower
wages to work in a nice place, have flexible
hours, or perform work that requires little
effort. Differences in wages that come from
these kinds of reasons are called compensating differentials.
The theory of labor demand is especially
important for this article. The core of that
theory is based on the observation that
hiring an additional employee (or employee
hour) will increase the profits of the firm as
long as the employee’s compensation is less
than the value of the additional output the
firm can produce after hiring the employee.
The latter quantity is called the value of
marginal product (VMP) and is calculated
by multiplying the additional employee’s
marginal product by the price of the firm’s
product. This relationship defines the
firm’s labor demand curve. Since the marginal product is likely to decrease as the
firm hires more labor (holding other inputs
fixed), the firm’s labor demand curve is
downward-sloping: A firm that must pay
higher wages will demand less labor. If
there are no impediments, a labor market
will reach equilibrium where supply
equals demand.
The theory of supply and demand does
a good job of explaining the broad outlines
of labor markets, but a closer look reveals
some cracks. This article concentrates on
the fact that (unlike wheat, for example)
the same worker behaves differently in different economic circumstances; the same
worker might, for example, work hard at $30
per hour but loaf at $7 per hour. The simple
supply-and-demand framework cannot
encompass this possibility, so different kinds
of models are needed.
worker motivation, labor economists have
focused largely on three core problems:
sorting potential employees, achieving
optimal performance on the job, and
regulating turnover.
Sorting Job Applicants
In the textbook supply-and-demand
approach to labor markets, sorting applicants
is assumed to be a simple problem. That
theory presumes that an employer knows
how productive an applicant will be if he
or she takes the job. An accounting firm
(or anybody else) knows that an accounting
major is likely to be a more effective
accountant than a high-school dropout.
But that kind of insight is only the tip of
the iceberg and would not help to land an
applicant a job in the accounting firm’s
personnel office. The difference between
good and bad employees often depends on
qualities that are difficult to discern (willingness to work hard, for example). If the
firm designs the right incentives, however,
it can encourage desirable applicants, even
though the firm cannot easily identify
them when they apply.
Performance on the Job
Workers’ behavior on the job can
disrupt the firm’s attempts to make money
in many ways. A surly worker might drive
away a customer. An employee who shows
up late might make it difficult for other
workers to do their own jobs. A worker
might be careless or simply not work very
hard. Workers might steal from their
employer. The list is virtually endless.
Beyond the obvious, several aspects
of these situations are important. First,
none of the examples just mentioned is
necessarily tied to any observable characteristic of a job applicant. Businesses use
an arsenal of screening devices to try to
avoid problems, but their effectiveness
is manifestly limited. To get optimal
performance from employees, the firm
cannot rely on applicant screening alone
but must also design effective incentives
for existing employees.
SPECIAL PROBLEMS IN
LABOR MARKETS
A central task of economic theory is to
boil a problem down to its essentials so
that it can be thoroughly understood and
carefully analyzed. In principle, after the
core of the problem is understood, economists turn their attention to the nuances
that separate their models (artificial
economies) from reality. In the area of
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
4
SEPTEMBER/OCTOBER 1997
Second, certain on-the-job problems
are particularly critical because employees
work together in most firms. A worker
who shows up 10 minutes late, for example,
is not a problem if his job is to sit in front
of a computer and write articles, but if he
works on an assembly line, he may force
several hundred workers to start work
10 minutes late.
Third, the size and complexity of most
workplaces make it impossible to detect all
negative behavior. This fact triggers a powerful principle: To deter any behavior that
is unlikely to be detected, punishment must
be disproportionate. For example, suppose
Joe likes hanging around the water cooler
enough that he would be willing to pay the
firm a dollar to be allowed this liberty for
an extra hour each day, but his manager
notices excess water cooler attendance
only one time in a hundred.1 To deter this
behavior, then, the firm must impose a
penalty greater than a dollar if Joe is
caught hanging around the water cooler.
This is, in our view, the central reason the
basic supply-and-demand model is unlikely
to be completely satisfactory in labor markets. The most severe punishment a firm
can impose is firing, but in the basic
supply-and-demand model, firing imposes
very little cost on the worker, because the
model assumes that markets are anonymous
and function quickly and efficiently. Thus
a terminated worker has no difficulty in
finding a comparable job.2
productive for some time. To the extent
that firms cannot shift these costs to the
new worker (through probationary wages,
for example), firm-specific human capital
is costly to the firm. Quit rates are not
entirely outside the firm’s control, however.
Compensation policies can provide incentives for workers to stay on the job.
Turnover
If the agency problem is related to the
worker’s productivity, there is an obvious
approach to solving it: Establish a direct
connection between the worker’s output
and his compensation. Many workers are
compensated in ways that resemble piece
rates: garment workers who are paid on
the basis of output, sales workers paid on
commission, auto mechanics in large dealerships whose pay is partly on a per-repair
basis, and agricultural workers whose pay
depends on the amount of fruit picked or
rows of grape vines pruned.
One pervasive problem in firms that
tie pay closely to some objective measure
of output is that they often get exactly
The Agency Problem
All of the problems mentioned in this
section are corollaries of the maxim, “If
you want something done right, you have
to do it yourself.” The problem confronting
the owner of a business is how to design
incentives that will induce the workers to
do it right or, more precisely, to behave in a
way that maximizes the firm’s profits. This
is an example of what economists call an
agency problem: A principal (in this case
the firm’s owner or manager) designs
incentives for an agent or agents (the
workers), who take actions that affect the
principal’s well-being. The agency problem
stems from the fact that there is a different
connection between the agent’s actions and
well-being than between the agent’s actions
and the principal’s well-being.3 For example,
it is in the firm’s interest for a worker to
work hard (the action preferred by management), but the worker may prefer to spend
the morning at the water cooler.
1
Perhaps because the manager
is usually golfing. See footnote
3 below.
2
Recent work on the dynamics
of labor markets uses matching
models in which the firm and
worker bargain over gains generated by a good match. If the
worker’s share is small, firing
costs the worker little. Even
when the worker’s share is
larger, so that termination is a
significant penalty, its credibility
as a disciplinary device is limited
because firing is costly to the
firm too.
3
The owner(s) of a large firm
face another agency problem:
how to get the manager of a
firm to act in the interest of the
owner(s). This problem has
also been extensively studied
under the heading of executive
compensation. See Jensen and
Murphy (1990).
PIECE RATES
Turnover can be very costly to the firm
for two reasons. First, isolating and hiring
a new worker can cost thousands of dollars
for some jobs. Firms that outsource part of
this activity to “head-hunters” (presumably
because they think it is cheaper than doing
it themselves) typically pay a commission
that is a substantial fraction of the new
worker’s annual pay. Second, new workers
almost always need to accumulate some
knowledge specific to the new job (firmspecific human capital). This process may
require explicit training, or it may just
mean that the new worker will not be fully
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
5
SEPTEMBER/OCTOBER 1997
4
The literature on incentive pay
in economics studies the limited
extent to which efficient
employment relationships can
be achieved in the face of this
kind of slippage. A nice summary of this and other issues in
incentive pay is Gibbons
(1996).
what they pay for: behavior that changes
the measure of output rather than output
itself (Baker, Gibbons, and Murphy, 1994).
Fraud and accounting tricks often allow
employees to manipulate the output measurement without changing output. Or,
perhaps worse, easily observable quantity
may rise at the expense of less apparent
quality. The dilemma is summarized by
Gibbons (1996): “When measured performance omits important dimensions of total
contribution [to the firm], firms understand
that they will ‘get what they pay for,’ and
so may choose weak incentives in preference
to strong but frequently dysfunctional
incentives.” In other words, firms facing
these types of distortions may choose to
use incentive systems that are less direct
and less precise than piece rates.
The biggest impediment to the
implementation of piece rates is that the
output of individual workers is not easily
measured in many jobs; reasonable,
objective measures of performance do
not exist. One reason is that it is usually
difficult, if not impossible, to separate a
particular worker’s performance from the
overall performance of a group or firm.
Inadequate output measurement makes
piece rates far less effective.
Firms’ motives for using “weak” incentives can be even deeper than obviously
defective or easily manipulated measurement
systems. Holmstrom and Milgrom (1994)
argue that when workers perform several
tasks, incentives must be finely balanced
to ensure that all the tasks get adequate
attention. But if, for example, one task is
easy to measure and another, equally
important, task is hard to measure (cooperation, for example), it will be impossible
to use “strong” incentives—piece rates—to
motivate performance on the first task
without also inducing the worker to neglect
the second task. Sometimes, therefore,
firms may forego the opportunity to use
piece rates (or use only weak ones), even
when they would ostensibly be feasible
and effective. Holmstrom and Milgrom
conclude that “the use of low powered
incentives within the firm, while sometimes
lamented as one of the major disadvantages
of internal organization, is also an important vehicle for inspiring cooperation and
coordination.”
Although a supervisor may be able to
judge whether the worker is doing a good
job over some period of time (we choose
fuzzy words deliberately) and set pay
accordingly, for two reasons this approach
is not really a piece rate. First, evaluation
by supervisors breaks the tight relationship
between performance and pay that true
piece rates can achieve in a simple environment.4 Second, it introduces a time
dimension to the relationship between
work and compensation that changes it
in fundamental ways from the simple immediate reward system of piece rates. The
remaining approaches discussed here
stress this time dimension.
PERFORMANCE BONDING
In the face of workers’ inclinations to
do various things contrary to the best
interests of the firm, it is useful to divide
compensation in two pieces. One piece is
the level of compensation that the worker
requires before agreeing to work for the
firm at all. This piece includes any
compensating differentials the firm must
pay. For the next three paragraphs (only)
we will call this component the base
wage. The second piece of compensation
convinces the worker to perform
optimally—to work hard, stay sober, be
unlikely to quit, and so forth. For the
moment we will call this the bonus. If
piece rates were feasible, this bonus could
be zero. It might also be zero if it is easy
to monitor the worker’s performance in
relevant ways. As we argued above, these
cases are far from universal.
The base wage does not help motivate
the worker, because it simply measures the
alternative value of his time. It does not
motivate him to do things he is disinclined
to do (work hard, for example). Compensating differentials reflect the market’s
valuation of things such as high effort, but
if the employer cannot perfectly monitor
the employee’s behavior, a compensating
differential will not ensure that high effort
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
6
SEPTEMBER/OCTOBER 1997
is forthcoming. Clearly, the bonus, also,
will not motivate workers if it is not conditional on performance in some way. So
the firm must have a scheme whereby the
worker is periodically evaluated and
receives the bonus only if the evaluation
suggests that his performance exceeds
some threshold. Suppose that the evaluation is reasonably honest and accurate
(closely related to the worker’s actual performance). If the bonus is big enough, it
will provide adequate incentive for the
worker to perform as the firm wants. How
big it needs to be will depend on how
likely it is that the firm’s evaluation will
detect suboptimal performance.
There is a flaw in this plan, however:
The worker’s compensation (base wage
plus bonus) may exceed the value of his
marginal product if the bonus is too large.
In this case, firms could simply decide it is
not profitable to hire workers whose compensation exceeds the value of their
marginal product and make no further
effort to solve the agency problem. But
here is a better idea: The firm could require
the worker to give it some money at the
beginning of the evaluation period and
promise to pay it back with interest at the
end, conditional on adequate performance.
Now the firm is free to hire workers up
to the point at which the value of the marginal product of labor equals the base wage
because the workers are paying their own
bonus. In economics jargon, they are
posting a bond to guarantee their own performance. The firm still must compensate
workers to do things they do not want to
do (pay a compensating differential, in
other words), but the bond guarantees that
the firm will get what it pays for (if the bond
is large enough to offset whatever temptations cause the firm’s agency problem).
At first this idea appears to be a case
of economic theory run amok. Jobs that
require an explicit bond, as just described,
are extremely rare, and this seems to be
conclusive evidence against this theoretical
approach. Indeed, Carmichael (1989) is
blunt about this fact: “I know of no labor
markets anywhere in the world or in
history where this practice has been
widespread.” But to write the idea off
would be to underestimate the ingenuity
of economic theorists.
Work–Life Incentives
Edward Lazear (1979, 1981, 1995)
has argued that actual compensation plans
implicitly use the bonding idea and, moreover, that recognizing this fact can help to
explain some features of labor markets that
otherwise appear quite odd. Lazear’s basic
insight is that if firms and workers have
full access to capital markets—that is,
if they are able to save and borrow
effectively—neither side should care
whether workers’ compensation exactly
equals the value of marginal product
(VMP) on any given day. Instead, both
care about the present value of wages
and VMP over the working life of the
employee. This observation suggests a
new strategy that makes the performance
bond an implicit part of compensation,
rather than an explicit up-front payment.
Lazear (1995) calls this approach “work–life
incentives.” The same idea goes by various
names, including “life-cycle incentives”
and “upward-sloping age-earnings profiles”
or “tenure-earnings profiles.”
Lazear poses a simple agency problem:
Suppose workers can work at either a high
or a low effort level, and that they are
indifferent among the options of working
hard for wage W H, working at a low effort
level for wage W L, or not having the job.5
W H and W L are the workers’ high- and
low-effort reservation wages. (In reality, of
course, firms must decide on an acceptable
effort level, but adding that decision would
not substantially change any part of this
article.) Their difference, e, measures the
monetary value to the worker of the extra
effort. Employees who work hard are
more productive than those who supply
low effort, so VMP H > VMPL. Suppose that
workers’ productivity at each effort level
does not change during their lifetimes.
A firm that could be sure its workers were
working hard would pay W H and hire
additional workers until VMP H fell to WH.
A firm that knew its workers to be shirkers
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
7
5
This effort-supply problem is frequently used because, despite
its extreme simplicity, it captures ideas like Lazear’s that
are valid for many different and
more complex situations.
SEPTEMBER/OCTOBER 1997
difference between them at any time s
between hiring and retirement is
Figure 1
Work–Life Incentives Wage Profile
Wage
WT
and is shown in Figure 2. This quantity is
the value of a job that pays wages Wt from
t= s until t=T. In Figure 2, the difference
first rises as the initial negative Wt – WH
terms get dropped off the beginning of
the sum, and the positive ones get less discounting because they are not so far in the
future (the term [1/(1+r)]t–s gets bigger as
t – s gets smaller). Eventually, however, the
terms getting dropped off the start of the
sum are positive, and there are fewer and
fewer terms to sum, so the difference falls.
By retirement, the difference falls to
WT – WH.
At any point during his working life, a
worker who chooses to work at low effort
gets a utility gain e, but gambles that
he will be caught (with probability d for
detection) and lose a valuable job.6 This
will be a good bet; that is, a risk-neutral
worker will shirk, if 7
W
W
H
WO
T
6
Of course, the firm has some
control over d. It should be
understood here as a stand-in
for how difficult in general it
is to monitor an employee’s
performance.
7
A risk-neutral worker is indifferent between accepting and
rejecting a fair bet. A riskaverse worker would require a
bigger gain from shirking to
accept a given risk to his job.
Because the worker does not
lose Ws if he shirks and is
caught in s (he gets paid up
until the day he is fired),
the wage profile must still
be sloped a little bit even
when d =1.
8
The worker always has an
incentive to shirk in T because
there is no stream of future
payments left to lose. The firm
could use a pension paid after T
to give the job value in T (and
before) as long as it could take
the pension away up to the
very last minute, if necessary.
t
would pay W L and hire until VMPL = W L.
Some firms will choose the latter strategy,
but if high effort is worth more to a particular firm than to workers (VMPH – VMPL >
e), the firm will want to choose a compensation mechanism that persuades the
worker to work at the high effort level.
These are the firms with agency problems.
For the reasons discussed above, paying
workers WH throughout their careers will
not by itself convince them to work hard,
even though their pay includes a compensating differential for high effort. Even a
threat of termination would do no good,
because their next best option is just as
desirable as a high-effort job at wage WH;
that is what we mean by a reservation
wage. In other words, the job itself has no
value to the worker. A firm following this
strategy gets low output for high wages, a
losing proposition.
Lazear observes that there is a simple
way to make the job valuable to the worker.
Consider the lifetime wage profile labeled
W in Figure 1, which has been tilted so
that the present value of wages paid on W
between hiring at date t = 0 and retirement
after date t = T equals the present value of
a constant wage WH, that is,
(1)
In Figure 2, the worker will work hard up
to time s**.
By adjusting the slope of the W
wage path (but leaving its present value
unchanged), the firm can make s** equal
T, thus giving workers incentives for adequate performance most of the time.8
Deferring compensation, as work–life
incentives do, also discourages quits among
current employees. An employee does not
receive full compensation for past work
until the end of his career; as a result, the
job continues to have value and there is
always an incentive to hang on a little
longer. For a similar reason, work–life
incentives also help to screen out applicants
who, for one reason or another, would be
more likely to quit: A worker who takes a
job for just a year or two at a firm that uses
work–life incentives is underpaid, since
wages are initially below W H.
where r is the interest rate. What happens
to the difference between the present value
of W and that of WH as time passes? The
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
8
SEPTEMBER/OCTOBER 1997
It is easy to see that work–life incentives
can solve a broad range of agency problems.
In fact, in principle this approach can be
applied simultaneously to every agency
problem mentioned in the introduction
except encouraging better job applicants.
The reason Lazear’s approach is so versatile
is that it works entirely by making the
worker’s job valuable enough that he will
not risk dismissal; it does not matter how
you interpret e, as long as the expected
loss from undesirable behavior [the righthand side of (1)] is larger.9 Tournaments
and efficiency wages function in the same
way. Why can’t Lazear’s approach help
improve a firm’s applicant pool? The job
does not have value until the worker posts
bond, which happens after hiring.
As a careful look at Figure 2 reveals,
the agency problem is not completely
solved even when the profile is adjusted
so that s** = T, because the value of the
job is created by the accumulation of
deferred pay, starting at zero. Initially,
therefore, the value of the job is less than
e/d, so some mechanism other than
work–life incentives must be used to motivate workers during this interval.10 The
firm could require the worker to post an
explicit bond at the beginning. But that
would remove a major attraction of
Lazear’s theory, that it does not require
outright (net) payments from workers to
firms, which are rare.
There is one last problem to wrap up.
Since wages are at their highest late in life
in Lazear’s model, workers have an incentive to hang on past T. The firm does not
want this to happen because these high
wages do not correspond to high current
productivity; they are deferred compensation for past productivity. But this is not a
flaw, it is a feature. Lazear (1979, 1995)
observed that this “problem” could serve
as an explanation of widespread mandatory
retirement policies—policies that force
employees to retire at a certain age, regardless of their productivity.
Mandatory retirement policies are now
illegal for most workers in the United States,
but Lazear (1995) shows how defined-benefit pensions (pensions that promise a set
Figure 2
Difference Between Present Values
of Wage Profiles
e/d
WT – W
H
0
monthly benefit, based on years of service
and rate of pay) can also be structured to
bring about timely retirement. Decreasing
life expectancy (as the worker ages) causes
the present value of any given benefit level
to decline as retirement age increases.
Since the firm sets the rate at which benefits increase with years of service, it can
therefore determine the age at which the
present value is maximized. If the worker
chooses to work past the age preferred by
the firm, the present value of his pension
starts to decrease, even if the monthly benefit level is still increasing. The worker is
thus given a strong financial incentive to
retire at the age preferred by the firm.
Another empirical implication of
Lazear’s model, perhaps obvious enough to
escape notice, is that earnings profiles slope
upward throughout a worker’s career, even
for workers who do not change jobs. This
matches what labor economists have found
in data on individual earnings histories.
The upward-sloping earnings histories in
the data do not seem to be fully explained
by increasing productivity (human capital)
as workers accumulate experience (Medoff
and Abraham, 1980). Lazear’s analysis
provides a supplementary reason for earnings to increase with experience.11
Tournaments
Malcomson (1984) developed the idea
that the internal hierarchy of a firm can be
used as an effective incentive system.12 In
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
9
s** T
s*
t
9
There are probably limits on
how large the right-hand side
of (1) can be made in practice.
We discuss these at the end of
this section.
10
Akerlof and Katz (1989) pursue the implications of this
observation. The problem disappears if we assume, as
Lazear (1979, 1981) does,
that shirking is detected with
certainty at each instant in a
continuous-time model. In that
case the parameter that corresponds to d would be infinite.
11
Details of the nexus between
seniority and wages are surveyed by Hutchens (1989).
12
Lazear and Rosen (1981) initiated the study of tournaments
in labor economics. Studying
the incentive effects of tournaments of the form, “The
employee of the year will get a
trip to Hawaii,” they showed
that in some circumstances
such tournaments could replicate the outcome of a piecerate system, but with the
advantage of needing information only about the relative
rankings of workers, instead of
their absolute productivity levels.
SEPTEMBER/OCTOBER 1997
13
The problem of motivating the
individual(s) at the top of the
hierarchy remains. This is,
again, the problem of executive
compensation mentioned in
footnote 3.
this type of model, a worker enters the
firm at some level in a pyramid of possible
jobs. The jobs at higher levels in the
pyramid are rarer and pay more than those
at the entry level. Periodically, the firm
will promote a fraction of the employees
from each level according to their ranking
in some evaluation process, so that jobs at
higher levels in effect become prizes in an
ongoing tournament. The firm may
supplement the prizes with terminations
for employees who are not promoted. The
chance of moving up and the competition
needed to do so provide strong incentives
for good performance.13
In a way similar to the work–life incentives described in the previous section, the
size and number of prizes and penalties are
set up so that the expected present value of
compensation during a worker’s career
equals the present value of his reservation
wage. The incentives then operate in
almost exactly the same way as work–life
incentives: When the worker enters the
hierarchy, he is initially paid less than the
value of his marginal product (thus accumulating a bond). Wage increases are not
certain in this model because luck (the
quality of co-workers, for example), as well
as effort, can influence success, but his
expected lifetime earnings profile slopes up.
High expected future income comes from a
chance at promotions rather than increasing
pay in the current job (as in Lazear’s model).
Our comments in the previous section
about quits apply here too.
Tournaments have some problems
similar to the “you get what you pay for”
problem that plagues piece rates. Because
promotions are based on relative evaluations,
workers may collude to reduce output
(though, as in other cartels, this strategy
is prone to defections) or spend time sabotaging each other’s chances for promotion
rather than working.
From an economist’s point of view, the
idea of hierarchies as incentive systems
shares an attractive feature with work–life
incentives. The logic of work–life incentives
simultaneously solves an agency problem
and provides an explanation for mandatory
retirement, a phenomenon that had puzzled
economists. Similarly, tournament models
provide a workable solution to an agency
problem, and they help explain why hierarchies exist at all, why firms often prefer
to promote existing employees rather than
to hire new ones, and why the variance of
earnings within an organization is greater
for employees with more seniority.
Problems with
Performance Bonding
Few economists would dispute that
mechanisms like those described in this
section exist, and that managers of firms are
aware of and try to exploit the incentives
that the mechanisms provide. Controversy
arises over whether compensation schemes
based on the bonding principle can be
pushed far enough to solve completely the
motivation problems that firms face.
This controversy is important because
bonding models allow firms to solve their
agency problems at no cost and without
altering the basic principles of supply and
demand in the labor market. Although
these models break the tight link between
wages and value of marginal product, firms
still end up equating the two, but they are
averaged over a worker’s lifetime or across
workers who enter a tournament. Therefore, ex ante decisions are not affected by
the use of performance bonding. Bonding
produces, in economists’ jargon, first-best
solutions. If first-best solutions exist—that
is, if bonding approaches can fully solve
the agency problem—they will presumably
be firms’ preferred approach. Barriers to
their use open the door to second-best
solutions like efficiency wages, which are
considered in the next section.
