Search Results
Search Results
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The Effects of Artificial Intelligence and Robotics on Employment and Wages in Korean Manufacturing Firms
This article analyzes the effects of two key automation technologies – artificial intelligence (AI) and robotics – on employment and wages in Korean manufacturing since the late 2010s. Drawing on firm-level data from the Survey of Business Activities and individual wage data from the Local Labor Force Survey, the analysis explores both firm- and worker-level impacts. Adoption of these technologies is concentrated in large firms within the electronics and automotive sectors. Robotics has been widely implemented, primarily for cost reduction, safety enhancement, and union avoidance, whereas AI adoption remains limited but is gradually expanding. The results reveal contrasting effects: AI adoption is associated with job creation and wage growth, while robotics tends to reduce both employment and wages – an outcome that diverges from findings in existing firm-level studies. These negative effects appear to stem from Korea’s institutional context, where automation – particularly robotics – is frequently employed to reduce labor costs rather than to enhance productivity, as well as from diminishing marginal returns in industries with long-standing automation. Importantly, these wage effects persist even when U.S.-based automation exposure measures are applied, suggesting broader applicability. However, the findings underscore that the economic impact of automation depends significantly on the motivations and strategies behind its adoption. In the case of Korean manufacturing, capital-biased automation driven by robotics has contributed mainly to labor displacement without generating substantial productivity gains, reflecting Acemoglu and Restrepo’s (2018) notion of ‘so-so automation.’
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Toward a Socioeconomic Company-Level Theory of Automation at Work
The current understanding of automation is dominated by “routine-biased technological change” (RBTC). This theory predicts a strong automation dynamic in jobs with high routine-task share and a polarization of employment structures. While RBTC theory has many merits, this paper develops a systematic critique of the theory and a counter-proposal of a socioeconomically grounded company-level theory of the automation of work. It distinguishes between feasibility conditions of automation, technology choices, and social outcomes. With regard to feasibility conditions, the relevant factor is not routine-task intensity but the interaction between product architecture (product complexity) and process complexity. Which technology choices are made in this feasibility space is in turn influenced by companies’ profit strategies and power relations between management and labor. The social outcomes of automation depend on these technology choices, but also on managerial strategies pursued in the restructuring of organizational roles and skills. These managerial strategies are shaped by national institutional systems.
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Automation and Its Impact on Productivity and Workers: Lessons from the History of the Car Industry
This article explores the historical development and impact of automation in the automotive industry, focusing on the production systems of Ford, Toyota, and Volkswagen, and addresses two key research questions: How has automation evolved over time? What are its effects on productivity and labor? Drawing on company archives, empirical fieldwork, and the existing literature, the study uses a case study approach. The findings reveal that automation progressed in uneven, layered trajectories rather than through disruptive leaps. While machining, press, and paint shops have become highly automated, final assembly remains largely manual. Automation’s influence on productivity has declined over time, with product complexity and shorter model cycles emerging as constraints. Employment effects are nuanced, and shaped by automation, outsourcing, and customization trends. Ultimately, the study cautions against deterministic views of technological change and highlights the persistent role of organizational and institutional factors. The transition to electric vehicles may trigger further automation – but not necessarily through disruptive technologies alone.
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Labor-atories of Digital Economies: Latin America as a Site of Struggles and Experimentation
This article argues that the digital labor developments and struggles are labor-atories of digital economies, with special focus in Latin America. This means that, on the one hand, capital is experimenting and updating forms of control and exploitation - through the long trajectory of informality and dependency and, on the other hand, workers are trying and experimenting forms of organizing and collectivities, also updating Latin American rich histories of organizing, solidarity economies and community technologies. The emphasis on “labor” means that these laboratories are products of class struggles and capital-labor relationships. The paper unpacks the argument with four short insights from ongoing research: 1) Latin America as not only of research site; 2) The updating of informality in the Latin American AI context; 3) Global implications of data work, AI value chains, and the cultural sector; 4) Digital solidarity economies as a Latin American response to the current digital labor scenario, including digital sovereignty and autonomy.
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The Automation of Management and the Multiplication of Labor: On the Role of Algorithmic Management in the Recomposition of Labor
Digital technologies are increasingly used to automatically organize, measure, and control labor in many sectors and industries. This article offers an analysis of how digital technologies, particularly algorithmic management, not only reshape the ways in which work is done and controlled but also drive profound transformations in the division and composition of labor. Drawing on qualitative and ethnographic studies of the gig economy, this research article demonstrates how the digital automation of management serves as a prerequisite for efficiently and flexibly incorporating highly heterogeneous workforces into production processes. This is first demonstrated by an analysis of the online gig economy and its capacity to integrate a wide range of geographically dispersed workers into digital production processes. Then, the paper examines the role of migrant labor in the urban gig economy, contending that in this context too, digital technologies and algorithmic management play a crucial role in the flexible and efficient inclusion of highly diverse workforces. This ultimately illustrates how digital technologies for automated management are integral to a multifaceted process of workforce heterogenization, a phenomenon that can be conceptualized within the framework of the multiplication of labor.
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Coordinating Digital Transformation: The Discursive Context of Production in the Knowledge Economy
This article introduces the concept of the “discursive context of production” in order to explain how the transition to the knowledge economy affects working conditions. Past episodes of economic adjustment saw national institutions in corporatist countries protect working conditions by facilitating coordination between employers and workers in the workplace. Where workers had the capacity to enforce these institutions, they succeeded, for instance, in defending against mass layoffs. Digital transformation, however, has led managers to adopt the market discourse of the knowledge economy, which allows them to dissuade workers from mobilizing. With their mechanisms for enforcement undermined, national institutions are less effective in protecting workers from employer discretion, thereby exposing them to the threat of job loss during economic adjustment. Relying on a case study of mass layoffs at a technology firm in Germany, this article uses process tracing to illustrate how discourse constitutes an important contextual feature that conditions the causal linkage between digital transformation and the ineffectiveness of national institutions. Understanding how digital transformation affects working conditions requires tracing how discursive change in the workplace reconfigures power relations between managers and workers.
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Algorithmic Management: From Technology to Politics and Theory
This article provides an overview of the concept of ‘algorithmic management’. This concept has played an important role as an organizing frame for empirical research seeking to demystify the role of labor platforms in intermediating paid work. More recently, this concept has helped shed light on the increasing use of computer algorithms to automate managerial tasks in conventional work settings. However, beyond platform work, most research is confined to warehousing, with only a few notable studies in manufacturing and retail. Moreover, most empirical investigations highlight the conditional nature of algorithmic management, with human managers retaining important functions. Only recently have studies begun to go beyond technical functions and consider how human elements (worker, manager, and technologist) shape such systems. Relatedly, the contingencies, moderations, and variations in algorithmic management have received insufficient consideration. These weaknesses result from a tendency to generalize from single case studies without adequately extending out from the case to theory, history, and geography, and not situating empirical research within a broader theoretically motivated research program. Workplace regime theory, with its focus on technology, power, and embeddedness, is presented as a remedy that enables algorithmic management research to account for variations while explaining regularities.
