untalented but successful olivier gergaud univ. de reims – univ. paris 1 joint with vincenzo...

Untalented but Successful

Olivier GERGAUDUniv. de Reims – Univ. Paris 1

Joint with

Vincenzo VERARDICRED, Namur

Réactions :

« On aimerait que Libé ouvre ses colonnes à des économistes qui bossent sur de vrais sujets, plutôt que de s'amuser dans la cour de récré », Victoria Beckham

« Laisser les économistes jouer dans la cour d'école c'est aussi leur permettre de repenser la théorie classique […] Il n'y a qu'en se posant des questions iconoclastes que la science économique avance », Captain Cavern

Rosen (1981) : talent basedSmall differences in talent can lead to large earnings differences and eventually to the emergence of superstars. Inferior talent is not a substitute for superior talent.

Adler (1985) : talent independentStars can even emerge among equally-talented individuals. Network effects.

Theory of Superstars: Fundamentals

Rosen (1981) : talent based

Inferior talent is not a substitute for superior talent.


Mother

Adler (1985): talent independent (network)


Mother

Adler (2005) p.3, “Superstars [...] are individuals who attain considerable prominence and success in their field and whose earnings as a result are significantly greater than the earnings of their competitors”.

Superstars phenomenon : Adler (2005)

Superstars phenomenon : Adler (2005)

"Superstars attain earnings significantly greater than their competitors"

Meaning: For a similar level of talent, some individuals will be valued more than others.

Talent: definition

Talent \Tal"ent\, n. [F., fr. L. talentum]

1. Among the ancient Greeks, a weight and a denomination of money equal to 60 min[ae] or 6,000 drachm[ae].

Webster's Revised Unabridged Dictionary (1913)

Talent: definition (cont.)

Webster's Revised Unabridged Dictionary (1913)

4. Intellectual ability, natural or acquired; mental endowment or capacity; skill in accomplishing; a special gift, particularly in business, art, or the like; faculty.

Are Rosen-MacDonald and Adler Theories of Superstars complementary?

Motivation

Emergence of superstars may be…

… The reward for a greater talent: à la Rosen (1981) Hamlen (1991) Hamlen (1994) Lucifora and Simmons (2003)

… Independent of talent: à la Adler (1985) Blass (1992) Chung and Cox (1994)

Empirical Evidence : Fuzzy


… The reward for a greater talent: à la Rosen (1981) Hamlen (1991) Hamlen (1994)

… Independent of talent: à la Adler Blass (1992) Chung and Cox (1994)


(Music) Link quality of voice and sales but find no evidence of Rosen’s effect


… The reward for a greater talent: à la Rosen (1981) Lucifora and Simmons (2003) :


(Soccer) convex structure of rewards but measure of talent questionable, endogenous (# of goals) : weak evidence of a Rosen effect

“If we think of the top three players as the best players in Italian football measured by our goals per season indicator, then […] our results are consistent with Rosen’s (1981) theory of superstars”, Lucifora and Simmons (03)


… The reward for a greater talent: à la Rosen (1981) Hamlen (1991); Hamlen (1994).

… Independent of talent: à la Adler (1985) Blass (1992)


(Baseball) finds evidence against Rosen: experience is more important than talent


… The reward for a greater talent: à la Rosen (1981) Hamlen (1991); Hamlen (1994).

… Independent of talent: à la Adler (1985) Chung and Cox (1994)


(Music) find evidence against Rosen, there is a snowball effect on CD sold, success is the result of a prob. mechanism which predicts that “artistic outputs will be concentrated among a few lucky individuals”

How to test theories of superstars?

Use data where:

Talent is objectively measurable There is no unobserved heterogeneity Superstars exist Rarity can be separated from talent Earning is measured with precision Role of agents or managers is negligible

In other words, use quasi-experimental data

Solution: Quasi-experimental dataset

Pokemon TCG

Solution: Quasi-experimental dataset

Pokemon TCG All characteristics of monsters are available Talent is objectively summarized in a one-

dimensional indicator Superstars are present Trading prices are available Objective rarity is provided There are no agents …

(i.e. )

Supply of the cards First-hand market: 19 decks in 2000

Not included: 65% [1,4] times: 32% > 4 times: 3% No individual prices. Bad cards >>> Very good cards.

