The effect of generative language models on job-posting volumes among primary-care physicians: evidence from a cross-sectional survey

[SYNTHETIC full text — teaching stand-in for an open-access PDF.]

1. Introduction
This paper studies the labour-market impact of the technology described in the
title. We study how the adoption of generative language models affects job-posting volumes among primary-care physicians. Using a cross-sectional survey covering the 2023–2024 period, we estimate the impact of access to generative language models on the outcome of interest. We find a modest but significant improvement. The results are robust to alternative specifications and are concentrated among less-experienced workers. We discuss implications for how generative language models reshapes the tasks and skills that make up these jobs.

3. Data and design
Design: survey. The sample is drawn from administrative and survey
sources over the study window. Table 3 reports the main estimate.

4. Results
Main estimate (Table 3, column 4): the point estimate is 0.168
(standard error 0.033) [SEE PAGE 7]. Effect class:
positive. Heterogeneity analysis on page 9 shows the effect concentrates
among less-experienced workers.

6. Conclusion
We discuss how the technology reshapes the tasks and skills composing these jobs.
