Template-Type: ReDIF-Paper 1.0

Author-Name: Emilio Colombo
Author-Email: emilio.colombo@unicatt.it

Author-Name: Fabio Mercorio
Author-Email: fabio.mercorio@unimib.it

Author-Name: Mario Mezzanzanica
Author-Email: mario.mezzanzanica@unimib.it

Author-Name: Antonio Serino
Author-Email: a.serino3@campus.unimib.it


Title: Towards the Terminator Economy: Assessing Job Exposure to AI through LLMs 
Abstract: There is no doubt that AI and AI-related technologies are reshaping jobs and related tasks, either by automating or by augmenting human skills in the workplace. Many researchers have tried to  estimate if, and to what extent, jobs and  tasks are exposed to the risk of being automatized by state-of-the-art AI-related technologies. Our work tackles this issue  through a data-driven approach: (i) developing a reproducible framework that uses several open-source large language models to assess the current capabilities of AI and robotics in performing work-related tasks; (ii) formalising and computing a measure of AI exposure by occupation, namely the TEAI (Task Exposure to AI) index. Our TEAI index is positively correlated with cognitive, problem-solving and management skills, while is negatively correlated with social skills. Our results show that about one-third of U.S. employment is highly exposed to AI, primarily in high-skill jobs, requiring graduate or postgraduate level of education. Using 4-year rolling regressions, we also find that AI exposure is positively associated with both employment and wage growth in the period 2003-2023, suggesting that AI has an overall positive effect on productivity.
Creation-Date: 2024

File-URL: http://dipartimenti.unicatt.it/diseis-wp_2401.pdf
File-Format: Application/pdf
Number: dis2401
Classification-JEL: J24, O33, O36
Handle: RePEc:dis:wpaper:dis2401