Econometric Analysis Of Health Data

Given extensive use of individual level data in Health Economics, it has become increasingly important to understand the microeconometric techniques available to applied researchers. The purpose of this book is to give readers convenient access to a collection of recent contributions that contain innovative applications of microeconometric methods to data on health and health care.

Contributions are selected from papers presented at the European Workshops on Econometrics and Health Economics and published in Health Economics. Topics covered include:
* Latent Variables
* Unobservable heterogeneity and selection problems
* Count data and survival analysis
* Flexible and semiparametric estimators for limited dependent variables
* Classical and simulation methods for panel data
* Publication marks the tenth anniversary of the Workshop series.
Doctoral students and researchers in health economics and microeconomics will find this book invaluable. Researchers in related fields such as labour economics and biostatistics will also find the content of use.

Andrew Jones, PhD (York), Professor of Economics at the University of York, UK, where he was Head of the Department of Economics and Related Studies between January 2011 and september 2015. He was responsible for the running of the MSc in Health Economics at York between 1994 and 2011. During that time there were over 500 graduates from more than 70 different countries. He has also supervised 23 PhD students. He is a joint editor of Health Economics. He edited the Elgar Companion to Health Economics which was published in 2006 with 50 concise chapters that review the state-of-the-art in the field.

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 Jim?nez-Mart?n, Jos? M. Labeaga, Maite Mart?nez-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 L?pez-Nicol?s).

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.