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Measuring Task-Level Technological Exposure: A Language Model Approach

Author

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  • Andre Mouton

    (Department of Economics, Wake Forest University)

Abstract

This paper develops methods and tools for measuring the exposure of occupational tasks to technological substitution. Patent abstracts and task statements are matched and classified by a small, open-source language model. The model is fine-tuned and validated against a foundation AI, achieving accuracy improvements of roughly 5X over conventional ‘word embedding’ approaches. Model fine-tuning and a rules-based match threshold are critical for realizing these gains. The approach replicates stylized facts about IT exposure, but diverges sharply from survey-based measures of AI automation risk, which systematically understate exposure among high-wage occupations. A living dataset and Python package allow researchers to measure exposure across user-defined technology and task categories, with minimal time lag and at fine temporal resolution.

Suggested Citation

  • Andre Mouton, 2026. "Measuring Task-Level Technological Exposure: A Language Model Approach," Working Papers 132, Wake Forest University, Economics Department.
    • Handle: RePEc:ris:wfuewp:022172
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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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