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 $α\approx 2$, 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.

翻译：利用Patreon月度收益数据，我们量化了平台注意力算法如何影响创作者经济中的收益集中度。Patreon是创作者通过忠实订阅者实现附加内容变现的工具，但其原生分发功能有限，因此其收益能较好地反映创作者在外部平台（Instagram、Twitch、YouTube、Twitter/X、Facebook及“仅限Patreon”内容）上获得的注意力。通过拟合幂律尾部以检验高度不平等的收益分布，我们得出三个关键发现：首先，跨年份与跨平台的收益尾部及分布呈现帕累托指数约为$α\approx 2$，更接近资本收入的集中特征而非劳动收入，符合复合增长的“富者愈富”动态（Barabási与Albert，1999）。其次，当算法将更多注意力向头部倾斜时，收益增益主要来自创作者“中产阶级”的份额缩减。第三，随时间推移，各社交媒体平台的创作者收入不平等趋于收敛至类似的厚尾（且日益集中）分布，这很可能源于算法推荐相对于通过社交图谱进行用户筛选的内容重要性上升。尽管Patreon数据仅代表这些平台创作者总收益的一小部分，但其为跨平台算法对收益集中度的影响提供了独特洞见。