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We discuss payment structures that induce honest reporting of private information by risk-neutral agents in settings involving multiple-choice questions. Such payment structures do not rely on the existence of ground-truth answers, but instead they rely on the assumption that agents exhibit social projection. Social projection is a strong form of the well-known psychological phenomenon called the false-consensus effect, where an agent believes that his private answer to a multiple-choice question is the most popular answer. From a theoretical perspective, we first show that when social projection holds true, honest reporting strictly maximizes an agent’s expected reward from a payment structure that simply compares agents’ reported answers and rewards agreements. Furthermore, we suggest how to induce honest reporting by taking the distance between reported answers into account when social projection is strong, i. e., when an agent believes that his private answer is more likely to be reported by a random peer than all the other answers combined. We also discuss how to derive the above results in terms of proper scoring rules. From an empirical perspective, we investigate the consequences of using a payment structure that rewards agreements in a content-analysis experiment on Amazon Mechanical Turk. We obtain some evidence that, under such a payment structure, agents report more accurate answers than when there are no direct incentives for honest reporting of private answers. Moreover, we find that priming agents by briefly mentioning the theoretical properties of the underlying payment structure results in even more accurate answers.