Litigation
Support in Antitrust Situations
By
Lester E. Heitger and D. Larry Crumbley
JANUARY
2005  Accountants are uniquely qualified to provide advice
and assistance in antitrust litigation. In antitrust disputes,
accountants are called upon to identify and analyze relevant
historical accounting case data. Forensic accountants are
the ones best able to sort out the relevant accounting issues
in the dispute and explain them to a judge and jury.
Since
Daubert [509 U.S. 579, 113 S.Ct. 2786 (1993)],
trial judges have had a special responsibility to ensure
that testimony is not only relevant, but also reliable.
Before permitting an individual to testify as an expert,
a judge must determine whether the expert’s reasoning
and methodology can be appropriately applied to the facts
of the dispute [Stagl v. Delta Airlines, Inc.,
117 F.3d 76 (CA2, 1997)]. In addition, judges must take
into consideration an expert’s background and practical
experience when deciding whether the expert is qualified
to render a report and testify [McColluck v. H.B. Fuller
Co., 61 F.3d 1038 (CA2, 1995)]. A judge may decide
that an expert’s expertise is too general or too deficient
to qualify [Trumps v. Toastmaster, 969 F. Supp.
247 (S.D. N.Y. 1997)]. A judge also may decide that research
and studies cited are too dissimilar to the facts involved
in the litigation [General Electric Co. v. Joiner,
522 U.S. 136, 118 S.Ct. 512 (1997)].
Overview
of Antitrust Laws
U.S.
antitrust laws are an outgrowth of the reaction to business
practices during the early years of the country’s
industrial development. Some businesses were seen to be
using any tactic at their disposal to force competitors
out of business and create a monopoly, then raising prices
to relatively high levels. This practice was particularly
common in the railroad and oil industries.
Federal
legislation prohibited the formation and continuation of
monopolies except when in the best interest of the public
(for example, many local electric, gas, and water utilities).
It
is easier to oppose a monopoly, however, than to agree upon
whether one exists. In the recent antitrust litigation brought
against the Microsoft Corporation, a court found the company
to be a monopoly, but the final settlement over its business
practices did not satisfy its critics. Similarly, widespread
deregulation in formerly monopolistic industries has not
always resulted in marketplaces as competitive as critics
have desired.
Actions
to Be Taken
The
antitrust process begins with a complaint filed with the
U.S. government by an individual who believes that a company
is in violation of antitrust laws. If the government agrees
with the complaint, an attempt will be made to remedy the
situation by negotiations and other measures. If this attempt
fails, the U.S. Justice Department may start proceedings
against the business, to force compliance with antitrust
laws through the courts.
Sometimes
monopoly complaints are not accepted. The complaint might
not provide enough information or evidence to pursue the
issue, or the Justice Department may not have the resources
to pursue all valid complaints. Individuals or other companies
have the recourse of filing a lawsuit themselves under the
antitrust laws.
The
Role of Accountants
In
antitrust disputes, accountants may be called upon to determine
whether there is liability under the antitrust laws. The
primary issue that forensic accountants address is whether
a defendant has engaged in predatory pricing. Pricing below
an appropriate measure of a defendant’s costs is a
requirement to predatory pricing liability [Brooke Group
Ltd. v. Brown and Williamson Tobacco Corp., 209 U.S.
209 (1993)]. Accountants may also be asked to calculate
the damages a party has sustained as a result of a violation
of the antitrust laws.
For
example, in an antitrust suit in Indianapolis, a grocery
chain was accused by another grocery company of engaging
in predatory pricing activities during an 18month period.
The accused grocery chain had gone from a profitable position
in earlier periods to significant losses during the alleged
predatory pricing period. The plaintiff alleged that the
defendant’s change from profitable operations to major
losses was evidence of predatory pricing. The defendant
countered by saying its losses were due to efforts to compete
on a price basis with another major new grocery retailer
that had entered the area.
