978-1118808948 Chapter 14 Lecture Note Part 1

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CHAPTER FOURTEEN

ASYMMETRIC INFORMATION

AND ORGANIZATIONAL DESIGN

OBJECTIVES

1. To show the strategic implications posed by asymmetric information.

(Asymmetric Information)

2. To understand the incentive conflicts (including moral hazard) inherent in the

relationship between principal and agent. (Principals, Agents, and Moral

Hazard)

3. To identify the factors influencing the nature and breadth of the firm.

(The Boundaries of the Firm)

4. To understand how decisions are delegated within organizations and to

consider the choice between centralized and decentralized decisions.

(Assigning Decision-Making Responsibilities)

5. To appreciate how firms monitor and reward the performance of workers,

individually and in teams. (Monitoring and Rewarding Performance)

6. To understand the conflicts posed by the separation of ownership and control

in modern corporations.

TEACHING SUGGESTIONS

I. Introduction and Motivation

There are many vivid illustrations of asymmetric information. Here are some

examples:

Example 1. Problem 11, Chapter 15 (slightly modified). Firm A is attempting to

acquire Firm T but is uncertain about T’s value. It judges that the firm’s value

under current management (call this vT) is in the range of $0 to $10 million, with

all values in between equally likely. Whatever the present value of the firm, A

estimates that it will be worth 50% more under its own management. (For instance,

if vT = $5 million, then vA = $7.5 million.) Firm A will make a price offer to

purchase the firm, which T’s management (knowing vT) will accept or reject. On

behalf of Firm A, what price would you name?

Discussion. This is a version of Akerlof’s classic “lemons” problem.

Surprisingly, no mutually beneficial transaction is possible, despite the seller’s

50% value advantage. Asymmetric information is the culprit. As buyer, most

students offer a price in the $5 million to $6 million range. They reckon that by

offering slightly more than the seller’s expected value ($5 million), they can induce

a sale and gain a profit (since A’s overall expected value is $7.5 million). Of

course, this reasoning is faulty in that it ignores the implications of asymmetric

information. When it receives A’s offer, Firm T knows precisely its value VT and

accepts or rejects the offer accordingly.

What’s wrong with an offer of $6 million? Clearly, Firm T will accept the offer

only if it knows the firm’s value under current management is less than this price.

Thus, if the offer is accepted, the firm’s current value is between $0 and $6 million,

or (1/2)($6 million) = $3 million on average. Even with the seller’s 50% value

advantage, the seller’s acquisition value is only (1.5)(3) = $4.5 million on average.

Because of adverse selection, Firm A tends to acquire low-value firms leaving it

with a conditional expected loss of $1.5 million. Its overall expected profit is: (.6)

(-1.5) = -$.9 million (since the offer is accepted 60% of the time. It is easy to check

that any price is loss making. The conditional expected acquisition value for price

P is: (1.5)(P/2) = .75P. In words, Firm A’s acquisition value is only 75% of the

price offered. Adverse selection prevents a profitable acquisition.

Additional comment. The instructor might ask students whether it would make

any difference if the target company (again knowing its value) were to name its

buyout price. The answer is no. A naïve analysis might envision high-value firms

naming high prices and low-value firms naming lower prices. The idea is that

named prices would be calibrated to underlying values. The problem is that

asymmetric information prevents targets from credibly signaling their true values.

If Firm A were to accept a high price at face value (believing this to signal a

high-value firm), a low-value firm would have an obvious incentive to name a high

price, as well. Signaling cannot work here; there is no way high-value firms can

distinguish themselves from their low-value counterparts.

2. Bidding for an Item of Unknown Value: The Winner’s Curse

Tell the class that you will conduct a sealed-bid auction for a valuable item. The

high bidder will pay his bid and win the item. The item is a jar of pennies.

Actually it’s easier to fill a plastic pickle jar with colored plastic Christmas

ornaments (little pinwheels about penny size) to spare the bother of dealing with

pennies. Fill the jar with a predetermined number of trinkets. (For instance, set the

number at 463 by using two packs of 250 trinkets minus 17.) Tell the class that the

winning bidder will receive the value of the jar where each trinket is redeemable

for a penny. Thus, though the students don’t know it the jar is worth $4.63. A buyer

with a winning bid of $3.50 would make a profit of $1.13. If the winning bid were

$6.00, the “winner” would lose $1.37.

Ask each student to write down 4 items: 1) an estimate of the value of the jar

(i.e. the number of trinkets translated into dollars); 2) a sealed bid for the jar; and

3) upper and lower bounds constructed to form a 90% confidence interval around

the true value. (Remind students what a confidence interval is.)

Discussion This example introduces complications posed by uncertainty. Each

bidder is uncertain about the value of the jar and also about the level of

competitors’ bids. Before revealing the true value of the jar, ask a number of

students to reveal their estimates. Students will be surprised by the wide dispersion

of estimates. Ask the class what the distribution might look like if it were plotted. A

number of students are sure to suggest a bell-shaped normal curve. By shows of

hands, find roughly the median of the class estimates and sketch a normal curve

with this mean. Then ask students how they bid. Compare bids to estimates to

emphasize the logic of placing bids below estimates. Note that the optimal degree

of shading involves a tradeoff between the chance of winning and the profit from

winning. (Finding an optimal solution is a complex problem.) Superimpose the bid

distribution next to the estimate distribution. Your graph should resemble Figure

13.3.

