CAREER：一种面向劳动力序列数据的基础模型
CAREER: A Foundation Model for Labor Sequence Data
AI总结：
本文提出职业序列基础模型 CAREER，先在2400万份简历序列上预训练，再在小型纵向调查数据上微调，提升了工作序列、工资等经济变量预测。
摘要：
劳动经济学家通常通过将预测模型拟合到规模较小、经过精心构建的纵向调查数据集来分析就业数据。尽管机器学习方法为这类问题带来了希望，但这些调查数据集规模过小，无法充分发挥其优势。近年来，大规模在线简历数据集也开始出现，提供了数百万人职业轨迹的相关数据。然而，标准计量经济学模型既无法利用这些数据的规模，也无法将其纳入调查数据分析。为此，我们开发了面向工作序列的基础模型 CAREER。CAREER 首先在大规模、被动收集的简历数据上进行拟合，随后在规模较小、整理质量更高的数据集上进行微调，以用于经济推断。我们将 CAREER 拟合到一个包含来自简历的2400万个工作序列的数据集，并在小型纵向调查数据集上对其进行调整。我们发现，CAREER 能够形成准确的工作序列预测，在三个广泛使用的经济学数据集上优于计量经济学基线。我们还发现，CAREER 可用于对其他下游变量形成良好预测。例如，将 CAREER 纳入工资模型后，其预测效果优于当前使用的计量经济学模型。
英文摘要：
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.
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