Evaluating the Discrete Choice and BN Methods to Estimate Labor Supply Functions

Estimated labor supply functions are important tools when designing an optimal income tax or calculating the effect of tax reforms. It is therefore of large importance to use estimation methods that give reliable results and to know their properties. In this paper Monte Carlo simulations are used to evaluate two different methods to estimate labor supply functions; the discrete choice method and a nonparametric method suggested in Blomquist and Newey (2002). The focus is on the estimators' ability to predict the hours of work for a given tax system and the change in hours of work when there is a tax reform. The simulations show that the DC method is quite sensitive to misspecifications of the likelihood function and to measurement errors in hours of work. A version of the Blomquist Newey method shows the overall best performance to predict the hours of work.