The most important criticisms of performance bonding fall into four categories:
imperfect financial markets, legal barriers,
cheating (moral hazard) problems, and
problems that come from hidden information. Explicit bond posting is rare in labor
markets. In fact, it is unusual to see firms
taking anything other than the job itself
from workers who are fired. In other words,
to the extent that bonding arrangements
are used by firms, the value of the bond is
somehow embedded in the job itself.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
10
SEPTEMBER/OCTOBER 1997
Understanding why explicit performance bonds are hardly ever used obviously
helps explain why firms might choose
roundabout practices like work–life
incentives and tournaments. The near
impossibility of using explicit performance
bonds is also important because there are
limits to the implicit bonding schemes
Lazear and others have proposed. For
example, it is easy to construct examples
in which adequate work–life incentives
(steep enough wage profiles) require negative wages early in a worker’s career. In
other words, it is not always possible to tilt
the wage path enough to get high effort
and avoid explicit payments from workers
to firms. Some other mechanism, such as
efficiency wages, may therefore be necessary
to change workers’ behavior sufficiently. So
why are explicit performance bonds so rare?
One reason may be that workers who
are just starting a job have difficulty
coming up with enough money to post an
explicit bond. This conjecture challenges
the assumption that workers can lend and
borrow freely. Instead, they are liquidity
constrained; they can save (lend), but their
borrowing ability is limited.
Dickens et al. (1989) discuss a second
reason explicit bonds may not be a useful
option: There are limits on the types of
contracts that governments will enforce.
In particular, under American and English
common law, courts refuse to enforce contract provisions they interpret as penalties
(as distinct from damages). When the
probability of detecting workers’
misbehavior is low, performance bonds
must be large because the disincentive to
workers comes from the expected loss (the
size of the bond times the probability of
losing it), not the actual loss. Courts will
typically not enforce contracts in which
workers forfeit bonds that are disproportionately large. The courts do not, however,
view firing as a penalty in this sense.
Therefore implicit bonding arrangements
are not limited by this legal standard.
Implicit bonding also does not require
explicit enumeration of the types and
quantities of undesirable behavior that
will result in penalties. Explicit contracts
would be limited to a relatively small set of
legally verifiable actions.14 The remaining
problems with performance bonding apply
to implicit bonds as well.
In addition to the common-law legal
principle just mentioned, many countries
have laws that interfere with the use of
performance bonds. In the United States,
for example: (1) mandatory retirement is
illegal for most workers; (2) minimum
wage laws interfere with firms’ ability to
pay very low wages to workers at the start
of their careers; and (3) employers are
required to vest workers in defined-benefit
pension plans after five years. (This makes
the job less valuable because it separates
claim to a pension from continuation of
the job.)
One problem with performance bonds
that stands out in most people’s minds is
cheating by the firm, an example of moral
hazard. If the worker’s performance were
objectively verifiable, the employer could
probably use piece rates or something like
them. In most jobs, though, performance
is judged, somewhat subjectively, by management. This gives the firm a clear
incentive to misrepresent the worker’s performance in order to keep the bond. This
is a compelling argument, but there are
some considerations that mitigate it.
First, since other workers usually have
their own subjective evaluation of a worker
who is fired, firms that regularly exploit
this opportunity may develop a bad reputation. If either existing workers or new
applicants recognize that there is a
substantial chance that they will lose their
bond even if they perform well, the bond
no longer provides the desired incentive.
In addition, workers would require compensation in some form, probably higher wages,
for the expected loss of the bond.
Second, promotion tournaments avoid
the problem to a certain extent in the following way: If firms use a fixed number
of prizes that will definitely be awarded
according to the relative rankings of
existing workers, the firms have no incentive to cheat. If they must fill the slots
anyway, they are happy to fill them with
the best workers. Of course, firms have
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
11
14
Hart (1995) contains a very
persuasive discussion about the
practical (and, thus, legal) limits of legal contracting.
SEPTEMBER/OCTOBER 1997
15
The information is private in the
technical sense that only the
firm knows it, and it cannot be
costlessly verified if the firm
chooses to reveal it. The latter
condition gives firms an opportunity for strategic misrepresentation.
an incentive to avoid filling the slots at all
(that is, awarding prizes) unless they serve
some further function in the organization,
but this ploy is easily observed by workers,
so it would quickly destroy the incentive
effects of the tournament. Ritter and
Taylor (1997) argue that seniority-based
layoffs have a similar advantage. Lower
layoff probabilities for more experienced
workers result in an upward-sloping experience-expected earnings profile, like that
achieved by tournaments, even if the profile of actual wages is flat. The firm does
not care which workers it lays off, since
each is paid a wage equal to the value of
his marginal product. It thus has no
incentive to cheat.
The final category of criticism is based
on two principles: (1) that workers will
insist on competitive rates of return on the
bonds they post and (2) that the firm has
better information about the rates of
return that workers will actually receive
than do the workers. If a business shuts
down, workers who have posted bonds
through low wages early in their careers
lose the entire value of their bond.
Similarly, if a firm hits a rough spot and
responds by eliminating higher-level positions to make itself more competitive,
prizes are removed from its promotion
tournament, lowering the expected payoff
to the bonds that workers posted by
accepting low wages in entry-level positions.
The first principle implies that workers
will expect to be compensated for events
like these. The second says that firms have
private information about how likely these
events are.15 Ritter and Taylor (1994)
show that in these circumstances, risky
firms (where workers insist on a higher
rate of return on their bonds) have an
incentive to pretend to be safe firms so
that they can pay lower rates of return on
the bonds. Workers, unable to distinguish
between the two, require a rate of return
above what they would demand from
known safe firms. This makes performance bonding costly and, therefore,
undesirable for safe firms, which separate
themselves from risky firms by paying
efficiency wages.
EFFICIENCY WAGES
Bonding mechanisms like work–life
incentives and tournaments can provide an
effective resolution to the agency problem
because they make jobs valuable to
workers. Workers have an investment for
which the return is tied to continuation of
the job. They are therefore less likely to
quit or to take actions that would result in
their dismissal.
How should firms proceed if any of
the economic or institutional reasons
discussed above limit their use of performance bonds? The most obvious solution
is to make jobs valuable in a direct manner—
by paying more. The firm’s strategy here
entails the use of a “carrot” and a “stick.”
As in the work–life incentives model, the
stick is the threat of dismissal. The carrot
is the promise of a high-paying job.
To see how this works, we return to
the simple effort-supply problem that
motivated our discussion of work–life
incentives. We assumed for simplicity
that workers could either work hard (high
effort) or shirk (loaf). Workers have reservation wages W H and W L for high and
low effort levels, which are related by
W H = W L + e. We call e the difference in
effort levels, but it is really the amount of
money that makes the worker indifferent
between high and low effort.
Each day, a worker must decide
whether to work hard or loaf. The consequences of this decision mirror those in
the work–life incentives model: If he loafs,
he gets immediate gratification worth e,
but the probability is d that he will get
caught, be fired, and lose a series of wages
that exceed his reservation wage. Thus he
will loaf at time s if
(2)
It is not a coincidence that (2) looks
the same as (1); they express the same
gamble for the worker. There are, however,
two differences that are not immediately
apparent from the inequality alone. In the
work–life incentives model, the worker
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
12
SEPTEMBER/OCTOBER 1997
posts a bond by accepting Wt < W H early in
the his career, so (1) does not hold at that
stage. A premise of the efficiency-wage literature is that, for one reason or another,
bonds cannot fully solve the agency problem.
The crudest efficiency-wage models assume
they cannot be used at all. In (2), therefore,
_ W H all the time, but in (1), Wt < W H
Wt >
at the beginning of the worker’s career.
An important consequence of not
allowing a bond to be posted is that it is
impossible both to solve the agency
problem and to match the present value of
a worker’s lifetime pay with the present
value of his reservation wage; to solve the
agency problem, the firm must pay an efficiency-wage premium. The efficiency wage
is the lowest wage that will induce high
effort, that is, the wage that would make
(2) into an equality. Because the wage premium reduces profits, paying efficiency
wages would be a second-best solution for
the firm if some form of performance
bonds could be used. Because it must pay
a wage premium, an efficiency-wage firm
demands less labor and produces less
output than an otherwise identical firm
that has no agency problem (or can solve
its problem with performance bonds).16
Our formulation in (1) and (2) highlights
that the problem might be solved by some
combination of performance bonds and
efficiency wages, depending on how
far the firm can push performancebonding strategies.
The second subtle difference between
(2) and (1) is in the interpretation of the
reservation wage, and it arises because
of the possibility of involuntary unemployment. We postpone discussing this until
the next section.
The sum in (2) is the present value of
efficiency-wage premiums—the value of
the job relative to the reservation wage. To
deter shirking, the firm must set the wage
high enough to make the present value at
least as great as e/d. Wages any higher
than that would cut unnecessarily into
profits. Thus the value of the job must
always equal e/d.17 Figure 3 shows the lifetime wage profiles that come out of the
efficiency-wage and work–life incentives
Figure 3
Work–Life Incentives and EfficiencyWage Profiles
Wage
Work–Life Incentives
Efficiency Wage
0
High-Effort
Reservation Wage
models using the same W H, e, and d.18
How do the solutions shown in Figure 3
change as the situation changes (across
firms, for example)? First, if monitoring is
more difficult (d is smaller) or more effort
is required (e is higher), the efficiency
wage will rise; larger carrots must be dangled to achieve optimal performance. In
performance bonding models, bigger
bonds are necessary—workers must give
the firm larger carrots to be dangled in
front of them. In the work–life incentives
model, this means that the wage profile
must be steeper, since the bond is accumulated during the phase in which the
worker is underpaid. (For the same
reason, s* increases.) A fall in d works on
the cost side of the worker’s mental
calculus. He recognizes that the chances
of “getting caught” have fallen, and therefore a bigger penalty is required to induce
him to forego a gain of e. An increase in e
is simply an increase in the benefit of
loafing and requires a more valuable job,
so again the wage profile is steeper.
Suppose there is always a chance that
the job will end for reasons unrelated to performance. The worker’s wife could get an
attractive job in a different city or the firm
could shrink. We have not built this
wrinkle into our simple versions of the
models, but it is easy to apply the logic of
the previous paragraph to see how this consideration affects the solutions. It all works
through the value of the job. If a job separa-
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
13
T
t
16
Typically, efficiency-wage models
assume that the firm still operates on its neoclassical labor
demand curve; that is, it hires
labor until the VMP equals the
wage. Its equilibrium VMP is
thus higher than the equilibrium
VMP of an otherwise identical
firm with no agency problem.
17
This means that effort-regulation efficiency-wage models
share the problem of inducing
high effort in T (when there are
no future wage premiums)
mentioned in footnote 8.
Similar strategies would solve
the problem. The problem
does not arise in many of the
other types of efficiency wage
models described below.
18
The slope of the work–life profile in Figure 3 is set so that
(1) holds with equality at T
(s** = T ). The efficiency
wage path, like the work–life
profile, assumes that the incentive problem is solved somehow in T. That being the case,
the value of the job is always
e/d, which gives an efficiency
wage of
r e
1+ r d
in every period.
SEPTEMBER/OCTOBER 1997
that firms always prefer to pay for high effort.
The competitive equilibrium wage is W HC,
where supply equals demand. This is also
the high-effort reservation wage; any worker
paid less than W HC would immediately move
into a comparable position with another
firm. No worker would care about losing
his job or whether his next job was working
hard for W HC or not so hard for some loweffort reservation wage (not shown).
Now introduce the agency problem.
For the reasons given in previous sections,
all workers would shirk at wage W HC, and
firms would not be getting their money’s
worth. In efficiency-wage models, firms
make jobs more valuable to deter shirking.
They do this individually by raising wages
and reducing their own labor demand.
Although they do not collude, their actions
move them collectively up the high-effort
demand curve to wage W E, where they
employ only NE workers. The reservation
wage is no longer W HC, because jobs are no
longer available at that wage. Instead, the
reservation wage in (2) is the wage that,
combined with a high effort level, would
make the worker as well off as remaining
unemployed with a chance of getting a
higher wage, W E, sometime in the future.
This reservation wage may be above or
below W HC, depending on the desirability
of unemployment (which depends on
things like the level of unemployment
insurance benefits).
In contrast to the competitive equilibrium, there is now involuntary unemployment N – NE in the sense that the workers
who are unemployed would be willing to
work at the prevailing wage. In the simple
supply-and-demand model, firms never
offer wages that differ much from the
market-clearing wage W HC. If wages were
above that level, workers who could not
find jobs would offer to work at less than
the going wage, bidding down the wage.
In the efficiency wage equilibrium, workers
without jobs cannot successfully underbid
their employed neighbors. Recall that efficiency wages are chosen so that (2) becomes
an equality. Suppose an unemployed worker
approaches a firm’s manager, offering to
work for less than the efficiency wage. The
Figure 4
Efficiency–Wage Equilibrium
Wage
Supply
W
W
E
High-Effort
Demand
HC
NE
N
Employment
tion is more likely, there is a larger chance
that the worker will never see some of the
high wages promised in the future. This
factor reduces the value of the job, so the
firm must either pay a higher efficiency
wage or require a larger bond. Using this
reasoning, a firm that finds itself paying efficiency wages might also find it profitable to
offer relatively stable employment, since
stable employment would reduce the
efficiency wage. Such a firm would
sometimes operate off its VMP curve.
Efficiency Wages and
Unemployment
We have described efficiency wages
from the standpoint of a single firm. When
Shapiro and Stiglitz (1984) first introduced
a close relative of the efficiency-wage
model presented above, their primary
focus was on the implications of this
model of compensation for unemployment
rates. This section presents the core of
their argument.
Suppose there are lots of identical
employers, each facing the same agency
problem—encouraging high effort. There
are also N workers who each supply one
unit of labor, inelastically. If there were
no agency problem, labor could be bought
and sold like wheat. The applicable supplyand-demand graph would look like Figure 4.
Suppose, as we have throughout this article,
that the marginal product of effort is so high
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
14
SEPTEMBER/OCTOBER 1997
manager would like to pay the lower wage.
But the manager understands that workers
who are paid less than the efficiency wage
will find it optimal to shirk, since lowering
the wage makes the right-hand side of (2)
less than the left-hand side. The unemployed
worker’s offer is therefore declined. Since
all firms behave in this same manner, unemployment persists in equilibrium. (When
firms differ, the result can be dual labor
markets, which we discuss shortly.)
It is not hard to see how performance
bonds would circumvent this problem and
eliminate the involuntary unemployment.
Suppose the unemployed worker approaches
a firm, offering to work at less than the
efficiency wage and offering to post a bond
to be forfeited if he is detected shirking. A
clever manager would understand that a
big enough bond would deter shirking.
The manager would accept this offer.
This last point leads to two additional
observations. First, firms that pay efficiency
wages will, whenever possible, want to also
use partial performance bonding. Worker
bonds complement efficiency wages in
coaxing high effort from workers, thus
reducing the efficiency-wage premium.
Second, efficiency wages and resulting
unemployment persist only to the extent
that firms cannot resolve the agency problem
by using performance bonds. If bonding
schemes were costless to implement, wages
would be bid down to the competitive level
and unemployment would disappear.
suggests that there are some highly paid,
stable jobs in which employees do work
that is complicated and hard to measure.
Many other jobs are characterized by simple
work, poor pay, no job security, and little
prospect of promotion. In short, as
Doeringer and Piore (1971) argue, the
American labor market seems to have a
dual labor market with a “primary sector”
of good jobs and a “secondary sector” of
less desirable jobs. Dual labor market theorists like Doeringer and Piore argue that
even hard-working, well-qualified workers
in the secondary sector often cannot find
employment in the primary sector. In a
dual labor market, good workers can be
stuck in bad jobs.
In the basic supply-and-demand
model, workers with equal ability and
training who are doing equally difficult or
distasteful work are paid the same. In this
model, there may well be poorly paid jobs,
but these jobs tend to have low-skill workers
doing easy work. The supply-and-demand
model predicts that equally productive
workers will have similar lifetime earnings.
The central idea of dual labor market
theory—that good workers can be stuck in
bad jobs—just doesn’t make sense in the
competitive model. Pure performancebonding models also envision a perfectly
competitive environment, so this observation applies there, too.
Bulow and Summers (1986) argue that
efficiency-wage models like the one we present here can provide an explanation for dual
labor markets. Imagine a labor market in
which all workers are identical but their jobs
differ. In some jobs, low effort is acceptable
or worker performance is easy to evaluate, so
firms can effectively pay piece rates. Workers
in this “secondary sector” receive a competitively determined wage. “Primary sector”
jobs, in contrast, have agency problems that
firms can resolve only by paying efficiency
wages. All workers would like to have one
of the valuable primary-sector jobs, but
many well-qualified workers will end up in
secondary-sector jobs.
Critics of dual labor market theories
argue that labor markets efficiently sort
workers into appropriate jobs, given their
Efficiency Wages and
Dual Labor Markets
The distinctive feature of efficiencywage jobs is that they are valuable from
the start; they are jobs that people want
but can’t easily get. The “carrot” that
elicits high effort in an efficiency-wage job
is the credible promise of high wages
extending into the future. Efficiency-wage
jobs also tend to offer stable employment.
In addition, firms that pay efficiency wages
might complement the efficiency-wage
policy with performance bonds, so these
jobs would have job ladders and pensions.
A casual look at jobs in the economy
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
15
SEPTEMBER/OCTOBER 1997
ability, training, and inclinations. They
argue that labor markets do not really produce primary and secondary sectors.
Instead, markets sort workers according to
characteristics that are not observable to
labor economists (like willingness to work
hard or cooperate with coworkers), creating
the illusion of dual labor markets.
Critics of efficiency-wage models also
point out that if there really is a secondary
sector, efficiency-wage models would not
imply unemployment. Instead, people who
could not get high-wage jobs would accept
low-wage ones. In fact, this outcome
depends on how the job search is modeled.
If workers cannot search efficiently for
primary-sector jobs while they are
employed, the equilibrium level of unemployment will make workers indifferent
between searching for a high-wage job
while unemployed and accepting a lowwage job. This might still be interpreted
as involuntary unemployment.
reducing labor turnover is an important
objective for managers. How does this
problem affect compensation policy?
An employee just starting out with a
firm typically won’t know very much
about nonwage features of the job. How
difficult will the work be? Is it interesting?
Are the working conditions pleasant? Will
he like his boss and colleagues? Once he
has spent time on the job, a typical worker
will learn about these aspects of the job,
and what he learns will affect his inclination
to stay with the firm or seek employment
elsewhere (while still employed). Indeed,
the decision to quit or stay hinges on the
value of the job (which in turn depends on
both wage and non-wage features of the job)
compared with the value of the alternative.
One option for the firm is a low-wage,
high-turnover strategy. The firm can
simply set the wage at the lowest level necessary to fill vacancies immediately, fully
understanding that many workers will quit
as they discover undesirable nonwage
aspects of the job. For firms with high
turnover costs, though, a better strategy
will be to reduce turnover by paying a wage
higher than necessary to fill open jobs.
As in the effort-regulation model,
employers pay workers more than their
reservation wages in order to alter their
behavior. In the labor-turnover model,
higher wages reduce recruiting and training
costs and generate a more experienced
labor force.
Salop (1979) establishes that when all
firms use this strategy, involuntary unemployment can persist in the economy.
Also, if firms’ turnover costs differ, the
market generates wage dispersion in
which workers of equal ability receive
different wages.
Attracting Good Workers. Adverseselection models (Weiss, 1980, 1990) are
based on another real-world problem that
firms frequently encounter. A manager
hiring a new worker wants to know how
smart, conscientious, congenial, and motivated—in short, how productive—the
worker is. The manager understands that
workers have differing levels of productivity
but can make only an informed guess
Other Efficiency-Wage Models
In the efficiency-wage model we
outlined above, firms get higher
productivity (less shirking) by paying
workers more than their reservation wage.
As we have seen, the market consequence of
this employment-relations strategy can be
dramatic. Most striking is the result that
firms will not cut wages in response to
involuntary unemployment, because cutting
wages would reduce productivity. The
effort-regulation problem we described is
only one of a number of agency problems
that have been addressed with efficiencywage models. The following hypotheses
about how efficiency wages might arise
differ from the widely used effort-supply
model in using only carrots and no sticks;
the firm does not use dismissals.
Controlling Turnover. For many
firms, orienting and training new employees
can be an expensive, time-consuming
activity. It can take months or even years
for workers to become fully adjusted and
productive in some work environments.
Since firms face a big loss when employees
join a firm only to quit a short time later,
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
16
SEPTEMBER/OCTOBER 1997
about the applicant’s productivity. Often
firms learn about workers’ productivity
only after the workers have been on the
job for some time. In an extreme case, in
which a firm can discern nothing about
the future productivity of workers, the
firm would have to resort to hiring at
random from the pool of applicants.
Now suppose that, in general, the
most productive workers also have the
best opportunities (as self-employed
workers or employees in other firms), so
that more productive workers have higher
reservation wages. Then if a firm offers
the lowest wage necessary to fill open positions, it will be choosing from among
applicants with generally low productivity.
As the firm increases the wage it offers, the
pool of applicants expands to include better
applicants, and the average productivity of
the pool increases. The firm’s optimal
strategy entails trading off higher wages
against increased average productivity.
Wage Norms. The models we have
discussed so far are based on the general
premise that workers act in their own narrowly defined interest. Akerlof (1982) set
out a “sociological” perspective on worker
behavior in which the employment
relationship is viewed as a “gift exchange.”
A firm that pays workers only the lowest
wage necessary to get them to show up for
work finds that workers reciprocate with
minimal effort. A firm that gives workers a
“gift” of higher wages (without requiring
higher effort) finds that workers reciprocate
with a “gift” of higher effort norms (which
are enforced, in part, by peers). The
model has characteristics similar to those
of the basic effort-regulation model, but
with behavioral foundations more similar
to those hypothesized by sociologists than
to the opportunistic utility maximization
favored by economists.
Annable (1988) advances a subtle
argument about the formation and rigidity
of wage norms, starting from the premise
that “it is a tenet of personnel management
that violations of established wage relationships will lead to worker dissatisfaction.”
The wage relationships are both intertemporal and interpersonal and are established
either spontaneously through “equity,
custom, and tradition” or by explicit coordination activity among workers. The
norms thus established translate into a
relationship between wages and effort
(broadly defined) that the firm will find
difficult to influence. The firm must therefore take this relationship as given if it
chooses the profit-maximizing wage, just
as in the simple effort-regulation model.
Annable argues that once a set of norms
has been established, they will tend to be
rigid because they are a public good for
workers; the benefits of the coordination
activity needed to change norms are
shared by all workers, not just those
bearing the cost of coordination.
Avoiding Unionization. Union organizing entails different costs and benefits
for workers and firms. The idea behind
union-threat models is that by voluntarily
giving workers one of the biggest benefits
of unionization—higher wages—the firm
can change the workers’ cost-benefit
calculus. Workers would still bear the cost
of unionization, but the marginal benefit
would be lower. If the nonwage costs of
unionization (less flexible employment
policies, for example) are much higher for
firms than the corresponding benefits to
workers, the firm would find it worthwhile
to follow this approach. Of course, the
firm must also believe that there is a
significant chance that a union will be
successfully organized if they do not act.
In the right circumstances (not in the
middle of an open unionization effort,
for example), the firm’s voluntary action
could also be interpreted in Akerlof’s gift
exchange framework. Workers, receiving
the “gift” of higher wages, believe their
employer is “fair” and see no need for
a union.
EMPIRICAL STUDIES
Economic theory is most compelling
when it provides plausible predictions of
widespread phenomena, such as mandatory
retirement, that are otherwise difficult to
explain. In this section, however, we
sample some of the more detailed
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
17
SEPTEMBER/OCTOBER 1997
19
Gibbons and Katz (1992) give
a bit more equivocal reading of
the evidence on inter-industry
differentials. Thaler (1989)
gives a concise overview.
