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Enregistrement W3017492405 · doi:10.11575/prism/37707

The Price of the Puck: Recommendations for Public Financing National Hockey League Arenas in North America

2019· article· en· W3017492405 sur OpenAlexaboutno aff
Isabelle Puppa

Notice bibliographique

RevueOpen MIND · 2019
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSports, Gender, and Society
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLeaguePolitical scienceBusinessFinance

Résumé

récupéré en direct d'OpenAlex

Public subsidization of private infrastructure has been a controversial policy. This topic becomes even more contentious in the area of professional sports stadiums and arenas. Governments use subsidies to stimulate production and increase employment. Yet, some argue that a $71 billion industry should not need assistance. Others argue that these subsidies fulfill their purpose, ensuring the existence of sports arenas. Every few years, a National Hockey League (NHL) team considers plans for a new arena. The median age of current arenas is 21.5 years, with most tenancy leases lasting 30 years. Recently, governments have split the cost of arenas with team owners. Historically, most arenas had been publicly owned and funded. Much literature exists about economic benefits of professional sport investments. From an economic perspective, funding a professional sports arena does not yield a positive return on investment. Economic benefits are inconsequential when compared to the initial investment capital. Also, funding an arena provides an opportunity cost. The opportunity cost becomes clear when cities lack other infrastructure or public supports. Controversy arises when a city chooses to invest in a sports arena rather than the best alternative use of funds. Nonetheless, sports arenas and teams provide non-economic benefits. The value of city pride and prestige from hosting a professional sports team is subjective but important to consider. Governments have tried to decrease public funding towards sports arenas. A key attempt occurred in the United States in 1986. In 1986, the federal government limited how much a private entity could contribute to pay off tax-exempt municipal bonds. The government assumed that cities would stop providing bonds if teams could not help pay off the debt. This assumption proved false. Cities continued to provide bonds even though it meant paying off a larger amount. Canadian governments also limit sports financing. Traditionally, the federal government only supports NHL arenas if they also serve a community event like the Olympics. For other arenas, funding must come from the provincial and municipal levels. More recently, Canadian governments have provided some NHL teams with operating subsidies. In addition, some cities and provinces provide infrastructure improvements and community revitalization levies. Assessing these financing trends involved researching every team in the NHL. No literature exists that examines the funding structure and features of all teams. This paper adds to the discussion by analyzing the purpose and value of public financing NHL arenas. This project also discusses fiscal policy and its impact on a team’s existence. Public subsidization of sports arenas is an important topic for many localities. Therefore, it is critical to have one source of comparable information. I first examined leases between governments and teams and construction records. This research helped me identify the most popular types of funding used in each country. I separated the countries because they differ in population size, currency rates, and legislation. Another comparison I made is privately-owned arenas versus publicly owned arenas. Most arenas are publicly owned. Generally, smaller cities received the highest percentage of public contribution towards capital costs. I discuss the reason behind this and pros and cons of publicly owning an arena. Examples in the United States and Canada are present throughout the paper. Analyzing the purpose of arena construction revealed a few trends. In the United States, the highest public contribution went to arenas to prevent team relocation. In Canada, most public funding went to arenas that replaced older facilities. In addition, I found that public financing arenas is more common in the United States than in Canada. Canada makes up a large number of total hockey fans and league revenues. However, lower tax rates, stronger currency value, and greater public support make the United States a more competitive environment for NHL teams. Still, the United States has had its fair share of unsuccessful NHL franchise teams. Later in this paper, I examine nine NHL teams that no longer exist today. After a city has a team and arena, it must ensure proper team management and high team performance. Otherwise, revenues will fall and the NHL can move the team out of the city. I conclude this paper with four options for policy makers in the United States and Canada. First, policy makers should consider city market size when deciding how to structure fiscal arrangements with the NHL. Although city size does not perfectly correlate with contribution, smaller cities generally need more public assistance. Second, policy makers should use present value when discussing public finances. Many cities in the United States grant arenas property tax exemptions. The value of a property tax exemption is not stated in construction costs. However, its value culminates to millions of dollars per year. Economic impact assessments must account for all forms of public contribution. Third, policy makers must track and systematically report its financing data. Research in this paper involved many sources of information. To learn from best practices, cities should track its contributions and outcomes. Finally, policy makers must consider if existing policies have unintended consequences. As I discuss in this paper, some federal policies have had unintended consequences on city decisions and are ineffective at achieving their goals.

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)0,014
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,202
Score d'incertitude au seuil0,402

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,025
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0060,003
Études des sciences et des technologies0,0060,003
Communication savante0,0110,011
Science ouverte0,0090,007
Intégrité de la recherche0,0290,012
Charge utile insuffisante (le modèle a refusé de juger)0,0350,008

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 Opus0,087
Tête enseignante GPT0,347
Écart entre enseignants0,260 · 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'étudeSans objet
Domainenon disponible
GenreAutre

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

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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