Papers › Inferring cumulative advantage from longitudinal records
Inferring cumulative advantage from longitudinal records
Alexandros Gelastopoulos, Lucas Sage, Arnout van de Rijt
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Inequality in human success may emerge through endogenous success-breeds-success dynamics but may also originate in pre-existing differences in talent. It is widely recognized that the skew in static frequency distributions of success implied by a cumulative advantage model is also consistent with a talent model. Studies have turned to longitudinal records of success, seeking to exploit the time dimension for adjudication. Here we show that success histories suffer from a similar identification problem as static distributional evidence. We prove that for any talent model there exists an analogous path dependent model that generates the same longitudinal predictions, and vice versa. We formally identify such twins for prominent models in the literature, in both directions. These results imply that longitudinal data previously interpreted to support a talent model equally well fits a model of cumulative advantage and vice versa.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections