"Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data

Ilan Strauss, Jangho Yang and Mariana Mazzucato

Papers from arXiv.org

Abstract: Using monthly Patreon earnings, we quantify how platform attention algorithms shape earnings concentration across creator economies. Patreon is a tool for creators to monetize additional content from loyal subscribers but offers little native distribution, so its earnings proxy well for the attention creators capture on external platforms (Instagram, Twitch, YouTube, Twitter/X, Facebook, and ``Patreon-only''). Fitting power-law tails to test for a highly unequal earnings distribution, we have three key findings. First, across years and platforms the earnings tail and distribution exhibits a Pareto exponent around $\alpha \approx 2$, closer to concentrated capital income than to labor income and consistent with a compounding, ``rich-get-richer'' dynamic (Barabasi and Albert 1999). Second, when algorithms tilt more attention toward the top, the gains are drawn disproportionately from the creator ``middle class''. Third, over time, creator inequality across social media platforms converge toward similarly heavy-tailed (and increasingly concentrated) distributions, plausibly as algorithmic recommendations rises in importance relative to user-filtered content via the social graph. While our Patreon-sourced data represents a small subset of total creator earnings on these platforms, it provides unique insight into the cross-platform algorithmic effects on earnings concentration.

Date: 2025-09, Revised 2025-11
New Economics Papers: this item is included in nep-pay
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2509.26523 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2509.26523

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().