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

TL;DR

This paper introduces LABOR-LLM, a large language model fine-tuned on occupational data to accurately predict workers' next jobs, outperforming previous models in labor transition predictions.

Contribution

It presents a novel approach of fine-tuning large language models with career data converted into text, enhancing occupational transition predictions beyond existing methods.

Findings

Fine-tuned LLMs outperform prior models in predicting next occupations.

Adding more diverse career data improves model performance.

Replacing occupational titles with codes reduces predictive accuracy.

Abstract

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. We convert tabular data from the survey into text files that resemble resumes and fine-tune the parameters of the foundation model, a large language model (LLM), using these text files with the objective of predicting the next token (word). The resulting fine-tuned LLM is used to calculate estimates of worker transition probabilities. Its predictive performance surpasses all prior models, both for the task of granularly predicting the next occupation as well as for specific tasks such as predicting whether the worker changes occupations or stays in the labor force. We quantify the value of fine-tuning and further show that by adding more career data from a different population, fine-tuning smaller LLMs (fewer parameters) surpasses the performance of fine-tuning larger models. When we omit the English language occupational title and replace it with a unique code, predictive performance declines.