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DTSTART:20070311T020000
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DTSTAMP:20260929T103307
DTSTART;TZID=America/Detroit:20261001T130000
DTEND;TZID=America/Detroit:20261001T142000
SUMMARY:Workshop / Seminar:Multimargin Selection in Leniency Designs
DESCRIPTION:Leniency designs use quasi-random assignment to decision makers with different treatment propensities to estimate causal effects. When decision makers weight unobserved case characteristics differently\, a higher (lower) overall treatment rate can mask a lower (higher) probability of treating some case types. I develop a model with finitely many latent regimes\, each representing a case type with its own potential outcome distributions and treatment probabilities that vary across decision-makers. The main identifying restriction is that\, within a regime and conditional on observed characteristics\, unobserved factors governing treatment decisions are independent of potential outcomes. Under the model\, observed outcome distributions admit a finite mixture representation\, and pairwise Wald estimands decompose into regime-specific treatment effects weighted by each regime’s share of the aggregate first stage. I give conditions for identifying the number of regimes\, their treatment effects\, and their weights using parametric restrictions on outcome distributions or auxiliary pretreatment measurements. I characterize when the weights are nonnegative and when they are constant across decision-maker pairs. I develop bootstrap tests of both restrictions and establish their pointwise asymptotic validity for a fixed number of regimes. Applying the model to Miami-Dade pretrial bail data\, I find that some of the more lenient groups of judges release fewer defendants of the case type for whom release most reduces prison time.
UID:151819-21912225@events.umich.edu
URL:https://events.umich.edu/event/151819
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Econometrics,Economics,seminar
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