Labor supply models: unobserved heterogeneity, nonparticipation and dynamics

This chapter is concerned with the identification and estimation of models of labor supply. The focus is on the key issues that arise from unobserved heterogeneity, nonparticipation and dynamics. We examine the simple ‘static’ labor supply model with proportional taxes and highlight the problems surrounding nonparticipation and missing wages. The difference in differences approach to estimation and identification is developed within the context of the labour supply model. We also consider the impact of incorporating nonlinear taxation and welfare programme participation. Family labor supply is looked at from botht e unitary and collective persepctives. Finally we consider intertemporal models focusing on the difficulties that arise with participation and heterogeneity

An Empirical Comparison of Three Inference Methods

In this paper, an empirical evaluation of three inference methods for uncertain reasoning is presented in the context of Pathfinder, a large expert system for the diagnosis of lymph-node pathology. The inference procedures evaluated are (1) Bayes' theorem, assuming evidence is conditionally independent given each hypothesis; (2) odds-likelihood updating, assuming evidence is conditionally independent given each hypothesis and given the negation of each hypothesis; and (3) a inference method related to the Dempster-Shafer theory of belief. Both expert-rating and decision-theoretic metrics are used to compare the diagnostic accuracy of the inference methods.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (UAI1988

Growth Economics and Reality

This paper questions current empirical practice in the study of growth. We argue that much of the modern empirical growth literature is based on assumptions concerning regressors, residuals, and parameters which are implausible both from the perspective of economic theory as well as from the perspective of the historical experiences of the countries under study. A number of these problems are argued to be forms of violations of an exchangeability assumption which underlies standard growth exercises. We show that relaxation of these implausible assumptions can be done by allowing for uncertainty in model specification. Model uncertainty consists of two types: theory uncertainty, which relates to which growth determinants should be included in a model, and heterogeneity uncertainty, which relates to which observations in a data set comprise draws from the same statistical model. We propose ways to account for both theory and heterogeneity uncertainty. Finally, using an explicit decision-theoretic framework, we describe how one can engage in policy-relevant empirical analysis.

A Structural Econometric Model of Consumer Demand at Pick-Your-Own Fruit Operations

This paper develops a fully structural econometric consumer demand model for goods which have time and monetary costs, and where time spent obtaining the goods also enters into the utility function. The model is used to analyze customers' decision to buy pick-your-own versus pre-harvested fruit at North Carolina pick-your-own fruit operations. The empirical application distinguishes the double effect of time as a resource constraint and also providing utility. Elasticity estimates show that strawberries sold at pick-your-own operations are price elastic, with pick-your-own fruit being less price elastic than pre-harvested fruit.Consumer/Household Economics,

Using rank data to estimate health state utility models

In this paper we report the estimation of conditional logistic regression models for the Health Utilities Index Mark 2 and the SF-6D, using ordinal preference data. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations. For both the HUI2 and the SF-6D, the models estimated using ordinal data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that ordinal data have the potential to provide useful insights into community health state preferences. However, important questions remain