Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 0

The DOI matches its Crossref record; the references it cites are not checked yet.

Working on an algorithm-controlled platform as a content creator: what explains unequal earnings on YouTube?

Verwiebe, Roland, Osorio-Krauter, Chiara, Fritsch, Nina-Sophie, Weißmann, Sarah, Philipp, Aaron, Buder, Claudia·2026·European Societies

Abstract

The rise of digital platforms like YouTube has transformed content creation into a lucrative yet precarious industry. We explore the inequality in earnings among content creators (CCs) by conducting a large-scale quantitative analysis of YouTube channels from Germany, Austria, and Switzerland. Our findings reveal a highly skewed earning distribution, with a Gini coefficient of 0.89, indicating extreme inequality. Detailed exploratory data analyses show to what extent sociostructural factors, including gender and age, and platform-specific variables, such as the number of subscribers and channel topic, influence earnings inequality among CCs. These findings underscore the presence of winner-take-all dynamics in the YouTube ecosystem. Despite the platform's potential for niche market success, the economic outcomes for CCs are highly polarized, reflecting broader inequalities present in the digital economy. This paper contributes to the understanding of social stratification in the creator economy, highlighting the need for further investigation into the mechanisms driving these inequalities.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1162/euso.a.42

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Metadata

Authors
Verwiebe, R., Osorio-Krauter, C., Fritsch, N.-S., Weißmann, S., Philipp, A., & Buder, C.
Venue
European Societies
Publisher
MIT Press
Published
2026-02-05
Pages
1-29
ISSN
  • 1461-6696
  • 1469-8307
Cited by
0
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Working on an algorithm-controlled platform as a content creator: what explains unequal earnings on YouTube? https://app.cochat.ai/papers/10.1162/euso.a.42
The paper
Verwiebe, R., Osorio-Krauter, C., Fritsch, N.-S., Weißmann, S., Philipp, A., & Buder, C. (2026). Working on an algorithm-controlled platform as a content creator: what explains unequal earnings on YouTube? European Societies. https://doi.org/10.1162/euso.a.42

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.