We also show that an IV strategy that uses a human-labeled sample to purge the measurement error in generated variables works poorly when the number of labels is small relative to the unlabeled data.

We provide an illustration of how bias correction increases the estimated impact of remote work on wages across occupations.

We provide a simple bias correction formula that applied researchers can easily use. This restores valid inference and has quantitatively important effects even when AI/LLM are extremely accurate.