Incentive-driven inattention

“Rational inattention” is becoming increasingly prominent in economic modeling, but there 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 choices observables. We fill this gap literature by exploiting unique dataset professional forecasters who update their inflation forecasts at days choice. In we observe how many (extensive margin updating), magnitude (intensive margin), and objective optimization (forecast accuracy). There are also “shifters” incentives: A contest that increases benefit accurate forecasting, release official reduces cost processing information. These features us link observables incentive parameters. structurally estimate model where decision endogenous latter outcome rational inattention The findings provide support key implication information-processing efforts react changing incentives. Counterfactuals reveal accuracy maximized if date coincides with information, aligning higher benefits lower costs attention.