Abstract
This chapter reexamines the impact of cultural and language diversity on the production of winning hockey games to discern the underlying source(s) driving the effects. It makes inferences based on various analyses of micro-level data that include information relating to player interactions, which suggest that the diversity effects are a result of lower off-ice communication costs rather than reduced cultural dominance of the domestic group or on-ice synergies amongst homogeneous players. The inferences are of general interest to managerial economists who can increase firm-level productivity by hiring employees that speak similar languages and share ideas regardless of their cultural background.
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Notes
- 1.
There is also a diversity literature that is unrelated to cultural diversity. Hamilton et al. (2003) examined the production efficiency of 288 garment company workers involved in a change from a primarily individual to a team environment, and found that, in terms of ability, more heterogeneous teams of workers were more productive than less heterogeneous teams. Leonard and Levine (2003) found that retail sales were lower among teams with diverse age range.
- 2.
See Alesina and Ferrara (2005) for a summary of additional studies analyzing the impact of cultural diversity on economic output.
- 3.
In general, European players are trained to focus more on basic skill development as compared to North American players who are trained more typically through playing games. While European players on average are considered to have better individual skills, North American players are considered to be better at physical play.
- 4.
Regular season overtime is played 4-on-4 compared to 5-on-5 during regulation play.
- 5.
Only one season of each player’s salary and their country of origin were obtained. One season of data provides a sufficient amount of observations to test the various hypotheses and to make inferences.
- 6.
Player ethnic categories are defined identically to those of Kahane et al. (2013). Players originating from the following countries are categorized as originating from Other countries: Austria, Denmark, France, Germany, Italy, Norway, Poland, and Switzerland.
- 7.
The ethnic specific player groups are defined by E P .
- 8.
Based on game rosters, a (home or visiting) team consisting of ten North American, five Russian, three Finish, and two Swedish players would have a HHI of \( 0.345\left(={\left(\raisebox{1ex}{$10$}\!\left/ \!\raisebox{-1ex}{$20$}\right.\right)}^2+{\left(\raisebox{1ex}{$5$}\!\left/ \!\raisebox{-1ex}{$20$}\right.\right)}^2+{\left(\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$20$}\right.\right)}^2+{\left(\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$20$}\right.\right)}^2\right) \). The European Share would be \( 0.50\left(=\left(\raisebox{1ex}{$5$}\!\left/ \!\raisebox{-1ex}{$20$}\right.\right)+\left(\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$20$}\right.\right)+\left(\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$20$}\right.\right)\right) \). Based on total game time on ice, a (home or visiting) team in which the North American, Russian, Finnish, and Swedish players played 130, 85, 60, and 25 min of the total game time (300 min) would have a HHI of \( 0.315\left(={\left(\raisebox{1ex}{$120$}\!\left/ \!\raisebox{-1ex}{$300$}\right.\right)}^2+{\left(\raisebox{1ex}{$85$}\!\left/ \!\raisebox{-1ex}{$300$}\right.\right)}^2+{\left(\raisebox{1ex}{$60$}\!\left/ \!\raisebox{-1ex}{$300$}\right.\right)}^2+{\left(\raisebox{1ex}{$25$}\!\left/ \!\raisebox{-1ex}{$300$}\right.\right)}^2\right) \). The European Share would be \( 0.567\left(=\left(\raisebox{1ex}{$85$}\!\left/ \!\raisebox{-1ex}{$300$}\right.\right)+\left(\raisebox{1ex}{$60$}\!\left/ \!\raisebox{-1ex}{$300$}\right.\right)+\left(\raisebox{1ex}{$25$}\!\left/ \!\raisebox{-1ex}{$300$}\right.\right)\right) \). Total game time on ice is the sum of the game time of the individual players. Deviations in the total game time on ice from 300 min (= 60 min × 5 players) exists from short-handed situations and overtime play.
- 9.
Based on players on the ice for each goal event a (home or visiting) team that had two North American, two Russian, and one Finnish player on the ice for a goal event would have a HHI of \( 0.360\left(={\left(\raisebox{1ex}{$2$}\!\left/ \!\raisebox{-1ex}{$5$}\right.\right)}^2+{\left(\raisebox{1ex}{$2$}\!\left/ \!\raisebox{-1ex}{$5$}\right.\right)}^2+{\left(\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$5$}\right.\right)}^2\right) \). The European Share would be \( 0.600\left(=\left(\raisebox{1ex}{$2$}\!\left/ \!\raisebox{-1ex}{$5$}\right.\right)+\left(\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$5$}\right.\right)\right) \). Only even-strength observations in which each team has five players on the ice are included in the analysis.
- 10.
The score margin fixed effects identify the score margin state of the game prior to the goal being scored (e.g., home team leading by two goals, home team leading by one goal, etc.).
- 11.
The majority of team season-games are played with one or two goaltenders. As a result, goaltender quality in both GM and LM is accounted for with the team fixed effects. Kahane (2005) does not find that goaltender quality metrics (i.e., save percentage) impacts winning beyond team effects, a finding that he attributes to increased goaltender quality across the league over time.
- 12.
We omit man-advantage observations from the analyses. Results that include man-advantage scenarios (i.e., power-player goals) that are controlled for with fixed effects are similar and support identical inferences.
- 13.
We note the coefficients of the marginal effect obtained from a logit regression model are similar and support identical conclusions to the coefficients and inferences presented in this paper.
- 14.
We note that team quality is controlled for by both payroll/player salaries and team fixed effects. Since we are using one season of data and team rosters are fairly constant throughout the season, team fixed effects provide for a strong control variable in terms of accounting for factors that can impact the likelihood of game outcomes beyond diversity. The lower R-squared values compared to previous research based on season games is a function of the more micro unit of observation rather than poorer model fit. Variations in game outcomes are more easily explained at the season level compared to the game level.
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Mongeon, K.P., Michael Boyle, J. (2017). The Source of the Cultural or Language Diversity Effects in the National Hockey League. In: Frick, B. (eds) Breaking the Ice. Sports Economics, Management and Policy, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-67922-8_6
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