Econometric Analysis of Health Data

List of Contributors.

Preface.

Introduction (Andrew M. Jones and Owen O'Donnell).

PART I: LATENT VARIABLES AND SELECTION PROBLEMS.

The demand for health: an empirical reformulation of the Grossman model. (Adam Wagstaff).

Health, health care and the environment: Econometric evidence from German micro data.(Manfred Erbsland, Walter Ried and Volker Ulrich).

Subjective health measures and state dependent reporting errors. (Marcel Kerkhofs and Maarten Lindeboom.)

The effect of smoking on health using a sequential self-selection model. (Kajal Lahiri and Jae G. Song).

PART II: COUNT DATA AND SURVIVAL ANALYSIS.

A comparison of alternative models of prescription drug utilization. (Paul V. Grootendorst).

Estimates of the use and costs of behavioural health care: a comparison of standard and finite mixture models. (Partha Deb and Ann M. Holmes).

An empirical analysis of the demand for physician services across the European Union. (Sergi Jiménez-Martín, José M. Labeaga, Maite Martínez-Granado).

Proportional treatment effects for count response panel data: Effects of binary exercise on health care demand. (Myoung-jae Lee and Saturo Kobayashi).

Estimating surgical volume-outcome relationships applying survival models: accounting for frailty and hospital fixed effects. (Barton H. Hamilton and Vivian H. Ho).

PART III: FLEXIBLE AND SEMIPARAMETIC ESTIMATORS.

Individual cigarette consumption and addiction: a flexible limited dependent variable approach. (Steven T. Yen and Andrew M. Jones).

Identifying demand for health resources using waiting times information. (Richard Blundell and Frank Windmeijer).

Non- and semiparametric estimation of age and time heterogeneity in repeated cross-sections: an application to self-reported morbidity and general practitioner utilisation. (David Parkin, Nigel Rice and Matthew Sutton).

PART IV: CLASSICAL AND SIMULATION METHODS FOR PANEL DATA.

Unobserved heterogeneity and censoring in the demand for health care. (Angel López-Nicolás).

A discrete random effects probit model with application to the demand for preventive care. (Partha Deb).

The use of long-term care services by the Dutch elderly. (France Portrait, Maarten Lindeboom and Dorly Deeg).

HMO selection and medical care costs: Bayesian MCMC estimation of a robust panel data probit model with survival. (Barton H. Hamilton).

Index.