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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 likelihood 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. |
See this page for the information about the project on the elicitation of truthful answers to sensitive survey questions.
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The software package that implements the proposed method is available here for download.
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