Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/251586 
Year of Publication: 
2022
Series/Report no.: 
IWH Discussion Papers No. 9/2022
Publisher: 
Halle Institute for Economic Research (IWH), Halle (Saale)
Abstract: 
Non-clearing goods markets are an important driver of capacity utilisation and total factor productivity (TFP). The trade-off between goods prices and household search effort is central to goods market matching and therefore drives TFP over the business cycle. In this paper, I develop a New-Keynesian DSGE model with capital utilisation, worker effort, and expand it with goods market search-and-matching (SaM) to model non-clearing goods markets. I conduct a horse-race between the different capacity utilisation channels using Bayesian estimation and capacity utilisation survey data. Models that include goods market SaM improve the data fit, while the capital utilisation and worker effort channels are rendered less important compared to the literature. It follows that TFP fluctuations increase for demand and goods market mismatch shocks, while they decrease for technology shocks. This pattern increases as goods market frictions increase and as prices become stickier. The paper shows the importance of non-clearing goods markets in explaining the difference between technology and TFP over the business cycle.
Subjects: 
Bayesian estimation
capacity utilisation
non-clearing goods markets
search-and-matching
total factor productivity
JEL: 
E22
E23
E3
J20
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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