Relative age effect

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The distribution of births according to month in the general population
The distribution, according to month of birth, of players involved in UEFA organised international youth soccer tournaments in 2010/11

The term relative age effect (RAE), also known as birthdate effect or birth date effect, is used to describe a bias, evident in the upper echelons of youth sport[1] and academia,[2][3] where participation is higher amongst those born early in the relevant selection period (and correspondingly lower amongst those born late in the selection period) than would be expected from the normalised distribution of live births. The selection period is usually the calendar year, the academic year or the sporting season. The difference in maturity - which can be extreme at young ages: a six-year old born in January is almost 17% older than a six-year old born in December in the same year[4] - causes a performance gap that persists over time.

The term month of birth bias is also used to describe the effect and season of birth bias is used to describe similar effects driven by different hypothesised mechanisms.

The bias results from the common use of age related systems, for organizing youth sports competition and academic cohorts, based on specific cut-off dates to establish eligibility for inclusion. Typically a child born after the cut-off date is included in a cohort and a child born before the cut-off date is excluded from it.

In sports[edit]

The most commonly used cut-off date for youth international sporting competition is 1 January. The IOC[5] and FIFA[6] and the 6 international football confederations (AFC, CAF, CONCACAF, CONMEBOL, OFC and UEFA[7]) all use 1 January as their administrative cut-off date when determining an athlete's eligibility to compete in youth competitions, children born before a specified cut-off date are excluded.

Malcolm Gladwell's book Outliers: The Story of Success and SuperFreakonomics by Steven Levitt and Stephen Dubner have popularised the issue in respect of Canadian ice-hockey players, European football players and US Major League baseball players.

The expected distribution of births in any given month across a population correlates closely to the number of days in the month, with February as the shortest month having the fewest births. The first graph shows the distribution of births, by month, for the European Union over the ten years from 2000 to 2009. There is a slight but clearly perceptible increase in the birth rate in the summer months.

A relative age effect is illustrated in the second graph by the month of birth distribution of over 4,000 youth players involved in the qualifying squads for U17, U19 and U21 tournaments organised by UEFA in 2010/11.

Research suggests that individuals born closer to the cut-off date are more likely to play professionally.[4][8][9][10]

In academia[edit]

Cut-off dates for academic cohort structuring, including the setting of academic years, are usually determined by national education authorities and tend to be based on autumn start dates, so August or September cut-off dates are common in the Northern Hemisphere and February or March cut-off dates are common in the Southern Hemisphere. This tendency reflects the historical need for children to be involved in summer-time agricultural work with school starting after harvesting.

Oxford University RAE profile in aggregate 2004/5 to 2013/14

A relative age effect in academia is illustrated in the third graph which shows the percent deviation from month of birth profile norms evident in graduations from Oxford University over a 10-year period. Academic relative age effects seem to be moderated by culture.[11]

A 2006 study finds that relative age affects student performance and has long-lasting effects on life outcomes. The authors find that "the youngest members of each cohort score 4–12 percentiles lower than the oldest members in grade four and 2–9 percentiles lower in grade eight… data from Canada and the United States show that the youngest members of each cohort are even less likely to attend university."[12]

A 2014 study finds that Italian students born in the early months of the year "are more likely to be tracked in more academic schools rather than in vocational schools."[13]

In leadership positions[edit]

A relative age effect has also been observed in the context of leadership. Studies have found an over-representation of people born just after the school entry cut-off date in a range of leadership positions. Such an over-representation starts in high-school leadership activities such as sports team captain or club president.[14] In the adult life, this over-representation has been observed in top managerial positions (CEOs of S&P 500 companies),[15] and in top political positions, both in the USA (senators and representatives),[16] and in Finland (MPs).[17]

In other spheres[edit]

Whilst an over-representation of early-born participation is evident in the aspirational fields of elite sport and education there is also evidence of a corresponding disproportionate over-representation of late-born children in epidemiologically defined cohorts exhibiting conditions such as ADHD,[18] schizophrenia[19][20] and obesity.[21] One study finds "that higher school starting age lowers the propensity to commit crime at young ages."[22] However, other studies failed to replicate relative age effects on temperament, mood, or physical development.[11]


  • Ability grouping – cohort selection by ability rather than chronological age.
  • Corrective Relative Age Adjustments – calculated adjustments to competitive outcomes designed to compensate for relative age advantage/disadvantage, for example by the re-calculation of sprint times according to relative age within a cohort.[23]
  • Cut-off date – an administratively defined date in the calendar which determines the eligibility of individuals to be included in a cohort, usually a competition or an academic year group. A birth date prior to the cut-off date excludes an individual and a birth date after the cut-off date includes an individual.
  • Multi-age grouping – the broadening of cohort selection to encompass multiple chronological age groups. For example, a Multi-age classroom.
  • Relative Age Advantage – a competitive advantage accruing to an individual due to being born early in a selection period. A relative age advantage can also accrue to a group consisting of a majority of relatively older individuals.[24]
  • Relative Age Bias – a tendency towards the prioritised selection of relatively older members of a cohort.
  • Relative Age Characteristic (of an individual) – the quality of having been born early or late in the selection period.
  • Relative Age Competition – competition designed to avoid cut-off date rules, such as ‘average team age’ or ‘bio-banded’ competition.[25]
  • Relative Age Disadvantage – a competitive disadvantage accruing to an individual due to being born late in a selection period. A relative age disadvantage can also accrue to a group consisting of a majority of relatively younger individuals.[24]
  • Relative Age Discrimination – indirect, systemic discrimination, disadvantaging relatively young individuals in a cohort and advantaging relatively old individuals in the same cohort.
  • Relative Age Effect – an observable effect resulting from a relationship between chronological age and an eligibility cut-off date used for cohort selection.
  • Relative Age Environment – the system of rules governing inclusion or exclusion from cohorts based on cut-off date eligibility rules.
  • Relative Age Quota – a quota system imposed on a cohort to establish an even distribution of chronological ages.
  • Relative Age Skew – statistically significant differing levels of participation observed amongst relatively older and relatively younger members of a cohort.
  • Selection Period – an administratively or culturally defined timeframe, such as a competition season or an academic year, established by reference to a cut-off date.


