On the Inefficiency of Social Learning

We study whether a social planner can restore efficient learning, defined as a finite expected number of incorrect actions, in the canonical sequential social learning model. For each agent, the planner chooses what social information to disclose and what action-contingent transfer to offer. For unbounded signal distributions with regular tails, we show that whenever learning is inefficient without intervention, no combination of disclosure and transfers can restore efficiency with a finite budget. In particular, disclosure alone cannot restore efficiency. We identify agents' incentives as the key source of this impossibility. The budget requirement, however, concerns the transfers offered rather than the payments made. We show that even under full disclosure, the planner can achieve efficient learning using transfers while keeping expenditure arbitrarily small. Thus, the need for an unlimited budget does not entail large payments.