Data Interpretation Issues Chapter

Chapter 10
Data Interpretation Issues
Learning Objectives
• Distinguish between random and
systematic errors
• State and describe sources of bias
• Identify techniques to reduce bias at the
design and analysis phases of a study
• Define what is meant by the term
confounding and provide three examples
• Describe methods to control confounding
Validity of Study Designs
• The degree to which the inference drawn
from a study, is warranted when account it

taken of the study, methods, the
representativeness of the study sample,
and the nature of the population from
which it is drawn.
Validity of Study Designs
• Two components of validity:
– Internal validity
– External validity
Internal Validity
• A study is said to have internal validity
when there have been proper selection of
study groups and a lack of error in
measurement.
• Concerned with the appropriate
measurement of exposure, outcome, and
association between exposure and

disease.
External Validity
• External validity implies the ability to
generalize beyond a set of observations to
some universal statement.
• A study is externally valid, or
generalizable, if it allows unbiased
inferences regarding some other target
population beyond the subjects in the
study.
Sources of Error in
Epidemiologic Research
• Random errors
• Systematic errors (bias)
Random Errors

• Reflect fluctuations around a true value of
a parameter because of sampling
variability.
Factors That Contribute to
Random Error
• Poor precision
• Sampling error
• Variability in measurement
Poor Precision
• Occurs when the factor being measured is
not measured sharply.
• Analogous to aiming a rifle at a target that
is not in focus.
• Precision can be increased by increasing
sample size or the number of
measurements.

• Example: Bogalusa Heart Study
Sampling Error
• Arises when obtained sample values
(statistics) differ from the values
(parameters) of the parent population.
• Although there is no way to prevent a
non-representative sample from
occurring, increasing the sample size
can reduce the likelihood of its
happening.
Variability in Measurement
• The lack of agreement in results from
time to time reflects random error
inherent in the type of measurement
procedure employed.

Bias (Systematic Errors)
• “Deviation of results or inferences
from the truth, or processes leading to
such deviation. Any trend in the
collection, analysis, interpretation,
publication, or review of data that can
lead to conclusions that are
systematically different from the
truth.”
Factors That Contribute to
Systematic Errors
• Selection bias
• Information bias
• Confounding
Selection Bias
• Refers to distortions that result from procedures
used to select subjects and from factors that
influence participation in the study.
• Arises when the relation between exposure and
disease is different for those who participate and
those who theoretically would be eligible for study
but do not participate.

• Example: Respondents to the Iowa Women’s
Health Study were younger, weighed less, and were
more likely to live in rural, less affluent counties than
nonrespondents.
Information Bias
• Can be introduced as a result of
measurement error in assessment of
both exposure and disease.
• Types of information bias:
– Recall bias: better recall among cases
than among controls.
• Example: Family recall bias
Information Bias (cont’d)
– Interviewer/abstractor bias--occurs
when interviewers probe more
thoroughly for an exposure in a case
than in a control.
– Prevarication (lying) bias--occurs when

participants have ulterior motives for
answering a question and thus may
underestimate or exaggerate an
exposure.
Confounding
• The distortion of the estimate of the
effect of an exposure of interest
because it is mixed with the effect of
an extraneous factor.
• Occurs when the crude and
adjusted measures of effect are not
equal (difference of at least 10%).
• Can be controlled for in the data
analysis.
Criteria of Confounders
• To be a confounder, an extraneous
factor must satisfy the following
criteria:
– Be a risk factor for the disease.

– Be associated with the exposure.
– Not be an intermediate step in the
causal path between exposure and
disease.
Simpson’s Paradox as an
Example of Confounding
• Simpson’s paradox means that an
association in observed subgroups of a
population may be reversed in the entire
population.
• Illustrated by examining the data (% of
black and gray hats) first according to two
individual tables and then by combining all
the hats on a single table.
Simpson’s Paradox (cont’d)
• When the hats are on separate tables, a
greater proportion of black hats than gray

hats on each table fit.
– On table 1:
• 90% of black hats fit
• 85% of gray hats fit
– On table 2:
• 15% of black hats fit
• 10% of gray hats fit
• When the man returns the next day
and all of the hats are on one table:
– 60% of gray hats fit (18 of 30)
– 40% of black hats fit (12 of 30)
Note that combining all of the hats on
one table is analogous to
confounding.

Examples of Confounding
• Air pollution and bronchitis are positively
associated. Both are influenced by
crowding, a confounding variable.
• The association between high altitude and
lower heart disease mortality also may be
linked to the ethnic composition of the
people in these regions.
Techniques to Reduce
Selection Bias
• Develop an explicit (objective) case
definition.
• Enroll all cases in a defined time and
region.
• Strive for high participation rates.
• Take precautions to ensure

representativeness.
Reducing Selection Bias Among
Cases
• Ensure that all medical facilities are thoroughly
canvassed.
• Develop an effective system for case
ascertainment.
• Consider whether all cases require medical
attention; consider possible strategies to
identify where else the cases might be
ascertained.
Reducing Selection Bias
Among Controls
• Compare the prevalence of the exposure
with other sources to evaluate credibility.
• Attempt to draw controls from a variety of

sources.
Techniques to Reduce
Information Bias
• Use memory aids; validate exposures.
• Blind interviewers as to subjects’ study status.
• Provide standardized training sessions and
protocols.
• Use standardized data collection forms.
• Blind participants as to study goals and
classification status.
• Try to ensure that questions are clearly
understood through careful wording and
pretesting.
Methods to Control
Confounding
• Prevention strategies--attempt to control confounding
through the study design itself.
• Three types of prevention strategies:
– Randomization

