Incentive-driven inattention

“Rational inattention” is becoming increasingly prominent in economic modeling, but there is little empirical evidence for its central premise-that the choice of attention results from a cost-benefit optimization. Observational data typically do not allow researchers to infer attention choices from observables. We fill this gap in the literature by exploiting a unique dataset of professional forecasters who update their inflation forecasts at days of their choice. In the data we observe how many forecasters update (extensive margin of updating), the magnitude of the update (intensive margin), and the objective of optimization (forecast accuracy). There are also “shifters” in incentives: A contest that increases the benefit of accurate forecasting, and the release of official data that reduces the cost of processing information. These features allow us to link observables to attention and incentive parameters. We structurally estimate a model where the decision to update and the magnitude of the update are endogenous and the latter is the outcome of a rational inattention optimization. The empirical findings provide support for the key implication of rational inattention that information-processing efforts react to changing incentives. Counterfactuals reveal that accuracy is maximized if the contest date coincides with the release of information, aligning higher benefits with lower costs of attention.