Abstract
We analyze the performance outcomes of National Hockey League (NHL) players over 18 seasons (1990–1991 to 2007–2008) as a function of the demographic conditions into which they were born. We have three main findings. First, larger birth cohorts substantially affect careers. A player born into a large birth cohort can expect an earnings loss of roughly 18% over the course of an average career as compared to a small birth cohort counterpart. The loss in earnings is driven chiefly by supply-side factors in the form of excess cohort competition and not quality differences since the performance of players (as measured by point totals for non-goalies) is actually significantly greater for players born into large birth cohorts. Performance-adjusted wage losses for those born in large birth cohorts are therefore greater than the raw estimates would suggest. Second, career effects differ by relative age. Those born in early calendar months (January to April) are more likely to make it into the NHL, but display significantly lower performance across all birth cohorts than later calendar births (September to December). In short, those in the top echelon of NHL achievement are drawn from fatter cohorts and later relative age categories, consistent with the need to be of greater relative talent in order to overcome significant early barriers (biases) in achievement. We find league expansions increase entry level salaries including the salaries of those born into larger birth cohorts, but they do not affect salaries of older players. Finally we find that the 2004–2005 lock-out appears to have muted the differentials in pay for large birth cohort players relative to their smaller birth cohort counterparts.
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- 1.
The baby-boom (and subsequent bust) was felt more in Canada than in the US where birth rates climbed to 25.3 per 1,000 of the population in the 1950s but fell only to 17 per 1,000 of the population in the 1980s.
- 2.
The actual cut-off dates do vary. This early year bias is of course not true in all sports since it depends on varying school entry date decisions. For example in Britain there is a higher percentage of September to December births in professional soccer because entry conditions into youth sports leagues are tied to the beginning of the school year.
- 3.
There is the special case of competing league formation as occurred in hockey under the banner of the World Hockey Association (WHA) which acted as a competitor for player talent for nearly 10 years from 1971 to 1979. If one imagines a 20 year old entry age for the average hockey player this lines up almost perfectly with the height of the baby-boom in North America, from 1951 to 1959. One could speculate that a supply-side push would allow a competing league to draw near equivalent talent. Once that baby-boom talent pool dries up and wages for marginal players begin to be bid up by rival leagues, the financial viability of the less established franchises in a league is likely to fade.
- 4.
Those born during a calendar year January to December are included in the same pool of those eligible to play on the same teams. Despite the potential of playing alongside someone who had almost a full year more of maturation, these systems still prevail in most amateur settings.
- 5.
Initially, only indirect evidence in support of the Easterlin hypothesis was advanced and other researchers that attempted to test the general idea behind it found mixed results (Pampel and Peters 1995).
- 6.
- 7.
This leads immediately to predictions across relative age (birth month) in terms of differences in prime-age versus rookie substitution elasticities (discussed in more detail in section “Cohort Size and Player Outcomes” below).
- 8.
The history of unrestricted free agency (UFA) in the NHL begins in 1995. From 1995 to 2004 unrestricted free agency usually began at age 31. Following the season-ending lockout of 2004–2005, a new collective bargaining agreement with a salary cap was implemented, resulting in a gradual lowering of the eligibility age for UFA status from 30 to 27, and the proviso that if a player completed seven full NHL seasons, he would be free-agent eligible prior to age 27 or whichever came first. See: http://spectorshockey.net/blog/is-the-era-of-building-through-unrestricted-free-agency-over/
- 9.
- 10.
The controls included in X it are ExperienceCat as a direct effect, non-goalie forward positional dummy (defensemen as excluded category), a bmi indicator (weight/height) and country of origin dummies.
- 11.
Total games played is used as an explanatory variable, replacing player performance used in Table 2.
- 12.
The draft is an annual meeting in which every franchise of the NHL selects players (in ascending order based on past season performance) from the amateur leagues where they meet draft eligibility requirements.
- 13.
This was the result of a demand made by the national Hockey League Players Association (NHLPA) in one of the first rounds of bargaining that did not involve Alan Eagleson as head of the NHLPA. Pay secrecy clearly favoured the NHL owners and this move was one reason NHL player salaries began to slowly converge to the rest of the North American player salaries in the 1990s and 2000s. Eagleson was convicted of fraud and collusion with owners in restraining player salary demands.
References
Arnett, J.J. (2000): Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), pp. 469–480.
Bachman, R., T.K. Bauer and P. David (2009): Cohort Wage Effects and Job Mobility Working Paper 5035, Bonn: Institute for the Study of Labor.
Barnsley, R.H., A.H. Thompson and P.E. Barnsley (1985): Hockey success and birthdate: The relative age effect. Canadian Association of Health, Physical Education and Recreation Journal, 51, pp. 23–28.
Berger, M.C. (1985): The Effect of Cohort Size on Earnings Growth: A Re-examination of the Evidence. Journal of Political Economy, 93(3), pp. 561–573.
Berger, M.C. (1989): Demographic cycles, cohort size, and earnings. Demography, 26(2), pp. 311–321.
Bloom, D.E., R.B. Freeman and S. Korenman (1987): The Labor Market Consequences of Generational Crowding. European Journal of Population, 3, pp. 131–176.
Brunello, G. (2010): The Effects of Cohort Size on European Earnings. Journal of Population Economics, 23(1), pp. 273–290.