(but often ambiguous) empirical evidence
that bears on these theories.
The simple competitive supply-anddemand model implies that wages depend
only on workers’ productivity and on
attributes of firms or jobs that make the
job more or less desirable. Characteristics
such as the firm’s size or the ease of monitoring employees should not affect
compensation. Suppose that a worker at
firm A is paid less than a worker with
comparable experience, skills, and so forth
at firm B. In the supply-and-demand model,
the firm A worker will go to firm B and
offer to work for slightly less than the current firm B worker. In the competitive
supply-and-demand paradigm, then, the
law of one price holds, because workers
arbitrage away price differences. This
observation forms the basis for most
econometric tests of the different compensation models.
The performance-bonding models predict some additional relationships between
wages and characteristics of workers and
firms. Lazear’s work–life incentives model,
for instance, predicts a positive relationship
between wages and job tenure (length of
time in present job) after controlling for
overall work experience (as well as characteristics such as education-related worker
productivity). The evidence on this relationship is supportive on balance, but it is
somewhat muddied by technical econometric issues (Hutchens, 1989).
Lazear’s theory also predicts that
delayed-payment arrangements and
collateral phenomena such as mandatory
retirement will not be present when
employees are easily monitored (a characteristic of the job, not the employee).
Hutchens (1987) bases a test on the
assumption that jobs involving repetitive
tasks are, on average, more easily monitored
and should therefore be characterized by
absence of high wages for more senior
workers, mandatory retirement, pensions,
and long job tenures. Despite the fact that
his measure of repetitive tasks is a very
noisy proxy for ease of monitoring,
Hutchens finds in the National Longitudinal
Survey that jobs with more repetitive tasks
are significantly less likely to exhibit the
characteristics predicted by Lazear’s theory.
Henry Ford is famous for deciding in
1914 to pay a wage well above the going
rate. Raff and Summers (1987), who
studied this episode intensively, say that
“On balance it seems fair to conclude that
Ford was able, by offering the five-dollar
day, to reduce the turnover among his
workers and to extract much more intensive, and generally productive, effort from
them.” Ford’s policy thus had the main
hallmarks of an efficiency wage: desirable
effects on workers’ behavior brought about
by wages above the level necessary to
fill vacancies.
A study by Krueger and Summers
(1988) is one of a number that examine
wage differentials across industries. The
principle here is that, by and large, the
industry in which a worker finds himself
should not affect his wages in a competitive
model. This observation applies to both
the simple supply-and-demand model and
the more sophisticated performancebonding models (as long as average age of
employees does not differ across industries).
They argue that systematically higher
wages for workers in one industry than in
another constitute evidence of efficiency
wages. Krueger and Summers show that
there are significant wage differentials
across industries and use various types
of data to argue that these cannot be attributed to employee demographics, human
capital differences, compensating differentials, or unions. Although the existence
of inter-industry wage differentials is not
direct evidence of efficiency wages,
Krueger and Summers seem to take the
position that, after all other reasonable
explanations have been ruled out, the only
possibility left is efficiency wages.19
Murphy and Topel (1990) point out that a
fully convincing explanation of interindustry wage differentials would link
wages to features of industries that,
according to efficiency-wage models,
should generate different wages.
Similar arguments have been made
about the so-called employer-size effect;
larger employers, on average, pay higher
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
18
SEPTEMBER/OCTOBER 1997
wages, which prove difficult to explain
without efficiency wages. Rebitzer and
Taylor (1995) pose a challenge to this line
of reasoning. They study law firms—organizations in which there are obvious and
dramatic promotion tournaments. Associates who are promoted to partner get very
large increases in income, creating the presumption that the performance bonds
created by the tournament are sufficient to
generate high levels of effort, low quit
rates, and so on. Rebitzer and Taylor show
that the employer-size effect persists even
in this environment, where the most
common reasons for efficiency wages
appear to be absent.
Cappelli and Chauvin (1991) test one
component of the efficiency-wage model.
Using data from a single multi-plant automobile manufacturer, they test directly
whether wage premiums result in lower
levels of disciplinary action. All workers
in their data were covered under the same
collective bargaining agreement and the
same disciplinary policies. By comparing
the wages specified in the contract (the
same for all plants) with the average
hourly wage for production work in each
plant’s Standard Metropolitan Statistical
Area, Cappelli and Chauvin measure the
wage premium paid at each plant. The
premiums varied from 0 to 100 percent.
They find fewer shirking-related
disciplinary actions at plants with higher
wage premiums. Their results provide
support for a connection between pay and
productivity. Because the firm was unionized, the existence of wage premiums does
not imply the presence of efficiency wages,
but the result does suggest that a union
wage premium, by making the job
valuable, acts as an efficiency wage. Of
course a union contract that also makes
disciplinary actions more difficult would
offset that effect.
Krueger (1991) compares compensation
at company-owned and franchise-owned
fast-food restaurants. This comparison
controls automatically for different characteristics of workers and jobs. The two
groups differ because managers of companyowned restaurants have less incentive
to monitor employees than do ownermanagers. Thus the two groups can be
presumed to have systematically different
levels of monitoring (that is, d is higher for
owner-operated restaurants). Krueger
finds a small wage premium and steeper
tenure-earnings profiles at companyowned outlets, results consistent with the
efficiency-wage model. The steeper profile
would also be implied by Lazear’s
work–life incentives model, but the
premium implies that the present value of
lifetime wages is higher at companyowned outlets, for which Lazear’s model
offers no rationale. Interestingly, the wage
premiums are much higher for low-level
managers than for regular workers. This
finding suggests that the incentive
problems faced in this industry are most
efficiently solved by paying efficiency
wages to supervisors to encourage more
effective monitoring of production
workers.
On the other hand, using data on
wages for narrowly defined occupations at
200 plants, Leonard (1987) finds that differing intensity of supervision across
plants does not lead to the wage variation
predicted by efficiency-wage models. We
find this evidence less compelling than
Krueger’s because the reason for variation
in supervision intensity is unobserved.
Without that information, it is difficult to
know whether other relevant factors are
really being held constant.
CONCLUSION
Employers and employees are often
inclined to pursue goals that are at crosspurposes. The focus of this article is on
economists’ hypotheses about how firms
resolve this problem, and on the implications of these solutions for the structure of
labor markets.
Piece rates or incentive pay plans provide powerful direct incentives but have
limited applicability. The performancebonding concept adds a valuable general
perspective on employment practices such
as job ladders, promotion tournaments,
mandatory retirement, and pension policy.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
19
SEPTEMBER/OCTOBER 1997
Hart, Oliver. Firms, Contracts, and Financial Structure, Clarendon
Press, 1995.
These models form an important link
between labor economics and the study of
firm organization. Still, there are numerous
legal, institutional, and economic impediments to the use of performance bonds, so
it seems likely that firms’ best efforts to use
this approach to motivating employees
often fall short of completely resolving
fundamental agency problems. Thus, even
though efficiency wages are a second-best
solution, they may often be needed as a
complementary incentive device. Further,
efficiency-wage theories present possible
explanations for a number of additional
labor market features, most notably involuntary unemployment.
Holmstrom, Bengt, and Paul Milgrom. “The Firm as an Incentive
System,” The American Economic Review (September 1994),
pp. 972-92.
Hutchens, Robert M. “Seniority, Wages and Productivity: A Turbulent
Decade,” Journal of Economic Perspectives (Fall 1989), pp. 49-64.
_______. “A Test of Lazear’s Theory of Delayed Payment Contracts,”
Journal of Labor Economics (October 1987, part 2),
pp. S153-70.
Jensen, Michael C., and Kevin J. Murphy. “CEO Incentives—It’s
Not How Much You Pay, but How,” Harvard Business Review
(May–June 1990), pp. 138-49.
Krueger, Alan B. “Ownership, Agency, and Wages: An Examination of
Franchising in the Fast Food Industry,” Quarterly Journal of Economics
(February 1991), pp. 75-101.
_______ and Lawrence H. Summers. “Efficiency Wages and
the Inter-Industry Wage Structure,” Econometrica (March 1988),
pp. 259-93.
REFERENCES
Akerlof, George A. “Labor Contracts as Partial Gift Exchange,” Quarterly
Journal of Economics (November 1982), pp. 543-69.
Lazear, Edward P. “Why Is There Mandatory Retirement?” Journal of
Political Economy (December 1979), pp. 1261-84.
_______ and Lawrence F. Katz. “Workers’ Trust Funds and the Logic
of Wage Profiles,” Quarterly Journal of Economics (August 1989), pp.
525-36.
_______. “Agency, Earnings Profiles, Productivity, and Hours
Restrictions,” The American Economic Review (September 1981),
pp. 606-20.
Annable, James. “Another Auctioneer is Missing,” Journal of
Macroeconomics (Winter 1988), pp. 1-26.
_______. Personnel Economics, The MIT Press, 1995.
_______ and Sherwin Rosen. “Rank-Order Tournaments
as Optimum Labor Contracts,” Journal of Political Economy
(October 1981), pp. 841-64.
Baker, George, Robert Gibbons, and Kevin J. Murphy. “Subjective
Performance Measures and Optimal Incentive Contracts,” Quarterly
Journal of Economics (1994), pp. 1125-56.
Leonard, Jonathan S. “Carrots and Sticks: Pay, Supervision, and
Turnover,” Journal of Labor Economics (October 1987), pp. S136-52.
Bulow, Jeremy I., and Lawrence H. Summers. “A Theory of Dual Labor
Markets with Application to Industrial Policy, Discrimination, and
Keynesian Unemployment,” Journal of Labor Economics (October
1986), pp. 376-414.
Malcomson, James M. “Work Incentives, Hierarchy, and Internal Labor
Markets,” Journal of Political Economy (June 1984), pp. 486-507.
Cappelli, Peter, and Keith Chauvin. “An Interplant Test of the Efficiency
Wage Hypothesis,” Quarterly Journal of Economics (August 1991),
pp. 769-87.
Medoff, James, and Katharine Abraham. “Experience, Performance,
and Earnings,” Quarterly Journal of Economics (December 1980),
pp. 703-36.
Carmichael, H. Lorne. “Self-Enforcing Contracts, Shirking, and Life Cycle
Incentives,” Journal of Economic Perspectives (Fall 1989), pp. 65-83.
Murphy, Kevin M., and Robert H. Topel. “Efficiency Wages
Reconsidered: Theory and Evidence,” Advances in the Theory and
Measurement of Unemployment, Yoram Weiss and Gideon Fishelson,
eds., St. Martin’s Press, 1990, pp. 204-40.
Dickens, William T., Lawrence F. Katz, Kevin Lang, and Lawrence H.
Summers. “Employee Crime and the Monitoring Puzzle,” Journal of
Labor Economics (July 1989), pp. 331-47.
Raff, Daniel M. G., and Lawrence H. Summers. “Did Henry Ford Pay
Efficiency Wages?” Journal of Labor Economics (October 1987),
pp. S57-86.
Doeringer, P. B., and M. J. Piore. Internal Labor Markets and Manpower
Analysis, Heath, 1991.
Gibbons, Robert. “Incentives and Careers in Organizations,” National
Bureau of Economic Research Working Paper 5705, August 1996.
Rebitzer, James, and Lowell J. Taylor. “Efficiency Wages and Employment
Rents: The Employer Size Wage Effect in the Job Market for Lawyers,”
Journal of Labor Economics (October 1995), pp. 678-708.
_______ and Lawrence F. Katz. “Does Unmeasured Ability Explain
Inter-industry Wage Differentials?” Review of Economic Studies (July
1992), pp. 515-35.
Ritter, Joseph A., and Lowell J. Taylor. “Workers as Creditors:
Performance Bonds and Efficiency Wages,” The American Economic
Review (June 1994), pp. 694-704.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
20
SEPTEMBER/OCTOBER 1997
_______ and _______. “Seniority-Based Layoffs as an Incentive
Device,” Federal Reserve Bank of St. Louis Working Paper 97-17A,
November 1997.
Shapiro, Carl, and Joseph Stiglitz. “Involuntary Unemployment as a
Worker Discipline Device,” The American Economic Review (June
1984), pp. 433-44.
Thaler, Richard H. “Anomalies: Inter-industry Wage Differentials,”
Journal of Economic Perspectives (Spring 1989), pp. 181-93.
Weiss, Andrew. “Job Queues and Layoffs in Labor Markets with Flexible
Wages,” Journal of Political Economy (June 1980), pp. 526-38.
_______. Efficiency Wages: Models of Unemployment, Layoffs, and
Wage Dispersion, Princeton University Press, 1990.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
21
SEPTEMBER/OCTOBER 1997
Christopher J. Neely is an economist at the Federal Reserve Bank of St. Louis. Kent A. Koch provided research assistance.
Technical
Analysis in
the Foreign
Exchange
Market: A
Layman’s Guide
Investors are concerned with “beating the
market,” earning the best return on their
money. Economists study technical
analysis in foreign exchange markets
because its success casts doubt on the efficient markets hypothesis, which holds that
publicly available information, like past
prices, should not help traders earn
unusually high returns. Instead, the
success of technical analysis suggests that
exchange rates are not always determined
by economic fundamentals like prices and
interest rates, but rather are driven away
from their fundamental values for long
periods by traders’ irrational expectations
of future exchange rate changes. These
swings away from fundamental values may
discourage international trade and investment by making the relative price of U.S.
and foreign goods and investments very
volatile. For example, when BMW decides
where to build an automobile factory, it
may choose poorly if fluctuating exchange
rates make it difficult or impossible to predict costs of production in the United
States relative to those in Germany.
Despite the widespread use of
technical analysis in foreign exchange
(and other) markets, economists have traditionally been very skeptical of its value.
Technical analysis has been dismissed by
some as astrology. In turn, technical
traders have frequently misunderstood
what economists have to say about asset
price behavior. What can the two learn
from each other? This article provides
an accessible treatment of recent research
on technical analysis in the foreign
exchange market.
Christopher J. Neely
Technical analysis suggests that a long-term rally
frequently is interrupted by a short-lived decline.
Such a dip, according to this view, reinforces the
original uptrend. Should the dollar fall below
1.5750 marks, dealers said, technical signals would
point to a correction that could pull the dollar back
as far as 1.55 marks before it rebounded.
Gregory L. White
Wall Street Journal
November 12, 1992
T
echnical analysis, which dates back a
century to the writings of Wall Street
Journal editor Charles Dow, is the use of
past price behavior to guide trading decisions in asset markets. For example, a
trading rule might suggest buying a currency if
its price has risen more than 1 percent from
its value five days earlier. Such rules are
widely used in stock, commodity, and (since
the early 1970s) foreign exchange markets.
More than 90 percent of surveyed foreign
exchange dealers in London report using
some form of technical analysis to inform
their trading decisions (Taylor and Allen,
1992). In fact, at short horizons—less than
a week—technical analysis predominates
over fundamental analysis, the use of other
economic variables like interest rates, and
prices in influencing trading decisions.
Investors and economists are interested
in technical analysis for different reasons.
A PRIMER ON TECHNICAL
ANALYSIS IN FOREIGN
EXCHANGE MARKETS
Technical analysis is a short-horizon
trading method; positions last a few hours
or days. Technical traders will not hold
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
23
SEPTEMBER/OCTOBER 1997
existence of trends: Trends in motion tend
to remain in motion unless acted upon by
another force. The third principle of technical analysis is that history repeats itself.
Asset traders will tend to react the same
way when confronted by the same
conditions. Technical analysts do not
claim their methods are magical; rather,
they take advantage of market psychology.
Following from these principles, the
methods of technical analysis attempt to
identify trends and reversals of trends.
These methods are explicitly extrapolative;
that is, they infer future price changes
from those of the recent past. Formal
methods of detecting trends are necessary
because prices move up and down around
the primary (or longer-run) trend. An
example of this movement is shown in
Figure 1, where the dollar/deutsche mark
($/DM) exchange rate fluctuates around an
apparent uptrend.2
To distinguish trends from shorter-run
fluctuations, technicians employ two types
of analysis: charting and mechanical rules.
Charting, the older of the two methods,
involves graphing the history of prices
over some period—determined by the
practitioner—to predict future patterns
in the data from the existence of past
patterns. Its advocates admit that this
subjective system requires the analyst to
use judgement and skill in finding and
interpreting patterns. The second type of
method, mechanical rules, imposes consistency and discipline on the technician by
requiring him to use rules based on mathematical functions of present and past
exchange rates.
Figure 1
Peaks, Troughs, Trends, Resistance
and Support Levels Illustrated for
the $/DM
$ per DM
0.72
0.70
Resistance level
Sell signal from a
0.5% filter rule
0.68
0.66
0.64
Local troughs
Trendline
Local peak
0.62
0.60
May
Buy signal from a 0.5% filter rule
Support level
June
July
Aug
Sept
1992
NOTES: Not all buy and sell signals from the filter rule are identified.
1
2
These principles and a much
more comprehensive treatment
of technical analysis are provided by Murphy (1986) and
Pring (1991). Rosenberg and
Shatz (1995) advocate the
use of technical analysis with
more economic explanation.
Figure 1 shows only closing
prices. In this, it differs from
most charts employed by technical traders, which might show
the opening, closing, and daily
trading range.
positions for months or years, waiting
for exchange rates to return to where fundamentals are pushing them. In contrast,
fundamental investors study the economic
determinants of exchange rates as a basis
for positions that typically last much
longer, for months or years. Some traders,
however, use technical analysis in
conjunction with fundamental analysis,
doubling their positions when technical
and fundamental indicators agree on the
direction of exchange rate movements.
Three principles guide the behavior
of technical analysts.1 The first is that
market action (prices and transactions
volume) “discounts” everything. In other
words, all relevant information about an
asset is incorporated into its price history,
so there is no need to forecast the
fundamental determinants of an asset’s
value. In fact, Murphy (1986) claims that
asset price changes often precede observed
changes in fundamentals. The second
principle is that asset prices move in
trends. Predictable trends are essential to
the success of technical analysis because
they enable traders to profit by buying
(selling) assets when the price is rising
(falling), or as technicians counsel, “the
trend is your friend.” Practitioners appeal
to Newton’s law of motion to explain the
Charting
To identify trends through the use of
charts, practitioners must first find peaks
and troughs in the price series. A peak is
the highest value of the exchange rate
within a specified period of time (a local
maximum), while a trough is the lowest
value the price has taken on within the
same period (a local minimum). A series
of peaks and troughs establishes downtrends
and uptrends, respectively. For example, as
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
24
SEPTEMBER/OCTOBER 1997
shown in Figure 1, an analyst may establish
an uptrend visually by connecting two
local troughs in the data. A trendline is
drawn below an apparent up trend or
above an apparent downtrend. As more
troughs touch the trendline without
violating it, the technician may place more
confidence in the validity of the trendline.
The angle of the trendline indicates the
speed of the trend, with steeper lines indicating faster appreciation (or depreciation)
of the foreign currency.
After a trendline has been established,
the technician trades with the trend,
buying the foreign currency if an uptrend is
signaled and selling the foreign currency if
a downtrend seems likely. When a market
participant buys a foreign currency in the
hope that it will go up in price, that participant is said to be long in the currency. The
opposite strategy, called shorting or selling
short, enables the participant to make
money if the foreign currency falls in price.
A short seller borrows foreign currency
today and sells it, hoping the price will fall
so that it can be bought back more cheaply
in the future.
Spotting the reversal of a trend is just
as important as detecting trends. Peaks
and troughs are important in identifying
reversals too. Local peaks are called resistance levels, and local troughs are called
support levels (see Figure 1). If the price
fails to break a resistance level (a local
peak) during an uptrend, that may be an
early indication that the trend may soon
reverse. If the exchange rate significantly
penetrates the trendline, that is considered
a more serious signal of a possible reversal.
Technicians identify several patterns
that are said to foretell a shift from a trend
in one direction to a trend in the opposite
direction. An example of the best-known
type of reversal formation, called “head
and shoulders,” is shown in Figure 2. The
head and shoulders reversal following an
uptrend is characterized by three local
peaks with the middle peak being the
largest of the three. The line between the
troughs of the shoulders is known as the
“neckline.” When the exchange rate
penetrates the neckline of a head and
Figure 2
The Head and Shoulders Reversal Pattern
Illustrated for the $/DM
$ per DM
0.66
Head
0.64
Left shoulder
0.62
Right shoulder
Exchange rate
penetrates the
neckline sell
signal
Neckline
0.60
0.58
0.56
Sept
Oct
Nov
Dec
Jan
Feb
1991-92
Mar
shoulders, the technician confirms a
reversal of the previous uptrend and
begins to sell the foreign currency. There
are several other similar reversal patterns,
including the V (single peak), the double
top (two similar peaks) and the triple
top (three similar peaks). The reversal
patterns of a downtrend are essentially
the mirrors of the reversal patterns for
the uptrend.
Mechanical Rules
Charting is very dependent on the
interpretation of the technician who is
drawing the charts and interpreting the
patterns. Subjectivity can permit emotions
like fear or greed to affect the trading
strategy. The class of mechanical trading
rules avoids this subjectivity and so is
more consistent and disciplined, but,
according to some technicians, it sacrifices
some information that a skilled chartist
might discern from the data. Mechanical
trading rules are even more explicitly
extrapolative than charting; they look for
trends and follow those trends. A wellknown type of mechanical trading rule is
the “filter rule,” or “trading range break”
rule which counsels buying (selling) a currency when it rises (falls) x percent above
(below) its previous local minimum (maximum). The size of the filter, x, which is
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
25
Apr
May
SEPTEMBER/OCTOBER 1997
exchange rate over a given number of
previous trading days. The length of the
moving average “window”—the number of
days in the moving average—governs
whether the moving average reflects long- or
short-run trends.3 Any moving average will
be smoother than the original exchangerate series, and long moving averages will
be smoother than short moving averages.
Figure 3 illustrates the behavior of a 5-day
and a 20-day moving average of the exchange
rate in relation to the exchange rate itself.
A typical moving average trading rule prescribes a buy (sell) signal when a short
moving average crosses a longer moving
average from below (above)—that is,
when the exchange rate is rising (falling)
relatively fast. Of course, the lengths of
the moving averages must be chosen by
the technician. The length of the short
moving average rule is sometimes chosen
to equal one, the exchange rate itself.
A final type of mechanical trading rule
is the class of “oscillators,” which are said
to be useful in non-trending markets, when
the exchange rate is not trending up or
down strongly. A simple type of oscillator
index, an example of which is shown in
Figure 4, is given by the difference between
two moving averages: the 5-day moving
average minus the 20-day moving average.
Oscillator rules suggest buying (selling) the
foreign currency when the oscillator index
takes an extremely low (high) value. Note
that the oscillator index, as a difference
between moving averages, also generates
buy/sell signals from a moving average rule
when the index crosses zero. That is,
when the short moving average becomes
larger than the long moving average, the
moving average rule will generate a buy
signal. By definition, this will happen
when the oscillator index goes from negative to positive. Therefore, an oscillator
chart is also useful for generating moving
average rule signals.
Figure 3
5- and 20-Day Moving Averages
$ per DM
0.65
Exchange rate
0.64
0.63
5-Day moving average
20-Day moving average
0.62
Sell signal, moving
average rule
0.61
0.60
Buy signal, moving average rule
0.59
Feb
Mar
Apr
May
Jun
1992
NOTES: These moving averages smooth the exchange rate and can be used to
generate buy and sell signals in the foreign exchange market.
