Information Speed Bumps in the Labour Market
This working group will take place on Zoom
The plain vanilla search and matching model has a flow persistence problem followinga negative productivity shock. This missing persistence, which goes hand in hand with a jobless recovery may be explained as the result of a productivity shock introducing a noisier environment in which workers and firms find it more difficult to determine changes to match productivity and thus to efficiently sort good from bad matches in the market. Such an environment limits the extent of learning about the idiosyncratic productivity changes to the match by reducing the informativeness of present observations. This leads to less dispersed estimated match productivity and reduced incentives to search for new jobs and new workers. This paper shows that survey data from Michigan Survey of Consumers and the NY Fed’s Survey of Consumer Expectations support this hypothesis. Furthermore it suggests mechanisms how this noisier environment may come about, and shows how reduced learning in recessions cancontribute to understanding a variety of recent observations about the labour market, including the breakdown of job ladders in recessions (Haltiwanger, Hyatt, Kahn, andMcEntarfer (2018), Moscarini and Postel-Vinay (2018)) and the procyclicality of wage dispersion. The increase in the posterior of the expected fundamentals can be interpreted as an increase in uncertainty following David, Hopenhayn, and Venkateswaran(2016). This kind of uncertainty can be more persistent than an increase in the volatility of idiosyncratic fundamental shocks and may have stronger effects on the economy a while after the aggregate shock, thereby providing a new view on how uncertainty affects the labour market.
Date:
26 May 2020, 13:00
Venue:
Venue to be announced
Speaker:
Philip Schnattinger (University of Oxford)
Organising department:
Department of Economics
Part of:
Macroeconomics Working Group
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Melis Clark