Buschemann, A., and Deutscher, C. (2011). “Did the 2005 collective bargaining agreement really improve team efficiency in the NHL?” International Journal of Sport Finance, 6(1), 298–306.
Deaner R.O., A. Lowen and S. Cobley (2013): Born at the Wrong Time: Selection Bias in the NHL Draft. PLoS ONE 8(2): e57753. doi:https://doi.org/10.1371/journal.pone.0057753
Easterlin, R.A. (1987): Birth and Fortune: The Impact of Numbers on Personal Welfare, Chicago: Chicago University Press.
Foot, D. and D. Stoffman (2001): Boom, Bust & Echo: Profiting from the Demographic Shift in the 21st Century, Toronto: Stoddart.
Freeman, R. (1979): The Effect of Demographic Factors on Age-Earnings Profiles. Journal of Human Resources, 14, pp. 289–318.
Frick, B. (2009). “Globalization and Fcator Mobility: The Impact of the “Bosman-Ruling” on Player Migration in Professional Soccer.” Journal of Sports Economics, 10(1): 88–106.
Gibbs, B.G., J.A. Jarvis and M.J. Dufur (2012): The rise of the underdog? The relative age effect reversal among Canadian-born NHL hockey players: A reply to Nolan and Howell. International Review for the Sociology of Sport, 47, pp. 644–649.
Gladwell, M (2008). Outliers: The Story of Success. New York: Little, Brown and Company.
Grondin S., P. Deshaies et L.P. Nault (1984): Trimestre de naissance et participation au hockey et au volleyball. Le revue Quebecoise de l’activite physique, 2, pp. 97–103.
Idson, T. and L H. Kahane (2000): Team Effects on Compensation: An Application to Salary Determination in the National Hockey League. Economic Inquiry, 28, pp. 345–357.
Kahn, L (1993). “Free Agency, Long-Term Contracts and Compensation in Major League Baseball: Estimates from Panel Data” The Review of Economics and Statistics, 75(1): 157–64.
Korenman, Sanders and David Neumark. (2000). “Cohort Crowding and Youth Labor Markets (A Cross-National Analysis)” in Youth Employment and Joblessness in Advanced Countries, David Blanchflower and Richard Freeman (eds), NBER: pp 57–106.
Morin, L.-P. (2013): Cohort Size and Youth Earnings: Evidence from a Quasi-Experiment, Working Paper, Vancouver School of Economics.
Murphy, K.M., M. Plant and F. Welch (1988): Cohort Size and Earnings in the United States, in: Economics of Changing Age Distributions in Developing Countries, ed. by R.D. Lee, W.B. Arthur and G. Rodgers, pp. 39–58, Oxford: Oxford University Press.
Musch J. and S. Grondin (2001): Unequal competition as an impediment to personal development: A review of the relative age effect in sport. Developmental Review, 21, pp. 147–167.
Musch, J. and R. Hay (1999): The relative age effecting soccer: Cross-cultural evidence for a systematic discrimination against children born late in the competition year. Sociology of Sport Journal, 16, pp. 54–64.
Nolan J, and G.Howell (2010) “Hockey success and birth date: The relative age effect revisited.” International Review for the Sociology of Sport 4(3): 507–512.
Pampel, F.C. and H.E. Peters (1995): The Easterlin Effect. Annual Review of Sociology, 21, pp. 163–194.
Rosen, S. (1972) “Learning and Experience in the Labor Market”, The Journal of Human Resources, 7, 3: 326–242.
Welch, F. (1979): Effects of Cohort Size on Earnings: The Baby Boom Babies’ Financial Bust. Journal of Political Economy, 87(5), pp. S65–S97.
Wright, R.E. (1991): Cohort Size and Earnings in Great Britain. Journal of Population Economics, 4(4), pp. 295–305.
Wattie N, Baker J, Cobley S, and Montelpare WJ (2007). “A historical examination of relative age effects in Canadian hockey players.” International Journal of Sport Psychology 38(2): 178–186.
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Appendix
Appendix
Data Sources
Our key dependent variable is individual player salaries. The USA Today Sports Salaries Database (http://content.usatoday.com/sportsdata/hockey/nhl/salaries/team/) provides player salaries by player by team going back to 2000. For earlier seasons we rely on a time-intensive search of the HockeyZonePlus database which allows one to view the salary history of an individual player since player salaries became public in 1989,Footnote 13 by entering the player’s last name (http://www.hockeyzoneplus.com/bizdb/nhl-salaries-search.htm). Historical player demographic and performance data was obtained from the official NHL league website (http://www.nhl.com/ice/playerstats.htm).
Birth rate data was obtained from the United Nations Statistics Division’s Demographic Yearbook (http://unstats.un.org/unsd/demographic/products/dyb/dyb2.htm) which provides crude birth rate data for the countries and the birth years present in our sample of players (1951–1989). Despite having 46 birth countries in our sample of NHL players, we collected birth rate data only for the following countries/regions (Canada, US, Sweden, Russia, Finland, Czech Republic, Slovakia, Former Soviet Republics, and Rest of Europe). A few players born in places like Jamaica or South Korea etc. where there is no history of amateur hockey, were tracked down and found to have been players brought up in Canada or the US and hence assigned birth rates for those countries in the sample period.
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Bryson, A., Gomez, R., Zhang, T. (2017). All-Star or Benchwarmer? Relative Age, Cohort Size and Career Success in the NHL. 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_4
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