The first reported case of COVID-19 in the United States occurred on January 21, 2020 in Snohomish County (WA), a suburb of Seattle. On March 11, the basketball game between the Utah Jazz and Oklahoma City Thunder was postponed prior to tip-off when a player for the Jazz tested positive for the coronavirus. The following day, the National Basketball Association (NBA) and the National Hockey League (NHL) both suspended play indefinitely. (Ultimately, both leagues resumed play and concluded their seasons without in-person attendance in a "bubble" environment later in the year.) In the intervening 50 days, there were 319 NBA games and 322 NHL games played in the U.S. and Canada with fans in attendance. This project proposes the collecting and matching daily, county-level data on the number of cases and deaths attributed to COVID-19 to game-level data for NBA and NHL franchises located in the U.S.
The primary contribution will be the empirical examination of the relationship, or lack thereof, between local rates of infection and consumer demand for attendance to professional sporting events. The theory of decision-making under uncertainty with risk aversion implies that the risk of exposure to the coronavirus would raise the expected "cost" of attendance, if there is a non-zero probability of infection, such that fewer fans should attend games in cities with a larger number of cases (deaths). Conversely, individuals may neglect probability when making an uncertain choice (neglect of probability) or underestimate the probability of a negative outcome (optimism bias) such that attendance would not respond to local intensity of infection. Controlling for other factors affecting game-level attendance, such as team quality, expected outcome, average ticket prices, and local weather conditions, this research will explore whether consumer demand for the NBA and NHL was affected by the number of new (or ongoing) cases and/or deaths attributed to COVID-19 using county-level data aggregated to the local Metropolitan Statistical Area (MSA). This project offers contributions to both the theory of consumer demand under uncertainty and the impact of information on consumer behavior in a public health context. In addition to the primary research question, the collection of multiple seasons of data (necessary to control for seasonality in consumer demand) for two different leagues and the inclusion of expected outcome and local weather conditions will contribute to the literature on fan demand in sports economics. A natural extension of this research will be the collection of data on local newspaper coverage of the coronavirus, either at the local, national, or global levels, in mediating the relationship between the local intensity of the pandemic and fan demand.
Experience with spreadsheet software
Completion of Econometrics (ECON 375) or similar advanced statistics course prior to Summer 2021
Knowledge of additional programming languages (Python, R, etc.) preferred
Number of Student Researchers
Applications open on 01/03/2021 and close on 03/22/2021