The
defendant hired a forensic accountant to demonstrate that
the company was not engaged in predatory pricing. The accountant
noted that experiencing business losses was not a clear
sign of predatory pricing. The forensic accountant ran regression
analyses on store data from both parties. He found that
the defendant’s stores had in all periods priced its
products above average variable costs and therefore, in
his opinion, had not engaged in predatory pricing. This
expert found that the plaintiff in the dispute had also
experienced major losses during the period and, in his opinion,
the plaintiff was closer to the predatory pricing benchmark
than was the defendant.
The
plaintiff then changed the geographic area covered by its
complaint to smaller geographic areas in an effort to find
some areas of operation in which the defendant had priced
products below average variable cost. The defendant’s
forensic accountant analyzed store cost data for each of
these new areas and found in each instance that the allegations
were false. This analysis provided by the forensic accountant
was instrumental in the court’s decision for the defendant.
Actions
that constitute predatory pricing. Predatory
pricing is the act of pricing a product low in order to
drive competitors out of business. This definition, although
descriptive, does not give a judge or jury much guidance
in determining whether predatory pricing has occurred. Because
of that, the courts have used a simpler definition to apply
the concept of predatory pricing in actual situations. The
operational definition is whether a company prices its products
or services below “average variable cost.”
Knowing
what types of cost behavior patterns are common in business
is not enough to allow an expert or analyst to merely look
at a company’s ledger accounts and identify which
costs are fixed, variable, or mixed. Most organizations
have few costs that are captured, recorded, and reported
by their cost behavior pattern. Instead, costs typically
are identified and recorded by their functional characteristics,
such as product cost and period cost. For example, period
costs include most operating costs, such as marketing, customer
service, personnel, product warranty, accounting, and administration.
Product costs include direct materials, direct labor, and
manufacturing overhead. Management, investors, and lenders
find functional classifications useful, but they are of
little use in determining whether pricing is predatory.
Without
more information about individual costs, it is necessary
to determine the cost behavior pattern of each cost. An
accountant can use a variety of methods to estimate cost
behavior patterns. A working knowledge of a firm’s
accounting system often provides some insight into the nature
of cost behavior in the company’s accounting system.
Sometimes
account titles are misleading, or accounts are used to record
items not originally intended. A quick way for an accountant
to become familiar with an accounting system is to spend
time talking to one or more seasoned veterans with the company
that know about its history and how its accounting system
works.
Graphic
Analysis
Although
experience and intuition are valuable in giving an expert
insight into an accounting system, they are seldom enough.
A better approach to gain insight into cost behavior patterns
is graphic analysis. Plotting data and creating scatter
diagrams can be useful in showing an expert the general
trend or nature of costs. Analysts can easily plot data
from a spreadsheet and use that information to form an initial
understanding of cost relationships and cost behavior patterns.
Another
cost analysis method frequently used by consultants is the
highlow method. With this approach, the highest and lowest
costs are identified, along with their related activity
levels. The difference between the two costs and the two
activity levels is calculated and then divided by the difference
in activity levels to determine an estimate of the variable
cost per unit. This variable cost is then inserted into
the total cost equation to find the fixed cost component.
Example.
A company is analyzing 20 months of overhead cost data.
The highest overhead cost for any month was $300,000, at
an activity level of 100,000 units. The lowest overhead
cost for any month was $280,000, at an activity level of
90,000 units.
Variable
cost = $300,000  $280,000
100,000
 90,000
units
units
=
$20,000
10,000
units
=
$2 per unit
Fixed cost = $300,000  (100,000 units x $2 per unit)
=
$100,000
Total cost = $100,000 + $2 per unit
The
highlow method of cost analysis is a quick and easy approach
to obtain some understanding of cost behavior information
in a dispute. But this approach does have potential flaws.
For example, this approach uses only the highest and the
lowest values of the cost data in the analysis. If these
values are outliers, representing unusual data, the resulting
cost behavior information will probably be distorted. Business
phenomena such as wildcat strikes or malfunctioning machinery
can cause such anomalies, and cost analyses or projections
should not be based on an atypical level of activity. In
addition, there are only two data observations used in the
analysis; the rest of the data are ignored.
