Record Details

Replication data for: Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

View Archive Info
 
 
Field Value
 
Title Replication data for: Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models
 
Identifier https://doi.org/10.7910/DVN/27083
 
Creator Imai, Kosuke; Park, Bethany; Greene, Kenneth F.
 
Publisher Harvard Dataverse
 
Description The list experiment, also known as the item count technique, is becoming increasingly popular as a survey methodology for eliciting truthful responses to sensitive questions. Recently, multivariate regression techniques have been developed to predict the unobserved response to sensitive questions using respondent characteristics. Nevertheless, no method exists for using this predicted response as an explanatory variable in another regression model. We address this gap by first improving the performance of a naive two-step estimator. Despite its simplicity, this improved two-step estimator can only be applied to linear models and is statistically inefficient. We therefore develop a maximum likel
ihood estimator that is fully efficient and applicable to a wide range of models. We use a simulation study to evaluate the empirical performance of the proposed methods. We also apply them to the Mexico 2012 Panel Study and examine whether vote-buying is associated with increased turnout and candidate approval. The proposed methods are implemented in open-source software.
 
Date 2012