  Labor economists regularly analyze employment data by fitting predictive
models to small, carefully constructed longitudinal survey datasets. Although
machine learning methods offer promise for such problems, these survey datasets
are too small to take advantage of them. In recent years large datasets of
online resumes have also become available, providing data about the career
trajectories of millions of individuals. However, standard econometric models
cannot take advantage of their scale or incorporate them into the analysis of
survey data. To this end we develop CAREER, a foundation model for job
sequences. CAREER is first fit to large, passively-collected resume data and
then fine-tuned to smaller, better-curated datasets for economic inferences. We
fit CAREER to a dataset of 24 million job sequences from resumes, and adjust it
on small longitudinal survey datasets. We find that CAREER forms accurate
predictions of job sequences, outperforming econometric baselines on three
widely-used economics datasets. We further find that CAREER can be used to form
good predictions of other downstream variables. For example, incorporating
CAREER into a wage model provides better predictions than the econometric
models currently in use.
