Daniel McFadden

McFadden shared the 2000 Nobel Memorial Prize in Economic Sciences with James Heckman for his development of theory and methods for analyzing discrete choice. His work explains how individuals choose among distinct alternatives like transportation modes, occupations, or housing locations.

The conditional logit model, developed by McFadden, is a statistical method for analyzing choices among discrete alternatives based on their characteristics. It estimates how the attributes of alternatives—such as travel time, cost, or convenience—affect the probability that a decision-maker will choose each option.

McFadden's discrete choice models are widely used to predict demand for different transportation modes, helping agencies design transit systems and forecast ridership. His methods were applied to analyze the Bay Area Rapid Transit (BART) system in San Francisco and have become standard tools in transportation economics and urban planning.

Choice-based sampling is a survey technique where researchers over-sample individuals who chose less common alternatives—like transit riders in a car-dominated region—to ensure adequate data for analysis. McFadden developed statistical corrections for the bias this introduces, making the method practical for studying rare choices.