The article evaluates whether GPT-3.5 Turbo can predict individual World Values Survey responses and whether agreement varies by country, demographics, topic, and number of response options. It uses Wave 6 (2010–2014) for the United States, Japan, Singapore, Brazil, South Africa, and Sweden. For each respondent, observed sex, age, education, class, ethnicity, and covariates are converted into a verbal profile; a target question is added, and the model is asked to reason before selecting an option, to avoid politically correct answers, and to respond in the questionnaire language for Sweden, Brazil, and Japan. The paper identifies the model only as GPT-3.5 Turbo, sets temperature to 0.2, and reports 100 simulations per sample or respondent, but it releases no snapshot, API date, analytic sample size by country or subgroup, total request count, complete prompts, generations, or code. The primary task compares a synthetic answer with the real answer in the same row using Cohen's kappa, Cramer's V, and raw agreement. In the main text, mean country kappas are 0.239 for the United States, 0.165 for Sweden, 0.063 for Singapore, 0.034 for Brazil, 0.024 for Japan, and 0.006 for South Africa: even the best result is low-to-modest agreement, not accurate recovery of public opinion. The supplement reports different values, 0.257, 0.134, 0.059, 0.039, 0.026, and 0.001, respectively, without reconciling them. From only six country observations, the authors correlate kappa with three binary indicators and report 0.971 for Western culture, 0.557 for developed economy, and 0.101 for English language. No intervals, p values, or multivariable model are provided, and culture overlaps with development; the coefficients also cannot be reconstructed exactly from either the rounded figure values or the supplementary table. These are exploratory six-case associations, not evidence that culture, development, or language causes the differences. For the United States, the supplement reports higher kappa for men than women (0.275 versus 0.257), White than Black respondents (0.274 versus 0.079), ages 65+ than 18–29 (0.310 versus 0.134), upper than working class (0.371 versus 0.172), and university than non-university education (0.293 versus 0.172). Subgroup sizes, uncertainty, declared weighting, and tests are absent; kappa also varies with prevalence and category composition, so these gaps are descriptive signals rather than adjusted bias estimates. In U.S. topic analyses, the environmental item reaches kappa 0.270 with five covariates and zero without them; the political item reaches 0.306 with ideology and 0.145 without it. This does not isolate topic difficulty because the question, outcome, and covariate set all change. The model generates 6.10 percentage points fewer responses defined as liberal for the environmental item and 16.33 points more for political voting, demonstrating topic dependence rather than one stable ideological leaning. Reducing voting from four options to two raises kappa from 0.109 to 0.306 and agreement from 29.21% to 65.92%, but it also excludes responses and changes prevalence, so option count is not isolated as the cause. A control changes only the country label for U.S. Wave 6 data to Japan and reduces kappa to 0.057; this establishes sensitivity to the label, not fidelity to Japan. U.S. Wave 7 yields 0.379 versus 0.306 for Wave 6, a difference the paper calls consistency without an equivalence analysis. The defensible contribution is evidence that profile-conditioned survey simulation has unequal and generally limited agreement whose errors depend on context and subgroup. It does not support replacing surveys, causally locating failures in training data, or treating generations as real public opinion.
Research question
With what fidelity does GPT-3.5 Turbo reproduce responses from the World Values Survey, how does that agreement vary across six countries and US subgroups, and how does it change according to topic and number of options?