Linear Probability Models of the Demand for Attributes with an Empirical Application to Estimating the Preferences of Legislators

We formulate and estimate a rigorously justified linear probability model of binary choices over alternatives characterized by unobserved attributes. We apply the model to estimate preferences of congressmen as expressed in their votes on bills. The effective dimension of the attribute space characterizing votes is larger than what has been estimated in recent influential studies of congressional voting by Poole and Rosenthal. Congressmen vote on more than ideology. Issue-specific attributes are an important determinant of congressional voting patterns. The estimated dimension is too large for the median voter model to describe congressional voting