– Restriction
– Matching
• Two types of analysis strategies:
– Stratification
– Multivariate techniques
Randomization
• Attempts to ensure equal distributions of the
confounding variable in each exposure
category.
• Advantages:
– Convenient, inexpensive; permits straightforward
data analysis.
• Disadvantages:
– Need control over the exposure and the ability to
assign subjects to study groups.
– Need large sample sizes.
Restriction
• May prohibit variation of the confounder in the

study groups.
– For example, restricting participants to a
narrow age category can eliminate age as a
confounder.
• Provides complete control of known
confounders.
• Unlike randomization, cannot control for
unknown confounders.
Matching
• Matches subjects in the study groups according
to the value of the suspected or known
confounding variable to ensure equal
distributions.
• Frequency matching--the number of cases with
particular match characteristics is tabulated.
• Individual matching--the pairing of one or more
controls to each case based on similarity in sex,
race, or other variables.
Matching (cont’d)
• Advantages:

– Fewer subjects are required than in
unmatched studies of the same hypothesis.
– May enhance the validity of a follow-up study.
• Disadvantages:
– Costly because extensive searching and
recordkeeping are required to find matches.
Two Analysis Strategies to
Control Confounding
• Stratification--analyses performed to evaluate
the effect of an exposure within strata (levels) of
the confounder.
• Multivariate techniques--use computers to
construct mathematical models that describe
simultaneously the influence of exposure and
other factors that may be confounding the
effect.

Advantages of Stratification
• Performing analyses within strata is a
direct and logical strategy.
• Minimum assumptions must be
satisfied for the analysis to be
appropriate.
• The computational procedure is
straightforward.
Disadvantages of Stratification
• Small numbers of observations in some
strata.
• A variety of ways to form strata with
continuous variables.
• Difficulty in interpretation when several
confounding factors must be evaluated.
• Categorization results in loss of
information.

Multivariate Techniques
• Advantages:
– Continuous variables do not need to be
converted to categorical variables.
– Allow for simultaneous control of several
exposure variables in a single analysis.
• Disadvantages:
– Potential for misuse.
Publication Bias
• Occurs because of the influence of
study results on the chance of
publication.
– Studies with positive results are more
likely to be published than studies with
negative results.

Publication Bias (cont’d)
• May result in a preponderance of
false-positive results in the
literature.
• Bias is compounded when
published studies are subjected to
meta-analysis.
Managerial Economics
Applications, Strategies and Tactics, 14e
James R. McGuigan
R. Charles Moyer
Frederick H. deB. Harris
© 2017 Cengage Learning® May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
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1
1
PART IV – PRICING & OUTPUT DECISIONS:
STRATEGY AND TACTICS

2
Chapter 12 –
Price and Output Determination:
Oligopoly
Chapter 12 – Price & Output Determination: Oligopoly
Overview (1 of 1)
OLIGOPOLISTIC MARKET STRUCTURES
INTERDEPENDENCIES IN OLIGOPOLISTIC INDUSTRIES
CARTELS AND OTHER FORMS OF COLLUSION
PRICE LEADERSHIP
THE KINKED DEMAND CURVE MODEL
AVOIDING PRICE WARS
3

Ch 12 – Oligopolistic Market Structures
(1 of 1)
An oligopoly is characterized by a relatively small number of
firms offering a similar product or service
Oligopoly products may be branded or unbranded
The number of firms is small enough that actions by any one
firm on price, output, etc. have a perceptible impact on the sales
of the others
Each firm knows that any new price cut or large promotional
campaign is likely to evoke a countermove from its rivals
Rival response expectations are the key to firm-level analysis
If rival firms are expected to match price increases and price
cuts, a share-of-the market demand curve may illustrate the
sales response to the pricing initiatives of the firm (See Figure
12.1)
4
Figure 12.1 –
Rival Response Expectations Determine Firm Demand

5
Figure 12.2 Browser Market Shares
6
Ch 12 – Oligopolistic Market Structures
Oligopoly in the U.S.: Relative Market Shares (1 of 1)
Much of U.S. industry is best classified as oligopolistic in
structure with a wide range of industry configurations, (Table
12.1)
At one extreme are dominant single firms in the markets for
razors, beer, etc.
In crackers, handsets, etc. two firms dominate
Sales in tires, U.S. auto & truck markets are dispersed across 6-
8 firms
Market shares are more dispersed in telecom equipment and
pharmaceuticals

7
Table 12.1 – Largest U.S. Market Shares in Oligopolistic
Industries
8
Table 12.2 – Market Share Distributions Over Time in Airlines,
Cereals, and Wide-Bodied Aircraft
9
Figure 12.3 – The Relative Sizes of Competitors in the Auto
Rental and Gasoline Industries

10
Ch 12 – Interdependencies in Oligopolistic Industries
The Cournot Model (1 of 1)
One standard approach to the interdependency problem among
oligopolists is merely to ignore it, for a firm to assume that its
competitors will act as if it does not exist
Because of the wide scope of oligopoly industry configurations
in Table 12.1, several simplifying models have been used to
describe oligopolists’ competitive behavior regarding price,
output, etc.
The Cournot oligopoly model asserts that each firm, in
determining its profit-maximizing output level assumes that the
other firm’s output will not change

