Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/280690 
Year of Publication: 
2023
Series/Report no.: 
I4R Discussion Paper Series No. 93
Publisher: 
Institute for Replication (I4R), s.l.
Abstract: 
Using a Diamond-Mortensen-Pissarides (DMP) model with noisy signals on worker-firm match quality calibrated on data from 30 US states for 1999 and 2017, Pries and Rogerson argue that improved screening may explain the decrease in short-term employment spells observed in the US labor market. Using a decomposition exercise in a "reduced form" model, the authors show that changes in short-term employment spells (δ₁ and δ₂) are almost entirely accounted for by changes in the rate of learning on match quality α and in the probability of a good match πᵍ. Then, using a decomposition exercise in a "structural" model, they show in their main calibration strategy that changes in δ₁ and δ₂ are mainly driven by changes in α and σϵ, parameters pertaining to learning about match quality. First, we reproduce the authors' codes in R and Python, two popular free open source programming languages. We find identical results to the paper. Second, we test the robustness of results to (1) using an earlier starting year, (2) adding additional states in the analysis, and (3) increasing the value of the 1999 mean vacancy duration parameter. The direction and relative size of the effect of each parameter on δ₁ and δ₂ is preserved in all robustness tests, corroborating the authors' argument.
JEL: 
E24
J23
J41
J63
M51
Document Type: 
Working Paper

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