In this paper, we describe a novel iterative procedure called SISTA to learn the underlying cost in optimal transport problems. SISTA is a hybrid between two classical methods, coordinate descent ("S"-inkhorn) and proximal gradient descent ("ISTA"). It alternates between a phase of exact...

We use a behavioural microsimulation model embedded in a numerical optimization procedure in order to identify optimal (social welfare maximizing) tax-transfer rules. We consider the class of tax-transfer rules consisting of a universal basic income and a tax defined by a 4th degree polynomial....

The traditional Becker/Arrow style model of discrimination depicts majority and minority and workers as perfectly substitutable inputs, implying that all workers have the same job assignment. The model is only appropriate for determining whether pay differences between, for example, whites and...

Using a sample of professional baseball players from 1871-2007, this paper aims at analyzing a longstanding empirical observation that married men earn significantly more than their single counterparts holding all else equal (the "marriage premium"). Baseball is a unique case study because it...

The relevance of sunk costs in decision making is one of the major sources of disagreement between neoclassical economists and behavioral economists. We test the importance of sunk costs by examining the role of a player's draft position on his playing time in the National Basketball...

This Chapter reviews evidence on discrimination in basketball, primarily examining studies on race but with some discussion of gender as well. I focus on discrimination in pay, hiring, and retention against black NBA players and coaches and pay disparities by gender among college coaches. There...

Can raising awareness of racial bias subsequently reduce that bias? We address this question by exploiting the widespread media attention highlighting racial bias among professional basketball referees that occurred in May 2007 following the release of an academic study. Using new data, we...