11
Ch 12 – Cartels and Other Forms of Collusion
(1 of 1)
Oligopolists sometimes reduce the inherent risk of being so
interdependent by either formally or informally agreeing to
cooperate or collude in decision-making; such agreements are
called cartels
Cartel – A formal or informal agreement among firms in an
oligopolistic industry that influences such issues s prices, total
industry output, market shares, and the division of profits
In general, collusive agreements are illegal in the U.S. and
Europe, but some exceptions exist
Prices and quotas of some agricultural products are set by
grower cooperatives
The IATA airlines flying transoceanic routes jointly set uniform
prices for these flights
Ocean shipping rates are set by hundreds of collusive
conferences on each major route
Illegal collusive arrangements also arise from time to time
12

Factors Affecting the Likelihood of Successful Collusion
(1 of 2)
Number and Size Distribution of Sellers
Collusion is less difficult as the number of firms involved
decreases
Product Heterogeneity
Products that are alike are homogenous, and price is the only
distinction that matters; for heterogeneous (differentiated)
products, cooperation is more difficult
Cost Structures
The more cost functions differ among competing firms, the
more difficult it will be
Size and Frequency of Orders
Effective collusion is more likely when orders are small,
frequent, and received regularly, as in the purchase of autos
13
Factors Affecting the Likelihood of Successful Collusion
(2 of 2)

Threat of Retaliation
An oligopolistic firm will be less tempted to grant secret price
concessions to selected customers if it feels that other cartel
members would detect those price reductions and then retaliate
Percentage of External Output
Most cartels contain the seeds of their own destruction
rising prices and profits attract the entry of new competitors
any increase in supply from outside the cartel means that larger
restrictions on output must be imposed on cartel members in
order to sustain price
14
Cartel Profit Maximization and the Allocation of Restricted
Output (1 of 1)
Under legal cartels & secret collusive agreements, firms attempt
to increase prices and profits above the level that would prevail
in the absence of collusion
The profit-maximization solution for a two-firm cartel, E and F,
is shown graphically in Figure 12.4
If the cartel maximizes its total profits, the market share

(quota)for each firm should be set where marginal cost of all
firms is identical and the industry (summed) MC = MR
The central problem for cartels is in monitoring these quotas,
detecting violations and effectively enforcing punishments
As a result, most cartels are unstable
The longevity of OPEC and the DeBeers diamond cartels is
exceptional
15
Figure 12.4 –
Price-Output Determination for a Two-Firm Cartel
16

Figure 12.5 –
How OPEC III Production Quotas Affected Crude Oil Prices
17
Figure 12.6 – Saudi Arabian Crude Oil Production
18
Figure 12.7 – Proven Oil Reserves
19
Figure 12.8 –
Components of the Price of Gasoline per Gallon (1990-2009)

20
Cartel Analysis: Algebraic Approach (1 of 3)
The profit-maximizing price and output levels for a two-firm
cartel can be determined algebraically when the demand and
cost functions are given:
21

22

23
Table 12.3 – Comparisons of Pricing, Output, and Profits for
Siemens & Thomson
24
Ch 12 – Price Leadership
(1 of 1)
Price leadership – A pricing strategy followed in many
oligopolistic industries. One firm normally announces all new
price changes. Either by an explicit or by an implicit agreement,
other firms in the industry regularly follow the pricing moves of

the industry leader
Barometric Price Leadership
One firm announces a change in price that it hopes will be
accepted by others
The leader must, however, be reasonably correct in its
interpretation of changing demand and cost conditions
The barometric price leader merely initiates a reaction to
changing market conditions that other firms find in their best
interests to follow
25
Ch 12 – Price Leadership
Dominant Firm Price Leadership (1 of 1)
One firm establishes itself as the leader because of its larger
size, customer loyalty, or lower cost structure in relation to
others
The leader may then act as a monopolist in its segment of the
market
The incentive for followers to accept the established price may
be a fear of cutthroat retaliation from a low-cost dominant firm,
or it may be simply a convenience
The price-output solution for the dominant-firm model is shown

in Figure 12.9
The dominant firm maximizes its profits by setting price and
output where marginal cost equals marginal revenue
26
Figure 12.9 –
Price-Output Determination for the Dominant Firm
27
Ch 12 – The Kinked Demand Curve Model
(1 of 1)
Sometimes when an oligopolist cuts its prices, competitors
quickly feel the decline in their sales, and are forced to match
the price reduction

Or, if one firm raises its prices, competitors gain customers by
maintaining their original prices, and have little motivation to
match a price increase
The demand curve facing an individual oligopolist would be far
more elastic for price increases than for price decreases (Figure
12.10)
This model explains why stable prices exist in some
oligopolistic industries
But the kinked demand model is incomplete in that it offers no
reason why the prevailing price level, rather than some other
one, is chosen
28
Figure 12.10 – The Kinked Demand Curve Model
29

