Using Auxiliary Data to Estimate Selection Bias Models, with an Application to Interest Group Use of the Direct Initiative Process

Recent work in survey research has made progress in estimatingmodels involving selection bias in a particularly difficultcircumstance—all nonrespondents are unit nonresponders,meaning that no data are available for them. These models arereasonably successful in circumstances where the dependent variableof interest is continuous, but they are less practical empiricallywhen it is latent and only discrete outcomes or choices areobserved. I develop a method in this article to estimate thesemodels that is much more practical in terms of estimation. Themodel uses a small amount of auxiliary information to estimatethe selection equation parameters, which are then held fixedwhile estimating the equation of interest parameters in a maximum-likelihoodsetting. After presenting Monte Carlo analyses to support themodel, I apply the technique to a substantive problem: Whichinterest groups are likely to to be involved in support of potentialinitiatives to achieve their policy goals?