LABOR-LLM: Language-Based Occupational Representations with Large Language Models

Understand

This paper builds an empirical model that predicts a worker's next occupation as a function of the worker's occupational history.

• Because histories are sequences of occupations, the covariate space is high-dimensional, and further, the outcome (the next occupation) is a discrete choice that can take on many values.
• To estimate the parameters of the model, we leverage an approach from generative artificial intelligence.
• Estimation begins from a ``foundation model'' trained on non-representative data and then ``fine-tunes'' the estimation using data about careers from a representative survey.