Monopsony and Outside Options

Schubert, Gregor; Stansbury, Anna; Taska, Bledi

doi:10.2139/ssrn.3599454

Cited by 53 publications

(52 citation statements)

References 51 publications

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“…We currently neglect wage dynamics to focus on labour market frictions due to worker-vacancy mismatches and because adding wages into the model requires vacancy data at the occupation level. While vacancy data are so far not publicly available, work is underway to prioritize data collection efforts to facilitate labour market research [46][47][48]. Finally, we have not considered the role of geography [12,49], cities [50] or the feedback effects from the production network [51], all of which are known to be important and would constitute crucial avenues for further research.…”

Section: Resultsmentioning

confidence: 99%

Occupational mobility and automation: a data-driven network model

Rio-Chanona

Mealy

Beguerisse-Díaz

et al. 2021

J. R. Soc. Interface.

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The potential impact of automation on the labour market is a topic that has generated significant interest and concern amongst scholars, policymakers and the broader public. A number of studies have estimated occupation-specific risk profiles by examining how suitable associated skills and tasks are for automation. However, little work has sought to take a more holistic view on the process of labour reallocation and how employment prospects are impacted as displaced workers transition into new jobs. In this article, we develop a data-driven model to analyse how workers move through an empirically derived occupational mobility network in response to automation scenarios. At a macro level, our model reproduces the Beveridge curve, a key stylized fact in the labour market. At a micro level, our model provides occupation-specific estimates of changes in short and long-term unemployment corresponding to specific automation shocks. We find that the network structure plays an important role in determining unemployment levels, with occupations in particular areas of the network having few job transition opportunities. In an automation scenario where low wage occupations are more likely to be automated than high wage occupations, the network effects are also more likely to increase the long-term unemployment of low-wage occupations.

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Section: Resultsmentioning

confidence: 99%

Occupational mobility and automation: a data-driven network model

Rio-Chanona

Mealy

Beguerisse-Díaz

et al. 2021

J. R. Soc. Interface.

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show abstract

“…) Identifying workers' outside options is notoriously challenging. See Lachowska (2016); Caldwell and Harmon (2019); Caldwell and Danieli (2018); Jäger et al (2020); Schubert, Stansbury, and Taska (2021); Di Addario et al (2021) for recent work on the effect of outside options on wages.…”

Section: Introductionmentioning

confidence: 99%

Worker Beliefs About Outside Options

et al. 2022

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“…To further investigate potential implications for labor market inequality, we assess whether labor market concentration affects the relationship between temperature and injuries. Previous literature finds evidence that more concentrated labor markets can lead to lower wages, and that low-skilled workers are more likely to work in more concentrated labor markets (Naidu et al, 2016;Azar et al, 2020;Schubert et al, 2020). All else equal, we might expect workers in more monopsonistic labor markets to tolerate greater workplace disamenities before terminating employment relationships and to have lower wages.…”

Section: Labor Market Concentrationmentioning

confidence: 90%