Figure 4
The Oscillator Index
Normalized difference in moving averages
1.0
0.8
0.6
0.4
0.2
0
–0.2
–0.4
–0.6
–0.8
–1.0
Oscillator rule sell signals
Moving
average rule
sell signal
Moving
average
rule buy
signal
Difference in
moving averages
Oscillator rule buy signal
Feb
Mar
Apr
May
Jun
1992
NOTES: The 5-day moving average minus the 20-day moving average can also be
used to generate buy and sell signals.
3
For example, the five-day moving average of an exchange
rate series is given by:
M(5)t =
1
5
4
i =0
S t-i
where S t denotes the closing
price of the spot exchange rate
at day t.
chosen by the technician from past experience, is generally between 0.5 percent and
3 percent. Figure 1 illustrates some of the
buy and sell signals generated by a filter
rule with filter size of 0.5 percent.
A second variety of mechanical trading
rule is the “moving average” class. Like
trendlines and filter rules, moving averages
bypass the short-run zigs and zags of the
exchange rate to permit the technician to
examine trends in the series. A moving
average is the average closing price of the
Other Kinds of Technical Analysis
Technical analysis is more complex
and contains many more techniques than
those described in this article. For
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
26
SEPTEMBER/OCTOBER 1997
example, many technical analysts assign a
special role to round numbers in support
or resistance levels. When the exchange
rate significantly crosses the level of 100
yen to the dollar, that is seen as an indication that further movement in the same
direction is likely.4 Other prominent types
of technical analysis use exotic mathematical concepts such as Elliot wave theory
and/or Fibonacci numbers.5 Finally,
traders sometimes use technical analysis of
one market’s price history to take positions
in another market, a practice called intermarket technical analysis.
the investment with the higher expected
return. While the U.S. and German
interest rates are known, the bank must
base its decision on its forecast of the rate
of appreciation of the DM. If market participants expect the return to investing in
the German money market to be higher
than that of investing in the U.S. money
market, they will all try to invest in the
German market, and none will invest in
the U.S. money market. Such a situation
would tend to drive down the German
return and raise the U.S. return until the
two were equalized. The excess return on a
German investment over an investment in
the U.S. money market (Rt DM), at date t,
from the point of view of a U.S. investor is
defined as
EFFICIENT MARKETS AND
TECHNICAL ANALYSIS
Technical analysts believe that their
methods will permit them to beat the
market. Economists have traditionally been
skeptical of the value of technical analysis,
affirming the theory of efficient markets
that holds that no strategy should allow
investors and traders to make unusual
returns except by taking excessive risk.6
(1)
RtDM ; itDM + D St – it $,
where itDM is the German overnight interest
rate, D St is the percentage rate of appreciation of the DM against the dollar overnight,
and it$ is the U.S. overnight interest rate.7
If market participants cared only about the
expected return on their investments, and
if their expectations about the change in the
exchange rate were not systematically wrong,
the expected excess return on foreign
exchange should equal zero, every day.
The assumption that market participants care only about the expected return
is too strong, of course. Surely, participants
also care about the risk of their investment.8
Risk can come from either the risk of default
on the loan or the risk of sharp changes in
the exchange rate, or both. If investing in
the German market is significantly riskier
than investing in the U.S. market, investors
must be compensated with a higher expected
return in the German market, or they will
not invest there. In that case, the expected
excess return would be positive and equal
to a risk premium. The expected riskadjusted excess return would be equal
to zero. That is,
Investing in the Foreign
Exchange Market
To understand the efficient markets
hypothesis in the context of foreign
exchange trading, consider the options
open to an American bank (or firm) that
temporarily has excess funds to be invested
overnight. The bank could lend that money
in the overnight bank money market,
known as the federal funds market. The
simple net return on each dollar invested
this way would be the overnight interest
rate on dollar deposits. The bank has other
investment options, though. It could
instead convert its money to a foreign currency (e.g., the deutsche mark), lend its
money in the overnight German money
market (at the German interest rate) and
then convert it back to dollars tomorrow.
This return is the sum of the German
overnight interest rate and the change in
the value of the DM. Which investment
should the bank choose? If the bank were
not concerned about risk, it would choose
(2)
E[RtDM] – RPt = 0,
where E[*] is a function that takes the
expected value of the term inside the
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
27
4
“The 100 yen level for the dollar is still a very big psychological barrier and it will take a few
tests before it breaks. But once
you break 100 yen, it’s not
going to remain there for long.
You’ll probably see it trade
between 102 and 106 for a
while,” said Jorge Rodriguez,
director of North American
Sales at Credit Suisse, as
reported by Creswell (1995).
5
Murphy (1986) discusses Elliot
wave theory, Fibonacci numbers, and many other technical
concepts.
6
Samuelson (1965) did seminal
theoretical work on the modern
theory of efficient markets.
7
The excess return may also be
considered the return to someone borrowing in dollars and
investing those dollars in
German investments.
8
Market participants may be
concerned about the liquidity
of their position as well as
the expected return and risk.
Liquidity is the ease with which
assets can be converted
into cash.
SEPTEMBER/OCTOBER 1997
brackets [*] and RPt is the risk premium
associated with the higher risk of lending
in the German market.
How do prices move in the hypothetical
efficient market? In an efficient market,
profit seekers trade in a way that causes
prices to move instantly in response to new
information, because any information that
makes an asset appear likely to become
more valuable in the future causes an
immediate price rise today. If prices do
move instantly in response to all new
information, past information, like prices,
does not help anyone make money. If
there were a way to make money with little
risk from past prices, speculators would
employ it until they bid away the money to
be made. For example, if the price of an
asset rose 10 percent every Wednesday,
speculators would buy strongly on Tuesday,
driving prices past the point where anyone
would think they could rise much further,
and so a fall would be likely. This situation
could not lead to a predictable pattern of
rises on Tuesday, though, because speculators would buy on Monday. Any pattern in
prices would be quickly bid away by market
participants seeking profits. Indeed, there
is considerable evidence that markets often
do work this way. Moorthy (1995) finds
that foreign exchange rates react very
quickly and efficiently to news of changes
in U.S. employment figures, for example.
Because the efficient markets hypothesis
is frequently misinterpreted, it is important
to clarify what the idea does not mean. It
does not mean that asset prices are unrelated
to economic fundamentals.10 Asset prices
may be based on fundamentals like the purchasing power of the U.S. dollar or German
mark. Similarly, the hypothesis does not
mean that an asset price fluctuates randomly
around its intrinsic (fundamental) value.
If this were the case, a trader could make
money by buying the asset when the price
was relatively low and selling it when it was
relatively high. Rather, “efficient markets”
means that at any point in time, asset prices
represent the market’s best guess, based on
all currently available information, as to the
fundamental value of the asset. Future price
changes, adjusted for risk, will be close to
unpredictable.
But if any pattern in prices is quickly
bid away, how does one explain the
Efficient Markets
9
There are a number of versions
of the efficient markets hypothesis. This version is close to
that put forward by Jensen
(1978).
10
For an example of an incorrect
interpretation of the efficient
markets hypothesis, see
Murphy (1986, p. 20-21) who
offers, “The theory is based on
the efficient markets hypothesis,
which holds that prices fluctuate
randomly about their intrinsic
value. . . . it’s just unrealistic to
believe that all price movement
is random.”
The idea that the expected risk-adjusted
excess return on foreign exchange is zero
implies a sensible statement of the efficient
markets hypothesis in the foreign exchange
context: Exchange rates reflect information
to the point where the potential excess returns
do not exceed the transactions costs of acting
(trading) on that information.9 In other
words, you can’t profit in asset markets
(like the foreign exchange market) by
trading on publicly available information.
This description of the efficient markets
hypothesis appears to be a restatement of
the first principle of technical analysis:
Market action (price and transactions
volume) discounts all information about
the asset’s value. There is, however, a subtle
but important distinction between the efficient markets hypothesis and technical
analysis: The efficient markets hypothesis
posits that the current exchange rate adjusts
to all information to prevent traders from
reaping excess returns, while technical
analysis holds that current and past price
movements contain just the information
needed to allow profitable trading.
What does this version of the efficient
markets hypothesis imply for technical
analysis? Under the efficient markets
hypothesis, only current interest rates and
risk factors help predict exchange rate
changes, so past exchange rates are of no
help in forecasting excess foreign exchange
returns—i.e., if the hypothesis holds, technical analysis will not work. Malkiel’s
summary of the attitude of many economists
toward technical analysis in the stock
market is based on similar reasoning:
The past history of stock prices cannot
be used to predict the future in any
meaningful way. Technical strategies
are usually amusing, often comforting,
but of no real value. (Malkiel, 1990,
p. 154.)
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
28
SEPTEMBER/OCTOBER 1997
apparent trends seen in charts of asset
prices like those in Figure 1? Believers in
efficient markets point out that completely
random price changes—like those generated
by flipping a coin—will produce price
series that seem to have trends (Malkiel,
1990, or Paulos, 1995). Under efficient
markets, however, traders cannot exploit
those trends to make money, since the
trends occur by chance and are as likely to
reverse as to continue at any point. (For
example, some families have—purely by
chance—strings of either boys or girls, yet
a family that already has four girls and is
expecting a fifth child still has only a 50
percent chance of having another girl.)
published and taken to indicate that
trading rule strategies can yield profits.
For example, there is a vast literature on
pricing anomalies in the equity markets,
summarized by Ball (1995) and Fortune
(1991), but Roll (1994) has found that
these aberrations are difficult to exploit in
practice; he suggests that they may be partially the result of data mining.
Trading Rules
With these considerations, two kinds
of trading rules have been commonly
tested: filter rules and moving average rules.
As a preceding section of this article
explained, filter rules give a buy signal
when the exchange rate rises x percent
over the previous recent minimum.
The analyst must make two choices to
construct a filter rule: First, how much
does the exchange rate have to rise, or
what is the size of the filter? Second, how
far back should the rule go in finding a
recent minimum? The filter rules studied
here will use filters from 0.5 percent to 3
percent and go back five business days to
find the extrema.12 A moving average rule
gives a buy signal when a short moving
average is greater than the long moving
average; otherwise it gives a sell signal.
This rule requires the researcher to choose
the lengths of the moving averages. The
moving average rules to be tested will use
short moving averages of 1 day and 5 days
and long moving averages of 10 days and
50 days. Both the filter rules and the
moving average rules are extrapolative, in
that they indicate that the trader should
buy when the exchange rate has been
rising and sell when it has been falling.
EVALUATING TECHNICAL
ANALYSIS
The efficient markets hypothesis
requires that past prices cannot be used
to predict exchange rate changes. If the
hypothesis is true, technical analysis
should not enable a trader to earn profits
without accepting unusual risk. This section examines how two common types of
trading rules are formulated and how the
returns generated by these rules are
measured. Problems inherent in testing
the rules, measuring risk, and drawing
conclusions about the degree of market
efficiency are discussed.11
Finding a Trading Rule
A basic problem in evaluating
technical trading strategies is that rules
requiring judgement and skill are impossible
to quantify and therefore unsuitable for
testing. A fair test requires fixed, objective,
commonly used trading rules to evaluate.
An “objective” rule does not rely on individual skill or judgement to determine buy
or sell decisions. The rule should be commonly used to reduce the problem of
drawing false conclusions from “data
mining”— a practice in which many
different rules are tested until, purely by
chance, some are found to be profitable
on the data set. Negative test results are
ignored, while positive results are
11
A number of previous studies
have documented evidence of
profitable technical trading rules
in the foreign exchange market:
Sweeney (1986); Levich and
Thomas (1993); Neely, Weller,
and Dittmar (1997).
12
As with most aspects of technical analysis, the choice of filter
size and window lengths has
been determined by practitioners through a process of trial
and error.
Profits
The trading rules switch between long
and short positions in the foreign currency.
Recall that a long position is a purchase
of foreign currency—a bet that it will go
up—while a short position is the reverse,
selling borrowed foreign currency now in
the hope that its value will fall. Denoting
the percentage change in the exchange rate
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
29
SEPTEMBER/OCTOBER 1997
Table 1
Technical Trading Rule Results for the $/DM
Moving Average Rule Results
Monthly
Standard
Deviation
Number of
Trades
Sharpe
Ratio
Estimated
CAPM
Beta
Standard
Error of
Est. Beta
Short MA
Long MA
Annual
Return
1
1
5
5
10
50
10
50
6.016
7.546
6.718
6.671
2.979
3.155
3.064
3.236
928
268
576
146
0.583
0.690
0.633
0.595
– 0.022
– 0.135
– 0.144
– 0.134
0.091
0.085
0.084
0.080
Filter
Annual
Return
Monthly
Standard
Deviation
Number of
Trades
Sharpe
Ratio
Estimated
CAPM
Beta
Standard
Error of
Est. Beta
0.005
0.010
0.015
0.020
0.025
0.030
5.739
6.438
3.323
1.934
0.839
– 1.541
3.057
2.951
3.255
3.348
3.236
3.578
1070
584
382
234
142
92
0.542
0.630
0.295
0.167
0.075
– 0.124
– 0.071
– 0.092
– 0.037
– 0.128
– 0.118
– 0.086
0.089
0.093
0.085
0.087
0.082
0.077
Filter Rule Results
NOTES: The first two columns of the top panel characterize the length of the short and long moving averages used in the moving-average
trading rule. The third column is the annualized asset return to the rule, while the fourth column is the monthly standard deviation of
the return. The fifth column is the number of trades over the 23-year sample. The sixth column is the Sharpe ratio, and the last two
columns provide the CAPM beta with the S&P 500 and the standard error of that estimate. The lower panel has a similar structure,
except that the first column characterizes the size of the filter used in the rule. All extrema for filter rules were measured over the
previous five business days.
($ per unit of foreign currency) from date t
to t+1 by DSt, and the domestic (foreign)
overnight interest rate by it$ (itDM), then the
overnight return from a long position is
approximately given by Equation 1:
13
14
The estimate of transactions
costs used here is consistent
with recent figures. Levich and
Thomas (1993) consider a
round-trip cost of 0.05 percent
realistic, as do Osler and Chang
(1995).
The exchange rate data were
obtained from DRI and were
collected at 4:00 p.m. local
time in London from Natwest
Markets and S&P Comstock.
Daily overnight interest rates
are collected by BIS at 9:00
a.m. London time. Interest
rates for Japan were unavailable before 3/1/82, so the
interest rates before this date
were set to 0 for the $/¥ case.
(1)
average of daily U.S. dollar bid and ask
quotes for the DM, yen, pound sterling, and
Swiss franc.14 All exchange rate data begin
on 3/1/74 and end on 4/10/97. These four
series are called $/DM, $/¥, $/£, and $/SF.
Because the results for the four exchange
rates were similar, full results from only
the $/DM will be reported in the tables.
Table 1 shows the annualized
percentage return, monthly standard
deviation (a measure of the volatility of
returns), number of trades per year, and
two measures of risk, the Sharpe ratio and
the CAPM beta, for each of the 10 trading
strategies for the $/DM. The Sharpe ratio
and CAPM betas are discussed in some
detail in the shaded insert. The mean
annual return to the 10 rules was 4.4 percent, and 38 of the 40 trading rules were
profitable (had positive excess return) over
the whole sample. These results cast doubt
on the efficient markets hypothesis, which
holds that no trading strategy should be
able to consistently earn positive excess
RtDM ; itDM + DSt – it$.
The return to a short position is the negative of the return to a long position. The
return to a trading rule over a period of
time is approximately the sum of daily
returns, minus transactions costs for each
trade. Transactions costs are set at 5 basis
points (0.05 percent) for each round trip in
the currency. A round trip is a move from
a long position to a short position and
back or vice-versa.13
Evidence from Ten Simple Technical
Trading Rules
Six filter rules and four moving average
rules were tested on data consisting of the
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
30
SEPTEMBER/OCTOBER 1997
returns. The number of trades over the
23-year sample varied substantially over the
10 rules, ranging from 4 trades per year
to almost 50 trades per year. The moving
average rules were somewhat more profitable
than the filter rules.
There is little evidence that these
excess returns are compensation for
bearing excessive risk. The first measure
of risk, the Sharpe ratio, is the mean
annual return divided by the mean annual
standard deviation. The moving average
rules had higher Sharpe ratios (0.6 vs.
0.25) than the filter rules. Six of the 10
Sharpe ratios are better than the 0.3
obtained by a buy-and-hold strategy in
the S&P 500 over approximately the same
period. This result indicates that the
average return to the rules is very good
compared to the risk involved in
following the rules.
The second measure of risk, the CAPM
betas, reflects the correlation between the
monthly trading rule returns and the
monthly returns to a broad portfolio of
risky assets (the S&P 500). Significantly
positive betas indicate that the rule is
bearing undiversifiable risk. These CAPM
betas estimated from the 10 rules generally
indicate negative correlation with the S&P
500 monthly returns. None of them is
significantly positive, statistically or economically. In other words, there is no systematic
risk in these rules that could explain the
positive excess returns.
Figure 5
One-Year Moving Average, ForwardLooking Excess Returns to the (1,10)
Moving Average Trading Rule
Moving Average of Annual Return
50
Excess return to $/DM
(1,10) moving average rule
40
30
20
10
0
–10
Excess return to
S&P 500 index
–20
–30
1974
76
78
80
82
84
86
88
90
March 1974-March 1996
up with the market on a daily basis. How
large would transactions costs have to be to
eliminate the excess return to the technical
rules? If we assume a 6 percent annual
excess return to the rule and 230 trades (10
trades a year), round-trip transactions costs
would have to be greater than 0.6 percent
to produce zero excess returns.
In addition to higher transactions
costs, individual investors following technical rules also must accept the risk that
such a strategy entails. Figure 5 illustrates
the risk by depicting, at monthly intervals,
the one-year-ahead excess return from
1974 through 1996 for the (1,10) moving
average rule on the $/DM and, for comparison, the total excess return on buying and
holding the S&P 500 index, a popular
measure of returns to a stock portfolio.
The figure shows that the excess returns to
both portfolios vary considerably at the
annual horizon, often turning negative.
While the technical trading rule excess
return is less variable than the S&P excess
return, it can still lead to significant losses
for some subperiods. Two ways to
measure losses over subperiods are the
maximal single-period loss (maximum
drawdown) and maximum loss in a
calendar year. Over the period from March
1974 through March 1997, the maximum
For Whom is Technical Trading
Appropriate?
The discussion of risk and returns suggests that technical analysis may be very
useful for banks and large financial firms
that can borrow and lend freely at the
overnight interbank interest rate and buy
and sell in the wholesale market for foreign
exchange, where transactions sizes are in
the millions of dollars. Technical trading is
much less useful for individuals, who
would face much higher transactions costs
and must consider the opportunity cost of
the time necessary to become an expert on
foreign exchange speculating and to keep
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
31
92
94
96
SEPTEMBER/OCTOBER 1997
that an investor could have lost by using the
moving average trading rule was –28.2 percent; this loss, which would have occurred
between March 7, 1995, and August 2, 1995
(a period of 149 days), translates into an
annual rate of – 69.2 percent. In other
words, an investor using this rule would
have lost almost 30 percent of his capital
over this five-month period. Similarly, the
maximum loss for this technical trading
rule in a complete calendar year was – 9.8
percent in 1995, but – 17.8 percent for the
S&P 500 in 1981.15
Perhaps the biggest obstacle to
exploiting technical rules is that while the
returns to stocks depend ultimately on the
profitability of the firms in which the stock
is held, the source of returns to technical
analysis is not well understood; therefore,
the investor does not know if the returns
will persist into the future or even if they
continue to exist at the present. Indeed,
Figure 5 shows that the post-1992 return
to the (1,10) moving average rule for the
$/DM has been negative.
the degree of inefficiency. Risk is notoriously
difficult to measure. In fact, a major area
of study for macro and financial economists
for the last 10 years has been to explain
why the return on stocks is so much higher
than that on bonds, a phenomenon called
the equity premium puzzle. Of course, at
least part of the answer is that stocks are
much riskier than bonds, but there is no
generally accepted model of risk that will
explain the size of the return difference.16
Defenders of the efficient markets hypothesis maintain that the discovery of an
apparently successful trading strategy may
not indicate market inefficiency but, rather,
that risk is not measured properly.
Another problem is that of “data
mining”: If enough rules are tested, some—
purely by chance—will produce excess
returns on the data. These rules may not
have been obvious to traders at the beginning of the sample. In fact, the rules tested
here are certainly subject to a data-mining
bias, since many of them had been shown
to be profitable on these exchange rates
over at least some of the subsample. Closely
related to the data-mining problem is the
tendency to publish research that overturns
the conventional wisdom on efficient
markets, rather than research that shows
technical analysis to be ineffective. One
solution to the data-mining problem is
suggested by Neely, Weller, and Dittmar
(1997), who apply genetic programming
techniques to the foreign-exchange market.
Genetic programming is a method by which
a computer searches through the space of
possible technical trading rules to find a
group of good rules (i.e., rules that generate
positive excess return). These good rules
are then tested on out-of-sample data to
see if they continue to generate positive
excess returns.
Do These Results Measure the
Degree of Market Efficiency?
15
The returns for complete calendar years were available from
1975 through 1995.
16
Kocherlakota (1996) and
Siegel and Thaler (1997) discuss the equity premium puzzle
extensively.
There are a number of problems associated with inferring the degree of market
efficiency from the apparent profitability of
these trading rules. The first problem is
the data. To test the profitability of a
trading rule, the researcher needs actual
prices and interest rates from a series of
simultaneous market transactions. Unfortunately, simultaneous quotes for daily
exchange rates and interest rates are not
generally available for a long time span.
For example, these exchange-rate data
were collected late in the afternoon, while
the interest rates were collected in the
morning. Although most economists
judge this problem to be very minor, some
argue that the trading rule decisions could
not have been executed at the exchange
rates and interest rates used.
The second problem is that without a
good model of how to price risk, positive
excess returns resulting from the use of
trading rules cannot be used to measure
RETHINKING THE EFFICIENT
MARKETS HYPOTHESIS
Early research in finance on the
efficient markets hypothesis was very
supportive; little evidence was found of
profitable trading rules after transactions
costs were accounted for (Fama, 1970).
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
32
SEPTEMBER/OCTOBER 1997
Empirical Reasons to Suspect Failure
of Efficient Markets
The success of technical trading rules
shown in the previous section is typical
of a number of later studies showing that
the simple efficient markets hypothesis
fails in important ways to describe how
the foreign exchange market actually functions. While these results did not surprise
market practitioners, they have helped
persuade economists to examine features
of the market like sequential trading,
asymmetric information, and the role of
risk that might explain the profitability of
technical analysis.
The miserable empirical performance
of standard exchange rate models is another
reason to suspect the failure of the efficient
markets hypothesis. In an important paper,
Meese and Rogoff (1983) persuasively
showed that no existing exchange rate model
could forecast exchange rate changes better
than a “no-change” guess at forecast horizons
of up to one year. This was true even when
the exchange rate models were given true
values of future fundamentals like output
and money. Although Mark (1995) and
others have demonstrated some forecasting
ability for these models at forecasting horizons greater than three years, no one has
been able to convincingly overturn the
Meese and Rogoff (1983) result despite 14
years of research. The efficient markets
hypothesis is frequently misinterpreted as
implying that exchange rate changes should
be unpredictable; that is, exchange rates
should follow a random walk. This is incorrect. Equation 2 shows that interest rate
differentials should have forecasting power
for exchange rate changes, leaving excess
returns unpredictable. There is, however,
convincing evidence that interest rates
are not good forecasters of exchange rate
changes.17 According to Frankel (1996),
this failure of exchange rate forecasting
leaves two possibilities:
The Paradox of Efficient Markets
Grossman and Stiglitz (1980) identified
a major theoretical problem with the
hypothesis termed the paradox of efficient
markets, which they developed in the context of equity markets. As applied to the
foreign exchange market, the argument
starts by noting that exchange rate returns
are determined by fundamentals like
national price levels, interest rates, and
public debt levels, and that information
about these variables is costly for traders
to gather and analyze. The traders must
be able to make some excess returns by
trading on this analysis, or they will not do
it. But if markets were perfectly efficient,
the traders would not be able to make
excess returns on any available information.