Now is the time to suggest the possibility of the winner’s curse by raising two

hypotheses. First, suppose estimates are unbiased, i.e. centered around the true

value of the item. (This seems reasonable.) Second, sup-pose that the estimates and

bids are quite dispersed. (Also reasonable.) These two points imply that the

highest bids (the shaded tail in Figure 13.3) are above the true value of the item.

On average, the winning bidder loses money on the acquisition. At this point, you

may ask some of the high bidders in the class, whether this argument worries them.

(Do they still want to play for real or only for fun?) Find the highest bid. Then,

reveal the value of the item. With 20 or more students in the class, nine times out

of ten, the winning bidder will fall prey to the winner’s curse, i.e. pay more than

the item’s value. The only exceptions occur if the class estimates are biased well

below the true number of trinkets. (To avoid this problem, try the trinkets in

different shaped jars, and see that the jar is 75% or more full.)

Having made the main point, you can raise some other questions. What factors

increase the likelihood and magnitude of the winner’s curse? As discussed in

Chapter 13, the winner’s curse increases with the degree of uncertainty and the

number of bidders. Does the winner’s curse happen in real life? Yes, bidding for

off-shore oil leases, competitive tender-offers, and free-agent bidding for athletes

are just three examples. How well did you (the student) gauge your degree of

uncertainty? Most individuals are overconfident of their estimation abilities. Ask

students if the true estimate fell between their lower and upper bounds. Typically,

less than half the students answer in the affirmative by a show of hands. By

construction, the intervals should bracket the true value 90 percent of the time.

Students are surprised that their knowledge is so uncertain.

II. Teaching the “Nuts and Bolts”

In using this chapter, the instructor has a choice of options. One approach is to

emphasize asymmetric information within market settings. Under this approach,

the focus is clearly put on material in the first half of the chapter. Applications to

organizational design might be mentioned briefly or omitted altogether. The second

approach focuses on the chapter as a whole. Asymmetric information, whether in

markets or within firms, is a cutting edge topic. Indeed, the eminent

microeconomists and game theorists, Paul Milgrom and John Roberts, have written

a wonderful text, Economics, Organization, and Management, Prentice Hall, 1992)

devoted exclusively to this topic. Time permitting, the instructor can guide

management students through a wealth of applications.

A. Asymmetric Information

While the chapter is already stocked with numerous, real-world examples, there are

many more applications. Possible examples include:

A Benefits Program We recommend posing this problem before the assigned

chapter reading where it is discussed. Students offer a range of “reasons” for the

policy losses including the zany moral hazard explanation that the generous

maternity policy has induced workers to have more babies! The correct,

adverse-selection reason quickly emerges and can be backed up with the

two-by-two table in the text. (The appearance of conditional probabilities from the

table is a good bridge from the concepts in Chapter 13.)

A Building Contract This is another good application from the text. The example

reinforces the important distinction between adverse selection and moral hazard.

Adverse selection occurs when the agent (whose interests are at odds with the

principal’s) holds unobservable or hidden information.

Moral hazard occurs when the agent (whose interests are at odds with the

principal’s) takes unobservable or hidden actions.

Other Short Examples involving Moral Hazard and Incentives. A host of

principal-agent applications fall into this category.

A. How should a company structure commissions for its direct sales force? What

portion should be paid in the form of a flat guaranteed wage? How much should

depend on performance? How should performance be measured? By number of

potential customers contacted? By the quantity of sales generated? In the latter

case, what if unobservable variables (unrelated to the salesperson’s expertise and

effort) influence sales? For instance, some salespeople happen to work in regions,

market segments, or for specific customers who have high demand for the firm’s

product. One partial solution is to base compensation on relative performance. The

best performers in a product segment or region earn the highest bonuses.

B. For many years, the U.S. government has offered low-cost (indeed, highly

subsidized) flood and disaster insurance to homeowners in coastal areas. The

intention is to offer affordable insurance against hurricanes and floods that

periodically destroy many of these homes. However, the predictable effect of the

program is to encourage excessive coastal development. Homes and structures

have been built and rebuilt in the most severely affected, high risk, coastal

locations. Economists have called for stricter regulation of coastal development

and for an end to subsidized federal programs. Realistic insurance premiums,

reflecting the true risks and costs of destruction, would restore incentives for

prudent development.

C. The practice of warranting the health of livestock shows the logical market

response to a potential problem of moral hazard. Livestock are prone to disease,

and sellers are much better informed about the health of their stock than buyers. To

remedy moral hazard, sellers commonly warrant their stock against disease for a

stipulated period of time after sale. (This is not a perfect remedy, because the

warrantee may reduce the buyer’s incentive to take precautions against disease.)