ArticleNature communications2026
How common depictions of wealth distributions can bias people to underestimate inequality.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- How common depictions of wealth distributions can bias people to underestimate inequality.Nature communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Economic inequality is a critical issue in elections and legislating, so having an informed electorate is vital to sensible policymaking. Unfortunately, misperceptions about the true extent of inequality abound. Here, we demonstrate that the most prevalent formats for representing economic distributions tend to dampen viewer impressions of inequality. We begin by showing that the most popular newspaper portrayals of inequality contain potentially biasing features. Next, in nine experiments (N = 3599 U.S. adults), we explore how these common representations of inequality evoke biased impressions and we test methods to mitigate these biases. Specifically, we document two biases in how people evaluate economic distributions. First, people are under-sensitive to differences in the size of identified population groups (e.g., top 1% vs. top 10%) such that their judgments become distorted by arbitrary ways in which a population is divided (partition dependence). Second, people's judgments under-weight information about intermediate groups relative to the most- and least-wealthy groups (middle neglect). We test ways to mitigate these biases and conclude by proposing presentation guidelines that promote more accurate impressions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.