{"title":"Effect of automation of routine and non-routine tasks on labour demand and wages","authors":"Arvind Upreti, V. Sridhar","doi":"10.1016/j.iimb.2024.09.001","DOIUrl":null,"url":null,"abstract":"<div><div>Artificial intelligence and associated technology advances have progressively diffused from routine tasks to non-routine tasks, causing disruptions in the labour market. In this work, we study the effect of automation on the labour market outcomes for low-skilled and high-skilled workers. We use the agent-based modelling approach to model firms and workers as rational agents with defined objective functions, endowments, and interactions. Using extensive simulations, we analyse the emergent phenomenon of employment levels and wage inequality in the labour market under varying scenarios. The key findings of our simulations indicate that automation of routine tasks increases wage inequality, whereas automation of non-routine tasks reduces it. Based on our results, we propose policy prescriptions regarding the job categories in which automation can be introduced for societal benefits, the skill enhancement programme needed for the workers, and guidelines on the redeployment of labour displaced through automation.</div></div>","PeriodicalId":46337,"journal":{"name":"IIMB Management Review","volume":"36 4","pages":"Pages 289-308"},"PeriodicalIF":1.7000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IIMB Management Review","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0970389624001368","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence and associated technology advances have progressively diffused from routine tasks to non-routine tasks, causing disruptions in the labour market. In this work, we study the effect of automation on the labour market outcomes for low-skilled and high-skilled workers. We use the agent-based modelling approach to model firms and workers as rational agents with defined objective functions, endowments, and interactions. Using extensive simulations, we analyse the emergent phenomenon of employment levels and wage inequality in the labour market under varying scenarios. The key findings of our simulations indicate that automation of routine tasks increases wage inequality, whereas automation of non-routine tasks reduces it. Based on our results, we propose policy prescriptions regarding the job categories in which automation can be introduced for societal benefits, the skill enhancement programme needed for the workers, and guidelines on the redeployment of labour displaced through automation.
期刊介绍：
IIMB Management Review (IMR) is a quarterly journal brought out by the Indian Institute of Management Bangalore. Addressed to management practitioners, researchers and academics, IMR aims to engage rigorously with practices, concepts and ideas in the field of management, with an emphasis on providing managerial insights, in a reader friendly format. To this end IMR invites manuscripts that provide novel managerial insights in any of the core business functions. The manuscript should be rigorous, that is, the findings should be supported by either empirical data or a well-justified theoretical model, and well written. While these two requirements are necessary for acceptance, they do not guarantee acceptance. The sole criterion for publication is contribution to the extant management literature.Although all manuscripts are welcome, our special emphasis is on papers that focus on emerging economies throughout the world. Such papers may either improve our understanding of markets in such economies through novel analyses or build models by taking into account the special characteristics of such economies to provide guidance to managers.
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