🤖 AI Summary
This paper investigates the causal effect of temporary deferral—due to hemoglobin levels falling below a clinical threshold—on subsequent blood donation intention. Standard regression discontinuity design (RDD) is invalid here, as healthcare workers may manipulate hemoglobin measurements, inducing endogenous manipulation of the running variable. To address this, the paper develops a partial-identification RDD framework tailored to discrete running variables and endogenous manipulation, yielding credible upper and lower bounds on the causal effect. The method jointly integrates manipulation detection, correction modeling, and discrete-threshold adjustment. Applied to real-world data, it delivers tight, robust estimates, validated through multiple sensitivity analyses. Key findings indicate that deferral significantly reduces subsequent donation participation. Methodologically, the study overcomes a critical limitation of RDD under endogenous manipulation, offering a novel quasi-experimental paradigm for settings where measurement manipulation is plausible.
📝 Abstract
Volunteer labor can temporarily yield lower benefits to charities than its costs. In such instances, organizations may wish to defer volunteer donations to a later date. Exploiting a discontinuity in blood donations' eligibility criteria, we show that deferring donors reduces their future volunteerism. In our setting, medical staff manipulates donors' reported hemoglobin levels over a threshold to facilitate donation. Such manipulation invalidates standard regression discontinuity design. To circumvent this issue, we propose a procedure for obtaining partial identification bounds where manipulation is present. Our procedure is applicable in various regression discontinuity settings where the running variable is manipulated and discrete.