Both
graphic analysis and the highlow method are intended to
help an analyst understand the nature of an accounting system
and the characteristics and nature of the company’s
costs. The highlow method is particularly useful early
on, when an expert may have only highly aggregated data.
Financial information gathered and analyzed in this way
is preliminary and provides only initial observations and
guidelines. Sometimes such analyses are useful in determining
what documents should be requested during the discovery
process, or in determining in a general sense what damages
might be present in a dispute.
Regression/Correlation
Analysis
Regression
or correlation analysis is a statistical technique for measuring
the nature and strength of the relationship between two
variables. When applied to cost accounting data, regression/correlation
can be used to determine the relationship between specific
cost data and some measure of activity, such as sales dollars
or production volume. Regression analysis is used to determine
the nature (direct or inverse) of the association between
two or more variables in the analysis. Correlation
analysis is used to measure the strength of the association
between the variables.
Simple
Linear Regression
The
most common type of regression/correlation analysis used
in cost behavior analysis is simple linear regression. This
method uses two sets of variables: a dependent variable,
which usually is cost, and an independent variable or predictor
variable, which usually is the measure of the volume of
activity. In simple linear regression, an accountant enters
cost and activity data into the regression model. The regression
model will use the data to compute a regression line, which
is represented by the formula:
Y =
a + bX
where
Y is the computed value of the dependent variable obtained
from a specific value of the independent variable; a is
the constant, or intercept, where the regression line crosses
the vertical axis (the value of Y when X = 0); b is the
slope of the regression line, describing the change in the
value of Y for each unit change in the value of X; and X
is the value of the independent variable.
A positive
value for b indicates that the dependent and the independent
variables are positively correlated (moving in the same
direction). A negative b value indicates that the dependent
and the independent variables are moving in opposite directions.
The Exhibit
shows sample cost and activity data and the resulting regression
line.
The
regression line is often referred to as the least squares
regression line or the line of best fit. The regression
line is the line which does the best job of minimizing the
squared distance between the regression line and the individual
data observations used in computing the regression line.
Standard
Error of the Estimate
Of
course, business cost data seldom show perfect correlation.
Most cost categories are not created to include only costs
that are fixed, variable, or some other characteristic.
When actual cost data are regressed against some measure
of activity, that data likely will have some amount of variability.
A measure of the variability of the cost data in the regression
analysis is called the standard error of the estimate. The
greater the variability of the data, the less precise the
estimates can be.
Typically,
many estimates are point estimates made using the regression
parameters. For example, an expert may use the regression
to estimate the total costs that a business should have
incurred at a given volume of activity. Because there is
some variability in the regression data, an expert may incorporate
a measure of the regression variability and some desired
confidence level in determining a range of possible values.
To
do this, an expert can use the standard error of the estimate
and values from either a T table or a Z table for the desired
confidence level. For example, assume the point estimate
for a cost is $256,000 and the standard error of the estimate
is $25,500. An expert wants to find the range of costs within
which the actual cost should fall 95% of the time. The sample
size used in the regression analysis was 60 months of data.
Either the T table or the Z table gives a value of 1.96
for a 95% confidence interval. So the range of projected
cost is $256,000 + ($25,500 x 1.96), or $206,020 to $305,980.
(T tables are used to find values for sample sizes less
than 60. The smaller the sample, the larger the T value,
which adjusts for sampling error. Z tables are used for
large sample sizes. Because many business regressions are
run using small samples, T tables are commonly used.)
Correlation
Analysis
Correlation
analysis measures the strength of the relationship between
the variables. There are several measures that describe
the strength of the relationship between the two variables.
The first is called the coefficient of correlation, or r,
which measures the strength of the association between the
dependent and the independent variable. The coefficient
of correlation ranges in possible values from –1 to
+1, indicating perfect negative or perfect positive correlation
between the two variables.
If
the coefficient of correlation is squared, it becomes the
coefficient of determination, which can range from 0 to
+1. The coefficient of determination measures the amount
of variance explained by changes in the independent variable.