Therefore, markets cannot be perfectly efficient in the sense of exchange rates’ always
being exactly where fundamentals suggest
they should be. Of course, one resolution
to this paradox is to recognize that market
analysts can recover the costs of some fundamental research by profiting from having
marginally better information than the rest
of the market on where the exchange rate
should be. In this case, the exchange rate
remains close enough to its fundamental
value to prevent less informed people from
profiting from the difference. Partly for
these reasons, Campbell, Lo, and MacKinlay
(1997) suggest that the debate about perfect efficiency is pointless and that it is
more sensible to evaluate the degree of
inefficiency than to test for absolute
efficiency.
• Fundamentals are not observed well
enough to allow forecasting of
exchange rates.
• Exchange rates are detached from
fundamentals by (possibly irrational)
swings in expectations about future
values of the exchange rate. These
fluctuations in exchange rates are
known as bubbles.18
Which of these possibilities is
more likely? One clue is given by the
relationship between exchange rates and
fundamentals when expectations about the
value of the exchange rate are very stable,
as they are under a fixed exchange rate
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
33
17
Engel (1995) reviews the
failure of this theory, called
uncovered interest parity.
18
Swings in expectations that
are subsequently justified by
changes in the exchange rate
are known as rational bubbles.
Swings that are not consistent
with the future path of exchange
rates are irrational bubbles.
SEPTEMBER/OCTOBER 1997
When Germany and the United States ceased
to fix their currencies in March 1973, the
variability in the real $/DM exchange rate
increased dramatically. This result suggests
that, contrary to the efficient markets
hypothesis, swings in investor expectations
may detach exchange rates from fundamental
values in the short run.
Figure 6
Monthly Percentage Changes in the
$/DM Real Exchange Rate
16
12
8
4
0
Why Do Bubbles Arise?
–4
If traders might profit by anticipating
swings in investor expectations, then the
efficient markets hypothesis needs significant adjustment. The structure of the
foreign exchange market has several
features that might help drive these swings
in expectations that produce bubbles.
Most foreign exchange transactions are
conducted by large commercial banks in
financial centers like London, New York,
Tokyo, and Singapore. These large banks
“make a market” in a currency by offering
to buy or sell large quantities (generally
more than $1 million) of currencies for a
specific price in another currency (e.g.,
the dollar) on request. The exchange rates
at which they are willing to buy or sell dollars are known as the bid and ask prices,
respectively. The market is highly competitive, and transactions occur 24 hours a day
over the telephone and automated trading
systems. The first feature of this market that
might influence technical trading is that specific transactions quantities and prices are
not public information; the market is nontransparent. But the bid and ask exchange
rates are easy to track, as banks freely quote
them to any participant. Second, the trades
take place sequentially—i.e., there is time to
learn from previous trades. Third, the participants in this market differ from one another
in the information they have and their willingness to tolerate risk.19 In other words, the
participants are heterogeneous.
How might these features combine to
produce bubbles? To the extent that some
participants are better informed about certain fundamentals than other agents (for
instance, they will know more about their
own and their customers’ demand for
foreign exchange), the trading behavior of
–8
–12
1962
66
70
74
78
82
86
90
94
98
NOTES: These changes become much more volatile after the end of the Bretton Woods
system of fixed exchange rates in March 1973. The vertical line denotes this break
date in the series. Data cover January 1960–February 1997.
19
It has long been assumed that
there is little or no private information in foreign exchange
markets, but this view has
been forcefully challenged with
respect to intraday trading by
Ito, Lyons, and Melvin (1997).
regime. A fixed exchange rate regime is
a situation in which a government is committed to maintaining the value of its
currency by manipulating monetary policy
and trading foreign exchange reserves.
Fixed exchange rate regimes are contrasted
to floating regimes, in which the government
has no such obligation. For example, most
countries in the European Union had a
type of fixed exchange rate regime, known
as a target zone, from 1979 through the
early 1990s. Fixed exchange rates anchor
investor sentiment about the future value
of a currency because of the government’s
commitment to stabilize its value. If
fundamentals, like goods prices, or expectations based on fundamentals, rather than
irrationally changing expectations, drive
the exchange rate, the relationship
between fundamentals and exchange rates
should be the same under a fixed exchange
rate regime as it is under a floating regime.
This is not the case. Countries that move
from floating exchange rates to fixed
exchange rates experience a dramatic
change in the relationship between prices
and exchange rates. Specifically, real
exchange rates (exchange rates adjusted
for inflation in both countries) are much
more volatile under floating exchange rate
regimes, where expectations are not tied
down by promises of government intervention. Figure 6 illustrates a typical case:
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
34
SEPTEMBER/OCTOBER 1997
the informed participants will reveal
some of their private information to the
uninformed agents. For example, if the
informed agents know of fundamental
forces that are likely to make the exchange
rate rise in the future, they are likely to buy
the foreign currency and thereby bid up the
publicly observed bid and ask prices. The
uninformed agents might infer from the rise
that the rate will continue to rise and, as
a result, they might buy more foreign
exchange, pushing the rate up themselves
in a self-fulfilling prophecy.20 This inference
from past price behavior is extrapolative technical analysis: It assumes that the exchange
rate will continue moving as it has in the
recent past. The uninformed traders may
continue to buy foreign exchange past the
point where it is supported by fundamentals.
Although this story is most plausible for very
high-frequency (intraday) trading, it might
also generate longer-term swings in the
exchange rate.
There are other explanations for
extrapolative trading that jettison the
assumption of rational behavior in favor of
the study of how people really make decisions. This field, called behavioral finance,
has concentrated on examples of seeming
irrationality in decision making. Two findings of this field are that (1) experimental
participants seem unusually optimistic
about their chances for success in games
and (2) the behavior and opinions of
members of a group tend to reinforce
common ideas or beliefs.21 For example,
members of a jury may become more confident about their individual verdicts if the
other members of the group agree.
Either explanation for extrapolative
trading implies that bubbles may be produced
by slow dissemination of private information
into the market, coupled with extrapolative
trading rules. There is some evidence to
support this explanation. Eichenbaum and
Evans (1995) found that foreign exchange
markets reacted gradually to money supply
shocks, over a period of many months,
instead of instantly incorporating the new
information. Surveys revealed that foreign
exchange market participants’ expectations
are extrapolative at horizons up to six
months. That is, if the exchange rate has
risen recently, market participants expect it
to continue to rise in the near future
(Frankel and Froot, 1987). Also, the success of extrapolative traders tends to feed
on itself. Frankel and Froot (1990) argue
that extrapolative traders’ success during
the early part of the large dollar appreciation of 1981-1985 convinced many other
traders to follow extrapolative rules,
driving the dollar up even further.
Central Bank Intervention
The other popular explanation for the
apparent profitability of technical trading
rules is that technical traders are able to
profit consistently from central bank intervention. Some central banks frequently
intervene (buy and sell currency) in the
foreign exchange market to move the
exchange rate to help influence other variables like employment or inflation.22
Because these actions are designed to control macroeconomic variables rather than
to make money, central banks may be
willing to take a loss on their trading.
Trading rule profits may represent a
transfer from central banks to technical
traders. Lebaron (1996) found that most
trading rule profits were generated on the
day before a U.S. intervention. Neely and
Weller (1997) find that “intelligent”
trading rules tend to trade against the Fed;
that is, they tend to buy dollars when they
find out the Fed is selling dollars. This
tantalizing story does not fit all the facts,
however. For example, Leahy (1995) finds
that U.S. foreign exchange operations
make positive profits, on average.23 This
finding is inconsistent with the idea that
central banks are giving money away to
technical traders.
20
Treynor and Ferguson (1985),
Brown and Jennings (1989),
Banerjee (1992), and Kirman
(1993) construct models of
behavior in which information
is inferred from the actions of
others. One easily understood
example is the problem of consumers who must choose
between two restaurants. One
seemingly sensible strategy for
choosing would be to go to the
more crowded restaurant on
the theory that it is likely to be
crowded because it has better
food. This phenomenon
depends on asymmetric
information.
21
Shiller (1988) and Shleifer and
Summers (1990) discuss
behavioral finance in more
detail. Ohanian (1996) considers the reasons for the collapse
of bubbles.
22
In the United States, the
Federal Reserve and the U.S.
Treasury generally collaborate
on foreign exchange intervention decisions, and operations
are conducted by the Federal
Reserve Bank of New York on
behalf of both.
23
See Szakmary and Mathur
(1996) for more on central
bank intervention and trading
rule profits.
Why Are the Profits Not
Arbitraged Away?
Whether the trends or inefficiencies in
exchange rates are created by swings in
expectations or by central bank intervention,
efficient market advocates would ask why
any predictable returns in exchange rates
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
35
SEPTEMBER/OCTOBER 1997
HOW TO MEASURE RISK?
The simplest widely used measure of risk is the Sharpe ratio or the ratio of the
average annual excess return to a measure of excess return volatility called the standard deviation. Higher Sharpe ratios are desirable because they indicate either higher
average excess returns or less volatility. A commonly used benchmark of a good
Sharpe ratio is that of the S&P 500, which Osler and Chang (1995) estimated to be
about 0.32 from March 1973 to March 1994.
A major drawback to Sharpe ratios is that they ignore an important idea in
finance: An investment is risky only to the extent that its return is correlated with
the return to a broad measure of the investments available. To see this, consider the
risk associated with holding a portfolio of assets whose returns are each individually
volatile but completely independent of each other. Each year, the assets in the portfolio that do unusually well will tend to offset those that do unusually poorly. The
portfolio as a whole will be much less risky than any of the individual assets. The
more assets in the portfolio, the less risky it will be. In fact, if enough of these independent assets are grouped together into a portfolio, the return on this portfolio
becomes certain. This means that investors do not need to be compensated for holding risky assets that are not correlated with all the other assets they can buy (the market portfolio), because the risk of each uncorrelated asset can be reduced to zero if
the portfolio contains a large enough variety of these assets. On the other hand,
assets for which returns are positively correlated with those of the other assets on
the market need a higher expected return to convince investors to hold them.
This idea motivates the second measure of riskiness, the CAPM beta: the coefficient from the linear regression of an asset’s (or trading rule’s) excess return on the
excess return of a proxy for the market portfolio, the return to a broad equity index
like the S&P 500. An estimated beta equal to zero means that the trading rule is
bearing no systematic risk, while significantly positive betas indicate that a trading
strategy is bearing some risk, and a beta equal to one means that the trading rule
moves closely with the market, so that following it requires the investor to accept
significant risk.
24
Both Shleifer and Summers
(1990) and Shleifer and
Vishny (1997) discuss the
importance of risk in speculating against bubbles.
25
Essentially the same argument
is presented more simply in
Shleifer and Summers (1990).
should not be arbitraged away. One answer
to this question is that speculators have
short horizons and are deterred from speculating against the trends by the risk that
such a strategy would incur. There are several reasons for this: First, traders typically
operate on margin, borrowing some of the
money with which they trade. With a limited line of credit, the borrowing costs
would add up if traders were not able to
turn a quick profit. Second, a trader’s
performance is typically evaluated on
relatively short horizons (less than a year).
Third, there may be institutional or legal
restrictions that prevent some types of
enterprises from taking on “excessive”
exchange risk. And finally, traders do not
know the equilibrium value of the exchange
rate with any certainty, so they cannot distinguish bubbles from movements in
fundamentals. Investors who bet on longrun reversion to fundamental values in
exchange rates may be wiped out by shortrun deviations away from those values.24
Explaining the success of technical
trading rules with bubbles begs one more
question: Why do destabilizing extrapolative
traders not lose their money? Friedman
(1953) showed that destabilizing speculation is doomed to lose money and so drive
the speculators out of the market. Friedman
argued that speculation can only destabilize
asset prices if the speculators consistently
buy when the asset price is above its equi-
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
36
SEPTEMBER/OCTOBER 1997
librium value (driving the price up further)
and sell when the asset price is below its
equilibrium value; as the destabilizing
speculators lose their money, he
maintained, they will have less effect on the
market. The corollary to this argument is
that all successful speculation is stabilizing.
Delong, Schleifer, Summers, and Waldman
(1989) constructed a “noise trader” model
that questioned this logic, however.25 They
showed that irrational (“noise”) traders
could create so much risk in asset markets
that the returns to those assets would have
to be unusually high for rational traders to
trade in them at all. In other words, the
irrational traders make unusually high
returns (on average) by foolishly pursuing
risky strategies. Some go out of business,
but, on average, this group increases its
market position.
transactions data or experimental work
on expectations formation may provide a
better understanding of market behavior.
CONCLUSION
Eichenbaum, Martin, and Charles L. Evans. “Some Empirical Evidence
on the Effects of Shocks to Monetary Policy on Exchange Rates,”
Quarterly Journal of Economics (November 1995), pp. 975-1009.
REFERENCES
Ball, Ray. “The Theory of Stock Market Efficiency: Accomplishments and
Limitations,” Journal of Applied Corporate Finance (Spring 1995),
pp. 4-17.
Banerjee, Abhijit V. “A Simple Model of Herd Behavior,” Quarterly
Journal of Economics (August 1992), pp. 797-817.
Brown, David P., and Robert H. Jennings. “On Technical Analysis,” The
Review of Financial Studies (1989), pp. 527-51.
Campbell, John Y., Andrew W. Lo, and Archie Craig MacKinlay. The
Econometrics of Financial Markets, Princeton University Press, 1997.
Creswell, Juli. “Currency Market Expects Rate Cut By Bank of Japan,”
Wall Street Journal, September 5, 1995, p. C16.
DeLong, J. Bradford, Andrei Shleifer, Lawrence H. Summers, and Robert
J. Waldmann. “Noise Trader Risk in Financial Markets,” Journal of
Political Economy (August 1990), pp. 703-38.
Technical analysis is the most widely
used trading strategy in the foreign exchange
market. Traders stake large positions on
their interpretations of patterns in the data.
Economists have traditionally rejected the
claims of technical analysts because of the
appealing logic of the efficient markets
hypothesis. More recently, however, the discovery of profitable technical trading rules
and other evidence against efficient markets
have led to a rethinking about the importance
of institutional features that might justify
extrapolative technical analysis such as private information, sequential trading, and
central bank intervention, as well as the
role of risk.
The weight of the evidence now
suggests that excess returns have been
available to technical foreign exchange
traders over long periods. Risk is hard to
define and measure, however, and this
difficulty has obscured the degree of inefficiency in the foreign exchange market.
There is no guarantee, of course, that technical rules will continue to generate excess
returns in the future; the excess returns
may be bid away by market participants.
Indeed, this may already be occurring.
Continued research on high-frequency
Engel, Charles. “Why is the Forward Exchange Rate Forecast Biased? A
Survey of Recent Evidence,” Federal Reserve Bank of Kansas City
Working Paper 95-06, September 1995.
Fama, Eugene F. “Efficient Capital Markets: A Review of Theory and
Empirical Work,” Journal of Finance (May 1970), pp. 383-417.
Fortune, Peter. “Stock Market Efficiency: An Autopsy?,” New England
Economic Review, Federal Reserve Bank of Boston (March/April
1991), pp. 18-40.
Frankel, Jeffrey. “How Well Do Foreign Exchange Markets Function:
Might a Tobin Tax Help?,” National Bureau of Economic Research
Working Paper 5422, January 1996.
_______ and Kenneth A. Froot. “Using Survey Data to Test Standard
Propositions Regarding Exchange Rate Expectations,” The American
Economic Review (March 1987), pp. 133-53.
_______ and _______. “Chartists, Fundamentalists and the
Demand for Dollars,” Private Behavior and Government Policy in
Interdependent Economies, Anthony S. Courakis and Mark Taylor,
eds., Clarendon Press, 1990.
Friedman, Milton. “The Case for Flexible Exchange Rates,” Essays in
Positive Economics, University of Chicago Press, 1953.
Grossman, Sanford J., and Joseph E. Stiglitz. “On the Impossibility of
Informationally Efficient Markets,” The American Economic Review
(June 1980), pp. 393-408.
Ito, Takatoshi, Richard K. Lyons, and Michael T. Melvin. “Is There Private
Information in the FX Market? The Tokyo Experiment,” National
Bureau of Economic Research Working Paper 5936, February 1997.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
37
SEPTEMBER/OCTOBER 1997
Paulos, John Allen. A Mathematician Reads the Newspaper,
Basic Books, 1995.
Jensen, Michael C. “Some Anomalous Evidence Regarding Market
Efficiency,” Journal of Financial Economics (June/September 1978),
pp. 95-101.
Pring, Martin J. Technical Analysis Explained, Third Edition,
McGraw-Hill, 1991.
Kirman, Alan. “Ants, Rationality and Recruitment,” Quarterly Journal of
Economics (February 1993), pp. 137-56.
Kocherlakota, Narayana R. “The Equity Premium: It’s Still a Puzzle,”
Journal of Economic Literature (March 1996), pp. 42-71.
Roll, Richard. “What Every CFO Should Know About Scientific Progress
in Financial Economics: What is Known, and What Remains to be
Resolved,” Financial Management (Summer 1994), pp. 69-75.
Leahy, Michael P. “The profitability of US intervention in the foreign
exchange markets,” Journal of International Money and Finance
(December 1995), pp. 823-44.
Rosenberg, Michael R., and Eric A. Shatz. The Merrill Lynch Guide to
Understanding and Using Technical Analysis, Merrill Lynch and Co.,
Global Securities Research & Economics Group, 1995.
Lebaron, Blake. “Technical Trading Rule Profitability and Central Bank
Intervention,” National Bureau of Economic Research Working Paper
5505, March 1996.
Samuelson, Paul A. “Proof that Properly Anticipated Prices Fluctuate
Randomly,” Industrial Management Review (1965), pp. 41-49.
Shiller, Robert J. “Fashions, Fads and Bubbles in Financial Markets,”
Knights, Raiders and Targets: The Impact of the Hostile Takeover,
J. Coffee, S. Ackerman, and L. Lowenstein, eds., Oxford University
Press, 1988. Reprinted in Market Volatility, by Robert J. Shiller,
MIT Press, 1989.
Levich, Richard M., and Lee R. Thomas. “The Significance of Technical
Trading-Rule Profits in the Foreign Exchange Market: A Bootstrap
Approach,” Journal of International Money and Finance (October
1993), pp. 451-74.
Malkiel, Burton G. A Random Walk Down Wall Street, Fifth Edition,
W. W. Norton & Company, 1990.
Shleifer, Andrei, and Lawrence Summers. “The Noise Trader Approach
to Finance,” Journal of Economic Perspectives (Spring 1990),
pp. 19-33.
Mark, Nelson C. “Exchange Rates and Fundamentals: Evidence on
Long-Horizon Predictability,” The American Economic Review
(March 1995), pp. 201-18.
_______ and Robert W. Vishny. “The Limits of Arbitrage,” Journal of
Finance (March 1997), pp. 35-55.
Meese, Richard A., and Kenneth Rogoff. “Empirical Exchange Rate
Models of the Seventies: Do They Fit out of Sample?,” Journal of
International Economics (February 1983), pp. 3-24.
Siegel, Jeremy J., and Richard H. Thaler. “Anomalies: The Equity
Premium Puzzle,” Journal of Economic Perspectives (Winter 1997),
pp. 191-200.
Moorthy, Vivek. “Efficiency Aspects of Exchange Rate Response to
News: Evidence from U.S. Employment Data,” Journal of International
Financial Markets, Institutions and Money (1995), pp. 1-18.
Sweeney, Richard J. “Beating the foreign exchange market,” Journal of
Finance (March 1986), pp. 163-82.
Murphy, John J. Technical Analysis of the Futures Markets, New York
Institute of Finance, Prentice-Hall, New York, 1986.
Szakmary, Andrew C., and Ike Mathur. “Central Bank Intervention and
Trading Rule Profits in Foreign Exchange Markets,” Journal of
International Money and Finance (August 1997), pp. 513-35.
Neely, Chris, and Paul Weller. “Technical Analysis and Central Bank
Intervention,” Federal Reserve Bank of St. Louis Working Paper 97002A, January 1997.
Taylor, Mark P., and Helen Allen. “The use of technical analysis in the
foreign exchange market,” Journal of International Money and
Finance (June 1992), pp. 304-14.
_______, _______, and Robert Dittmar. “Is Technical Analysis
Profitable in the Foreign Exchange Market? A Genetic Programming
Approach,” Forthcoming in Journal of Financial and Quantitative
Analysis (December 1997).
Treynor, Jack L., and Robert Ferguson. “In Defense of Technical
Analysis,” Journal of Finance (July 1985), pp. 757-73.
Ohanian, Lee E. “When the Bubble Bursts: Psychology or
Fundamentals?,” Business Review, Federal Reserve Bank of
Philadelphia (January/February 1996), pp. 3-13.
Osler, Carol L., and P. H. Kevin Chang. “Head and Shoulders: Not Just a
Flaky Pattern,” Federal Reserve Bank of New York Staff Paper 4,
August 1995.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
38
SEPTEMBER/OCTOBER 1997
Michael R. Pakko is an economist at the Federal Reserve Bank of St. Louis. Kelly M. Morris and Eran Segev provided research assistance.
The Business
Cycle and
Chain-Weighted
GDP: Has Our
Perspective
Changed?
Figure 1
Quarterly Real GDP Growth
1987 Dollars and Chain-Weighted 1992 Dollars
Percent (ar)
8
6
4
2
0
–2
–4
Michael R. Pakko
–6
1989
I
n early 1996 the Bureau of Economic
Analysis released data from an extensive
revision to its measures of aggregate economic activity in the United States. The
new data and methodology, which represent the first comprehensive revision of the
National Income and Product Accounts
(NIPA) since 1991, reflect more substantial changes than have many previous
revisions.1 While past revisions have
changed some definitions and statistics
and have incorporated newly available
source data, the most recent revision
includes an even more important change:
the move from a fixed base-year measure
to a chained index, which is said to have
significantly altered the way we view
recent economic performance.
One analysis in the New York Times
even speculated that the outcome of the
1992 election might have been different if
we had known then what we know now
about the 1990-91 recession:
1990
1991
1987 Dollars
1992
1993
1994
Chain-Weighted 1992 dollars
it actually did, might have changed the
impression among many voters that
President Bush did not care.2
Figure 1 illustrates the effect of the
changes on the 1990-91 recession and subsequent recovery. During the three quarters
of negative economic growth, real GDP registered a 1.8 percent average annual rate of
decline under the old methodology. The
revised figures show an average rate of
decline of more than 2.7 percent. Moreover, the new figures indicate a slower
growth rate in the period of economic
recovery that followed the recession. The
old figures showed a 2.8 percent growth
rate from the first quarter of 1991 to the
first quarter of 1994, while the new figures
indicate 2.5 percent growth.
Two limitations of the old system of
measurement have been cited to support
the claim of superiority for the new chainweighting approach: First, fixed weights
fail to measure the effects of shifting
demand in response to relative price
changes—known as “substitution bias.”
Second, the periodic re-basing required by
the fixed-weight system of measurement
implies a “rewriting of economic history.”3
The revisions suggest that the downturn of 1990-91 was nearly twice as
deep as Washington knew at the time,
raising the possibility that the Federal
Reserve and the White House would
have reacted more aggressively to dig
the economy out of a recession. Such
a response, coming much earlier than
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
39
1
This was the tenth such “comprehensive” revision. See
Parker and Seskin (1996).
2
Passell (1996).
3
E.g., Samuelson (1996).
