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Issues in the Industrial Organization of Health Markets

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Issues in the Industrial Organization of Health Markets Issues in the Industrial Organization of Health Markets Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Kevin E. Pflum, B.S., M.A. Graduate Program in Economics The Ohio State University 2011 Dissertation Committee: Dan Levin, Advisor Paul Healy Matthew Lewis © Copyright by Kevin E. Pflum 2011 Abstract In the first chapter, a theoretical model of pharmaceutical sample dispensation is developed. We currently know very little about how samples are used and the reasons often presented in the medical literature do not have a theoretical founda- tion. The premise of the model developed in this chapter is that sample dispensation developed to mitigate the risk of trying a drug that, due to extreme idiosyncrasy in the consumer-to-drug matching, may have no therapeutic value to a consumer. Cconsumers are rational and in choosing, or not choosing, a drug so are maximiz- ing the total, discounted expected utility of their choice. A firm’s sampling strategy depends on the degree of information available in the market. When the efficacy is commonly known the sampling decision is not monotonic in a drug's efficacy. With asymmetric information, however, sample dispensation acts as a signal of efficacy and is monotonic. The signaling effect also causes the firm to dispense samples for lower efficacies than is optimal with symmetric information. The circumstances causing sampling to increase consumer welfare are explored. The second chapter focuses on the problem of inducing socially preferred levels of quality when quality represents a dimension to a firm’s good or service that cannot be directly contracted upon. The existing mechanism design literature on regula- tion treats quantity and quality as interchangeable or observable. The inseparability ii and non-contractibility of quality and quantity are explicitly modeled and show that the optimal regulatory policy may generate a different outcome than has been pre- viously suggested. Many markets possessing unverifiable quality often contain a mix of for-profit and not-for profit firms; therefore, the firm’s objective is modeled as a combination of profit- and output-maximization with a break-even constraint. The firm’s ability to manipulate demand by adjusting quality can result in an under- or over-supply relative to the first-best level, even for a pure profit-maximizer. A firm’s informational advantage may be completely attenuated, however, when the firm is a pure output-maximizer and the regulator removes the demand response to price by paying directly for the good. Finally, the third chapter examines the effect of hospital system membership on the bargaining outcome between a hospital and a managed care organization (MCO). Previous research has explored whether system hospitals secure higher reimbursement rates by exploiting local market concentration|or market power|to increase their value to MCO networks and negotiate a higher reimbursement rate. However, it is also possible that system hospitals are able to leverage their system membership in the bargaining game in some way in order to extract a higher percentage of their value to an MCO network. We find that system hospitals that are highly concen- trated within a patient market both generate a higher surplus from contracting with a MCO and extract a higher proportion of the surplus. We also find that hospitals| particularly for-profits—belonging to systems operating in multiple patient markets have higher bargaining power suggesting that it is insufficient to only focus on local patient markets when evaluating the potential effect of a merger. Our results indicate iii that a majority of the price difference between system and non-system hospitals is attributable to differences in their bargaining power. iv To Paula. v I owe a debt of gratitude to many people who have made this dissertation possible. First and foremost I must thank my fianc´e,Paula Cordero Salas, who stood by me and encouraged me throughout the dissertation writing process. The process of getting a Ph.D. can push anybody to their limits and Paula has stood by me through the best and worst of the process. Without her love and support this dissertation may have never been completed. Special thanks to my committee. First to my advisor, Professor Dan Levin, whose guidance and encouragement helped me to develop and focus my research. To Profes- sor Matthew Lewis with whom I have spent many hours discussing this dissertation and from whom I have learned a tremendous amount. Last, but by no means least, to Professor Paul Healy who has gone above and beyond to provide guidance in all aspects of the Ph.D. process. Reflecting his generosity Professor Healy has provided me financial support so that can I focus on my dissertation, throughout which he has provided invaluable advice