Ch 12 – Avoiding Price Wars
(1 of 1)
To sustain profitability, oligopoly firms must avoid discounting
tactics in their high-margin business
The ready-to-eat (RTE) Cereal, beer, camera film, DVD
industries & more have experienced classic price wars
Growing the Market
One key to avoiding price wars is to attempt to grow the market
Pepsi cannot hope to get rid of Coke
Each rival must anticipate aggressive discounting designed to
attract the other firm’s customers
It is better to maintain high prices and focus on opening new
markets
30
Customer Segmentation with Revenue Management (1 of
1)
If low-cost new entrants attack a major airline, one effective
response involves matching prices to a targeted customer

segment and controlling how much capacity is released for sale
to that segment
“fencing” restrictions such as 7-day advance-purchase
requirements, and Saturday night stayovers prove crucial in
segmenting the price-sensitive discretionary traveler from the
regular business expense account customer
Incumbent carriers can “meet the competition” in these
restricted fare classes while reserving sufficient capacity for
those who desire to pay for the reliability, convenience, and
change-order responsiveness of business-class and full-coach
seats
Established competitors can maintain high prices on segments &
routes, etc.
31
Reference Prices and Framing Effects (1 of 1)
Product line extensions can reduce gainshare price discounting
by providing reference prices and framing effects that help sell
the mid-range product at undiscounted prices
Consumers of unbranded products typically remember the last
price they encountered

Branded products, however, trigger much longer reference
pricing
Discounting with a major branded item tends to etch in the
customer’s mind a new lowball price that can be expected
thereafter for months or years’
Therefore, better to introduce a super-premium product offered
at price points well above your traditional product; loyal
customers will remember
32
What Went Right? ● What Went Wrong?
Good-Better-Best Product Strategy at Marriott Corp & Kodak
Marriott responded to fierce price competition by introducing
upscale, high-quality, mid-range, and down-market product
lines to its customers.
Length of stay, proximity to the central business district, and
room type effectively segmented Marriott’s customer base
Marriott has no problem getting a substantial price premium for
hotel rooms with sitting areas in central business districts &
convention hotels
In the late 1990s, Kodak pursued a similar concept with
discounted Funtime Film and the Kodak disposable camera

Disposable cameras for everyday use made this film highly
accessible
Kodak’s Royal Gold film provided exceptional picture
resolution in many lighting conditions, and could memorialize
subtleties of expression, at a premium price
33
The Role of Innovation (1 of 1)
Rather than matching price cuts, a higher-priced brand can
highlight conspicuous product innovations
Sony’s Mavica was an easy-to-use point-and-shoot digital
camera that recorded images onto thumb drives, which popped
out of the camera and loaded into any PC for easy editing,
storing and printing
While competitor Kodak was improving picture resolution to
justify expensive and complicated home printer hardware
peripherals using Kodak chemicals and paper, Sony simplified
the prices and increased customer value.
Mavica earned a premium price relative to its competitors

34
Figure 12.11 –
Segmented Oligopoly with Extreme Brand Loyalty
35
Matching Price Cuts with Increased Advertising (1 of 1)
Perhaps the best way to avoid a price was is to not start one
If someone else starts a price war, often the best response is
simply to match the competition and then accentuate non-price
elements of the marketing mix by increasing services or
advertising
A final key to avoiding price wars comes through the tactical
insights often available from game theory analysis:
Being able to identify a rival’s payoffs using competitor
surveillance helps predict the competitor’s response to one’s
own price cuts
In other circumstances, cooperative high-price outcomes may
arise out of mutual interest
Recognizing the detailed structure of the pricing “game” can be
a first step in altering the competitive environment

36
Managerial Economics
Applications, Strategies and Tactics, 14e
James R. McGuigan
R. Charles Moyer
Frederick H. deB. Harris
1
1

PART IV – PRICING & OUTPUT DECISIONS:
STRATEGY AND TACTICS
2
Chapter 11 –
Price and Output Determination:
Monopoly and Dominant Firms
Chapter 11 – Price & Output Determination: Monopoly and
Dominant Firms Overview (1 of 1)
MONOPOLY DEFINED
SOURCES OF MARKET POWER FOR A MONOPOLIST
PRICE AND OUTPUT DETERMINATION FOR A
MONOPOLIST
THE OPTIMAL MARKUP, CONTRIBUTION MARGIN, AND
CONTRIBUTION MARGIN PERCENTAGE
REGULATED MONOPOLIES
THE ECONOMIC RATIONALE FOR REGULATION

3
Ch 11 – Monopoly Defined
(1 of 1)
Monopoly is defined as a market structure with significant
barriers to entry in which a single firm produces a highly
differentiated product
Without any close substitutes for the product, the demand curve
for a monopolist is often an entire relevant market demand
Just as purely competitive market structures are rare, so too
pure monopoly markets are rare
4
Ch 11 – Sources of Market Power for a Monopolist
(1 of 1)
Monopolist or near-monopoly dominant firms enjoy several
sources of market power
A firm may possess a patent or copyright that prevents others
from producing the same product
A firm may control critical resources
A third source may be a government-authorized franchise

Monopoly power also happens in natural monopolies because of
significant economies of scale over a wide range of output
5
Increasing Returns from Network Effects (1 of 2)
These can also be a source of monopoly market power
Marketing and promotions are generally subject to diminishing
returns; See Figure 11.1; example: Microsoft and Apple
Sales penetration curve – An S-shaped curve relating current
market share to the probability of adoption by the next garget
customer, reflecting the presence of increasing returns
By achieving more than 30% acceptance in the marketplace, the
technology becomes the industry standard
Achieving greater than 30% share, leads to increasing returns in
marketing caused by a network effect that displaces other
competitors