Second-hand market: Retailers, specialized games shops or websites Individual cards Individual prices. SCRYE: Median price charged by retail outlets

across the US and Canada.

A sample card

Number

NameHit Points

Type

Power

Level

Pokemon Data

Attack Cost

Weakness and

Resistance

12

Attack Damage

A sample card

Number

NameHit Points

Type

Power

Level

Pokemon Data

Attack Cost

Weakness and

Resistance

76

Attack Damage

Advantages of our dataset

Talent (Level): fully observable, totally objective, explicitly provided in the cards.

Supply : exogenously controlled by a single firm.

Price : measure of success, proxy for consumers' preferences.

13$

Rosen effect 20$

4152

Convexity: « Greater magnification of

the earnings-talent gradient increasing

sharply near the top of the scale. »

Adler effect?

12 20

1.5$

0.5$

Snowball effect: «Superstars may emerge

even among equally-talented individuals»

The empirical procedure

First step: Estimate a Robust Hedonic Price Function for the

Pokémon TCG

Second step: Detect outliers via a simple (robust) analysis of the

residuals and determine the fair price for all individuals

Third step: Graph the overprized individuals

Fourth step: Estimate a Reweighted Hedonic Price Function for the

Pokémon TCG

Classical regression

Z

P

OLS

Masking effect

Superstar

Superstar

Superstar

Swamping effect

Robust regression

Z

P

Robust regression

Superstar

Superstar

Superstar

LTS regression : Intuition

Decide a percentage of trimming (α%) that indicates the degree of resistance to outliers.

Run OLS on all samples having (1-α%) of the data

Choose the regression with the best fit

LTS regression: example

N = 100 & trimming 25%

Number of sub samples:

Number of sub samples: 2.42 1023

100 100!

75 75!25!

2.42 1023??

Fast-LTS algorithm:

based on the min # ofsub samples guaranteeingat least one non-corrupt sample

The Specification

i 0 i 1 i 2 i 3 i 4 i iLog(p )= +Z +SET +SUP +RAR +f(LEVEL )+

Rosen effect

Creature's characteristics

Card's setting

Supply conditions

Rarity

Observed priceConvex

The Specification


The Specification

Estimate robust residuals (ri ) by LTS


The Specification

Estimate robust residuals (ri ) by LTS

Overpricing can be estimated by:

2

iˆ

-r -i 2

i

p-1=exp 1

p̂


How to test Rosen effect?

1. Run a reweighted least squares hedonic price function

2. Check if we observe a convexity

Results

… …

10 20 30 40 50 60 70 80 90 100

15

20

25

30

35

40Graphically

Talent

Pri

ce

GyaradosCharizard

Predicted by Rosen

Blastoise

Predicted by Adler

Venusaur

Identifying Superstars (year 2000)• Who is sold at more than 25% of its fair price?

Pikachu

Squirtle

Charizard

Blastoise

Alakazam

-100

Fair Price

100

200

300

400

Und

erp

ricin

dg

& O

verp

ricin

g (

%)

0.0

1.0

2.0

3.0

4.0

5D

ensi

ty

0 20 40 60 80Talent

Evolution of Superstars

Alakazam

Blastoise

Charizard

Pikachu

Squirtle

25

50

75

Fair Price

100

125

200

300

150

175

225

250

275

-25

-50

-75

-100

Ove

rpric

ing

& U

nder

pric

ing

(%

)

03-

00

07-

00

09-

00

11-

00

01-

01

04-

02

10-

02

01-

03

Period

Conclusion: Innovations

Original Dataset: Collectible cards

Robust estimation methods

Original Results…

Conclusion: Original Results

Rosen (81) and Adler (85): complementary

Untalented but Successful: why not !!!

“We are all made of stars”

Untalented but Successful

Olivier GERGAUDUniversité de Reims

Joint with

Vincenzo VERARDICRED - Namur

Electrode

Pikachu

Talented - Unsuccessful

Untalented - Successful

On the job market !

untalented but successful olivier gergaud univ. de reims – univ. paris 1 joint with vincenzo...

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