Learning and the Size of the Government Spending Multiplier

This paper examines the government spending multiplier when economic agents form their expectations based on an adaptive learning scheme. The learning mechanism is such that the agents forecast future values of forward-looking variables using a linear function of an information set that does not contain the fiscal shock. Our impulse response analysis shows that the effects of a government spending shock change substantially when the rational expectations hypothesis is replaced by this learning mechanism. In contrast to the dynamics under rational expectations, a government spending shock in a small-scale new Keynesian DSGE model with this adaptive learning mechanism crowds in private consumption and is associated with a positive comovement between real wages and hours worked. The learning model also relies less on consumption-leisure non-separability in utility and price stickiness to deliver high output multipliers, as opposed to the rational expectations benchmark. In the baseline calibration, the multiplier under learning is nearly twice as large as under rational expectations. These results are robust to a richer specification, irrespective of the financing strategy. An alternative adaptive learning model, where agents know the future path of taxes implied by the government spending shock, leads to results that differ to a large extent from those of the benchmark learning model and are largely incompatible with most of the empirical evidence