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  4. ^ a b "Long-term relative age effect: Evidence from Italian football". Retrieved 2016-04-23.
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  8. ^ Musch, Jochen; Hay, Roy (1999). "The Relative Age Effect in Soccer: Cross-Cultural Evidence for a Systematic Discrimination against Children Born Late in the Competition Year". Sociology of Sport Journal. 16: 54–64. doi:10.1123/ssj.16.1.54.
  9. ^ "The Duke Orthopaedic Journal - Jaypee Journals - Jaypee Brother". Retrieved 2016-04-23.
  10. ^ Böheim, René; Lackner, Mario (2012). "Returns to education in professional football". Economics Letters. 114 (3): 326–8. doi:10.1016/j.econlet.2011.11.009. S2CID 37186318. SSRN 1835304.
  11. ^ a b Jeronimus, Bertus F; Stavrakakis, Nikolaos; Veenstra, René; Oldehinkel, Albertine J (2015). "Relative Age Effects in Dutch Adolescents: Concurrent and Prospective Analyses". PLOS ONE. 10 (6): e0128856. doi:10.1371/journal.pone.0128856. PMC 4468064. PMID 26076384.
  12. ^ Bedard, K; Dhuey, E (2006). "The Persistence of Early Childhood Maturity: International Evidence of Long-Run Age Effects". The Quarterly Journal of Economics. 121 (4): 1437–72. doi:10.1093/qje/121.4.1437. JSTOR 25098831.
  13. ^ Ponzo, Michela; Scoppa, Vincenzo (2014). "The long-lasting effects of school entry age: Evidence from Italian students". Journal of Policy Modeling. 36 (3): 578–99. doi:10.1016/j.jpolmod.2014.04.001.
  14. ^ Dhuey, Elizabeth; Lipscomb, Stephen (2008). "What makes a leader? Relative age and high school leadership". Economics of Education Review. 27 (2): 173–83. CiteSeerX doi:10.1016/j.econedurev.2006.08.005.
  15. ^ Du, Qianqian; Gao, Huasheng; Levi, Maurice D (2012). "The relative-age effect and career success: Evidence from corporate CEOs". Economics Letters. 117 (3): 660–2. doi:10.1016/j.econlet.2012.08.017.
  16. ^ Muller, Daniel; Page, Lionel (2016). "Born leaders: Political selection and the relative age effect in the US Congress". Journal of the Royal Statistical Society, Series A (Statistics in Society). 179 (3): 809–29. doi:10.1111/rssa.12154.
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  18. ^ Morrow, R. L; Garland, E. J; Wright, J. M; MacLure, M; Taylor, S; Dormuth, C. R (2012). "Influence of relative age on diagnosis and treatment of attention-deficit/hyperactivity disorder in children". Canadian Medical Association Journal. 184 (7): 755–62. doi:10.1503/cmaj.111619. PMC 3328520. PMID 22392937.
  19. ^ Davies, G; Welham, J; Chant, D; Torrey, E. F; McGrath, J (2003). "A Systematic Review and Meta-analysis of Northern Hemisphere Season of Birth Studies in Schizophrenia". Schizophrenia Bulletin. 29 (3): 587–93. doi:10.1093/oxfordjournals.schbul.a007030. PMID 14609251.
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  21. ^ Tanaka, Hisako; Sei, Masako; Quang Binh, Tran; Munakata, Hokuma; Yuasa, Kyoko; Nakahori, Yutaka (2007). "Correlation of month and season of birth with height, weight and degree of obesity of rural Japanese children". The Journal of Medical Investigation. 54 (1–2): 133–9. doi:10.2152/jmi.54.133. PMID 17380024.
  22. ^ Landersø, Rasmus; Nielsen, Helena Skyt; Simonsen, Marianne (2017). "School Starting Age and the Crime-age Profile" (PDF). The Economic Journal. 127 (602): 1096–118. doi:10.1111/ecoj.12325. S2CID 155576753. SSRN 2984362.
  23. ^ Romann, M; Cobley, S (2015). "Relative Age Effects in Athletic Sprinting and Corrective Adjustments as a Solution for Their Removal". PLOS ONE. 10 (4): e0122988. Bibcode:2015PLoSO..1022988R. doi:10.1371/journal.pone.0122988. PMC 4386815. PMID 25844642.
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  25. ^ Reeves, M; Enright, K; Dowling, J; Roberts, S (2018). "Stakeholders' understanding and perceptions of bio-banding in junior-elite football training" (PDF). Soccer & Society. 19: 1166–1182. doi:10.1080/14660970.2018.1432384. S2CID 148894870.

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