Economic Impact of the 2012 “6 Hours of Warrior Creek” Mountain Bike Race

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How to cite this study

Schiller, A., and J. Whitehead. 2013. Economic Impact of the 2012 ‘6 Hours of Warrior Creek’ Mountain Bike Race. Boone, NC: Center for Economic Research and Policy Analysis at Appalachian State University.

Overview

Two factors most strongly predicted racers’ total spending at an endurance mountain bike race in North Carolina: whether they visited other tourist attractions during their trip and how many nights they stayed. Having more people in the racer’s party was also associated with higher total spending, while income had almost no effect on spending.

Relevance

This research is relevant for race organizers and others interested in using events for economic development, and who are interested in the most effective strategies for increasing the economic impact of events. The results can be used to target marketing or which aspects of the event to promote.

Although the sample size is small (n=94) the researchers had a relatively high response rate and there is no reason to suspect the results are biased.

Location

The race is held in Boomer, North Carolina, located in central North Carolina about an hour from Winston-Salem, North Carolina. The population of Boomer was 2,158 in 2010.

Trail Type

This study considered the economic impact of the 6 Hours of Warrior Creek, an endurance mountain bike race during which participants complete as many 12.9 mile loops as possible in six hours.

Purpose

The purpose of this study is to identify how different factors related to out-of-town racers’ travel might affect the economic impact of a mountain bike race. Race promoters can use this information to target marketing efforts and potentially increase their event’s economic impact.

The study was commissioned by the Brushy Mountain Cyclists Club, which organizes the race.

Findings

  • Respondents spent an average of $296 during their trip.
  • Whether or not a racer visited other tourist attractions had the biggest effect on their total spending in the area. Holding income, number of nights, and all other variables constant, racers who visited other attractions spent 46 percent, or $136, more than those who did not visit other attractions.
  • Every additional night a racer spent in the area increased their total spending by 34 percent, or $101 for the average racer.
  • Every additional person in the racer’s party—including family and friends—increased total spending by 9 percent, or $27 for the average racer.
  • Differences in participant income had a negligible effect on total spending.

Methods

The authors sent an online survey to the 368 participants in the race; they omitted local residents and incomplete survey responses from the sample, for a final sample size of 94. The survey gathered data on income, the number of people in the party, the number of nights spent in the area, whether they visited tourist attractions in the area, and how much they spent on food, lodging, travel, tourist activities, and other expenses.

To identify the main drivers of racer spending, the authors estimated a regression model using both a linear and double log specification, with total spending as the dependent variable and the number of nights spent in the area, the number of people in the party, income, and whether they visited attractions as the explanatory variables.

Added to library on December 29, 2015