SEPTEMBER/OCTOBER 1997
But has the revision to NIPA data
really changed our view of the fluctuations
and economic co-movements known collectively as the “business cycle”? Both
casual observation and more rigorous
empirical examination reveal that the revisions to GDP and its components have had
very little effect on the empirical regularities that characterize economic fluctuations.
After first describing the methodological changes and rationales underlying the
recent NIPA data revisions, this article discusses the differences between the revised
data and the previously reported data. It
then presents a comparison of the basic
business cycle regularities as measured
under the old and new systems.
explicitly reflected in the national
accounts. More importantly, government
expenditures have been separated into
categories for current expenditures and government investment expenditures. The
addition of the latter category—which
classifies government expenditures for
structures and equipment as investment—
provides a more complete measure of
investment, including purchases by both
the public and private sectors. The change
also makes figures for U.S. investment
more comparable to those of other countries by treating government investment
in a way that is more consistent with the
International System of National Accounts.
By far the most significant and widely
discussed revision to the NIPA data is the
new methodology for reporting real output
and its components: the “chained” dollar
series. The previously used “constantdollar” measures were based on prices for
a specific base year. (Prior to the most
recent revision, the base year was 1987.)
The new method uses contemporaneous
price data to produce estimates of growth
rates for real output and its components.
The prices are then chained together to
form a series that is independent of the
choice of any particular base year. This
approach mitigates the two related weaknesses of the constant-dollar approach:
“substitution bias” and “rewriting
economic history.”
REVISIONS TO NIPA DATA
4
For a complete description of
the statistical and methodological revisions to the NIPA
accounts, see United States
Department of Commerce–
Bureau of Economic Analysis
(1995).
The revisions to NIPA included new
and revised source data, new methodological procedures, the new chain-weighted
methodology for reporting real output and
its components, and an updated base year.4
Some of the more routine changes
reflect the incorporation of new and
revised source data. For example, the
revisions to the data on non-durable consumption expenditures for 1993 and 1994
are based on newly available information
on retail sales from the 1993 Annual Retail
Trade Survey, while the revisions to data
on nondurable goods are based on the
results of a 1987 input-output analysis
constructed by the Commerce Department.
Revisions to the services consumption data
are based on direct estimates of rental payments for tenant-occupied dwellings, taken
from a 1991 Residential Finance Survey.
In addition, the Commerce department has incorporated some new methodological procedures. For example, it has
replaced the previously used straight-line
method of estimating depreciation with
one based on studies of the prices of used
equipment and structures in resale markets.
There are also two significant changes
in the classification of government expenditures. Federal Government contributions
to civilian and military retirement programs
(rather than benefits paid out) are now
Substitution Bias
A fundamental purpose of the new
chain-weighting methodology is to avoid
the problem known as “substitution bias.”
As relative prices rise and fall, purchasers
tend to substitute less expensive items for
ones on which prices have gone up. In a
fixed-weight system, items with falling
prices continue to constitute a large share
of the total, because this system is based
on their historically higher prices. Similarly,
although sales tend to decline for goods
and services items with rising prices, these
items continue to have low weights because
of their historically lower prices. Consequently, a fixed-weight index tends to
overstate growth in periods after the base
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
40
SEPTEMBER/OCTOBER 1997
CONSTRUCTION OF CHAIN-WEIGHTED INDEXES
The purpose of the change to chain-weighting in the NIPA accounts is to
improve the separation of price changes and quantity changes in the overall
measurement of nominal (current dollar) magnitudes. Specifically, real and
nominal measures are related by
Real GDP =
Nominal GDP
,
P
where P is some measure of the aggregate price level. Previously, this separation
was accomplished by using implicit price deflators to measure P. Implicit price
deflators are examples of what is known as a Paasche price index, calculated as
follows:
N
∑p q
it it
Pt =
P
i =1
N
∑p
,
ib qit
i =1
where p and q denote quantities and prices, respectively; and the subscripts t and b
denote measures in the current period and in a base period, respectively.
An alternative price measure is known as a Laspeyres price index, the formula
for which is
N
∑p q
it ib
Pt =
L
i =1
N
∑p
.
ib qib
i =1
Each of these indexes suffers from substitution bias, giving an imperfect
measure of the price level. The Laspeyeres index tends to overstate price changes,
while the Paasche index tends to understate them.
One way of adjusting for these errors is to take an average of the two. The geometric average of a Laspeyres and a Paasche index is known as a Fisher Index:
Pt F = Pt P x Pt L .
Although this measure is largely free of systematic substitution bias, it is still
subject to the problem of base-year sensitivity. History is rewritten with changes in
the base year, since each of the component price indexes uses a fixed base year.
The new chain-weighted GDP accounts use a variant of the Fisher index, in which
the base year is not fixed. Instead, the previous year is taken as the base. This makes
the price index interpretable as a percentage rate of change from the previous year.
The percentage changes can then be “chained” together and expressed either as an
index number or in terms of some particular reference period.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
41
SEPTEMBER/OCTOBER 1997
prices, caused growth to be overstated.
Computers and related equipment were
not the only items contributing to this
effect, but their role was significant: For
instance, it has been estimated that computers accounted for about three-fifths of
the 1.1 percentage point overstatement of
GDP in the fourth quarter of 1994.5
The tendency of the old 1987-dollar
GDP series to understate growth before
1987 and to overstate growth after 1987
can be seen in Figure 2, which shows
GDP growth from 1960 to 1995.
Measured according to the old fixed-baseyear measure, GDP growth was 3.1
percent before 1987, and 2.3 percent
after. With the new chain-weighted measure, the growth rate measures 3.4
percent prior to 1987, and only 1.8
percent after 1987.
Figure 2
Real GDP
1987 Dollars and Chain-Weighted 1992 Dollars
Trillions of Dollars
8
1.8%
7
6
3.4%
5
4
2.3%
3
3.1%
2
1
1960
1967
1974
1987 Dollars
1981
1988
1995
Chain-Weighted 1992 dollars
Figure 3
A Changing View of
the 1974-75 Recession
Rewriting Economic History
Quarterly Real GNP Growth
Percent Change (ar)
10
5
0
–5
–10
1972
1973
1972 Dollars
5
Landefeld (1995).
6
Jain (1996).
7
Prowse (1996).
8
Ulan (1994).
9
The BEA switched from GNP to
GDP as the featured measure of
output in the 1991 comprehensive revisions. Because figures
for GDP are unavailable for previous base-year series, GNP is
used in Figure 3.
1974
1982 Dollars
1975
1987 Dollars
1976
1977
Chain-Weighted 1992 dollars
year—and understate growth in periods
before the base year. The move to a
“Fisher Ideal” index, implicit in the new
chained figures, largely solves this problem.
(See shaded box p. 41: "Construction of •••:
Chain-Weighted Indexes.”)
The problem of substitution bias
became prominent in the context of computer equipment. Compared to the 1987
base year, prices of computers and
peripherals fell dramatically in the early
1990s, while sales of such equipment rose
sharply. As a result, the volume of
computer sales, valued at high 1987
An additional problem of the old
fixed-weight methodology was that
rebasing the weights resulted in “rewriting macroeconomic history every 5
years.”6 For example, one columnist has
observed that “each time [the base year]
shifts, the 1973-75 recession looks a little
different.”7 In general, the effect of the old
rebasing has been characterized as
providing “a very distorted view of
economic history.”8
Although the new measure continues
to be expressed relative to a specific base
year—now 1992—the chain-weighting
approach makes the choice of a base year
nothing more than a matter of normalization. Price and quantity changes are
calculated as percentage changes from
one year to the next and then “chained”
together in a sequence. As a result, future
“Construction
of base year will no longer
updates of the
result in a revised view of the past,
because the growth rates on which the
chain-weighted figures are based will not
be subject to subsequent revision.
Figure 3 illustrates the rewriting of
history for one episode—the 1973-75
recession and subsequent recovery. It
shows a comparison between the growth
rates of real GNP reported at the time of
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
42
SEPTEMBER/OCTOBER 1997
the 1973-75 recession and the revised figures after each of three revisions to the
base year.9 It is clear that each time the
NIPA accounts were rebased, the pattern
of output growth was revised. However,
the most significant change took place in
the initial comprehensive revision, which
rebased from the original 1972 dollars to
1982 dollars. The subsequent rebasing
to 1987 dollars had little additional
effect on the pattern of real GNP growth.
Perhaps surprisingly, even the chainweighted measure of real GNP growth
does not differ markedly from the 1982
or 1987 fixed-weighted measures, in
spite of the fact that the chained figures
use prices and quantities from the early
1970s, much as the original 1972-based
figures did.
Although rebasing changes the pattern
of real GDP growth, another important
factor in the periodic revision of the NIPA is
the incorporation of improved source data.
New source data is typically integrated by
the time of the first rebasing (or in the
annual benchmark revisions), implying
noticeable changes to the series. Relative to
the original 1972-based data shown in
Figure 3, the first base year shows the most
significant alteration in the pattern of measured economic activity. Subsequent
updates show modest changes. Hence,
Figure 3 suggests that revisions to the base
year are less important than to the incorporation of new source data in initial
benchmark revisions.
Figure 3 also shows that although the
pattern of GNP growth changed from one
revision to another, the overall pattern
did not change markedly. In particular,
the timing of the onset of recession in late
1973 and the recovery in early 1975 was
not affected by revisions. History was
re-written with each revision, but the fundamental features of that episode in the
business cycle remained essentially unchanged. This observation leads naturally to a basic question: Do data
revisions—even those as fundamental as
the recent switch to chain-weighted
GDP—alter our overall perspective on
business cycles?
Figure 4
Real GDP and H-P Trend
Chain-Weighted 1992 Dollars
Trillions of Dollars
7
6
5
4
3
2
1
1960
1965
1970
1975
1980
H-P Trend
1985
1990
1995
1990
1995
Real GDP
H-P Deviations from Trend
Percent
6
4
2
0
–2
–4
–6
1960
1965
1970
1975
1980
1985
NOTE: Shaded bars indicate recessions.
CYCLICAL PATTERNS
UNDER THE OLD AND
NEW METHODS
Each business cycle seems to have its
own unique features, yet certain regularities
characterize the general nature of economic
fluctuations. For various measures of economic activity, key issues about cyclical
behavior revolve around the degree of variability, the persistence of fluctuations, and
co-movement with other economic indicators. Some economic indicators are more
variable than total output, some are less
variable; some tend to move in the same
direction as output (i.e., they are procyclical)
while others tend to move in the opposite
direction (countercyclical). These empirical
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
43
10
Formally, the trend component
isolated by the H-P filter is
given by the equation:
∑ {( y
T
min
t
t =0
[(
− ytτ
)
+ λ ytτ+1 − ytτ
(
)
)]
2
− ytτ − ytτ−1 ,
where ytτ is the trend component of the time series yt , and λ
is a smoothing parameter. For
applications with quarterly economic data, a value of λ=1600
is typically used.
SEPTEMBER/OCTOBER 1997
Figure 5
Real GDP Fluctuations
1987 Dollars and Chain-Weighted 1992 Dollars
Percent
4
2
0
-2
-4
1960
1965
1970
1975
1987 Dollars
11
This approach of comparing various “second moments” of
time series has been popularized in the “real business
cycle” literature; e.g. Kydland
and Prescott (1982); King,
Plosser, and Rebelo (1988).
As in Figures 5 and 6, the data
are logged and detrended by
means of the H-P filter. Two
series—investment and net
exports—are not logged, but
taken as a ratio to GDP in order
to convert to proportionate
measures before detrending.
12
The countercyclicality of prices
is contrary to conventional wisdom and has been an issue of
some controversy. See Kydland
and Prescott (1992).
1980
1985
1990
1995
Chain-Weighted 1992 dollars
regularities of business cycles are fundamental to economists’ attempts to
understand and explain them.
One way of approaching an answer to
the question of how revisions affect our
understanding of business cycles is to
examine various measures and components
of output after adjusting for long-term
growth trends. This section uses a method of
isolating the cyclical components, known as
the Hodrick-Prescott (H-P) filter, to examine
the cyclical behavior of economic variables.
The H-P filter represents a statistical method
of fitting a smooth trend line to an economic
time series. The deviations from the trend
line are then interpreted as the cyclical
component of the series.10
Figure 4 (p. 43) illustrates a way
that the H-P filter separates the trend components from the cyclical components of
GDP. Shaded sections in the lower panel
of Figure 4 indicate periods of recession as
identified by the National Bureau of Economic Research. Note that the periods
identified as economic downturns by the
H-P filtering technique correspond closely
to the official recession episodes.
Figure 5 compares the H-P cyclical
component of fixed-weighted and chainweighted GDP. It is clear from the figure that
the broad pattern of cyclical fluctuations in
real GDP is affected very little by the
change from fixed-weighted 1987 dollars
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
44
to chain-weighted 1992 dollars, particularly
for the 1970s and early 1980s. To the
extent that differences do appear, they
occur mostly in the late 1980s and 1990s
(where recent benchmark and data-source
revisions are important) and in the early
1960s (where fixed 1987 weights are likely
to be particularly misleading due to the
passage of time).
Figure 6 provides a similar comparison
for the major components of GDP. For all
four components, the change from fixedweighted to chain-weighted measures makes
little discernable difference. In all four
cases, revisions to data for the late 1980s
and 1990s are most prominent.
Table 1 provides a more formal examination of the behavior of various components of
GDP, presenting standard deviations, autocorrelations, and cross correlations with GDP.11
The first row of numbers for each variable
shows the previously reported statistics based
on the fixed-weight measures in 1987 dollars.
The second number is the corresponding statistic for chain-weighted 1992 dollars.
The overall patterns are clearly robust
to the methodological changes. As measured by standard deviation, consumption
of nondurables and services is only about
half as variable as output, while durablegoods consumption and investment are
about three times as variable. Each of
these measures shows a strong positive
correlation with output—i.e., each is procyclical. Government spending is about
as variable as GDP, and largely uncorrelated
to the business cycle. Net exports are
countercyclical, due primarily to the
strong procyclicality of imports. Under
either measurement system, the price
level is countercyclical.12 These overall
patterns are clear for both the fixedweighted and chain-weighted data.
Table 1 (pp. 46-47) also reports
probability measures (in italics) for
testing the hypothesis that there is any
significant difference between the statistics for fixed-weight and chain-weighted
figures. 13 A probability value near zero
would indicate that the chain-weighted
statistic was significantly less than the
fixed-weight statistic, while a value near one
SEPTEMBER/OCTOBER 1997
Figure 6
Fluctuations in Components of Real GDP
Consumption Expenditures
Percent
Fixed Investment
Percent
3
15
2
10
1
5
0
0
–1
–5
–2
–0
–3
1960
65
70
75
80
85
90
95
Government Spending
Percent
–5
1960
65
70
75
80
85
90
95
65
70
75
80
85
90
95
Net Exports
Percent
6
2
4
1
2
0
0
–2
–4
–1
–6
–8
1960
–2
65
70
75
80
85
90
95
1987 Dollars
would indicate the opposite. There are no
cases at all for which the probability values
indicate a statistically significant difference
between the measures at conventional
significance levels (< 0.05 or > 0.95).
Table 2 (p. 48) provides information on
the relationship of the two measures of
GDP and other non-NIPA economic indicators. As in Table 1, the differences that arise
as a result of the switch to chain-weighting
are minor. Measures of employment remain
strongly procyclical, while wages and prices
are countercyclical. With the exception of
non-borrowed reserves, measures of money
(including total reserves, the monetary
base, M1 and M2) are procyclical. None of
the comparisons between the fixed-weight
method and chain-weighting in Table 2 is
statistically significant.
1960
1992 Chain-Weighted Dollars
index. Most importantly, the chain-weighting
approach corrects for the “substitution bias”
in which measures of long-term growth
become increasingly distorted as they move
away from a fixed base year. It also resolves
the issue of “rewriting history” every time
there is a comprehensive revision to the
national accounts. However, the switch to
the new approach has little or no effect on an
overall evaluation of the fluctuations and comovements among economic variables that
constitute the business cycle.
13
The probability values for differences in standard deviations are
derived from standard F-tests.
For autocorrelations and crosscorrelations with GDP, the
reported statistic is based on
Z=
N −3
2 2
1 + ρi
x ln
1 − ρi
1+ ρ j
− ln
,
1 − ρ j
which is normally distributed
under the hypothesis that the
correlations, ρ i and ρ j , are
identical.
CONCLUSION
The new chain-weighted GDP measure is
conceptually superior to the old fixed-weight
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
45
SEPTEMBER/OCTOBER 1997
Table 1
Comparisons of the Cyclical Properties of NIPA Data
1987 Dollars and Chained 1992 Dollars — H-P Filtered
Variable
GDP
Standard Deviations
Percent
Ratio to GDP
1.63
1.00
1.71
1.00
0.611
0.85
Autocorrelations
2
3
0.65
4
0.43
0.23
0.84
0.62
0.39
0.19
0.614
0.661
0.655
0.635
Consumption
Nondurables
plus services
0.84
0.52
0.86
0.67
0.50
0.28
0.87
0.51
0.86
0.68
0.50
0.29
0.500
0.440
0.500
0.464
Consumption
Services
0.68
0.42
0.80
0.62
0.46
0.29
0.71
0.41
0.582
0.600
Consumption
Nondurables
0.82
0.65
0.50
0.32
0.315
0.339
0.333
0.392
1.20
0.74
0.84
0.63
0.43
0.20
1.24
0.72
0.84
0.62
0.41
0.18
0.500
0.554
0.580
0.568
0.76
0.60
0.40
0.23
0.576
Consumption
Durables
5.01
3.08
5.01
2.93
0.500
0.77
0.60
0.41
0.24
0.421
0.500
0.461
0.465
Fixed
nonresidential
investment
5.22
3.21
0.89
0.71
0.48
0.23
5.00
2.93
0.89
0.70
0.46
0.21
0.500
0.565
0.584
Change in
business
inventories
0.52
0.32
0.43
0.17
0.01
– 0.15
0.47
0.27
– 0.14
Government
purchases
1.65
1.63
0.400
0.46
0.18
0.02
0.466
0.467
0.466
1.02
0.86
0.72
0.58
0.43
0.95
0.85
0.70
0.58
0.43
0.621
0.631
0.500
0.500
0.64
0.52
0.38
0.22
4.37
2.69
4.38
2.56
0.505
Imports
0.51
0.37
0.19
0.545
0.538
0.602
3.16
0.75
0.54
0.35
0.18
5.29
3.09
0.74
0.54
0.37
0.21
0.574
0.500
0.425
0.398
0.89
0.76
0.64
0.49
0.50
0.31
0.44
0.26
0.226
Price deflator
0.62
0.608
5.14
0.567
Net exports
0.569
0.378
0.276
0.471
Exports
NOTE: The first row of numbers for
each variable refers to 1987
dollars, the second to chainweighted 1993 dollars, and the
third (in italics) is a normal test
statistic, extreme values of
which represent rejection of the
hypothesis that the two measures are equal (see text).
1
0.87
0.73
0.62
0.46
0.769
0.712
0.608
0.626
0.89
0.55
0.91
0.79
0.64
0.46
0.89
0.52
0.93
0.81
0.66
0.48
0.139
0.323
0.387
0.416
0.500
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
46
SEPTEMBER/OCTOBER 1997
Table 1 continued
Comparisons of The Cyclical Properties of NIPA Data
1987 Dollars and Chained 1992 Dollars — H-P Filtered
Cross-Correlations with Y(t+ j)
12
GDP
0
–1
–2
–4
–8
– 12
0.23
0.65
0.85
1.00
0.85
0.65
0.23
– 0.33
– 0.47
– 0.32
0.19
0.62
0.84
1.00
0.84
0.62
0.19
– 0.32
– 0.42
0.635
0.661
0.614
NA
0.614
0.661
0.635
– 0.41
– 0.32
0.17
0.56
0.73
0.86
0.83
0.71
0.37
– 0.24
– 0.42
– 0.40
– 0.32
0.12
0.52
0.71
0.84
0.81
0.69
0.37
– 0.20
– 0.39
0.664
0.680
0.635
0.725
0.693
0.627
0.500
0.463
0.500
.
