使用基础模型估计工资差异
Estimating Wage Disparities Using Foundation Models
AI总结：
本文研究如何微调基础模型以解决社会科学中的估计问题，提出缓解遗漏变量偏差的算法，并在性别工资分解中验证其能比标准模型解释更多工资差距。
摘要：
基础模型的兴起标志着机器学习的范式转变：不再从头训练专门模型，而是先在海量数据集上训练基础模型，然后将其适应或微调以在较小数据集上进行预测。最初为文本开发的基础模型，在预测社会科学数据方面也表现出色。然而，尽管社会科学中的许多估计问题将预测作为中间步骤，它们最终需要不同的成功标准。在本文中，我们开发了微调基础模型以执行这些估计问题的方法。我们首先刻画了当基础模型仅被微调以最大化预测精度时可能出现的遗漏变量偏差。然后，我们提供了一组新的微调条件，在此条件下从基础模型得出的估计量具有根号 n 一致性。基于这一理论，我们开发了新的微调算法，在经验上缓解这种遗漏变量偏差。为了验证我们的想法，我们研究了性别工资分解。这是计量经济学中的一个统计估计问题，目标是将性别工资差距分解为能和不能由工人职业历史解释的组成部分。分解工资差距的经典方法采用简单的工资预测模型，这些模型以职业历史的粗略总结为条件，可能遗漏了对解释差距很重要的因素。相反，我们使用定制构建的基础模型来分解性别工资差距，该模型捕获了职业历史的更丰富表示。使用来自收入动态面板研究的数据，我们发现职业历史解释的性别工资差距比标准计量经济学模型能够测量的更多，并且我们识别了被标准模型遗漏但对解释工资差距很重要的职业历史要素。
英文摘要：
The rise of foundation models marks a paradigm shift in machine learning: instead of training specialized models from scratch, foundation models are first trained on massive datasets before being adapted or fine-tuned to make predictions on smaller datasets. Initially developed for text, foundation models have also excelled at making predictions about social science data. However, while many estimation problems in the social sciences use prediction as an intermediate step, they ultimately require different criteria for success. In this paper, we develop methods for fine-tuning foundation models to perform these estimation problems. We first characterize an omitted variable bias that can arise when a foundation model is only fine-tuned to maximize predictive accuracy. We then provide a novel set of conditions for fine-tuning under which estimates derived from a foundation model are root-n-consistent. Based on this theory, we develop new fine-tuning algorithms that empirically mitigate this omitted variable bias. To demonstrate our ideas, we study gender wage decomposition. This is a statistical estimation problem from econometrics where the goal is to decompose the gender wage gap into components that can and cannot be explained by career histories of workers. Classical methods for decomposing the wage gap employ simple predictive models of wages which condition on coarse summaries of career history that may omit factors that are important for explaining the gap. Instead, we use a custom-built foundation model to decompose the gender wage gap, which captures a richer representation of career history. Using data from the Panel Study of Income Dynamics, we find that career history explains more of the gender wage gap than standard econometric models can measure, and we identify elements of career history that are omitted by standard models but are important for explaining the wage gap.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
