Truth-telling between Salespeople and their Managers (The Search for a Non-Truth-Telling...

Truth-telling between Salespeople and their Managers(The Search for a Non-Truth-Telling Equilibrium)

December 4, 2007

Presentation for: MGT 703: Experimental Economics

Yale School of Management 20071204_In_Class_Presentation.ppt 2

Motivation

Experimental setup

Results from in-class experiment

Critique and Advice– I need your help …

Agenda


What is the context?

Managers rely on salespeople for this information– What are sales going to be?– Who is buying? (What? How much?)

The manager represents the company– How much should the company produce?– Strong incentives to not over-/under-produce

We see successful sales organizations in reality– Salespeople cooperate with their managers

Salespeople represent the “front lines” of most profit-making companies– Most interaction with customers– Privy to the most information “on the ground”


Where’s the problem?

Managers do not have the same incentives– Growth of company is very important– Maximize efficiencies– Minimize wasted effort/production (losses)

Sales force has strong incentive to sell more– Salespeople typically receive two-part (or more complex) compensation schemes

- Big bonus incentives for matching/exceeding sales targets- Some bonus part of salary expectations

Managers also have to set targets– Salespeople often have better information– Revealing information can compromise the salesperson’s

bonus


Motivation for Experiment…

Everyone has an incentive not to cooperate

Salespeople would like to keep information to themselves– Make targets lower– Earn larger bonuses

Managers would like to know more information– Make accurate targets– Run the company most efficiently

- Sales force bonuses are also a “cost”

But, in reality, companies survive Is there equilibrium behavior? What mechanism drives this?

Can we study the problem in a lab setting?


Experimental Setup: The Base Case

Step 1: Salespeople see a private signal, and generate a sales “estimate” which is provided to their manager

Step 2: The Manager creates a sales target based on that signal – (As well as information observed in prior periods)

Step 3: Payoffs are calculated and observed by both the Salesperson and the Manager

Salesperson's Total Salary $10k

$20 (Actual Sales Sales Target) if Actual Sales > Sales Target

0 otherwise

Bonus

Bonus

| Actual Sales Sales Target |Manager's Total Salary $10k $4k 1

Actual Sales


Experimental Setup: Advanced Cases…

Used to make the problem more realistic

Case 1: Salespeople allowed to (privately) invest extra effort to grow sales– Potential for increasing bonus

Case 2: In addition to Salesperson’s effort, Manager now allowed to provide “side-payment”– Manager makes (binding) decision up-front– Salesperson receives incentive to tell truth

- “Insurance” against lost bonus- Proxy for other benefits that a manager may bestow in reality

Experiment proceeded in stages– Allow subjects time to understand incentives

(learning)


Results: Did people get the idea?

0%

5%

10%

15%

20%

25%

30%

35%

Normalized Shading, Adjustment and Effort Exerted(Normalizations relative to Signal, Estimate or relative to Max Allowable Investment - $1k, respectively). N=8

Base Case With EffortEffort &

Side-payment

% Shading (relative to Private Signal)

% Adjustment (relative to Estimate)

% Effort (relative to $1000 max)


Results: Were results tending to stability?

0%

5%

10%

15%

20%

25%

30%

35%

40%

1 2 3 4 5 6 1 2 3 4 1 2

Normalized Shading, Adjustment and Effort Exerted(Normalizations relative to Signal, Estimate or relative to Max Allowable Investment - $1k, respectively). N=8


Side-payment



Tit-for-tat?

Stability?

Outliergroup

No s-p in last period


Results: Were results tending to stability?

0%

5%

10%

15%

20%

25%

30%

35%

1 2 3 4 5 6 1 2 3 4 1 2

Normalized Shading, Adjustment and Effort Exerted(Normalizations relative to Signal, Estimate or relative to Max Allowable Investment - $1k, respectively). N=8 (N=7 in Effort & Side-Payment Case)


Side-payment



Outlier group removed


Results: No changes in manager accuracy…

0%

5%

10%

15%

20%

25%

30%

35%

1 2 3 4 5 6 1 2 3 4 1 20%

20%

40%

60%

80%

100%

Normalized Shading, Adjustment and Effort ExertedN=8 (N=7 in Effort & Side-Payment Case). Min Accuracy 73%, Max Accuracy 96%.


Side-payment

Beh

avio

ral

Metr

ics M

an

ag

er A

ccu

racy


Results: Within-group variations…

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

A B C D E F G H A B C D E F G H A B C D E F G H

Within-group Variation: Shading & Adjustment(Normalizations relative to Signal or Estimate, respectively). N=8

Beh

avio

ral

Metr

ics


Side-payment

+142%




Results: Within-group variations…

0%

10%

20%

30%

40%

50%

60%

A B C D E F G H A B C D E F G H

Within-group Variation: Effort Exerted/Side-PaymentsN=8. Y indicates offer of side-payment in round 1 (of 2). No side-payments were offered in round 2.

With EffortEffort &

Side-payment

$ E

ffort

“In

veste

d”/$

1000 Y Y Y Y Y


0%

10%

20%

30%

40%

50%

-20% -10% 0% 10% 20% 30% 40%

0%

10%

20%

30%

40%

50%

-20% -10% 0% 10% 20% 30% 40%

0%

10%

20%

30%

40%

50%

-20% -10% 0% 10% 20% 30% 40%

Results: Behavioral Correlation

Shading & Adjustment Between TreatmentsN=8. Red = Base Case; Blue = Effort; Green = Side-Payments (Some data points not on chart).

% A

dju

stm

en

t b

y M

an

ag

er

% Shading by Salesperson

Treatment or learning effects?


Results: Review

Considerable within-group variation

However, some reason to believe that there is a “stable” outcome– Even in base case, coordination seems to evolve– Very low shading/adjustment in side-payment case

- After excluding outliers

Shading and adjustment are positively correlated– Means that experiment is being understood– Unclear as to whether learning or treatment effects are taking place

Most significant tests are rejected (n too small)

Not enough time for “stable” behaviors to evolve (t too small)


Critiques and Advice…

Experimental setup– Noise in private signal may not be necessary– Insufficient inclusion of “growth” motive

- Currently “normalized” out – is this an oversimplification?– Random walk DGP

- Would an extra condition (Targett>Targett-1) be more appropriate?

In analysis, I have not (yet) discussed payoffs to either group

Other items?

What am I missing in the experimental setup/analysis?

What mechanism might help sustain ‘equilibrium’ behavior observed in reality?

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