English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Instrumental variable (IV) strategies are widely used in political science toestablish causal relationships. However, the identifying assumptions requiredby an IV design are demanding, and it remains challenging for researchers toassess their validity. In this paper, we replicate 67 papers published in threetop journals in political science during 2010-2022 and identify severaltroubling patterns. First, researchers often overestimate the strength of theirIVs due to non-i.i.d. errors, such as a clustering structure. Second, the mostcommonly used t-test for the two-stage-least-squares (2SLS) estimates oftenseverely underestimates uncertainty. Using more robust inferential methods, wefind that around 19-30% of the 2SLS estimates in our sample are underpowered.Third, in the majority of the replicated studies, the 2SLS estimates are muchlarger than the ordinary-least-squares estimates, and their ratio is negativelycorrelated with the strength of the IVs in studies where the IVs are notexperimentally generated, suggesting potential violations of unconfoundednessor the exclusion restriction. To help researchers avoid these pitfalls, weprovide a checklist for better practice.

How Much Should We Trust Instrumental Variable Estimates in Political Science? Practical Advice Based on Over 60 Replicated Studies