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Abstract: Although the most commonly used quantitative measures of corruption are highly correlated, we find that these measures do not accurately track changes in corruption within a given country over time. Many causal inference research designs, such as difference-in-difference designs and dynamic panel instrumental variable models, rely on such within-country changes to identify causal relationships. As a result, we argue that findings based on changes in corruption within countries should be interpreted with caution. We show that factor scores extracting the common signal from multiple measures should be preferred to any single measure of corruption when studying changes within a country over time.

Bio: Dr. Justin Esarey is an Associate Professor of Politics and International Affairs at Wake Forest University. He received his PhD in Political Science from Florida State University in 2008 following a BA in Political Science and BS in Economics from Bowling Green State University in 2002. His area of specialization is political methodology, especially hypothesis testing and the scientific ecosystem. His current substantive projects study the relationship between corruption and women's participation in government. Dr. Esarey is currently co-editor of PS: Political Science and Politics and the Principal Investigator of the International Methods Colloquium project.

Learn more about Dr. Justin E. Esarey at his website.