Research

I introduce a discrete Taylor rule that mirrors the institutional practice of adjusting policy rates in 25-basis-point steps. Re-estimating several examples from the literature shows that this rule reduces residuals by up to one-third, suggesting smaller monetary policy shocks than previously estimated. Embedding the rule in a New-Keynesian model and additionally restricting interest rate adjustments to two predetermined dates per quarter yields further insights. First, impulse responses change substantially, exhibiting kinks and delayed returns to steady state. These effects are heterogeneous across the output gap and inflation and with respect to the shock size. Second, in a simulated U.S. business cycle, the interest rate deviates on average by 30 basis points from the linear-rule benchmark, and discreteness accounts for 2.07% of output-gap variance. In contrast, infrequent rate adjustments reduce volatility by making monetary policy more active on average.

We introduce a task-based framework for modeling production in which certain tasks are too complex for many workers to perform. In such an environment, workers’ wages may significantly diverge from their relative productivities: Workers with marginally higher skill levels may obtain a large additional wage premium on top of the skill premium, which we call complexity premium. We apply our framework to explain past employment and wage polarization and estimate model parameters for the U.S. labor market between 2001 and 2019. Beyond a rising skill level and skill premium, we find that the complexity of tasks increases and employees performing more complex tasks earn a significant complexity premium, which accounts for up to 43 percent of their wages. Finally, we explore the effects of artificial intelligence and find it may aggravate wage inequality, with an ambiguous effect on complexity premia.