Market for Answers: Integrating Сommunity Information in Survey Design (with Alena Buinskaya)

We propose a framework for survey design as a market interaction. In our model, respondents choose answer options closest to their true beliefs, forming demand, while crowdsourced contributors supply options under both monetary and non-monetary incentive schemes. This framework yields two theoretically grounded metrics for evaluating the quality of answer sets for multiple-choice questions: (1) consumer surplus, measured as the semantic similarity between the chosen option and the respondent's true beliefs, and (2) market share, defined as the probability of selecting any option from the set. We propose a method for estimating demand that leverages text embeddings, a recent advance in natural language processing. To validate the model, we conduct an experiment and find that the demand model successfully predicts respondent choices. On the supply side, we find that while monetary incentives improve crowdsourced options, supplier characteristics—particularly their own beliefs about the question of interest—play a larger role. Our results suggest that selecting an appropriate crowdsourcing population may be more important than incentive design for obtaining high-quality survey options.