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Record 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 on OpenAlexaboutno aff
Isabelle Puppa

Bibliographic record

VenueOpen MIND · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsLeaguePolitical scienceBusinessFinance

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.202
Threshold uncertainty score0.402

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0060.003
Scholarly communication0.0110.011
Open science0.0090.007
Research integrity0.0290.012
Insufficient payload (model declined to judge)0.0350.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.087
GPT teacher head0.347
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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