Summary of Estimating Wage Disparities Using Foundation Models, by Keyon Vafa et al.
Estimating Wage Disparities Using Foundation Models
by Keyon Vafa, Susan Athey, David M. Blei
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Econometrics (econ.EM); Methodology (stat.ME); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an alternative methodology for decomposing the gender wage gap using powerful foundation models, such as large language models. The authors argue that classical methods suffer from omitted variable bias (OVB), where covariates correlated with both gender and wages are not included in the model. They develop fine-tuning algorithms to mitigate OVB and demonstrate the effectiveness of their approach using data from the Panel Study of Income Dynamics. By leveraging complex, high-dimensional inputs, foundation models can capture a richer representation of career history than simple models and predict wages more accurately. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to understand why men and women are paid differently. Right now, we don’t fully understand how people’s past experiences affect their salaries. The authors suggest using special kinds of computer models that can handle lots of information to better understand this relationship. They show that these models can be trained in a way that reduces errors and provides a more accurate picture of how career history affects the gender wage gap. |
Keywords
* Artificial intelligence * Fine tuning
