Risk and punishment revisited: Errors in variables and in the lab - Christoph Engel; Oliver Kirchkamp (Max Planck Institute for Research on Collective Goods, 2016)
We provide an example for an errors in variables problem which might be often neglected but which is quite common in lab experimental practice: In one task, attitude towards risk is measured, in another task participants behave in a way that can possibly be explained by their risk attitude. How should we deal with inconsistent behaviour in the risk task? Ignoring these observations entails two biases: An errors in variables bias and a selection bias. We argue that inconsistent observations should be exploited to address the errors in variables problem, which can easily be done within a Bayesian framework.
Keywords: C91; D43; L41; 330; risk; lab experiment; public good; errors in variables; Bayesian inference