Nonparametric Analysis of Labour Supply Using Random Fields

This paper studies labour supply in panel data by means of random fields. In doing so it describes a way of uniting classical revealed preference techniques and econometric prediction by means of a best linear, unbiased prediction procedure based on Goldberger (1962) and known as Weiner-Kolmogorov prediction or Universal Kriging in the spatial statistics literature. This, it is argued, retains the best features of both revealed preference and statistical approaches. In an application to the consumption and labour supply decisions of NYC taxi drivers this paper makes a number of empirical points: first that behaviour which is, on the basis of conventional revealed preference-based measures, rational, can be shown to be highly economically implausible; secondly that modelling labour supply using parsimonious relevant conditioning can solve the puzzle by providing predictions which match the data, are theoretically-consistent yet are behaviourally and economically sensible; thirdly it shows that modelling behaviour at the level at the which the theory is designed to apply (which is to say at the level of the individual) can given greater insights into behaviour and heterogeneity than modelling population moments or quantiles; lastly than the practice of assuming monotonic scalar heterogeneity when modelling cross sectional data may give a strongly misleading impression of both behaviour and preference heterogeneity.