The closeness of preferences in a preference profile has mainly been measured by aggregating the distances between each pair of preferences. We argue in this paper that some important information is lost in this process and we opt for considering the profile as a whole when constructing such a measure. With this idea in mind, we propose axioms a cohesiveness measure should satisfy and show that these properties fully characterize a new family of measures.

We study experimentally in the laboratory the situation when individuals have to report their private information about a variable to a public authority that then makes inference about the true values given a known variable using a regression technique. It is assumed that individuals prefer this predicted value to be as close as possible to their true value. Consistent with the theoretical literature, we show that subjects misrepresent their private information more when an ordinary least squares regression is implemented than (...) when the so-called resistant line estimator is employed. The latter extends the median voter theorem to the two-dimensional setting and belongs to the family of robust estimation techniques. In fact, we find that OLS involves serious biases but the RL estimation is empirically unbiased. Furthermore, subjects never earn less when the RL is applied. (shrink)