6
Figure 11.1 How the Adoption of a Technology Leads to
Increasing Returns
7
Increasing Returns from Network Effects (2 of 2)
But monopoly is seldom assured for 3 reasons:
First, a higher price point for innovative new products can
offset cost savings from increasing returns of a competitor
(Example: Apple)
Second network effects tend to occur in technology-based
industries that have experienced falling input prices; Figure
11.2
Third, technology products whose primary value lies in their
intellectual property have revenue sources dependent on
renewals of governmental licensures and product standards
Firms try to get around the inflection point of Figure 11.1 and

achieve increasing returns by free trials for a limited time, or
giving the technology away if it can be bundled with other
revenue-generating product offerings
8
Figure 11.2 How Declining Component Costs Led to Falling
Product Prices in the Computer and Telecom Industries
9
What Went Right at Microsoft but Wrong at Apple Computer
Historically, Apple Computer hovered at 7-10% market share in
the U.S. personal computer industry, never coming close to the

30% inflection point for declining selling costs (See Fig 11.1).
Apple attempted to become an industry standard in several PC
submarkets, like desktop publishing, journalism, advertising &
entertainment.
Also, after defending its GUI code for 2 decades, Apple
reversed course, and began licensing agreements with MS &
IBM, to achieve the widespread adoption of Mac programming;
this strategy led to success in smart phones
Although Apple’s GUI code was superior to the original MS
code, the superior product lost to the product that first reached
increasing returns - MS
10
Pilot Error at Palm
Despite having 80% of the handheld operating system market
and despite producing 60% of the handheld hardware at its peak
in 2000, Palm has now lost most of its market share to its rivals.
Palm grew so fast (165% year-over-year sales increases) that it
gave little attention to operational issues such as managing the
supply of inputs and forecasting demand.
It mistimed the announcement of its m500 product upgrades,
which were delayed by supply chain bottlenecks, and Palm’s
customers stopped buying older models.
Handspring, Sony, HP, MS’s Pocket PC and Blackberry drove
prices lower and offered newer product features
Almost overnight, excess Palm IV and V inventories piled up on
shelves; Palm took a $300 million write-down on its inventory
losses and its stock price fell from $25 to $2 per share.

11
Ch 11 – Price and Output Determination for a Monopolist
Spreadsheet Approach: Profit v. Revenue Maximization…
(1 of 1)
Table 11.1 shows the demand projections for daily sales of Polo
golf shirts at an outlet store
Sales floor personnel are paid a salary plus commission based
on their sales
Such an employee wants the price to continue dropping as long
as total sales revenue rises (MR remains positive up to and
including 14 shirts/day at $25.79; any fewer shirts, and total
revenue would be smaller, reducing commissions
The store manager & parent company are concerned that the
14th shirt imposes a unit operating loss of -$24; (MR in column
4 falls below the variable cost in column 5)
Not until the price is raised and MR is increased back to $28
will operating losses be eliminated

12
Table 11.1 – Ralph Lauren Polo Golf Shirts (Per Color, Per
Store, Per Day)
13
Graphical Approach (1 of 1)
Figure 11.3 shows the price-output decision for a profit-
maximizing monopolist
Just as in pure competition, profit is maximized at the price and
output combination where MC = MR
If the demand curve were of the form
14

Figure 11.3 – The Price and Output Determination of a Pure
Monopoly
15
Algebraic Approach (1 of 3)
Profit Maximization for a Theme Park Restaurant
16

Profit Maximization for a Theme Park Restaurant (cont.)
17
Profit Maximization for a Theme Park Restaurant (cont.)

18
The Importance of the Price Elasticity of Demand (1 of 2)
19
The Importance of the Price Elasticity of Demand (1 of 2)

20
Ch 11 – The Optimal Markup, Contribution Margin, &
Contribution Margin Percentage
(1 of 1)
Value proposition – A statement of the specific source(s) of
perceived value, the value driver(s), for customers in a target
market
21
Table 11.2 – Optimal Prices, Markups, and Margins

22
Figure 11.4 – Value Creation in the Strategy Map for
Natureview Farms Yogurt
23
Contribution Margin Percentage (1 of 1)
Gross Profit Margins
Gross profit margin – Revenue minus the sum of variable cost
plus direct fixed cost, also known as direct costs of goods sold
in manufacturing
Components of the Margin
Contribution margins and gross profit margins differ across
industries and across firms within the same industry for many
reasons
Some industries are more capital intensive than others
Differences in margins reflect differences in advertising,
promotion & selling costs
Differences in gross margins arise because of differential
overhead in some businesses
Finally, after accounting for any differences in the indirect
fixed costs of capital equipment, advertising, selling expenses
and overhead, the remaining differences in profit margins
reflect differential profitability

24
Monopolists and Capacity Investments
Because monopolists do not face the discipline of strong
competition, they tend to install excess capacity or fail to install
enough capacity
Even regulated monopolies over or underinvest generating
capacity
Limit Pricing
Maximizing short-run profits may not necessarily maximize the
long-run profits of the firm
The monopolist firm may decide instead to engage in limit
pricing where it charges a lower price to discourage entry into
the industry by potential rivals
The firm foregoes some of its short-run monopoly profits in
order to sustain its monopoly position

25
Figure 11.5 – Limit-Pricing Strategy
26
Figure 11.6 – The Effect of Pricing Strategies on Profit Streams
as a Patent Expires
27
Using Limit Pricing to Hamper the Sales of Generic Drugs
Patent protection is the key to financial success in the
pharmaceutical industry
Rather than limit pricing of BMS’s Capoten, a hypertension
drug, BMX maintained the price per pill to the end of the 20
year patent protection
Competition from generics was swift and disastrously effective

