Main description:

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 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 practise 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.

Contents:

CONTENTS PREFACE ACKNOWLEDGEMENTS INTRODUCTION PART I: DATA DESCRIPTION 1. DATA AND SURVEY DESIGN 1.1 THE HEALTH AND LIFESTYLE SURVEY (HALS) 1.2 THE BRITISH HOUSEHOLD PANEL SURVEY (BHPS) 1.3 THE EUROPEAN COMMUNITY HOUSEHOLD PANEL (ECHP) 1.4 THE CANADIAN NATIONAL POPULATION HEALTH SURVEY (NPHS) 1.5 THE WHO MULTI-COUNTRY STUDY (WHO-MCS) 1.6 OVERVIEW 2. DESCRIBING THE DYNAMICS OF HEALTH 2.1 INTRODUCTION 2.2 GRAPHICAL ANALYSIS 2.3 TABULATING THE DATA 3. INEQUALITY IN HEALTH UTILITY AND SELF-ASSESSED HEALTH 3.1 INTRODUCTION 3.2 DISTRIBUTIONAL ANALYSIS 3.3 REGRESSION ANALYSIS OF HUI: ORDINARY LEAST SQUARES (OLS) 3.4 REGRESSION ANALYSIS OF SAH: ORDERED PROBIT MODEL 3.5 COMBINED ANALYSIS OF HUI AND SAH: INTERVAL REGRESSION 3.6 OVERVIEW PART II: CATEGORICAL DATA 4. BIAS IN SELF-REPORTED DATA 4.1 INTRODUCTION 4.2 VIGNETTES 4.3 STANDARD ORDERED PROBIT MODEL 4.4 USING VIGNETTES TO CONTROL FOR HETEROGENEOUS REPORTING 5. HEALTH AND LIFESTYLES 5.1 INTRODUCTION 5.2 HALS DATA AND SAMPLE 5.3 DESCRIPTIVE ANALYSIS 5.4 ESTIMATION STRATEGY AND RESULTS 5.5 DISCUSSION PART III: SURVIVAL DATA 6. SMOKING AND MORTALITY 6.1 INTRODUCTION 6.2 BASIC CONCEPTS OF SURVIVAL ANALYSIS 6.3 THE HALS DATA 6.4 SURVIVAL DATA IN HALS 6.5 DESCRIPTIVE STATISTICS 6.6 DURATION MODELS 7. HEALTH AND RETIREMENT 7.1 INTRODUCTION 7.2 PREPARING AND SUMMARIZING THE DATA 7.3 THE ENDOGENEITY OF HEALTH 7.4 EMPIRICAL APPROACH TO DURATION MODELLING 7.5 STOCK SAMPLING AND DISCRETE-TIME HAZARD ANALYSIS 7.6 OVERVIEW PART IV: PANEL DATA 8. HEALTH AND WAGES 8.1 INTRODUCTION 8.2 BHPS SAMPLE AND VARIABLES 8.3 EMPIRICAL MODEL AND ESTIMATION 8.4 DISCUSSION 9. MODELLING THE DYNAMICS OF HEALTH 9.1 INTRODUCTION 9.2 STATIC MODELS 9.3 DYNAMIC MODELS 10. NON-RESPONSE AND ATTRITION BIAS 10.1 INTRODUCTION 10.2 TESTING FOR NON-RESPONSE BIAS 10.3 ESTIMATION 11. MODELS FOR HEALTH CARE USE 11.1 INTRODUCTION 11.2 THE POISSON MODEL 11.3 THE NEGATIVE BINOMIAL MODEL 11.4 ZERO INFLATED MODELS 11.5 HURDLE MODELS 11.6 FINITE MIXTURE/LATENT CLASS MODELS 11.7 LATENT CLASS HURDLE MODEL BIBLIOGRAPHY INDEX