Research

Recent advances in automation have raised concerns about job displacement and increased interest in social protection policies. However, public support for such measures is not uniformly distributed across cases of job loss. This study argues that gender norms, rather than economic vulnerability alone, explain support for social protection in response to automation-driven layoffs. Using a survey experiment in South Korea, we show that automation-driven job loss increases support for an ex-ante protective measure (e.g., Automation Tax) only when male workers are affected. This selective protection reflects the male-breadwinner model, which views male labor as more essential to household income and male job loss as more socially disruptive. The disparity in social policy preferences by the laid-off worker’s gender profile is pronounced among individuals who hold sexist attitudes. Our findings reveal how gendered beliefs about labor value shape social protection preferences, highlighting identity-based biases in responses to economic change.

The climate crisis is an urgent challenge, yet public support for fossil fuel taxation remains low. Existing research highlights how material interests, such as concerns about the regressive nature of consumption taxes like fuel taxes, contribute significantly to this limited support. This perspective aligns with the literature on the emerging ‘eco-social divide,’ which highlights tensions between environmental and equity concerns, yet potential synergies remain understudied. While social policies may ease resistance by protecting displaced workers, we know little about whether they actually do. We examine what drives public support for a fossil fuel tax designed to reduce carbon emissions but likely to displace workers, focusing on the mediating role of the welfare state. Drawing on original survey data from the ‘Eco-Social’ UniMi project (10,500 respondents across seven European countries), we find that income anxiety and dissatisfaction with national social policies significantly reduce support for fossil fuel taxation. Moreover, individuals facing high employment risk are less supportive of carbon taxation when dissatisfied with social policies, but their support increases as satisfaction with the welfare state improves. These findings suggest that confidence in the welfare state can offset economic concerns driving opposition to climate policy, thereby easing the ‘green backlash.’

Many of the richest social data come as open-ended, write-in responses. Occupational data are a prime example, yet the difficulty of coding such free-text entries has long limited research. We address this challenge by fine-tuning a large language model (LLM) to classify millions of write-in occupation fields into a standardized taxonomy. Treating classification as a text-generation task, we supplement standard occupational descriptions with a large corpus of alternative job titles, generating synthetic data for infrequent occupations. The resulting classifier matches or exceeds the accuracy of the US government’s benchmark tool, especially in the long tail of rare occupations. We use this method to construct the Occupational Partisanship in the United States (OPUS) dataset, which assigns partisan scores to nearly all occupations in the O*NET standard classification. Two findings illustrate its potential: (i) occupation predicts partisan identity as well as, or better than, county of residence; and (ii) majority-female occupations lean consistently Democratic. Our approach provides a low-cost, generalizable framework for classifying write-in data and a new empirical lens on the political structure of the American labor market.

How do domestic institutions shape whether societies adjust to labour-market pressures through migration or automation, and how do these legacies influence public perceptions of such change? This article advances an institutional conditioning framework linking industrial-relations structures to both elite strategies and mass attitudes toward economic transformation. Study 1 traces policy developments in the United Kingdom and Sweden (2000–2020) to show that weak unions and decentralised bargaining in the UK enabled reliance on low-cost migrant labour, whereas strong unions and coordinated bargaining in Sweden limited this dependence and fostered a tech-savvy strategy supported by retraining and social partnership. Study 2, an original survey experiment (n = 1,050), reveals that these institutional legacies extend to public opinion. Swedes perceive automation-led job loss as more legitimate and less deserving of regulation than migration-led displacement, while Britons display no comparable difference. A mechanism test further shows that Swedes’ belief in the inevitability of automation—and their view that it is less disrespectful to workers—partly explains this gap. Together, the studies demonstrate how industrial relations not only steer policy adjustment but also shape citizens’ normative frames for judging technological and demographic change, highlighting the political foundations of legitimacy in European labour-market governance.