In contrast, Eli Lilly chose limit pricing & advertising for
Prozac and Claritin
Smaller margins and a slower decline of market share could
achieve higher profitability over a longer period
28
Ch 11 – Regulated Monopolies
(1 of 1)
Several important industries in the U.S. operate as regulated
monopolies, including electric power companies, natural gas
companies and communications companies
Public utilities – A group of firms, mostly in the electric power,
natural gas, and communications industries, that are closely
regulated by one or more government agencies. The agencies
control entry into the business, set prices, establish product
quality standards, and influence these total profits that may be
earned by the firms subject to scale economies

29
Electric Power Companies (1 of 1)
Electric power is made available to the consumer through a
production process characterized by 3 stages
Power is generated in generating plants
Power is transmitted from the generating site to the locality
where it is used
The power is distributed to individual users
Integrated firms that carry out all 3 stages of production are
usually regulated by state public utility commissions
These commissions set the rates to be charged to consumers
The firms normally receive exclusive rights to serve localities
through franchisees granted by local governing bodies
As a result, electric power firms have well-defined markets
within which they are the sole provider of output
Finally, the FERC has the authority to set rates on power that
crosses state lines, and on wholesale power sales
30
The Public Service Company of New Mexico
This firm provides electric power service and natural gas
distribution services to most of New Mexico’s population
Although PNM was authorized to earn a return of 12.5% on

common equity, it was unable to do so
Faced with high growth in demand, PNM joined other regional
utilities in the construction of several large coal-fired plants
and a nuclear power plant, but load growth did not materialize
as expected
The projects were plagued by cost overruns, delays and costly
safety modifications
At completion, PNM found itself with a capacity in excess of
80% of peak demand for which it could not recover
The regulatory process does not ensure that a firm will earn its
authorized return
31
Natural Gas Companies (1 of 1)
This highly regulated industry is also a 3-stage process
Production of the gas in the field
Transportation to the consuming locality through pipelines
Distribution to the final user
FERC historically set the field price of natural gas, but
regulation at the wellhead has been effectively phased out
Today, FERC overseas the interstate transportation of gas by
approving pipeline routes and by controlling the wholesale rates
charged by pipeline firms to distribution firms
The distribution function may be carried out by a private firm or
a municipal government agency
In either event, the rates charged to final users are also subject
to regulatory control

32
Ch 11 – The Economic Rationale for Regulation
Natural Monopoly Argument (1 of 1)
Firms operating in the regulated sector are often natural
monopolies in which a single supplier emerges because of a
production process characterized by massive economies of scale
Natural monopoly - An industry in which maximum economic
efficiency is obtained when the firm produces, distributes, and
transmits all of the commodity or service produced in that
industry. The production of natural monopolists is typically
characterized by increasing returns to scale throughout the
relevant range of output
33
Figure 11.7 – The Price-Output Determination of a Natural
Monopoly

34
Chapter 9
Measures of Effect
Learning Objectives
• Explain the meeting of absolute and relative
effects
• Calculate and interpret the following
measures: risk difference, population risk
difference, etiologic fraction, and population
etiologic fraction
• Discuss the role of statistical tests in
epidemiologic research

• Apply Hill’s criteria for evaluation of
epidemiologic associations
Effect Measure
• A quantity that measures the effect of
a factor on the frequency or risk of a
health outcome
Three Effect Measures
• Attributable Fractions
– Measure the fraction of cases due to a
factor.
• Risk and Rate Differences
– Measure the amount a factor adds to
the risk or rate of a disease.
• Risk and Rate Ratio
– Measure the amount by which a factor
multiplies the risk or rate of disease.

Absolute vs. Relative Effects
• Absolute
– Attributable risk is also known as a rate
difference or risk difference.
– Population risk difference
• Relative
– Relative risk
– Etiologic fraction
– Population etiologic fraction
Risk Difference (Attributable
Risk)
• Risk difference--the difference
between the incidence rate of
disease in the exposed group (Ie)
and the incidence rate of disease in
the nonexposed group (Ine).

• Risk difference = Ie - Ine
Calculation of Risk Difference
• For women younger than age 75, the
incidence (Ie) of hip fractures per 100,000
person-days was highest in the winter
(0.41), and the incidence (Ine) was lowest
in the summer (0.29). The risk difference
between the two seasons (Ie - Ine) was 0.41
- 0.29, or 0.12 per 100,000 person-days.
Population Risk Difference
• Measures the benefit to the
population derived by modifying a
risk factor.
Etiologic Fraction
• Defined as the proportion of the rate
in the exposed group that is due to
the exposure.
• Also termed attributable proportion or

attributable fraction.
Population Etiologic Fraction
• Provides an indication of the effect of
removing a particular exposure on the
burden of disease in the population.
• Also termed attributable fraction in the
population.
Statistical Measures of Effect
• Significance tests
• The P value
• Confidence interval
Null Hypothesis
• Underlying all statistical tests is a null
hypothesis, which states that there is
no difference among the groups being

compared.
• The parameters may consist of the
prevalence or incidence of disease in
the population.
Significance Tests
• Used to decide whether to reject or fail to reject
a null hypothesis.
• Involves computation of a test statistic, which is
compared with a critical value obtained from
statistical tables.
• The critical value is set by the significance level
of the test.
• The significance level is the chance of rejecting
the null hypothesis when, in fact, it is true.
The P Value
• Indicates the probability that the

findings observed could have
occurred by chance alone.
• However, a nonsignificant difference
is not necessarily attributable to
chance alone.
The P Value (cont’d)
• Possible meaning of nonsignificant
differences: For studies with a small
sample size the sampling error may
be large, which can lead to a
nonsignificant test even if the
observed difference is caused by a
real effect.
Confidence Interval (CI)
• A computed interval of values that, with a
given probability, contains the true value

of the population parameter.
• The degree of confidence is usually stated
as a percentage; commonly the 95% CI is
used.
• Influenced by variability of the data and
sample size.
Clinical vs. Statistical
Significance
• While small differences in disease frequency or
low magnitudes of relative risk (RR) may be
significant, they may have no clinical
significance.
• Conversely, with small sample sizes, large
differences or measures of effect may be
clinically important and worthy of additional
study.

