Incentive-driven Inattention

“Rational inattention” is becoming increasingly prominent in economic modelling, 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 information acquisition. 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 model fits the data and gives realistic predictions. We find that shifts in incentives affect both extensive and intensive margins, but the shift in benefits from the contest has the largest aggregate effect. Counterfactuals reveal that accuracy is maximized if the contest coincides with the release of information, aligning higher benefits with lower costs of attention.