This paper discusses and illustrates identification problems in personality psychology. The measures used by psychologists to infer traits are based on behaviors, broadly defined. These behaviors are produced from multiple traits interacting with incentives in situations. In general, measures...

This paper considers the problem of making inferences about the effects of a program on multiple outcomes when the assignment of treatment status is imperfectly randomized. By imperfect randomization we mean that treatment status is reassigned after an initial randomization on the basis of...

This paper examines the correlated random coefficient model. It extends the analysis of Swamy (1971, 1974), who pioneered the uncorrelated random coefficient model in economics. We develop the properties of the correlated random coefficient model and derive a new representation of the variance...

This paper reviews the problems and potential benefits of integrating personality psychology into economics. Economists have much to learn from and contribute to personality psychology. -- personality psychology ; behavioral economics ; identification ; causality

Haavelmo's seminal 1943 paper is the first rigorous treatment of causality. In it, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are de fined using hypothetical models that assign variation to some of the inputs determining...

This paper studies how the risk of divorce affects the human capital decisions of a young couple. We consider a setting where complete specialization (one of the spouses uses up all the education resources) is optimal with no divorce risk. Symmetry in education (both spouses receive an equal...

We compare the performance of maximum likelihood (ML) and simulated method of moments (SMM) estimation for dynamic discrete choice models. We construct and estimate a simplified dynamic structural model of education that captures some basic features of educational choices in the United States in...

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the...