0.463
0.364
0.303
0.383
– 0.46
– 0.24
0.21
0.51
0.65
0.80
0.79
0.69
0.36
– 0.24
– 0.45
– 0.46
– 0.24
0.18
0.49
0.64
0.76
0.77
0.67
0.38
– 0.19
– 0.41
0.602
0.587
0.556
0.802
0.664
0.621
0.424
– 0.34
– 0.35
0.12
0.53
0.71
0.82
0.77
0.65
0.34
– 0.20
– 0.35
– 0.30
– 0.34
0.06
0.49
0.68
0.80
0.75
0.63
0.32
– 0.19
– 0.33
0.692
0.673
0.685
0.685
0.652
0.610
0.574
0.356
Consumption
Durables
1
– 0.33
0.500
Consumption
Nondurables
2
– 0.42
0.461
Consumption
Services
4
– 0.47
0.303
Consumption
Nondurables
plus services
8
0.500
0.463
0.332
0.466
0.342
0.426
– 0.43
– 0.52
– 0.08
0.38
0.59
0.80
0.77
0.68
0.47
– 0.05
– 0.29
– 0.40
– 0.50
– 0.08
0.39
0.61
0.81
0.78
0.67
0.44
– 0.06
– 0.29
0.500
0.461
0.398
0.407
0.418
0.560
Fixed
nonresidential
investment
– 0.53
0.382
– 0.28
0.49
0.79
0.85
0.80
0.60
0.36
– 0.05
– 0.40
– 0.33
– 0.50
– 0.28
0.45
0.78
0.85
0.81
0.60
0.35
– 0.07
– 0.40
– 0.32
0.665
0.585
0.500
0.407
0.500
0.538
0.566
0.500
Change in
business
inventories
– 0.17
– 0.41
– 0.15
0.20
0.42
0.61
0.44
0.33
0.12
0.00
– 0.14
– 0.13
– 0.40
– 0.16
0.18
0.42
0.65
0.50
0.37
0.12
0.00
– 0.12
Government
purchases
– 0.02
0.368
0.367
– 0.08
Net exports
Price deflator
0.461
0.534
0.568
0.500
0.291
0.40
0.34
0.18
0.13
0.08
0.262
0.353
0.500
0.533
0.500
0.500
0.463
0.433
– 0.01
– 0.07
– 0.14
– 0.18
– 0.12
– 0.03
– 0.12
– 0.22
– 0.16
0.35
0.37
0.26
0.22
0.16
0.05
0.388
0.243
0.221
0.251
0.310
– 0.03
0.29
0.47
0.45
0.41
0.27
0.05
– 0.18
– 0.43
– 0.49
– 0.10
– 0.09
0.23
0.42
0.45
0.42
0.29
0.10
– 0.12
– 0.36
– 0.45
– 0.10
0.691
Imports
0.500
0.623
0.685
0.691
Exports
0.411
0.703
0.370
0.433
0.635
0.335
0.632
0.697
0.500
0.460
0.429
0.339
0.306
0.246
– 0.42
– 0.52
0.06
0.48
0.70
0.75
0.65
0.50
0.28
– 0.09
– 0.30
0.500
– 0.38
– 0.48
– 0.06
– 0.30
0.03
0.45
0.68
0.75
0.66
0.51
0.29
0.347
0.329
0.598
0.624
0.624
0.500
0.442
0.456
0.464
0.39
0.64
0.24
– 0.12
– 0.31
– 0.44
– 0.51
– 0.53
– 0.52
– 0.29
0.14
0.33
0.57
0.21
– 0.12
– 0.31
– 0.43
– 0.49
– 0.50
– 0.48
– 0.26
0.15
0.716
0.820
0.51
0.49
– 0.04
– 0.34
– 0.46
– 0.58
– 0.67
– 0.72
– 0.64
– 0.10
0.35
0.48
0.49
– 0.07
– 0.40
– 0.55
– 0.66
– 0.70
– 0.70
– 0.56
– 0.06
0.34
0.629
0.500
0.603
0.598
0.500
0.718
0.500
0.459
0.842
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
47
0.860
0.413
0.680
0.368
0.369
0.329
0.150
0.401
0.394
0.369
0.500
0.466
0.537
SEPTEMBER/OCTOBER 1997
Table 2
Cross-Correlations of Non-NIPA Variables with GDP
Variable
12
Employment
– 0.45
– 0.17
– 0.43
– 0.18
0.419
Average weekly hours
8
–2
–4
0.55
0.83
0.90
0.84
0.64
0.42
0.03
– 0.44
– 0.4
0.51
0.81
0.88
0.82
0.63
0.41
0.02
– 0.41
– 0.44
0.693
0.788
0.703
0.555
0.540
0.533
– 0.09
0.34
0.60
0.77
0.71
0.55
0.23
– 0.25
– 0.23
– 0.24
– 0.48
– 0.12
– 0.24
– 0.20
0.32
0.58
0.77
0.72
0.57
0.23
0.599
0.574
0.600
0.500
0.433
0.405
0.500
0.381
– 12
– 0.48
0.500
0.677
–8
– 0.26
0.430
0.534
Cross-Correlations with Y(t+ j)
2
1
0
–1
4
0.465
0.377
0.397
Total work effort
– 0.44
– 0.3
0.41
0.76
0.89
0.91
0.75
0.52
0.09
– 0.45
– 0.43
(Employment x Hours)
– 0.42
– 0.31
0.38
0.74
0.87
0.9
0.75
0.53
0.09
– 0.43
– 0.41
Average hourly earnings
CPI
Nonborrowed reserves
Total reserves
0.420
0.536
0.29
0.16
– 0.04
– 0.17
– 0.23
– 0.28
– 0.39
– 0.47
– 0.49
– 0.26
0.500
0.455
0.500
0.419
– 0.05
– 0.16
– 0.21
– 0.25
– 0.35
– 0.43
– 0.46
– 0.25
0.420
0.10
0.12
0.533
0.46
0.54
0.11
– 0.25
– 0.42
– 0.59
– 0.71
– 0.77
– 0.70
– 0.18
0.33
0.44
0.50
0.11
– 0.23
– 0.39
– 0.55
– 0.68
– 0.73
– 0.67
– 0.17
0.30
0.582
0.675
0.09
0.19
– 0.21
– 0.29
– 0.27
– 0.18
– 0.01
0.08
0.21
– 0.17
– 0.29
– 0.29
– 0.20
– 0.05
0.533
0.432
0.500
0.366
0.466
0.430
0.431
0.383
0.395
0.312
0.351
0.315
0.339
0.374
0.465
0.11
0.467
0.224
0.320
0.466
0.609
0.15
0.27
0.27
0.02
0.10
0.22
0.24
0.04
0.500
0.571
0.568
0.630
0.663
0.670
0.605
0.434
– 0.07
0.04
– 0.03
0.03
0.09
0.17
0.24
0.27
0.23
0.04
– 0.07
– 0.05
0.07
– 0.01
0.03
0.07
0.13
0.19
0.21
0.17
0.01
– 0.03
0.402
0.434
0.500
0.566
0.633
0.668
0.701
0.698
– 0.19
0.04
0.11
0.25
0.30
0.36
0.32
0.26
0.16
– 0.09
– 0.20
– 0.18
0.08
0.12
0.23
0.26
0.31
0.26
0.19
0.14
– 0.08
– 0.20
0.370
0.467
0.570
0.640
0.679
0.706
0.729
0.567
0.467
0.598
0.370
0.500
– 0.09
– 0.06
– 0.04
0.09
0.19
0.30
0.36
0.36
0.28
0.05
– 0.19
– 0.06
– 0.01
– 0.03
0.08
0.16
0.25
0.30
0.30
0.21
0.03
– 0.15
0.533
0.601
0.673
0.711
0.711
0.731
0.566
– 0.14
– 0.40
– 0.26
0.03
0.20
0.39
0.52
0.57
0.48
0.06
– 0.26
– 0.13
– 0.39
0.339
0.467
0.367
– 0.26
– 0.26
0.04
0.22
0.40
0.53
0.57
0.48
0.05
0.461
0.500
0.467
0.431
0.461
0.455
0.500
0.500
0.533
– 0.04
0.22
0.48
0.48
0.45
0.34
0.08
– 0.21
– 0.55
– 0.58
– 0.09
– 0.05
0.18
0.43
0.46
0.43
0.34
0.10
– 0.17
– 0.52
– 0.55
– 0.08
0.635
0.699
0.584
0.581
0.500
0.434
0.466
3-month T-bill rate
0.676
0.632
0.401
M2
0.769
0.27
0.466
M1
0.647
0.571
0.434
Monetary base
0.616
0.533
0.366
NOTE: See note to Table 1.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
48
0.364
0.358
0.500
0.467
SEPTEMBER/OCTOBER 1997
REFERENCES
Jain, P. “Feature: The Chain-weighted GDP Measure,” Prudential
Economics (January 1996), pp. 14-17.
King, Robert G., Charles I. Plosser, and Sergio T. Rebelo. “Production,
Growth and Business Cycles: I. The Basic Neoclassical Model,” Journal
of Monetary Economics (1988), pp. 195-232.
Kydland, Finn E., and Edward C. Prescott. “Business Cycles: Real Facts
and a Monetary Myth,” Quarterly Review, Federal Reserve Bank of
Minneapolis (Spring 1990), pp. 3-18.
_____ and _____. “Time to Build and Aggregate Fluctuations,”
Econometrica (1982), pp. 1345-70.
Landefeld, J. Steven. “BEA’s Featured Measure of Output and Prices,”
NABE News 113 (September 1995).
Parker, Robert P., and Eugene P. Seskin. “Improved Estimates of the
National Income and Product Accounts for 1959-95: Results of the
Comprehensive Revision,” Survey of Current Business,
(January/February 1996), pp. 1-31.
Passell, Peter. “Maybe It Wasn’t the Economy in the 1992 Election,”
New York Times, Jan. 20, 1996.
Prowse, Michael. “Lies, Damned Lies, and the US Commerce Department’s
New Way of Measuring GDP,” Financial Times, Jan. 20-21, 1996.
Samuelson, Robert J. “Rewriting Economic History,” The Washington
Post, Feb. 28, 1996.
Ulan, Michael. “Is the Current Business Cycle Different? Does How We
Measure Matter?” Business Economics (April 1994), pp. 41-47.
United States Department of Commerce–Bureau of Economic Analysis,
“Gross Domestic Product: Third Quarter 1995 (preliminary); Corporate
Profits, Third Quarter 1995 (preliminary); and Revised Estimates,
1959-95,” Survey of Current Business (November/December
1995), pp. 1-47.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
49
SEPTEMBER/OCTOBER 1997
Katrin Wesche is an assistant professor of economics at the Institut für Internationale Wirtschaftspolitik, Universität Bonn in Bonn, Germany.
Cindy Gleit provided research assistance. The author is grateful to Dan Thornton for his invaluable help, and to Barry Jones and Travis
Nesmith, whose comments substantially improved this paper.
The Demand for
Divisia Money
in a Core
Monetary Union
constructed for each country, and the
European aggregate is taken to be the
average of the national indexes. In the
direct method, the components are added
across countries, and weighted averages of
national interest rates are used to obtain
the user cost for each component. Neither
approach is strictly consistent with aggregation theory, because aggregation by
averaging national Divisia implicitly
assumes perfect substitutability across
indexes, and the summation of monetary
assets across countries requires that assets,
denominated in different currencies, be
perfect substitutes.
Aggregation over different national
moneys should employ appropriate
methods.3 European monetary aggregation
that uses indexes for monetary services is
particularly attractive because such indexes
can account for the different paces of
financial innovation in the countries of
Europe. The main contribution of this
paper is to apply the aggregation theoretic
framework consistently to money holdings
of European residents.4 The first section
presents the definition of the Divisia index.
The second derives a European Divisia
index. In the third, the Divisia index and
a simple-sum measure of European money
are compared and analyzed.
Katrin Wesche
T
he ratification of the Maastricht Treaty
and the agreement on the constitution
of the European Central Bank have
given rise to a number of papers investigating the demand for money in Europe.
In most of this work, conventional simplesum aggregates have been used to measure
the quantity of money in the European
Union.1 However, proponents of the
aggregation theoretic approach to the
demand for money argue that simple-sum
measures lack adequate theoretical foundations and fail to capture the theoretical
notion of money. This is especially true
for broad monetary aggregates, which
include components that are imperfect
substitutes for transactions media. The
use of simple-sum aggregates in the investigation of European money demand,
therefore, is questionable—especially since
the European Central Bank will presumably target a broad monetary aggregate.
Some research on European money
demand has considered aggregation theory.
For example, Fase and Winder (1994) and
Fase (1996) compute European Divisia
monetary indexes2 for different groups of
countries in the European Union and find
that European money demand is fairly
stable. A similar result is obtained by
Monticelli and Papi (1996), who construct
a currency-equivalent index proposed by
Rotemberg, Driscoll, and Poterba (1995).
Both studies construct indexes by
using the direct and the indirect methods.
In the indirect method, an index is
THE DIVISIA MONETARY
INDEX
Most writers define money according to
the functions it performs.5 Monetary assets
serve as a medium of transaction, a store of
value, and a unit of account, with the
medium-of-transaction function being crucial for distinguishing monetary assets from
other financial assets. It has, however,
become commonplace for monetary aggregates to include financial assets that are not
mutually exchangeable.6 For example, savings and time deposits are included in the
M2 monetary aggregate, despite the fact that
they cannot be used to make transactions.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
51
1
See, e.g., Kremers and Lane
(1990), Monticelli and StraussKahn (1991), Artis et al.
(1993).
2
The term Divisia index is used
throughout the paper to refer to
the Törnqvist-Theil discrete time
approximation to the continuous time index suggested by
Divisia (1926).
3
Marquez (1987) tackles this
problem by applying the aggregation approach to money
demand in an open economy.
But since his focus is on the
holdings decisions of residents,
only residents’ holdings of foreign currency are included. The
same is the case in the study
by Ewis and Fisher (1984),
who find strong substitutability
between domestic and foreign
monetary assets with a
translog utility model.
4
This paper focuses on the consumer’s problem. For models
applicable to firms and financial
intermediaries, see Barnett
(1987).
5
See Osborne (1992) for a
more detailed survey on different approaches to the definition
of money.
6
In accordance with aggregation
theory, a monetary aggregate
is defined over a weakly separable block in the utility function. This definition is rarely
implemented because tests for
blockwise weak separability are
biased towards rejection; a single rejection in the data renders
the formation of a separable
group impossible. For a discussion of separability tests and
applications to U.S. monetary
data, see Swofford and
Whitney (1986, 1987, 1988,
1994).
SEPTEMBER/OCTOBER 1997
7
Barry Jones and Travis Nesmith
point out that the aggregation
approach provides a “negative”
definition of monetary services
by specifying that the store-ofvalue function is not a monetary service. Nevertheless, as
the Divisia index intends to
measure a monetary services
flow (and given the difficulties
with testing for weak separability), some a priori idea of
which assets contain a monetary services component is useful for determining which assets
to include in a monetary aggregate (see Barnett, 1982,
p. 697).
8
Aggregation of real monetary
assets is equivalent to aggregating nominal assets and
deflating the monetary services
index afterwards (see
Anderson, Jones, and Nesmith,
1997b).
9
Actually, the single-period utility
function is a special case. The
solution to the single-period utility function is equivalent to an
intertemporal optimization if
current-period monetary assets
are weakly separable from the
other decision variables in the
consumer’s utility function.
10 Money
is included in the utility
function, since it provides services such as convenience, liquidity, and information. For the
equivalence of putting money
into the utility function or solely
into the budget constraint, see
Barnett, Fisher, and Serletis
(1992, p. 2093). Barnett
(1987) shows that with
money entering the production
function as durable physical
capital, the user cost formula
also applies for firms. The user
costs also have the same form
for financial intermediaries, if
no reserve requirements are
present.
The idea in the aggregation approach
is to extract the store-of-value function
from all financial assets, so that what
remains are the “monetary services” for
the assets.7 It is assumed that the store-ofvalue characteristic of an asset is reflected
by its investment yield and that one asset,
called the benchmark asset, provides only
the store-of-value function and no other.
In addition, instead of simply adding such
assets together, as it is done in simple-sum
aggregation, the theoretical approach of
aggregation creates an index of monetary
services that has microeconomic foundations. While conventional monetary
aggregates are derived in a simple
accounting procedure from the banking
sector’s balance sheet, the theoretical
approach, or Divisia index, is based on the
optimizing behavior of economic agents.
One way to see how this aggregation theory
approach compares with simple-sum
aggregation is to assume that individuals
maximize a utility function composed of a
number of real monetary assets, Mi /p*, 8
and commodities that are directly consumed,
Cj.9 That is, consumers maximize
the optimal quantities of consumption
goods and optimal total expenditures on
monetary assets. In the second stage, the
monetary expenditures are allocated
among specific monetary assets. The solution to the maximization problem that
uses the two-step approach is identical to
the one that uses a one-step approach so
long as the marginal rate of substitution
between any two monetary assets does not
depend on the quantities of commodities
consumed (Barnett, Fisher, and Serletis,
1992). This condition, referred to as
blockwise weak separability, is necessary
for economic aggregation.12 If this condition is satisfied, the monetary aggregate
behaves like a single economic good for
which a demand function exits.13 Under
these assumptions, M is the monetary
aggregate that we desire to measure.
The discrete-time approximation to the
continuous-time Divisia index is exact for
a function that can provide a second-order
approximation to any arbitrary aggregator
function, M, and therefore belongs to the
class of superlative indexes, as defined by
Diewert (1976).14 The growth rate of the
Divisia index is defined as
M M
M
U = U 1 , 2 ,…, I , C1, C2 ,…, CJ ,
p* p*
p*
log Qt - log Qt - 1
N
subject to a budget constraint, where p* is
a true cost-of-living index.10 The I monetary assets commonly include assets that
are used directly in transactions—i.e., cash
and checkable deposits—but may include
other financial assets such as saving and
time deposits as well.
The aggregation approach assumes
that there exists an aggregator function,
=
∑ s (log
it
i =1
sit =
M
Mit
– log i*,t - 1 ,
*
pt
pt - 1
(
1
(sit + si ,t - 1 ).
2
with the expenditure shares
sit =
M M
M 11
M = f 1 , 2 , …, I .
p* p*
p *
π it Mit
K
∑ π kt Mkt
.
k =1
In the aggregation approach, money is
regarded as a durable good that yields services in facilitating transactions and
providing liquidity. The user cost, pit , for
monetary services therefore can be derived
in a fashion analogous to that used to derive
the user cost for a durable consumption
good (see Donovan, 1978; Barnett, 1978,
1987).15 For a durable consumption good,
the one-period holding cost, or rental price,
The utility function can be rewritten as
U = F (M , C1, C2 ,…, CJ ),
so that the demand for money can be separated from the demand for consumption
goods. Consumers can be seen as allocating
their budget in two stages (Green, 1964).
In the first stage, the consumer chooses
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
52
SEPTEMBER/OCTOBER 1997
is given by the cost of the purchase of
the good in the current period less the
discounted expected resale value of the
depreciated good in the next period.16 The
opportunity cost of a component monetary
asset is measured by the user cost, pit , of
the ith monetary asset, defined by
π it = pt*
financial assets across countries. Namely,
the value of component assets changes as
exchange rates vary. Hence, the aggregation approach must be modified to
account for expected changes in the
exchange rate.
The stock of monetary assets is
redefined to account for currencies of
different denominations. That is, the representative consumer is assumed to hold
real monetary assets, denominated in different European currencies,
Rt - rit
,
1 + Rt
which is a function of the difference
between the own rate of return on the ith
asset, ri , and the return on a so-called
benchmark asset, R. The benchmark asset
is assumed to provide no monetary services
and is used only to transfer wealth between
periods. The user cost is larger, the smaller
is the own rate of return. The own rate
of return on cash is taken to be zero and
therefore cash has the highest user cost.
On the other hand, a monetary asset
earning the benchmark’s rate of return
would not contribute to the growth of
the index in that period.17
The aggregation approach does not
consider aggregation over a diverse population of individuals. To deal with the
problem, it uses the concept of a representative consumer. In essence, the behavior
of the representative consumer is assumed
to reflect the average behavior of the population. Researchers frequently employ the
representative agent methodology to avoid
the problems that can arise from aggregation over a diverse group of individuals
(see Phlips, 1974, p. 100). The assumption of a representative consumer is very
restrictive, but it is assumed in simple-sum
aggregation as well.
Mik,t / ek,t
pt*
,
where Mik is the ith monetary asset denominated in the kth country’s currency and e k
is the kth country’s exchange rate relative
to a weighted currency basket like the ECU
(see Wesche, 1996 for details). As it is
assumed that the representative consumer
allocates his consumption expenditure on
European consumption goods, the true
cost-of-living price index, p*, is defined
in terms of this bundle of European
consumption goods.
In addition, the own rate of return, rik ,
of a component monetary asset has to take
account of the expected depreciation or
appreciation of the respective currency relative to the weighted exchange rate. The
nominal user cost for the European Divisia
index thus becomes
R − r +δe
π ik,t = p t ik,t k,t ,
1 + Rt
*
t
with
EUROPEAN MONETARY
AGGREGATION
δ ek,t =
eke,t +1− ek,t
ek,t +1
being the expected depreciation of the kth
country’s currency and Rt = max(Rk,t – dke,te)
the European benchmark yield, which is
the highest yield on a portfolio of European
bonds, corrected for expected depreciation
of the exchange rate. The main difference
between the user cost in the multiplecountry framework and the single-country
case is that the user cost reflects the
expected capital gain (or loss) on money
To derive a European monetary aggregate, researchers assume that consumers
hold a diversified portfolio of European
currencies with different degrees of
liquidity.18 Nevertheless, in contrast to
the computation of a Divisia index for a
single country, an additional difficulty
arises when the Divisia index is applied to
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
53
11 Technically,
M is the function
evaluated at its optimal point.
12 The
precise conditions under
which aggregation is valid are
stated in Anderson, Jones, and
Nesmith (1997a).
13 The
condition of blockwise
weak separability can be tested
by examining the consistency
of consumer choices. A violation of consistency would occur
if consumers chose a different
market basket even if prices
remained unchanged.
14 In
fact, the continuous time
Divisia index is always exact.
In contrast, the simple-sum is
the exact index if, and only if,
all of the component assets are
perfect substitutes. Moreover,
the simple-sum has no statistical
properties in any case.
15 The
user-cost formula is derived
from a dynamic budget constraint. User costs are correct
even if aggregation is not valid.
16 Even
if the good is held more
than one period, it can be
assumed that the holder sells
the good to himself at the end
of each period (see Donovan,
1978).
17 This
implies that the asset does
not provide any marginal utility
in that period (Barnett, 1996).
18 This
says nothing about the
substitutability of different
national moneys. Indeed, failure of the representative consumer to react in his portfolio
composition to exchange-rate
changes indicates that different
currencies are not close substitutes.
SEPTEMBER/OCTOBER 1997
19 Unless
otherwise indicated, all
data are from the International
Monetary Fund’s International
Financial Statistics.
20 The
weighted exchange rate
uses GDP weights, converted
with purchasing power parities
from the OECD (1990).
holdings that results from exchange-rate
fluctuations. A capital gain caused by an
appreciation of the exchange rate is treated
like the interest yield of a monetary asset.
Though national currencies have different
user costs, consumers hold all of them
because they are imperfect substitutes. If
they were perfect substitutes, the representative consumer would hold only the
currency with the lowest user cost.
the benchmark rate, although even longterm bonds are not completely illiquid.
To construct a European benchmark rate,
we assume that bonds denominated in
different currencies perform the same
function — i.e., the transfer of wealth
between periods. So the benchmark rate
becomes the highest national interest rate,
corrected for expected depreciation.23
In theory, the benchmark yield is the
maximum expected holding-period yield
in the economy (Barnett, Fisher, and
Serletis, 1992).24 Any asset that yields
monetary or liquidity services must earn
less interest than the benchmark asset. In
reality, however, interest rates on time
deposits are often higher than long-term
rates. This would cause the user costs to
become negative if the long-term rate is
taken to be the benchmark rate.
To avoid negative user costs, which
make no sense, two types of adjustments
have been used. In the first, the user cost
is augmented by its minimum value. This
approach can be interpreted as a “liquidity
mark-up,” since data on the theoretically
correct benchmark yield are difficult to
identify. This method is arbitrary, however,
as the particular minimum value depends
on the sample period. In the second
approach, the asset yielding the highest
return in the period is taken to be the
benchmark asset. Fisher, Hudson, and
Pradhan (1993) argue that in principle the
benchmark asset should not provide monetary services and, therefore, an asset that
is included as money in a previous time
period should not be used later as the
benchmark. Thus in some periods an asset
will have a zero user cost and a resulting
zero contribution to the growth rate of the
monetary index. Only results for the
index obtained with the second method
are presented here.25 It is generally
assumed that M1 earns no interest. For
the interest rate on quasi money, I use
the money market rate.26
Figures 1a to 1c show the user cost for
narrow money and quasi money for each
of the three countries. The user costs for
M1 are very similar for all countries after
1987 because of the convergence of nom-
21 Here
I construct the Divisia
index in a single stage. One
could also (with the appropriate
separability assumptions) use a
two-stage aggregation
approach in which the consumer first allocates his expenditures among monetary assets
in different national currencies,
and then among monetary
assets denominated in the
same currency with different
degrees of liquidity. Though
this approach has more restrictive assumptions, it would be
advantageous if higher-quality
national Divisia indexes could
be used.
22 Following
the literature, aggregation is performed over the
components of the official
aggregates and implicitly
assumes that weak separability
is satisfied (see also Thornton
and Yue, 1992; Fisher, Hudson,
and Pradhan, 1993; or Gaab
and Mullineux, 1996).
23 Expected depreciation is proxied
by actual depreciation, assuming
zero uncertainty and agents
with rational expectations. These
assumptions are restrictive and
do not hold in practice. However,
because expected depreciation
enters in the numerator and
denominator of the user cost,
errors may cancel out partially.
To capture uncertainty, the user
cost could be adjusted by an
additional term reflecting the
interest-rate and exchange-rate
risk (see Barnett and Liu,
1995). This aspect, however,
is neglected here.
Construction of the Index
The countries investigated are Germany,
France, and the Netherlands, the most
likely candidates for a core monetary union.
A currency union without Germany and
France is inconceivable, since these two
countries are the driving forces behind
European unification. The Netherlands,
being the only country for which the
narrow exchange rate targets currently
apply, has close economic relations with
Germany as well as with France. Data are
quarterly from 1973:1 to 1994:4.19
The simple-sum European money
stock is converted with current exchange
rates and expressed in a weighted currency.20
As in Fase and Winder (1994) and Monticelli and Papi (1996), aggregation is
performed over two different groups of
monetary assets: narrow money (M1) and
quasi money (M3-M1) as defined in the
International Financial Statistics.21,22 The
income variable, gross domestic product
(GDP), is also converted into a weighted
currency. The European price index, used
to deflate the simple-sum and the Divisia
aggregates, is obtained through aggregation of national consumer price indexes
with GDP weights, based on current
exchange rates.
Identifying the benchmark asset is difficult. Conceptually, the benchmark asset
offers no transactions services and can be
used only to transfer wealth between
periods. Moreover, in order to be comparable to monetary assets, the benchmark
asset should be capital-certain, and its
yield should not include a risk premium
(see Fisher, Hudson, and Pradhan, 1993).
