Large Language Models Substantially Compress Well-Being Dispersion but Largely Preserve Its Socioeconomic Structure

Research using large language models (LLMs) to generate synthetic populations shows that model outputs compress human variation. We ask whether this compression also distorts where dispersion is concentrated across social groups. Using 93,901 respondents from 66 countries and territories in Wave 7 of the World Values Survey, we compare observed life satisfaction with predictions from six LLMs. All six substantially understate overall dispersion. Yet after normalizing for these scale differences, they largely reproduce the human income gradient in well-being inequality: lower-income groups remain relatively more heterogeneous than higher-income groups. The pattern is robust to country fixed effects and alternative weighting schemes, and extends directionally to employment, education, and perceived control. In a separately preregistered mechanism experiment using Claude Sonnet 5, counterfactually increasing income strongly raises predicted mean life satisfaction but produces essentially no linear change in predicted dispersion. Removing income from the respondent profile leaves the negative dispersion gradient almost unchanged; the same pattern appears with Gemini and DeepSeek, although somewhat attenuated. These findings show that LLMs can substantially compress well-being dispersion while preserving much of its socioeconomic structure, and that this structure does not depend on the explicit income cue alone.