What does this research mean for the field?

Ignoring the subjective choices and behaviors of researchers in econometric frameworks leads to highly biased and unreliable findings, including inflated estimates and understated uncertainty. Novelty: ClaimNovelty.CONTRADICTORY. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

To examine how incorporating realistic researcher behavior into econometric frameworks alters the bias, precision, and validity of common empirical estimators.

September 2, 2026Journal of Economic LiteratureOpen Access

Humans in the Loop: The Next Frontier in the Credibility Revolution

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Authors

MSMegan T. StevensonUniversity of Virginia JFJoshua B. FischmanUniversity of Virginia

Discussion

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Implication

Methodological analysis reveals severe estimator bias and understated uncertainty in empirical economics, highlighting the necessity of modeling human researcher behavior in econometric analysis.

Key Points

To examine how incorporating realistic researcher behavior into econometric frameworks alters the bias, precision, and validity of common empirical estimators.
Modeled econometric estimator performance under plausible assumptions regarding human researcher decision-making and specification selection.
Evaluated low-power estimators, specifically instrumental variables, across varying first-stage F-statistic thresholds up to 200.
Analyzed the effects of first-stage F-statistic threshold testing and researcher-driven variation on standard error calculations.
Instrumental variable estimators exhibit severe bias under realistic human researcher behavior, persisting even at a first-stage F-statistic of 200.
Threshold testing on the first-stage F-statistic reduces estimator bias, contradicting recent econometric claims.
Standard errors systematically understate empirical uncertainty by failing to account for variance arising from researchers' subjective analytical decisions.

Cite This Study

Stevenson et al. (2026) studied this question.

synapsesocial.com/papers/6a97e20ec562ede874ec616a https://doi.org/10.1257/jel.20261775

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September 2, 2026Journal of Economic LiteratureOpen Access

Humans in the Loop: The Next Frontier in the Credibility Revolution

View Full Paper

Ask AI

Bookmark

Authors

MSMegan T. StevensonUniversity of Virginia JFJoshua B. FischmanUniversity of Virginia

Discussion

Member takes

Implication

Methodological analysis reveals severe estimator bias and understated uncertainty in empirical economics, highlighting the necessity of modeling human researcher behavior in econometric analysis.

Key Points

To examine how incorporating realistic researcher behavior into econometric frameworks alters the bias, precision, and validity of common empirical estimators.
Modeled econometric estimator performance under plausible assumptions regarding human researcher decision-making and specification selection.
Evaluated low-power estimators, specifically instrumental variables, across varying first-stage F-statistic thresholds up to 200.
Analyzed the effects of first-stage F-statistic threshold testing and researcher-driven variation on standard error calculations.
Instrumental variable estimators exhibit severe bias under realistic human researcher behavior, persisting even at a first-stage F-statistic of 200.
Threshold testing on the first-stage F-statistic reduces estimator bias, contradicting recent econometric claims.
Standard errors systematically understate empirical uncertainty by failing to account for variance arising from researchers' subjective analytical decisions.

Cite This Study

Stevenson et al. (2026) studied this question.

synapsesocial.com/papers/6a97e20ec562ede874ec616a https://doi.org/10.1257/jel.20261775

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