Statistical Power
• The ability of a study to demonstrate
an association if one exists.
• Determined by:
– Frequency of the condition under study.
– Magnitude of the effect.
– Study design.
– Sample size.
Evaluating Epidemiologic
Associations
• Five key questions to be asked:
– Could the association have been observed by
chance?
• Determined through the use of statistical tests.
– Could the association be due to bias?
• Bias refers to systematic errors, i.e., how samples
were selected or how data was analyzed.

Evaluating Epidemiologic
Associations (cont’d)
• Could other confounding variables have
accounted for the observed relationship?
• To whom does this association apply?
– Representativeness of sample
– Participation rates
• Does the association represent a cause-
and-effect relationship?
– Considers criteria of causality.
Types of Associations between
Factors and Outcomes
• Not statistically associated
(independent)
• Statistically associated

Statistical Association
• When a factor and outcome are
statistically associated, the
relationship can be:
– Non-causal
– Causal
• Indirect
• Direct
Multiple Causality
• Also referred to as multifactorial
etiology.
• “…requirement that more than one
factor be present for disease to
develop…”
Models of Multiple Causality
• Epidemiologic triangle
• Web of causation, e.g., in avian

influenza
• Wheel model, e.g., childhood lead
poisoning
• Pie model, e.g., lung cancer
Chapter 8
Experimental Study
Designs
Learning Objectives (abridged)
• State how study designs compare with respect
to validity of causal inference
• Distinguish between a controlled experiment
and a quasi-experiment
• Describe the scope of intervention studies
• Define the term controlled clinical trials and give
examples

• Explain the phases in testing a new drug or
vaccine
Learning Objectives (abridged)
• Discuss blinding and crossover in
clinical trials.
• Define what is meant by community
trials.
• Discuss ethical aspects of
experimentation with human subjects.
True Experimental Studies
• Most convincing for conferring
evidence of associations between
risk factors and outcomes
• Manipulation of study factor and
randomization of subjects
• An example is a randomized clinical

trial.
Women’s Health Initiative
• Hormone Replacement Therapy (HRT)
– Epidemiologic studies had shown that HRT
use had significant benefits against coronary
heart disease.
– Clinical trials had failed to demonstrate any
benefit.
– Large body of epidemiologic research had
observed that women who took HRT had
elevated risks of breast cancer.
Women’s Health Initiative
• Hormone Replacement Therapy (HRT)
– To resolve the question of risks versus benefits of
HRT, a clinical trial was conducted.
– Demonstrated that:

• the epidemiologic findings on cancer were
generally accurate
• the benefits on cardiovascular disease had
been overestimated
– Results
• Use of HRT decreased 40%-80% after the trial
was stopped
Quasi-
Experiment/Community Trial
• Ranked immediately below
controlled experiments in rigor
• Investigator is unable to randomly
allocate subjects to the conditions.
• There may be contamination across
the conditions of the study.

Intervention Studies
• An investigation involving intentional
change in some aspect of the status
of subjects
• Used to test efficacy of preventive or
therapeutic measures
• Manipulation of the study factor and
randomization of study subjects
Intervention Studies
• Two categories:
– Clinical trials (focus on the individual)
– Community trial or community
intervention (focus on the group or
community.
• NOTE: Controlled clinical trials may
be conducted both at the individual
and community levels.

Clinical Trials: Definition
• A research activity that involves the
administration of a test regimen to
humans to evaluate its efficacy and safety
• Wide variation in usage:
– The first use of the term was for studies in
humans without any control treatment
– Now denotes a rigorously designed and
executed experiment involving RANDOM
ALLOCATION of test and control treatments
Characteristics of Clinical Trials
• Carefully designed and rigidly enforced
protocol
• Tightly controlled in terms of eligibility,
delivery of the intervention, and monitoring
out outcomes

• Duration ranges from days to years
• Participation is generally restricted to a
highly selected group of individuals.
Characteristics of Clinical Trials
• Once subjects agree to participate,
they are randomly assigned to one of
the study groups, e.g., intervention or
control (placebo)
History of Clinical Trials
• In 1537, Ambroise Paré applied
experimental treatment for battlefield
wounds.
• East India Shipping Company (1600)
found that lemon juice protected against
scurvy.
• James Lind (1747) used the concurrently

treated control group method.
History of Clinical Trials
• Edward Jenner’s efforts to develop a
smallpox vaccine in the late 18th century
• Most recent historical developments
include the use of multicenter trials.
– Instrumental in the development of
treatments for infectious diseases and
recently in chronic diseases that are of
noninfectious origin
Prophylactic and Therapeutic
Trials
• A prophylactic trial evaluates the
effectiveness of a substance that is used
to prevent disease; it can also involve a
prevention program.

