Large Language Models and Sectoral Employment in Sweden: A Multi-Cutoff Difference-in-Differences Analysis - Wolff Perez,Leo; Torstensson,Teo (2025)
This thesis investigates whether recent Large Language Models (LLMs)—notably ChatGPT—have influenced sectoral employment in Sweden. Using a multi-cutoff Difference-in-Differences (DiD) design centered on the dates November 2022 (initial ChatGPT release), September 2023, March 2024, we compare employment changes in high vs. low-exposure sectors classified via exposure metrics from Eloundou et al. (2023). Analysis of monthly panel data (2021–2024) finds no statistically significant divergence in employment trends between high- and low-exposure sectors post November 2022. While point estimates suggest mild positive effects for high-exposure industries, these lack robust significance but may reflect employer expectations that LLMs will augment, rather than entirely replace, human labor. Sweden’s strong labor institutions and gradual tech adoption may temper large-scale disruption, underscoring the need for longer-term monitoring. This study contributes a Swedish-specific assessment of LLMs’ labor impact, employs multiple DiD cutoffs to capture staggered AI adoption, and provides policy insights on how institutional features can mediate emerging AI technologies.
Keywords: Employment; LLM; AI; DiD