Reference-Dependent Preferences : Evidence from Marathon Runners, ric J. Allen, Patricia M. Dechow, University of California, Berkeley, Devin G. Pope, George Wu, University of Chicago
▻https://www.researchgate.net/publication/301571201_Reference-Dependent_Preferences_Evidence_from_Marathon_Runne
▻https://www.researchgate.net/profile/George-Wu-7/publication/301571201/figure/fig2/AS:601871815299079@1520508831816/Distribution-of-Marathon-Finishing-Times-n-9-789-093.png
l’abstract est un peu (!) abscons, mais les graphiques sont suffisamment parlants : l’attraction pour les « nombres ronds » influe sur les performances… Très bel exemple, assez fascinant.
(article d’avril 2016)
Abstract
Theories of reference-dependent preferences propose that individuals evaluate outcomes as gains or losses relative to a neutral reference point. We test for reference dependence in a large data set of marathon finishing times (n = 9;789;093). Models of reference-dependent preferences such as prospect theory predict bunching of finishing times at reference points. We provide visual and statistical evidence that round numbers (e.g., a four-hour marathon) serve as reference points in this environment and as a result produce significant bunching of performance at these round numbers. Bunching is driven by planning and adjustments in effort provision near the finish line and cannot be explained by explicit rewards (e.g., qualifying for the Boston Marathon), peer effects, or institutional features (e.g., pacesetters).
les autres temps « ronds »
▻https://www.researchgate.net/profile/George-Wu-7/publication/301571201/figure/fig3/AS:601871815307267@1520508831839/Distribution-of-the-Number-of-Finishers-Around-Round-Number-Reference-
aux temps intermédiaires
▻https://www.researchgate.net/profile/George-Wu-7/publication/301571201/figure/fig4/AS:601871815278592@1520508831868/Histogram-of-Extrapolated-Finishing-Times-Based-on-Intermediate-Splits
Distribution of the Number of Finishers Around Round Number Reference Point and the Fitted Counterfactual Distribution
c’est bien le comportement des coureurs qui se modifie, pas un biais d’enregistrement au joli arrondi…
▻https://www.researchgate.net/profile/George-Wu-7/publication/301571201/figure/fig6/AS:601871815286791@1520508831916/Percentage-of-Marathoners-Who-Speed-Up-Over-the-Last-2195-Kilometers-a
Percentage of Marathoners Who Speed Up Over the Last 2.195 Kilometers (a) Runners on 3:45 to 4:15 pace through 40 kilometers
stats sur presque 10 M de parcours achevés !
The data used in this paper were obtained from various public websites. In total, we have finishing times for 9,789,093 marathon finishes. The full sample contains data from 1970–2013 (88.97% of data are from 2000 or later) for 6,888 different marathon-years. For some of our analysis, we will focus on a smaller sample of 873,674 finishing times with complete 10-kilometer, half-marathon, 30-kilometer, and 40-kilometer split times. We refer to this smaller sam-ple as the “full-split sample.”