heterogeneity and the winner’s curse

Heterogeneity and the Winner’s Curse

Mike Huwyler

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

Contemporary Examples of the Winner’s Curse

• Professional sports (free agency)

• Initial public offerings– Google example

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

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)

Dataset

• 6,000 eBay auctions

• Bidder, auction, seller, and product characteristics

• Corvettes (all different models)– Most popular car sold on eBay

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

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)

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)

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

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

Adjustment #1

• Low R-squared values– Addition of three new variables: year, color

(dummy), and model (dummy)– Adjusted R-squared increased– Same Results

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

Real World Applications

• Can bidders improve their situation?– Education– Personality– Third Parties

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