9 Conclusions
This paper has provided an introduction to the basic analytics of discrete hours labour supply modelling. Special attention was given to model specification, estimation and microsimulation. The paper has given several numerical examples to illustrate the more technical exposition of the methodologies used in this research field. It is suggested that the approach offers much potential for further interesting and valuable applications and extensions.
Several developments are occurring with regard to the specification of the different random error terms in the utility function, which are aimed at increasing the flexibility of the labour supply model. Alternative models relax the assumption of particular restrictive patterns in the variance-covariance matrices of the error terms in use, such as independence between the different labour supply choices. An increase in computing power has made some of these extensions feasible, although they are often still quite burdensome to carry out.
One area related to the discussion in this paper, that has received little attention in the literature so far, is concerned with the evaluation of simulation outcomes. When using discrete choice labour supply models in simulation, the outcomes of analyses are probabilistic in nature. Measures of welfare, inequality or poverty which can deal with these probabilistic outcomes need further development.[47]
Notes
- [47]Creedy, Kalb and Scutella (2003) propose an approach for calculating inequality and poverty measures in a discrete choice microsimulation setting.
