1. Heterogeneity and the Winner’s Curse Mike Huwyler

2. What is the Winner’s Curse? Win the auction, but overpay relative to true value Three assumptions: – Imperfect information scenario – Common values setting – Impact of increasing bidder count

3. Contemporary Examples of the Winner’s Curse Professional sports (free agency) Initial public offerings – Google example

4. Online Setting Is it still an imperfect information scenario? – Feedback, product reviews, other listings Diverse range of participants – Income and experience Increased number of participants

5. Literature Review Uncertainty – Product misrepresentation (Jin and Kato, 2002) Pictures (Hou et. al, 2009) Product quality (Adams et. al, 2011) Timing strategies – “Sniping” (Easley and Wood, 2005) Secret reserve prices (Bajari and Hortascu, 2003)

6. Dataset 6,000 eBay auctions Bidder, auction, seller, and product characteristics Corvettes (all different models) – Most popular car sold on eBay

7. Tests Divide dataset into experience and income groupings Primary test – Relationship b/w bid amount and bidder count Secondary tests – Relationship b/w bid amount and individual and product characteristics

8. Hypotheses Goal: Determine how different individuals respond to the winner’s curse – Do bidders optimally respond to an increase in the number of bidders? Hypotheses: – High income and high experience bidders should respond optimally – Secondary test results will be mixed (horizontal vs. vertical characteristics)

9. Regression Model For three experience and three income groupings (low, medium, and high): – Y 1 = β 0 + β 1 x 1 + β 2 x 2 + β 3 x 3 + β 4 x 4 + β 5 x 5 + β 6 x 6 + ϵ 1 Dependent variable = bid amount Independent variables = number of bidders, bidder income/experience, seller feedback, vehicle mileage, vehicle condition (dummy), vehicle transmission (dummy)

10. Experience Model Results Hypothesis partially supported Negative, statistically significant relationship b/w bid amount and number of bidders for ALL experience groups Secondary tests mixed – Universal response to mileage, condition – Seller feedback more important to high experienced bidders

11. Income Model Results Hypothesis fully supported Negative, statistically significant relationship b/w bid amount and number of bidders for high income; Positive, insignificant for low income Secondary tests remain mixed

12. Adjustment #1 Low R-squared values – Addition of three new variables: year, color (dummy), and model (dummy) – Adjusted R-squared increased – Same Results

13. Adjustment #2 Switch number of bidders to auction length – Proxy for the expected number of bidders Results support experience hypothesis, conflict with previous income findings – Negative relationship b/w bid amount and auction length for medium and high experienced bidders, positive relationship for low experienced – Income models scrapped

14. Real World Applications Can bidders improve their situation? – Education – Personality – Third Parties

15. Future Adjustments As results indicate, model is far from perfect Future adjustments would include: – Interaction model – More accurate way to represent expected number of bidders – Examine different products – New bidder variables (education) – Split dataset into quantiles, not by standard deviation