A Structural Labour Supply Model with Nonparametric Preferences

Nonparametric techniques are usually seen as a statistic device for data description and exploration, and not as a tool for estimating models with a richer economic structure, which are often required for policy analysis. This paper presents an example where nonparametric flexibility can be attained in a fully structural model. A structural labour supply model with a nonparametric specification of preferences is introduced, which can be used for the analysis of all sorts of (non-linear) tax and benefits changes. Moreover, the model can deal with several other problems in estimation of structural labour supply models, such as non-convex tax rules, benefits, unobserved wages of non-workers, and model coherency. The utility maximization problem is solved by discretizing the budget set and choosing the optimal leisure and income combination from a finite set of alternatives. The direct utility function is approximated with a series expansion. For a given length of the expansion, the model is estimated by smooth simulated maximum likelihood. The wage equation is estimated jointly with the labour supply model, and measurement errors in wage rates are allowed for. The model is estimated with Dutch data on labour supply of married females, for various lengths of the series expansion. Estimates of labour supply elasticities and effects of a proposed tax reform suggest that the results do not change much once the order of the series expansion is extended beyond two, even though the second order model is statistically rejected against higher order models. Monte Carlo simulations are used to show that the estimation strategy has remarkably good finite sample properties for the size of our sample. On the other hand they lead to some concern about the potential bias to measurement error in the hours variable