Heterogeneity and the Winner’s Curse

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): • Y1 = β0 + β1x1 + β2x2 + β3x3 + β4x4 + β5x5 + β6x6 + ϵ1 • 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