Difference-in-Differences Models

A Win or a Flop? Identifying and Estimating Unintended Protest Costs in Measuring Success Outcomes

Kimberly Turner (Southern Illinois University, Carbondale)

Abstract: How we measure protest success and how to identify and measure the lagged effects of movements has long besieged the field. Estimating the full impact of a movement, both its intended and unintended consequences, is undermined by the lack of consensus on...

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Changing the Dialogue: Candidate Position-Taking in Primary Elections

Rachel Porter, Sarah Treul and Maura McDonald (University of North Carolina)

Abstract: The record-high number of women who ran for the U.S. Congress during the midterm elections led many journalists to...

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Causal Inference in Difference-in-Differences Designs under Uncertainty in Counterfactual Trends

Thomas Leavitt (Columbia University)

Abstract: Difference-in-Differences (DID) is a popular method for design-based causal inference. Design-based methods typically quantify uncertainty in inferences from a sample to a population via a sampling mechanism and from observed to counterfactual outcomes via an assignment mechanism. The...

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(How) Do Elections Build States? Evidence from Liberian Electoral Administration

Jeremy Bowles (Harvard University)

Abstract: In contexts where the state otherwise has limited reach, effective electoral administration permits the projection of state authority and increases levels of state-citizen interaction....

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