Empirical Essays in Health and Education Economics [Elektronische Ressource] / Amelie Catherine Wuppermann. Betreuer: Joachim Winter

Wuppermann, Amelie

Informations

• Publié par: ludwig-maximilians-universitat_munchen
• Publié le: 01 janvier 2011
• Nombre de lectures: 24
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Empirical Essays
in
Health and Education Economics
Inaugural-Dissertation
zur Erlangung des Grades
Doctor oeconomiae publicae (Dr. oec. publ.)
an der Ludwig-Maximilians-Universität München
2010
vorgelegt von
Amelie Catherine Wuppermann
Referent: Prof. Dr. Joachim Winter
Korreferent: Prof. Dr. Florian Heiss
Promotionsabschlussberatung: 1. Juni 2011Acknowledgements
First and foremost, I would like to thank my supervisor, Joachim Winter. I am grateful
for his critical questions, constructive comments and general support that have guided
me while writing this dissertation and beyond.
I would further like to thank my co-supervisor, Florian Heiss, for his guidance and
continuous encouragement during the past years. Thanks also go to Ludger Wößmann
for advice on the fourth chapter of this dissertation and for agreeing to serve on my
committee.
In addition, I am indebted to many people for their help with writing this disserta-
tion. In particular, I would like to thank my coauthors, Helmut Farbmacher and Guido
Schwerdt, for the collaboration that has been and continues to be a source of inspi-
ration and motivation. I would further like to thank Luc Bissonnette, Nicolas Sauter,
Beatrice Scheubel and Hannes Schwandt for valuable suggestions and comments and
Peter Ihle and Ingrid Schubert for data support.
I would like to thank Klaus Schmidt for convincing me to spend an academic year at
the University of Wisconsin-Madison and my professors and fellow-students at the eco-
nomics department in Madison for the challenging and inspiring academic experience.
I would also like to thank Dana Goldman and the Bing Center for Health Economics
for giving me the opportunity to write part of this dissertation at RAND. Without
my stays in Madison and at the RAND Corporation in Santa Monica this dissertation
would not have been the same.
Finally, I gratefully acknowledge financial support from the Deutsche Forschungs-
gemeinschaft through GRK 801 and from the German Academic Exchange Service.Contents
Preface 1
1 Do I know more than my body can tell? 8
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2 Empirical Literature on Adverse Selection . . . . . . . . . . . . . . . . 11
1.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4 Estimation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.6 Robustness Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Appendix: Inverse Probability Weighting . . . . . . . . . . . . . . . . . . . . 36
2 Do they know what’s at risk? 38
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.2 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4 Estimation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.4.1 Calculation of Objective Risk . . . . . . . . . . . . . . . . . . . 44
2.4.2 Comparison Subjective and Objective Risk . . . . . . . . . . . . 47
2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.6 Robustness Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3 Heterogeneous Effects of Copayments 58
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2 Econometric Framework . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Contents iii
3.4.1 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.4.2 Average Effects and one-way Heterogeneities . . . . . . . . . . . 65
3.4.3 Simultaneous Evaluation of . . . . . . . . . . . . 67
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Appendix: Marginal Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4 Is traditional teaching really all that bad? 75
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.2 Literature on Teaching Practices . . . . . . . . . . . . . . . . . . . . . . 77
4.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.4 Estimation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.6 Robustness Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Appendix: Selection on Unobservables . . . . . . . . . . . . . . . . . . . . . 100
Bibliography 104List of Tables
1.1 Descriptives – ELSA Wave 0 . . . . . . . . . . . . . . . . . . . . . . . . 13
1.2 Health Measures – ELSA Wave 0 . . . . . . . . . . . . . . . . . . . . . 15
1.3 Prevalence of Different Longstanding Illnesses – Wave 0 . . . . . . . . . 16
1.4 Information Used in Underwriting in US and UK Insurance Markets . . 20
1.5 Private Information on Mortality Risk – Women . . . . . . . . . . . . . 22
1.6 Private ony Risk – Men . . . . . . . . . . . . . . 23
1.7 Private Information on Health Risk – Women . . . . . . . . . . . . . . 24
1.8 Private on Health Risk – Men . . . . . . . . . . . . . . . . 25
1.9 Robustness – Event by 2002 . . . . . . . . . . . . . . . . . . . . . . . . 28
1.10 – Subjective Life Expectancy . . . . . . . . . . . . . . . . . 29
1.11 Selection Mechanisms – Bivariate Probit . . . . . . . . . . . . . . . . . 37
2.1 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.2 Subjective and Objective Risk . . . . . . . . . . . . . . . . . . . . . . . 45
2.3 Duration Model for Disease Onsets in HRS . . . . . . . . . . . . . . . . 48
2.4 Differences Subjective and Objective Risk . . . . . . . . . . . . . . . . 50
2.5 Robustness Analyses I . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.6 II . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.7 Results for Smokers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.1 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.2 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.3 Marginal Effects after Probit, FMM Probit and Bivariate Probit . . . . 66
3.4 FMM Bivariate Probit – Coefficients and Marginal Effects . . . . . . . 67
3.5 FMM Bivariate Probit – Marginal Effects on Joint Probabilities . . . . 69
4.1 Descriptive Statistics – Teacher variables . . . . . . . . . . . . . . . . . 82
4.2 Student, School and Class Variables by Intensity of Lecture Style Teaching 83
4.3 Teacher Variables by Intensity of Lecture Style Teaching . . . . . . . . 84Contents v
4.4 Estimation Results OLS . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.5 First Difference . . . . . . . . . . . . . . . . . . . . 91
4.6 Robustness Checks I . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.7 Checks II: Absolute Time Specification . . . . . . . . . . . 97
4.8 Descriptive Statistics – Class Characteristics . . . . . . . . . . . . . . . 102
4.9e – Class (cont.) . . . . . . . . . . . 103List of Figures
1.1 SRH and Future Health Events - Men . . . . . . . . . . . . . . . . . . . 18
1.2 SRH and Future Health Events - Women . . . . . . . . . . . . . . . . . 18
1.3 Predicted Probability Death - Men . . . . . . . . . . . . . . . . . . . . 31
1.4y Death - Women . . . . . . . . . . . . . . . . . . . 32
1.5 Predicted Probability Major Condition - Men . . . . . . . . . . . . . . 32
1.6y Major - Women . . . . . . . . . . . . . 33
3.1 Development of GP and Specialist Visits Over Time . . . . . . . . . . . 63
3.2 Predicted Probabilities in 2002 . . . . . . . . . . . . . . . . . . . . . . . 70Preface
Health and education are two fields of economic research that are of relevance to
the political agenda in most developed countries. Governments play a major role in
organizing and financing health care and the education system. Market imperfections
in both areas are put forward as rationale for government intervention (Poterba, 1996).
Imperfections in the health care market result mostly from informational asymme-
tries. As future health care needs are to a large extent uncertain, a high proportion of
individuals demand insurance. Asymmetric distributions of information between the
insurer and the user of health care, however, complicate the organization of health
insurance. In particular, adverse selection and moral hazard may occur.
When the individuals who buy insurance are heterogeneous in terms of the insured
risk and when they have more information on their risk than the insurer, adverse
selection may result in market failure. Given a choice between different insurance
products, high risk individuals who know about their risk tend to choose generous
coverage. Correspondingly, individual