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Estimating wage disparities using foundation models.

Keyon Vafa1, Susan Athey2,3, David M Blei4,5

  • 1Harvard Data Science Initiative, Harvard University, Cambridge, MA 02138.

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Summary
This summary is machine-generated.

Foundation models can be fine-tuned for social science estimation tasks, addressing omitted variable bias. New methods reveal career history

Keywords:
econometricsfoundation modelslabor economicsmachine learning

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Area of Science:

  • Machine Learning and Social Sciences
  • Econometrics and Causal Inference

Background:

  • Foundation models, initially for text, now excel in social science data prediction.
  • Standard fine-tuning minimizes predictive error, but social science estimation requires different success criteria.
  • Omitted variable bias is a key challenge when using predictive models for estimation in social sciences.

Purpose of the Study:

  • To develop methods for fine-tuning foundation models for social science estimation problems.
  • To characterize and mitigate omitted variable bias in fine-tuned foundation models.
  • To apply these methods to estimate the gender wage gap using richer career history data.

Main Methods:

  • Characterized omitted variable bias in standard foundation model fine-tuning.
  • Developed theoretical conditions for [Formula: see text]-consistent estimates from fine-tuned foundation models.
  • Created novel fine-tuning algorithms to empirically reduce omitted variable bias.
  • Utilized a custom foundation model for in-depth career history representation.

Main Results:

  • Identified conditions for reliable estimation using fine-tuned foundation models.
  • Empirically demonstrated mitigation of omitted variable bias.
  • Found that career history explains more of the gender wage gap than standard econometric models suggest.
  • Highlighted specific career history elements omitted by traditional models but crucial for explaining the wage gap.

Conclusions:

  • Fine-tuning foundation models with specialized algorithms can overcome limitations of standard predictive approaches in social science estimation.
  • Richer representations of variables, like career history, are essential for accurate estimation of social phenomena such as the gender wage gap.
  • The developed methods offer a more nuanced understanding of complex social issues by reducing bias in estimation.