• A therapeutic trial involves the study of
curative drugs or a new surgical procedure
to improve the patient’s health.
Outcomes of Clinical Trials
• Referred to as clinical end points
• May include rates of disease, death, or
recovery
• The outcome of interest is measured in
the intervention and control arms of the
trial to evaluate efficacy--these must be
measured in a comparable manner.
Examples of Clinical Trials
• Medical Research Council Vitamin
Study—studied role of folic acid in
preventing neural tube defects.
• South Bronx, NY, STD Program—

evaluated effectiveness of education
efforts to prevent spread of sexually
transmitted diseases (STDs).
Blinding (Masking)
• To maintain the integrity of a study and
reduce the potential for bias, the
investigator may utilize one of two popular
approaches:
–Single-blind design: subject unaware of
group assignment
–Double-blind design: Neither subject nor
experimenter is aware of group
assignment
Phases of Clinical Trials
• Before a vaccine, drug, or treatment can
be licensed for general use, it must go

through several stages of development.
• This lengthy process requires balance to:
– protect the public from a potentially
deleterious vaccine
– satisfy the urgent needs for new vaccines
Stages in the Development of A
Vaccination Program
• Pre-licensing evaluation of vaccine
– Phase I trials: Safety of adult volunteers
– Phase II trials: Immunogenicity and reactogenicity in
the target population.
– Phase III trials: protective efficacy
• Post-licensing evaluation
– Safety and efficacy of vaccine
– Disease surveillance
– Serologic surveillance
– Measurement of vaccine coverage

Phase IV Trials
• There can be more than three phases in a
clinical trial.
• Phase IV trials involve post-marketing
research to gather more information about
risks and benefits of a drug.
Randomization
• Method of choice for assigning subjects to
the treatment or control conditions of a
clinical trial.
• Non-random assignment may cause
mixing of the effects of the intervention
with differences (e.g., demographic)
among the participants of the trial.
Crossover Designs
• Any change of treatment for a patient in a
clinical trial involving a switch of study

treatments
• In planned crossovers a protocol is
developed in advance, and the patient
may serve as his or her own control.
• Unplanned crossovers exist for various
reasons, such as patient’s request to
change treatment.
Ethical Aspects of Human
Experimentation
• Benefits must outweigh risks.
• Ethical issues:
– Informed consent
– Withholding treatment known to be effective
– Protective the interests of the individual
patient
– Monitoring for side effects
– Deciding when to withdraw a patient
Reporting the Results of

Clinical Trials
• The CONSORT Statement is a
protocol that guides the reporting of
randomized trials by providing a 22-
item checklist and a flowchart.
Summary of Clinical Trials
• Strengths:
– Provide the greatest control over:
• the amount of exposure
• the timing and frequency of exposure
• the period of observation
– Ability to randomize reduces the likelihood
that groups will differ significantly.
Summary of Clinical Trials
(cont’d)
• Limitations:

– Artificial setting
– Limited scope of potential impact
– Adherence to protocol is difficult to
enforce
– Ethical dilemmas
Community Trials
• Community intervention trials determine the
potential benefit of new policies and programs
• Intervention: Any program or other planned
effort designed to produce changes in a target
population
• Community refers to a defined unit, e.g., a
county, state, or school district
Community Trials (cont’d)
• Start by determining eligible communities and
their willingness to participate

• Collect baseline measures of the problem to be
addressed in the intervention and control
communities
• Use a variety of measures, e.g., disease rates,
knowledge, attitudes, and practices
Community Trials (cont’d)
• Communities are randomized and followed over
time
• Outcomes of interest are measured
Examples of Community Trials
• North Karelia Project
• Minnesota Heart Health Program
• Stanford Five-City Project
• Pawtucket Heart Health Program
• Community Intervention Trial for Smoking
Cessation (COMMIT)

• Project Respect
Summary of Community Trials:
Advantages
• They represent the only way to estimate
directly the impact of change in behavior
or modifiable exposure on the incidence of
disease.
Summary of Community Trials:
Disadvantages
• They are inferior to clinical trials with respect to
ability to control entrance into study, delivery of
the intervention, and monitoring of outcomes.
• Fewer study units are capable of being
randomized, which affects comparability.
• They are affected by population dynamics,
secular trends, and nonintervention influences.
Four Stages of Evaluation

• Formative: Will all plans and procedures
work as conceived?
• Process: Is the program serving the
target group as planned?
• Impact: Has the program produced any
changes among the target group?
• Outcome: Did the program accomplish
its ultimate goal?
Overview of Quasi-Experimental
Study Designs
Type of Study Design Group(s) Pretest Intervention Posttest
Posttest only Intervention O X X
(has only one group)
Pretest/Posttest Intervention X X X
(has only one group)
Pretest/Posttest/Control Intervention X X X
(has two groups) Control X O X

Solomon Four-Group Intervention 1 X X X
(has four groups) Intervention 2 O X X
Control 1 X O X
Control 2 O O X
Note. O = not used; X = used.
Quasi-Experimental Designs
• Posttest only--observations are made only
after the program has been delivered.
• Pretest/Posttest--baseline and follow-up
observations are made.
• Pretest/Postest/Control--observations are
made in both intervention and control
groups before and after the program.
Quasi-Experimental Designs
(cont’d)
• Solomon Four-Group assignment:

– Used to overcome the Hawthorne Effect.
– Uses four equivalent groups, two
intervention and two control:
• Two are observed before and after intervention.
• Two are observed only after intervention.

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