1. Truth-telling between Salespeople and their Managers (The Search for a Non-Truth-Telling Equilibrium) December 4, 2007 Presentation for: MGT 703: Experimental Economics

2. 20071204_In_Class_Presentation.ppt 1 Yale School of Management Motivation Experimental setup Results from in-class experiment Critique and Advice – I need your help … Agenda

3. 20071204_In_Class_Presentation.ppt 2 Yale School of Management 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”

4. 20071204_In_Class_Presentation.ppt 3 Yale School of Management 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

5. 20071204_In_Class_Presentation.ppt 4 Yale School of Management 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?

6. 20071204_In_Class_Presentation.ppt 5 Yale School of Management 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

7. 20071204_In_Class_Presentation.ppt 6 Yale School of Management 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)

8. 20071204_In_Class_Presentation.ppt 7 Yale School of Management Results: Did people get the idea? Normalized Shading, Adjustment and Effort Exerted (Normalizations relative to Signal, Estimate or relative to Max Allowable Investment - $1k, respectively). N=8 Base CaseWith Effort Effort & Side-payment % Shading (relative to Private Signal) % Adjustment (relative to Estimate) % Effort (relative to $1000 max)

9. 20071204_In_Class_Presentation.ppt 8 Yale School of Management Results: Were results tending to stability? Normalized Shading, Adjustment and Effort Exerted (Normalizations relative to Signal, Estimate or relative to Max Allowable Investment - $1k, respectively). N=8 Base CaseWith Effort Effort & Side-payment % Shading (relative to Private Signal) % Adjustment (relative to Estimate) Tit-for-tat? Stability? Outlier group No s-p in last period

10. 20071204_In_Class_Presentation.ppt 9 Yale School of Management Results: Were results tending to stability? 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) Base CaseWith Effort Effort & Side-payment % Shading (relative to Private Signal) % Adjustment (relative to Estimate) Outlier group removed

11. 20071204_In_Class_Presentation.ppt 10 Yale School of Management Results: No changes in manager accuracy… Normalized Shading, Adjustment and Effort Exerted N=8 (N=7 in Effort & Side-Payment Case). Min Accuracy 73%, Max Accuracy 96%. Base CaseWith Effort Effort & Side-payment Behavioral Metrics Manager Accuracy

12. 20071204_In_Class_Presentation.ppt 11 Yale School of Management Results: Within-group variations… Within-group Variation: Shading & Adjustment (Normalizations relative to Signal or Estimate, respectively). N=8 Behavioral Metrics Base CaseWith Effort Effort & Side-payment +142% % Shading (relative to Private Signal) % Adjustment (relative to Estimate)

13. 20071204_In_Class_Presentation.ppt 12 Yale School of Management Results: Within-group variations… Within-group Variation: Effort Exerted/Side-Payments N=8. Y indicates offer of side-payment in round 1 (of 2). No side-payments were offered in round 2. With Effort Effort & Side-payment $ Effort “Invested”/$1000 YYYYY

14. 20071204_In_Class_Presentation.ppt 13 Yale School of Management Results: Behavioral Correlation Shading & Adjustment Between Treatments N=8. Red = Base Case; Blue = Effort; Green = Side-Payments (Some data points not on chart). % Adjustment by Manager % Shading by Salesperson Treatment or learning effects?

15. 20071204_In_Class_Presentation.ppt 14 Yale School of Management 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)

16. 20071204_In_Class_Presentation.ppt 15 Yale School of Management 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 (Target t >Target t-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?