When unionisation is profitable for firms in network industries

Fanti, Luciano; Buccella, Domenico

doi:10.1111/meca.12249

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Formal unionisation rates are low in technology companies, though higher in academia [18]. However, it is clear that the AI community has substantial bargaining power, of the type described in our opening quote.…”

Section: Worker Organizing and Bargainingmentioning

confidence: 85%

Activism by the AI Community

Belfield

2020

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

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The artificial intelligence (AI) community has recently engaged in activism in relation to their employers, other members of the community, and their governments in order to shape the societal and ethical implications of AI. It has achieved some notable successes, but prospects for further political organising and activism are uncertain. We survey activism by the AI community over the last six years; apply two analytical frameworks drawing upon the literature on epistemic communities, and worker organising and bargaining; and explore what they imply for the future prospects of the AI community. Success thus far has hinged on a coherent shared culture, and high bargaining power due to the high demand for a limited supply of AI 'talent'. Both are crucial to the future of AI activism and worthy of sustained attention.

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Section: Worker Organizing and Bargainingmentioning

confidence: 85%

Activism by the AI Community

Belfield

2020

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

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“…Recent applications includeAlbert and Meckl (2003),Bhattacharyya and Gupta (2015),Buccella and Wang (2021),Chaudhuri (2016),Cogliano et al (2016), de Pinto and Lingen (2019),Dobbelaere and Kiyota (2018),Falch (2001),Fanti and Buccella (2019),Fuest and Thum (2001),Jayadev (2007),Julius (2009),Kraft et al (2011),Lechthaler and Snower (2012),Merkl and Schmitz (2011),Montagna and Nocco (2015),Meccheri and Fanti (2018) Sattinger and Hartog (2013),Tavani (2012Tavani ( , 2013 andZiesemer (2005).…”

mentioning

confidence: 99%

The Nash bargaining solution in labor market analysis

Skillman

2022

Metroeconomica

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Two alternative strategic foundations have been identified for applications of the Nash bargaining solution in economic modelling. However, neither interpretation is consistent with treating “threat point” or disagreement payoffs as individual outside options, a salient feature of bargaining relationships in most labor market settings. This paper uses a matching and strategic bargaining model to examine the determination of bargaining power weights and threat points in the asymmetric Nash bargaining solution. The key result establishes a strategic foundation for applying this solution to market settings with endogenously determined outside options, and shows that bargaining weights depend on outside market conditions only when the matching prospects of one side of the market or the other are relatively high.

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When unionisation is profitable for firms in network industries

Cited by 2 publications

References 34 publications

Activism by the AI Community

Activism by the AI Community

The Nash bargaining solution in labor market analysis

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