Events at CERGE-EI

Anushka Mitra, M.A.

The University of Texas at Austin, United States

Abstract: The unemployment rate remains persistently high after recessions even after job losses subside. Standard search and matching models have diffculty capturing this pattern. In this paper, I argue that noise shocks, which capture agents’ expectational errors due to the noise in received signals about the persistence of aggregate productivity, can generate substantial persistence in the unemployment rate. I first identify these noise shocks using a novel structural VAR and find that unemployment would have recovered to its pre-recession level 7 quarters earlier in the absence of noise shocks in the 1968-2019 period. I then set-up a general equilibrium search and matching model with on-the-job search, endogenous search effort and wage rigidity and consider three shocks: a permanent productivity shock; a transitory productivity shock and a noise shock. The model calibrated to target standard moments and disciplined to match impulse responses identified through SVAR predicts 6 quarters longer recoveries in unemployment compared to a model without imperfect information and noise shocks. It also predicts 23 percent more volatility in unemployment and vacancies. These results are generated mainly through two channels. First, responses to persistent productivity shocks are more persistent as it takes time for agents to learn whether a shock is persistent or not. Second, noise shocks provide an additional source of persistence, which are amplified through on-the-job search and firms’ vacancy posting decisions.

JEL Classification: E24, E32, E70
Keywords: Imperfect Information, Labor Market, Business Cycles

Full Text: Imperfect Information and Slow Recoveries in the Labor Market