Labour Market Dynamics in RBC Models

This paper explores the ability of a large set of RBC type models to explain aggregate US data by examining how well the rst-order conditions (FOCs) from each model t the data. Typically, the residuals from the FOC for hours worked are large in magnitude (more volatile than total hours), very highly persistent, and stay away from zero for long periods of time. This pattern suggests that standard RBC models are unable to capture the dynamics in the joint behaviour of consumption, output and hours that exists in the US data. We show that models which generate dynamic terms in the FOC for hours worked are able to capture this feature of the data by exploring a RBC model augmented by learning by doing which has been shown to have such a dynamic FOC. The results are remarkable. The residuals from the hours FOC are much less volatile than total hours and display no persistence. Less conclusive results emerge from models with habit formation in preferences which also yield dynamic FOCs for the labour input. We conclude that an additional dynamic component in the FOCs is essential to better capture the dynamics in the data and future research using the RBC structure should explore models that deliver it.