Large Language Models as Synthetic Respondents for Economic Preferences: A Global Audit

Large-scale surveys are central to the study of economic preferences but are costly and slow to conduct. Recent advances in large language models (LLMs) raise the question of whether synthetic respondents can substitute for human participants. We address this question by running a virtual version of the Global Preference Survey (GPS), a large cross-national instrument that elicits six experimentally validated economic preferences in nationally representative samples from 74 countries and 42 languages. Using a one-to-one, persona-based prompting protocol that embeds respondents’ demographics, location, local language, and country-specific incentives, eight leading LLMs predict human economic preferences for approximately 70,000 individuals in the original GPS. We find systematic but modest alignment with human data at the individual level, with LLMs capturing many of the demographic gradients in gender, age, and cognitive ability. However, alignment deteriorates with aggregation. LLMs only partially recover cultural heterogeneity and show limited correspondence with country-level preference structure. Probing sources of misalignment, we show that country-level performance covaries with economic development and digital infrastructure, but not with population size or the number of internet users. These results indicate that while LLMs can mirror certain forms of individual- and group-level heterogeneity in economic preferences, they are not reliable substitutes for representative surveys, macro-level inference, or policy evaluation. LLM-based synthetic respondents may nonetheless aid early-stage hypothesis generation, study-design diagnostics, provided their use is accompanied by careful validation and attention to developmental and cultural biases.