MétaCan
Menu
Retour à la cohorte
Enregistrement W335767545

Implications of State Income Tax Policy on NBA Franchise Success: Tax Policy, Professional Sports, and Collective Bargaining

2011· article· en· W335767545 sur OpenAlexaboutno aff
Timothy E. Zimmer

Notice bibliographique

RevueThe Sport Journal · 2011
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueSports Analytics and Performance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSalaryRevenueCollective bargainingBasketballLeagueTicketBusinessRevenue sharingTax revenueTotal revenueEconomicsMarketingLabour economicsFinanceMarket economyPublic economics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Introduction The National Basketball Association (NBA) is a sports entertainment enterprise with yearly revenues surpassing $4 billion (5). The majority of these revenues are derived from ticket sales, merchandising and television revenues. The distribution of these revenues between franchises and players has been negotiated and is governed by the Collective Bargaining Agreement (CBA). The current version of the CBA was implemented before the 1984-85 season and was most recently re-negotiated prior to the 2005 season. The current CBA contract expires following the 2010-2011 NBA season, but league owners have the option to extend the agreement through the 2011-2012 NBA season (5). A large component of the CBA is the provision of a salary cap. The salary cap dictates a fixed percentage of league revenues which are to be paid to players in terms of salaries and benefits. NBA teams are presented with a yearly salary cap number to be used as player compensation. This amount can only be exceeded utilizing certain exceptions as further defined by the CBA. One justification for the salary cap is the concept that it is designed to benefit middle and small market teams. It is argued that larger market teams have significantly more ability to profit from ticket, merchandising and television revenues. This advantage could be used to enlist top talent by paying salaries far exceeding those of smaller markets. In using superior financial resources to lure and retain better talent (players, coaches and management), it is feared that larger market teams could dominate the league over a prolonged period. The salary cap system, it is argued, should allow every NBA franchise an equal opportunity in acquiring and obtaining comparable resources. While not a perfect system, the CBA should work to distribute resources (player skill, coaching talent and management expertise) more evenly throughout the league. While the CBA only governs player salaries, the even distribution of quality players throughout the league should also dissuade quality coaches and management from concentrating and distribute them throughout the league. League ownership believes that an equal chance of team success should promote larger game attendance and provide for a healthier competitive balance in the league. However, these goals have repeatedly been disputed in research (7, 3, 9) which have found increased disparity of play after the imposition of revenue sharing amongst teams and other results inconsistent with stated goals. This paper will extend this research by examining potential causes of the breakdown between the intended goals of the CBA and its results. In assessing NBA franchise success, the incentive structure facing potential resources (players, coaches, management) should be examined. The different tax environment of NBA franchises is a potential variable which could disrupt league parity. It is argued in this paper that resources are influenced by the financial incentives created by varying state income tax rates applied to the differing NBA franchises based on location. The implications of these findings could have impacts on future CBA negotiations. Methodolgy The study examines the potential for state tax income tax policy to influence NBA team success. The model employs data for eleven years (2000 through 2010) of previous NBA seasons. Also included are the rates (in percentage terms) for the individual states top marginal tax brackets for these eleven years. The basketball data was assembled using information from a sports database website (6). The income tax bracket data was derived using information from the tax foundation website (10). For ease of computation and data gathering, only the top marginal tax bracket was used. As NBA salaries escalate, the importance of lower tax brackets becomes nominal. The data from the Canadian-based team was removed. The examination is on the impact of income taxes on player decisions, which in the United States will be uniform at the federal level and vary only at the state level. …

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,039
Score d'incertitude au seuil0,703

Scores Codex et Gemma par catégorie

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

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 Opus0,030
Tête enseignante GPT0,257
Écart entre enseignants0,228 · 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 tête enseignante, 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

Citations1
Publié2011
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueThe Sport JournalMême sujetSports Analytics and PerformanceTravaux en français237 207