and support. Both inside and outside of the classroom Professor Healy is a role model whose example I hope to one day be able to live up to. Many other people in the department deserve my gratitude. Professors Lixin Ye, Huanxing Yang, Jim Peck, and Yaron Azrieli have all provided valuable feedback and guidance at various stages of this dissertation. I am also grateful to numerous participants in seminars as the Ohio State University, the University of Michigan Ross School of Business, Kent State University, Bowling Green State University, Miami University, the University of Alabama, the Federal Trade Commission, and Charles vi Rivers Associates as well as participants as the 2010 and 2011 International Industrial Organization conferences. I would like to thank the Alumni association for providing me funds through the Alumni Grant for Scholarly Research to purchase some of the data used in this dissertation. Finally, I wish to thank my family and friends. My parents, Tony and Nancy Pflum, have always supported by goals and believed in me. Their support forged my desire to achieve all that I could in life and I owe them everything. vii Vita 1998 . .B.S. Mathematics, University of Victo- ria 1999-2000 . Network Associate, Good Samaritan Hospital 2000-2005 . Software Engineer, Strategic Analysis, Inc. 2005 . .M.A. Economics, New York University 2006 . .Senior Consultant, Booz Allen Hamil- ton 2006-2007 . University Fellowship, The Ohio State University 2007-2009 . Graduate Teaching Assistant, The Ohio State University 2009 . .Department Citation for Excellence in Teaching, The Ohio State University 2009-2011 . Graduate Research Assistant, The Ohio State University Fields of Study Major Field: Economics Studies in: Industrial Organization Microeconomic Theory viii Table of Contents Page Abstract . ii Dedication . .v Vita......................................... viii List of Tables . xii List of Figures . xiv 1. Utilizing Free Samples in Pharmaceutical Markets . .1 1.1 Background . .9 1.2 The Model . 13 1.3 Prices . 19 1.3.1 Prices without Sampling . 19 1.3.2 Prices with Sampling . 21 1.4 Equilibrium Sampling . 25 1.4.1 Symmetric Information . 25 1.4.2 Asymmetric Information . 28 1.5 Sampling with Informed and Uninformed Consumers . 33 1.6 Welfare . 37 1.6.1 Consumer Surplus . 37 1.6.2 Socially Efficient Sampling . 41 1.7 Discussion and Conclusion . 42 ix 2. Regulating a Monopolist with Unverifiable Quality . 48 2.1 The General Model . 58 2.1.1 Quality-Adjusted Cost and Value . 61 2.2 Social Optimum . 63 2.3 Regulating with a Marketed Good . 65 2.3.1 Symmetric Information about θ ................ 66 2.3.2 Asymmetric Information about θ ............... 70 2.4 A Nonmarketed Good . 84 2.4.1 Symmetric Information about θ ................ 84 2.4.2 Asymmetric Information about θ ............... 87 2.5 Asymmetric Information about Demand . 90 2.6 Concluding Remarks . 94 3. Diagnosing Hospital System Bargaining Power in Managed Care Networks 98 3.1 Background . 103 3.1.1 Market Structure . 103 3.1.2 Contract Negotiations . 106 3.2 Related Literature . 107 3.3 Model for Estimation . 112 3.3.1 Willingness-to-Pay . 112 3.3.2 Hospital Cost . 115 3.3.3 Bargaining . 117 3.4 Data . 124 3.5 Results . 128 3.5.1 Demand . 128 3.5.2 Cost . 131 3.5.3 Bargaining Power . 134 3.5.4 Predicted Managed Care Patients and Bargaining Power . 144 3.6 Market Power Versus Bargaining Power . 145 3.7 Conclusions . 147 Appendices 166 A. Chapter 1 Proofs . 166 B. Chapter 2 Proofs . 173 x Bibliography . 180 xi List of Tables Table Page 1.1 10 Most Prescribed Therapeutic Categories, 2008 . 45 1.2 10 Most Sampled Prescription Drugs, 2008 . 46 1.3 Most Sampled Statins, 2008 . 47 3.1 All Hospital Characteristics . 149 3.2 Private Hospital Operation Characteristics . 149 3.3 Hospital Systems Operating in California . 150 3.4 Top 10 Managed Care Companies . 151 3.5 Discharges . 153 3.6 Included Services by Diagnostic Category . 154 3.7 Demand Estimates . 155 3.8 Incremental Patient Value for Hospital System Membership . 156 3.9 Cost Estimates: Variable Cost Characteristics . 157 3.10 Cost Estimates: Fixed Cost Characteristics . 158 3.11 Estimated Cost Differential for Not-For-Profit and For-Profit Hospitals 159 xii 3.12 Estimated Cost Differential for System and Non-System Hospitals . 160 3.13 Determinants of Bargaining Power . 161 3.14 System Characteristics and Bargaining Power . 162 3.15 System Characteristics and Bargaining Power Adjusted for System Market Power . 163 3.16 Determinants of Bargaining Power and Patient Size . 164 3.17 Markup of Average Daily Reimbursement . 165 xiii List of Figures Figure Page 2.1 Graphical Depiction of Proposition 10 . 80 3.1 California Managed Care Organizations Ordered by Market Share . 152 xiv Chapter 1: Utilizing Free Samples in Pharmaceutical Markets In 1919 Mahon Kline of GlaxoSmithKline pioneered a new form of pharmaceutical marketing by mailing drug samples directly to physicians across the United States.
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