Research

This paper investigates the impact of artificial intelligence (AI) on hiring and employment, using the universe of job postings published by the Swedish Public Employment Service from 2014-2022 and universal register data for Sweden. We construct a detailed measure of AI exposure according to occupational content and find that establishments exposed to AI are more likely to hire AI workers. Survey data further indicate that AI exposure  aligns with greater use of AI services. Importantly, rather than displacing non-AI workers, AI exposure is positively associated with increased hiring for both AI and non-AI roles. In the absence of substantial productivity gains that might account for this increase, we interpret the positive link between AI exposure and non-AI hiring as evidence that establishments are using AI to augment existing roles and expand task capabilities, rather than to replace non-AI workers.

This paper assesses whether workers who develop and apply artificial intelligence experience a earnings premium. I link skill requirements specified in job vacancies to the individuals ultimately hired to fill those positions using a combination of Swedish job vacancy and matched employer-employee register data. By identifying positions that explicitly necessitate AI skills, this paper seeks to determine if a earnings premium is associated with these skills while controlling for other individual attributes. 

Findings suggest a significant earnings premium for individuals hired to positions requiring AI skills. Discerning between AI developers and AI users, the results indicate that the former group experiences a stronger earnings premium. The premium is partly driven by workers being hired into high-wage industries. However, transitioning into roles requiring AI skills does not result in additional earnings increases, indicating that firms do not engage in wage competition for these workers.

This paper provides new evidence on how automation transforms firms’ demand for skills, not by changing the occupational composition, but by reshaping what existing jobs require. Using matched data on firm-level automation investments and detailed job ads from Denmark, we extract multidimensional skill profiles through natural language processing. Guided by a task-based framework, we decompose changes in skill demand into within- and between-occupation components and find that within-occupation adjustments dominate. Automation increases the demand for soft skills in professional and managerial roles and reduces the reliance on routine hard skills in production jobs. Register data confirm parallel shifts in workforce composition: increased experience in high-skill occupations and intensified retraining among lower-skilled workers. Our findings highlight that automation reconfigures work from within, with important implications for training policy and labor market resilience.

We unbox developments in artificial intelligence (AI) to estimate how exposure to these developments affect firm-level labour demand, using detailed register data from Denmark, Portugal and Sweden over two decades. Based on data on AI capabilities and occupational work content, we develop and validate a time-variant measure for occupational exposure to AI across subdomains of AI, such as language modelling. According to the model, white collar occupations are most exposed to AI, and especially white collar work that entails relatively little social interaction. We illustrate its usefulness by applying it to near-universal data on firms and individuals from Sweden, Denmark, and Portugal, and estimating firm labour demand regressions. We find a positive (negative) association between AI exposure and labour demand for high-skilled white (blue) collar work. Overall, there is an up-skilling effect, with the share of white-collar to blue collar workers increasing with AI exposure. Exposure to AI within the subdomains of image and language are positively (negatively) linked to demand for high-skilled white collar (blue collar) work, whereas other AI-areas are heterogeneously linked to groups of workers.

Artificial intelligence (AI) is expected to reshape labor markets, yet causal evidence remains scarce. We exploit a novel Swedish subsidy program that encouraged small and mid-sized firms to adopt AI. Using a synthetic difference-in-differences design comparing awarded and non-awarded firms, we find that AI subsidies led to a sustained increase in job postings over five years, without negatively affecting net employment. This pattern reflects broad-based hiring signals across AI and non-AI occupations, concentrated in white-collar roles. Our findings align with task-based models of automation, in which AI adoption reconfigures work and spurs demand for new skills, but hiring frictions and the need for complementary investments delay workforce expansion.