The yield on government bonds is taken as
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
54
SEPTEMBER/OCTOBER 1997
Figures 1a-1c
Figures 2a-2c
User Cost for Narrow
Money and Quasi Money
(Percent)
Annual Growth Rate of
Narrow Money and
Quasi Money (Percent)
Germany
Germany
25
25
20
20
15
15
10
10
5
5
0
–5
0
74
76
78
80
82
84
86
88
90
92
74
94
France
France
25
20
20
15
76
78
80
82
84
86
88
90
92
94
10
15
5
10
0
5
24 To
–5
0
–10
74
76
78
80
82
84
86
88
90
92
74
94
76
Netherlands
Netherlands
20
25
78
80
82
84
86
88
90
92
94
78
80
82
84
86
88
90
92
94
20
15
15
10
10
5
5
0
0
74
76
78
80
82
84
86
88
90
92
–5
94
74
— UC Quasi Money — UC Narrow Money
76
— Quasi Money
inal interest rates during the “hard” period
of the European Monetary System following
the Basle-Nyborg agreement in 1987.
Even the widening of the exchange rate
bands in 1993 had almost no effect on the
user cost, since neither the French franc
nor the Dutch guilder depreciated significantly against the German mark.
The user cost of quasi money is
surprisingly low for France because the
short-term interest rate in France is
relatively high—often higher than the government bond yield. Consequently, the
French money market rate is frequently
— Narrow Money
the benchmark rate. After the establishment of the European Monetary System,
the user costs for the three countries narrowed considerably, indicating progress in
monetary and financial integration.
Figures 2a to 2c show the growth of
the monetary components in the three
countries. The German Unification is
denoted by the sharp spike in money
growth rates in 1990, with M1 growing
more rapidly than quasi money. Money
growth in France declined steadily after
the beginning of the ’80s. After German
Unification, France had to follow a very
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
55
be comparable, interest
rates should be holding-period
adjusted; liquidity premia are
generally higher on longer
maturity assets. This can be
done by estimating a yield
curve adjustment (see e.g.,
Anderson, Jones, and Nesmith,
1997b, or Farr and Johnson,
1985). Unfortunately, in the
International Financial Statistics
no data on the yield curve for
government bonds are available. The own rates on monetary assets, however, are
comparable as they refer to
the same holding period.
25 Which
of these two adjustments for negative user costs is
used makes no qualitative difference for the empirical
results. To avoid taking logarithms of zero, a very small
constant of less than a basis
point was further added to the
user costs (see Anderson,
Jones, and Nesmith, 1997b).
26 A
deposit rate would have been
preferable but was not available for all countries over the
sample period.
SEPTEMBER/OCTOBER 1997
Table 1
Figures 3a, 3b
Nominal Growth Rates of Monetary
Aggregates
Annual Growth Rates
Divisia and M1
QM3
M3
M3-M1
M1
Sample 73:1-94:4
Mean
Standard deviation
7.39
2.56
7.69
2.30
7.66
2.43
7.74
3.08
Sample 73:1-78:4
Mean
Standard deviation
8.79
2.44
9.36
1.26
9.01
1.41
10.04
3.49
Sample 79:1-86:4
Mean
Standard deviation
8.17
2.12
8.56
1.97
8.81
2.25
8.07
2.11
Sample 87:1-90:2
Mean
Standard deviation
5.80
0.98
5.57
1.34
5.27
1.62
6.15
1.22
Sample 90:3-94:4
Mean
Standard deviation
Percent
20
M1
15
10
5
Divisia
0
–5
74
76
78
80
82
5.56
1.57
5.67
1.55
86
88
90
92
94
86
88
90
92
94
Divisia and M3
Percent
14
M3
12
5.38
2.50
84
10
5.34
2.63
8
6
NOTES: “QM3” denotes the Divisia aggregate, “M3” the simple-sum monetary aggregate,
“M1” narrow money, and “M3–M1” quasi money for Germany, France, and the
Netherlands. Annual growth rates in percent.
4
Divisia
2
0
74
27 These
characteristics are also
found by Gaab and Mullineux
(1996), and by Issing et al.
(1993) for Germany, and by
Gaiotti (1994) for Italy.
restrictive monetary policy to support its
exchange rate. This is reflected in the
sharp drop in M1 growth in 1990, and in
the relatively slow money growth and
quasi money growth thereafter. Money
growth slowed over the sample period in
the Netherlands, although no clear effect
of German Unification is seen. This is not
surprising, since the Netherlands did not
experience an exchange-rate crisis.
Substitutability between narrow money
and quasi money appears to be high for all
these countries, but particularly so for the
Netherlands. This is especially true at the
beginning of the sample period when the
growth rates of narrow money and quasi
money moved in opposite directions. The
user costs for non-interest-bearing money
are highest in France because, on average,
France had higher inflation in the first part
of the sample leading to exchange-rate
depreciation against both of the other
currencies.
Figures 3a and 3b compare the annual
growth rates of the European Divisia
indexes and the traditional simple-sum
aggregates. From 1982 on, the growth
76
78
80
82
84
rates of the Divisia index and M1 were very
close. As the money market rate is used as
own rate on quasi money, the user cost for
quasi money is presumably too low because
time and savings deposits in general earn an
interest rate below the money market rate.
Consequently, the share of quasi money in
the index is biased downwards, and the
Divisia aggregate behaves much like M1.
Table 1 shows descriptive statistics
for the whole sample period as well as for
different subperiods. Like the national
Divisia indexes for 10 European countries
computed by Fase and Winder (1994),
nominal Divisia money shows a lower
average growth rate and a higher standard
deviation than simple-sum M3.27
Differences between the growth rates of
the Divisia index and the traditional aggregates are not significant for any sample
period. From 1987 onwards, the growth
rate of all aggregates fell considerably.
Even after German Unification, money
growth was lower than in every other subsample, despite the rise in the German
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
56
SEPTEMBER/OCTOBER 1997
Table 2
Unit Root Tests
Variable
QM3R
M3R
EXPR
GDPR
PQ
GBY
ADF Level
ADF 1. Diff.
–2.858
–3.075
–4.409
–3.306
–1.954
–2.257
–3.781
–3.303
–5.631
–3.318
–5.433
–3.619
Regression
T
T
T
T
C
C
C
C
C
C
N
N
Conclusion
unit root
unit root
trend stationary
unit root
unit root
unit root
NOTES: “QM3R“ is real Divisia money, “M3R” real simple-sum money. “EXPR” denotes real expenditure on consumption and monetary
services, “GDPR” real gross domestic product, “PQ” the price dual to the Divisia index, and “GBY” the government bond yield.
Except for the government bond yield, all variables are in logs. The sample period is 1973:1 to 1994:4. “ADF Level” and “ADF
1. Diff.” are the Augmented Dickey-Fuller test statistics for the levels and the first differences of the variables, respectively. The
“Regression” column shows the specification of the test, with the first entry referring to the test of the levels, and the second
entry the test of the first differences of the variables. “T” indicates the inclusion of a trend and a constant, “C” the inclusion of a
constant only, and “N” a test without trend and constant. All tests include four lags. Critical values are –3.464 for the tests
including a trend and a constant, –2.896 for the tests with a constant only, and –1.946 for the tests without trend and constant
(MacKinnon, 1991).
money stock. This rise was compensated
by a very slow money growth in France.
In general, all monetary aggregates show
the same picture and are consistent with
the lower inflation and the more stable
exchange rates that have prevailed in
Europe since the mid-’80s.
Furthermore, expenditures on monetary
services are not included in GDP but would
be included in the representative household’s
budget constraint. Similar considerations
apply to the opportunity cost variable
frequently used in money demand
estimations. Modeling the demand for
Divisia money in the conventional way is
justifiable from a policymaker’s perspective.
A measure of money is useful to the policymaker only insofar as it conveys information
about the behavior of objective variables,
such as prices and output (see Pill and
Pradhan, 1994).
Two different money demand equations
for Divisia money are estimated here: The
first one uses expenditures on consumption
and monetary services as the income variable 29 and the Divisia price dual 30 as
opportunity cost. The second uses GDP
and an interest rate as regressors. These
regressions are compared to a conventional
simple-sum money demand function.
Money Demand
Analysts often assess the performance
of a Divisia index by estimating a demand
function for Divisia money and comparing
it to the money demand function for a
simple-sum aggregate.28 Money demand
functions generally include real income
and an interest rate as explanatory variables.
However, Barnett (1996) argues that these
variables are inconsistent with demand
theory. The Divisia index is derived from a
utility maximization framework; hence,
the demand for Divisia money should be
modeled according to demand theory as
the first stage of the budget allocation, in
which the agent allocates his expenditures
among consumption goods and monetary
services. However, national income does
not correspond to the representative
agent’s income as it appears in the budget
constraint. For example, GDP contains
components such as investment that do
not appear in the budget constraint.
28 See,
e.g., Gaab and Mullineux
(1996), and Barnett (1982).
29 Data
on private consumption
expenditures are from the
OECD National Accounts. Data
were converted to 1990 prices
for France (1980 prices) and
Germany (1991 prices). For
the Netherlands, consumption
data from 1973:1 to 1976:4
were extrapolated with GDP
data. As for Germany, pre-unification data are seasonally
adjusted and post-unification
data are not; these were adjusted by a regression on three
seasonal dummies and a constant.
30 The
EMPIRICAL RESULTS
Before the model is specified, the time
series are tested for their order of integration. Table 2 presents the results of the
unit root tests. Most variables are
integrated of order one; only real expenditures seem to be trend-stationary in levels.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
57
price dual is computed by
dividing total expenditure on
monetary assets by the Divisia
quantity index. As the Divisia
index is computed over real
monetary assets, the corresponding price dual is nominal
(see Anderson, Jones, and
Nesmith, 1997b).
SEPTEMBER/OCTOBER 1997
Divisia aggregate than the interest rate elasticity of simple-sum M3. Nevertheless, if
the Divisia aggregate is regressed on GDP
and the government bond yield, the results
are almost identical to those obtained with
M3. Stationarity of the residuals is tested
with a Dickey-Fuller test. Residuals are
stationary at the 5 percent level for both of
the Divisia regressions but not for M3.
Next, the dynamic adjustment to the
long-run relationship is modeled. Dynamic
models are specified according to the
general-to-specific approach, starting with
four lags of each variable. Insignificant terms
have been eliminated. Table 4 shows the
final specifications, including the errorcorrection term, a dummy for German monetary union, and four seasonal dummies.
Each of the dynamic models is
satisfactory. For M3, lagged changes of
real GDP have no significant effect. In all
equations, the error-correction term is
highly significant. For the Divisia aggregate,
about 20 percent of the deviation from
equilibrium is corrected each quarter,
whereas the error-correction term for
M3 is slightly lower.
Table 3
Estimation Results for the Long-Run Relation
Variable
Constant
EXPR/GDPR
PQ/GBY
Adj. R2
Durbin-Watson
Dickey-Fuller
QM3R
QM3R
M3R
–22.002
0.875
–0.224
–23.519
0.869
–0.015
–8.722
0.971
–0.013
0.975
0.514
–3.935
0.977
0.468
–4.203
0.988
0.326
–3.228
NOTES: All regressions include a dummy for German Unification, which takes the value of 1 from
1990:3 on, and seasonal dummies. The first column shows the regression of the real Divisia
aggregate on real expenditure and the price dual. The second column regresses the real
Divisia aggregate on real GDP and the government bond yield. The third column gives the
results for real M3, real GDP, and the government bond yield. The last line shows the
Dickey-Fuller test statistic for stationarity of the residuals. The critical value for the 5 percent level is –3.840 (McKinnon, 1991). See also notes to Table 2.
31 In
fact, parameter estimates
are superconsistent: they converge asymptotically against
the true parameter values at an
even faster rate than in usual
OLS regressions. Small sample
bias, however, can be severe
(see Banerjee et al., 1993).
32 The
Engle-Granger method is
less efficient than the Johansen
approach, since the long-run
relation is estimated without
the information in the dynamic
adjustment. Moreover, with
more than two variables, testing for the existence of multiple
cointegrating vectors is impossible. On the other hand, the
Johansen method is often very
sensitive to the lag choice.
33 T-values
are not shown, since
their distribution is nonstandard.
Money demand is estimated with the
Engle-Granger method, which uses ordinary
least squares to estimate the long-run relation. Though non-stationary variables are
involved in the regression, parameter estimates remain consistent.31 If cointegration
exists—that is, if the variables move together
in the long run—the residuals must be stationary. Nevertheless, they may exhibit
autocorrelation or non-normality because
the dynamic adjustment is not modeled in
the first step.32
Results for the long-run relations are
shown in Table 3. Three different equations
are estimated. The first column shows
the results for the Divisia money demand
regression, including the real Divisia quantity index, expenditures on consumption
and monetary services, and the price dual.
The second column regresses the Divisia
index on GDP and the government bond
yield. The third column gives the results
for a conventional money demand equation
for M3. All regressions include four seasonal
dummies and a dummy for German Unification that takes the value of one from
the third quarter of 1990 onwards and
zero elsewhere.
The income elasticity is close to unity
in all three regressions, though the point
estimate for the Divisia equations is slightly
lower than that of simple-sum M3.33 The
price dual elasticity is much higher for the
CONCLUSION
The Divisia index has microeconomic
foundations and empirically performs
better than the simple-sum M3. While the
aggregation approach regards money as a
durable consumption good yielding a flow
of services, simple-sum aggregation treats
money as a component of wealth in a
simple accounting procedure. In this paper,
a consistent framework for the aggregation
of monetary assets in different currencies
has been developed. With completely
fixed exchange rates, the European Divisia
index equals the conventional Divisia index,
since depreciation vanishes. If a common
currency is introduced, monetary assets of
the same degree of liquidity become indistinguishable for the consumer and can be
aggregated across countries by simplesum aggregation.
The advantage of the Divisia index is
likely to be important during the transition
to monetary union, because this index can
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
58
SEPTEMBER/OCTOBER 1997
take account of increased exchange-rate
stability. Moreover, it can cope better
with financial innovation. The move to a
currency union will liberalize financial
markets and increase competition in the
banking sector, and will presumably lead
to new financial products in those countries
where markets are still regulated. As
payments systems still differ among the
European countries, the Divisia index may
give a more appropriate indication of
liquidity in Europe until a completely integrated financial market has developed (see
Spencer, 1995). Even after the financial
markets have been completely integrated,
the Divisia index would continue to be more
valid than simple-sum measures, because
substitution effects between assets with
different degrees of liquidity will remain.
Though the Divisia index performs
slightly better, the empirical differences
between the Divisia index and simple-sum
M3 with regard to money demand are small.
This lack of striking findings is probably a
result of the degree of disaggregation, since
the breakup into narrow money and quasi
money is a very crude one. Nevertheless,
the Divisia index of European monetary
services may provide additional insight
into money demand during the period of
transition to monetary union. With more
disaggregated data on monetary assets and
the corresponding interest rates, the
performance of the Divisia index relative
to simple-sum indexes would likely
improve. Therefore, the European Monetary Institute should monitor Divisia
aggregates in addition to M3 during the
transition to a monetary union.
Table 4
Dynamic Equations
Variable
Constant
DM3R(–1)
DQM3R(–4)/
DM3R(–4)
DEXPR
DGDPR(–1)
DPQ/DGBY
RES(–1)
Adj. R2
SEE
Durbin-Watson
DQM3R
DQM3R
DM3R
–0.170)
(–4.994)
–0.019)
(–4.369)
0.410)
(5.060)
0.314)
(3.508)
0.391)
(4.582)
–0.007)
(–2.209)
0.194)
(2.179)
0.189)
(2.140)
–0.077)
(–4.023)
–0.206)
(–4.011)
0.305)
(1.772)
–0.009)
(–2.988)
–0.221)
(–4.070)
–0.006)
(–4.023)
–0.144)
(–4.011)
0.824)
0.013)
1.902)
0.832)
0.012)
1.931)
0.773)
0.008)
2.003)
NOTES: The dynamic equations include a dummy for German Unification, which takes the value of 1
in 1990:3, and seasonal dummies. “D” means first differences, (–1) and (–4) indicate that
the variable is lagged one and four quarters, respectively. See also notes to Tables 2 and 3.
Banerjee, Anindya, Juan J. Dolado, John W. Galbraith, and David F.
Hendry. Co-Integration, Error Correction, and the Econometric Analysis
of Non-Stationary Data, Oxford University Press, 1993.
Barnett, William A. “The User Cost of Money,” Economics Letters
(vol. 1, 1978), pp. 145-49.
_______. Consumer Demand and Labor Supply: Goods, Monetary
Assets, and Time, Amsterdam: North-Holland, 1981.
_______. “The Optimal Level of Monetary Aggregation,” Journal of
Money, Credit, and Banking (November 1982), pp. 687-711.
_______. “The Microeconomic Theory of Aggregation,” New
Approaches to Monetary Economics: Proceedings of the Second
International Symposium in Economic Theory and Econometrics,
William A. Barnett and Kenneth J. Singleton, eds., Cambridge
University Press, 1987, pp. 115-68.
REFERENCES
_______. “Which Road Leads to Stable Money Demand?,” forthcoming in The Economic Journal.
Anderson, Richard G., Barry E. Jones, and Travis D. Nesmith. “Monetary
Aggregation Theory and Statistical Index Numbers,” this Review
(January/February 1997a), pp. 31-51.
_______, Douglas Fisher, and Apostolos Serletis. “Consumer Theory
and the Demand for Money,” Journal of Economic Literature,
(December 1992), pp. 2086-119.
Anderson, Richard G., Barry E. Jones, and Travis D. Nesmith. “Building
New Monetary Services Indices: Concepts, Data and Methods,” this
Review (January/February 1997b), pp. 53-82.
_______, and Yi Liu. “The CAPM-Extended Divisia Monetary
Aggregate with Exact Tracking Under Risk,” Working Paper No. 194,
Washington University, St. Louis, 1995.
Artis, Michael J., Robin C. Bladen-Hovell, and Wenda Zhang. “A
European Money Demand Function,” Policy Issues in the Operation of
Currency Unions, Paul R. Masson, and Mark P. Taylor, eds., Cambridge
University Press, 1993, pp. 240-65.
Diewert, W. Erwin. “Exact and Superlative Index Numbers,” Journal of
Econometrics, (May 1976), pp. 115-45.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
59
SEPTEMBER/OCTOBER 1997
Divisia, François. “L’indice Monétaire et la Théorie de la Monnaie,”
Revue d’Economie Politique (1925), pp. 980-1008.
Phlips, Louis. Applied Consumption Analysis, Amsterdam:
North-Holland, 1974.
Donovan, Donald J. “Modeling the Demand for Liquid Assets: An
Application to Canada,” International Monetary Fund Staff Papers
(December 1978), pp. 676-704.
Pill, Huw, and Mahmood Pradhan. “Monetary Aggregation: A
Reconciliation of Theory and Central Bank Practice,” International
Monetary Fund Working Paper 118 (October 1994).
Ewis, Nabil A., and Douglas Fisher. “The Translog Utility Function and the
Demand for Money in the United States,” Journal of Money, Credit,
and Banking (February 1984), pp. 35-52.
Rotemberg, Julio J., John C. Driscoll, and James M. Poterba. “Money,
Output, and Prices: Evidence from a New Monetary Aggregate,”
Journal of Business and Economic Statistics (January 1995),
pp. 67-83.
Farr, Helen T., and Deborah Johnson. “Revisions in the Monetary Services
(Divisia) Indexes of the Monetary Aggregates,” Staff Study 147,
Board of Governors of the Federal Reserve System, December 1985.
Spencer, Peter. “Should the ECB Adopt a Divisia Monetary Aggregate?,”
paper presented at the CEPR/Irving Fisher Society Conference: What
Monetary Policy for the European Central Bank?, 1995.
Fase, Martin M. G. “Divisia Aggregates and the Demand for Money in
Core EMU,” De Nederlandsche Bank Staff Reports No. 5, 1996.
Swofford, James L., and Gerald A. Whitney. “Flexible Functional Forms
and The Utility Approach to the Demand for Money: A Nonparametric
Analysis,” Journal of Money, Credit, and Banking (August 1986),
pp. 383-89.
_______, and C. C. A. Winder. “Money Demand within EMU: An
Analysis with the Divisia Measure,” De Nederlandsche Bank Quarterly
Bulletin (September 1994), pp. 25-55.
Fisher, Paul G., Suzanne Hudson, and Mahmood Pradhan. “Divisia
Measures of Money,” Quarterly Bulletin, Bank of England, (May
1993), pp. 240-52.
_______, and _______. “Nonparametric Tests of Utility
Maximization and Weak Separability for Consumption, Leisure and
Money,” Review of Economics and Statistics (August 1987),
pp. 458-64.
Gaab, Werner, and Andy Mullineux. “Financial Innovation, Monetary
Aggregates and Monetary Policy in the UK and Germany,”
Finanzmärkte, Finanzinnovationen und Geldpolitik, Schriften des
Vereins für Socialpolitik, Bd. 242, D. Duwendag, ed., Berlin: Duncker
und Humblot, 1996, pp. 73-110.
_______, and _______. “A Comparison of Nonparametric Tests of
Weak Separability for Annual and Quarterly Data on Consumption,
Leisure, and Money,” Journal of Business and Economic Statistics
(April 1988), pp. 241-46.
Gaiotti, Eugenio. “Measuring Money with a Divisia Index: An Application to
Italy,” Temi di Discussione del Servizio Studi 223, Banca d’Italia, 1994.
_______, and _______. “A Revealed Preference Test for Weakly
Separable Utility Maximization with Incomplete Adjustment,” Journal
of Econometrics (January/February 1994), pp. 235-49.
Green, H. A. John. Aggregation in Economic Analysis: An Introductory
Survey, Princeton University Press, 1964.
Thornton, Daniel L., and Piyu Yue. “An Extended Series of Divisia
Monetary Aggregates,” this Review (November/December 1992),
pp. 35-52.
Issing, Otmar, Karl-Heinz Tödter, Heinz Herrmann, and Hans-Eggert
Reimers. “Zinsgewichtete Geldmengenaggregate und M3 – ein
Vergleich,” Kredit und Kapital (Vol. 26, Issue 1, 1993), pp. 1-21.
Wesche, Katrin. “Aggregating Money Demand in Europe with a Divisia
Index,” Discussion Paper No. B-393, SFB 303, Universität Bonn, 1996.
Kremers, Jeroen J. M., and Timothy D. Lane. “Economic and Monetary
Integration and the Aggregate Demand for Money in the EMS,”
International Monetary Fund Staff Papers (December 1990),
pp. 777-805.
MacKinnon, James G. “Critical Values for Cointegration Tests,” Long-Run
Economic Relationships, Robert F. Engle and Clive W. J. Granger, eds.,
Oxford University Press, 1991, pp. 267-76.
Marquez, Jaime. “Money Demand in Open Economies: A Divisia
Application to the U.S. Case,” New Approaches to Monetary
Economics, Proceedings of the Second International Symposium in
Economic Theory and Econometrics, William A. Barnett, and Kenneth
J. Singleton, eds., Cambridge University Press, 1987, pp. 183-99.
OECD. Purchasing Power Parities and Real Expenditures – GK Results,
Vol. 2, Paris: Organisation for Economic Co-operation and
Development, 1993.
Osborne, Dale K. “Defining Money,” The New Palgrave Dictionary of
Money and Finance, Peter Newman, Murray Milgate, and John
Eatwell, eds., Macmillan, 1992, pp. 602-06.
F E D E R A L R E S E R V E B A N K O F S T. L O U I S
60
@FedFRASER