R: Endowment Effect for Sports Cards

SportsCards {AER} | R Documentation

## Endowment Effect for Sports Cards

### Description

Trading sports cards: Does ownership increase the value of goods to consumers?

### Usage

    data("SportsCards")

### Format

A data frame containing 148 observations on 9 variables.

good

  factor. Was the individual given good A or B (see below)?

dealer

  factor. Was the individual a dealer?

permonth

  number of trades per month reported by the individual.

years

  number of years that the individual has been trading.

income

  factor indicating income group (in 1000 USD).

gender

  factor indicating gender.

education

  factor indicating highest level of education (8th grade or less, high school, 2-year college, other post-high school, 4-year college or graduate school).

age

  age in years.

trade

  factor. Did the individual trade the good he was given for the other good?

### Details

`SportsCards` contains data from 148 randomly selected traders who attended a trading card show in Orlando, Florida, in 1998. Traders were randomly given one of two sports collectables, say good A or good B, that had approximately equal market value. Those receiving good A were then given the option of trading good A for good B with the experimenter; those receiving good B were given the option of trading good B for good A with the experimenter. Good A was a ticket stub from the game that Cal Ripken Jr. set the record for consecutive games played, and Good B was a souvenir from the game that Nolan Ryan won his 300th game.

### Source

Online complements to Stock and Watson (2007).

### References

List, J.A. (2003). Does Market Experience Eliminate Market Anomalies? Quarterly Journal of Economcis, 118, 41–71.

Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

### See Also

`StockWatson2007`

### Examples

    data("SportsCards")
    summary(SportsCards)

    plot(trade ~ permonth, data = SportsCards,
      ylevels = 2:1, breaks = c(0, 5, 10, 20, 30, 70))
    plot(trade ~ years, data = SportsCards,
      ylevels = 2:1, breaks = c(0, 5, 10, 20, 60))

[Package AER version 1.2-5 Index]