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Enregistrement W2007828165 · doi:10.1353/nin.0.0069

Player Pay and Productivity in the Reserve Clause and Collusion Eras

2009· article· en· W2007828165 sur OpenAlexvenueno aff
Michael Haupert

Notice bibliographique

RevueNine · 2009
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueSports Analytics and Performance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSalaryRevenueLabour economicsWageMonopsonyAgency (philosophy)Value (mathematics)EconomicsCollusionMinimum wageTest (biology)ProductivityBusinessDemographic economicsAccountingMarket economyMicroeconomicsSociology

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION Professional athletes are among the highest paid workers in the American economy. Their minimum salaries are several times that of the average American salary, and the average wages are an even greater multiple. Among professional athletes, professional baseball players fare particularly well. They enjoy a minimum salary of $390,000 per year, an average salary exceeding $2 million per year, guaranteed contracts, and arguably the strongest union in American history. Stories of the financial escapades of professional baseball teams and the salaries they pay their employees are common in the sports and business press. It was not always this way. It was not until the mid-1970s when professional baseball players blazed the trail for all professional athletes by gaining the right to bargain competitively for their wages. Before that time, players were bound to their original employer for the duration of their careers. The labor market for ballplayers was a classic monopsony--a market in which there is a single employer and a large number of potential employees. Theory suggests that labor will be exploited in such a market. Previous literature suggests this is the case, but a comprehensive test of this theory using data from the pre-free agency period has not previously been undertaken. Whenever a worker is paid a wage less than the value of his or her contribution to the firm's total revenue, we say that the worker is being exploited. The degree of depends on the amount by which the worker is underpaid relative to his or her contribution to the firm's revenue. The contribution a worker makes to the firm's revenue is called marginal revenue product, and is discussed later. Exploitation of workers can occur in many circumstances, but is more likely to occur in circumstances when workers are unable to bargain for their wages. This is exactly the situation that existed in Major League Baseball (MLB) prior to free agency. Because players were tied to one team, they had limited ability to bargain for their wages, thus it was easier for team owners to exploit them by paying them a wage less than what they were actually worth. Players would tolerate this exploitation because even though they were paid less than their value to the team, the salary they were paid was usually higher than what they could have earned in any other line of work. Baseball salaries in the 1920s, while not as outrageous as today, were still substantially higher than the average wage. (1) Since the dawning of free agency, there have been numerous studies of the baseball player labor market. (2) These previous works have focused on issues such as labor exploitation, the effect of competitive labor markets on competitiveness in the league, and the impact of competition for players on league structure and financial stability. This earlier research has produced some interesting results, including models to calculate the financial contribution of individual players to their teams and the degree of labor exploitation. One of the most famous studies, and the one I use for comparison in this research, is by Andrew Zimbalist, who measured the rate of of MLB players from 1984-86. (3) Baseball fans will immediately recognize this as the famous collusion era. While that is an issue of some concern for measuring labor rates, it is not of particular concern in my work. For this study, the Zimbalist results serve as a reasonable comparison, as I will discuss later. One thing missing in all of this previous work, however, has been a historical foundation for these studies. No work has been done using player salaries for the years prior to 1968 because of a lack of sufficient data. None of these previous studies has covered a time period of more than ten years, and no study to date has used actual team revenue information to calculate a player's financial value to his team and measure the team's of his labor. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,041
Score d'incertitude au seuil0,136

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,012
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,002
Études des sciences et des technologies0,0020,003
Communication savante0,0060,003
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0410,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,223
Écart entre enseignants0,195 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2009
Routes d'admission1
Résumé présentoui

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