The Lives of Others: Predicting Donations with Non-Choice Responses

Abstract

There is significant variation in the percentage of adults registered as organ donors across the United States. Some of this variation may be due to characteristics of the sign-up process, in particular the form that is used when state residents renew or apply for their driver's licenses. However, it is difficult to model and predict the success of the different forms with typical methods, due to the exceptionally large feature space and the limited data. To surmount this problem, I apply a methodology that uses data on subjective non-choice reactions to predict choices. I find that active (ie yes-no) framing of the designation question decreases designation rates by 2-3 percentage points relative to an opt-in framing. Additionally, I show that this methodology can predict behavior in an experimental setting involving social motives where we have good structural benchmarks. More generally, this methodology can be used to perform policy pseudo-experiments where field experiments would prove prohibitively expensive or difficult.