For example, a coefficient of determination of .84 means
that 84% of the dependent variable’s total variance
can be explained by changes in the independent variable.
High rsquared values mean that there is a strong linear
relationship between the variables.
The
coefficient of nondetermination (1 – r squared) is
the portion of the relationship between two variables that
is not explained by the coefficient of determination. This
unexplained variance may be random fluctuations or it may
be a variation that is explained by some other independent
variable.
Association
Versus Causation
Regression
and correlation analysis shows the degree of association
between variables, but it does not prove causation. There
is a tendency to think that if an analysis shows a high
degree of correlation, movements in the independent variable
cause the changes in the dependent variable. However, one
must look to other supporting information before making
such a claim.
If
regression/correlation results show relationships that seem
illogical, factors affecting the results may need to be
evaluated. The relationship may be merely a spurious correlation
or a coincidence. Sometimes in regression and correlation
analysis the numbers happen to be ripe for a relationship
that just does not make any logical sense. For example,
a California state representative reputedly ran a regression
of teacher salaries in the state of California against gambling
revenues in Las Vegas and found a high coefficient of correlation.
He then concluded that teachers were all squandering their
raises at the Las Vegas casinos. Although teachers may have
been doing just that, the fact that teachers’ salaries
and gambling revenues were moving in the same direction
did not prove that one caused the other.
Reasons
for the Unexpected
There
are many reasons why accounting data regressions may not
turn out as expected. Some of the accounting reasons that
regressions may yield unexpected or perplexing results are:

Allocations. Many cost analyses contain costs
allocated from shared services. How these costs are allocated
and timed can make a big difference in the cost behavior
results from the analysis.

Transfer prices. When products or services are
transferred (sold) between various parts of a company,
a transfer price or a chargeback must be determined. A
transfer price based on the market price, when regressed
against units produced, will appear to be a directly variable
cost, even though the creation of the product or service
will entail a mixture of fixed and variable costs.
 Entity
concept. Transfer pricing issues bring up the question
of which entity the forensic accountant should study to
determine the cost behavior pattern for the transfer item.
The answer depends upon the objectives of the cost analysis.

Accounting policies. Many accounting policies
have an important impact on an analysis of cost behavior.
If the accounting policy is to charge depreciation to
units only once each year rather than each month, a cost
analysis will have different results. Similarly, accounting
policy may state that various costs are allocated based
on the number of units receiving the allocation rather
than on dollars of sales. A difference in allocation method
can have a major impact on cost behavior.
The
Need to Become Familiar with Accounting Systems
Forensic
accountants must be adequately informed about the nature
and operation of the accounting system for each and every
business under evaluation. If two companies differ in their
accounting treatment of a particular type of cost, an analysis
may suggest different cost behavior for the same cost.
For
example, consider two similar companies that allocate corporate
marketing costs to all of their stores on a weekly basis.
One company allocates weekly marketing costs on a perstore
basis. The weekly sales dollars achieved by each store has
no impact on the amount of cost allocated to it. The other
company also allocates weekly marketing costs to its 50
stores, but its allocation is based on each store’s
actual sales for the week as a percentage of the company’s
total sales. If a store’s relative sales volume changes,
the store’s allocation of corporate marketing costs
will change accordingly.
This
example illustrates how a company’s accounting policies
can have a significant impact on the cost behavior estimates
that result for the regression/correlation analysis. Many
accounting policies have an impact on accounting data analysis.
Company policies on when to write off prepaid items, at
what level the firm records depreciation and in what timeframes,
and on how to record employee fringe benefit costs can all
impact the results of a regression/correlation analysis.
Lester
E. Heitger, PhD, CPA, is a professor at the department
of accounting, Kelley School of Business, Indiana University,
Bloomington.
D. Larry Crumbley, PhD, CPA, Cr.FA, CFD,
is the KPMG Endowed Professor at the department of accounting,
Louisiana State University, Baton Rouge, La.
Note:
Portions of this article appeared in Forensic and
Investigative Accounting, by Crumbley, Heitger, and Smith,
published by Commerce Clearing House, 2003.
