Andrew M. Jones, Nigel Rice, Teresa Bago d'Uva, Silvia Balia
Published February 28, 2007
Textbook - 352 Pages - 24 B/W Illustrations
ISBN 9780415397711 - CAT# RU97715
Series: Routledge Advanced Texts in Economics and Finance
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Large-scale survey datasets, in particular complex survey designs such as panel data, provide a rich source of information for health economists. They offer the scope to control for individual heterogeneity and to model the dynamics of individual behaviour. However the measures of outcome used in health economics are often qualitative or categorical. These create special problems for estimating econometric models. The dramatic growth in computing power over recent years has been accompanied by the development of methods that help to solve these problems. This book provides a practical guide to the skills required to put these techniques into practice.
This book illustrates practical applications of these methods using data on health from, among others, the British Health and Lifestyle Survey (HALS), the British Household Panel Survey (BHPS), the European Community Household Panel (ECHP) and the WHO Multi-Country Survey (WHO-MCS). Assuming a familiarity with the basic syntax and structure of Stata, this book presents and explains the statistical output using empirical case studies rather than general theory.
Never before has a health economics text brought theory and practice together and this book will be of great benefit to applied economists, as well as advanced undergraduate and post graduate students in health economics and applied econometrics.
Introduction Part 1: Data Description 1. Data and Survey Design 2. Describing the Dynamics of Health 3. Inequality in Health Utility and Self-assessed Health Part 2: Categorical Data 4. Bias in Self-reported Data 5. Health and Lifestyles Part 3: Survival Data 6. Smoking and Mortality 7. Health and Retirement Part 4: Panel Data 8. Health and Wages 9. Modelling the Dynamics of Health 10. Non-Response and Attrition Bias 11. Models for Health Care Use
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